Gas Ventilation Management and Accident Prevention Analysis Of Mine Safety Engineering Based on Deep Learning Evaluation

Abstract

The gas data collected at the coal mining site is characterized by nonlinearity, high dimension and fuzzy complexity. At present, a lot of information and laws hidden behind the massive data have not been developed, and the prediction of gas emission is still insufficient in terms of reliability, accuracy and timeliness. Deep learning can complete the feature extraction and conversion of massive multi-source heterogeneous data information under the mine through the processing stage of multiple internal nonlinear layers, and can achieve the purpose of prediction through independent learning, providing technical support for the prediction and decision-making of gas accidents. Ventilation management of coal mine safety engineering is one of the most important contents of coal mine safety management at present, which is related to the safety of coal mine underground operation and the safety of life and property of construction personnel. Coal mine safety production is not only the fundamental meaning of coal mine enterprise operation, but also the content that can not be ignored to ensure social stability and harmony. This paper will make a detailed analysis of the current situation of ventilation management of coal mine safety engineering based on the in-depth learning evaluation, and its purpose is to study measures to strengthen the efficiency of ventilation management of coal mine safety engineering.