AI 协作日志 | AI 优化算法逻辑，调拨系统需求预测模块精度提升实践

// 多源数据采集服务框架
class DataCollector {
  constructor() {
    this.sources = new Map(); // 数据源配置
    this.cache = new RedisCache(); // Redis缓存实例
  }

  // 添加数据源配置
  addSource(sourceConfig) {
    const { name, type, endpoint, refreshInterval } = sourceConfig;
    this.sources.set(name, {
      type,
      endpoint,
      refreshInterval,
      lastUpdated: null
    });
  }

  // 统一数据采集方法
  async collectData(sourceName, options = {}) {
    const source = this.sources.get(sourceName);
    if (!source) throw new Error(`数据源 ${sourceName} 未配置`);
    
    // 检查缓存中是否有有效数据
    const cacheKey = `data:${sourceName}`;
    const cachedData = await this.cache.get(cacheKey);
    
    if (cachedData && !options.forceRefresh) {
      return cachedData;
    }
    
    // 根据数据类型采用不同采集策略
    let rawData;
    switch (source.type) {
      case 'rest-api':
        rawData = await this.fetchFromAPI(source.endpoint);
        break;
      case 'database':
        rawData = await this.queryDatabase(source.endpoint);
        break;
      case 'streaming':
        rawData = await this.subscribeStream(source.endpoint);
        break;
      default:
        throw new Error(`不支持的数据类型: ${source.type}`);
    }
    
    // 数据处理与缓存
    const processedData = this.processRawData(rawData, source.type);
    await this.cache.set(cacheKey, processedData, source.refreshInterval);
    
    return processedData;
  }

  // 多源数据融合处理
  async integrateMultiSourceData() {
    const salesData = await this.collectData('historical-sales');
    const weatherData = await this.collectData('weather-forecast');
    const promotionData = await this.collectData('promotion-schedule');
    const socialMediaData = await this.collectData('social-media-sentiment');
    
    // 基于时间戳进行数据对齐与融合
    return this.alignAndMergeData(
      salesData,
      weatherData,
      promotionData,
      socialMediaData
    );
  }
}

// LSTM需求预测模型实现
class DemandForecastModel {
  constructor(modelConfig) {
    this.config = {
      lookbackWindow: modelConfig.lookbackWindow || 60,
      hiddenUnits: modelConfig.hiddenUnits || 128,
      dropoutRate: modelConfig.dropoutRate || 0.2,
      learningRate: modelConfig.learningRate || 0.001
    };
    
    this.model = this.buildModel();
    this.scaler = new DataScaler();
  }

  // 构建LSTM神经网络结构
  buildModel() {
    const model = tf.sequential();
    
    // 输入层：接收lookbackWindow时间步长的特征数据
    model.add(tf.layers.lstm({
      units: this.config.hiddenUnits,
      inputShape: [this.config.lookbackWindow, 10], // 10个特征维度
      returnSequences: true,
      kernelRegularizer: tf.regularizers.l2({l2: 0.01})
    }));
    
    // Dropout层防止过拟合
    model.add(tf.layers.dropout({rate: this.config.dropoutRate}));
    
    // 第二LSTM层
    model.add(tf.layers.lstm({
      units: this.config.hiddenUnits * 0.8,
      returnSequences: false,
      kernelRegularizer: tf.regularizers.l2({l2: 0.01})
    }));
    
    // 全连接层
    model.add(tf.layers.dense({units: 64, activation: 'relu'}));
    model.add(tf.layers.dropout({rate: this.config.dropoutRate / 2}));
    
    // 输出层：预测未来7天的需求
    model.add(tf.layers.dense({units: 7, activation: 'linear'}));
    
    // 编译模型
    model.compile({
      optimizer: tf.train.adam(this.config.learningRate),
      loss: 'meanSquaredError',
      metrics: ['mae', 'mape']
    });
    
    return model;
  }

  // 多步时间序列预测
  async predictFutureDemand(inputData) {
    // 数据标准化处理
    const scaledData = this.scaler.transform(inputData);
    
    // 构建时间窗口数据
    const windowData = this.createLookbackWindows(scaledData);
    
    // 使用模型进行预测
    const predictions = this.model.predict(windowData);
    
    // 反标准化得到实际预测值
    return this.scaler.inverseTransform(predictions);
  }

  // 模型训练与验证
  async trainModel(trainingData, validationData, callbacks) {
    const {xTrain, yTrain} = this.prepareTrainingData(trainingData);
    const {xVal, yVal} = this.prepareTrainingData(validationData);
    
    const trainingHistory = await this.model.fit(xTrain, yTrain, {
      epochs: 100,
      batchSize: 32,
      validationData: [xVal, yVal],
      callbacks: callbacks || [
        tf.callbacks.earlyStopping({monitor: 'val_loss', patience: 10}),
        tf.callbacks.reduceLROnPlateau({monitor: 'val_loss', factor: 0.2, patience: 5})
      ]
    });
    
    return trainingHistory;
  }
}

// 基于GNN的需求预测增强模型
class GNNDemandModel {
  constructor() {
    this.productGraph = new ProductGraph();
    this.gnnLayers = 2;
    this.nodeEmbeddingDim = 64;
  }

  // 构建产品-门店关系图
  buildProductStoreGraph(salesData, storeLocations) {
    // 创建基本图结构
    const graph = {
      nodes: [],
      edges: [],
      features: []
    };
    
    // 添加门店节点
    storeLocations.forEach(store => {
      graph.nodes.push({id: store.id, type: 'store'});
      graph.features.push(this.extractStoreFeatures(store));
    });
    
    // 添加产品节点
    salesData.products.forEach(product => {
      graph.nodes.push({id: product.id, type: 'product'});
      graph.features.push(this.extractProductFeatures(product));
    });
    
    // 添加边（基于历史销售关系）
    salesData.transactions.forEach(transaction => {
      graph.edges.push({
        source: transaction.storeId,
        target: transaction.productId,
        weight: this.calculateEdgeWeight(transaction),
        type: 'sells'
      });
    });
    
    // 添加产品-产品关联边（替代/互补关系）
    this.addProductRelationships(graph, salesData);
    
    return graph;
  }

  // GNN消息传递实现
  async applyGNNMessagePassing(graph, iterations = 3) {
    // 初始化节点嵌入
    let nodeEmbeddings = graph.features;
    
    for (let i = 0; i < iterations; i++) {
      const newEmbeddings = [];
      
      // 对每个节点应用消息传递
      for (let nodeIdx = 0; nodeIdx < graph.nodes.length; nodeIdx++) {
        const neighborEmbs = this.aggregateNeighborMessages(nodeIdx, graph, nodeEmbeddings);
        const newEmb = this.updateNodeEmbedding(nodeEmbeddings[nodeIdx], neighborEmbs);
        newEmbeddings.push(newEmb);
      }
      
      nodeEmbeddings = newEmbeddings;
    }
    
    return nodeEmbeddings;
  }

  // 基于注意力机制的邻居消息聚合
  aggregateNeighborMessages(nodeIdx, graph, currentEmbeddings) {
    const node = graph.nodes[nodeIdx];
    const neighbors = this.findNeighbors(nodeIdx, graph);
    
    // 计算注意力权重
    const attentionWeights = neighbors.map(neighbor => {
      return this.calculateAttention(
        currentEmbeddings[nodeIdx],
        currentEmbeddings[neighbor.nodeIdx],
        graph.edges[neighbor.edgeIdx].weight
      );
    });
    
    // 加权聚合邻居消息
    const aggregatedMessage = tf.tidy(() => {
      const neighborEmbs = neighbors.map((neighbor, idx) => {
        const emb = tf.tensor(currentEmbeddings[neighbor.nodeIdx]);
        return emb.mul(tf.scalar(attentionWeights[idx]));
      });
      
      return tf.sum(neighborEmbs, 0);
    });
    
    return aggregatedMessage;
  }
}

// 集成预测模型（LSTM + GNN）
class EnsembleDemandModel {
  constructor() {
    this.lstmModel = new DemandForecastModel();
    this.gnnModel = new GNNDemandModel();
    this.blenderModel = this.buildBlenderModel();
  }

  // 构建模型融合器
  buildBlenderModel() {
    const model = tf.sequential();
    
    model.add(tf.layers.dense({
      units: 64,
      inputShape: [this.lstmModel.config.hiddenUnits + this.gnnModel.nodeEmbeddingDim],
      activation: 'relu'
    }));
    
    model.add(tf.layers.dropout({rate: 0.3}));
    
    model.add(tf.layers.dense({
      units: 32,
      activation: 'relu'
    }));
    
    model.add(tf.layers.dense({
      units: 7, // 预测未来7天
      activation: 'linear'
    }));
    
    model.compile({
      optimizer: 'adam',
      loss: 'meanSquaredError'
    });
    
    return model;
  }

  // 集成预测
  async predictIntegratedDemand(salesData, weatherData, graphData) {
    // 并行获取不同模型的预测
    const [lstmPredictions, gnnEmbeddings] = await Promise.all([
      this.lstmModel.predictFutureDemand(salesData, weatherData),
      this.gnnModel.applyGNNMessagePassing(graphData)
    ]);
    
    // 融合特征
    const blendedFeatures = this.blendFeatures(lstmPredictions, gnnEmbeddings);
    
    // 使用融合器生成最终预测
    const finalPredictions = this.blenderModel.predict(blendedFeatures);
    
    return finalPredictions;
  }

  // 动态权重调整
  calculateDynamicWeights(recentAccuracy) {
    // 基于各模型近期准确率动态调整权重
    const totalAccuracy = recentAccuracy.lstm + recentAccuracy.gnn;
    return {
      lstmWeight: recentAccuracy.lstm / totalAccuracy,
      gnnWeight: recentAccuracy.gnn / totalAccuracy
    };
  }
}