Application Resource Pre-loading via Trigger Probability Prediction
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Solution Overview
Problem
As smartphones install increasingly powerful applications, storage space becomes a concern, leading to potential system operation issues, and existing methods for predicting application usage are not efficient in reducing network dependencies and storage management.
Innovation Solution
A method for application processing that involves obtaining historical operation information, calculating triggering probability values for applications, and pre-downloading resource files for applications with high probabilities, allowing for faster loading and reducing storage space usage by selectively downloading resources based on probability and storage capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If resource files of all applications are pre-downloaded and cached, then application loading speed is improved, but storage space consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-downloading and caching resource files of applications that are predicted to be used soon. The prediction module analyzes historical operation information to identify target applications, and the resource file download module proactively downloads their resource files before user request, thereby improving application loading speed when users actually access these applications.
Solution Approach 2:
Instead of uniformly pre-downloading resource files for all applications, the system applies local quality by selectively downloading resource files only for predicted target applications based on their individual usage probabilities. This differentiated approach ensures that storage space is consumed only for applications likely to be used, rather than wasting storage on applications that will not be accessed.
2Volume of stationary object
If resource files are downloaded on-demand without prediction, then storage space is saved, but application loading speed deteriorates due to network dependency
Solution Approach 1:
The system performs preliminary actions by pre-downloading and caching resource files of applications that are predicted to be used soon. The prediction module analyzes historical operation information to identify target applications, and the resource file download module proactively downloads their resource files before user request, thereby improving application loading speed when users actually access these applications.
3Productivity
If existing application prediction methods are used, then some prediction capability is achieved, but network dependency and storage management efficiency remain insufficient
Solution Approach 1:
The system segments the application resource management process into distinct functional modules: a prediction module that analyzes historical operation information to predict target applications, and a resource file download module that executes downloads based on predictions. This segmentation allows independent optimization of prediction accuracy and download management, improving overall system efficiency and reliability without excessive network dependency.
Data Source
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AI summary
An application processing method, a storage medium, and an electronic device. The method comprises: acquiring historical operating information of the electronic device, and acquiring trigger probability values of a plurality of applications in an application platform according to the historical operating information (201); setting the application of which the trigger probability value is greater than a first preset probability value as a target application to obtain at least one target application (202); downloading a resource file of the target application, and caching the resource file in a storage area of the application platform (203); and upon detecting a trigger operation of the target application, loading the corresponding resource file stored in the storage area (204).