Research on performance of gas volume fraction early-warning based on spatio-temporal graph convolutional network
To enhance the performance of gas volume fraction early-warning,a gas volume fraction early-warning model in-tegrating the spatio-temporal features(STGCN)was proposed.The graph neural network was taken as the foundational frame-work for unified training and inference of multiple sensors on the same working face,and the spatio-temporal characteristics of gas volume fraction data were captured through graph convolution.On this basis,a grading early-warning method of gas volume fraction was proposed,which extended the prediction to the grading early-warning.The results show that STGCN achieves bet-ter accuracy and efficiency in gas volume concentration prediction and early-warning tasks.The research results can provide a reference for the prevention and control of mine gas disasters.
NETL
NSTL
万方数据
维普
