Optical Network Multi-fault Localization based on Network Topology and Knowledge Graph
As the optical network structure becomes larger and more complex, optical network faults are more likely to occur. After a network fault occurs, due to the derivative characteristics of network alarms, the root cause alarms will generate multiple derivative alarms. Therefore, after network faults oc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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《光通信研究》编辑部
2022-08-01
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| Series: | Guangtongxin yanjiu |
| Subjects: | |
| Online Access: | http://www.gtxyj.com.cn/thesisDetails#10.13756/j.gtxyj.2022.04.006&lang=zh |
| _version_ | 1811211435381882880 |
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| author | Jian-xing HAN Zhuo-tong LI Yin-ji JING Yong-li ZHAO Jie ZHANG |
| author_facet | Jian-xing HAN Zhuo-tong LI Yin-ji JING Yong-li ZHAO Jie ZHANG |
| author_sort | Jian-xing HAN |
| collection | DOAJ |
| description | As the optical network structure becomes larger and more complex, optical network faults are more likely to occur. After a network fault occurs, due to the derivative characteristics of network alarms, the root cause alarms will generate multiple derivative alarms. Therefore, after network faults occur, the network management system will receive alarm storms. Due to the complex relationship between faults and alarms, the difficulty of locating network faults, especially multiple faults, has also risen sharply. In response to this problem, the knowledge graph technology that is good at managing massive amounts of information and revealing the characteristics of data is introduced into optical networks. The alarm knowledge graph contains rich relationships between alarms, which can be completed by combining Graph Neural Network (GNN) technology. The knowledge-guided automatic reasoning of the root cause of network faults is in line with the fault location ideas of the operation and maintenance personnel in the operation and maintenance process. Further, the network topology information is added in the fault location process, and the knowledge dimension of the knowledge graph is improved. The limitation of the single-fault scenario is lifted, and a high accuracy rate is obtained in the multi-fault location scenario. |
| first_indexed | 2024-04-12T05:13:00Z |
| format | Article |
| id | doaj.art-c75ee020353b4faf868e29a41a069b9e |
| institution | Directory Open Access Journal |
| issn | 1005-8788 |
| language | zho |
| last_indexed | 2024-04-12T05:13:00Z |
| publishDate | 2022-08-01 |
| publisher | 《光通信研究》编辑部 |
| record_format | Article |
| series | Guangtongxin yanjiu |
| spelling | doaj.art-c75ee020353b4faf868e29a41a069b9e2022-12-22T03:46:43Zzho《光通信研究》编辑部Guangtongxin yanjiu1005-87882022-08-0142730,5110.13756/j.gtxyj.2022.04.0061005-8788(2022)04-0027-04Optical Network Multi-fault Localization based on Network Topology and Knowledge GraphJian-xing HANZhuo-tong LIYin-ji JINGYong-li ZHAOJie ZHANGAs the optical network structure becomes larger and more complex, optical network faults are more likely to occur. After a network fault occurs, due to the derivative characteristics of network alarms, the root cause alarms will generate multiple derivative alarms. Therefore, after network faults occur, the network management system will receive alarm storms. Due to the complex relationship between faults and alarms, the difficulty of locating network faults, especially multiple faults, has also risen sharply. In response to this problem, the knowledge graph technology that is good at managing massive amounts of information and revealing the characteristics of data is introduced into optical networks. The alarm knowledge graph contains rich relationships between alarms, which can be completed by combining Graph Neural Network (GNN) technology. The knowledge-guided automatic reasoning of the root cause of network faults is in line with the fault location ideas of the operation and maintenance personnel in the operation and maintenance process. Further, the network topology information is added in the fault location process, and the knowledge dimension of the knowledge graph is improved. The limitation of the single-fault scenario is lifted, and a high accuracy rate is obtained in the multi-fault location scenario.http://www.gtxyj.com.cn/thesisDetails#10.13756/j.gtxyj.2022.04.006&lang=zhknowledge graphoptical networksfault localization |
| spellingShingle | Jian-xing HAN Zhuo-tong LI Yin-ji JING Yong-li ZHAO Jie ZHANG Optical Network Multi-fault Localization based on Network Topology and Knowledge Graph Guangtongxin yanjiu knowledge graph optical networks fault localization |
| title | Optical Network Multi-fault Localization based on Network Topology and Knowledge Graph |
| title_full | Optical Network Multi-fault Localization based on Network Topology and Knowledge Graph |
| title_fullStr | Optical Network Multi-fault Localization based on Network Topology and Knowledge Graph |
| title_full_unstemmed | Optical Network Multi-fault Localization based on Network Topology and Knowledge Graph |
| title_short | Optical Network Multi-fault Localization based on Network Topology and Knowledge Graph |
| title_sort | optical network multi fault localization based on network topology and knowledge graph |
| topic | knowledge graph optical networks fault localization |
| url | http://www.gtxyj.com.cn/thesisDetails#10.13756/j.gtxyj.2022.04.006&lang=zh |
| work_keys_str_mv | AT jianxinghan opticalnetworkmultifaultlocalizationbasedonnetworktopologyandknowledgegraph AT zhuotongli opticalnetworkmultifaultlocalizationbasedonnetworktopologyandknowledgegraph AT yinjijing opticalnetworkmultifaultlocalizationbasedonnetworktopologyandknowledgegraph AT yonglizhao opticalnetworkmultifaultlocalizationbasedonnetworktopologyandknowledgegraph AT jiezhang opticalnetworkmultifaultlocalizationbasedonnetworktopologyandknowledgegraph |