Energy storage safety level evaluation system based on big data analysis

Abstract

This invention belongs to the field of big data analysis and evaluation, specifically relating to a big data analysis-based energy storage safety level evaluation system. The system includes: a high-precision data acquisition device for acquiring weak physical signals such as electrochemical noise; an electrochemical noise analysis device that uses a time-frequency domain joint analysis algorithm to extract feature vectors characterizing internal physical evolution; a dynamic baseline drift compensation device that constructs a health trend space and generates an adaptive evaluation baseline boundary, calculating real-time deviation indicators; a big data analysis and evaluation device that integrates feature vectors and deviation, using a deep learning model to output a quantitative value of the safety level; and a safety early warning execution device that executes control strategies and outputs fault location information based on physical mechanisms. This invention improves the accuracy of hazard identification by capturing microscopic fluctuation signals and introducing a compensation mechanism, achieving interpretable proactive operation and maintenance.

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