Analysis of Gas Emission Prediction Model Based on Mining Internet of Things
In response to the problems of inaccurate data and unscientific information processing and analysis methods in traditional gas emission monitoring methods,the mining Internet of Things method is adopted to monitor and study the gas and ventilation data in coal mines.By using multi-sensor data comprehensive processing technology and ventilation network optimization methods,the availability and accuracy of coal mine ventilation and gas data are improved.Based on this,a PCA-MLR-RBF gas emission prediction model is established to effectively improve the accuracy of coal mine gas emission prediction,ensure the scientificity of underground ventilation and gas data monitoring,and thus improve the safety factor of coal mine production.
mining Internet of Thingsgas emissionPCA-MLR-RBF prediction model
NETL
NSTL
万方数据
