Neural Window Shading Control for Adaptive Light and Visibility
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Solution Overview
Problem
Existing window shading systems require manual adjustment to control sunlight and visibility, which is inefficient and lacks adaptability to changing environmental conditions.
Innovation Solution
A window shading system that uses sensors and a processor to learn user preferences and autonomously adjust shading settings based on environmental data, employing artificial neural networks and reinforcement learning to optimize tint levels, blind slat angles, and coverage percentages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustment is used to control window shading settings, then the system is simple and easy to manufacture, but the adaptability to changing environmental conditions deteriorates
Solution Approach 1:
The window shading system autonomously adjusts its own settings by detecting environmental conditions (light intensity, temperature) through sensors and automatically modifying blind slat angles and coverage without requiring manual user intervention. The system serves itself by making decisions based on sensor data and user preferences stored in memory.
Solution Approach 2:
The patent replaces manual mechanical adjustment with an automated control system that uses sensors to detect environmental conditions and a processor to automatically adjust blind slat angles and coverage. This substitution of mechanical manual operation with sensor-based automated control resolves the contradiction between simplicity and adaptability.
2Ease of operation
If automated sensor-based control is implemented, then the adaptability to environmental conditions improves, but the device complexity increases
Solution Approach 1:
The system automatically detects environmental conditions and adjusts shading settings without requiring user operation, thereby improving ease of operation. The automated control eliminates the need for manual intervention while the system manages itself based on sensor inputs and stored preferences.
Solution Approach 2:
The control system integrates multiple functions including environmental sensing, data processing, automatic decision-making based on user preferences, and actuation of shading mechanisms. This multi-functionality consolidates what would otherwise require separate systems into a single integrated controller, managing the complexity while providing comprehensive automated operation.
3Reliability
If frequent manual adjustments are made to optimize visibility and brightness, then the user comfort improves, but the loss of time increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring environmental conditions and proactively adjusting shading settings before user discomfort occurs. The automated system anticipates the need for adjustment and executes it immediately based on sensor data, eliminating the time users would otherwise spend making manual adjustments.
Solution Approach 2:
The system implements continuous feedback loops where sensors detect environmental conditions (light intensity, temperature), the processor compares current settings against user preferences, and automatic adjustments are made to maintain optimal comfort levels. This closed-loop feedback ensures consistent user comfort without requiring repeated manual interventions.
Data Source
AI summary
Included is a method for adjusting window shade settings of a window shade. At least one sensor captured environmental data of surroundings. A processor actuates at least one window shading setting to be applied to the window shade based on at least one of the environmental data, window shade setting preferences of a user, and at least one input received by an application of a communication device paired with the processor.