With the continuous development of the logistics industry, the relevant technology is becoming more mature, the system is increasingly perfect, and the accuracy requirements for the logistics monitoring system are becoming higher and higher. Traditional logistics monitoring is usually manually recorded, which not only wastes manpower and is inefficient, but also cannot meet the current scale and intelligent development requirements of the logistics industry. The purpose of this article is to study modern logistics monitoring systems based on sensor networks and big data. Based on the in-depth study of three technologies of sensor network, big data and logistics technology, this article integrates the entire warehousing and distribution logistics process, and builds a monitoring system framework combining sensor network and big data based on the wireless sensor network software and hardware platform. Combining the two key methods studied in this paper, the information management process of traditional logistics operations such as outbound and inbound, warehousing positioning and monitoring, and distribution monitoring management are implemented, and the monitoring system is tested on this basis. The test results prove that this system has certain deployment reference significance and practical application value, and can help to improve the informationization and intelligence level of logistics management to a certain extent. In this paper, by simulating 250 concurrent users, the server CPU usage is 56.37%, the server memory usage is 57.13%, and the response time is 1944 ms.

随着物流业的不断发展，相关技术越来越成熟，系统日益完善，对物流监控系统的精度要求也越来越高。传统的物流监控通常是手工记录的，不仅浪费人力，效率低下，而且不能满足当前物流行业的规模和智能化发展要求。本文的目的是研究基于传感器网络和大数据的现代物流监控系统。在对传感器网络，大数据和物流技术这三种技术进行深入研究的基础上，本文将整个仓储和配送物流过程进行了整合，建立了基于无线传感器网络软硬件平台的传感器网络与大数据融合的监控系统框架。结合本文研究的两种关键方法，实现了传统物流作业的出入库，仓储定位与监控，配送监控管理等信息管理流程，并在此基础上对监控系统进行了测试。测试结果证明，该系统具有一定的部署参考意义和实际应用价值，可以在一定程度上帮助提高物流管理的信息化和智能水平。本文通过模拟250个并发用户，服务器CPU使用率为56.37％，服务器内存使用率为57.13％，响应时间为1944 ms。