关键词: 矿山安全风险, 双重预防机制, 智能分级, 信息系统, 大数据分析, 智能矿山

Abstract: Under the guidance of relevant national policies,mines have built safety supervision systems from the two dimensions of risk grading control and on-site safety inspection,but the risk grading control and hidden hazard investigation and governance are relatively independent and lack effective connections. In order to solve the problems of weak risk identification and single risk grading method in the management process of mine safety dual prevention system, this paper constructs a closed-loop management system of the whole process of dual prevention system based on combing the existing business process of dual prevention system,combined with big data analysis technology and accident tree analysis method. Through the collection and statistical analysis of underground metal mine accident data,the accident tree method is used to analyze the potential risks of inducing various accidents,and the accident severity is used as the evaluation basis to guide the risk level evaluation. Big data analysis method,Chinese co-occurrence analysis and fuzzy cluster analysis model were used to process and analyze hidden danger data,and a risk grading method suitable for the actual situation of the mine was formed. Conduct system analysis and design according to the business content of the closed-loop management system of the whole business process,and complete the system development on this basis. The system adopts the cross-screen development method of combining computer terminals, mobile terminals,and large data screens to realize the application of all scenarios of risk grading management and control,hidden danger investigation and governance. The results show that the system realizes the closed management mode and multi-scenario application of risk grading control,hidden danger investigation and governance,and risk intelligent grading, which achieves the purpose of dynamic,standardized and intelligent grading of security risks.

Key words: mine safety risk,dual prevention,intelligence grading,information system,big data analysis,intelligent mine