基于信息优化融合的煤矿井下环境安全监测技术

针对传统的传感器监测矿井环境存在评判可靠性低、数据单一的缺点,提出一种基于多参数、两级信息优化融合的安全监测方案.该方法综合了瓦斯、粉尘、CO、风速、温度对矿井环境的影响,利用多传感器采集其参数,首先用Bayes理论对剔除了疏忽误差的同质数据源进行一级融合,形成矿井环境特征向量,然后用灰色理论进行灰色关联度分析的二级融合,根据融合后的关联度判断环境安全等级,实现对矿井环境监测参数的优化和整合.研究表明,该方法充分利用多种有效监测数据,既实现了同质数据源的优化,又从整体上考虑了异质数据源的互补性,提高了监测系统的可靠性、全面性.

Environmental safety monitoring based on information optimization fusion in coal mines

Aimed at improving conventional mine environment-monitoring method suffering from lower judgment certainty and less sufficient data, this paper proposes a new safe monitor way based on multi parameters and two stage information optimization fusion. This method synthesizes gas, dust, CO, wind speed and temperature, and uses multi sensors to collect parameters, and the method firstly involves using Bayes estimate theory to fuse the homogeneous data for the first time and generate characteristic vector of mine environmental, and then comparing it with standard characteristic vector of mine environmental to obtain grey associated degree for the second data fusion time. According to grey associated degree, it is possible to judge the environmental safe level, optimize and integrate environmental monitored parameters. This method, characterized by sufficiently utilizing the effective monitored data, optimizing homogeneous data, and considering the complementation of the different data source makes possible an improvement in the reliability and entirety of monitor system.