Memory detection model training method and memory detection method and device

Abstract

The invention discloses a memory detection model training method. The method is applied to fault detection, and comprises the steps of obtaining a memory state historical data set, generating a real fault label set according to the memory state historical data set, generating a plurality of to-be-trained feature subsets according to the memory state historical data set, and obtaining a predictionfault label subset corresponding to the to-be-trained feature subset through the to-be-trained memory detection sub-model, and generating a memory detection sub-model according to the to-be-trained memory detection sub-model if the prediction fault label subset and the real fault label subset meet a model verification condition. The invention further discloses a memory detection device. Accordingto the memory detection model provided by the invention, the memory fault condition can be predicted according to the granularity of the memory module level, and the health condition and the risk level of the memory are fully considered, so that the fault positioning accuracy of memory detection is improved.

Description

[0001] This application is a divisional application of a Chinese patent application submitted to the China Patent Office on September 26, 2019, with the application number 201910918511.4, and the title of the invention is "a memory detection model training method, memory detection method and device". technical field [0002] The present application relates to the field of management tools, in particular to a method for training a memory detection model, a method and a device for memory detection. Background technique [0003] With the development of science and technology, computers have entered thousands of households. The hardware system of a computer is composed of an arithmetic unit, a controller, a memory, an input device, and an output device. The memory in the computer is divided into internal memory and external memory. Memory is used to store programs and data that are currently in use, or that will be used at any time. Once there is an error or failure in the mem...

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Problems solved by technology

[0005] However, the above-mentioned fault matching model can only predict UE at the granularity of the system level. The prediction method based on the system level has a relatively large granularity. Therefore, it is difficult to locate a specific memory module, resulting in low memory detection accuracy.

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