Method and control unit for controlling a household appliance
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Solution Overview
Problem
Controlling multiple household appliances within a household can be cumbersome for users, and existing intelligent appliances do not adequately address efficiency and user comfort.
Innovation Solution
A control unit utilizing a machine-learning based prediction entity to anticipate user behavior and control appliances based on usage data, including calendar, device, and environmental data, to optimize operation and settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a control unit uses machine-learning algorithms to predict user behavior and automatically control appliances, then user comfort and efficiency are enhanced, but device complexity increases
Solution Approach 1:
The control unit automatically collects usage data, trains prediction entities, and adjusts appliance operations without requiring user intervention. The system serves itself by autonomously learning user behavior patterns and making control decisions, thereby enhancing user comfort while managing complexity through automation.
Solution Approach 2:
The control unit performs preliminary data collection and prediction entity training in advance to prepare for future usage scenarios. By pre-processing usage data and building prediction models before they are needed for actual control decisions, the system enhances responsiveness and user comfort while organizing complexity into manageable preparatory stages.
2Measurement precision
If the control unit collects and processes extensive usage data from multiple sources, then prediction accuracy improves, but loss of time for data processing increases
Solution Approach 1:
The control unit continuously collects and pre-processes usage data from multiple sources in the background, organizing it into training datasets before prediction is needed. This preliminary data preparation ensures high prediction accuracy when needed while minimizing processing delays during actual usage scenarios.
Solution Approach 2:
The data collection and processing operations run continuously in the background without interrupting appliance operations. Usage data is accumulated and processed in an ongoing manner, ensuring that prediction accuracy improves over time without causing time loss during critical appliance usage moments.
3Adaptability or versatility
If the prediction entity is trained continuously with new usage data, then adaptability to user behavior changes improves, but loss of time for retraining increases
Solution Approach 1:
The control unit implements periodic retraining of prediction entities at scheduled intervals rather than continuously. Usage data is accumulated over periods and used to retrain prediction models periodically, allowing the system to adapt to user behavior changes while minimizing interruptions and time loss associated with frequent retraining operations.
Solution Approach 2:
The control unit performs partial retraining by updating prediction entities with only the most recent or most relevant usage data rather than retraining with the complete historical dataset each time. This approach maintains adaptability to user behavior changes while significantly reducing the time required for each retraining operation.
Data Source
Figure 1~3
AI summary
A control unit (110) configured to control a household appliance (120) is described. The control unit (110) is configured to determine usage data regarding a user (141) and regarding actual usage of the household appliance (120) by the user (141). Furthermore, the control unit (110) is configured to train a prediction entity (200) based on the usage data using a machine-learning algorithm, such that the prediction entity (200) is configured to predict an upcoming usage (205) of the appliance (102) based on input data (201, 202, 203, 204). Furthermore, the control unit (110) is configured to use the prediction entity (200) for controlling the household appliance (120).