Service anomaly warning method and device based on time sequence

Abstract

The invention discloses a service anomaly warning method and device based on time sequence. The method includes: acquiring multiple true values of a preset warning index in an appointed time period according to preset time intervals; determining multiple predicted values of the preset warning index in the time period according to a predicting model which is obtained by fitting according to the time sequence of historical true values of the preset warning index; according the true values in the time period and the predicted values in the time period, determining whether to perform service anomaly warning in the time period or not; after the warning cycle where the time period is located is finished, adding the true values of the preset warning index in the warning cycle into the historicaltrue values of the preset warning index so as to update the predicting model. By the method and device, warning efficiency can be increased, and false warning and warning failure can be reduced.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a method and device for reminding service abnormalities based on time series. Background technique [0002] With the development of computer technology, terminal devices can provide users with more and more services, such as personalized mobile phone operating systems, personalized mobile phone desktops, and the like. For service developers, setting up a service monitoring and alarm system is one of the effective means to ensure the high availability of services. When a problem occurs in the service, if the developer can find it in the first place and repair the service, it is a good way to stop the loss in time. method. [0003] The current service monitoring and alarm system mainly relies on the subjective experience of the service developer to set the threshold of the reminder indicator, and reminds when the reminder indicator of the service exceeds the corres...

AI Technical Summary

Problems solved by technology

[0004] However, when setting the threshold of reminder indicators based on the subjective experience of developers, not only does it take a lot of practice to determine a more reasonable threshold of reminder indicators, but also when the internal or external environment on which the service depends changes, it needs to be based on the new Adjusting the threshold based on subjective experience is not only inefficient, but also has serious false negatives and false negatives.

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