A graph computation optimization method and system based on multicentrism metric and global optimal ranking

By using a graph computation optimization method based on multi-centrality metrics and global optimal ranking, the problems of slow query response and high dynamic update overhead in the construction machinery industry cloud map platform were solved, achieving efficient and stable adaptation to multiple business scenarios and improving the overall performance of the industry cloud map platform.

CN121615737BActive Publication Date: 2026-06-30HUNAN GUOZHONG ZHILIAN CONSTR MASCH RES INST CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUNAN GUOZHONG ZHILIAN CONSTR MASCH RES INST CO LTD
Filing Date
2025-12-05
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing cloud-based platforms for the construction machinery industry suffer from slow query response and high overhead for dynamic updates when processing knowledge graphs with tens of millions of edges. Existing graph computation optimization technologies fail to effectively incorporate industry characteristics, resulting in low computational efficiency and an inability to meet the demands of real-time processing and multiple business scenarios.

Method used

We employ a graph computing optimization method based on multi-centrality metrics and global optimal ranking. By constructing multiple candidate sorting sequences and combining them with parallel computing architecture and industry chain weights, we generate efficient sequence subsets and optimize strategies through a dynamic feedback mechanism to adapt to the query needs of different business scenarios.

Benefits of technology

It significantly improves the query efficiency and stability of the industry cloud map platform, reduces preprocessing overhead, meets the real-time and accuracy requirements of supply chain risk monitoring and precise investment promotion, and adapts to the multi-dimensional attributes of the construction machinery industry.

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Abstract

This invention discloses a graph computing optimization method and system based on multi-centrality metrics and globally optimal ranking. The method first calculates the multi-centrality metric of vertices based on an industry knowledge graph, generating candidate ranking sequences adapted to different business operations, and then filters out efficient sequence subsets through performance testing. Subsequently, a parallel computing architecture and a business-region combined partitioning strategy are employed to perform a breadth-first search in parallel, starting from key nodes, generating locally optimal sequences. By incorporating sequence similarity calculations with industry chain weights, the filtered efficient sequence subsets are merged with the locally optimal sequences to form a globally optimal sequence. This effectively overcomes the limitations of traditional single ranking in adaptability across multiple business scenarios and significantly reduces computational complexity. The constructed globally optimal sequence demonstrates higher accuracy and adaptability in businesses such as supply chain risk identification and precise investment attraction, providing an efficient and stable graph computing solution for industrial cloud platforms in the equipment manufacturing field.
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