Light Fixture Monitoring to Prevent Sensor-Driven Cycling
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Solution Overview
Problem
Conventional smart streetlights rely on sensor data for remote monitoring and control, but inaccuracies in sensing ambient light due to external sources can lead to inappropriate lighting decisions, resulting in inefficient energy use and potential safety issues.
Innovation Solution
A monitoring module that analyzes sensor data to determine if a light fixture is operating outside expected parameters, temporarily disabling monitoring and allowing the light to remain on or adjust intensity to prevent excessive cycling, and re-enabling monitoring after a finite period to address faulty sensor data from external sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the monitoring system continuously monitors sensor data to detect faulty operation, then the reliability of lighting control is improved, but the system may react to inaccurate sensor data from external light sources causing excessive cycling
Solution Approach 1:
The system performs preliminary analysis of sensor data patterns to detect potential faults before they cause excessive cycling. By monitoring for abnormal patterns (such as rapid on-off transitions) and preemptively disabling monitoring when faults are detected, the system prevents operational instability while maintaining continuous oversight capability.
Solution Approach 2:
The system uses feedback from sensor data analysis to dynamically adjust monitoring behavior. When sensor data indicates faulty operation patterns, the system feeds this information back to disable further monitoring actions, preventing the system from reacting to inaccurate data while maintaining the ability to detect future faults.
2Productivity
If the system reacts to sensor data by turning the light on or off, then lighting control responsiveness is improved, but energy wastage increases due to excessive cycling caused by external light sources
Solution Approach 1:
The system analyzes sensor data patterns in advance to identify faulty operation before executing lighting control actions. By detecting abnormal patterns (such as rapid cycling) and preemptively disabling monitoring, the system prevents energy-wasting responses to inaccurate sensor data while maintaining responsive control for legitimate lighting needs.
Solution Approach 2:
The system applies partial monitoring by selectively disabling monitoring functions when faults are detected, rather than completely shutting down control. This allows the system to maintain necessary lighting responsiveness while avoiding excessive energy consumption from reacting to faulty sensor data.
3Reliability
If the monitoring system operates continuously to ensure safety, then operational reliability is improved, but the system complexity increases due to fault detection and monitoring management
Solution Approach 1:
The system uses preliminary pattern recognition to detect faulty operation before it causes problems. By implementing simple threshold-based detection for abnormal patterns (such as rapid on-off transitions) and preemptively disabling monitoring when faults are detected, the system maintains high operational reliability with relatively simple monitoring logic.
Data Source
AI summary
Methods for remotely monitoring a status of a light fixture including monitoring sensor data associated with the light fixture; analyzing the monitored sensor data to determine if the light fixture is operating outside expected parameters associated with the light fixture; and altering the status of the light fixture for a finite period of time if it is determined that the light fixture is operating outside expected parameters are provide. Related monitoring modules and computer program products are also provided.