Early Warning Technology in Mine Production Based on BP Neural Network Model
Mine production is characterized by high dynamics,rapid time variation,chaos,and randomness.The massive data from human,equipment,environment,and management in the production process have multi-source heterogeneous characteristics,which increase the uncertainty of safety risk assessment.This paper uses intelligent algorithms to simulate and model the spatial production scenario,establishes an early warning index system for fac-tors affecting mine safety,and quantifies and preprocesses the indicators.Through the training and testing of the preprocessed index data model using the BP neural network model,the test results show that with the increase of it-eration times,the convergence and accuracy of the model are greatly improved,which can provide information for mine safety risk assessment decisions to the greatest extent.It provides technical support for improving the efficien-cy and depth of coal mine safety monitoring.
big dataBP neural networkmine productionearly warning technology
万方数据
维普
