# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 5 Experiments
### Discussion  
The Intelligent Mine Gas State Decision-Making System developed in this study demonstrates excellent performance in intelligent Q&A, intelligent judgment, and report generation, providing robust technical support for mine safety management. In terms of functionality, the experimental data show that the system achieves a high level of accuracy and consistency in intelligent Q&A, demonstrating its ability to accurately understand and respond to complex safety production issues. The intelligent judgment module exhibits efficient decision-making and data processing capabilities, capable of making accurate alarm judgments based on real-time gas data and providing reasons for alarms along with handling suggestions. This is crucial for the early prevention and timely management of potential safety hazards. Regarding report generation, the automatic report generation module significantly reduces the time required compared to traditional manual report writing, which is critical for the quick response needs in mine safety management. Additionally, the automatically generated reports maintain a very low level of grammatical errors, outperforming potentially error-prone manual reports due to human factors, and excel in textual accuracy. Moreover, the experimental results indicate that the hybrid multi-agent orchestration algorithm adopted by the system outperforms single centralized or distributed algorithms across various performance metrics, fully validating its effectiveness and superiority. While centralized algorithms ensure orderly task execution and rational resource utilization, their lack of flexibility may impede response speed and efficiency when dealing with complex and dynamic local environments. In contrast, distributed algorithms are capable of quick responses and adapting to environmental changes but may face difficulties in coordinating overall tasks, resulting in inconsistencies and inaccuracies. The hybrid multi-agent orchestration algorithm implemented in the system effectively combines the advantages of both centralized and distributed algorithms, achieving an organic integration of global task planning and local task execution. However, the system also has some limitations. Firstly, while the use of large language models (LLMs) has enhanced the overall performance of the system, it also increases system complexity and operational costs, suggesting the need for further optimization of response time. Secondly, although the experimental results indicate that the system performs well in a simulated experimental environment, the lack of information from tests conducted in actual mining conditions limits a comprehensive assessment of the system's performance under dynamic and complex conditions. Furthermore, the accuracy of the report prediction component needs improvement. The current approach combines only simple time series models with LLMs, and the predictive results are not yet ideal. Lastly, existing automatic evaluation tools have limitations in processing lengthy answers, and reliance on expert evaluation may introduce subjective bias. To overcome the existing limitations, future research can focus on the following improvement directions:  
* Lightweight LLM Model Research: Explore key technologies such as model compression, quantization, and fine-tuning to reduce model complexity, enhance system response speed, and effectively lower operational costs while ensuring model performance. * Actual Environment Testing: Design and implement system tests in a real mining environment to comprehensively assess its performance under dynamic and complex conditions. This can be achieved through collaboration with coal mining companies to conduct on-site pilot projects, ensuring that the system can adapt to various challenges encountered in actual operations. * Construction of Complex Prediction Models: Integrate deep learning and other technologies to build more accurate prediction models, improving the accuracy and reliability of predictive results.