Noise processing method and device of washing machine, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a noise processing method and device of a washing machine, electronic equipment and a storage medium. According to the technical scheme provided by the embodiment of the invention, the preset washing noise threshold value of the target time period is determined by determining the target time period of the current washing machine operation time; sound wave signals and mechanical vibration signals during operation of the washing machine are collected, a noise source is separated from the sound wave signals and the mechanical vibration signals based on a frequency domain analysis algorithm, and the noise decibel value of the current noise source is determined; comparing a washing noise threshold value based on the noise decibel value, under the condition that the noise decibel value is larger than the washing noise threshold value, adjusting the drum rotating speed and the water level height of the washing machine according to the noise source, and prolonging the washing duration according to the adjusted drum rotating speed and the adjusted water level height. By means of the technical means, a better noise control effect can be achieved while the washing performance is guaranteed, and then the use experience of a user is improved.

Description

Technical Field

[0001] This application relates to the field of electrical appliance technology, and in particular to a method, apparatus, electronic device, and storage medium for noise reduction of a washing machine. Background Technology

[0002] Currently, washing machines often generate significant operating noise during daily washing operations, especially during high-speed spin-drying or intensive washing phases. This noise primarily originates from the combined effects of various mechanical vibrations and acoustic effects, such as motor operation, water flow impact, and clothes beating against the drum. During periods of high ambient noise, such as daytime, the operating noise of the washing machine usually does not significantly disturb users. However, during quieter times, such as nighttime, the noise of the same intensity becomes more prominent, potentially affecting not only users' rest but also causing disputes with neighbors due to noise propagation. Therefore, most washing machines employ noise control strategies based on fixed noise thresholds or predefined operating modes, such as limiting the maximum spin speed or using specific vibration damping structures to reduce noise.

[0003] However, existing noise reduction solutions for washing machines are difficult to adapt to actual environmental needs. Simply limiting the maximum speed or using specific vibration reduction structures to reduce noise often results in either insufficient noise suppression, failure to meet noise reduction requirements, or excessive noise reduction leading to decreased washing efficiency, unreasonably extended washing time, or poor cleaning effect. Summary of the Invention

[0004] This application provides a noise reduction method, apparatus, electronic device, and storage medium for a washing machine. It can intelligently adjust the noise output of the washing machine according to the usage period while ensuring washing performance, and implement stricter noise control according to different scenarios, thereby achieving a more reasonable balance between noise suppression and washing performance. This solves the technical problem of the imbalance between noise suppression and washing performance in washing machines.

[0005] In a first aspect, embodiments of this application provide a noise reduction method for a washing machine, comprising: Determine the target time period in which the current washing machine is operating, and determine the preset washing noise threshold for the target time period; The microphone array and vibration sensor set up in the washing machine collect sound wave signals and mechanical vibration signals during the operation of the washing machine. Based on the frequency domain analysis algorithm, the noise source is separated from the sound wave signal and mechanical vibration signal, and the noise decibel value of the current noise source is determined. The noise source includes motor noise, water flow impact sound and clothes beating sound. Based on the comparison of noise decibel values ​​with the washing noise threshold, when the noise decibel value is greater than the washing noise threshold, the drum speed and water level of the washing machine are adjusted according to the noise source, and the washing time is extended according to the adjusted drum speed and water level.

[0006] Furthermore, noise sources are separated from acoustic and mechanical vibration signals based on frequency domain analysis algorithms, including: Fast Fourier Transform is performed on acoustic wave signals and mechanical vibration signals based on frequency domain analysis algorithms to generate corresponding spectrum diagrams; Based on the spectrum diagram, the noise feature spectrum library is queried. If the spectrum diagram matches the noise feature spectrum in the noise feature spectrum library, the noise source is determined according to the matched noise feature spectrum. The noise feature spectrum library stores the corresponding noise feature spectrum in advance according to motor noise, water flow impact sound and clothing pattering sound.

[0007] Furthermore, based on the spectrogram, the noise characteristic spectrum is queried, including: Calculate the feature similarity between the spectrogram and the noise feature spectrum of the noise feature spectrum library in a specified frequency band, and determine whether the spectrogram and the noise feature spectrum of the noise feature spectrum library match based on the feature similarity.

[0008] Furthermore, adjusting the washing machine's drum speed and water level based on the noise source includes: Identify sub-decibels of motor noise, water flow impact sound, and clothing slapping sound; Based on the decibel value, excessive noise is determined from motor noise, water flow impact sound, and clothes beating sound, and the drum speed and water level of the washing machine are adjusted accordingly.

[0009] Furthermore, adjusting the washing machine's drum speed and water level based on excessive noise levels includes: Set adjustment coefficients for the washing machine's drum speed and water level based on the type of excessive noise, and adjust the washing machine's drum speed and water level based on these adjustment coefficients.

[0010] Furthermore, the washing time is extended based on the adjusted drum speed and water level, including: Based on the adjusted drum speed and water level after the reduction, the compensation time required for the washing machine to reach the preset washing target is calculated, and the washing time is extended based on the compensation time.

[0011] Furthermore, determine the target time period in which the current washing machine is operating, including: Obtain the current washing machine operation time, compare the operation time with the pre-stored time period division rules, and determine the target time period in which the current washing machine operation time is located based on the comparison results. The target time period includes at least daytime, evening, and late night.

[0012] In a second aspect, embodiments of this application provide a noise reduction device for a washing machine, comprising: The threshold determination module is used to determine the target time period in which the current washing machine is operating and to determine the preset washing noise threshold for the target time period. The noise separation module is used to collect sound wave signals and mechanical vibration signals during the operation of the washing machine based on the microphone array and vibration sensor set in the washing machine. Based on the frequency domain analysis algorithm, the noise source is separated from the sound wave signal and mechanical vibration signal, and the noise decibel value of the current noise source is determined. The noise sources include motor noise, water flow impact sound and clothes beating sound. The adjustment module is used to compare the noise decibel value with the washing noise threshold. If the noise decibel value is greater than the washing noise threshold, the drum speed and water level of the washing machine are adjusted according to the noise source, and the washing time is extended according to the adjusted drum speed and water level.

[0013] In a third aspect, embodiments of this application provide an electronic device, including: Memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the noise reduction method for the washing machine as described in the first aspect.

[0014] In a fourth aspect, embodiments of this application provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the noise reduction method for a washing machine as described in the first aspect.

[0015] This application embodiment determines the target time period of the current washing machine operation and sets a preset washing noise threshold for that time period. Based on the microphone array and vibration sensors installed in the washing machine, it collects sound wave signals and mechanical vibration signals during washing machine operation. Using a frequency domain analysis algorithm, it separates the noise source from the sound wave signals and mechanical vibration signals, determining the noise decibel value of the current noise source. The noise source includes motor noise, water flow impact sound, and clothes beating sound. Based on the noise decibel value compared to the washing noise threshold, if the noise decibel value exceeds the threshold, it adjusts the washing machine's drum speed and water level according to the noise source, and extends the washing time based on the adjusted drum speed and water level. By employing the above technical means, by separating the noise source from the washing machine's sound wave signals and mechanical vibration signals, adjusting the washing machine's drum speed and water level based on the noise source, and extending the washing time based on the adjusted drum speed and water level, it is possible to intelligently adjust the washing machine's noise output according to the usage period while ensuring washing performance. This implements stricter noise control, achieving better noise control while ensuring washing performance, thereby improving the user experience. Attached Figure Description

[0016] Figure 1 This is a flowchart of a noise reduction method for a washing machine provided in Embodiment 1 of this application; Figure 2 This is a flowchart illustrating the determination of noise sources in an embodiment of this application; Figure 3 This is a flowchart illustrating the adjustment of drum speed and water level in an embodiment of this application. Figure 4 This is a schematic diagram of the structure of a noise reduction device for a washing machine provided in Embodiment 2 of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0018] Example 1: Figure 1 A flowchart of a noise reduction method for a washing machine according to Embodiment 1 of this application is provided. The noise reduction method provided in this embodiment can be executed by a noise reduction device for the washing machine. This noise reduction device can be implemented through software and / or hardware. The noise reduction device can consist of two or more physical entities, or it can consist of a single physical entity. Generally, the noise reduction device for the washing machine can be a controller or other processing device.

[0019] The following description uses the washing machine controller as an example to illustrate the noise reduction method implemented in the washing machine. (Refer to...) Figure 1 The noise reduction methods for this washing machine specifically include: S110. Determine the target time period in which the current washing machine is operating, and determine the preset washing noise threshold for the target time period.

[0020] In this application, during the noise control process of a washing machine, the washing machine obtains the current time through a built-in real-time clock or network synchronization function, and matches the target time period according to predefined time period categories (such as daytime, evening, or late night). Different noise thresholds are preset for each time period; for example, a lower noise limit may be set for nighttime to suit a quiet environment. The system quickly determines the applicable noise control standard by querying these pre-stored parameters. By adaptively configuring washing noise thresholds for different time periods, the washing machine can adaptively adjust its noise tolerance according to the actual usage scenario.

[0021] Based on the pre-configured target time period and corresponding washing noise threshold, when the washing machine starts its operation program, the controller first queries the current time and matches it with the time period definition in the database. This accurately determines the current target time period and its corresponding maximum allowable noise limit, i.e., the washing noise threshold. For example, it might be set to 50 decibels in the evening and a more lenient 65 decibels during the day. This provides a dynamic and lifestyle-friendly baseline for washing machine noise control, ensuring that subsequent noise reduction measures are closely linked to environmental needs, fundamentally improving the product's intelligence and user experience.

[0022] Optionally, the target time period in which the current washing machine operation time falls can be determined, including: Obtain the current washing machine operation time, compare the operation time with the pre-stored time period division rules, and determine the target time period in which the current washing machine operation time is located based on the comparison results. The target time period includes at least daytime, evening, and late night.

[0023] Specifically, the washing machine controller accurately obtains the current year, month, day, hour, minute, and second information as the current washing machine operation time through its built-in hardware real-time clock (RTC) module or through a network connection (such as Wi-Fi) and the Network Time Protocol (NTP) service. Then, the controller accesses a pre-stored time period segmentation rule table in its non-volatile memory. This table defines the time ranges corresponding to different target time periods (such as daytime, evening, and late night periods), for example, daytime (06:00-17:00), evening (17:00-22:00), and late night (22:00-06:00 the next day). These rules can be factory presets or customized by the user through an application or control panel according to their own habits. After obtaining the current time, the controller compares and performs logical judgments (i.e., checks which interval the current time falls into) with each time interval in the rule table to uniquely determine the specific target time period of the current operation. This transforms abstract time information into time period labels with clear noise control meaning, providing precise washing noise thresholds for subsequent dynamic loading of different noise thresholds and execution of differentiated noise reduction strategies.

[0024] In one embodiment, the washing machine can provide predefined time periods via a companion mobile app, and even allow users to plot custom noise tolerance curves. For example, it can set a quiet mode for weekday lunch breaks, or allow users to input specific schedules (such as nap times for infants) in the app. The system incorporates this information into the time period segmentation rules to generate a highly personalized noise control strategy. Optionally, the controller can also obtain user location authorization via the app. When it detects that the user's mobile phone (representing the user) is outside a certain range from home, it automatically adopts a more lenient noise threshold to maximize washing efficiency. Conversely, when it detects that the user is at home and in a rest area, it adopts the strictest quiet standard. This allows for a deeper integration with the user's real-life scenarios.

[0025] S120. The microphone array and vibration sensor set in the washing machine collect the sound wave signal and mechanical vibration signal when the washing machine is running. Based on the frequency domain analysis algorithm, the noise source is separated from the sound wave signal and mechanical vibration signal, and the noise decibel value of the current noise source is determined. The noise source includes motor noise, water flow impact sound and clothes beating sound.

[0026] Furthermore, during the washing machine's operation, the controller collects sound wave signals and mechanical vibration signals in real time to isolate noise sources. Specifically, an array of multiple microphones is placed at key locations inside and outside the washing machine to capture airborne sound waves from all directions, while high-precision vibration sensors are directly installed on the motor shaft, drum bearings, and housing structure to monitor mechanical vibrations transmitted through solids. These analog signals are then sent to a digital signal processor after high-speed analog-to-digital conversion. Frequency domain analysis algorithms convert the time-domain signals into a frequency-domain spectrum, and then, through spectral feature analysis, the mixed signals are decomposed and classified into specific noise sources.

[0027] For example, motor operation typically generates electromagnetic and mechanical noise at specific frequencies, the sound of water flowing and impacting exhibits broadband turbulence characteristics, while the sound of clothes slapping is characterized by irregular high-energy impact signals. Ultimately, the controller identifies these noise sources, thereby precisely quantifying the decibel value of each noise component. This achieves refined and source-based separation of complex noise structures, providing indispensable data for subsequent precise control.

[0028] Optionally, refer to Figure 2 Based on frequency domain analysis algorithms, noise sources are separated from acoustic signals and mechanical vibration signals, including: S1201. Based on the frequency domain analysis algorithm, perform fast Fourier transform on the acoustic wave signal and the mechanical vibration signal to generate the corresponding spectrum. S1202. Based on the spectrum diagram, query the noise feature spectrum library. If the spectrum diagram matches the noise feature spectrum in the noise feature spectrum library, determine the noise source according to the matched noise feature spectrum. The noise feature spectrum library stores the corresponding noise feature spectrum in advance according to motor noise, water flow impact sound and clothing pattering sound.

[0029] The raw time-domain sound waves and vibration signals acquired by the microphone array and vibration sensors are preprocessed (e.g., filtered and windowed). Then, the Fast Fourier Transform (FFT) algorithm is applied to convert these time-domain signals into frequency-domain signals, generating a spectrum diagram that clearly shows the distribution of signal energy at different frequencies. Subsequently, the controller compares this real-time generated spectrum diagram with a pre-built noise feature spectrum library stored in the washing machine control unit. This feature library is a representative spectrum template extracted through a large number of preliminary experiments in a controlled environment, collecting the electromagnetic and mechanical noise of the motor at different speeds, the impact noise of water flow at different water levels and velocities, and the slapping noise of different types of clothing at different drum speeds. During the query matching process, the algorithm calculates the similarity between the real-time spectrum and each template (motor, water flow, clothing) in the database (e.g., using cross-correlation or Euclidean distance algorithms). When a specific frequency band of the real-time spectrum (e.g., the mid-to-low frequency peak of motor noise, the broadband characteristics of water flow noise, and the high-frequency transient pulses of clothing beating) highly matches the characteristics of a certain template, the match is considered successful. This accurately identifies the source of the current dominant noise, providing a data foundation for subsequent implementation of targeted and differentiated noise reduction strategies, and avoiding misjudgments and ineffective adjustments.

[0030] Optionally, querying the noise feature spectrum based on the spectrogram includes: Calculate the feature similarity between the spectrogram and the noise feature spectrum of the noise feature spectrum library in a specified frequency band, and determine whether the spectrogram and the noise feature spectrum of the noise feature spectrum library match based on the feature similarity.

[0031] After generating the real-time noise spectrum, considering that the energy of different noise sources is concentrated in specific frequency bands, global comparison would introduce a large amount of interference from irrelevant frequencies, reducing the accuracy of identification. Therefore, this application predefines specified frequency bands strongly correlated with various noise sources (e.g., motor noise's main energy is concentrated in the mid-to-low frequency range (e.g., 100-500Hz) and may contain specific electromagnetic harmonic components; water flow impact noise exhibits a wide-band white noise characteristic (the frequency band may span 500Hz-5kHz); and the slapping of clothing zippers may present high-frequency, transient spike pulses (e.g., >5kHz)). Then, within these pre-defined narrow-band frequency bands that best characterize a certain type of noise, the feature similarity between the real-time spectrum and the spectra of various noise templates in the feature library is calculated. This includes calculating the cross-correlation coefficient (measuring the similarity of waveform shape) or Euclidean distance (measuring numerical closeness) of two spectral sequences within the specified frequency band, and setting a similarity threshold (e.g., a correlation coefficient higher than 0.8 is considered a match). By segmenting frequency bands, calculating quantified similarity metrics, and comparing thresholds, this method reliably determines which noise template in the feature library best matches the real-time spectrum, thus accurately pinpointing the dominant noise source. This improves the anti-interference capability and accuracy of noise source identification. By focusing on the most characteristic frequency bands and employing quantified similarity judgment, it effectively avoids misjudgments caused by background noise or other irrelevant frequency components, providing a highly reliable decision-making basis for subsequent implementation of the most effective noise reduction adjustments.

[0032] S130. Based on the comparison of the noise decibel value with the washing noise threshold, if the noise decibel value is greater than the washing noise threshold, adjust the drum speed and water level of the washing machine according to the noise source, and extend the washing time according to the adjusted drum speed and water level.

[0033] The controller then continuously compares the calculated decibel values ​​of noise sources (such as the sound of clothes slapping) with the dynamic thresholds obtained above. If an exceedance is detected, the controller adaptively adjusts the drum speed and water level based on the identified noise source. For example, if the noise source is clothes slapping, the drum speed is gradually reduced until the noise falls below the threshold, and the water level setting is increased. If the noise source is water flow impact, the inlet valve may be adjusted to raise the water level while reducing the speed, altering the flow field to reduce turbulence and splashing noise. Based on this, the controller calculates washing efficiency using the adjusted drum speed and water level. This calculation can be based on a pre-built washing efficiency model. This model, based on extensive experimental data, accurately estimates the decrease in mechanical force caused by changes in speed and water level, and precisely calculates the compensation washing time required to achieve the same washing ratio, thus automatically extending the program duration.

[0034] By transforming noise control from a passive, performance-sacrificing constraint into a proactive, intelligent optimization process, a balance between the two core needs of quietness and cleanliness is ultimately achieved without the user's awareness.

[0035] Optionally, refer to Figure 3 Adjusting the washing machine's drum speed and water level based on the noise source includes: S1301, Identify the sub-decibels of motor noise, water flow impact sound, and clothing slapping sound; S1302. Determine the excessive noise from motor noise, water flow impact sound and clothes beating sound based on the decibel value, and adjust the drum speed and water level of the washing machine based on the excessive noise.

[0036] Based on the identified noise sources, the independent sub-decibels (dB) values ​​for the motor, water flow, and clothing patter were first calculated. The energy of the signals within these specific frequency bands was calculated; acoustically, this energy value is proportional to the sound power generated by the noise source. The signal energy value, expressed as voltage, was collected by the sensors and converted into a sound pressure level in Pascals using a pre-calibrated conversion coefficient (obtained through laboratory calibration, establishing the relationship between the electrical signal amplitude and the actual sound pressure). Then, the standard formula for sound pressure level, Lp = 20 * log10(P / Pref), was applied, where P is the calculated effective sound pressure of the noise source, and Pref is the reference sound pressure (typically 20 μPa), ultimately calculating the independent sub-decibelel value Lp for each noise source.

[0037] Furthermore, by comparing the sub-decibels of each noise source with the aforementioned washing noise thresholds, the specific cause of the overall noise exceeding the standard can be accurately determined, that is, which one or more noise sources contributed the most excessive sound energy. If motor noise is identified as the dominant source of excessive noise, the control unit will prioritize instructing the motor driver to reduce the drum speed, because the high-frequency electromagnetic noise and mechanical howling of the motor are strongly positively correlated with the speed, and reducing the speed can directly suppress the noise at its source. If water flow impact noise is identified as the dominant source of excessive noise, the system will, while considering the speed, focus more on adjusting the water inlet valve to reduce the water level, because reducing the water level can reduce the kinetic energy of water falling from a height and impacting clothes and the drum wall, thereby effectively weakening the broadband noise generated by turbulence and splashing. If clothes slapping noise is identified as the dominant source of excessive noise, the control strategy will again focus on reducing the drum speed, because reducing the speed will reduce the centrifugal force, preventing clothes from being carried to the highest point and then falling, thereby fundamentally avoiding the generation of high-frequency, high-energy impact noise. This precise control mechanism based on the decomposition of noise source contribution can achieve the best noise reduction effect with minimal performance cost (adjusting only the most relevant parameters), and avoid the excessive extension of washing time and the decline in cleaning effect caused by blindly reducing the speed across the board.

[0038] Optionally, adjusting the washing machine's drum speed and water level based on excessive noise includes: Set adjustment coefficients for the washing machine's drum speed and water level based on the type of excessive noise, and adjust the washing machine's drum speed and water level based on these adjustment coefficients.

[0039] Based on a pre-established and optimized noise control mapping table developed through extensive experiments, once the type of excessive noise (motor, water flow, or clothes beating) and its severity (e.g., exceeding the threshold by 3 or 5 dB) are determined, the control unit consults this mapping table. This table pre-sets a set of adjustment coefficients for drum speed and water level for each noise source at different levels of exceedance. These coefficients are attenuation factors between 0 and 1; for example, 0.9 represents a reduction to 90% of the original value. These coefficients are the optimal solutions derived from in-depth analysis of the physical mechanisms of noise and numerous washing experiments. For example, for motor noise, the speed adjustment coefficient will be set to a larger value (e.g., directly applying a coefficient of 0.85 to significantly reduce speed), while the water level adjustment coefficient may be 1 (i.e., remain unchanged). Conversely, for water flow impact noise, the water level adjustment coefficient will become the primary adjustment target (e.g., applying a coefficient of 0.8 to lower the water level), while the speed coefficient will be adjusted less significantly. After obtaining this specific set of coefficients, they are multiplied by the reference speed and water level values ​​set in the washing machine's current original program to calculate the new target speed and target water level to be executed immediately (new speed = reference speed × speed adjustment coefficient, new water level = reference water level × water level adjustment coefficient). This achieves standardization, optimization, and automation of the control process, ensuring that noise is suppressed while interference with washing performance is minimized.

[0040] Optionally, the washing time can be extended based on the adjusted drum speed and water level, including: Based on the adjusted drum speed and water level after the reduction, the compensation time required for the washing machine to reach the preset washing target is calculated, and the washing time is extended based on the compensation time.

[0041] This application considers that the washing effect mainly depends on the combined effects of mechanical action (the tumbling force determined by the drum speed), chemical action (detergent), and thermal action (water temperature). When the speed or water level is reduced to reduce noise, the mechanical action will be significantly weakened. To compensate for this performance loss, the controller first calculates a current mechanical force index in real time based on the adjusted new speed and water level (e.g., using a set formula that comprehensively considers factors such as speed (related to kinetic energy), water level (affecting the drop height of clothes and fluid resistance), and clothing load). Then, this calculated real-time mechanical force index is compared with the baseline mechanical force index under the original program settings to obtain an efficiency reduction ratio. Next, the controller queries its internal washing dynamics model, which defines the theoretical time required to achieve the target washing ratio under a specific mechanical force index, thereby accurately calculating the additional compensation time required to compensate for the efficiency reduction caused by noise reduction (compensation time = original program time × (baseline mechanical force index / current mechanical force index - 1)). Ultimately, the controller adds this compensation time to the remaining time of the current washing program, automatically extending the washing duration.

[0042] Through an intelligent time compensation mechanism, the mechanical energy lost due to parameter adjustment is made up for with extra time. Ultimately, while maintaining the preset washing target, the operating noise is significantly reduced, thus resolving the inherent contradiction between noise suppression and washing performance.

[0043] In one embodiment, the controller can incorporate a lightweight machine learning module to continuously record the noise spectrum and corresponding operating parameters (such as spin speed, load, and preset clothing type) separated during each wash cycle. When the spectral characteristics of a certain type of noise (such as motor noise) deviate from the template in the library for an extended period exceeding a certain threshold, the controller can prompt the user for one-click calibration, or automatically update the local feature library with new spectral characteristics under cloud authorization. This allows the washing machine to adapt to noise changes caused by different home environments, component aging, and new types of clothing, maintaining a very high recognition accuracy over the long term.

[0044] In one embodiment, the controller can also utilize the washing machine's built-in weighing and material detection functions (such as analysis via infrared sensors) to predict the noise risk level of the current wash (e.g., lightweight synthetic fabrics have a low noise risk, while heavy denim and fabrics with fasteners have a high risk). Based on the prediction, a better "spin-water level" time curve can be proactively selected during the startup phase, thereby suppressing noise at its source and reducing significant adjustments during operation.

[0045] In addition, the washing machine's display screen or accompanying app provides real-time visualizations (such as a spectrum graph) showing the results of noise source separation, the relationship between the current noise level and the threshold, and the adjustments the system is making. After the wash cycle, a noise reduction report is generated, summarizing the noise control effectiveness of the wash and the extended time required to achieve quiet operation. This allows users to clearly perceive the value of intelligent noise reduction, enhancing their trust in the product and their satisfaction.

[0046] The above-mentioned method involves determining the target time period of the current washing machine operation and setting a preset washing noise threshold for that period. Based on the microphone array and vibration sensors installed in the washing machine, sound wave signals and mechanical vibration signals are collected during operation. A frequency domain analysis algorithm is used to separate the noise source from these signals, determining the decibel value of the noise source. The noise sources include motor noise, water flow impact sound, and clothes beating sound. The decibel value is compared to the washing noise threshold. If the noise decibel value exceeds the threshold, the drum speed and water level are adjusted according to the noise source, and the washing time is extended based on the adjusted drum speed and water level. By employing this technique, by separating the noise source from the sound wave and mechanical vibration signals, adjusting the drum speed and water level accordingly, and extending the washing time based on the adjusted drum speed and water level, the noise output of the washing machine can be intelligently adjusted according to the usage period while ensuring washing performance. This implements stricter noise control, achieving better noise control while maintaining washing performance, thereby improving the user experience.

[0047] Example 2: Based on the above embodiments, Figure 4 This is a schematic diagram of a noise reduction device for a washing machine according to Embodiment 2 of this application. (Reference) Figure 4 The noise reduction device for the washing machine provided in this embodiment specifically includes: The threshold determination module 21 is used to determine the target time period in which the current washing machine is operating and to determine the preset washing noise threshold for the target time period. The noise separation module 22 is used to collect sound wave signals and mechanical vibration signals during the operation of the washing machine based on the microphone array and vibration sensor set in the washing machine. Based on the frequency domain analysis algorithm, the noise source is separated from the sound wave signal and mechanical vibration signal, and the noise decibel value of the current noise source is determined. The noise source includes motor noise, water flow impact sound and clothes beating sound. The adjustment module 23 is used to compare the noise decibel value with the washing noise threshold. When the noise decibel value is greater than the washing noise threshold, the drum speed and water level of the washing machine are adjusted according to the noise source, and the washing time is extended according to the adjusted drum speed and water level.

[0048] Specifically, noise sources are separated from acoustic signals and mechanical vibration signals based on frequency domain analysis algorithms, including: Fast Fourier Transform is performed on acoustic wave signals and mechanical vibration signals based on frequency domain analysis algorithms to generate corresponding spectrum diagrams; Based on the spectrum diagram, the noise feature spectrum library is queried. If the spectrum diagram matches the noise feature spectrum in the noise feature spectrum library, the noise source is determined according to the matched noise feature spectrum. The noise feature spectrum library stores the corresponding noise feature spectrum in advance according to motor noise, water flow impact sound and clothing pattering sound.

[0049] Specifically, querying the spectrum of noise characteristics based on the spectrogram includes: Calculate the feature similarity between the spectrogram and the noise feature spectrum of the noise feature spectrum library in a specified frequency band, and determine whether the spectrogram and the noise feature spectrum of the noise feature spectrum library match based on the feature similarity.

[0050] Specifically, adjusting the washing machine's drum speed and water level based on the noise source includes: Identify sub-decibels of motor noise, water flow impact sound, and clothing slapping sound; Based on the decibel value, excessive noise is determined from motor noise, water flow impact sound, and clothes beating sound, and the drum speed and water level of the washing machine are adjusted accordingly.

[0051] Specifically, adjusting the washing machine's drum speed and water level based on excessive noise includes: Set adjustment coefficients for the washing machine's drum speed and water level based on the type of excessive noise, and adjust the washing machine's drum speed and water level based on these adjustment coefficients.

[0052] Specifically, the washing time is extended based on the adjusted drum speed and water level, including: Based on the adjusted drum speed and water level after the reduction, the compensation time required for the washing machine to reach the preset washing target is calculated, and the washing time is extended based on the compensation time.

[0053] Specifically, determining the target time period in which the current washing machine is operating includes: Obtain the current washing machine operation time, compare the operation time with the pre-stored time period division rules, and determine the target time period in which the current washing machine operation time is located based on the comparison results. The target time period includes at least daytime, evening, and late night.

[0054] The above-mentioned method involves determining the target time period of the current washing machine operation and setting a preset washing noise threshold for that period. Based on the microphone array and vibration sensors installed in the washing machine, sound wave signals and mechanical vibration signals are collected during operation. A frequency domain analysis algorithm is used to separate the noise source from these signals, determining the decibel value of the noise source. The noise sources include motor noise, water flow impact sound, and clothes beating sound. The decibel value is compared to the washing noise threshold. If the noise decibel value exceeds the threshold, the drum speed and water level are adjusted according to the noise source, and the washing time is extended based on the adjusted drum speed and water level. By employing this technique, by separating the noise source from the sound wave and mechanical vibration signals, adjusting the drum speed and water level accordingly, and extending the washing time based on the adjusted drum speed and water level, the noise output of the washing machine can be intelligently adjusted according to the usage period while ensuring washing performance. This implements stricter noise control, achieving better noise control while maintaining washing performance, thereby improving the user experience.

[0055] The noise reduction device for a washing machine provided in Embodiment 2 of this application can be used to execute the noise reduction method for a washing machine provided in Embodiment 1 above, and has corresponding functions and beneficial effects.

[0056] Example 3: This application provides an electronic device in embodiment three, referring to... Figure 5 The electronic device includes a processor 31, a memory 32, a communication module 33, an input device 34, and an output device 35. The electronic device may have one or more processors and one or more memories. The processor, memory, communication module, input device, and output device of the electronic device can be connected via a bus or other means.

[0057] Memory, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the noise treatment method of a washing machine described in any embodiment of this application (e.g., various modules in the noise treatment device of a washing machine). Memory may primarily include a program storage area and a data storage area, wherein the program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, memory may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0058] The communication module is used for data transmission.

[0059] The processor executes various functional applications and data processing of the device by running software programs, instructions, and modules stored in memory, thereby realizing the noise reduction method of the washing machine described above.

[0060] Input devices can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of the device. Output devices may include display devices such as displays.

[0061] The electronic device provided above can be used to perform the noise reduction method of the washing machine provided in Embodiment 1 above, and has the corresponding functions and beneficial effects.

[0062] Example 4: This application embodiment also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a noise reduction method for a washing machine. The noise reduction method includes: determining the target time period in which the current washing machine is operating; determining a preset washing noise threshold for the target time period; collecting sound wave signals and mechanical vibration signals from the washing machine based on a microphone array and vibration sensors installed in the washing machine; separating noise sources from the sound wave signals and mechanical vibration signals based on a frequency domain analysis algorithm; determining the noise decibel value of the current noise source, including motor noise, water flow impact sound, and clothes beating sound; comparing the noise decibel value with the washing noise threshold; and, if the noise decibel value is greater than the washing noise threshold, adjusting the drum speed and water level of the washing machine according to the noise source, and extending the washing time according to the adjusted drum speed and water level.

[0063] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a first computer system in which the program is executed, or it may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0064] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the noise reduction method of the washing machine as described above, but can also perform related operations in the noise reduction method of the washing machine provided in any embodiment of this application.

[0065] The noise processing device, storage medium, and electronic device of the washing machine provided in the above embodiments can execute the noise processing method of the washing machine provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the noise processing method of the washing machine provided in any embodiment of this application.

[0066] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method for noise reduction in a washing machine, characterized in that, include: Determine the target time period in which the current washing machine is operating, and determine the preset washing noise threshold for the target time period; The microphone array and vibration sensor set in the washing machine collect sound wave signals and mechanical vibration signals during the operation of the washing machine. Based on the frequency domain analysis algorithm, the noise source is separated from the sound wave signal and the mechanical vibration signal, and the noise decibel value of the current noise source is determined. The noise source includes motor noise, water flow impact sound and clothes beating sound. Based on the noise decibel value compared with the washing noise threshold, if the noise decibel value is greater than the washing noise threshold, the drum speed and water level of the washing machine are adjusted according to the noise source, and the washing time is extended according to the adjusted drum speed and water level.

2. The noise reduction method for a washing machine according to claim 1, characterized in that, The frequency domain analysis algorithm for separating noise sources from the acoustic signal and the mechanical vibration signal includes: The acoustic wave signal and the mechanical vibration signal are subjected to fast Fourier transform based on the frequency domain analysis algorithm to generate the corresponding spectrum. Based on the spectrum diagram, a noise feature spectrum library is queried. If the spectrum diagram matches the noise feature spectrum in the noise feature spectrum library, the noise source is determined according to the matched noise feature spectrum. The noise feature spectrum library stores the corresponding noise feature spectra in advance for motor noise, water flow impact sound and clothing pattering sound.

3. The noise reduction method for a washing machine according to claim 2, characterized in that, The querying of noise feature spectrum based on the spectrogram includes: Calculate the feature similarity between the spectrogram and the noise feature spectrum of the noise feature spectrum library in a specified frequency band, and determine whether the spectrogram matches the noise feature spectrum of the noise feature spectrum library based on the feature similarity.

4. The noise reduction method for a washing machine according to claim 1, characterized in that, The step of adjusting the washing machine drum speed and water level according to the noise source includes: Identify the sub-decibels of the motor noise, the water flow impact sound, and the clothing slapping sound; Based on the sub-decibel value, excessive noise is determined from the motor noise, the water flow impact sound, and the clothes beating sound, and the drum speed and water level of the washing machine are adjusted based on the excessive noise.

5. The noise reduction method for a washing machine according to claim 4, characterized in that, The adjustment of the washing machine's drum speed and water level based on the excessive noise includes: Set adjustment coefficients for the washing machine's drum speed and water level based on the type of excessive noise, and adjust the washing machine's drum speed and water level based on the adjustment coefficients.

6. The noise reduction method for a washing machine according to claim 1, characterized in that, The method of extending the washing time based on the adjusted drum speed and water level includes: Based on the adjusted drum speed and water level after the reduction, the compensation time required for the washing machine to reach the preset washing target is calculated, and the washing time is extended based on the compensation time.

7. The noise reduction method for a washing machine according to claim 1, characterized in that, Determining the target time period in which the current washing machine is operating includes: The current washing machine operation time is obtained, and the operation time is compared with the pre-stored time period division rules. Based on the comparison result, the target time period in which the current washing machine operation time is located is determined. The target time period includes at least daytime, evening, and late night.

8. A noise reduction device for a washing machine, characterized in that, include: The threshold determination module is used to determine the target time period in which the current washing machine is operating and to determine the preset washing noise threshold for the target time period. The noise separation module is used to collect sound wave signals and mechanical vibration signals during the operation of the washing machine based on the microphone array and vibration sensor set in the washing machine, separate the noise source from the sound wave signal and the mechanical vibration signal based on the frequency domain analysis algorithm, and determine the noise decibel value of the current noise source. The noise source includes motor noise, water flow impact sound and clothes beating sound. The adjustment module is used to compare the noise decibel value with the washing noise threshold, and when the noise decibel value is greater than the washing noise threshold, adjust the drum speed and water level of the washing machine according to the noise source, and extend the washing time according to the adjusted drum speed and water level.

9. An electronic device, characterized in that, include: Memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the noise reduction method of the washing machine as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the noise reduction method of the washing machine as described in any one of claims 1-7.

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