风电场运营商始终致力于优化维修策略，以期在提高资源利用率的同时控制运营与维修成本。然而，传统维修决策方法不仅耗时，且在应对多样化场景时表现出灵活性与适应性不足的局限性。为此，本研究针对这些挑战，利用大语言模型（Large language model，LLM）来理解涉及多种故障模式和维修特征的维修决策问题，规划和生成相应的维修策略。本文提出了一种基于标注数据监督微调的风电场维修决策领域专用大语言模型，命名为LLM4M。该模型以小参数量LLM为基础模型，在大规模维修规划数据集上进行训练，能够为风电场生成最优的维修策略。实验结果表明，与多个成熟的大参数量LLM相比，微调后的LLM4M展现出显著的准确性，其生成的策略与最优策略的误差约为2%；LLM4M的泛化能力也取得了显著成效，当模型正确生成维修策略时，维修成本与最优解的偏差仅约5%。此外，本研究还观察到了训练中的相变现象，这为维修领域开发专用大语言模型提供了重要指导。

Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs. However, conventional maintenance decision-making approaches are time-consuming and have poor flexibility and adaptability to various scenarios. This study addressed these challenges by using a large language model (LLM) to understand, generate, and plan maintenance strategies for wind farms characterized by various failure modes and maintenance costs. A labelled-data-supervised fine-tuning LLM for maintenance, named LLM4M, is proposed. The proposed LLM4M model is trained on an extensive dataset of mathematical programs for maintenance to generate optimal strategies for wind farms. Compared with other large parameter LLMs, the fine-tuned LLM4M model demonstrates remarkable accuracy, with an error of approximately 2% from the optimal strategy. In addition, the generalization of the proposed LLM4M model has achieved remarkable results. If the LLM4M model correctly generates the maintenance strategy, the maintenance cost deviates from the optimal solution by only approximately 5%. Furthermore, phase transition behavior is observed, which provides considerable guidance for the development of domain-specific LLMs for the maintenance domain.