# MIRA-ChatGLM: A Fine-Tuned Large Language Model for Intelligent Risk Assessment in Coal Mining
## 2 Related Work
### Current Research Status of Intelligent Assessment Systems  
Coal mine risk assessment is a critical component for ensuring the safe operation of mines, with a primary focus on monitoring gas concentrations, such as methane and carbon monoxide, and providing early risk warnings [4]. Traditional coal mine risk assessment methods often rely on rule-based expert systems, which analyze sensor data to identify potential hazards, such as elevated gas concentrations or leaks, and trigger alarms based on predefined thresholds. However, as mining conditions become increasingly complex, these methods face growing limitations in adaptability and accuracy.  
In recent years, data-driven machine learning methods have been introduced into coal mine risk assessment. Traditional machine learning algorithms, such as random forests and support vector machines (SVMs) [5; 6], have been employed to analyze gas concentration data, aiding in the identification of risk patterns. Meanwhile, deep learning techniques, known for their robust feature extraction capabilities, have further advanced intelligent coal mine research. For example, long short-term memory (LSTM) networks and gated recurrent units (GRUs) have been applied to time-series predictions of gas concentrations, significantly improving the accuracy of risk forecasting by capturing temporal dependencies in the data [7; 8]. However, these methods primarily focus on unimodal data inputs, making it challenging to integrate multi-dimensional data--such as gas measurements, environmental parameters, and equipment states--for comprehensive risk assessment.  
To overcome the limitations of single-source data, multi-dimensional data fusion techniques have gradually been introduced into coal mine risk analysis research [9]. By combining data from various sensors--such as methane, carbon monoxide, oxygen, and gas concentrations--researchers have developed more sophisticated models [10; 11]. These models not only enhance the robustness of risk assessments but also improve system adaptability to complex scenarios [12]. Nevertheless, the processing of multi-dimensional data imposes higher demands on computational resources and data labeling quality. In real-world coal mining environments, complex operational conditions and limited annotated data pose significant challenges to the adoption of such methods.  
Additionally, intelligent algorithms have garnered significant attention in coal mine risk assessments. For instance, [PERSON] et al. utilized t-SNE to process high-dimensional gas incident data and employed genetic algorithms to optimize SVMs, achieving notable improvements in accuracy and efficiency [13]. Similarly, [PERSON] et al. developed an integratedtime-series evaluation model based on multi-source dynamic detection and data fusion. This model was successfully implemented in practical coal mine settings, providing reliable and precise dynamic hazard warnings [14]. However, most existing studies focus on optimizing individual algorithms or modules, lacking a comprehensive exploration of systematic risk assessment. Such limitations may result in the omission of critical information, thereby reducing reliability in real-world applications.  
Notably, with the rapid development of artificial intelligence, pre-trained large language models (LLMs) have demonstrated promising potential in coal mine risk assessment due to their powerful generalization capabilities in multimodal tasks. These models can semantically interpret multimodal data and leverage instruction learning and knowledge transfer to support complex decision-making tasks, paving the way for enhanced intelligence in coal mine operations.