This invention relates to the field of
artificial intelligence technology in mining, and particularly to a method, apparatus, and equipment for fine-tuning and
distillation of a
coal mine underground model. The method includes: initial supervised fine-tuning of a teacher model and a target model using a precisely labeled dataset; performing multi-channel parallel
inference on an unlabeled dataset using the initial teacher model and the target model, and constructing a high-quality
distillation dataset after consistency
verification and quality screening; fusing precisely
labeled data, publicly available general data, and
distillation data to form a comprehensive
training set, and performing secondary supervised fine-tuning of the target model using multi-loss fusion; iteratively updating and quality-evaluating the distillation dataset based on the optimized target model, and performing enhanced fine-tuning using a thought chain
inference template to obtain a final model adapted to edge devices. This invention effectively utilizes multi-source underground data and reduces labeling costs, significantly improving the model's adaptability,
inference efficiency, and robustness in
anomaly detection in complex underground environments, achieving a synergistic optimization of high accuracy and
low resource consumption.