基于大型物联网技术的电商仓库检测

E-commerce warehouse detection based on large Internet of Things technology

针对当前检测电商仓库容量或流量精度不高的问题,在大型物联网环境下设计基于决策树主成分分析的电商仓库检测方法。首先基于电子标签技术构建电商仓库存储系统的物联网构架模型,采用超高频射频识别技术追踪电商仓库系统的物流流量,并提取信息特征;在C4.5决策树模型下,分析仓库物流信息特征主成分,实现对电商仓库吞吐量和容量的准确检测和预估;最后通过实验进行性能测试。实验结果表明,采用该方法对仓库的吞吐量预测和物资收发数据实时检测的精度较高,提高了电商仓库的物资收发效率。

Since the detection accuracy of the current e-commerce warehouse capacity and flow is low, a new e-commerce warehouse detection method based on decision tree principal component analysis was designed under the large Internet of Things environment. The Internet of Things framework model of the e-commerce warehouse storage system was constructed based on the RFID technology. The ultrahigh frequency RFID technology is used to trace the logistics flow of the e-commerce warehouse sys- tem and extract the information feature. The principal component of the warehouse logistics information characteristics is ana- lyzed in C4.5 decision tree model to detect and estimate the throughput and capacity of the e-commerce warehouse accurately. The performance of the method was tested with experiments. The experimental results show that the method has high real-time detection accuracy of the warehouse throughput prediction and materials transceiving data, and can improve the materials trans- ceiving efficiency of the e-commerce warehouse.