Noise reduction method and electronic device

Abstract

The application provides a noise reduction method and an electronic device. The noise reduction method provided by the application detects noise data by using a target sensor arranged on an outdoor unit, and obtains a frequency domain signal corresponding to the noise data; determines a frequency reference value of the noise data based on the frequency domain signal; and determines whether to perform noise reduction processing based on the frequency reference value and a frequency threshold value, wherein the frequency threshold value is determined based on user feedback data. The noise reduction method provided by the application adjusts the frequency threshold value in real time based on the user feedback data, thereby performing noise judgment, and can improve the noise of the open air conditioner outdoor unit in real time and improve the comfort.

Description

Technical Field

[0001] This application relates to the field of air conditioning noise reduction technology, and in particular to a noise reduction method and electronic equipment. Background Technology

[0002] As living standards improve, there are increasingly higher requirements not only for indoor air conditioning noise levels but also for the noise levels of outdoor air conditioning units. This is particularly evident in areas with high population density and high air conditioning usage, where the noise from the outdoor unit significantly impacts the comfort of air conditioning use. Summary of the Invention

[0003] This application mainly provides a noise reduction method and an electronic device. The noise reduction method of this application can improve the noise of the outdoor unit of the air conditioner in real time and improve comfort.

[0004] Firstly, the first technical solution adopted in this application is: providing a noise reduction method, comprising: Noise data is detected using a target sensor installed on the outdoor unit, and the corresponding frequency domain signal of the noise data is obtained. Determine the frequency reference value of noise data based on frequency domain signals; Whether to perform noise reduction processing is determined based on frequency reference values ​​and frequency thresholds, where the frequency thresholds are determined based on user feedback data.

[0005] In one embodiment, determining a frequency reference value for noise data based on a frequency domain signal includes: The frequency domain signal is frequency-weighted to obtain the total noise value; The frequency reference value is calculated based on the total noise value and the noise peak value.

[0006] In one embodiment, a frequency reference value is calculated based on the total noise value and the noise peak value, including: Calculate the first difference between the total noise value and the noise peak value; A frequency reference value is obtained based on the difference between a preset coefficient and a first value, wherein the preset coefficient is determined based on user feedback data.

[0007] In one embodiment, the method further includes: The frequency domain signal is divided into multiple frequency domain segments, and the noise peak value corresponding to each frequency domain segment is determined.

[0008] In one embodiment, a frequency reference value is calculated based on the total noise value and the noise peak value, including: Calculate the total noise value and the first difference between each noise peak value; The first difference is weighted and summed using the corresponding preset coefficients to obtain the frequency reference value. Each first difference corresponds to a preset coefficient, which is determined based on user feedback data.

[0009] In one embodiment, determining whether to perform noise reduction processing based on a frequency reference value and a frequency threshold includes: When the frequency reference value is greater than or equal to the first frequency threshold, noise reduction processing is performed; when the frequency reference value is greater than or equal to the second frequency threshold and less than the first frequency threshold, a prompt signal is issued, wherein the second frequency threshold is less than the first frequency threshold.

[0010] In one embodiment, in response to user feedback data meeting preset conditions, the current preset coefficient and / or the current frequency threshold are updated.

[0011] In one embodiment, the method further includes: Detection mode switching signal; In response to the mode switching signal, the following steps are performed: noise data is detected using the target sensor installed on the outdoor unit.

[0012] In one embodiment, the mode switching signal is generated based on an input signal or a temperature signal, the temperature signal being generated in response to the indoor temperature reaching a set temperature.

[0013] Secondly, the second technical solution adopted in this application is: to provide an electronic device, comprising: Target sensor, used to detect noise data; The processor is used to obtain the frequency domain signal corresponding to the noise data based on the noise data; determine the frequency reference value of the noise data based on the frequency domain signal; and determine whether to perform noise reduction processing based on the frequency reference value and the frequency threshold, wherein the frequency threshold is determined based on user feedback data.

[0014] The beneficial effects of this application are as follows: The noise reduction method provided by this application utilizes a target sensor installed on the outdoor unit to detect noise data and obtain the corresponding frequency domain signal; determines the frequency reference value of the noise data based on the frequency domain signal; and determines whether to perform noise reduction processing based on the frequency reference value and a frequency threshold, wherein the frequency threshold is determined based on user feedback data. This noise reduction method adjusts the frequency threshold in real time based on user feedback data to determine noise levels, thereby improving the noise level of the outdoor unit when the air conditioner is running and enhancing comfort. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the first embodiment of the noise reduction method of this application; Figure 2 These are frequency domain signals at different locations on the compressor; Figure 3 for Figure 1 A flowchart illustrating an embodiment of step S12; Figure 4 This is a flowchart illustrating the second embodiment of the noise reduction method of this application; Figure 5 This is a schematic diagram of the structure of an embodiment of the electronic device of this application. Detailed Implementation

[0017] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0018] The terms "first," "second," and "third" used in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature.

[0019] In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.

[0020] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0023] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0024] See Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the noise reduction method of this application, specifically including: Step S11: Use the target sensor installed on the outdoor unit to detect noise data and obtain the frequency domain signal corresponding to the noise data.

[0025] It should be noted that a target sensor is installed on the outdoor unit to detect noise data. The noise data mainly includes compressor noise, pipe vibration noise, and other mechanical vibration noise. Because the sound frequencies are different, in one embodiment, the target sensor can be installed only on the compressor to collect compressor noise, pipe vibration noise, and other mechanical vibration noise. In another embodiment, the target sensor can be installed on the outdoor unit casing near the compressor cavity. In other embodiments, target sensors can be installed on the compressor and pipes respectively; the specific location is not limited.

[0026] Noise data is detected using a target sensor. The detected noise data is a time-varying boost value, represented as a time-domain waveform. A mode converter transforms the noise data into a discrete digital signal, which is then transmitted to a processor. The processor uses a Fourier transform algorithm to analyze the time-domain waveform, obtaining the noise amplitude and phase information for each frequency component, thus yielding a frequency-domain signal. This frequency-domain signal characterizes the noise intensity corresponding to each frequency. Specifically, for example... Figure 2 As shown. Figure 2 The red and blue curves represent the frequency domain signals at different locations on the compressor, such as the front and rear positions of the compressor.

[0027] Step S12: Determine the frequency reference value of the noise data based on the frequency domain signal.

[0028] In one embodiment, combined with Figure 3 Step S12 includes: Step S121: Perform frequency weighting on the frequency domain signal to obtain the total noise value.

[0029] Step S122: Calculate the frequency reference value based on the total noise value and the noise peak value.

[0030] In one embodiment, a frequency-weighted algorithm is used to weight the frequency domain signal to calculate the total noise value. The frequency-weighted algorithm, for example, is an A-weighted algorithm, which converts the frequency domain signal into an A-weighted sound level (dB) that more closely approximates the subjective loudness of the human ear, thereby obtaining the total noise value.

[0031] In one embodiment, a first difference between the total noise value and the noise peak value, i.e., ΔP, is calculated. I =PP SI Where P represents the total noise value, P SI This represents the noise peak value, which is the maximum noise level in the frequency domain signal, ΔP. I This represents the first difference. A frequency reference value is obtained based on the preset coefficient and the first difference, i.e., ΔP = bΔP. I Where ΔP represents the frequency reference value, and b represents the preset coefficient, which is determined based on user feedback data.

[0032] In one embodiment, the frequency domain signal is divided into multiple frequency domain segments, and the noise peak value corresponding to each frequency domain segment is determined. The first difference between the total noise value and each noise peak value is calculated; the first difference is weighted and summed using corresponding preset coefficients to obtain a frequency reference value, wherein each first difference corresponds to a preset coefficient, and the preset coefficients are determined based on user feedback data.

[0033] For example, the frequency domain signal is divided into two frequency domain segments according to the frequency range, such as below 200Hz (denoted as frequency domain segment S1) and 2000~4000Hz (denoted as frequency domain segment S2), for a total of two frequency segments. The maximum noise intensity in each frequency domain segment is taken as the noise peak value corresponding to that frequency domain segment, denoted as P. S1 P S2 Calculate the difference between the total noise value and the first noise peak value, denoted as ΔP. i =PP Si (i=1, 2). This calculation yields two initial differences: ΔP1 and ΔP2. These initial differences are then weighted and summed using corresponding preset coefficients to obtain the frequency reference value. It should be noted that each initial difference corresponds to a preset coefficient, which is determined based on user feedback data. The frequency reference value is calculated as: ΔP = b1ΔP1 + b2ΔP2.

[0034] For example, the frequency domain signal is divided into four frequency domain segments according to the frequency range: below 500Hz (denoted as frequency domain segment S1), 500~2000Hz (denoted as frequency domain segment S2), 2000~5000Hz (denoted as frequency domain segment S3), and 5000Hz (denoted as frequency domain segment S4), for a total of four frequency segments. The maximum noise intensity in each frequency domain segment is taken as the noise peak value corresponding to that frequency domain segment, denoted as P. S1 P S2 P S3 P S4 Calculate the difference between the total noise value and the first noise peak value, denoted as ΔP. i =PP Si(i=1, 2, 3, 4). This calculation yields four first differences: ΔP1, ΔP2, ΔP3, and ΔP4. These first differences are then weighted and summed using corresponding preset coefficients to obtain the frequency reference value. It should be noted that each first difference corresponds to a preset coefficient, which is determined based on user feedback data. The frequency reference value is calculated as follows: ΔP = b1ΔP1 + b2ΔP2 + b3ΔP3 + b4ΔP4.

[0035] In one embodiment, to improve the accuracy of frequency reference value calculation, a constant term can be introduced into the calculation method. Specifically, the frequency reference value is calculated as follows: ΔP = b1ΔP1 + b2ΔP2 + b3ΔP3 + b4ΔP4 + b5. b5 is a constant term, which is also determined based on user feedback data.

[0036] Step S13: Determine whether to perform noise reduction processing based on the frequency reference value and the frequency threshold, wherein the frequency threshold is determined based on user feedback data.

[0037] In one embodiment, noise reduction is performed in response to a frequency reference value being greater than or equal to a first frequency threshold. It should be noted that the first frequency threshold is determined based on user feedback data. Let the first frequency threshold be denoted as a3. If ΔP ≥ a3, it is determined that the current noise level is high and the sound quality is unacceptable, at which point noise reduction is performed. Noise reduction processing may include, for example, reducing the compressor frequency, or adjusting the fan speed, etc., and is not specifically limited to any particular method.

[0038] In one embodiment, a second frequency threshold is determined based on user feedback data. A prompt signal is issued when a frequency reference value is greater than or equal to the second frequency threshold and less than a first frequency threshold, wherein the second frequency threshold is less than the first frequency threshold. Specifically, the second frequency threshold is denoted as a1. If a1 ≤ ΔP < a3, a prompt signal is issued. The prompt signal is used to prompt the user whether to perform noise reduction.

[0039] In one specific embodiment, a third frequency threshold is determined based on user feedback data. This third frequency threshold is greater than the second frequency threshold a1 and less than the first frequency threshold a3, and is denoted as a2. If ΔP < a1, the current noise intensity is low, and the noise quality is excellent (4 points), requiring no noise reduction. If a1 ≤ ΔP < a2, the noise quality is good (3 points). If a2 ≤ ΔP < a3, the noise quality is acceptable (2 points). If ΔP ≥ a3, the current noise is considered high, and the sound quality is unacceptable (1 point). Understandably, if ΔP ≥ a3, the system automatically performs noise reduction. If a1 ≤ ΔP < a2 or a2 ≤ ΔP < a3, a prompt signal is generated to alert the user, allowing the user to choose whether to perform noise reduction. If the user chooses noise reduction, the system executes the noise reduction operation in response to the user's instruction. If no user instruction is received, noise reduction is not performed.

[0040] It should be noted that the aforementioned first frequency threshold a3, second frequency threshold a1, and third frequency threshold a2 are determined based on user feedback data, as are the preset coefficients b1, b2, b3, b4, and b5. Specifically, the product features an interactive window where users can rate the noise level in real-time each time they use the air conditioner. The system receives and records this data, generating user feedback data. Based on this data, the system monitors the frequency reference value ΔP in real-time and performs data fitting with the ratings, generating and adjusting a3, a2, a1, b1, b2, b3, b4, and b5 in real-time. This ensures the system better reflects the actual usage environment, air conditioner operating status, and user habits.

[0041] Specifically, each time the air conditioner runs, it calculates the first difference, for example, four first differences: ΔP1, ΔP2, ΔP3, and ΔP4. Initially, these differences are weighted and summed using default preset coefficients b1, b2, b3, and b4. After calculating the frequency reference value ΔP, the user provides a score in the interactive window (S=4 for excellent; S=3 for good; S=2 for acceptable; S=1 for unacceptable). If S=4, automatic noise reduction is triggered. After each score, the corresponding data for this frequency reference value calculation is used as a sample. After accumulating N sets of valid samples, a dataset is formed. Understandably, the dataset includes all the initial differences and scores obtained from each calculation.

[0042] It should be noted that user ratings are considered as an evaluation of the overall noise quality. A relationship is established between the user score and the calculated first difference, specifically expressed as follows: ; Specifically, a higher score indicates lower noise and better noise quality, while a lower frequency reference value ΔP indicates better noise quality. Therefore, the score and the frequency reference value ΔP are negatively correlated.

[0043] The relationship between the frequency reference value ΔP and the first difference is further established, specifically expressed as follows: ; The above relationships were fitted using the least squares method, and b1, b2, b3, b4, and b5 were calculated.

[0044] Specifically, based on the above, if ΔP < a1, it indicates that the current noise intensity is low and the noise quality is excellent (Score = 4 points), and noise reduction is not required. If a1 ≤ ΔP < a2, it indicates that the noise quality is good (Score = 3 points). If a2 ≤ ΔP < a3, it indicates that the noise quality is acceptable (Score = 2 points). If ΔP ≥ a3, it is determined that the current noise is high and the sound quality is unacceptable (Score = 1 point). Therefore, using the calculated values ​​b1, b2, b3, b4, and b5, the frequency reference value ΔP of all samples is calculated, and they are grouped according to their corresponding scores. Specifically, samples with Score = 4 are grouped together, samples with Score = 3 are grouped together, samples with Score = 2 are grouped together, and samples with Score = 1 are grouped together.

[0045] The minimum value of ΔP in the sample with Score=4 is used as the lower bound of the second frequency threshold a1. In one embodiment, the second frequency threshold a1 is equal to the minimum value of ΔP in the sample group; or, in one embodiment, the second frequency threshold a1 is slightly smaller than the minimum value of ΔP in the sample group.

[0046] The minimum value of ΔP in the sample with Score=3 is used as the lower bound of the third frequency threshold a2. In one embodiment, the third frequency threshold a2 is equal to the minimum value of ΔP in the sample group; or, in one embodiment, the third frequency threshold a2 is slightly smaller than the minimum value of ΔP in the sample group.

[0047] The minimum value of ΔP in the sample with Score=2 is used as the lower bound of the first frequency threshold a3. In one embodiment, the first frequency threshold a3 is equal to the minimum value of ΔP in the sample group; or, in one embodiment, the first frequency threshold a3 is slightly smaller than the minimum value of ΔP in the sample group.

[0048] It should be noted that user habits may change with time, season, age, and hearing ability. Therefore, for every K new samples, b1, b2, b3, b4, and b5 are recalculated, and a3, a2, and a1 are also recalculated.

[0049] In one embodiment, to avoid drastic fluctuations in a3, a2, and a1, a sliding window can be used to calculate a3, a2, and a1 from the M data points within the sliding window. Alternatively, an exponentially weighted moving average can be used to calculate a3, a2, and a1.

[0050] In one embodiment, in response to user feedback data meeting preset conditions, the current preset coefficient and / or the current frequency threshold are updated.

[0051] Specifically, assuming the air conditioner's factory default values ​​are b=[0.25, 0.25, 0.25, 0.25], a1=10, a2=20, and a3=30. During the first week of use, users frequently give scores of 1-2, and the calculated frequency reference value ΔP is generally higher than 10, indicating that the first frequency threshold a3 is too high. In this case, the first frequency threshold a3 can be lowered, and after refitting, b=[0.3, 0.3, 0.2, 0.2]. At this point, users are more sensitive to low frequencies, and a1=6, a2=15, and a3=25. This setting allows the system to adapt to different users' noise sensitivities and usage environments, achieving a personalized noise reduction strategy. In actual deployment, after accumulating a certain number of samples, a3, a2, a1 and b1, b2, b3, b4, b5 are automatically adjusted according to the above process, initially using the factory default values. In this embodiment, the preset condition is that user feedback data is continuous or frequently identical.

[0052] In one embodiment, if a certain number of samples are accumulated during the calculation process using the current b=[0.3, 0.3, 0.2, 0.2] and a1=6, a2=15, a3=25, then a3, a2, a1, b1, b2, b3, b4, b5 are re-fitted and calculated based on the sample data within a preset time window. In this embodiment, the preset condition is that the accumulated user feedback data reaches a preset value.

[0053] In some embodiments, the noise of the outdoor unit of the air conditioner mainly consists of fan, compressor, and mechanical vibration noise. Fan noise has a highly linear relationship with its rotational speed. The compressor, as the main vibration source, has a significant impact on mechanical vibration noise, but its noise has a highly non-linear relationship with frequency and is greatly affected by operating conditions (pressure difference, refrigerant quantity, and temperature). Therefore, this application combines user feedback data to adjust preset coefficients and frequency thresholds in real time, enabling real-time feedback and adjustment of noise during actual operation, thereby improving the air conditioner's sound quality and enhancing user comfort.

[0054] In one embodiment, combined with Figure 4 , Figure 4 This is a flowchart illustrating a second embodiment of the noise reduction method of this application. Before step S11, this embodiment further includes: Step S10: Detecting a mode switching signal. After detecting the mode switching signal, step S11 is executed.

[0055] Specifically, after the air conditioner starts working, it continuously monitors the mode switching signal. If a mode switching signal is detected, step S11 is executed. In one embodiment, the mode switching signal is generated based on the input signal and / or the temperature signal.

[0056] For example, the mode switching signal can be input by the user. For instance, after the air conditioner is turned on, the air conditioner control panel or controller pops up a prompt box asking the user whether to enter adaptive mode. If the user confirms, a mode switching signal is generated in response to the user's input signal, and step S11 is executed.

[0057] For example, the mode switching signal can be generated from a temperature signal. For instance, after the air conditioner is turned on, it executes the corresponding mode, such as heating or cooling. When the indoor temperature reaches the set temperature, a temperature signal is generated. At this time, in response to the temperature signal, a mode switching signal is generated, and step S11 is executed to enter the adaptive mode.

[0058] In other embodiments, users may also define other conditions for entering adaptive mode, and there are no specific limitations.

[0059] Combination Figure 5 This application also provides an electronic device 100, which includes a target sensor 101 and a processor 102. The target sensor 101 is used to detect noise data. The processor 102 is used to obtain a frequency domain signal corresponding to the noise data based on the noise data; determine a frequency reference value of the noise data based on the frequency domain signal; and determine whether to perform noise reduction processing based on the frequency reference value and a frequency threshold, wherein the frequency threshold is determined based on user feedback data.

[0060] In one embodiment, the processor 102 is used to perform frequency weighting on the frequency domain signal to obtain a total noise value; and to calculate a frequency reference value based on the total noise value and the noise peak value.

[0061] In one embodiment, the processor 102 is used to calculate a first difference between the total noise value and the noise peak value; and to obtain a frequency reference value based on a preset coefficient and the first difference, wherein the preset coefficient is determined based on user feedback data.

[0062] In one embodiment, the processor 102 is configured to divide the frequency domain signal into multiple frequency domain segments and determine the noise peak value corresponding to each frequency domain segment.

[0063] In one embodiment, the processor 102 is used to calculate the first difference between the total noise value and each noise peak value; and to obtain a frequency reference value by weighting and summing the first differences using corresponding preset coefficients, wherein each first difference corresponds to a preset coefficient, and the preset coefficients are determined based on user feedback data.

[0064] In one embodiment, the processor 102 is configured to perform noise reduction processing in response to a frequency reference value being less than or equal to a first frequency threshold.

[0065] In one embodiment, the processor 102 is configured to issue a prompt signal in response to a frequency reference value being less than or equal to a second frequency threshold and greater than a first frequency threshold, wherein the second frequency threshold is greater than the first frequency threshold.

[0066] In one embodiment, the processor 102 is used to detect a mode switching signal; in response to the mode switching signal, the processor performs the following steps: detecting noise data using a target sensor located on the outdoor unit; the mode switching signal is generated based on an input signal or a temperature signal, the temperature signal being generated in response to the indoor temperature reaching a set temperature.

[0067] Understandably, the electronic device in this application is an air conditioner. By combining user feedback data to adjust preset coefficients and frequency thresholds in real time, it can provide real-time feedback and adjustment of noise during actual operation, thereby improving the sound quality of the air conditioner and enhancing user comfort.

[0068] The above are merely embodiments of this application and do not limit the scope of patent protection of this application. Any equivalent structural or procedural changes made using the content of this application’s specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of this application.

Claims

1. A noise reduction method, characterized in that, include: Noise data is detected using a target sensor installed on the outdoor unit, and the frequency domain signal corresponding to the noise data is obtained. Determine the frequency reference value of the noise data based on the frequency domain signal; Whether to perform noise reduction processing is determined based on the frequency reference value and the frequency threshold, wherein the frequency threshold is determined based on user feedback data.

2. The method according to claim 1, characterized in that, Determining the frequency reference value of the noise data based on the frequency domain signal includes: The frequency domain signal is frequency-weighted to obtain the total noise value; The frequency reference value is calculated based on the total noise value and the noise peak value.

3. The method according to claim 2, characterized in that, The frequency reference value is calculated based on the total noise value and the noise peak value, including: Calculate the first difference between the total noise value and the noise peak value; The frequency reference value is obtained based on a preset coefficient and the first difference, wherein the preset coefficient is determined based on the user feedback data.

4. The method according to claim 2, characterized in that, The method further includes: The frequency domain signal is divided into multiple frequency domain segments, and the noise peak value corresponding to each frequency domain segment is determined.

5. The method according to claim 4, characterized in that, The frequency reference value is calculated based on the total noise value and the noise peak value, including: Calculate the first difference between the total noise value and each of the noise peak values; The first difference is weighted and summed using corresponding preset coefficients to obtain the frequency reference value, wherein each first difference corresponds to a preset coefficient, and the preset coefficient is determined based on the user feedback data.

6. The method according to claim 1, characterized in that, Determining whether to perform noise reduction processing based on the frequency reference value and frequency threshold includes: When the frequency reference value is greater than or equal to the first frequency threshold, noise reduction processing is performed; When the frequency reference value is greater than or equal to the second frequency threshold and less than the first frequency threshold, a prompt signal is issued, wherein the second frequency threshold is less than the first frequency threshold.

7. The method according to claim 5, characterized in that, In response to user feedback data meeting preset conditions, the current preset coefficients and / or the current frequency thresholds are updated.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Detection mode switching signal; In response to the mode switching signal, the following steps are performed: noise data is detected using a target sensor installed on the outdoor unit.

9. The method according to claim 8, characterized in that, The mode switching signal is generated based on the input signal or the temperature signal, and the temperature signal is generated in response to the indoor temperature reaching the set temperature.

10. An electronic device, characterized in that, include: Target sensor, the target sensor being used to detect noise data; A processor is configured to obtain a frequency domain signal corresponding to the noise data based on the noise data. Determine the frequency reference value of the noise data based on the frequency domain signal; Whether to perform noise reduction processing is determined based on the frequency reference value and the frequency threshold, wherein the frequency threshold is determined based on user feedback data.

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