Disk fault prediction method and system based on time sequence feature processing and model optimization

Abstract

The invention discloses a disk fault prediction method based on time sequence feature processing and model optimization. The disk fault prediction method is characterized in that the method comprisesthe following steps: obtaining SMART attribute data of a disk and a timestamp of the SMART attribute data; acquiring expansion data according to the acquired standard value and the original value of the SMART attribute data of the disk and the timestamp of the SMART attribute data; selecting a plurality of features from the extended data and the standard value and the original value of the SMART attribute data by using a principal component analysis method; and constructing a multi-dimensional matrix, inputting the obtained multi-dimensional matrix into the trained random forest model to obtain a fault prediction result of the disk, and updating the random forest model according to the obtained fault prediction result of the disk to obtain an updated random forest model. According to the method, time sequence feature processing and model optimization are utilized, so that the technical problem that the accuracy of disk fault prediction is relatively low due to the fact that the incidence relation between SMART attributes is not considered in the existing SMART technology is solved.

Description

technical field [0001] The invention belongs to the field of storage technology, and more specifically relates to a disk failure prediction method and system based on time series feature processing and model optimization. Background technique [0002] According to Microsoft, disk failures account for 78% of all hardware damage in its data centers. There are many factors that cause disk failure and thus affect disk reliability, including disk temperature, humidity, load level, running time, potential sector error failure, etc. These factors may cause disk abnormality and cause the loss of recorded user data. [0003] In order to solve the above problems, in recent years, people have proposed to use the self-monitoring, analysis and reporting technology (SMART) technology of the disk to actively predict the disk failure, which monitors the important health attributes of the disk Indicators are recorded, and health thresholds are set for each indicator; if any indicator is low...

AI Technical Summary

Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a disk failure prediction method and system based on time-series feature processing and model optimization, the purpose of which is to solve the problem of existing SMART Due to the lack of consideration of the relationship between SMART attributes in the technology, it will lead to low technical problems in the accuracy of disk failure prediction, and because the feature analysis of massive historical data is not considered, it cannot effectively track the latest damage in real time. Disk technical issues

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