Research on methane monitoring mode of coal mining face based on edge computing

In view of the shortage of existing methane monitoring points in the coal face,and it can notmeet the the needs of methane sensing,analysis and early w arning in the coal face of smart mine construction. In combination with the edge of computing technology,mine Internet of things environment sensing technology and da ta transmission related technologies, analysis methane micropower perception,network transmission,monitoring terminal power supply and sensing location service issu es,a methane monitoring terminal that integratesmicro-power methane perception,self-powered supply,self-positioning,and self-organizing network data transmission wa s proposed. With the analysis of the work area regional edge gateway,mine-end edge servers and cloud big data,a system architecture of methane edge monitoring mo de was established based on cloud-edge and end to solve the problem of methane sensing and transmission based on edge computing in the face area.Taking the 100 hydraulic supports and 2 end support of coal faces as an example,a methane monitoring scheme based on edge calculation was designed in the Cartesian two-dimensi onal coordinate system to realize the monitoring of large amounts of data in the two-dimensional spatial range of methane concentration. The realization method of the methane concentration digital field with full coverage in the coal face area and the visual display method of the cloud image were proposed. On the basis of the spatial f ield of methane concentration and the cloud image display,two early warning modes and ideas for methane analysis based on big data support were proposed. One is t 0 perform correlation analysis of regional space field before and after methane over-limit concentration,and extract the corresponding methane over-limit in the coal fac e. The distribution characteristics and changing rules of the spatial field use the regional concentration field to detect the methane concentration over-limit problem as e arly as possible. Another one is to use a big data analysis tool to solve the problem of abnormal manifestation below the methane monitoring alarm threshold.