Research on Optimization of Monitoring Nodes Based on the Entropy Weight Method for Underground Mining Ventilation

Abstract

Air pressure monitoring is the basis of mining-intelligent ventilation. In order to optimize the coverage of monitoring nodes, the node importance in the ventilation network was taken as the optimization basis in this study. Two evaluation indexes of the extent of node coverage and the influence degree of nodes were obtained by analyzing the influence degree of node air pressure. The entropy weight method (EWM) was used to weigh the evaluation indexes to obtain the importance of all nodes in the ventilation network. A node layout method with node importance as the optimization of air pressure-monitoring nodes was proposed. The minimum distance correlation between the limited monitoring nodes and the monitored nodes was set as the constraint condition, and any air pressure monitoring node could only monitor its adjacent nodes. The nodes with high node importance were selected as air pressure-monitoring nodes in turn until the coverage of air pressure-monitoring nodes in the ventilation network was maximized. By applying the entropy weight method (EWM) and the clustering algorithm (CA) to the case mine, the research results show that the application of the entropy weight method (EWM) to optimize the air pressure-monitoring nodes was more feasible than the clustering algorithm (CA). The coverage rate was 81.6% at different constraint values, and the maximum coverage rate was 92.1%, which meets the needs of arranging the least air pressure-monitoring nodes to monitor the maximum range of air pressure changes and can carry out full coverage monitoring of mine air pressure.