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1. 中国联合网络通信有限公司研究院,北京 100048
2. 中国联合网络通信有限公司广东省分公司,广东 广州 510660
3. 中国联合网络通信集团有限公司,北京 100032
[ "韩赛(1988- ),女,博士,中国联合网络通信有限公司研究院高级工程师,主要研究方向为网络智能、自智网络等" ]
[ "张冬月(1995- ),女,中国联合网络通信有限公司研究院助理工程师,主要研究方向为网络智能等" ]
[ "王泽林(1983- ),男,中国联合网络通信有限公司研究院高级工程师,主要研究方向为IP、云网、白盒、SDN技术等" ]
[ "王光全(1968- ),男,中国联合网络通信有限公司研究院教授级高级工程师,主要研究方向为通信网络的规划、新技术演进、标准制定等" ]
[ "李奥(1994- ),女,中国联合网络通信有限公司研究院助理工程师,主要研究方向为网络智能等" ]
[ "方遒铿(1980- ),男,中国联合网络通信有限公司广东省分公司工程师,主要研究方向为光网络、IP承载等" ]
[ "马红兵(1967- ),男,中国联合网络通信集团有限公司科技创新部总经理、正高级工程师,主要研究方向为无线通信领域新技术研究、标准制定、技术试验等" ]
网络出版日期:2022-11,
纸质出版日期:2022-11-20
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韩赛, 张冬月, 王泽林, 等. 跨专业承载网络智能运维研究与应用[J]. 电信科学, 2022,38(11):113-122.
Sai HAN, Dongyue ZHANG, Zelin WANG, et al. Research and applications on intelligent operations of cross-professional carrying network[J]. Telecommunications science, 2022, 38(11): 113-122.
韩赛, 张冬月, 王泽林, 等. 跨专业承载网络智能运维研究与应用[J]. 电信科学, 2022,38(11):113-122. DOI: 10.11959/j.issn.1000-0801.2022269.
Sai HAN, Dongyue ZHANG, Zelin WANG, et al. Research and applications on intelligent operations of cross-professional carrying network[J]. Telecommunications science, 2022, 38(11): 113-122. DOI: 10.11959/j.issn.1000-0801.2022269.
随着人工智能技术和网络日趋紧密地融合,未来网络的运营和生产应该是全面数字化、自动化和智能化的。目前跨专业网络故障定位主要依赖各专业运维人员分别进行分析和派单,导致排障时间长、重复派单等问题。为了节省人力工作,借助自动化和人工智能技术,研发了一套应用于实际现网的跨专业网络智能运维系统。该系统可对IPRAN和OTN关联的拓扑信息以及实时的IPRAN和OTN告警数据进行综合分析,精准定位根因告警,确定故障位置,实现自动派单。该系统构建了跨专业的故障自动诊断能力,将故障定位时间由传统的2 min缩短为几十毫秒,极大减少故障处理时间和人工工作,可覆盖现网大部分故障种类。
With the increasingly close integration of artificial intelligence technology and networks
the operation and production of networks in the future should be fully digitalized
automated and intelligent.At present
fault locating of cross-professional network mainly relies on operation and maintenance staff of each professional network to analyze and dispatch orders
resulting in long trouble clearing time and repeated orders.In order to save manual work
with the help of automation and artificial intelligent technologies
a cross-professional network intelligent operation and maintenance system was developed and applied in real network.The associated topology information of IPRAN and OTN
along with the real-time IPRAN and OTN alarm data were analyzed
the root cause alarm was located accurately
the fault location was determined
and dispatch order was achieved automatically.A automatic cross-professional fault diagnosis capability was built by the system
which reduced the fault locating time from traditional two minutes to tens of milliseconds
therefore
the manual work was reduced by more than 90%
and 95% fault types of the existing network could be covered.
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