Intelligent Evaluation Method of Pressure Relief Gas Drainage in Goaf Based on IoT Perception

Abstract

The pressure relief gas drainage in goaf is the main control method of mine gas. This paper has been designed to study how to analyze and study the gas drainage of goaf pressure relief based on the perception layer of the Internet of Things. The intelligent evaluation of pressure relief gas drainage in goaf is described. This paper has raised the problem of gas extraction, which is based on the Internet of Things, so it has elaborated on the data-level fusion-related algorithms for sensing coal mine safety, and the case design and analysis of the prediction model and intelligent evaluation have been carried out. Aiming at the problem of intelligent grading of gas drainage evaluation in goaf, data preprocessing is performed on the drainage metering data. Using a deep learning evaluation method based on a convolutional neural network (CNN), an intelligent evaluation model is constructed for gas extraction. Compared with the classification model of the shallow neural network, the CNN classification model is more suitable for gas intelligence evaluation and has higher accuracy due to the good learning ability and accuracy of the deep neural network. When the learning rate is 0.1 and the batch is 256, the prediction effect of the CNN pressure relief gas intelligent classification model is the best, which can effectively provide classification results.