基于多源异构信息耦合的煤岩界面识别技术研究  

Research on coal-rock interface recognition technology based on multi-source heterogeneous information coupling

作　　者：李彦明[1,2] 孙利海 LI Yanming;SUN Lihai(State Key Laboratory of the Gas Disaster Detecting,Preventing and Emergency Controlling,Chongqing 400037,China;CCTEG Chongqing Research Institute,Chongqing 400039,China)

机构地区：[1]瓦斯灾害监控与应急技术国家重点实验室,重庆400037 [2]中煤科工集团重庆研究院有限公司,重庆400039

出　　处：《矿业安全与环保》2022年第5期6-10,共5页Mining Safety & Environmental Protection

摘　　要：针对煤矿井下钻孔作业场景特点,分别选取了基于煤岩图像特征和基于钻进参数反馈的识别方法。为了提高煤岩界面识别准确性,对前述的2种识别方法采用深度学习模型实现了2种异构信息的耦合识别。耦合识别模型基于神经网络模型构建,以CNN卷积神经网络为基础,扩展得到深度残差神经网络模型,其结构主要由骨干网络、颈部网络、头部网络3个部分构成。通过数据库模型训练,得到最终识别方法。通过在煤矿井下进行现场钻孔试验,验证了识别方法的准确性,其中煤岩界面识别准确率达92%。According to the characteristics of underground drilling scene in coal mine,the recognition methods based on the image features of coal-rock and the feedback of drilling parameters were selected.In order to improve the accuracy of coal-rock interface recognition,the deep learning model was used to realize the coupling recognition of two heterogeneous information.The coupling recognition model was constructed based on neural network mode.Based on CNN convolution neural network,the depth residual neural network model was extended,and its structure was mainly composed of backbone network,neck network and head network.Through the training of database model,the final recognition method was obtained.Finally,accuracy of the identification method was verified by in-situ drilling test in underground coal mine,and the recognition accuracy of coal-rock interface was up to 92%.

关 键 词：自动化钻进 煤岩识别 图像识别 钻进参数 神经网络模型 耦合识别 

分 类 号：TD823.97[矿业工程—煤矿开采]