Air Conditioner Adaptive Control Using Historical Adjustment Feedback
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Solution Overview
Problem
Existing air conditioner control methods are inefficient as they require frequent user adjustments to maintain desired environmental parameters, leading to discomfort and energy inefficiency, as they lack adaptive intelligence to adjust based on changing conditions.
Innovation Solution
An air conditioner control method that pairs current ambient temperatures with optimal operational parameters learned from historical data, allowing for intelligent selection and storage of optimal settings based on continuous running time and modification frequency, ensuring accurate and comfortable operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic control method is used to simplify operation, then ease of operation is improved, but control accuracy deteriorates because it merely replicates user settings without considering whether the control is proper
Solution Approach 1:
The system collects user adjustment operations during air conditioner running, counts the adjustment frequency, and uses this feedback to determine whether to store operational parameters as optimal. When adjustment frequency exceeds a threshold, the parameters are not stored, ensuring only truly optimal parameters are saved for automatic control.
Solution Approach 2:
The system automatically learns and stores optimal operational parameters by observing user adjustment behaviors. The control unit self-updates the optimal parameter database based on accumulated running data and adjustment frequency analysis, eliminating the need for manual re-programming by users.
2Measurement precision
If user manually adjusts operational parameters to adapt to changing environment, then control accuracy is improved, but ease of operation deteriorates due to complicated and time-consuming modifications
Solution Approach 1:
The system pre-stores optimal operational parameters for various ambient temperatures based on historical running data and user adjustment patterns. When the air conditioner starts, it automatically selects the most suitable pre-stored parameters according to current ambient temperature, avoiding the need for manual adjustments.
Solution Approach 2:
The system dynamically changes the stored optimal parameters based on ambient temperature conditions. By categorizing parameters according to temperature ranges and automatically selecting appropriate sets, the system adapts to environmental changes without requiring user intervention.
3Adaptability or versatility
If optimal operational parameters are stored for all ambient temperatures, then adaptability is improved, but device complexity increases due to data storage and selection requirements
Solution Approach 1:
The system segments optimal parameters into different sets categorized by ambient temperature ranges. Instead of storing all possible parameters uniformly, it divides them into temperature-specific groups, simplifying the storage structure and selection process while maintaining comprehensive adaptability.
Solution Approach 2:
The system performs preliminary categorization of operational parameters according to ambient temperature conditions during the data storage phase. This pre-organization enables efficient retrieval and automatic selection based on current temperature, reducing the computational complexity during operation.
4Adaptability or versatility
If frequent adjustments of operational parameters are allowed during running, then adaptability is improved, but loss of time increases due to repeated modifications
Solution Approach 1:
The system monitors and counts the number of user adjustments during each running cycle. When the adjustment count exceeds a predetermined threshold, the system determines that the parameters should not be stored as optimal, preventing future unnecessary adjustments and saving time.
Solution Approach 2:
The system performs preliminary validation of operational parameters by analyzing user adjustment behavior before storing them. This pre-screening ensures that only stable, optimal parameters are stored, preventing future frequent adjustments and reducing time loss.
Data Source
Figure 1
AI summary
An air conditioner control method includes: obtaining a current ambient temperature when the air conditioner is being powered on, and selecting a current operational parameter corresponding to the current ambient temperature from stored optimal operational parameters which are paired with historical ambient temperatures; performing a control process on the air conditioner according to the current operational parameter; wherein a one-to-one correspondence between the historical ambient temperatures and the optimal operational parameters obtained by a process including: obtaining a continuous running time since this start, how many times operational parameters being modified since this start and a ambient temperature since this start as receiving a power-off signal; obtaining an allowed modification times of operational parameters during the running duration, which corresponds to the continuous running time since this start, if the times of the operational parameters being modified obtained is not greater than the allowed modification times, pairing the optimal operational parameter with the ambient temperature to generate and store the one-to-one correspondence between the optimal operational parameters and the historical ambient temperatures; if the times of the operational parameters being modified obtained is greater than the allowed modification times identified, terminating the setting of the optimal operational parameters. This method could improve the accuracy of air conditioner control and make the user feel more comfortable.