Data flow transfer behavior mixed hierarchical evaluation method and system based on multi-dimensional feature fusion and expert rule cooperation

By combining multi-dimensional feature fusion with expert rule collaboration, the risk assessment problem of data flow behavior in vehicle-road-cloud business scenarios is solved, enabling accurate risk identification and continuous optimization, and supporting refined security management.

CN122220945APending Publication Date: 2026-06-16BEIJING INST OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING INST OF TECH
Filing Date
2026-03-17
Publication Date
2026-06-16

AI Technical Summary

Technical Problem

Existing technologies for risk assessment of data flow in vehicle-road-cloud business scenarios suffer from problems such as a single assessment dimension, fragmented rules and models, and coarse-grained grading results, which makes it impossible to accurately identify complex risk patterns and achieve precise security control.

Method used

It employs a multi-dimensional feature fusion and expert rule collaboration approach, which extracts time, frequency, scale, and content features by collecting raw information on data flow behavior in real time, combines expert rule base and intelligent analysis model for risk assessment, and determines the final hazard level through collaborative decision-making algorithm, and has the ability to continuously evolve.

Benefits of technology

It achieves comprehensive and accurate risk assessment of data flow behavior, combining determinism and adaptability. The output results are interpretable and have continuous evolution capabilities, enabling it to adapt to new attacks and continuous optimization, and supporting refined security management strategies.

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Abstract

The application discloses a data flow transfer behavior mixed hierarchical evaluation method and system based on multi-dimensional feature fusion and expert rule cooperation, and belongs to the technical field of data security. The method comprises the following steps: collecting original information of data flow transfer behavior, performing feature extraction and feature fusion, matching the fused features with an expert rule library, obtaining a hazard level judgment result set, constructing an intelligent analysis model, taking the fused features and the hazard level judgment result set as inputs, performing model reasoning, outputting a risk assessment vector, determining the final hazard level of the data flow transfer behavior through a cooperative decision algorithm based on the hazard level judgment result set and the risk assessment vector, collecting feedback information on the final hazard level, and optimizing and updating the intelligent analysis model and the expert rule library by using the feedback information. The application fuses multi-dimensional features, cooperates rules and models, realizes risk assessment on data flow transfer behavior, has a continuous evolution capability, and realizes safety management and control.
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