Random Forest Preloading for Application Launch Speed
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Solution Overview
Problem
Current preloading methods for smart terminal applications are inefficient as they often preload too many resources, leading to increased power consumption and storage occupancy, which affects the terminal's fluency and accuracy in predicting the next application to launch.
Innovation Solution
A method using a random forest prediction model based on historical usage regularity to determine whether to preload an application, considering current state feature information such as network switching, hotspot connection, and battery charging state, to optimize resource allocation and improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If many applications are preloaded in advance, then application launch speed is improved, but storage space occupancy increases and power consumption increases
Solution Approach 1:
The system performs preliminary actions by preloading applications in advance based on prediction models. The random forest prediction model identifies which applications are likely to be launched soon, and only those applications are preloaded into memory, achieving fast launch speed without loading all possible applications
Solution Approach 2:
The system changes the parameter of preloading scope from 'all applications' to 'predicted applications only'. By using historical usage data to dynamically adjust which applications are preloaded, the system optimizes the balance between launch speed and power consumption
2Speed
If many applications are preloaded in advance, then application launch speed is improved, but storage space occupancy increases
Solution Approach 1:
The system performs preliminary actions by preloading applications in advance based on prediction models. The random forest prediction model identifies which applications are likely to be launched soon, and only those applications are preloaded into memory, achieving fast launch speed without loading all possible applications
Solution Approach 2:
Instead of preloading all applications (excessive action), the system preloads only the subset of applications predicted to be needed soon (partial action). This partial preloading approach maintains launch speed benefits while significantly reducing storage space occupancy
3Speed
If traditional preloading methods are used, then application launch speed is improved, but prediction accuracy of next application decreases
Solution Approach 1:
The system implements feedback mechanisms by continuously collecting actual application launch data and using it to retrain and update the random forest prediction model. This feedback loop improves prediction accuracy over time, allowing the system to better predict which applications will be launched next and adjust preloading strategies accordingly
Solution Approach 2:
The system replaces traditional mechanical preloading methods (based on simple rules or user profiles) with an intelligent prediction system using random forest machine learning models. This substitution enables more accurate prediction of application launch patterns, improving both prediction accuracy and preloading efficiency
Data Source
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AI summary
A method for preloading an application, a storage medium, and a terminal device are provided. The method includes the following. In response to a target application being detected to be closed, current state feature information of a terminal device is acquired. The current state feature information is input into a random forest prediction model corresponding to the target application, where the random forest prediction model is generated based on a usage regularity of the target application corresponding to historical state feature information of the terminal device. Whether to preload the target application is determined according to a prediction result of the random forest prediction model.