第301集SLB高级特性与智能运维架构实战：自适应调度、故障自愈与企业级智能负载均衡解决方案

/**
 * 自适应调度引擎
 */
@Component
public class AdaptiveSchedulingEngine {

    private final Map<String, ServerMetrics> serverMetrics = new ConcurrentHashMap<>();
    private final Map<String, AlgorithmConfig> algorithmConfigs = new ConcurrentHashMap<>();
    private final ScheduledExecutorService scheduler = Executors.newScheduledThreadPool(4);

    /**
     * 自适应调度算法
     */
    public ServerNode adaptiveSchedule(RequestContext context) {
        // 获取当前负载情况
        LoadMetrics currentLoad = getCurrentLoadMetrics();

        // 选择最优算法
        SchedulingAlgorithm algorithm = selectOptimalAlgorithm(currentLoad);

        // 执行调度
        ServerNode selectedNode = algorithm.schedule(context, getAvailableNodes());

        // 记录调度结果
        recordSchedulingResult(context, selectedNode, algorithm);

        return selectedNode;
    }

    /**
     * 选择最优算法
     */
    private SchedulingAlgorithm selectOptimalAlgorithm(LoadMetrics metrics) {
        if (metrics.getCpuUsage() > 80) {
            return new LeastConnectionAlgorithm();
        } else if (metrics.getResponseTime() > 1000) {
            return new ResponseTimeBasedAlgorithm();
        } else if (metrics.getErrorRate() > 5) {
            return new HealthBasedAlgorithm();
        } else {
            return new WeightedRoundRobinAlgorithm();
        }
    }

    /**
     * 动态调整算法参数
     */
    @Scheduled(fixedRate = 30000)
    public void adjustAlgorithmParameters() {
        LoadMetrics metrics = getCurrentLoadMetrics();

        // 根据负载情况调整算法参数
        if (metrics.getCpuUsage() > 70) {
            adjustWeightDistribution();
        }

        if (metrics.getResponseTime() > 800) {
            adjustTimeoutSettings();
        }

        if (metrics.getErrorRate() > 3) {
            adjustHealthCheckFrequency();
        }
    }
}

/**
 * 故障自愈引擎
 */
@Component
public class FaultSelfHealingEngine {

    private final HealthCheckService healthCheckService;
    private final TrafficShiftService trafficShiftService;
    private final AutoScalingService autoScalingService;
    private final CircuitBreakerService circuitBreakerService;

    /**
     * 故障检测与自愈
     */
    @EventListener
    public void handleFaultDetection(FaultDetectionEvent event) {
        FaultType faultType = event.getFaultType();
        String serverId = event.getServerId();

        switch (faultType) {
            case SERVER_DOWN:
                handleServerDown(serverId);
                break;
            case HIGH_LATENCY:
                handleHighLatency(serverId);
                break;
            case HIGH_ERROR_RATE:
                handleHighErrorRate(serverId);
                break;
            case RESOURCE_EXHAUSTION:
                handleResourceExhaustion(serverId);
                break;
            default:
                handleUnknownFault(serverId, faultType);
        }
    }

    /**
     * 处理服务器宕机
     */
    private void handleServerDown(String serverId) {
        // 1. 立即从负载均衡中移除
        removeFromLoadBalancer(serverId);

        // 2. 启动故障转移
        startFailover(serverId);

        // 3. 尝试自动恢复
        scheduleAutoRecovery(serverId);

        // 4. 触发告警
        triggerAlert("SERVER_DOWN", serverId);
    }

    /**
     * 处理高延迟
     */
    private void handleHighLatency(String serverId) {
        // 1. 降低权重
        reduceServerWeight(serverId, 0.5);

        // 2. 启用熔断器
        circuitBreakerService.enableCircuitBreaker(serverId);

        // 3. 启动性能优化
        startPerformanceOptimization(serverId);

        // 4. 监控恢复情况
        monitorRecovery(serverId);
    }

    /**
     * 处理高错误率
     */
    private void handleHighErrorRate(String serverId) {
        // 1. 启用熔断器
        circuitBreakerService.enableCircuitBreaker(serverId);

        // 2. 降低流量分配
        reduceTrafficAllocation(serverId, 0.3);

        // 3. 启动错误分析
        startErrorAnalysis(serverId);

        // 4. 尝试自动修复
        attemptAutoFix(serverId);
    }

    /**
     * 处理资源耗尽
     */
    private void handleResourceExhaustion(String serverId) {
        // 1. 触发自动扩缩容
        autoScalingService.scaleOut(serverId);

        // 2. 临时降低权重
        reduceServerWeight(serverId, 0.2);

        // 3. 启用资源监控
        enableResourceMonitoring(serverId);

        // 4. 优化资源使用
        optimizeResourceUsage(serverId);
    }
}

/**
 * 基于机器学习的调度算法
 */
@Component
public class MLBasedSchedulingAlgorithm implements SchedulingAlgorithm {

    private final MLModelService mlModelService;
    private final FeatureExtractor featureExtractor;
    private final PredictionService predictionService;

    /**
     * 机器学习调度
     */
    @Override
    public ServerNode schedule(RequestContext context, List<ServerNode> availableNodes) {
        // 1. 提取特征
        FeatureVector features = featureExtractor.extractFeatures(context, availableNodes);

        // 2. 预测最优节点
        ServerNode predictedNode = predictionService.predictOptimalNode(features);

        // 3. 验证预测结果
        if (isPredictionValid(predictedNode, availableNodes)) {
            return predictedNode;
        }

        // 4. 回退到传统算法
        return fallbackToTraditionalAlgorithm(context, availableNodes);
    }

    /**
     * 特征提取
     */
    private FeatureVector extractFeatures(RequestContext context, List<ServerNode> nodes) {
        FeatureVector features = new FeatureVector();

        // 请求特征
        features.addFeature("request_size", context.getRequestSize());
        features.addFeature("request_type", context.getRequestType().ordinal());
        features.addFeature("user_location", context.getUserLocation());
        features.addFeature("time_of_day", LocalTime.now().getHour());

        // 服务器特征
        for (ServerNode node : nodes) {
            features.addFeature("cpu_usage_" + node.getId(), node.getCpuUsage());
            features.addFeature("memory_usage_" + node.getId(), node.getMemoryUsage());
            features.addFeature("response_time_" + node.getId(), node.getAvgResponseTime());
            features.addFeature("error_rate_" + node.getId(), node.getErrorRate());
        }

        return features;
    }

    /**
     * 模型训练
     */
    @Scheduled(cron = "0 0 2 * * ?") // 每天凌晨2点训练
    public void trainModel() {
        // 1. 收集训练数据
        List<TrainingData> trainingData = collectTrainingData();

        // 2. 数据预处理
        ProcessedData processedData = preprocessData(trainingData);

        // 3. 模型训练
        MLModel newModel = mlModelService.trainModel(processedData);

        // 4. 模型验证
        if (validateModel(newModel)) {
            // 5. 更新模型
            mlModelService.updateModel(newModel);
        }
    }
}

/**
 * 基于地理位置的智能调度
 */
@Component
public class GeoBasedSchedulingAlgorithm implements SchedulingAlgorithm {

    private final GeoLocationService geoLocationService;
    private final NetworkLatencyService networkLatencyService;
    private final CDNService cdnService;

    /**
     * 地理位置调度
     */
    @Override
    public ServerNode schedule(RequestContext context, List<ServerNode> availableNodes) {
        // 1. 获取用户地理位置
        GeoLocation userLocation = geoLocationService.getLocation(context.getClientIp());

        // 2. 计算到各节点的网络延迟
        Map<String, Long> latencyMap = calculateLatency(userLocation, availableNodes);

        // 3. 选择最优节点
        ServerNode optimalNode = selectOptimalNode(latencyMap, availableNodes);

        // 4. 考虑CDN加速
        if (shouldUseCDN(context)) {
            return selectCDNNode(userLocation);
        }

        return optimalNode;
    }

    /**
     * 计算网络延迟
     */
    private Map<String, Long> calculateLatency(GeoLocation userLocation, List<ServerNode> nodes) {
        Map<String, Long> latencyMap = new HashMap<>();

        for (ServerNode node : nodes) {
            // 1. 计算地理距离
            double distance = calculateDistance(userLocation, node.getLocation());

            // 2. 估算网络延迟
            long estimatedLatency = estimateNetworkLatency(distance);

            // 3. 获取实际延迟（如果有缓存）
            Long actualLatency = networkLatencyService.getCachedLatency(
                userLocation, node.getLocation());

            // 4. 使用实际延迟或估算延迟
            latencyMap.put(node.getId(), actualLatency != null ? actualLatency : estimatedLatency);
        }

        return latencyMap;
    }

    /**
     * 选择最优节点
     */
    private ServerNode selectOptimalNode(Map<String, Long> latencyMap, List<ServerNode> nodes) {
        return nodes.stream()
            .min((node1, node2) -> {
                Long latency1 = latencyMap.get(node1.getId());
                Long latency2 = latencyMap.get(node2.getId());

                // 综合考虑延迟和服务器负载
                double score1 = calculateScore(node1, latency1);
                double score2 = calculateScore(node2, latency2);

                return Double.compare(score1, score2);
            })
            .orElse(nodes.get(0));
    }

    /**
     * 计算综合评分
     */
    private double calculateScore(ServerNode node, Long latency) {
        double latencyScore = 1.0 / (1.0 + latency / 1000.0); // 延迟评分
        double loadScore = 1.0 - node.getCpuUsage() / 100.0; // 负载评分
        double healthScore = 1.0 - node.getErrorRate() / 100.0; // 健康评分

        return latencyScore * 0.4 + loadScore * 0.3 + healthScore * 0.3;
    }
}

/**
 * 智能监控系统
 */
@Component
public class IntelligentMonitoringSystem {

    private final MetricsCollector metricsCollector;
    private final AnomalyDetector anomalyDetector;
    private final AlertManager alertManager;
    private final AutoRemediationService autoRemediationService;

    /**
     * 智能监控主循环
     */
    @Scheduled(fixedRate = 5000)
    public void intelligentMonitoring() {
        // 1. 收集指标
        Map<String, Object> metrics = metricsCollector.collectMetrics();

        // 2. 异常检测
        List<Anomaly> anomalies = anomalyDetector.detectAnomalies(metrics);

        // 3. 处理异常
        for (Anomaly anomaly : anomalies) {
            handleAnomaly(anomaly);
        }

        // 4. 预测性维护
        performPredictiveMaintenance(metrics);
    }

    /**
     * 处理异常
     */
    private void handleAnomaly(Anomaly anomaly) {
        // 1. 评估异常严重程度
        Severity severity = evaluateSeverity(anomaly);

        // 2. 自动修复尝试
        if (severity == Severity.LOW || severity == Severity.MEDIUM) {
            boolean fixed = autoRemediationService.attemptAutoFix(anomaly);
            if (fixed) {
                log.info("异常自动修复成功: {}", anomaly);
                return;
            }
        }

        // 3. 发送告警
        alertManager.sendAlert(anomaly, severity);

        // 4. 记录异常
        recordAnomaly(anomaly);
    }

    /**
     * 预测性维护
     */
    private void performPredictiveMaintenance(Map<String, Object> metrics) {
        // 1. 预测服务器故障
        List<String> predictedFailures = predictServerFailures(metrics);

        // 2. 预测性能瓶颈
        List<String> predictedBottlenecks = predictPerformanceBottlenecks(metrics);

        // 3. 预测容量需求
        CapacityPrediction capacityPrediction = predictCapacityNeeds(metrics);

        // 4. 执行预防性措施
        executePreventiveMeasures(predictedFailures, predictedBottlenecks, capacityPrediction);
    }

    /**
     * 预测服务器故障
     */
    private List<String> predictServerFailures(Map<String, Object> metrics) {
        List<String> predictedFailures = new ArrayList<>();

        // 基于历史数据和当前指标预测故障
        for (String serverId : getServerIds()) {
            double failureProbability = calculateFailureProbability(serverId, metrics);

            if (failureProbability > 0.7) {
                predictedFailures.add(serverId);

                // 发送预测告警
                alertManager.sendPredictiveAlert("SERVER_FAILURE_PREDICTED", serverId, failureProbability);
            }
        }

        return predictedFailures;
    }
}

/**
 * 自动扩缩容系统
 */
@Component
public class AutoScalingSystem {

    private final ScalingPolicyService scalingPolicyService;
    private final ResourceManager resourceManager;
    private final LoadBalancerService loadBalancerService;
    private final CostOptimizationService costOptimizationService;

    /**
     * 自动扩缩容决策
     */
    @Scheduled(fixedRate = 30000)
    public void autoScalingDecision() {
        // 1. 收集当前负载指标
        ScalingMetrics metrics = collectScalingMetrics();

        // 2. 评估扩缩容需求
        ScalingDecision decision = evaluateScalingNeeds(metrics);

        // 3. 执行扩缩容操作
        if (decision.getAction() != ScalingAction.NO_ACTION) {
            executeScalingAction(decision);
        }

        // 4. 成本优化
        optimizeScalingCosts(metrics);
    }

    /**
     * 评估扩缩容需求
     */
    private ScalingDecision evaluateScalingNeeds(ScalingMetrics metrics) {
        ScalingDecision decision = new ScalingDecision();

        // CPU使用率评估
        if (metrics.getAvgCpuUsage() > 80) {
            decision.setAction(ScalingAction.SCALE_OUT);
            decision.setTargetInstances(calculateTargetInstances(metrics, ScalingAction.SCALE_OUT));
        } else if (metrics.getAvgCpuUsage() < 30) {
            decision.setAction(ScalingAction.SCALE_IN);
            decision.setTargetInstances(calculateTargetInstances(metrics, ScalingAction.SCALE_IN));
        }

        // 响应时间评估
        if (metrics.getAvgResponseTime() > 1000) {
            if (decision.getAction() == ScalingAction.NO_ACTION) {
                decision.setAction(ScalingAction.SCALE_OUT);
                decision.setTargetInstances(calculateTargetInstances(metrics, ScalingAction.SCALE_OUT));
            }
        }

        // 错误率评估
        if (metrics.getErrorRate() > 5) {
            decision.setAction(ScalingAction.SCALE_OUT);
            decision.setTargetInstances(calculateTargetInstances(metrics, ScalingAction.SCALE_OUT));
        }

        return decision;
    }

    /**
     * 执行扩缩容操作
     */
    private void executeScalingAction(ScalingDecision decision) {
        switch (decision.getAction()) {
            case SCALE_OUT:
                executeScaleOut(decision.getTargetInstances());
                break;
            case SCALE_IN:
                executeScaleIn(decision.getTargetInstances());
                break;
            case NO_ACTION:
                // 无需操作
                break;
        }
    }

    /**
     * 执行扩容
     */
    private void executeScaleOut(int targetInstances) {
        // 1. 创建新实例
        List<String> newInstances = resourceManager.createInstances(targetInstances);

        // 2. 配置新实例
        for (String instanceId : newInstances) {
            configureNewInstance(instanceId);
        }

        // 3. 添加到负载均衡
        loadBalancerService.addInstances(newInstances);

        // 4. 健康检查
        waitForInstancesHealthy(newInstances);

        // 5. 记录扩容事件
        recordScalingEvent(ScalingAction.SCALE_OUT, newInstances);
    }

    /**
     * 执行缩容
     */
    private void executeScaleIn(int targetInstances) {
        // 1. 选择要移除的实例
        List<String> instancesToRemove = selectInstancesToRemove(targetInstances);

        // 2. 从负载均衡中移除
        loadBalancerService.removeInstances(instancesToRemove);

        // 3. 等待连接排空
        waitForConnectionsDrained(instancesToRemove);

        // 4. 销毁实例
        resourceManager.destroyInstances(instancesToRemove);

        // 5. 记录缩容事件
        recordScalingEvent(ScalingAction.SCALE_IN, instancesToRemove);
    }
}

/**
 * 智能缓存系统
 */
@Component
public class IntelligentCacheSystem {

    private final CacheManager cacheManager;
    private final CacheAnalyticsService analyticsService;
    private final CacheOptimizationService optimizationService;

    /**
     * 智能缓存策略
     */
    public Object getWithIntelligentCaching(String key, Supplier<Object> dataLoader) {
        // 1. 尝试从缓存获取
        Object cached = cacheManager.get(key);
        if (cached != null) {
            // 更新访问统计
            analyticsService.recordCacheHit(key);
            return cached;
        }

        // 2. 缓存未命中，加载数据
        analyticsService.recordCacheMiss(key);
        Object data = dataLoader.get();

        // 3. 智能缓存决策
        if (shouldCache(key, data)) {
            // 4. 计算TTL
            long ttl = calculateIntelligentTTL(key, data);

            // 5. 存储到缓存
            cacheManager.put(key, data, ttl);
        }

        return data;
    }

    /**
     * 智能缓存决策
     */
    private boolean shouldCache(String key, Object data) {
        // 1. 数据大小检查
        if (getDataSize(data) > MAX_CACHE_SIZE) {
            return false;
        }

        // 2. 访问频率检查
        double accessFrequency = analyticsService.getAccessFrequency(key);
        if (accessFrequency < MIN_ACCESS_FREQUENCY) {
            return false;
        }

        // 3. 数据热度检查
        double dataHotness = calculateDataHotness(key, data);
        if (dataHotness < MIN_HOTNESS_THRESHOLD) {
            return false;
        }

        return true;
    }

    /**
     * 计算智能TTL
     */
    private long calculateIntelligentTTL(String key, Object data) {
        // 1. 基础TTL
        long baseTTL = getBaseTTL(data);

        // 2. 访问频率调整
        double accessFrequency = analyticsService.getAccessFrequency(key);
        long frequencyAdjustment = (long) (baseTTL * accessFrequency);

        // 3. 数据更新频率调整
        double updateFrequency = analyticsService.getUpdateFrequency(key);
        long updateAdjustment = (long) (baseTTL / updateFrequency);

        // 4. 系统负载调整
        double systemLoad = getSystemLoad();
        long loadAdjustment = (long) (baseTTL * (1.0 - systemLoad));

        // 5. 综合计算
        long finalTTL = baseTTL + frequencyAdjustment - updateAdjustment + loadAdjustment;

        return Math.max(MIN_TTL, Math.min(MAX_TTL, finalTTL));
    }

    /**
     * 缓存预热
     */
    @Scheduled(cron = "0 0 6 * * ?") // 每天凌晨6点预热
    public void cacheWarmup() {
        // 1. 分析访问模式
        Map<String, Double> accessPatterns = analyticsService.analyzeAccessPatterns();

        // 2. 预测热点数据
        List<String> hotKeys = predictHotKeys(accessPatterns);

        // 3. 预热缓存
        for (String key : hotKeys) {
            preloadCache(key);
        }

        // 4. 优化缓存配置
        optimizationService.optimizeCacheConfiguration();
    }
}

/**
 * 智能连接池优化
 */
@Component
public class IntelligentConnectionPoolOptimizer {

    private final ConnectionPoolManager poolManager;
    private final ConnectionMetricsCollector metricsCollector;
    private final ConnectionPoolAnalyzer analyzer;

    /**
     * 连接池优化主循环
     */
    @Scheduled(fixedRate = 60000) // 每分钟优化一次
    public void optimizeConnectionPools() {
        // 1. 收集连接池指标
        Map<String, ConnectionPoolMetrics> metrics = metricsCollector.collectMetrics();

        // 2. 分析连接池性能
        Map<String, OptimizationRecommendation> recommendations = analyzer.analyzePerformance(metrics);

        // 3. 执行优化
        for (Map.Entry<String, OptimizationRecommendation> entry : recommendations.entrySet()) {
            String poolName = entry.getKey();
            OptimizationRecommendation recommendation = entry.getValue();

            executeOptimization(poolName, recommendation);
        }
    }

    /**
     * 执行优化
     */
    private void executeOptimization(String poolName, OptimizationRecommendation recommendation) {
        switch (recommendation.getType()) {
            case ADJUST_POOL_SIZE:
                adjustPoolSize(poolName, recommendation.getNewPoolSize());
                break;
            case ADJUST_TIMEOUT:
                adjustTimeout(poolName, recommendation.getNewTimeout());
                break;
            case ADJUST_VALIDATION:
                adjustValidation(poolName, recommendation.getValidationInterval());
                break;
            case ENABLE_MONITORING:
                enableMonitoring(poolName);
                break;
        }
    }

    /**
     * 调整连接池大小
     */
    private void adjustPoolSize(String poolName, int newSize) {
        ConnectionPool pool = poolManager.getPool(poolName);

        // 1. 验证新大小
        if (!isValidPoolSize(newSize)) {
            log.warn("无效的连接池大小: {}", newSize);
            return;
        }

        // 2. 平滑调整
        int currentSize = pool.getCurrentSize();
        if (newSize > currentSize) {
            // 扩容：逐步增加
            int step = Math.min(5, newSize - currentSize);
            pool.expandPool(step);
        } else if (newSize < currentSize) {
            // 缩容：等待连接释放后减少
            pool.shrinkPool(currentSize - newSize);
        }

        // 3. 记录调整
        recordPoolSizeAdjustment(poolName, currentSize, newSize);
    }

    /**
     * 动态连接池配置
     */
    @Component
    public class DynamicConnectionPoolConfig {

        private final Map<String, ConnectionPoolConfig> configs = new ConcurrentHashMap<>();

        /**
         * 动态更新配置
         */
        public void updateConfig(String poolName, ConnectionPoolConfig newConfig) {
            // 1. 验证配置
            validateConfig(newConfig);

            // 2. 平滑更新
            ConnectionPoolConfig currentConfig = configs.get(poolName);
            if (currentConfig != null) {
                smoothUpdateConfig(poolName, currentConfig, newConfig);
            } else {
                configs.put(poolName, newConfig);
            }
        }

        /**
         * 平滑更新配置
         */
        private void smoothUpdateConfig(String poolName, ConnectionPoolConfig current, ConnectionPoolConfig target) {
            // 1. 计算差异
            ConfigDiff diff = calculateConfigDiff(current, target);

            // 2. 分步更新
            if (diff.hasPoolSizeChange()) {
                updatePoolSizeGradually(poolName, current.getMaxPoolSize(), target.getMaxPoolSize());
            }

            if (diff.hasTimeoutChange()) {
                updateTimeoutGradually(poolName, current.getConnectionTimeout(), target.getConnectionTimeout());
            }

            // 3. 更新最终配置
            configs.put(poolName, target);
        }
    }
}

/**
 * 智能运维核心服务
 */
@Service
public class IntelligentOpsCoreService {

    private final AnomalyDetectionService anomalyDetectionService;
    private final RootCauseAnalysisService rootCauseAnalysisService;
    private final AutoRemediationService autoRemediationService;
    private final PredictiveAnalysisService predictiveAnalysisService;

    /**
     * 智能运维主流程
     */
    @EventListener
    public void handleIntelligentOps(MonitoringEvent event) {
        // 1. 异常检测
        List<Anomaly> anomalies = anomalyDetectionService.detectAnomalies(event.getMetrics());

        // 2. 根因分析
        for (Anomaly anomaly : anomalies) {
            RootCause rootCause = rootCauseAnalysisService.analyzeRootCause(anomaly);

            // 3. 影响评估
            ImpactAssessment impact = assessImpact(anomaly, rootCause);

            // 4. 自动修复
            if (impact.getSeverity() <= ImpactLevel.MEDIUM) {
                boolean fixed = autoRemediationService.attemptAutoFix(anomaly, rootCause);
                if (fixed) {
                    log.info("异常自动修复成功: {}", anomaly);
                    continue;
                }
            }

            // 5. 人工介入
            escalateToHuman(anomaly, rootCause, impact);
        }

        // 6. 预测性分析
        performPredictiveAnalysis(event.getMetrics());
    }

    /**
     * 预测性分析
     */
    private void performPredictiveAnalysis(Map<String, Object> metrics) {
        // 1. 预测系统故障
        List<Prediction> failurePredictions = predictiveAnalysisService.predictFailures(metrics);

        // 2. 预测性能瓶颈
        List<Prediction> bottleneckPredictions = predictiveAnalysisService.predictBottlenecks(metrics);

        // 3. 预测容量需求
        CapacityPrediction capacityPrediction = predictiveAnalysisService.predictCapacity(metrics);

        // 4. 执行预防性措施
        executePreventiveMeasures(failurePredictions, bottleneckPredictions, capacityPrediction);
    }

    /**
     * 执行预防性措施
     */
    private void executePreventiveMeasures(List<Prediction> failurePredictions, 
                                         List<Prediction> bottleneckPredictions, 
                                         CapacityPrediction capacityPrediction) {

        // 1. 处理故障预测
        for (Prediction prediction : failurePredictions) {
            if (prediction.getConfidence() > 0.8) {
                executePreventiveMaintenance(prediction);
            }
        }

        // 2. 处理瓶颈预测
        for (Prediction prediction : bottleneckPredictions) {
            if (prediction.getConfidence() > 0.7) {
                executePerformanceOptimization(prediction);
            }
        }

        // 3. 处理容量预测
        if (capacityPrediction.getConfidence() > 0.75) {
            executeCapacityPlanning(capacityPrediction);
        }
    }
}

/**
 * 智能告警系统
 */
@Component
public class IntelligentAlertSystem {

    private final AlertRuleEngine ruleEngine;
    private final AlertAggregationService aggregationService;
    private final AlertSuppressionService suppressionService;
    private final AlertEscalationService escalationService;

    /**
     * 智能告警处理
     */
    public void processAlert(Alert alert) {
        // 1. 告警规则匹配
        List<AlertRule> matchedRules = ruleEngine.matchRules(alert);

        // 2. 告警聚合
        Alert aggregatedAlert = aggregationService.aggregateAlert(alert, matchedRules);

        // 3. 告警抑制
        if (suppressionService.shouldSuppress(aggregatedAlert)) {
            log.info("告警被抑制: {}", aggregatedAlert);
            return;
        }

        // 4. 告警升级
        AlertLevel targetLevel = escalationService.determineEscalationLevel(aggregatedAlert);

        // 5. 发送告警
        sendAlert(aggregatedAlert, targetLevel);

        // 6. 记录告警
        recordAlert(aggregatedAlert);
    }

    /**
     * 智能告警规则引擎
     */
    @Component
    public class IntelligentAlertRuleEngine {

        private final Map<String, AlertRule> rules = new ConcurrentHashMap<>();
        private final RuleOptimizationService optimizationService;

        /**
         * 动态规则优化
         */
        @Scheduled(cron = "0 0 1 * * ?") // 每天凌晨1点优化
        public void optimizeRules() {
            // 1. 分析告警历史
            Map<String, AlertStatistics> alertStats = analyzeAlertHistory();

            // 2. 识别无效规则
            List<String> ineffectiveRules = identifyIneffectiveRules(alertStats);

            // 3. 优化规则参数
            for (String ruleId : ineffectiveRules) {
                optimizeRuleParameters(ruleId, alertStats.get(ruleId));
            }

            // 4. 添加新规则
            addNewRules(alertStats);
        }

        /**
         * 智能阈值调整
         */
        public void adjustThresholds(String metricName, Map<String, Object> historicalData) {
            // 1. 分析历史数据分布
            DataDistribution distribution = analyzeDataDistribution(historicalData);

            // 2. 计算最优阈值
            Threshold optimalThreshold = calculateOptimalThreshold(distribution);

            // 3. 更新规则
            updateRuleThreshold(metricName, optimalThreshold);
        }
    }
}