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采用分批估计理论和BP神经网络对井下多传感器信息进行两级融合的瓦斯监测系统设计方案能够大大提高数据测量的准确性、可靠性,并能实时、客观地给出矿井安全状况综合评价。利用多个煤矿的井下环境实测数据进行仿真实验,结果表明该方法是可行的。
The design scheme of gas monitoring system based on batch estimation theory and BP neural network is two-level fusion of downhole multi-sensor information, which can greatly improve the accuracy and reliability of data measurement and give a comprehensive evaluation of mine safety status in real time and objectively. The simulation experiment of the downhole environment in several coal mines shows that the method is feasible.