Research on the integrated "cloud-edge-end" intelligent and precise management and control technology system for coal mine disasters
-
摘要:
构建煤矿灾害智能化精准管控体系有助于提高事故预测预警准确度,实现智能化风险研判。现有研究大多聚焦单一灾害进行监测预警技术或算法优化,缺乏多灾害监测预警协同机制和事故应急联动下的避灾路径规划,且数据传输时延大,管控效率较低。提出了一种煤矿灾害“云边端”一体化智能精准管控技术体系,介绍了该体系架构和监控预警数据流转与交互机制,从数据精准感知、边缘计算、云平台3个角度重点分析了该体系的关键技术,包括:在端侧,研发瓦斯、火灾、粉尘、顶板等多灾害智能传感器,构建基于IPv6的5G+4G+WiFi6的高速低时延通信网络,优化感知设备及联动控制装备部署方案;在边侧,建立基于深度学习AdaTT模型的煤矿重大灾害数据融合分析模型,研发矿用AI视频分析设备以实现安全隐患图像识别,开发基于边缘计算驱动的煤矿工作面协同管控技术;在云侧,采用数字孪生技术实现可视化推演,基于Delphi理论和深度学习模型实现煤矿重大灾害安全态势分析,设计灾害环境下的路径时变网络路径规划算法。基于该技术体系开发了煤矿灾害融合监控预警与管控数字化决策平台,并在平顶山天安煤业股份有限公司十二矿成功应用,显著提高了多灾种风险分析决策效率与智能管控水平。
Abstract:Constructing an intelligent and precise disaster management system for coal mines helps improve the accuracy of accident prediction and early warning, enabling intelligent risk assessment. Existing research mainly focuses on monitoring and early warning technologies or algorithm optimization for individual disasters, lacking a coordinated mechanism for multi-disaster monitoring and early warning, as well as disaster avoidance path planning under emergency response. Additionally, data transmission latency is high, and management efficiency remains low. An integrated "cloud-edge-end" intelligent and precise management and control technology system for coal mine disasters was proposed in this study. The system architecture and the data flow and interaction mechanism for monitoring and early warning were introduced. Key technologies were analyzed from three perspectives: precise data perception, edge computing, and cloud platform. On the end side, intelligent sensors for multiple disasters, including gas, fire, dust, and roof hazards, were developed. A high-speed, low-latency communication network based on IPv6 and a 5G+4G+WiFi6 framework was established, and the deployment of sensing devices and linked control equipment was optimized. On the edge side, a coal mine major disaster data fusion analysis model based on the deep learning AdaTT model was developed. AI-powered video analysis devices for mining applications were designed to enable image-based hazard identification. Additionally, a coalface collaborative management and control technology driven by edge computing was developed. On the cloud side, digital twin technology was applied for visual simulation, while coal mine major disaster safety situation analysis was conducted using Delphi theory and deep learning models. Furthermore, a time-varying network path planning algorithm was designed for disaster environments. Based on the technical system, a coal mine disaster fusion monitoring and intelligent decision-making platform was developed and successfully applied at the No.12 Mine, Pingdingshan Tian'an Coal Mining Co., Ltd. The platform significantly improves the efficiency of multi-disaster risk analysis decision-making and the level of intelligent management and control.
-
-
-
[1] 王斌国. 面向矿山瓦斯预警应用的多元多尺度数据融合方法研究[D]. 青岛:山东科技大学,2019. WANG Binguo. Research on multi-element and multi-scale data fusion method for mine gas early warning application[D]. Qingdao:Shandong University of Science and Technology,2019.
[2] ANANI A,ADEWUYI S O,RISSO N,et al. Advancements in machine learning techniques for coal and gas outburst prediction in underground mines[J]. International Journal of Coal Geology,2024,285. DOI: 10.1016/J.COAL.2024.104471.
[3] YANG Li,FANG Xin,WANG Xue,et al. Risk prediction of coal and gas outburst in deep coal mines based on the SAPSO-ELM algorithm[J]. International Journal of Environmental Research and Public Health,2022,19(19). DOI: 10.3390/IJERPH191912382.
[4] 刘程,孙东玲,邓飞,等. 煤矿多灾害智能防治与智能监控技术[J]. 智能矿山,2022,3(7):105-114. LIU Cheng,SUN Dongling,DENG Fei,et al. Intelligent prevention and control of multiple disasters in coal mines and intelligent monitoring technology[J]. Journal of Intelligent Mine,2022,3(7):105-114.
[5] 孙继平,孙雁宇,范伟强. 基于可见光和红外图像的矿井外因火灾识别方法[J]. 工矿自动化,2019,45(5):1-5,21. SUN Jiping,SUN Yanyu,FAN Weiqiang. Mine exogenous fire identification method based on visible light and infrared image[J]. Industry and Mine Automation,2019,45(5):1-5,21.
[6] 李光宇,李守军,缪燕子. 基于机器视觉和灰色模型的矿井外因火灾辨识与定位方法[J]. 矿业安全与环保,2023,50(2):82-87. LI Guangyu,LI Shoujun,MIAO Yanzi. Identification and positioning method of mine external fire based on machine vision and grey model[J]. Mining Safety and Environmental Protection,2023,50(2):82-87.
[7] 胡纪年,李雨成,李俊桥,等. 基于CNN的矿井外因火灾火源定位方法研究[J]. 中国安全生产科学技术,2024,20(3):134-140. HU Jinian,LI Yucheng,LI Junqiao,et al. Study on localization method of mine exogenous fire source based on CNN[J]. Journal of Safety Science and Technology,2024,20(3):134-140.
[8] 王树斌,王旭,闫世平,等. 基于Transformer的矿井内因火灾时间序列预测方法[J]. 工矿自动化,2024,50(3):65-70,91. WANG Shubin,WANG Xu,YAN Shiping,et al. Transformer based time series prediction method for mine internal caused fire[J]. Journal of Mine Automation,2024,50(3):65-70,91.
[9] 裴晓东,姚志远,王亮,等. 基于Unity3D的矿井火灾监测与防治虚拟仿真试验系统[J]. 中国安全科学学报,2024,34(3):109-116. PEI Xiaodong,YAO Zhiyuan,WANG Liang,et al. Virtual simulation experimental system for mine fire monitoring and prevention based on Unity3D[J]. China Safety Science Journal,2024,34(3):109-116.
[10] 周福宝,郑丽娜,冯子康,等. 基于振荡天平原理的矿工个体粉尘连续监测仪的研制[J]. 煤炭学报,2024,49(2):876-884. ZHOU Fubao,ZHENG Lina,FENG Zikang,et al. Development of a miner personal dust continuous monitor based on the principle of oscillating balance[J]. Journal of China Coal Society,2024,49(2):876-884.
[11] 陈建阁,李德文,王杰,等. 基于静电感应法的粉尘质量浓度检测装置优化[J]. 煤炭学报,2022,47(7):2668-2677. CHEN Jiange,LI Dewen,WANG Jie,et al. Optimization of dust concentration detection device based on electrostatic induction method[J]. Journal of China Coal Society,2022,47(7):2668-2677.
[12] 陈建阁,李德文,许江,等. 基于光散射法无动力粉尘质量浓度检测技术[J]. 煤炭学报,2023,48(增刊1):149-158. CHEN Jiange,LI Dewen,XU Jiang,et al. Detection technology of unpowered dust concentration based on light scattering method[J]. Journal of China Coal Society,2023,48(S1):149-158.
[13] 毛德兵,尹希文,张会军. 我国煤矿顶板灾害防治与监测监控技术[J]. 煤炭科学技术,2013,41(9):105-108,121. MAO Debing,YIN Xiwen,ZHANG Huijun. Coal mine roof disaster prevention and monitoring technology in China[J]. Coal Science and Technology,2013,41(9):105-108,121.
[14] WANG Ke,ZHUANG Xinwei,ZHAO Xiaohu,et al. Roof pressure prediction in coal mine based on grey neural network[J]. IEEE Access,2020,8:117051-117061.
[15] WEI Mingsheng,TONG Minming,HAO Jifei,et al. Detection of coal dust in a mine using optical tomography[J]. International Journal of Mining Science and Technology,2012,22(4):523-527.
[16] 陈晓晶. 基于“云−边−端”协同的煤矿火灾智能化防控体系建设[J]. 煤炭科学技术,2022,50(12):136-143. CHEN Xiaojing. Construction of intelligent coal mine fire prevention and control system based on "cloud-edge-end" collaboration[J]. Coal Science and Technology,2022,50(12):136-143.
[17] 王文娟. 瓦斯传感器的封装改进与检测方法研究[D]. 徐州:中国矿业大学,2015. WANG Wenjuan. Research on package improvement and detection method of gas sensor[D]. Xuzhou:China University of Mining and Technology,2015.
[18] 王昊,李杰,郑闯凯,等. 煤矿采场顶板灾害预警技术研究进展及展望[J]. 矿业安全与环保,2024,51(2):46-52. WANG Hao,LI Jie,ZHENG Chuangkai,et al. Research progress and prospect of early warning technology of coal mine stope roof disaster[J]. Mining Safety & Environmental Protection,2024,51(2):46-52.
[19] 肖双双, 马亚洁, 李卫炎, 等. 我国露天矿粉尘防治理论技术近20 a研究进展与展望[J]. 金属矿山, 2023(7): 40-56. XIAO Shuangshuang, MA Yajie, LI Weiyan, et al. Study progress and prospect on theory and technology for dust prevention and control in open-pit mine of China in the past 20 years[J]. Metal Mine, 2023(7): 40-56.
[20] 丁雨生. 可调谐激光光谱吸收式瓦斯预警系统研究[D]. 淮南:安徽理工大学,2017. DING Yusheng. Study on tunable laser spectrum absorption gas early warning system[D]. Huainan:Anhui University of Science & Technology,2017.
[21] 陈强强. 基于TDLAS煤矿瓦斯浓度监测系统的研究[D]. 西安:西安科技大学,2010. CHEN Qiangqiang. Research on coal mine gas concentration monitoring system based on TDLAS[D]. Xi'an:Xi'an University of Science and Technology,2010.
[22] 史经灿. 粉尘浓度测量仪检定装置的粉尘均匀性控制研究[D]. 徐州:中国矿业大学,2022. SHI Jingcan. Study on dust uniformity control of dust concentration measuring instrument calibration device[D]. Xuzhou:China University of Mining and Technology,2022.
[23] ZHANG Hao,NIE Wen,LIANG Yu,et al. Development and performance detection of higher precision optical sensor for coal dust concentration measurement based on Mie scattering theory[J]. Optics and Lasers in Engineering,2021,1442. DOI: 10.1016/J.OPTLASENG.2021.106642.
[24] 范培全,魏爱玲,张冬晨,等. 700 MHz频段5G网络性能分析与建设方案建议[J]. 电信科学,2022,38(5):158-164. DOI: 10.11959/j.issn.1000-0801.2022091 FAN Peiquan,WEI Ailing,ZHANG Dongchen,et al. Performance analysis and construction scheme proposal of 5G network at 700 MHz band[J]. Telecommunications Science,2022,38(5):158-164. DOI: 10.11959/j.issn.1000-0801.2022091