Energy Consumption Regulation via Machine Learning
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Solution Overview
Problem
Current power control software lacks advanced control 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 configurations based on usage patterns, including processor power, backlight, and battery charging modes, with self-learning to optimize energy use.
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 usage patterns, device operational data, and power consumption characteristics. Through this self-service mechanism, the system autonomously identifies optimal power settings without requiring users to manually configure multiple individual parameters, thereby maintaining comprehensive power management capability while eliminating operational complexity
Solution Approach 2:
The system dynamically adjusts power management parameters based on learned usage patterns and operational conditions. By continuously monitoring device state and automatically modifying power settings according to actual usage scenarios, the system adapts to different operational contexts without presenting static configuration options to users, thus preserving versatility while improving ease of operation
2Device complexity
If power control software uses static settings, then device complexity is reduced, but adaptability deteriorates and energy efficiency is suboptimal
Solution Approach 1:
The system transitions from static power settings to dynamic adaptation through continuous learning. By implementing real-time monitoring of usage patterns and automatically adjusting power configurations based on learned behavioral models, the system achieves adaptability without significantly increasing user-facing complexity, as the learning process occurs autonomously in the background
Solution Approach 2:
The system establishes a feedback loop where power consumption data and usage patterns are continuously collected, analyzed, and used to refine power management decisions. This feedback mechanism enables the system to adapt to changing operational conditions and optimize energy efficiency dynamically, overcoming the limitations of static settings while maintaining manageable complexity through automated control
3Adaptability or versatility
If power control software requires manual user configuration, then adaptability is improved, but ease of operation deteriorates and time consumption increases
Solution Approach 1:
The system performs preliminary learning and analysis of usage patterns automatically during initial operation phases. By pre-configuring power settings based on early learned behaviors before users need to make manual adjustments, the system provides customized power management adaptability while eliminating the time users would otherwise spend on configuration
Solution Approach 2:
The system autonomously performs the configuration task that would otherwise require manual user input. Through self-learning mechanisms that monitor and analyze usage patterns, the system automatically generates and applies optimized power settings, providing customization capability while completely eliminating configuration time for users
4Power
If power control software consumes system resources, then processing capability is improved, but productivity deteriorates due to reduced system performance
Solution Approach 1:
The system implements partial learning and optimization by focusing computational resources on the most impactful power management decisions rather than analyzing every system parameter. By applying machine learning selectively to critical power consumption areas and using simplified models where sufficient, the system maintains adequate processing capability while minimizing the performance overhead associated with continuous full-system analysis
Data Source
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AI summary
The present invention relates to an energy consumption regulation method (200). The method (200) includes executing (201) the following steps by a processor: continuously collecting (202) 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 (203) 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 (204) 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.