# Research on Grading Evaluation of Coal and Gas Dynamic Disasters Based on Fuzzy Mathematics
## 1 Introduction  
Coal mining continues to be the cornerstone of energy utilization in China. In 2023, the nation's total raw coal production reached 4.71 billion tons, with Guizhou and Yunnanprovinces in Southwest China contributing 4.70% to the national output [1; 2]. Although this proportion is relatively low, Southwest China ranks among the regions that are the most significantly impacted by coal and gas dynamic disasters (CGDDs). Notably, several severe coal and gas outburst accidents have occurred in recent years, including incidents at the Rong'an Coal Mine in Yunnan (April 2022), the Guli Coal Mine in Guizhou (March 2023), the Pingdingshan Coal Mine in Yunnan (June 2024), and the Gotout Coal Mine in Yunnan (August 2024) [3; 4]. The region's coal deposits predominantly consist of high-metamorphic anthracite, characterized by complex geological structures, uneven stress distribution, and high gas emissions. Furthermore, as regional coal mines transition into medium- and deep-well mining, the interaction between stress and gas has intensified, thereby increasing the risk of composite dynamic disasters [5]. Therefore, it is essential to develop a grading evaluation method for CGDDs to enhance risk assessment and mitigation strategies. Significant progress has been made in understanding and monitoring CGDDs, particularly in their mechanisms, prediction, and early warning technologies. Simulation systems and experiments based on the CSIRO prominence model have enhanced theoretical research, confirming that gas expansion energy is the primary driver of CGDDs, while geological structures create favorable conditions for gas outbursts during mining. CGDDs typically progress through three stages: preparation, initiation, and development. These stages are governed by the stress states in outburst cavities and the gas pressure gradients in coal pores and fractures [6; 7; 8; 9; 10]. Early prediction of CGDDs in China relied on field measurements of key indicators, such as the initial gas emission velocity from boreholes and drill cuttings volume. and manual monitoring of gas concentration served as the primary early warning method. Advancements in mine safety monitoring systems have since enabled real-time gas concentration monitoring and automatic over-limit alarms, improving early warning capabilities [11; 12; 13]. For mining-induced stress monitoring, techniques, such as acoustic emission, electromagnetic radiation, microseismic monitoring, and integrated acoustic-electric-gas systems, are widely used [14; 15; 16; 17; 18]. In gas dynamic emission monitoring, methods have evolved from manual calculations to automated real-time monitoring using mine safety systems [19; 20]. More recently, mathematical modeling and machine learning have been applied to analyze gas emission patterns, further enhancing prediction accuracy [21]. Despite significant progress in CGDD monitoring, limitations remain in detecting precursor signals and establishing reliable early warning indicators. Traditional single-factor methods, which rely on intermittent, point-based predictions, lack adaptability and fail to capture the complex, real-time evolution of CGDDs. Additionally, multi-source data fusion remains underexplored, and the interactions between multiple influencing factors are not well understood. The coal mine environment is highly complex, with external factors affecting individual monitoring indicators, making it difficult to improve prediction accuracy and reduce false alarms. Moreover, current monitoring systems struggle to respond effectively to sudden CGDD events, and a real-time dynamic risk assessment framework has yet to be fully developed. In recent years, fuzzy mathematics has proven highly effective in uncertainty analysis and multi-factor evaluation, offering distinct advantages in geological disaster prediction. CGDDs are a complex process influenced by geological conditions, mining disturbances, and gas dynamics, making traditional single-indicator models insufficient for capturing their evolution [22; 23; 24].