Behavior Analysis and Safety Management of Workers in Limited Space Based on Machine Learning

The purpose of this study is to explore the design of a limited space worker behavior analysis and safety management system based on machine learning. By using the methods of machine learning, behavior recognition, process monitoring and safety warning, an innovative behavior analysis and safety management system for workers in limited space is proposed. The system learns the operator's behavior through training model to realize the prediction and risk assessment of the operator's behavior. By using fault diagnosis and anomaly detection methods, we can realize early identification and warning of potentially dangerous behaviors by analyzing operators' operational data. The process monitoring system monitors the operation process of the operator in real time to ensure the safety of the operation. Finally, combined with human movement recognition technology, it provides an effective solution for the safety management of workers in limited space.