Adaptive Lighting Routine Adjustment via State Comparison
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Solution Overview
Problem
Existing lighting control systems are limited in their ability to adjust the execution of routines in response to a broad range of exceptional situations, typically only allowing adjustments in predefined scenarios.
Innovation Solution
A system comprising a receiver, a transmitter, and a processor that learns the states of portable devices at the start time of a routine over multiple executions, determines expected states, and adjusts the routine's execution based on differences between expected and current states, allowing for adjustments in a wide variety of exceptional situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a routine is executed automatically based on predefined conditions, then the automation level is improved, but the adaptability to exceptional situations deteriorates
Solution Approach 1:
The system continuously monitors the current state of portable devices and compares it with the expected state learned from historical data. This feedback mechanism enables the system to detect deviations from normal patterns and automatically adjust routine execution accordingly, resolving the contradiction by maintaining high automation while adapting to exceptional situations through real-time state comparison
Solution Approach 2:
The system performs preliminary learning of expected states for portable devices during a learning mode before actual routine execution. By pre-establishing what normal device states should be, the system can quickly identify exceptional situations without requiring complex real-time decision-making, thus maintaining automation while improving adaptability
2Adaptability or versatility
If the system monitors and learns device states to determine expected patterns, then the adaptability is improved, but the complexity of the system increases
Solution Approach 1:
The system automatically learns and determines the expected states of portable devices through continuous monitoring during a learning phase, without requiring manual configuration or user intervention. This self-learning capability improves adaptability while minimizing the increase in system complexity by automating the pattern recognition process
Solution Approach 2:
The system uses a general-purpose learning mechanism that can adapt to different types of portable devices and routines without requiring device-specific or routine-specific customization. This universal approach allows the system to handle diverse scenarios while maintaining relatively simple system architecture
Data Source
AI summary
A method comprises, in a learning mode, learning (101) a state of one or more portable devices at a start time associated with a routine for a plurality of executions of the routine over a period of time based on received signals and determining (103) one or more expected states of the one or more portable devices at the start time associated with the routine based on the learned state. The method further comprises, in an adjustment mode, determining (105) a current state of the one or more portable devices at the start time of the routine for a current execution of the routine, determining (107) whether the current state differs from the expected state, and adjusting (109) the current execution of the routine in dependence on a difference between these states. The current execution of the routine comprises transmission of at least one light command.