黔货云仓商品销售数据集

1、仓库主体名称已脱敏处理，符合《数据安全法》与《个人信息保护法》，数据分级授权访问，敏感字段仅限特定机构调用；2、通过NLP技术将不同表述的商品名称统一，如“遵义辣椒”与“辣椒（遵义产）”合并，将商品名称归一化；3、基于国家商务部标准（SBT11236-2023）对商品品类进行多级分类，如一级分类“茶叶”，二级分类“绿茶”；4、基于时间序列LSTM模型将发货量数据进行归一化处理，转化为适合 LSTM 模型输入的格式，如按照时间步长进行切片。搭建 LSTM 网络结构，设置合适的隐藏层节点数、层数等参数，利用训练数据对模型进行训练，通过反向传播算法不断调整模型权重，以最小化预测误差，再结合季节性因子，例如春节、端午、中秋等传统节日期间是商品销售高峰期，可以提前优化备货计划，确保在发货高峰前提前与供应商沟通，保证货源充足，同时避免过度备货导致库存积压，减少仓储成本和知识产权风险。

1. The main name of the warehouse has been desensitized, which complies with the Data Security Law of the People's Republic of China and the Personal Information Protection Law of the People's Republic of China. The data adopts hierarchical authorized access, and sensitive fields are only callable by designated institutions. 2. Commodity names with different expressions are unified via NLP technologies. For example, "Zunyi chili peppers" and "chili peppers (produced in Zunyi)" are merged to normalize commodity names. 3. Commodity categories are classified at multiple levels based on the national Ministry of Commerce standard (SBT11236-2023). For instance, the first-level category is "tea", and the second-level category is "green tea". 4. The shipment volume data is normalized using the time-series Long Short-Term Memory (LSTM) model and converted into a format suitable for LSTM model input, such as slicing according to time steps. An LSTM network structure is then built, with appropriate parameters including the number of hidden layer nodes and layers set. The model is trained using the training dataset, and the model weights are continuously adjusted via backpropagation algorithms to minimize prediction errors. Furthermore, combined with seasonal factors (e.g., traditional festivals such as Spring Festival, Dragon Boat Festival, and Mid-Autumn Festival, which are peak periods for commodity sales), the stock preparation plan can be optimized in advance: communicate with suppliers ahead of the shipping peak to ensure sufficient supply, while avoiding overstocking that leads to inventory backlog, thereby reducing warehousing costs and intellectual property risks.

搜集汇总

背景与挑战

背景概述

黔货云仓商品销售数据集是一个3GB规模、月更新的数据集，用于优化仓库管理、物流路径和消费偏好分析，包含商品名称、类目、单量等字段。