储能电池系统多源数据综合安全评价模型构建方法及系统
By constructing a Transformer architecture based on a multi-head attention mechanism and a multivariate statistical analysis-based safety evaluation model for energy storage batteries, the problem of insufficient multi-source data correlation processing in existing technologies is solved, enabling high-precision safety evaluation of energy storage battery systems and improving the accuracy and reliability of the evaluation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING SIFANG JIBAO ENG TECH
- Filing Date
- 2025-05-27
- Publication Date
- 2026-07-17
AI Technical Summary
Existing safety evaluation methods for energy storage batteries are based on single data sources, which makes it difficult to handle the complex relationships between multiple data sources. This results in insufficient accuracy and reliability of the evaluation results, failing to meet the needs of high-precision safety evaluation.
A Transformer architecture with a multi-head attention mechanism is adopted, combined with multivariate statistical analysis and cloud model, to construct a comprehensive safety evaluation model for energy storage battery systems based on multi-source data. The model processes multivariate statistical analysis, uses the multi-head attention mechanism to focus on different feature subspaces of the data in parallel, and optimizes hyperparameters through a subtraction optimizer algorithm improved by the golden sine.
It enables a comprehensive and accurate safety evaluation of energy storage battery systems, improves the accuracy and reliability of the evaluation, better captures the complex correlations of multi-source data, and enhances the performance and reliability of the model.
Smart Images
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