Decision Tree Application Cleaning for Mobile Device Memory
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Solution Overview
Problem
Electronic devices, such as smartphones, experience reduced memory and increased CPU occupancy when background applications are not cleared, leading to slower performance and faster power consumption.
Innovation Solution
An application cleaning method that collects multi-dimensional features of applications, constructs a decision tree model based on information gain ratios, and predicts whether an application can be cleaned up, allowing for automatic cleaning to improve device fluency and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If background applications are kept running, then application availability is improved, but memory availability deteriorates and CPU occupancy increases
Solution Approach 1:
The system automatically monitors application states, collects multi-dimensional features, and makes cleaning decisions without user intervention. The electronic device self-manages background applications by using the decision tree model to predict cleanability and automatically terminate unsuitable applications, resolving the contradiction between maintaining application availability and preserving memory resources.
Solution Approach 2:
The system implements a feedback mechanism where application running states are continuously monitored, features are collected, and cleaning decisions are made based on the decision tree model predictions. This closed-loop feedback system dynamically adjusts background application management to balance memory availability with application accessibility.
2Adaptability or versatility
If background applications are kept running, then application availability is improved, but device performance deteriorates
Solution Approach 1:
The system autonomously evaluates application performance impact and makes cleaning decisions to maintain device productivity. By continuously monitoring multi-dimensional features and using the decision tree model, the system self-regulates background applications to prevent performance degradation while preserving necessary application availability.
3Adaptability or versatility
If background applications are kept running, then application availability is improved, but power consumption increases
Solution Approach 1:
The system automatically manages power consumption by monitoring application states and making intelligent cleaning decisions. The decision tree model predicts cleanability based on collected features, enabling the device to self-optimize power usage while maintaining necessary application availability in the background.
Data Source
AI summary
Disclosed is an application cleaning method and device, a storage medium and an electronic device. In the embodiments of the present disclosure, the method involves: collecting multi-dimensional features of an application as samples, and constructing a sample set of the application; according to information gain ratios of the features with regard to sample classification, carrying out sample classification on the sample set so as to construct a decision tree model of the application; according to a prediction time, collecting corresponding multi-dimensional features of the application as prediction samples; and according to the prediction samples and the decision tree model, predicting whether the application can be cleaned.