# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 2 Related Work
### Development of Mine Gas Monitoring Technology  
Mine gas safety monitoring, as a core research area in the mining sector, aims to achieve precise monitoring of gas concentrations through the integration of advanced technological methods and management strategies, effectively preventing gas-related incidents to ensure the safety of miners and the security of mining assets. With the rapid advancement of information technology, intelligent detection has gradually become a vital approach to enhancing the level of mine gas safety monitoring.  
In recent years, scholars, both domestically and internationally, have conducted extensive and in-depth research in the field of intelligent mine gas safety monitoring. [PERSON] et al. [12] introduced digital twin technology as an innovative approach to intelligent management, creating a digital replica of the physical mining entity. This technology facilitates real-time monitoring, behavior simulation, and performance prediction of the mining production environment. It can accurately identify potential gas accumulation and leakage risks, offering a new perspective and efficient tools for mine gas safety monitoring. In the realm of big data analysis technology [13; 14; 15], researchers have conducted deep mining and analysis of vast amounts of gas monitoring data, uncovering patterns and trends in gas emissions. This provides a scientific basis for judgment in gas safety monitoring and significantly enhances the precision and efficiency of gas safety detection. In the domain of artificial intelligence and machine learning technologies, [PERSON] et al. [16] developed a risk assessment model for unsafe behaviors in coal mine gas explosion accidents, based on the HFACS-GE framework and Bayesian networks. This model identifies risk paths and key factors, providing technical support for gas explosion risk management. [PERSON] et al. [17] proposed a prediction method for coal and gas outbursts based on GWO-SVM and optimized the parameters using the random forest algorithm, thereby enhancing prediction accuracy and robustness. The application of these technologies enables deep learning and pattern recognition of coal mine safety data, allowing for effective prediction and evaluation of safety risks. This provides timely safety warnings for mining enterprises, helping to reduce incident rates. Furthermore, the integration of the Internet of Things (IoT) technology [18; 19; 20; 21] in mine safety production management has achieved interconnectivity among equipment, sensors, and systems. By collecting and transmitting gas data in real time, IoT offers reliable data support for gas safety monitoring, enabling mining companies to precisely monitor gas conditions and promptly respond to anomalies.  
Despite the positive impact of the aforementioned technologies on mine gas safety monitoring, the current level of intelligence in research still needs to be enhanced. While some systems have begun integrating artificial intelligence technologies, there remains room for improvement, especially in areas such as data processing, in-depth analysis, and decision support. The functionalities of these systems require further strengthening and optimization.