AI-Driven Dwelling Automation for Adaptive Light and Blind Control
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Solution Overview
Problem
Existing home automation systems require manual programming and frequent adjustments due to seasonal light intensity changes, leading to inefficiency and loss of precision.
Innovation Solution
A residential automation system with an AI evaluation and control unit that forms a neural network using presence, light intensity, and time data to automatically control lighting and blinds, learning user behavior and adapting to changing habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual programming and configuration of scenes is implemented in home automation systems, then the system can control lighting and blinds according to user needs, but the user has to spend significant time and effort to program and continuously adjust the scenes
Solution Approach 1:
The system employs machine learning algorithms that automatically learn user behavior patterns and preferences without manual programming. The system observes and analyzes user interactions with lighting and blinds, then autonomously adjusts scenes based on learned patterns, eliminating the need for users to manually program or continuously adjust settings
Solution Approach 2:
The system continuously monitors user behavior and system performance, using this feedback to refine and improve its automatic control decisions. By analyzing patterns in user interactions and environmental data, the system adapts its control strategies over time, maintaining high automation while minimizing user intervention
2Reliability
If fixed scenes are programmed to control lighting and blinds, then the system can execute predetermined actions, but the scenes lose precision when seasonal light intensity changes occur
Solution Approach 1:
The system transitions from static, fixed scenes to dynamic, adaptive control. Machine learning models continuously process environmental data including light intensity and time of day, enabling the system to automatically adjust lighting and blind control parameters in response to seasonal changes and varying user needs, maintaining precision without requiring reprogramming
Solution Approach 2:
The system autonomously adapts to seasonal changes by learning from environmental data and user behavior patterns. It automatically adjusts scene parameters based on observed patterns in light intensity variations and user preferences across different seasons, eliminating the need for manual scene adjustments
3Adaptability or versatility
If multiple sensors and actuators are integrated for comprehensive room automation, then the system can monitor and control various parameters, but the system complexity increases
Solution Approach 1:
The system integrates multiple sensors (presence, light intensity, time) and actuators (lighting control, blind control) into a unified automated control architecture. Machine learning algorithms process data from all sensors and coordinate control actions across all actuators, managing complexity through integrated intelligence rather than separate control systems
Solution Approach 2:
The machine learning system autonomously manages the complexity of coordinating multiple sensors and actuators. It automatically processes sensor data, determines appropriate control actions, and executes coordinated responses without requiring manual configuration or intervention, making the complex system easy to use
Data Source
Figure 1~2
AI summary
A residential automation system R is presented, comprising a presence sensor P for detecting presence data relating to a living being M, a brightness sensor H for detecting light intensity data, a time sensor T for detecting time data, in particular with regard to the time and the date, wherein the presence data, the light intensity data and the time data are stored in a data memory S of the residential automation system R, wherein the residential automation system R comprises a light control actuator whose states can be read out and controlled, as well as a readable and controllable blind control actuator.What is special about this is that the residential automation system R comprises an AI evaluation and control unit KI, which controls the light control actuator L and the blind control actuator J within the framework of an automatic control process on the basis of the presence data, light intensity data and time data evaluated by the AI evaluation and control unit KI, whereby the AI evaluation and control unit KI forms a neural network into which both the presence data, the light intensity data and the time data as well as the states of the readable and controllable light control actuator L and the readable and controllable blind control actuator J are input.