Claude Code监控运维详解：构建生产环境智能化运维最佳实践

# 企业级智能日志分析系统
class IntelligentLogAnalysisSystem:
    """智能日志聚合分析系统"""
    
    def __init__(self):
        self.log_collector = EnhancedLogCollector()
        self.ai_analyzer = AILogAnalyzer()
        self.pattern_detector = LogPatternDetector()
        self.alert_correlator = AlertCorrelationEngine()
    
    async def comprehensive_log_intelligence_analysis(self) -> Dict:
        """执行全面的智能日志分析"""
        
        print("📊 启动企业级智能日志分析...")
        
        analysis_results = {
            "analysis_timestamp": datetime.now().isoformat(),
            "log_volume_summary": {},
            "anomaly_detection_results": {},
            "pattern_recognition_insights": {},
            "root_cause_analysis": {},
            "predictive_alerts": {}
        }
        
        # 1. 日志量和质量统计
        log_volume_analysis = await self.analyze_log_volume_and_quality()
        analysis_results["log_volume_summary"] = log_volume_analysis
        
        # 2. AI驱动异常检测
        anomaly_results = await self.ai_driven_anomaly_detection()
        analysis_results["anomaly_detection_results"] = anomaly_results
        
        # 3. 智能模式识别
        pattern_insights = await self.intelligent_pattern_recognition()
        analysis_results["pattern_recognition_insights"] = pattern_insights
        
        # 4. 根因分析
        if anomaly_results.get("critical_anomalies"):
            root_cause_results = await self.ai_root_cause_analysis(
                anomaly_results["critical_anomalies"]
            )
            analysis_results["root_cause_analysis"] = root_cause_results
        
        # 5. 预测性告警生成
        predictive_alerts = await self.generate_predictive_alerts(analysis_results)
        analysis_results["predictive_alerts"] = predictive_alerts
        
        return analysis_results
    
    async def ai_driven_anomaly_detection(self) -> Dict:
        """AI驱动的异常检测"""
        
        anomaly_results = {
            "detection_models_used": [],
            "critical_anomalies": [],
            "warning_anomalies": [],
            "trend_anomalies": [],
            "behavioral_anomalies": []
        }
        
        # 1. 时间序列异常检测
        time_series_anomalies = await self.detect_time_series_anomalies()
        anomaly_results["critical_anomalies"].extend(time_series_anomalies.get("critical", []))
        anomaly_results["detection_models_used"].append("lstm_time_series_detector")
        
        # 2. 日志内容语义异常检测
        semantic_anomalies = await self.detect_semantic_anomalies()
        anomaly_results["warning_anomalies"].extend(semantic_anomalies.get("warnings", []))
        anomaly_results["detection_models_used"].append("bert_semantic_analyzer")
        
        # 3. 用户行为异常检测
        behavioral_anomalies = await self.detect_behavioral_anomalies()
        anomaly_results["behavioral_anomalies"] = behavioral_anomalies
        anomaly_results["detection_models_used"].append("isolation_forest_behavior")
        
        # 4. 系统性能异常检测
        performance_anomalies = await self.detect_performance_anomalies()
        anomaly_results["trend_anomalies"] = performance_anomalies
        anomaly_results["detection_models_used"].append("prophet_performance_forecaster")
        
        return anomaly_results
    
    async def intelligent_pattern_recognition(self) -> Dict:
        """智能模式识别"""
        
        pattern_insights = {
            "error_patterns": {},
            "performance_patterns": {},
            "security_patterns": {},
            "business_patterns": {}
        }
        
        # 错误模式识别
        pattern_insights["error_patterns"] = await self.recognize_error_patterns()
        
        # 性能模式识别  
        pattern_insights["performance_patterns"] = await self.recognize_performance_patterns()
        
        # 安全威胁模式识别
        pattern_insights["security_patterns"] = await self.recognize_security_patterns()
        
        # 业务异常模式识别
        pattern_insights["business_patterns"] = await self.recognize_business_patterns()
        
        return pattern_insights
    
    async def recognize_error_patterns(self) -> Dict:
        """识别错误模式"""
        
        # 获取最近24小时的错误日志
        error_logs = await self.get_error_logs(time_range="24h")
        
        error_patterns = {
            "frequent_errors": {},
            "error_cascades": [],
            "new_error_types": [],
            "error_correlation": {}
        }
        
        # 频繁错误分析
        error_frequency = {}
        for log in error_logs:
            error_type = self.extract_error_type(log)
            if error_type:
                error_frequency[error_type] = error_frequency.get(error_type, 0) + 1
        
        # 识别高频错误
        error_patterns["frequent_errors"] = {
            error_type: count for error_type, count in error_frequency.items() 
            if count > 10  # 超过10次的错误
        }
        
        # 错误级联分析
        error_patterns["error_cascades"] = await self.detect_error_cascades(error_logs)
        
        # 新出现的错误类型
        error_patterns["new_error_types"] = await self.identify_new_error_types(error_logs)
        
        # 错误相关性分析
        error_patterns["error_correlation"] = await self.analyze_error_correlations(error_logs)
        
        return error_patterns
    
    async def ai_root_cause_analysis(self, critical_anomalies: List[Dict]) -> Dict:
        """AI根因分析"""
        
        root_cause_results = {
            "primary_root_causes": [],
            "contributing_factors": [],
            "impact_analysis": {},
            "resolution_recommendations": []
        }
        
        for anomaly in critical_anomalies:
            # 使用AI模型进行根因分析
            analysis = await self.perform_ai_root_cause_analysis(anomaly)
            
            root_cause_results["primary_root_causes"].append({
                "anomaly_id": anomaly["id"],
                "root_cause": analysis["root_cause"],
                "confidence": analysis["confidence"],
                "evidence": analysis["evidence"]
            })
            
            # 分析影响范围
            impact = await self.analyze_impact_scope(anomaly, analysis)
            root_cause_results["impact_analysis"][anomaly["id"]] = impact
            
            # 生成解决建议
            recommendations = await self.generate_resolution_recommendations(analysis)
            root_cause_results["resolution_recommendations"].extend(recommendations)
        
        return root_cause_results
    
    async def perform_ai_root_cause_analysis(self, anomaly: Dict) -> Dict:
        """执行AI根因分析"""
        
        # 收集相关上下文信息
        context_data = await self.gather_analysis_context(anomaly)
        
        # AI分析提示构建
        analysis_prompt = f"""
        基于以下异常情况和系统上下文，请进行根因分析：
        
        异常详情：
        - 类型: {anomaly['type']}
        - 严重程度: {anomaly['severity']}
        - 发生时间: {anomaly['timestamp']}
        - 影响范围: {anomaly.get('scope', 'unknown')}
        
        系统上下文：
        - CPU使用率: {context_data.get('cpu_usage', 'N/A')}%
        - 内存使用率: {context_data.get('memory_usage', 'N/A')}%
        - 网络延迟: {context_data.get('network_latency', 'N/A')}ms
        - 错误日志模式: {context_data.get('error_patterns', [])}
        - 最近部署: {context_data.get('recent_deployments', [])}
        
        请提供：
        1. 最可能的根本原因
        2. 置信度评分 (0-1)
        3. 支持证据
        4. 建议的解决方案
        """
        
        # 调用AI分析 - 这里使用稳定的Yoretea Claude Code
        try:
            ai_response = await self.call_claude_analysis(analysis_prompt)
            
            return {
                "root_cause": ai_response.get("root_cause", "Unknown"),
                "confidence": ai_response.get("confidence", 0.5),
                "evidence": ai_response.get("evidence", []),
                "resolution_steps": ai_response.get("resolution_steps", [])
            }
        except Exception as e:
            print(f"⚠️ AI根因分析失败: {str(e)}")
            return {
                "root_cause": "AI分析不可用，需要人工分析",
                "confidence": 0.0,
                "evidence": ["AI服务连接失败"],
                "resolution_steps": ["检查AI服务状态", "启动人工分析流程"]
            }

# 使用示例
log_analysis_system = IntelligentLogAnalysisSystem()

# 执行智能日志分析
analysis_results = await log_analysis_system.comprehensive_log_intelligence_analysis()
print(f"📊 智能日志分析完成:")
print(f"  检测到关键异常: {len(analysis_results['anomaly_detection_results'].get('critical_anomalies', []))}个")
print(f"  识别模式洞察: {len(analysis_results['pattern_recognition_insights'])}个类别")
print(f"  生成预测告警: {len(analysis_results['predictive_alerts'])}个")

class IntelligentDistributedTracingSystem:
    """智能分布式链路追踪系统"""
    
    def __init__(self):
        self.tracer_config = self.load_intelligent_tracer_config()
        self.jaeger_client = self.initialize_enhanced_jaeger()
        self.trace_analyzer = AITraceAnalyzer()
        self.performance_optimizer = PerformanceOptimizer()
    
    async def trace_claude_intelligent_request(self, user_request: Dict) -> Dict:
        """智能追踪Claude请求全链路"""
        
        from opentelemetry import trace
        from opentelemetry.trace import Status, StatusCode
        
        tracer = trace.get_tracer(__name__)
        
        with tracer.start_as_current_span("claude_intelligent_request") as main_span:
            # 设置主Span的智能属性
            main_span.set_attributes({
                "claude.user_id": user_request.get("user_id"),
                "claude.request_id": user_request.get("request_id"),
                "claude.input_complexity": self.calculate_input_complexity(user_request.get("prompt", "")),
                "claude.model_version": user_request.get("model_version", "default"),
                "claude.task_category": self.categorize_task(user_request.get("task_type", "general")),
                "claude.user_tier": user_request.get("user_tier", "standard"),
                "claude.expected_quality": user_request.get("quality_requirement", "standard")
            })
            
            try:
                # 1. 智能输入预处理追踪
                with tracer.start_as_current_span("intelligent_input_preprocessing") as preprocess_span:
                    preprocess_start = time.time()
                    
                    # 输入安全检查
                    security_check = await self.perform_security_check(user_request["prompt"])
                    
                    # 输入优化处理
                    processed_input = await self.intelligent_input_preprocessing(
                        user_request["prompt"], 
                        user_request.get("context", {})
                    )
                    
                    preprocess_span.set_attributes({
                        "preprocessing.duration_ms": (time.time() - preprocess_start) * 1000,
                        "preprocessing.input_length": len(user_request["prompt"]),
                        "preprocessing.output_length": len(processed_input),
                        "preprocessing.security_score": security_check.get("score", 1.0),
                        "preprocessing.optimization_applied": processed_input != user_request["prompt"],
                        "preprocessing.language_detected": security_check.get("language", "unknown")
                    })
                
                # 2. AI模型推理智能追踪
                with tracer.start_as_current_span("intelligent_model_inference") as inference_span:
                    inference_start = time.time()
                    
                    # 选择最优模型
                    optimal_model = await self.select_optimal_model(processed_input, user_request)
                    
                    # 执行推理
                    inference_result = await self.perform_intelligent_inference(
                        processed_input, 
                        optimal_model,
                        user_request
                    )
                    
                    inference_span.set_attributes({
                        "inference.duration_ms": (time.time() - inference_start) * 1000,
                        "inference.model_selected": optimal_model["name"],
                        "inference.model_version": optimal_model["version"],
                        "inference.tokens_processed": inference_result.get("tokens_processed", 0),
                        "inference.confidence_score": inference_result.get("confidence", 0.0),
                        "inference.quality_score": inference_result.get("quality_score", 0.0),
                        "inference.cache_hit": inference_result.get("cache_hit", False),
                        "inference.resource_usage": inference_result.get("resource_usage", {})
                    })
                
                # 3. 智能输出后处理追踪
                with tracer.start_as_current_span("intelligent_output_postprocessing") as postprocess_span:
                    postprocess_start = time.time()
                    
                    # 输出质量检查
                    quality_assessment = await self.assess_output_quality(
                        inference_result, 
                        user_request
                    )
                    
                    # 输出优化处理
                    final_output = await self.intelligent_output_postprocessing(
                        inference_result,
                        quality_assessment,
                        user_request
                    )
                    
                    postprocess_span.set_attributes({
                        "postprocessing.duration_ms": (time.time() - postprocess_start) * 1000,
                        "postprocessing.output_length": len(final_output.get("response", "")),
                        "postprocessing.quality_score": quality_assessment.get("score", 0.0),
                        "postprocessing.optimization_applied": quality_assessment.get("optimization_applied", False),
                        "postprocessing.user_satisfaction_prediction": quality_assessment.get("satisfaction_prediction", 0.5)
                    })
                
                # 4. 设置成功状态和综合指标
                main_span.set_status(Status(StatusCode.OK))
                total_duration = time.time() - time.time()
                main_span.set_attributes({
                    "claude.total_duration_ms": total_duration * 1000,
                    "claude.success": True,
                    "claude.response_length": len(final_output.get("response", "")),
                    "claude.overall_quality": final_output.get("overall_quality", 0.0),
                    "claude.user_experience_score": self.calculate_user_experience_score(final_output),
                    "claude.business_value_score": self.calculate_business_value(final_output, user_request)
                })
                
                return final_output
                
            except Exception as e:
                # 智能错误处理和记录
                main_span.record_exception(e)
                main_span.set_status(Status(StatusCode.ERROR, str(e)))
                main_span.set_attributes({
                    "claude.error": True,
                    "claude.error_type": type(e).__name__,
                    "claude.error_message": str(e),
                    "claude.error_category": self.categorize_error(e),
                    "claude.recovery_suggestion": self.suggest_error_recovery(e)
                })
                
                # 尝试智能错误恢复
                recovery_attempt = await self.attempt_intelligent_recovery(e, user_request)
                if recovery_attempt.get("success"):
                    main_span.set_attributes({
                        "claude.auto_recovery": True,
                        "claude.recovery_method": recovery_attempt.get("method")
                    })
                    return recovery_attempt.get("result")
                
                raise e
    
    async def analyze_distributed_trace_intelligence(self, trace_id: str) -> Dict:
        """智能分析分布式链路"""
        
        print(f"🔍 执行智能链路分析: {trace_id}")
        
        # 1. 获取完整链路数据
        trace_data = await self.get_enhanced_trace_data(trace_id)
        
        # 2. AI驱动性能分析
        ai_performance_analysis = await self.ai_performance_analysis(trace_data)
        
        # 3. 智能瓶颈识别
        intelligent_bottlenecks = await self.identify_intelligent_bottlenecks(trace_data)
        
        # 4. 自动优化建议生成
        optimization_recommendations = await self.generate_ai_optimization_recommendations(
            trace_data, ai_performance_analysis, intelligent_bottlenecks
        )
        
        # 5. 用户体验影响评估
        ux_impact_assessment = await self.assess_user_experience_impact(trace_data)
        
        analysis_result = {
            "trace_id": trace_id,
            "analysis_timestamp": datetime.now().isoformat(),
            "ai_performance_analysis": ai_performance_analysis,
            "intelligent_bottlenecks": intelligent_bottlenecks,
            "optimization_recommendations": optimization_recommendations,
            "ux_impact_assessment": ux_impact_assessment,
            "trace_intelligence_score": self.calculate_trace_intelligence_score(trace_data),
            "business_impact": self.assess_business_impact(trace_data)
        }
        
        return analysis_result
    
    async def generate_intelligent_performance_dashboard(self) -> Dict:
        """生成智能性能监控仪表板"""
        
        dashboard_data = {
            "intelligent_overview": {
                "ai_performance_score": await self.calculate_ai_performance_score(),
                "user_satisfaction_prediction": await self.predict_user_satisfaction(),
                "system_health_ai_assessment": await self.ai_assess_system_health(),
                "optimization_opportunities_count": await self.count_optimization_opportunities()
            },
            
            "service_intelligence": {},
            "endpoint_intelligence": {},
            "user_experience_intelligence": {},
            "predictive_insights": {}
        }
        
        # 服务智能分析
        services = await self.get_all_services()
        for service in services:
            dashboard_data["service_intelligence"][service] = {
                "ai_health_score": await self.calculate_service_ai_health_score(service),
                "performance_trend": await self.analyze_performance_trend(service),
                "optimization_potential": await self.assess_optimization_potential(service),
                "user_impact_score": await self.calculate_user_impact_score(service)
            }
        
        # 端点智能洞察
        critical_endpoints = await self.get_critical_endpoints()
        for endpoint in critical_endpoints:
            dashboard_data["endpoint_intelligence"][endpoint] = {
                "intelligent_performance_analysis": await self.analyze_endpoint_intelligence(endpoint),
                "user_journey_impact": await self.assess_user_journey_impact(endpoint),
                "business_value_contribution": await self.calculate_business_value_contribution(endpoint)
            }
        
        # 用户体验智能洞察
        dashboard_data["user_experience_intelligence"] = {
            "satisfaction_score_prediction": await self.predict_satisfaction_scores(),
            "journey_completion_forecast": await self.forecast_journey_completion(),
            "pain_point_identification": await self.identify_user_pain_points()
        }
        
        # 预测性洞察
        dashboard_data["predictive_insights"] = {
            "performance_degradation_risks": await self.predict_performance_risks(),
            "capacity_shortage_forecasts": await self.forecast_capacity_needs(),
            "optimization_impact_predictions": await self.predict_optimization_impacts()
        }
        
        return dashboard_data

# 使用示例
tracing_system = IntelligentDistributedTracingSystem()

# 智能链路追踪
user_request = {
    "user_id": "user_12345",
    "request_id": "req_67890", 
    "prompt": "帮我优化这段Python代码的性能",
    "model_version": "claude-3-sonnet",
    "task_type": "code_optimization",
    "user_tier": "premium",
    "quality_requirement": "high"
}

# 执行智能追踪
traced_result = await tracing_system.trace_claude_intelligent_request(user_request)
print(f"💡 智能追踪完成: {traced_result.get('overall_quality', 0):.2f} 质量分数")

# 生成智能性能仪表板
performance_dashboard = await tracing_system.generate_intelligent_performance_dashboard()
print(f"📊 AI性能评分: {performance_dashboard['intelligent_overview']['ai_performance_score']:.2f}")
print(f"😊 用户满意度预测: {performance_dashboard['intelligent_overview']['user_satisfaction_prediction']:.1%}")

class EnterpriseIntelligentFaultRecoverySystem:
    """企业级智能故障自愈系统"""
    
    def __init__(self):
        self.fault_detector = AIFaultDetectionEngine()
        self.recovery_orchestrator = IntelligentRecoveryOrchestrator()
        self.knowledge_base = DynamicFaultKnowledgeBase()
        self.business_impact_analyzer = BusinessImpactAnalyzer()
        self.recovery_validator = RecoveryValidationEngine()
    
    async def comprehensive_fault_detection_and_recovery(self) -> Dict:
        """全面的故障检测和智能恢复"""
        
        print("🔧 启动企业级智能故障检测与自愈...")
        
        recovery_session = {
            "session_id": f"recovery_{int(time.time())}",
            "start_time": datetime.now().isoformat(),
            "detected_faults": [],
            "recovery_actions": [],
            "business_impact_assessment": {},
            "recovery_success_rate": 0.0,
            "lessons_learned": []
        }
        
        # 1. 多层次智能故障检测
        detected_faults = await self.multi_tier_intelligent_fault_detection()
        recovery_session["detected_faults"] = detected_faults
        
        if not detected_faults:
            print("✅ 系统健康状态良好，未检测到故障")
            return recovery_session
        
        # 2. 故障优先级智能排序
        prioritized_faults = await self.intelligent_fault_prioritization(detected_faults)
        
        # 3. 业务影响智能评估
        business_impact = await self.assess_comprehensive_business_impact(prioritized_faults)
        recovery_session["business_impact_assessment"] = business_impact
        
        # 4. 智能恢复策略制定
        recovery_plan = await self.formulate_intelligent_recovery_plan(
            prioritized_faults, business_impact
        )
        
        # 5. 执行智能故障恢复
        successful_recoveries = 0
        for fault in prioritized_faults:
            if fault.get("severity") == "critical" or fault.get("auto_recoverable", False):
                recovery_result = await self.execute_intelligent_fault_recovery(fault)
                recovery_session["recovery_actions"].append(recovery_result)
                
                if recovery_result.get("success", False):
                    successful_recoveries += 1
        
        # 6. 计算恢复成功率
        total_recovery_attempts = len(recovery_session["recovery_actions"])
        if total_recovery_attempts > 0:
            recovery_session["recovery_success_rate"] = successful_recoveries / total_recovery_attempts
        
        # 7. 系统健康验证
        post_recovery_health = await self.comprehensive_system_health_validation()
        recovery_session["post_recovery_health"] = post_recovery_health
        
        # 8. 更新知识库和学习
        learning_insights = await self.extract_recovery_learning_insights(recovery_session)
        recovery_session["lessons_learned"] = learning_insights
        await self.update_fault_recovery_knowledge_base(learning_insights)
        
        return recovery_session
    
    async def multi_tier_intelligent_fault_detection(self) -> List[Dict]:
        """多层次智能故障检测"""
        
        all_detected_faults = []
        
        # 第1层：基础设施智能监控
        infrastructure_faults = await self.detect_infrastructure_intelligent_faults()
        all_detected_faults.extend(infrastructure_faults)
        
        # 第2层：应用服务智能分析
        application_faults = await self.detect_application_intelligent_faults()
        all_detected_faults.extend(application_faults)
        
        # 第3层：业务流程智能检测
        business_faults = await self.detect_business_process_intelligent_faults()
        all_detected_faults.extend(business_faults)
        
        # 第4层：用户体验智能监控
        user_experience_faults = await self.detect_user_experience_intelligent_faults()
        all_detected_faults.extend(user_experience_faults)
        
        # 第5层：安全威胁智能识别
        security_faults = await self.detect_security_threat_intelligent_faults()
        all_detected_faults.extend(security_faults)
        
        # AI故障关联分析
        correlated_faults = await self.perform_intelligent_fault_correlation(all_detected_faults)
        
        return correlated_faults
    
    async def execute_intelligent_fault_recovery(self, fault: Dict) -> Dict:
        """执行智能故障恢复"""
        
        recovery_execution = {
            "fault_id": fault["fault_id"],
            "fault_type": fault["type"],
            "recovery_start_time": datetime.now().isoformat(),
            "recovery_strategy": fault.get("recommended_strategy", "unknown"),
            "ai_confidence": fault.get("ai_confidence", 0.5),
            "steps_executed": [],
            "success": False,
            "recovery_duration_ms": 0,
            "business_continuity_impact": {}
        }
        
        recovery_start = time.time()
        
        try:
            # 根据故障类型执行相应的智能恢复策略
            strategy = fault.get("recommended_strategy", "default_recovery")
            
            if strategy == "intelligent_service_restart":
                await self.execute_intelligent_service_restart(fault, recovery_execution)
                
            elif strategy == "smart_load_redistribution":
                await self.execute_smart_load_redistribution(fault, recovery_execution)
                
            elif strategy == "adaptive_resource_scaling":
                await self.execute_adaptive_resource_scaling(fault, recovery_execution)
                
            elif strategy == "intelligent_dependency_healing":
                await self.execute_intelligent_dependency_healing(fault, recovery_execution)
                
            elif strategy == "ai_driven_configuration_rollback":
                await self.execute_ai_driven_configuration_rollback(fault, recovery_execution)
                
            elif strategy == "predictive_capacity_adjustment":
                await self.execute_predictive_capacity_adjustment(fault, recovery_execution)
                
            else:
                # 使用AI生成自定义恢复策略
                custom_strategy = await self.generate_ai_custom_recovery_strategy(fault)
                await self.execute_custom_recovery_strategy(custom_strategy, recovery_execution)
            
            # 恢复效果验证
            recovery_verification = await self.verify_intelligent_recovery_success(
                fault, recovery_execution
            )
            
            recovery_execution["success"] = recovery_verification["verified"]
            recovery_execution["verification_results"] = recovery_verification
            
            # 业务连续性影响评估
            business_continuity_impact = await self.assess_business_continuity_impact(
                fault, recovery_execution
            )
            recovery_execution["business_continuity_impact"] = business_continuity_impact
            
        except Exception as e:
            recovery_execution["success"] = False
            recovery_execution["error"] = str(e)
            recovery_execution["error_category"] = self.categorize_recovery_error(e)
            
            # 记录恢复失败并尝试备用策略
            await self.log_recovery_failure_and_escalate(fault, e)
            
            # 尝试备用恢复策略
            if fault.get("fallback_strategies"):
                fallback_result = await self.attempt_fallback_recovery(fault, e)
                recovery_execution["fallback_attempted"] = True
                recovery_execution["fallback_result"] = fallback_result
        
        recovery_execution["recovery_duration_ms"] = (time.time() - recovery_start) * 1000
        recovery_execution["recovery_end_time"] = datetime.now().isoformat()
        
        # 发送恢复状态通知
        await self.send_intelligent_recovery_notification(recovery_execution)
        
        return recovery_execution
    
    async def generate_ai_custom_recovery_strategy(self, fault: Dict) -> Dict:
        """生成AI自定义恢复策略"""
        
        # 收集故障上下文信息
        fault_context = await self.gather_comprehensive_fault_context(fault)
        
        # 构建AI分析提示
        recovery_strategy_prompt = f"""
        基于以下故障信息和系统状态，请制定智能恢复策略：
        
        故障详情：
        - 类型: {fault['type']}
        - 严重程度: {fault['severity']}
        - 影响范围: {fault.get('impact_scope', 'unknown')}
        - 发生时间: {fault['timestamp']}
        - 持续时间: {fault.get('duration', 'unknown')}
        
        系统上下文：
        - 当前负载: {fault_context.get('current_load', 'unknown')}
        - 资源使用情况: {fault_context.get('resource_usage', {})}
        - 依赖服务状态: {fault_context.get('dependency_status', {})}
        - 最近变更: {fault_context.get('recent_changes', [])}
        - 类似历史故障: {fault_context.get('similar_historical_faults', [])}
        
        业务影响：
        - 受影响用户数: {fault_context.get('affected_users', 0)}
        - 业务功能影响: {fault_context.get('business_functions_impacted', [])}
        - 预估财务损失: {fault_context.get('estimated_financial_impact', 'unknown')}
        
        请提供：
        1. 详细的恢复步骤
        2. 每个步骤的预估执行时间
        3. 风险评估和缓解措施
        4. 验证恢复成功的检查点
        5. 如果失败的备选方案
        """
        
        try:
            # 调用AI生成恢复策略
            ai_strategy = await self.call_claude_recovery_analysis(recovery_strategy_prompt)
            
            return {
                "strategy_name": "ai_generated_custom_recovery",
                "confidence": ai_strategy.get("confidence", 0.7),
                "recovery_steps": ai_strategy.get("recovery_steps", []),
                "estimated_duration": ai_strategy.get("estimated_duration", "unknown"),
                "risk_assessment": ai_strategy.get("risk_assessment", {}),
                "validation_checkpoints": ai_strategy.get("validation_checkpoints", []),
                "fallback_options": ai_strategy.get("fallback_options", []),
                "business_impact_mitigation": ai_strategy.get("business_impact_mitigation", [])
            }
            
        except Exception as e:
            print(f"⚠️ AI恢复策略生成失败: {str(e)}")
            return {
                "strategy_name": "fallback_manual_intervention",
                "confidence": 0.0,
                "recovery_steps": ["联系运维团队进行手动干预"],
                "estimated_duration": "30-60分钟",
                "risk_assessment": {"level": "high", "reason": "AI分析不可用"},
                "requires_manual_intervention": True
            }
    
    async def generate_intelligent_fault_recovery_report(self) -> Dict:
        """生成智能故障恢复报告"""
        
        report = {
            "report_timestamp": datetime.now().isoformat(),
            "reporting_period": "last_24_hours",
            "executive_summary": {},
            "detailed_fault_analysis": {},
            "recovery_performance_metrics": {},
            "business_impact_summary": {},
            "improvement_recommendations": [],
            "ai_insights_and_predictions": {}
        }
        
        # 获取最近24小时的故障和恢复数据
        recent_fault_data = await self.get_comprehensive_fault_data(hours=24)
        
        # 生成执行摘要
        report["executive_summary"] = {
            "total_faults_detected": len(recent_fault_data),
            "critical_faults": len([f for f in recent_fault_data if f.get("severity") == "critical"]),
            "auto_recovery_success_rate": self.calculate_auto_recovery_success_rate(recent_fault_data),
            "mean_time_to_detection": self.calculate_mttd(recent_fault_data),
            "mean_time_to_recovery": self.calculate_mttr(recent_fault_data),
            "business_continuity_score": self.calculate_business_continuity_score(recent_fault_data),
            "ai_decision_accuracy": self.calculate_ai_decision_accuracy(recent_fault_data)
        }
        
        # 详细故障分析
        report["detailed_fault_analysis"] = {
            "fault_categories": self.categorize_faults(recent_fault_data),
            "root_cause_distribution": self.analyze_root_cause_distribution(recent_fault_data),
            "failure_patterns": await self.identify_failure_patterns(recent_fault_data),
            "correlation_insights": await self.generate_correlation_insights(recent_fault_data)
        }
        
        # 恢复性能指标
        recovery_actions = [f.get("recovery_action") for f in recent_fault_data if f.get("recovery_action")]
        report["recovery_performance_metrics"] = {
            "average_recovery_time": self.calculate_average_recovery_time(recovery_actions),
            "recovery_strategy_effectiveness": self.analyze_strategy_effectiveness(recovery_actions),
            "ai_vs_manual_recovery_comparison": self.compare_ai_vs_manual_recovery(recovery_actions),
            "recovery_cost_analysis": self.calculate_recovery_costs(recovery_actions)
        }
        
        # 业务影响汇总
        report["business_impact_summary"] = {
            "total_downtime_minutes": self.calculate_total_downtime(recent_fault_data),
            "affected_user_count": self.calculate_affected_users(recent_fault_data),
            "revenue_impact_estimate": self.estimate_revenue_impact(recent_fault_data),
            "customer_satisfaction_impact": self.assess_satisfaction_impact(recent_fault_data),
            "sla_compliance_status": self.assess_sla_compliance(recent_fault_data)
        }
        
        # AI洞察和预测
        report["ai_insights_and_predictions"] = {
            "predicted_failure_risks": await self.predict_future_failure_risks(),
            "optimization_opportunities": await self.identify_optimization_opportunities(),
            "capacity_planning_recommendations": await self.generate_capacity_recommendations(),
            "proactive_maintenance_suggestions": await self.suggest_proactive_maintenance()
        }
        
        # 改进建议
        report["improvement_recommendations"] = await self.generate_comprehensive_improvement_recommendations(
            recent_fault_data, report
        )
        
        return report

# 使用示例
fault_recovery_system = EnterpriseIntelligentFaultRecoverySystem()

# 执行企业级故障检测和恢复
recovery_results = await fault_recovery_system.comprehensive_fault_detection_and_recovery()
print(f"🔧 故障检测与恢复完成:")
print(f"  检测故障数: {len(recovery_results['detected_faults'])}")
print(f"  执行恢复操作: {len(recovery_results['recovery_actions'])}")
print(f"  恢复成功率: {recovery_results['recovery_success_rate']:.1%}")

# 生成智能故障恢复报告
recovery_report = await fault_recovery_system.generate_intelligent_fault_recovery_report()
print(f"📊 24小时故障恢复报告:")
print(f"  总故障数: {recovery_report['executive_summary']['total_faults_detected']}")
print(f"  自动恢复成功率: {recovery_report['executive_summary']['auto_recovery_success_rate']:.1%}")
print(f"  MTTR: {recovery_report['executive_summary']['mean_time_to_recovery']} 分钟")
print(f"  AI决策准确率: {recovery_report['executive_summary']['ai_decision_accuracy']:.1%}")

class IntelligentCapacityPlanningSystem:
    """智能容量规划和性能优化系统"""
    
    def __init__(self):
        self.forecasting_engine = AIForecastingEngine()
        self.performance_analyzer = IntelligentPerformanceAnalyzer()
        self.cost_optimizer = SmartCostOptimizer()
        self.capacity_orchestrator = CapacityOrchestrator()
    
    async def comprehensive_capacity_intelligence_analysis(self) -> Dict:
        """全面的智能容量分析"""
        
        print("🎯 启动企业级智能容量规划分析...")
        
        capacity_analysis = {
            "analysis_timestamp": datetime.now().isoformat(),
            "current_capacity_utilization": {},
            "predictive_capacity_forecasts": {},
            "performance_optimization_opportunities": {},
            "cost_optimization_recommendations": {},
            "intelligent_scaling_strategies": {},
            "business_growth_capacity_planning": {}
        }
        
        # 1. 当前容量利用率智能分析
        current_utilization = await self.analyze_current_capacity_utilization()
        capacity_analysis["current_capacity_utilization"] = current_utilization
        
        # 2. AI驱动的容量需求预测
        capacity_forecasts = await self.generate_ai_capacity_forecasts()
        capacity_analysis["predictive_capacity_forecasts"] = capacity_forecasts
        
        # 3. 性能优化机会识别
        performance_opportunities = await self.identify_performance_optimization_opportunities()
        capacity_analysis["performance_optimization_opportunities"] = performance_opportunities
        
        # 4. 成本优化智能建议
        cost_optimizations = await self.generate_intelligent_cost_optimizations()
        capacity_analysis["cost_optimization_recommendations"] = cost_optimizations
        
        # 5. 智能扩容策略制定
        scaling_strategies = await self.formulate_intelligent_scaling_strategies(
            current_utilization, capacity_forecasts
        )
        capacity_analysis["intelligent_scaling_strategies"] = scaling_strategies
        
        # 6. 业务增长容量规划
        business_capacity_planning = await self.plan_business_growth_capacity(capacity_forecasts)
        capacity_analysis["business_growth_capacity_planning"] = business_capacity_planning
        
        return capacity_analysis
    
    async def generate_ai_capacity_forecasts(self) -> Dict:
        """生成AI驱动的容量预测"""
        
        forecasting_results = {
            "short_term_forecasts": {},  # 1-7天
            "medium_term_forecasts": {}, # 1-3个月
            "long_term_forecasts": {},   # 3-12个月
            "seasonal_pattern_analysis": {},
            "business_event_impact_predictions": {}
        }
        
        # 收集历史数据用于预测
        historical_data = await self.collect_comprehensive_historical_data()
        
        # 短期预测 (1-7天)
        short_term_data = await self.generate_short_term_capacity_forecast(historical_data)
        forecasting_results["short_term_forecasts"] = {
            "cpu_demand_forecast": short_term_data["cpu"],
            "memory_demand_forecast": short_term_data["memory"],
            "storage_demand_forecast": short_term_data["storage"],
            "network_demand_forecast": short_term_data["network"],
            "user_load_forecast": short_term_data["user_load"],
            "confidence_scores": short_term_data["confidence"]
        }
        
        # 中期预测 (1-3个月)
        medium_term_data = await self.generate_medium_term_capacity_forecast(historical_data)
        forecasting_results["medium_term_forecasts"] = {
            "infrastructure_scaling_needs": medium_term_data["scaling_needs"],
            "technology_upgrade_requirements": medium_term_data["upgrade_needs"],
            "team_capacity_planning": medium_term_data["team_scaling"],
            "budget_allocation_recommendations": medium_term_data["budget_planning"]
        }
        
        # 长期预测 (3-12个月)
        long_term_data = await self.generate_long_term_capacity_forecast(historical_data)
        forecasting_results["long_term_forecasts"] = {
            "strategic_infrastructure_roadmap": long_term_data["infrastructure_roadmap"],
            "technology_evolution_planning": long_term_data["tech_evolution"],
            "business_expansion_capacity": long_term_data["business_expansion"],
            "sustainability_and_efficiency_targets": long_term_data["sustainability"]
        }
        
        # 季节性模式分析
        seasonal_analysis = await self.analyze_seasonal_capacity_patterns(historical_data)
        forecasting_results["seasonal_pattern_analysis"] = seasonal_analysis
        
        # 业务事件影响预测
        business_event_predictions = await self.predict_business_event_capacity_impact()
        forecasting_results["business_event_impact_predictions"] = business_event_predictions
        
        return forecasting_results
    
    async def execute_intelligent_performance_optimization(self) -> Dict:
        """执行智能性能优化"""
        
        optimization_results = {
            "optimization_session_id": f"opt_{int(time.time())}",
            "pre_optimization_baseline": {},
            "optimization_actions_executed": [],
            "post_optimization_performance": {},
            "performance_improvements": {},
            "cost_impact_analysis": {},
            "user_experience_impact": {}
        }
        
        # 1. 建立性能基线
        baseline_metrics = await self.establish_performance_baseline()
        optimization_results["pre_optimization_baseline"] = baseline_metrics
        
        # 2. 识别优化机会
        optimization_opportunities = await self.identify_intelligent_optimization_opportunities()
        
        # 3. 制定优化执行计划
        optimization_plan = await self.create_intelligent_optimization_plan(
            baseline_metrics, optimization_opportunities
        )
        
        # 4. 执行优化策略
        for optimization in optimization_plan["prioritized_optimizations"]:
            if optimization.get("auto_executable", False):
                execution_result = await self.execute_performance_optimization(optimization)
                optimization_results["optimization_actions_executed"].append(execution_result)
        
        # 5. 验证优化效果
        post_optimization_metrics = await self.measure_post_optimization_performance()
        optimization_results["post_optimization_performance"] = post_optimization_metrics
        
        # 6. 计算性能改进
        performance_improvements = await self.calculate_performance_improvements(
            baseline_metrics, post_optimization_metrics
        )
        optimization_results["performance_improvements"] = performance_improvements
        
        # 7. 分析成本影响
        cost_impact = await self.analyze_optimization_cost_impact(
            optimization_results["optimization_actions_executed"]
        )
        optimization_results["cost_impact_analysis"] = cost_impact
        
        # 8. 评估用户体验影响
        ux_impact = await self.assess_user_experience_optimization_impact(
            baseline_metrics, post_optimization_metrics
        )
        optimization_results["user_experience_impact"] = ux_impact
        
        return optimization_results

# 使用示例  
capacity_planning_system = IntelligentCapacityPlanningSystem()

# 执行智能容量规划分析
capacity_analysis = await capacity_planning_system.comprehensive_capacity_intelligence_analysis()
print(f"🎯 智能容量规划分析完成:")
print(f"  短期预测置信度: {capacity_analysis['predictive_capacity_forecasts']['short_term_forecasts'].get('confidence_scores', {}).get('average', 'N/A')}")
print(f"  识别优化机会: {len(capacity_analysis['performance_optimization_opportunities'])}个")
print(f"  成本优化建议: {len(capacity_analysis['cost_optimization_recommendations'])}项")

# 执行性能优化
optimization_results = await capacity_planning_system.execute_intelligent_performance_optimization()
print(f"⚡ 智能性能优化完成:")
print(f"  执行优化操作: {len(optimization_results['optimization_actions_executed'])}项")
print(f"  性能改进幅度: {optimization_results['performance_improvements'].get('overall_improvement', 'N/A')}")
print(f"  成本节约: {optimization_results['cost_impact_analysis'].get('estimated_monthly_savings', 'N/A')}")