Ceiling Fan Learning Mode for User-Adaptive Speed Control
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Solution Overview
Problem
Existing fan technologies adjust speed based on ambient conditions like temperature, but they do not account for user preferences, leading to discomfort as users may find different speeds comfortable at varying temperatures.
Innovation Solution
A fan with a learning mode that allows users to input desired fan speeds for specific environmental conditions, enabling the controller to automatically adjust speeds based on user preferences and adapt to changes in conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If fan speed is automatically adjusted based on sensed temperature, then the fan responds to environmental changes, but user comfort preferences are not accounted for
Solution Approach 1:
The system performs preliminary actions by having users manually adjust fan speed during a learning period before automatic control is fully engaged. These preliminary manual adjustments are stored and used to establish user preferences, which then guide subsequent automatic speed adjustments based on temperature sensing.
Solution Approach 2:
The system incorporates feedback mechanisms where user manual adjustments to fan speed are detected and stored as preference data. This feedback loop allows the controller to learn and adapt to user preferences over time, combining automatic temperature-based control with personalized user comfort requirements.
2Reliability
If fan speed is locked to temperature regulation, then environmental control is achieved, but adaptability to different users and conditions is reduced
Solution Approach 1:
The system transitions from a static, fixed temperature-to-speed mapping to a dynamic control strategy. The fan speed control becomes adaptive, continuously adjusting based on both temperature conditions and learned user preferences. The system can modify its behavior over time as it learns different user preferences for different temperature ranges.
Solution Approach 2:
The control system changes the parameter mapping between temperature and fan speed from a fixed relationship to a variable relationship. Instead of a single temperature corresponding to a single speed, the system maintains multiple possible speed values for each temperature based on learned user preferences, allowing flexible adaptation to different users and conditions.
3Ease of operation
If manual fan speed control is used, then user preferences are captured, but automatic response to environmental changes is lost
Solution Approach 1:
The system uses manual control as a preliminary action during an initialization or learning phase. Users manually adjust the fan to their preferred speed at current temperature conditions, and the system stores these preferences. After this preliminary manual input phase, the system transitions to automatic control that incorporates these learned preferences.
Solution Approach 2:
The system enables users to self-service the configuration of their preferences through manual adjustments. The controller automatically detects these manual adjustments and uses them to program the optimal fan speed for various temperature conditions, eliminating the need for complex setup procedures while capturing user preferences.
Data Source
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AI summary
A fan, such as a ceiling fan, includes a learning mode of operation. This learning mode may permit a user to input a desired speed for the fan for a given condition, such as ambient temperature, and adjustments for other conditions would be automatically determined based on the user input. A subsequent selection of fan speed at that condition (such as, for example, for a different user) or a different condition setting would also be obtained, either during initialization or later, and then used as an updated measure of the desired fan speed for the condition. Related methods of controlling the operation of a fan are also disclosed.