A noise reduction method and a washing apparatus

Abstract

This invention discloses a noise reduction method and a washing device, relating to the field of household appliance technology. The method includes: acquiring a sound field model based on a user-selected washing mode, wherein the sound field model characterizes the ideal noise data generated by the washing device during operation in the washing mode; generating and playing a noise-reduced wave based on the sound field model; during the operation of the washing mode, collecting current environmental parameters and determining whether secondary noise reduction is needed based on these parameters; if secondary noise reduction is needed, extracting residual noise data from the current environmental parameters, updating the sound field model using the residual noise data, and returning to the step of generating and playing the noise-reduced wave based on the sound field model. The solution provided by this invention can proactively intervene in the noise generated by the washing device when it starts up, and can provide real-time feedback and adjustments based on the noise reduction effect during the operation of the washing device to further reduce noise, thereby improving the user experience.

Description

Technical Field

[0001] This invention relates to the field of household appliance technology, and in particular to a noise reduction method and washing equipment. Background Technology

[0002] With the increasing popularity of washing equipment such as washing machines and dishwashers, and the rising demands of users for quality of life, reducing the noise of washing equipment has become an important issue that needs to be addressed.

[0003] Existing washing equipment typically employs two noise reduction methods: passive noise reduction and active noise reduction. Passive noise reduction primarily involves wrapping the inner tank with sound-insulating material, utilizing the material's sound insulation properties to reduce noise. Active noise reduction, on the other hand, utilizes the principle of destructive interference. It generates sound waves with the same amplitude but opposite phase to the noise, meaning the peak of the noise wave meets the trough of the anti-phase wave, causing the combined sound wave to have a phase close to 0, thus canceling out the noise.

[0004] However, passive noise reduction is often only effective for high-frequency noise and has little effect on controlling mid- and low-frequency noise. Active noise reduction, on the other hand, requires capturing and analyzing the noise signals generated by the washing equipment during operation before generating an inverted wave, which introduces a certain delay and prevents the noise reduction effect from reaching its optimal level, thus affecting the user experience. Summary of the Invention

[0005] This invention provides a noise reduction method and washing equipment that can actively intervene in the noise generated when the washing equipment is started, and can also provide real-time feedback and adjustment based on the noise reduction effect during the operation of the washing equipment to further reduce noise and improve the user experience.

[0006] According to one aspect of the present invention, a noise reduction method is provided, applied to a washing device, the method comprising: acquiring a sound field model according to a washing mode selected by a user, wherein the sound field model is used to characterize the ideal noise data generated by the washing device when operating in the washing mode; generating and playing a noise-reduced wave according to the sound field model; during the operation of the washing mode, collecting current environmental parameters and determining whether secondary noise reduction is required based on the current environmental parameters; if secondary noise reduction is required, extracting residual noise data from the current environmental parameters, updating the sound field model using the residual noise data, and returning to execute the step of generating and playing a noise-reduced wave according to the sound field model.

[0007] Optionally, the method also includes: if secondary noise reduction is not required, returning to the step of collecting current environmental parameters during the operation of the washing mode.

[0008] Optionally, based on the washing mode selected by the user, a sound field model is obtained, including: obtaining a pre-stored mapping table and determining the sound field model corresponding to the washing mode from the mapping table; or, sending model request information to the server, wherein the model request information includes the identification information of the washing mode; and receiving model feedback information sent by the server, wherein the model feedback information includes a sound field model or a link with a sound field model.

[0009] Optionally, the washing equipment is equipped with multiple audio devices; after obtaining the sound field model, the method further includes: determining the target audio device corresponding to the sound field model; controlling the target audio device to turn on; and controlling the remaining audio devices other than the target audio device to turn off.

[0010] Optionally, the audio device includes a microphone and a speaker; wherein the microphone is located in at least one of the following locations: on the base of the washing machine, next to the motor of the washing machine, next to the water pipe of the washing machine, and on the outside of the inner tank of the washing machine; and the speaker is located in at least one of the following locations: various corners of the washing machine, and the side of the washing machine facing the user.

[0011] Optionally, determine whether secondary noise reduction is needed based on the current environmental parameters, including: if the user has set noise reduction requirements, and the current environmental parameters do not meet the noise reduction requirements, then it is determined that secondary noise reduction is needed; if the current environmental parameters meet the noise reduction requirements, then it is determined that secondary noise reduction is not needed; if the user has not set noise reduction requirements, obtain the pre-stored baseline requirements; if the current environmental parameters do not meet the baseline requirements, then it is determined that secondary noise reduction is needed; if the current environmental parameters meet the baseline requirements, then it is determined that secondary noise reduction is not needed.

[0012] Optionally, residual noise data is extracted from the current environmental parameters, including: performing signal analysis on the current environmental parameters to obtain an environmental spectrum; identifying multiple frequency components from the environmental spectrum and dividing the multiple frequency components into noise components and non-noise components; and weighting the spectral bandwidths corresponding to the multiple frequency components to obtain residual noise data, wherein the weight corresponding to the noise component is greater than the weight corresponding to the non-noise component.

[0013] Optionally, signal analysis is performed on the current environmental parameters to obtain an environmental spectrum diagram, including: sampling the current environmental parameters to obtain a sampled signal; segmenting the sampled signal using a preset window and performing a Fourier transform on the segmented sampled signal; and plotting the environmental spectrum diagram based on the Fourier transform results of the segmented sampled signal.

[0014] Optionally, the sound field model can be updated using residual noise data, including: determining superimposed noise data based on residual noise data and ideal noise data; and reconstructing the sound field using the superimposed noise data to obtain the updated sound field model.

[0015] According to another aspect of the present invention, a noise reduction device is provided, configured in a washing machine; the noise reduction device includes: a model acquisition module, a noise reduction module, a playback module, a data acquisition module, a decision module, and a model optimization module; the model acquisition module is used to acquire a sound field model according to the washing mode selected by the user, wherein the sound field model is used to characterize the ideal noise data generated by the washing machine when it runs in the washing mode; the noise reduction module is used to generate a noise-reduced wave according to the sound field model; the playback module is used to play the noise-reduced wave; the data acquisition module is used to acquire current environmental parameters during the operation of the washing mode; the decision module is used to determine whether secondary noise reduction is needed based on the current environmental parameters; the model optimization module is used to extract residual noise data from the current environmental parameters and update the sound field model using the residual noise data if secondary noise reduction is needed, so that the noise reduction module re-executes the step of generating a noise-reduced wave according to the sound field model.

[0016] According to another aspect of the present invention, a washing device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the noise reduction method of any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the noise reduction method of any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, the computer program product including a computer program that, when executed by a processor, implements the noise reduction method of any embodiment of the present invention.

[0019] The technical solution of this invention obtains a sound field model matching the user-selected washing mode, generates and plays a noise-reduced wave based on the sound field model, and collects current environmental parameters during the washing mode operation. It further determines whether secondary noise reduction is needed based on these parameters. If secondary noise reduction is required, residual noise data is extracted from the current environmental parameters, and the sound field model is updated using this data. The process then returns to the previous step of generating and playing the noise-reduced wave based on the sound field model. On one hand, the washing device can quickly match the sound field model and generate and play the noise-reduced wave when the user selects a washing mode, thus proactively intervening in the noise generated when the washing device starts, avoiding the delay caused by analyzing noise signals. On the other hand, during the washing mode operation, it can also collect current environmental parameters to determine whether secondary noise reduction is needed, achieving real-time feedback and continuous optimization of the noise reduction effect, thereby improving the user experience.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a noise reduction method provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart illustrating a noise reduction method provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram showing the location of an audio device installed inside a washing device according to Embodiment 2 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of a noise reduction device provided in Embodiment 3 of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "current," "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart illustrating a noise reduction method provided in Embodiment 1 of the present invention. This embodiment is applicable to scenarios involving noise reduction processing during the operation of washing equipment. The method can be executed by a noise reduction device, which can be implemented in hardware and / or software and can be configured within the washing equipment. Figure 1 As shown, the method includes:

[0031] S110. Based on the washing mode selected by the user, obtain the sound field model, wherein the sound field model is used to characterize the ideal noise data generated by the washing equipment when it is running in the washing mode.

[0032] In this invention, the washing equipment can be any device with washing functions, such as a washing machine, dishwasher, washer-dryer combo, shoe washer, etc. The washing equipment may be equipped with an interactive unit to enable interaction between the equipment and the user. Specifically, the interactive unit can be a communication device (interacting with the user through information transmission and reception with a terminal device), or it can be a control button or a touch-sensitive display panel. Furthermore, the washing equipment also includes an audio device capable of playing and capturing sound. The number of audio devices can be one or multiple.

[0033] Washing equipment is typically equipped with multiple washing modes to meet the cleaning needs of different types of items and stains. For example, a washer-dryer combo may include a standard washing mode, a gentle washing mode, and a quick washing mode; a dishwasher may include a pre-rinse mode, an energy-saving washing mode, a quick washing mode, a heavy-duty washing mode, and a standard washing mode. This invention does not impose any specific limitations on these modes.

[0034] Under different washing modes, operating parameters such as water pressure, water level, and motor speed may vary during the washing process, resulting in different noise levels and sources from the washing equipment. To quickly reduce noise and avoid delays caused by noise signal analysis, this invention configures a sound field model for each washing mode to facilitate rapid generation of noise-reduced waves. In one embodiment, one sound field model corresponds to one washing mode, and the sound field model characterizes the ideal noise data generated by the washing equipment when operating according to its corresponding washing mode.

[0035] In one embodiment, the sound field model can be pre-stored in the washing device. When the user selects a washing mode, the washing device can directly match the sound field model corresponding to the washing mode.

[0036] In this embodiment, the sound field model is pre-stored in the washing equipment, which enables a fast acquisition speed of the sound field model, thereby further reducing the noise reduction delay.

[0037] In another implementation, the sound field model can be stored on a server, and the washing device can request the sound field model from the server after the user selects a washing mode.

[0038] Compared to the previous implementation, storing the sound field model on the server will take some time to retrieve, but this solution will make it easier to maintain the sound field model (such as version updates, parameter adjustments, etc.).

[0039] S120. Generate and play noise-reduced waves based on the sound field model.

[0040] Since the sound field model can characterize the ideal noise data generated by the washing equipment when it is running in the washing mode, the washing equipment can quickly generate noise reduction waves based on the sound field model.

[0041] Specifically, the method for generating noise-reducing waves based on a sound field model in washing equipment can include: the washing equipment determining the propagation path, amplitude, and phase of each noise source in the ideal noise data based on the sound field model; generating noise-reducing waves corresponding to each noise source, wherein the noise-reducing waves have the same amplitude as their corresponding noise sources, but opposite propagation paths and phases. By utilizing the principle of destructive interference, the phase of the synthesized sound wave from the noise and the noise-reducing waves tends to 0, thus achieving noise reduction.

[0042] In addition, after the washing equipment generates noise-reducing waves, these waves can be played back through an audio device.

[0043] S130. During the operation of the washing mode, collect the current environmental parameters and determine whether secondary noise reduction is needed based on the current environmental parameters.

[0044] As can be seen from steps S110 and S120, when the user selects a washing mode, the washing equipment can quickly generate and play noise-reduced waves by matching the sound field model. This allows for proactive intervention in the noise generated when the washing equipment starts up, avoiding the delay problem caused by analyzing the noise signal. However, since the sound field model represents the ideal noise data generated by the washing equipment when running in the washing mode, in actual applications, the actual noise generated may deviate from the ideal noise data due to changes in the placement and quantity of the items being washed, resulting in an unsatisfactory noise reduction effect. Therefore, further optimization of the noise reduction effect is needed during the operation of the washing mode.

[0045] The specific optimization process can be as follows: During the operation of the washing mode, the current environmental parameters are collected, and it is determined whether secondary noise reduction is required based on the current environmental parameters. If secondary noise reduction is not required, it means that the current noise reduction effect is relatively ideal. At this time, the washing equipment can not perform any operation (i.e., end the process) or continue to monitor the current environmental parameters (i.e., return to the step of collecting the current environmental parameters during the operation of the washing mode). If secondary noise reduction is required, it means that the current noise reduction effect is not ideal. At this time, the washing equipment needs to perform secondary noise reduction operation (i.e., continue to execute the following step S140).

[0046] In one embodiment, current environmental parameters can be collected via an audio device.

[0047] S140. If secondary noise reduction is required, extract residual noise data from the current environmental parameters, update the sound field model using the residual noise data, and return to execute the step of generating and playing the noise-reduced wave based on the sound field model.

[0048] When secondary noise reduction is required, the washing equipment can first extract residual noise data from the current environmental parameters, then use the residual noise data to update the sound field model, and return to step S120. This achieves real-time feedback and continuous optimization of the noise reduction effect, thereby improving the user experience.

[0049] For example, a washing device can use a signal separation method to extract residual noise data from current environmental parameters.

[0050] The technical solution of this invention obtains a sound field model matching the user-selected washing mode, generates and plays a noise-reduced wave based on the sound field model, and collects current environmental parameters during the washing mode operation. It further determines whether secondary noise reduction is needed based on these parameters. If secondary noise reduction is required, residual noise data is extracted from the current environmental parameters, and the sound field model is updated using this data. The process then returns to the previous step of generating and playing the noise-reduced wave based on the sound field model. On one hand, the washing device can quickly match the sound field model and generate and play the noise-reduced wave when the user selects a washing mode, thus proactively intervening in the noise generated when the washing device starts, avoiding the delay caused by analyzing noise signals. On the other hand, during the washing mode operation, it can also collect current environmental parameters to determine whether secondary noise reduction is needed, achieving real-time feedback and continuous optimization of the noise reduction effect, thereby improving the user experience.

[0051] Example 2

[0052] Figure 2 This is a flowchart illustrating a noise reduction method provided in Embodiment 2 of the present invention. Based on Embodiment 1, this embodiment provides a method for obtaining the sound field model, a logic for determining whether secondary noise reduction is needed, and a method for updating the sound field model. For example... Figure 2 As shown, the method includes:

[0053] S210. Based on the washing mode selected by the user, obtain the sound field model, wherein the sound field model is used to characterize the ideal noise data generated by the washing equipment when it is running in the washing mode.

[0054] In this invention, the washing equipment can be any device with a washing function, such as a washing machine, dishwasher, washer-dryer combo, shoe washer, etc. The washing equipment is equipped with an audio device. The number of audio devices can be one or multiple.

[0055] In one embodiment, the noise sources of the washing equipment are mainly divided into two categories: one is the continuous low-frequency noise generated when the power components (such as the washing motor and drain motor) are running, and the other is the turbulence noise generated when the water flow impacts the washing items and the inner tank of the washing equipment. Therefore, in order to better collect noise and play noise-reduced waves, the number of audio devices can be set to multiple, thereby realizing multi-channel audio processing.

[0056] For example, when there are multiple audio devices, the audio devices include a microphone and a speaker. The microphone is located in at least one of the following locations: on the base of the washing machine, next to the motor of the washing machine, next to the water pipe of the washing machine, and on the outside of the inner tank of the washing machine; the speaker is located in at least one of the following locations: various corners of the washing machine, and on the side of the washing machine facing the user.

[0057] Figure 3 This is a schematic diagram showing the location of an audio device installed inside a washing device according to Embodiment 2 of the present invention. Figure 3 As shown, the audio device includes a microphone and a speaker. The microphone includes a base microphone and an internal microphone, with the base microphones distributed on the base of the washing machine. Figure 3 Some basic microphones are placed at the four corners of the washing machine base, while others are placed near the washing machine's motors (such as the washing motor and drain motor) and water pipes. Internal microphones are distributed around the outside of the washing machine's inner drum, primarily used to collect noise generated within the drum. The loudspeakers include corner speakers and front speakers, with corner speakers positioned in various corners of the washing machine (such as...). Figure 3 The washing machine is positioned at each of its eight corners (as shown), ensuring that noise reduction waves can propagate in all directions. The front speakers are located on the side of the washing machine facing the user (e.g., at the eight corners). Figure 3 The washing machine door panel shown is located near the monitor / control panel, and the front speaker is positioned facing the user to ensure optimized noise levels in the user's direction.

[0058] In one alternative implementation, the method for "obtaining the sound field model according to the user-selected washing mode" in step S210 may include the following step:

[0059] Step a: Obtain the pre-stored mapping table and determine the sound field model corresponding to the washing mode from the mapping table.

[0060] Table 1 Mapping Relationship Table

[0061] Washing modes Sound field model Washing mode 1 Sound field model m1 Washing mode 2 Sound field model m2 Washing mode 3 Sound field model m3 …… …… Washing mode n Sound field model mn

[0062] Table 1 shows an example of a pre-stored mapping table in a washing machine. As shown in Table 1, the washing machine includes n washing modes, each corresponding to a sound field model. When a user selects a washing mode, the corresponding sound field model can be determined from the mapping table.

[0063] In another alternative implementation, the method of "obtaining the sound field model according to the user-selected washing mode" in step S210 may include the following two steps:

[0064] Step b: Send model request information to the server, wherein the model request information includes the identification information of the washing mode.

[0065] The model request information is used to request the sound field model corresponding to the washing mode from the server. The washing mode identification information is a unique and clear identifier that allows the server to recognize the washing mode. For example, the washing mode identification information can be the washing mode identification code, the washing mode name, etc.

[0066] Step c: Receive model feedback information sent by the server, wherein the model feedback information includes a sound field model or a link to a sound field model.

[0067] The model feedback information includes a sound field model or a link containing a sound field model. When the model feedback information includes a sound field model, the washing device can directly parse the sound field model from the model feedback information; when the model feedback information includes a link containing a sound field model, the washing device can download the sound field model through the link, thus saving signaling overhead.

[0068] Optionally, since the washing equipment may contain a large number of audio devices, not all of them may be needed for noise reduction in some washing modes. Therefore, from an energy-saving perspective, a correspondence between the sound field model and the audio devices can be established. That is, after obtaining the sound field model, the target audio device corresponding to the sound field model is determined; the target audio device is turned on, and the other audio devices are turned off. In this way, resources can be saved and the lifespan of the audio devices can be extended while ensuring noise reduction effect.

[0069] In one embodiment, the correspondence between the washing mode, the sound field model, and the target audio device can be established through multiple experiments before the washing equipment leaves the factory.

[0070] S220. Generate noise-reduced waves based on the sound field model.

[0071] Since the sound field model can characterize the ideal noise data generated by the washing equipment when it is running in the washing mode, the washing equipment can quickly generate noise reduction waves based on the sound field model.

[0072] Specifically, the method for generating noise-reducing waves based on a sound field model in washing equipment can include: the washing equipment determining the propagation path, amplitude, and phase of each noise source in the ideal noise data based on the sound field model; generating noise-reducing waves corresponding to each noise source, wherein the noise-reducing waves have the same amplitude as their corresponding noise sources, but opposite propagation paths and phases. By utilizing the principle of destructive interference, the phase of the synthesized sound wave from the noise and the noise-reducing waves tends to 0, thus achieving noise reduction.

[0073] S230, play noise-reduced waveform.

[0074] In one embodiment, the washing device can play noise-reduced waves through a speaker.

[0075] S240: Determine if the washing mode has finished running. If yes, end the process; otherwise, continue to S250.

[0076] Since the sound field model represents the ideal noise data generated by the washing equipment during washing mode, in actual applications, the actual noise may deviate from the ideal noise data due to changes in the placement and quantity of the items being washed, resulting in unsatisfactory noise reduction effects. Therefore, further optimization of the noise reduction effect is needed during washing mode operation.

[0077] Specifically, further optimization of the noise reduction effect needs to be performed before the washing mode has finished running. Therefore, it is first necessary to determine whether the washing mode has finished running. If the washing mode has finished running, no more noise will be generated, and the process can be ended; if the washing mode has not finished running, continue to execute the following step S250.

[0078] S250: Collect current environmental parameters and determine whether secondary noise reduction is needed based on the current environmental parameters. If yes, continue to S260; otherwise, return to S240.

[0079] In one embodiment, current environmental parameters can be collected via a microphone.

[0080] Methods for determining whether secondary noise reduction is needed based on current environmental parameters may include: if the user has set noise reduction requirements, and the current environmental parameters do not meet the noise reduction requirements, then secondary noise reduction is determined to be necessary; if the current environmental parameters meet the noise reduction requirements, then secondary noise reduction is determined not to be necessary; if the user has not set noise reduction requirements, then a pre-stored baseline requirement is obtained; if the current environmental parameters do not meet the baseline requirement, then secondary noise reduction is determined to be necessary; if the current environmental parameters meet the baseline requirement, then secondary noise reduction is determined not to be necessary.

[0081] In this way, users can meet their own noise reduction needs, improve the interactivity of the washing equipment, and thus enhance the user experience.

[0082] S260. Extract residual noise data from the current environmental parameters.

[0083] Specifically, the method for "extracting residual noise data from current environmental parameters" in step S260 may include the following three steps:

[0084] Step d: Perform signal analysis on the current environmental parameters to obtain the environmental spectrum.

[0085] 1) Sample the current environmental parameters to obtain the sampled signal.

[0086] The environmental parameters captured by the microphone are typically analog signals, which are usually quite weak. Therefore, before entering the analog-to-digital converter (ADC), the signal can be amplified by an analog signal amplifier to increase its strength. Then, an analog filter is used to remove noise outside the set frequency range. For example, a high-pass filter can be used to remove high-frequency interference in addition to the characteristic noise of the washing machine, and a low-pass filter can be used to remove low-frequency environmental noise. Finally, the analog signal is converted into a digital signal by the ADC for subsequent processing.

[0087] After converting analog signals to digital signals, preprocessing of the current environmental parameters (including but not limited to filtering, gain control, and noise reduction) can be performed. Filtering can employ bandpass filters to retain the main noise frequency band (50Hz to 2kHz) during the washing machine's operation, enhancing noise source analysis; IIR (Infinite Impulse Response) or FIR (Finite Impulse Response) filters can be used to remove signals outside the washing machine's operating noise frequency range. Gain control can adjust the signal strength to prevent excessively strong or weak signals from causing processing distortion. Noise reduction includes at least one of time-domain noise reduction, frequency-domain noise reduction, and CNN (Convolutional Neural Network) noise reduction to further reduce background noise or interference signals.

[0088] In one embodiment, the sampling frequency for sampling the current environmental parameters can be set to 44.1 kHz to ensure complete signal acquisition.

[0089] It should be noted that since there may be multiple microphones, with one microphone corresponding to one channel, synchronization of multiple channels is necessary to ensure the accuracy of subsequent signal processing. Specifically, this can be achieved by synchronizing acquisition through a shared clock to ensure time alignment of multi-channel signals; or by performing time delay compensation on the signals from each microphone to align the arrival times of different sound sources and ensure the accuracy of sound field analysis.

[0090] 2) Use a preset window to segment the sampled signal and perform Fourier transform on the segmented sampled signal.

[0091] The sampled signal is relatively long. By segmenting the sampled signal using a preset window and performing a Fourier transform on the segmented sampled signal, the error caused by the signal boundary during segmentation can be reduced.

[0092] Assuming the sampled signal is x[n] and the preset window is a Hanning window, the segmentation of the sampled signal can be achieved using the following formula: X w [n] = x[n]·w[n].

[0093] Among them, X w[n] represents the signal after processing through a preset window, and x[n] is the window function. n is the index of the current sampling point (for example, if the microphone records dishwasher noise 1000 times per second for a total of 2 seconds, then there are 2000 sampling points. When processing the first sampling point, n=1, when processing the second sampling point, n=2, and so on), 0≤n≤N-1; N is the length of the preset window, that is, the number of sampling points processed at one time. The value of N is usually a power of 2.

[0094] Performing a Fourier transform on the segmented sampled signal converts the time-domain signal into a frequency-domain signal, allowing for frequency distribution analysis. The specific transformation formula is as follows:

[0095] 3) Draw the environmental spectrum diagram based on the Fourier transform results of the segmented sampled signals.

[0096] Step e: Identify multiple frequency components from the environmental spectrum diagram and divide the multiple frequency components into noise components and non-noise components.

[0097] Based on the environmental spectrum diagram, it is possible to identify which noise and non-noise components were generated in this sampled signal, and then identify the amplitude and phase of the noise and non-noise components.

[0098] Step f: Weight the spectral bandwidths corresponding to multiple frequency components to obtain residual noise data, wherein the weight of the noise component is greater than the weight of the non-noise component.

[0099] By weighting the spectral bandwidths corresponding to multiple frequency components, irrelevant noise can be suppressed, thereby ensuring the accurate extraction of residual noise data.

[0100] S270, Update the sound field model using the residual noise data, and return to execute S220.

[0101] Specifically, the method of "updating the sound field model using residual noise data" in step S270 may include the following two steps:

[0102] Step g: Determine the superimposed noise data based on the residual noise data and the ideal noise data.

[0103] Step h: Reconstruct the sound field using superimposed noise data to obtain an updated sound field model.

[0104] Specifically, when using superimposed noise data for sound field reconstruction, the microphones corresponding to the sound field model can perform adaptive beamforming to adjust the weight of each microphone, thereby enhancing the signal direction while further suppressing interference from noise sources.

[0105] The formula for MVDR (Minimum Variance Distortionless Response Beamforming) beamforming is: Where R is the covariance matrix of the received signal, and a(θ) is the array manifold vector in the target direction.

[0106] Sound field reconstruction can be performed using the Finite Difference Method (FDTD), which simulates the propagation of sound waves in space by discretizing both space and time. Discrete space: The three-dimensional space is discretized into grid points, and acoustic parameters such as sound pressure and velocity are calculated at these grid points. Discrete time: Time is discretized into small steps, and the propagation and attenuation of sound waves over time are simulated using difference equations. Iterative calculation: Based on the initial boundary conditions and the excitation of the sound source, the sound field distribution at each time step is calculated iteratively. Through simulation, combined with the geometric model and acoustic transmission equations, a three-dimensional sound field distribution map of the noise source can be obtained, characterizing the propagation and reflection of sound waves inside and outside the washing equipment space.

[0107] The technical solution of this invention obtains a sound field model matching the user-selected washing mode, generates and plays a noise-reduced wave based on the sound field model, and collects current environmental parameters during the washing mode operation. It further determines whether secondary noise reduction is needed based on these parameters. If secondary noise reduction is required, residual noise data is extracted from the current environmental parameters, and the sound field model is updated using this data. The process then returns to the previous step of generating and playing the noise-reduced wave based on the sound field model. On one hand, the washing device can quickly match the sound field model and generate and play the noise-reduced wave when the user selects a washing mode, thus proactively intervening in the noise generated when the washing device starts, avoiding the delay caused by analyzing noise signals. On the other hand, during the washing mode operation, it can also collect current environmental parameters to determine whether secondary noise reduction is needed, achieving real-time feedback and continuous optimization of the noise reduction effect, thereby improving the user experience.

[0108] Example 3

[0109] Figure 4 This is a schematic diagram of a noise reduction device according to Embodiment 3 of the present invention. The noise reduction device is configured in a washing machine. Figure 4 As shown, the device includes: a model acquisition module 401, a noise reduction module 402, a playback module 403, a data acquisition module 404, a decision-making module 405, and a model optimization module 406.

[0110] The model acquisition module 401 is used to acquire a sound field model based on the washing mode selected by the user. The sound field model is used to characterize the ideal noise data generated by the washing equipment when it is running in the washing mode.

[0111] The noise reduction module 402 is used to generate noise-reduced waves based on the sound field model;

[0112] Playback module 403 is used to play noise-reduced waves;

[0113] The data acquisition module 404 is used to collect current environmental parameters during the operation of the washing mode;

[0114] Decision module 405 is used to determine whether secondary noise reduction is needed based on the current environmental parameters;

[0115] The model optimization module 406 is used to extract residual noise data from the current environmental parameters and update the sound field model using the residual noise data if secondary noise reduction is required, so that the noise reduction module 402 can re-execute the step of generating noise-reduced waves according to the sound field model.

[0116] Optionally, if secondary noise reduction is not required, the acquisition module 404 will re-execute the step of acquiring the current environmental parameters during the operation of the washing mode.

[0117] Optionally, the model acquisition module 401 is specifically used to acquire a pre-stored mapping relationship table and determine the sound field model corresponding to the washing mode from the mapping relationship table; or, send model request information to the server, wherein the model request information includes the identification information of the washing mode; and receive model feedback information sent by the server, wherein the model feedback information includes a sound field model or a link with a sound field model.

[0118] Optionally, the washing equipment is equipped with multiple audio devices; the playback module 403 is also used to determine the target audio device corresponding to the sound field model; control the target audio device to turn on, and control the other audio devices except the target audio device to turn off.

[0119] Optionally, the audio device includes a microphone and a speaker; wherein the microphone is located in at least one of the following locations: on the base of the washing machine, next to the motor of the washing machine, next to the water pipe of the washing machine, and on the outside of the inner tank of the washing machine; and the speaker is located in at least one of the following locations: various corners of the washing machine, and the side of the washing machine facing the user.

[0120] Optionally, the decision module 405 is specifically used to determine whether secondary noise reduction is required if the current environmental parameters do not meet the noise reduction requirements when the user has set noise reduction requirements; and whether secondary noise reduction is not required if the current environmental parameters meet the noise reduction requirements. If the user has not set noise reduction requirements, the module obtains the pre-stored baseline requirements; if the current environmental parameters do not meet the baseline requirements, the module determines whether secondary noise reduction is required; and if the current environmental parameters meet the baseline requirements, the module determines that secondary noise reduction is not required.

[0121] Optionally, the model optimization module 406 is specifically used to perform signal analysis on the current environmental parameters to obtain an environmental spectrum; identify multiple frequency components from the environmental spectrum and divide the multiple frequency components into noise components and non-noise components; weight the spectral bandwidths corresponding to the multiple frequency components to obtain residual noise data, wherein the weight corresponding to the noise component is greater than the weight corresponding to the non-noise component.

[0122] Optionally, the model optimization module 406 is specifically used to sample the current environmental parameters to obtain the sampled signal; to segment the sampled signal using a preset window, and to perform Fourier transform on the segmented sampled signal; and to draw an environmental spectrum diagram based on the Fourier transform result of the segmented sampled signal.

[0123] Optionally, the model optimization module 406 is specifically used to determine the superimposed noise data based on the residual noise data and the ideal noise data; and to reconstruct the sound field using the superimposed noise data to obtain the updated sound field model.

[0124] The noise reduction device provided in the embodiments of the present invention can execute the noise reduction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0125] Example 4

[0126] This invention also provides a washing device that integrates electronic equipment for implementing the above-mentioned noise reduction method. Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0127] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0128] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0129] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as noise reduction methods.

[0130] In some embodiments, the noise reduction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the noise reduction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the noise reduction method by any other suitable means (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0134] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0135] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0136] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0137] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the noise reduction method provided in any embodiment of this invention.

[0138] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0139] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0140] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A noise reduction method, characterized in that, Applied to washing equipment, the method includes: Based on the washing mode selected by the user, a sound field model is obtained, wherein the sound field model is used to characterize the ideal noise data generated by the washing device when it is running in the washing mode; Based on the sound field model, generate and play a noise-reduced wave; During the operation of the washing mode, current environmental parameters are collected, and it is determined whether secondary noise reduction is needed based on the current environmental parameters. If secondary noise reduction is required, residual noise data is extracted from the current environmental parameters, and the sound field model is updated using the residual noise data. Then, the process returns to the step of generating and playing the noise-reduced wave based on the sound field model.

2. The noise reduction method according to claim 1, characterized in that, The method further includes: If secondary noise reduction is not required, return to the step of collecting current environmental parameters during the operation of the washing mode.

3. The noise reduction method according to claim 1, characterized in that, The step of obtaining the sound field model based on the user-selected washing mode includes: Obtain a pre-stored mapping table, and determine the sound field model corresponding to the washing mode from the mapping table; or, Send model request information to the server, wherein the model request information includes the identification information of the washing mode; receive model feedback information sent by the server, wherein the model feedback information includes the sound field model or a link containing the sound field model.

4. The noise reduction method according to claim 1, characterized in that, The washing equipment is equipped with multiple audio devices; After obtaining the sound field model, the method further includes: Determine the target audio device corresponding to the sound field model; Control the target audio device to turn on, and control all other audio devices except the target audio device to turn off.

5. The noise reduction method according to claim 4, characterized in that, The audio device includes a microphone and a speaker; wherein... The microphone is located in at least one of the following positions: on the base of the washing device, next to the motor of the washing device, next to the water pipe of the washing device, and on the outside of the inner tank of the washing device; The speaker is located in at least one of the following positions: at each corner of the washing machine, or on the side of the washing machine facing the user.

6. The noise reduction method according to claim 1, characterized in that, The step of determining whether secondary noise reduction is needed based on the current environmental parameters includes: If the user has noise reduction requirements, and the current environmental parameters do not meet the noise reduction requirements, then it is determined that secondary noise reduction is required; if the current environmental parameters meet the noise reduction requirements, then it is determined that secondary noise reduction is not required. If the user has not set noise reduction requirements, the system retrieves the pre-stored baseline requirements; if the current environmental parameters do not meet the baseline requirements, it determines that secondary noise reduction is required; if the current environmental parameters meet the baseline requirements, it determines that secondary noise reduction is not required.

7. The noise reduction method according to claim 1, characterized in that, The step of extracting residual noise data from the current environmental parameters includes: Signal analysis is performed on the current environmental parameters to obtain an environmental spectrum diagram; Multiple frequency components are identified from the environmental spectrum diagram, and the multiple frequency components are divided into noise components and non-noise components. The residual noise data is obtained by weighting the spectral bandwidths corresponding to the multiple frequency components, wherein the weights corresponding to the noise components are greater than the weights corresponding to the non-noise components.

8. The noise reduction method according to claim 7, characterized in that, The step of performing signal analysis on the current environmental parameters to obtain an environmental spectrum includes: The current environmental parameters are sampled to obtain a sampled signal; The sampled signal is segmented using a preset window, and a Fourier transform is performed on the segmented sampled signal. The environmental spectrum is plotted based on the Fourier transform results of the segmented sampled signals.

9. The noise reduction method according to claim 1, characterized in that, Updating the sound field model using the residual noise data includes: Based on the residual noise data and the ideal noise data, the superimposed noise data is determined; The sound field is reconstructed using the superimposed noise data to obtain an updated sound field model.

10. A washing device, characterized in that, The washing equipment includes: At least one processor; and a memory communicatively connected to said at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the noise reduction method according to any one of claims 1-9.

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