AI-Powered App Restriction for Mobile Power Optimization
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Solution Overview
Problem
Intelligent terminals, such as smartphones and tablets, face significant power consumption issues due to various applications running in the foreground and background, leading to shortened battery life, with some applications consuming excessive power unnecessarily.
Innovation Solution
An AI-powered method that collects and analyzes application operation data to predict usage patterns and restrict background activities of applications, classifying them based on usage frequency and implementing corresponding power-saving measures, such as freezing or upgrading applications, to optimize CPU, DDR, and GPU frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If applications run in the background to provide services, then user experience is improved, but power consumption increases
Solution Approach 1:
The patent applies dynamics by making the background application restriction policy adaptive rather than static. The AI model dynamically adjusts restriction levels based on predicted user behavior patterns, learning from historical data to determine when background activities should be allowed or restricted. This enables the system to optimize power consumption while maintaining user experience according to actual usage scenarios.
Solution Approach 2:
The system implements self-service through autonomous AI-driven decision making. The AI model automatically analyzes operation data, predicts user behavior, and determines restriction policies without requiring manual user intervention. The system serves itself by autonomously optimizing its own power management strategy based on learned patterns, reducing the need for user configuration while achieving personalized power saving.
2Use of energy by moving object
If AI analysis is performed on operation data to predict usage patterns, then power consumption optimization is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex AI analysis system into distinct functional modules: data collection module, AI analysis module, and policy execution module. Each module has a specific responsibility - collecting operation data, analyzing patterns and predicting usage, and executing restriction policies respectively. This modular architecture reduces system complexity by making each component independent and manageable while maintaining the overall optimization capability.
Data Source
AI summary
The disclosure discloses an intelligent terminal energy saving method based on artificial intelligence (AI) prediction. The method includes: collecting application (APP)-related operation data on the intelligent terminal; carrying out AI analysis on the APP-related operation data collected, to predict timing and a restriction measurement to restrict an APP in a background; and adopting the restriction measurement to restrict the APP in the background at the timing predicted. Corresponding to the method, the disclosure further discloses an intelligent terminal energy saving device based on AI prediction. Using the technical schemes disclosed in the disclosure, the power consumption of applications on a portable intelligent terminal can be reduced, and the battery life can be extended, without affecting the user experience.