In the heart of China’s coal mining industry, a revolutionary technology is poised to transform safety and efficiency. Researchers from the School of Safety Engineering at China University of Mining and Technology, led by Liu Yubing, have developed an integrated “cloud-edge-end” intelligent and precise management and control technology system for coal mine disasters. This innovative system, published in the journal ‘Gong-kuang zidonghua’ (which translates to ‘Mining Automation’), promises to enhance the accuracy of accident prediction, early warning, and intelligent risk assessment, potentially saving lives and reducing downtime in one of the world’s most dangerous industries.
The new system addresses several critical challenges in coal mine safety. Traditional approaches often focus on individual disasters, such as gas leaks, fires, or roof collapses, but lack a coordinated mechanism for multi-disaster monitoring and early warning. Liu Yubing explains, “Existing technologies often operate in silos, leading to high data transmission latency and low management efficiency. Our system integrates multiple disaster monitoring and response mechanisms into a cohesive, intelligent platform.”
At the core of this technology is a sophisticated architecture that leverages the strengths of cloud, edge, and end computing. On the end side, intelligent sensors detect multiple hazards, including gas, fire, dust, and roof instability. These sensors are connected through a high-speed, low-latency communication network based on IPv6 and a 5G+4G+WiFi6 framework, ensuring rapid data transmission and device optimization.
Edge computing plays a crucial role in processing this data. Liu Yubing’s team developed a coal mine major disaster data fusion analysis model using the deep learning AdaTT model. This model, combined with AI-powered video analysis devices, enables real-time hazard identification and response. “By processing data at the edge, we can significantly reduce latency and improve the accuracy of our predictions,” Liu Yubing notes.
On the cloud side, digital twin technology creates visual simulations of the mine, allowing for comprehensive safety situation analysis. Deep learning models and Delphi theory are employed to assess and mitigate risks, while a time-varying network path planning algorithm ensures safe evacuation routes in emergency situations.
The practical application of this technology is already evident. The coal mine disaster fusion monitoring and intelligent decision-making platform has been successfully implemented at the No.12 Mine, Pingdingshan Tian’an Coal Mining Co., Ltd. The results are impressive: a significant improvement in multi-disaster risk analysis decision-making and a higher level of intelligent management and control.
For the energy sector, the implications are profound. As coal remains a critical component of the global energy mix, enhancing safety and efficiency in coal mines is not just a matter of operational excellence but also of economic and environmental sustainability. By reducing the risk of accidents and minimizing downtime, this technology can lead to more stable energy supplies and lower operational costs.
Looking ahead, Liu Yubing’s research could pave the way for similar integrated systems in other high-risk industries, such as oil and gas, mining, and even manufacturing. The “cloud-edge-end” approach, with its emphasis on real-time data processing and intelligent decision-making, represents a significant leap forward in industrial safety and efficiency.
As the energy sector continues to evolve, technologies like this will be crucial in meeting the dual challenges of increasing demand and stringent safety regulations. The future of coal mining, and indeed many other industries, may well be shaped by the innovative work of researchers like Liu Yubing and their groundbreaking “cloud-edge-end” systems.