基于矿山物联网的瓦斯涌出预测模型分析
Analysis of Gas Emission Prediction Model Based on Mining Internet of Things
李常青1
作者信息
- 1. 山西焦煤东曲煤矿通风区,山西 太原 030200
- 折叠
摘要
针对传统瓦斯涌出量监测方法存在数据不准确、信息处理与分析手段不科学等问题,采用矿山物联网的方法监测研究煤矿井下瓦斯与通风数据,通过采用多传感器数据综合处理技术与通风网络解算的优化方法,提高煤矿井下通风与瓦斯数据的可用性与准确性,并基于此建立PCA-MLR-RBF瓦斯涌出量预测模型,以有效提高煤矿井下瓦斯涌出量预测精度,保证井下通风与瓦斯数据监测的科学性,从而提高煤矿生产安全系数.
Abstract
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.
关键词
矿山物联网/瓦斯涌出量/PCA-MLR-RBF预测模型Key words
mining Internet of Things/gas emission/PCA-MLR-RBF prediction model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据