开源社区AI智能化运维探索OA

[Objective]This study proposes and validates an LLM-driven intelligent Operations-and-Main-tenance(O&M)framework for the OpenHarmony open-source community,designed to man-age the surge in code submissions and multimodal maintenance demands and to accelerate fault localization and resolution during the compilation and static-analysis stages.[Methods]The framework combines Qwen LLMs with an Adversarial-Collaboration Retrieval Augmentation(AC-RAG)pipeline and Retrieval-Augmented Chain-of-Thought(RA-CoT)fine-tuning.Curat-ed multimodal logs support incremental pre-training and LoRA adaptation for domain align-ment,while a built-in AI assistant provides real-time Q&A and automated fixes within DevOps.[Results]Online deployment indicates that the framework substantially shortens the diagnosis-and-repair cycle while significantly reducing extensive manual intervention.It enhances collab-orative efficiency,preserves code quality,and ultimately saves the community several hundred person-months of O&M effort annually.