Design of an intelligent recognition and early warning system for underground coal mine personnel safety behavior
煤矿井下人员安全行为智能识别与预警系统设计

摘要

煤矿井下作业环境复杂多变，人员安全行为管控面临诸多挑战，传统监控方式存在响应滞后、识别精度不足等问题。为此，设计煤矿井下人员安全行为智能识别与预警系统，通过融合机器视觉、物联网和人工智能技术，实现对井下人员违规行为的实时监测、智能识别及主动预警。系统构建多维度感知网络，采集人员操作姿态、设备状态及环境参数，利用深度学习模型进行行为特征分析与风险评估，形成从数据采集到预警处置的闭环管理。实践表明，该系统可有效提升安全监管效率，降低事故发生率，为煤矿井下安全生产提供技术支撑。

Abstract

The underground coal mine environment is complex and ever-changing, posing significant challenges for personnel safety behavior management. Traditional monitoring methods suffer from issues such as delayed response and insufficient recognition accuracy. To address these challenges, this study designs an intelligent recognition and early warning system for underground coal mine personnel safety behavior. By integrating machine vision, IoT, and AI technologies, the system enables real-time monitoring, intelligent identification, and proactive alerts for personnel violations. The system establishes a multi-dimensional sensing network to collect operational postures, equipment status, and environmental parameters. Through deep learning models, it performs behavioral pattern analysis and risk assessment, forming a closed-loop management system from data collection to early warning and response. Practical applications demonstrate that this system effectively enhances safety supervision efficiency, reduces accident rates, and provides technical support for underground coal mine production safety.