Operation and maintenance fault diagnosis and analysis method based on subgraph matching and distributed query

Abstract

The invention discloses an operation and maintenance fault diagnosis and analysis method based on subgraph matching and distributed query, which comprises the following steps of: 1) establishing a fault handling measure retrieval model of a knowledge graph based on a subgraph matching method; 2) based on similarity calculation of a graph structure and semantic information, sorting the result sub-graphs to obtain an optimal query result; (3) optimization is carried out based on a Top-k query model, and a distributed query method is used for increasing the query speed; 4) grading the operation and maintenance alarm data and screening related network element attributes; 5) regularizing the processing steps of each fault based on the large-scale intelligent operation and maintenance knowledge graph; and 6) directly calling an entity-relation-entity object in an intelligent operation and maintenance decision analysis module based on the knowledge graph platform in the steps 1), 2) and 3), and finally forming a one-key operation and maintenance fault diagnosis and analysis state.According to the method, the availability problem and the efficiency problem in the prior art are solved through optimization in the directions of sub-graph matching, retrieval algorithm, distributed processing and the like.

Description

technical field [0001] The invention relates to the field of intelligent retrieval and analysis, in particular to an operation and maintenance fault diagnosis and analysis method based on subgraph matching and distributed query. Background technique [0002] With the continuous development of artificial intelligence, intelligent retrieval and analysis methods based on knowledge graphs are gradually applied to search engines, education, medical care, smart grids and other fields. Extract semantic information such as entities, attributes, and relationships from data in various fields through extraction technology, build a knowledge base through knowledge fusion, knowledge processing and other technologies, and then realize the retrieval and analysis services required by users through matching analysis between entities. At the same time, the knowledge map adopts the format of ontology terminology and semantic expression, and has a standardized and standard conceptual model, whi...

AI Technical Summary

Problems solved by technology

[0004] First of all, most of the traditional knowledge graph query models require the query results to accurately match the user's query. However, due to the noise data in the knowledge graph, these models will miss the query results that the user is interested in, and there is a problem of poor usability.

[0005] Secondly, in order to speed up the query, traditional knowledge graph query algorithms generally use graph index technology, but the data scale of the intelligent operation and maintenance knowledge graph in this project is large, and it takes a lot of time and space to establish a graph index

[0006] Finally, due to the complex and large-scale intelligent operation and maintenance knowledge graph network, it is necessary to implement the query process in a distributed manner. However, the traditional distributed graph data processing platform is not optimized for the execution process of the knowledge graph query, and there is a problem of low execution efficiency. question

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