柔性供应链软件开发：云原生技术融合实践教程

引言：供应链数字化转型的必然选择

在当今全球商业环境中，供应链正面临着前所未有的挑战与机遇。市场需求波动加剧、全球化布局复杂化、消费者期望不断提升，这些因素共同推动着传统供应链向数字化、智能化方向转型。柔性供应链作为应对不确定性的关键策略，其核心在于通过技术手段构建能够快速响应变化的动态系统。而云原生技术，以其弹性、可扩展和敏捷的特性，成为实现柔性供应链的理想技术架构。本教程将深入探讨如何将云原生技术融合到柔性供应链软件开发中，提供从理论到实践的完整指南。

第一章：柔性供应链的核心特征与技术需求

1.1 柔性供应链的五大维度

柔性供应链并非单一概念，而是包含多个维度的综合能力体系：

• 数量柔性：能够快速调整生产与配送规模，应对需求波动
• 交付柔性：适应不同交付时间要求和紧急订单处理
• 产品柔性：支持产品配置变更和新产品快速引入
• 路由柔性：在供应链中断时快速调整物流路径
• 混合柔性：在同一生产线上处理不同产品的能力

1.2 技术实现的关键需求

要实现上述柔性特征，供应链软件需要具备以下技术能力：

• 实时数据处理：对供应链各环节数据实时采集与分析
• 模块化架构：各功能模块可独立开发、部署和扩展
• 弹性计算能力：根据负载自动调整资源分配
• 多系统集成：与ERP、WMS、TMS等现有系统无缝对接
• 智能决策支持：基于AI/ML的预测与优化能力

第二章：云原生技术栈与供应链融合架构

2.1 云原生核心组件解析

云原生技术为柔性供应链提供了理想的技术基础：

• 容器化技术：Docker实现应用环境标准化，确保开发与生产环境一致性
• 编排系统：Kubernetes管理容器化应用的部署、扩展和运维
• 微服务架构：将供应链功能拆分为独立服务，提高系统灵活性
• 服务网格：Istio等服务网格技术处理服务间通信，增强可观察性
• 无服务器计算：应对突发流量，优化资源利用率

2.2 供应链云原生参考架构

基于云原生的柔性供应链系统可采用分层架构设计：

表现层：响应式Web界面、移动应用、API网关
业务层：订单管理微服务、库存管理微服务、物流跟踪微服务、供应商协作微服务
数据层：实时数据流处理、分布式数据库、数据湖、缓存系统
基础设施层：容器平台、服务网格、CI/CD流水线、监控告警系统

第三章：实践开发教程：构建柔性库存管理模块

3.1 环境准备与项目初始化

首先，我们需要设置开发环境：

# 安装必要工具
brew install kubectl docker helm minikube

# 启动本地Kubernetes集群
minikube start --driver=docker

# 创建项目结构
mkdir flexible-supply-chain
cd flexible-supply-chain
mkdir -p inventory-service/{src,deploy,test}

3.2 微服务设计与实现

以库存管理微服务为例，展示如何实现柔性功能：

// InventoryService.java - 库存管理核心服务
@RestController
@RequestMapping("/api/inventory")
public class InventoryService {
    
    @Autowired
    private InventoryRepository repository;
    
    @Autowired
    private KafkaTemplate<String, String> kafkaTemplate;
    
    // 柔性库存分配算法
    @PostMapping("/allocate")
    public ResponseEntity<AllocationResult> allocateInventory(
            @RequestBody AllocationRequest request) {
        
        // 实时库存检查
        InventoryStatus status = repository.getRealTimeStatus(
            request.getProductId(), request.getWarehouseId());
        
        // 动态分配逻辑：考虑优先级、地理位置、库存周转率
        AllocationStrategy strategy = selectStrategy(
            request.getPriority(), status);
        
        AllocationResult result = strategy.execute(request, status);
        
        // 发布库存变更事件
        kafkaTemplate.send("inventory-events", 
            createInventoryEvent(result));
        
        return ResponseEntity.ok(result);
    }
    
    // 基于规则的策略选择器
    private AllocationStrategy selectStrategy(Priority priority, 
                                              InventoryStatus status) {
        if (priority == Priority.URGENT) {
            return new CrossWarehouseAllocation();
        } else if (status.getTurnoverRate() > 0.8) {
            return new HighTurnoverStrategy();
        } else {
            return new StandardAllocation();
        }
    }
}

3.3 容器化部署与配置

创建Dockerfile和Kubernetes部署文件：

# Dockerfile
FROM openjdk:11-jre-slim
COPY target/inventory-service.jar /app.jar
EXPOSE 8080
ENTRYPOINT ["java", "-jar", "/app.jar"]

# inventory-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: inventory-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: inventory
  template:
    metadata:
      labels:
        app: inventory
    spec:
      containers:
      - name: inventory
        image: inventory-service:1.0
        ports:
        - containerPort: 8080
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        env:
        - name: DB_HOST
          valueFrom:
            configMapKeyRef:
              name: app-config
              key: database.host
---
apiVersion: v1
kind: Service
metadata:
  name: inventory-service
spec:
  selector:
    app: inventory
  ports:
  - port: 80
    targetPort: 8080
  type: LoadBalancer

第四章：弹性扩展与自动化运维实践

4.1 基于Kubernetes的自动扩缩容

通过Horizontal Pod Autoscaler实现根据负载自动调整服务实例数量：

# hpa-inventory.yaml
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: inventory-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: inventory-service
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

4.2 服务网格实现智能路由

使用Istio实现流量管理、故障恢复和A/B测试：

# inventory-virtualservice.yaml
apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: inventory-route
spec:
  hosts:
  - inventory-service
  http:
  - match:
    - headers:
        x-user-type:
          exact: premium
    route:
    - destination:
        host: inventory-service
        subset: v2
      weight: 100
  - route:
    - destination:
        host: inventory-service
        subset: v1
      weight: 90
    - destination:
        host: inventory-service
        subset: v2
      weight: 10

4.3 持续部署流水线配置

使用GitLab CI/CD实现自动化部署：

# .gitlab-ci.yml
stages:
  - build
  - test
  - deploy

variables:
  IMAGE_TAG: $CI_REGISTRY_IMAGE:$CI_COMMIT_SHORT_SHA

build-job:
  stage: build
  script:
    - mvn clean package
    - docker build -t $IMAGE_TAG .
    - docker push $IMAGE_TAG

deploy-dev:
  stage: deploy
  environment:
    name: development
  script:
    - kubectl config use-context dev-cluster
    - kubectl set image deployment/inventory-service inventory=$IMAGE_TAG
    - kubectl rollout status deployment/inventory-service
  only:
    - develop

deploy-prod:
  stage: deploy
  environment:
    name: production
  script:
    - kubectl config use-context prod-cluster
    - kubectl apply -f k8s/production/
    - kubectl rollout status deployment/inventory-service
  only:
    - master
  when: manual

第五章：监控、优化与安全实践

5.1 全链路可观察性建设

构建完整的监控体系：

# monitoring-stack.yaml
# 使用Prometheus + Grafana + Jaeger构建监控体系
apiVersion: monitoring.coreos.com/v1
kind: Prometheus
metadata:
  name: supply-chain-monitor
spec:
  serviceMonitorSelector:
    matchLabels:
      app: inventory
  resources:
    requests:
      memory: 400Mi
---
apiVersion: opentelemetry.io/v1alpha1
kind: OpenTelemetryCollector
metadata:
  name: trace-collector
spec:
  mode: deployment
  config: |
    receivers:
      jaeger:
        protocols:
          grpc:
    exporters:
      jaeger:
        endpoint: jaeger-all-in-one:14250
        insecure: true
    service:
      pipelines:
        traces:
          receivers: [jaeger]
          exporters: [jaeger]

5.2 性能优化策略

针对供应链场景的特殊优化：

1. 数据库优化：使用读写分离、缓存策略（Redis）、分库分表
2. API响应优化：实施GraphQL替代RESTful API，减少过度获取
3. 事件驱动架构：使用Kafka解耦服务，提高系统响应能力
4. 边缘计算：在仓库节点部署轻量级计算，减少数据传输延迟

5.3 安全最佳实践

确保供应链数据安全：

• 零信任架构：每个请求都需要验证，无论来源
• 秘密管理：使用HashiCorp Vault或Kubernetes Secrets管理敏感信息
• 网络策略：通过NetworkPolicy限制Pod间通信
• API安全：实施OAuth 2.0、JWT令牌和API速率限制

第六章：案例研究：全球零售企业的柔性供应链改造

6.1 挑战与目标

某全球零售企业面临以下挑战：

• 季节性需求波动剧烈，峰值达平时的5倍
• 全球供应商超过2000家，协调困难
• 库存周转率低，仓储成本高企
• 系统响应慢，订单处理需数小时

6.2 实施路径与成果

通过18个月的云原生改造：

1. 第一阶段（6个月）：核心库存管理系统微服务化，部署到Kubernetes平台
2. 第二阶段（6个月）：引入事件驱动架构，实现实时库存同步
3. 第三阶段（6个月）：添加AI预测模块，优化库存分配

成果指标：

• 系统弹性提升：自动扩展应对300%流量增长
• 订单处理时间：从4小时缩短至15分钟
• 库存周转率：提高35%
• 运营成本：降低28%

结语：面向未来的柔性供应链技术趋势

云原生技术与柔性供应链的融合不仅是当前数字化转型的解决方案，更是面向未来智能供应链的基础。随着5G、物联网、边缘计算和人工智能技术的进一步发展，供应链系统将变得更加智能、自适应和抗脆弱。开发者和架构师需要持续关注以下趋势：

1. AI驱动的自主供应链：系统能够自我学习、预测和决策
2. 区块链增强的可追溯性：提供不可篡改的全程追溯能力
3. 数字孪生技术：创建物理供应链的虚拟映射，进行模拟与优化
4. 可持续供应链：通过技术手段减少碳足迹，实现绿色运营

柔性供应链的云原生实践之旅是一个持续演进的过程。通过本教程介绍的方法论和实践指南，企业可以逐步构建起适应不确定时代的弹性供应链系统，在日益复杂的商业环境中保持竞争优势。记住，技术实施的成功不仅取决于工具选择，更取决于组织文化、流程适配和持续改进的承诺。

第七章：云原生供应链的数据治理与智能分析

7.1 数据湖与实时分析架构

数据分层治理策略

柔性供应链需要处理多源异构数据，建立有效的数据治理体系至关重要：

# data-governance-pipeline.yaml
apiVersion: data.fluid.io/v1alpha1
kind: Dataset
metadata:
  name: supply-chain-data
spec:
  mounts:
  - mountPoint: s3://supply-chain-raw/
    name: raw
    path: "/raw"
  - mountPoint: s3://supply-chain-processed/
    name: processed
    path: "/processed"
  - mountPoint: s3://supply-chain-curated/
    name: curated
    path: "/curated"
  dataGovernance:
    retentionPolicy:
      raw: 30d
      processed: 180d
      curated: 365d
    accessControl:
      - role: data-engineer
        permissions: [read, write]
      - role: business-analyst
        permissions: [read]

实时流处理管道

使用Apache Flink处理供应链实时数据流：

// RealTimeInventoryStream.java
public class RealTimeInventoryStream {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = 
            StreamExecutionEnvironment.getExecutionEnvironment();
        
        // 从Kafka读取实时库存事件
        DataStream<InventoryEvent> inventoryStream = env
            .addSource(new FlinkKafkaConsumer<>(
                "inventory-events",
                new InventoryEventSchema(),
                properties))
            .name("inventory-source");
        
        // 实时库存预警处理
        DataStream<InventoryAlert> alertStream = inventoryStream
            .keyBy(InventoryEvent::getWarehouseId)
            .process(new InventoryAlertProcessFunction())
            .name("alert-processor");
        
        // 动态安全库存计算
        DataStream<SafetyStockUpdate> safetyStockStream = inventoryStream
            .keyBy(InventoryEvent::getProductId)
            .window(SlidingEventTimeWindows.of(Time.days(7), Time.hours(1)))
            .process(new SafetyStockCalculator())
            .name("safety-stock-calculator");
        
        // 输出到多个目的地
        alertStream.addSink(new KafkaSink<>("inventory-alerts"));
        safetyStockStream.addSink(new ElasticsearchSink<>());
        
        env.execute("Real-time Inventory Analytics");
    }
}

7.2 供应链知识图谱构建

图数据库建模

使用Neo4j构建供应链实体关系网络：

// 创建供应链知识图谱
CREATE CONSTRAINT ON (s:Supplier) ASSERT s.id IS UNIQUE;
CREATE CONSTRAINT ON (p:Product) ASSERT p.sku IS UNIQUE;
CREATE CONSTRAINT ON (w:Warehouse) ASSERT w.code IS UNIQUE;

// 构建供应商-产品-仓库关系网络
MATCH (s:Supplier {id: $supplierId})
MERGE (p:Product {sku: $sku, name: $productName})
MERGE (w:Warehouse {code: $warehouseCode, location: $location})
CREATE (s)-[:SUPPLIES {leadTime: $leadTime, cost: $unitCost}]->(p)
CREATE (p)-[:STORED_AT {quantity: $quantity, safetyStock: $safetyStock}]->(w)
CREATE (w)-[:SHIPS_TO {transitTime: $transitTime}]->(:Region {name: $regionName});

// 查询多级供应链网络
MATCH path = (s:Supplier)-[:SUPPLIES*1..3]->(p:Product)
              -[:STORED_AT]->(w:Warehouse)
WHERE p.category = 'Electronics'
RETURN s.name AS Supplier, 
       collect(DISTINCT p.name) AS Products,
       w.location AS WarehouseLocation,
       length(path) AS SupplyChainDepth
ORDER BY SupplyChainDepth;

图神经网络预测

使用GNN进行供应链风险预测：

# supply_chain_gnn.py
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv

class SupplyChainGNN(torch.nn.Module):
    def __init__(self, num_features, hidden_channels, num_classes):
        super().__init__()
        self.conv1 = GCNConv(num_features, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, hidden_channels)
        self.conv3 = GCNConv(hidden_channels, hidden_channels)
        self.lin = torch.nn.Linear(hidden_channels, num_classes)
    
    def forward(self, x, edge_index, edge_attr):
        # 节点特征：供应商可靠性、地理位置、产能等
        # 边特征：运输时间、成本、历史履约率等
        x = self.conv1(x, edge_index, edge_attr)
        x = F.relu(x)
        x = F.dropout(x, p=0.5, training=self.training)
        
        x = self.conv2(x, edge_index, edge_attr)
        x = F.relu(x)
        
        x = self.conv3(x, edge_index, edge_attr)
        x = self.lin(x)
        
        return F.log_softmax(x, dim=1)
    
    def predict_risk(self, supply_chain_graph):
        """预测供应链节点风险"""
        self.eval()
        with torch.no_grad():
            logits = self.forward(
                supply_chain_graph.x,
                supply_chain_graph.edge_index,
                supply_chain_graph.edge_attr
            )
            risk_scores = torch.argmax(logits, dim=1)
            return risk_scores.numpy()

第八章：多云与混合云部署策略

8.1 跨云供应链架构设计

多云服务网格配置

使用Anthos或Istio多集群实现跨云部署：

# multi-cloud-mesh.yaml
apiVersion: networking.istio.io/v1beta1
kind: ServiceEntry
metadata:
  name: cross-cloud-services
spec:
  hosts:
  - inventory-service.aws.amazonaws.com
  - logistics-service.gcp.googleapis.com
  - supplier-portal.azure.microsoft.com
  ports:
  - number: 443
    name: https
    protocol: HTTPS
  resolution: DNS
  location: MESH_EXTERNAL
---
apiVersion: networking.istio.io/v1beta1
kind: DestinationRule
metadata:
  name: cross-cloud-load-balancing
spec:
  host: "*.amazonaws.com"
  trafficPolicy:
    loadBalancer:
      simple: ROUND_ROBIN
    connectionPool:
      tcp:
        maxConnections: 100
        connectTimeout: 30ms
      http:
        http2MaxRequests: 1000
        maxRequestsPerConnection: 10
    outlierDetection:
      consecutive5xxErrors: 10
      interval: 5s
      baseEjectionTime: 30s
      maxEjectionPercent: 50

数据同步与一致性保障

实现跨云数据同步：

// CrossCloudDataSync.java
@Component
public class CrossCloudDataSync {
    
    @Autowired
    private CloudSyncCoordinator coordinator;
    
    @Value("${cloud.providers}")
    private List<String> cloudProviders;
    
    @Scheduled(fixedDelay = 30000)
    public void syncInventoryData() {
        Map<String, InventorySnapshot> snapshots = 
            cloudProviders.parallelStream()
                .collect(Collectors.toMap(
                    provider -> provider,
                    this::fetchInventorySnapshot
                ));
        
        // 使用CRDT进行冲突解决
        MergedInventory merged = coordinator.mergeSnapshots(snapshots);
        
        // 异步推送到所有云
        cloudProviders.forEach(provider -> 
            asyncUpdateInventory(provider, merged)
        );
    }
    
    private InventorySnapshot fetchInventorySnapshot(String provider) {
        // 从不同云提供商获取数据
        switch (provider) {
            case "aws":
                return fetchFromDynamoDB();
            case "azure":
                return fetchFromCosmosDB();
            case "gcp":
                return fetchFromFirestore();
            default:
                throw new IllegalArgumentException("Unknown provider");
        }
    }
}

8.2 边缘计算集成

边缘节点部署模式

在仓库、零售店等边缘位置部署轻量级服务：

# edge-inventory-service.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: edge-inventory-service
  labels:
    app: edge-inventory
spec:
  replicas: 50  # 部署到50个边缘位置
  selector:
    matchLabels:
      app: edge-inventory
  template:
    metadata:
      labels:
        app: edge-inventory
    spec:
      nodeSelector:
        node-type: edge
      containers:
      - name: inventory
        image: edge-inventory:1.0
        resources:
          requests:
            memory: "128Mi"
            cpu: "100m"
        env:
        - name: EDGE_LOCATION_ID
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        - name: OFFLINE_MODE
          value: "true"
        volumeMounts:
        - name: local-storage
          mountPath: /data
      volumes:
      - name: local-storage
        hostPath:
          path: /var/edge-data
          type: DirectoryOrCreate

边缘-云协同处理

实现边缘计算与云中心的智能协同：

# edge_cloud_orchestrator.py
import asyncio
from typing import Dict, Optional
from dataclasses import dataclass
from enum import Enum

class SyncStrategy(Enum):
    REAL_TIME = "real_time"
    BATCH = "batch"
    LAZY = "lazy"

@dataclass
class EdgeNode:
    node_id: str
    location: str
    connectivity_score: float
    last_sync: float
    local_data: Dict

class EdgeCloudOrchestrator:
    def __init__(self, cloud_endpoint: str):
        self.cloud_endpoint = cloud_endpoint
        self.edge_nodes: Dict[str, EdgeNode] = {}
        self.sync_queue = asyncio.Queue()
    
    async def register_edge_node(self, node: EdgeNode):
        """注册边缘节点"""
        self.edge_nodes[node.node_id] = node
        
        # 根据连接质量选择同步策略
        if node.connectivity_score > 0.8:
            strategy = SyncStrategy.REAL_TIME
        elif node.connectivity_score > 0.3:
            strategy = SyncStrategy.BATCH
        else:
            strategy = SyncStrategy.LAZY
        
        await self.configure_sync_strategy(node.node_id, strategy)
    
    async def adaptive_sync(self, node_id: str, data: Dict):
        """自适应数据同步"""
        node = self.edge_nodes[node_id]
        
        # 判断数据优先级
        priority = self.calculate_priority(data)
        
        if priority == "high" and node.connectivity_score > 0.5:
            # 实时同步关键数据
            await self.real_time_sync(node_id, data)
        elif priority == "medium":
            # 批量同步
            await self.batch_sync(node_id, data)
        else:
            # 延迟同步
            await self.queue_for_later_sync(node_id, data)
    
    async def real_time_sync(self, node_id: str, data: Dict):
        """实时同步到云端"""
        try:
            async with aiohttp.ClientSession() as session:
                async with session.post(
                    f"{self.cloud_endpoint}/sync/realtime",
                    json={
                        "node_id": node_id,
                        "data": data,
                        "timestamp": time.time()
                    }
                ) as response:
                    if response.status == 200:
                        node = self.edge_nodes[node_id]
                        node.last_sync = time.time()
        except Exception as e:
            # 同步失败，降级到本地存储
            await self.store_locally(node_id, data)

第九章：供应链韧性测试与混沌工程

9.1 韧性测试框架设计

故障注入测试套件

构建全面的供应链系统韧性测试：

# chaos-testing-suite.yaml
apiVersion: chaos-mesh.org/v1alpha1
kind: ChaosExperiment
metadata:
  name: supply-chain-resilience-test
spec:
  duration: "1h"
  tests:
  - name: network-latency-test
    type: NetworkChaos
    spec:
      action: delay
      mode: all
      selector:
        namespaces: ["supply-chain"]
        labelSelectors:
          app: ["inventory", "logistics"]
      delay:
        latency: "500ms"
        correlation: "50"
        jitter: "100ms"
  
  - name: database-failure-test
    type: PodChaos
    spec:
      action: pod-failure
      mode: one
      selector:
        namespaces: ["supply-chain"]
        labelSelectors:
          app: "inventory-db"
      duration: "5m"
  
  - name: cloud-service-outage
    type: AWSChaos
    spec:
      action: ec2-stop
      region: us-east-1
      instanceIds: ["i-1234567890abcdef0"]
      duration: "10m"
  
  - name: dependency-degradation
    type: StressChaos
    spec:
      mode: one
      selector:
        namespaces: ["supply-chain"]
        labelSelectors:
          app: "payment-service"
      stressors:
        cpu:
          workers: 4
          load: 90
          duration: "3m"
  
  metrics:
    successCriteria:
      - name: order-processing-success-rate
        threshold: 95%
      - name: system-recovery-time
        threshold: 5m
      - name: data-consistency-rate
        threshold: 99.9%

自动化韧性测试流水线

集成到CI/CD流程中的自动化测试：

// Jenkinsfile
pipeline {
    agent any
    
    stages {
        stage('Build') {
            steps {
                sh 'mvn clean package'
                sh 'docker build -t supply-chain-app .'
            }
        }
        
        stage('Resilience Tests') {
            parallel {
                stage('Chaos Testing') {
                    steps {
                        sh '''
                        kubectl apply -f chaos-experiments/
                        
                        # 运行混沌实验
                        chaos run chaos-experiment.yaml
                        
                        # 验证系统行为
                        python verify_resilience.py 
                            --metrics-order-success-rate=95 
                            --metrics-recovery-time=300
                        '''
                    }
                }
                
                stage('Load Testing') {
                    steps {
                        sh '''
                        # 模拟峰值负载
                        k6 run --vus 1000 --duration 10m 
                            load-test.js
                        
                        # 验证弹性扩展
                        kubectl get hpa -w
                        '''
                    }
                }
            }
        }
        
        stage('Recovery Validation') {
            steps {
                sh '''
                # 验证故障恢复后数据一致性
                python verify_data_consistency.py 
                    --tolerance=0.001
                
                # 验证服务级别目标
                python verify_slos.py 
                    --availability=99.95 
                    --latency-p99=200ms
                '''
            }
        }
    }
    
    post {
        always {
            // 清理混沌实验
            sh 'kubectl delete -f chaos-experiments/'
            
            // 生成韧性报告
            sh 'python generate_resilience_report.py'
            
            // 归档测试结果
            archiveArtifacts artifacts: 'reports/*.html'
        }
    }
}

9.2 自适应韧性策略

基于AI的故障预测与自愈

实现智能的故障预测和自动恢复：

# adaptive_resilience_engine.py
import numpy as np
from sklearn.ensemble import IsolationForest
from datetime import datetime, timedelta
import pandas as pd

class AdaptiveResilienceEngine:
    def __init__(self):
        self.anomaly_detector = IsolationForest(
            contamination=0.1,
            random_state=42
        )
        self.failure_patterns = self.load_failure_patterns()
        self.recovery_strategies = self.load_recovery_strategies()
    
    def predict_failure(self, metrics: pd.DataFrame) -> dict:
        """预测潜在故障"""
        # 特征工程
        features = self.extract_features(metrics)
        
        # 异常检测
        anomalies = self.anomaly_detector.predict(features)
        
        # 模式匹配
        predictions = []
        for idx, is_anomaly in enumerate(anomalies):
            if is_anomaly == -1:
                pattern = self.match_failure_pattern(
                    features.iloc[idx]
                )
                if pattern:
                    predictions.append({
                        'component': pattern['component'],
                        'failure_type': pattern['type'],
                        'confidence': pattern['confidence'],
                        'eta': pattern.get('eta', '1h'),
                        'suggested_action': pattern['action']
                    })
        
        return {
            'predictions': predictions,
            'timestamp': datetime.now(),
            'metrics_analyzed': len(metrics)
        }
    
    def execute_recovery(self, failure_prediction: dict):
        """执行自动恢复"""
        strategy = self.select_recovery_strategy(
            failure_prediction
        )
        
        if strategy['type'] == 'auto_scale':
            self.auto_scale_component(
                failure_prediction['component'],
                strategy['scale_factor']
            )
        elif strategy['type'] == 'traffic_reroute':
            self.reroute_traffic(
                failure_prediction['component'],
                strategy['alternative_components']
            )
        elif strategy['type'] == 'degraded_mode':
            self.enable_degraded_mode(
                failure_prediction['component'],
                strategy['degraded_features']
            )
        
        # 记录恢复操作
        self.log_recovery_action(failure_prediction, strategy)
    
    def auto_scale_component(self, component: str, scale_factor: float):
        """自动扩缩容"""
        # 获取当前副本数
        current_replicas = self.get_replica_count(component)
        
        # 计算目标副本数
        target_replicas = max(
            2,  # 最小副本数
            int(current_replicas * scale_factor)
        )