Wavelet Neural Network Display Status Adjustment
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Solution Overview
Problem
Conventional display status adjustment methods for mobile devices are either limited in functionality, only adjusting brightness, or require manual settings of multiple parameters, leading to energy consumption and low accuracy.
Innovation Solution
A display status adjustment method utilizing a wavelet neural network that collects environmental parameters such as gravitational acceleration, light, position, and temperature, and adjusts display settings like brightness, resolution, and refresh rate based on analysis, incorporating a pre-built wavelet neural network model for intelligent and accurate adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic brightness adjustment through light sensor is used, then brightness adjustment is achieved, but the function remains simple and limited to only brightness
Solution Approach 1:
The patent applies multi-functionality by enabling a single automatic adjustment system to control multiple display parameters (brightness, resolution, refresh rate, contrast, gamma) simultaneously. The wavelet neural network processes environmental information to generate comprehensive display status adjustments, transforming a single-function brightness adjustment system into a multi-parameter display optimization system that adapts to different usage scenarios.
2Adaptability or versatility
If manual adjustment of multiple parameters is used, then more parameters can be set, but energy is consumed and accuracy is low
Solution Approach 1:
The system applies self-service by automatically adjusting multiple display parameters without requiring user intervention. The wavelet neural network autonomously processes environmental information (light intensity, ambient temperature, device orientation) and directly controls display parameters (brightness, resolution, refresh rate), eliminating the need for manual user adjustments and reducing energy consumption associated with user interaction and processing.
Solution Approach 2:
The patent implements feedback mechanisms where sensors continuously monitor environmental conditions (light sensor for ambient light, temperature sensor for ambient temperature, accelerometer for device orientation) and feed this information to the wavelet neural network. The network processes this feedback and dynamically adjusts display parameters accordingly, creating a closed-loop control system that adapts in real-time to changing environmental conditions.
3Adaptability or versatility
If manual parameter setting is used, then multiple parameters can be adjusted, but accuracy is low due to manual input limitations
Solution Approach 1:
The patent replaces manual mechanical adjustment (user interface interactions, slider movements, button presses) with an intelligent automated system. The wavelet neural network substitutes human decision-making with algorithmic processing of sensor data, enabling more precise and consistent parameter adjustments. This substitution transforms a manual, imprecise adjustment process into an automated, high-precision control system that can process environmental data and make optimal display parameter selections.
Data Source
AI summary
The present invention provides a display status adjustment method, a display status adjustment device and a display device, which belongs to the field of display technology and can solve the problem that the existing display status adjustment method for a display is too simple and has low accuracy. The display status adjustment method of the present invention comprises steps of: collecting information parameter of an environment where a display is located; inputting the collected information parameter to a pre-built wavelet neural network model for analysis, and obtaining a display status in which the display is to display; and adjusting display status of the display based on analysis result.