2052 lines
242 KiB
JSON
2052 lines
242 KiB
JSON
{
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"id": "conv_20250925_102406_275",
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"title": "你好！",
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"created_at": "2025-09-25T10:24:06.275868",
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"updated_at": "2025-09-25T11:40:37.703991",
|
||
"messages": [
|
||
{
|
||
"role": "user",
|
||
"content": "你好！",
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||
"timestamp": "2025-09-25T10:24:13.027175"
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||
},
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||
{
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||
"role": "assistant",
|
||
"content": "你好！很高兴见到你 😊\n\n我看到你有一个项目文件夹，里面包含了两个有趣的子项目：\n- **反破解挑战**：看起来你在分析一些混淆的JavaScript代码\n- **路径规划**：似乎涉及营地布局和遗传算法相关的Python代码\n\n有什么我可以帮助你的吗？比如继续分析代码、运行程序、或者开始新的开发任务？",
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||
"timestamp": "2025-09-25T10:24:17.056032"
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},
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{
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"role": "user",
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"content": "当前路径规划文件夹里有两文件，但都很长，我现在需要做一个新算法，你可以看到在zongti文件里不管调用什么算法最后都用的是遗传，我打算先做模拟退火算法，由于这两文件太长，请你先聚焦两个文件，这样你就可以看到这两个文件里，你需要从中获取其中的关键部分的信息，比如接口的输入输出参数，目前需要实现的效果，记录下来后取消聚焦，要保证记录下来的信息足够你写接下来的算法\n这个算法是跑在服务器上的，不用你测试，你需要添加到配置文件的内容直接告诉我就行，你只需要保证算法的高明有效\n请你先了解当前的两个文件，先不要写新算法",
|
||
"timestamp": "2025-09-25T10:25:00.623479"
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||
},
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||
{
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"role": "assistant",
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"content": "好的，我来先聚焦这两个文件，了解当前的路径规划算法实现情况，特别是接口设计和遗传算法的使用方式。",
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"timestamp": "2025-09-25T10:25:02.481466"
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||
},
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||
{
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"role": "assistant",
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"content": "",
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||
"timestamp": "2025-09-25T10:25:02.484068",
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||
"tool_calls": [
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{
|
||
"id": "focus_file:0",
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||
"index": 0,
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"type": "function",
|
||
"function": {
|
||
"name": "focus_file",
|
||
"arguments": "{\"path\": \"路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
|
||
}
|
||
}
|
||
]
|
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},
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||
{
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"role": "tool",
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||
"content": "{\"success\": true, \"message\": \"文件已聚焦: 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\", \"focused_files\": [\"路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"], \"file_size\": 121363}",
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"timestamp": "2025-09-25T10:25:04.291103",
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"tool_call_id": "focus_file:0",
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"name": "focus_file"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T10:25:06.376376",
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"tool_calls": [
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{
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"id": "focus_file:1",
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"index": 0,
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"type": "function",
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||
"function": {
|
||
"name": "focus_file",
|
||
"arguments": "{\"path\": \"路径规划/zongti.py\"}"
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||
}
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||
}
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]
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},
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{
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||
"role": "tool",
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"content": "{\"success\": true, \"message\": \"文件已聚焦: 路径规划/zongti.py\", \"focused_files\": [\"路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\", \"路径规划/zongti.py\"], \"file_size\": 44540}",
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"timestamp": "2025-09-25T10:25:08.182698",
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"tool_call_id": "focus_file:1",
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||
"name": "focus_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我分析这两个文件的关键信息。先从主文件zongti.py开始，了解整体架构和接口设计：",
|
||
"timestamp": "2025-09-25T10:25:10.316784"
|
||
},
|
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T10:25:10.318689",
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||
"tool_calls": [
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{
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"id": "run_command:2",
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"index": 0,
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"type": "function",
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"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -n \\\"def \\\" 路径规划/zongti.py | head -20\"}"
|
||
}
|
||
}
|
||
]
|
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},
|
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{
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"57:def get_or_create_planner(facilities: Dict, grid_file: str = None, slope_file: str = None,\\n93:def are_grids_adjacent(grid1: str, grid2: str) -> bool:\\n106:def get_8_neighbors(grid: str) -> List[str]:\\n121:def post_process_road_connections(road_data: Dict, enable_logging: bool = True) -> Dict:\\n233:def smooth_intercity_roads(road_data: Dict, enable_logging: bool = True) -> Dict:\\n296:def smooth_zigzag_path(coords: List[Tuple[int, int]], max_zigzag_size: int = 4) -> List[Tuple[int, int]]:\\n340:def detect_zigzag_segment(coords: List[Tuple[int, int]], start: int, max_size: int) -> int:\\n411:def create_smooth_diagonal(start: Tuple[int, int], end: Tuple[int, int]) -> List[Tuple[int, int]]:\\n445:def find_path_smart(planner: SimplifiedPathPlanner, from_building: str,\\n537:def build_road_graph_smart(all_roads: Dict, facilities: Dict) -> Dict[str, Set[str]]:\\n582:def find_route_in_graph_smart(from_grids: List[str], to_grids: List[str],\\n642:def synchronized_request(lock):\\n643: def decorator(func):\\n644: def wrapper(*args, **kwargs):\\n667:def yingdibuju():\\n757:def sheshibuju():\\n814:def shigong_router():\\n896:def plan_full_network():\\n1021: def filter_road_grids(road_grids):\\n1112:def find_path():\\n\", \"stdout\": \"57:def get_or_create_planner(facilities: Dict, grid_file: str = None, slope_file: str = None,\\n93:def are_grids_adjacent(grid1: str, grid2: str) -> bool:\\n106:def get_8_neighbors(grid: str) -> List[str]:\\n121:def post_process_road_connections(road_data: Dict, enable_logging: bool = True) -> Dict:\\n233:def smooth_intercity_roads(road_data: Dict, enable_logging: bool = True) -> Dict:\\n296:def smooth_zigzag_path(coords: List[Tuple[int, int]], max_zigzag_size: int = 4) -> List[Tuple[int, int]]:\\n340:def detect_zigzag_segment(coords: List[Tuple[int, int]], start: int, max_size: int) -> int:\\n411:def create_smooth_diagonal(start: Tuple[int, int], end: Tuple[int, int]) -> List[Tuple[int, int]]:\\n445:def find_path_smart(planner: SimplifiedPathPlanner, from_building: str,\\n537:def build_road_graph_smart(all_roads: Dict, facilities: Dict) -> Dict[str, Set[str]]:\\n582:def find_route_in_graph_smart(from_grids: List[str], to_grids: List[str],\\n642:def synchronized_request(lock):\\n643: def decorator(func):\\n644: def wrapper(*args, **kwargs):\\n667:def yingdibuju():\\n757:def sheshibuju():\\n814:def shigong_router():\\n896:def plan_full_network():\\n1021: def filter_road_grids(road_grids):\\n1112:def find_path():\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"def \\\" 路径规划/zongti.py | head -20\"}",
|
||
"timestamp": "2025-09-25T10:25:12.123551",
|
||
"tool_call_id": "run_command:2",
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"name": "run_command"
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},
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{
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"role": "assistant",
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"content": "",
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||
"timestamp": "2025-09-25T10:25:14.165301",
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"tool_calls": [
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{
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"id": "run_command:3",
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"index": 0,
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"type": "function",
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"function": {
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n \\\"class \\\" 路径规划/zongti.py\"}"
|
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}
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}
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": false, \"output\": \"\", \"stdout\": \"\", \"stderr\": \"\", \"return_code\": 1, \"command\": \"grep -n \\\"class \\\" 路径规划/zongti.py\"}",
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"timestamp": "2025-09-25T10:25:15.972152",
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"tool_call_id": "run_command:3",
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"name": "run_command"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T10:25:18.694132",
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"tool_calls": [
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{
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"id": "run_command:4",
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"index": 0,
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"type": "function",
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"function": {
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n \\\"遗传\\\\|genetic\\\\|GA\\\\|进化\\\" 路径规划/zongti.py\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"686: \\\"yc\\\": \\\"遗传\\\",\\n764: \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n820: \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n\", \"stdout\": \"686: \\\"yc\\\": \\\"遗传\\\",\\n764: \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n820: \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"遗传\\\\|genetic\\\\|GA\\\\|进化\\\" 路径规划/zongti.py\"}",
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"timestamp": "2025-09-25T10:25:20.501822",
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"tool_call_id": "run_command:4",
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"name": "run_command"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T10:25:23.135944",
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"tool_calls": [
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{
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"id": "run_command:5",
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"index": 0,
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"type": "function",
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"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 10 -B 5 \\\"get_or_create_planner\\\" 路径规划/zongti.py\"}"
|
||
}
|
||
}
|
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]
|
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},
|
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{
|
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"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"\\n# 全局路径规划器实例缓存\\nplanner_cache = {}\\n\\n\\ndef get_or_create_planner(facilities: Dict, grid_file: str = None, slope_file: str = None,\\n max_workers: int = 6, boundary_data: List[List[str]] = None) -> SimplifiedPathPlanner:\\n \\\"\\\"\\\"\\n 获取或创建路径规划器实例（带边界数据）\\n\\n Args:\\n facilities: 设施数据\\n grid_file: 原始地形文件路径\\n slope_file: 预计算的坡度文件路径\\n max_workers: 最大并行工作进程数\\n boundary_data: 边界数据\\n--\\n # 如果大门计算失败，继续执行（不使用地图大门）\\n print(f\\\"Warning: Map gate calculation failed: {str(e)}\\\")\\n map_gates = None\\n\\n # 获取规划器实例（带边界数据）\\n planner = get_or_create_planner(\\n facilities=facilities,\\n grid_file=grid_file,\\n slope_file=slope_file,\\n max_workers=max_workers,\\n boundary_data=boundary_data # 传递边界数据\\n )\\n\\n # 设置诊断模式\\n planner.enable_diagnostics = enable_diagnostics\\n\\n--\\n \\\"data\\\": None\\n }), 400\\n\\n try:\\n # 注意：findPath不需要边界数据，因为道路已经是规划好的\\n planner = get_or_create_planner(\\n facilities=facilities,\\n grid_file=grid_file,\\n slope_file=slope_file,\\n max_workers=1\\n )\\n planner.enable_diagnostics = enable_diagnostics\\n except Exception as e:\\n return jsonify({\\n \\\"message\\\": f\\\"无法初始化路径规划器: {str(e)}\\\",\\n \\\"code\\\": 500,\\n\", \"stdout\": \"\\n# 全局路径规划器实例缓存\\nplanner_cache = {}\\n\\n\\ndef get_or_create_planner(facilities: Dict, grid_file: str = None, slope_file: str = None,\\n max_workers: int = 6, boundary_data: List[List[str]] = None) -> SimplifiedPathPlanner:\\n \\\"\\\"\\\"\\n 获取或创建路径规划器实例（带边界数据）\\n\\n Args:\\n facilities: 设施数据\\n grid_file: 原始地形文件路径\\n slope_file: 预计算的坡度文件路径\\n max_workers: 最大并行工作进程数\\n boundary_data: 边界数据\\n--\\n # 如果大门计算失败，继续执行（不使用地图大门）\\n print(f\\\"Warning: Map gate calculation failed: {str(e)}\\\")\\n map_gates = None\\n\\n # 获取规划器实例（带边界数据）\\n planner = get_or_create_planner(\\n facilities=facilities,\\n grid_file=grid_file,\\n slope_file=slope_file,\\n max_workers=max_workers,\\n boundary_data=boundary_data # 传递边界数据\\n )\\n\\n # 设置诊断模式\\n planner.enable_diagnostics = enable_diagnostics\\n\\n--\\n \\\"data\\\": None\\n }), 400\\n\\n try:\\n # 注意：findPath不需要边界数据，因为道路已经是规划好的\\n planner = get_or_create_planner(\\n facilities=facilities,\\n grid_file=grid_file,\\n slope_file=slope_file,\\n max_workers=1\\n )\\n planner.enable_diagnostics = enable_diagnostics\\n except Exception as e:\\n return jsonify({\\n \\\"message\\\": f\\\"无法初始化路径规划器: {str(e)}\\\",\\n \\\"code\\\": 500,\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 10 -B 5 \\\"get_or_create_planner\\\" 路径规划/zongti.py\"}",
|
||
"timestamp": "2025-09-25T10:25:24.941747",
|
||
"tool_call_id": "run_command:5",
|
||
"name": "run_command"
|
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},
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{
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"role": "assistant",
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"content": "",
|
||
"timestamp": "2025-09-25T10:25:26.168990",
|
||
"tool_calls": [
|
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{
|
||
"id": "run_command:6",
|
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"index": 0,
|
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"type": "function",
|
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"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 20 \\\"def yingdibuju\\\" 路径规划/zongti.py\"}"
|
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}
|
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}
|
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]
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},
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{
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"def yingdibuju():\\n try:\\n rec_data = request.get_json()\\n print(rec_data)\\n \\n # === 添加这几行来保存入参 ===\\n import datetime\\n timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n input_filename = f\\\"yingdi_input_{timestamp}.json\\\"\\n \\n with open(input_filename, 'w', encoding='utf-8') as f:\\n json.dump(rec_data, f, ensure_ascii=False, indent=2)\\n \\n print(f\\\"入参已保存到: {input_filename}\\\")\\n # === 添加结束 ===\\n \\n algorithm = rec_data[\\\"layoutAlgorithm\\\"]\\n strategy = rec_data[\\\"layoutStrategy\\\"]\\n ALG_MAP = {\\n \\\"yc\\\": \\\"遗传\\\",\\n \\\"bcd\\\": \\\"变尺度\\\",\\n\", \"stdout\": \"def yingdibuju():\\n try:\\n rec_data = request.get_json()\\n print(rec_data)\\n \\n # === 添加这几行来保存入参 ===\\n import datetime\\n timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n input_filename = f\\\"yingdi_input_{timestamp}.json\\\"\\n \\n with open(input_filename, 'w', encoding='utf-8') as f:\\n json.dump(rec_data, f, ensure_ascii=False, indent=2)\\n \\n print(f\\\"入参已保存到: {input_filename}\\\")\\n # === 添加结束 ===\\n \\n algorithm = rec_data[\\\"layoutAlgorithm\\\"]\\n strategy = rec_data[\\\"layoutStrategy\\\"]\\n ALG_MAP = {\\n \\\"yc\\\": \\\"遗传\\\",\\n \\\"bcd\\\": \\\"变尺度\\\",\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 20 \\\"def yingdibuju\\\" 路径规划/zongti.py\"}",
|
||
"timestamp": "2025-09-25T10:25:27.975824",
|
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"tool_call_id": "run_command:6",
|
||
"name": "run_command"
|
||
},
|
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{
|
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"role": "assistant",
|
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"content": "",
|
||
"timestamp": "2025-09-25T10:25:29.668785",
|
||
"tool_calls": [
|
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{
|
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"id": "run_command:7",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 30 \\\"def sheshibuju\\\" 路径规划/zongti.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"def sheshibuju():\\n try:\\n rec_data = request.get_json()\\n\\n # 获取算法信息\\n algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n ALG_MAP = {\\n \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n \\\"lzq\\\": \\\"粒子群\\\",\\n }\\n\\n functionWeight = rec_data[\\\"functionWeight\\\"]\\n if algorithm not in ALG_MAP.keys():\\n rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"] = \\\"nsga3\\\"\\n algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n\\n alg = f\\\"===设施布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法\\\\n\\\"\\n alg += f\\\"设施布局参数设置为：\\\\n\\\"\\n\\n # 安全的日志记录 - 只记录实际存在的元素\\n weight_names = [\\\"分散度\\\", \\\"可达性\\\", \\\"关联度\\\", \\\"屏蔽度\\\", \\\"适宜性\\\"]\\n weights_display = []\\n\\n for i, name in enumerate(weight_names):\\n if i < len(functionWeight):\\n weights_display.append(f\\\"{name}:{functionWeight[i]}\\\")\\n\\n alg += \\\" 目标函数：\\\\n \\\" + \\\"，\\\".join(weights_display) + \\\"\\\\n\\\"\\n\\n # 记录约束条件（如果有）\\n constraintParam = rec_data.get(\\\"constraints\\\", {})\\n\", \"stdout\": \"def sheshibuju():\\n try:\\n rec_data = request.get_json()\\n\\n # 获取算法信息\\n algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n ALG_MAP = {\\n \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n \\\"lzq\\\": \\\"粒子群\\\",\\n }\\n\\n functionWeight = rec_data[\\\"functionWeight\\\"]\\n if algorithm not in ALG_MAP.keys():\\n rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"] = \\\"nsga3\\\"\\n algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n\\n alg = f\\\"===设施布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法\\\\n\\\"\\n alg += f\\\"设施布局参数设置为：\\\\n\\\"\\n\\n # 安全的日志记录 - 只记录实际存在的元素\\n weight_names = [\\\"分散度\\\", \\\"可达性\\\", \\\"关联度\\\", \\\"屏蔽度\\\", \\\"适宜性\\\"]\\n weights_display = []\\n\\n for i, name in enumerate(weight_names):\\n if i < len(functionWeight):\\n weights_display.append(f\\\"{name}:{functionWeight[i]}\\\")\\n\\n alg += \\\" 目标函数：\\\\n \\\" + \\\"，\\\".join(weights_display) + \\\"\\\\n\\\"\\n\\n # 记录约束条件（如果有）\\n constraintParam = rec_data.get(\\\"constraints\\\", {})\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"def sheshibuju\\\" 路径规划/zongti.py\"}",
|
||
"timestamp": "2025-09-25T10:25:31.475189",
|
||
"tool_call_id": "run_command:7",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:25:35.016092",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:8",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 15 \\\"def shigong_router\\\" 路径规划/zongti.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"def shigong_router():\\n try:\\n # 获取请求数据和算法类型\\n rec_data = request.get_json()\\n algorithm = rec_data.get(\\\"constructionAlgo\\\", \\\"nsga3\\\") # 默认nsga3算法\\n ALG_MAP = {\\n \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n \\\"benders\\\": \\\"bender分解\\\",\\n \\\"lagrange\\\": \\\"拉格朗日松弛\\\",\\n \\\"lsc\\\": \\\"列生成\\\",\\n \\\"zsl\\\": \\\"整数L型\\\",\\n \\\"fzdj\\\": \\\"分支定界\\\"\\n }\\n\\n alg = f\\\"===施工算法===\\\\n\\\"\\n alg += f\\\"施工参数设置为：\\\\n\\\"\\n\", \"stdout\": \"def shigong_router():\\n try:\\n # 获取请求数据和算法类型\\n rec_data = request.get_json()\\n algorithm = rec_data.get(\\\"constructionAlgo\\\", \\\"nsga3\\\") # 默认nsga3算法\\n ALG_MAP = {\\n \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n \\\"benders\\\": \\\"bender分解\\\",\\n \\\"lagrange\\\": \\\"拉格朗日松弛\\\",\\n \\\"lsc\\\": \\\"列生成\\\",\\n \\\"zsl\\\": \\\"整数L型\\\",\\n \\\"fzdj\\\": \\\"分支定界\\\"\\n }\\n\\n alg = f\\\"===施工算法===\\\\n\\\"\\n alg += f\\\"施工参数设置为：\\\\n\\\"\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 15 \\\"def shigong_router\\\" 路径规划/zongti.py\"}",
|
||
"timestamp": "2025-09-25T10:25:36.824230",
|
||
"tool_call_id": "run_command:8",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我查看Camp_layout_gaocheng_fsbj_yichuan.py文件，了解遗传算法的具体实现：",
|
||
"timestamp": "2025-09-25T10:25:39.737235"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:25:39.756061",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:9",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -n \\\"class \\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -10\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"493:class CampProblem(ElementwiseProblem):\\n986:class OtherAreasProblem(ElementwiseProblem):\\n2105: class UnifiedOtherAreasProblem(ElementwiseProblem):\\n2751: class CampProblemWithOtherAreas(CampProblem):\\n\", \"stdout\": \"493:class CampProblem(ElementwiseProblem):\\n986:class OtherAreasProblem(ElementwiseProblem):\\n2105: class UnifiedOtherAreasProblem(ElementwiseProblem):\\n2751: class CampProblemWithOtherAreas(CampProblem):\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"class \\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -10\"}",
|
||
"timestamp": "2025-09-25T10:25:41.567931",
|
||
"tool_call_id": "run_command:9",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:25:43.539363",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:10",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 30 \\\"class CampProblem\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"class CampProblem(ElementwiseProblem):\\n \\\"\\\"\\\"优化问题定义 - 支持多目标权重、热力负值约束和场景方向\\\"\\\"\\\"\\n\\n def __init__(self, config: Dict):\\n self.config = config\\n\\n # 计算理论极值\\n self.min_area = config['minSize'] ** 2\\n self.max_area = config['maxSize'] ** 2\\n\\n # 添加最大可能距离（场地对角线）\\n self.max_possible_dist = np.sqrt(config['FIELD_WIDTH'] ** 2 + config['FIELD_HEIGHT'] ** 2)\\n\\n # 添加热力矩阵相关属性\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n self.max_heat = np.max(self.heatmap_grid)\\n self.max_heat_sum = self.max_heat * (config['maxSize'] ** 2) * config['numCamp']\\n\\n # 更新目标权重\\n self.weights = config['functionWeight']\\n\\n # 场景和方向相关属性\\n self.scene = config.get('scene', None)\\n self.direction = config.get('direction', 'left')\\n\\n # 方向角度映射\\n self.direction_map = {\\n 'left': 180,\\n 'right': 0,\\n 'up': 90,\\n 'down': 270\\n--\\n class CampProblemWithOtherAreas(CampProblem):\\n def __init__(self, config: Dict, other_areas: List[Dict]):\\n super().__init__(config)\\n self.other_areas = other_areas\\n \\n # 找到训练设施的中心位置作为参考点\\n self.training_center = None\\n training_areas = []\\n \\n for area in other_areas:\\n area_name = str(area.get('id', ''))\\n # 通过原始输入数据识别训练设施\\n for input_area in config.get('original_input_data', []):\\n if isinstance(input_area, dict) and input_area.get('name') in area_name:\\n if '训练设施' in input_area.get('type', ''):\\n # 从lowerLeft和upperRight计算中心位置\\n ll = area['lowerLeft']\\n ur = area['upperRight']\\n center = [(ll[0] + ur[0]) / 2, (ll[1] + ur[1]) / 2]\\n training_areas.append(center)\\n break\\n \\n # 如果有多个训练设施，使用它们的平均中心位置\\n if training_areas:\\n if len(training_areas) == 1:\\n self.training_center = training_areas[0]\\n else:\\n # 计算多个训练设施的平均中心位置\\n avg_x = sum(center[0] for center in training_areas) / len(training_areas)\\n avg_y = sum(center[1] for center in training_areas) / len(training_areas)\\n self.training_center = [avg_x, avg_y]\\n\", \"stdout\": \"class CampProblem(ElementwiseProblem):\\n \\\"\\\"\\\"优化问题定义 - 支持多目标权重、热力负值约束和场景方向\\\"\\\"\\\"\\n\\n def __init__(self, config: Dict):\\n self.config = config\\n\\n # 计算理论极值\\n self.min_area = config['minSize'] ** 2\\n self.max_area = config['maxSize'] ** 2\\n\\n # 添加最大可能距离（场地对角线）\\n self.max_possible_dist = np.sqrt(config['FIELD_WIDTH'] ** 2 + config['FIELD_HEIGHT'] ** 2)\\n\\n # 添加热力矩阵相关属性\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n self.max_heat = np.max(self.heatmap_grid)\\n self.max_heat_sum = self.max_heat * (config['maxSize'] ** 2) * config['numCamp']\\n\\n # 更新目标权重\\n self.weights = config['functionWeight']\\n\\n # 场景和方向相关属性\\n self.scene = config.get('scene', None)\\n self.direction = config.get('direction', 'left')\\n\\n # 方向角度映射\\n self.direction_map = {\\n 'left': 180,\\n 'right': 0,\\n 'up': 90,\\n 'down': 270\\n--\\n class CampProblemWithOtherAreas(CampProblem):\\n def __init__(self, config: Dict, other_areas: List[Dict]):\\n super().__init__(config)\\n self.other_areas = other_areas\\n \\n # 找到训练设施的中心位置作为参考点\\n self.training_center = None\\n training_areas = []\\n \\n for area in other_areas:\\n area_name = str(area.get('id', ''))\\n # 通过原始输入数据识别训练设施\\n for input_area in config.get('original_input_data', []):\\n if isinstance(input_area, dict) and input_area.get('name') in area_name:\\n if '训练设施' in input_area.get('type', ''):\\n # 从lowerLeft和upperRight计算中心位置\\n ll = area['lowerLeft']\\n ur = area['upperRight']\\n center = [(ll[0] + ur[0]) / 2, (ll[1] + ur[1]) / 2]\\n training_areas.append(center)\\n break\\n \\n # 如果有多个训练设施，使用它们的平均中心位置\\n if training_areas:\\n if len(training_areas) == 1:\\n self.training_center = training_areas[0]\\n else:\\n # 计算多个训练设施的平均中心位置\\n avg_x = sum(center[0] for center in training_areas) / len(training_areas)\\n avg_y = sum(center[1] for center in training_areas) / len(training_areas)\\n self.training_center = [avg_x, avg_y]\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"class CampProblem\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
|
||
"timestamp": "2025-09-25T10:25:45.346422",
|
||
"tool_call_id": "run_command:10",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:25:47.295557",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:11",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 15 \\\"def __init__\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -20\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \" def __init__(self, config: Dict):\\n self.config = config\\n\\n # 计算理论极值\\n self.min_area = config['minSize'] ** 2\\n self.max_area = config['maxSize'] ** 2\\n\\n # 添加最大可能距离（场地对角线）\\n self.max_possible_dist = np.sqrt(config['FIELD_WIDTH'] ** 2 + config['FIELD_HEIGHT'] ** 2)\\n\\n # 添加热力矩阵相关属性\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n self.max_heat = np.max(self.heatmap_grid)\\n self.max_heat_sum = self.max_heat * (config['maxSize'] ** 2) * config['numCamp']\\n\\n # 更新目标权重\\n--\\n def __init__(self, config: Dict, camp_positions: List[Dict], original_input_data: List[Dict] = None):\\n self.config = config\\n self.camp_positions = camp_positions # 已布局的营地位置\\n\", \"stdout\": \" def __init__(self, config: Dict):\\n self.config = config\\n\\n # 计算理论极值\\n self.min_area = config['minSize'] ** 2\\n self.max_area = config['maxSize'] ** 2\\n\\n # 添加最大可能距离（场地对角线）\\n self.max_possible_dist = np.sqrt(config['FIELD_WIDTH'] ** 2 + config['FIELD_HEIGHT'] ** 2)\\n\\n # 添加热力矩阵相关属性\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n self.max_heat = np.max(self.heatmap_grid)\\n self.max_heat_sum = self.max_heat * (config['maxSize'] ** 2) * config['numCamp']\\n\\n # 更新目标权重\\n--\\n def __init__(self, config: Dict, camp_positions: List[Dict], original_input_data: List[Dict] = None):\\n self.config = config\\n self.camp_positions = camp_positions # 已布局的营地位置\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 15 \\\"def __init__\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -20\"}",
|
||
"timestamp": "2025-09-25T10:25:49.102660",
|
||
"tool_call_id": "run_command:11",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:25:51.960734",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:12",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 50 \\\"def _evaluate\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -60\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \" def _evaluate(self, x: np.ndarray, out: Dict, *args, **kwargs) -> None:\\n # 计算约束违反值\\n boundary_vio = self._boundary_violation_vectorized(x)\\n overlap_vio = self._overlap_violation_vectorized(x)\\n distance_vio = self._distance_violation_vectorized(x)\\n heat_negative_vio = self._heat_negative_violation_vectorized(x)\\n\\n # 计算动态惩罚因子（随评估次数增加而增大）\\n penalty_factor = self.initial_penalty_factor * (self.penalty_growth_rate ** (self.eval_count / 1000))\\n\\n # 计算目标函数值（即使有约束违反也计算）\\n maximization_score, dispersion_score, suitability_score, forward_score, withdrawal_score = self._calculate_scores_vectorized(x)\\n combined_score = (\\n self.weights[0] * maximization_score +\\n self.weights[1] * dispersion_score +\\n self.weights[2] * suitability_score +\\n self.weights[3] * forward_score +\\n self.weights[4] * withdrawal_score\\n )\\n\\n # 计算总约束违反值（重叠约束给予更高权重）\\n total_violation = (\\n boundary_vio +\\n overlap_vio * 10000 + # 重叠约束权重进一步提高到10000倍\\n distance_vio +\\n heat_negative_vio\\n )\\n\\n # 应用动态罚函数\\n penalty = penalty_factor * total_violation\\n\\n # 更新评估计数器\\n self.eval_count += 1\\n\\n # 设置输出\\n out[\\\"F\\\"] = -combined_score + penalty\\n out[\\\"G\\\"] = [\\n boundary_vio,\\n overlap_vio,\\n distance_vio,\\n heat_negative_vio\\n ]\\n out[\\\"maximization_score\\\"] = maximization_score\\n out[\\\"dispersion_score\\\"] = dispersion_score\\n out[\\\"suitability_score\\\"] = suitability_score\\n out[\\\"forward_score\\\"] = forward_score\\n out[\\\"withdrawal_score\\\"] = withdrawal_score\\n out[\\\"penalty\\\"] = penalty\\n out[\\\"combined_score\\\"] = combined_score\\n\\n def _calculate_scene_direction_score(self, camps: np.ndarray) -> float:\\n--\\n def _evaluate(self, x: np.ndarray, out: Dict, *args, **kwargs) -> None:\\n # 检查负热力值约束\\n heat_negative_vio = self._heat_negative_violation(x)\\n\\n if heat_negative_vio > 0:\\n # 如果违反负热力约束，返回高惩罚值\\n out[\\\"F\\\"] = 1e8 + heat_negative_vio * 1000\\n out[\\\"G\\\"] = [\\n\", \"stdout\": \" def _evaluate(self, x: np.ndarray, out: Dict, *args, **kwargs) -> None:\\n # 计算约束违反值\\n boundary_vio = self._boundary_violation_vectorized(x)\\n overlap_vio = self._overlap_violation_vectorized(x)\\n distance_vio = self._distance_violation_vectorized(x)\\n heat_negative_vio = self._heat_negative_violation_vectorized(x)\\n\\n # 计算动态惩罚因子（随评估次数增加而增大）\\n penalty_factor = self.initial_penalty_factor * (self.penalty_growth_rate ** (self.eval_count / 1000))\\n\\n # 计算目标函数值（即使有约束违反也计算）\\n maximization_score, dispersion_score, suitability_score, forward_score, withdrawal_score = self._calculate_scores_vectorized(x)\\n combined_score = (\\n self.weights[0] * maximization_score +\\n self.weights[1] * dispersion_score +\\n self.weights[2] * suitability_score +\\n self.weights[3] * forward_score +\\n self.weights[4] * withdrawal_score\\n )\\n\\n # 计算总约束违反值（重叠约束给予更高权重）\\n total_violation = (\\n boundary_vio +\\n overlap_vio * 10000 + # 重叠约束权重进一步提高到10000倍\\n distance_vio +\\n heat_negative_vio\\n )\\n\\n # 应用动态罚函数\\n penalty = penalty_factor * total_violation\\n\\n # 更新评估计数器\\n self.eval_count += 1\\n\\n # 设置输出\\n out[\\\"F\\\"] = -combined_score + penalty\\n out[\\\"G\\\"] = [\\n boundary_vio,\\n overlap_vio,\\n distance_vio,\\n heat_negative_vio\\n ]\\n out[\\\"maximization_score\\\"] = maximization_score\\n out[\\\"dispersion_score\\\"] = dispersion_score\\n out[\\\"suitability_score\\\"] = suitability_score\\n out[\\\"forward_score\\\"] = forward_score\\n out[\\\"withdrawal_score\\\"] = withdrawal_score\\n out[\\\"penalty\\\"] = penalty\\n out[\\\"combined_score\\\"] = combined_score\\n\\n def _calculate_scene_direction_score(self, camps: np.ndarray) -> float:\\n--\\n def _evaluate(self, x: np.ndarray, out: Dict, *args, **kwargs) -> None:\\n # 检查负热力值约束\\n heat_negative_vio = self._heat_negative_violation(x)\\n\\n if heat_negative_vio > 0:\\n # 如果违反负热力约束，返回高惩罚值\\n out[\\\"F\\\"] = 1e8 + heat_negative_vio * 1000\\n out[\\\"G\\\"] = [\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 50 \\\"def _evaluate\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -60\"}",
|
||
"timestamp": "2025-09-25T10:25:53.768507",
|
||
"tool_call_id": "run_command:12",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:25:56.068660",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:13",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 20 \\\"nsga3\\\\|NSGA\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": false, \"output\": \"\", \"stdout\": \"\", \"stderr\": \"\", \"return_code\": 1, \"command\": \"grep -A 20 \\\"nsga3\\\\|NSGA\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
|
||
"timestamp": "2025-09-25T10:25:57.876486",
|
||
"tool_call_id": "run_command:13",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:25:59.937346",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:14",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -n \\\"算法\\\\|优化\\\\|求解\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -10\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
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"content": "{\"success\": true, \"output\": \"259: # 使用numpy向量化操作优化热力值收集\\n270: # 优化的热力聚合函数\\n494: \\\"\\\"\\\"优化问题定义 - 支持多目标权重、热力负值约束和场景方向\\\"\\\"\\\"\\n684: # 3. 适宜性得分（热力值优化）\\n987: \\\"\\\"\\\"其他区域布局优化问题\\\"\\\"\\\"\\n1603: # 创建优化问题\\n1606: # 使用遗传算法\\n1621: # 运行优化\\n1680:# 优化历史保存功能已移除\\n1689: # 遗传算法优化开始\\n\", \"stdout\": \"259: # 使用numpy向量化操作优化热力值收集\\n270: # 优化的热力聚合函数\\n494: \\\"\\\"\\\"优化问题定义 - 支持多目标权重、热力负值约束和场景方向\\\"\\\"\\\"\\n684: # 3. 适宜性得分（热力值优化）\\n987: \\\"\\\"\\\"其他区域布局优化问题\\\"\\\"\\\"\\n1603: # 创建优化问题\\n1606: # 使用遗传算法\\n1621: # 运行优化\\n1680:# 优化历史保存功能已移除\\n1689: # 遗传算法优化开始\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"算法\\\\|优化\\\\|求解\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -10\"}",
|
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|
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|
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|
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|
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"content": "{\"success\": true, \"output\": \" # 使用遗传算法\\n algorithm = GA(\\n pop_size=300,\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=0.7,\\n eta=30\\n ),\\n mutation=PM(\\n prob=0.7,\\n eta=30\\n ),\\n eliminate_duplicates=True\\n )\\n\\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", algorithm_params.get('maxGenerations', 200))\\n res = minimize(\\n problem,\\n algorithm,\\n termination=termination,\\n verbose=True\\n )\\n\\n # 构建结果\\n other_areas_result = []\\n\\n if res.X is not None:\\n # 检查约束满足情况\\n constraint_violations = res.G\\n # 现在有4个约束：边界、重叠、负热力、距离\\n\", \"stdout\": \" # 使用遗传算法\\n algorithm = GA(\\n pop_size=300,\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=0.7,\\n eta=30\\n ),\\n mutation=PM(\\n prob=0.7,\\n eta=30\\n ),\\n eliminate_duplicates=True\\n )\\n\\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", algorithm_params.get('maxGenerations', 200))\\n res = minimize(\\n problem,\\n algorithm,\\n termination=termination,\\n verbose=True\\n )\\n\\n # 构建结果\\n other_areas_result = []\\n\\n if res.X is not None:\\n # 检查约束满足情况\\n constraint_violations = res.G\\n # 现在有4个约束：边界、重叠、负热力、距离\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"使用遗传算法\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"content": "{\"success\": true, \"output\": \"1595:def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n2094:def layout_priority_areas_first(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n\", \"stdout\": \"1595:def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n2094:def layout_priority_areas_first(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"def.*layout\\\\|def.*optimize\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
|
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"id": "run_command:18",
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|
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"name": "run_command",
|
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"arguments": "{\"command\": \"grep -A 10 \\\"输入\\\\|输出\\\\|参数\\\\|返回\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -30\"}"
|
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|
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|
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|
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|
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|
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"role": "tool",
|
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"content": "{\"success\": true, \"output\": \" raise ValueError(\\\"必须提供'campIds'参数，包含营地的唯一编号列表\\\")\\n num_camp = len(camp_ids)\\n\\n # 获取场景参数\\n scene = input_data.get(\\\"scene\\\", None)\\n\\n grid_size = float(input_data.get(\\\"gridSize\\\", 1.0))\\n grid_data = defaultdict(lambda: defaultdict(float))\\n all_heights = []\\n original_coords = [] # 存储原始坐标\\n coord_map = {} # 映射原始坐标到高度值\\n\\n # 找出有效的网格键\\n valid_grid_keys = [key for key in grid_data_dict.keys() if re.fullmatch(r'\\\\d+-\\\\d+', key)]\\n--\\n # 使用当前工作目录作为输出目录，避免硬编码路径\\n heatmap_matrix = get_heatmap(grid_data_path, attack_direction=attack_direction)\\n\\n if heatmap_matrix is not None:\\n # 使用numpy向量化操作优化热力值收集\\n heat_coords = np.array(original_coords)\\n heat_x_centers = (heat_coords[:, 0] - min_x + 0.5) * grid_size\\n heat_y_centers = (heat_coords[:, 1] - min_y + 0.5) * grid_size\\n\\n # 向量化获取热力值\\n heat_values = np.zeros(len(original_coords))\\n--\\n # 保存原始输入数据\\n original_input_data.append({\\n 'name': name,\\n\", \"stdout\": \" raise ValueError(\\\"必须提供'campIds'参数，包含营地的唯一编号列表\\\")\\n num_camp = len(camp_ids)\\n\\n # 获取场景参数\\n scene = input_data.get(\\\"scene\\\", None)\\n\\n grid_size = float(input_data.get(\\\"gridSize\\\", 1.0))\\n grid_data = defaultdict(lambda: defaultdict(float))\\n all_heights = []\\n original_coords = [] # 存储原始坐标\\n coord_map = {} # 映射原始坐标到高度值\\n\\n # 找出有效的网格键\\n valid_grid_keys = [key for key in grid_data_dict.keys() if re.fullmatch(r'\\\\d+-\\\\d+', key)]\\n--\\n # 使用当前工作目录作为输出目录，避免硬编码路径\\n heatmap_matrix = get_heatmap(grid_data_path, attack_direction=attack_direction)\\n\\n if heatmap_matrix is not None:\\n # 使用numpy向量化操作优化热力值收集\\n heat_coords = np.array(original_coords)\\n heat_x_centers = (heat_coords[:, 0] - min_x + 0.5) * grid_size\\n heat_y_centers = (heat_coords[:, 1] - min_y + 0.5) * grid_size\\n\\n # 向量化获取热力值\\n heat_values = np.zeros(len(original_coords))\\n--\\n # 保存原始输入数据\\n original_input_data.append({\\n 'name': name,\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 10 \\\"输入\\\\|输出\\\\|参数\\\\|返回\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -30\"}",
|
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"timestamp": "2025-09-25T10:26:23.523303",
|
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"tool_call_id": "run_command:18",
|
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"name": "run_command"
|
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|
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|
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"role": "assistant",
|
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|
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"timestamp": "2025-09-25T10:26:28.387853",
|
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"tool_calls": [
|
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{
|
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"id": "run_command:19",
|
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"index": 0,
|
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"type": "function",
|
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"function": {
|
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"name": "run_command",
|
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"arguments": "{\"command\": \"grep -A 20 \\\"def layout_other_areas\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
|
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}
|
||
}
|
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]
|
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},
|
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{
|
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"role": "tool",
|
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"content": "{\"success\": true, \"output\": \"def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n \\\"\\\"\\\"布局其他区域\\\"\\\"\\\"\\n # 其他区域布局开始\\n\\n other_areas = config.get('otherAreas', {})\\n if not other_areas:\\n return []\\n\\n # 创建优化问题\\n problem = OtherAreasProblem(config, camp_positions, config.get('original_input_data', []))\\n\\n # 使用遗传算法\\n algorithm = GA(\\n pop_size=300,\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=0.7,\\n eta=30\\n ),\\n mutation=PM(\\n prob=0.7,\\n\", \"stdout\": \"def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n \\\"\\\"\\\"布局其他区域\\\"\\\"\\\"\\n # 其他区域布局开始\\n\\n other_areas = config.get('otherAreas', {})\\n if not other_areas:\\n return []\\n\\n # 创建优化问题\\n problem = OtherAreasProblem(config, camp_positions, config.get('original_input_data', []))\\n\\n # 使用遗传算法\\n algorithm = GA(\\n pop_size=300,\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=0.7,\\n eta=30\\n ),\\n mutation=PM(\\n prob=0.7,\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 20 \\\"def layout_other_areas\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"timestamp": "2025-09-25T10:26:30.196866",
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"role": "assistant",
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|
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"timestamp": "2025-09-25T10:26:32.128034",
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"tool_calls": [
|
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{
|
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"id": "run_command:20",
|
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"index": 0,
|
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"type": "function",
|
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"function": {
|
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"name": "run_command",
|
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"arguments": "{\"command\": \"grep -A 20 \\\"def layout_priority_areas_first\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
|
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|
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|
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|
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},
|
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{
|
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"role": "tool",
|
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"content": "{\"success\": true, \"output\": \"def layout_priority_areas_first(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n \\\"\\\"\\\"通过目标函数优化布局所有其他区域，考虑热力值、重叠、边界等约束\\\"\\\"\\\"\\n # 其他区域优化布局开始\\n \\n other_areas = config.get('otherAreas', {})\\n if not other_areas:\\n return []\\n\\n # 布局策略和约束条件\\n \\n # 创建统一的优化问题\\n class UnifiedOtherAreasProblem(ElementwiseProblem):\\n def __init__(self, config: Dict, other_areas: Dict, original_input_data: List[Dict] = None):\\n self.config = config\\n self.other_areas = other_areas\\n self.other_areas_details = config.get('otherAreasDetails', {}) # 新增：获取详细区域信息\\n self.original_input_data = original_input_data or []\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n \\n # 网格参数\\n self.agg_rows, self.agg_cols = self.heatmap_grid.shape\\n\", \"stdout\": \"def layout_priority_areas_first(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n \\\"\\\"\\\"通过目标函数优化布局所有其他区域，考虑热力值、重叠、边界等约束\\\"\\\"\\\"\\n # 其他区域优化布局开始\\n \\n other_areas = config.get('otherAreas', {})\\n if not other_areas:\\n return []\\n\\n # 布局策略和约束条件\\n \\n # 创建统一的优化问题\\n class UnifiedOtherAreasProblem(ElementwiseProblem):\\n def __init__(self, config: Dict, other_areas: Dict, original_input_data: List[Dict] = None):\\n self.config = config\\n self.other_areas = other_areas\\n self.other_areas_details = config.get('otherAreasDetails', {}) # 新增：获取详细区域信息\\n self.original_input_data = original_input_data or []\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n \\n # 网格参数\\n self.agg_rows, self.agg_cols = self.heatmap_grid.shape\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 20 \\\"def layout_priority_areas_first\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
|
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"content": "{\"success\": true, \"output\": \" 'aspect': float(aspect_grid[i][j]),\\n 'slope': float(slope_grid[i][j])\\n })\\n\\n # 更新目标权重处理 - 现在是5个目标（最大性、分散度、适宜性、前出性、撤出性）\\n functionWeight = input_data.get(\\\"functionWeight\\\")\\n if functionWeight is None:\\n # 如果有场景，给前出性和撤出性分配权重\\n if scene in ['YJFJ', 'JJCS']:\\n functionWeight = [0.2, 0.2, 0.2, 0.2, 0.2] # 平均分配权重\\n else:\\n functionWeight = [0.33, 0.33, 0.34, 0.0, 0.0] # 无场景时保持原权重\\n else:\\n # 处理不同长度的权重数组\\n if len(functionWeight) == 3:\\n # 3个权重：最大性、分散度、适宜性\\n if scene in ['YJFJ', 'JJCS']:\\n # 重新分配权重，给前出性和撤出性各分配15%权重\\n total = sum(functionWeight)\\n scene_weight = 0.3 # 前出性+撤出性总共30%\\n remaining = 1.0 - scene_weight\\n functionWeight = [\\n functionWeight[0] * remaining / total,\\n functionWeight[1] * remaining / total,\\n functionWeight[2] * remaining / total,\\n 0.15, # 前出性权重\\n 0.15 # 撤出性权重\\n ]\\n else:\\n functionWeight.extend([0.0, 0.0]) # 无场景时，前出性和撤出性权重为0\\n elif len(functionWeight) == 4:\\n # 4个权重：最大性、分散度、适宜性、场景方向（兼容旧版本）\\n if scene in ['YJFJ', 'JJCS']:\\n # 将场景方向权重分配给前出性和撤出性\\n scene_weight = functionWeight[3]\\n functionWeight = [\\n functionWeight[0],\\n functionWeight[1],\\n functionWeight[2],\\n scene_weight * 0.5, # 前出性权重\\n scene_weight * 0.5 # 撤出性权重\\n ]\\n else:\\n functionWeight.append(0.0) # 添加撤出性权重为0\\n elif len(functionWeight) == 5:\\n # 5个权重：最大性、分散度、适宜性、前出性、撤出性\\n pass # 直接使用\\n else:\\n # 其他情况，使用默认权重\\n if scene in ['YJFJ', 'JJCS']:\\n functionWeight = [0.2, 0.2, 0.2, 0.2, 0.2]\\n else:\\n functionWeight = [0.33, 0.33, 0.34, 0.0, 0.0]\\n \\n # 验证权重总和\\n if abs(sum(functionWeight) - 1.0) > 1e-5:\\n raise ValueError(\\\"目标权重总和必须为1\\\")\\n\\n # 热力矩阵计算 - 每次都重新计算，不缓存\\n if functionWeight[2] > 0:\\n try:\\n direction_map = {\\n 'left': 180,\\n 'right': 0,\\n 'up': 90,\\n 'down': 270\\n }\\n direction_str = input_data.get('constraints', {}).get('direction', 'left')\\n attack_direction = direction_map.get(direction_str.lower(), 0)\\n\\n # 每次都重新计算热力图，不使用缓存\\n # 使用当前工作目录作为输出目录，避免硬编码路径\\n heatmap_matrix = get_heatmap(grid_data_path, attack_direction=attack_direction)\\n\\n if heatmap_matrix is not None:\\n--\\n config.update({\\n 'FIELD_WIDTH': field_width,\\n 'FIELD_HEIGHT': field_height,\\n 'gridSize': grid_size,\\n 'max_slope': float(np.max(slope_grid)),\\n 'WEIGHT_MAXIMIZATION': float(functionWeight[0]), # 最大性权重\\n 'WEIGHT_DISPERSION': float(functionWeight[1]), # 分散度权重\\n 'WEIGHT_SUITABILITY': float(functionWeight[2]), # 适宜性权重\\n 'WEIGHT_FORWARD': float(functionWeight[3]), # 前出性权重\\n 'WEIGHT_WITHDRAWAL': float(functionWeight[4]), # 撤出性权重\\n 'minDistance': float(constraints.get(\\\"minDistance\\\", 5.0)),\\n 'minSize': float(constraints.get(\\\"minSize\\\", 50.0)),\\n 'maxSize': float(constraints.get(\\\"maxSize\\\", 120.0)),\\n 'numCamp': num_camp,\\n 'campIds': camp_ids,\\n 'otherAreas': other_areas_dict, # 使用转换后的字典格式\\n 'otherAreasDetails': other_areas_details, # 保存详细的区域信息\\n 'original_input_data': original_input_data, # 保存原始输入数据（列表格式）\\n 'vehicle_parking_groups': vehicle_parking_groups, # 新增：车辆停放设施分组信息\\n 'direction': constraints.get('direction', None),\\n 'scene': scene, # 新增场景字段\\n 'functionWeight': functionWeight\\n })\\n\\n print(\\\"=== 配置验证通过 ===\\\")\\n return config\\n\\n except Exception as e:\\n raise\\n\\n\\nclass CampProblem(ElementwiseProblem):\\n \\\"\\\"\\\"优化问题定义 - 支持多目标权重、热力负值约束和场景方向\\\"\\\"\\\"\\n\\n def __init__(self, config: Dict):\\n self.config = config\\n\\n--\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n self.max_heat = np.max(self.heatmap_grid)\\n self.max_heat_sum = self.max_heat * (config['maxSize'] ** 2) * config['numCamp']\\n\\n # 更新目标权重\\n self.weights = config['functionWeight']\\n\\n # 场景和方向相关属性\\n self.scene = config.get('scene', None)\\n self.direction = config.get('direction', 'left')\\n\\n # 方向角度映射\\n self.direction_map = {\\n 'left': 180,\\n 'right': 0,\\n 'up': 90,\\n 'down': 270\\n }\\n self.attack_angle = self.direction_map.get(self.direction.lower(), 0)\\n\\n # 预计算网格参数以加速\\n\", \"stdout\": \" 'aspect': float(aspect_grid[i][j]),\\n 'slope': float(slope_grid[i][j])\\n })\\n\\n # 更新目标权重处理 - 现在是5个目标（最大性、分散度、适宜性、前出性、撤出性）\\n functionWeight = input_data.get(\\\"functionWeight\\\")\\n if functionWeight is None:\\n # 如果有场景，给前出性和撤出性分配权重\\n if scene in ['YJFJ', 'JJCS']:\\n functionWeight = [0.2, 0.2, 0.2, 0.2, 0.2] # 平均分配权重\\n else:\\n functionWeight = [0.33, 0.33, 0.34, 0.0, 0.0] # 无场景时保持原权重\\n else:\\n # 处理不同长度的权重数组\\n if len(functionWeight) == 3:\\n # 3个权重：最大性、分散度、适宜性\\n if scene in ['YJFJ', 'JJCS']:\\n # 重新分配权重，给前出性和撤出性各分配15%权重\\n total = sum(functionWeight)\\n scene_weight = 0.3 # 前出性+撤出性总共30%\\n remaining = 1.0 - scene_weight\\n functionWeight = [\\n functionWeight[0] * remaining / total,\\n functionWeight[1] * remaining / total,\\n functionWeight[2] * remaining / total,\\n 0.15, # 前出性权重\\n 0.15 # 撤出性权重\\n ]\\n else:\\n functionWeight.extend([0.0, 0.0]) # 无场景时，前出性和撤出性权重为0\\n elif len(functionWeight) == 4:\\n # 4个权重：最大性、分散度、适宜性、场景方向（兼容旧版本）\\n if scene in ['YJFJ', 'JJCS']:\\n # 将场景方向权重分配给前出性和撤出性\\n scene_weight = functionWeight[3]\\n functionWeight = [\\n functionWeight[0],\\n functionWeight[1],\\n functionWeight[2],\\n scene_weight * 0.5, # 前出性权重\\n scene_weight * 0.5 # 撤出性权重\\n ]\\n else:\\n functionWeight.append(0.0) # 添加撤出性权重为0\\n elif len(functionWeight) == 5:\\n # 5个权重：最大性、分散度、适宜性、前出性、撤出性\\n pass # 直接使用\\n else:\\n # 其他情况，使用默认权重\\n if scene in ['YJFJ', 'JJCS']:\\n functionWeight = [0.2, 0.2, 0.2, 0.2, 0.2]\\n else:\\n functionWeight = [0.33, 0.33, 0.34, 0.0, 0.0]\\n \\n # 验证权重总和\\n if abs(sum(functionWeight) - 1.0) > 1e-5:\\n raise ValueError(\\\"目标权重总和必须为1\\\")\\n\\n # 热力矩阵计算 - 每次都重新计算，不缓存\\n if functionWeight[2] > 0:\\n try:\\n direction_map = {\\n 'left': 180,\\n 'right': 0,\\n 'up': 90,\\n 'down': 270\\n }\\n direction_str = input_data.get('constraints', {}).get('direction', 'left')\\n attack_direction = direction_map.get(direction_str.lower(), 0)\\n\\n # 每次都重新计算热力图，不使用缓存\\n # 使用当前工作目录作为输出目录，避免硬编码路径\\n heatmap_matrix = get_heatmap(grid_data_path, attack_direction=attack_direction)\\n\\n if heatmap_matrix is not None:\\n--\\n config.update({\\n 'FIELD_WIDTH': field_width,\\n 'FIELD_HEIGHT': field_height,\\n 'gridSize': grid_size,\\n 'max_slope': float(np.max(slope_grid)),\\n 'WEIGHT_MAXIMIZATION': float(functionWeight[0]), # 最大性权重\\n 'WEIGHT_DISPERSION': float(functionWeight[1]), # 分散度权重\\n 'WEIGHT_SUITABILITY': float(functionWeight[2]), # 适宜性权重\\n 'WEIGHT_FORWARD': float(functionWeight[3]), # 前出性权重\\n 'WEIGHT_WITHDRAWAL': float(functionWeight[4]), # 撤出性权重\\n 'minDistance': float(constraints.get(\\\"minDistance\\\", 5.0)),\\n 'minSize': float(constraints.get(\\\"minSize\\\", 50.0)),\\n 'maxSize': float(constraints.get(\\\"maxSize\\\", 120.0)),\\n 'numCamp': num_camp,\\n 'campIds': camp_ids,\\n 'otherAreas': other_areas_dict, # 使用转换后的字典格式\\n 'otherAreasDetails': other_areas_details, # 保存详细的区域信息\\n 'original_input_data': original_input_data, # 保存原始输入数据（列表格式）\\n 'vehicle_parking_groups': vehicle_parking_groups, # 新增：车辆停放设施分组信息\\n 'direction': constraints.get('direction', None),\\n 'scene': scene, # 新增场景字段\\n 'functionWeight': functionWeight\\n })\\n\\n print(\\\"=== 配置验证通过 ===\\\")\\n return config\\n\\n except Exception as e:\\n raise\\n\\n\\nclass CampProblem(ElementwiseProblem):\\n \\\"\\\"\\\"优化问题定义 - 支持多目标权重、热力负值约束和场景方向\\\"\\\"\\\"\\n\\n def __init__(self, config: Dict):\\n self.config = config\\n\\n--\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n self.max_heat = np.max(self.heatmap_grid)\\n self.max_heat_sum = self.max_heat * (config['maxSize'] ** 2) * config['numCamp']\\n\\n # 更新目标权重\\n self.weights = config['functionWeight']\\n\\n # 场景和方向相关属性\\n self.scene = config.get('scene', None)\\n self.direction = config.get('direction', 'left')\\n\\n # 方向角度映射\\n self.direction_map = {\\n 'left': 180,\\n 'right': 0,\\n 'up': 90,\\n 'down': 270\\n }\\n self.attack_angle = self.direction_map.get(self.direction.lower(), 0)\\n\\n # 预计算网格参数以加速\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -B 5 -A 15 \\\"functionWeight\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
|
||
"timestamp": "2025-09-25T10:26:55.409899",
|
||
"tool_call_id": "run_command:23",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我取消聚焦，整理分析结果：",
|
||
"timestamp": "2025-09-25T10:26:58.417790"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:26:58.425471",
|
||
"tool_calls": [
|
||
{
|
||
"id": "unfocus_file:24",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "unfocus_file",
|
||
"arguments": "{\"path\": \"路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"message\": \"已取消文件聚焦: 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\", \"remaining_focused\": [\"路径规划/zongti.py\"]}",
|
||
"timestamp": "2025-09-25T10:27:00.233123",
|
||
"tool_call_id": "unfocus_file:24",
|
||
"name": "unfocus_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:27:10.869868",
|
||
"tool_calls": [
|
||
{
|
||
"id": "unfocus_file:25",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "unfocus_file",
|
||
"arguments": "{\"path\": \"路径规划/zongti.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"message\": \"已取消文件聚焦: 路径规划/zongti.py\", \"remaining_focused\": []}",
|
||
"timestamp": "2025-09-25T10:27:12.678414",
|
||
"tool_call_id": "unfocus_file:25",
|
||
"name": "unfocus_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "基于对两个文件的深入分析，我整理出了关键信息：\n\n## 🎯 关键接口信息总结\n\n### 📋 **输入参数结构**\n```json\n{\n \"layoutAlgorithm\": \"yc\", // 算法选择：yc=遗传, bcd=变尺度\n \"layoutStrategy\": \"策略名称\",\n \"functionWeight\": [0.33, 0.33, 0.34, 0.0, 0.0], // 5个目标权重\n \"constraints\": {\n \"minDistance\": 5.0, // 最小间距\n \"minSize\": 50.0, // 最小尺寸 \n \"maxSize\": 120.0, // 最大尺寸\n \"direction\": \"left\" // 攻击方向：left/right/up/down\n },\n \"scene\": \"YJFJ\", // 场景：YJFJ/JJCS/其他\n \"campIds\": [\"camp1\", \"camp2\"], // 营地ID列表\n \"otherAreas\": {...}, // 其他区域配置\n \"gridSize\": 1.0, // 网格大小\n \"heatmap_grid\": [...] // 热力矩阵\n}\n```\n\n### 🎯 **目标函数权重 (5个维度)**\n1. **最大性权重** (index 0): 营地最大化覆盖\n2. **分散度权重** (index 1): 营地间分散程度 \n3. **适宜性权重** (index 2): 基于热力值的适宜性\n4. **前出性权重** (index 3): 场景相关的前出性能\n5. **撤出性权重** (index 4): 场景相关的撤出性能\n\n### 🔧 **约束条件**\n- **边界约束**: 营地不能超出场地边界\n- **重叠约束**: 营地间不能重叠 (权重×10000倍惩罚)\n- **距离约束**: 营地间最小距离要求\n- **负热力约束**: 不能在负热力区域布局\n\n### 📊 **算法配置参数**\n```python\n# 遗传算法当前配置\nalgorithm = GA(\n pop_size=300, # 种群大小\n sampling=FloatRandomSampling(), # 浮点随机采样\n crossover=SBX(prob=0.7, eta=30), # SBX交叉\n mutation=PM(prob=0.7, eta=30), # 多项式变异\n eliminate_duplicates=True # 消除重复个体\n)\ntermination = get_termination(\"n_gen\", 200) # 200代终止\n```\n\n### 🎮 **场景支持**\n- **YJFJ/JJCS场景**: 启用前出性和撤出性目标\n- **普通场景**: 仅使用最大性、分散度、适宜性\n\n### 📈 **输出格式**\n算法返回优化后的营地位置列表，包含每个营地的坐标、尺寸等布局信息。\n\n---\n\n## 💡 **模拟退火算法设计要点**\n\n基于以上分析，模拟退火算法需要：\n\n1. **状态表示**: 编码营地位置坐标和尺寸\n2. **目标函数**: 实现相同的5目标加权组合\n3. **约束处理**: 实现相同的4种约束惩罚机制\n4. **邻域操作**: 设计有效的状态扰动策略\n5. **退火调度**: 设计合适的温度下降策略\n\n你希望我继续实现模拟退火算法吗？",
|
||
"timestamp": "2025-09-25T10:27:47.152169"
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "好的，请你编写吧，你只需要保证代码的准确性和高效性，不需要你测试，我会自己去测试\n你需要加入到配置文件的信息单独建一个文件\n创建一个zongti的副本，修改其中的调用逻辑，模拟退火可以真的调用\n如果你写到一半发现缺失信息，可以继续查阅这两个代码\n请开始写吧",
|
||
"timestamp": "2025-09-25T10:31:10.874034"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "好的，我来开始实现模拟退火算法。首先创建配置文件，然后修改zongti的副本来支持模拟退火算法调用。",
|
||
"timestamp": "2025-09-25T10:31:22.718757"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:31:22.722715",
|
||
"tool_calls": [
|
||
{
|
||
"id": "create_file:26",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "create_file",
|
||
"arguments": "{\"path\": \"路径规划/simulated_annealing_config.py\", \"file_type\": \"py\", \"annotation\": \"模拟退火算法配置文件\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/simulated_annealing_config.py\", \"size\": 0}",
|
||
"timestamp": "2025-09-25T10:31:24.533349",
|
||
"tool_call_id": "create_file:26",
|
||
"name": "create_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:31:35.726654",
|
||
"tool_calls": [
|
||
{
|
||
"id": "create_file:27",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "create_file",
|
||
"arguments": "{\"path\": \"路径规划/simulated_annealing_optimizer.py\", \"file_type\": \"py\", \"annotation\": \"模拟退火算法核心实现\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/simulated_annealing_optimizer.py\", \"size\": 0}",
|
||
"timestamp": "2025-09-25T10:31:37.538061",
|
||
"tool_call_id": "create_file:27",
|
||
"name": "create_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:31:47.162763",
|
||
"tool_calls": [
|
||
{
|
||
"id": "create_file:28",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "create_file",
|
||
"arguments": "{\"path\": \"路径规划/zongti_sa.py\", \"file_type\": \"py\", \"annotation\": \"支持模拟退火算法的zongti修改版\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/zongti_sa.py\", \"size\": 0}",
|
||
"timestamp": "2025-09-25T10:31:48.973059",
|
||
"tool_call_id": "create_file:28",
|
||
"name": "create_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在开始编写配置文件：",
|
||
"timestamp": "2025-09-25T10:32:31.391853"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:32:31.399226",
|
||
"tool_calls": [
|
||
{
|
||
"id": "modify_file:29",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "modify_file",
|
||
"arguments": "{\"path\": \"路径规划/simulated_annealing_config.py\", \"operation\": \"append\", \"content\": \"\\\"\\\"\\\"\\n模拟退火算法配置文件\\n包含算法参数、调度策略和问题定义\\n\\\"\\\"\\\"\\n\\nfrom dataclasses import dataclass\\nfrom typing import Dict, List, Tuple, Optional, Callable\\nimport numpy as np\\n\\n\\n@dataclass\\nclass SAConfig:\\n \\\"\\\"\\\"模拟退火算法配置\\\"\\\"\\\"\\n \\n # 基本参数\\n initial_temperature: float = 1000.0 # 初始温度\\n final_temperature: float = 0.1 # 终止温度\\n cooling_rate: float = 0.95 # 冷却速率\\n max_iterations: int = 10000 # 最大迭代次数\\n \\n # 邻域搜索参数\\n perturbation_scale: float = 0.1 # 扰动幅度比例\\n adaptive_perturbation: bool = True # 自适应扰动\\n min_perturbation: float = 0.01 # 最小扰动幅度\\n max_perturbation: float = 0.3 # 最大扰动幅度\\n \\n # 接受准则参数\\n acceptance_function: str = 'metropolis' # 接受函数: metropolis/boltzmann\\n adaptive_temperature: bool = True # 自适应温度调整\\n \\n # 约束处理参数\\n penalty_initial: float = 1000.0 # 初始惩罚因子\\n penalty_growth_rate: float = 1.1 # 惩罚因子增长率\\n penalty_adaptive: bool = True # 自适应惩罚\\n \\n # 收敛参数\\n convergence_threshold: float = 1e-6 # 收敛阈值\\n stagnation_limit: int = 1000 # 停滞限制\\n \\n # 输出参数\\n verbose: bool = True # 详细输出\\n save_history: bool = True # 保存历史记录\\n history_interval: int = 100 # 历史记录间隔\\n\\n\\n@dataclass\\nclass SAPerturbationConfig:\\n \\\"\\\"\\\"扰动配置\\\"\\\"\\\"\\n \\n # 位置扰动\\n position_perturbation: float = 0.1 # 位置扰动比例\\n position_adaptive: bool = True # 自适应位置扰动\\n \\n # 尺寸扰动 \\n size_perturbation: float = 0.05 # 尺寸扰动比例\\n size_adaptive: bool = True # 自适应尺寸扰动\\n \\n # 扰动策略\\n strategy: str = 'mixed' # 策略: position/size/mixed\\n probability_position: float = 0.7 # 位置扰动概率\\n probability_size: float = 0.3 # 尺寸扰动概率\\n\\n\\nclass SACoolingSchedule:\\n \\\"\\\"\\\"冷却调度策略\\\"\\\"\\\"\\n \\n @staticmethod\\n def exponential_cooling(temp: float, rate: float, iteration: int) -> float:\\n \\\"\\\"\\\"指数冷却\\\"\\\"\\\"\\n return temp * rate\\n \\n @staticmethod\\n def linear_cooling(temp: float, rate: float, iteration: int, initial_temp: float) -> float:\\n \\\"\\\"\\\"线性冷却\\\"\\\"\\\"\\n return temp - (initial_temp * (1 - rate))\\n \\n @staticmethod\\n def adaptive_cooling(temp: float, rate: float, iteration: int, \\n acceptance_rate: float, target_rate: float = 0.44) -> float:\\n \\\"\\\"\\\"自适应冷却 - 根据接受率调整冷却速率\\\"\\\"\\\"\\n if acceptance_rate > target_rate:\\n # 接受率过高，加快冷却\\n return temp * (rate - 0.02)\\n elif acceptance_rate < target_rate * 0.8:\\n # 接受率过低，减慢冷却\\n return temp * (rate + 0.02)\\n else:\\n # 接受率合适，正常冷却\\n return temp * rate\\n \\n @staticmethod\\n def logarithmic_cooling(temp: float, iteration: int, alpha: float = 10.0) -> float:\\n \\\"\\\"\\\"对数冷却\\\"\\\"\\\"\\n return temp / (1 + alpha * np.log(1 + iteration))\\n\\n\\nclass SAAcceptanceCriterion:\\n \\\"\\\"\\\"接受准则\\\"\\\"\\\"\\n \\n @staticmethod\\n def metropolis(delta_energy: float, temperature: float) -> bool:\\n \\\"\\\"\\\"Metropolis准则\\\"\\\"\\\"\\n if delta_energy <= 0:\\n return True\\n probability = np.exp(-delta_energy / temperature)\\n return np.random.random() < probability\\n \\n @staticmethod\\n def boltzmann(delta_energy: float, temperature: float) -> bool:\\n \\\"\\\"\\\"Boltzmann准则\\\"\\\"\\\"\\n if delta_energy <= 0:\\n return True\\n probability = 1 / (1 + np.exp(delta_energy / temperature))\\n return np.random.random() < probability\\n \\n @staticmethod\\n def threshold_acceptance(delta_energy: float, temperature: float, threshold: float = 0.1) -> bool:\\n \\\"\\\"\\\"阈值接受\\\"\\\"\\\"\\n return delta_energy < threshold * temperature\\n\\n\\n# 默认配置实例\\nDEFAULT_SA_CONFIG = SAConfig()\\nDEFAULT_PERTURBATION_CONFIG = SAPerturbationConfig()\\n\\n# 算法映射\\nACCEPTANCE_FUNCTIONS = {\\n 'metropolis': SAAcceptanceCriterion.metropolis,\\n 'boltzmann': SAAcceptanceCriterion.boltzmann,\\n 'threshold': SAAcceptanceCriterion.threshold_acceptance\\n}\\n\\nCOOLING_SCHEDULES = {\\n 'exponential': SACoolingSchedule.exponential_cooling,\\n 'linear': SACoolingSchedule.linear_cooling,\\n 'adaptive': SACoolingSchedule.adaptive_cooling,\\n 'logarithmic': SACoolingSchedule.logarithmic_cooling\\n}\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
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|
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"role": "tool",
|
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"content": "{\"success\": true, \"path\": \"路径规划/simulated_annealing_config.py\", \"size\": 4158, \"mode\": \"a\"}",
|
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"timestamp": "2025-09-25T10:32:33.207239",
|
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"tool_call_id": "modify_file:29",
|
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"name": "modify_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在编写核心的模拟退火优化器：",
|
||
"timestamp": "2025-09-25T10:34:32.934089"
|
||
},
|
||
{
|
||
"role": "assistant",
|
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"content": "",
|
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"timestamp": "2025-09-25T10:34:32.941955",
|
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"tool_calls": [
|
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{
|
||
"id": "modify_file:30",
|
||
"index": 0,
|
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"type": "function",
|
||
"function": {
|
||
"name": "modify_file",
|
||
"arguments": "{\"path\": \"路径规划/simulated_annealing_optimizer.py\", \"operation\": \"append\", \"content\": \"\\\"\\\"\\\"\\n模拟退火算法优化器\\n用于营地布局优化问题\\n\\\"\\\"\\\"\\n\\nimport numpy as np\\nimport time\\nimport copy\\nfrom typing import Dict, List, Tuple, Optional, Any\\nfrom dataclasses import dataclass\\nfrom abc import ABC, abstractmethod\\n\\nfrom Camp_layout_gaocheng_fsbj_yichuan import CampProblem, OtherAreasProblem, UnifiedOtherAreasProblem\\nfrom simulated_annealing_config import (\\n SAConfig, SAPerturbationConfig, DEFAULT_SA_CONFIG, DEFAULT_PERTURBATION_CONFIG,\\n ACCEPTANCE_FUNCTIONS, COOLING_SCHEDULES\\n)\\n\\n\\n@dataclass\\nclass SASolution:\\n \\\"\\\"\\\"模拟退火解\\\"\\\"\\\"\\n x: np.ndarray # 决策变量\\n fitness: float # 适应度值\\n objectives: Dict[str, float] # 各目标函数值\\n constraints: Dict[str, float] # 约束违反值\\n penalty: float # 惩罚项\\n temperature: float = 0.0 # 当前温度\\n iteration: int = 0 # 当前迭代\\n\\n\\nclass SAStateManager:\\n \\\"\\\"\\\"状态管理器\\\"\\\"\\\"\\n \\n def __init__(self, problem, config: SAConfig):\\n self.problem = problem\\n self.config = config\\n self.current_solution = None\\n self.best_solution = None\\n self.temperature = config.initial_temperature\\n self.iteration = 0\\n self.acceptance_count = 0\\n self.total_trials = 0\\n self.history = []\\n self.start_time = time.time()\\n \\n def initialize_solution(self) -> SASolution:\\n \\\"\\\"\\\"初始化解\\\"\\\"\\\"\\n # 使用随机采样初始化\\n x = np.random.uniform(self.problem.xl, self.problem.xu)\\n fitness, objectives, constraints, penalty = self.evaluate_solution(x)\\n \\n solution = SASolution(\\n x=x.copy(),\\n fitness=fitness,\\n objectives=objectives,\\n constraints=constraints,\\n penalty=penalty,\\n temperature=self.temperature,\\n iteration=self.iteration\\n )\\n \\n self.current_solution = solution\\n self.best_solution = copy.deepcopy(solution)\\n return solution\\n \\n def evaluate_solution(self, x: np.ndarray) -> Tuple[float, Dict, Dict, float]:\\n \\\"\\\"\\\"评估解的质量\\\"\\\"\\\"\\n out = {}\\n self.problem._evaluate(x, out)\\n \\n fitness = float(out[\\\"F\\\"][0]) if isinstance(out[\\\"F\\\"], np.ndarray) else float(out[\\\"F\\\"])\\n \\n objectives = {\\n 'maximization': out.get(\\\"maximization_score\\\", 0),\\n 'dispersion': out.get(\\\"dispersion_score\\\", 0),\\n 'suitability': out.get(\\\"suitability_score\\\", 0),\\n 'forward': out.get(\\\"forward_score\\\", 0),\\n 'withdrawal': out.get(\\\"withdrawal_score\\\", 0),\\n 'combined': out.get(\\\"combined_score\\\", 0)\\n }\\n \\n constraints = {\\n 'boundary': out[\\\"G\\\"][0] if len(out[\\\"G\\\"]) > 0 else 0,\\n 'overlap': out[\\\"G\\\"][1] if len(out[\\\"G\\\"]) > 1 else 0,\\n 'distance': out[\\\"G\\\"][2] if len(out[\\\"G\\\"]) > 2 else 0,\\n 'heat_negative': out[\\\"G\\\"][3] if len(out[\\\"G\\\"]) > 3 else 0\\n }\\n \\n penalty = out.get(\\\"penalty\\\", 0)\\n \\n return fitness, objectives, constraints, penalty\\n \\n def generate_neighbor(self, perturbation_config: SAPerturbationConfig) -> np.ndarray:\\n \\\"\\\"\\\"生成邻域解\\\"\\\"\\\"\\n current_x = self.current_solution.x.copy()\\n n_vars = len(current_x)\\n \\n # 自适应扰动幅度\\n if perturbation_config.adaptive_perturbation:\\n acceptance_rate = self.acceptance_count / max(1, self.total_trials)\\n scale = perturbation_config.position_perturbation * (1 + 0.5 * (0.5 - acceptance_rate))\\n scale = np.clip(scale, perturbation_config.min_perturbation, perturbation_config.max_perturbation)\\n else:\\n scale = perturbation_config.position_perturbation\\n \\n # 根据策略选择扰动方式\\n if perturbation_config.strategy == 'position':\\n # 仅位置扰动\\n perturbation = np.random.normal(0, scale, n_vars)\\n new_x = current_x + perturbation\\n \\n elif perturbation_config.strategy == 'size':\\n # 仅尺寸扰动（假设后一半变量是尺寸）\\n perturbation = np.zeros(n_vars)\\n size_start = n_vars // 2\\n perturbation[size_start:] = np.random.normal(0, scale, n_vars - size_start)\\n new_x = current_x + perturbation\\n \\n else: # mixed\\n # 混合扰动\\n perturbation = np.random.normal(0, scale, n_vars)\\n \\n # 以一定概率对某些维度进行更大扰动\\n if np.random.random() < perturbation_config.probability_size:\\n # 尺寸扰动（假设后一半变量是尺寸）\\n size_start = n_vars // 2\\n size_perturbation = np.random.normal(0, perturbation_config.size_perturbation, \\n n_vars - size_start)\\n perturbation[size_start:] += size_perturbation\\n \\n new_x = current_x + perturbation\\n \\n # 边界处理\\n new_x = np.clip(new_x, self.problem.xl, self.problem.xu)\\n \\n return new_x\\n \\n def accept_solution(self, new_solution: SASolution) -> bool:\\n \\\"\\\"\\\"判断是否接受新解\\\"\\\"\\\"\\n delta_energy = new_solution.fitness - self.current_solution.fitness\\n \\n acceptance_func = ACCEPTANCE_FUNCTIONS.get(self.config.acceptance_function, \\n ACCEPTANCE_FUNCTIONS['metropolis'])\\n \\n accept = acceptance_func(delta_energy, self.temperature)\\n \\n self.total_trials += 1\\n if accept:\\n self.acceptance_count += 1\\n self.current_solution = new_solution\\n \\n # 更新最优解\\n if new_solution.fitness < self.best_solution.fitness:\\n self.best_solution = copy.deepcopy(new_solution)\\n \\n return accept\\n \\n def update_temperature(self):\\n \\\"\\\"\\\"更新温度\\\"\\\"\\\"\\n if self.config.cooling_schedule == 'adaptive':\\n acceptance_rate = self.acceptance_count / max(1, self.total_trials)\\n cooling_func = COOLING_SCHEDULES['adaptive']\\n self.temperature = cooling_func(self.temperature, self.config.cooling_rate, \\n self.iteration, acceptance_rate)\\n else:\\n cooling_func = COOLING_SCHEDULES.get(self.config.cooling_schedule, \\n COOLING_SCHEDULES['exponential'])\\n if self.config.cooling_schedule == 'linear':\\n self.temperature = cooling_func(self.temperature, self.config.cooling_rate, \\n self.iteration, self.config.initial_temperature)\\n elif self.config.cooling_schedule == 'logarithmic':\\n self.temperature = cooling_func(self.temperature, self.iteration)\\n else:\\n self.temperature = cooling_func(self.temperature, self.config.cooling_rate, self.iteration)\\n \\n def record_history(self):\\n \\\"\\\"\\\"记录历史信息\\\"\\\"\\\"\\n if self.config.save_history and self.iteration % self.config.history_interval == 0:\\n history_entry = {\\n 'iteration': self.iteration,\\n 'temperature': self.temperature,\\n 'current_fitness': self.current_solution.fitness,\\n 'best_fitness': self.best_solution.fitness,\\n 'acceptance_rate': self.acceptance_count / max(1, self.total_trials),\\n 'objectives': self.best_solution.objectives,\\n 'constraints': self.best_solution.constraints,\\n 'penalty': self.best_solution.penalty\\n }\\n self.history.append(history_entry)\\n \\n def check_convergence(self) -> bool:\\n \\\"\\\"\\\"检查收敛条件\\\"\\\"\\\"\\n # 温度终止\\n if self.temperature < self.config.final_temperature:\\n return True\\n \\n # 最大迭代次数\\n if self.iteration >= self.config.max_iterations:\\n return True\\n \\n # 收敛阈值\\n if len(self.history) >= 2:\\n recent_improvement = abs(self.history[-1]['best_fitness'] - self.history[-2]['best_fitness'])\\n if recent_improvement < self.config.convergence_threshold:\\n return True\\n \\n return False\\n\\n\\nclass SimulatedAnnealingOptimizer:\\n \\\"\\\"\\\"模拟退火优化器主类\\\"\\\"\\\"\\n \\n def __init__(self, config: SAConfig = None, perturbation_config: SAPerturbationConfig = None):\\n self.config = config or DEFAULT_SA_CONFIG\\n self.perturbation_config = perturbation_config or DEFAULT_PERTURBATION_CONFIG\\n self.state_manager = None\\n self.problem = None\\n \\n def optimize(self, problem, initial_solution: Optional[np.ndarray] = None) -> Dict:\\n \\\"\\\"\\\"执行优化\\\"\\\"\\\"\\n self.problem = problem\\n self.state_manager = SAStateManager(problem, self.config)\\n \\n # 初始化解\\n if initial_solution is not None:\\n # 使用提供的初始解\\n fitness, objectives, constraints, penalty = self.state_manager.evaluate_solution(initial_solution)\\n initial_sol = SASolution(\\n x=initial_solution.copy(),\\n fitness=fitness,\\n objectives=objectives,\\n constraints=constraints,\\n penalty=penalty,\\n temperature=self.config.initial_temperature\\n )\\n self.state_manager.current_solution = initial_sol\\n self.state_manager.best_solution = copy.deepcopy(initial_sol)\\n else:\\n # 随机初始化\\n self.state_manager.initialize_solution()\\n \\n # 优化主循环\\n start_time = time.time()\\n \\n while not self.state_manager.check_convergence():\\n self.state_manager.iteration += 1\\n \\n # 生成邻域解\\n new_x = self.state_manager.generate_neighbor(self.perturbation_config)\\n fitness, objectives, constraints, penalty = self.state_manager.evaluate_solution(new_x)\\n \\n new_solution = SASolution(\\n x=new_x.copy(),\\n fitness=fitness,\\n objectives=objectives,\\n constraints=constraints,\\n penalty=penalty,\\n temperature=self.state_manager.temperature,\\n iteration=self.state_manager.iteration\\n )\\n \\n # 接受判断\\n self.state_manager.accept_solution(new_solution)\\n \\n # 更新温度\\n self.state_manager.update_temperature()\\n \\n # 记录历史\\n self.state_manager.record_history()\\n \\n # 输出进度\\n if self.config.verbose and self.state_manager.iteration % 100 == 0:\\n print(f\\\"Iteration {self.state_manager.iteration}: \\\"\\n f\\\"T={self.state_manager.temperature:.4f}, \\\"\\n f\\\"Best={self.state_manager.best_solution.fitness:.6f}, \\\"\\n f\\\"Current={self.state_manager.current_solution.fitness:.6f}, \\\"\\n f\\\"AcceptRate={self.state_manager.acceptance_count/max(1, self.state_manager.total_trials):.3f}\\\")\\n \\n # 优化结束\\n end_time = time.time()\\n \\n # 构建结果\\n result = {\\n 'success': True,\\n 'x': self.state_manager.best_solution.x,\\n 'fitness': self.state_manager.best_solution.fitness,\\n 'objectives': self.state_manager.best_solution.objectives,\\n 'constraints': self.state_manager.best_solution.constraints,\\n 'penalty': self.state_manager.best_solution.penalty,\\n 'iterations': self.state_manager.iteration,\\n 'time': end_time - start_time,\\n 'temperature_history': [h['temperature'] for h in self.state_manager.history],\\n 'fitness_history': [h['best_fitness'] for h in self.state_manager.history],\\n 'acceptance_rate': self.state_manager.acceptance_count / max(1, self.state_manager.total_trials),\\n 'history': self.state_manager.history\\n }\\n \\n if self.config.verbose:\\n print(f\\\"\\\\nOptimization completed!\\\")\\n print(f\\\"Best fitness: {result['fitness']:.6f}\\\")\\n print(f\\\"Iterations: {result['iterations']}\\\")\\n print(f\\\"Time: {result['time']:.2f}s\\\")\\n print(f\\\"Final acceptance rate: {result['acceptance_rate']:.3f}\\\")\\n \\n return result\\n\\n\\ndef create_sa_optimizer(algorithm_params: Dict) -> SimulatedAnnealingOptimizer:\\n \\\"\\\"\\\"根据算法参数创建模拟退火优化器\\\"\\\"\\\"\\n \\n # 解析算法参数\\n sa_params = algorithm_params.get('sa_params', {})\\n \\n # 创建配置\\n config = SAConfig(\\n initial_temperature=sa_params.get('initial_temperature', 1000.0),\\n final_temperature=sa_params.get('final_temperature', 0.1),\\n cooling_rate=sa_params.get('cooling_rate', 0.95),\\n max_iterations=sa_params.get('max_iterations', 10000),\\n cooling_schedule=sa_params.get('cooling_schedule', 'exponential'),\\n acceptance_function=sa_params.get('acceptance_function', 'metropolis'),\\n verbose=sa_params.get('verbose', True)\\n )\\n \\n perturbation_config = SAPerturbationConfig(\\n position_perturbation=sa_params.get('position_perturbation', 0.1),\\n size_perturbation=sa_params.get('size_perturbation', 0.05),\\n strategy=sa_params.get('perturbation_strategy', 'mixed'),\\n adaptive_perturbation=sa_params.get('adaptive_perturbation', True)\\n )\\n \\n return SimulatedAnnealingOptimizer(config, perturbation_config)\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
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"content": "{\"success\": true, \"path\": \"路径规划/simulated_annealing_optimizer.py\", \"size\": 13232, \"mode\": \"a\"}",
|
||
"timestamp": "2025-09-25T10:34:34.748969",
|
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"tool_call_id": "modify_file:30",
|
||
"name": "modify_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在创建支持模拟退火的zongti修改版。首先让我查看原始zongti的结构：",
|
||
"timestamp": "2025-09-25T10:34:41.523128"
|
||
},
|
||
{
|
||
"role": "assistant",
|
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"content": "",
|
||
"timestamp": "2025-09-25T10:34:41.530759",
|
||
"tool_calls": [
|
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{
|
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"id": "run_command:31",
|
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"index": 0,
|
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"type": "function",
|
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"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"head -50 路径规划/zongti.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
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{
|
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"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"from flask import Flask, request, jsonify\\nfrom flask_cors import CORS\\nimport traceback\\nfrom typing import Dict, List, Tuple, Optional, Set\\nfrom Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# from shigong_benders_v3 import main_shigong_benders\\n# from shigong_lagelangri_v3 import main_shigong_lag\\n# from shigong_lieshengcheng_v3 import main_shigong_cg\\n# from shigong_zhengshuLxing_v3 import main_shigong_ils\\n# from shigong_fenzhidingjie_v3 import main_shigong_branch\\n# from path_finder import build_graph, find_shortest_path\\n# from baidy_gongshi_layout_v2 import gs_layout\\n# import logging\\n# import sys\\nimport os\\n# 修改导入：使用新的路径规划模块\\nfrom path_planning import SimplifiedPathPlanner, MapGateCalculator\\nfrom baidy_gongshi_layout_main import ShelterLayoutOptimizer\\nimport threading # 添加线程支持\\nfrom creatImageLayers import createLayers\\nimport json\\nfrom collections import deque\\n\\napp = Flask(__name__)\\nCORS(app)\\n\\n# 为每个接口创建独立的锁\\nyingdi_lock = threading.Lock() # 营地布局锁\\nsheshi_lock = threading.Lock() # 设施布局锁\\nshigong_lock = threading.Lock() # 施工锁\\ndaolu_lock = threading.Lock() # 道路规划锁\\nfind_path_lock = threading.Lock() # 路径查找锁\\ngongshi_lock = threading.Lock() # 工事布局锁\\nimage_lock = threading.Lock() # 绘图锁\\ndamen_lock = threading.Lock() # 大门锁\\n\\nimport logging\\n\\nlogging.basicConfig(level=logging.DEBUG)\\n\\n# 施工算法映射表\\nSHIGONG_ALGORITHMS = {\\n \\\"nsga3\\\": main_shigong_yichuan,\\n \\\"benders\\\": main_shigong_yichuan,\\n \\\"lagrange\\\": main_shigong_yichuan,\\n \\\"lsc\\\": main_shigong_yichuan,\\n \\\"zsl\\\": main_shigong_yichuan,\\n \\\"fzdj\\\": main_shigong_yichuan\\n\", \"stdout\": \"from flask import Flask, request, jsonify\\nfrom flask_cors import CORS\\nimport traceback\\nfrom typing import Dict, List, Tuple, Optional, Set\\nfrom Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# from shigong_benders_v3 import main_shigong_benders\\n# from shigong_lagelangri_v3 import main_shigong_lag\\n# from shigong_lieshengcheng_v3 import main_shigong_cg\\n# from shigong_zhengshuLxing_v3 import main_shigong_ils\\n# from shigong_fenzhidingjie_v3 import main_shigong_branch\\n# from path_finder import build_graph, find_shortest_path\\n# from baidy_gongshi_layout_v2 import gs_layout\\n# import logging\\n# import sys\\nimport os\\n# 修改导入：使用新的路径规划模块\\nfrom path_planning import SimplifiedPathPlanner, MapGateCalculator\\nfrom baidy_gongshi_layout_main import ShelterLayoutOptimizer\\nimport threading # 添加线程支持\\nfrom creatImageLayers import createLayers\\nimport json\\nfrom collections import deque\\n\\napp = Flask(__name__)\\nCORS(app)\\n\\n# 为每个接口创建独立的锁\\nyingdi_lock = threading.Lock() # 营地布局锁\\nsheshi_lock = threading.Lock() # 设施布局锁\\nshigong_lock = threading.Lock() # 施工锁\\ndaolu_lock = threading.Lock() # 道路规划锁\\nfind_path_lock = threading.Lock() # 路径查找锁\\ngongshi_lock = threading.Lock() # 工事布局锁\\nimage_lock = threading.Lock() # 绘图锁\\ndamen_lock = threading.Lock() # 大门锁\\n\\nimport logging\\n\\nlogging.basicConfig(level=logging.DEBUG)\\n\\n# 施工算法映射表\\nSHIGONG_ALGORITHMS = {\\n \\\"nsga3\\\": main_shigong_yichuan,\\n \\\"benders\\\": main_shigong_yichuan,\\n \\\"lagrange\\\": main_shigong_yichuan,\\n \\\"lsc\\\": main_shigong_yichuan,\\n \\\"zsl\\\": main_shigong_yichuan,\\n \\\"fzdj\\\": main_shigong_yichuan\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"head -50 路径规划/zongti.py\"}",
|
||
"timestamp": "2025-09-25T10:34:43.339199",
|
||
"tool_call_id": "run_command:31",
|
||
"name": "run_command"
|
||
},
|
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{
|
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"role": "assistant",
|
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"content": "",
|
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"timestamp": "2025-09-25T10:34:48.210810",
|
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"tool_calls": [
|
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{
|
||
"id": "run_command:32",
|
||
"index": 0,
|
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"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 10 \\\"ALG_MAP.*=\\\" 路径规划/zongti.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \" ALG_MAP = {\\n \\\"yc\\\": \\\"遗传\\\",\\n \\\"bcd\\\": \\\"变尺度\\\",\\n \\\"jy\\\": \\\"鲸鱼\\\",\\n \\\"lq\\\": \\\"狼群\\\",\\n \\\"nd\\\": \\\"拟牛顿\\\",\\n \\\"yhc\\\": \\\"萤火虫\\\",\\n \\\"tdf\\\": \\\"梯度\\\",\\n \\\"mnth\\\": \\\"模拟退火\\\",\\n }\\n\\n--\\n ALG_MAP = {\\n \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n \\\"lzq\\\": \\\"粒子群\\\",\\n }\\n\\n functionWeight = rec_data[\\\"functionWeight\\\"]\\n if algorithm not in ALG_MAP.keys():\\n rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"] = \\\"nsga3\\\"\\n algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n\\n alg = f\\\"===设施布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法\\\\n\\\"\\n--\\n ALG_MAP = {\\n \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n \\\"benders\\\": \\\"bender分解\\\",\\n \\\"lagrange\\\": \\\"拉格朗日松弛\\\",\\n \\\"lsc\\\": \\\"列生成\\\",\\n \\\"zsl\\\": \\\"整数L型\\\",\\n \\\"fzdj\\\": \\\"分支定界\\\"\\n }\\n\\n alg = f\\\"===施工算法===\\\\n\\\"\\n alg += f\\\"施工参数设置为：\\\\n\\\"\\n\", \"stdout\": \" ALG_MAP = {\\n \\\"yc\\\": \\\"遗传\\\",\\n \\\"bcd\\\": \\\"变尺度\\\",\\n \\\"jy\\\": \\\"鲸鱼\\\",\\n \\\"lq\\\": \\\"狼群\\\",\\n \\\"nd\\\": \\\"拟牛顿\\\",\\n \\\"yhc\\\": \\\"萤火虫\\\",\\n \\\"tdf\\\": \\\"梯度\\\",\\n \\\"mnth\\\": \\\"模拟退火\\\",\\n }\\n\\n--\\n ALG_MAP = {\\n \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n \\\"lzq\\\": \\\"粒子群\\\",\\n }\\n\\n functionWeight = rec_data[\\\"functionWeight\\\"]\\n if algorithm not in ALG_MAP.keys():\\n rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"] = \\\"nsga3\\\"\\n algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n\\n alg = f\\\"===设施布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法\\\\n\\\"\\n--\\n ALG_MAP = {\\n \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n \\\"benders\\\": \\\"bender分解\\\",\\n \\\"lagrange\\\": \\\"拉格朗日松弛\\\",\\n \\\"lsc\\\": \\\"列生成\\\",\\n \\\"zsl\\\": \\\"整数L型\\\",\\n \\\"fzdj\\\": \\\"分支定界\\\"\\n }\\n\\n alg = f\\\"===施工算法===\\\\n\\\"\\n alg += f\\\"施工参数设置为：\\\\n\\\"\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 10 \\\"ALG_MAP.*=\\\" 路径规划/zongti.py\"}",
|
||
"timestamp": "2025-09-25T10:34:50.021679",
|
||
"tool_call_id": "run_command:32",
|
||
"name": "run_command"
|
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},
|
||
{
|
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"role": "assistant",
|
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"content": "",
|
||
"timestamp": "2025-09-25T10:34:54.657681",
|
||
"tool_calls": [
|
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{
|
||
"id": "run_command:33",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 30 \\\"def yingdibuju\\\" 路径规划/zongti.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"def yingdibuju():\\n try:\\n rec_data = request.get_json()\\n print(rec_data)\\n \\n # === 添加这几行来保存入参 ===\\n import datetime\\n timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n input_filename = f\\\"yingdi_input_{timestamp}.json\\\"\\n \\n with open(input_filename, 'w', encoding='utf-8') as f:\\n json.dump(rec_data, f, ensure_ascii=False, indent=2)\\n \\n print(f\\\"入参已保存到: {input_filename}\\\")\\n # === 添加结束 ===\\n \\n algorithm = rec_data[\\\"layoutAlgorithm\\\"]\\n strategy = rec_data[\\\"layoutStrategy\\\"]\\n ALG_MAP = {\\n \\\"yc\\\": \\\"遗传\\\",\\n \\\"bcd\\\": \\\"变尺度\\\",\\n \\\"jy\\\": \\\"鲸鱼\\\",\\n \\\"lq\\\": \\\"狼群\\\",\\n \\\"nd\\\": \\\"拟牛顿\\\",\\n \\\"yhc\\\": \\\"萤火虫\\\",\\n \\\"tdf\\\": \\\"梯度\\\",\\n \\\"mnth\\\": \\\"模拟退火\\\",\\n }\\n\\n strategy_MAP = {\\n \\\"jzbj\\\": \\\"集中布局\\\",\\n\", \"stdout\": \"def yingdibuju():\\n try:\\n rec_data = request.get_json()\\n print(rec_data)\\n \\n # === 添加这几行来保存入参 ===\\n import datetime\\n timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n input_filename = f\\\"yingdi_input_{timestamp}.json\\\"\\n \\n with open(input_filename, 'w', encoding='utf-8') as f:\\n json.dump(rec_data, f, ensure_ascii=False, indent=2)\\n \\n print(f\\\"入参已保存到: {input_filename}\\\")\\n # === 添加结束 ===\\n \\n algorithm = rec_data[\\\"layoutAlgorithm\\\"]\\n strategy = rec_data[\\\"layoutStrategy\\\"]\\n ALG_MAP = {\\n \\\"yc\\\": \\\"遗传\\\",\\n \\\"bcd\\\": \\\"变尺度\\\",\\n \\\"jy\\\": \\\"鲸鱼\\\",\\n \\\"lq\\\": \\\"狼群\\\",\\n \\\"nd\\\": \\\"拟牛顿\\\",\\n \\\"yhc\\\": \\\"萤火虫\\\",\\n \\\"tdf\\\": \\\"梯度\\\",\\n \\\"mnth\\\": \\\"模拟退火\\\",\\n }\\n\\n strategy_MAP = {\\n \\\"jzbj\\\": \\\"集中布局\\\",\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"def yingdibuju\\\" 路径规划/zongti.py\"}",
|
||
"timestamp": "2025-09-25T10:34:56.468440",
|
||
"tool_call_id": "run_command:33",
|
||
"name": "run_command"
|
||
},
|
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{
|
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"role": "assistant",
|
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"content": "",
|
||
"timestamp": "2025-09-25T10:34:59.914315",
|
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"tool_calls": [
|
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{
|
||
"id": "run_command:34",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 50 \\\"layout_other_areas\\\\|layout_priority_areas_first\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -30\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n \\\"\\\"\\\"布局其他区域\\\"\\\"\\\"\\n # 其他区域布局开始\\n\\n other_areas = config.get('otherAreas', {})\\n if not other_areas:\\n return []\\n\\n # 创建优化问题\\n problem = OtherAreasProblem(config, camp_positions, config.get('original_input_data', []))\\n\\n # 使用遗传算法\\n algorithm = GA(\\n pop_size=300,\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=0.7,\\n eta=30\\n ),\\n mutation=PM(\\n prob=0.7,\\n eta=30\\n ),\\n eliminate_duplicates=True\\n )\\n\\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", algorithm_params.get('maxGenerations', 200))\\n res = minimize(\\n problem,\\n\", \"stdout\": \"def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n \\\"\\\"\\\"布局其他区域\\\"\\\"\\\"\\n # 其他区域布局开始\\n\\n other_areas = config.get('otherAreas', {})\\n if not other_areas:\\n return []\\n\\n # 创建优化问题\\n problem = OtherAreasProblem(config, camp_positions, config.get('original_input_data', []))\\n\\n # 使用遗传算法\\n algorithm = GA(\\n pop_size=300,\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=0.7,\\n eta=30\\n ),\\n mutation=PM(\\n prob=0.7,\\n eta=30\\n ),\\n eliminate_duplicates=True\\n )\\n\\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", algorithm_params.get('maxGenerations', 200))\\n res = minimize(\\n problem,\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 50 \\\"layout_other_areas\\\\|layout_priority_areas_first\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -30\"}",
|
||
"timestamp": "2025-09-25T10:35:01.725356",
|
||
"tool_call_id": "run_command:34",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我复制zongti.py并创建支持模拟退火的版本：",
|
||
"timestamp": "2025-09-25T10:35:33.217775"
|
||
},
|
||
{
|
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"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:35:33.224935",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:35",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"cp 路径规划/zongti.py 路径规划/zongti_sa.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"\", \"stdout\": \"\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"cp 路径规划/zongti.py 路径规划/zongti_sa.py\"}",
|
||
"timestamp": "2025-09-25T10:35:35.034038",
|
||
"tool_call_id": "run_command:35",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在修改zongti_sa.py，添加模拟退火支持。首先添加导入：",
|
||
"timestamp": "2025-09-25T10:36:33.729382"
|
||
},
|
||
{
|
||
"role": "assistant",
|
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"content": "",
|
||
"timestamp": "2025-09-25T10:36:33.737554",
|
||
"tool_calls": [
|
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{
|
||
"id": "run_command:36",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -n \\\"from Camp_main import\\\" 路径规划/zongti_sa.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"5:from Camp_main import camp_layout_main\\n\", \"stdout\": \"5:from Camp_main import camp_layout_main\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"from Camp_main import\\\" 路径规划/zongti_sa.py\"}",
|
||
"timestamp": "2025-09-25T10:36:35.548607",
|
||
"tool_call_id": "run_command:36",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:36:44.375506",
|
||
"tool_calls": [
|
||
{
|
||
"id": "modify_file:37",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "modify_file",
|
||
"arguments": "{\"path\": \"路径规划/zongti_sa.py\", \"operation\": \"replace\", \"old_text\": \"from Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\", \"content\": \"from Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# 模拟退火算法导入\\nfrom simulated_annealing_optimizer import create_sa_optimizer\\nfrom Camp_layout_gaocheng_fsbj_yichuan import layout_other_areas, layout_priority_areas_first\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/zongti_sa.py\", \"size\": 44707, \"mode\": \"w\", \"replacements\": 1}",
|
||
"timestamp": "2025-09-25T10:36:46.184444",
|
||
"tool_call_id": "modify_file:37",
|
||
"name": "modify_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在修改yingdibuju函数，添加模拟退火算法支持：",
|
||
"timestamp": "2025-09-25T10:36:52.107897"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:36:52.117210",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:38",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -n -A 20 \\\"algorithm = rec_data\\\\[\\\\\\\"layoutAlgorithm\\\\\\\"\\\\]\\\" 路径规划/zongti_sa.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"686: algorithm = rec_data[\\\"layoutAlgorithm\\\"]\\n687- strategy = rec_data[\\\"layoutStrategy\\\"]\\n688- ALG_MAP = {\\n689- \\\"yc\\\": \\\"遗传\\\",\\n690- \\\"bcd\\\": \\\"变尺度\\\",\\n691- \\\"jy\\\": \\\"鲸鱼\\\",\\n692- \\\"lq\\\": \\\"狼群\\\",\\n693- \\\"nd\\\": \\\"拟牛顿\\\",\\n694- \\\"yhc\\\": \\\"萤火虫\\\",\\n695- \\\"tdf\\\": \\\"梯度\\\",\\n696- \\\"mnth\\\": \\\"模拟退火\\\",\\n697- }\\n698-\\n699- strategy_MAP = {\\n700- \\\"jzbj\\\": \\\"集中布局\\\",\\n701- \\\"fsbj\\\": \\\"分散布局\\\"\\n702- }\\n703-\\n704- constraintParam = rec_data[\\\"constraints\\\"]\\n705- alg = f\\\"===营地布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法，实行{strategy_MAP[strategy]}策略\\\\n\\\"\\n706- alg += f\\\"营区布局参数设置为：\\\\n\\\"\\n\", \"stdout\": \"686: algorithm = rec_data[\\\"layoutAlgorithm\\\"]\\n687- strategy = rec_data[\\\"layoutStrategy\\\"]\\n688- ALG_MAP = {\\n689- \\\"yc\\\": \\\"遗传\\\",\\n690- \\\"bcd\\\": \\\"变尺度\\\",\\n691- \\\"jy\\\": \\\"鲸鱼\\\",\\n692- \\\"lq\\\": \\\"狼群\\\",\\n693- \\\"nd\\\": \\\"拟牛顿\\\",\\n694- \\\"yhc\\\": \\\"萤火虫\\\",\\n695- \\\"tdf\\\": \\\"梯度\\\",\\n696- \\\"mnth\\\": \\\"模拟退火\\\",\\n697- }\\n698-\\n699- strategy_MAP = {\\n700- \\\"jzbj\\\": \\\"集中布局\\\",\\n701- \\\"fsbj\\\": \\\"分散布局\\\"\\n702- }\\n703-\\n704- constraintParam = rec_data[\\\"constraints\\\"]\\n705- alg = f\\\"===营地布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法，实行{strategy_MAP[strategy]}策略\\\\n\\\"\\n706- alg += f\\\"营区布局参数设置为：\\\\n\\\"\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n -A 20 \\\"algorithm = rec_data\\\\[\\\\\\\"layoutAlgorithm\\\\\\\"\\\\]\\\" 路径规划/zongti_sa.py\"}",
|
||
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"content": "{\"success\": true, \"output\": \"689: \\\"yc\\\": \\\"遗传\\\",\\n690- \\\"bcd\\\": \\\"变尺度\\\",\\n691- \\\"jy\\\": \\\"鲸鱼\\\",\\n692- \\\"lq\\\": \\\"狼群\\\",\\n693- \\\"nd\\\": \\\"拟牛顿\\\",\\n694- \\\"yhc\\\": \\\"萤火虫\\\",\\n695- \\\"tdf\\\": \\\"梯度\\\",\\n696- \\\"mnth\\\": \\\"模拟退火\\\",\\n697- }\\n698-\\n699- strategy_MAP = {\\n700- \\\"jzbj\\\": \\\"集中布局\\\",\\n701- \\\"fsbj\\\": \\\"分散布局\\\"\\n702- }\\n703-\\n704- constraintParam = rec_data[\\\"constraints\\\"]\\n705- alg = f\\\"===营地布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法，实行{strategy_MAP[strategy]}策略\\\\n\\\"\\n706- alg += f\\\"营区布局参数设置为：\\\\n\\\"\\n707- alg += f\\\"\\\"\\\" 目标函数：\\\\n 面积最大：{rec_data[\\\"functionWeight\\\"][0]}，分散度：{rec_data[\\\"functionWeight\\\"][1]}，禁区约束：{rec_data[\\\"functionWeight\\\"][2]}\\\\n\\\"\\\"\\\"\\n708- alg += f\\\"\\\"\\\" 约束条件：\\\\n 最大长：{constraintParam[\\\"maxSize\\\"]}，最大宽：{constraintParam[\\\"maxSize\\\"]}，最小长：{constraintParam[\\\"minSize\\\"]}，最小宽：{constraintParam[\\\"minSize\\\"]}，营区最小距离：{constraintParam[\\\"minDistance\\\"]}\\\"\\\"\\\"\\n709-\\n710- logging.debug(alg)\\n711- rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n712- final_data = camp_layout_main(rec_data)\\n713-\\n714- # 确保返回格式包含三个一级字段\\n715- response = {\\n716- \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n717- \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\n718- \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n719- \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n\", \"stdout\": \"689: \\\"yc\\\": \\\"遗传\\\",\\n690- \\\"bcd\\\": \\\"变尺度\\\",\\n691- \\\"jy\\\": \\\"鲸鱼\\\",\\n692- \\\"lq\\\": \\\"狼群\\\",\\n693- \\\"nd\\\": \\\"拟牛顿\\\",\\n694- \\\"yhc\\\": \\\"萤火虫\\\",\\n695- \\\"tdf\\\": \\\"梯度\\\",\\n696- \\\"mnth\\\": \\\"模拟退火\\\",\\n697- }\\n698-\\n699- strategy_MAP = {\\n700- \\\"jzbj\\\": \\\"集中布局\\\",\\n701- \\\"fsbj\\\": \\\"分散布局\\\"\\n702- }\\n703-\\n704- constraintParam = rec_data[\\\"constraints\\\"]\\n705- alg = f\\\"===营地布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法，实行{strategy_MAP[strategy]}策略\\\\n\\\"\\n706- alg += f\\\"营区布局参数设置为：\\\\n\\\"\\n707- alg += f\\\"\\\"\\\" 目标函数：\\\\n 面积最大：{rec_data[\\\"functionWeight\\\"][0]}，分散度：{rec_data[\\\"functionWeight\\\"][1]}，禁区约束：{rec_data[\\\"functionWeight\\\"][2]}\\\\n\\\"\\\"\\\"\\n708- alg += f\\\"\\\"\\\" 约束条件：\\\\n 最大长：{constraintParam[\\\"maxSize\\\"]}，最大宽：{constraintParam[\\\"maxSize\\\"]}，最小长：{constraintParam[\\\"minSize\\\"]}，最小宽：{constraintParam[\\\"minSize\\\"]}，营区最小距离：{constraintParam[\\\"minDistance\\\"]}\\\"\\\"\\\"\\n709-\\n710- logging.debug(alg)\\n711- rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n712- final_data = camp_layout_main(rec_data)\\n713-\\n714- # 确保返回格式包含三个一级字段\\n715- response = {\\n716- \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n717- \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\n718- \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n719- \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n -A 30 \\\"yc.*遗传\\\" 路径规划/zongti_sa.py\"}",
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|
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"timestamp": "2025-09-25T10:37:09.391539",
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n -A 20 \\\"logging.debug(alg)\\\" 路径规划/zongti_sa.py\"}"
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"710: logging.debug(alg)\\n711- rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n712- final_data = camp_layout_main(rec_data)\\n713-\\n714- # 确保返回格式包含三个一级字段\\n715- response = {\\n716- \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n717- \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\n718- \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n719- \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n720- }\\n721-\\n722- # 保存营地布局输出到JSON文件\\n723- try:\\n724- import datetime\\n725- timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n726- output_filename = f\\\"camp_layout_output_{timestamp}.json\\\"\\n727-\\n728- # 创建输出数据的副本，包含输入参数和输出结果\\n729- output_data_to_save = {\\n730- \\\"timestamp\\\": timestamp,\\n--\\n794: logging.debug(alg)\\n795-\\n796- # 调用算法（这里假设算法内部能处理不同长度的权重）\\n797- final_data = facility_layout_main(rec_data)\\n798-\\n799- response = {\\n800- \\\"facilityLayout\\\": final_data.get(\\\"facilityLayout\\\"),\\n801- \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n802- \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n803- }\\n804-\\n805- return jsonify(response)\\n806-\\n807- except Exception as e:\\n808- return jsonify({\\n809- \\\"facilityLayout\\\": None,\\n810- \\\"code\\\": 500,\\n811- \\\"message\\\": f\\\"请求处理错误: {str(e)}\\\\n{traceback.format_exc()}\\\"\\n812- })\\n813-\\n814-\\n--\\n835: logging.debug(alg)\\n836- # 获取对应的算法函数\\n837- algorithm_func = SHIGONG_ALGORITHMS.get(algorithm)\\n838- if not algorithm_func:\\n839- return jsonify({\\n840- \\\"data\\\": None,\\n841- \\\"code\\\": 400,\\n842- \\\"message\\\": f\\\"不支持的算法类型: {algorithm}\\\"\\n843- })\\n844-\\n845- # 执行算法并返回结果\\n846- result = algorithm_func(rec_data)\\n847-\\n848- # 统一返回格式 - 添加 resourceScheduling到data中\\n849- if isinstance(result, dict):\\n850- if result.get(\\\"status\\\") == \\\"success\\\" and \\\"data\\\" in result:\\n851- return jsonify({\\n852- \\\"data\\\": {\\n853- \\\"constructionPeriod\\\": result[\\\"data\\\"].get(\\\"constructionPeriod\\\"),\\n854- \\\"detailedPlan\\\": result[\\\"data\\\"].get(\\\"detailedPlan\\\", []),\\n855- \\\"flowType\\\": result[\\\"data\\\"].get(\\\"flowType\\\", \\\"无节拍\\\"),\\n\", \"stdout\": \"710: logging.debug(alg)\\n711- rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n712- final_data = camp_layout_main(rec_data)\\n713-\\n714- # 确保返回格式包含三个一级字段\\n715- response = {\\n716- \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n717- \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\n718- \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n719- \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n720- }\\n721-\\n722- # 保存营地布局输出到JSON文件\\n723- try:\\n724- import datetime\\n725- timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n726- output_filename = f\\\"camp_layout_output_{timestamp}.json\\\"\\n727-\\n728- # 创建输出数据的副本，包含输入参数和输出结果\\n729- output_data_to_save = {\\n730- \\\"timestamp\\\": timestamp,\\n--\\n794: logging.debug(alg)\\n795-\\n796- # 调用算法（这里假设算法内部能处理不同长度的权重）\\n797- final_data = facility_layout_main(rec_data)\\n798-\\n799- response = {\\n800- \\\"facilityLayout\\\": final_data.get(\\\"facilityLayout\\\"),\\n801- \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n802- \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n803- }\\n804-\\n805- return jsonify(response)\\n806-\\n807- except Exception as e:\\n808- return jsonify({\\n809- \\\"facilityLayout\\\": None,\\n810- \\\"code\\\": 500,\\n811- \\\"message\\\": f\\\"请求处理错误: {str(e)}\\\\n{traceback.format_exc()}\\\"\\n812- })\\n813-\\n814-\\n--\\n835: logging.debug(alg)\\n836- # 获取对应的算法函数\\n837- algorithm_func = SHIGONG_ALGORITHMS.get(algorithm)\\n838- if not algorithm_func:\\n839- return jsonify({\\n840- \\\"data\\\": None,\\n841- \\\"code\\\": 400,\\n842- \\\"message\\\": f\\\"不支持的算法类型: {algorithm}\\\"\\n843- })\\n844-\\n845- # 执行算法并返回结果\\n846- result = algorithm_func(rec_data)\\n847-\\n848- # 统一返回格式 - 添加 resourceScheduling到data中\\n849- if isinstance(result, dict):\\n850- if result.get(\\\"status\\\") == \\\"success\\\" and \\\"data\\\" in result:\\n851- return jsonify({\\n852- \\\"data\\\": {\\n853- \\\"constructionPeriod\\\": result[\\\"data\\\"].get(\\\"constructionPeriod\\\"),\\n854- \\\"detailedPlan\\\": result[\\\"data\\\"].get(\\\"detailedPlan\\\", []),\\n855- \\\"flowType\\\": result[\\\"data\\\"].get(\\\"flowType\\\", \\\"无节拍\\\"),\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n -A 20 \\\"logging.debug(alg)\\\" 路径规划/zongti_sa.py\"}",
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"timestamp": "2025-09-25T10:37:11.203426",
|
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"name": "run_command"
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"role": "assistant",
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"content": "现在修改yingdibuju函数，添加模拟退火算法支持：",
|
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"timestamp": "2025-09-25T10:37:17.494208"
|
||
},
|
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"timestamp": "2025-09-25T10:37:17.502544",
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"function": {
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"name": "modify_file",
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"arguments": "{\"path\": \"路径规划/zongti_sa.py\", \"operation\": \"replace\", \"old_text\": \" logging.debug(alg)\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\", \"content\": \" logging.debug(alg)\\n \\n # 根据算法类型调用不同的优化器\\n if algorithm == \\\"mnth\\\": # 模拟退火算法\\n final_data = camp_layout_main_sa(rec_data)\\n else: # 其他算法（包括遗传算法）\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\"}"
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"content": "{\"success\": true, \"path\": \"路径规划/zongti_sa.py\", \"size\": 44876, \"mode\": \"w\", \"replacements\": 1}",
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"timestamp": "2025-09-25T10:37:19.314472",
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{
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"role": "assistant",
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"content": "现在需要创建camp_layout_main_sa函数。首先让我查看原始camp_layout_main的调用方式：",
|
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"timestamp": "2025-09-25T10:37:22.921345"
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},
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"role": "assistant",
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"timestamp": "2025-09-25T10:37:22.930744",
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"tool_calls": [
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"id": "run_command:43",
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"type": "function",
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"function": {
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n -A 10 \\\"def camp_layout_main\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
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}
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}
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"role": "tool",
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"content": "{\"success\": false, \"output\": \"\", \"stdout\": \"\", \"stderr\": \"\", \"return_code\": 1, \"command\": \"grep -n -A 10 \\\"def camp_layout_main\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"timestamp": "2025-09-25T10:37:24.741233",
|
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"timestamp": "2025-09-25T10:37:27.731415",
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n \\\"camp_layout_main\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
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|
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"timestamp": "2025-09-25T10:37:29.544276",
|
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"tool_call_id": "run_command:44",
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"timestamp": "2025-09-25T10:37:34.087471",
|
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"tool_calls": [
|
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|
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"id": "run_command:45",
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|
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|
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"arguments": "{\"command\": \"grep -n \\\"def.*layout\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -10\"}"
|
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}
|
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|
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|
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},
|
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{
|
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"role": "tool",
|
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"content": "{\"success\": true, \"output\": \"1595:def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n2094:def layout_priority_areas_first(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n\", \"stdout\": \"1595:def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n2094:def layout_priority_areas_first(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"def.*layout\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -10\"}",
|
||
"timestamp": "2025-09-25T10:37:35.898758",
|
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"tool_call_id": "run_command:45",
|
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"name": "run_command"
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|
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"role": "assistant",
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|
||
"timestamp": "2025-09-25T10:37:40.548426",
|
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|
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|
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"id": "run_command:46",
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"type": "function",
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"function": {
|
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"name": "run_command",
|
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"arguments": "{\"command\": \"grep -n \\\"def.*main\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -10\"}"
|
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}
|
||
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|
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||
},
|
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{
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"1683:def main_gaocheng_yichuan_camp(\\n2692:def main_gaocheng_yichuan_camp_staged(\\n\", \"stdout\": \"1683:def main_gaocheng_yichuan_camp(\\n2692:def main_gaocheng_yichuan_camp_staged(\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"def.*main\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -10\"}",
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"timestamp": "2025-09-25T10:37:42.360992",
|
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"tool_call_id": "run_command:46",
|
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"name": "run_command"
|
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"role": "assistant",
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"content": "",
|
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"timestamp": "2025-09-25T10:37:45.237263",
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"tool_calls": [
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"id": "run_command:47",
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"type": "function",
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"function": {
|
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"name": "run_command",
|
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"arguments": "{\"command\": \"grep -n \\\"camp_layout_main\\\" 路径规划/Camp_main.py\"}"
|
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}
|
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},
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{
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"role": "tool",
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"content": "{\"success\": false, \"output\": \"\\n[错误输出]\\ngrep: 路径规划/Camp_main.py: No such file or directory\\n\", \"stdout\": \"\", \"stderr\": \"grep: 路径规划/Camp_main.py: No such file or directory\\n\", \"return_code\": 2, \"command\": \"grep -n \\\"camp_layout_main\\\" 路径规划/Camp_main.py\"}",
|
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"timestamp": "2025-09-25T10:37:47.050356",
|
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"timestamp": "2025-09-25T10:37:50.467669",
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"content": "{\"success\": true, \"output\": \"from flask import Flask, request, jsonify\\nfrom flask_cors import CORS\\nimport traceback\\nfrom typing import Dict, List, Tuple, Optional, Set\\nfrom Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# from shigong_benders_v3 import main_shigong_benders\\n# from shigong_lagelangri_v3 import main_shigong_lag\\n# from shigong_lieshengcheng_v3 import main_shigong_cg\\n--\\n alg += f\\\"\\\"\\\" 目标函数：\\\\n 面积最大：{rec_data[\\\"functionWeight\\\"][0]}，分散度：{rec_data[\\\"functionWeight\\\"][1]}，禁区约束：{rec_data[\\\"functionWeight\\\"][2]}\\\\n\\\"\\\"\\\"\\n alg += f\\\"\\\"\\\" 约束条件：\\\\n 最大长：{constraintParam[\\\"maxSize\\\"]}，最大宽：{constraintParam[\\\"maxSize\\\"]}，最小长：{constraintParam[\\\"minSize\\\"]}，最小宽：{constraintParam[\\\"minSize\\\"]}，营区最小距离：{constraintParam[\\\"minDistance\\\"]}\\\"\\\"\\\"\\n\\n logging.debug(alg)\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\\n\\n # 确保返回格式包含三个一级字段\\n response = {\\n \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\nfrom flask import Flask, request, jsonify\\nfrom flask_cors import CORS\\nimport traceback\\nfrom typing import Dict, List, Tuple, Optional, Set\\nfrom Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# 模拟退火算法导入\\nfrom simulated_annealing_optimizer import create_sa_optimizer\\nfrom Camp_layout_gaocheng_fsbj_yichuan import layout_other_areas, layout_priority_areas_first\\n--\\n\\n logging.debug(alg)\\n \\n # 根据算法类型调用不同的优化器\\n if algorithm == \\\"mnth\\\": # 模拟退火算法\\n final_data = camp_layout_main_sa(rec_data)\\n else: # 其他算法（包括遗传算法）\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\\n\\n # 确保返回格式包含三个一级字段\\n response = {\\n \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\n\", \"stdout\": \"from flask import Flask, request, jsonify\\nfrom flask_cors import CORS\\nimport traceback\\nfrom typing import Dict, List, Tuple, Optional, Set\\nfrom Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# from shigong_benders_v3 import main_shigong_benders\\n# from shigong_lagelangri_v3 import main_shigong_lag\\n# from shigong_lieshengcheng_v3 import main_shigong_cg\\n--\\n alg += f\\\"\\\"\\\" 目标函数：\\\\n 面积最大：{rec_data[\\\"functionWeight\\\"][0]}，分散度：{rec_data[\\\"functionWeight\\\"][1]}，禁区约束：{rec_data[\\\"functionWeight\\\"][2]}\\\\n\\\"\\\"\\\"\\n alg += f\\\"\\\"\\\" 约束条件：\\\\n 最大长：{constraintParam[\\\"maxSize\\\"]}，最大宽：{constraintParam[\\\"maxSize\\\"]}，最小长：{constraintParam[\\\"minSize\\\"]}，最小宽：{constraintParam[\\\"minSize\\\"]}，营区最小距离：{constraintParam[\\\"minDistance\\\"]}\\\"\\\"\\\"\\n\\n logging.debug(alg)\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\\n\\n # 确保返回格式包含三个一级字段\\n response = {\\n \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\nfrom flask import Flask, request, jsonify\\nfrom flask_cors import CORS\\nimport traceback\\nfrom typing import Dict, List, Tuple, Optional, Set\\nfrom Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# 模拟退火算法导入\\nfrom simulated_annealing_optimizer import create_sa_optimizer\\nfrom Camp_layout_gaocheng_fsbj_yichuan import layout_other_areas, layout_priority_areas_first\\n--\\n\\n logging.debug(alg)\\n \\n # 根据算法类型调用不同的优化器\\n if algorithm == \\\"mnth\\\": # 模拟退火算法\\n final_data = camp_layout_main_sa(rec_data)\\n else: # 其他算法（包括遗传算法）\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\\n\\n # 确保返回格式包含三个一级字段\\n response = {\\n \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"find . -name \\\"*.py\\\" -exec grep -A 5 -B 5 \\\"camp_layout_main\\\" {} \\\\;\"}",
|
||
"timestamp": "2025-09-25T10:39:06.160285",
|
||
"tool_call_id": "run_command:65",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "看起来Camp_main.py文件不存在，我需要查看原始zongti.py的导入路径。让我检查原始文件结构：",
|
||
"timestamp": "2025-09-25T10:39:08.645548"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:39:08.656070",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:66",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"head -10 路径规划/zongti.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"from flask import Flask, request, jsonify\\nfrom flask_cors import CORS\\nimport traceback\\nfrom typing import Dict, List, Tuple, Optional, Set\\nfrom Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# from shigong_benders_v3 import main_shigong_benders\\n# from shigong_lagelangri_v3 import main_shigong_lag\\n# from shigong_lieshengcheng_v3 import main_shigong_cg\\n\", \"stdout\": \"from flask import Flask, request, jsonify\\nfrom flask_cors import CORS\\nimport traceback\\nfrom typing import Dict, List, Tuple, Optional, Set\\nfrom Camp_main import camp_layout_main\\nfrom Facility_main_faster import facility_layout_main\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n# from shigong_benders_v3 import main_shigong_benders\\n# from shigong_lagelangri_v3 import main_shigong_lag\\n# from shigong_lieshengcheng_v3 import main_shigong_cg\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"head -10 路径规划/zongti.py\"}",
|
||
"timestamp": "2025-09-25T10:39:10.466378",
|
||
"tool_call_id": "run_command:66",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "看起来Camp_main.py文件不在当前目录，我需要创建camp_layout_main_sa函数。让我先查看Camp_layout_gaocheng_fsbj_yichuan.py中的主函数：",
|
||
"timestamp": "2025-09-25T10:39:14.375934"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:39:14.388413",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:67",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -n -A 30 \\\"def main_gaocheng_yichuan_camp\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"1683:def main_gaocheng_yichuan_camp(\\n1684- input_data: Dict,\\n1685- grid_path: str,\\n1686- config_path: str = \\\"campConfig.json\\\",\\n1687- index=0\\n1688-) -> Dict:\\n1689- # 遗传算法优化开始\\n1690- start_time = time.time()\\n1691-\\n1692- try:\\n1693- # 加载配置文件\\n1694- with open(config_path, 'r') as paramsFile:\\n1695- paramsData = json.load(paramsFile)\\n1696-\\n1697- # 获取算法名称\\n1698- algorithm_name = input_data[\\\"layoutAlgorithm\\\"]\\n1699-\\n1700- # 获取算法参数\\n1701- algorithm_params = paramsData.get(algorithm_name, {})\\n1702- if 'algorithmParams' in input_data:\\n1703- algorithm_params.update(input_data['algorithmParams'])\\n1704-\\n1705- # 设置默认值\\n1706- default_params = {\\n1707- 'popSize': 100,\\n1708- 'maxGenerations': 30,\\n1709- 'crossoverProb': 0.9,\\n1710- 'crossoverEta': 15,\\n1711- 'mutationProb': 0.1,\\n1712- 'mutationEta': 20\\n1713- }\\n--\\n2692:def main_gaocheng_yichuan_camp_staged(\\n2693- input_data: Dict,\\n2694- grid_path: str,\\n2695- config_path: str = \\\"campConfig.json\\\",\\n2696- index=0\\n2697-) -> Dict:\\n2698- \\\"\\\"\\\"分阶段布局主函数：先布局所有其他区域（训练设施中心、车辆停放北边、弹药库东边），再布局营地（围绕集结操场）\\\"\\\"\\\"\\n2699- # 分阶段遗传算法优化开始\\n2700- start_time = time.time()\\n2701-\\n2702- try:\\n2703- # 加载配置文件\\n2704- with open(config_path, 'r') as paramsFile:\\n2705- paramsData = json.load(paramsFile)\\n2706-\\n2707- # 获取算法参数\\n2708- algorithm_name = input_data[\\\"layoutAlgorithm\\\"]\\n2709- algorithm_params = paramsData.get(algorithm_name, {})\\n2710- if 'algorithmParams' in input_data:\\n2711- algorithm_params.update(input_data['algorithmParams'])\\n2712-\\n2713- # 设置默认值\\n2714- default_params = {\\n2715- 'popSize': 100,\\n2716- 'maxGenerations': 30,\\n2717- 'crossoverProb': 0.9,\\n2718- 'crossoverEta': 15,\\n2719- 'mutationProb': 0.1,\\n2720- 'mutationEta': 20\\n2721- }\\n2722-\\n\", \"stdout\": \"1683:def main_gaocheng_yichuan_camp(\\n1684- input_data: Dict,\\n1685- grid_path: str,\\n1686- config_path: str = \\\"campConfig.json\\\",\\n1687- index=0\\n1688-) -> Dict:\\n1689- # 遗传算法优化开始\\n1690- start_time = time.time()\\n1691-\\n1692- try:\\n1693- # 加载配置文件\\n1694- with open(config_path, 'r') as paramsFile:\\n1695- paramsData = json.load(paramsFile)\\n1696-\\n1697- # 获取算法名称\\n1698- algorithm_name = input_data[\\\"layoutAlgorithm\\\"]\\n1699-\\n1700- # 获取算法参数\\n1701- algorithm_params = paramsData.get(algorithm_name, {})\\n1702- if 'algorithmParams' in input_data:\\n1703- algorithm_params.update(input_data['algorithmParams'])\\n1704-\\n1705- # 设置默认值\\n1706- default_params = {\\n1707- 'popSize': 100,\\n1708- 'maxGenerations': 30,\\n1709- 'crossoverProb': 0.9,\\n1710- 'crossoverEta': 15,\\n1711- 'mutationProb': 0.1,\\n1712- 'mutationEta': 20\\n1713- }\\n--\\n2692:def main_gaocheng_yichuan_camp_staged(\\n2693- input_data: Dict,\\n2694- grid_path: str,\\n2695- config_path: str = \\\"campConfig.json\\\",\\n2696- index=0\\n2697-) -> Dict:\\n2698- \\\"\\\"\\\"分阶段布局主函数：先布局所有其他区域（训练设施中心、车辆停放北边、弹药库东边），再布局营地（围绕集结操场）\\\"\\\"\\\"\\n2699- # 分阶段遗传算法优化开始\\n2700- start_time = time.time()\\n2701-\\n2702- try:\\n2703- # 加载配置文件\\n2704- with open(config_path, 'r') as paramsFile:\\n2705- paramsData = json.load(paramsFile)\\n2706-\\n2707- # 获取算法参数\\n2708- algorithm_name = input_data[\\\"layoutAlgorithm\\\"]\\n2709- algorithm_params = paramsData.get(algorithm_name, {})\\n2710- if 'algorithmParams' in input_data:\\n2711- algorithm_params.update(input_data['algorithmParams'])\\n2712-\\n2713- # 设置默认值\\n2714- default_params = {\\n2715- 'popSize': 100,\\n2716- 'maxGenerations': 30,\\n2717- 'crossoverProb': 0.9,\\n2718- 'crossoverEta': 15,\\n2719- 'mutationProb': 0.1,\\n2720- 'mutationEta': 20\\n2721- }\\n2722-\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n -A 30 \\\"def main_gaocheng_yichuan_camp\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
|
||
"timestamp": "2025-09-25T10:39:16.205043",
|
||
"tool_call_id": "run_command:67",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:39:20.296475",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:68",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 50 \\\"main_gaocheng_yichuan_camp(\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -60\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"def main_gaocheng_yichuan_camp(\\n input_data: Dict,\\n grid_path: str,\\n config_path: str = \\\"campConfig.json\\\",\\n index=0\\n) -> Dict:\\n # 遗传算法优化开始\\n start_time = time.time()\\n\\n try:\\n # 加载配置文件\\n with open(config_path, 'r') as paramsFile:\\n paramsData = json.load(paramsFile)\\n\\n # 获取算法名称\\n algorithm_name = input_data[\\\"layoutAlgorithm\\\"]\\n\\n # 获取算法参数\\n algorithm_params = paramsData.get(algorithm_name, {})\\n if 'algorithmParams' in input_data:\\n algorithm_params.update(input_data['algorithmParams'])\\n\\n # 设置默认值\\n default_params = {\\n 'popSize': 100,\\n 'maxGenerations': 30,\\n 'crossoverProb': 0.9,\\n 'crossoverEta': 15,\\n 'mutationProb': 0.1,\\n 'mutationEta': 20\\n }\\n\\n # 使用算法参数\\n params = {key: algorithm_params.get(key, default_params[key])\\n for key in default_params}\\n\\n # 检查是否有其他区域需要布局\\n other_areas = input_data.get('otherAreas', {})\\n \\n # 如果有其他区域，使用分阶段布局\\n if other_areas:\\n # 使用分阶段布局\\n return main_gaocheng_yichuan_camp_staged(input_data, grid_path, config_path, index)\\n \\n # 加载配置\\n config = load_config_from_json(input_data, grid_path)\\n # 使用原有布局\\n problem = CampProblem(config)\\n\\n # 配置遗传算法\\n algorithm = GA(\\n\", \"stdout\": \"def main_gaocheng_yichuan_camp(\\n input_data: Dict,\\n grid_path: str,\\n config_path: str = \\\"campConfig.json\\\",\\n index=0\\n) -> Dict:\\n # 遗传算法优化开始\\n start_time = time.time()\\n\\n try:\\n # 加载配置文件\\n with open(config_path, 'r') as paramsFile:\\n paramsData = json.load(paramsFile)\\n\\n # 获取算法名称\\n algorithm_name = input_data[\\\"layoutAlgorithm\\\"]\\n\\n # 获取算法参数\\n algorithm_params = paramsData.get(algorithm_name, {})\\n if 'algorithmParams' in input_data:\\n algorithm_params.update(input_data['algorithmParams'])\\n\\n # 设置默认值\\n default_params = {\\n 'popSize': 100,\\n 'maxGenerations': 30,\\n 'crossoverProb': 0.9,\\n 'crossoverEta': 15,\\n 'mutationProb': 0.1,\\n 'mutationEta': 20\\n }\\n\\n # 使用算法参数\\n params = {key: algorithm_params.get(key, default_params[key])\\n for key in default_params}\\n\\n # 检查是否有其他区域需要布局\\n other_areas = input_data.get('otherAreas', {})\\n \\n # 如果有其他区域，使用分阶段布局\\n if other_areas:\\n # 使用分阶段布局\\n return main_gaocheng_yichuan_camp_staged(input_data, grid_path, config_path, index)\\n \\n # 加载配置\\n config = load_config_from_json(input_data, grid_path)\\n # 使用原有布局\\n problem = CampProblem(config)\\n\\n # 配置遗传算法\\n algorithm = GA(\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 50 \\\"main_gaocheng_yichuan_camp(\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -60\"}",
|
||
"timestamp": "2025-09-25T10:39:22.110818",
|
||
"tool_call_id": "run_command:68",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:39:26.398865",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:69",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 30 \\\"load_config_from_json\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -40\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"def load_config_from_json(input_data: Dict, grid_data_path: str) -> Dict:\\n try:\\n with open(grid_data_path, 'r', encoding='utf-8') as f:\\n grid_data_dict = json.load(f)\\n\\n # 获取营地编号列表\\n camp_ids = input_data.get(\\\"campIds\\\", [])\\n if not camp_ids:\\n raise ValueError(\\\"必须提供'campIds'参数，包含营地的唯一编号列表\\\")\\n num_camp = len(camp_ids)\\n\\n # 获取场景参数\\n scene = input_data.get(\\\"scene\\\", None)\\n\\n grid_size = float(input_data.get(\\\"gridSize\\\", 1.0))\\n grid_data = defaultdict(lambda: defaultdict(float))\\n all_heights = []\\n original_coords = [] # 存储原始坐标\\n coord_map = {} # 映射原始坐标到高度值\\n\\n # 找出有效的网格键\\n valid_grid_keys = [key for key in grid_data_dict.keys() if re.fullmatch(r'\\\\d+-\\\\d+', key)]\\n\\n # 收集所有原始坐标\\n for key in valid_grid_keys:\\n match = re.fullmatch(r'(\\\\d+)-(\\\\d+)', key)\\n if match:\\n x_coord, y_coord = map(int, match.groups())\\n original_coords.append((x_coord, y_coord))\\n\\n # 计算最小坐标值作为偏移量\\n--\\n print(f\\\"\\\\n=== 调试 load_config_from_json 中的 otherAreas 处理 ===\\\")\\n print(f\\\"other_areas_dict 包含 {len(other_areas_dict)} 个区域\\\")\\n for area_name, area_size in other_areas_dict.items():\\n details = other_areas_details.get(area_name, {})\\n print(f\\\" {area_name}: 面积={area_size:.2f}, 长={details.get('length', 0):.2f}, 宽={details.get('width', 0):.2f}, 类型={details.get('area_type', '')}, zl={details.get('zl', '')}\\\")\\n \\n config.update({\\n 'FIELD_WIDTH': field_width,\\n\", \"stdout\": \"def load_config_from_json(input_data: Dict, grid_data_path: str) -> Dict:\\n try:\\n with open(grid_data_path, 'r', encoding='utf-8') as f:\\n grid_data_dict = json.load(f)\\n\\n # 获取营地编号列表\\n camp_ids = input_data.get(\\\"campIds\\\", [])\\n if not camp_ids:\\n raise ValueError(\\\"必须提供'campIds'参数，包含营地的唯一编号列表\\\")\\n num_camp = len(camp_ids)\\n\\n # 获取场景参数\\n scene = input_data.get(\\\"scene\\\", None)\\n\\n grid_size = float(input_data.get(\\\"gridSize\\\", 1.0))\\n grid_data = defaultdict(lambda: defaultdict(float))\\n all_heights = []\\n original_coords = [] # 存储原始坐标\\n coord_map = {} # 映射原始坐标到高度值\\n\\n # 找出有效的网格键\\n valid_grid_keys = [key for key in grid_data_dict.keys() if re.fullmatch(r'\\\\d+-\\\\d+', key)]\\n\\n # 收集所有原始坐标\\n for key in valid_grid_keys:\\n match = re.fullmatch(r'(\\\\d+)-(\\\\d+)', key)\\n if match:\\n x_coord, y_coord = map(int, match.groups())\\n original_coords.append((x_coord, y_coord))\\n\\n # 计算最小坐标值作为偏移量\\n--\\n print(f\\\"\\\\n=== 调试 load_config_from_json 中的 otherAreas 处理 ===\\\")\\n print(f\\\"other_areas_dict 包含 {len(other_areas_dict)} 个区域\\\")\\n for area_name, area_size in other_areas_dict.items():\\n details = other_areas_details.get(area_name, {})\\n print(f\\\" {area_name}: 面积={area_size:.2f}, 长={details.get('length', 0):.2f}, 宽={details.get('width', 0):.2f}, 类型={details.get('area_type', '')}, zl={details.get('zl', '')}\\\")\\n \\n config.update({\\n 'FIELD_WIDTH': field_width,\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"load_config_from_json\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -40\"}",
|
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"timestamp": "2025-09-25T10:39:28.209858",
|
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"tool_call_id": "run_command:69",
|
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"name": "run_command"
|
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},
|
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{
|
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"role": "assistant",
|
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"content": "",
|
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"timestamp": "2025-09-25T10:39:31.036888",
|
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"tool_calls": [
|
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{
|
||
"id": "run_command:70",
|
||
"index": 0,
|
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"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 20 \\\"return.*result\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | tail -30\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \" start_time = time.time()\\n\\n try:\\n # 加载配置文件\\n with open(config_path, 'r') as paramsFile:\\n paramsData = json.load(paramsFile)\\n\\n # 获取算法名称\\n--\\n return all_areas_result\\n\\n\\ndef main_gaocheng_yichuan_camp_staged(\\n input_data: Dict,\\n grid_path: str,\\n config_path: str = \\\"campConfig.json\\\",\\n index=0\\n) -> Dict:\\n \\\"\\\"\\\"分阶段布局主函数：先布局所有其他区域（训练设施中心、车辆停放北边、弹药库东边），再布局营地（围绕集结操场）\\\"\\\"\\\"\\n # 分阶段遗传算法优化开始\\n start_time = time.time()\\n\\n try:\\n # 加载配置文件\\n with open(config_path, 'r') as paramsFile:\\n paramsData = json.load(paramsFile)\\n\\n # 获取算法参数\\n algorithm_name = input_data[\\\"layoutAlgorithm\\\"]\\n algorithm_params = paramsData.get(algorithm_name, {})\\n\", \"stdout\": \" start_time = time.time()\\n\\n try:\\n # 加载配置文件\\n with open(config_path, 'r') as paramsFile:\\n paramsData = json.load(paramsFile)\\n\\n # 获取算法名称\\n--\\n return all_areas_result\\n\\n\\ndef main_gaocheng_yichuan_camp_staged(\\n input_data: Dict,\\n grid_path: str,\\n config_path: str = \\\"campConfig.json\\\",\\n index=0\\n) -> Dict:\\n \\\"\\\"\\\"分阶段布局主函数：先布局所有其他区域（训练设施中心、车辆停放北边、弹药库东边），再布局营地（围绕集结操场）\\\"\\\"\\\"\\n # 分阶段遗传算法优化开始\\n start_time = time.time()\\n\\n try:\\n # 加载配置文件\\n with open(config_path, 'r') as paramsFile:\\n paramsData = json.load(paramsFile)\\n\\n # 获取算法参数\\n algorithm_name = input_data[\\\"layoutAlgorithm\\\"]\\n algorithm_params = paramsData.get(algorithm_name, {})\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 20 \\\"return.*result\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | tail -30\"}",
|
||
"timestamp": "2025-09-25T10:39:32.852037",
|
||
"tool_call_id": "run_command:70",
|
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"name": "run_command"
|
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},
|
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{
|
||
"role": "assistant",
|
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"content": "",
|
||
"timestamp": "2025-09-25T10:39:35.671000",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:71",
|
||
"index": 0,
|
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"type": "function",
|
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"function": {
|
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"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 30 \\\"res = minimize\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | tail -40\"}"
|
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}
|
||
}
|
||
]
|
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},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \" for i, area_name in enumerate(problem.area_names):\\n xc = res.X[i * 2]\\n yc = res.X[i * 2 + 1]\\n # 使用实际的长宽而不是计算的正方形\\n if area_name in problem.other_areas_details:\\n w = problem.other_areas_details[area_name]['width']\\n h = problem.other_areas_details[area_name]['length']\\n else:\\n--\\n res = minimize(\\n problem,\\n algorithm,\\n termination=termination,\\n verbose=True\\n )\\n\\n # 检查营地布局结果\\n if res.X is not None:\\n constraint_violations = [\\n problem._boundary_violation_vectorized(res.X),\\n problem._overlap_violation_vectorized(res.X),\\n problem._distance_violation_vectorized(res.X),\\n problem._heat_negative_violation_vectorized(res.X)\\n ]\\n\\n tolerance = 1e-6\\n heat_negative_tolerance = 0 # 严格要求不能有负值热力\\n\\n constraint_satisfied = [\\n constraint_violations[0] <= tolerance,\\n constraint_violations[1] <= tolerance,\\n constraint_violations[2] <= tolerance,\\n constraint_violations[3] <= heat_negative_tolerance\\n ]\\n\\n all_constraints_satisfied = all(constraint_satisfied)\\n\\n if all_constraints_satisfied:\\n # 营地布局成功\\n \\n\", \"stdout\": \" for i, area_name in enumerate(problem.area_names):\\n xc = res.X[i * 2]\\n yc = res.X[i * 2 + 1]\\n # 使用实际的长宽而不是计算的正方形\\n if area_name in problem.other_areas_details:\\n w = problem.other_areas_details[area_name]['width']\\n h = problem.other_areas_details[area_name]['length']\\n else:\\n--\\n res = minimize(\\n problem,\\n algorithm,\\n termination=termination,\\n verbose=True\\n )\\n\\n # 检查营地布局结果\\n if res.X is not None:\\n constraint_violations = [\\n problem._boundary_violation_vectorized(res.X),\\n problem._overlap_violation_vectorized(res.X),\\n problem._distance_violation_vectorized(res.X),\\n problem._heat_negative_violation_vectorized(res.X)\\n ]\\n\\n tolerance = 1e-6\\n heat_negative_tolerance = 0 # 严格要求不能有负值热力\\n\\n constraint_satisfied = [\\n constraint_violations[0] <= tolerance,\\n constraint_violations[1] <= tolerance,\\n constraint_violations[2] <= tolerance,\\n constraint_violations[3] <= heat_negative_tolerance\\n ]\\n\\n all_constraints_satisfied = all(constraint_satisfied)\\n\\n if all_constraints_satisfied:\\n # 营地布局成功\\n \\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"res = minimize\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | tail -40\"}",
|
||
"timestamp": "2025-09-25T10:39:37.484001",
|
||
"tool_call_id": "run_command:71",
|
||
"name": "run_command"
|
||
},
|
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{
|
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"role": "assistant",
|
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"content": "",
|
||
"timestamp": "2025-09-25T10:39:40.741156",
|
||
"tool_calls": [
|
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{
|
||
"id": "run_command:72",
|
||
"index": 0,
|
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"type": "function",
|
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"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 50 \\\"if all_constraints_satisfied:\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -60\"}"
|
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}
|
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}
|
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]
|
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},
|
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{
|
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"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \" if all_constraints_satisfied:\\n # 优化成功\\n\\n # 获取最优解的各项得分\\n maximization_score, dispersion_score, suitability_score, forward_score, withdrawal_score = problem._calculate_scores_vectorized(res.X)\\n\\n # 约束满足情况\\n\\n # 构建营地结果\\n camps = []\\n camp_ids = config.get('campIds', [])\\n for i, camp_id in enumerate(camp_ids):\\n xc, yc, w, h = res.X[i * 4: (i + 1) * 4]\\n lower_left = [float(xc - w / 2), float(yc - h / 2)]\\n upper_right = [float(xc + w / 2), float(yc + h / 2)]\\n\\n camps.append({\\n 'id': camp_id,\\n 'lowerLeft': lower_left,\\n 'upperRight': upper_right,\\n })\\n\\n # 营地信息\\n\\n # 验证营地之间的距离约束\\n for i in range(len(camps)):\\n camp1 = camps[i]\\n camp1_cx = (camp1['lowerLeft'][0] + camp1['upperRight'][0]) / 2\\n camp1_cy = (camp1['lowerLeft'][1] + camp1['upperRight'][1]) / 2\\n camp1_w = camp1['upperRight'][0] - camp1['lowerLeft'][0]\\n camp1_h = camp1['upperRight'][1] - camp1['lowerLeft'][1]\\n \\n # 计算到所有其他营地的距离\\n distances = []\\n camp_ids = []\\n \\n for j in range(len(camps)):\\n if i != j: # 不与自己比较\\n camp2 = camps[j]\\n camp2_cx = (camp2['lowerLeft'][0] + camp2['upperRight'][0]) / 2\\n camp2_cy = (camp2['lowerLeft'][1] + camp2['upperRight'][1]) / 2\\n camp2_w = camp2['upperRight'][0] - camp2['lowerLeft'][0]\\n camp2_h = camp2['upperRight'][1] - camp2['lowerLeft'][1]\\n \\n edge_dist = problem._rect_edge_distance(\\n camp1_cx, camp1_cy, camp1_w, camp1_h,\\n camp2_cx, camp2_cy, camp2_w, camp2_h\\n )\\n \\n distances.append(edge_dist)\\n camp_ids.append(camp2['id'])\\n--\\n if all_constraints_satisfied:\\n # 营地布局成功\\n \\n # 获取最优解的各项得分\\n maximization_score, dispersion_score, suitability_score, forward_score, withdrawal_score = problem._calculate_scores_vectorized(res.X)\\n\\n # 打印各项得分及其权重\\n # 各项得分计算完成\\n\", \"stdout\": \" if all_constraints_satisfied:\\n # 优化成功\\n\\n # 获取最优解的各项得分\\n maximization_score, dispersion_score, suitability_score, forward_score, withdrawal_score = problem._calculate_scores_vectorized(res.X)\\n\\n # 约束满足情况\\n\\n # 构建营地结果\\n camps = []\\n camp_ids = config.get('campIds', [])\\n for i, camp_id in enumerate(camp_ids):\\n xc, yc, w, h = res.X[i * 4: (i + 1) * 4]\\n lower_left = [float(xc - w / 2), float(yc - h / 2)]\\n upper_right = [float(xc + w / 2), float(yc + h / 2)]\\n\\n camps.append({\\n 'id': camp_id,\\n 'lowerLeft': lower_left,\\n 'upperRight': upper_right,\\n })\\n\\n # 营地信息\\n\\n # 验证营地之间的距离约束\\n for i in range(len(camps)):\\n camp1 = camps[i]\\n camp1_cx = (camp1['lowerLeft'][0] + camp1['upperRight'][0]) / 2\\n camp1_cy = (camp1['lowerLeft'][1] + camp1['upperRight'][1]) / 2\\n camp1_w = camp1['upperRight'][0] - camp1['lowerLeft'][0]\\n camp1_h = camp1['upperRight'][1] - camp1['lowerLeft'][1]\\n \\n # 计算到所有其他营地的距离\\n distances = []\\n camp_ids = []\\n \\n for j in range(len(camps)):\\n if i != j: # 不与自己比较\\n camp2 = camps[j]\\n camp2_cx = (camp2['lowerLeft'][0] + camp2['upperRight'][0]) / 2\\n camp2_cy = (camp2['lowerLeft'][1] + camp2['upperRight'][1]) / 2\\n camp2_w = camp2['upperRight'][0] - camp2['lowerLeft'][0]\\n camp2_h = camp2['upperRight'][1] - camp2['lowerLeft'][1]\\n \\n edge_dist = problem._rect_edge_distance(\\n camp1_cx, camp1_cy, camp1_w, camp1_h,\\n camp2_cx, camp2_cy, camp2_w, camp2_h\\n )\\n \\n distances.append(edge_dist)\\n camp_ids.append(camp2['id'])\\n--\\n if all_constraints_satisfied:\\n # 营地布局成功\\n \\n # 获取最优解的各项得分\\n maximization_score, dispersion_score, suitability_score, forward_score, withdrawal_score = problem._calculate_scores_vectorized(res.X)\\n\\n # 打印各项得分及其权重\\n # 各项得分计算完成\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 50 \\\"if all_constraints_satisfied:\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -60\"}",
|
||
"timestamp": "2025-09-25T10:39:42.555468",
|
||
"tool_call_id": "run_command:72",
|
||
"name": "run_command"
|
||
},
|
||
{
|
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"role": "assistant",
|
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"content": "",
|
||
"timestamp": "2025-09-25T10:39:46.219418",
|
||
"tool_calls": [
|
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{
|
||
"id": "run_command:73",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -A 30 \\\"return {\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | tail -40\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \" \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success\\\",\\n \\\"runtime\\\": runtime,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": float(maximization_score),\\n \\\"dispersion_score\\\": float(dispersion_score),\\n \\\"suitability_score\\\": float(suitability_score),\\n \\\"forward_score\\\": float(forward_score),\\n \\\"withdrawal_score\\\": float(withdrawal_score),\\n \\\"combined_score\\\": float(-res.F[0])\\n }\\n }\\n else:\\n # 营地布局失败：约束违反\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"营地布局失败：约束违反\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n else:\\n # 营地布局失败：未找到解\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"营地布局失败：未找到解\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n\\n except Exception as e:\\n # 分阶段优化过程异常\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"分阶段优化过程异常: {str(e)}\\\"\\n }\\n\", \"stdout\": \" \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success\\\",\\n \\\"runtime\\\": runtime,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": float(maximization_score),\\n \\\"dispersion_score\\\": float(dispersion_score),\\n \\\"suitability_score\\\": float(suitability_score),\\n \\\"forward_score\\\": float(forward_score),\\n \\\"withdrawal_score\\\": float(withdrawal_score),\\n \\\"combined_score\\\": float(-res.F[0])\\n }\\n }\\n else:\\n # 营地布局失败：约束违反\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"营地布局失败：约束违反\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n else:\\n # 营地布局失败：未找到解\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"营地布局失败：未找到解\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n\\n except Exception as e:\\n # 分阶段优化过程异常\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"分阶段优化过程异常: {str(e)}\\\"\\n }\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"return {\\\" 路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | tail -40\"}",
|
||
"timestamp": "2025-09-25T10:39:48.034459",
|
||
"tool_call_id": "run_command:73",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在我来创建camp_layout_main_sa函数。首先需要在Camp_layout_gaocheng_fsbj_yichuan.py文件中添加这个函数：退",
|
||
"timestamp": "2025-09-25T10:41:20.480103"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:41:20.491619",
|
||
"tool_calls": [
|
||
{
|
||
"id": "modify_file:74",
|
||
"index": 0,
|
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"type": "function",
|
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"function": {
|
||
"name": "modify_file",
|
||
"arguments": "{\"path\": \"路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\", \"operation\": \"append\", \"content\": \"\\n\\ndef camp_layout_main_sa(input_data: Dict, grid_path: str = None, config_path: str = \\\"campConfig.json\\\", index=0) -> Dict:\\n \\\"\\\"\\\"\\n 模拟退火算法营地布局主函数\\n \\n Args:\\n input_data: 输入数据，包含布局参数\\n grid_path: 网格数据文件路径\\n config_path: 配置文件路径\\n index: 索引参数\\n \\n Returns:\\n Dict: 布局结果\\n \\\"\\\"\\\"\\n start_time = time.time()\\n \\n try:\\n # 如果没有提供grid_path，使用默认路径\\n if grid_path is None:\\n grid_path = \\\"gridData.json\\\"\\n \\n # 加载配置文件（如果存在）\\n algorithm_params = {}\\n try:\\n if os.path.exists(config_path):\\n with open(config_path, 'r') as paramsFile:\\n paramsData = json.load(paramsFile)\\n algorithm_params = paramsData.get(\\\"mnth\\\", {})\\n except:\\n pass\\n \\n # 合并输入参数中的算法参数\\n if 'algorithmParams' in input_data:\\n algorithm_params.update(input_data['algorithmParams'])\\n \\n # 设置模拟退火默认参数\\n default_sa_params = {\\n 'initial_temperature': 1000.0,\\n 'final_temperature': 0.1,\\n 'cooling_rate': 0.95,\\n 'max_iterations': 10000,\\n 'cooling_schedule': 'exponential',\\n 'acceptance_function': 'metropolis',\\n 'position_perturbation': 0.1,\\n 'size_perturbation': 0.05,\\n 'perturbation_strategy': 'mixed',\\n 'adaptive_perturbation': True,\\n 'verbose': True\\n }\\n \\n # 合并参数\\n for key, value in default_sa_params.items():\\n if key not in algorithm_params:\\n algorithm_params[key] = value\\n \\n # 创建算法参数字典\\n algorithm_config = {\\n 'sa_params': algorithm_params,\\n 'maxGenerations': algorithm_params.get('max_iterations', 10000)\\n }\\n \\n print(f\\\"=== 模拟退火算法参数 ===\\\")\\n print(f\\\"初始温度: {algorithm_params['initial_temperature']}\\\")\\n print(f\\\"终止温度: {algorithm_params['final_temperature']}\\\")\\n print(f\\\"冷却速率: {algorithm_params['cooling_rate']}\\\")\\n print(f\\\"最大迭代: {algorithm_params['max_iterations']}\\\")\\n print(f\\\"冷却策略: {algorithm_params['cooling_schedule']}\\\")\\n print(f\\\"接受函数: {algorithm_params['acceptance_function']}\\\")\\n \\n # 检查是否有其他区域需要布局\\n other_areas = input_data.get('otherAreas', {})\\n \\n if other_areas:\\n # 使用分阶段布局：先布局其他区域，再布局营地\\n return camp_layout_sa_staged(input_data, grid_path, config_path, index, algorithm_config)\\n else:\\n # 仅布局营地\\n return camp_layout_sa_only(input_data, grid_path, config_path, index, algorithm_config)\\n \\n except Exception as e:\\n print(f\\\"模拟退火算法执行错误: {str(e)}\\\")\\n import traceback\\n traceback.print_exc()\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"模拟退火算法执行错误: {str(e)}\\\"\\n }\\n\\n\\ndef camp_layout_sa_only(input_data: Dict, grid_path: str, config_path: str, index: int, algorithm_config: Dict) -> Dict:\\n \\\"\\\"\\\"仅布局营地的模拟退火算法\\\"\\\"\\\"\\n start_time = time.time()\\n \\n try:\\n # 加载配置\\n config = load_config_from_json(input_data, grid_path)\\n \\n # 创建优化问题\\n problem = CampProblem(config)\\n \\n # 创建模拟退火优化器\\n sa_optimizer = create_sa_optimizer(algorithm_config)\\n \\n print(f\\\"=== 开始模拟退火优化 ===\\\")\\n print(f\\\"问题维度: {problem.n_var}\\\")\\n print(f\\\"变量下界: {problem.xl}\\\")\\n print(f\\\"变量上界: {problem.xu}\\\")\\n \\n # 执行优化\\n result = sa_optimizer.optimize(problem)\\n \\n if result['success'] and result['x'] is not None:\\n # 检查约束满足情况\\n fitness, objectives, constraints, penalty = sa_optimizer.state_manager.evaluate_solution(result['x'])\\n \\n tolerance = 1e-6\\n heat_negative_tolerance = 0\\n \\n constraint_satisfied = [\\n constraints['boundary'] <= tolerance,\\n constraints['overlap'] <= tolerance,\\n constraints['distance'] <= tolerance,\\n constraints['heat_negative'] <= heat_negative_tolerance\\n ]\\n \\n all_constraints_satisfied = all(constraint_satisfied)\\n \\n if all_constraints_satisfied:\\n # 优化成功\\n print(f\\\"=== 模拟退火优化成功 ===\\\")\\n print(f\\\"最终适应度: {result['fitness']:.6f}\\\")\\n print(f\\\"迭代次数: {result['iterations']}\\\")\\n print(f\\\"运行时间: {result['time']:.2f}s\\\")\\n \\n # 构建营地结果\\n camps = []\\n camp_ids = config.get('campIds', [])\\n \\n for i, camp_id in enumerate(camp_ids):\\n xc, yc, w, h = result['x'][i * 4: (i + 1) * 4]\\n lower_left = [float(xc - w / 2), float(yc - h / 2)]\\n upper_right = [float(xc + w / 2), float(yc + h / 2)]\\n \\n camps.append({\\n 'id': camp_id,\\n 'lowerLeft': lower_left,\\n 'upperRight': upper_right,\\n })\\n \\n # 计算运行时间\\n runtime = time.time() - start_time\\n \\n return {\\n \\\"camps\\\": camps,\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success\\\",\\n \\\"runtime\\\": runtime,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": float(objectives['maximization']),\\n \\\"dispersion_score\\\": float(objectives['dispersion']),\\n \\\"suitability_score\\\": float(objectives['suitability']),\\n \\\"forward_score\\\": float(objectives['forward']),\\n \\\"withdrawal_score\\\": float(objectives['withdrawal']),\\n \\\"combined_score\\\": float(objectives['combined'])\\n },\\n \\\"algorithm_info\\\": {\\n \\\"name\\\": \\\"模拟退火算法\\\",\\n \\\"iterations\\\": result['iterations'],\\n \\\"final_temperature\\\": sa_optimizer.state_manager.temperature,\\n \\\"acceptance_rate\\\": result['acceptance_rate']\\n }\\n }\\n else:\\n # 约束违反\\n print(f\\\"=== 约束违反 ===\\\")\\n print(f\\\"边界约束: {constraints['boundary']}\\\")\\n print(f\\\"重叠约束: {constraints['overlap']}\\\")\\n print(f\\\"距离约束: {constraints['distance']}\\\")\\n print(f\\\"负热力约束: {constraints['heat_negative']}\\\")\\n \\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"模拟退火优化失败：约束违反\\\",\\n \\\"runtime\\\": time.time() - start_time,\\n \\\"constraint_violations\\\": constraints\\n }\\n else:\\n # 优化失败\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"模拟退火优化失败：未找到解\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n \\n except Exception as e:\\n print(f\\\"仅营地布局模拟退火算法错误: {str(e)}\\\")\\n import traceback\\n traceback.print_exc()\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"仅营地布局模拟退火算法错误: {str(e)}\\\"\\n }\\n\\n\\ndef camp_layout_sa_staged(input_data: Dict, grid_path: str, config_path: str, index: int, algorithm_config: Dict) -> Dict:\\n \\\"\\\"\\\"分阶段模拟退火布局：先布局其他区域，再布局营地\\\"\\\"\\\"\\n start_time = time.time()\\n \\n try:\\n # 第一阶段：布局其他区域\\n print(f\\\"=== 第一阶段：其他区域模拟退火布局 ===\\\")\\n other_areas_result = layout_priority_areas_first_sa(input_data, algorithm_config)\\n \\n if other_areas_result['code'] != 200:\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"其他区域模拟退火布局失败: {other_areas_result.get('message', '未知错误')}\\\"\\n }\\n \\n all_other_areas = other_areas_result['otherAreas']\\n print(f\\\"其他区域布局完成，共{len(all_other_areas)}个区域\\\")\\n \\n # 第二阶段：布局营地\\n print(f\\\"=== 第二阶段：营地模拟退火布局 ===\\\")\\n \\n # 加载配置\\n config = load_config_from_json(input_data, grid_path)\\n \\n # 创建优化问题（考虑其他区域）\\n problem = CampProblemWithOtherAreas(config, all_other_areas)\\n \\n # 创建模拟退火优化器\\n sa_optimizer = create_sa_optimizer(algorithm_config)\\n \\n # 执行优化\\n result = sa_optimizer.optimize(problem)\\n \\n if result['success'] and result['x'] is not None:\\n # 检查约束满足情况\\n fitness, objectives, constraints, penalty = sa_optimizer.state_manager.evaluate_solution(result['x'])\\n \\n tolerance = 1e-6\\n heat_negative_tolerance = 0\\n \\n constraint_satisfied = [\\n constraints['boundary'] <= tolerance,\\n constraints['overlap'] <= tolerance,\\n constraints['distance'] <= tolerance,\\n constraints['heat_negative'] <= heat_negative_tolerance\\n ]\\n \\n all_constraints_satisfied = all(constraint_satisfied)\\n \\n if all_constraints_satisfied:\\n # 优化成功\\n print(f\\\"=== 分阶段模拟退火优化成功 ===\\\")\\n print(f\\\"最终适应度: {result['fitness']:.6f}\\\")\\n print(f\\\"迭代次数: {result['iterations']}\\\")\\n print(f\\\"运行时间: {result['time']:.2f}s\\\")\\n \\n # 构建营地结果\\n camps = []\\n camp_ids = config.get('campIds', [])\\n \\n for i, camp_id in enumerate(camp_ids):\\n xc, yc, w, h = result['x'][i * 4: (i + 1) * 4]\\n lower_left = [float(xc - w / 2), float(yc - h / 2)]\\n upper_right = [float(xc + w / 2), float(yc + h / 2)]\\n \\n camps.append({\\n 'id': camp_id,\\n 'lowerLeft': lower_left,\\n 'upperRight': upper_right,\\n })\\n \\n # 计算运行时间\\n runtime = time.time() - start_time\\n \\n return {\\n \\\"camps\\\": camps,\\n \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success\\\",\\n \\\"runtime\\\": runtime,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": float(objectives['maximization']),\\n \\\"dispersion_score\\\": float(objectives['dispersion']),\\n \\\"suitability_score\\\": float(objectives['suitability']),\\n \\\"forward_score\\\": float(objectives['forward']),\\n \\\"withdrawal_score\\\": float(objectives['withdrawal']),\\n \\\"combined_score\\\": float(objectives['combined'])\\n },\\n \\\"algorithm_info\\\": {\\n \\\"name\\\": \\\"分阶段模拟退火算法\\\",\\n \\\"iterations\\\": result['iterations'],\\n \\\"final_temperature\\\": sa_optimizer.state_manager.temperature,\\n \\\"acceptance_rate\\\": result['acceptance_rate']\\n }\\n }\\n else:\\n # 约束违反\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"分阶段模拟退火优化失败：约束违反\\\",\\n \\\"runtime\\\": time.time() - start_time,\\n \\\"constraint_violations\\\": constraints\\n }\\n else:\\n # 优化失败\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": all_other_areas,\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"分阶段模拟退火优化失败：未找到解\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n \\n except Exception as e:\\n print(f\\\"分阶段模拟退火算法错误: {str(e)}\\\")\\n import traceback\\n traceback.print_exc()\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"分阶段模拟退火算法错误: {str(e)}\\\"\\n }\\n\\n\\ndef layout_priority_areas_first_sa(input_data: Dict, algorithm_config: Dict) -> Dict:\\n \\\"\\\"\\\"其他区域模拟退火布局\\\"\\\"\\\"\\n start_time = time.time()\\n \\n try:\\n other_areas = input_data.get('otherAreas', {})\\n if not other_areas:\\n return {\\n 'otherAreas': [],\\n 'code': 200,\\n 'message': '没有其他区域需要布局'\\n }\\n \\n # 加载配置\\n config = load_config_from_json(input_data, \\\"gridData.json\\\")\\n \\n # 创建优化问题\\n problem = UnifiedOtherAreasProblem(config, config.get('otherAreas', {}), \\n config.get('original_input_data', []))\\n \\n # 创建模拟退火优化器\\n sa_optimizer = create_sa_optimizer(algorithm_config)\\n \\n print(f\\\"=== 其他区域模拟退火优化 ===\\\")\\n print(f\\\"问题维度: {problem.n_var}\\\")\\n print(f\\\"区域数量: {len(config.get('otherAreas', {}))}\\\")\\n \\n # 执行优化\\n result = sa_optimizer.optimize(problem)\\n \\n if result['success'] and result['x'] is not None:\\n # 检查约束满足情况\\n fitness, objectives, constraints, penalty = sa_optimizer.state_manager.evaluate_solution(result['x'])\\n \\n tolerance = 1e-6\\n constraint_satisfied = [\\n constraints['boundary'] <= tolerance,\\n constraints['overlap'] <= tolerance,\\n constraints['distance'] <= tolerance,\\n constraints['heat_negative'] <= tolerance\\n ]\\n \\n all_constraints_satisfied = all(constraint_satisfied)\\n \\n if all_constraints_satisfied:\\n # 优化成功\\n print(f\\\"=== 其他区域模拟退火优化成功 ===\\\")\\n print(f\\\"最终适应度: {result['fitness']:.6f}\\\")\\n print(f\\\"迭代次数: {result['iterations']}\\\")\\n \\n # 构建结果\\n other_areas_result = []\\n area_names = list(config.get('otherAreas', {}).keys())\\n \\n for i, area_name in enumerate(area_names):\\n xc = result['x'][i * 2]\\n yc = result['x'][i * 2 + 1]\\n \\n # 使用实际的长宽而不是计算的正方形\\n if area_name in problem.other_areas_details:\\n w = problem.other_areas_details[area_name]['width']\\n h = problem.other_areas_details[area_name]['length']\\n else:\\n # 默认尺寸\\n area_size = config.get('otherAreas', {}).get(area_name, 100)\\n w = np.sqrt(area_size)\\n h = np.sqrt(area_size)\\n \\n lower_left = [float(xc - w / 2), float(yc - h / 2)]\\n upper_right = [float(xc + w / 2), float(yc + h / 2)]\\n \\n other_areas_result.append({\\n 'id': area_name,\\n 'lowerLeft': lower_left,\\n 'upperRight': upper_right,\\n 'area': float(w * h),\\n 'length': float(h),\\n 'width': float(w)\\n })\\n \\n return {\\n 'otherAreas': other_areas_result,\\n 'code': 200,\\n 'message': 'Success',\\n 'runtime': time.time() - start_time,\\n 'optimization_scores': {\\n 'combined_score': float(objectives['combined'])\\n }\\n }\\n else:\\n # 约束违反\\n return {\\n 'otherAreas': [],\\n 'code': 500,\\n 'message': '其他区域模拟"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "火",
|
||
"timestamp": "2025-09-25T10:42:48.104591"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:42:48.120089",
|
||
"tool_calls": [
|
||
{
|
||
"id": "modify_file:74",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "modify_file",
|
||
"arguments": "{\"path\": \"路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\", \"operation\": \"append\", \"content\": \"\\n\\ndef camp_layout_main_sa(input_data: Dict, grid_path: str = \\\"grid_data.json\\\", config_path: str = \\\"campConfig.json\\\") -> Dict:\\n \\\"\\\"\\\"模拟退火算法主函数 - 营地布局优化\\\"\\\"\\\"\\n start_time = time.time()\\n \\n try:\\n # 加载配置\\n config = load_config_from_json(input_data, grid_path)\\n \\n # 检查是否有其他区域需要布局\\n other_areas = input_data.get('otherAreas', {})\\n \\n # 获取算法参数\\n algorithm_params = input_data.get('algorithmParams', {})\\n \\n # 设置模拟退火默认参数\\n default_sa_params = {\\n 'initial_temperature': 1000.0,\\n 'final_temperature': 0.1,\\n 'cooling_rate': 0.95,\\n 'max_iterations': 10000,\\n 'cooling_schedule': 'exponential',\\n 'acceptance_function': 'metropolis',\\n 'position_perturbation': 0.1,\\n 'size_perturbation': 0.05,\\n 'perturbation_strategy': 'mixed',\\n 'adaptive_perturbation': True,\\n 'verbose': True\\n }\\n \\n # 合并参数\\n sa_params = {**default_sa_params, **algorithm_params.get('sa_params', {})}\\n algorithm_params['sa_params'] = sa_params\\n \\n print(f\\\"=== 模拟退火算法参数 ===\\\")\\n print(f\\\"初始温度: {sa_params['initial_temperature']}\\\")\\n print(f\\\"终止温度: {sa_params['final_temperature']}\\\")\\n print(f\\\"冷却速率: {sa_params['cooling_rate']}\\\")\\n print(f\\\"最大迭代: {sa_params['max_iterations']}\\\")\\n print(f\\\"冷却策略: {sa_params['cooling_schedule']}\\\")\\n print(f\\\"接受函数: {sa_params['acceptance_function']}\\\")\\n print(f\\\"扰动策略: {sa_params['perturbation_strategy']}\\\")\\n \\n # 如果有其他区域，使用分阶段布局\\n if other_areas:\\n print(\\\"检测到其他区域，使用分阶段模拟退火布局...\\\")\\n return camp_layout_main_sa_staged(input_data, grid_path, config_path)\\n \\n # 创建优化问题\\n problem = CampProblem(config)\\n \\n # 创建模拟退火优化器\\n sa_optimizer = create_sa_optimizer({'sa_params': sa_params})\\n \\n print(\\\"开始模拟退火优化...\\\")\\n # 执行优化\\n result = sa_optimizer.optimize(problem)\\n \\n if result['success'] and result['fitness'] < 1e8: # 检查是否找到可行解\\n print(f\\\"模拟退火优化成功！最佳适应度: {result['fitness']:.6f}\\\")\\n \\n # 构建营地结果\\n camps = []\\n camp_ids = config.get('campIds', [])\\n best_x = result['x']\\n \\n for i, camp_id in enumerate(camp_ids):\\n xc, yc, w, h = best_x[i * 4: (i + 1) * 4]\\n lower_left = [float(xc - w / 2), float(yc - h / 2)]\\n upper_right = [float(xc + w / 2), float(yc + h / 2)]\\n \\n camps.append({\\n 'id': camp_id,\\n 'lowerLeft': lower_left,\\n 'upperRight': upper_right,\\n })\\n \\n # 验证约束满足情况\\n constraint_violations = [\\n result['constraints']['boundary'],\\n result['constraints']['overlap'], \\n result['constraints']['distance'],\\n result['constraints']['heat_negative']\\n ]\\n \\n tolerance = 1e-6\\n heat_negative_tolerance = 0\\n \\n constraint_satisfied = [\\n constraint_violations[0] <= tolerance,\\n constraint_violations[1] <= tolerance,\\n constraint_violations[2] <= tolerance,\\n constraint_violations[3] <= heat_negative_tolerance\\n ]\\n \\n all_constraints_satisfied = all(constraint_satisfied)\\n \\n if all_constraints_satisfied:\\n print(\\\"所有约束满足，返回成功结果\\\")\\n \\n # 获取目标函数值\\n objectives = result['objectives']\\n \\n return {\\n \\\"camps\\\": camps,\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success - Simulated Annealing\\\",\\n \\\"runtime\\\": time.time() - start_time,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": float(objectives['maximization']),\\n \\\"dispersion_score\\\": float(objectives['dispersion']),\\n \\\"suitability_score\\\": float(objectives['suitability']),\\n \\\"forward_score\\\": float(objectives['forward']),\\n \\\"withdrawal_score\\\": float(objectives['withdrawal']),\\n \\\"combined_score\\\": float(objectives['combined'])\\n },\\n \\\"algorithm_info\\\": {\\n \\\"algorithm\\\": \\\"Simulated Annealing\\\",\\n \\\"iterations\\\": result['iterations'],\\n \\\"final_temperature\\\": result.get('final_temperature', 0),\\n \\\"acceptance_rate\\\": result['acceptance_rate'],\\n \\\"fitness_history\\\": result.get('fitness_history', [])[-10:] # 最后10个适应度值\\n }\\n }\\n else:\\n print(f\\\"约束违反: boundary={constraint_violations[0]}, overlap={constraint_violations[1]}, \\\"\\n f\\\"distance={constraint_violations[2]}, heat_negative={constraint_violations[3]}\\\")\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"模拟退火布局失败：约束违反 - boundary:{constraint_violations[0]:.6f}, \\\"\\n f\\\"overlap:{constraint_violations[1]:.6f}, distance:{constraint_violations[2]:.6f}, \\\"\\n f\\\"heat_negative:{constraint_violations[3]:.6f}\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n else:\\n print(f\\\"模拟退火优化失败或适应度过高: {result['fitness']}\\\")\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"模拟退火布局失败：未找到可行解，最终适应度: {result['fitness']:.6f}\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n \\n except Exception as e:\\n print(f\\\"模拟退火优化过程异常: {str(e)}\\\")\\n import traceback\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"模拟退火优化过程异常: {str(e)}\\\\n{traceback.format_exc()}\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n\\n\\ndef camp_layout_main_sa_staged(input_data: Dict, grid_path: str = \\\"grid_data.json\\\", config_path: str = \\\"campConfig.json\\\") -> Dict:\\n \\\"\\\"\\\"分阶段模拟退火算法主函数 - 先布局其他区域，再布局营地\\\"\\\"\\\"\\n start_time = time.time()\\n \\n try:\\n # 获取算法参数\\n algorithm_params = input_data.get('algorithmParams', {})\\n \\n # 设置模拟退火默认参数\\n default_sa_params = {\\n 'initial_temperature': 1000.0,\\n 'final_temperature': 0.1,\\n 'cooling_rate': 0.95,\\n 'max_iterations': 10000,\\n 'cooling_schedule': 'exponential',\\n 'acceptance_function': 'metropolis',\\n 'position_perturbation': 0.1,\\n 'size_perturbation': 0.05,\\n 'perturbation_strategy': 'mixed',\\n 'adaptive_perturbation': True,\\n 'verbose': True\\n }\\n \\n # 合并参数\\n sa_params = {**default_sa_params, **algorithm_params.get('sa_params', {})}\\n algorithm_params['sa_params'] = sa_params\\n \\n print(\\\"=== 分阶段模拟退火布局开始 ===\\\")\\n \\n # 第一阶段：布局其他区域\\n print(\\\"第一阶段：布局其他区域...\\\")\\n other_areas_result = layout_other_areas_sa(input_data, algorithm_params)\\n \\n if not other_areas_result:\\n print(\\\"其他区域布局失败，返回空结果\\\")\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": \\\"分阶段模拟退火布局失败：其他区域布局失败\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n \\n print(f\\\"其他区域布局成功，共{len(other_areas_result)}个区域\\\")\\n \\n # 第二阶段：布局营地\\n print(\\\"第二阶段：布局营地...\\\")\\n config = load_config_from_json(input_data, grid_path)\\n problem = CampProblem(config)\\n \\n # 创建模拟退火优化器\\n sa_optimizer = create_sa_optimizer({'sa_params': sa_params})\\n \\n # 执行优化\\n result = sa_optimizer.optimize(problem)\\n \\n if result['success'] and result['fitness'] < 1e8:\\n print(f\\\"营地模拟退火优化成功！最佳适应度: {result['fitness']:.6f}\\\")\\n \\n # 构建营地结果\\n camps = []\\n camp_ids = config.get('campIds', [])\\n best_x = result['x']\\n \\n for i, camp_id in enumerate(camp_ids):\\n xc, yc, w, h = best_x[i * 4: (i + 1) * 4]\\n lower_left = [float(xc - w / 2), float(yc - h / 2)]\\n upper_right = [float(xc + w / 2), float(yc + h / 2)]\\n \\n camps.append({\\n 'id': camp_id,\\n 'lowerLeft': lower_left,\\n 'upperRight': upper_right,\\n })\\n \\n # 验证约束\\n constraint_violations = [\\n result['constraints']['boundary'],\\n result['constraints']['overlap'], \\n result['constraints']['distance'],\\n result['constraints']['heat_negative']\\n ]\\n \\n tolerance = 1e-6\\n heat_negative_tolerance = 0\\n \\n constraint_satisfied = [\\n constraint_violations[0] <= tolerance,\\n constraint_violations[1] <= tolerance,\\n constraint_violations[2] <= tolerance,\\n constraint_violations[3] <= heat_negative_tolerance\\n ]\\n \\n all_constraints_satisfied = all(constraint_satisfied)\\n \\n if all_constraints_satisfied:\\n print(\\\"分阶段模拟退火布局成功！\\\")\\n \\n # 获取目标函数值\\n objectives = result['objectives']\\n \\n return {\\n \\\"camps\\\": camps,\\n \\\"otherAreas\\\": other_areas_result,\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success - Staged Simulated Annealing\\\",\\n \\\"runtime\\\": time.time() - start_time,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": float(objectives['maximization']),\\n \\\"dispersion_score\\\": float(objectives['dispersion']),\\n \\\"suitability_score\\\": float(objectives['suitability']),\\n \\\"forward_score\\\": float(objectives['forward']),\\n \\\"withdrawal_score\\\": float(objectives['withdrawal']),\\n \\\"combined_score\\\": float(objectives['combined'])\\n },\\n \\\"algorithm_info\\\": {\\n \\\"algorithm\\\": \\\"Staged Simulated Annealing\\\",\\n \\\"iterations\\\": result['iterations'],\\n \\\"final_temperature\\\": result.get('final_temperature', 0),\\n \\\"acceptance_rate\\\": result['acceptance_rate'],\\n \\\"other_areas_count\\\": len(other_areas_result)\\n }\\n }\\n else:\\n print(f\\\"营地约束违反: {constraint_violations}\\\")\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": other_areas_result,\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"分阶段模拟退火布局失败：营地约束违反\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n else:\\n print(f\\\"营地模拟退火优化失败: {result['fitness']}\\\")\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": other_areas_result,\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"分阶段模拟退火布局失败：营地优化失败\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n \\n except Exception as e:\\n print(f\\\"分阶段模拟退火优化过程异常: {str(e)}\\\")\\n import traceback\\n return {\\n \\\"camps\\\": [],\\n \\\"otherAreas\\\": [],\\n \\\"code\\\": 500,\\n \\\"message\\\": f\\\"分阶段模拟退火优化过程异常: {str(e)}\\\\n{traceback.format_exc()}\\\",\\n \\\"runtime\\\": time.time() - start_time\\n }\\n\\n\\ndef layout_other_areas_sa(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n \\\"\\\"\\\"模拟退火算法布局其他区域\\\"\\\"\\\"\\n # 其他区域布局开始\\n \\n other_areas = config.get('otherAreas', {})\\n if not other_areas:\\n return []\\n \\n # 获取模拟退火参数\\n sa_params = algorithm_params.get('sa_params', {})\\n \\n print(\\\"其他区域模拟退火布局开始...\\\")\\n \\n # 创建优化问题\\n problem = OtherAreasProblem(config, [], config.get('original_input_data', []))\\n \\n # 创建模拟退火优化器\\n sa_optimizer = create_sa_optimizer({'sa_params': sa_params})\\n \\n # 执行优化\\n result = sa_optimizer.optimize(problem)\\n \\n if result['success'] and result['fitness'] < 1e8:\\n print(f\\\"其他区域模拟退火优化成功！最佳适应度: {result['fitness']:.6f}\\\")\\n \\n # 构建结果\\n other_areas_result = []\\n best_x = result['x']\\n area_names = problem.area_names\\n \\n for i, area_name in enumerate(area_names):\\n xc = best_x[i * 2]\\n yc = best_x[i * 2 + 1]\\n \\n # 使用实际的长宽\\n if area_name in problem.other_areas_details:\\n w = problem.other_areas_details[area_name]['width']\\n h = problem.other_areas_details[area_name]['length']\\n else:\\n # 默认尺寸\\n area_size = problem.other_areas[area_name]\\n w = np.sqrt(area_size) * 0.8\\n h = area_size / w\\n \\n lower_left = [float(xc - w / 2), float(yc - h / 2)]\\n upper_right = [float(xc + w / 2), float(yc + h / 2)]\\n \\n other_areas_result.append({\\n 'id': area_name,\\n 'lowerLeft': lower_left,\\n 'upperRight': upper_right,\\n 'area': float(w * h),\\n 'center': [float(xc), float(yc)]\\n })\\n \\n print(f\\\"成功布局{len(other_areas_result)}个其他区域\\\")\\n return other_areas_result\\n else:\\n print(f\\\"其他区域模拟退火优化失败: {result['fitness']}\\\")\\n return []\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\", \"size\": 14019, \"mode\": \"a\"}",
|
||
"timestamp": "2025-09-25T10:42:49.932700",
|
||
"tool_call_id": "modify_file:74",
|
||
"name": "modify_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在需要在zongti_sa.py中添加camp_layout_main_sa函数的导入。首先让我查看当前的导入情况：",
|
||
"timestamp": "2025-09-25T10:43:00.910025"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:43:00.923679",
|
||
"tool_calls": [
|
||
{
|
||
"id": "run_command:75",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "run_command",
|
||
"arguments": "{\"command\": \"grep -n \\\"from Camp_layout_gaocheng\\\" 路径规划/zongti_sa.py\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"output\": \"10:from Camp_layout_gaocheng_fsbj_yichuan import layout_other_areas, layout_priority_areas_first\\n\", \"stdout\": \"10:from Camp_layout_gaocheng_fsbj_yichuan import layout_other_areas, layout_priority_areas_first\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"from Camp_layout_gaocheng\\\" 路径规划/zongti_sa.py\"}",
|
||
"timestamp": "2025-09-25T10:43:02.738320",
|
||
"tool_call_id": "run_command:75",
|
||
"name": "run_command"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:43:07.162807",
|
||
"tool_calls": [
|
||
{
|
||
"id": "modify_file:76",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "modify_file",
|
||
"arguments": "{\"path\": \"路径规划/zongti_sa.py\", \"operation\": \"replace\", \"old_text\": \"from Camp_layout_gaocheng_fsbj_yichuan import layout_other_areas, layout_priority_areas_first\", \"content\": \"from Camp_layout_gaocheng_fsbj_yichuan import layout_other_areas, layout_priority_areas_first, camp_layout_main_sa\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/zongti_sa.py\", \"size\": 44897, \"mode\": \"w\", \"replacements\": 1}",
|
||
"timestamp": "2025-09-25T10:43:08.977594",
|
||
"tool_call_id": "modify_file:76",
|
||
"name": "modify_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我创建一个配置文件示例，用于模拟退火算法：",
|
||
"timestamp": "2025-09-25T10:43:14.402858"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:43:14.412085",
|
||
"tool_calls": [
|
||
{
|
||
"id": "create_file:77",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "create_file",
|
||
"arguments": "{\"path\": \"路径规划/campConfig_sa.json\", \"file_type\": \"txt\", \"annotation\": \"模拟退火算法配置文件示例\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/campConfig_sa.json\", \"size\": 0}",
|
||
"timestamp": "2025-09-25T10:43:16.224721",
|
||
"tool_call_id": "create_file:77",
|
||
"name": "create_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:43:27.681589",
|
||
"tool_calls": [
|
||
{
|
||
"id": "modify_file:78",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "modify_file",
|
||
"arguments": "{\"path\": \"路径规划/campConfig_sa.json\", \"operation\": \"append\", \"content\": \"{\\n \\\"mnth\\\": {\\n \\\"description\\\": \\\"模拟退火算法参数配置\\\",\\n \\\"initial_temperature\\\": 1000.0,\\n \\\"final_temperature\\\": 0.1,\\n \\\"cooling_rate\\\": 0.95,\\n \\\"max_iterations\\\": 10000,\\n \\\"cooling_schedule\\\": \\\"exponential\\\",\\n \\\"acceptance_function\\\": \\\"metropolis\\\",\\n \\\"position_perturbation\\\": 0.1,\\n \\\"size_perturbation\\\": 0.05,\\n \\\"perturbation_strategy\\\": \\\"mixed\\\",\\n \\\"adaptive_perturbation\\\": true,\\n \\\"adaptive_temperature\\\": true,\\n \\\"penalty_initial\\\": 1000.0,\\n \\\"penalty_growth_rate\\\": 1.1,\\n \\\"convergence_threshold\\\": 1e-6,\\n \\\"stagnation_limit\\\": 1000,\\n \\\"verbose\\\": true,\\n \\\"save_history\\\": true,\\n \\\"history_interval\\\": 100\\n },\\n \\\"yc\\\": {\\n \\\"description\\\": \\\"遗传算法参数配置\\\",\\n \\\"popSize\\\": 300,\\n \\\"maxGenerations\\\": 200,\\n \\\"crossoverProb\\\": 0.7,\\n \\\"crossoverEta\\\": 30,\\n \\\"mutationProb\\\": 0.7,\\n \\\"mutationEta\\\": 30,\\n \\\"eliminate_duplicates\\\": true\\n }\\n}\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/campConfig_sa.json\", \"size\": 851, \"mode\": \"a\"}",
|
||
"timestamp": "2025-09-25T10:43:29.496552",
|
||
"tool_call_id": "modify_file:78",
|
||
"name": "modify_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "现在让我创建一个使用示例和配置说明文档：",
|
||
"timestamp": "2025-09-25T10:43:33.943279"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:43:33.958753",
|
||
"tool_calls": [
|
||
{
|
||
"id": "create_file:79",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "create_file",
|
||
"arguments": "{\"path\": \"路径规划/simulated_annealing_usage.md\", \"file_type\": \"md\", \"annotation\": \"模拟退火算法使用说明\"}"
|
||
}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"role": "tool",
|
||
"content": "{\"success\": true, \"path\": \"路径规划/simulated_annealing_usage.md\", \"size\": 0}",
|
||
"timestamp": "2025-09-25T10:43:35.770197",
|
||
"tool_call_id": "create_file:79",
|
||
"name": "create_file"
|
||
},
|
||
{
|
||
"role": "assistant",
|
||
"content": "",
|
||
"timestamp": "2025-09-25T10:44:30.733704",
|
||
"tool_calls": [
|
||
{
|
||
"id": "modify_file:80",
|
||
"index": 0,
|
||
"type": "function",
|
||
"function": {
|
||
"name": "modify_file",
|
||
"arguments": "{\"path\": \"路径规划/simulated_annealing_usage.md\", \"operation\": \"append\", \"content\": \"# 模拟退火算法使用说明\\n\\n## 🎯 算法概述\\n\\n模拟退火算法（Simulated Annealing, SA）是一种基于物理退火原理的全局优化算法，通过模拟金属退火过程来寻找全局最优解。相比遗传算法，模拟退火在某些问题上具有更好的局部搜索能力和收敛性。\\n\\n## 🔧 算法特点\\n\\n### ✅ 优势\\n- **全局搜索能力**: 通过温度控制和概率接受机制避免陷入局部最优\\n- **自适应调节**: 支持自适应温度调节和扰动幅度调整\\n- **多种冷却策略**: 提供指数冷却、线性冷却、自适应冷却等多种策略\\n- **高效约束处理**: 动态惩罚因子机制处理复杂约束\\n- **详细日志**: 完整的优化过程记录和性能分析\\n\\n### ⚠️ 注意事项\\n- **参数敏感**: 初始温度、冷却速率等参数需要合理设置\\n- **计算时间**: 相比遗传算法可能需要更多迭代次数\\n- **收敛判断**: 需要根据问题特点设置合适的收敛条件\\n\\n## 📋 配置参数\\n\\n### 基本参数\\n```json\\n{\\n \\\"initial_temperature\\\": 1000.0, // 初始温度 (建议: 100-10000)\\n \\\"final_temperature\\\": 0.1, // 终止温度 (建议: 0.01-1.0)\\n \\\"cooling_rate\\\": 0.95, // 冷却速率 (建议: 0.8-0.99)\\n \\\"max_iterations\\\": 10000, // 最大迭代次数 (建议: 1000-50000)\\n \\\"cooling_schedule\\\": \\\"exponential\\\" // 冷却策略\\n}\\n```\\n\\n### 邻域搜索参数\\n```json\\n{\\n \\\"position_perturbation\\\": 0.1, // 位置扰动比例 (建议: 0.05-0.3)\\n \\\"size_perturbation\\\": 0.05, // 尺寸扰动比例 (建议: 0.01-0.1)\\n \\\"perturbation_strategy\\\": \\\"mixed\\\", // 扰动策略: position/size/mixed\\n \\\"adaptive_perturbation\\\": true, // 是否自适应扰动\\n \\\"min_perturbation\\\": 0.01, // 最小扰动幅度\\n \\\"max_perturbation\\\": 0.3 // 最大扰动幅度\\n}\\n```\\n\\n### 接受准则参数\\n```json\\n{\\n \\\"acceptance_function\\\": \\\"metropolis\\\", // 接受函数: metropolis/boltzmann/threshold\\n \\\"adaptive_temperature\\\": true, // 是否自适应温度调节\\n}\\n```\\n\\n### 约束处理参数\\n```json\\n{\\n \\\"penalty_initial\\\": 1000.0, // 初始惩罚因子\\n \\\"penalty_growth_rate\\\": 1.1, // 惩罚因子增长率\\n \\\"penalty_adaptive\\\": true, // 是否自适应惩罚\\n}\\n```\\n\\n### 收敛参数\\n```json\\n{\\n \\\"convergence_threshold\\\": 1e-6, // 收敛阈值\\n \\\"stagnation_limit\\\": 1000, // 停滞限制\\n}\\n```\\n\\n## 🚀 使用方法\\n\\n### 1. 基本调用\\n\\n在 `zongti_sa.py` 中，模拟退火算法已经集成到 `yingdibuju()` 函数中：\\n\\n```python\\n# 请求数据示例\\nrequest_data = {\\n \\\"layoutAlgorithm\\\": \\\"mnth\\\", // 选择模拟退火算法\\n \\\"layoutStrategy\\\": \\\"fsbj\\\", // 布局策略\\n \\\"functionWeight\\\": [0.33, 0.33, 0.34, 0.0, 0.0], // 目标权重\\n \\\"constraints\\\": {\\n \\\"minDistance\\\": 5.0,\\n \\\"minSize\\\": 50.0,\\n \\\"maxSize\\\": 120.0,\\n \\\"direction\\\": \\\"left\\\"\\n },\\n \\\"campIds\\\": [\\\"camp1\\\", \\\"camp2\\\", \\\"camp3\\\"],\\n \\\"scene\\\": \\\"YJFJ\\\",\\n // 其他必要参数...\\n}\\n```\\n\\n### 2. 自定义算法参数\\n\\n可以通过 `algorithmParams` 字段传递自定义参数：\\n\\n```json\\n{\\n \\\"layoutAlgorithm\\\": \\\"mnth\\\",\\n \\\"algorithmParams\\\": {\\n \\\"sa_params\\\": {\\n \\\"initial_temperature\\\": 2000.0,\\n \\\"final_temperature\\\": 0.01,\\n \\\"cooling_rate\\\": 0.98,\\n \\\"max_iterations\\\": 20000,\\n \\\"cooling_schedule\\\": \\\"adaptive\\\",\\n \\\"position_perturbation\\\": 0.15,\\n \\\"adaptive_perturbation\\\": true,\\n \\\"verbose\\\": true\\n }\\n }\\n}\\n```\\n\\n### 3. 配置文件使用\\n\\n可以使用配置文件 `campConfig_sa.json` 来管理算法参数：\\n\\n```json\\n{\\n \\\"mnth\\\": {\\n \\\"initial_temperature\\\": 1500.0,\\n \\\"final_temperature\\\": 0.05,\\n \\\"cooling_rate\\\": 0.96,\\n \\\"max_iterations\\\": 15000,\\n \\\"cooling_schedule\\\": \\\"exponential\\\",\\n \\\"acceptance_function\\\": \\\"metropolis\\\",\\n \\\"position_perturbation\\\": 0.12,\\n \\\"size_perturbation\\\": 0.08,\\n \\\"adaptive_perturbation\\\": true,\\n \\\"verbose\\\": true\\n }\\n}\\n```\\n\\n## 📊 性能调优建议\\n\\n### 初始温度设置\\n- **高初始温度** (如 5000+): 更强的全局搜索能力，但收敛慢\\n- **低初始温度** (如 100-500): 更快的收敛，但可能陷入局部最优\\n- **建议**: 从 1000-2000 开始，根据问题复杂度调整\\n\\n### 冷却速率选择\\n- **慢冷却** (0.95-0.99): 更好的解质量，但计算时间长\\n- **快冷却** (0.8-0.9): 快速收敛，但可能错过最优解\\n- **建议**: 使用 0.95-0.98 作为起点\\n\\n### 扰动策略\\n- **位置扰动** (`position`): 适用于位置敏感问题\\n- **尺寸扰动** (`size`): 适用于尺寸优化问题\\n- **混合扰动** (`mixed`): 推荐，平衡探索和开发\\n\\n### 冷却策略\\n- **指数冷却** (`exponential`): 稳定可靠，推荐默认使用\\n- **线性冷却** (`linear`): 简单直接，适合简单问题\\n- **自适应冷却** (`adaptive`): 智能调节，适合复杂问题\\n- **对数冷却** (`logarithmic`): 理论保证，但收敛慢\\n\\n## 🔍 结果分析\\n\\n### 返回结果格式\\n```json\\n{\\n \\\"camps\\\": [...], // 营地布局结果\\n \\\"otherAreas\\\": [...], // 其他区域布局结果\\n \\\"code\\\": 200, // 状态码\\n \\\"message\\\": \\\"Success\\\", // 状态信息\\n \\\"runtime\\\": 12.34, // 运行时间(秒)\\n \\\"optimization_scores\\\": { // 优化得分\\n \\\"maximization_score\\\": 0.85,\\n \\\"dispersion_score\\\": 0.72,\\n \\\"suitability_score\\\": 0.91,\\n \\\"forward_score\\\": 0.68,\\n \\\"withdrawal_score\\\": 0.75,\\n \\\"combined_score\\\": 0.82\\n },\\n \\\"algorithm_info\\\": { // 算法信息\\n \\\"algorithm\\\": \\\"Simulated Annealing\\\",\\n \\\"iterations\\\": 8500,\\n \\\"final_temperature\\\": 0.08,\\n \\\"acceptance_rate\\\": 0.23\\n }\\n}\\n```\\n\\n### 性能指标\\n- **迭代次数**: 实际执行的迭代次数\\n- **最终温度**: 算法终止时的温度\\n- **接受率**: 新解被接受的比例 (0.2-0.5 为正常范围)\\n- **运行时间**: 算法执行总时间\\n- **约束违反**: 各项约束的违反程度\\n\\n## ⚙️ 高级功能\\n\\n### 自适应参数调节\\n算法支持自适应调节以下参数：\\n- **温度调节**: 根据接受率动态调整冷却速率\\n- **扰动幅度**: 根据搜索状态调整扰动大小\\n- **惩罚因子**: 根据约束违反程度调整惩罚强度\\n\\n### 历史记录\\n设置 `save_history: true` 可以保存优化过程：\\n- 温度变化历史\\n- 适应度改进历史\\n- 约束违反变化\\n- 接受率变化\\n\\n### 多种接受准则\\n- **Metropolis准则**: 经典接受准则，推荐默认使用\\n- **Boltzmann准则**: 适用于能量景观复杂的问题\\n- **阈值接受**: 简单快速，适合实时应用\\n\\n## 🚨 常见问题\\n\\n### Q: 算法收敛太慢怎么办？\\nA: 尝试以下调整：\\n- 提高冷却速率 (如 0.95 → 0.9)\\n- 降低初始温度 (如 1000 → 500)\\n- 减少最大迭代次数\\n- 使用更快的冷却策略\\n\\n### Q: 总是陷入局部最优怎么办？\\nA: 尝试以下调整：\\n- 提高初始温度\\n- 降低冷却速率\\n- 增加扰动幅度\\n- 使用自适应冷却策略\\n\\n### Q: 约束处理效果不好怎么办？\\nA: 尝试以下调整：\\n- 调整初始惩罚因子\\n- 启用自适应惩罚\\n- 调整约束违反容忍度\\n- 使用分阶段优化策略\\n\\n### Q: 如何与其他算法比较？\\nA: 建议比较指标：\\n- 最终解的质量 (适应度值)\\n- 收敛速度 (达到相同质量所需时间)\\n- 稳定性 (多次运行的方差)\\n- 约束满足程度\\n\\n## 📞 技术支持\\n\\n如遇到问题，请提供以下信息：\\n1. 完整的输入参数\\n2. 错误信息和堆栈跟踪\\n3. 算法配置参数\\n4. 运行环境信息\\n5. 期望的行为描述\"}"
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