Control method and device of twin-tub washing machine, twin-tub washing machine and storage medium

Abstract

The invention provides a control method and device of a twin-tub washing machine, the twin-tub washing machine and a storage medium. The method comprises the steps of obtaining operation noise of the twin-tub washing machine, and determining whether the operation noise is larger than a noise threshold value or not; under the condition that the operation noise is larger than the noise threshold value, operation parameters of a first barrel and a second barrel of the double-barrel washing machine are obtained; control parameters of the twin-tub washing machine are determined according to the operation parameters of the first tub, the operation parameters of the second tub and the operation noise, the control parameters are adopted to control the twin-tub washing machine so as to eliminate resonance of the twin-tub washing machine, and the control parameters comprise the control duration and the control rotating speed. The problems that an existing twin-tub washing machine is prone to causing the resonance phenomenon, the service life of equipment is shortened, and user experience is affected are solved.

Description

Technical Field

[0001] The present application relates to the technical field of twin-tub washing machine control, and more specifically, to a control method and device for a twin-tub washing machine, a twin-tub washing machine, and a computer-readable storage medium. Background Art

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

[0003] Current twin-tub washing machines on the market are prone to resonance during the spin cycle 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 difference in the natural frequencies of the inner and outer tubs causes 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 can also damage clothing during the wash process, seriously affecting the user experience. Summary of the Invention

[0004] The main purpose of this application is to provide a control method and device for a twin-tub washing machine, a twin-tub washing machine and a computer-readable storage medium, so as to at least solve the problem that the existing twin-tub washing machine is prone to resonance, which shortens the service life of the equipment and affects the user experience.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a control method for a twin-tub washing machine is provided, comprising: obtaining the operating noise of the twin-tub washing machine, and determining whether the operating noise is greater than a noise threshold; when the operating noise is greater than the noise threshold, respectively obtaining the operating parameters of the first tub and the second tub of the twin-tub washing machine; determining the control parameters of the twin-tub washing machine according to the operating parameters of the first tub, the operating parameters of the second tub and the operating noise, and controlling the twin-tub washing machine using the control parameters to eliminate the resonance of the twin-tub washing machine, wherein the control parameters include control duration and control speed.

[0006] Optionally, 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, including: constructing a resonance optimization model, wherein the resonance optimization model is trained using multiple sets of training data, and each set of training data in the multiple sets of training data includes: historical operating parameters of the first tub and the second tub, historical operating noise, and the control parameters corresponding to the historical operating parameters and the historical operating noise, obtained 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 time; and / or controlling the second tub to operate at a second control speed for a second control time.

[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 respectively 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, and obtaining the relative phase of the operating parameters of the first tub and the relative phase of 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 barrel and the center of gravity position data of the second barrel are determined respectively through an image processing algorithm, including: obtaining the barrel images of the first barrel and the second barrel respectively, and determining the coordinate data of each clothing pixel point in the barrel image; and determining the center of gravity position data of the first barrel and the center of gravity position data of the second barrel respectively based on the coordinate average value of the coordinate data of each clothing pixel point.

[0010] Optionally, obtaining 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 dehydration modes, and determining the rotational speeds of the first tub and the second tub; when the operating modes of the first tub and the second tub are both dehydration modes and the rotational speeds of the first tub and the second tub are equal, obtaining the operating noise of the twin-tub washing machine.

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

[0012] According to another aspect of the present application, a control device for a twin-tub washing machine is provided, comprising: a first acquisition unit for acquiring the operating noise of the twin-tub washing machine and determining whether the operating noise is greater than a noise threshold; a second acquisition unit for respectively acquiring the operating parameters of the first tub and the second tub of the twin-tub washing machine when the operating noise is greater than the noise threshold; a first control unit for 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 controlling 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 the present application, a twin-tub washing machine is provided, comprising a controller, wherein the controller is configured to execute any one of the control methods for the twin-tub washing machine.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute any one of the control methods for the twin-tub washing machine.

[0015] The technical solution of this application is applied to a twin-tub washing machine. The system obtains the operating noise of the machine and determines whether the noise exceeds a threshold. If the noise exceeds the threshold, the system obtains the operating parameters of the first and second tubs of the machine. The system then determines control parameters based on the operating parameters of the first and second tubs, as well as the noise, and controls the machine using the control parameters to eliminate resonance. The control parameters include a control duration and a control speed. By monitoring the operating noise of the machine and providing real-time feedback on the noise level, the system can quickly identify resonance issues. If the noise exceeds a set threshold, the system adjusts the speed of the machine based on the operating parameters and the noise to optimize the phase difference between the first and second tubs. This eliminates centrifugal force generated by the rotation of the two tubs, thereby preventing resonance. This reduces the noise level of the machine, improves the user experience, and increases the machine's service life. This solves the problem of resonance in existing twin-tub washing machines, which can shorten the machine's service life and negatively impact the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings that constitute part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on 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 provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic flow chart of a control method for a twin-tub washing machine according to an embodiment of the present application is shown;

[0019] Figure 3 A schematic flow chart of a control method for a twin-tub washing machine according to an embodiment of the present application is shown;

[0020] Figure 4 A schematic diagram showing the reverse rotation of the twin tubs of a twin tub washing machine provided according to an embodiment of the present application is shown;

[0021] Figure 5 A schematic diagram illustrating identification of the center of gravity of clothing provided in accordance with an embodiment of the present application is shown;

[0022] Figure 6 shows a schematic diagram of phase difference provided according to an embodiment of the present application;

[0023] Figure 7shows a schematic diagram of centrifugal force provided according to an embodiment of the present application;

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

[0025] Figure 9 shows a structural diagram of a neural network model provided according to an embodiment of the present application;

[0026] Figure 10 A structural block diagram of a control device for a twin-tub washing machine provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] As introduced in the background technology, the existing twin-tub washing machine is prone to cause resonance phenomenon, which shortens the service life of the equipment and affects the user experience. In order to solve the problem that the existing twin-tub washing machine is prone to cause resonance phenomenon, which shortens the service life of the equipment and affects the user experience, the embodiments of the present application provide a control method and device for a twin-tub washing machine, a twin-tub washing machine and a computer-readable storage medium.

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

[0032] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a 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 double-tub washing machine according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0033] The memory 104 can be used to store computer programs, such as application software programs and modules, such as the computer program corresponding to the control method for a twin-tub washing machine in an embodiment of the present invention. The processor 102 executes the computer programs stored in the memory 104 to execute various functional applications and data processing, thereby implementing the aforementioned method. The memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located from the processor 102, which can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The transmission device 106 is used to receive or transmit data via a network. Specific examples of such networks may include a wireless network provided by the mobile terminal's telecommunications 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 enable communication 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] In this embodiment, a control method for a twin-tub washing machine running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0035] Figure 2 Flowchart of the control method of the double-tub washing machine according to the embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0036] Step S201, obtaining the operating noise of the twin-tub washing machine, and determining whether the operating noise is greater than a noise threshold;

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

[0038] In addition, the operating noise is collected by a sound sensor installed on the twin-tub washing machine, which is used 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, respectively obtaining operating parameters of the first tub and the second tub of the twin-tub washing machine;

[0040] Among them, the rotation directions of the first barrel and the second barrel are opposite, that is, the first barrel can rotate clockwise and the second barrel can rotate counterclockwise. The operating parameters of the first barrel and the second barrel may include relative phase, rotation speed and clothing weight, wherein the relative phase is collected by the encoder installed on the motor shaft corresponding to the first barrel and the second barrel.

[0041] Step S203, 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, and controlling the twin-tub washing machine using the control parameters to eliminate the resonance of the twin-tub washing machine, wherein the control parameters include control duration and control speed.

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

[0043] Among them, the rotation speed and control time of one of the barrels in the twin-barrel washing machine can be controlled to solve the resonance problem of the twin-barrel washing machine. The rotation speed and control time of both barrels in the twin-barrel washing machine can also be controlled at the same time to solve the resonance problem of the twin-barrel washing machine. Compared with controlling one barrel, controlling both barrels at the same time will shorten the running time of the two barrels in the twin-barrel washing machine to adjust the phase.

[0044] Through this embodiment, by applying the above steps S201, S202, and S203, 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 rotation speed of the twin-tub washing machine is adjusted according to the operating parameters and operating noise of the twin-tub washing machine to achieve an optimal phase difference between the first and second tubs of the twin-tub washing machine, eliminating the centrifugal force when the two tubs rotate, thereby preventing resonance in the twin-tub washing machine and reducing the operating noise of the twin-tub washing machine. This improves the user experience and also increases the service life of the twin-tub washing machine. Therefore, the problem that existing twin-tub washing machines are prone to resonance, which shortens the equipment life and affects the user experience, is solved.

[0045] During the specific implementation process, the control parameters of the double-barrel washing machine are determined based on the operating parameters of the first barrel, the operating parameters of the second barrel and the operating noise, including: constructing a resonance optimization model, wherein the resonance optimization model is trained using multiple sets of training data, and each set of training data in the multiple sets of training data includes: historical operating parameters of the first barrel and the second barrel, historical operating noise and the control parameters corresponding to the historical operating parameters and the historical operating noise obtained within a historical time period; the operating parameters of the first barrel, the operating parameters of the second barrel and the operating noise are input into the resonance optimization model to obtain the control parameters of the double-barrel washing machine.

[0046] This method uses machine learning technology to train a model through a large amount of historical operating data, so that it can predict and output the best control parameters to achieve resonance optimization of the twin-barrel washing machine during the dehydration stage. By learning the relationship between different operating parameters and noise levels, the model can intelligently adjust the control speed and control duration to achieve the best phase difference between the first barrel and the second barrel, thereby reducing vibration and noise. In terms of effect, through the dynamic adjustment of the machine learning model, this embodiment can significantly improve the operating efficiency and user experience of the twin-barrel washing machine, and avoid equipment loss and energy waste due to resonance. In addition, by introducing a more complex neural network architecture or combining it with a deep learning algorithm, the prediction accuracy and generalization ability of the model can be further improved to adapt to a wider range of operating environments and clothing types.

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

[0048] This method effectively controls resonance in a twin-tub washing machine by dynamically adjusting the speed and duration of the two tubs. By varying the speed and duration of at least one of the two tubs, the phase difference between the two tubs can be optimized in real time, ensuring optimal vibration and noise suppression during the spin cycle. This embodiment significantly reduces vibration and noise during the high-speed spin cycle of a twin-tub washing machine, 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 respectively 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, and obtaining the relative phase of the operating parameters of the first tub and the relative phase of 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 encoder feedback, to accurately determine the center of gravity of clothing within the two wash tubs and subsequently calculate the relative phase of the two tubs. By analyzing the clothing distribution image, the system determines the center of gravity of the clothing. Combined with the angle information read by the encoder, it calculates the phase difference between the two tubs, providing critical data for subsequent control. Effectively, this embodiment ensures precise phase control during the high-speed spin cycle of a twin-tub washing machine, effectively reducing vibration and noise, and improving washing quality and user experience.

[0051] Furthermore, the center of gravity position data of the first barrel and the center of gravity position data of the second barrel are respectively determined by an image processing algorithm, including: obtaining the barrel images of the first barrel and the second barrel respectively, and determining the coordinate data of each pixel point of the clothes in the barrel images; and determining the center of gravity position data of the first barrel and the center of gravity position data of the second barrel respectively by taking the coordinate average value of the above coordinate data of each pixel point of the clothes.

[0052] Among them, the image inside the barrel is an image of the distribution of clothes in the barrel taken by the camera. The two camera modules are located outside the lids of the two barrels. After the lids are closed, they are located in the middle of the inner barrel and can capture a top view of the two barrels.

[0053] This method uses a camera module to capture images of the interior of the tub, then uses image processing techniques to analyze the distribution of clothing and calculate its center of gravity. By analyzing the coordinate data of clothing pixels in the image and calculating their average value, the center of gravity of the clothing is determined, providing a basis for subsequent phase difference calculations. Effectively, this embodiment accurately identifies the distribution of clothing within the tub, ensuring optimal phase control during high-speed spin cycles, reducing vibration and noise, and improving washing quality and user experience.

[0054] Furthermore, obtaining 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 dehydration modes, and determining the rotational speeds of the first tub and the second tub; when the operating modes of the first tub and the second tub are both dehydration modes and the rotational speeds of the first tub and the second tub are equal, obtaining the operating noise of the twin-tub washing machine.

[0055] This method monitors the operating noise of a twin-tub washing machine in real time, using an acoustic sensor to capture noise signatures and determine whether it exceeds a preset threshold, thereby triggering subsequent control processes. This method identifies the presence of resonance issues when the twin-tub washing machine is spinning at high speed, using real-time feedback on noise levels to quickly identify potential resonance issues. This embodiment ensures that when both tubs are spinning at the same speed, timely measures are taken to reduce system vibration and noise, preventing negative impacts on the machine's service life and 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 dehydration modes, the method further includes: when the rotational speeds of the first tub and the second tub in the twin-tub washing machine are both greater than a rotational speed threshold, determining that the first tub and the second tub in the twin-tub washing machine are both in the dehydration mode.

[0057] Among them, the speed threshold can be set according to the speed of the twin-tub washing machine in the dehydration mode. By detecting the speeds of the first tub and the second tub, it is determined whether the first tub and the second tub are both in the dehydration mode. When the first tub and the second tub are both in the dehydration mode, the speeds of the first tub and the second tub are high, which is prone to resonance.

[0058] Specifically, after the twin-tub washing machine is controlled by adopting the control parameters, the method further includes: controlling the rotation speed of the twin-tub washing machine to return to a normal operating rotation speed.

[0059] After completing operation under specific control parameters, this method restores the speed of the twin-tub washing machine to its preset normal operating speed through the control system, ensuring the continuity and efficiency of the washing process. By dynamically adjusting the speed, when the relative phase difference between the first and second tubs reaches the optimal phase difference (180°), the twin-tub washing machine can quickly return to normal washing after eliminating resonance, avoiding impacting the overall washing process. This embodiment ensures that the twin-tub washing machine continues to operate efficiently and stably after eliminating resonance, improving washing results and user experience. Furthermore, by introducing speed smoothing technology, the speed recovery process is smooth, avoiding unnecessary impacts on the washing process and equipment caused by sudden speed changes.

[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 the machine learning model 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 rotation speed, clothing weight, noise level, encoder readings, etc.

[0063] 3. Training model:

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

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

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

[0067] 4) Introducing momentum terms can speed up model learning while reducing oscillations and improving stability.

[0068] By using an adaptive optimization algorithm, the model achieves higher training accuracy in a shorter time, enabling it to more quickly find the optimal speed adjustment strategy. Furthermore, the adaptive learning rate helps overcome local optimal solutions, allowing the model to maintain good generalization performance even in complex operating conditions.

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

[0070] 1. Build a deep learning prediction model: Use a convolutional neural network (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 patterns of clothing distribution over time, especially the concentration and dispersion patterns of clothing.

[0073] 4. Real-time prediction and correction: During the washing cycle, the camera monitors the washing cycle in real time, inputs the current image into the prediction model, and obtains the clothing distribution prediction 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 approach predicts and corrects potential imbalances earlier, maintaining low vibration and noise levels throughout the wash cycle. This early intervention avoids the strong vibrations that could be caused by uneven clothing distribution early in the spin cycle, significantly improving the smoothness and quietness of the entire wash process and significantly enhancing the user experience.

[0076] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the control method of the double-tub washing machine of the present 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, which sets the twin tubs to rotate in opposite directions and monitors the noise level in real time during the dehydration stage at the same speed. If the noise exceeds the limit, the center of gravity of the clothes is obtained through image recognition, and the relative phase of the two tubs is calculated. The encoder value, rotation speed, clothing weight and vibration noise are input into the machine learning model, and the left and right tub rotation speed adjustment values ​​and adjustment time are output. The twin tub rotation speeds are dynamically adjusted to achieve the optimal phase difference, thereby reducing vibration and noise and improving user experience. Figure 3 Specifically, it includes the following contents:

[0078] The left and right washing drums rotate in clockwise and counterclockwise directions respectively. Figure 4 Each drum is driven by an independent permanent magnet synchronous motor, which achieves high-precision control. The motor's built-in encoder provides real-time speed and position feedback, forming a closed-loop control loop that ensures a speed error of ≤±1 revolution per minute (rpm). The motor supports millisecond-level direction switching (for example, switching from forward to reverse), and can predict vibration trends and adjust phase differences in advance to enhance vibration compensation.

[0079] When both drums are running at the same speed and entering the dehydration phase, the system monitors the noise level in real time. If the noise level exceeds a preset threshold, the system uses an image processing algorithm to determine the center of gravity of the garments in each drum. Based on this center of gravity data, the system calculates the relative phase of the two drums. Subsequently, the encoder's real-time value, current speed, load weight, and vibration noise parameters are input into a pre-trained machine learning model. Using the machine learning algorithm, the neural network model outputs the speed adjustment values ​​and adjustment times for the left and right drums. Based on this calculation, the system dynamically adjusts the speeds of the left and right drums to achieve the optimal phase difference (180°), effectively reducing system vibration and noise.

[0080] The neural network model can be divided into three stages: parameter acquisition stage, model training stage and model application stage.

[0081] 1. Parameter collection stage:

[0082] The double-tub laundry weight g1, g2, rotation speed v, noise interval α, and the values ​​θ1, θ2 of the double-tub encoder are all key parameters that determine the double-tub rotation speed v1, v2 and the duration t of the change, so these parameters must be included in the parameter collection stage.

[0083] 1. Before washing begins, if the two tubs have the same speed and the dehydration phases overlap, record the set speed v. After washing begins, obtain the weights g1 and g2 of the clothes in the two tubs by weighing.

[0084] 2. Sound sensors are installed on the left and right sides of the dual-tub outer tub to capture the noise characteristics generated by the high-speed operation of the washing tub in real time. Two camera modules are located on the outside of the tub lid and, when the door is closed, directly in the center of the inner tub, capturing a bird's-eye view of both tubs. During the spin cycle, the system monitors the current noise range in real time, using built-in sound sensors and data acquisition modules to accurately capture the noise characteristics generated during the spin cycle. If the noise range α is detected to be within the preset threshold (70dB), the system continues the current spin cycle. If the noise exceeds the preset threshold, the system uses an image processing algorithm to determine the center of gravity of the clothing in the two tubs and determine the values ​​θ1 and θ2 of the dual-tub encoders.

[0085] 3. Use the camera to capture the distribution of clothes inside the tube. Figure 5 The black diagonal lines represent clothing, which is an irregular shape. Assuming the center of the bucket is at coordinate (0,0), image processing is used to determine the coordinates of each pixel in the clothing image. The center of mass (or geometric center) is the average value of all pixel coordinates. The coordinates of the center of mass (xc, yc) can be calculated using the following formula:

[0086] .

[0087] Where N is the total number of pixels in the image, xi and yi are the coordinates of the i-th pixel. If there are holes or discontinuous areas in the image, the pixels in these areas will not be counted 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 accurate position and speed control. After completing the center of mass position calculation, the center of mass needs to be geometrically associated with the edge of the inner barrel. The specific operation is: extend from the center of mass position to the edge of the inner barrel and determine the specific coordinate point x at the edge, such as Figure 5 As shown. In the top view, with the top of the barrel as 0°, the encoders installed on the two barrels can measure the angles (relative phases) θ1 and θ2 between the first barrel and the second barrel, and use the angle data to calculate the relative phase of the clothes. Figure 6 As shown in the figure, when the angles 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] 2. Model training stage:

[0090] Model Description: The model's inputs are the laundry weights g1 and g2 of the twin tubs, their rotational speed v, noise α, and the values ​​of the twin tub encoders θ1 and θ2. The outputs are the controlled rotational speeds v1 and v2 of the twin tubs and the controlled duration t. This is a multiple-input, multiple-output (MIMO) problem, so in this example, a neural network model is used to implement its functionality.

[0091] A massive and diverse dataset, covering different dual-tub laundry weights g1, g2, speeds v, noise intervals α, dual-tub encoder values ​​θ1, θ2, and their corresponding controlled speeds v1, v2, and control duration t (control duration), is sent to the model via a network transmission protocol for training. The core goal of model training is to establish a mapping relationship between the dual-tub laundry weights g1, g2, speed v, noise interval α, dual-tub encoder values ​​θ1, θ2, the dual-tub speeds v1, v2, and the control duration t.

[0092] The model training process uses the dual-drum laundry weight g1, g2, speed v, noise interval α, dual-drum encoder values ​​θ1, θ2, dual-drum speed v1, v2, and change duration t. These parameters are derived from the parameter collection phase of the previous design. This phase involved extensive repetitive experiments and data logging, providing a solid data foundation for subsequent model training. The laundry weight is used to calculate centrifugal force, and the dual-drum encoder values ​​θ1, θ2 are used to calculate the phase difference between the two drums. The speed v, phase difference, and centrifugal force are used to derive the adjusted dual-drum control speeds v1, v2, and control duration t. During this process, the noise interval α serves as a dynamic adjustment parameter for feedback control of the model: when the detected noise level exceeds 70dB, the system dynamically adjusts the dual-drum speeds until the noise level drops below a preset threshold, thus achieving closed-loop control.

[0093] like Figure 7 As shown in the figure, the two drums (pulsator A and pulsator B) rotate in opposite directions. When the two drums run at the same speed v and the initial phase difference is 0°, the centrifugal force Fa generated is in the same direction as Fb, resulting in the superposition of the resultant force, which causes system vibration. By adjusting the phase difference to 180°, the centrifugal force Fa generated by the two drums can be in the opposite direction to Fb, thereby achieving the resultant force offset and significantly reducing system vibration. Further combined with the weight of the clothes, it can be calculated to determine the centrifugal force Fa generated by the two drums at a specific phase difference. Under these conditions, the resultant force is minimized, thus achieving dynamic balance of the system.

[0094] According to the dynamic principle of phase difference change, the relationship between the speed difference and time that satisfies the conditions is derived as follows: Assume that the control speeds of the two barrels are ω1 and ω2 (unit: rpm), the control time is t (seconds), and the change of the target phase difference is t. The phase difference change satisfies: ;

[0095] In practice, ensure that the speed is within the motor's allowable range (e.g., 0 < ω < 1200 rpm) to avoid overspeed damage. Set a minimum adjustment time limit for the motor's response time (e.g., tmin ≥ 0.1 seconds) to prevent transient shocks.

[0096] For example, assuming the laundry weight is the same, the initial phase difference between the two washing tubs is 90°, and the speed is 800 rpm. Now, by adjusting the rotation speed of the two tubs and changing the time, the phase difference between the two tubs is made 180°.

[0097] ;

[0098] 1) If (Speed ​​difference ): .

[0099] 2) If (Speed ​​difference ): .

[0100] Choosing a smaller Δω reduces energy consumption but requires a longer 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 data θ1, θ2, α, v, g1, and g2 are input into the model, which then outputs the modified dual-bucket control speeds v1 and v2 and the control duration t. After the duration expires, the synchronized speed is restored. This process continuously improves the model's prediction accuracy and generalization capabilities, providing strong support for the practical application of dual-bucket intelligent control systems.

[0102] 3. Model application stage:

[0103] 1. Application example: Before washing starts, if the two tubs select the same speed mode, record the set speed v. After washing starts, obtain the weight of the clothes in the two tubs g1 and g2 by weighing. During the dehydration phase, dynamically identify the noise interval α and the encoder values ​​θ1 and θ2. Figure 8 As shown, the collected θ1, θ2, α, v, g1, and g2 are input into the model, and the model calculates and outputs the changed speed v1, v2 of the double barrel and the changed duration t through learning, as shown in Figure 9 The input and output parameters of the model are shown in Table 1. After the running duration is completed, the synchronous speed is restored.

[0104] Table 1

[0105]

[0106] This embodiment uses clockwise and counterclockwise rotation modes for the dual barrels, respectively. When both barrels rotate at the same speed and enter the dehydration phase, 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 of the clothing in the two barrels and calculates the relative phase of the two barrels based on the center of gravity data. Subsequently, the real-time value of the encoder, the current speed, the weight of the clothing, and other parameters such as vibration noise are input into a pre-trained machine learning model. Through the machine learning algorithm, the model will output the speed value and adjustment time that need to be adjusted for the left and right barrels. Based on the calculation results, the system will dynamically adjust the speed of the left and right barrels to achieve the optimal phase difference between the two barrels, thereby effectively reducing system vibration and noise, ensuring the stability of the washing process and user experience.

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

[0108] The following is an introduction to the control device of the twin-tub washing machine provided in the embodiment of the present application.

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

[0110] The first acquiring unit 1001 is configured to acquire the operating noise of the twin-tub washing machine and determine whether the operating noise is greater than a noise threshold;

[0111] The second acquiring unit 1002 is configured to acquire 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 above-mentioned twin-tub washing machine according to the above-mentioned operating parameters of the above-mentioned first tub, the above-mentioned operating parameters of the above-mentioned second tub and the above-mentioned operating noise, and use the above-mentioned control parameters to control the above-mentioned twin-tub washing machine to eliminate the resonance of the above-mentioned twin-tub washing machine, wherein the above-mentioned control parameters include control time and control speed.

[0113] In this embodiment, a first acquisition unit is configured to acquire the operating noise of a twin-tub washing machine and determine whether the operating noise exceeds a noise threshold. A second acquisition unit is configured to acquire the operating parameters of the first and second tubs of the twin-tub washing machine, respectively, if the operating noise exceeds the noise threshold. A first control unit is configured to determine control parameters of the twin-tub washing machine based on the operating parameters of the first and second tubs, as well as the operating noise, and to control the twin-tub washing machine using the control parameters to eliminate resonance in the twin-tub washing machine. The control parameters include a control duration and a control speed. By monitoring the operating noise of the twin-tub washing machine and providing real-time feedback on the noise level, the system can quickly identify resonance issues in the twin-tub washing machine. If the operating noise exceeds a set noise threshold, the system adjusts the speed of the twin-tub washing machine based on the operating parameters and the operating noise to achieve an optimal phase difference between the first and second tubs of the twin-tub washing machine. This eliminates centrifugal force during the rotation of the two tubs, thereby preventing resonance in the twin-tub washing machine and reducing the operating noise of the twin-tub washing machine, thereby improving the user experience and extending the service life of the twin-tub washing machine. Therefore, the problem that the existing twin-tub washing machine is prone to resonance, which shortens the service life of the equipment and affects the user experience is solved.

[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 above-mentioned resonance optimization model is trained using multiple sets of training data, and each set of training data in the above-mentioned multiple sets of training data includes: the historical operating parameters, historical operating noise and the above-mentioned control parameters corresponding to the above-mentioned historical operating parameters and the above-mentioned historical operating noise of the above-mentioned first tub and the above-mentioned second tub obtained within a historical time period; the first input module is used to input the above-mentioned operating parameters of the above-mentioned first tub, the above-mentioned operating parameters of the above-mentioned second tub and the above-mentioned operating noise into the above-mentioned resonance optimization model to obtain the above-mentioned control parameters of the above-mentioned double-tub washing machine.

[0115] An optional solution is that the first control unit also includes a first control module and a second control module; the first control module is used to control the above-mentioned first barrel to operate at a first control speed for a first control time; the second control module is used to control the above-mentioned second barrel to operate at a second control speed for a second control time.

[0116] An optional solution, the second acquisition unit includes a first determination module and a second input module; the first determination module is used to determine the center of gravity position data of the first barrel and the center of gravity position data of the second barrel respectively through an image processing algorithm; the second input module is used to input the center of gravity position data of the first barrel and the center of gravity position data of the second barrel into the encoder respectively, to obtain the relative phase of the above-mentioned operating parameters of the first barrel and the relative phase of the above-mentioned operating parameters of the second barrel, wherein the above-mentioned encoder is installed on the motor shaft of the above-mentioned double-barrel washing machine.

[0117] An optional solution, the first determination module includes an acquisition submodule and a determination submodule; the acquisition submodule is used to respectively acquire the barrel images of the above-mentioned first barrel and the above-mentioned second barrel, and determine the coordinate data of each clothing pixel point in the above-mentioned barrel images; the determination submodule is used to respectively determine the above-mentioned center of gravity position data of the above-mentioned first barrel and the above-mentioned center of gravity position data of the above-mentioned second barrel based on the coordinate average value of the above-mentioned coordinate data of each of the above-mentioned clothing pixel points.

[0118] An optional solution, the first acquisition unit includes a second determination module and an acquisition module; the second determination module is used to determine whether the operating modes of the first tub and the second tub in the above-mentioned twin-tub washing machine are both dehydration modes, and to determine the rotational speeds of the first tub and the second tub; the acquisition module is used to obtain the above-mentioned operating noise of the above-mentioned twin-tub washing machine when the above-mentioned operating modes of the above-mentioned first tub and the above-mentioned second tub are both dehydration modes and the above-mentioned rotational speeds of the above-mentioned first tub and the above-mentioned second tub are equal.

[0119] In an optional solution, the first acquisition unit also includes a third determination module, which is used to determine whether the operating modes of the first barrel and the second barrel in the above-mentioned twin-barrel washing machine are both dehydration modes. The above-mentioned method also includes: when the rotational speeds of the above-mentioned first barrel and the above-mentioned second barrel in the above-mentioned twin-barrel washing machine are both greater than the rotational speed threshold, determining that the above-mentioned first barrel and the above-mentioned second barrel in the above-mentioned twin-barrel washing machine are both in the above-mentioned dehydration mode.

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

[0121] The processor contains a core, which retrieves the corresponding program unit from memory. One or more cores can be configured, and by adjusting core parameters, this solves the problem of resonance in existing twin-tub washing machines, which shortens the device's lifespan and affects the user experience.

[0122] The memory may include non-permanent memory in a computer-readable medium, 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. The controller is used to execute the control method of the twin-tub washing machine.

[0124] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is executed, the device where the computer-readable storage medium is located is controlled to execute the control method of the twin-tub washing machine.

[0125] An embodiment of the present invention provides a processor, which is used to run a program, wherein the control method of the twin-tub washing machine is executed when the program is run.

[0126] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and runnable on the processor. When the processor executes the program, at least the steps of the above-mentioned control method for a twin-tub washing machine are implemented.

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

[0128] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes at least the steps of the control method of the above-mentioned twin-tub washing machine.

[0129] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, can be centralized on a single computing device, or can be distributed across a network of multiple computing devices. They can be implemented using program code executable by the computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described herein can be performed in a different order than that shown, or 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 appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

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

[0135] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0136] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0137] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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 "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0139] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A control method for a twin-tub washing machine, characterized in that: include: Obtaining operating noise of a twin-tub washing machine, and determining whether the operating noise is greater than a noise threshold; When the operating noise is greater than the noise threshold, respectively obtaining operating parameters of the first tub and the second tub of the twin-tub washing machine, the operating parameters including the laundry weight, 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 a resonance optimization model to obtain control parameters of the twin-tub washing machine, and the twin-tub washing machine is controlled using the control parameters to eliminate the resonance of the twin-tub washing machine, wherein the control parameters include control time and control speed, and wherein the resonance optimization model is a neural network model.

2. The method according to claim 1, characterized in that Before inputting the resonance optimization model according to the operating parameters of the first barrel, the operating parameters of the second barrel, and the operating noise, the method further includes: Construct the 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 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, obtained within a historical time period.

3. The method according to claim 1, characterized in that Using the control parameters to control the twin-tub washing machine includes: controlling the first barrel to operate at a first controlled speed for a first controlled time; and / or, The second barrel is controlled to rotate at a second controlled speed for a second controlled time period.

4. The method according to claim 1, wherein Respectively obtaining operating parameters of the first tub and the second tub of the twin-tub washing machine includes: Determining the center of gravity position data of the first barrel and the center of gravity position data of the second barrel respectively through an image processing algorithm; 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 of the operating parameters of the first tub and the relative phase of the operating parameters of the second tub, wherein the encoder is installed on the motor shaft of the double-tub washing machine.

5. The method according to claim 4, characterized in that Determining the center-of-gravity position data of the first barrel and the center-of-gravity position data of the second barrel respectively by an image processing algorithm includes: Obtaining images of the first tub and the second tub respectively, and determining coordinate data of each pixel point of clothing in the images of the first tub and the second tub; The center-of-gravity position data of the first tub and the center-of-gravity position data of the second tub are respectively determined based on the average coordinate value of the coordinate data of each pixel point of the clothing.

6. The method according to claim 1, characterized in that Get the operating noise of the twin-tub washing machine, including: Determining whether the operation mode of the first tub and the second tub of the twin-tub washing machine is both a spin mode, and determining the rotation speeds of the first tub and the second tub; When the operation modes of the first tub and the second tub are both the dehydration mode and the rotation speeds of the first tub and the second tub are equal, the operation noise of the twin-tub washing machine is obtained.

7. The method according to claim 6, characterized in that After determining whether the operation modes of the first tub and the second tub of the twin-tub washing machine are both dehydration modes, the method further includes: 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, it is determined that the first tub and the second tub in the twin-tub washing machine are both in the dehydration mode.

8. A control device for a twin-tub washing machine, characterized in that: include: a first acquiring unit, configured to acquire an operating noise of the twin-tub washing machine and determine whether the operating noise is greater than a noise threshold; a second acquiring unit, configured to acquire 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 including the laundry weight, rotation speed, and relative phase of the first tub and the second tub; A first control unit is used 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 of the twin-tub washing machine, and use the control parameters to control the twin-tub washing machine to eliminate the resonance of the twin-tub washing machine, wherein the control parameters include control time and control speed, and wherein the resonance optimization model is a neural network model.

9. A twin-tub washing machine, characterized in that: The twin-tub washing machine includes a controller configured to execute the control method for the twin-tub washing machine according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the control method for the twin-tub washing machine according to any one of claims 1 to 7.

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