矿山风险动态预警与管控评价研究

Research on Dynamic Warning and Control Evaluation of Mining Risks

摘要: 矿山生产环境复杂，风险因素多且相互耦合，传统安全管理手段难以实现事前有效防控。为此，本文构建了一套融合人防与技防数据的矿山风险动态预警及管控评价模型。基于双重预防机制，建立了覆盖“人–机–环–管”全要素的主体责任履责指标体系；针对风险的时序动态特性，对LSTM神经网络进行了改进，并设计了多层级动态预警机制；采用熵权法与灰色关联理论，提出了企业安全风险管控水平的综合评价方法。实验结果显示，所提模型在预测精度与风险评价客观性方面表现良好，能够实现风险的早期识别与精准干预。

Abstract: The mining production environment is complex, with numerous and intercoupled risk factors, making it difficult for traditional safety management methods to achieve effective preemptive control. To address this, this paper constructs a dynamic risk early warning and control evaluation model for mines that integrates human and technical prevention data. Based on a dual prevention mechanism, a subject responsibility performance indicator system covering all elements of “human-machine-environment-management” has been established; We have improved the LSTM neural network based on the temporal dynamic characteristics of risks and designed a multi-level dynamic warning mechanism; A comprehensive evaluation method for the level of enterprise safety risk control is proposed using entropy weight method and grey correlation theory. Experimental results demonstrate that the proposed model performs well in prediction accuracy and risk assessment objectivity, enabling early risk identification and precise intervention.