3D Streetlight Setpoint Matching for Geographic Illumination Control
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Solution Overview
Problem
Current computing systems are inadequate in designing and managing smart streetlights due to limited processing capabilities and back-end systems, lacking effective solutions for optimizing streetlight performance based on geographic location and illumination needs.
Innovation Solution
A processor system that identifies the 3D location, orientation, and height of streetlights, accesses lighting performance data, and performs interpolation to generate a substantive performance layer (SPL) compared to a target lighting layer (TLL), determining absolute illumination differences to correlate a particular lighting configuration, which can include hardware and software adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If current computing systems are used for smart streetlight design and management, then system simplicity is maintained, but processing capabilities and back-end system performance are severely limited
Solution Approach 1:
The patent transitions from traditional 2D lighting design to 3D spatial modeling with substantive performance layers that incorporate height, orientation, and geographic location dimensions. This enables comprehensive analysis of light distribution in three-dimensional space, significantly enhancing processing capabilities for smart streetlight management.
Solution Approach 2:
The patent introduces an intermediary computing system that acts as a bridge between streetlight hardware and management software. This intermediary layer provides advanced processing capabilities for analyzing lighting performance data, generating performance layers, and optimizing streetlight configurations without requiring complex modifications to existing streetlight infrastructure.
2Measurement precision
If comprehensive lighting performance analysis is performed across multiple areas, then illumination accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the analysis area into multiple discrete zones or areas around each streetlight. By segmenting the space and analyzing lighting performance in each segment separately, the system achieves comprehensive coverage and high measurement precision while enabling parallel processing to reduce overall computation time.
Solution Approach 2:
The patent performs preliminary calculations of lighting performance data using photometric information and streetlight parameters before conducting full analysis. This preliminary action pre-computes baseline values and intermediate results that accelerate the subsequent detailed illumination analysis across multiple areas, reducing total processing time.
3Area of stationary object
If interpolation is used to identify illumination at additional areas, then coverage completeness is improved, but calculation complexity increases
Solution Approach 1:
The patent uses interpolation to create virtual copies of measured lighting performance data at locations where direct measurements are not available. By copying and extrapolating data from nearby measured points, the system achieves complete spatial coverage without requiring complex direct measurements at every location, maintaining calculation efficiency.
4Manufacturing precision
If multiple performance layers are generated and compared against target layers, then optimization precision is improved, but data processing requirements increase
Solution Approach 1:
The patent generates substantive performance layers that capture local lighting characteristics at different spatial locations and orientations. By creating localized performance data that reflects specific conditions at each area, the system achieves high optimization precision for streetlight configuration while enabling targeted analysis that reduces overall data processing requirements compared to uniform global analysis.
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 system enables precise optimization of streetlight configurations to meet target illumination goals, adapting to geographic variations and external factors, reducing the need for constant user intervention and improving lighting efficiency.
Implementation Method 1
perform interpolation to identify substantive illumination for the streetlight at third and fourth areas on the ground
Implementation Method 2
use the inverse square law to identify the illumination for the streetlight at first and second areas on the ground
Data Source
AI summary
Computer-based systems are disclosed that provide technological advances in electronic setpoint management and infrastructure. Present principles may be applied to streetlight configuration and management as well as to other applied technologies. Thus, in one aspect an apparatus includes a processor and storage with instructions. The instructions are executable to identify substantive performance data for a device, access setpoint data associated the device, and compare the substantive performance data to the setpoint data to determine a difference between the substantive setpoint data and the setpoint data. The instructions are also executable to correlate the difference to a particular configuration for the device. The instructions are then executable to store the substantive performance data, setpoint data, and the particular configuration for the device. The stored data is then used for training a machine learning model, which is then deployed to infer additional configurations for smart streetlights and other smart devices.