A novel edge computing architecture for intelligent coal mining system

The global coal industry, as an important basic industry, has strongly supported the stable and rapid development of the international economy and society. Although the level of automation in the context of coal mining has reached a very high degree, coal mine accidents such as roof collapse, side falling accidents, gas outburst potential, etc. continue to take place. Therefore, lots of intelligent systems are installed into collieries. For example, intelligent safety analysis technology represented by deep learning has been widely implemented in coal mine safety. The intelligent coal mining applications are always computing resource sensitive. They are usually deployed in the cloud centres that are located on the ground. As we all know, coal mining applications such as coal mine safety requires a comprehensive consideration of the mining, transportation, ventilation, hydrology, geology and other integrated factors. The transmission of all detecting data about these factors especially for multimedia from the underground face to the cloud computing centre is time-consuming. However, coal mine accidents always happen in a short span of time. This long transmission time is unacceptable for coal mine safety. In this paper, we propose a novel edge computing based intelligent processing architecture that integrates Internet of Things (IoT), fifth generation (5G), and Edge computing technologies for the coal mining intelligent system. Experiments are conducted on a deep learning based video fire prediction algorithm to prove the effectiveness of the architecture