煤矿安全2026，Vol.57Issue(3)：227-236,10.DOI:10.13347/j.cnki.mkaq.20250603
基于边缘计算的顶板压力监测关键技术研究
Research on key technologies of roof pressure monitoring based on edge computing
摘要
Abstract
With the increase of coal mining depth,roof accidents have become one of the main hidden dangers threatening mine safety.Traditional roof pressure monitoring technology generally relies on ground central stations for data processing,which has problems such as large data transmission delay,high bandwidth peak occupancy,and weak real-time warning capabilities,making it difficult to meet the urgent needs of dynamic monitoring in complex underground environments.Therefore,we propose a roof pres-sure monitoring technology based on edge computing.By sinking the computing power to the edge nodes of the network,the local real-time processing and intelligent analysis of pressure data are realized,and the response speed and reliability of the monitoring system are significantly improved.Firstly,for the roof pressure monitoring scenario,an edge computing node with multi-source het-erogeneous data fusion capability is designed.The node integrates high-precision pressure sensors,hydraulic support piston expan-sion and contraction,and environmental parameter acquisition modules,and combines with adaptive filtering algorithms and light-weight anomaly detection models,to complete data preprocessing and preliminary feature extraction,effectively reducing the amount of redundant data uploaded.Secondly,a three-level distributed monitoring architecture of"perception layer-edge layer-cloud plat-form"has been constructed.The edge layer achieves dynamic trend prediction and risk grading warning through collaborative com-puting,while the cloud platform is responsible for global data storage and model optimization iteration,balancing real-time and long-term data analysis needs.In addition,a dynamic scheduling strategy for edge resources based on deep reinforcement learning is pro-posed to optimize computing task allocation and energy management,ensuring the long-term stable operation of edge nodes in com-plex underground environments.The experimental results show that compared with the traditional cloud computing mode,the edge computing scheme has the shortest local transmission path and local data computing capability.The wireless transmission power of the pressure sensor is reduced from 10 dbm to 4 dbm,significantly reducing the energy consumption of the equipment.At the same time,the success rate of wireless data transmission is increased to 100%,and the warning time of dangerous states is increased from 8.2 min to 3.5 min,which improves the real-time warning capability of roof pressure monitoring.关键词
边缘计算/顶板压力监测/多源数据融合/动态调度/矿山安全Key words
edge computing/roof pressure monitoring/multi-source data fusion/dynamic scheduling/mine safety分类
矿业与冶金引用本文复制引用
刘立仁,霍雷敏,谷民帅,陈博,袁强,王志伟,黄友胜..基于边缘计算的顶板压力监测关键技术研究[J].煤矿安全,2026,57(3):227-236,10.基金项目
天地科技股份有限公司科技创新创业资金专项资助项目(2024-TD-ZD013-05) （2024-TD-ZD013-05）
