AI训练异常诊断方法、电子设备及存储介质
By calculating the causal influence coefficient and conducting bidirectional causal association analysis, this approach addresses the shortcomings of existing AI training anomaly diagnosis methods, which cannot accurately distinguish between training data and storage device issues, thus achieving more accurate anomaly diagnosis.
CN122132218BActive Publication Date: 2026-07-17AXD (ANXINDA) MEMORY TECH CO LTD +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- AXD (ANXINDA) MEMORY TECH CO LTD
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing AI training anomaly diagnosis methods cannot accurately distinguish between training data problems and storage device problems, leading to misjudgments of the cause of the anomaly.
Method used
By calculating the first causal influence coefficient of the data quality feature vector on the storage performance feature vector and the second causal influence coefficient of the storage performance feature vector on the data quality feature vector, anomaly diagnosis results are generated, and bidirectional causal association analysis is performed.
Benefits of technology
It enables accurate diagnosis of training data problems and storage device problems, improving the accuracy and adaptability of anomaly diagnosis.
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Abstract
本发明公开了一种AI训练异常诊断方法、电子设备及存储介质，该方法包括获取训练数据和存储设备的运行日志；基于训练数据确定数据质量特征向量；基于运行日志确定存储性能特征向量；计算数据质量特征向量对存储性能特征向量的第一因果影响系数和存储性能特征向量对数据质量特征向量的第二因果影响系数；根据第一因果影响系数和第二因果影响系数生成异常诊断结果。本申请通过计算数据质量特征向量对存储性能特征向量的第一因果影响系数和存储性能特征向量对数据质量特征向量的第二因果影响系数，根据第一因果影响系数和第二因果影响系数生成异常诊断结果，能够对训练数据问题和存储设备问题进行双向因果关联分析，从而准确生成异常诊断结果。
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