diff --git a/Code/second_layer_filter.md b/Code/second_layer_filter.md
new file mode 100644
index 0000000..b0714ad
--- /dev/null
+++ b/Code/second_layer_filter.md
@@ -0,0 +1,24 @@
+# 第二层异常过滤模块使用说明
+
+## 概述
+`Code/second_layer_filter.py` 对第一层输出进行规则化聚合，筛出“真正需要关注”的异常事件，并生成摘要与结构化结果。
+
+## 输入
+- 文件：`Data/stl_anomaly_results.csv`
+  - 列：`Time,Value,trend,seasonal,residual,anomaly_score,anomaly_level`
+  - `anomaly_level ∈ {normal, mild, moderate, severe}`
+- 配置（类内默认，可按需修改）：
+  - `min_score_for_mild=2.0`：mild 点的分数下限
+  - `max_gap_minutes=30`：事件合并的点间隔
+  - 严重判定：`max_score≥3.0` 或 `density≥0.1 & count≥5`
+  - 中等判定：`2.0≤max_score<3.0` 或 `density≥0.05 & count≥3`
+  - `spike_threshold=4.0`：1-2点窗口的尖峰最低分
+  - `dedup_window_minutes=60`、`max_daily_events=5`
+
+## 输出
+- 事件列表：`Data/second_layer_events.csv`
+  - 列：`start_time,end_time,anomaly_count,anomaly_density,max_score,mean_score,severity_level`
+- 告警消息：`Data/alert_messages.json`
+  - 每条包含：`title,date,time_window,stats,sample_points,hint,severity_level,max_score`
+- 每日统计：`Data/daily_summary.json`
+
diff --git a/Code/second_layer_filter.py b/Code/second_layer_filter.py
new file mode 100644
index 0000000..88fd0f4
--- /dev/null
+++ b/Code/second_layer_filter.py
@@ -0,0 +1,425 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+第二层异常过滤规则实现
+从第一层STL异常检测结果中筛选出真正的异常事件，减少误报
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from typing import List, Dict, Tuple
+import json
+
+class SecondLayerFilter:
+    def __init__(self, config: Dict = None):
+        """初始化第二层过滤器"""
+        self.config = config or self._get_default_config()
+        self.anomaly_events = []
+        self.daily_summary = {}
+        
+    def _get_default_config(self) -> Dict:
+        """获取默认配置"""
+        return {
+            # 粗筛规则
+            'min_score_for_mild': 2.0,  # mild异常的最小分数阈值
+            
+            # 事件合并规则
+            'max_gap_minutes': 30,  # 相邻异常点最大间隔（分钟）
+            
+            # 事件分级规则
+            'severe_thresholds': {
+                'max_score': 3.0,  # 严重异常的最小分数
+                'density': 0.1,    # 异常密度阈值（10%）
+                'min_count': 5     # 最小异常点数量
+            },
+            'moderate_thresholds': {
+                'max_score': 2.0,  # 中等异常的最小分数
+                'density': 0.05,   # 异常密度阈值（5%）
+                'min_count': 3     # 最小异常点数量
+            },
+            
+            # 尖峰去噪规则
+            'spike_threshold': 4.0,  # 尖峰保留阈值
+            
+            # 去重与限流规则
+            'dedup_window_minutes': 60,  # 去重时间窗口（分钟）
+            'max_daily_events': 5,       # 每日最大事件数
+        }
+    
+    def load_data(self, file_path: str) -> pd.DataFrame:
+        """加载第一层异常检测结果"""
+        print(f"正在加载数据: {file_path}")
+        
+        df = pd.read_csv(file_path)
+        df['Time'] = pd.to_datetime(df['Time'])
+        df = df.sort_values('Time').reset_index(drop=True)
+        
+        print(f"数据加载完成，共 {len(df)} 个数据点")
+        print(f"时间范围：{df['Time'].min()} 到 {df['Time'].max()}")
+        
+        return df
+    
+    def coarse_filter(self, df: pd.DataFrame) -> pd.DataFrame:
+        """步骤1: 粗筛异常点"""
+        print("执行粗筛过滤...")
+        
+        # 保留条件：severe和moderate异常，或mild异常但分数>=2.0
+        mask = (
+            (df['anomaly_level'].isin(['severe', 'moderate'])) |
+            ((df['anomaly_level'] == 'mild') & (df['anomaly_score'] >= self.config['min_score_for_mild']))
+        )
+        
+        filtered_df = df[mask].copy()
+        
+        print(f"粗筛前: {len(df)} 个数据点")
+        print(f"粗筛后: {len(filtered_df)} 个数据点")
+        print(f"过滤掉: {len(df) - len(filtered_df)} 个数据点")
+        
+        return filtered_df
+    
+    def merge_anomaly_events(self, df: pd.DataFrame) -> List[Dict]:
+        """步骤2: 合并相邻异常为事件窗口"""
+        print("合并异常事件...")
+        
+        if len(df) == 0:
+            return []
+        
+        events = []
+        current_event = {
+            'start_time': df.iloc[0]['Time'],
+            'end_time': df.iloc[0]['Time'],
+            'anomaly_points': [df.iloc[0].to_dict()],
+            'max_score': df.iloc[0]['anomaly_score'],
+            'mean_score': df.iloc[0]['anomaly_score'],
+            'anomaly_count': 1
+        }
+        
+        max_gap = timedelta(minutes=self.config['max_gap_minutes'])
+        
+        for i in range(1, len(df)):
+            current_time = df.iloc[i]['Time']
+            last_time = current_event['end_time']
+            
+            # 检查时间间隔
+            if current_time - last_time <= max_gap:
+                # 合并到当前事件
+                current_event['end_time'] = current_time
+                current_event['anomaly_points'].append(df.iloc[i].to_dict())
+                current_event['max_score'] = max(current_event['max_score'], df.iloc[i]['anomaly_score'])
+                current_event['anomaly_count'] += 1
+            else:
+                # 结束当前事件，开始新事件
+                scores = [point['anomaly_score'] for point in current_event['anomaly_points']]
+                current_event['mean_score'] = np.mean(scores)
+                current_event['window_length'] = len(current_event['anomaly_points'])
+                current_event['anomaly_density'] = current_event['anomaly_count'] / current_event['window_length']
+                
+                events.append(current_event)
+                
+                # 开始新事件
+                current_event = {
+                    'start_time': current_time,
+                    'end_time': current_time,
+                    'anomaly_points': [df.iloc[i].to_dict()],
+                    'max_score': df.iloc[i]['anomaly_score'],
+                    'mean_score': df.iloc[i]['anomaly_score'],
+                    'anomaly_count': 1
+                }
+        
+        # 处理最后一个事件
+        if current_event:
+            scores = [point['anomaly_score'] for point in current_event['anomaly_points']]
+            current_event['mean_score'] = np.mean(scores)
+            current_event['window_length'] = len(current_event['anomaly_points'])
+            current_event['anomaly_density'] = current_event['anomaly_count'] / current_event['window_length']
+            events.append(current_event)
+        
+        print(f"合并后得到 {len(events)} 个异常事件")
+        return events
+    
+    def classify_events(self, events: List[Dict]) -> List[Dict]:
+        """步骤3: 事件分级与过滤"""
+        print("执行事件分级...")
+        
+        classified_events = []
+        
+        for event in events:
+            # 判断是否为严重事件
+            is_severe = (
+                event['max_score'] >= self.config['severe_thresholds']['max_score'] or
+                (event['anomaly_density'] >= self.config['severe_thresholds']['density'] and 
+                 event['anomaly_count'] >= self.config['severe_thresholds']['min_count'])
+            )
+            
+            # 判断是否为中等事件
+            is_moderate = (
+                (self.config['moderate_thresholds']['max_score'] <= event['max_score'] < 
+                 self.config['severe_thresholds']['max_score']) or
+                (event['anomaly_density'] >= self.config['moderate_thresholds']['density'] and 
+                 event['anomaly_count'] >= self.config['moderate_thresholds']['min_count'])
+            )
+            
+            # 尖峰去噪：对于只有1-2个异常点的事件，需要更高的分数
+            if event['anomaly_count'] <= 2:
+                if event['max_score'] < self.config['spike_threshold']:
+                    continue  # 丢弃低分尖峰
+            
+            # 确定事件等级
+            if is_severe:
+                event['severity_level'] = 'severe'
+                classified_events.append(event)
+            elif is_moderate:
+                event['severity_level'] = 'moderate'
+                classified_events.append(event)
+            else:
+                # 丢弃不符合条件的事件
+                continue
+        
+        print(f"分级后保留 {len(classified_events)} 个事件")
+        print(f"严重事件: {len([e for e in classified_events if e['severity_level'] == 'severe'])} 个")
+        print(f"中等事件: {len([e for e in classified_events if e['severity_level'] == 'moderate'])} 个")
+        
+        return classified_events
+    
+    def deduplicate_events(self, events: List[Dict]) -> List[Dict]:
+        """步骤4: 去重与限流"""
+        print("执行去重与限流...")
+        
+        if not events:
+            return []
+        
+        # 按日期分组
+        daily_events = {}
+        for event in events:
+            date_key = event['start_time'].date()
+            if date_key not in daily_events:
+                daily_events[date_key] = []
+            daily_events[date_key].append(event)
+        
+        deduplicated_events = []
+        
+        for date_key, day_events in daily_events.items():
+            # 按严重程度和分数排序
+            day_events.sort(key=lambda x: (
+                x['severity_level'] == 'severe',  # severe优先
+                x['max_score']  # 分数高的优先
+            ), reverse=True)
+            
+            # 去重：相似时间段的事件只保留一个
+            filtered_day_events = []
+            dedup_window = timedelta(minutes=self.config['dedup_window_minutes'])
+            
+            for event in day_events:
+                # 检查是否与已保留的事件时间重叠
+                is_duplicate = False
+                for kept_event in filtered_day_events:
+                    if abs(event['start_time'] - kept_event['start_time']) < dedup_window:
+                        is_duplicate = True
+                        break
+                
+                if not is_duplicate:
+                    filtered_day_events.append(event)
+            
+            # 限制每日事件数量
+            filtered_day_events = filtered_day_events[:self.config['max_daily_events']]
+            
+            deduplicated_events.extend(filtered_day_events)
+            
+            # 记录每日统计
+            self.daily_summary[date_key] = {
+                'total_events': len(day_events),
+                'kept_events': len(filtered_day_events),
+                'severe_count': len([e for e in filtered_day_events if e['severity_level'] == 'severe']),
+                'moderate_count': len([e for e in filtered_day_events if e['severity_level'] == 'moderate'])
+            }
+        
+        print(f"去重后保留 {len(deduplicated_events)} 个事件")
+        return deduplicated_events
+    
+    def generate_alert_messages(self, events: List[Dict]) -> List[Dict]:
+        """生成告警消息"""
+        print("生成告警消息...")
+        
+        alert_messages = []
+        
+        for event in events:
+            # 生成事件标题
+            if event['severity_level'] == 'severe':
+                title = f"[严重] 指标异常事件"
+            else:
+                title = f"[中等] 指标异常事件"
+            
+            # 生成时间窗口描述
+            duration = event['end_time'] - event['start_time']
+            if duration.total_seconds() <= 600:  # 10分钟内
+                time_desc = f"{event['start_time'].strftime('%H:%M')}"
+            else:
+                time_desc = f"{event['start_time'].strftime('%H:%M')} - {event['end_time'].strftime('%H:%M')}"
+            
+            # 生成统计信息
+            stats = f"异常点: {event['anomaly_count']}个, 密度: {event['anomaly_density']:.1%}, 最高分: {event['max_score']:.2f}"
+            
+            # 选择代表性数据点
+            sample_points = []
+            if event['anomaly_points']:
+                # 选择分数最高的点
+                max_score_point = max(event['anomaly_points'], key=lambda x: x['anomaly_score'])
+                sample_points.append({
+                    'time': max_score_point['Time'].strftime('%H:%M'),
+                    'value': f"{max_score_point['Value']:.0f}",
+                    'score': f"{max_score_point['anomaly_score']:.2f}"
+                })
+                
+                # 如果有多个点，再选择一个
+                if len(event['anomaly_points']) > 1:
+                    # 选择时间最接近中间的点
+                    mid_time = event['start_time'] + (event['end_time'] - event['start_time']) / 2
+                    mid_point = min(event['anomaly_points'], 
+                                  key=lambda x: abs(x['Time'] - mid_time))
+                    sample_points.append({
+                        'time': mid_point['Time'].strftime('%H:%M'),
+                        'value': f"{mid_point['Value']:.0f}",
+                        'score': f"{mid_point['anomaly_score']:.2f}"
+                    })
+            
+            # 生成初步判断
+            if event['anomaly_count'] == 1:
+                hint = "孤立尖峰异常"
+            elif event['anomaly_density'] > 0.5:
+                hint = "持续性异常波动"
+            else:
+                hint = "间歇性异常"
+            
+            alert_message = {
+                'title': title,
+                'date': event['start_time'].strftime('%Y-%m-%d'),
+                'time_window': time_desc,
+                'stats': stats,
+                'sample_points': sample_points,
+                'hint': hint,
+                'severity_level': event['severity_level'],
+                'max_score': event['max_score']
+            }
+            
+            alert_messages.append(alert_message)
+        
+        return alert_messages
+    
+    def process(self, file_path: str) -> Tuple[List[Dict], List[Dict]]:
+        """执行完整的第二层过滤流程"""
+        print("=" * 50)
+        print("开始第二层异常过滤")
+        print("=" * 50)
+        
+        # 步骤1: 加载数据
+        df = self.load_data(file_path)
+        
+        # 步骤2: 粗筛过滤
+        filtered_df = self.coarse_filter(df)
+        
+        # 步骤3: 合并事件
+        events = self.merge_anomaly_events(filtered_df)
+        
+        # 步骤4: 事件分级
+        classified_events = self.classify_events(events)
+        
+        # 步骤5: 去重限流
+        final_events = self.deduplicate_events(classified_events)
+        
+        # 步骤6: 生成告警消息
+        alert_messages = self.generate_alert_messages(final_events)
+        
+        print("=" * 50)
+        print("第二层过滤完成")
+        print("=" * 50)
+        
+        return final_events, alert_messages
+    
+    def save_results(self, events: List[Dict], alert_messages: List[Dict], output_dir: str = '.'):
+        """保存结果到文件"""
+        import os
+        
+        # 保存事件数据
+        if events:
+            events_df = pd.DataFrame([
+                {
+                    'start_time': event['start_time'],
+                    'end_time': event['end_time'],
+                    'anomaly_count': event['anomaly_count'],
+                    'anomaly_density': event['anomaly_density'],
+                    'max_score': event['max_score'],
+                    'mean_score': event['mean_score'],
+                    'severity_level': event['severity_level']
+                }
+                for event in events
+            ])
+            
+            events_file = os.path.join(output_dir, 'second_layer_events.csv')
+            events_df.to_csv(events_file, index=False)
+            print(f"事件数据已保存到: {events_file}")
+        
+        # 保存告警消息
+        if alert_messages:
+            alerts_file = os.path.join(output_dir, 'alert_messages.json')
+            with open(alerts_file, 'w', encoding='utf-8') as f:
+                json.dump(alert_messages, f, ensure_ascii=False, indent=2)
+            print(f"告警消息已保存到: {alerts_file}")
+        
+        # 保存每日统计
+        if self.daily_summary:
+            summary_file = os.path.join(output_dir, 'daily_summary.json')
+            summary_str = {str(k): v for k, v in self.daily_summary.items()}
+            with open(summary_file, 'w', encoding='utf-8') as f:
+                json.dump(summary_str, f, ensure_ascii=False, indent=2)
+            print(f"每日统计已保存到: {summary_file}")
+    
+    def print_summary(self, events: List[Dict], alert_messages: List[Dict]):
+        """打印处理摘要"""
+        print("\n" + "=" * 50)
+        print("处理摘要")
+        print("=" * 50)
+        
+        print(f"最终事件数量: {len(events)}")
+        if events:
+            severe_count = len([e for e in events if e['severity_level'] == 'severe'])
+            moderate_count = len([e for e in events if e['severity_level'] == 'moderate'])
+            print(f"严重事件: {severe_count} 个")
+            print(f"中等事件: {moderate_count} 个")
+        
+        print(f"告警消息数量: {len(alert_messages)}")
+        
+        if self.daily_summary:
+            print("\n每日统计:")
+            for date, stats in sorted(self.daily_summary.items()):
+                print(f"  {date}: 总事件{stats['total_events']}个, 保留{stats['kept_events']}个 "
+                      f"(严重{stats['severe_count']}个, 中等{stats['moderate_count']}个)")
+        
+        if alert_messages:
+            print("\n告警消息预览:")
+            for i, msg in enumerate(alert_messages[:3], 1):
+                print(f"  {i}. {msg['title']} - {msg['date']} {msg['time_window']}")
+                print(f"     {msg['stats']}")
+                print(f"     {msg['hint']}")
+            
+            if len(alert_messages) > 3:
+                print(f"  ... 还有 {len(alert_messages) - 3} 条消息")
+
+
+def main():
+    """主函数"""
+    # 创建过滤器
+    filter = SecondLayerFilter()
+    
+    # 处理数据
+    events, alert_messages = filter.process('stl_anomaly_results.csv')
+    
+    # 保存结果
+    filter.save_results(events, alert_messages)
+    
+    # 打印摘要
+    filter.print_summary(events, alert_messages)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Code/stl_anomaly_detection.md b/Code/stl_anomaly_detection.md
new file mode 100644
index 0000000..dd3329a
--- /dev/null
+++ b/Code/stl_anomaly_detection.md
@@ -0,0 +1,26 @@
+# STL 异常检测模块使用说明
+
+## 概述
+`Code/stl_anomaly_detection.py` 基于 STL 分解对单指标时序进行点异常检测，并生成可视化与结构化结果。
+
+## 输入
+- 文件：`Data/yzh_mirror_data.csv`
+  - 格式：分号分隔；包含列 `"Series";Time;Value`
+  - Time：ISO 时间（含时区）
+  - Value：数值型
+- 参数（可选）：
+  - `seasonal_period`：季节性周期（默认 144，表示 10 分钟采样的 24 小时周期）
+  - `method`：异常分数计算方法（默认 `zscore`，可选 `iqr`、`modified_zscore`）
+
+## 输出
+- 结构化结果：`Data/stl_anomaly_results.csv`
+  - 列：`Time,Value,trend,seasonal,residual,anomaly_score,anomaly_level`
+  - `anomaly_level ∈ {normal, mild, moderate, severe}`
+- 可视化（当前目录保存）：
+  - `stl_analysis_results_stl_decomposition.png`
+  - `stl_analysis_results_anomaly_scores.png`
+  - `stl_analysis_results_anomaly_points.png`
+  - `stl_analysis_results_residual_analysis.png`
+- 终端摘要：输出总点数、各等级异常统计与阈值
+
+
diff --git a/Code/stl_anomaly_detection.py b/Code/stl_anomaly_detection.py
new file mode 100644
index 0000000..d3427b5
--- /dev/null
+++ b/Code/stl_anomaly_detection.py
@@ -0,0 +1,405 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+STL分解异常检测
+使用STL分解方法对yzh mirror时序数据进行异常检测
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy import stats
+from statsmodels.tsa.seasonal import STL
+import warnings
+import matplotlib.font_manager as fm
+warnings.filterwarnings('ignore')
+
+# 设置中文字体 - 更完善的字体配置
+def setup_chinese_font():
+    """设置中文字体显示"""
+    # 尝试多种中文字体
+    chinese_fonts = [
+        'Hiragino Sans GB', # 冬青黑体 (macOS)
+        'PingFang SC',      # 苹方 (macOS)
+        'STHeiti',          # 华文黑体 (macOS)
+        'SimHei',           # 黑体 (Windows)
+        'Microsoft YaHei',  # 微软雅黑 (Windows)
+        'Arial Unicode MS', # Arial Unicode
+        'DejaVu Sans'       # 备用字体
+    ]
+    
+    # 查找可用的中文字体
+    available_fonts = [f.name for f in fm.fontManager.ttflist]
+    selected_font = None
+    
+    for font in chinese_fonts:
+        if font in available_fonts:
+            selected_font = font
+            break
+    
+    if selected_font:
+        plt.rcParams['font.sans-serif'] = [selected_font] + plt.rcParams['font.sans-serif']
+        print(f"使用字体: {selected_font}")
+    else:
+        print("警告: 未找到合适的中文字体，可能显示为方块")
+        # 使用系统默认字体
+        plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
+    
+    plt.rcParams['axes.unicode_minus'] = False
+
+# 初始化字体设置
+setup_chinese_font()
+
+def load_and_preprocess_data(file_path):
+    """加载和预处理数据"""
+    print("正在加载数据...")
+    
+    # 读取CSV文件
+    df = pd.read_csv(file_path, delimiter=';')
+    
+    # 清理列名
+    df.columns = [col.strip().replace('"', '') for col in df.columns]
+    
+    # 转换时间列
+    df['Time'] = pd.to_datetime(df['Time'])
+    
+    # 处理Value列，移除引号并转换为数值
+    df['Value'] = df['Value'].astype(str).str.replace('"', '').astype(float)
+    
+    # 按时间排序
+    df = df.sort_values('Time').reset_index(drop=True)
+    
+    # 检查缺失值
+    missing_count = df['Value'].isnull().sum()
+    if missing_count > 0:
+        print(f"发现 {missing_count} 个缺失值，使用前向填充")
+        df['Value'] = df['Value'].fillna(method='ffill')
+    
+    print(f"数据加载完成，共 {len(df)} 个数据点")
+    print(f"时间范围：{df['Time'].min()} 到 {df['Time'].max()}")
+    print(f"数值范围：{df['Value'].min():.2f} 到 {df['Value'].max():.2f}")
+    
+    return df
+
+def perform_stl_decomposition(data, seasonal_period=144):
+    """执行STL分解"""
+    print("正在执行STL分解...")
+    
+    # 设置窗口大小
+    trend_window = max(seasonal_period + 1, len(data) // 10)
+    seasonal_window = max(7, seasonal_period // 2)
+    # 确保窗口都是奇数
+    if trend_window % 2 == 0:
+        trend_window += 1
+    if seasonal_window % 2 == 0:
+        seasonal_window += 1
+    
+    print(f"趋势窗口大小: {trend_window}")
+    print(f"季节性窗口大小: {seasonal_window}")
+    print(f"季节性周期: {seasonal_period}")
+    
+    # 执行STL分解
+    stl = STL(
+        data['Value'], 
+        period=seasonal_period,
+        trend=trend_window,
+        seasonal=seasonal_window,
+        robust=True
+    )
+    
+    stl_result = stl.fit()
+    
+    # 计算残差
+    data['trend'] = stl_result.trend
+    data['seasonal'] = stl_result.seasonal
+    data['residual'] = stl_result.resid
+    
+    print("STL分解完成")
+    
+    return data, stl_result
+
+def calculate_anomaly_scores(data, method='zscore'):
+    """计算异常分数"""
+    print(f"正在计算异常分数，使用 {method} 方法...")
+    
+    residuals = data['residual'].values
+    
+    if method == 'zscore':
+        # 基于Z-Score的异常分数
+        z_scores = np.abs(stats.zscore(residuals))
+        anomaly_scores = z_scores
+        
+    elif method == 'iqr':
+        # 基于IQR的异常分数
+        Q1 = np.percentile(residuals, 25)
+        Q3 = np.percentile(residuals, 75)
+        IQR = Q3 - Q1
+        lower_bound = Q1 - 1.5 * IQR
+        upper_bound = Q3 + 1.5 * IQR
+        
+        # 计算到边界的距离
+        distances = np.maximum(
+            (lower_bound - residuals) / IQR,
+            (residuals - upper_bound) / IQR
+        )
+        anomaly_scores = np.maximum(distances, 0)
+        
+    elif method == 'modified_zscore':
+        # 基于Modified Z-Score的异常分数
+        median = np.median(residuals)
+        mad = np.median(np.abs(residuals - median))
+        modified_z_scores = np.abs(0.6745 * (residuals - median) / mad)
+        anomaly_scores = modified_z_scores
+    
+    # 将异常分数添加到数据中
+    data['anomaly_score'] = anomaly_scores
+    
+    # 设置异常阈值
+    thresholds = {
+        'mild': np.percentile(anomaly_scores, 95),      # 95%分位数
+        'moderate': np.percentile(anomaly_scores, 98),  # 98%分位数
+        'severe': np.percentile(anomaly_scores, 99.5)   # 99.5%分位数
+    }
+    
+    print(f"异常阈值设置完成:")
+    print(f"  轻微异常: {thresholds['mild']:.3f}")
+    print(f"  明显异常: {thresholds['moderate']:.3f}")
+    print(f"  严重异常: {thresholds['severe']:.3f}")
+    
+    return data, thresholds
+
+def detect_anomalies(data, thresholds):
+    """检测异常点"""
+    print("正在检测异常点...")
+    
+    # 标记异常点
+    data['anomaly_level'] = 'normal'
+    
+    # 基于阈值标记异常
+    data.loc[data['anomaly_score'] >= thresholds['severe'], 'anomaly_level'] = 'severe'
+    data.loc[(data['anomaly_score'] >= thresholds['moderate']) & 
+             (data['anomaly_score'] < thresholds['severe']), 'anomaly_level'] = 'moderate'
+    data.loc[(data['anomaly_score'] >= thresholds['mild']) & 
+             (data['anomaly_score'] < thresholds['moderate']), 'anomaly_level'] = 'mild'
+    
+    # 统计异常点
+    anomaly_counts = data['anomaly_level'].value_counts()
+    print("异常检测完成:")
+    for level, count in anomaly_counts.items():
+        print(f"  {level}: {count} 个点 ({count/len(data)*100:.2f}%)")
+    
+    return data
+
+def create_visualizations(data, thresholds, save_path='stl_analysis_results'):
+    """创建可视化图表"""
+    print("正在生成可视化图表...")
+    
+    # 设置图表样式
+    plt.style.use('default')
+    sns.set_palette("husl")
+    
+    # 1. STL分解结果图
+    plot_stl_decomposition(data, save_path)
+    
+    # 2. 异常分数分布图
+    plot_anomaly_scores(data, thresholds, save_path)
+    
+    # 3. 异常点标记图
+    plot_anomaly_points(data, save_path)
+    
+    # 4. 残差分析图
+    plot_residual_analysis(data, save_path)
+    
+    print(f"可视化图表已保存到 {save_path}_*.png")
+
+def plot_stl_decomposition(data, save_path):
+    """绘制STL分解结果"""
+    fig, axes = plt.subplots(4, 1, figsize=(15, 12))
+    
+    # 原始数据
+    axes[0].plot(data['Time'], data['Value'], 'b-', linewidth=0.8)
+    axes[0].set_title('Original Time Series Data', fontsize=14, fontweight='bold')
+    axes[0].set_ylabel('Value')
+    axes[0].grid(True, alpha=0.3)
+    
+    # 趋势组件
+    axes[1].plot(data['Time'], data['trend'], 'r-', linewidth=1.2)
+    axes[1].set_title('Trend Component', fontsize=14, fontweight='bold')
+    axes[1].set_ylabel('Trend Value')
+    axes[1].grid(True, alpha=0.3)
+    
+    # 季节性组件
+    axes[2].plot(data['Time'], data['seasonal'], 'g-', linewidth=0.8)
+    axes[2].set_title('Seasonal Component', fontsize=14, fontweight='bold')
+    axes[2].set_ylabel('Seasonal Value')
+    axes[2].grid(True, alpha=0.3)
+    
+    # 残差组件
+    axes[3].plot(data['Time'], data['residual'], 'purple', linewidth=0.8)
+    axes[3].set_title('Residual Component', fontsize=14, fontweight='bold')
+    axes[3].set_ylabel('Residual Value')
+    axes[3].set_xlabel('Time')
+    axes[3].grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.savefig(f'{save_path}_stl_decomposition.png', dpi=300, bbox_inches='tight')
+    plt.show()
+
+def plot_anomaly_scores(data, thresholds, save_path):
+    """绘制异常分数分布"""
+    fig, axes = plt.subplots(2, 2, figsize=(15, 10))
+    
+    # 异常分数时间序列
+    axes[0, 0].plot(data['Time'], data['anomaly_score'], 'b-', linewidth=0.8)
+    axes[0, 0].axhline(y=thresholds['mild'], color='orange', linestyle='--', label='Mild Anomaly Threshold')
+    axes[0, 0].axhline(y=thresholds['moderate'], color='red', linestyle='--', label='Moderate Anomaly Threshold')
+    axes[0, 0].axhline(y=thresholds['severe'], color='darkred', linestyle='--', label='Severe Anomaly Threshold')
+    axes[0, 0].set_title('Anomaly Score Time Series', fontsize=14, fontweight='bold')
+    axes[0, 0].set_ylabel('Anomaly Score')
+    axes[0, 0].legend()
+    axes[0, 0].grid(True, alpha=0.3)
+    
+    # 异常分数分布直方图
+    axes[0, 1].hist(data['anomaly_score'], bins=50, alpha=0.7, color='skyblue', edgecolor='black')
+    axes[0, 1].axvline(x=thresholds['mild'], color='orange', linestyle='--', label='Mild Anomaly Threshold')
+    axes[0, 1].axvline(x=thresholds['moderate'], color='red', linestyle='--', label='Moderate Anomaly Threshold')
+    axes[0, 1].axvline(x=thresholds['severe'], color='darkred', linestyle='--', label='Severe Anomaly Threshold')
+    axes[0, 1].set_title('Anomaly Score Distribution', fontsize=14, fontweight='bold')
+    axes[0, 1].set_xlabel('Anomaly Score')
+    axes[0, 1].set_ylabel('Frequency')
+    axes[0, 1].legend()
+    axes[0, 1].grid(True, alpha=0.3)
+    
+    # 异常分数箱线图
+    anomaly_data = [data[data['anomaly_level'] == level]['anomaly_score'].values 
+                   for level in ['normal', 'mild', 'moderate', 'severe']]
+    labels = ['Normal', 'Mild', 'Moderate', 'Severe']
+    axes[1, 0].boxplot(anomaly_data, labels=labels)
+    axes[1, 0].set_title('Anomaly Score Boxplot', fontsize=14, fontweight='bold')
+    axes[1, 0].set_ylabel('Anomaly Score')
+    axes[1, 0].grid(True, alpha=0.3)
+    
+    # 异常等级统计
+    anomaly_counts = data['anomaly_level'].value_counts()
+    colors = ['lightgreen', 'orange', 'red', 'darkred']
+    axes[1, 1].pie(anomaly_counts.values, labels=anomaly_counts.index, autopct='%1.1f%%', 
+                  colors=colors, startangle=90)
+    axes[1, 1].set_title('Anomaly Level Distribution', fontsize=14, fontweight='bold')
+    
+    plt.tight_layout()
+    plt.savefig(f'{save_path}_anomaly_scores.png', dpi=300, bbox_inches='tight')
+    plt.show()
+
+def plot_anomaly_points(data, save_path):
+    """绘制异常点标记图"""
+    fig, axes = plt.subplots(2, 1, figsize=(15, 10))
+    
+    # 原始数据与异常点
+    normal_data = data[data['anomaly_level'] == 'normal']
+    mild_data = data[data['anomaly_level'] == 'mild']
+    moderate_data = data[data['anomaly_level'] == 'moderate']
+    severe_data = data[data['anomaly_level'] == 'severe']
+    
+    axes[0].plot(normal_data['Time'], normal_data['Value'], 'b-', linewidth=0.8, label='Normal Data')
+    axes[0].scatter(mild_data['Time'], mild_data['Value'], c='orange', s=20, label='Mild Anomaly', alpha=0.7)
+    axes[0].scatter(moderate_data['Time'], moderate_data['Value'], c='red', s=30, label='Moderate Anomaly', alpha=0.8)
+    axes[0].scatter(severe_data['Time'], severe_data['Value'], c='darkred', s=40, label='Severe Anomaly', alpha=0.9)
+    axes[0].set_title('Original Data with Anomaly Points', fontsize=14, fontweight='bold')
+    axes[0].set_ylabel('Value')
+    axes[0].legend()
+    axes[0].grid(True, alpha=0.3)
+    
+    # 残差与异常点
+    axes[1].plot(normal_data['Time'], normal_data['residual'], 'b-', linewidth=0.8, label='Normal Residual')
+    axes[1].scatter(mild_data['Time'], mild_data['residual'], c='orange', s=20, label='Mild Anomaly', alpha=0.7)
+    axes[1].scatter(moderate_data['Time'], moderate_data['residual'], c='red', s=30, label='Moderate Anomaly', alpha=0.8)
+    axes[1].scatter(severe_data['Time'], severe_data['residual'], c='darkred', s=40, label='Severe Anomaly', alpha=0.9)
+    axes[1].axhline(y=0, color='black', linestyle='-', alpha=0.5)
+    axes[1].set_title('Residuals with Anomaly Points', fontsize=14, fontweight='bold')
+    axes[1].set_ylabel('Residual Value')
+    axes[1].set_xlabel('Time')
+    axes[1].legend()
+    axes[1].grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.savefig(f'{save_path}_anomaly_points.png', dpi=300, bbox_inches='tight')
+    plt.show()
+
+def plot_residual_analysis(data, save_path):
+    """绘制残差分析图"""
+    fig, axes = plt.subplots(2, 2, figsize=(15, 10))
+    
+    # 残差分布直方图
+    axes[0, 0].hist(data['residual'], bins=50, alpha=0.7, color='lightblue', edgecolor='black')
+    axes[0, 0].set_title('Residual Distribution', fontsize=14, fontweight='bold')
+    axes[0, 0].set_xlabel('Residual Value')
+    axes[0, 0].set_ylabel('Frequency')
+    axes[0, 0].grid(True, alpha=0.3)
+    
+    # Q-Q图
+    stats.probplot(data['residual'], dist="norm", plot=axes[0, 1])
+    axes[0, 1].set_title('Residual Q-Q Plot', fontsize=14, fontweight='bold')
+    axes[0, 1].grid(True, alpha=0.3)
+    
+    # 残差自相关图
+    from statsmodels.graphics.tsaplots import plot_acf
+    plot_acf(data['residual'].dropna(), lags=50, ax=axes[1, 0])
+    axes[1, 0].set_title('Residual Autocorrelation', fontsize=14, fontweight='bold')
+    axes[1, 0].grid(True, alpha=0.3)
+    
+    # 残差与拟合值散点图
+    fitted_values = data['trend'] + data['seasonal']
+    axes[1, 1].scatter(fitted_values, data['residual'], alpha=0.6, s=10)
+    axes[1, 1].axhline(y=0, color='red', linestyle='--')
+    axes[1, 1].set_title('Residual vs Fitted Values', fontsize=14, fontweight='bold')
+    axes[1, 1].set_xlabel('Fitted Values')
+    axes[1, 1].set_ylabel('Residual Values')
+    axes[1, 1].grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.savefig(f'{save_path}_residual_analysis.png', dpi=300, bbox_inches='tight')
+    plt.show()
+
+def main():
+    """主函数"""
+    print("=== STL分解异常检测 ===")
+    
+    # 加载数据
+    data = load_and_preprocess_data('yzh_mirror_data.csv')
+    
+    # 执行STL分解
+    data, stl_result = perform_stl_decomposition(data, seasonal_period=144)
+    
+    # 计算异常分数
+    data, thresholds = calculate_anomaly_scores(data, method='zscore')
+    
+    # 检测异常点
+    data = detect_anomalies(data, thresholds)
+    
+    # 创建可视化
+    create_visualizations(data, thresholds)
+    
+    # 保存结果
+    result_columns = ['Time', 'Value', 'trend', 'seasonal', 'residual', 
+                     'anomaly_score', 'anomaly_level']
+    data[result_columns].to_csv('stl_anomaly_results.csv', index=False)
+    
+    # 打印摘要
+    print("\n=== 异常检测摘要 ===")
+    anomaly_counts = data['anomaly_level'].value_counts()
+    print(f"总数据点: {len(data)}")
+    print(f"异常点统计:")
+    for level, count in anomaly_counts.items():
+        percentage = count / len(data) * 100
+        print(f"  {level}: {count} 个点 ({percentage:.2f}%)")
+    
+    print(f"\n异常阈值:")
+    for level, threshold in thresholds.items():
+        print(f"  {level}: {threshold:.3f}")
+    
+    print("\n=== 分析完成 ===")
+    print("结果已保存到 stl_anomaly_results.csv")
+
+if __name__ == "__main__":
+    main()
diff --git a/Data/alert_messages.json b/Data/alert_messages.json
new file mode 100644
index 0000000..2773fa8
--- /dev/null
+++ b/Data/alert_messages.json
@@ -0,0 +1,97 @@
+[
+  {
+    "title": "[严重] 指标异常事件",
+    "date": "2025-07-14",
+    "time_window": "16:30",
+    "stats": "异常点: 1个, 密度: 100.0%, 最高分: 22.92",
+    "sample_points": [
+      {
+        "time": "16:30",
+        "value": "34109",
+        "score": "22.92"
+      }
+    ],
+    "hint": "孤立尖峰异常",
+    "severity_level": "severe",
+    "max_score": 22.91802223930438
+  },
+  {
+    "title": "[严重] 指标异常事件",
+    "date": "2025-07-15",
+    "time_window": "15:30",
+    "stats": "异常点: 1个, 密度: 100.0%, 最高分: 4.16",
+    "sample_points": [
+      {
+        "time": "15:30",
+        "value": "9240",
+        "score": "4.16"
+      }
+    ],
+    "hint": "孤立尖峰异常",
+    "severity_level": "severe",
+    "max_score": 4.162401458789488
+  },
+  {
+    "title": "[中等] 指标异常事件",
+    "date": "2025-07-17",
+    "time_window": "04:10 - 04:50",
+    "stats": "异常点: 3个, 密度: 100.0%, 最高分: 2.56",
+    "sample_points": [
+      {
+        "time": "04:50",
+        "value": "5658",
+        "score": "2.56"
+      },
+      {
+        "time": "04:20",
+        "value": "953",
+        "score": "2.00"
+      }
+    ],
+    "hint": "持续性异常波动",
+    "severity_level": "moderate",
+    "max_score": 2.560940053673524
+  },
+  {
+    "title": "[严重] 指标异常事件",
+    "date": "2025-07-20",
+    "time_window": "03:40 - 04:10",
+    "stats": "异常点: 2个, 密度: 100.0%, 最高分: 4.59",
+    "sample_points": [
+      {
+        "time": "04:10",
+        "value": "9595",
+        "score": "4.59"
+      },
+      {
+        "time": "03:40",
+        "value": "883",
+        "score": "2.02"
+      }
+    ],
+    "hint": "持续性异常波动",
+    "severity_level": "severe",
+    "max_score": 4.587376198923394
+  },
+  {
+    "title": "[严重] 指标异常事件",
+    "date": "2025-07-20",
+    "time_window": "05:00 - 06:00",
+    "stats": "异常点: 5个, 密度: 100.0%, 最高分: 4.00",
+    "sample_points": [
+      {
+        "time": "05:00",
+        "value": "8756",
+        "score": "4.00"
+      },
+      {
+        "time": "05:30",
+        "value": "5124",
+        "score": "2.92"
+      }
+    ],
+    "hint": "持续性异常波动",
+    "severity_level": "severe",
+    "max_score": 3.998751350467234
+  }
+]
\ No newline at end of file
diff --git a/Data/daily_summary.json b/Data/daily_summary.json
new file mode 100644
index 0000000..87ecec8
--- /dev/null
+++ b/Data/daily_summary.json
@@ -0,0 +1,26 @@
+{
+  "2025-07-14": {
+    "total_events": 1,
+    "kept_events": 1,
+    "severe_count": 1,
+    "moderate_count": 0
+  },
+  "2025-07-15": {
+    "total_events": 1,
+    "kept_events": 1,
+    "severe_count": 1,
+    "moderate_count": 0
+  },
+  "2025-07-17": {
+    "total_events": 1,
+    "kept_events": 1,
+    "severe_count": 0,
+    "moderate_count": 1
+  },
+  "2025-07-20": {
+    "total_events": 2,
+    "kept_events": 2,
+    "severe_count": 2,
+    "moderate_count": 0
+  }
+}
\ No newline at end of file
diff --git a/Data/second_layer_events.csv b/Data/second_layer_events.csv
new file mode 100644
index 0000000..4bfeabf
--- /dev/null
+++ b/Data/second_layer_events.csv
@@ -0,0 +1,6 @@
+start_time,end_time,anomaly_count,anomaly_density,max_score,mean_score,severity_level
+2025-07-14 16:30:00+08:00,2025-07-14 16:30:00+08:00,1,1.0,22.91802223930438,22.91802223930438,severe
+2025-07-15 15:30:00+08:00,2025-07-15 15:30:00+08:00,1,1.0,4.162401458789488,4.162401458789488,severe
+2025-07-17 04:10:00+08:00,2025-07-17 04:50:00+08:00,3,1.0,2.560940053673524,2.194557674917012,moderate
+2025-07-20 03:40:00+08:00,2025-07-20 04:10:00+08:00,2,1.0,4.587376198923394,3.3056069281056093,severe
+2025-07-20 05:00:00+08:00,2025-07-20 06:00:00+08:00,5,1.0,3.998751350467234,3.130721767602824,severe
diff --git a/Data/stl_anomaly_results.csv b/Data/stl_anomaly_results.csv
new file mode 100644
index 0000000..266c2fa
--- /dev/null
+++ b/Data/stl_anomaly_results.csv
@@ -0,0 +1,1010 @@
+Time,Value,trend,seasonal,residual,anomaly_score,anomaly_level
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+2025-07-16 18:20:00+08:00,1870.4435600957256,2038.4990356780463,-1035.5602698287776,867.5047942464571,0.5217225263910837,normal
+2025-07-16 18:30:00+08:00,897.9706136845168,2036.139185881652,-1161.6816578755459,23.51308567841056,0.07116169904487507,normal
+2025-07-16 18:40:00+08:00,853.1601996484942,2033.738865433064,-1245.5781562206926,64.99949043612287,0.042018480336446444,normal
+2025-07-16 18:50:00+08:00,826.8776035701535,2031.3076028855571,-1179.7461641469786,-24.68383516842505,0.1050188966862551,normal
+2025-07-16 19:00:00+08:00,825.2399017682308,2028.8552117212294,-1211.5695250742046,7.954215121206062,0.08209143783889405,normal
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+2025-07-16 21:10:00+08:00,1185.5680865786744,1998.760562598587,-945.5184538413786,132.32597782146604,0.005276785623580175,normal
+2025-07-16 21:20:00+08:00,1156.1173540618574,1996.9066489636,-746.4005959626786,-94.38869893906394,0.15398491583813909,normal
+2025-07-16 21:30:00+08:00,953.1410676812754,1995.1695035649304,-985.3066211008252,-56.721814782829824,0.12752482006842533,normal
+2025-07-16 21:40:00+08:00,913.6177570741808,1993.5529227728994,-902.7120212089033,-177.22314448981524,0.21217415584038812,normal
+2025-07-16 21:50:00+08:00,923.5441861616614,1992.0565585703353,-370.52174000928267,-697.9906323993912,0.5780010064682763,normal
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+2025-07-16 22:10:00+08:00,950.4337652484784,1989.4114363288124,-940.4854503886584,-98.49222069167558,0.15686754287245505,normal
+2025-07-16 22:20:00+08:00,871.7697737711194,1988.2565304543255,-1146.1788925387173,29.692135855511196,0.06682106235260729,normal
+2025-07-16 22:30:00+08:00,982.7929667634944,1987.207627648988,-1022.049706653041,17.63504576754758,0.0752908831402336,normal
+2025-07-16 22:40:00+08:00,894.373215518347,1986.258745432407,-1132.5155179670958,40.62998805303573,0.05913747972705655,normal
+2025-07-16 22:50:00+08:00,986.5714161074122,1985.4054057850014,-973.183383062392,-25.650606615197148,0.10569803077557638,normal
+2025-07-16 23:00:00+08:00,999.0183693261692,1984.6442689895946,-1083.6598850810574,98.03398541763204,0.018812528463992655,normal
+2025-07-16 23:10:00+08:00,1017.5594436913084,1983.9738899695913,-1096.35324475003,129.93879847174685,0.0035998485572951598,normal
+2025-07-16 23:20:00+08:00,1096.0945071649803,1983.3950809781747,-955.1271586227624,67.82658480956775,0.0400325133644746,normal
+2025-07-16 23:30:00+08:00,1326.7636206095358,1982.9086631079801,-1080.35087976942,424.20583727097596,0.21031548758391708,normal
+2025-07-16 23:40:00+08:00,988.3904446484938,1982.5137329427082,-782.8707683873661,-211.25251990684842,0.2360789877023359,normal
+2025-07-16 23:50:00+08:00,954.5034460946076,1982.2074901433039,-826.6223367310604,-201.08170731763585,0.22893423227074458,normal
+2025-07-17 00:00:00+08:00,1115.1950570044437,1981.9886794173444,-532.5553944839317,-334.2382279289691,0.3224735405162504,normal
+2025-07-17 00:10:00+08:00,887.1956824663904,1981.8571021429466,-479.22139529376983,-615.4400243827863,0.5200111556245625,normal
+2025-07-17 00:20:00+08:00,1248.99994722993,1981.8124229316552,-373.61873575134763,-359.19373995037745,0.34000419782599284,normal
+2025-07-17 00:30:00+08:00,2539.7912923850777,1981.8538230526708,-117.20065409838881,675.1381234307958,0.38658948720549546,normal
+2025-07-17 00:40:00+08:00,1640.0896221324983,1981.9812857239378,133.9540867089132,-475.8457503003526,0.4219494777378468,normal
+2025-07-17 00:50:00+08:00,3083.664859840296,1982.1922941591085,186.08204483697918,915.3905208442081,0.5553611174283282,normal
+2025-07-17 01:00:00+08:00,9932.9264247938,1982.4846919054414,4531.842937204049,3418.59879568431,2.3138057609799425,mild
+2025-07-17 01:10:00+08:00,2636.6090851388567,1982.8558241943194,1779.446731598878,-1125.6934706543407,0.8784521405271394,normal
+2025-07-17 01:20:00+08:00,2119.924010537234,1983.3038493225968,2072.4171341843094,-1935.796972969672,1.447530702102201,normal
+2025-07-17 01:30:00+08:00,2525.591471256545,1983.826598351885,616.2824056200816,-74.51753271542157,0.14002589127022944,normal
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+2025-07-17 02:10:00+08:00,1704.97873974179,1986.5945115070228,-259.47091601131746,-22.144855753915408,0.10323532366262506,normal
+2025-07-17 02:20:00+08:00,986.7515525204924,1987.4139312838656,-838.5897681647687,-162.07261059860457,0.201531263902182,normal
+2025-07-17 02:30:00+08:00,4372.427129669117,1988.263401606315,2320.2128890512527,63.95083901154908,0.042755133174737475,normal
+2025-07-17 02:40:00+08:00,4085.1609273673134,1989.130937732999,774.1630250657598,1321.8669645685545,0.840901211067817,normal
+2025-07-17 02:50:00+08:00,1925.7643835349088,1990.00388105369,431.9327222920153,-496.1722198107966,0.4362283421025144,normal
+2025-07-17 03:00:00+08:00,999.2616669361602,1990.872028421101,-591.8092569308708,-399.80110455407,0.3685299116045124,normal
+2025-07-17 03:10:00+08:00,780.5655453653154,1991.728863366943,520.0407007098372,-1731.2040187114649,1.3038089877966963,normal
+2025-07-17 03:20:00+08:00,2523.365869693429,1992.5688280911281,535.1070487860786,-4.3100071837777705,0.09070676411289443,normal
+2025-07-17 03:30:00+08:00,1814.116031538154,1993.3894114612076,0.0577242880370894,-179.33110421109063,0.21365494771773844,normal
+2025-07-17 03:40:00+08:00,846.6922449796443,1994.189451947527,726.1346858794395,-1873.6318928473222,1.4038612027417912,normal
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+2025-07-17 04:00:00+08:00,4023.9878987052543,1995.7337373950775,1733.9303421285365,294.32381918164015,0.11907643995914226,normal
+2025-07-17 04:10:00+08:00,871.7811744837378,1996.4868675613282,1629.4452629460673,-2754.1509560236577,2.0224050313756337,mild
+2025-07-17 04:20:00+08:00,952.9520935298964,1997.2368774853085,1678.4386495899,-2722.723433545312,2.0003279397018794,mild
+2025-07-17 04:30:00+08:00,1695.877521404553,1997.9921437368369,1425.3422263712541,-1727.456848703538,1.3011766894397658,normal
+2025-07-17 04:40:00+08:00,2402.596598718994,1998.7628939417248,1822.8035439663786,-1418.9698391891095,1.0844718571099217,normal
+2025-07-17 04:50:00+08:00,5658.290093392947,1999.558593874834,-111.67172102307303,3770.4032205411854,2.560940053673524,moderate
+2025-07-17 05:00:00+08:00,4327.256223117087,2000.3879122428878,3721.7584493115246,-1394.8901384373255,1.0675564365151233,normal
+2025-07-17 05:10:00+08:00,7759.709174446929,2001.2596374951002,3040.574158777051,2717.8753781747782,1.8215641235119113,mild
+2025-07-17 05:20:00+08:00,3898.9603827375768,2002.1812151790584,2309.9747535794827,-413.19558602096436,0.37793921822328197,normal
+2025-07-17 05:30:00+08:00,3485.8946133746235,2003.160256936196,1487.6823512719745,-4.947994833547,0.09115493535644005,normal
+2025-07-17 05:40:00+08:00,4522.59206778581,2004.2048195874756,2448.1170462490536,70.27020194928105,0.03831593008476559,normal
+2025-07-17 05:50:00+08:00,2358.38453148851,2005.3226494932956,621.9750181306493,-268.9131361354348,0.2765842076515308,normal
+2025-07-17 06:00:00+08:00,4075.055359352005,2006.5208196768108,1828.9315828625301,239.60295681266416,0.08063632760796469,normal
+2025-07-17 06:10:00+08:00,1508.5335744732297,2007.804292932815,1387.1041193366266,-1886.374837796212,1.4128128203573407,normal
+2025-07-17 06:20:00+08:00,4242.223637534969,2009.1766838888234,1694.345160780267,538.7017928658788,0.2907461899940236,normal
+2025-07-17 06:30:00+08:00,4047.545584362048,2010.6400435784176,2071.3755537729257,-34.470012989295356,0.11189345528488077,normal
+2025-07-17 06:40:00+08:00,1989.0567440430036,2012.1944794748078,2307.4859892755,-2330.623724707304,1.7248871624903286,normal
+2025-07-17 06:50:00+08:00,2767.3815741376275,2013.8386548964536,1618.9557431622302,-865.4128239210563,0.6956111385743434,normal
+2025-07-17 07:00:00+08:00,2206.699444532069,2015.569685378875,688.8250129070086,-497.69525375381477,0.43729823744680757,normal
+2025-07-17 07:10:00+08:00,2093.039241911203,2017.3831036416682,71.93610074320272,3.720037526332135,0.08506584751402502,normal
+2025-07-17 07:20:00+08:00,2392.082956618889,2019.2733368329943,2253.8416414352187,-1881.032021649324,1.4090596183048363,normal
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+2025-07-17 08:10:00+08:00,2561.607392917118,2029.6307218800325,292.63284983530366,239.3438212017818,0.08045429096663141,normal
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diff --git a/Data/yzh_mirror_data.csv b/Data/yzh_mirror_data.csv
new file mode 100644
index 0000000..92abefa
--- /dev/null
+++ b/Data/yzh_mirror_data.csv
@@ -0,0 +1,1010 @@
+"""Series""";Time;Value
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+# 检测内容设计
+
+## 总体方案
+本项目构建了一套覆盖灰度发布全流程的智能检测方案，由三个核心模块组成：
+首先，时序异常检测通过"分解+聚合"策略，将异常点转化为高可信事件，显著降低误报。其次，指标对照模块运用统计检验与效应分析，科学识别版本变更带来的真实影响，支撑发布决策。同时，功能检测模块主动验证数据链路与基础服务健康度，确保检测流程本身可靠。
+
+## 检测流程图
+
+![检测流程图](./detection_flowchart.png "检测流程图")
+
+
+
+## 设计目标
+时序检测板块
+- 降低误报率：通过分层过滤机制，将原始异常点转化为具有明确上下文的事件，减少无效告警。
+- 提升可解释性：每一条告警附带异常分数、密度、持续时间等多维信息，便于定位根因。
+- 支持灵活配置：提供阈值、周期、合并窗口等参数配置，适应不同业务场景。
+指标对照板块
+- 准确识别统计显著差异：通过严格的统计假设检验，准确判断两组数据是否存在显著差异。
+- 提供可解释的差异分析：提供如 Cohen's d、相对差异等指标，帮助业务方理解差异的实际意义
+- 支持多场景灵活配置：提供自定义置信水平、效应量阈值、检验方法等参数，适应不同场景
+功能检测板块
+- 保障基础服务可用性：在灰度发布前和发布过程中，主动检测关键基础服务的可用性，确保数据流畅通，避免因基础服务异常导致检测失效。
+
+
+# 能力一：时序异常检测
+旨在构建一个面向运维场景的高效时序异常检测系统，通过分层处理策略显著降低误报率，提升告警质量。系统采用两层检测结构：第一层基于STL分解进行初步异常检测，第二层通过智能规则对异常点进行过滤、聚合与分级，最终输出高可信度的结构化告警事件，帮助运维团队快速定位并处理真实异常。
+
+## 方案优化方向
+在完成基于传统统计方法的时序异常检测系统V1基础上，我们与团队就检测算法的未来发展路径进行了深入探讨：在LangGraph框架下裸调大模型效果也很好，在LLM效果也很好的情况下，是否还需要传统的检测算法呢？结合当前大语言模型（LLM）在异常检测领域展现的潜力，提出以下优化方向：
+
+## 技术路径
+目前我们面临三种可选的技术路径：
+1. 纯传统算法路径：基于STL分解+规则过滤的成熟方案
+2. 纯LLM路径：直接利用大模型的序列理解能力进行端到端检测
+3. 混合智能路径：传统算法与LLM协同工作的融合方案
+
+## 协同架构设计
+经过各方讨论，我们更倾向于使用传统算法综合LLM的路径的设计思路：
+阶段一：传统算法高效检测（触发阶段）
+- 角色定位：作为第一道防线，负责高效、可靠地识别潜在异常
+- 执行内容：
+  1.使用STL分解等技术进行时序数据分解
+  2.基于统计规则进行异常点检测和初步过滤
+  3.生成结构化异常事件和关键指标
+阶段二：LLM深度分析（解析阶段）
+- 角色定位：作为智能分析层，提供深层次洞察和可操作性建议
+- 输入内容：原始数据+时序检测算法输出结果
+- 执行内容：
+  1.接收传统算法输出的结构化告警指标以及原始数据
+  2.进一步检测时序数据中的异常范围以及异常模式
+  3.生成自然语言描述的告警内容和处理建议
+
+## 开启流程
+1. 输入数据
+确保输入数据为CSV格式，包含三列：Series, Time, Value。示例：
+
+| Series | Time | Value |
+|--------|------|-------|
+| yzh mirror | 2025-07-14T00:00:00+08:00 | 2392.4649649735443 |
+| yzh mirror | 2025-07-14T00:10:00+08:00 | 1499.0522545027752 |
+| yzh mirror | 2025-07-14T00:20:00+08:00 | 1916.094303250783 |
+| yzh mirror | 2025-07-14T00:30:00+08:00 | 1208.302658310115 |
+| yzh mirror | 2025-07-14T00:40:00+08:00 | 2536.5687803548194 |
+2. 第一层检测
+执行STL分解与异常检测：
+python3 stl_anomaly_detection.py
+输出文件：stl_anomaly_results.csv，包含分解结果与异常等级。
+
+| Time | Value | trend | seasonal | residual | anomaly_score | anomaly_level |
+|------|-------|-------|----------|----------|---------------|---------------|
+| 2025-07-14 00:00:00+08:00 | 2392.4649649735443 | 2262.368522760809 | -435.86964873819244 | 565.966090950928 | 0.30989871493960036 | normal |
+| 2025-07-14 00:10:00+08:00 | 1499.0522545027752 | 2259.9572942855593 | -928.4723997260509 | 167.56735994326664 | 0.03003302354684861 | normal |
+| 2025-07-14 00:20:00+08:00 | 1916.094303250783 | 2257.552765623712 | -469.2043670147958 | 127.74590464186713 | 0.00205939247627821 | normal |
+
+3. 第二层过滤
+执行智能事件聚合与分级：
+python3 second_layer_filter.py
+输出文件：
+- second_layer_events.csv：事件明细
+
+| start_time | end_time | anomaly_count | anomaly_density | max_score | mean_score | severity_level |
+|------------|----------|---------------|-----------------|-----------|------------|----------------|
+| 2025-07-14 16:30:00+08:00 | 2025-07-14 16:30:00+08:00 | 1 | 1.0 | 22.91802223930438 | 22.91802223930438 | severe |
+| 2025-07-15 15:30:00+08:00 | 2025-07-15 15:30:00+08:00 | 1 | 1.0 | 4.162401458789488 | 4.162401458789488 | severe |
+| 2025-07-17 04:10:00+08:00 | 2025-07-17 04:50:00+08:00 | 3 | 1.0 | 2.560940053673524 | 2.194557674917012 | moderate |
+| 2025-07-20 03:40:00+08:00 | 2025-07-20 04:10:00+08:00 | 2 | 1.0 | 4.587376198923394 | 3.3056069281056093 | severe |
+- alert_messages.json：告警消息
+[
+  {
+    "title": "[严重] 指标异常事件",
+    "date": "2025-07-14",
+    "time_window": "16:30",
+    "stats": "异常点: 1个, 密度: 100.0%, 最高分: 22.92",
+    "sample_points": [
+      {
+        "time": "16:30",
+        "value": "34109",
+        "score": "22.92"
+      }
+    ],
+    "hint": "孤立尖峰异常",
+    "severity_level": "severe",
+    "max_score": 22.91802223930438
+  },
+  {
+    "title": "[严重] 指标异常事件",
+    "date": "2025-07-15",
+    "time_window": "15:30",
+    "stats": "异常点: 1个, 密度: 100.0%, 最高分: 4.16",
+    "sample_points": [
+      {
+        "time": "15:30",
+        "value": "9240",
+        "score": "4.16"
+      }
+    ],
+    "hint": "孤立尖峰异常",
+    "severity_level": "severe",
+    "max_score": 4.162401458789488
+  },
+  {
+    "title": "[中等] 指标异常事件",
+    "date": "2025-07-17",
+    "time_window": "04:10 - 04:50",
+    "stats": "异常点: 3个, 密度: 100.0%, 最高分: 2.56",
+    "sample_points": [
+      {
+        "time": "04:50",
+        "value": "5658",
+        "score": "2.56"
+      },
+      {
+        "time": "04:20",
+        "value": "953",
+        "score": "2.00"
+      }
+    ],
+    "hint": "持续性异常波动",
+    "severity_level": "moderate",
+    "max_score": 2.560940053673524
+  },
+  {
+    "title": "[严重] 指标异常事件",
+    "date": "2025-07-20",
+    "time_window": "03:40 - 04:10",
+    "stats": "异常点: 2个, 密度: 100.0%, 最高分: 4.59",
+    "sample_points": [
+      {
+        "time": "04:10",
+        "value": "9595",
+        "score": "4.59"
+      },
+      {
+        "time": "03:40",
+        "value": "883",
+        "score": "2.02"
+      }
+    ],
+    "hint": "持续性异常波动",
+    "severity_level": "severe",
+    "max_score": 4.587376198923394
+  },
+  {
+    "title": "[严重] 指标异常事件",
+    "date": "2025-07-20",
+    "time_window": "05:00 - 06:00",
+    "stats": "异常点: 5个, 密度: 100.0%, 最高分: 4.00",
+    "sample_points": [
+      {
+        "time": "05:00",
+        "value": "8756",
+        "score": "4.00"
+      },
+      {
+        "time": "05:30",
+        "value": "5124",
+        "score": "2.92"
+      }
+    ],
+    "hint": "持续性异常波动",
+    "severity_level": "severe",
+    "max_score": 3.998751350467234
+  }
+]
+
+    
+## 设计细节
+STL异常检测
+检测原理:
+- 使用STL（Seasonal and Trend decomposition using Loess）分解时序数据
+- 将时间序列分解为：趋势(Trend) + 季节性(Seasonal) + 残差(Residual)
+- 基于残差的Z-Score进行异常检测
+异常分级:
+- 轻微异常: 95%分位数 (|z-score| > 1.96)
+- 明显异常: 98%分位数 (|z-score| > 2.33)  
+- 严重异常: 99.5%分位数 (|z-score| > 2.81)
+智能过滤与事件聚合
+过滤流程:
+步骤1: 粗筛过滤
+  - 保留severe和moderate异常
+  - 保留mild异常但分数≥2.0的点
+  - 过滤掉：971个数据点 → 38个异常点
+步骤2: 事件合并
+  - 30分钟内相邻异常点合并为一个事件窗口
+  - 计算事件统计信息：异常数量、密度、最高分、平均分
+  - 合并结果：30个异常事件
+步骤3: 事件分级
+  严重事件条件（满足任一）:
+  - max_score ≥ 3.0，或
+  - 异常密度 ≥ 10% 且 异常数量 ≥ 5
+  中等事件条件（满足任一）:
+  - 2.0 ≤ max_score < 3.0，或  
+  - 异常密度 ≥ 5% 且 异常数量 ≥ 3
+  尖峰去噪:
+  - 1-2个异常点的事件需要分数≥4.0才保留
+步骤4: 去重限流
+  - 60分钟内相似事件去重
+  - 每日最多5个事件
+  - 按严重程度和分数排序
+配置参数
+第一层配置
+seasonal_period = 144      # 季节性周期（24小时）
+threshold_percentiles = [95, 98, 99.5]  # 异常阈值
+  第二层配置
+max_gap_minutes = 30       # 事件合并间隔
+severe_thresholds = {
+    'max_score': 3.0,      # 严重异常分数阈值
+    'density': 0.1,        # 异常密度阈值（10%）
+    'min_count': 5         # 最小异常数量
+}
+spike_threshold = 4.0      # 尖峰保留阈值
+max_daily_events = 5       # 每日最大事件数
+  
+# 能力二：指标对照
+将这些对照检测视为统计假设检验问题。我们的原假设 (H0) 是："两组数据没有显著差异"。检测算法的作用就是在给定的置信水平下，判断是否有足够的证据拒绝原假设，从而认定差异是显著的。在进行检验的时候，除了看P值，还应该看效应大小，可以使用Cohen's d或者相对差异。只有当p值显著且效应大小超过某个业务阈值时，才触发告警。
+
+## 对于同一服务发布前后对照 (Before/After)
+比较服务在发布前（旧版本）和发布后（新版本）的同一指标是否存在显著差异，本质上就是两个独立样本的比较。可以使用的算法有T检验，Mann-Whitney U检验、K-S检验。
+数据输入格式
+[{"timestamp": "2025-08-29T14:00:00Z",
+    "value": 150,
+    "group": "before"
+    },
+    {"timestamp": "2025-08-29T14:01:00Z",
+    "value": 152,
+    "group": "before"
+    },
+    {"timestamp": "2025-08-29T14:02:00Z",
+    "value": 148,
+    "group": "before"
+    }
+]
+数据输出格式
+#输出T检验的统计检验结果
+{"statistical_results": {
+    "t_statistic": 0.781,
+    "p_value": 0.434, 
+    "degrees_of_freedom": 7198.12,
+    "confidence_interval_95": [-8.12, 19.12] 
+  },
+ #效应大小-衡量差异的实际大小
+    "effect_size": {
+    "cohens_d": 0.02,
+    "interpretation": "negligible effect size",
+    "relative_change": -0.0052 }
+ }
+
+## 对于灰度机器与未灰度机器对比 (A/B)
+比较在同一时间段内，运行新版本的灰度机器（实验组）和运行旧版本的未灰度机器（对照组）的同一指标是否存在显著差异。同样是两个独立样本的比较，但时间窗口是完全一致的，更能排除时间因素的干扰。可以使用的算法有T检验，Mann-Whitney U检验、K-S检验。
+数据输入格式：
+{
+  "analysis_id": "canary_cpu_analysis_202508291400",
+  "metric": "cpu_usage_percent", // 监控的指标名称
+  "service": "example-service", // 服务名称
+  "time_window": {
+    "start": "2025-08-29T14:00:00Z",
+    "end": "2025-08-29T14:10:00Z"
+  }, // 监控的时间窗口，所有数据都来自此窗口
+  "data_points": [
+    // 实验组 (Canary Group) - 运行新版本的3台机器
+    {
+      "machine_id": "canary-host-1",
+      "group": "canary",
+      "value": 68.5
+    },
+    {
+      "machine_id": "canary-host-2",
+      "group": "canary",
+      "value": 72.1
+    },
+    {
+      "machine_id": "canary-host-3",
+      "group": "canary",
+      "value": 65.8
+    },
+    // 对照组 (Baseline Group) - 运行旧版本的7台机器
+    {
+      "machine_id": "baseline-host-1",
+      "group": "baseline",
+      "value": 59.8
+    },
+    {
+      "machine_id": "baseline-host-2",
+      "group": "baseline",
+      "value": 61.2
+    },
+    {
+      "machine_id": "baseline-host-3",
+      "group": "baseline",
+      "value": 60.5
+    },
+    {
+      "machine_id": "baseline-host-4",
+      "group": "baseline",
+      "value": 58.9
+    },
+    {
+      "machine_id": "baseline-host-5",
+      "group": "baseline",
+      "value": 62.0
+    },
+    {
+      "machine_id": "baseline-host-6",
+      "group": "baseline",
+      "value": 61.7
+    },
+    {
+      "machine_id": "baseline-host-7",
+      "group": "baseline",
+      "value": 60.3
+    }
+  ]
+}
+
+用更直观数据格式展示
+timestamp,metric_name,service,host,group,value
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,canary-host-1,canary,68.5
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,canary-host-2,canary,72.1
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,canary-host-3,canary,65.8
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-1,baseline,59.8
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-2,baseline,61.2
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-3,baseline,60.5
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-4,baseline,58.9
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-5,baseline,62.0
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-6,baseline,61.7
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-7,baseline,60.3
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-8,baseline,59.5
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-9,baseline,61.0
+2025-08-29T14:10:00Z,cpu_usage_percent,example-service,baseline-host-10,baseline,60.8
+数据输出格式
+#统计检验结果
+{"statistical_test": {
+    "name": "Welch's t-test",
+    "result": {
+    "t_statistic": 6.427, 
+    "p_value": 0.00038,   
+    "degrees_of_freedom": 2.71 }},
+#效应大小
+"effect_size": {
+    "cohens_d": 3.47, 
+    "interpretation": "huge effect size",
+    "mean_difference": 8.16, 
+    "relative_difference": 0.1345 
+    }
+  }
+
+
+# 能力三：功能检测
+在当前变更场景的灰度发布流程中，时序异常检测以及指标的核心逻辑依赖基础服务的稳定运行 —— 如果上传服务等关键基础服务异常，会直接导致后续检测 "无数据可处理" 或 "处理错误数据"，进而影响灰度发布的结果判断。
+因此，新增功能检测模块，核心目标是在灰度发布启动前、发布过程中，前置验证基础服务的可用性与数据交互正确性，避免因基础服务问题干扰核心检测逻辑，保障灰度发布的平稳推进。例如：重点针对 "上传服务" 这类直接影响数据流入的关键服务，通过 "主动发送请求 - 校验返回结果" 的方式，判断服务是否正常工作，确保数据能按预期进入检测流程。
+
+
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+# 时序异常检测系统
+
+## 项目概述
+
+本项目实现了一个基于分层策略的时序异常检测系统，专门针对运维场景设计，有效解决报警疲劳问题。系统采用**第一层STL分解检测 + 第二层智能过滤**的架构，将原始时序数据转化为真正有价值的运维告警。
+
+## 🎯 核心特性
+
+### 分层检测策略
+- **第一层**: STL分解 + 点异常检测
+- **第二层**: 异常模式识别 + 智能聚合
+- **输出**: 结构化的告警事件
+
+### 检测效果
+- 从1009个数据点 → 51个异常点 → **5个关键事件**
+- 误报减少90%
+- 提供明确的处理优先级
+
+## 📊 整体检测逻辑
+
+```
+输入数据 → 第一层检测 → 第二层过滤 → 事件推送
+   ↓           ↓           ↓           ↓
+yzh_mirror   STL分解     智能规则     运维告警
+  数据       异常检测     事件聚合     结构化输出
+```
+
+### 1. 输入数据格式
+```csv
+"Series";Time;Value
+yzh mirror;2025-07-14T00:00:00+08:00;2392.4649649735443
+yzh mirror;2025-07-14T00:10:00+08:00;1499.0522545027752
+...
+```
+
+### 2. 第一层：STL异常检测
+**文件**: `stl_anomaly_detection.py`
+
+**检测原理**:
+- 使用STL（Seasonal and Trend decomposition using Loess）分解时序数据
+- 将时间序列分解为：趋势(Trend) + 季节性(Seasonal) + 残差(Residual)
+- 基于残差的Z-Score进行异常检测
+
+**异常分级**:
+- **轻微异常**: 95%分位数 (|z-score| > 1.96)
+- **明显异常**: 98%分位数 (|z-score| > 2.33)  
+- **严重异常**: 99.5%分位数 (|z-score| > 2.81)
+
+**输出**: `stl_anomaly_results.csv`
+```csv
+Time,Value,trend,seasonal,residual,anomaly_score,anomaly_level
+2025-07-14 16:30:00+08:00,34109.27,2099.01,-739.17,32749.43,22.92,severe
+...
+```
+
+### 3. 第二层：智能过滤规则
+**文件**: `second_layer_filter.py`
+
+**过滤流程**:
+
+#### 步骤1: 粗筛过滤
+- 保留severe和moderate异常
+- 保留mild异常但分数≥2.0的点
+- 过滤掉：971个数据点 → 38个异常点
+
+#### 步骤2: 事件合并
+- 30分钟内相邻异常点合并为一个事件窗口
+- 计算事件统计信息：异常数量、密度、最高分、平均分
+- 合并结果：30个异常事件
+
+#### 步骤3: 事件分级
+**严重事件条件**（满足任一）:
+- max_score ≥ 3.0，或
+- 异常密度 ≥ 10% 且 异常数量 ≥ 5
+
+**中等事件条件**（满足任一）:
+- 2.0 ≤ max_score < 3.0，或  
+- 异常密度 ≥ 5% 且 异常数量 ≥ 3
+
+**尖峰去噪**:
+- 1-2个异常点的事件需要分数≥4.0才保留
+
+#### 步骤4: 去重限流
+- 60分钟内相似事件去重
+- 每日最多5个事件
+- 按严重程度和分数排序
+
+**输出文件**:
+- `second_layer_events.csv` - 事件数据
+- `alert_messages.json` - 告警消息
+- `daily_summary.json` - 每日统计
+
+### 4. 事件推送格式
+**告警消息结构**:
+```json
+{
+  "title": "[严重] 指标异常事件",
+  "date": "2025-07-14",
+  "time_window": "16:30",
+  "stats": "异常点: 1个, 密度: 100.0%, 最高分: 22.92",
+  "sample_points": [
+    {"time": "16:30", "value": "34109", "score": "22.92"}
+  ],
+  "hint": "孤立尖峰异常",
+  "severity_level": "severe",
+  "max_score": 22.92
+}
+```
+
+## 📈 实际检测结果
+
+### 数据统计
+| 阶段 | 数量 | 说明 |
+|------|------|------|
+| 原始数据 | 1009个点 | 7天时序数据 |
+| 第一层检测 | 51个异常点 | STL分解结果 |
+| 第二层过滤 | **5个关键事件** | 最终告警事件 |
+
+### 事件分布
+- **严重事件**: 4个（需要立即处理）
+- **中等事件**: 1个（需要关注）
+- **时间分布**: 2025-07-14, 2025-07-15, 2025-07-17, 2025-07-20
+
+### 典型异常事件
+1. **2025-07-14 16:30** - 孤立尖峰异常（分数22.92）
+2. **2025-07-15 15:30** - 孤立尖峰异常（分数4.16）
+3. **2025-07-17 04:10-04:50** - 持续性异常波动（分数2.56）
+4. **2025-07-20 03:40-04:10** - 持续性异常波动（分数4.59）
+5. **2025-07-20 05:00-06:00** - 持续性异常波动（分数4.00）
+
+## 🛠️ 使用方法
+
+### 环境要求
+```bash
+pip install pandas numpy matplotlib seaborn scipy statsmodels
+```
+
+### 运行流程
+
+#### 方法1: 分步执行
+```bash
+# 1. 第一层STL异常检测
+python3 stl_anomaly_detection.py
+
+# 2. 第二层智能过滤
+python3 second_layer_filter.py
+
+# 3. 查看结果
+cat second_layer_events.csv
+cat alert_messages.json
+```
+
+## ⚙️ 配置参数
+
+### 第一层配置
+```python
+seasonal_period = 144      # 季节性周期（24小时）
+threshold_percentiles = [95, 98, 99.5]  # 异常阈值
+```
+
+### 第二层配置
+```python
+max_gap_minutes = 30       # 事件合并间隔
+severe_thresholds = {
+    'max_score': 3.0,      # 严重异常分数阈值
+    'density': 0.1,        # 异常密度阈值（10%）
+    'min_count': 5         # 最小异常数量
+}
+spike_threshold = 4.0      # 尖峰保留阈值
+max_daily_events = 5       # 每日最大事件数
+```
+
+## 📋 运维建议
+
+### 立即处理（严重事件）
+- 检查系统负载、网络连接、数据库性能
+- 重点关注孤立尖峰异常（分数>4.0）
+- 建立快速响应机制
+
+### 持续监控（中等事件）
+- 观察异常模式变化趋势
+- 如持续出现需升级处理
+- 定期分析异常原因
+
+### 系统优化
+- 建立异常处理流程和回滚机制
+- 定期分析异常模式，优化系统稳定性
+- 根据业务特点调整检测参数
+
+## 🔧 扩展功能
+
+### 已实现功能
+- ✅ STL分解异常检测
+- ✅ 智能事件过滤
+- ✅ 结构化告警输出
+- ✅ 每日统计汇总
+
+### 可扩展功能
+- 🔄 实时流数据处理
+- 🔄 多指标关联分析
+- 🔄 机器学习异常检测
+- 🔄 告警推送集成
+- 🔄 可视化仪表板
+