基于图像识别的煤矿井下安全管控技术
Safety control technology of coal mine based on image recognition
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摘要: 根据煤矿安全生产的需求,研究基于图像识别的煤矿井下重点区域安全管控技术,利用机器学习算法和计算机视觉技术,同时结合人员管理数据、设备运行数据进行数据的分析,经过联动分析,数据挖掘,可实现对井下人员行为、煤量的监测和管控,形成目标风险预控的知识库,并进行了井下的实验验证。实验数据表明:系统平台的实时分析响应时间小于2 s,识别率大于98%,系统可以有效的实现井下人员、煤量等动目标的安全管控。Abstract: According to the demands of coal mine safety production, the safety management and control technology of key area in coal mine based on image recognition is studied. Machine learning algorithm and computer vision technology are used to analyze the data combined with personnel management data and equipment operation data. Through linkage analysis and data mining, the monitoring and control of underground personnel behavior and coal quantity can be realized, and a knowledge base for target risk pre-control can be formed, which is also verified by underground experiments. The experimental data show that the real-time analysis response time of the system platform is less than 2 s, and the recognition rate is more than 98%. The system can effectively realize the safety control of moving targets such as underground personnel and coal quantity.
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