A method, system, and storage medium for constructing a heterogeneous log and standard rule knowledge base for vehicle network security.

By processing heterogeneous data related to vehicle network security through a dual-channel pipeline, a bottom-level vector fact base and an upper-level graph rule base are constructed, which solves the problems of log semantic sparsity and standard logic discontinuity, and achieves more accurate security event retrieval and compliance reasoning.

CN122309720APending Publication Date: 2026-06-30HARBIN INSTITUTE OF TECHNOLOGY (SHENZHEN) (INSTITUTE OF SCIENCE AND TECHNOLOGY INNOVATION HARBIN INSTITUTE OF TECHNOLOGY SHENZHEN)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HARBIN INSTITUTE OF TECHNOLOGY (SHENZHEN) (INSTITUTE OF SCIENCE AND TECHNOLOGY INNOVATION HARBIN INSTITUTE OF TECHNOLOGY SHENZHEN)
Filing Date
2026-06-03
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing technologies suffer from sparse log semantics and broken standard logic when dealing with heterogeneous logs and standard rules in vehicle-to-everything (V2X) security, making it difficult to effectively build a high-quality knowledge base and resulting in inaccurate security event retrieval and difficulties in compliance reasoning.

Method used

A dual-channel knowledge base construction pipeline is adopted to process semi-structured and unstructured data respectively. Through semantic serialization and graphing methods, a bottom-level vector fact base and an upper-level graph rule base are constructed to achieve logical alignment and semantic fusion of data.

Benefits of technology

It improves the accuracy and interpretability of security audits, enabling better comparison of on-site violations with static compliance standards and enhancing the recall effect of searches.

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Abstract

This invention provides a method, system, and storage medium for constructing a heterogeneous log and standard rule knowledge base for vehicle network security. The method includes: Step 1: Identifying the data source format, including semi-structured vehicle network operational data and unstructured vehicle network compliance data; Step 2: Employing a dual-channel knowledge base construction pipeline to process both semi-structured and unstructured vehicle network data, standardizing discrete log features and extracting standard graph paths in a constrained manner, and constructing a data foundation with a dual-modal separation and logical alignment between a bottom-level vector fact base and an upper-level graph rule base. The beneficial effects are: by constructing a dual-path fusion architecture of a log semantic enhancement pipeline and a standard rule knowledge base, logical alignment of heterogeneous knowledge is achieved at the retrieval level, solving the problems of log semantic sparsity and standard logical discontinuity in existing technologies, enabling security audits to more accurately compare on-site violations with static compliance standards.
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