Key points are not available for this paper at this time.
The efficient management and utilization of coal mine equipment maintenance knowledge is an indispensable foundation for advancing the establishment of intelligent mines. This knowledge has problems such as scattered, low sharing, and insufficient management, which restricts the development of coal mine intelligence. For the above-mentioned problems, a large language model for the maintenance of coal mine equipment based on multi-source text (XCoalChat) was proposed to better manage and utilize the existing massive knowledge of coal mine equipment maintenance. The dataset of coal mine equipment maintenance based on ReliableCEMK-Self-Instruction was constructed to obtain a wide and diverse amount of knowledge through sample generation. Aiming at the illusory problem of the large language model, a knowledge graph enhancement method based on the “Coal Mine Equipment Maintenance System—Full Life Cycle—Specification” was proposed to improve the knowledge density. A triple-LoRA fine-tuning mechanism and DPO direct preference optimization method were introduced into the top of the baseline model, which guarantees that XCoalChat can handle multiple Q XCoalChat also took the least reasoning time on average. XCoalChat outperformed other mainstream large language models, which verify that XCoalChat is an effective large language model in the field of coal mine equipment maintenance.
Cao et al. (Sun,) studied this question.