Adaptive Power Configuration for Usage-Based Energy Control
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Solution Overview
Problem
Current power control software lacks advanced settings, is complex for non-technical users, relies on static settings, and consumes system resources, leading to suboptimal power savings and reduced system stability.
Innovation Solution
An energy consumption regulation method and system using machine learning to dynamically adjust power settings based on usage patterns, including processor power, backlight, and battery charging modes, with self-learning to optimize energy configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If power control software provides many individual setting options for different components, then power management capability is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The system performs self-learning by automatically monitoring and analyzing user operation patterns, device usage states, and power consumption data. Based on this learned information, the system autonomously configures optimal power settings for different components without requiring manual user intervention, thereby maintaining high power management capability while eliminating the complexity of manual configuration.
Solution Approach 2:
The system dynamically adjusts power management parameters based on learned usage patterns and current device states. By continuously monitoring operation data and adapting power settings in real-time, the system achieves versatile power management while presenting a simple interface to users who don't need to understand or configure individual parameters.
2Adaptability or versatility
If power control software provides advanced control options, then power management capability is improved, but device complexity increases
Solution Approach 1:
The system automatically performs the complex task of analyzing usage patterns and configuring power settings through self-learning. This eliminates the need for users to navigate complex configuration interfaces or understand advanced power management concepts, while the system internally maintains sophisticated control capabilities.
Solution Approach 2:
The system pre-configures optimal power settings based on learned usage patterns before users need them. By anticipating power management needs through continuous learning and preparation, the system provides advanced functionality without requiring users to engage with complex configuration processes.
3Device complexity
If power control software uses static settings, then device complexity is reduced, but adaptability deteriorates and energy savings become suboptimal
Solution Approach 1:
The system transitions from static to dynamic power management by continuously learning usage patterns and automatically adjusting power settings in real-time. This dynamic adaptation allows the system to optimize energy consumption based on actual device usage while maintaining a simple configuration interface for users.
Solution Approach 2:
The system implements a feedback loop by continuously monitoring device operation data, analyzing usage patterns, and using this information to automatically adjust power settings. This closed-loop approach enables the system to adapt to changing usage conditions and optimize energy savings without requiring complex manual configuration.
4Adaptability or versatility
If power control software relies on manual user adjustment, then adaptability is improved, but ease of operation deteriorates and time consumption increases
Solution Approach 1:
The system automatically performs power optimization by learning and analyzing user patterns, eliminating the need for manual user intervention. The system independently configures and adjusts power settings based on learned information, thereby achieving customized power management while saving users significant time and effort.
Solution Approach 2:
The system uses feedback from continuous monitoring of device usage and power consumption to automatically adjust settings. This automated feedback-driven optimization provides personalized power management without requiring users to spend time manually configuring or adjusting parameters.
5Ease of operation
If power control software consumes system resources for automation, then ease of operation is improved, but productivity deteriorates due to reduced system performance
Solution Approach 1:
The system applies partial automation by focusing computational resources on learning and optimizing only the most impactful power management decisions. Rather than continuously analyzing all system parameters, the system selectively monitors and adjusts key components, thereby maintaining ease of operation while minimizing the impact on overall system performance.
Data Source
AI summary
The present invention relates to an energy consumption regulation method. The method includes executing the following steps by a processor: continuously collecting and learning a plurality of first operation data about an electronic device, thereby automatically distinguishing an operation of the electronic device into a plurality of operation periods; automatically configuring a different plurality of energy configurations for the electronic device according to the different plurality of operation periods to regulate a maximum power of the processor, an operation mode of a backlight unit, a charging mode of a battery, and a peripheral energy management mode; and continuously collecting and learning a plurality of second operation data about the electronic device and autonomously updating and adjusting the plurality of operation periods and the plurality of energy configurations based on the plurality of second operation data.