Adaptive Scene Trigger Sequencing for Easier Site Automation
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Solution Overview
Problem
Existing environmental control systems are complex to program and manage, particularly when multiple scenes are required, often necessitating technical expertise for routine changes, which increases system complexity and requires specialized skills.
Innovation Solution
A relational database and automation server system that uses genetic learning algorithms to create and prioritize scenes based on user interactions, allowing for automatic refinement and optimization of scene triggers and configurations through a structured dialogue process with installers, enabling easier management and adaptation of environmental control settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple scenes are programmed to handle different times of day and exceptions, then the system can accommodate diverse environmental control needs, but the programming complexity and system complexity increase significantly
Solution Approach 1:
The system automatically generates scene configurations by monitoring user interactions with environmental control devices. Instead of requiring manual programming of multiple scenes and exceptions, the system self-learns the user's preferences and automatically creates appropriate scene groupings, thereby reducing programming complexity while maintaining adaptability
Solution Approach 2:
The system continuously monitors user behavior feedback (how users manually control devices at different times) and uses this feedback to refine and optimize scene configurations. This iterative learning process allows the system to adapt to user needs without requiring complex manual reprogramming
2Reliability
If exceptions to scene programs are programmed to account for various scenarios, then the system can handle edge cases, but the remote controls require multiple activation options which further adds to overall system complexity
Solution Approach 1:
The system automatically handles exceptions by monitoring user interactions and identifying patterns that represent exceptional scenarios. Rather than requiring pre-programmed exception handlers, the system self-configures appropriate responses based on observed user behavior, maintaining reliability while simplifying operation
3Adaptability or versatility
If scene programs are made changeable to accommodate user preferences, then the system becomes more adaptable, but changing scene programs requires service by technicians with special skills which increases system complexity
Solution Approach 1:
The system enables users to dynamically modify scene configurations through automatic learning of their preferences. Users can change scene programs simply by interacting with devices differently, and the system automatically adapts the scene configurations without requiring technician intervention, thus maintaining adaptability while dramatically improving ease of operation
Data Source
AI summary
Disclosed is a method and apparatus for an environmental control system in which a genetic learning algorithm creates scenes and scene triggers and in which a fitness function scores the scenes through end-user interaction.