Design of mine air quality evaluation system based on improved random forests

In order to meet the demand for underground air quality monitoring by the mine frequency conversion ventilation system, a mine air quality evaluation method based on improved random forest is proposed, aiming at accurately monitoring the air quality inside the mine and providing an important reference for the frequency conversion ventilation system, so as to effectively safeguard the safe production and the miners' health. Firstly, analyze the sources of pollutants in the air of mines and their potential hazards to the health of miners, and the five main pollutants, namely carbon monoxide (CO), sulfur dioxide (SO2), hydrogen sulfide (H2S), nitrogen dioxide (NO2) and dust, are selected as the evaluation factors. Secondly, the standard for evaluating underground air quality was established, based on which an air quality evaluation rating system was set up and the corresponding data set was constructed accordingly. In this study, an improved random forest algorithm using AUC values is used to carry out a comprehensive evaluation of mine air quality. The experimental results show that the improved algorithm performs better than the original algorithm, with a minimum generalization error of only 0.0177 and a maximum classification accuracy of 97.72% for the test data. The method can better achieve the evaluation of air quality under the mine, with high robustness and stability. It provides new ideas and methods for the construction of smart mines and the evaluation of air quality in mines.