Longwall Face Roof Disaster Prediction Algorithm Based on Data Model Driving

Abstract Hydraulic support is the main equipment used for surrounding rock control at fully mechanized mining face. Data such as load and posture of hydraulic support are important basis for predicting roof disasters. This paper summarized and analyzed the status of coal mine safety accidents and the main influencing factors of roof disasters, and proposed the monitoring information characteristic parameters of roof disasters based on support posture-load changes. According to the monitoring load data of the Yanghuopan coal mine, the data feature decomposition method was used to effectively extract the trend item and cycle period item of the load, and the period weighting characteristics of the longwall face was obtained. Some various algorithms were used to model and analyze the monitoring data, which can better realize the single point prediction and the prediction of one support cycle. The accuracy of different algorithms was evaluated. Different data models had better single point prediction effects on the support load. SARIMA model was better than ARIMA model for load prediction in one support cycle, but the prediction effect of these two algorithms in one fracture cycle was poor. Therefore, we proposed a hydraulic support load prediction method based on multiple data cutting and hydraulic support load template library, and constructed the technical framework of roof disaster intelligent prediction platform based on this method, which can perform the prediction and early warning of roof disaster according to the load and posture monitoring information of hydraulic support.