Control methods and devices for twin-tub washing machines, twin-tub washing machines and storage media

Abstract

This application provides a control method, apparatus, washing machine, and storage medium for a twin-tub washing machine. The method includes: acquiring the operating noise of the twin-tub washing machine and determining whether the operating noise exceeds a noise threshold; if the operating noise exceeds the noise threshold, acquiring the operating parameters of the first and second tubs of the twin-tub washing machine respectively; determining control parameters for the twin-tub washing machine based on the operating parameters of the first and second tubs and the operating noise; and using the control parameters to control the twin-tub washing machine to eliminate resonance. The control parameters include control duration and control speed. This solves the problem that existing twin-tub washing machines are prone to resonance, which shortens the equipment's lifespan and affects the user experience.

Description

Technical Field

[0001] This application relates to the field of twin-tub washing machine control technology, and more specifically, to a control method, apparatus, twin-tub washing machine and computer-readable storage medium for a twin-tub washing machine. Background Technology

[0002] With the improvement of the smart home ecosystem, washing machines, as intelligent home appliances, have been continuously upgraded in terms of function and performance to meet diversified needs, and have become one of the important intelligent devices in modern families.

[0003] Currently, twin-tub washing machines on the market are prone to resonance during the spin-drying stage due to the difference in centrifugal force generated by the high-speed rotation of the inner and outer tubs. Specifically, when a twin-tub washing machine operates at high speed, the inherent frequency difference between the inner and outer tubs leads to the superposition of vibration energy, resulting in significant mechanical vibration and noise pollution. This resonance phenomenon not only shortens the lifespan of the equipment but may also damage clothes during the washing process, seriously affecting the user experience. Summary of the Invention

[0004] The main objective of this application is to provide a control method, apparatus, twin-tub washing machine and computer-readable storage medium for a twin-tub washing machine, so as to at least solve the problem that existing twin-tub washing machines are prone to resonance, which leads to a shortened lifespan of the equipment and affects the user experience.

[0005] To achieve the above objectives, according to one aspect of this application, a control method for a twin-tub washing machine is provided, comprising: acquiring the operating noise of the twin-tub washing machine and determining whether the operating noise is greater than a noise threshold; if the operating noise is greater than the noise threshold, acquiring the operating parameters of the first tub and the second tub of the twin-tub washing machine respectively; determining control parameters of the twin-tub washing machine based on the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise, and using the control parameters to control the twin-tub washing machine to eliminate resonance of the twin-tub washing machine, wherein the control parameters include control duration and control speed.

[0006] Optionally, determining the control parameters of the twin-tub washing machine based on the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise includes: constructing a resonance optimization model, wherein the resonance optimization model is trained using multiple sets of training data, each set of training data including: historical operating parameters of the first tub and the second tub, historical operating noise, and control parameters corresponding to the historical operating parameters and the historical operating noise acquired within a historical time period; and inputting the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise into the resonance optimization model to obtain the control parameters of the twin-tub washing machine.

[0007] Optionally, the control parameters are used to control the twin-tub washing machine, including: controlling the first tub to operate at a first control speed for a first control duration; and / or controlling the second tub to operate at a second control speed for a second control duration.

[0008] Optionally, the operating parameters of the first tub and the second tub of the twin-tub washing machine are obtained respectively, including: determining the center of gravity position data of the first tub and the center of gravity position data of the second tub through an image processing algorithm; inputting the center of gravity position data of the first tub and the center of gravity position data of the second tub into an encoder respectively to obtain the relative phase in the operating parameters of the first tub and the relative phase in the operating parameters of the second tub, wherein the encoder is installed on the motor shaft of the twin-tub washing machine.

[0009] Optionally, the center of gravity position data of the first bucket and the center of gravity position data of the second bucket are determined by image processing algorithms, including: acquiring images inside the first bucket and the second bucket respectively, and determining the coordinate data of each clothing pixel in the images inside the buckets; and determining the center of gravity position data of the first bucket and the center of gravity position data of the second bucket respectively by using the average coordinate data of each clothing pixel.

[0010] Optionally, acquiring the operating noise of a twin-tub washing machine includes: determining whether the operating modes of the first tub and the second tub in the twin-tub washing machine are both spin-drying modes, and determining the rotation speeds of the first tub and the second tub; acquiring the operating noise of the twin-tub washing machine when the operating modes of the first tub and the second tub are both spin-drying modes and the rotation speeds of the first tub and the second tub are equal.

[0011] Optionally, after determining whether the operating modes of the first tub and the second tub in the twin-tub washing machine are both spin-drying modes, the method further includes: if the rotation speeds of the first tub and the second tub in the twin-tub washing machine are both greater than a rotation speed threshold, determining that the first tub and the second tub in the twin-tub washing machine are both in the spin-drying mode.

[0012] According to another aspect of this application, a control device for a twin-tub washing machine is provided, comprising: a first acquisition unit, configured to acquire the operating noise of the twin-tub washing machine and determine whether the operating noise is greater than a noise threshold; a second acquisition unit, configured to acquire the operating parameters of the first tub and the second tub of the twin-tub washing machine respectively when the operating noise is greater than the noise threshold; and a first control unit, configured to determine control parameters of the twin-tub washing machine based on the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise, and to control the twin-tub washing machine using the control parameters to eliminate resonance of the twin-tub washing machine, wherein the control parameters include control duration and control speed.

[0013] According to another aspect of this application, a twin-tub washing machine is provided, the twin-tub washing machine including a controller for executing any of the control methods of the twin-tub washing machine described above.

[0014] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the control methods of the twin-tub washing machine described above.

[0015] By applying the technical solution of this application, the operating noise of a twin-tub washing machine is acquired, and it is determined whether the operating noise exceeds a noise threshold. If the operating noise exceeds the noise threshold, the operating parameters of the first and second tubs of the twin-tub washing machine are acquired separately. Based on the operating parameters of the first and second tubs and the operating noise, the control parameters of the twin-tub washing machine are determined, and the twin-tub washing machine is controlled using these control parameters to eliminate resonance. The control parameters include control duration and control speed. By monitoring the operating noise of the twin-tub washing machine and based on real-time feedback of the noise level, the system can quickly identify resonance problems in the twin-tub washing machine. When the operating noise exceeds a set noise threshold, the speed of the twin-tub washing machine is adjusted according to the operating parameters and operating noise to ensure that the phase difference between the first and second tubs is at the optimal phase difference. This eliminates the centrifugal force when the two tubs rotate, thereby preventing resonance in the twin-tub washing machine, reducing operating noise, improving user experience, and extending the service life of the twin-tub washing machine. Therefore, this solves the problem that existing twin-tub washing machines are prone to resonance, which shortens the equipment's lifespan and affects user experience. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for executing a control method for a twin-tub washing machine according to an embodiment of this application is shown.

[0018] Figure 2 A schematic flowchart of a control method for a twin-tub washing machine according to an embodiment of this application is shown.

[0019] Figure 3 A flowchart illustrating a specific control method for a twin-tub washing machine according to an embodiment of this application is shown.

[0020] Figure 4 A schematic diagram of the twin tubs rotating in opposite directions in a twin-tub washing machine according to an embodiment of this application is shown;

[0021] Figure 5 A schematic diagram of clothing center of gravity identification provided according to an embodiment of this application is shown;

[0022] Figure 6 A schematic diagram of the phase difference provided according to an embodiment of this application is shown;

[0023] Figure 7A schematic diagram of centrifugal force provided according to an embodiment of this application is shown;

[0024] Figure 8 A schematic diagram of the model processing flow provided according to an embodiment of this application is shown;

[0025] Figure 9 A neural network model structure diagram according to an embodiment of this application is shown;

[0026] Figure 10 A structural block diagram of a control device for a twin-tub washing machine according to an embodiment of this application is shown. Detailed Implementation

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that 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.

[0030] As described in the background section, existing twin-tub washing machines are prone to resonance, which can shorten the lifespan of the equipment and affect the user experience. To address the problem of resonance in existing twin-tub washing machines, which can shorten the lifespan of the equipment and affect the user experience, embodiments of this application provide a control method, apparatus, twin-tub washing machine, and computer-readable storage medium for a twin-tub washing machine.

[0031] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0032] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a control method of a twin-tub washing machine according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0033] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the control method of the twin-tub washing machine in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0034] This embodiment provides a control method for a twin-tub washing machine that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] Figure 2 This is a flowchart of a control method for a twin-tub washing machine according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0036] Step S201: Obtain the operating noise of the twin-tub washing machine and determine whether the operating noise is greater than the noise threshold.

[0037] The noise threshold can be set according to the resonance condition of the washing machine, for example, it can be set to 70dB. The noise threshold can be used to determine whether a twin-tub washing machine is resonating.

[0038] In addition, the operating noise is collected by a sound sensor installed on the twin-tub washing machine to capture the noise characteristics generated by the washing tub during high-speed operation in real time.

[0039] Step S202: When the operating noise is greater than the noise threshold, the operating parameters of the first tub and the second tub of the twin-tub washing machine are obtained respectively.

[0040] The first and second tubs rotate in opposite directions, meaning the first tub can rotate clockwise and the second tub can rotate counterclockwise. The operating parameters of the first and second tubs can include relative phase, rotational speed, and weight of the clothes. The relative phase is acquired by encoders installed on the motor shafts corresponding to the first and second tubs.

[0041] Step S203: Determine the control parameters of the twin-tub washing machine based on the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise, and use the control parameters to control the twin-tub washing machine to eliminate the resonance of the twin-tub washing machine. The control parameters include control duration and control speed.

[0042] Among them, the control duration in the control parameters is the duration for which the first tub and / or the second tub are controlled to operate at a controlled speed. The control parameters can make the phase difference between the two tubs in the twin-tub washing machine reach the optimal phase difference (180°).

[0043] Among them, the resonance problem of twin-tub washing machines can be solved by controlling the speed and control time of one of the tubs. Alternatively, the resonance problem can be solved by controlling the speed and control time of both tubs simultaneously. Controlling both tubs simultaneously will shorten the running time for the two tubs to adjust their phases compared to controlling one tub.

[0044] In this embodiment, by applying steps S201, S202, and S203, and monitoring the operating noise of the twin-tub washing machine, the system can quickly identify resonance problems based on real-time feedback of noise levels. When the operating noise exceeds a set noise threshold, the system adjusts the rotation speed of the twin-tub washing machine according to its operating parameters and noise level. This ensures the phase difference between the first and second tubs is optimal, eliminating centrifugal force during rotation and preventing resonance. Simultaneously, it reduces operating noise, improves user experience, and extends the machine's lifespan. Therefore, it solves the problem of existing twin-tub washing machines easily experiencing resonance, which shortens equipment lifespan and negatively impacts user experience.

[0045] In the specific implementation process, the control parameters of the twin-tub washing machine are determined based on the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise. This includes: constructing a resonance optimization model, wherein the resonance optimization model is trained using multiple sets of training data, and each set of training data includes historical operating parameters, historical operating noise, and control parameters corresponding to the historical operating parameters and historical operating noise acquired within a historical time period; and inputting the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise into the resonance optimization model to obtain the control parameters of the twin-tub washing machine.

[0046] This method utilizes machine learning technology to train a model using a large amount of historical operating data, enabling it to predict and output optimal control parameters to optimize resonance during the spin-drying stage of a twin-tub washing machine. By learning the correlation between different operating parameters and noise levels, the model intelligently adjusts the control speed and duration to achieve the optimal phase difference between the first and second tubs, thereby reducing vibration and noise. In terms of effectiveness, through the dynamic adjustment of the machine learning model, this embodiment significantly improves the operating efficiency and user experience of twin-tub washing machines, avoiding equipment damage and energy waste caused by resonance. Furthermore, by introducing more complex neural network architectures or combining deep learning algorithms, the model's prediction accuracy and generalization ability can be further improved to adapt to a wider range of operating environments and clothing types.

[0047] Specifically, the control parameters described above are used to control the twin-tub washing machine, including: controlling the first tub to operate at a first control speed for a first control duration; and / or controlling the second tub to operate at a second control speed for a second control duration.

[0048] This method effectively controls the resonance of a twin-tub washing machine by dynamically adjusting the control speed and duration of the two tubs. By changing the control speed and duration of at least one tub, the phase difference between the two tubs can be optimized in real time, ensuring optimal vibration and noise suppression during the spin-drying stage. This embodiment can significantly reduce vibration and noise in twin-tub washing machines during the high-speed spin-drying stage, improving washing efficiency and user experience.

[0049] More specifically, the operating parameters of the first tub and the second tub of the twin-tub washing machine are obtained respectively, including: determining the center of gravity position data of the first tub and the center of gravity position data of the second tub through an image processing algorithm; inputting the center of gravity position data of the first tub and the center of gravity position data of the second tub into an encoder respectively to obtain the relative phase in the operating parameters of the first tub and the relative phase in the operating parameters of the second tub, wherein the encoder is installed on the motor shaft of the twin-tub washing machine.

[0050] This method utilizes image processing technology, combined with real-time feedback from the encoder, to accurately obtain the center-of-gravity positions of the clothes in the two washing tubs, and then calculates the relative phase between the two tubs. By analyzing the clothing distribution image, the system can determine the center of gravity of the clothes, and then, combined with the angle information read by the encoder, calculate the phase difference between the two tubs, providing crucial data for subsequent control. In terms of effectiveness, this embodiment ensures precise phase control of the twin-tub washing machine during the high-speed spin-drying stage, effectively reducing vibration and noise, and improving washing performance and user experience.

[0051] Further, the center of gravity position data of the first bucket and the center of gravity position data of the second bucket are determined by image processing algorithms, including: acquiring images of the inside of the first bucket and the second bucket respectively, and determining the coordinate data of each clothing pixel in the images of the inside of the buckets; and determining the center of gravity position data of the first bucket and the center of gravity position data of the second bucket respectively by using the average coordinate data of the coordinate data of each clothing pixel.

[0052] The image inside the bucket is a picture of the distribution of clothes inside the bucket, captured by a camera. The two camera modules are located outside the lids of the two buckets, and are positioned in the center of the inner bucket after the lids are closed, allowing them to capture a top-down view of the two buckets.

[0053] This method captures images of the inside of the washing machine using a camera module, analyzes the distribution of the clothes using image processing technology, and calculates the center of gravity of the clothes. By analyzing the coordinate data of the pixels on the clothes in the image and calculating their average value, the center of gravity of the clothes is determined, providing a basis for subsequent phase difference calculations. In terms of effectiveness, this embodiment can accurately identify the distribution of clothes inside the machine, ensuring optimal phase control during the high-speed spin-drying stage, reducing vibration and noise, and improving washing performance and user experience.

[0054] Furthermore, acquiring the operating noise of the twin-tub washing machine includes: determining whether the operating modes of the first tub and the second tub in the twin-tub washing machine are both spin-drying modes, and determining the rotation speeds of the first tub and the second tub; acquiring the operating noise of the twin-tub washing machine when the operating modes of the first tub and the second tub are both spin-drying modes and the rotation speeds of the first tub and the second tub are equal.

[0055] This method monitors the operating noise of a twin-tub washing machine in real time, using sound sensors to capture noise characteristics and determine whether the noise exceeds a preset threshold, thereby triggering subsequent control processes. It identifies potential resonance issues by providing real-time feedback on noise levels when the twin-tub washing machine is operating at high speed. This embodiment ensures that measures are taken promptly to reduce system vibration and noise during simultaneous spin-drying of both tubs, avoiding negative impacts on the lifespan of the twin-tub washing machine and the user experience.

[0056] Furthermore, after determining whether the operating modes of the first tub and the second tub in the twin-tub washing machine are both spin-drying modes, the method further includes: if the rotation speeds of the first tub and the second tub in the twin-tub washing machine are both greater than a rotation speed threshold, then determining that the first tub and the second tub in the twin-tub washing machine are both in spin-drying mode.

[0057] The speed threshold can be set according to the specific speed of the twin-tub washing machine in spin-drying mode. It is determined whether the first and second tubs are both in spin-drying mode by detecting the speed of the first and second tubs. When the first and second tubs are both in spin-drying mode, the speed of the first and second tubs is relatively high, which can easily cause resonance.

[0058] Specifically, after controlling the twin-tub washing machine with the above-mentioned control parameters, the method further includes: controlling the rotation speed of the twin-tub washing machine to return to the normal operating speed.

[0059] After completing operation under specific control parameters, this method restores the spindle speed of the twin-tub washing machine to the preset normal operating speed through the control system, ensuring the continuity and efficiency of the washing process. By dynamically adjusting the spindle speed, when the relative phase difference between the first and second tubs of the twin-tub washing machine reaches the optimal phase difference (180°), it can quickly return to normal washing state after eliminating resonance, avoiding affecting the overall washing process. This embodiment ensures that the twin-tub washing machine continues to operate efficiently and stably after eliminating resonance, improving washing effect and user experience. In addition, by introducing spindle speed smoothing transition technology, the smoothness of the spindle speed recovery process can be ensured, avoiding unnecessary shocks to the washing process and equipment caused by sudden changes in spindle speed.

[0060] In addition, this embodiment also includes a dynamic optimization mechanism for adaptive machine learning algorithms, such as the Adam algorithm, to improve the convergence speed and performance of machine learning models during training. The specific implementation steps are as follows:

[0061] 1. Initialize model parameters: Set initial values ​​for the weights and biases of the neural network model.

[0062] 2. Collect training data: Collect a large amount of sample data from actual washing machine operation, including but not limited to speed, weight of clothes, noise level, encoder readings, etc.

[0063] 3. Training the model:

[0064] 1) Divide the dataset into a training set and a validation set.

[0065] 2) Use the Adam optimization algorithm to update model parameters, learn iteratively through the training set, and monitor the risk of overfitting using the validation set.

[0066] 3) Dynamically adjust the learning rate, that is, gradually reduce the learning rate as training progresses to improve convergence accuracy.

[0067] 4) Introducing a momentum term accelerates model learning while reducing oscillations and improving stability.

[0068] By employing an adaptive optimization algorithm, the model can achieve higher training accuracy in a shorter time, thus finding the optimal speed adjustment strategy more quickly. Furthermore, the adaptive learning rate helps overcome the problem of local optima, enabling the model to maintain good generalization performance even under complex operating conditions.

[0069] This embodiment also includes using deep learning to predict clothing distribution. In the initial stage of washing, in addition to determining the center of gravity of the clothing through image recognition, deep learning technology can be used to predict the changing trend of clothing distribution throughout the entire washing cycle. This is specifically achieved through the following steps:

[0070] 1. Build a deep learning prediction model: Use convolutional neural networks (CNN) to analyze and learn the distribution patterns of clothes during the washing process.

[0071] 2. Data collection and preprocessing: Collect images of clothing distribution in the washing machine at different washing stages, including before, during and after washing.

[0072] 3. Model training: Through a large amount of training data, the model learns the pattern of clothing distribution over time, especially the pattern of clothing concentration and dispersion.

[0073] 4. Real-time prediction and correction: During the washing cycle, the current image is input into the prediction model through real-time monitoring via camera to obtain predictions of clothing distribution at several future time points.

[0074] 5. Optimize vibration control strategy: Based on the prediction results, adjust the rotation speed and phase difference of the left and right drums in advance to cope with the upcoming changes in clothing distribution and reduce the additional vibration and noise caused by them.

[0075] This method can predict and adjust potential imbalances earlier, thus maintaining low vibration and noise levels throughout the washing cycle. By intervening in advance, it avoids strong vibrations that may be caused by uneven distribution of clothes in the early stages of the spin cycle, significantly improving the smoothness and quietness of the entire washing process and greatly enhancing the user experience.

[0076] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the control method for the twin-tub washing machine of this application will be described in detail below with reference to specific embodiments.

[0077] This embodiment relates to a specific control method for a twin-tub washing machine. By setting the two tubs to rotate in opposite directions, noise levels are monitored in real time during the spin-drying stage at the same rotation speed. If the noise exceeds limits, the center of gravity of the clothes is obtained through image recognition, and the relative phase of the two tubs is calculated. Encoder values, rotation speed, clothes weight, and vibration noise are input into a machine learning model, which outputs adjustment values ​​and adjustment times for the left and right tub rotation speeds. The rotation speeds of the two tubs are dynamically adjusted to achieve the optimal phase difference, thereby reducing vibration and noise and improving the user experience. Figure 3 As shown, it specifically includes the following:

[0078] The left and right washing drums rotate clockwise and counterclockwise respectively, such as... Figure 4 As shown, each drum is driven by an independent permanent magnet synchronous motor, enabling high-precision control: the motor's built-in encoder provides real-time feedback on speed and position, forming a closed-loop control that ensures a speed error of ≤±1 rpm. The motor supports millisecond-level direction switching (such as switching from forward to reverse), and can adjust the phase difference in advance by predicting vibration trends, enhancing the cancellation effect.

[0079] When both buckets are rotating at the same speed and both are in the dehydration stage, the system noise level is monitored in real time. Once the noise level exceeds a preset threshold, the system uses an image processing algorithm to obtain the center of gravity position of the clothes inside the two buckets and calculates the relative phase of the two buckets based on the center of gravity data. Subsequently, the real-time encoder value, current rotation speed, load weight, vibration noise, and other parameters are input into a pre-trained machine learning model. Through the machine learning algorithm, the neural network model will output the required rotation speed values ​​and adjustment times for the left and right buckets. Based on the calculation results, the system will dynamically adjust the rotation speed of the left and right buckets to achieve the optimal phase difference (180°), thereby effectively reducing system vibration and noise.

[0080] Neural network models can be explained in three stages: parameter acquisition, model training, and model application.

[0081] I. Parameter Acquisition Stage:

[0082] The weights of the clothes in the twin drums, g1 and g2, the rotational speed, v, the noise range, and the values ​​of the twin drum encoders, θ1 and θ2, are all key parameters that determine the rotational speeds v1 and v2 of the twin drums and the duration of their change, t. Therefore, these parameters must be included in the parameter acquisition phase.

[0083] 1. Before the washing begins, if the two tubs rotate at the same speed and the spin-drying time overlaps, record the set rotation speed v. After the washing begins, weigh the clothes in both tubs to obtain the weights g1 and g2.

[0084] 2. Sound sensors are installed on the left and right sides of the outer tub of the twin-tub washing machine to capture the noise characteristics generated during high-speed operation in real time. Two camera modules are located outside the tub lid, positioned in the center of the inner tub when the lid is closed, allowing for top-down views of both tubs. During the spin-drying stage, the system monitors the current noise level in real time, accurately capturing the noise characteristics generated during spin-drying through the built-in sound sensors and data acquisition module. If the detected noise level α does not exceed the preset threshold (70dB), the system continues the current spin-drying process. If the detected noise exceeds the preset threshold, the system uses an image processing algorithm to obtain the center of gravity positions of the clothes inside the two tubs and determines the values ​​θ1 and θ2 of the twin-tub encoders.

[0085] 3. The distribution of tubular clothing was captured by a camera, and... Figure 5 It can be seen that the black diagonal lines represent clothing, which is an irregular shape. Assuming the exact center of the bucket is at coordinates (0,0), image processing is used to obtain the coordinates of each pixel in the clothing image. The centroid (or geometric center) is the average of the coordinates of all pixels. The coordinates of the centroid (xc, yc) can be calculated using the following formula:

[0086] .

[0087] Where N is the total number of pixels in the image, and xi and yi are the coordinates of the i-th pixel. If there are holes or discontinuous regions in the image, the pixels in these regions will not be included in the centroid calculation to ensure accuracy.

[0088] 4. The encoder is installed on the motor shaft to measure the rotation angle, speed, and direction, providing real-time feedback to help the control system achieve precise position and speed control. After calculating the center of mass position, it is necessary to geometrically correlate the center of mass with the edge of the inner tub. Specifically, this involves extending from the center of mass position towards the edge of the inner tub and determining a specific coordinate point x at the edge, such as... Figure 5 As shown. In the top view, with the top of the tub as 0°, encoders installed on the two tubs respectively can measure the angles (relative phases) θ1 and θ2 between the first and second tubs, and use this angle data to calculate the relative phase of the clothing. Figure 6 As shown, when θ1 and θ2 are the same, the relative phase difference is 0; when θ1 = 0° and θ2 = 180°, the relative phase difference is 180°.

[0089] II. Model Training Phase:

[0090] Model Description: The model's inputs are the weights g1 and g2 of the clothing in the two drums, the rotational speed v, the noise α, and the encoder values ​​θ1 and θ2 of the two drums. The outputs are the control rotational speeds v1 and v2 of the two drums and the control duration t. This is a multiple-input multiple-output problem; therefore, a neural network model is used in this embodiment to implement its functionality.

[0091] A massive and diverse dataset, encompassing the weights g1 and g2 of clothing in different dual-bucket systems, rotational speed v, noise range α, encoder values ​​θ1 and θ2 of the dual-bucket systems, and their corresponding control rotational speeds v1 and v2 and control duration t, is transmitted to the model via a network protocol for model training. The core objective of model training is to establish the mapping relationship between the weights g1 and g2 of clothing in the dual-bucket systems, rotational speed v, noise range α, encoder values ​​θ1 and θ2 of the dual-bucket systems, and the rotational speeds v1 and v2 of the dual-bucket systems, as well as the varying duration t.

[0092] The clothing weights g1 and g2, rotational speed v, noise range α, encoder values ​​θ1 and θ2, and rotational speeds v1 and v2, as well as the duration of the change, used in the model training process, all originated from the data acquisition phase designed in the early stage. This phase involved a large number of repetitive experiments and data recordings, providing a solid data foundation for subsequent model training. The clothing weight was used to calculate the centrifugal force, and the encoder values ​​θ1 and θ2 were used to calculate the phase difference between the two buckets. The rotational speed v, phase difference, and centrifugal force can be used to derive the adjusted control rotational speeds v1 and v2 and the control duration t of the two buckets. In this process, the noise range α serves as a dynamic adjustment parameter for feedback control of the model: when the detected noise value exceeds 70 dB, the system will dynamically adjust the rotational speed of the two buckets until the noise value drops below the preset threshold, thereby achieving closed-loop control.

[0093] like Figure 7 As shown, the two drums (impact wheel A and impact wheel B) rotate in opposite directions. When the two drums operate at the same rotational speed v and the initial phase difference is 0°, the resulting centrifugal forces Fa and Fb are in the same direction, leading to superposition of the resultant forces and causing system vibration. By adjusting the phase difference to 180°, the centrifugal forces Fa and Fb generated by the two drums can be made to be in opposite directions, thus canceling out the resultant forces and significantly reducing system vibration. Furthermore, considering the weight of the clothing, the optimal phase difference between the two drums can be calculated. This minimizes the resultant force, thereby achieving dynamic equilibrium of the system.

[0094] Based on the dynamic principle of phase difference change, the following formula is derived to describe the relationship between the rotational speed difference and time that satisfies the conditions: Let the control rotational speeds of the two barrels be ω1 and ω2 (unit: rpm), the control duration be t (seconds), and t be the change in the target phase difference. The phase difference change satisfies: ;

[0095] In practice, it is necessary to ensure that the speed is within the motor's allowable range (e.g., 0 < ω < 1200 rpm) to avoid damage from overspeeding. The motor response time should be limited to a minimum adjustment time (e.g., tmin ≥ 0.1 seconds) to prevent instantaneous shocks.

[0096] For example: Assuming the clothes have the same weight, the two washing tubs have an initial phase difference of 90° and a speed of 800 rpm. Now, by adjusting the rotation speed and time of the two tubs, the phase difference between the two tubs can be made to 180°.

[0097] ;

[0098] 1) If (speed difference) ): .

[0099] 2) If (speed difference) ): .

[0100] Choosing a smaller Δω can reduce energy consumption, but requires more time. The combination of speed difference and adjustment time can be flexibly designed, and machine learning can be used to meet phase synchronization requirements under different operating conditions.

[0101] The collected values ​​θ1, θ2, α, v, g1, and g2 are input into the model. The model outputs the modified control speeds v1 and v2 of the dual-barrel system and the control duration t. After the duration is completed, the synchronous speed is restored. Through this process, the model's prediction accuracy and generalization ability can be continuously improved, providing strong support for the practical application of dual-barrel intelligent control systems.

[0102] III. Model Application Stage:

[0103] 1. Application Example: Before the wash cycle begins, if both tubs are selected with the same spin speed, the set spin speed v is recorded. After the wash cycle begins, the weights of the clothes in both tubs, g1 and g2, are obtained by weighing. During the spin-drying stage, the noise range α and encoder values ​​θ1 and θ2 are dynamically identified. For example... Figure 8 As shown, the collected θ1, θ2, α, v, g1, and g2 are input into the model. The model learns and calculates the output of the changed rotational speeds v1 and v2 of the double barrels and the duration of the change t, as shown. Figure 9 As shown in the figure; the specific input and output parameters of the model are shown in Table 1. After the running time is completed, the synchronous speed is restored.

[0104] Table 1

[0105]

[0106] This embodiment employs clockwise and counterclockwise rotation modes for the two tubs respectively. When both tubs rotate at the same speed and are in the spin-drying stage, the system noise level is monitored in real time. Once the noise level exceeds a preset threshold, the system uses an image processing algorithm to obtain the center of gravity positions of the clothes in both tubs and calculates the relative phase between them based on this data. Subsequently, the real-time encoder value, current rotation speed, clothing weight, and vibration noise parameters are input into a pre-trained machine learning model. The machine learning algorithm outputs the required rotation speed values ​​and adjustment times for both tubs. Based on the calculation results, the system dynamically adjusts the rotation speeds of the two tubs to achieve the optimal phase difference, effectively reducing system vibration and noise, ensuring the stability of the washing process and a better user experience.

[0107] This application also provides a control device for a twin-tub washing machine. It should be noted that the control device for the twin-tub washing machine in this application can be used to execute the control method for a twin-tub washing machine provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0108] The control device for a twin-tub washing machine provided in the embodiments of this application will be described below.

[0109] Figure 10 This is a schematic diagram of the control device for a twin-tub washing machine according to an embodiment of this application. Figure 10 As shown, the device includes:

[0110] The first acquisition unit 1001 is used to acquire the operating noise of the twin-tub washing machine and determine whether the operating noise is greater than the noise threshold.

[0111] The second acquisition unit 1002 is used to acquire the operating parameters of the first tub and the second tub of the twin-tub washing machine respectively when the operating noise is greater than the noise threshold.

[0112] The first control unit 1003 is used to determine the control parameters of the twin-tub washing machine based on the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise, and to control the twin-tub washing machine using the control parameters to eliminate the resonance of the twin-tub washing machine. The control parameters include control duration and control speed.

[0113] In this embodiment, the first acquisition unit is used to acquire the operating noise of the twin-tub washing machine and determine whether the operating noise exceeds a noise threshold; the second acquisition unit is used to acquire the operating parameters of the first and second tubs of the twin-tub washing machine respectively when the operating noise exceeds the noise threshold; the first control unit is used to determine the control parameters of the twin-tub washing machine based on the operating parameters of the first and second tubs and the operating noise, and to control the twin-tub washing machine using the control parameters to eliminate resonance. The control parameters include control duration and control speed. By monitoring the operating noise of the twin-tub washing machine and based on real-time feedback of the noise level, the system can quickly identify the resonance problem in the twin-tub washing machine. When the operating noise exceeds a set noise threshold, the system adjusts the speed of the twin-tub washing machine according to the operating parameters and operating noise to ensure that the phase difference between the first and second tubs is at the optimal phase difference, eliminating the centrifugal force when the two tubs rotate, thereby preventing resonance in the twin-tub washing machine, reducing the operating noise, improving the user experience, and extending the service life of the twin-tub washing machine. Therefore, this solves the problem that existing twin-tub washing machines are prone to resonance, which can shorten the lifespan of the equipment and affect the user experience.

[0114] As an optional solution, the first control unit includes a construction module and a first input module; the construction module is used to construct a resonance optimization model, wherein the resonance optimization model is trained using multiple sets of training data, each set of training data including: historical operating parameters and historical operating noise of the first tub and the second tub, and the control parameters corresponding to the historical operating parameters and the historical operating noise acquired within a historical time period; the first input module is used to input the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise into the resonance optimization model to obtain the control parameters of the twin-tub washing machine.

[0115] In one optional embodiment, the first control unit further includes a first control module and a second control module; the first control module is used to control the first bucket to operate at a first control speed for a first control duration; and the second control module is used to control the second bucket to operate at a second control speed for a second control duration.

[0116] In one optional embodiment, the second acquisition unit includes a first determining module and a second input module; the first determining module is used to determine the center of gravity position data of the first tub and the center of gravity position data of the second tub respectively through an image processing algorithm; the second input module is used to input the center of gravity position data of the first tub and the center of gravity position data of the second tub respectively into an encoder to obtain the relative phase in the operating parameters of the first tub and the relative phase in the operating parameters of the second tub, wherein the encoder is installed on the motor shaft of the twin-tub washing machine.

[0117] In one optional scheme, the first determining module includes an acquisition submodule and a determining submodule; the acquisition submodule is used to acquire images of the inside of the first bucket and the second bucket respectively, and determine the coordinate data of each clothing pixel in the images of the inside of the bucket; the determining submodule is used to determine the center of gravity position data of the first bucket and the center of gravity position data of the second bucket respectively based on the average coordinate data of the coordinate data of each clothing pixel.

[0118] In one optional embodiment, the first acquisition unit includes a second determining module and an acquisition module; the second determining module is used to determine whether the operating modes of the first tub and the second tub in the twin-tub washing machine are both spin-drying modes, and to determine the rotation speeds of the first tub and the second tub; the acquisition module is used to acquire the operating noise of the twin-tub washing machine when the operating modes of the first tub and the second tub are both spin-drying modes and the rotation speeds of the first tub and the second tub are equal.

[0119] In an optional embodiment, the first acquisition unit further includes a third determining module, which, after determining whether the operating modes of the first tub and the second tub in the twin-tub washing machine are both spin-drying modes, further includes: determining that the first tub and the second tub in the twin-tub washing machine are both in spin-drying mode when the rotation speeds of the first tub and the second tub in the twin-tub washing machine are both greater than a rotation speed threshold.

[0120] The control device for the aforementioned twin-tub washing machine includes a processor and a memory. The first acquisition unit, the second acquisition unit, the first control unit, etc., are all stored as program units in the memory, and the processor executes the program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the various modules may be located in different processors in any combination.

[0121] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured. By adjusting kernel parameters, the resonance phenomenon that commonly occurs in existing twin-tub washing machines can be addressed, which shortens the lifespan of the device and negatively impacts the user experience.

[0122] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0123] This embodiment provides a twin-tub washing machine, which includes a controller for executing the control method of the twin-tub washing machine described above.

[0124] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the control method for the twin-tub washing machine.

[0125] This invention provides a processor for running a program, wherein the program executes the control method for the twin-tub washing machine.

[0126] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements at least the steps of the control method for a twin-tub washing machine described above.

[0127] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0128] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform a program that initializes a control method step for at least the above-described twin-tub washing machine.

[0129] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0135] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0136] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0139] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A control method for a twin-tub washing machine, characterized in that, include: The operating noise of a twin-tub washing machine is obtained, and it is determined whether the operating noise is greater than a noise threshold. When the operating noise is greater than the noise threshold, the operating parameters of the first tub and the second tub of the twin-tub washing machine are obtained respectively. The operating parameters include the weight of clothes, rotation speed and relative phase of the first tub and the second tub. The operating parameters of the first tub, the operating parameters of the second tub, and the operating noise are input into the resonance optimization model to obtain the control parameters of the twin-tub washing machine. The control parameters are then used to control the twin-tub washing machine to eliminate the resonance of the twin-tub washing machine. The control parameters include control duration and control speed. The resonance optimization model is a neural network model. Obtain the operating noise of the twin-tub washing machine, including: Determine whether the operating mode of the first tub and the second tub in the twin-tub washing machine is both spin-dry mode, and determine the rotation speed of the first tub and the second tub; The operating noise of the twin-tub washing machine is obtained when both the first tub and the second tub are in the spin-drying mode and the rotation speeds of the first tub and the second tub are equal.

2. The method according to claim 1, characterized in that, Before optimizing the operating parameters of the first bucket, the operating parameters of the second bucket, and the operating noise input resonance model, the method further includes: The resonance optimization model is constructed, wherein the resonance optimization model is trained using multiple sets of training data, and each set of training data includes the following acquired within a historical time period: historical operating parameters and historical operating noise of the first bucket and the second bucket, and the control parameters corresponding to the historical operating parameters and the historical operating noise.

3. The method according to claim 1, characterized in that, Controlling the twin-tub washing machine using the aforementioned control parameters includes: Control the first barrel to operate at a first control speed for a first control duration; And / or, Control the second barrel to operate at a second control speed for a second control duration.

4. The method according to claim 1, characterized in that, Obtain the operating parameters of the first and second tubs of the twin-tub washing machine, respectively, including: The center of gravity position data of the first bucket and the center of gravity position data of the second bucket are determined by image processing algorithms. The center of gravity position data of the first tub and the center of gravity position data of the second tub are respectively input into the encoder to obtain the relative phase in the operating parameters of the first tub and the relative phase in the operating parameters of the second tub, wherein the encoder is installed on the motor shaft of the twin-tub washing machine.

5. The method according to claim 4, characterized in that, The center of gravity positions of the first bucket and the second bucket are determined using image processing algorithms, including: The images inside the first bucket and the second bucket are obtained respectively, and the coordinate data of each clothing pixel in the images inside the bucket are determined. The center of gravity position data of the first bucket and the center of gravity position data of the second bucket are determined by the average coordinate data of the coordinate data of each of the clothing pixels.

6. The method according to claim 1, characterized in that, After determining whether both the first tub and the second tub in the twin-tub washing machine are in spin-dry mode, the method further includes: If the rotational speeds of both the first and second tubs in the twin-tub washing machine are greater than the rotational speed threshold, then it is determined that both the first and second tubs in the twin-tub washing machine are in the spin-drying mode.

7. A control device for a twin-tub washing machine, characterized in that, include: The first acquisition unit is used to acquire the operating noise of the twin-tub washing machine and determine whether the operating noise is greater than a noise threshold. The second acquisition unit is used to acquire the operating parameters of the first tub and the second tub of the twin-tub washing machine respectively when the operating noise is greater than the noise threshold. The operating parameters include the weight of clothes, rotation speed and relative phase of the first tub and the second tub. The first control unit is configured to input the operating parameters of the first tub, the operating parameters of the second tub, and the operating noise into a resonance optimization model to obtain control parameters for the twin-tub washing machine, and to use the control parameters to control the twin-tub washing machine to eliminate resonance of the twin-tub washing machine. The control parameters include control duration and control speed, and the resonance optimization model is a neural network model. The first acquisition unit includes a second determining module and an acquisition module. The second determining module is used to determine whether the operating modes of the first tub and the second tub in the twin-tub washing machine are both spin-drying modes, and to determine the rotation speeds of the first tub and the second tub. The acquisition module is used to acquire the operating noise of the twin-tub washing machine when the operating modes of the first tub and the second tub are both spin-drying modes and the rotation speeds of the first tub and the second tub are equal.

8. A twin-tub washing machine, characterized in that, The twin-tub washing machine includes a controller, which is used to execute the control method of the twin-tub washing machine according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the control method of the twin-tub washing machine according to any one of claims 1 to 6.

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