Social Sensing Lighting Control for Dynamic Scene Adaptation
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Solution Overview
Problem
Current lighting control systems lack the ability to dynamically and efficiently manage lighting scenes for events based on real-time social media data, leading to inefficient energy use, high costs, and limited flexibility in lighting landmark venues.
Innovation Solution
A lighting control system that utilizes data mining from social media to determine lighting attributes, allowing for the generation and updating of lighting scenes in real-time, with minimal processor power and memory usage, enabling dynamic and flexible dimming and color control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional lighting control systems are used for landmark illumination, then lighting scenes can be created for events, but energy consumption is high and costs are significant
Solution Approach 1:
The system dynamically adjusts lighting scenes based on real-time social media data mining, transitioning from static pre-programmed lighting to adaptive, event-driven illumination. The controller continuously monitors social media content, extracts relevant information, and modifies lighting attributes accordingly, enabling the system to respond flexibly to changing event conditions and audience reactions.
Solution Approach 2:
The system implements a feedback loop where social media content is continuously analyzed to determine lighting attributes. The controller obtains data from social media, determines lighting attributes based on this data, and adjusts the lighting scenes accordingly. This closed-loop feedback mechanism enables the system to learn from audience reactions and event progression, optimizing energy usage while maintaining high adaptability.
2Adaptability or versatility
If lighting scenes are updated in real-time based on social media data, then lighting adaptability improves, but processor power and memory requirements increase
Solution Approach 1:
The system extracts only the relevant information from vast amounts of social media data using targeted data mining techniques. Instead of processing entire social media feeds, the controller selectively extracts content related to the specific event or lighting scene, significantly reducing the computational burden while maintaining high adaptability to event-specific requirements.
Solution Approach 2:
The system performs partial data processing by focusing on specific keywords, hashtags, and content types relevant to the current lighting event. Rather than analyzing all social media content, the system applies selective filtering and processing only to portions of data that are likely to influence lighting decisions, thereby reducing processor energy consumption while maintaining sufficient adaptability.
3Reliability
If traditional lighting designs are used for landmarks, then lighting scenes can be created, but there is no feedback mechanism to optimize lighting based on event characteristics
Solution Approach 1:
The system incorporates social media data mining as a feedback mechanism that continuously monitors audience reactions, event progression, and environmental conditions. This feedback informs real-time adjustments to lighting scenes, improving the reliability and effectiveness of lighting designs. The feedback loop includes obtaining social media data, determining lighting attributes from this data, and adapting lighting scenes accordingly.
Solution Approach 2:
The system serves multiple functions within a unified platform: it creates lighting scenes, monitors social media content, extracts relevant data, determines lighting attributes, and adjusts illumination in real-time. This multi-functionality consolidates what would otherwise require separate systems into a single integrated solution, managing complexity while improving reliability through comprehensive event-responsive lighting control.
Data Source
AI summary
A lighting control system (1) comprising at least one lighting module (2) and a controller (3), the controller (3) being configured to cause the at least one lighting module to create a lighting scene for a lighting calendar event, the controller (3) further being configured to obtain first data by data mining social media content relating to the lighting calendar event, determine first lighting attributes from the first data, based on the first lighting attributes, control the at least one lighting module to generate at least one lighting scene and apply the at least one lighting scene for the lighting calendar event, obtain second data by data mining social media content relating to the applied at least one lighting scene, determine second lighting attributes from the second data, and based on the second lighting attributes control the at least one lighting module to generate at least one updated lighting scene and apply the at least one updated lighting scene for the lighting calendar event.