Research status and development trend of intelligent monitoring and early warning technology in coal mine
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摘要: 从物联网、大数据、云计算及人工智能方面综述了我国煤矿智能监测与预警技术的研究现状。针对煤矿智能监测技术的实际应用情况,指出了煤矿智能监测与预警存在井下煤矿监测设备前兆信息采集可靠性差、云端平台集成应用融合深度不够、数据库安全性较弱、人工智能技术在煤矿监测中的应用尚不成熟等问题。展望了煤矿智能监测与预警技术的发展趋势:煤矿智能监测系统应用石墨烯/氧化石墨烯光纤传感器可实现多源信息感知,提升前兆信息采集的可靠性;研究多技术深度交叉融合技术,以探究监测数据的深层价值;构建基于区块链技术的煤矿监测数据库,保证数据库不可篡改,且具有较好的读写性能;研发具备自适应和深度学习的煤矿智能安全监测预警系统,实现矿井自动监测、智能预警。Abstract: The research status of intelligent monitoring and early warning technology in coal mine in China was summarized from the aspects of Internet of things, big data, cloud computing and artificial intelligence.According to practical application of intelligent monitoring technology in coal mine, the problems of intelligent monitoring and early warning in coal mine was points out, including poor reliability of precursory information collection of underground coal mine monitoring equipment, insufficient depth of cloud platform integrated application and fusion, weak database security, and immature application of artificial intelligence technology in coal mine monitoring.The development trend of intelligent monitoring and early warning technology in coal mine was prospected: the application of graphene/graphene oxide optical fiber sensors in intelligent monitoring system of coal mine can realize multi-source information perception and improve the reliability of precursory information collection; studying multi-technology deep cross fusion to explore deep value of monitoring data; constructing coal mine monitoring database based on block chain technology, ensuring that the database cannot be tampered and has good read and write performance; developing intelligent safety monitoring and warning system with adaptive and deep learning, realizing mine automatic monitoring and intelligent early warning.
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2. 马天,姜梅,杨嘉怡,张杰慧,丁旭涵. 基于多特征融合时差网络的带式输送机区域违规行为识别. 工矿自动化. 2024(07): 115-122 . 本站查看
3. 李占利,权锦成,靳红梅. 基于3D-Attention与多尺度的矿井人员行为识别算法. 国外电子测量技术. 2023(07): 95-104 . 百度学术
4. 潘德泰,李贵亮,何启远,祁鸣露,陈其超,吴川彬. 基于计算机视觉的电网输变配环节配电线路巡检系统. 电子设计工程. 2023(17): 85-89 . 百度学术
5. 程德强,寇旗旗,江鹤,徐飞翔,宋天舒,王晓艺,钱建生. 全矿井智能视频分析关键技术综述. 工矿自动化. 2023(11): 1-21 . 本站查看
6. 王宇,于春华,陈晓青,宋家威. 基于多模态特征融合的井下人员不安全行为识别. 工矿自动化. 2023(11): 138-144 . 本站查看
7. 杨春雨,张鑫. 煤矿机器人环境感知与路径规划关键技术. 煤炭学报. 2022(07): 2844-2872 . 百度学术
8. 饶天荣,潘涛,徐会军. 基于交叉注意力机制的煤矿井下不安全行为识别. 工矿自动化. 2022(10): 48-54 . 本站查看
9. 黄瀚,程小舟,云霄,周玉,孙彦景. 基于DA-GCN的煤矿人员行为识别方法. 工矿自动化. 2021(04): 62-66 . 本站查看
10. 党伟超,史云龙,白尚旺,高改梅,刘春霞. 基于条件变分自编码器的井下配电室巡检行为检测. 工矿自动化. 2021(12): 98-105 . 本站查看
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