Presence Sensor Placement via Lighting Control History Analysis
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Solution Overview
Problem
Consumers often struggle to determine the optimal location for installing presence sensors or light switches in networked lighting systems, especially in homes where the purpose of the sensor can vary across different locations.
Innovation Solution
A system and method that analyze the control history of lighting devices to detect regularly occurring sequences of manual light control actions, determining the most beneficial location for the presence sensor or light switch based on these patterns, and providing tailored user guidance for installation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a presence sensor is installed without knowing the optimal location, then the installation can proceed, but the sensor may not effectively trigger desired light control actions
Solution Approach 1:
The system performs preliminary analysis of control history data before the actual sensor installation to identify optimal locations. By examining past manual light control actions and detecting regularly occurring sequences, the system determines where a presence sensor would be most effective, allowing the installer to place the sensor at the predetermined optimal location rather than guessing or using generic placement guidelines
2Reliability
If control history analysis is performed to determine optimal sensor location, then sensor placement effectiveness is improved, but system complexity increases
Solution Approach 1:
The lighting system analyzes its own control history data to determine optimal presence sensor locations. The system uses its existing operational data - records of manual light control actions already being performed - to autonomously identify where a presence sensor would provide the most benefit, eliminating the need for external tools or complex installation procedures
Solution Approach 2:
The system uses feedback from historical control actions to inform future sensor placement decisions. By analyzing patterns in past manual light control actions and detecting regularly occurring sequences, the system learns from its own operational history to determine where automated control would be most valuable
3Measurement precision
If manual light control actions are analyzed to detect patterns, then optimal sensor location is identified, but time and computational resources are consumed
Solution Approach 1:
The system analyzes control history data to detect regularly occurring sequences of manual light control actions, focusing on identifying patterns rather than examining every single control action in detail. By detecting sequences rather than analyzing individual actions exhaustively, the system achieves sufficient accuracy for determining optimal sensor locations without consuming excessive time or computational resources
Data Source
AI summary
A system (21) is configured to obtain a control history of one or more lighting devices (35,36) of a networked lighting system and detect a regularly occurring sequence of manual light control actions (91.92) based on the control history. The control history describes light control actions and the sequence of manual light control actions comprises a light control action associated with a location. The system is further configured to determine a location for a presence sensor or light switch based on the location associated with the light control action such that the network lighting system is able to trigger one or more of the manual light control actions at an appropriate moment when the presence sensor or light switch is placed at the determined location and output the determined location to a user to facilitate the installation of the presence sensor or light switch in the networked lighting system.