Refrigerator Ambient Noise Control via Dynamic Frequency Adjustment
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Solution Overview
Problem
Conventional refrigerators do not effectively consider ambient noise when determining their operating state to minimize noise levels, leading to inefficient noise reduction strategies.
Innovation Solution
A refrigerator equipped with a microphone and processor that identifies ambient noise levels, adjusts compressor and fan motor frequencies based on noise ratios, and utilizes a neural network model to determine optimal operating states for reduced noise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If the refrigerator operates in a specific driving state to reduce noises, then the noise level is reduced, but the refrigerator cannot adapt to varying ambient noise conditions
Solution Approach 1:
The refrigerator transitions from a static driving state to a dynamic one by continuously monitoring ambient noise levels through a microphone and adjusting its compressor and fan motor operations in real-time. The processor dynamically selects from multiple driving state information sets based on current noise conditions, enabling the system to adapt its noise reduction strategy to varying environmental acoustic conditions.
Solution Approach 2:
The system implements a feedback mechanism where the microphone continuously captures ambient noise, the processor analyzes the noise level and spectrum characteristics, and then adjusts the driving state accordingly. This closed-loop control allows the refrigerator to respond to changing noise environments by selecting appropriate driving states that optimize noise reduction while maintaining cooling performance.
2Object-generated harmful factors
If the refrigerator actively identifies optimal driving states based on ambient noise, then noise reduction efficiency is improved, but the device complexity increases
Solution Approach 1:
The refrigerator performs self-diagnosis and self-adjustment by using its own microphone to monitor ambient noise and automatically selecting appropriate driving states without external intervention. The processor independently analyzes noise characteristics and determines optimal operating parameters, enabling the system to manage its own noise profile through autonomous decision-making based on environmental feedback.
Solution Approach 2:
The patent replaces complex mechanical noise control mechanisms with electronic and software-based solutions. Instead of using multiple physical dampers or adjustable mechanical components, the system uses a microphone for noise detection, a processor for analysis, and software algorithms to select driving states, thereby achieving noise reduction through intelligent control rather than mechanical means.
3Object-generated harmful factors
If the refrigerator adjusts compressor and fan motor frequencies dynamically, then noise levels are minimized, but the control precision requirements increase
Solution Approach 1:
The system changes operational parameters (compressor frequency and fan motor rotation frequency) based on ambient noise conditions. By adjusting these parameters dynamically according to the selected driving state, the refrigerator optimizes its noise output while maintaining effective cooling performance. The processor selects from predefined frequency sets that balance noise reduction with cooling efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution allows for dynamic adjustment of operating states to minimize noise levels based on ambient conditions, enhancing noise reduction efficiency and user experience.
Implementation Method 1
based on receiving an ambient noise through the microphone while operating according to the first driving state information
Data Source
AI summary
A refrigerator includes a microphone and at least one processor configured to identify a first driving noise level corresponding to first driving state information of the refrigerator, based on receiving an ambient noise through the microphone while operating according to the first driving state information, identify a reception noise level based on the received ambient noise, identify an external noise level based on the reception noise level and the first driving noise level, and based on a ratio of the external noise level to the first driving noise level being smaller than a threshold ratio, drive the refrigerator based on second driving state information corresponding to a lower noise level than the first driving state information.