Application management method, mobile terminal and computer-readable storage medium

Abstract

The invention discloses an application management method. The method comprises the following steps: obtaining current application scene information, a pre-stored first application probability of each application program applied under each application scene, and a second application probability of each application program applied under each application scene during triggering cleaning of background application programs; inputting the current application scene information, all the first application probabilities and all the second application probabilities into a preset Bayesian network probability calculating model and calculating a third application probability of each background application program applied under the current application scene; and closing the background application programs with third application probabilities being smaller than a preset probability threshold value. The invention also discloses a mobile terminal and a computer-readable storage medium. Through the application management method, the accuracy of application management of the mobile terminal can be improved.

Description

technical field [0001] The present invention relates to the technical field of mobile terminals, in particular to an application management method, a mobile terminal and a computer-readable storage medium. Background technique [0002] In order to optimize system power consumption, heat generation, memory usage efficiency, and improve user satisfaction, mobile terminals often need to optimize applications running in the background of the operating system. However, the current background application optimization process often kills all background applications or manages background applications according to the Task killing mechanism of the LRU algorithm. The LRU algorithm only considers the recent usage of the application and does not consider the user's usage habits, which may cause background applications When the user switches back to the application, it will cause problems such as increased application startup delay, increased energy consumption, and lost state, resulting...

AI Technical Summary

Problems solved by technology

However, the current background application optimization process often kills all background applications or manages background applications according to the Task killing mechanism of the LRU algorithm. The LRU algorithm only considers the recent usage of the application and does not consider the user's usage habits, which may cause background applications Incorrectly terminated, when the user switches back to the application, it will cause problems such as increased application startup delay, increased energy consumption, lost state, etc., resulting in a bad user experience

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