Proximity Detection Threshold Adaptation for Lighting Nodes
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Solution Overview
Problem
Manually adjusting proximity signal thresholds for different devices is complex and time-consuming, requiring a dynamic adjustment method without explicit user interaction.
Innovation Solution
A proximity-detecting device that receives wireless identification signals from lighting nodes, determines signal strength, and adjusts proximity thresholds based on pre-stored criteria associated with node identification information, enabling dynamic adaptation of proximity areas and controlling lighting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of proximity signal thresholds is performed, then the proximity detection accuracy can be optimized, but the system complexity and time consumption increase significantly
Solution Approach 1:
The system automatically adjusts proximity thresholds by monitoring signal strength values and detecting when they exceed predefined thresholds. The control unit autonomously updates threshold values based on received signal characteristics without requiring manual user intervention, thereby maintaining detection accuracy while eliminating the complexity of manual adjustment.
Solution Approach 2:
The system continuously monitors received signal strength values and uses this feedback to dynamically adjust proximity thresholds. When signal strength exceeds a threshold, the system updates the threshold value based on the measured signal characteristics, creating a closed-loop control system that adapts to changing environmental conditions automatically.
2Measurement precision
If manual adjustment of proximity signal thresholds is performed, then the proximity detection accuracy can be optimized, but the time consumption increases significantly
Solution Approach 1:
The control unit automatically performs threshold adjustments by monitoring signal strength and updating thresholds based on predefined criteria. This self-service mechanism eliminates the need for manual user intervention, thereby maintaining optimized detection accuracy while reducing the time required for threshold configuration to near-zero.
Solution Approach 2:
The system pre-defines threshold update criteria and signal strength monitoring parameters in advance. When signal conditions meet these pre-established criteria, the system automatically executes threshold updates without requiring real-time manual decision-making, thereby maintaining accuracy while minimizing adjustment time.
3Adaptability or versatility
If dynamic adjustment of proximity thresholds is enabled, then the adaptability to different lighting nodes is improved, but the device complexity increases
Solution Approach 1:
The control unit automatically adapts to different lighting nodes by monitoring their specific signal characteristics and adjusting thresholds individually based on each node's emission properties. This self-service adaptation mechanism enables the system to handle diverse lighting nodes without requiring complex manual configuration for each device.
Solution Approach 2:
The system applies different threshold values and update criteria specific to each lighting node based on its individual signal characteristics. By customizing threshold parameters locally for each node rather than using a universal threshold, the system achieves high adaptability while keeping the adjustment logic relatively simple and node-specific.
4Productivity
If dynamic adjustment of proximity thresholds is enabled, then the operational efficiency is improved, but the device complexity increases
Solution Approach 1:
The system continuously monitors signal strength values and uses this feedback to automatically update proximity thresholds in real-time. This feedback-driven approach enables the system to adapt to changing operational conditions dynamically, improving productivity by eliminating manual reconfiguration while the automated feedback loop manages the processing complexity.
Solution Approach 2:
The system transitions from static, manually-configured thresholds to dynamic, automatically-adjusted thresholds that adapt in real-time based on signal conditions. This dynamic approach improves operational efficiency by enabling the system to respond automatically to changing environments, while the automation of the adjustment process prevents complexity from becoming unmanageable.
Data Source
AI summary
The invention is directed to a proximity-detecting device configured to determine a signal-strength value (S) indicative of a received signal power amount of an identification signal of an originating lighting node that comprises lighting-node identification information and that is received via a signal input unit (102). It also comprises a proximity-detection unit (106) configured, upon determining that the received signal-strength value exceeds a predetermined proximity threshold value (Sth), to generate and provide a proximity-information signal (P) indicative of the proximity-detecting device being within a proximity area. It also comprises a threshold-determination unit (108) configured, upon determining fulfilment of a pre-stored threshold-updating criterion (C) comprising one or more updating-conditions associated with the lighting-node identification information, to update the proximity threshold value for the given originating lighting node, for redefining the adaptation.