AI 创作日记 | AI 驱动的 SKU 优化，DeepSeek赋能新零售企业库存的智能进阶

class DemandPredictor:
    def __init__(self):
        self.temporal_model = TimeTransformer()  # 时间序列模型
        self.spatial_model = SpaceGNN()         # 图神经网络
        self.fusion_layer = FusionNet()         # 融合层
        
    def predict(self, features):
        # 时间维度特征
        time_feat = self.temporal_model(features['history'])
        
        # 空间维度特征
        space_feat = self.spatial_model(features['geo_matrix'])
        
        # 多模态融合
        fused = self.fusion_layer(time_feat, space_feat)
        
        return fused

# 示例：预测华东地区下月防晒霜需求
pred = DemandPredictor().predict({
    'history': last_3years_sales,
    'geo_matrix': regional_relations
})

def optimize_sku(allocation):
    # 构建多目标优化问题
    problem = {
        'variables': allocation_matrix,
        'constraints': [
            {'type': 'max_service_level', 'threshold': 0.95},
            {'type': 'min_trans_cost', 'limit': budget},
            {'type': 'safety_stock', 'value': safety_level}
        ],
        'objective': 'max_profit'
    }
    
    # 使用遗传算法求解
    solution = genetic_algorithm(problem)
    
    # 生成调拨方案
    return generate_transfer_plan(solution)

# 实时执行示例
replenishment_plan = optimize_sku(current_allocation)

# 数据预处理
def preprocess_data(data):
    # 数据清洗：去除缺失值
    data = remove_missing_values(data)
    # 数据转换：将分类变量转换为数值变量
    data = convert_categorical_variables(data)
    # 数据归一化：将数值变量缩放到[0, 1]区间
    data = normalize_numerical_variables(data)
    return data

# 示例调用
raw_data = collect_data()
processed_data = preprocess_data(raw_data)

# 伪代码：模型训练
def train_model(processed_data):
    # 划分数据集
    train_data, val_data, test_data = split_data(processed_data)
    # 定义模型结构
    model = define_deepseek_model()
    # 训练模型
    model.train(train_data, val_data)
    # 评估模型
    performance = model.evaluate(test_data)
    return model

# 示例调用
model = train_model(processed_data)

# SKU需求预测
def predict_demand(model, new_data):
    # 对新数据进行预处理
    new_processed_data = preprocess_data(new_data)
    # 使用模型进行预测
    demand_predictions = model.predict(new_processed_data)
    return demand_predictions

# 示例调用
new_data = collect_new_data()
demand_predictions = predict_demand(model, new_data)

# 库存决策优化
def optimize_inventory(demand_predictions, current_inventory, inventory_costs):
    # 计算补货数量
    replenishment_quantities = calculate_replenishment_quantities(demand_predictions, current_inventory)
    # 计算补货时间
    replenishment_times = calculate_replenishment_times(demand_predictions, current_inventory)
    # 计算安全库存水平
    safety_stock_levels = calculate_safety_stock_levels(demand_predictions, inventory_costs)
    return replenishment_quantities, replenishment_times, safety_stock_levels

# 示例调用
current_inventory = get_current_inventory()
inventory_costs = get_inventory_costs()
replenishment_quantities, replenishment_times, safety_stock_levels = optimize_inventory(demand_predictions, current_inventory, inventory_costs)

class DataValidator:
    @staticmethod
    def detect_anomalies(data):
        # 离群值检测
        if abs(zscore(data)) > 3:
            mark_as_abnormal()
        
        # 季节性检验
        if not passes_seasonal_test(data):
            adjust_seasonality_factor()
        
        # 因果关系验证
        if granger_causality(features, target) < 0.05:
            remove_feature()

# 自动数据清洗流水线
raw_data >> DataValidator() >> FeatureEngineer()

class ModelMonitor:
    def __init__(self):
        self.drift_detector = DriftDetector()
        self.performance_matrix = {}
        
    def daily_check(self):
        # 数据漂移检测
        if self.drift_detector.detect():
            trigger_retraining()
            
        # 业务指标验证
        if service_level < 0.9:
            adjust_optimization_weights()
            
        # 资源消耗监控
        if inference_cost > budget:
            switch_to_light_model()