雷鸣,郑荣,张成根,周斌,马翻红

• 1:中国联合网络通信有限公司湖南省分公司

摘要(Abstract)：

为应对5G/云网融合时代下网络运维在规模、复杂性和实时性方面挑战，文章提出一种面向规模化交付的AIOps平台构建方法，设计一个兼具电信级可靠性与云原生弹性的五层架构，重点构建融合动态拓扑与领域知识的故障根因定位模型，实现从数据治理、智能分析到自动化闭环的全链路支撑，并创新性地提出“分阶段、场景驱动、组织流程适配”的规模化交付方法论，旨在推动AIOps从技术试点走向跨域协同的生产化部署，为网络运维智能化转型提供系统化实施路径。

关键词(KeyWords)： 智能运维(AIOps);通信网络;故障根因定位;知识图谱

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 雷鸣,郑荣,张成根,周斌,马翻红

参考文献(References)：

• [1]COLAKOVIC A, HADZIALIC M. Internet of things(IOT):A review of Enabling Technologies, challenges, and open research issues[J]. Comput. Netw. 2018,144:17-39
• [2]MAO B, TANG F, KAWAMOTO Y, et al. AI Models for Green Communications Towards 6G. IEEE Communications Surveys&Tutorials[J]. 2022,24:210-247
• [3]MAO B, LIU J, WU Y, et al. Security and Privacy on6G Network Edge:A Survey. IEEE Communications Surveys&Tutorials[J].2023,25:1095-1127
• [4]ALI A S, BADDELEY M, BARIAH L, et al. Performance analysis and evaluation of RF jamming in IoT networks[C]//. In Proceedings of the GLOBECOM 2022-2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022:2745-2751
• [5]LUNARDI W T, LOPEZ M A, GIACALONE J P. ARCADE:Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection[J]. IEEE Trans. Netw.Serv. Manag. 2023,20:1305-1318
• [6]GABRIEL R P, NORTHROP L M, SCHMIDT D C, et al. UltraLarge-Scale Systems[C]//Companion to the21th Annual ACM SIGPLAN Conference on ObjectOriented Programming, Systems, Languages, and Applications, OOPSLA 2006, Portland, Oregon, USA
• [7]Summary of the Amazon S3 Service Disruption in the Northern Virginia(US-EAST-1)Region[EB/OL].[2026-02-26]. https://aws.amazon.com/message/41926/
• [8]XIANG Z, GUO D, LI Q. Detecting mobile advanced persistent threats based on large-scale DNS logs[J].Computers&Security.2020(96):101933
• [9]HOULE J J, ROSEEN R M, BALLESTERO T P, et al.Comparison of Maintenance Cost, Labor Demands,and System Performance for LID and Conventional Stormwater Management[J]. Journal of Environmental Engineering 2013(139):932-938
• [10]CHENG Q, SAHOO D, SAHA A, et al. AI for IT Operations(AIOps)on Cloud Platforms:Reviews[EB/OL].[2026-05-27]. https://arxiv.org/abs/2304.04661
• [11]DANG Y, LIN Q, HUANG P. AIOps:Real-world challenges and researchinnovations[C]//Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering:Companion Proceedings(ICSE-Companion)(ICSE'19), Montreal,QC, Canada,2019:4-5
• [12]Lerner A. AIOps Platforms—Gartner. 2017[EB/OL].[2026-02-26]. https://blogs.gartner.com/andrewlerner/2017/08/09/aiops-platforms
• [13]LEVIN A, GARION S, KOLODNER E K, et al. AIOps for a cloud object storage service[C]//Proceedings of the 2019 IEEE International Congress on Big Data(BigDataCongress), Milan, Italy,2019:165-169
• [14]WANG H, ZHANG H. AIOPS prediction for hard drive failures based on stacking ensemble model[C]//Proceedings of the 2020 10th Annual Computing and Communication Workshop and Conference(CCWC),2020:0417-0423
• [15]RIJAL L, COLOMO-PALACIOS R, SANCHEZ-GORDON M. AIOps:A multivocal literature review[C]//Artificial Intelligence for Cloud and Edge Computing, Internet of Things, 2022
• [16]NOTARO P. CARDOSO J, GERNDT M. A Survey of AIOps Methods for Failure Management[J]. ACM transactions on intelligent systems and technology2021,12(6):1-45
• [17]HUTTER F, KOTTHOFF L, VANSCHOREN J.Automated Machine Learning, The Springer Series on Challenges in Machine Learning[EB/OL].[2026-02-26]. https://library.oapen.org/bitstream/hand le/20.500.12657/23012/1/1007149.pdf
• [18]HOSPEDALES T, ANTONIOU, A, MICAELLI P, et al. Meta-Learning in Neural Networks:A Survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021(44):5149-5169
• [19]GUI J Y, MA Z N, ZHOU H, et al. Deep Anomaly Detection of Temporal Heterogeneous Data in AIOps:A Survey[J]. Frontiers of Information Technology&Electronic Engineering, 2025,26(9):1551-1576
• [20]胡记伟,赵明明,李旺,等.知识图谱增强的大语言模型辅助通信网络运维[J].计算机应用,2025,45(S2):46-51
• [21]ZHANG L, TONG X, LI Z, et al. A Survey of AIOps in the Era of Large Language Models[J]. ACM Computing Surveys,2026,58(2):1-35