Data platform fault positioning system and method based on knowledge graph

CN120803797AActive Publication Date: 2025-10-17上海市大数据中心
5 Cites -1 Cited by

Patent Information

Application Number
CN202511299851.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-12
Publication Date
2025-10-17
Estimated Expiration
2045-09-12

AI Technical Summary

Technical Problem

Existing data platform fault location technology cannot effectively screen out historical samples with large differences in structure, load, and configuration in platform transformation scenarios, resulting in redundant selections for fault root cause location and reduced accuracy, and is unable to adapt to the fault location needs of post-transformation scenarios.

Method used

By defining the trusted migration factor (CTF), the matching degree between historical samples and current scenarios is quantified from four dimensions: structural similarity, load similarity, configuration similarity, and time decay. Valid historical samples are screened out, and the entity relationship network of the data platform is constructed using the knowledge graph. Combined with the entity vulnerability index and the final score ranking, the root cause of the fault can be accurately identified.

Benefits of technology

It achieves accurate reuse of historical fault experience in platform transformation scenarios, avoids interference from invalid samples, improves the accuracy and efficiency of fault location, and supports continuous optimization.

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Abstract

The invention discloses a data platform fault positioning system and method based on a knowledge graph, and relates to the technical field of data analysis, and the method comprises the following steps: defining a core index and an entity based on an e-commerce order data platform link, collecting historical sample data before and after platform transformation, constructing a data platform knowledge graph by using a graph database after standardization processing; based on the standardized assets, quantifying the matching degree of the historical samples and the current scene, and classifying according to threshold values to obtain an effective historical sample library; when the index is abnormal, locking an initial entity and tracing an upstream to form a root cause candidate set, screening high-risk candidates by using an entity vulnerability index, sorting by using a final score, and outputting first N items as fault root causes; effective samples are supplemented regularly, a fault template is extracted, and continuous optimization is achieved. According to the method, the situation that in the prior art, when fault positioning is carried out after platform transformation, historical data are difficult to effectively utilize due to different reference values can be effectively improved.
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Citation Information

Patent Citations

  • Real-time root cause analysis method based on operation and maintenance knowledge graph

    CN116225760A

  • Microservice fault positioning method and device based on causal inference and knowledge graph

    CN120179509A

  • Building safety risk identification method of large language model-assisted knowledge graph

    CN120542919A

  • Industrial robot operation maintenance management method and device based on improved knowledge graph

    CN120563090A

  • Data-driven offline and online integrated simulation system and method for power distribution network

    WO2023115842A1

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