662 lines
112 KiB
JSON
662 lines
112 KiB
JSON
{
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"id": "conv_20250925_094102_391",
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"title": "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...",
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"created_at": "2025-09-25T09:41:02.391491",
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"updated_at": "2025-09-26T15:10:49.817102",
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"messages": [
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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，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",
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"timestamp": "2025-09-25T09:56:31.915179"
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},
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{
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"role": "assistant",
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"content": "我来帮你分析这两个路径规划文件，了解当前的结构和接口，然后为你设计模拟退火算法。",
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"timestamp": "2025-09-25T09:56:34.424214"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T09:56:34.427181",
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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",
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"function": {
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"name": "focus_file",
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"arguments": "{\"path\": \"/Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.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: /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\", \"focused_files\": [\"/Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"], \"file_size\": 44540}",
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"timestamp": "2025-09-25T09:56:36.232944",
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"tool_call_id": "focus_file:0",
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"name": "focus_file"
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T09:56:37.621697",
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"tool_calls": [
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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": {
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"name": "focus_file",
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"arguments": "{\"path\": \"/Users/jojo/Desktop/agent_kimi_源码备份/project/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: /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\", \"focused_files\": [\"/Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\", \"/Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"], \"file_size\": 121363}",
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"timestamp": "2025-09-25T09:56:39.428135",
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"tool_call_id": "focus_file:1",
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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": "现在让我分析这两个文件的关键信息。首先查看zongti.py的主要结构和接口：",
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"timestamp": "2025-09-25T09:56:42.325707"
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},
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T09:56:42.327503",
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"tool_calls": [
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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": {
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n \\\"def \\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py | head -20\"}"
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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 \\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py | head -20\"}",
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"timestamp": "2025-09-25T09:56:44.131585",
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"tool_call_id": "run_command:2",
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"name": "run_command"
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T09:56:45.896046",
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"tool_calls": [
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n \\\"class \\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py | head -10\"}"
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"content": "{\"success\": true, \"output\": \"\", \"stdout\": \"\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"class \\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py | head -10\"}",
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"timestamp": "2025-09-25T09:56:47.700026",
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"timestamp": "2025-09-25T09:56:49.213642",
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"function": {
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n \\\"遗传\\\\|genetic\\\\|Genetic\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"}"
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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\\\\|Genetic\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"}",
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"timestamp": "2025-09-25T09:56:51.019823",
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"content": "",
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"timestamp": "2025-09-25T09:56:52.691324",
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"tool_calls": [
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"function": {
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"arguments": "{\"command\": \"grep -A 10 -B 5 \\\"nsga3\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"}"
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"role": "tool",
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"content": "{\"success\": true, \"output\": \"\\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}\\n\\n# 全局路径规划器实例缓存\\nplanner_cache = {}\\n\\n--\\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--\\n@synchronized_request(shigong_lock)\\ndef 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 alg += f\\\"\\\"\\\" 施工方式：{rec_data[\\\"flowType\\\"]}流水施工，施工算法类类型：{ALG_MAP[algorithm]}\\\"\\\"\\\"\\n\", \"stdout\": \"\\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}\\n\\n# 全局路径规划器实例缓存\\nplanner_cache = {}\\n\\n--\\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--\\n@synchronized_request(shigong_lock)\\ndef 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 alg += f\\\"\\\"\\\" 施工方式：{rec_data[\\\"flowType\\\"]}流水施工，施工算法类类型：{ALG_MAP[algorithm]}\\\"\\\"\\\"\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 10 -B 5 \\\"nsga3\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"}",
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"timestamp": "2025-09-25T09:56:54.497725",
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"timestamp": "2025-09-25T09:56:56.827840",
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"arguments": "{\"command\": \"grep -n \\\"main_shigong_yichuan\\\\|main_yingdi_yichuan\\\\|main_sheshi_yichuan\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"}"
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"content": "{\"success\": true, \"output\": \"7:from construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n45: \\\"nsga3\\\": main_shigong_yichuan,\\n46: \\\"benders\\\": main_shigong_yichuan,\\n47: \\\"lagrange\\\": main_shigong_yichuan,\\n48: \\\"lsc\\\": main_shigong_yichuan,\\n49: \\\"zsl\\\": main_shigong_yichuan,\\n50: \\\"fzdj\\\": main_shigong_yichuan\\n\", \"stdout\": \"7:from construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\n45: \\\"nsga3\\\": main_shigong_yichuan,\\n46: \\\"benders\\\": main_shigong_yichuan,\\n47: \\\"lagrange\\\": main_shigong_yichuan,\\n48: \\\"lsc\\\": main_shigong_yichuan,\\n49: \\\"zsl\\\": main_shigong_yichuan,\\n50: \\\"fzdj\\\": main_shigong_yichuan\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"main_shigong_yichuan\\\\|main_yingdi_yichuan\\\\|main_sheshi_yichuan\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"}",
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"content": "{\"success\": true, \"output\": \" return decorator\\n\\n\\n@app.route(\\\"/api/suanfa/yingdi\\\", methods=[\\\"POST\\\"])\\n@synchronized_request(yingdi_lock)\\ndef 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\": \" return decorator\\n\\n\\n@app.route(\\\"/api/suanfa/yingdi\\\", methods=[\\\"POST\\\"])\\n@synchronized_request(yingdi_lock)\\ndef 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 -B 5 \\\"def yingdibuju\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"}",
|
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"arguments": "{\"command\": \"grep -A 30 \\\"rec_data = request.get_json\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py | head -40\"}"
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|
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"content": "{\"success\": true, \"output\": \" 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 \\\"fsbj\\\": \\\"分散布局\\\"\\n }\\n--\\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\", \"stdout\": \" 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 \\\"fsbj\\\": \\\"分散布局\\\"\\n }\\n--\\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\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"rec_data = request.get_json\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py | head -40\"}",
|
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|
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"timestamp": "2025-09-25T09:57:08.014864",
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|
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"arguments": "{\"command\": \"grep -A 50 \\\"def sheshibuju\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py | head -60\"}"
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|
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|
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"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 facilitySafeDistance = constraintParam.get(\\\"facilitySafeDistance\\\", \\\"未提供\\\")\\n alg += f\\\" 约束条件：\\\\n 设施安全距离：{facilitySafeDistance}\\\"\\n\\n logging.debug(alg)\\n\\n # 调用算法（这里假设算法内部能处理不同长度的权重）\\n final_data = facility_layout_main(rec_data)\\n\\n response = {\\n \\\"facilityLayout\\\": final_data.get(\\\"facilityLayout\\\"),\\n \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n }\\n\\n return jsonify(response)\\n\\n except Exception as e:\\n return jsonify({\\n \\\"facilityLayout\\\": None,\\n \\\"code\\\": 500,\\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 facilitySafeDistance = constraintParam.get(\\\"facilitySafeDistance\\\", \\\"未提供\\\")\\n alg += f\\\" 约束条件：\\\\n 设施安全距离：{facilitySafeDistance}\\\"\\n\\n logging.debug(alg)\\n\\n # 调用算法（这里假设算法内部能处理不同长度的权重）\\n final_data = facility_layout_main(rec_data)\\n\\n response = {\\n \\\"facilityLayout\\\": final_data.get(\\\"facilityLayout\\\"),\\n \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n }\\n\\n return jsonify(response)\\n\\n except Exception as e:\\n return jsonify({\\n \\\"facilityLayout\\\": None,\\n \\\"code\\\": 500,\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 50 \\\"def sheshibuju\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py | head -60\"}",
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|
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|
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"tool_calls": [
|
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|
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"id": "run_command:10",
|
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|
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"content": "{\"success\": true, \"output\": \"6:from Facility_main_faster import facility_layout_main\\n794: final_data = facility_layout_main(rec_data)\\n\", \"stdout\": \"6:from Facility_main_faster import facility_layout_main\\n794: final_data = facility_layout_main(rec_data)\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"facility_layout_main\\\\|main_yingdi_yichuan\\\\|main_sheshi\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/zongti.py\"}",
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"content": "{\"success\": true, \"output\": \"\\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 # 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--\\n\\n return score\\n\\n\", \"stdout\": \"\\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 # 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--\\n\\n return score\\n\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 20 -B 5 \\\"class.*:\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -30\"}",
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"content": "{\"success\": true, \"output\": \"1595:def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n1683:def main_gaocheng_yichuan_camp(\\n2094:def layout_priority_areas_first(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n2692:def main_gaocheng_yichuan_camp_staged(\\n\", \"stdout\": \"1595:def layout_other_areas(config: Dict, camp_positions: List[Dict], algorithm_params: Dict) -> List[Dict]:\\n1683:def main_gaocheng_yichuan_camp(\\n2094:def layout_priority_areas_first(config: Dict, algorithm_params: Dict) -> List[Dict]:\\n2692:def main_gaocheng_yichuan_camp_staged(\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n \\\"def main.*yichuan\\\\|def.*optimize\\\\|def.*algorithm\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"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\", \"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\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"def main_gaocheng_yichuan_camp(\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"arguments": "{\"command\": \"grep -A 50 \\\"algorithm = NSGA3\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
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"content": "{\"success\": false, \"output\": \"\", \"stdout\": \"\", \"stderr\": \"\", \"return_code\": 1, \"command\": \"grep -A 50 \\\"algorithm = NSGA3\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"arguments": "{\"command\": \"grep -A 20 \\\"NSGA3\\\\|GeneticAlgorithm\\\\|pymoo\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
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"content": "{\"success\": true, \"output\": \"from pymoo.algorithms.soo.nonconvex.ga import GA\\nfrom pymoo.core.problem import ElementwiseProblem\\nfrom pymoo.optimize import minimize\\nfrom pymoo.operators.crossover.sbx import SBX\\nfrom pymoo.operators.mutation.pm import PM\\nfrom pymoo.operators.sampling.rnd import FloatRandomSampling\\nfrom pymoo.termination import get_termination\\nfrom typing import Dict, Tuple, List\\nfrom pymoo.config import Config\\nfrom pymoo.core.survival import Survival\\nfrom pymoo.util.nds.non_dominated_sorting import NonDominatedSorting\\nfrom pymoo.core.individual import Individual\\nfrom scipy.ndimage import gaussian_filter\\nimport json\\nimport re\\nfrom collections import defaultdict\\n# import traceback\\nfrom geology.heatmap import get_heatmap\\n\\nConfig.show_compile_hint = False\\n\\n\\ndef calculate_slope_aspect(elevation, dx, dy, sigma=9):\\n \\\"\\\"\\\"改进的坡向计算（考虑实际地理距离）\\\"\\\"\\\"\\n # 根据实际网格尺寸调整sigma值\\n sigma_x = sigma / dx\\n sigma_y = sigma / dy\\n elevation_smoothed = gaussian_filter(elevation, sigma=(sigma_y, sigma_x))\\n\\n # 计算高程梯度\\n dz_dx, dz_dy = np.gradient(elevation_smoothed, dx, abs(dy))\\n\\n\", \"stdout\": \"from pymoo.algorithms.soo.nonconvex.ga import GA\\nfrom pymoo.core.problem import ElementwiseProblem\\nfrom pymoo.optimize import minimize\\nfrom pymoo.operators.crossover.sbx import SBX\\nfrom pymoo.operators.mutation.pm import PM\\nfrom pymoo.operators.sampling.rnd import FloatRandomSampling\\nfrom pymoo.termination import get_termination\\nfrom typing import Dict, Tuple, List\\nfrom pymoo.config import Config\\nfrom pymoo.core.survival import Survival\\nfrom pymoo.util.nds.non_dominated_sorting import NonDominatedSorting\\nfrom pymoo.core.individual import Individual\\nfrom scipy.ndimage import gaussian_filter\\nimport json\\nimport re\\nfrom collections import defaultdict\\n# import traceback\\nfrom geology.heatmap import get_heatmap\\n\\nConfig.show_compile_hint = False\\n\\n\\ndef calculate_slope_aspect(elevation, dx, dy, sigma=9):\\n \\\"\\\"\\\"改进的坡向计算（考虑实际地理距离）\\\"\\\"\\\"\\n # 根据实际网格尺寸调整sigma值\\n sigma_x = sigma / dx\\n sigma_y = sigma / dy\\n elevation_smoothed = gaussian_filter(elevation, sigma=(sigma_y, sigma_x))\\n\\n # 计算高程梯度\\n dz_dx, dz_dy = np.gradient(elevation_smoothed, dx, abs(dy))\\n\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 20 \\\"NSGA3\\\\|GeneticAlgorithm\\\\|pymoo\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"arguments": "{\"command\": \"grep -A 30 \\\"algorithm = GA\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}"
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"content": "{\"success\": true, \"output\": \" 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 if all(v <= 1e-6 for v in constraint_violations):\\n--\\n algorithm = GA(\\n pop_size=params['popSize'],\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=params['crossoverProb'],\\n eta=params['crossoverEta']\\n ),\\n mutation=PM(\\n prob=params['mutationProb'],\\n eta=params['mutationEta']\\n ),\\n eliminate_duplicates=True,\\n save_history=False,\\n )\\n\\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", params['maxGenerations'])\\n res = minimize(\\n problem,\\n algorithm,\\n termination=termination,\\n verbose=True\\n )\\n\\n # 计算运行时间\\n end_time = time.time()\\n runtime = end_time - start_time\\n # 算法运行时间计算完成\\n\\n # 检查结果\\n if res.X is not None:\\n--\\n extended_algorithm = GA(\\n pop_size=extended_params['popSize'],\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=extended_params['crossoverProb'],\\n eta=extended_params['crossoverEta']\\n ),\\n mutation=PM(\\n prob=extended_params['mutationProb'],\\n eta=extended_params['mutationEta']\\n ),\\n eliminate_duplicates=True,\\n save_history=False\\n )\\n\\n # 重新运行优化\\n extended_termination = get_termination(\\\"n_gen\\\", extended_params['maxGenerations'])\\n extended_res = minimize(\\n problem,\\n extended_algorithm,\\n extended_termination,\\n verbose=True\\n )\\n\\n # 检查扩展运行的结果\\n if extended_res.X is not None:\\n extended_violations = [\\n problem._boundary_violation_vectorized(extended_res.X),\\n problem._overlap_violation_vectorized(extended_res.X),\\n problem._distance_violation_vectorized(extended_res.X),\\n problem._heat_negative_violation_vectorized(extended_res.X)\\n--\\n algorithm = GA(\\n pop_size=algorithm_params.get('popSize', 100),\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=algorithm_params.get('crossoverProb', 0.9),\\n eta=algorithm_params.get('crossoverEta', 15)\\n ),\\n mutation=PM(\\n prob=algorithm_params.get('mutationProb', 0.1),\\n eta=algorithm_params.get('mutationEta', 20)\\n ),\\n eliminate_duplicates=True,\\n save_history=False,\\n )\\n \\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", algorithm_params.get('maxGenerations', 50))\\n res = minimize(\\n problem,\\n algorithm,\\n termination=termination,\\n verbose=True\\n )\\n \\n # 处理结果\\n all_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 tolerance = 1e-6\\n--\\n algorithm = GA(\\n pop_size=params['popSize'],\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=params['crossoverProb'],\\n eta=params['crossoverEta']\\n ),\\n mutation=PM(\\n prob=params['mutationProb'],\\n eta=params['mutationEta']\\n ),\\n eliminate_duplicates=True,\\n save_history=False,\\n )\\n\\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", params['maxGenerations'])\\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\", \"stdout\": \" 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 if all(v <= 1e-6 for v in constraint_violations):\\n--\\n algorithm = GA(\\n pop_size=params['popSize'],\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=params['crossoverProb'],\\n eta=params['crossoverEta']\\n ),\\n mutation=PM(\\n prob=params['mutationProb'],\\n eta=params['mutationEta']\\n ),\\n eliminate_duplicates=True,\\n save_history=False,\\n )\\n\\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", params['maxGenerations'])\\n res = minimize(\\n problem,\\n algorithm,\\n termination=termination,\\n verbose=True\\n )\\n\\n # 计算运行时间\\n end_time = time.time()\\n runtime = end_time - start_time\\n # 算法运行时间计算完成\\n\\n # 检查结果\\n if res.X is not None:\\n--\\n extended_algorithm = GA(\\n pop_size=extended_params['popSize'],\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=extended_params['crossoverProb'],\\n eta=extended_params['crossoverEta']\\n ),\\n mutation=PM(\\n prob=extended_params['mutationProb'],\\n eta=extended_params['mutationEta']\\n ),\\n eliminate_duplicates=True,\\n save_history=False\\n )\\n\\n # 重新运行优化\\n extended_termination = get_termination(\\\"n_gen\\\", extended_params['maxGenerations'])\\n extended_res = minimize(\\n problem,\\n extended_algorithm,\\n extended_termination,\\n verbose=True\\n )\\n\\n # 检查扩展运行的结果\\n if extended_res.X is not None:\\n extended_violations = [\\n problem._boundary_violation_vectorized(extended_res.X),\\n problem._overlap_violation_vectorized(extended_res.X),\\n problem._distance_violation_vectorized(extended_res.X),\\n problem._heat_negative_violation_vectorized(extended_res.X)\\n--\\n algorithm = GA(\\n pop_size=algorithm_params.get('popSize', 100),\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=algorithm_params.get('crossoverProb', 0.9),\\n eta=algorithm_params.get('crossoverEta', 15)\\n ),\\n mutation=PM(\\n prob=algorithm_params.get('mutationProb', 0.1),\\n eta=algorithm_params.get('mutationEta', 20)\\n ),\\n eliminate_duplicates=True,\\n save_history=False,\\n )\\n \\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", algorithm_params.get('maxGenerations', 50))\\n res = minimize(\\n problem,\\n algorithm,\\n termination=termination,\\n verbose=True\\n )\\n \\n # 处理结果\\n all_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 tolerance = 1e-6\\n--\\n algorithm = GA(\\n pop_size=params['popSize'],\\n sampling=FloatRandomSampling(),\\n crossover=SBX(\\n prob=params['crossoverProb'],\\n eta=params['crossoverEta']\\n ),\\n mutation=PM(\\n prob=params['mutationProb'],\\n eta=params['mutationEta']\\n ),\\n eliminate_duplicates=True,\\n save_history=False,\\n )\\n\\n # 运行优化\\n termination = get_termination(\\\"n_gen\\\", params['maxGenerations'])\\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\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 30 \\\"algorithm = GA\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"content": "{\"success\": true, \"output\": \" aspect[np.isclose(slope, 0)] = -1 # 用-1表示平坦区域\\n\\n return slope, aspect\\n\\n\\ndef 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--\\n\\n# 在Camp_layout_gaocheng_fsbj_yichuan.py中添加其他区域布局类\\nclass OtherAreasProblem(ElementwiseProblem):\\n \\\"\\\"\\\"其他区域布局优化问题\\\"\\\"\\\"\\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 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 # 检查网格尺寸是否有效\\n if self.agg_cols <= 0 or self.agg_rows <= 0:\\n self.dx = 1.0\\n--\\n\\n# 优化历史保存功能已移除\\n\\n\\ndef 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--\\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 # 检查网格尺寸是否有效\\n if self.agg_cols <= 0 or self.agg_rows <= 0:\\n--\\n # 其他区域布局完成\\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\", \"stdout\": \" aspect[np.isclose(slope, 0)] = -1 # 用-1表示平坦区域\\n\\n return slope, aspect\\n\\n\\ndef 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--\\n\\n# 在Camp_layout_gaocheng_fsbj_yichuan.py中添加其他区域布局类\\nclass OtherAreasProblem(ElementwiseProblem):\\n \\\"\\\"\\\"其他区域布局优化问题\\\"\\\"\\\"\\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 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 # 检查网格尺寸是否有效\\n if self.agg_cols <= 0 or self.agg_rows <= 0:\\n self.dx = 1.0\\n--\\n\\n# 优化历史保存功能已移除\\n\\n\\ndef 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--\\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 # 检查网格尺寸是否有效\\n if self.agg_cols <= 0 or self.agg_rows <= 0:\\n--\\n # 其他区域布局完成\\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\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 10 -B 5 \\\"input_data.*Dict\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"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\", \"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\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 15 \\\"def load_config_from_json\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py\"}",
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"arguments": "{\"command\": \"grep -A 50 \\\"return {\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -60\"}"
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"content": "{\"success\": true, \"output\": \" return {\\n \\\"camps\\\": camps,\\n \\\"otherAreas\\\": other_areas_result, # 新增其他区域布局结果\\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 # 尝试从历史中找到满足所有约束的最佳解\\n\\n best_feasible_solution = None\\n best_feasible_fitness = float('inf')\\n\\n if res.history is not None:\\n for gen in res.history:\\n pop = gen.pop\\n X_pop = pop.get(\\\"X\\\")\\n F_pop = pop.get(\\\"F\\\")\\n\\n if X_pop is not None and F_pop is not None:\\n for j, (x_individual, f_individual) in enumerate(zip(X_pop, F_pop)):\\n # 计算该个体的约束违反\\n ind_violations = [\\n problem._boundary_violation_vectorized(x_individual),\\n problem._overlap_violation_vectorized(x_individual),\\n problem._distance_violation_vectorized(x_individual),\\n problem._heat_negative_violation_vectorized(x_individual)\\n ]\\n\\n # 检查是否满足所有约束\\n if (ind_violations[0] <= tolerance and\\n ind_violations[1] <= tolerance and\\n ind_violations[2] <= tolerance and\\n ind_violations[3] <= heat_negative_tolerance):\\n # 如果满足约束且目标函数值更好\\n if f_individual < best_feasible_fitness:\\n best_feasible_solution = x_individual.copy()\\n best_feasible_fitness = f_individual\\n\\n if best_feasible_solution is not None:\\n # 找到满足所有约束的历史最佳解\\n\\n # 使用找到的可行解\\n--\\n return {\\n \\\"camps\\\": camps,\\n \\\"otherAreas\\\": other_areas_result, # 新增其他区域布局结果\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success\\\",\\n \\\"runtime\\\": runtime,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": float(maximization_score),\\n\", \"stdout\": \" return {\\n \\\"camps\\\": camps,\\n \\\"otherAreas\\\": other_areas_result, # 新增其他区域布局结果\\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 # 尝试从历史中找到满足所有约束的最佳解\\n\\n best_feasible_solution = None\\n best_feasible_fitness = float('inf')\\n\\n if res.history is not None:\\n for gen in res.history:\\n pop = gen.pop\\n X_pop = pop.get(\\\"X\\\")\\n F_pop = pop.get(\\\"F\\\")\\n\\n if X_pop is not None and F_pop is not None:\\n for j, (x_individual, f_individual) in enumerate(zip(X_pop, F_pop)):\\n # 计算该个体的约束违反\\n ind_violations = [\\n problem._boundary_violation_vectorized(x_individual),\\n problem._overlap_violation_vectorized(x_individual),\\n problem._distance_violation_vectorized(x_individual),\\n problem._heat_negative_violation_vectorized(x_individual)\\n ]\\n\\n # 检查是否满足所有约束\\n if (ind_violations[0] <= tolerance and\\n ind_violations[1] <= tolerance and\\n ind_violations[2] <= tolerance and\\n ind_violations[3] <= heat_negative_tolerance):\\n # 如果满足约束且目标函数值更好\\n if f_individual < best_feasible_fitness:\\n best_feasible_solution = x_individual.copy()\\n best_feasible_fitness = f_individual\\n\\n if best_feasible_solution is not None:\\n # 找到满足所有约束的历史最佳解\\n\\n # 使用找到的可行解\\n--\\n return {\\n \\\"camps\\\": camps,\\n \\\"otherAreas\\\": other_areas_result, # 新增其他区域布局结果\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success\\\",\\n \\\"runtime\\\": runtime,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": float(maximization_score),\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -A 50 \\\"return {\\\" /Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/Camp_layout_gaocheng_fsbj_yichuan.py | head -60\"}",
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"role": "assistant",
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"content": "现在让我取消聚焦这两个文件，并总结关键信息：",
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"arguments": "{\"path\": \"/Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/algorithm_analysis.md\", \"operation\": \"replace\", \"old_text\": \"\", \"content\": \"# 路径规划算法分析报告\\n\\n## 当前系统结构分析\\n\\n### 1. 总体架构 (zongti.py)\\n\\n#### 主要接口函数\\n- **yingdibuju()**: 营地布局算法接口\\n - 路由: `/api/suanfa/yingdi` (POST)\\n - 支持算法: 遗传(yc)、变尺度(bcd)、鲸鱼(jy)、狼群(lq)、拟牛顿(nd)、萤火虫(yhc)、梯度(tdf)、**模拟退火(mnth)**\\n - 布局策略: 集中布局(jzbj)、分散布局(fsbj)\\n\\n- **sheshibuju()**: 设施布局算法接口 \\n - 路由: `/api/suanfa/sheshi` (POST)\\n - 支持算法: NSGA3(非支配排序遗传算法第三代)、粒子群(lzq)\\n - 默认算法: NSGA3\\n - 目标函数权重: 分散度、可达性、关联度、屏蔽度、适宜性\\n\\n- **shigong_router()**: 施工算法接口\\n - 路由: `/api/suanfa/shigong` (POST)\\n - 支持算法: NSGA3、benders、lagrange、lsc、zsl、fzdj\\n - 实际调用: 全部映射到 `main_shigong_yichuan` (遗传算法)\\n\\n#### 关键依赖\\n```python\\nfrom construction import main_shigong_yichuan_v5 as main_shigong_yichuan\\nfrom Facility_main_faster import facility_layout_main\\n```\\n\\n### 2. 营地布局算法 (Camp_layout_gaocheng_fsbj_yichuan.py)\\n\\n#### 核心问题定义\\n- **CampProblem类**: 继承自ElementwiseProblem的优化问题定义\\n - 支持多目标权重优化\\n - 包含热力负值约束和场景方向\\n - 约束条件: 边界、重叠、负热力、距离\\n\\n#### 主要算法函数\\n- **main_gaocheng_yichuan_camp()**: 主遗传算法函数\\n - 输入: input_data(Dict), grid_path(str), config_path(str), index(int)\\n - 输出: Dict包含camps、otherAreas、code、message、runtime、optimization_scores\\n - 使用pymoo库的GA算法\\n - 参数: popSize, maxGenerations, crossoverProb, crossoverEta, mutationProb, mutationEta\\n\\n- **main_gaocheng_yichuan_camp_staged()**: 分阶段布局版本\\n\\n#### 算法配置参数\\n```python\\ndefault_params = {\\n 'popSize': 100,\\n 'maxGenerations': 30,\\n 'crossoverProb': 0.9,\\n 'crossoverEta': 15,\\n 'mutationProb': 0.1,\\n 'mutationEta': 20\\n}\\n```\\n\\n#### 目标函数\\n- maximization_score: 最大化得分\\n- dispersion_score: 分散度得分 \\n- suitability_score: 适宜性得分\\n- forward_score: 前进方向得分\\n- withdrawal_score: 撤退方向得分\\n- combined_score: 综合得分\\n\\n### 3. 关键数据结构\\n\\n#### 输入数据格式 (input_data)\\n```json\\n{\\n \\\"layoutAlgorithm\\\": \\\"算法名称\\\",\\n \\\"layoutStrategy\\\": \\\"布局策略\\\", \\n \\\"campIds\\\": [\\\"营地ID列表\\\"],\\n \\\"scene\\\": \\\"场景参数\\\",\\n \\\"gridSize\\\": 网格大小,\\n \\\"functionWeight\\\": [权重列表],\\n \\\"algorithmParams\\\": {算法参数}\\n}\\n```\\n\\n#### 输出数据格式\\n```json\\n{\\n \\\"camps\\\": [营地布局结果],\\n \\\"otherAreas\\\": [其他区域布局结果],\\n \\\"code\\\": 200,\\n \\\"message\\\": \\\"Success\\\",\\n \\\"runtime\\\": 运行时间,\\n \\\"optimization_scores\\\": {\\n \\\"maximization_score\\\": 浮点数,\\n \\\"dispersion_score\\\": 浮点数,\\n \\\"suitability_score\\\": 浮点数,\\n \\\"forward_score\\\": 浮点数,\\n \\\"withdrawal_score\\\": 浮点数,\\n \\\"combined_score\\\": 浮点数\\n }\\n}\\n```\\n\\n### 4. 当前问题\\n\\n1. **算法单一性**: 虽然接口支持多种算法，但实际实现都使用遗传算法\\n2. **硬编码映射**: 所有算法都映射到同一个遗传算法实现\\n3. **缺乏算法特性**: 不同算法的独特优势没有体现\\n\\n### 5. 模拟退火算法实现需求\\n\\n#### 需要实现的功能\\n1. 创建独立的模拟退火算法实现\\n2. 保持与现有接口兼容\\n3. 支持相同的目标函数和约束条件\\n4. 提供算法特有的参数配置\\n\\n#### 算法参数需求\\n- 初始温度 (initial_temperature)\\n- 终止温度 (final_temperature) \\n- 降温速率 (cooling_rate)\\n- 每个温度的迭代次数 (iterations_per_temperature)\\n- 邻域搜索参数 (neighborhood_params)\\n\\n#### 集成要求\\n- 保持现有输入输出格式\\n- 支持相同的目标函数权重\\n- 兼容现有的约束处理机制\\n- 提供详细的优化过程信息\"}"
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|
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"content": "基于对系统的深入分析，我现在为你设计并实现一个高效的模拟退火算法。让我创建模拟退火算法的核心实现：",
|
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"timestamp": "2025-09-25T09:58:36.277952"
|
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"arguments": "{\"path\": \"/Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/simulated_annealing.py\", \"operation\": \"replace\", \"old_text\": \"\", \"content\": \"\\\"\\\"\\\"\\n模拟退火算法实现 - 用于营地布局优化\\n基于物理退火原理，通过控制温度参数实现全局搜索到局部搜索的平滑过渡\\n\\\"\\\"\\\"\\n\\nimport numpy as np\\nimport json\\nimport time\\nimport random\\nimport math\\nfrom typing import Dict, List, Tuple, Optional\\nfrom dataclasses import dataclass\\n\\n\\n@dataclass\\nclass SimulatedAnnealingParams:\\n \\\"\\\"\\\"模拟退火算法参数配置\\\"\\\"\\\"\\n initial_temperature: float = 1000.0\\n final_temperature: float = 0.1\\n cooling_rate: float = 0.95\\n iterations_per_temperature: int = 100\\n max_stagnation_iterations: int = 50\\n acceptance_threshold: float = 0.001\\n neighborhood_size: float = 0.1\\n adaptive_cooling: bool = True\\n reheating_enabled: bool = True\\n reheating_threshold: float = 0.05\\n\\n\\nclass CampLayoutSolution:\\n \\\"\\\"\\\"营地布局解决方案类\\\"\\\"\\\"\\n \\n def __init__(self, positions: np.ndarray, config: Dict):\\n self.positions = positions.copy()\\n self.config = config\\n self.fitness_scores = {}\\n self.total_fitness = 0.0\\n self.constraint_violations = {}\\n self.is_feasible = True\\n \\n def copy(self):\\n \\\"\\\"\\\"创建解决方案的深拷贝\\\"\\\"\\\"\\n new_solution = CampLayoutSolution(self.positions, self.config)\\n new_solution.fitness_scores = self.fitness_scores.copy()\\n new_solution.total_fitness = self.total_fitness\\n new_solution.constraint_violations = self.constraint_violations.copy()\\n new_solution.is_feasible = self.is_feasible\\n return new_solution\\n\\n\\nclass CampLayoutProblemSA:\\n \\\"\\\"\\\"营地布局问题定义 - 适配模拟退火算法\\\"\\\"\\\"\\n \\n def __init__(self, config: Dict):\\n self.config = config\\n \\n # 基础参数\\n self.num_camps = config.get('numCamp', 5)\\n self.field_width = config.get('FIELD_WIDTH', 100)\\n self.field_height = config.get('FIELD_HEIGHT', 100)\\n self.min_size = config.get('minSize', 5)\\n self.max_size = config.get('maxSize', 15)\\n self.grid_size = config.get('gridSize', 1.0)\\n \\n # 目标函数权重\\n self.weights = config.get('functionWeight', [0.2, 0.2, 0.2, 0.2, 0.2])\\n \\n # 热力图数据\\n self.heatmap_grid = np.array(config.get('heatmap_grid', np.ones((100, 100))))\\n self.max_heat = np.max(self.heatmap_grid)\\n \\n # 约束参数\\n self.min_distance = config.get('minDistance', 10.0)\\n self.safe_distance = config.get('safeDistance', 5.0)\\n \\n # 计算理论极值\\n self.min_area = self.min_size ** 2\\n self.max_area = self.max_size ** 2\\n self.max_possible_dist = np.sqrt(self.field_width**2 + self.field_height**2)\\n \\n def generate_initial_solution(self) -> CampLayoutSolution:\\n \\\"\\\"\\\"生成初始解 - 使用启发式方法\\\"\\\"\\\"\\n positions = np.zeros((self.num_camps, 4)) # x, y, width, height\\n \\n # 使用网格化初始布局避免重叠\\n grid_cols = int(np.ceil(np.sqrt(self.num_camps)))\\n grid_rows = int(np.ceil(self.num_camps / grid_cols))\\n \\n cell_width = self.field_width / grid_cols\\n cell_height = self.field_height / grid_rows\\n \\n for i in range(self.num_camps):\\n row = i // grid_cols\\n col = i % grid_cols\\n \\n # 在网格单元内随机位置\\n x = col * cell_width + random.uniform(0.1, 0.9) * cell_width\\n y = row * cell_height + random.uniform(0.1, 0.9) * cell_height\\n \\n # 随机尺寸\\n size = random.uniform(self.min_size, self.max_size)\\n \\n positions[i] = [x, y, size, size]\\n \\n solution = CampLayoutSolution(positions, self.config)\\n self.evaluate_solution(solution)\\n return solution\\n \\n def evaluate_solution(self, solution: CampLayoutSolution) -> float:\\n \\\"\\\"\\\"评估解决方案的适应度\\\"\\\"\\\"\\n positions = solution.positions\\n total_score = 0.0\\n violations = {}\\n \\n # 1. 适宜性得分 (基于热力图)\\n suitability_score = self._calculate_suitability_score(positions)\\n solution.fitness_scores['suitability'] = suitability_score\\n total_score += self.weights[0] * suitability_score\\n \\n # 2. 分散度得分 (营地间平均距离)\\n dispersion_score = self._calculate_dispersion_score(positions)\\n solution.fitness_scores['dispersion'] = dispersion_score\\n total_score += self.weights[1] * dispersion_score\\n \\n # 3. 边界约束得分\\n boundary_score, boundary_violation = self._calculate_boundary_score(positions)\\n solution.fitness_scores['boundary'] = boundary_score\\n violations['boundary'] = boundary_violation\\n total_score += self.weights[2] * boundary_score\\n \\n # 4. 重叠约束得分\\n overlap_score, overlap_violation = self._calculate_overlap_score(positions)\\n solution.fitness_scores['overlap'] = overlap_score\\n violations['overlap'] = overlap_violation\\n total_score += self.weights[3] * overlap_score\\n \\n # 5. 距离约束得分\\n distance_score, distance_violation = self._calculate_distance_score(positions)\\n solution.fitness_scores['distance'] = distance_score\\n violations['distance'] = distance_violation\\n total_score += self.weights[4] * distance_score\\n \\n solution.total_fitness = total_score\\n solution.constraint_violations = violations\\n solution.is_feasible = all(v <= 1e-6 for v in violations.values())\\n \\n return total_score\\n \\n def _calculate_suitability_score(self, positions: np.ndarray) -> float:\\n \\\"\\\"\\\"计算适宜性得分 - 基于热力图\\\"\\\"\\\"\\n total_heat = 0.0\\n total_area = 0\\n \\n for pos in positions:\\n x, y, width, height = pos\\n \\n # 转换为网格坐标\\n grid_x = int(x / self.grid_size)\\n grid_y = int(y / self.grid_size)\\n grid_w = int(width / self.grid_size)\\n grid_h = int(height / self.grid_size)\\n \\n # 确保不超出边界\\n grid_x = max(0, min(grid_x, self.heatmap_grid.shape[1] - 1))\\n grid_y = max(0, min(grid_y, self.heatmap_grid.shape[0] - 1))\\n grid_w = max(1, min(grid_w, self.heatmap_grid.shape[1] - grid_x))\\n grid_h = max(1, min(grid_h, self.heatmap_grid.shape[0] - grid_y))\\n \\n # 计算热力值\\n heat_sum = np.sum(self.heatmap_grid[grid_y:grid_y+grid_h, grid_x:grid_x+grid_w])\\n area = grid_w * grid_h\\n \\n total_heat += heat_sum\\n total_area += area\\n \\n if total_area > 0:\\n avg_heat = total_heat / total_area\\n return avg_heat / self.max_heat if self.max_heat > 0 else 0.0\\n return 0.0\\n \\n def _calculate_dispersion_score(self, positions: np.ndarray) -> float:\\n \\\"\\\"\\\"计算分散度得分 - 基于营地间平均距离\\\"\\\"\\\"\\n total_distance = 0.0\\n num_pairs = 0\\n \\n for i in range(self.num_camps):\\n for j in range(i + 1, self.num_camps):\\n x1, y1, _, _ = positions[i]\\n x2, y2, _, _ = positions[j]\\n \\n distance = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)\\n total_distance += distance\\n num_pairs += 1\\n \\n if num_pairs > 0:\\n avg_distance = total_distance / num_pairs\\n return avg_distance / self.max_possible_dist\\n return 0.0\\n \\n def _calculate_boundary_score(self, positions: np.ndarray) -> Tuple[float, float]:\\n \\\"\\\"\\\"计算边界约束得分和违反量\\\"\\\"\\\"\\n total_violation = 0.0\\n \\n for pos in positions:\\n x, y, width, height = pos\\n \\n # 计算边界违反\\n left_violation = max(0, -x)\\n right_violation = max(0, (x + width) - self.field_width)\\n top_violation = max(0, -y)\\n bottom_violation = max(0, (y + height) - self.field_height)\\n \\n total_violation += left_violation + right_violation + top_violation + bottom_violation\\n \\n # 归一化违反量\\n max_possible_violation = self.num_camps * 2 * max(self.field_width, self.field_height)\\n normalized_violation = total_violation / max_possible_violation if max_possible_violation > 0 else 0.0\\n \\n # 得分 = 1 - 违反量\\n score = max(0.0, 1.0 - normalized_violation)\\n return score, normalized_violation\\n \\n def _calculate_overlap_score(self, positions: np.ndarray) -> Tuple[float, float]:\\n \\\"\\\"\\\"计算重叠约束得分和违反量\\\"\\\"\\\"\\n total_overlap_area = 0.0\\n \\n for i in range(self.num_camps):\\n for j in range(i + 1, self.num_camps):\\n x1, y1, w1, h1 = positions[i]\\n x2, y2, w2, h2 = positions[j]\\n \\n # 计算矩形重叠\\n left = max(x1, x2)\\n right = min(x1 + w1, x2 + w2)\\n top = max(y1, y2)\\n bottom = min(y1 + h1, y2 + h2)\\n \\n if left < right and top < bottom:\\n overlap_area = (right - left) * (bottom - top)\\n total_overlap_area += overlap_area\\n \\n # 归一化重叠面积\\n max_possible_overlap = self.num_camps * self.max_area\\n normalized_overlap = total_overlap_area / max_possible_overlap if max_possible_overlap > 0 else 0.0\\n \\n # 得分 = 1 - 重叠率\\n score = max(0.0, 1.0 - normalized_overlap)\\n return score, normalized_overlap\\n \\n def _calculate_distance_score(self, positions: np.ndarray) -> Tuple[float, float]:\\n \\\"\\\"\\\"计算距离约束得分和违反量\\\"\\\"\\\"\\n total_violation = 0.0\\n \\n for i in range(self.num_camps):\\n for j in range(i + 1, self.num_camps):\\n x1, y1, _, _ = positions[i]\\n x2, y2, _, _ = positions[j]\\n \\n distance = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)\\n \\n if distance < self.min_distance:\\n violation = self.min_distance - distance\\n total_violation += violation\\n \\n # 归一化违反量\\n max_possible_violation = self.num_camps * self.min_distance\\n normalized_violation = total_violation / max_possible_violation if max_possible_violation > 0 else 0.0\\n \\n # 得分 = 1 - 违反率\\n score = max(0.0, 1.0 - normalized_violation)\\n return score, normalized_violation\\n\\n\\nclass SimulatedAnnealingOptimizer:\\n \\\"\\\"\\\"模拟退火优化器\\\"\\\"\\\"\\n \\n def __init__(self, problem: CampLayoutProblemSA, params: SimulatedAnnealingParams):\\n self.problem = problem\\n self.params = params\\n self.current_solution = None\\n self.best_solution = None\\n self.temperature = params.initial_temperature\\n self.iteration = 0\\n self.stagnation_count = 0\\n self.history = []\\n \\n def optimize(self, max_iterations: Optional[int] = None) -> CampLayoutSolution:\\n \\\"\\\"\\\"执行模拟退火优化\\\"\\\"\\\"\\n if max_iterations is None:\\n max_iterations = self._calculate_max_iterations()\\n \\n # 生成初始解\\n self.current_solution = self.problem.generate_initial_solution()\\n self.best_solution = self.current_solution.copy()\\n \\n self.history = [{\\n 'iteration': 0,\\n 'temperature': self.temperature,\\n 'current_fitness': self.current_solution.total_fitness,\\n 'best_fitness': self.best_solution.total_fitness,\\n 'acceptance_rate': 1.0\\n }]\\n \\n acceptance_count = 0\\n total_transitions = 0\\n \\n for self.iteration in range(1, max_iterations + 1):\\n # 生成邻域解\\n neighbor_solution = self._generate_neighbor_solution()\\n \\n # 评估邻域解\\n neighbor_fitness = self.problem.evaluate_solution(neighbor_solution)\\n current_fitness = self.current_solution.total_fitness\\n \\n # 决定是否接受新解\\n if self._accept_solution(current_fitness, neighbor_fitness):\\n self.current_solution = neighbor_solution\\n acceptance_count += 1\\n \\n # 更新最佳解\\n if neighbor_fitness > self.best_solution.total_fitness:\\n self.best_solution = neighbor_solution.copy()\\n self.stagnation_count = 0\\n else:\\n self.stagnation_count += 1\\n else:\\n self.stagnation_count += 1\\n \\n total_transitions += 1\\n \\n # 记录历史\\n self.history.append({\\n 'iteration': self.iteration,\\n 'temperature': self.temperature,\\n 'current_fitness': self.current_solution.total_fitness,\\n 'best_fitness': self.best_solution.total_fitness,\\n 'acceptance_rate': acceptance_count / total_transitions if total_transitions > 0 else 0.0\\n })\\n \\n # 自适应降温\\n if self.params.adaptive_cooling:\\n acceptance_rate = acceptance_count / total_transitions if total_transitions > 0 else 0.0\\n self._adaptive_cooling(acceptance_rate)\\n else:\\n self.temperature *= self.params.cooling_rate\\n \\n # 检查终止条件\\n if self._should_terminate():\\n break\\n \\n # 重置计数器\\n if self.iteration % self.params.iterations_per_temperature == 0:\\n acceptance_count = 0\\n total_transitions = 0\\n \\n return self.best_solution\\n \\n def _calculate_max_iterations(self) -> int:\\n \\\"\\\"\\\"计算最大迭代次数\\\"\\\"\\\"\\n # 基于温度下降计算\\n temp_ratio = self.params.initial_temperature / self.params.final_temperature\\n num_temperature_steps = int(np.ceil(np.log(temp_ratio) / np.log(1.0 / self.params.cooling_rate)))\\n return num_temperature_steps * self.params.iterations_per_temperature\\n \\n def _generate_neighbor_solution(self) -> CampLayoutSolution:\\n \\\"\\\"\\\"生成邻域解\\\"\\\"\\\"\\n neighbor = self.current_solution.copy()\\n \\n # 随机选择扰动类型\\n perturbation_type = random.choice(['position', 'size', 'swap'])\\n \\n if perturbation_type == 'position':\\n # 位置扰动\\n camp_idx = random.randint(0, self.problem.num_camps - 1)\\n dx = random.uniform(-self.params.neighborhood_size * self.problem.field_width,\\n self.params.neighborhood_size * self.problem.field_width)\\n dy = random.uniform(-self.params.neighborhood_size * self.problem.field_height,\\n self.params.neighborhood_size * self.problem.field_height)\\n \\n neighbor.positions[camp_idx, 0] += dx\\n neighbor.positions[camp_idx, 1] += dy\\n \\n elif perturbation_type == 'size':\\n # 尺寸扰动\\n camp_idx = random.randint(0, self.problem.num_camps - 1)\\n size_change = random.uniform(-self.params.neighborhood_size * self.problem.max_size,\\n self.params.neighborhood_size * self.problem.max_size)\\n \\n neighbor.positions[camp_idx, 2] += size_change # width\\n neighbor.positions[camp_idx, 3] += size_change # height\\n \\n elif perturbation_type == 'swap':\\n # 交换两个营地的位置\\n if self.problem.num_camps >= 2"
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"arguments": "{\"path\": \"/Users/jojo/Desktop/agent_kimi_源码备份/project/路径规划/simulated_annealing.py\", \"operation\": \"replace\", \"old_text\": \"\", \"content\": \"\\\"\\\"\\\"\\n模拟退火算法实现 - 用于营地布局优化\\n基于物理退火原理，通过控制温度参数实现全局搜索到局部搜索的平滑过渡\\n\\\"\\\"\\\"\\n\\nimport numpy as np\\nimport random\\nimport math\\nimport time\\nimport json\\nfrom typing import Dict, List, Tuple, Optional\\nfrom dataclasses import dataclass\\n\\n\\n@dataclass\\nclass SimulatedAnnealingParams:\\n \\\"\\\"\\\"模拟退火算法参数配置\\\"\\\"\\\"\\n initial_temperature: float = 1000.0\\n final_temperature: float = 0.1\\n cooling_rate: float = 0.95\\n iterations_per_temperature: int = 100\\n max_stagnation_iterations: int = 50\\n acceptance_threshold: float = 0.001\\n neighborhood_size: float = 0.1\\n adaptive_cooling: bool = True\\n reheating_enabled: bool = True\\n reheating_threshold: float = 0.8\\n\\n\\nclass CampLayoutSolution:\\n \\\"\\\"\\\"营地布局解决方案\\\"\\\"\\\"\\n \\n def __init__(self, positions: np.ndarray, config: Dict):\\n \\\"\\\"\\\"\\n 初始化解决方案\\n Args:\\n positions: 营地位置数组，形状为(n_camps, 3) [x, y, size]\\n config: 配置字典\\n \\\"\\\"\\\"\\n self.positions = positions.copy()\\n self.config = config\\n self.n_camps = len(positions)\\n self.objective_values = None\\n self.constraint_violations = None\\n self.fitness_score = None\\n \\n def copy(self) -> 'CampLayoutSolution':\\n \\\"\\\"\\\"创建解决方案的深拷贝\\\"\\\"\\\"\\n new_solution = CampLayoutSolution(self.positions.copy(), self.config)\\n new_solution.objective_values = self.objective_values.copy() if self.objective_values is not None else None\\n new_solution.constraint_violations = self.constraint_violations.copy() if self.constraint_violations is not None else None\\n new_solution.fitness_score = self.fitness_score\\n return new_solution\\n\\n\\nclass CampLayoutObjective:\\n \\\"\\\"\\\"营地布局目标函数计算器\\\"\\\"\\\"\\n \\n def __init__(self, config: Dict):\\n self.config = config\\n self.weights = np.array(config.get('functionWeight', [0.2, 0.2, 0.2, 0.2, 0.2]))\\n self.heatmap_grid = np.array(config['heatmap_grid'])\\n self.max_possible_dist = np.sqrt(config['FIELD_WIDTH'] ** 2 + config['FIELD_HEIGHT'] ** 2)\\n self.max_heat = np.max(self.heatmap_grid)\\n \\n # 网格参数\\n self.agg_rows, self.agg_cols = self.heatmap_grid.shape\\n self.dx = config.get('field_width_real', 1000.0) / self.agg_cols\\n self.dy = config.get('field_height_real', 1000.0) / self.agg_rows\\n \\n # 约束参数\\n self.min_size = config.get('minSize', 10)\\n self.max_size = config.get('maxSize', 50)\\n self.min_distance = config.get('minDistance', 30)\\n \\n def evaluate(self, solution: CampLayoutSolution) -> Tuple[float, Dict]:\\n \\\"\\\"\\\"\\n 评估解决方案\\n Returns:\\n fitness_score: 适应度分数（越小越好）\\n detailed_scores: 详细评分字典\\n \\\"\\\"\\\"\\n positions = solution.positions\\n \\n # 计算各目标分量\\n scores = {}\\n \\n # 1. 分散度得分 (Dispersion)\\n scores['dispersion'] = self._calculate_dispersion(positions)\\n \\n # 2. 适宜性得分 (Suitability) - 基于热力图\\n scores['suitability'] = self._calculate_suitability(positions)\\n \\n # 3. 关联度得分 (Connectivity) - 基于距离\\n scores['connectivity'] = self._calculate_connectivity(positions)\\n \\n # 4. 屏蔽度得分 (Shielding) - 地形屏蔽\\n scores['shielding'] = self._calculate_shielding(positions)\\n \\n # 5. 可达性得分 (Accessibility) - 道路可达性\\n scores['accessibility'] = self._calculate_accessibility(positions)\\n \\n # 计算加权和\\n weighted_sum = sum(scores[key] * weight for key, weight in \\n zip(['dispersion', 'suitability', 'connectivity', 'shielding', 'accessibility'], self.weights))\\n \\n # 约束违反惩罚\\n constraint_penalty = self._calculate_constraint_violation(positions)\\n \\n # 总适应度（越小越好）\\n fitness_score = -weighted_sum + constraint_penalty * 1000\\n \\n solution.objective_values = scores\\n solution.fitness_score = fitness_score\\n \\n return fitness_score, scores\\n \\n def _calculate_dispersion(self, positions: np.ndarray) -> float:\\n \\\"\\\"\\\"计算分散度得分\\\"\\\"\\\"\\n if len(positions) < 2:\\n return 0.0\\n \\n # 计算所有营地间的平均距离\\n total_distance = 0.0\\n count = 0\\n \\n for i in range(len(positions)):\\n for j in range(i + 1, len(positions)):\\n dist = np.sqrt((positions[i][0] - positions[j][0]) ** 2 + \\n (positions[i][1] - positions[j][1]) ** 2)\\n total_distance += dist\\n count += 1\\n \\n avg_distance = total_distance / count if count > 0 else 0.0\\n # 归一化到0-1范围\\n return min(avg_distance / self.max_possible_dist, 1.0)\\n \\n def _calculate_suitability(self, positions: np.ndarray) -> float:\\n \\\"\\\"\\\"计算适宜性得分（基于热力图）\\\"\\\"\\\"\\n total_suitability = 0.0\\n \\n for pos in positions:\\n x, y, size = pos\\n \\n # 转换为网格坐标\\n grid_x = int(x / self.dx)\\n grid_y = int(y / self.dy)\\n \\n # 确保在网格范围内\\n grid_x = max(0, min(grid_x, self.agg_cols - 1))\\n grid_y = max(0, min(grid_y, self.agg_rows - 1))\\n \\n # 获取该位置的热力值（假设热力值越高越适宜）\\n heat_value = self.heatmap_grid[grid_y, grid_x]\\n suitability = heat_value / self.max_heat if self.max_heat > 0 else 0.0\\n \\n total_suitability += suitability\\n \\n return total_suitability / len(positions) if len(positions) > 0 else 0.0\\n \\n def _calculate_connectivity(self, positions: np.ndarray) -> float:\\n \\\"\\\"\\\"计算关联度得分\\\"\\\"\\\"\\n # 简化的连通性计算：基于营地间的连接性\\n if len(positions) < 2:\\n return 1.0\\n \\n # 计算最小生成树的平均边长\\n from scipy.spatial.distance import pdist, squareform\\n \\n coords = positions[:, :2] # 只取x,y坐标\\n distances = squareform(pdist(coords))\\n \\n # 使用Kruskal算法的简化版本\\n n = len(positions)\\n edges = []\\n for i in range(n):\\n for j in range(i + 1, n):\\n edges.append((distances[i][j], i, j))\\n \\n edges.sort()\\n \\n # 并查集\\n parent = list(range(n))\\n \\n def find(x):\\n if parent[x] != x:\\n parent[x] = find(parent[x])\\n return parent[x]\\n \\n def union(x, y):\\n px, py = find(x), find(y)\\n if px != py:\\n parent[px] = py\\n \\n total_length = 0.0\\n edge_count = 0\\n \\n for dist, i, j in edges:\\n if find(i) != find(j):\\n union(i, j)\\n total_length += dist\\n edge_count += 1\\n if edge_count == n - 1:\\n break\\n \\n # 归一化\\n avg_length = total_length / edge_count if edge_count > 0 else 0.0\\n return 1.0 - min(avg_length / self.max_possible_dist, 1.0)\\n \\n def _calculate_shielding(self, positions: np.ndarray) -> float:\\n \\\"\\\"\\\"计算屏蔽度得分\\\"\\\"\\\"\\n # 基于地形的屏蔽效果计算\\n # 这里使用简化的模型：假设某些方向有更好的屏蔽效果\\n \\n shielding_score = 0.0\\n for pos in positions:\\n x, y = pos[0], pos[1]\\n \\n # 计算相对于场地中心的方位\\n center_x = self.config['FIELD_WIDTH'] / 2\\n center_y = self.config['FIELD_HEIGHT'] / 2\\n \\n # 基于位置的屏蔽效果（简化模型）\\n # 边缘位置有更好的屏蔽效果\\n dist_from_center = np.sqrt((x - center_x) ** 2 + (y - center_y) ** 2)\\n max_dist_from_center = np.sqrt(center_x ** 2 + center_y ** 2)\\n \\n shielding = dist_from_center / max_dist_from_center\\n shielding_score += shielding\\n \\n return shielding_score / len(positions) if len(positions) > 0 else 0.0\\n \\n def _calculate_accessibility(self, positions: np.ndarray) -> float:\\n \\\"\\\"\\\"计算可达性得分\\\"\\\"\\\"\\n # 基于到场地边界的距离计算可达性\\n field_width = self.config['FIELD_WIDTH']\\n field_height = self.config['FIELD_HEIGHT']\\n \\n accessibility_score = 0.0\\n for pos in positions:\\n x, y = pos[0], pos[1]\\n \\n # 计算到四个边界的最近距离\\n dist_to_left = x\\n dist_to_right = field_width - x\\n dist_to_bottom = y\\n dist_to_top = field_height - y\\n \\n min_dist_to_boundary = min(dist_to_left, dist_to_right, dist_to_bottom, dist_to_top)\\n \\n # 归一化\\n max_possible_dist = min(field_width, field_height) / 2\\n accessibility = 1.0 - (min_dist_to_boundary / max_possible_dist)\\n \\n accessibility_score += accessibility\\n \\n return accessibility_score / len(positions) if len(positions) > 0 else 0.0\\n \\n def _calculate_constraint_violation(self, positions: np.ndarray) -> float:\\n \\\"\\\"\\\"计算约束违反的惩罚值\\\"\\\"\\\"\\n total_violation = 0.0\\n n_camps = len(positions)\\n \\n # 1. 边界约束\\n for i, pos in enumerate(positions):\\n x, y, size = pos\\n half_size = size / 2\\n \\n # 检查是否超出边界\\n if x - half_size < 0 or x + half_size > self.config['FIELD_WIDTH']:\\n total_violation += abs(x - half_size) if x - half_size < 0 else abs(x + half_size - self.config['FIELD_WIDTH'])\\n \\n if y - half_size < 0 or y + half_size > self.config['FIELD_HEIGHT']:\\n total_violation += abs(y - half_size) if y - half_size < 0 else abs(y + half_size - self.config['FIELD_HEIGHT'])\\n \\n # 2. 重叠约束\\n for i in range(n_camps):\\n for j in range(i + 1, n_camps):\\n x1, y1, size1 = positions[i]\\n x2, y2, size2 = positions[j]\\n \\n dist = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)\\n min_dist = (size1 + size2) / 2 + self.min_distance\\n \\n if dist < min_dist:\\n total_violation += (min_dist - dist)\\n \\n # 3. 尺寸约束\\n for pos in positions:\\n size = pos[2]\\n if size < self.min_size or size > self.max_size:\\n total_violation += abs(size - self.min_size) if size < self.min_size else abs(size - self.max_size)\\n \\n return total_violation\\n\\n\\nclass SimulatedAnnealingOptimizer:\\n \\\"\\\"\\\"模拟退火优化器\\\"\\\"\\\"\\n \\n def __init__(self, params: SimulatedAnnealingParams = None):\\n self.params = params or SimulatedAnnealingParams()\\n self.current_solution = None\\n self.best_solution = None\\n self.current_temperature = None\\n self.iteration = 0\\n self.stagnation_count = 0\\n self.history = []\\n \\n def optimize(self, initial_solution: CampLayoutSolution, objective: CampLayoutObjective,\\n callback=None) -> Tuple[CampLayoutSolution, Dict]:\\n \\\"\\\"\\\"\\n 执行模拟退火优化\\n \\n Returns:\\n best_solution: 最优解\\n optimization_info: 优化过程信息\\n \\\"\\\"\\\"\\n start_time = time.time()\\n \\n # 初始化\\n self.current_solution = initial_solution.copy()\\n self.best_solution = initial_solution.copy()\\n self.current_temperature = self.params.initial_temperature\\n \\n # 评估初始解\\n current_fitness, current_scores = objective.evaluate(self.current_solution)\\n best_fitness = current_fitness\\n \\n # 记录历史\\n self.history = [{\\n 'iteration': 0,\\n 'temperature': self.current_temperature,\\n 'fitness': current_fitness,\\n 'best_fitness': best_fitness\\n }]\\n \\n # 主优化循环\\n while self.current_temperature > self.params.final_temperature and self.stagnation_count < self.params.max_stagnation_iterations:\\n \\n temperature_accepted = False\\n \\n for _ in range(self.params.iterations_per_temperature):\\n self.iteration += 1\\n \\n # 生成邻域解\\n neighbor_solution = self._generate_neighbor(self.current_solution)\\n \\n # 评估邻域解\\n neighbor_fitness, neighbor_scores = objective.evaluate(neighbor_solution)\\n \\n # 计算接受概率\\n delta_fitness = neighbor_fitness - current_fitness\\n \\n # 接受准则\\n if delta_fitness < 0 or self._accept_worse_solution(delta_fitness):\\n self.current_solution = neighbor_solution\\n current_fitness = neighbor_fitness\\n temperature_accepted = True\\n \\n # 更新最优解\\n if current_fitness < best_fitness:\\n self.best_solution = self.current_solution.copy()\\n best_fitness = current_fitness\\n self.stagnation_count = 0\\n \\n # 记录历史\\n self.history.append({\\n 'iteration': self.iteration,\\n 'temperature': self.current_temperature,\\n 'fitness': current_fitness,\\n 'best_fitness': best_fitness,\\n 'accepted': temperature_accepted\\n })\\n \\n # 回调函数\\n if callback:\\n callback(self.iteration, self.current_temperature, current_fitness, best_fitness)\\n \\n # 更新停滞计数\\n if not temperature_accepted:\\n self.stagnation_count += 1\\n \\n # 降温\\n self._update_temperature()\\n \\n # 计算优化信息\\n end_time = time.time()\\n optimization_info = {\\n 'runtime': end_time - start_time,\\n 'total_iterations': self.iteration,\\n 'final_temperature': self.current_temperature,\\n 'initial_fitness': self.history[0]['fitness'],\\n 'final_fitness': best_fitness,\\n 'improvement': self.history[0]['fitness'] - best_fitness,\\n 'stagnation_iterations': self.stagnation_count,\\n 'history': self.history\\n }\\n \\n return self.best_solution, optimization_info\\n \\n def _generate_neighbor(self, solution: CampLayoutSolution) -> CampLayoutSolution:\\n \\\"\\\"\\\"生成邻域解\\\"\\\"\\\"\\n neighbor = solution.copy()\\n positions = neighbor.positions\\n \\n # 随机选择扰动方式\\n perturbation_type = random.choice(['position', 'size', 'swap'])\\n \\n if perturbation_type == 'position' and len(positions) > 0:\\n # 随机选择一个营地进行位置扰动\\n camp_idx = random.randint(0, len(positions) - 1)\\n \\n # 计算扰动幅度（随温度自适应调整）\\n temperature_factor = self.current_temperature / self.params.initial_temperature\\n perturbation_scale = self.params.neighborhood_size * temperature_factor\\n \\n # 添加高斯扰动\\n dx = random.gauss(0, perturbation_scale * self.config.get('FIELD_WIDTH', 1000))\\n dy = random.gauss(0, perturbation_scale * self.config.get('FIELD"
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