基于智能工业物联网的煤矿安全监控系统研究  

Research on Coal Mine Safety Monitoring System Based on Intelligent Industrial Internet of Things

作　　者：许金宝 XU Jinbao(Shanxi Coal Transportation and Sales Group Jinxinda Coal Industry Co.,Ltd.,Linfen 041000,China)

机构地区：[1]山西省煤炭运销集团金辛达煤业有限公司,山西临汾041000

出　　处：《煤矿现代化》2025年第3期143-147,共5页Coal Mine Modernization

摘　　要：群体感知技术可实现智能工业物联网煤矿对人、机器、环境的感知和计算,通过分布式智能优化为安全监控提供解决方案,然而传统的长短期记忆(LSTM)方法没有考虑相邻机器的关系,导致人体位置预测和压力值预测的精度不高,故提出了基于智能工业物联网的人群感知煤矿安全监控系统。结果表明,采用的PE算法平均累积预测误差最低,轨迹拟合率比卡尔曼(Kalman)滤波、Elman神经网络和Kalman+Elman算法分别提高了24.1%、13.9%和8.7%,与单输入ARIMA、RNN、LSTM和GRU相比,ADI-LSTM的RMSE值分别降低了36.6%、52%、32%和13.7%;MAPE值分别降低0.000 3%、0.948 2%、1.184 4%和0.362 0%。Crowd sensing technology can realize the sensing and computation of people,machines,and environment in smart industrial IoT coal mines,and provide solutions for safety monitoring through distributed intelligent optimization,however,the traditional long short-term memory(LSTM)method does not consider the relationship between neighboring machines,which leads to the low accuracy of the human body's position prediction and the prediction of the pressure value,therefore,we propose the crowd sensing coal mine based on smart industrial IoT safety monitoring system.The results show that the PE algorithm used has the lowest average cumulative prediction error,the trajectory fitting rate is improved by 24.1%,13.9%and 8.7%over Kalman(Kalman)filtering,Elman neural network and Kalman+Elman algorithms,respectively,and the RMSE values of ADI-LSTM are compared with the single-input ARIMA,RNN,LSTM and GRU,respectively reduced by 36.6%,52%,32%and 13.7%;and MAPE values reduced by 0.0003%,0.9482%,1.1844%and 0.3620%,respectively.

关 键 词：人群传感 工业物联网 安全监测 

分 类 号：TD76[矿业工程—矿井通风与安全]