LAUSR

Abstract With the increasing demand for energy in China, more and more coal mines are mined. At the same time, along with the safety and health risks brought by occupational exposure in mine operation, it is very important to assess the risk of toxic substances in the working environment. The purpose of this study is to evaluate and analyze the health risk of occupational exposure to toxic chemicals in coal mines based on deep learning algorithm and mathematical model. In this study, the working environment and employees of four working areas in a mine in Shanxi Province were selected as the research objects, and the gas in the working environment was sampled. Through the methods of mathematical evaluation model and single factor evaluation grade, the lung condition of employees and the content of harmful gas components were calculated, and the risk index of the area was obtained. The experimental results show that in the range of 0.56kg/s-3.06kg/s, the induced airflow velocity of chute increases by 54%, and the induced airflow velocity of guide chute increases by 53%. It shows that the increase of induced airflow velocity increases significantly with the increase of coal cutting amount, and the content of harmful substances in the working environment is higher. It is concluded that the deep learning mathematical model in this study is very effective and accurate in the health risk assessment caused by toxic chemicals in coal mines, and contributes to the risk assessment. It is very important for the safety management of the workers in the mine.