Ceiling Fan Learning Mode for User-Preference-Based Speed Control
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Solution Overview
Problem
Existing fans do not account for user preferences in fan speed adjustments based on ambient conditions, leading to discomfort as temperature or humidity changes, as they regulate fan speed independently without considering individual user preferences.
Innovation Solution
A learning mode in fans that allows users to input desired speeds for specific ambient conditions, with a controller adjusting fan speeds automatically based on user inputs, enabling personalized speed settings for different conditions, including temperature, humidity, or both, and allowing for continuous updates based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If fan speed is adjusted automatically based on sensed temperature, then the fan responds to environmental changes, but user preferences for fan speed are not taken into account
Solution Approach 1:
The system performs preliminary actions by having users input their preferred fan speeds for various temperature conditions during initialization. This stored preference data is then used to guide automatic adjustments, ensuring both automation and user preference adaptation are achieved.
Solution Approach 2:
The system incorporates feedback mechanisms where user preferences are continuously considered during operation. The controller references stored preference data and adjusts fan speeds accordingly, creating a feedback loop that maintains both automation and adaptability to user needs.
2Stability of the object's composition
If fan speed is regulated in a lock-step fashion to temperature changes, then the fan responds consistently to environmental conditions, but comfort is not optimized for different users
Solution Approach 1:
The system applies local quality by storing different fan speed preferences for different users or conditions. Instead of a single uniform response, the system maintains multiple preference profiles that can be selectively applied, allowing consistent temperature response while adapting to individual user comfort needs.
Solution Approach 2:
The system transitions from a static, fixed fan speed control to a dynamic system that can adapt between different user preferences. The controller dynamically selects appropriate fan speeds based on the current user and temperature conditions, maintaining stability in response while enabling adaptability across different users.
3Adaptability or versatility
If multiple user preferences are stored for different conditions, then user-specific comfort is achieved, but the system complexity increases
Solution Approach 1:
The system uses copying by storing simplified preference data representations rather than complex control algorithms. Each user preference is captured as a straightforward mapping between temperature ranges and fan speeds, creating lightweight data structures that are easy to store and retrieve without adding significant system complexity.
Data Source
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.