Abstract: The current status of methane monitoring in the existing fully-mechanized coal mining faces was analysed from both system monitoring and manual detection. In view of the problems of the existing methane sensor on the working face, such as large volume, high power consumption, difficulty in movement, maintenance and high equipment cost, the Internet of Things technology based on the concept of edge computing four key technologies including micro-power data acquisition methane data collection, micro-power sensing terminal positioning, wireless data transmission and terminal self-powered technologies are proposed to develop a micro-power methane sensing terminal and the specific technical indicators and performance requirements of four key technologies are put forward. In view of the lack of methane monitoring points in the working face area of the monitoring system, the real-time, readability and sharing of manual methane data are insufficient, based on the three main characteristics of methane gushing from coal falling in the working face area, methane gushing from coal walls and methane gushing from gobs, combined with the distribution characteristics of measured methane data from the on-site production team and maintenance team, a layout plan of 26 IoT methane sensing terminals in the fully-mechanized mining face area is proposed. Based on 26 real-time methane monitoring data points, in order to more intuitively and clearly show the distribution of methane in the working face area,interpolating the limited methane monitoring data to form a digital field of methane concentration in the working face area. In the Cartesian two-dimensional coordinate system, the digital field realization methodsin the X and Y directions of the working surface area are optimized and verified.The results show that this method greatly improves the readability and real-time performance of methane monitoring data in the working face area. It provides other scholars with a high real-time, wide coverage, and highly readable expression of methane concentration at the working face for further regional methane analysis and early warning.