基于改进RBF数据融合算法的煤矿井下安全监控研究

ResearchonCoalMineUndergroundSafetyMonitoringBasedonImprovedRBFDataFusionAlgorithm

摘要:

为了解决煤矿井下施工安全性低的问题,利用改进RBF数据融合算法优化设计煤矿井下安全监控方法。在煤矿井下环境下安装瓦斯、温湿度等传感器设备,将其安装在确定的测点位置上。利用改进RBF数据融合算法采集并处理传感数据,根据数据融合结果,考虑瓦斯浓度、温湿度、矿压等环境参数,得出煤矿井下安全性的监测结果。考虑煤矿井下人员实时位置,确定井下安全监控方向,在安全性监测结果的驱动下,利用设计的安全监控器,实现煤矿井下安全监控工作。通过效果测试实验得出结论：与传统监控方法相比,优化设计方法的瓦斯与温度监控误差分别降低了1.005mg/m3和5.65℃,同时安全监控范围得到明显扩大。

Abstract:

In order to solve the problem of low safety in coal mine underground construction, an improved RBF data fusion algorithm is used to optimize the design of coal mine underground safety monitoring methods. Install gas, temperature and humidity sensors and equipment in the underground environment of coal mines, and install them at the designated measurement points. Using the improved RBF data fusion algorithm to collect and process sensing data, based on the data fusion results, considering environmental parameters such as gas concentration, temperature and humidity, and mine pressure, the monitoring results of coal mine underground safety are obtained. Considering the real-time location of underground personnel in the coal mine, determine the direction of underground safety monitoring, and use the designed safety monitor to achieve underground safety monitoring work driven by the safety monitoring results. Through effectiveness testing experiments, it can be concluded that compared with traditional monitoring methods, the optimized design method reduces gas and temperature monitoring errors by 1.005mg/m, respectively 3 And 5.65 ℃, while the safety monitoring range has been significantly expanded.