Lighting Unit Localization via Global Background Model
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Solution Overview
Problem
Current methods for localizing and assessing lighting unit assets in lighting systems are inefficient and inaccurate, relying on subjective human inspection or two-dimensional data analysis, which is labor-intensive and prone to noise due to irregularities in local maxima and sensor asynchrony.
Innovation Solution
A system utilizing a simulation platform with a photometric database and a Gaussian mixture model to simulate and analyze lighting unit output, building a global background model for comparison with observational data to detect faults and notify users, with the option to augment sparse data using simulated information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human inspection is used to assess lighting unit performance and detect faults, then subjective judgment can be applied, but the process is labor intensive and time consuming
Solution Approach 1:
The patent replaces manual human inspection with an automated system that uses sensors to collect illuminance data and a processor to analyze the data. The processor compares observed illuminance data against expected values to automatically detect faults, eliminating the need for human inspectors to physically examine each lighting unit while maintaining or improving detection accuracy.
Solution Approach 2:
The lighting system performs self-diagnosis by automatically collecting its own performance data through integrated sensors and comparing it against predetermined thresholds. The system can autonomously identify when a lighting unit is malfunctioning without requiring external human intervention, enabling the system to monitor and assess itself continuously.
2Productivity
If vehicle- or drone-mounted sensing devices are used to collect geolocation and illuminance measurements, then automated data acquisition is achieved, but the methods are inefficient and inaccurate due to noise from irregular local maxima and sensor asynchrony
Solution Approach 1:
The patent introduces a global background model as an intermediary that synthesizes data from multiple sensors and time points. This model acts as a mediator that reconciles asynchronous sensor readings and filters out noise from irregular local maxima by comparing observations against the synthesized background model, thereby improving both efficiency and accuracy.
Solution Approach 2:
The system performs preliminary data processing by collecting and synthesizing illuminance data from multiple sensors over time to build a global background model before making fault detection decisions. This preliminary action of aggregating and processing data in advance allows the system to filter noise and resolve asynchrony issues before the actual comparison and detection phase.
3Measurement precision
If two-dimensional data analysis is used to localize lighting units by comparing geo-located assets to local maximum illuminance values, then localization can be achieved, but the method is noisy due to irregularity of local maxima and asynchrony between different sensors
Solution Approach 1:
The patent transitions from two-dimensional spatial analysis to four-dimensional analysis by incorporating both spatial coordinates and temporal information. The system collects illuminance data over time at multiple locations and synthesizes this spatio-temporal data into a global background model, adding the time dimension to resolve asynchrony issues and improve localization accuracy beyond what static two-dimensional analysis can achieve.
Data Source
AI summary
A method (400) for analyzing output of lighting units (10) in a lighting system (100) includes the steps of: (i) simulating (430), based on data from a photometric database (310), the output of a lighting unit; (ii) receiving and storing (420), from a database (330) of historical information, historical observed data about the output of the lighting unit; (iii) receiving (450) observed data (36) about the output of the lighting unit; (iv) generating (440) a model of the lighting system based at least in part on the simulated output of the lighting unit and the historical observed data about the output of the lighting unit, wherein the model comprises localization information for the lighting unit; and (v) comparing (470) the received observed data about the output of the lighting unit to the generated model, wherein a fault is detected if the observed data varies from the generated model by a predetermined amount.