A large rotating unit micro-service component recommendation method based on a knowledge graph

By combining a knowledge graph-based water wave model and a graph neural network, the problem of insufficient feature extraction in the recommendation of microservice components for large rotating units was solved, which improved the accuracy of high-order feature representation and component recommendation, and enhanced the automatic matching capability of the recommendation system.

CN116955848BActive Publication Date: 2026-07-07JILIN UNIVERSITY
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Patent Information

Application Number
CN202310960467.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-08-01
Publication Date
2026-07-07
Estimated Expiration
2043-08-01

AI Technical Summary

Technical Problem

Existing knowledge graph recommendation algorithms suffer from problems such as insufficient feature extraction and random sampling of domain information, which leads to reduced recommendation accuracy, especially in the recommendation of microservice components of large rotating units, where effective matching and recommendation are difficult.

Method used

A knowledge graph-based microservice component recommendation method for large rotating units is adopted. The method uses a water wave model to perform high-order modeling of functional description features, combines the message passing mechanism of graph neural networks to aggregate neighborhood information, utilizes the latent information in the knowledge graph to perform component feature representation and preference propagation, and adopts an importance-based sampling method to generate high-order feature representations to improve recommendation accuracy.

Benefits of technology

It improves the accuracy and efficiency of microservice component recommendations for large rotating units, enhances the automatic matching capability under functional descriptions, maximizes the utilization of knowledge graph information, and improves recommendation performance.

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Abstract

This invention discloses a method for recommending microservice components for large rotating airframes based on knowledge graphs. The method comprises the following steps: Step 1: Obtaining the functional description and component interaction matrix, as well as knowledge graph information; Step 2: Obtaining the entity set and triplet set; Step 3: Calculating the similarity probability value between the component embedding vector of the triplet and the entity; Step 4: Obtaining the feature vector representation of the functional description at each layer; Step 5: Obtaining the final high-order representation of the functional description; Step 6: Obtaining the entity set and triplet set; Step 7: Normalizing the weights; Step 8: Obtaining the component feature vector; Step 9: Obtaining the final component feature vector representation; Step 10: Predicting the degree of preference of the target functional description for the target component. Beneficial effects: Automatically acquiring latent information from the knowledge graph while enriching the functional description representation and component representation, performing high-order modeling of the functional description features, and obtaining high-order component representations.
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