Adaptive-based active suppression method and system for dishwasher fan noise

Abstract

The present application relates to the technical field of control, and especially relates to a kind of based on adaptive dish-washing machine fan noise active inhibition method and system, method includes: the fan noise signal and ambient sound signal of dish-washing machine are collected, the time window corresponding to each collection time is constructed;The target noise value of current collection time is calculated, the similarity value of ambient sound signal and fan noise signal in the time window of current collection time is calculated, the difference between the sound pressure level of fan noise signal and target noise value of current collection time is calculated, based on similarity value, the sound pressure level of all ambient sound signals in the time window of current collection time and the sound pressure level of fan noise signal adjust the aforementioned difference, obtain comprehensive error, according to comprehensive error adjustment PID controller's parameter, make PID controller control dish-washing machine's fan noise.The present application can realize dynamic, intelligent optimal balance between the two originally contradictory goals of silence and efficiency.

Description

Technical Field

[0001] This invention relates to the field of control technology. In particular, it relates to an adaptive method and system for actively suppressing dishwasher fan noise. Background Technology

[0002] With the popularization of smart home technology, dishwashers have become an essential appliance in modern households. Hot air drying has become the mainstream drying solution for dishwashers due to its high efficiency, but the noise generated by the fan, as a core component, during operation is becoming increasingly prominent, seriously affecting the user experience. Limited by hardware structure, material costs, and physical space, achieving noise reduction solely through hardware optimization has reached its limit. Currently, noise control of dishwasher fans generally adopts the traditional PID control method, which involves monitoring fan noise and adjusting its speed to achieve noise reduction.

[0003] Chinese patent application CN120704431A discloses a method, device, equipment, and computer storage medium for controlling the fan speed of a smart home appliance. The method obtains the exhaust temperature, cold outlet temperature, and ambient temperature of the smart home appliance, and generates a first temperature relationship and a second temperature relationship based on the exhaust temperature, cold outlet temperature, and ambient temperature. If the first temperature relationship and the second temperature relationship meet preset energy-saving conditions, the smart home appliance is controlled to enter the fan speed adjustment mode, and the fan speed of the smart home appliance is flexibly adjusted to reduce the noise generated when the smart home appliance is used.

[0004] Existing control methods typically reduce noise generated during appliance use by directly lowering the fan speed. However, excessively reducing the fan speed can severely compromise the dishwasher's drying efficiency and negatively impact the user experience. Summary of the Invention

[0005] To address the technical problem that the above-mentioned control methods cannot reduce fan noise while ensuring drying efficiency, the present invention provides solutions in the following aspects.

[0006] In the first aspect, the adaptive active noise suppression method for dishwasher fans includes:

[0007] The dishwasher's fan noise signal and ambient sound signal are collected and preprocessed at the same frequency, and a time window corresponding to each collection moment is constructed with each collection moment as the endpoint.

[0008] The comprehensive error at the current acquisition time is calculated, and the parameters of the PID controller are adjusted according to the comprehensive error. The PID controller controls the fan noise of the dishwasher according to the adjusted parameters.

[0009] The calculation method for the comprehensive error at the current acquisition time includes: calculating the target noise value at the current acquisition time based on the sound pressure levels of all ambient sound signals within the time window of the current acquisition time, the sound pressure levels of all ambient sound signals within the time window of the previous acquisition time, and the sound pressure level of the ambient sound signal at the previous acquisition time; calculating the similarity value between the ambient sound signals and the fan noise signals within the time window of the current acquisition time; calculating the difference between the sound pressure level of the fan noise signal and the target noise value at the current acquisition time; and adjusting the difference based on the aforementioned similarity value, the sound pressure levels of all ambient sound signals within the time window of the current acquisition time, and the sound pressure level of the fan noise signal to obtain the comprehensive error.

[0010] Preferably, the method for calculating the target noise value at the current acquisition time includes: calculating the average sound pressure level of all ambient sound signals within the time window of the previous acquisition time, calculating the standard deviation of the sound pressure level of all ambient sound signals within the time window of the current acquisition time, and weightedly fusing the aforementioned average value with the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation to obtain the target noise value at the current acquisition time.

[0011] Preferably, the weighted fusion of the aforementioned average value and the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation includes: calculating a first weight of the aforementioned average value based on the standard deviation, wherein the first weight is positively correlated with the standard deviation; calculating a second weight of the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation, wherein the second weight is negatively correlated with the standard deviation; and calculating a weighted sum of the aforementioned average value and the sound pressure level of the ambient sound signal at the previous acquisition time based on the first weight and the second weight.

[0012] Preferably, the calculation method for the first weight and the second weight includes: setting a first sensitivity coefficient, calculating the product of the standard deviation of the sound pressure level of all ambient sound signals within the time window of the current acquisition time and the first sensitivity coefficient, performing negative correlation normalization on the product to obtain a first normalized value; taking the difference between 1 and the first normalized value as the first weight, and taking the first normalized value as the second weight.

[0013] Preferably, the step of calculating the similarity value between the ambient sound signal and the fan noise signal within the time window of the current acquisition time includes: obtaining the spectrum of the ambient sound signal and the spectrum of the fan noise signal within the time window of the current acquisition time, calculating the cosine similarity between the spectrum of the ambient sound signal and the spectrum of the fan noise signal, and obtaining the similarity value.

[0014] Preferably, the step of adjusting the aforementioned difference based on the similarity value, the sound pressure level of all ambient sound signals within the current acquisition time window, and the sound pressure level of the fan noise signal to obtain the comprehensive error includes: performing positive correlation normalization on the similarity value to obtain a second normalized value; calculating the ratio of the standard deviation of the sound pressure level of all ambient sound signals within the current acquisition time window to the standard deviation of the sound pressure level of all fan noise signals within the current acquisition time window, setting a second sensitivity coefficient, calculating the product of the aforementioned ratio and the second sensitivity coefficient, performing negative correlation normalization on the product to obtain a third normalized value; and using the product of the difference between the sound pressure level of the fan noise signal at the current acquisition time and the target noise value, and the second and third normalized values ​​as the comprehensive error.

[0015] Preferably, adjusting the parameters of the PID controller based on the comprehensive error includes: setting a third sensitivity coefficient, calculating the product of the comprehensive error and the third sensitivity coefficient, and performing negative correlation normalization on the product using a bounded nonlinear adjustment function to obtain a fourth normalized value; calculating the sum of 1 and the fourth normalized value to obtain the proportional coefficient of the PID controller at the previous acquisition time, and multiplying the proportional coefficient at the previous acquisition time with the aforementioned sum to obtain the adjusted proportional coefficient.

[0016] Secondly, an adaptive dishwasher fan noise active suppression system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned adaptive dishwasher fan noise active suppression method is implemented.

[0017] The present invention has the following effects:

[0018] 1. This invention replaces the traditional fixed threshold control by calculating the dynamic target noise value in real time. The system can automatically adjust its control target according to the strength of the ambient sound. In a quiet environment, it forces the fan to run at low speed and quietly, while in a noisy environment, it allows the fan to speed up appropriately to ensure efficiency. This allows the fan noise to be intelligently hidden in the background noise of the environment. It successfully breaks through the traditional technical bottleneck that noise reduction must sacrifice drying efficiency. It achieves a dynamic and intelligent optimal balance between the two originally contradictory goals of quietness and high efficiency, and significantly improves the user's subjective quietness in different acoustic environments.

[0019] 2. This invention introduces a multi-dimensional perception error mechanism by calculating the comprehensive error, breaking through the limitations of traditional methods that rely solely on sound pressure difference. Based on the sound masking effect, when the similarity between the fan noise and the ambient sound is high, the sound pressure control can be relaxed, which is more in line with the auditory perception characteristics of the human ear, achieving a leap from physical noise reduction to perceptual noise reduction. When the external ambient sound is unstable, the over-response of the controller can be suppressed, thereby effectively avoiding repeated oscillations in the fan speed and enhancing the robustness of the system in real and complex environments. Attached Figure Description

[0020] Figure 1 This is a flowchart of steps S1-S2 in the adaptive active noise suppression method for dishwasher fans according to an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of steps S20-S22 in the adaptive active noise suppression method for dishwasher fans according to an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0023] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0024] In the same environment, the presence of a certain sound (the masked sound) can be interfered with by other sounds (masking sounds), reducing people's perception of that sound. This phenomenon is called the sound masking effect. This phenomenon has the following rules: weaker sounds are masked by louder sounds; sounds with similar frequencies have the strongest masking effect; and complex ambient sounds have the strongest masking effect on sounds.

[0025] From the perspective of auditory perception principles, the human ear's perception of sound relies primarily on its relative difference from background noise rather than its absolute intensity. When the fan noise is dynamically adjusted to be slightly lower than the ambient sound, the ambient sound naturally masks the fan noise through the sound masking effect, causing the two to merge into the same background sound field. This prevents the fan noise from being preferentially identified by the auditory system due to its abruptness. Simultaneously, the human ear's adaptive mechanism gradually reduces its sensitivity to continuous, stable background noise. Therefore, this invention adjusts the controller parameters by analyzing ambient sound and fan noise.

[0026] Reference Figure 1 The adaptive active noise suppression method for dishwasher fans includes steps S1-S2, as detailed below:

[0027] S1: Collect and preprocess the fan noise signal and ambient sound signal of the dishwasher at the same frequency, and construct the time window corresponding to each collection moment with each collection moment as the endpoint.

[0028] The system uses an acoustic acquisition module built into the dishwasher to collect real-time fan noise signals and ambient sound signals during dishwasher operation. The fan noise acquisition point is located at the air duct outlet or near the fan to reflect the actual noise output when the fan is working; the ambient sound acquisition point is located on the edge of the dishwasher's outer shell or door to obtain the overall sound field characteristics of the user's environment.

[0029] The acoustic acquisition module collects fan noise signals and ambient sound signals every 10 seconds. A sliding window is set up, and the fan noise signals and ambient sound signals collected in the last 5 minutes are extracted as the end point of each acquisition time to form the time window corresponding to each acquisition time.

[0030] After the collected fan noise signal and ambient sound signal are converted from analog to digital, they are first bandpass filtered to remove background interference in non-target frequency bands, and then the filtered signal is denoised to obtain the preprocessed fan noise signal and ambient sound signal.

[0031] S2: Calculate the comprehensive error at the current acquisition time, and adjust the parameters of the PID controller according to the comprehensive error. The PID controller controls the fan noise of the dishwasher according to the adjusted parameters.

[0032] Reference Figure 2 Step S2 includes steps S20-S22, as follows:

[0033] S20: Calculate the target noise value at the current acquisition time based on the sound pressure level of all ambient sound signals within the time window of the current acquisition time, the sound pressure level of all ambient sound signals within the time window of the previous acquisition time, and the sound pressure level of the ambient sound signal at the previous acquisition time.

[0034] Based on the processed fan noise signal and ambient sound signal, the sound pressure level corresponding to the fan noise signal and the sound pressure level corresponding to the ambient sound signal are calculated respectively. The process of calculating the sound pressure level is existing technology and will not be described in detail here.

[0035] The method for calculating the target noise value at the current acquisition time includes: calculating the average sound pressure level of all ambient sound signals within the time window of the previous acquisition time; calculating the standard deviation of the sound pressure level of all ambient sound signals within the time window of the current acquisition time; and weightedly fusing the aforementioned average value with the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation to obtain the target noise value at the current acquisition time.

[0036] The weighted fusion of the aforementioned average value and the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation includes: calculating a first weight of the aforementioned average value based on the standard deviation, wherein the first weight is positively correlated with the standard deviation; calculating a second weight of the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation, wherein the second weight is negatively correlated with the standard deviation; and calculating a weighted sum of the aforementioned average value and the sound pressure level of the ambient sound signal at the previous acquisition time based on the first weight and the second weight.

[0037] The calculation methods for the first weight and the second weight include: setting a first sensitivity coefficient, calculating the product of the standard deviation of the sound pressure level of all ambient sound signals within the time window of the current acquisition time and the first sensitivity coefficient, performing negative correlation normalization on the product to obtain a first normalized value; taking the difference between 1 and the first normalized value as the first weight, and taking the first normalized value as the second weight.

[0038] The formula for calculating the target noise value at the current acquisition time is expressed as follows:

[0039]

[0040] In the formula, Indicates the first The target noise value at each acquisition time; Indicates the first The standard deviation of the sound pressure level of all ambient sound signals within a time window of each acquisition time; Indicates the first The average sound pressure level of all ambient sound signals within a time window of each acquisition time; Indicates the first The sound pressure level of the ambient sound signal at each acquisition time; The first sensitivity coefficient is taken as an empirical value. To prevent standard deviation When it is large, Convergence too quickly leads to drastic changes in the first and second weights; Represented by natural numbers An exponential function with base 1.

[0041] Indicates the first weight. Indicates the second weight, standard deviation The larger it is, the more likely it is to be the first The ambient sound signal fluctuates significantly within the time window of the first acquisition time, indicating instability. Using the sound pressure level of the ambient sound signal at each acquisition moment as an instantaneous value reduces its reliability and reference value. Therefore, when calculating the target noise value, it is necessary to reduce the second weight and increase the first weight to make the calculation result more dependent on a stable historical trend (i.e., the first...). The average sound pressure level of all ambient sound signals within the time window of the sampling time (i.e., the average sound pressure level of the first sampling time), rather than the potentially distorted instantaneous value (i.e., the first sampling time). The sound pressure level of the ambient sound signal at each acquisition moment is used to improve the accuracy and robustness of the target noise value.

[0042] S21: Calculate the similarity value between the ambient sound signal and the fan noise signal within the time window of the current acquisition time, calculate the difference between the sound pressure level of the fan noise signal and the target noise value at the current acquisition time, and adjust the aforementioned difference based on the aforementioned similarity value, the sound pressure level of all ambient sound signals and the sound pressure level of the fan noise signal within the time window of the current acquisition time to obtain the comprehensive error.

[0043] Calculating the similarity value between the ambient sound signal and the fan noise signal within the time window of the current acquisition time includes: obtaining the spectrum of the ambient sound signal and the spectrum of the fan noise signal within the time window of the current acquisition time, calculating the cosine similarity between the spectrum of the ambient sound signal and the spectrum of the fan noise signal, and obtaining the similarity value. In this embodiment, all ambient sound signals within the time window of the current acquisition time are first spliced ​​into a whole audio segment according to the acquisition time, and then a Fourier transform is performed on the whole audio segment to obtain the spectrum of the ambient sound signal within the time window. The spectrum of the fan noise signal is obtained using the same method.

[0044] The calculation method for the comprehensive error includes: performing positive correlation normalization on the similarity value to obtain a second normalized value; calculating the ratio of the standard deviation of the sound pressure level of all ambient sound signals within the time window of the current acquisition time to the standard deviation of the sound pressure level of all fan noise signals within the time window of the current acquisition time, setting a second sensitivity coefficient, calculating the product of the aforementioned ratio and the second sensitivity coefficient, and performing negative correlation normalization on this product to obtain a third normalized value; and using the product of the difference between the sound pressure level of the fan noise signal at the current acquisition time and the target noise value, along with the second and third normalized values, as the comprehensive error. The specific formula is as follows:

[0045]

[0046] In the formula, Indicates the first The combined error at each acquisition time; Indicates the first The sound pressure level of the fan noise signal at each acquisition time; Indicates the first The target noise value at each acquisition time; Indicates the first Cosine similarity between the spectrum of the ambient sound signal and the spectrum of the fan noise signal within a time window of each acquisition moment; This represents the standard normalization function; Indicates the first The standard deviation of the sound pressure level of all ambient sound signals within a time window of each acquisition time; Indicates the first The standard deviation of the sound pressure level of all fan noise signals within a time window of each acquisition time; This represents the second sensitivity coefficient, taken as an empirical value. In order to prevent When it is large, The convergence was too fast, which caused the overall error to change drastically.

[0047] Characterizing the first The difference in sound pressure level between the fan noise and the target noise at each acquisition time, i.e., the baseline error; cosine similarity. The larger the value, the closer the spectrum of the ambient sound signal is to the spectrum of the fan noise signal, the stronger the sound masking effect, and the less likely the fan noise is to be detected. In this case, the basic error should be reduced proportionally to avoid unnecessary control actions and improve control smoothness.

[0048] The larger the sound pressure level, the more unstable the ambient sound is, and the lower the sensitivity of the human ear to the fan noise. This allows for a larger sound pressure deviation. In this case, the overall error should be reduced to prevent the controller from over-responding in noisy and fluctuating environments, effectively suppress the repeated oscillations of the fan speed, and enhance the robustness of the system.

[0049] S22: Adjust the parameters of the PID controller based on the comprehensive error, and control the fan noise of the dishwasher based on the adjusted parameters.

[0050] The parameters of a PID (Proportional-Integral-Derivative) controller directly determine the system's response speed, steady-state accuracy, and stability. The proportional coefficient is used to improve the system's response speed. When the overall error is large, the proportional term will have a greater control effect, prompting the system to quickly approach the target noise value. However, if the proportional coefficient is too large, the adjustment will be too aggressive, leading to system oscillation.

[0051] Adjusting the PID controller parameters based on the comprehensive error includes: setting a third sensitivity coefficient; calculating the product of the comprehensive error and the third sensitivity coefficient; and applying a bounded nonlinear adjustment function to negatively normalize this product to obtain a fourth normalized value; calculating the sum of 1 and the fourth normalized value to obtain the proportional coefficient of the PID controller at the previous acquisition time; and multiplying the proportional coefficient at the previous acquisition time with the aforementioned sum to obtain the adjusted proportional coefficient. In this embodiment, the bounded nonlinear adjustment function uses a hyperbolic tangent function. It is understood that in other embodiments, an algebraic sigmoid function may also be used.

[0052] The specific formula for calculating the adjusted scaling factor is as follows:

[0053]

[0054] In the formula, Indicates the first The proportional coefficient corresponding to each acquisition moment; Indicates the first The proportional coefficient corresponding to each acquisition moment; Indicates the first The combined error at each acquisition time; This represents the third sensitivity coefficient, taken as an empirical value. Used for control The convergence speed; Represents the hyperbolic tangent function, used to... Mapped to Within the interval, and the output function value is consistent with the overall error. It shows a negative correlation.

[0055] like If it is negative, it means that the first If the sound pressure level of the fan noise signal at a given time is less than the target noise value, the system can increase the fan speed to bring the sound pressure level of the fan noise signal closer to the target noise value. This maximizes drying efficiency and shortens the task duration without causing significant interference to the user.

[0056] like If it is positive, then it means that the first... If the sound pressure level of the fan noise signal at a certain sampling time exceeds the target noise value, the system needs to reduce the fan speed to reduce the fan noise and ensure the user's auditory comfort.

[0057] The PID controller adjusts the dishwasher's fan speed according to the adjusted proportional coefficient, thereby bringing the fan noise close to the target noise level.

[0058] This invention utilizes the sound masking effect to dynamically hide fan noise within the ambient background noise, significantly improving the user's subjective quiet experience in different acoustic environments. It breaks through the traditional dilemma that noise reduction must sacrifice efficiency, and achieves an intelligent trade-off of maximizing equipment operating efficiency while ensuring auditory comfort.

[0059] This application also discloses an adaptive active noise suppression system for dishwasher fans. The system includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the adaptive active noise suppression method for dishwasher fans according to the above embodiments of the present invention is implemented.

[0060] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0061] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An adaptive active noise suppression method for dishwasher fans, characterized in that, include: The dishwasher's fan noise signal and ambient sound signal are collected and preprocessed at the same frequency, and a time window corresponding to each collection moment is constructed with each collection moment as the endpoint. The comprehensive error at the current acquisition time is calculated, and the parameters of the PID controller are adjusted according to the comprehensive error. The PID controller controls the fan noise of the dishwasher according to the adjusted parameters. The calculation method for the comprehensive error at the current acquisition time includes: calculating the target noise value at the current acquisition time based on the sound pressure levels of all ambient sound signals within the time window of the current acquisition time, the sound pressure levels of all ambient sound signals within the time window of the previous acquisition time, and the sound pressure level of the ambient sound signal at the previous acquisition time; calculating the similarity value between the ambient sound signals and the fan noise signals within the time window of the current acquisition time, and performing positive correlation normalization on the similarity value to obtain a second normalized value; calculating the ratio of the standard deviation of the sound pressure levels of all ambient sound signals within the time window of the current acquisition time to the standard deviation of the sound pressure levels of all fan noise signals within the time window of the current acquisition time, setting a second sensitivity coefficient, calculating the product of the aforementioned ratio and the second sensitivity coefficient, and performing negative correlation normalization on the product to obtain a third normalized value; calculating the difference between the sound pressure level of the fan noise signal at the current acquisition time and the target noise value, and using the product of this difference with the second and third normalized values ​​as the comprehensive error; The parameters of the PID controller are adjusted based on the comprehensive error, including: setting the third sensitivity coefficient, calculating the product of the comprehensive error and the third sensitivity coefficient, and performing negative correlation normalization on the product using a bounded nonlinear adjustment function to obtain the fourth normalized value; calculating the sum of 1 and the fourth normalized value to obtain the proportional coefficient of the PID controller at the previous acquisition time, and multiplying the proportional coefficient at the previous acquisition time with the aforementioned sum to obtain the adjusted proportional coefficient.

2. The adaptive active noise suppression method for dishwasher fans according to claim 1, characterized in that, The method for calculating the target noise value at the current acquisition time includes: calculating the average sound pressure level of all ambient sound signals within the time window of the previous acquisition time; calculating the standard deviation of the sound pressure level of all ambient sound signals within the time window of the current acquisition time; and weightedly fusing the aforementioned average value with the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation to obtain the target noise value at the current acquisition time.

3. The adaptive active noise suppression method for dishwasher fans according to claim 2, characterized in that, The weighted fusion of the aforementioned average value and the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation includes: calculating a first weight of the aforementioned average value based on the standard deviation, wherein the first weight is positively correlated with the standard deviation; calculating a second weight of the sound pressure level of the ambient sound signal at the previous acquisition time based on the standard deviation, wherein the second weight is negatively correlated with the standard deviation; and calculating a weighted sum of the aforementioned average value and the sound pressure level of the ambient sound signal at the previous acquisition time based on the first weight and the second weight.

4. The adaptive active noise suppression method for dishwasher fans according to claim 3, characterized in that, The calculation method for the first weight and the second weight includes: setting a first sensitivity coefficient, calculating the product of the standard deviation of the sound pressure level of all ambient sound signals within the time window of the current acquisition time and the first sensitivity coefficient, performing negative correlation normalization on the product to obtain a first normalized value; taking the difference between 1 and the first normalized value as the first weight, and taking the first normalized value as the second weight.

5. The adaptive active noise suppression method for dishwasher fans according to claim 1, characterized in that, The calculation of the similarity value between the ambient sound signal and the fan noise signal within the time window of the current acquisition time includes: obtaining the spectrum of the ambient sound signal and the spectrum of the fan noise signal within the time window of the current acquisition time, calculating the cosine similarity between the spectrum of the ambient sound signal and the spectrum of the fan noise signal, and obtaining the similarity value.

6. An adaptive active noise suppression system for dishwasher fans, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the adaptive active noise suppression method for dishwasher fans according to any one of claims 1-5.

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