Lighting Control System Using Neural Networks for Target Distribution
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Solution Overview
Problem
Current lighting systems with multiple controllable light sources require complex manual adjustments to achieve a desired target light distribution, lacking an efficient method for automatic control based on user-defined scenarios.
Innovation Solution
A method and system that automatically generate control commands for lighting systems by defining a target light distribution, using influence data to optimize parameters such as brightness and color across multiple light sources, employing neural networks and multi-dimensional optimization algorithms to minimize colorimetric differences between predicted and target light distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustment of multiple light source parameters is used, then lighting system functionality is achieved, but system complexity and operation difficulty increase dramatically
Solution Approach 1:
The control unit automatically determines control commands for multiple light sources based on a target light distribution, eliminating the need for manual parameter adjustment. The system self-configures by processing the target distribution and generating appropriate control signals for each light source's parameters (intensity, color temperature, beam direction).
Solution Approach 2:
The control unit acts as an intermediary between the user's target light distribution specification and the actual light source parameters. It translates the high-level target distribution into detailed control commands for multiple controllable parameters of individual light sources, simplifying the user interface while maintaining full system functionality.
2Adaptability or versatility
If manual control of lighting parameters is used, then lighting scenarios can be created, but time consumption and operational effort increase
Solution Approach 1:
The control unit automatically generates control commands for multiple light sources based on the target light distribution without requiring manual intervention for parameter setting. The system autonomously processes the target distribution and configures all light source parameters, dramatically reducing setup and operation time.
Solution Approach 2:
The system pre-calculates the optimal parameter settings for multiple light sources based on the target light distribution before execution. By determining all control commands in advance through automated processing, the system eliminates time-consuming manual adjustments during actual lighting scenario implementation.
3Extent of automation
If automatic control based on environmental conditions is used, then lighting adaptation is achieved, but inability to achieve user-defined target distributions occurs
Solution Approach 1:
The control unit receives target light distribution specifications from users and automatically processes this feedback to generate appropriate control commands. The system continuously adapts the lighting configuration based on the target distribution, comparing the current state with the desired state and adjusting light source parameters accordingly to achieve user-defined scenarios.
Solution Approach 2:
The control unit dynamically adjusts the parameters of multiple light sources (intensity, color temperature, beam direction) based on the target light distribution. This dynamic control enables the system to transition between different lighting scenarios while maintaining full adaptability to user-defined requirements through automated real-time parameter optimization.
Data Source
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AI summary
The invention relates to a method of controlling a lighting system with multiple controllable light sources 3a, 3b and a system therefor. According to a first as¬ pect, influence data of the lighting system are obtained, which data represent the effect of one or more of the light sources 3a, 3b on the illumination of one or more sections of an illuminated environment. In an optimization method, sets of control commands are continuously determined, a predicted light distribution for these control commands is determined from the influence data, and a colorimetric difference between the predicted light distribution and a target light distribution is determined. A plurality of adjustment steps are performed to minimize the colorimetric difference. According to a second aspect, a neural network is trained with the influ¬ ence data and a set of control commands for controlling the lighting system is deter¬ mined with the use of the neural network.