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为了更好地解决目前煤矿自燃火灾探测的误报、漏报及方法单一等问题,提出了一种基于机器视觉的多传感器融合的煤矿自燃火灾智能预警系统。通过构建的煤矿井下视觉网络系统对煤矿自燃可疑区域进行图像采集、分析和处理,结合温湿度传感器、光感传感器在特定环境深度融合技术下采集的煤矿环境参数信息,并将数据信息实时地传输到控制中心。实现了对煤矿自燃火灾的实时监测、预警功能,使煤矿自燃火灾的监测预警的准确性大大提高,具有很好的应用前景。
In order to solve the problems such as false positives, false positives and single methods of self-ignited fire detection in coal mine, a multi-sensor fusion intelligent early-warning system for coal mine spontaneous combustion based on machine vision is proposed. Through the constructed underground mine visual network system, the image acquisition, analysis and processing of suspicious areas of coal spontaneous combustion were carried out. The environmental parameters of coal mine collected under the environment fusion technology of temperature, humidity and light sensors were combined, and the data information was transmitted in real time To the control center. It realizes the real-time monitoring and early-warning function of coal spontaneous combustion fire and greatly improves the accuracy of monitoring and early-warning of spontaneous combustion fire in coal mine. It has good application prospect.