开源社区AI智能化运维探索

doi:10.11871/jfdc.issn.2096-742X.2026.02.011

数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (2): 141-153.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.02.011

doi: 10.11871/jfdc.issn.2096-742X.2026.02.011

开源社区AI智能化运维探索

任旭东^1,^2,^*(),孟广浩²,张乐天²,齐宝玮¹,王意明¹

¹ 华为技术有限公司开源与开发者发展部，广东深圳 518129
² 清华大学深圳国际研究生院，广东深圳 518055

收稿日期:2025-05-05 出版日期:2026-04-20 发布日期:2026-04-23
通讯作者: *任旭东（E-mail: rxd21@mails.tsinghua.edu.cn）
作者简介:任旭东，华为公司首席开源联络官、开源与开发者发展部部长，CNCF基金会董事、开放原子开源基金会开源安全委员会副主席。毕业于上海交通大学计算机系，并于北京大学汇丰商学院获得高级工商管理硕士学位（EMBA）。2000年加入华为，曾在研发、市场、产品管理等多个岗位积累了丰富经验。现阶段主要负责以开源为手段推进产业生态构建与协同创新。
本文主要贡献：项目负责人，提出智能化运维体系建设的顶层设计思路，并撰写论文。
REN Xudong is the Chief Open Source Liaison Officer at Huawei and the Director of the Open Source and Developer Relations Department. He serves as a Governing Board Member of the CNCF and Vice Chair of the Open Source Security Committee of the OpenAtom Open Source Foundation. He holds a bachelor's degree in Computer Science from Shanghai Jiao Tong University and an Executive MBA from the Peking University HSBC Business School. Since joining Huawei in 2000, he has accumulated extensive experience across R&D, marketing, and product management. Currently, he leads efforts to advance industry ecosystems and collaborative innovation through open source initiatives.
In this paper, he serves as the project lead, proposing the top-level design framework for the intelligent O&M system and contributing to the writing of the manuscript.
E-mail: rxd21@mails.tsinghua.edu.cn

Exploration of AI Intelligent Operation and Maintenancein Open Source Community

REN Xudong^1,^2,^*(),MENG Guanghao²,ZHANG Letian²,QI Baowei¹,WANG Yiming¹

¹ Open Source and Developer Development Department, Huawei Technologies, Shenzhen, Guangdong 518129, China
² Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China

Received:2025-05-05 Online:2026-04-20 Published:2026-04-23

摘要/Abstract

摘要：

【目的】 本文旨在构建并验证一套面向OpenHarmony开源社区的AI智能化运维体系，以应对日益激增的代码提交与多模态运维需求，提升代码编译与静态检查的定位-修复效率。【方法】 系统采用Qwen大语言模型，融合对抗性协作检索增强（AC RAG）与思维链微调（RA CoT）策略；通过流水线日志多模态采集-清洗-标注构建训练数据，辅以增量预训练与LoRA微调实现领域适配，并在DevOps流程中嵌入AI助手提供实时问答与修复建议。 【结果】 线上部署表明，该体系显著缩短了问题定位-修复周期，减少了大量人工介入；在保持代码质量的同时，大幅提升社区协作效率，每年可为社区节约数百人月的运维成本。

关键词: OpenHarmony, 开源社区, 大模型, AI智能化

Abstract:

[Objective] This study proposes and validates an LLM-driven intelligent Operations-and-Maintenance (O&M) framework for the OpenHarmony open-source community, designed to manage 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. Curated multimodal logs support incremental pre-training and LoRA adaptation for domain alignment, 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 collaborative efficiency, preserves code quality, and ultimately saves the community several hundred person-months of O&M effort annually.

Key words: openHarmony, open source community, large language model, artificial intelligence

任旭东, 孟广浩, 张乐天, 齐宝玮, 王意明. 开源社区AI智能化运维探索[J]. 数据与计算发展前沿, 2026, 8(2): 141-153.

REN Xudong, MENG Guanghao, ZHANG Letian, QI Baowei, WANG Yiming. Exploration of AI Intelligent Operation and Maintenancein Open Source Community[J]. Frontiers of Data and Computing, 2026, 8(2): 141-153, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.02.011.

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参考文献 32

[1]	STALLMAN R M. The GNU manifesto[M]. Computers, Ethics, & Society. USA: Oxford University Press, Inc., 1990: 308-317.
[2]	RAYMOND E S. The cathedral and the bazaar: musings on Linux and open source by an accidental revolutionary[M]. USA: O’Reilly & Associates, Inc., 2001: 23-49.
[3]	FELLER J, FITZGERALD B. A framework analysis of the open source software development paradigm[C]. In. Association for Information Systems. Proceedings of the Twenty First International Conference on Information Systems. USA. 2000: 58-69.
[4]	LERNER J, TIROLE J. Some Simple Economics of Open Source[J]. The Journal of Industrial Economics, 2002, 50 (2): 197-234. doi: 10.1111/joie.2002.50.issue-2
[5]	OLATUNjI S O, IDREES S U, AL-GHAMDI Y S, et al. Mining software repositories-a comparative analysis[J]. International Journal of Computer Science and Network Security, 2010, 10(8): 161-174.
[6]	HARS A, OU S. Working for free? Motivations of participating in opensource projects[C]. In. Proceedings of the 34th Annual Hawaii International Conference on System Sciences. 2001: 9 pp.
[7]	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
[8]	HE K, ZHANG X, REN S, et al. Deep Residual Learning for Image Recognition[C]. In. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016: 770-778.
[9]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]. In. Curran Associates Inc. Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY, USA. 2017: 6000-6010.
[10]	BROWN T, MANN B, RYDER N, et al. Language Models are Few-Shot Learners[C]. In. Larochelle H, Ranzato M, Hadsell R et al. Curran Associates, Inc. Advances in Neural Information Processing Systems. 2020: 1877-1901.
[11]	ACHIAM J, ADLER S, AGARWAL S, et al. Gpt-4 technical report[J]. arXiv preprint arXiv:2303.08774. 2023.
[12]	TEAM G, ANIL R, BORGEAUD S, et al. Gemini: a family of highly capable multimodal models[J]. arXiv preprint arXiv:2312.11805, 2023.
[13]	ROUMELIOTIS K I, TSELIKAS N D, NASIOPOULOS D K. Llama 2: Early Adopters’ Utilization of Meta’s New Open-Source Pretrained Model[J]. 2023. DOI:10.20944/preprints202307.2142.v1.
[14]	LIU A, FENG B, XUE B, et al. Deepseek-v3 technical report[J]. arXiv preprint arXiv:2412.19437, 2024.
[15]	YANG A, XIAO B, WANG B, et al. Baichuan 2:Open large-scale language models[J/OL]. arXiv preprint arXiv: 2309.10305, 2023.
[16]	WANG T, ZHANG W, YE C, et al. FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing[J]. IEEE Transactionson Systems, Man, and Cybernetics: Systems, 2016, 46(1): 61-75.
[17]	LI X, ZHANG W. Deep Learning-Based Partial Domain Adaptation Method on Intelligent Machinery Fault Diagnostics[J]. IEEE Transactions on Industrial Electronics, 2021, 68(5): 4351-4361. doi: 10.1109/TIE.41
[18]	DU M, LI F, ZHENG G, et al. DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning[C]. In. Association for Computing Machinery. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York, NY, USA. 2017: 1285-1298.
[19]	KUMAR S. Artificial Intelligence in Software Engineering: A Systematic Exploration of AI-Driven Development[J]. International Journal of Innovative Research in Science Engineering and Technology, 2024, 13(6): 11903-11913.
[20]	DESMOND O C. AI-Powered DevOps: Leveraging machine intelligence for seamless CI/CD and infrastructure optimization[J]. International Journal of Science and Research Archive, 2022, 6(2): 094-107. doi: 10.30574/ijsra
[21]	Mnih V, Badia A P, Mirza M et al. Asynchronous methods for deep reinforcement learning[C]. In. JMLR. org. Proceedings of the 33rd International Conference on International Conference on Machine Learning-Volume 48. 2016: 1928-1937.
[22]	IZACARD G, GRAVE E. Distilling knowledge from reader to retriever for question answering[J]. arXiv preprint arXiv:2012.04584, 2020.
[23]	RACKAUCKAS Z. Rag-fusion: a new take on retrieval-augmented generation[J]. arXiv preprint arXiv:2402.03367, 2024
[24]	GAO L, MA X, LIN J, et al. Precise zero-shot dense retrieval without relevance labels[C]// Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023: 1762-1777.
[25]	WANG Z, LIU A, LIN H, et al. Rat: Retrieval augmented thoughts elicit context-aware reasoning in lo-ng-horizon generation[J]. arXiv preprint arXiv:2403. 05313, 2024.
[26]	WEI J, WANG X, SCHUURMANS D, et al. Chain-of-thought prompting elicits reasoning in large language models[C]. In. Curran Associates Inc. Proceedings of the 36th International Conference on Neural Information Processing Systems. Red Hook, NY, USA. 2022: 1-14.
[27]	罗远波, 孙嘉, 陶俐芝. 面向时间序列和时空数据分析的大模型研究进展[J]. 科技导报, 2025, 43(18): 48-56. doi: 10.3981/j.issn.1000-7857.2025.05.00037
[28]	NIE Y, NGUYEN N H, SINTHONG P, et al. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers[C]// The Eleventh International Conference on Learning Representations.
[29]	ZHANG Y, YAN J. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting[C]// The eleventh international conference on learning representations. 2023.
[30]	WU H, HU T, LIU Y, et al. Timesnet: Temporal 2d-variation modeling for general time series analysis[J]. arXiv preprint arXiv:2210.02186, 2022.
[31]	WANG H, PENG J, HUANG F, et al. Micn: Multi-scale local and global context modeling for long-term series forecasting[C]// The eleventh international conference on learning representations. 2023.
[32]	ZHOU T, NIU P, WANG X, et al. One fits all: power general time series analysis by pretrained LM[C]. In. Curran Associates Inc. Proceedings of the 37th International Conference on Neural Information Processing Systems. Red Hook, NY, USA. 2023: 1-34.

模型	准确率
Qwen1.5-32B	61.2%
Qwen1.5-32B + RA-COT	73.5%
Qwen1.5-32B + RA-COT + AC-RAG	81.7%

模型	准确率
Qwen3-32B	70.2%
Qwen3-32B + RA-COT	78.8%
Qwen3-32B + RA-COT + AC-RAG	86.4%

模型	F1-score
Qwen1.5-32B	51.2%
Qwen1.5-32B + 多模态拼接	72.5%
Qwen1.5-32B + 跨模态知识蒸馏	82.9%

开源社区AI智能化运维探索

Exploration of AI Intelligent Operation and Maintenancein Open Source Community

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