Coal Engineering College, Shanxi Datong University
Mining Engineering College, China University of Mining and Technology

随着大部分煤矿进入深部开采阶段,矿山动力灾害事故频发,其中冒顶事故占比尤为突出。若监测预警不全面,极易对矿井人员及设备造成严重后果,产生巨大的经济损失。针对顶板事故详细介绍了几种监测预警系统,并对监测预警系统未来的发展进行了展望,重点阐述了基于神经网络的监测预警系统,通过神经网络对监测数据进行降维、训练,之后建立数学模型对其进行分析和预测,并利用5G、大数据、云平台等先进技术对其进行实时预警,从而提高智能化监测预警的准确性和稳定性。

As most coal mines enter the deep mining stage, mine power disasters and accidents occur frequently,with roof falling accidents accounting for a particularly prominent proportion. If the monitoring and early warning arenot comprehensive, it is highly likely to cause serious consequences to mine personnel and equipment, resulting inhuge economic losses. Several monitoring and early warning systems for roof accidents were introduced in detail,and the future development of the monitoring and early warning system was prospected, focusing on the monitoringand early warning system based on neural network. The monitoring data was dimensionally reduced and trainedthrough neural network, and then a mathematical model was established to analyze and predict it, and realtimeearly warning was conducted using advanced technologies such as 5G, big data, cloud platform, etc., thusimproving the accuracy and stability of intelligent monitoring and early warning.

韩辰辉,鲁杰,刘震林,等.煤矿顶板智能监测系统研究现状与发展趋势[J].煤矿机电,2023,44(4):49-53.doi:10.16545/j.cnki.cmet.2023.04.009