Prediction Method of Coal and Gas Outburst Intensity Based on Digital Twin and Deep Learning - Wang, Zhiquan (Frontiers in Energy Research, 2022)
Digital twin can well solve complex problems, especially in the case of mechanical failures. Digital twin technology can be applied in 3D IoT smart factories, new smart city construction, smart medical care, digital energy, digital archives, warehousing and logistics visualization and other fields. Deep learning covers a wide range of applications and is extremely common. This paper discusses the application of the two in the risk prediction of coal and gas outburst strength. This paper firstly describes the method of predicting coal and gas outburst intensity. For example, the BP neural network algorithm applied to the prediction of coal and gas outburst intensity in deep learning, the air flow control system model of digital twin for coal mines, and the risk assessment algorithm of coal and gas outburst intensity in coal mines based on grey relational analysis, and various ways to predict risk. And the system model is designed in this paper. Combined with the Formula, this paper describes the process of predicting risk in detail, and then conducts experiments based on digital twin and deep learning to predict coal and gas outburst intensity. In this paper, digital twin is used to systematically design coal and gas outburst intensity prediction, and a neural network prediction model based on optimized quantum gate nodes is established. In this paper, the practical application experiment and result analysis of the optimization algorithm in the coal and gas outburst prediction model are carried out, and the conclusion is drawn. After QGNN is optimized by the sdPSO algorithm, the error is extremely small, only 2.0914, and the specific value of the prediction accuracy in practical applications is as high as 95%. The experimental data verifies the feasibility of digital twin and deep learning technology in the prediction of coal and gas outburst intensity.