Memory fault diagnosis method based on machine learning

The invention discloses a memory fault diagnosis method based on machine learning, and the method comprises the steps: determining a feature data set corresponding to at least one fault feature of a to-be-detected memory, constructing a fault diagnosis model, and training the fault diagnosis model according to the feature data set to obtain an optimized fault diagnosis model, wherein the fault diagnosis model is a neural network model; and acquiring feature data of the to-be-detected memory in real time, and performing fault diagnosis according to the feature data and the optimized fault diagnosis model to obtain an analysis result. The technical problems of low fault diagnosis efficiency and low system operation safety and reliability in the prior art are solved. 本申请公开了一种基于机器学习的存储器故障诊断方法，该方法包括：确定待检测存储器至少一种故障特征对应的特征数据集，构建故障诊断模型，根据所述特征数据集对所述故障诊断模型进行训练得到优化后的故障诊断模型，其中，所述故障诊断模型为神经网络模型；实时获取所述待检测存储器的特征数据，根据所述特征数据和所述优化后的故障诊断模型进行故障诊断得到分析结果。本申请解决了现有技术中故障诊断的效率以及系统运行的安全性和可靠性较低的技术问题。