From 0112087be4941341415819490188ca2f23a4142c Mon Sep 17 00:00:00 2001
From: jxxghp <jxxghp@gmail.com>
Date: Sun, 8 Jun 2025 10:51:59 +0800
Subject: [PATCH] refactor #4407

---
 .github/workflows/issues.yml |  30 ++
 app/core/config.py           |   2 +
 app/helper/memory.py         | 632 ++++++++++++++++++++++++++++++-----
 app/log.py                   |  18 +
 4 files changed, 607 insertions(+), 75 deletions(-)
 create mode 100644 .github/workflows/issues.yml

diff --git a/.github/workflows/issues.yml b/.github/workflows/issues.yml
new file mode 100644
index 00000000..bd92c0c2
--- /dev/null
+++ b/.github/workflows/issues.yml
@@ -0,0 +1,30 @@
+name: Close inactive issues
+on:
+ workflow_dispatch:
+
+ schedule:
+   # Github Action 只支持 UTC 时间。
+   # '0 18 * * *' 对应 UTC 时间的 18:00，也就是中国时区 (UTC+8) 的第二天凌晨 02:00。
+   - cron: "0 18 * * *"
+
+jobs:
+ close-issues:
+   runs-on: ubuntu-latest
+   permissions:
+     issues: write
+     pull-requests: write
+   steps:
+     - uses: actions/stale@v5
+       with:
+         # 标记 stale 标签时间
+         days-before-issue-stale: 30
+         # 关闭 issues 标签时间
+         days-before-issue-close: 14
+         # 自定义标签名
+         stale-issue-label: "stale"
+         stale-issue-message: "此问题已过时，因为它已打开 30 天且没有任何活动。"
+         close-issue-message: "此问题已关闭，因为它在标记为 stale 后，已处于无更新状态 14 天。"
+         # 忽略所有的 Pull Request，只处理 Issue
+         days-before-pr-stale: -1
+         days-before-pr-close: -1
+         repo-token: ${{ secrets.GITHUB_TOKEN }}
\ No newline at end of file
diff --git a/app/core/config.py b/app/core/config.py
index 5b1a4257..1445b201 100644
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -249,6 +249,8 @@ class ConfigModel(BaseModel):
     BIG_MEMORY_MODE: bool = False
     # 内存使用监控
     MEMORY_MONITOR_ENABLE: bool = False
+    # 启用详细内存分析
+    MEMORY_DETAILED_ANALYSIS: bool = False
     # 全局图片缓存，将媒体图片缓存到本地
     GLOBAL_IMAGE_CACHE: bool = False
     # 是否启用编码探测的性能模式
diff --git a/app/helper/memory.py b/app/helper/memory.py
index 080aa3f3..816dd395 100644
--- a/app/helper/memory.py
+++ b/app/helper/memory.py
@@ -1,9 +1,16 @@
 import gc
-import psutil
+import linecache
+import os
+import sys
 import threading
 import time
-from typing import Optional, Callable, Any
+import tracemalloc
+from collections import defaultdict, deque
+from datetime import datetime, timedelta
 from functools import wraps
+from typing import Optional, Callable, Any, Dict, List
+
+import psutil
 
 from app.core.config import settings
 from app.core.event import eventmanager, Event
@@ -13,73 +20,524 @@ from app.schemas.types import EventType
 from app.utils.singleton import Singleton
 
 
+class MemoryAnalyzer:
+    """
+    内存分析器，用于分析内存使用详情
+    """
+
+    _analyzing_depth = 15  # 默认分析深度
+
+    def __init__(self):
+        self.memory_history = deque(maxlen=100)  # 保留最近100次内存记录
+        self.module_memory = defaultdict(list)  # 模块内存使用记录
+        self._analyzing = False
+        # 创建专门的内存日志记录器
+        self._memory_logger = logger.get_logger("memory_analysis")
+
+    def start_analyzing(self):
+        """
+        开始内存分析
+        """
+        if not self._analyzing:
+            tracemalloc.start(self._analyzing_depth)
+            self._analyzing = True
+            self._memory_logger.info("内存分析器已启动")
+            logger.info("内存分析器已启动")
+
+    def stop_analyzing(self):
+        """
+        停止内存分析
+        """
+        if self._analyzing:
+            tracemalloc.stop()
+            self._analyzing = False
+            self._memory_logger.info("内存分析器已停止")
+            logger.info("内存分析器已停止")
+
+    def record_memory_snapshot(self, tag: str = ""):
+        """
+        记录内存快照
+        :param tag: 快照标签
+        """
+        if not self._analyzing:
+            return None
+
+        try:
+            snapshot = tracemalloc.take_snapshot()
+            top_stats = snapshot.statistics('lineno')
+
+            # 记录当前时间和内存使用
+            current_time = datetime.now()
+            memory_info = MemoryHelper.get_memory_usage()
+
+            # 记录基本信息到内存日志
+            self._memory_logger.info(f"[{tag}] 内存快照 - RSS: {memory_info['rss']:.1f}MB, "
+                                     f"系统使用率: {memory_info['system_percent']:.1f}%")
+
+            # 分析最大内存使用的代码行
+            top_memory_lines = []
+            for index, stat in enumerate(top_stats[:10]):
+                try:
+                    # 安全地访问traceback属性
+                    if hasattr(stat, 'traceback') and stat.traceback:
+                        filename = getattr(stat.traceback, 'filename', 'unknown')
+                        lineno = getattr(stat.traceback, 'lineno', 0)
+                        size_mb = stat.size / 1024 / 1024
+
+                        # 获取代码行内容
+                        try:
+                            line_content = linecache.getline(filename, lineno).strip()
+                        except Exception as e:
+                            line_content = f"无法读取代码行：{e}"
+
+                        top_memory_lines.append({
+                            'filename': os.path.basename(filename) if filename != 'unknown' else 'unknown',
+                            'lineno': lineno,
+                            'size_mb': size_mb,
+                            'line_content': line_content
+                        })
+
+                        # 记录详细的内存使用信息到内存日志
+                        if size_mb > 1.0:  # 只记录大于1MB的内存使用
+                            self._memory_logger.debug(f"[{tag}] 内存使用: {os.path.basename(filename)}:{lineno} "
+                                                      f"使用 {size_mb:.2f}MB - {line_content[:50]}")
+
+                except Exception as e:
+                    self._memory_logger.debug(f"处理内存统计项时出错: {e}")
+                    continue
+
+            # 记录到历史
+            snapshot_record = {
+                'timestamp': current_time,
+                'tag': tag,
+                'memory_info': memory_info,
+                'top_memory_lines': top_memory_lines
+            }
+
+            self.memory_history.append(snapshot_record)
+            return snapshot_record
+
+        except Exception as e:
+            self._memory_logger.error(f"记录内存快照失败: {e}")
+            logger.error(f"记录内存快照失败: {e}")
+            return None
+
+    def get_memory_trend(self, minutes: int = 30) -> List[Dict]:
+        """
+        获取内存使用趋势
+        :param minutes: 获取最近多少分钟的数据
+        :return: 内存趋势数据
+        """
+        cutoff_time = datetime.now() - timedelta(minutes=minutes)
+        trend_data = [
+            record for record in self.memory_history
+            if record['timestamp'] >= cutoff_time
+        ]
+
+        if trend_data:
+            self._memory_logger.info(f"获取内存趋势数据: 最近{minutes}分钟内有{len(trend_data)}条记录")
+
+        return trend_data
+
+    def get_top_memory_files(self, limit: int = 10) -> List[Dict]:
+        """
+        获取内存使用最多的文件
+        :param limit: 返回数量限制
+        :return: 文件内存使用统计
+        """
+        if not self._analyzing:
+            return []
+
+        try:
+            snapshot = tracemalloc.take_snapshot()
+            top_stats = snapshot.statistics('filename')
+
+            result = []
+            for stat in top_stats[:limit]:
+                try:
+                    if hasattr(stat, 'traceback') and stat.traceback:
+                        filename = getattr(stat.traceback, 'filename', 'unknown')
+                        size_mb = stat.size / 1024 / 1024
+
+                        file_info = {
+                            'filename': os.path.basename(filename) if filename != 'unknown' else 'unknown',
+                            'full_path': filename,
+                            'size_mb': size_mb,
+                            'count': stat.count
+                        }
+                        result.append(file_info)
+
+                        # 记录到内存日志
+                        if size_mb > 0.5:  # 只记录大于0.5MB的文件
+                            self._memory_logger.debug(f"文件内存使用: {file_info['filename']} "
+                                                      f"使用 {size_mb:.2f}MB ({stat.count} 次分配)")
+
+                except Exception as e:
+                    self._memory_logger.debug(f"处理文件统计项时出错: {e}")
+                    continue
+
+            if result:
+                self._memory_logger.info(f"获取内存使用最多的{len(result)}个文件")
+
+            return result
+
+        except Exception as e:
+            self._memory_logger.error(f"获取文件内存统计失败: {e}")
+            logger.error(f"获取文件内存统计失败: {e}")
+            return []
+
+    def analyze_memory_leaks(self) -> Dict:
+        """
+        分析可能的内存泄漏
+        :return: 内存泄漏分析结果
+        """
+        if len(self.memory_history) < 5:
+            return {'status': 'insufficient_data', 'message': '数据不足，无法分析'}
+
+        try:
+            # 分析内存增长趋势
+            recent_records = list(self.memory_history)[-10:]
+            memory_values = [record['memory_info']['rss'] for record in recent_records]
+
+            # 计算内存增长率
+            if len(memory_values) > 1:
+                growth_rate = (memory_values[-1] - memory_values[0]) / len(memory_values)
+
+                # 记录分析结果到内存日志
+                self._memory_logger.info(f"内存泄漏分析: 平均增长率 {growth_rate:.2f}MB/次")
+
+                # 每次检查增长超过10MB
+                if growth_rate > 10:
+                    result = {
+                        'status': 'potential_leak',
+                        'growth_rate_mb': growth_rate,
+                        'message': f'检测到潜在内存泄漏，平均每次检查增长 {growth_rate:.2f}MB'
+                    }
+                    self._memory_logger.warning(f"⚠️ 潜在内存泄漏: {result['message']}")
+                    return result
+                elif growth_rate > 5:
+                    result = {
+                        'status': 'high_growth',
+                        'growth_rate_mb': growth_rate,
+                        'message': f'内存增长较快，平均每次检查增长 {growth_rate:.2f}MB'
+                    }
+                    self._memory_logger.warning(f"⚠️ 内存增长较快: {result['message']}")
+                    return result
+
+            self._memory_logger.info("内存使用正常，未检测到异常增长")
+            return {'status': 'normal', 'message': '内存使用正常'}
+
+        except Exception as e:
+            self._memory_logger.error(f"分析内存泄漏失败: {e}")
+            logger.error(f"分析内存泄漏失败: {e}")
+            return {'status': 'error', 'message': f'分析失败: {str(e)}'}
+
+
 class MemoryHelper(metaclass=Singleton):
     """
     内存管理工具类，用于监控和优化内存使用
     """
-    
+
     def __init__(self):
-        self._memory_threshold = 512  # 内存使用阈值(MB)
-        self._check_interval = 300   # 检查间隔(秒)
+        # 内存使用阈值(MB)
+        self._memory_threshold = 512
+        # 检查间隔(秒)
+        self._check_interval = 300
         self._monitoring = False
         self._monitor_thread: Optional[threading.Thread] = None
-        
+        self._analyzer = MemoryAnalyzer()
+        # 是否启用详细日志
+        self._detailed_logging = False
+        # 创建专门的内存日志记录器
+        self._memory_logger = logger.get_logger("memory_monitor")
+
+    @property
+    def analyzer(self):
+        return self._analyzer
+
     @staticmethod
     def get_memory_usage() -> dict:
         """
         获取当前内存使用情况
         """
-        process = psutil.Process()
-        memory_info = process.memory_info()
-        system_memory = psutil.virtual_memory()
-        
-        return {
-            'rss': memory_info.rss / 1024 / 1024,  # MB
-            'vms': memory_info.vms / 1024 / 1024,  # MB
-            'percent': process.memory_percent(),
-            'system_percent': system_memory.percent,
-            'system_available': system_memory.available / 1024 / 1024 / 1024  # GB
-        }
-    
+        try:
+            process = psutil.Process()
+            memory_info = process.memory_info()
+            system_memory = psutil.virtual_memory()
+
+            return {
+                'rss': memory_info.rss / 1024 / 1024,  # MB
+                'vms': memory_info.vms / 1024 / 1024,  # MB
+                'percent': process.memory_percent(),
+                'system_percent': system_memory.percent,
+                'system_available': system_memory.available / 1024 / 1024 / 1024,  # GB
+                'system_total': system_memory.total / 1024 / 1024 / 1024,  # GB
+                'system_used': system_memory.used / 1024 / 1024 / 1024  # GB
+            }
+        except Exception as e:
+            logger.error(f"获取内存使用情况失败: {e}")
+            return {
+                'rss': 0, 'vms': 0, 'percent': 0,
+                'system_percent': 0, 'system_available': 0,
+                'system_total': 0, 'system_used': 0
+            }
+
+    def get_detailed_memory_info(self) -> Dict:
+        """
+        获取详细的内存信息
+        """
+        try:
+            process = psutil.Process()
+
+            # 获取更详细的进程内存信息
+            try:
+                memory_full_info = process.memory_full_info()
+                detailed_info = {
+                    'uss': memory_full_info.uss / 1024 / 1024,  # 进程独占内存 MB
+                    'pss': memory_full_info.pss / 1024 / 1024,  # 进程按比例共享内存 MB  
+                    'swap': memory_full_info.swap / 1024 / 1024,  # 交换内存 MB
+                }
+            except (psutil.AccessDenied, AttributeError) as e:
+                self._memory_logger.debug(f"获取详细内存信息失败: {e}")
+                detailed_info = {}
+
+            # 获取垃圾回收信息
+            gc_info = {}
+            try:
+                for generation in range(3):
+                    gc_info[f'gen_{generation}'] = gc.get_count()[generation]
+            except Exception as e:
+                self._memory_logger.debug(f"获取垃圾回收信息失败: {e}")
+
+            # 获取对象统计
+            object_counts = {}
+            try:
+                # 统计主要对象类型的数量
+                for obj_type in [list, dict, tuple, set, str, int, float]:
+                    try:
+                        object_counts[obj_type.__name__] = len([obj for obj in gc.get_objects()
+                                                                if type(obj) is obj_type])
+                    except Exception as e:
+                        self._memory_logger.debug(f"统计对象类型 {obj_type.__name__} 失败: {e}")
+                        continue
+            except Exception as e:
+                self._memory_logger.debug(f"获取对象统计失败: {e}")
+
+            detailed_result = {
+                'basic': self.get_memory_usage(),
+                'detailed': detailed_info,
+                'gc_info': gc_info,
+                'object_counts': object_counts,
+                'thread_count': threading.active_count(),
+                'fd_count': len(process.open_files()) if hasattr(process, 'open_files') else 0
+            }
+
+            # 记录详细信息到内存日志
+            basic = detailed_result['basic']
+            self._memory_logger.info(f"详细内存信息获取 - RSS: {basic['rss']:.1f}MB, "
+                                     f"线程数: {detailed_result['thread_count']}, "
+                                     f"文件描述符: {detailed_result['fd_count']}")
+
+            return detailed_result
+
+        except Exception as e:
+            self._memory_logger.error(f"获取详细内存信息失败: {e}")
+            logger.error(f"获取详细内存信息失败: {e}")
+            return {
+                'basic': self.get_memory_usage(),
+                'detailed': {},
+                'gc_info': {},
+                'object_counts': {},
+                'thread_count': 0,
+                'fd_count': 0
+            }
+
+    def get_module_memory_usage(self) -> Dict[str, float]:
+        """
+        获取各模块的内存使用情况（估算）
+        """
+        module_memory = {}
+
+        try:
+            # 统计已导入模块的大小
+            for module_name, module in sys.modules.items():
+                if module and hasattr(module, '__file__') and module.__file__:
+                    try:
+                        # 估算模块内存使用（通过模块中的对象数量）
+                        objects = []
+                        if hasattr(module, '__dict__'):
+                            objects = list(module.__dict__.values())
+
+                        # 粗略估算：每个对象平均占用内存
+                        estimated_size = len(objects) * 0.001  # MB
+                        module_memory[module_name] = estimated_size
+                    except Exception as e:
+                        self._memory_logger.debug(f"获取模块 {module_name} 内存使用失败: {e}")
+                        continue
+
+            # 按内存使用量排序，返回前20个
+            sorted_modules = sorted(module_memory.items(), key=lambda x: x[1], reverse=True)
+            top_modules = dict(sorted_modules[:20])
+
+            # 记录到内存日志
+            self._memory_logger.info(f"模块内存统计完成，共分析 {len(module_memory)} 个模块，"
+                                     f"前5个模块: {list(top_modules.keys())[:5]}")
+
+            return top_modules
+
+        except Exception as e:
+            self._memory_logger.error(f"获取模块内存使用失败: {e}")
+            logger.error(f"获取模块内存使用失败: {e}")
+            return {}
+
     def force_gc(self, generation: Optional[int] = None) -> int:
         """
         强制执行垃圾回收
         :param generation: 垃圾回收代数，None表示所有代数
         :return: 回收的对象数量
         """
-        before_memory = self.get_memory_usage()
-        logger.info(f"开始强制垃圾回收，当前内存使用: {before_memory['rss']:.2f}MB")
+        try:
+            before_memory = self.get_memory_usage()
+            self._memory_logger.info(f"开始强制垃圾回收，当前内存使用: {before_memory['rss']:.2f}MB")
+
+            if generation is not None:
+                collected = gc.collect(generation)
+                self._memory_logger.info(f"执行第{generation}代垃圾回收")
+            else:
+                collected = gc.collect()
+                self._memory_logger.info("执行全代垃圾回收")
+
+            after_memory = self.get_memory_usage()
+            memory_freed = before_memory['rss'] - after_memory['rss']
+
+            if memory_freed > 0:
+                self._memory_logger.info(f"垃圾回收完成: 回收对象 {collected} 个, 释放内存 {memory_freed:.2f}MB")
+            else:
+                self._memory_logger.info(f"垃圾回收完成: 回收对象 {collected} 个, 内存无明显释放")
+
+            # 记录内存快照
+            if self._detailed_logging:
+                self._analyzer.record_memory_snapshot("after_gc")
+
+            return collected
+
+        except Exception as e:
+            self._memory_logger.error(f"执行垃圾回收失败: {e}")
+            logger.error(f"执行垃圾回收失败: {e}")
+            return 0
 
-        if generation is not None:
-            collected = gc.collect(generation)
-        else:
-            collected = gc.collect()
-            
-        after_memory = self.get_memory_usage()
-        memory_freed = before_memory['rss'] - after_memory['rss']
-        
-        if memory_freed > 0:
-            logger.info(f"垃圾回收完成: 回收对象 {collected} 个, 释放内存 {memory_freed:.2f}MB")
-            
-        return collected
-    
     def check_memory_and_cleanup(self) -> bool:
         """
         检查内存使用量，如果超过阈值则执行清理
         :return: 是否执行了清理
         """
-        memory_info = self.get_memory_usage()
-        current_memory_mb = memory_info['rss']
-        
-        if current_memory_mb > self._memory_threshold:
-            logger.warning(f"内存使用超过阈值: {current_memory_mb:.1f}MB > {self._memory_threshold:.1f}MB, 开始清理...")
-            self.force_gc()
-            
-            # 再次检查清理效果
-            after_memory = self.get_memory_usage()
-            logger.info(f"清理后内存: {after_memory['rss']:.1f}MB")
-            return True
-        return False
+        try:
+            memory_info = self.get_memory_usage()
+            current_memory_mb = memory_info['rss']
+
+            # 记录常规检查到内存日志
+            self._memory_logger.debug(f"常规内存检查 - RSS: {current_memory_mb:.1f}MB, "
+                                      f"阈值: {self._memory_threshold}MB, "
+                                      f"系统使用率: {memory_info['system_percent']:.1f}%")
+
+            # 记录内存快照
+            if self._detailed_logging:
+                self._analyzer.record_memory_snapshot("routine_check")
+
+            if current_memory_mb > self._memory_threshold:
+                self._memory_logger.warning(f"内存使用超过阈值: {current_memory_mb:.1f}MB > {self._memory_threshold:.1f}MB, 开始清理...")
+
+                # 详细记录高内存使用情况
+                if self._detailed_logging:
+                    self.get_detailed_memory_info()
+                    self._memory_logger.info(f"高内存使用详细信息记录完成")
+
+                    # 记录内存使用最多的文件
+                    top_files = self._analyzer.get_top_memory_files(10)
+                    if top_files:
+                        self._memory_logger.info("内存使用最多的文件:")
+                        for file_info in top_files:
+                            self._memory_logger.info(f"  {file_info['filename']}: {file_info['size_mb']:.2f}MB")
+
+                self.force_gc()
+
+                # 再次检查清理效果
+                after_memory = self.get_memory_usage()
+                self._memory_logger.info(f"清理后内存: {after_memory['rss']:.1f}MB")
+
+                # 检查是否可能存在内存泄漏
+                leak_analysis = self._analyzer.analyze_memory_leaks()
+                if leak_analysis['status'] != 'normal':
+                    self._memory_logger.warning(f"内存泄漏分析: {leak_analysis['message']}")
+
+                return True
+            return False
+
+        except Exception as e:
+            self._memory_logger.error(f"内存检查和清理失败: {e}")
+            logger.error(f"内存检查和清理失败: {e}")
+            return False
+
+    def generate_memory_report(self) -> Dict:
+        """
+        生成详细的内存使用报告
+        """
+        try:
+            self._memory_logger.info("开始生成内存使用报告")
+
+            report = {
+                'timestamp': datetime.now().isoformat(),
+                'basic_info': self.get_memory_usage(),
+                'detailed_info': self.get_detailed_memory_info(),
+                'module_memory': self.get_module_memory_usage(),
+                'memory_trend': self._analyzer.get_memory_trend(30),
+                'top_memory_files': self._analyzer.get_top_memory_files(10),
+                'leak_analysis': self._analyzer.analyze_memory_leaks(),
+                'gc_stats': {
+                    'thresholds': gc.get_threshold(),
+                    'counts': gc.get_count(),
+                    'stats': gc.get_stats() if hasattr(gc, 'get_stats') else None
+                }
+            }
+
+            # 记录报告摘要到内存日志
+            basic = report['basic_info']
+            trend_count = len(report['memory_trend'])
+            files_count = len(report['top_memory_files'])
+
+            self._memory_logger.info(f"内存报告生成完成 - RSS: {basic['rss']:.1f}MB, "
+                                     f"趋势记录: {trend_count}条, 文件统计: {files_count}个, "
+                                     f"泄漏状态: {report['leak_analysis']['status']}")
+
+            return report
+
+        except Exception as e:
+            self._memory_logger.error(f"生成内存报告失败: {e}")
+            logger.error(f"生成内存报告失败: {e}")
+            return {
+                'timestamp': datetime.now().isoformat(),
+                'error': str(e),
+                'basic_info': self.get_memory_usage()
+            }
+
+    def enable_detailed_logging(self, enable: bool = True):
+        """
+        启用/禁用详细日志记录
+        :param enable: 是否启用
+        """
+        try:
+            self._detailed_logging = enable
+            if enable:
+                self._analyzer.start_analyzing()
+                self._memory_logger.info("已启用详细内存日志记录和分析")
+                logger.info("已启用详细内存日志记录")
+            else:
+                self._analyzer.stop_analyzing()
+                self._memory_logger.info("已禁用详细内存日志记录和分析")
+                logger.info("已禁用详细内存日志记录")
+        except Exception as e:
+            self._memory_logger.error(f"切换详细日志记录状态失败: {e}")
+            logger.error(f"切换详细日志记录状态失败: {e}")
 
     @eventmanager.register(EventType.ConfigChanged)
     def handle_config_changed(self, event: Event):
@@ -103,12 +561,18 @@ class MemoryHelper(metaclass=Singleton):
             return
         if self._monitoring:
             return
-            
+
+        # 初始化内存分析器
         self._monitoring = True
         self._monitor_thread = threading.Thread(target=self._monitor_loop, daemon=True)
         self._monitor_thread.start()
-        logger.info(f"内存监控已启动 - 阈值: {self._memory_threshold}MB, 检查间隔: {self._check_interval}秒")
-    
+
+        # 启用详细分析（如果配置允许）
+        if settings.MEMORY_DETAILED_ANALYSIS:
+            self.enable_detailed_logging(True)
+
+        self._memory_logger.info(f"内存监控已启动 - 阈值: {self._memory_threshold}MB, 检查间隔: {self._check_interval}秒")
+
     def stop_monitoring(self):
         """
         停止内存监控
@@ -116,36 +580,58 @@ class MemoryHelper(metaclass=Singleton):
         self._monitoring = False
         if self._monitor_thread:
             self._monitor_thread.join(timeout=5)
-            logger.info("内存监控已停止")
-    
+
+        # 停止详细分析
+        self.enable_detailed_logging(False)
+
+        self._memory_logger.info("内存监控已停止")
+
     def _monitor_loop(self):
         """
         内存监控循环
         """
+        self._memory_logger.info("内存监控循环开始")
         while self._monitoring:
             try:
+                # 执行常规检查
                 self.check_memory_and_cleanup()
+
+                # 每10次检查生成一次详细报告
+                if self._detailed_logging and hasattr(self, '_check_count'):
+                    self._check_count = getattr(self, '_check_count', 0) + 1
+                    if self._check_count % 10 == 0:
+                        report = self.generate_memory_report()
+                        self._memory_logger.info(f"第{self._check_count}次检查 - 内存使用报告: "
+                                                 f"RSS={report['basic_info']['rss']:.1f}MB, "
+                                                 f"系统使用率={report['basic_info']['system_percent']:.1f}%")
+
                 time.sleep(self._check_interval)
             except Exception as e:
+                self._memory_logger.error(f"内存监控出错: {e}")
                 logger.error(f"内存监控出错: {e}")
-                time.sleep(60)  # 出错后等待1分钟再继续
-    
+                # 出错后等待1分钟再继续
+                time.sleep(60)
+
+        self._memory_logger.info("内存监控循环结束")
+
     def set_threshold(self, threshold_mb: int):
         """
         设置内存使用阈值
         :param threshold_mb: 内存阈值，单位MB（500-4096之间）
         """
+        old_threshold = self._memory_threshold
         self._memory_threshold = max(512, min(4096, threshold_mb))
-        logger.info(f"内存阈值已设置为: {self._memory_threshold}MB")
-    
+        self._memory_logger.info(f"内存阈值已从 {old_threshold}MB 更新为: {self._memory_threshold}MB")
+
     def set_check_interval(self, interval: int):
         """
         设置检查间隔
         :param interval: 检查间隔，单位秒（最少60秒）
         """
+        old_interval = self._check_interval
         self._check_interval = max(60, interval)
-        logger.info(f"内存检查间隔已设置为: {self._check_interval}秒")
-    
+        self._memory_logger.info(f"内存检查间隔已从 {old_interval}秒 更新为: {self._check_interval}秒")
+
     def get_threshold(self) -> int:
         """
         获取当前内存阈值
@@ -160,36 +646,32 @@ def memory_optimized(force_gc_after: bool = False, log_memory: bool = False):
     :param force_gc_after: 函数执行后是否强制垃圾回收
     :param log_memory: 是否记录内存使用情况
     """
+
+    memory_logger = logger.get_logger("memory_monitor")
+
     def decorator(func: Callable) -> Callable:
         @wraps(func)
         def wrapper(*args, **kwargs) -> Any:
+            memory_helper = MemoryHelper()
+
             if settings.MEMORY_MONITOR_ENABLE:
                 if log_memory:
-                    before_memory = MemoryHelper().get_memory_usage()
-                    logger.info(f"{func.__name__} 执行前内存: {before_memory['rss']:.1f}MB")
+                    before_memory = memory_helper.get_memory_usage()
+                    memory_logger.info(f"{func.__name__} 执行前内存: {before_memory['rss']:.1f}MB")
+                memory_helper.analyzer.record_memory_snapshot(f"before_{func.__name__}")
+
             try:
                 result = func(*args, **kwargs)
                 return result
             finally:
                 if settings.MEMORY_MONITOR_ENABLE:
                     if force_gc_after:
-                        MemoryHelper().force_gc()
+                        memory_helper.force_gc()
                     if log_memory:
-                        after_memory = MemoryHelper().get_memory_usage()
-                        logger.info(f"{func.__name__} 执行后内存: {after_memory['rss']:.1f}MB")
-        
+                        after_memory = memory_helper.get_memory_usage()
+                        memory_logger.info(f"{func.__name__} 执行后内存: {after_memory['rss']:.1f}MB")
+                    memory_helper.analyzer.record_memory_snapshot(f"after_{func.__name__}")
+
         return wrapper
+
     return decorator
-
-
-def clear_large_objects(*objects):
-    """
-    清理大型对象的辅助函数
-    """
-    for obj in objects:
-        if hasattr(obj, 'clear') and callable(obj.clear):
-            obj.clear()
-        elif hasattr(obj, '__dict__'):
-            obj.__dict__.clear()
-        del obj
-    gc.collect()
diff --git a/app/log.py b/app/log.py
index 1247c45a..a855830a 100644
--- a/app/log.py
+++ b/app/log.py
@@ -99,6 +99,24 @@ class LoggerManager:
     # 线程锁
     _lock = threading.Lock()
 
+    def get_logger(self, name: str) -> logging.Logger:
+        """
+        获取一个指定名称的、独立的日志记录器。
+        创建一个独立的日志文件，例如 'diag_memory.log'。
+        :param name: 日志记录器的名称，也将用作文件名。
+        :return: 一个配置好的 logging.Logger 实例。
+        """
+        # 使用名称作为日志文件名
+        logfile = f"{name}.log"
+        with LoggerManager._lock:
+            # 检查是否已经创建过这个 logger
+            _logger = self._loggers.get(logfile)
+            if not _logger:
+                # 如果没有，就使用现有的 __setup_logger 来创建一个新的
+                _logger = self.__setup_logger(log_file=logfile)
+                self._loggers[logfile] = _logger
+        return _logger
+
     @staticmethod
     def __get_caller():
         """