面向煤矿安全的智能安全帽设计与应用  

Intelligent Safety Helmet Design and Application for Coal Mine Safety

作　　者：丁一 李玉丽[1] DING Yi;LI Yu-li(Jilin Jianzhu University,Changchun 130118,China)

机构地区：[1]吉林建筑大学,吉林长春130118

出　　处：《电脑与电信》2025年第1期65-69,共5页Computer & Telecommunication

摘　　要：提出了一种基于STM32的矿用智能安全帽,集成DHT11、MAX30102和煤矿专用防爆型MQ-4C与MQ-2传感器,分别检测环境温湿度、矿工心率血氧、甲烷浓度以及烟雾浓度。数据经STM32处理后在OLED屏显示,超标或异常时报警。采用IGWO-LSTM算法模型来预测甲烷浓度,以保障煤矿井下安全。通过实验测试,IGWO-LSTM算法模型在甲烷浓度预测中表现优异,其决定系数R^(2)达到0.97073,均方根误差RMSE为0.63257,均方误差MSE为0.40,预测精度高,能够提前发现甲烷浓度的异常变化,具有良好的应用前景。该智能安全帽系统显著提升了矿井安全管理的效率和可靠性,减少了事故发生率和因生产中断导致的经济损失。This design proposes a mining intelligent safety helmet based on STM32,integrating DHT11,MAX30102,and coal mine specific explosion-proof MQ-4C and MQ-2 sensors to detect environmental temperature and humidity,miner's heart rate,blood oxygen,methane concentration,and smoke concentration,respectively.The data is processed by STM32 and displayed on the OLED screen,and an alarm is triggered when it exceeds the standard or is abnormal.The IGWS-LSTM algorithm model is adopted to predict methane concentration to ensure safety underground in coal mines.Through experimental testing,the IGWS-LSTM algorithm model has shown excellent performance in predicting methane concentration,with a determination co‐efficient R^(2) of 0.97073,a root mean square error RMSE of 0.63257,and a mean square error MSE of 0.40.It has high predic‐tion accuracy and can detect abnormal changes in methane concentration in advance,with good application prospects.The intel‐ligent safety helmet system significantly improves the efficiency and reliability of mine safety management,reducing the inci‐dence of accidents and economic losses caused by production interruptions.

关 键 词：安全帽 甲烷气体 STM32 IGWO-LSTM 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TU714[自动化与计算机技术—控制科学与工程]