Method for establishing applications-to-be preloaded prediction model based on preorder usage sequence of foreground application, storage medium, and terminal

A non-transitory computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to carry out actions, comprising:

• collecting a first preorder usage sequence of a first application currently running as sample usage information, in response to an operation switching an application previously running in the foreground to the first application;
• monitoring applications launched within a time-window from a starting point of collecting the sample usage information and having a preset length and setting a preset number of applications launched first in the applications launched within a time-window as sample labels for the sample usage information;
• and
• training a predetermined machine learning model based on the sample usage information and the sample labels for the sample usage information to obtain an application prediction model,
• wherein setting the preset number of applications launched first in the applications launched within the time-window as the sample labels for the sample usage information is executed by the processor to carry out actions comprises:
• allocating probabilities to the preset number of applications launched first in the applications launched within the time-window according to intervals from the start time point to launching time points of the applications launched first;
• and
• setting the preset number of applications and the probabilities of the preset number of applications as the sample labels for the sample usage information.

A terminal, comprising:

• at least one processor;
• and
• a computer readable storage, coupled to the at least one processor and storing at least one computer executable instruction thereon which, when executed by the at least one processor, cause the at least one processor to carry out actions, comprising:
• collecting a first preorder usage sequence of a first application currently running as sample usage information, in response to an operation switching an application previously running in the foreground to the first application;
• monitoring applications launched within a time-window from a starting point of collecting the sample usage information and having a preset length and setting a preset number of applications launched first in the applications launched within a time-window as sample labels for the sample usage information;
• and
• training a predetermined machine learning model based on the sample usage information and the sample labels for the sample usage information to obtain an application prediction model,
• wherein setting the preset number of applications launched first in the applications launched within the time-window as the sample labels for the sample usage information is caused to carry out actions comprises:
• allocating probabilities to the preset number of applications launched first in the applications launched within the time-window according to intervals from the start time point to launching time points of the applications launched first;
• and
• setting the preset number of applications and the probabilities of the preset number of applications as the sample labels for the sample usage information.