煤矿岗前人员不安全状态智能检测系统研究——以红柳林煤矿为例

为从根本上降低煤矿从业人员实施不安全行为的概率，在充分考虑煤矿从业人员不安全个体状态是诱发其不安全行为的主要原因的基础上，设计开发一套矿工不安全状态智能检测系统。首先，对2007—2022年期间各高危行业安全事故调查报告和专家研究进行归纳，总结得出煤矿从业人员不安全状态的影响因素，构建包括生理状态、心理状态以及个体能力状态在内的3个一级指标和15个二级指标体系；其次，通过相应的表征模型对不安全状态影响因素进行深入剖析；最后，基于构建的从业人员不安全状态倾向数据库，建立煤矿岗前不安全状态智能检测系统。研究结果表明：在下井工作前对从业人员个体状态进行智能检测，能够实现矿工不安全状态“早发现、早干预”,能够有效地降低人因事故发生率，研究结果对煤炭行业安全管理具有重要的参考价值。

Study on intelligent detection system for unsafe state of pre-job personnel in coal mine : taking Hongliulin Coal Mine as example

In order to fundamentally reduce the probability of unsafe behavior of coal mine employees,an intelligent detection system for the unsafe state of miners was designed and developed on the basis of fully considering that the unsafe individual state of coal mine employees was the main cause of unsafe behavior.Firstly,the investigation reports and expert research on the safety accidents in various high-risk industries from 2007 to 2022 were concluded,then the influencing factors of the unsafe state of coal mine employees were summarized,and an index system of 3 first-level indexes and 15 second-level indexes including the physiological state,psychological state and individual ability state was constructed.Secondly,the influencing factors of unsafe state were analyzed in depth through the corresponding characterization model.Finally,based on the constructed unsafe state tendency database of employees,an intelligent detection system for the pre-job unsafe state in coal mine was established.The results showed that the intelligent detection on the individual state of the employees before underground working could realize the “early detection and early intervention” on the unsafe state of miners,effectively reduce the incidence of human accidents.The results have important reference value for the safety management of the coal industry.