基于NB-IOT的矿下环境监测及瓦斯涌出量研究

Research on Environment Monitoring and Gas Emission under Mine Based on NB-IOT

针对煤矿井下环境监测存在传输信号较差、工作面瓦斯涌出量难以预测的问题,提出基于NB-IOT的矿下环境监测系统及瓦斯涌出量预测模型。首先,针对矿井下温湿度、烟雾、一氧化碳、瓦斯气体等监测指标,利用NB-IOT无线传感网络技术与云平台相结合的方式,建立NB-IOT矿下环境监测系统。其次,结合监测的瓦斯气体数据与相关影响因素,利用主成分多元线性回归方法建立瓦斯涌出量预测模型。最后,将主成分多元线性回归与普通多元线性回归模型的预测精度进行对比,结果表明,主成分多元线性回归模型的最大值与最小值相对误差准确度分别提升50.47%、83.58%,验证了模型的可行性与准确性。

Aiming at the problems of poor transmission signal and difficult prediction of gas emission in coal mine underground environment monitoring,the environment monitoring system and gas emission prediction model based on NB-IOT are proposed.Firstly,according to the monitoring indexes of temperature and humidity,smoke,carbon monoxide,gas and so on,NB-IOT underground environment monitoring system is established by combining NB-IOT wireless sensor network technology with cloud platform.Secondly,combined with the monitoring gas data and related influencing factors,the principal component multiple linear regression method is used to establish the prediction model of gas emission.Finally,the prediction accuracy of principal component multiple linear regression model and ordinary multiple linear regression model is compared.The results show that the relative error accuracy of maximum and minimum value of principal component multiple linear regression model is improved by 50.47%and 83.58%respectively,which verifies the feasibility and accuracy of the model.