基于BP神经网络的矿工不安全心理预警研究

A study of unsafe psychological warning for miners based on BP neural network

矿工不安全心理是煤矿人因事故的险兆,为识别该险兆,进行了矿工不安全心理预警研究。基于扎根理论,对选取的47篇初始文献进行编码处理,构建了包括3个核心范畴、8个主范畴的矿工不安全心理预警指标体系,通过问卷调查获取数据,运用CRITIC-熵权法确定其指标权重。在此基础上,构建出矿工不安全心理的“8-20-4”3层BP神经网络预警模型。试验结果表明:矿工不安全心理受到个体、作业和组织层面影响,其中,工作负荷、身体素质、工作环境、人际关系是关键诱发因素;提出的预警模型准确率达到93.7%,能有效识别矿工不安全心理等级,并针对矿工心理预警结果提出了“预警-对策”机制。

The unsafe psychological state of miners is a significant precursor to accidents in coal mines.To identify this precursor,a study on the early warning of miners’unsafe psychological states was conducted based on grounded theory,47 initial documents were coded to establish a warning indicator system comprising 3 core categories and 8 main categories related to miners’unsafe psychological states.Data were collected through a questionnaire survey,and the CRITIC-Entropy method was employed to determine the weights of the indicators subsequently,a three-layer BP neural network early warning model,structured as“8-20-4”was developed for miners’unsafe psychological states.Experimental results indicate that the unsafe psychological state of miners is influenced by individual,operational,and organizational factors,with workload,physical condition,work environment,and interpersonal relationships identified as key triggering factors.The proposed early warning model achieved an accuracy rate of 93.7%,effectively identifying the levels of miners’unsafe psychological states.Moreover,a“warning-countermeasure”mechanism was proposed based on the psychological warning results for miners.