226 lines
20 KiB
JSON
226 lines
20 KiB
JSON
{
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"id": "conv_20250925_154000_043",
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"title": "请你看看zongti文件，里面有一个不管调用什么算法都使用遗传，现在我把模拟退火写好了，请你求改总体...",
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"created_at": "2025-09-25T15:40:00.043777",
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"updated_at": "2025-09-25T15:52:06.506306",
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"messages": [
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{
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"role": "user",
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"content": "请你看看zongti文件，里面有一个不管调用什么算法都使用遗传，现在我把模拟退火写好了，请你求改总体文件，让调用mnth时可以正常调用，其他依然是遗传，不需要测试",
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"timestamp": "2025-09-25T15:41:02.968880"
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},
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{
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"role": "assistant",
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"content": "我来帮你修改zongti文件，让它在调用mnth时使用模拟退火算法，其他情况仍然使用遗传算法。",
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"timestamp": "2025-09-25T15:41:15.392259"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T15:41:15.395820",
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"tool_calls": [
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{
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"id": "read_file:0",
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"index": 0,
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"type": "function",
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"function": {
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"name": "read_file",
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"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\": false, \"requires_confirmation\": true, \"message\": \"\\u9605\\u8bfb\\u5de5\\u5177\\u53ea\\u80fd\\u7528\\u4e8e\\u9605\\u8bfb\\u5c0f\\u6587\\u4ef6\\u3001\\u4e34\\u65f6\\u6587\\u4ef6\\u3001\\u4e0d\\u91cd\\u8981\\u7684\\u6587\\u4ef6\\u3002\\u5982\\u679c\\u8981\\u67e5\\u770b\\u6838\\u5fc3\\u6587\\u4ef6\\u3001\\u9700\\u8981\\u591a\\u6b21\\u4fee\\u6539\\u7684\\u6587\\u4ef6\\u3001\\u91cd\\u8981\\u7684\\u6587\\u4ef6\\uff0c\\u8bf7\\u4f7f\\u7528\\u805a\\u7126\\u529f\\u80fd\\u3002\\u8bf7\\u786e\\u8ba4\\u4f7f\\u7528\\u9605\\u8bfb\\u8fd8\\u662f\\u805a\\u7126\\uff1f\", \"instruction\": \"\\u8bf7\\u4f7f\\u7528 confirm_read_or_focus \\u5de5\\u5177\\u6765\\u9009\\u62e9\\u64cd\\u4f5c\\u65b9\\u5f0f\\uff0c\\u6587\\u4ef6\\u8def\\u5f84: \\u8def\\u5f84\\u89c4\\u5212/zongti.py\", \"file_path\": \"\\u8def\\u5f84\\u89c4\\u5212/zongti.py\"}",
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"timestamp": "2025-09-25T15:41:17.203553",
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"tool_call_id": "read_file:0",
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"name": "read_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": "2025-09-25T15:41:23.902363",
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"tool_calls": [
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{
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"id": "confirm_read_or_focus:1",
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"index": 0,
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"type": "function",
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"function": {
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"name": "confirm_read_or_focus",
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"arguments": "{\"file_path\": \"路径规划/zongti.py\", \"choice\": \"focus\", \"reason\": \"这是一个核心代码文件，需要修改算法调用逻辑，会频繁查看和编辑\"}"
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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, \"action\": \"focus\", \"message\": \"\\u6587\\u4ef6\\u5df2\\u805a\\u7126: \\u8def\\u5f84\\u89c4\\u5212/zongti.py\", \"focused_files\": [\"\\u8def\\u5f84\\u89c4\\u5212/zongti.py\"], \"file_size\": 44540}",
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"timestamp": "2025-09-25T15:41:25.708604",
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"tool_call_id": "confirm_read_or_focus:1",
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"name": "confirm_read_or_focus"
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},
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{
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"role": "assistant",
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"content": "现在我来查看文件内容，找到需要修改的部分：",
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"timestamp": "2025-09-25T15:41:31.601797"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T15:41:31.606871",
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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": {
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"name": "run_command",
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"arguments": "{\"command\": \"grep -n \\\"mnth\\\\|遗传\\\\|模拟退火\\\" 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\\\",\\n693: \\\"mnth\\\": \\\"模拟退火\\\",\\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\\\",\\n693: \\\"mnth\\\": \\\"模拟退火\\\",\\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 \\\"mnth\\\\|遗传\\\\|模拟退火\\\" 路径规划/zongti.py\"}",
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"timestamp": "2025-09-25T15:41:33.411735",
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"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": "让我查看算法调用的具体逻辑：",
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"timestamp": "2025-09-25T15:41:37.651695"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T15:41:37.654973",
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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 -A 10 -B 10 \\\"def.*algorithm\\\\|def.*solve\\\\|遗传算法\\\" 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\": \"754-\\n755-@app.route(\\\"/api/suanfa/sheshi\\\", methods=[\\\"POST\\\"])\\n756-@synchronized_request(sheshi_lock)\\n757-def sheshibuju():\\n758- try:\\n759- rec_data = request.get_json()\\n760-\\n761- # 获取算法信息\\n762- algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n763- ALG_MAP = {\\n764: \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n765- \\\"lzq\\\": \\\"粒子群\\\",\\n766- }\\n767-\\n768- functionWeight = rec_data[\\\"functionWeight\\\"]\\n769- if algorithm not in ALG_MAP.keys():\\n770- rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"] = \\\"nsga3\\\"\\n771- algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n772-\\n773- alg = f\\\"===设施布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法\\\\n\\\"\\n774- alg += f\\\"设施布局参数设置为：\\\\n\\\"\\n--\\n810-\\n811-\\n812-@app.route('/api/suanfa/shigong', methods=['POST'])\\n813-@synchronized_request(shigong_lock)\\n814-def shigong_router():\\n815- try:\\n816- # 获取请求数据和算法类型\\n817- rec_data = request.get_json()\\n818- algorithm = rec_data.get(\\\"constructionAlgo\\\", \\\"nsga3\\\") # 默认nsga3算法\\n819- ALG_MAP = {\\n820: \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n821- \\\"benders\\\": \\\"bender分解\\\",\\n822- \\\"lagrange\\\": \\\"拉格朗日松弛\\\",\\n823- \\\"lsc\\\": \\\"列生成\\\",\\n824- \\\"zsl\\\": \\\"整数L型\\\",\\n825- \\\"fzdj\\\": \\\"分支定界\\\"\\n826- }\\n827-\\n828- alg = f\\\"===施工算法===\\\\n\\\"\\n829- alg += f\\\"施工参数设置为：\\\\n\\\"\\n830- 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\": \"754-\\n755-@app.route(\\\"/api/suanfa/sheshi\\\", methods=[\\\"POST\\\"])\\n756-@synchronized_request(sheshi_lock)\\n757-def sheshibuju():\\n758- try:\\n759- rec_data = request.get_json()\\n760-\\n761- # 获取算法信息\\n762- algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n763- ALG_MAP = {\\n764: \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n765- \\\"lzq\\\": \\\"粒子群\\\",\\n766- }\\n767-\\n768- functionWeight = rec_data[\\\"functionWeight\\\"]\\n769- if algorithm not in ALG_MAP.keys():\\n770- rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"] = \\\"nsga3\\\"\\n771- algorithm = rec_data[\\\"facilityData\\\"][\\\"facilityAlgorithm\\\"]\\n772-\\n773- alg = f\\\"===设施布局===\\\\n算法切换为{ALG_MAP[algorithm]}算法\\\\n\\\"\\n774- alg += f\\\"设施布局参数设置为：\\\\n\\\"\\n--\\n810-\\n811-\\n812-@app.route('/api/suanfa/shigong', methods=['POST'])\\n813-@synchronized_request(shigong_lock)\\n814-def shigong_router():\\n815- try:\\n816- # 获取请求数据和算法类型\\n817- rec_data = request.get_json()\\n818- algorithm = rec_data.get(\\\"constructionAlgo\\\", \\\"nsga3\\\") # 默认nsga3算法\\n819- ALG_MAP = {\\n820: \\\"nsga3\\\": \\\"非支配排序遗传算法第三代\\\",\\n821- \\\"benders\\\": \\\"bender分解\\\",\\n822- \\\"lagrange\\\": \\\"拉格朗日松弛\\\",\\n823- \\\"lsc\\\": \\\"列生成\\\",\\n824- \\\"zsl\\\": \\\"整数L型\\\",\\n825- \\\"fzdj\\\": \\\"分支定界\\\"\\n826- }\\n827-\\n828- alg = f\\\"===施工算法===\\\\n\\\"\\n829- alg += f\\\"施工参数设置为：\\\\n\\\"\\n830- 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 -n -A 10 -B 10 \\\"def.*algorithm\\\\|def.*solve\\\\|遗传算法\\\" 路径规划/zongti.py\"}",
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"timestamp": "2025-09-25T15:41:39.460383",
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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": "让我查找实际的算法调用逻辑：",
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"timestamp": "2025-09-25T15:41:43.545497"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T15:41:43.550167",
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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 -A 20 -B 5 \\\"mnth\\\\|yc\\\" 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\": \"681- # === 添加结束 ===\\n682- \\n683- algorithm = rec_data[\\\"layoutAlgorithm\\\"]\\n684- strategy = rec_data[\\\"layoutStrategy\\\"]\\n685- ALG_MAP = {\\n686: \\\"yc\\\": \\\"遗传\\\",\\n687- \\\"bcd\\\": \\\"变尺度\\\",\\n688- \\\"jy\\\": \\\"鲸鱼\\\",\\n689- \\\"lq\\\": \\\"狼群\\\",\\n690- \\\"nd\\\": \\\"拟牛顿\\\",\\n691- \\\"yhc\\\": \\\"萤火虫\\\",\\n692- \\\"tdf\\\": \\\"梯度\\\",\\n693: \\\"mnth\\\": \\\"模拟退火\\\",\\n694- }\\n695-\\n696- strategy_MAP = {\\n697- \\\"jzbj\\\": \\\"集中布局\\\",\\n698- \\\"fsbj\\\": \\\"分散布局\\\"\\n699- }\\n700-\\n701- constraintParam = rec_data[\\\"constraints\\\"]\\n702- 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\\\"\\n703- alg += f\\\"营区布局参数设置为：\\\\n\\\"\\n704- 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\\\"\\\"\\\"\\n705- 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\\\"]}\\\"\\\"\\\"\\n706-\\n707- logging.debug(alg)\\n708: rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n709- final_data = camp_layout_main(rec_data)\\n710-\\n711- # 确保返回格式包含三个一级字段\\n712- response = {\\n713- \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n714- \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\n715- \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n716- \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n717- }\\n718-\\n719- # 保存营地布局输出到JSON文件\\n720- try:\\n721- import datetime\\n722- timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n723- output_filename = f\\\"camp_layout_output_{timestamp}.json\\\"\\n724-\\n725- # 创建输出数据的副本，包含输入参数和输出结果\\n726- output_data_to_save = {\\n727- \\\"timestamp\\\": timestamp,\\n728- \\\"input_data\\\": rec_data,\\n\", \"stdout\": \"681- # === 添加结束 ===\\n682- \\n683- algorithm = rec_data[\\\"layoutAlgorithm\\\"]\\n684- strategy = rec_data[\\\"layoutStrategy\\\"]\\n685- ALG_MAP = {\\n686: \\\"yc\\\": \\\"遗传\\\",\\n687- \\\"bcd\\\": \\\"变尺度\\\",\\n688- \\\"jy\\\": \\\"鲸鱼\\\",\\n689- \\\"lq\\\": \\\"狼群\\\",\\n690- \\\"nd\\\": \\\"拟牛顿\\\",\\n691- \\\"yhc\\\": \\\"萤火虫\\\",\\n692- \\\"tdf\\\": \\\"梯度\\\",\\n693: \\\"mnth\\\": \\\"模拟退火\\\",\\n694- }\\n695-\\n696- strategy_MAP = {\\n697- \\\"jzbj\\\": \\\"集中布局\\\",\\n698- \\\"fsbj\\\": \\\"分散布局\\\"\\n699- }\\n700-\\n701- constraintParam = rec_data[\\\"constraints\\\"]\\n702- 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\\\"\\n703- alg += f\\\"营区布局参数设置为：\\\\n\\\"\\n704- 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\\\"\\\"\\\"\\n705- 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\\\"]}\\\"\\\"\\\"\\n706-\\n707- logging.debug(alg)\\n708: rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n709- final_data = camp_layout_main(rec_data)\\n710-\\n711- # 确保返回格式包含三个一级字段\\n712- response = {\\n713- \\\"camps\\\": final_data.get(\\\"camps\\\", []),\\n714- \\\"otherAreas\\\": final_data.get(\\\"otherAreas\\\", []),\\n715- \\\"code\\\": final_data.get(\\\"code\\\", 500),\\n716- \\\"message\\\": final_data.get(\\\"message\\\", \\\"未知错误\\\")\\n717- }\\n718-\\n719- # 保存营地布局输出到JSON文件\\n720- try:\\n721- import datetime\\n722- timestamp = datetime.datetime.now().strftime(\\\"%Y%m%d_%H%M%S\\\")\\n723- output_filename = f\\\"camp_layout_output_{timestamp}.json\\\"\\n724-\\n725- # 创建输出数据的副本，包含输入参数和输出结果\\n726- output_data_to_save = {\\n727- \\\"timestamp\\\": timestamp,\\n728- \\\"input_data\\\": rec_data,\\n\", \"stderr\": \"\", \"return_code\": 0, \"command\": \"grep -n -A 20 -B 5 \\\"mnth\\\\|yc\\\" 路径规划/zongti.py\"}",
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"timestamp": "2025-09-25T15:41:45.355236",
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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": "user",
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"content": "你聚焦了后明明可以直接看到文件的完整内容，你为什么不直接看在哪？",
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"timestamp": "2025-09-25T15:42:05.278408"
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},
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{
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"role": "user",
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"content": "你聚焦了后明明可以直接看到文件的完整内容，你为什么不直接看在哪？而是调用一堆工具？",
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"timestamp": "2025-09-25T15:42:57.422622"
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},
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{
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"role": "assistant",
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"content": "你说得对，我聚焦了文件后可以直接看到完整内容。让我现在直接查看文件，找到需要修改的部分。\n\n从文件内容可以看到，在`yingdibuju`函数中，虽然定义了算法映射表支持\"mnth\"（模拟退火），但在第708行强制将算法改为了\"yc\"（遗传）：\n\n```python\nrec_data[\"layoutAlgorithm\"] = \"yc\"\n```\n\n这导致无论传入什么算法，最终都会使用遗传算法。现在我来修改这个逻辑，让\"mnth\"能正常调用模拟退火，其他情况仍然使用遗传算法。",
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"timestamp": "2025-09-25T15:43:15.034504"
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},
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{
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"role": "assistant",
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"content": "",
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"timestamp": "2025-09-25T15:43:15.037813",
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"tool_calls": [
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||
{
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"id": "modify_file:5",
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"index": 0,
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"type": "function",
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"function": {
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"name": "modify_file",
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"arguments": "{\"path\": \"路径规划/zongti.py\", \"operation\": \"replace\", \"old_text\": \" logging.debug(alg)\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\", \"new_text\": \" logging.debug(alg)\\n # 只有当算法不是mnth（模拟退火）时才强制改为yc（遗传）\\n if algorithm != \\\"mnth\\\":\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\"}"
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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, \"error\": \"replace操作需要提供content参数\"}",
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"timestamp": "2025-09-25T15:43:16.845406",
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"tool_call_id": "modify_file:5",
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"name": "modify_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": "2025-09-25T15:43:30.479311",
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"tool_calls": [
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"arguments": "{\"path\": \"路径规划/zongti.py\", \"old_text\": \" logging.debug(alg)\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\", \"operation\": \"replace\", \"content\": \" logging.debug(alg)\\n # 只有当算法不是mnth（模拟退火）时才强制改为yc（遗传）\\n if algorithm != \\\"mnth\\\":\\n rec_data[\\\"layoutAlgorithm\\\"] = \\\"yc\\\"\\n final_data = camp_layout_main(rec_data)\"}"
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"content": "修改完成！我已经成功修改了`zongti.py`文件，主要变化是：\n\n**修改内容：**\n- 在第708行附近，将原来的强制遗传算法逻辑改为条件判断\n- 现在只有当算法不是\"mnth\"（模拟退火）时，才会强制改为\"yc\"（遗传）\n- 如果算法是\"mnth\"，则保持原样，让模拟退火算法可以正常调用\n\n**修改后的逻辑：**\n```python\n# 只有当算法不是mnth（模拟退火）时才强制改为yc（遗传）\nif algorithm != \"mnth\":\n rec_data[\"layoutAlgorithm\"] = \"yc\"\n```\n\n这样修改后：\n- 当调用`mnth`时，会使用你写好的模拟退火算法\n- 其他所有算法（yc、bcd、jy、lq、nd、yhc、tdf）仍然会使用遗传算法\n\n修改完成，不需要测试，按照你的要求直接交付。",
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"timestamp": "2025-09-25T15:43:47.244633"
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