# MIRA-ChatGLM: A Fine-Tuned Large Language Model for Intelligent Risk Assessment in Coal Mining
## 5 Discussion  
In the field of intelligent risk assessment for coal mines, traditional risk evaluation primarily relies on expert-driven manual analysis and judgment. This process is not only time-consuming and labor-intensive but also heavily dependent on the expertise of individuals, making it susceptible to subjective biases. Furthermore, manual assessments struggle to provide rapid responses and handle large-scale data, failing to meet the efficiency demands of modern mine safety management. With advancements in technology, existing large-scale models have begun to extract environmental and risk factors from input data to improve assessment accuracy. However, these models often lack the ability to generate detailed risk management and emergency response recommendations. Mine managers and safety personnel are more concerned with obtaining specific countermeasures and management suggestions for each risk factor, as these insights are critical for enhancing mine safety and preventing accidents. However, current intelligent technologies in the coal mining domain are primarily focused on risk prediction and early warning, rather than direct risk assessment. As shown in Table 9, a comparison with existing methods highlights the unique advantages of the proposed MIRA-ChatGLM method in several aspects. Compared to traditional expert systems that rely on rules and risk evaluation methods based on a single data type, MIRA-ChatGLM is more effective at handling multidimensional data, integrating factors such as gas concentration, sensor location, and sensor equipment status, thereby providing more accurate and comprehensive risk assessments. Additionally, MIRA-ChatGLM employs a large language model, fine-tuned with instructions, which efficiently processes complex multisource data, reduces dependence on large volumes of labeled data, and significantly enhances both the efficiency and accuracy of the evaluation. While existing methods can still provide certain risk assessments in specific scenarios, they have limitations when dealing with complex mining environments, multidimensional data fusion, and limited labeled data. Therefore, MIRA-ChatGLM demonstrates greater adaptability and practical application potential in intelligent risk assessment, decision support, and emergency response scenarios. In order to meet the demand for intelligent risk assessment in coal mining operations, we have integrated various data sources, including mining safety protocols, alarm data collected from on-site sensors, alarm causes, and disposal measures, to construct a comprehensive dataset. By training on a large volume of historical alarm data, the model is able to accurately assess risks based on information provided by the sensors, such as measurement point types, locations, and maximum alarm data. It can also analyze the causes of alarms and propose corresponding disposal suggestions. The primary objective of this study is to explore the practical application of large language models in coal mine risk assessment. By fine-tuning the GLM-4-9B-chat base model, we developed the MIRA-ChatGLM, which is capable of automatically generating mining risk assessment results and providing detailed response strategies for specific scenarios. The development of MIRA-ChatGLM fills the gap in the application of large language models in the coal mine risk assessment field. Compared to traditional manual assessment methods, MIRA-ChatGLM significantly improves assessment efficiency, ensures the consistency and professionalism of results, and reduces human error. This approach assists mine managers and safety personnel in obtaining more comprehensive and accurate risk information, thereby providing strong support for the development of efficient safety management plans and emergency response strategies. MIRA-ChatGLM, trained on data from specific mines, is capable of offering personalized risk suggestions based on the unique conditions of different mines, further enhancing the effectiveness of safety management and the timeliness of responses.