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wess09
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76
alas.py
76
alas.py
@ -298,39 +298,50 @@ class AzurLaneAutoScript:
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from module.base.utils import save_image
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from module.handler.sensitive_info import (handle_sensitive_image,
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handle_sensitive_logs)
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if self.config.Error_SaveError:
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# LLM Error Analysis - 放到最前面，防止后面截图保存时二次崩溃导致分析没跑
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try:
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if hasattr(self, 'config') and getattr(self.config, 'Error_LlmAnalysis', False):
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from module.llm import analyze_exception
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import sys
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_, exc_value, _ = sys.exc_info()
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if exc_value is not None:
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analyze_exception(self.config, exc_value)
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except Exception as e:
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logger.error(f'LLM Analysis failed: {e}')
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if getattr(self.config, 'Error_SaveError', False):
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config_folder = pathlib.Path(f"./log/error/{self.config_name}")
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folder = config_folder.joinpath(str(int(time.time() * 1000)))
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folder.mkdir(parents=True, exist_ok=True)
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logger.warning(f'保存错误日志: {folder}')
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for data in self.device.screenshot_deque:
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image_time = datetime.strftime(data['time'], '%Y-%m-%d_%H-%M-%S-%f')
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image = handle_sensitive_image(data['image'])
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save_image(image, f'{folder}/{image_time}.png')
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with open(logger.log_file, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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start = 0
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for index, line in enumerate(lines):
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line = line.strip(' \r\t\n')
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if re.match('^═{15,}$', line):
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start = index
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lines = lines[start - 2:]
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lines = handle_sensitive_logs(lines)
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with open(f'{folder}/log.txt', 'w', encoding='utf-8') as f:
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f.writelines(lines)
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self.keep_last_errlog(config_folder, self.config.Error_SaveErrorCount)
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# LLM Error Analysis
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if self.config.Error_LlmAnalysis:
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try:
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from module.llm import analyze_exception
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import sys
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_, exc_value, _ = sys.exc_info()
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if exc_value is not None:
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analyze_exception(self.config, exc_value)
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except Exception as e:
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logger.error(f'LLM Analysis failed: {e}')
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try:
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# 只在已经初始化了设备时才尝试保存截图，避免按需初始化时二次崩溃
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if 'device' in self.__dict__:
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for data in self.device.screenshot_deque:
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image_time = datetime.strftime(data['time'], '%Y-%m-%d_%H-%M-%S-%f')
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image = handle_sensitive_image(data['image'])
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save_image(image, f'{folder}/{image_time}.png')
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except Exception as e:
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logger.error(f"Save error screenshot failed: {e}")
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try:
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with open(logger.log_file, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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start = 0
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for index, line in enumerate(lines):
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line = line.strip(' \r\t\n')
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if re.match('^═{15,}$', line):
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start = index
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lines = lines[start - 2:]
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lines = handle_sensitive_logs(lines)
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with open(f'{folder}/log.txt', 'w', encoding='utf-8') as f:
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f.writelines(lines)
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except Exception as e:
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logger.error(f"Save error logs failed: {e}")
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self.keep_last_errlog(config_folder, getattr(self.config, 'Error_SaveErrorCount', 0))
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def restart(self):
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from module.handler.login import LoginHandler
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@ -844,6 +855,15 @@ class AzurLaneAutoScript:
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logger.error("调度器循环中发生意外的全局异常！")
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import traceback
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logger.error(traceback.format_exc()) # 打印完整的错误堆栈
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# 即使没有达到重启或失败上限，也第一时间自动请求分析崩溃原因
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try:
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if hasattr(self, 'config') and getattr(self.config, 'Error_LlmAnalysis', False):
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from module.llm import analyze_exception
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analyze_exception(self.config, e)
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except Exception as ex:
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logger.error(f'LLM Analysis failed: {ex}')
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logger.warning(
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f">>> 这是第 {consecutive_global_failures} 次连续全局失败，共 {MAX_GLOBAL_FAILURES} 次。"
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)
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@ -456,7 +456,7 @@
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},
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"LlmModel": {
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"type": "input",
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"value": "Nvidia/deepseek-ai/deepseek-v3.1",
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"value": "Nvidia/qwen/qwen2.5-coder-32b-instruct",
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"valuetype": "str"
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}
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},
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@ -2,6 +2,10 @@ import traceback
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import os
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from module.logger import logger
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import hashlib
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_analyzed_errors_cache = {}
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def analyze_exception(config, e):
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"""
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Analyze the exception using LLM.
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@ -12,6 +16,26 @@ def analyze_exception(config, e):
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"""
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if not hasattr(config, 'Error_LlmAnalysis') or not config.Error_LlmAnalysis:
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return
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tb = ''.join(traceback.format_exception(type(e), e, e.__traceback__))
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# 错误去重：计算当前堆栈报错的哈希值，如果最近已经分析过，直接跳过，防止浪费 API 和时间
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error_hash = hashlib.md5(tb.encode('utf-8')).hexdigest()
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if error_hash in _analyzed_errors_cache:
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cached_result = _analyzed_errors_cache[error_hash]
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model = getattr(config, 'Error_LlmModel', 'gpt-4o-mini')
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logger.hr('LLM 错误分析', level=1)
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logger.info('该错误已被 LLM 分析过，直接复用上次的分析结果以节省 API...')
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logger.info(f"\n[LLM 分析报告 (由 {model} 提供, 复用缓存)]\n{cached_result}\n")
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logger.hr('LLM 分析结束', level=1)
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return
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# 加入缓存占位符，防止等待期间如果又疯狂连续报错导致重复发送请求
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_analyzed_errors_cache[error_hash] = "该错误正在被 LLM 分析中或上次分析失败，暂无结果。"
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# 控制缓存大小，防止内存泄漏（保留最近 50 个报错记录）
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if len(_analyzed_errors_cache) > 50:
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_analyzed_errors_cache.clear()
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_analyzed_errors_cache[error_hash] = "该错误正在被 LLM 分析中或上次分析失败，暂无结果。"
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api_key = getattr(config, 'Error_LlmApiKey', '')
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api_base = getattr(config, 'Error_LlmApiBase', 'https://api.openai.com/v1')
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@ -75,7 +99,9 @@ def analyze_exception(config, e):
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)
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analysis = response.choices[0].message.content.strip()
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logger.info(f"\n[LLM 分析报告]\n{analysis}\n")
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# 覆写真正的成果进字典
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_analyzed_errors_cache[error_hash] = analysis
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logger.info(f"\n[LLM 分析报告 (由 {model} 提供)]\n{analysis}\n")
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logger.hr('LLM 分析结束', level=1)
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except ImportError:
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