设备层（Device Layer）

最靠近数据源的一层，运行在资源受限的设备上：

// 示例：IoT温度传感器
class TemperatureSensor {
  private buffer: number[] = [];
  private readonly BUFFER_SIZE = 100;
  private readonly THRESHOLD = 50;

  // 本地预处理：减少上传数据量
  async collectReading(temp: number): Promise<void> {
    this.buffer.push(temp);

    // 1. 异常检测（本地）
    if (temp > this.THRESHOLD) {
      await this.sendAlert({ temp, timestamp: Date.now(), severity: "high" });
    }

    // 2. 数据压缩：只上传统计信息
    if (this.buffer.length >= this.BUFFER_SIZE) {
      const summary = {
        avg: this.average(this.buffer),
        min: Math.min(...this.buffer),
        max: Math.max(...this.buffer),
        count: this.buffer.length
      };
      await this.uploadToEdge(summary);
      this.buffer = [];
    }
  }

  private average(arr: number[]): number {
    return arr.reduce((a, b) => a + b, 0) / arr.length;
  }
}

边缘层（Edge Layer）

部署在网络边缘的计算节点，例如基站、CDN节点、工厂网关：

// 边缘节点：聚合多个设备的数据
class EdgeNode {
  private devices = new Map<string, DeviceState>();
  private localCache = new LRUCache<string, any>(1000);
  private mlModel: TensorFlowModel;

  constructor() {
    // 在边缘运行轻量级ML模型
    this.mlModel = this.loadOptimizedModel();
  }

  async processDeviceData(deviceId: string, data: SensorData): Promise<void> {
    // 1. 本地缓存热数据
    this.localCache.set(deviceId, data);

    // 2. 实时分析
    const anomaly = await this.detectAnomaly(data);
    if (anomaly) {
      await this.handleAnomaly(deviceId, data, anomaly);
    }

    // 3. 聚合上报云端（低频）
    this.updateAggregation(deviceId, data);
  }

  // 在边缘运行AI推理
  private async detectAnomaly(data: SensorData): Promise<Anomaly | null> {
    const prediction = await this.mlModel.predict(data);
    if (prediction.confidence > 0.9) {
      return {
        type: prediction.class,
        confidence: prediction.confidence,
        timestamp: Date.now()
      };
    }
    return null;
  }

  // 本地处理，避免往返云端的延迟
  private async handleAnomaly(
    deviceId: string,
    data: SensorData,
    anomaly: Anomaly
  ): Promise<void> {
    // 立即响应
    await this.sendControlCommand(deviceId, { action: "shutdown", reason: anomaly.type });

    // 通知云端（异步）
    this.notifyCloud({ deviceId, data, anomaly }).catch(err => {
      console.error("Failed to notify cloud:", err);
    });
  }
}

云层（Cloud Layer）

集中式云端负责长期存储、批处理分析、模型训练：

// 云端：全局视图与智能决策
class CloudOrchestrator {
  private dataLake: DataLakeService;
  private mlPipeline: MLTrainingPipeline;
  private edgeRegistry = new Map<string, EdgeNodeInfo>();

  // 从所有边缘节点收集数据
  async aggregateEdgeData(): Promise<GlobalInsights> {
    const allData = await Promise.all(
      Array.from(this.edgeRegistry.values()).map(edge =>
        this.fetchEdgeData(edge.id)
      )
    );

    return this.analyzeGlobal(allData);
  }

  // 在云端训练模型，部署到边缘
  async trainAndDeploy(): Promise<void> {
    // 1. 从数据湖读取历史数据
    const trainingData = await this.dataLake.query({
      timeRange: { start: Date.now() - 30 * 24 * 3600 * 1000, end: Date.now() },
      limit: 10_000_000
    });

    // 2. 训练模型
    const model = await this.mlPipeline.train(trainingData, {
      architecture: "efficientnet-lite",  // 轻量级，适合边缘部署
      epochs: 100,
      batchSize: 256
    });

    // 3. 量化模型（减小体积）
    const quantizedModel = await this.quantizeModel(model, "int8");

    // 4. 分发到所有边缘节点
    await this.deployToEdge(quantizedModel);
  }

  private async deployToEdge(model: Model): Promise<void> {
    const deployments = Array.from(this.edgeRegistry.values()).map(async edge => {
      try {
        await this.pushModelToEdge(edge.id, model);
        console.log("Deployed to edge node:", edge.id);
      } catch (err) {
        console.error("Deployment failed for edge:", edge.id, err);
      }
    });

    await Promise.allSettled(deployments);
  }
}

1. 资源受限的计算

边缘设备的计算、内存、存储远小于云端，需要优化算法：

// 模型优化技术
class ModelOptimizer {
  // 量化：FP32 -> INT8，减少4倍模型大小
  async quantizeModel(model: Model): Promise<QuantizedModel> {
    return await tf.quantization.quantize(model, {
      inputRange: [-1, 1],
      outputRange: [0, 255],
      dtype: "int8"
    });
  }

  // 剪枝：移除不重要的神经元
  async pruneModel(model: Model, sparsity = 0.5): Promise<Model> {
    // 移除权重接近0的连接
    for (const layer of model.layers) {
      const weights = layer.getWeights();
      const threshold = this.calculateThreshold(weights, sparsity);
      const prunedWeights = weights.map(w => 
        Math.abs(w) < threshold ? 0 : w
      );
      layer.setWeights(prunedWeights);
    }
    return model;
  }

  // 知识蒸馏：用小模型模仿大模型
  async distillModel(
    teacherModel: Model,
    studentArchitecture: ModelArchitecture
  ): Promise<Model> {
    const student = this.buildModel(studentArchitecture);

    // 训练student去预测teacher的输出
    await this.trainWithDistillation(student, teacherModel, {
      temperature: 3,  // 软化概率分布
      alpha: 0.7       // 蒸馏损失权重
    });

    return student;
  }
}

2. 分布式协调

云-边-端三层之间的同步、一致性、容错：

// 边缘节点的同步协议
class EdgeSyncProtocol {
  private vectorClock: VectorClock;
  private conflictResolver: ConflictResolver;

  async syncWithCloud(): Promise<void> {
    // 1. 收集本地更新
    const localUpdates = await this.getLocalUpdates();

    // 2. 上传到云端
    const cloudResponse = await this.uploadUpdates(localUpdates);

    // 3. 检测冲突
    const conflicts = this.detectConflicts(
      localUpdates,
      cloudResponse.remoteUpdates
    );

    // 4. 解决冲突
    if (conflicts.length > 0) {
      const resolved = await this.conflictResolver.resolve(conflicts);
      await this.applyResolution(resolved);
    }

    // 5. 合并远程更新
    await this.mergeRemoteUpdates(cloudResponse.remoteUpdates);
  }

  // 向量时钟：检测因果关系
  private detectConflicts(
    local: Update[],
    remote: Update[]
  ): Conflict[] {
    const conflicts: Conflict[] = [];

    for (const l of local) {
      for (const r of remote) {
        if (l.key === r.key) {
          const relation = this.vectorClock.compare(l.version, r.version);
          if (relation === "concurrent") {
            conflicts.push({ local: l, remote: r });
          }
        }
      }
    }

    return conflicts;
  }
}

3. 网络不稳定

边缘环境网络可能断连、高延迟、低带宽，需要容错设计：

// 弹性通信：处理网络故障
class ResilientCommunication {
  private messageQueue: PersistentQueue;
  private retryPolicy: ExponentialBackoff;

  async sendToCloud(message: Message): Promise<void> {
    // 1. 先入队（持久化）
    await this.messageQueue.enqueue(message);

    // 2. 尝试发送
    try {
      await this.trySend(message);
      await this.messageQueue.dequeue(message.id);
    } catch (err) {
      // 网络失败，消息留在队列中
      console.warn("Send failed, will retry:", err);
    }
  }

  // 后台重试机制
  async startRetryLoop(): Promise<void> {
    while (true) {
      const pending = await this.messageQueue.peek();
      if (!pending) {
        await this.sleep(1000);
        continue;
      }

      try {
        await this.trySend(pending);
        await this.messageQueue.dequeue(pending.id);
        this.retryPolicy.reset();
      } catch (err) {
        const delay = this.retryPolicy.nextDelay();
        console.log("Retry in", delay, "ms");
        await this.sleep(delay);
      }
    }
  }

  private async trySend(message: Message): Promise<void> {
    const response = await fetch(this.cloudEndpoint, {
      method: "POST",
      body: JSON.stringify(message),
      timeout: 5000  // 5秒超时
    });

    if (!response.ok) {
      throw new Error("HTTP " + response.status);
    }
  }
}

案例1：智能工厂

工业4.0场景中的边缘计算应用：

// 工厂边缘网关
class FactoryEdgeGateway {
  private plcConnections: Map<string, PLCConnection>;
  private timeSeriesDB: InfluxDB;
  private alertEngine: RuleEngine;

  async monitorProductionLine(): Promise<void> {
    // 连接所有PLC（可编程逻辑控制器）
    for (const [id, plc] of this.plcConnections) {
      plc.on("data", async (data: PLCData) => {
        // 1. 本地存储（时序数据库）
        await this.timeSeriesDB.write({
          measurement: "machine_metrics",
          tags: { machine_id: id },
          fields: data,
          timestamp: Date.now()
        });

        // 2. 实时规则检查
        const alerts = await this.alertEngine.evaluate(data, {
          rules: [
            { condition: "temperature > 80", action: "alert", severity: "high" },
            { condition: "vibration > 5", action: "shutdown", severity: "critical" }
          ]
        });

        // 3. 立即响应（无需云端）
        for (const alert of alerts) {
          if (alert.action === "shutdown") {
            await plc.sendCommand({ type: "EMERGENCY_STOP" });
          }
          await this.notifyOperator(alert);
        }

        // 4. 周期性上报云端（每分钟）
        if (Date.now() % 60000 < 100) {
          await this.uploadSummaryToCloud();
        }
      });
    }
  }
}

价值：

• 响应时间从云端的200ms降至<10ms
• 即使云端断连，工厂仍可安全运行
• 减少90%的上行带宽

案例2：自动驾驶

车辆作为移动边缘节点：

// 车载边缘计算单元
class VehicleEdgeCompute {
  private sensors: SensorArray;
  private perceptionModel: ObjectDetectionModel;
  private planningEngine: PathPlanningEngine;
  private v2xCommunication: V2XRadio;

  async autonomousDriveLoop(): Promise<void> {
    while (true) {
      const startTime = performance.now();

      // 1. 传感器融合（本地，实时）
      const sensorData = await this.sensors.read();
      const fused = this.fuseSensorData(sensorData);

      // 2. 感知（在车载GPU运行）
      const objects = await this.perceptionModel.detect(fused.camera);
      const obstacles = this.combineWithLidar(objects, fused.lidar);

      // 3. 路径规划（本地，低延迟）
      const currentPose = fused.gps;
      const plannedPath = await this.planningEngine.plan({
        currentPose,
        obstacles,
        destination: this.destination
      });

      // 4. 执行控制（必须<10ms）
      await this.executeControl(plannedPath);

      // 5. V2X通信（与附近车辆/基础设施）
      await this.v2xCommunication.broadcast({
        position: currentPose,
        velocity: fused.speed,
        plannedPath: plannedPath.next(5)  // 未来5秒轨迹
      });

      // 6. 云端辅助（异步，非关键路径）
      this.requestCloudAssist({
        location: currentPose,
        needsHDMap: this.isMapOutdated(),
        needsRouting: this.isNearDestination()
      }).catch(() => {});  // 云端失败不影响驾驶

      const elapsed = performance.now() - startTime;
      if (elapsed > 100) {
        console.warn("Loop took too long:", elapsed, "ms");
      }
    }
  }
}

关键点：

• 感知-规划-控制循环必须在本地完成（<100ms）
• 云端只提供非实时服务（高精地图、全局路由）
• V2X边缘通信实现车辆协同

1. 边缘AI

在边缘运行复杂AI模型，得益于硬件进步：

// 利用专用AI芯片（如Nvidia Jetson，Google Coral）
class EdgeAIRuntime {
  private accelerator: NPU | TPU | VPU;

  async runInference(input: Tensor): Promise<Prediction> {
    // 在边缘运行YOLO v8（目标检测）
    const output = await this.accelerator.execute(this.model, input);

    // 后处理
    const detections = this.postprocess(output);

    return detections;
  }

  // 性能：Jetson Orin可达275 TOPS，足以运行大模型
}

2. 5G与边缘

5G的低延迟（<1ms）和MEC（Multi-Access Edge Computing）：

// 5G基站集成MEC服务器
class MECPlatform {
  // 部署在基站的边缘应用
  async deploy App(app: EdgeApp): Promise<void> {
    const containerImage = await this.buildImage(app);
    const pod = await this.k8s.deploy({
      image: containerImage,
      resources: { cpu: "4", memory: "8Gi" },
      affinity: {
        // 调度到特定基站
        nodeSelector: { "mec.zone": app.targetZone }
      }
    });

    // 配置网络切片，确保低延迟
    await this.configure5GSlice({
      latency: "1ms",
      bandwidth: "100Mbps",
      reliability: "99.999%"
    });
  }
}

3. 联邦学习

在边缘训练模型，保护隐私：

# 联邦学习：数据不动，模型动
class FederatedLearning:
    def train(self, edge_nodes, global_model):
        for round in range(100):
            # 1. 分发全局模型到所有边缘节点
            for node in edge_nodes:
                node.set_model(global_model)
            
            # 2. 各节点用本地数据训练
            local_updates = []
            for node in edge_nodes:
                update = node.train_local(epochs=5)
                local_updates.append(update)
            
            # 3. 聚合更新（FedAvg）
            global_model = self.federated_averaging(local_updates)
        
        return global_model
    
    def federated_averaging(self, updates):
        # 加权平均（按数据量加权）
        total_samples = sum(u.num_samples for u in updates)
        avg_weights = {}
        for key in updates[0].weights:
            avg_weights[key] = sum(
                u.weights[key] * u.num_samples / total_samples
                for u in updates
            )
        return Model(avg_weights)

优势：

• 数据永不离开边缘，保护隐私
• 利用分布式数据，提升模型泛化能力
• Apple、Google已用于键盘预测、语音识别