Artificial intelligence-based flash particle test data processing method and device

By collecting and analyzing Flash particle test data, creating dynamic reasoning models and generating structured knowledge graphs, the problem of difficulty in mining temporal correlation logic in Flash particle testing is solved. This enables early positioning and accurate prediction of particle stability degradation trends, improving the efficiency of product iteration guidance.

CN121765670BActive Publication Date: 2026-05-05SHENZHEN CHIP TESTING TECH CO LTD
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Patent Information

Application Number
CN202610248349.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-02
Publication Date
2026-05-05
Estimated Expiration
2046-03-02

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively uncover the temporal correlation logic of particle response behavior in Flash particle testing, making it impossible to pinpoint the decline trend of particle stability in advance. There are information barriers between test data and design optimization, and fragmented data is insufficient to guide product iteration.

Method used

By collecting particle response behavior data from particle testing tasks stored in Flash memory, extracting time-series correlation logic information, creating a dynamic reasoning model based on behavior sequences, generating hidden evolution trajectories, and combining test log data to generate a structured test knowledge graph, we can achieve in-depth mining of particle performance evolution characteristics and prediction of degradation trends.

Benefits of technology

It breaks through the limitations of traditional single-feature judgment, realizes the early location of the stability degradation trend of Flash particles, improves the efficiency and accuracy of test data to design optimization, breaks down information barriers, and provides a brand-new dimension of lifetime prediction.

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Abstract

This application discloses an artificial intelligence-based method and device for processing Flash particle test data. The method includes: collecting particle response behavior data from Flash storage particle test tasks and extracting time-series correlation logic information; creating a dynamic reasoning model based on an adapted behavior sequence analysis algorithm to mine the evolutionary patterns under different instruction requests and generate hidden evolutionary trajectories; deriving the degradation trend prediction results through particle performance evolution characteristics, converting trend features into semantic knowledge nodes and establishing a semantic mapping relationship with failure types; and generating a structured test knowledge graph by combining test log data. This application breaks through the limitations of traditional single feature judgment, provides a new dimension for Flash particle lifetime prediction, and breaks down the information barrier between test data and design optimization, enabling efficient transformation from data to decision-making.
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