Objective To improve production efficiency, reduce cost, and enhance decision support, it is necessary to establish an efficient IoT (the Internet of Things) data system for CNC (the computer numerical control) equipment cluster in a rail passenger car factory.

Method With an introduction to DAAS (data acquisition and analysis system) of the CNC machining equipment cluster, a BP (back propagation) neural network model based on WOA (the whale optimization algorithm) is proposed, and a data analysis system for the CNC machining equipment cluster of car bodies and bogies based on WOA-BP neural network is established. Through data collection, processing and analysis technologies, the system's performance is examined in terms of monitoring and optimizing equipment operating status, production efficiency and fault early warning.

Result&Conclusion Compared with the BP neural network model, the WOA-BP neural network model reduces the average prediction error by 51.15%, which gives it greater advantages in data analysis for the CNC machining equipment cluster of car bodies and bogies. The WOA-BP neural network model can effectively provide early warning of the machining equipment faults for car bodies and bogies, reduce unplanned downtime, thus improving the work efficiency.