Enhancing Coal Mine Safety Monitoring Algorithm using Graph Computing Techniques

Coal mines are one of the pillars of industrial development, and their production safety issues have an important impact on social economy. Due to the complexity of industrial big data, the processing and prediction of coal mine safety monitoring data have always been highly challenging. For example, coal mine safety is generally related to multiple environmental variables such as temperature, methane concentration, and wind speed, and these data are usually distributed in multiple sensors monitoring data. Researchers need to deeply analyze the relationship between the data and model accordingly. Therefore, this paper proposes a coal mine safety monitoring algorithm based on graph computing. This algorithm abstracts sensors and the relationships between them into nodes and edges, and then converts sensor data into graph data. On this basis, the algorithm processes data through a heterogeneous graph neural network and achieves prediction of sensor monitoring values. Through this algorithm, coal mine safety practitioners can accurately predict sensor monitoring values and respond promptly, thereby improving coal mine production safety.