| Citation: |
LIU Yubing, LI Yiteng, LI Zhonghui, et al. Research on the integrated "cloud-edge-end" intelligent and precise management and control technology system for coal mine disasters[J]. Journal of Mine Automation，2025，51（3）：105-112, 164. DOI: 10.13272/j.issn.1671-251x.2024110084
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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.
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