Automated Luminaire Identification via Light Pattern Detection
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Solution Overview
Problem
Current methods for identifying and assigning luminaire locations in large lighting control systems are labor-intensive and prone to errors, especially in IoT-based systems where manual interaction is extensive and physical media like installation drawings can be lost or damaged, necessitating an automated solution for quick commissioning and reduced manual efforts.
Innovation Solution
A system comprising a luminaire with LEDs, a gateway, and a sensor subsystem that communicates with the luminaire and server to create a virtual map of luminaire positions by measuring and comparing light patterns and environmental data, allowing for automatic identification and group assignment, even in large ecosystems like entire buildings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification methods (detachable ID stickers, barcodes, service pins) are used for luminaire identification, then each device can be individually identified, but the commissioning time and labor expenditure increase significantly for large-scale installations
Solution Approach 1:
The luminaire automatically performs identification by emitting a unique light pattern (wink sequence) that encodes its address and location information. The commissioning tool detects this self-emitted signal, eliminating the need for manual observation and recording by technicians.
Solution Approach 2:
The patent replaces manual mechanical processes (technicians physically observing luminaire winks, recording addresses on drawings or devices) with an automated optical detection system. The commissioning tool uses sensors to detect light patterns and automatically captures device information, substituting human labor with machine-based detection.
2Reliability
If manual device identification and group assignment is performed, then correct operational configuration can be achieved, but the process becomes excessively time-consuming and error-prone in large-scale IoT lighting systems
Solution Approach 1:
The system implements automated feedback loops where the commissioning tool detects luminaire responses, automatically processes the detected information, and assigns devices to groups without manual intervention. This closed-loop process eliminates human errors while maintaining configuration accuracy.
Solution Approach 2:
The luminaire pre-configures its identification signal with embedded address and location data before commissioning begins. When detected, this pre-prepared information is automatically processed, eliminating the need for technicians to manually gather and record device details during commissioning.
3Loss of information
If physical media (installation drawings with attached ID stickers) are used for device identification, then location mapping can be achieved, but the media can be lost or damaged and requires extensive manual handling
Solution Approach 1:
Instead of relying on physical copies (drawings with stickers) that can be lost or damaged, the system creates and maintains a digital copy of the installation information. The commissioning tool automatically captures device locations and creates a persistent digital record that can be stored and retrieved without physical media.
Solution Approach 2:
The patent extracts the identification information from physical media and embeds it directly into the luminaire's digital identity. The device's address and location data are stored in its memory and transmitted electronically, removing the dependency on external physical carriers like stickers and drawings.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces commissioning time by automating luminaire location identification and group assignment, enhancing efficiency and accuracy, even in large-scale installations where manual methods would be excessively time-consuming and error-prone.
Implementation Method 1
The sensor subsystem is configured to detect light patterns emitted by luminaires when winking on and off
Data Source
AI summary
The disclosed devices, systems, and methods may be used to automatically identify, locate, and assign luminaires into groups such that lighting systems may be more efficiently configured, used, and maintained especially in large buildings, etc. For example, a wink function may be used with a system of sensors which are capable of detecting light patterns from individual and groups of luminaires to form virtual maps of luminaire locations which may be correlated with actual luminaire floor plans to efficiently identify, locate, and group the luminaires.