Self-configuring Public Lighting Device with Video Sensor
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Solution Overview
Problem
Current public lighting systems require human intervention to configure lighting parameters based on location and environmental changes, which is inefficient and labor-intensive, and do not adapt automatically to real-time conditions.
Innovation Solution
A system comprising public lighting devices with video sensors and digital processing elements that use predictive models to determine environmental and presence information, allowing for automatic adjustment of lighting parameters without human intervention, using a 'Yolo' type multilayer neural network for real-time presence detection and adaptive lighting control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human intervention is used to configure lighting parameters based on location, then lighting can be customized for different environments, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The lighting device performs self-configuration by automatically capturing images with its integrated camera, analyzing the environment using embedded processing units, and adjusting its lighting parameters without requiring manual intervention from operators or technicians
Solution Approach 2:
The device pre-configures its lighting parameters before actual operation by first analyzing the installation environment through camera capture and image processing, determining optimal lighting settings in advance based on the detected scene characteristics
2Manufacturing precision
If manual configuration is performed for each lighting device, then specific lighting requirements can be met, but operational complexity increases
Solution Approach 1:
Each lighting device autonomously determines its own optimal parameters by capturing and analyzing its specific installation environment, eliminating the need for complex manual configuration procedures while maintaining precise lighting parameters tailored to each location
Solution Approach 2:
The configuration process is divided into independent automated steps performed by each device: image capture, environmental analysis, parameter determination, and self-adjustment, allowing each lighting device to be configured independently without requiring centralized manual control
3Use of energy by moving object
If lighting parameters are manually adjusted, then lighting can be optimized for specific locations, but energy management efficiency decreases
Solution Approach 1:
The lighting device continuously monitors its installation environment through camera capture and automatically adjusts its lighting parameters in real-time based on detected changes, creating a closed-loop feedback system that optimizes energy consumption without requiring manual intervention
Solution Approach 2:
The lighting parameters are dynamically adjusted based on real-time environmental conditions detected by the camera and processing units, allowing the system to adapt to changing scenes and optimize energy usage automatically rather than remaining static
Data Source
Figure 1a~2b
Figure 2c~3
AI summary
[System for public lighting comprising at least one public lighting device (10) suitable for installation on the roadway, comprising at least one video sensor (12) providing a video stream composed of a series of digital images, said images representing an environment (120) of said device; and at least one lighting element (11) configurable in luminous intensity and lighting area (110) according to parameters; and at least one digital processing element (20) comprising a first predictive model (21) configured to determine environmental information from said series of images, and a second predictive model (22) to determine presence information within the environment and configured to issue a command to modify the parameters of the lighting element according to environmental information and presence information.