Spatio-temporal diagram neural network gas concentration prediction method based on spatio-temporal data

Abstract

The invention discloses a spatio-temporal diagram neural network gas concentration prediction method based on spatio-temporal data, and the method specifically comprises the following steps: 1, collecting gas concentration data in a mine through gas monitoring points arranged in the mine, and taking the gas concentration data as a gas data concentration data set; 2, generating a space diagram structure of the mine monitoring points; 3, processing the gas concentration data collected by the mine monitoring points as a time sequence to obtain a gas concentration time sequence, and establishing a training sample set of a space-time diagram neural network according to the obtained gas concentration time sequence; 4, using the sample training sample set of the space-time diagram neural network to construct a space-time diagram neural network gas concentration prediction model; and 5, outputting a gas concentration prediction result. According to the method, the problem of low prediction precision caused by the fact that a traditional neural network gas concentration prediction model does not consider the space-time characteristics of actually measured gas data is solved.

Description

technical field [0001] The invention belongs to the technical field of mine gas concentration detection, and relates to a gas concentration prediction method based on a spatiotemporal graph neural network based on spatiotemporal data. Background technique [0002] Gas outburst is one of the main disasters in coal resource mining projects. Accurate prediction of gas concentration changes in the mining area is the key to preventing gas outburst disasters. The internal mechanism of gas outburst is very complex, and its mechanism model is still unclear. At present, methods such as neural network, chaos and nonlinear theory, and gray theory are mainly used to predict gas concentration, and gas outburst disasters are judged by predicting gas emission in mining areas. [0003] The traditional neural network prediction model uses a large amount of measured historical data to realize the prediction of gas concentration by using the time series prediction method. Although traditiona...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a spatiotemporal graph neural network gas concentration prediction method based on spatiotemporal data, which solves the problem that the traditional neural network gas concentration prediction model does not consider the spatiotemporal characteristics of the measured gas data, resulting in low prediction accuracy

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