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煤矿自燃火灾探测过程中,由于单一的探测方法存在很大的局限性,煤炭自燃火灾探测易出现漏报或误报的情况。针对这一现状,结合多种探测方法,利用其互补性,通过分析多传感器信息融合技术的原理、融合级别和具体融合方法,提出了基于小波神经网络的多传感器信息融合技术应用于煤炭自燃火灾的监测,建立一个煤炭自燃监测的综合评判系统,有效提高煤炭自燃火灾监测的准确性。
During the process of spontaneous combustion fire detection in coal mine, due to the limitation of a single detection method, it is easy for the spontaneous combustion fire detection of coal to be prone to omission or false alarm. In view of this situation, combined with a variety of detection methods, the use of their complementary nature, through the analysis of the principle of multi-sensor information fusion technology, fusion level and the specific fusion method, a multi-sensor information fusion technology based on wavelet neural network is proposed for coal spontaneous combustion The establishment of a comprehensive evaluation system of coal spontaneous combustion monitoring to effectively improve the accuracy of coal spontaneous combustion monitoring.