###### Abstract  
In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy-Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety. airflow field measurement; metal mine ventilation system; machine learning; prediction model; convolutional neural networks +
Footnote †: journal: Medical  
Therefore, many researchers have optimized mine ventilation [10; 11]. For example, [PERSON] [12] uses modern mathematical methods to improve the automatic design planning of mine ventilation systems and discusses the prospect of genetic methods for analyzing alternative ventilation systems in the design stage. In addition, new monitoring technology greatly facilitates the monitoring of mine airflow parameters [13; 14; 15]. For example, [PERSON] et al. proposed an efficient method for optimizing sensors' layout to measure underground ventilation networks accurately [16]. Therefore, the limited quantitative data and expensive monitoring techniques hinder the accurate evaluation of mine ventilation effects and safety status. As a result, practitioners have simulated mine ventilation parameters from the simulation perspective [17]. Some three-dimensional simulation system suitable for all kinds of mines have been developed, such as network solutions [18]. The purpose of mine intelligent ventilation is to realize mine air supply according to demand through intelligent monitoring and perception, intelligent decision-making and intelligent regulation, stable and economical continuous delivery of fresh air to the mine for personnel to breathe, and the dilution and discharge of harmful gasses and dust [19]. In addition, it improves mine climate conditions, and disaster relief has a certain role in the intelligent regulation of airflow [20]. The mine ventilation system is a very complex network structure [21].