软件供应链风险识别方法、系统及计算机可读存储介质
By integrating multi-dimensional algorithms to extract features and assess risks in software supply chain components, the problem of insufficient multi-dimensional risk identification in existing technologies is solved, realizing intelligent and dynamic risk identification and assessment, and improving the accuracy and efficiency of detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG PONSHINE INFORMATION TECH CO LTD
- Filing Date
- 2026-04-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for risk identification in the software supply chain suffer from problems such as limited detection dimensions, delayed risk assessment, high false alarm rates, and insufficient algorithm intelligence, making it difficult to achieve intelligent, dynamic, and multi-dimensional risk identification and assessment.
A multi-head attention mechanism is adopted, which integrates graph attention network with time decay factor, graph structure features, temporal sequence features and text semantic features. Combined with Transformer encoder, graph convolutional network and BERT model, feature extraction and risk assessment of component data are performed to generate comprehensive score and classify risk level.
It enables intelligent, dynamic, and multi-dimensional risk identification of software components, with comprehensive detection, high accuracy, and privacy protection. It can be integrated into CI/CD processes, significantly improving the accuracy and efficiency of risk identification.
Smart Images
Figure CN122020674B_ABST