Dual-model training disk fault prediction method, system and device and storage medium

• Main Authors: ZHOU ZEXIANG, YIN WEI, LIU PAN
• Format: Patent
• Language: Chinese; English
• Published: 29.03.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

The invention discloses a disk fault prediction method, system and device based on double-model training and a storage medium. The method comprises the steps of collecting disk data; performing data preprocessing on the disk data, and taking a preset feature parameter as an initial feature; taking the disk data and the initial features as a data set, and performing LSTM deep recurrent neural network algorithm model training; detecting a disk fault caused by loss based on the trained LSTM deep recurrent neural network algorithm model; substituting smart information of all disks of the system into the static model every preset time interval, and then judging abnormity through a box graph method to obtain a prediction result; wherein the prediction result is in a multi-classification vector form, and the prediction result with the maximum probability is selected as output. Through the processing scheme disclosed by the invention, the prediction accuracy can be improved, and the consumption performance and time o