Spin-drying control method and apparatus for washing machines, computer-readable storage medium

Abstract

This invention discloses a method and apparatus for controlling the dehydration of a washing machine, as well as a computer-readable storage medium. The method includes: upon determining that dehydration of clothes to be dehydrated in the inner drum of the washing machine is to begin, acquiring image data of the inner drum and the weight of the clothes to be dehydrated; inputting the image data into a parameter determination model to process the image data and obtain state parameters of the clothes to be dehydrated in the inner drum; inputting the state parameters and weight of the clothes into a dehydration strategy determination model to process the state parameters and weight of the clothes and obtain a dehydration control strategy for dehydrating the washing machine; and controlling the washing machine to perform the dehydration operation according to the dehydration control strategy to ensure that the water content in the inner drum is below a water level threshold. This invention solves the technical problem in related technologies where repeated eccentric detection is usually required during the dehydration process, leading to poor dehydration efficiency.

Description

Technical Field

[0001] This invention relates to the field of home appliance control technology, and more specifically, to a method and apparatus for controlling the spin-drying of a washing machine, and a computer-readable storage medium. Background Technology

[0002] Currently, common methods for optimizing the spin-drying process in front-loading washing machines involve manual pre-input or the addition of a recognition device where the user places each garment in front of a camera for identification, providing the washing machine with the material information of the clothes to be washed. The spin-drying process is then optimized based on the material, the weight before and after spin-drying, etc., aiming to complete the process quickly. However, relying solely on the material without considering the varying amounts and distribution of clothes, which can lead to different spin-centering patterns during spin-drying, makes it impossible to predict the spin speed increase curve during the pre-spin-drying and main spin-drying stages. This results in repeated detection of spin centering issues and fails to effectively simplify the spin-drying process. Furthermore, whether manually pre-inputting or using a recognition device, obtaining material information from the washing machine significantly increases the learning and operational costs for users, leading to negative consequences.

[0003] There is currently no effective solution to the problem that the aforementioned technologies typically require repeated eccentricity detection during the washing machine's spin-drying process, resulting in poor spin-drying efficiency. Summary of the Invention

[0004] This invention provides a method and apparatus for controlling the dehydration of a washing machine, as well as a computer-readable storage medium, to at least solve the technical problem in related technologies that usually require repeated eccentricity detection during the dehydration process of a washing machine, resulting in poor dehydration efficiency.

[0005] According to one aspect of the present invention, a spin-drying control method for a washing machine is provided, comprising: a first acquisition step, wherein, upon determining that spin-drying of clothes to be spun in the inner drum of the washing machine is to begin, image data of the inner drum and the weight of the clothes to be spun; and a second acquisition step, wherein the image data is input into a parameter determination model to process the image data using the parameter determination model to obtain clothing state parameters of the clothes to be spun in the inner drum, wherein the parameter determination model is trained using multiple sets of first training data through a deep learning method, each set of the multiple sets of first training data including: sample image data and sample clothing state parameters corresponding to the sample image data. The third step involves inputting the garment state parameters and garment weight into a dehydration strategy determination model. This model processes the garment state parameters and garment weight to obtain a dehydration control strategy for the washing machine. The dehydration strategy determination model is trained using multiple sets of second training data via deep learning. Each set of second training data includes: sample garment state parameters, sample garment weight, and a sample dehydration control strategy corresponding to the sample garment state parameters and sample garment weight. The control step involves controlling the washing machine to perform a dehydration operation according to the dehydration control strategy, so that the water content in the inner drum is lower than a water level threshold.

[0006] Optionally, when it is determined that the dehydration process for the clothes to be spun in the inner drum of the washing machine will begin, acquiring image data of the inner drum and the weight of the clothes to be spun includes: determining that the dehydration process for the clothes to be spun in the inner drum will begin when the drainage operation of the washing machine is determined to have ended; controlling the washing machine to perform a shaking operation to shake the clothes to be spun in the inner drum, wherein the shaking operation is used to make the looseness of the clothes to be spun higher than a looseness threshold; controlling the washing machine to perform the dehydration operation at a preset speed and counting the cumulative time of the washing machine performing the dehydration operation at the preset speed; when the cumulative time reaches a predetermined time, controlling the washing machine to stop performing the dehydration operation at the preset speed and acquiring the image data and the weight of the clothes.

[0007] Optionally, the image data includes first image data and second image data. Obtaining the image data of the inner drum and the weight of the clothes to be spun includes: acquiring the first image data of the inner drum using a first image acquisition device and acquiring the second image data of the inner drum using a second image acquisition device, wherein the first image acquisition device is an image acquisition device installed directly in front of the inner drum, and the second image acquisition device is an image acquisition device installed above the front or rear of the inner drum; and weighing the clothes to be spun using a weighing module built into the washing machine to obtain the weight of the clothes to be spun.

[0008] Optionally, the image data is input into a parameter determination model to process the image data and obtain the garment state parameters of the garments to be dehydrated in the inner drum. This includes: inputting the first image data into the parameter determination model to process the first image data and obtain the stacking height of the garments to be dehydrated in the inner drum; inputting the second image data into the parameter determination model to process the second image data and obtain the unfolded area and distribution state of the garments to be dehydrated in the inner drum, wherein the distribution state includes the position and uniformity of the garments to be dehydrated in the inner drum; and integrating the stacking height, the unfolded area, and the distribution state to obtain the garment state parameters of the garments to be dehydrated in the inner drum.

[0009] Optionally, the dehydration operation includes a pre-dehydration operation and a main dehydration operation. Controlling the washing machine to perform the dehydration operation according to the dehydration control strategy to lower the water content in the inner drum below a water threshold includes: dividing the dehydration control strategy into a pre-dehydration control strategy and a main dehydration control strategy, wherein the pre-dehydration control strategy controls the pre-dehydration stage of the washing machine's dehydration process, and the main dehydration control strategy controls the main dehydration stage of the washing machine's dehydration process; the pre-dehydration stage is the dehydration stage preceding the main dehydration stage; and determining the pre-dehydration acceleration curve of the inner drum's rotation speed according to the pre-dehydration control strategy, wherein the pre-dehydration acceleration curve refers to... The predicted acceleration curve of the rotation speed during the pre-spinning stage; controlling the washing machine to execute the pre-spinning operation according to the pre-spinning control strategy to obtain the actual pre-spinning acceleration curve of the rotation speed, wherein the pre-spinning operation refers to the spin-drying operation performed during the pre-spinning stage; comparing the actual pre-spinning acceleration curve with the pre-spinning acceleration curve to obtain a comparison result; determining a target main spin-drying control strategy for controlling the main spin-drying stage based on the comparison result; controlling the washing machine to execute the main spin-drying operation according to the target main spin-drying control strategy to make the water content in the inner drum lower than the water content threshold, wherein the main spin-drying operation refers to the spin-drying operation performed during the main spin-drying stage.

[0010] Optionally, determining a target main dehydration control strategy for controlling the main dehydration stage based on the comparison result includes: determining the main dehydration control strategy as the target main dehydration control strategy when the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve; acquiring the current image data of the inner drum and the current weight of the clothes to be dehydrated when the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve, wherein the current image data refers to the data of the inner drum after the washing machine has completed the pre-dehydration operation, and the current weight of the clothes refers to the weight of the clothes to be dehydrated after the washing machine has completed the pre-dehydration operation; updating the image data input to the parameter determination model to the current image data, and updating the weight of the clothes input to the dehydration strategy determination model to the current weight of the clothes, and then sequentially executing the second acquisition step and the third acquisition step to obtain the target main dehydration control strategy for controlling the main dehydration stage.

[0011] Optionally, the spin-drying control method of the washing machine further includes: after obtaining the garment state parameters corresponding to the image data using the parameter determination model each time, optimizing the parameter determination model based on the image data and the garment state parameters to obtain an optimized parameter determination model; and after obtaining the spin-drying control strategy corresponding to the garment state parameters and the garment weight using the spin-drying strategy determination model each time, optimizing the spin-drying strategy determination model based on the garment state parameters, the garment weight, and the spin-drying control strategy to obtain an optimized spin-drying strategy determination model.

[0012] According to another aspect of the present invention, a spin-drying control device for a washing machine is also provided, comprising: a first acquisition unit, configured to perform a first acquisition step, acquiring image data of the inner drum and the weight of the clothes to be spun when it is determined that spin-drying treatment of clothes to be spun in the inner drum of the washing machine is to begin; and a second acquisition unit, configured to perform a second acquisition step, inputting the image data into a parameter determination model to process the image data using the parameter determination model to obtain clothing state parameters of the clothes to be spun in the inner drum, wherein the parameter determination model is trained using a deep learning method using multiple sets of first training data, each set of the multiple sets of first training data including: sample image data and sample clothing state corresponding to the sample image data. The system includes: a third acquisition unit, used to execute a third acquisition step, inputting the garment state parameters and the garment weight into a dehydration strategy determination model, and using the dehydration strategy determination model to process the garment state parameters and the garment weight to obtain a dehydration control strategy for dehydrating the washing machine. The dehydration strategy determination model is trained using multiple sets of second training data through deep learning. Each set of second training data includes: sample garment state parameters, sample garment weight, and a sample dehydration control strategy corresponding to the sample garment state parameters and the sample garment weight. A control unit is used to execute a control step, controlling the washing machine to perform a dehydration operation according to the dehydration control strategy, so that the water content in the inner drum is lower than a water level threshold.

[0013] Optionally, the first acquisition unit includes: a first determining module, configured to determine, upon determining that the draining operation of the washing machine has ended, to begin the dehydration process on the clothes to be spun in the inner drum; a first control module, configured to control the washing machine to perform a shaking operation to shake the clothes to be spun in the inner drum, wherein the shaking operation is used to make the looseness of the clothes to be spun higher than a looseness threshold; a second control module, configured to control the washing machine to perform the dehydration operation at a preset speed and to count the cumulative time of the washing machine performing the dehydration operation at the preset speed; and a third control module, configured to control the washing machine to stop performing the dehydration operation at the preset speed when the cumulative time reaches a predetermined time, and to acquire the image data and the weight of the clothes.

[0014] Optionally, the image data includes first image data and second image data. The first acquisition unit includes: an acquisition module, used to acquire the first image data of the inner drum using a first image acquisition device and to acquire the second image data of the inner drum using a second image device, wherein the first image acquisition device is an image acquisition device installed directly in front of the inner drum, and the second image acquisition device is an image acquisition device installed above the front or rear of the inner drum; and a first acquisition module, used to weigh the clothes to be spun using a weighing module built into the washing machine to obtain the weight of the clothes to be spun.

[0015] Optionally, the second acquisition unit includes: a second acquisition module, configured to input the first image data into the parameter determination model, so as to process the first image data using the parameter determination model to obtain the stacking height of the clothes to be dehydrated in the inner drum; a third acquisition module, configured to input the second image data into the parameter determination model, so as to process the second image data using the parameter determination model to obtain the unfolded area and distribution state of the clothes to be dehydrated in the inner drum, wherein the distribution state includes the position and uniformity of the clothes to be dehydrated in the inner drum; and a fourth acquisition module, configured to integrate the stacking height, the unfolded area, and the distribution state to obtain the clothing state parameters of the clothes to be dehydrated in the inner drum.

[0016] Optionally, the dehydration operation includes a pre-dehydration operation and a main dehydration operation. The control unit includes: a fifth acquisition module, used to divide the dehydration control strategy to obtain a pre-dehydration control strategy and a main dehydration control strategy, wherein the pre-dehydration control strategy is used to control the pre-dehydration stage in the washing machine's dehydration process, and the main dehydration control strategy is used to control the main dehydration stage in the washing machine's dehydration process, and the pre-dehydration stage is the dehydration stage preceding the main dehydration stage; a second determination module, used to determine the pre-dehydration acceleration curve of the inner drum's rotation speed according to the pre-dehydration control strategy, wherein the pre-dehydration acceleration curve refers to the predicted acceleration curve of the rotation speed in the pre-dehydration stage; and a fourth control module. The system comprises five modules: a first module for controlling the washing machine to perform the pre-spinning operation according to the pre-spinning control strategy, and a second module for obtaining the actual pre-spinning speed-up curve of the rotation speed, wherein the pre-spinning operation refers to the spin-drying operation performed in the pre-spinning stage; a third module for comparing the actual pre-spinning speed-up curve with the pre-spinning speed-up curve to obtain a comparison result; a fourth module for determining the target main spin-drying control strategy for controlling the main spin-drying stage based on the comparison result; and a fifth module for controlling the washing machine to perform the main spin-drying operation according to the target main spin-drying control strategy, so that the water content in the inner drum is lower than the water content threshold, wherein the main spin-drying operation refers to the spin-drying operation performed in the main spin-drying stage.

[0017] Optionally, the third determining module includes: a first determining submodule, configured to determine the main dehydration control strategy as the target main dehydration control strategy when the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve; a first acquiring submodule, configured to acquire the current image data of the inner drum and the current weight of the clothes to be dehydrated when the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve, wherein the current image data refers to the data of the inner drum after the washing machine has completed the pre-dehydration operation, and the current weight of the clothes refers to the weight of the clothes to be dehydrated after the washing machine has completed the pre-dehydration operation; and a second acquiring submodule, configured to update the image data input to the parameter determining model to the current image data and update the weight of the clothes input to the dehydration strategy determining model to the current weight of the clothes, and then sequentially execute the second acquiring step and the third acquiring step to obtain the target main dehydration control strategy for controlling the main dehydration stage.

[0018] Optionally, the spin-drying control device of the washing machine further includes: a first optimization unit, configured to optimize the parameter determination model based on the image data and the garment state parameters after each time the garment state parameters corresponding to the image data are obtained using the parameter determination model, to obtain an optimized parameter determination model; and a second optimization unit, configured to optimize the spin-drying strategy determination model based on the garment state parameters, the garment weight, and the spin-drying control strategy after each time the spin-drying strategy determination model is obtained using the spin-drying strategy determination model, to obtain an optimized spin-drying strategy determination model.

[0019] According to another aspect of the present invention, a spin-drying control system for a washing machine is also provided, wherein the spin-drying control system for the washing machine uses any of the spin-drying control methods described above.

[0020] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any of the above-described spin-drying control methods for a washing machine.

[0021] According to another aspect of the present invention, a processor is also provided, the processor being configured to run a program, wherein the program, when running, executes any of the above-described spin-drying control methods for a washing machine.

[0022] According to another aspect of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, perform any of the above-described spin-drying control methods for a washing machine.

[0023] In this embodiment of the invention, the first acquisition step involves acquiring image data of the inner drum and the weight of the clothes to be spun when it is determined that the dehydration process for the clothes to be spun in the inner drum of the washing machine has begun. The second acquisition step involves inputting the image data into a parameter determination model to process the image data and obtain the state parameters of the clothes to be spun in the inner drum. The parameter determination model is trained using multiple sets of first training data through deep learning. Each set of first training data includes: sample image data and sample clothes state parameters corresponding to the sample image data. The third acquisition step... The garment state parameters and weight are input into the dehydration strategy determination model. The model processes these parameters to obtain a dehydration control strategy for the washing machine. The dehydration strategy determination model is trained using multiple sets of second training data through deep learning. Each set of second training data includes: sample garment state parameters, sample garment weight, and a sample dehydration control strategy corresponding to the sample garment state parameters and weight. The control step involves controlling the washing machine to perform the dehydration operation according to the dehydration control strategy to reduce the water content in the inner drum below a water threshold. The above technical solution achieves the goal of acquiring and recognizing images of clothes in the inner drum before the actual spin-drying control of the washing machine, analyzing and processing them using a corresponding model to obtain a spin-drying control strategy, and controlling the washing machine to spin-dry according to the strategy. This realizes the technical effect of directly analyzing and determining the spin-drying control strategy before spin-drying, avoiding repeated eccentric detection during the spin-drying process, improving the spin-drying efficiency of the washing machine, and solving the technical problem that related technologies usually require repeated eccentric detection during the spin-drying process, resulting in poor spin-drying efficiency. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0025] Figure 1 This is a hardware structure block diagram of a mobile terminal for a washing machine spin-drying control method according to an embodiment of the present invention.

[0026] Figure 2 This is a flowchart of a washing machine spin-drying control method according to an embodiment of the present invention;

[0027] Figure 3 This is a flowchart of an optional spin-drying control method for a washing machine according to an embodiment of the present invention;

[0028] Figure 4This is a schematic diagram of the original image of the clothing to be dehydrated according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of the identification and labeling of clothing to be dehydrated according to an embodiment of the present invention;

[0030] Figure 6 This is a schematic diagram illustrating the model determination using a training dehydration strategy according to an embodiment of the present invention;

[0031] Figure 7 This is a schematic diagram of a spin-drying control device for a washing machine according to an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] As described in the background section, related technologies typically require repeated eccentricity detection during the spin-drying process of a washing machine, resulting in poor spin-drying efficiency. To address these shortcomings, embodiments of the present invention provide a spin-drying control method and apparatus for a washing machine, as well as a computer-readable storage medium.

[0035] 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.

[0036] The methods and embodiments provided in this invention can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1This is a hardware structure block diagram of a mobile terminal for a washing machine spin-drying control method 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.

[0037] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the washing machine spin-drying control method 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 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 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.

[0038] According to an embodiment of the present invention, a method embodiment of a spin-drying control method for a washing machine is provided. 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. Furthermore, 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.

[0039] Figure 2This is a flowchart of a washing machine spin-drying control method according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0040] Step S202, the first acquisition step, when it is determined that the dehydration process of the clothes to be spun in the inner drum of the washing machine will begin, acquire image data of the inner drum and the weight of the clothes to be spun.

[0041] Optionally, the above image data may include, but is not limited to, first image data and second image data.

[0042] The first and second image data here were captured by image acquisition devices placed at different locations inside the washing machine drum.

[0043] According to the above embodiments of the present invention, in step S202, when it is determined that the dehydration process of the clothes to be spun in the inner drum of the washing machine will begin, acquiring image data of the inner drum and the weight of the clothes to be spun includes: when it is determined that the drainage operation of the washing machine has ended, determining that the dehydration process of the clothes to be spun in the inner drum will begin; controlling the washing machine to perform a shaking operation to shake the clothes to be spun in the inner drum, wherein the shaking operation is used to make the looseness of the clothes to be spun higher than a looseness threshold; controlling the washing machine to perform a dehydration operation at a preset speed, and counting the cumulative time of the washing machine performing the dehydration operation at the preset speed; when the cumulative time reaches a predetermined time, controlling the washing machine to stop performing the dehydration operation at the preset speed, and acquiring image data and the weight of the clothes.

[0044] The following is combined Figure 3 The embodiments of the present invention will be described in detail below. Figure 3 This is a flowchart of an optional spin-drying control method for a washing machine according to an embodiment of the present invention.

[0045] like Figure 3 As shown, before the dehydration stage begins after drainage, a pre-dehydration treatment stage is first required, which involves a low-speed shaking program to shake the clothes in the drum as much as possible, and a low-speed dehydration program is uniformly executed for a period of time (i.e., a predetermined duration) to reduce the influence of other irrelevant or redundant features when analyzing the subsequent process.

[0046] According to the above embodiments of the present invention, in step S202, acquiring image data of the inner drum and the weight of the clothes to be spun includes: acquiring first image data of the inner drum using a first image acquisition device and acquiring second image data of the inner drum using a second image acquisition device, wherein the first image acquisition device is an image acquisition device installed directly in front of the inner drum, and the second image acquisition device is an image acquisition device installed above the front or rear of the inner drum; and weighing the clothes to be spun using a weighing module built into the washing machine to obtain the weight of the clothes to be spun.

[0047] Optionally, the aforementioned image acquisition devices may include, but are not limited to, devices that can be used to acquire images, such as cameras and infrared sensors.

[0048] The following is combined Figure 4 The embodiments of the present invention will be described in detail below. Figure 4 This is a schematic diagram of the original image of the clothing to be dehydrated according to an embodiment of the present invention.

[0049] As above Figure 3 As shown, after the pre-processing stage is completed, the two cameras inside the inner drum can be controlled to take pictures of the clothes inside, in order to obtain images such as... Figure 4 The image data shown can also control the built-in weighing module in the washing machine to weigh the clothes to be spun, so as to obtain the weight of the clothes to be spun in g.

[0050] It should be noted that, Figure 4 The image data shown is only the data captured by one of the two cameras. The two cameras are set at different positions in the inner tube and are both used to obtain the distribution of the clothes inside the tube. The only difference is the shooting angle.

[0051] The first camera (i.e., the first image acquisition device) can be installed directly in front of the inner drum of the washing machine to capture images of the clothes inside the drum. The second camera (i.e., the second image acquisition device) can be installed at a higher position in front or behind the inner drum of the washing machine to capture image data of the inner drum from a downward angle.

[0052] Step S204, the second acquisition step, inputs the image data into the parameter determination model to process the image data using the parameter determination model to obtain the garment state parameters of the garments to be dehydrated in the inner drum. The parameter determination model is trained using multiple sets of first training data through deep learning. Each set of multiple sets of first training data includes: sample image data and sample garment state parameters corresponding to the sample image data.

[0053] The following is combined Figure 5 The embodiments of the present invention will be described in detail below. Figure 5This is a schematic diagram of the identification and labeling of clothing to be dehydrated according to an embodiment of the present invention.

[0054] In this embodiment, the trained parameters can be used to determine the model pair. Figure 4 The image data shown is analyzed and labeled to obtain the corresponding clothing status parameters.

[0055] The parameter determination model here uses a large number of similar models. Figure 5 The labeled images shown are obtained by training the Mobi leU-net model. A large number of already labeled images are used to train the Mobi leU-net model, and the trained model can then interpret given images... Figure 4 The original image of clothes in the washing machine drum is used to identify the stacking height h, unfolded area s, and distribution parameters θ of the clothes in the drum through model calculation, so as to predict the acceleration node curve of the drum dehydration in the future.

[0056] In the above embodiments of the present invention, image data is input into a parameter determination model to process the image data and obtain the garment state parameters of the garments to be dehydrated in the inner drum. This includes: inputting first image data into the parameter determination model to process the first image data and obtain the stacking height of the garments to be dehydrated in the inner drum; inputting second image data into the parameter determination model to process the second image data and obtain the unfolded area and distribution state of the garments to be dehydrated in the inner drum, wherein the distribution state includes the position and uniformity of the garments to be dehydrated in the inner drum; and integrating the stacking height, unfolded area, and distribution state to obtain the garment state parameters of the garments to be dehydrated in the inner drum.

[0057] Specifically, a first camera is installed at the front of the washing machine, capturing images of the area in front of the inner drum. Based on the images captured by this first camera, semantic segmentation technology using computer vision is applied, and a large number of images are labeled to create a dataset. This dataset is then trained on a Mobi leU-net model of a convolutional neural network (CNN). The trained model can identify the segments of clothes and the washing machine, as well as the current stacking thickness h of the clothes in the washing machine, from real-time captured images. A second camera is installed at a higher position at the front or rear of the inner drum, capturing images of the inner drum from a downward angle. Based on the images captured by this second camera, semantic segmentation technology using computer vision is applied, and a large number of images are labeled to create a dataset. This dataset is then trained on a Mobi leU-net model of a convolutional neural network (CNN). The leU-net model is trained to identify the segments of clothes and the washing machine from real-time captured images, as well as the unfolded area s of the clothes in the inner drum sidewall. It is worth noting that when clothes in the drum have the same stacking height h and unfolded area s, the state of the clothes in the inner drum cannot be uniquely determined, and different distribution states of clothes will have different effects on the subsequent spin-drying control process. Therefore, in addition to stacking height and unfolded area, another variable needs to be introduced to describe the current distribution state of clothes in the inner drum. Thus, in this embodiment of the invention, a clothing distribution state parameter θ is introduced. In addition to obtaining the area parameter through the second camera, the distribution state parameter θ of clothes in the drum needs to be calculated using semantic segmentation technology.

[0058] Step S206, the third acquisition step, involves inputting the garment state parameters and garment weight into the dehydration strategy determination model. The dehydration strategy determination model is then used to process the garment state parameters and garment weight to obtain a dehydration control strategy for the washing machine. The dehydration strategy determination model is trained using multiple sets of second training data through deep learning. Each set of second training data includes: sample garment state parameters, sample garment weight, and a sample dehydration control strategy corresponding to the sample garment state parameters and sample garment weight.

[0059] In this embodiment, a trained dehydration strategy determination model can be used to analyze and process the stacking height h, unfolded area s, distribution state θ, and weight g of the clothes obtained in the above steps. This allows for the prediction of the acceleration node process of the pre-dehydration and main dehydration stages of the clothes in the drum through the calculation of multi-layer neurons. In other words, it is the dehydration control strategy that controls the washing machine to perform dehydration.

[0060] The following is combined Figure 6 The training process of the dehydration strategy determination model in the above embodiments of the present invention will be described in detail. Figure 6This is a schematic diagram illustrating the model determination using a training dehydration strategy according to an embodiment of the present invention.

[0061] Furthermore, image segmentation and feature point extraction using convolutional neural networks (CNNs) require processing on a remote server. This is primarily because remote servers offer advantages such as high-performance computing resources, efficient data processing capabilities, flexible scalability, high stability and reliability, stringent security, and efficient network connectivity, thereby meeting the demands of large-scale image processing and analysis. Therefore, in this embodiment of the invention, the CNN Mobi can be implemented by deploying a remote server. The leU-net model is used for initial model training and recognition tasks during actual washing machine operation, and is also used to train and compute the BP neural network. In actual use, after the preprocessing is completed, the two built-in cameras take pictures of the clothes in the drum. After obtaining the photos, the photos are transmitted to a remote server. Relying on the high performance of the remote server, the photos can be processed quickly. After receiving the photos, the remote server inputs them into the semantic segmentation model. Through the segmentation processing of the model, the stacking height h, unfolded area s, and distribution state θ of the clothes in the washing machine drum are obtained. The weight g of the clothes in the drum can be calculated locally by the weighing module in the washing machine body. The weight g of the clothes in the drum is also sent to the remote server. After receiving the parameter g, the remote server inputs the other three parameters obtained by itself into the prediction model BP neural network. The prediction model predicts the acceleration node process of the pre-dehydration and main dehydration stages of the clothes in the drum through the calculation of multiple layers of neurons based on the four parameters.

[0062] Specifically, historical operating data of the washing machine can be collected, and a large number of images of the inner drum can be used as input to the dataset. Figure 4 The Mobi leU-net convolutional neural network shown is used to train this network. The trained network can calculate the stacking height h, unfolded area s, weight g, and current distribution state θ of the clothes in the drum, and obtain the corresponding subsequent program execution flow (including prediction of acceleration nodes in the pre-dehydration and main dehydration stages). The stacking height h, unfolded area s, weight g, and current distribution state θ of the clothes in the drum are considered as a data point. The collected data is then cleaned and preprocessed to remove noise, outliers, and redundant information. Finally, the data is labeled to clarify the subsequent execution flow corresponding to each data point. A dataset consisting of a large number of data points is then input into... Figure 6 The BP neural network shown is used to train the network, and the model parameters are adjusted to optimize the prediction performance.

[0063] Step S208, Control Step: Control the washing machine to perform a spin-drying operation according to the spin-drying control strategy so that the water content in the inner drum is lower than the water content threshold.

[0064] In this embodiment, the washing machine can be controlled to perform dehydration according to the dehydration control strategy obtained in the above steps to complete the dehydration operation.

[0065] According to the above embodiments of the present invention, in step S208, the dehydration operation includes a pre-dehydration operation and a main dehydration operation. Controlling the washing machine to perform the dehydration operation according to a dehydration control strategy to ensure that the water content in the inner drum is below a water content threshold includes: dividing the dehydration control strategy to obtain a pre-dehydration control strategy and a main dehydration control strategy, wherein the pre-dehydration control strategy is used to control the pre-dehydration stage in the washing machine's dehydration process, and the main dehydration control strategy is used to control the main dehydration stage in the washing machine's dehydration process; the pre-dehydration stage is the dehydration stage preceding the main dehydration stage; and determining the pre-dehydration acceleration rate of the inner drum's rotation speed according to the pre-dehydration control strategy. The curves are as follows: the pre-spinning speed-up curve refers to the predicted speed-up curve during the pre-spinning stage; the washing machine is controlled to perform pre-spinning operation according to the pre-spinning control strategy to obtain the actual pre-spinning speed-up curve, where the pre-spinning operation refers to the spin-drying operation performed during the pre-spinning stage; the actual pre-spinning speed-up curve is compared with the pre-spinning speed-up curve to obtain the comparison result; based on the comparison result, the target main spin-drying control strategy for controlling the main spin-drying stage is determined; the washing machine is controlled to perform the main spin-drying operation according to the target main spin-drying control strategy to make the water content in the inner drum lower than the water content threshold, where the main spin-drying operation refers to the spin-drying operation performed during the main spin-drying stage.

[0066] Specifically, according to the method described above, during actual dehydration, the actual captured images inside the drum are input into the Mobi leU-net network model. After model calculation, h, s, θ and the obtained g value are input into the BP neural network for prediction, resulting in the pre-dehydration acceleration process curve corresponding to the current amount and state of the clothes. Then, based on the predicted acceleration node process, the rotation speed acceleration curve in the pre-dehydration stage is determined (for example, assuming the predicted acceleration curve has three rotation speed adjustment nodes: 200 rpm -> 400 rpm -> 500 rpm). After the pre-dehydration stage is completed, the actual running results can be compared with the predicted results to further determine the control method for the main dehydration stage.

[0067] In the above embodiments of the present invention, determining the target main dehydration control strategy for controlling the main dehydration stage based on the comparison results includes: if the comparison results indicate that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve, determining the main dehydration control strategy as the target main dehydration control strategy; if the comparison results indicate that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve, acquiring the current image data of the inner drum and the current weight of the clothes to be dehydrated, wherein the current image data refers to the data of the inner drum after the washing machine has completed the pre-dehydration operation, and the current weight of the clothes refers to the weight of the clothes to be dehydrated after the washing machine has completed the pre-dehydration operation; updating the image data input to the parameter determination model to the current image data, and updating the weight of the clothes input to the dehydration strategy determination model to the current weight of the clothes, and then sequentially executing the second acquisition step and the third acquisition step to obtain the target main dehydration control strategy for controlling the main dehydration stage.

[0068] Specifically, if the actual operation results of the pre-dehydration stage are consistent with the predicted results (the effect of eccentricity may cause the actual speed-up process to differ from the predicted process), then the speed-up node process of the main dehydration stage will start directly according to the previous prediction results (for example, assuming that the predicted speed-up curve has four speed adjustment nodes: 600 rpm -> 800 rpm -> 1000 rpm -> 1200 rpm) until the end of the dehydration stage (the speed-up node process curves of pre-dehydration and main dehydration may contain n speed adjustment nodes); if the actual operation results of the pre-dehydration stage differ from the predicted results, then the pre-processing, image capture, remote processing, process prediction, and other stages will be repeated according to the current state of the clothes in the inner drum, and the pre-dehydration stage will be skipped directly to execute the newly predicted speed-up node process of the main dehydration stage until the end of the dehydration stage.

[0069] In an optional embodiment of the present invention, the spin-drying control method of the washing machine further includes: after obtaining the clothing state parameters corresponding to the image data using the parameter determination model each time, optimizing the parameter determination model based on the image data and the clothing state parameters to obtain an optimized parameter determination model; and after obtaining the spin-drying control strategy corresponding to the clothing state parameters and clothing weight using the spin-drying strategy determination model each time, optimizing the spin-drying strategy determination model based on the clothing state parameters, clothing weight, and spin-drying control strategy to obtain an optimized spin-drying strategy determination model.

[0070] Specifically, in the process of controlling the dehydration process of a washing machine using the parameter determination model and the dehydration strategy determination model obtained from training, the two models can be continuously adaptively optimized using the actual data used in each control process to improve the performance of the models.

[0071] As described above, the technical solution provided by the above embodiments of the present invention can achieve the following: First, in the case of determining that the washing machine is starting to spin-dry the clothes to be spun, image data of the inner drum and the weight of the clothes to be spun are acquired; Second, in the case of the image data being input into a parameter determination model, the image data is processed using the parameter determination model to obtain the state parameters of the clothes to be spun in the inner drum. The parameter determination model is trained using multiple sets of first training data through deep learning. Each set of first training data includes: sample image data and sample clothes state parameters corresponding to the sample image data; Third, in the case of the clothes state parameters and the clothes weight being input into a spin-drying strategy determination model, the spin-drying strategy determination model is processed using the spin-drying strategy determination model to obtain the spin-drying parameters of the washing machine. The dehydration control strategy is implemented by training a model using multiple sets of second training data through deep learning. Each set of second training data includes: sample clothing state parameters, sample clothing weight, and a sample dehydration control strategy corresponding to the sample clothing state parameters and sample clothing weight. The control steps involve controlling the washing machine to perform dehydration according to the dehydration control strategy, so that the water content in the inner drum is lower than the water content threshold. This achieves the goal of acquiring and recognizing images of the clothing in the inner drum before formally controlling the dehydration of the washing machine, analyzing and processing them using the corresponding model, obtaining the dehydration control strategy, and controlling the washing machine to perform dehydration according to the strategy. This achieves the technical effect of directly analyzing and determining the dehydration control strategy before dehydration, avoiding repeated eccentric detection during the washing machine's dehydration process, and improving the dehydration efficiency of the washing machine.

[0072] Therefore, the technical solution provided by the above embodiments of the present invention solves the technical problem in the related art that it is usually necessary to repeatedly perform eccentric detection during the spin-drying process of a washing machine, resulting in poor spin-drying efficiency.

[0073] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0074] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0075] According to an embodiment of the present invention, a spin-drying control device for a washing machine for implementing the above-described spin-drying control method is also provided. Figure 7 This is a schematic diagram of a spin-drying control device for a washing machine according to an embodiment of the present invention, as shown below. Figure 7 As shown, the device includes: a first acquisition unit 71, a second acquisition unit 73, a third acquisition unit 75, and a control unit 77. The spin-drying control device of this washing machine will now be described in detail.

[0076] The first acquisition unit 71 is used to perform the first acquisition step, which, when it is determined that the dehydration process of the clothes to be spun in the inner drum of the washing machine is to begin, acquires image data of the inner drum and the weight of the clothes to be spun.

[0077] The second acquisition unit 73 is used to perform the second acquisition step, inputting the image data into the parameter determination model, so as to process the image data using the parameter determination model to obtain the garment state parameters of the garments to be dehydrated in the inner drum. The parameter determination model is trained using multiple sets of first training data through deep learning. Each set of multiple sets of first training data includes: sample image data and sample garment state parameters corresponding to the sample image data.

[0078] The third acquisition unit 75 is used to perform the third acquisition step, inputting the garment state parameters and garment weight into the dehydration strategy determination model, so as to use the dehydration strategy determination model to process the garment state parameters and garment weight to obtain the dehydration control strategy for the washing machine to dehydrate. The dehydration strategy determination model is trained using multiple sets of second training data through deep learning. Each set of second training data includes: sample garment state parameters, sample garment weight, and sample dehydration control strategy corresponding to the sample garment state parameters and sample garment weight.

[0079] The control unit 77 is used to execute control steps to control the washing machine to perform a spin-drying operation according to the spin-drying control strategy so that the water content in the inner drum is lower than the water content threshold.

[0080] It should be noted that the first acquisition unit 71, the second acquisition unit 73, the third acquisition unit 75 and the control unit 77 mentioned above correspond to steps S202 to S208 in the above embodiments. The four units and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments.

[0081] As can be seen from the above, in the solution described in the above embodiments of the present invention, the first acquisition unit can be used to perform the first acquisition step, acquiring image data of the inner drum and the weight of the clothes to be spun when it is determined that the dehydration process of the clothes to be spun in the inner drum of the washing machine is to begin; then, the second acquisition unit can be used to perform the second acquisition step, inputting the image data into the parameter determination model, so as to process the image data using the parameter determination model to obtain the state parameters of the clothes to be spun in the inner drum, wherein the parameter determination model is trained using multiple sets of first training data through deep learning methods, and each set of multiple sets of first training data includes: sample image data and sample clothes state parameters corresponding to the sample image data; then, the third acquisition unit can be used to perform the third acquisition step, inputting the clothes state parameters and the clothes weight into the dehydration strategy determination model, so as to process the clothes state parameters and the clothes weight using the dehydration strategy determination model. A dehydration control strategy for the washing machine is obtained. The dehydration strategy determination model is trained using multiple sets of second training data through deep learning. Each set of second training data includes: sample clothing state parameters, sample clothing weight, and a sample dehydration control strategy corresponding to the sample clothing state parameters and sample clothing weight. Finally, the control unit executes the control steps to control the washing machine to perform dehydration operation according to the dehydration control strategy, so that the water content in the inner drum is lower than the water content threshold. This achieves the goal of acquiring and recognizing images of the clothes in the inner drum before the actual dehydration control of the washing machine, analyzing and processing them using the corresponding model, obtaining the dehydration control strategy, and controlling the washing machine to perform dehydration according to the strategy. This realizes the technical effect of directly analyzing and determining the dehydration control strategy before dehydration, avoiding repeated eccentric detection during the washing machine dehydration process, and improving the dehydration efficiency of the washing machine.

[0082] Therefore, the technical solution provided by the above embodiments of the present invention solves the technical problem in the related art that it is usually necessary to repeatedly perform eccentric detection during the spin-drying process of a washing machine, resulting in poor spin-drying efficiency.

[0083] In an optional embodiment of the present invention, the first acquisition unit includes: a first determining module, configured to determine, upon determining that the draining operation of the washing machine has ended, to begin the spin-drying process on the clothes to be spun in the inner drum; a first control module, configured to control the washing machine to perform a shaking operation to shake the clothes to be spun in the inner drum, wherein the shaking operation is used to make the looseness of the clothes to be spun higher than a looseness threshold; a second control module, configured to control the washing machine to perform the spin-drying operation at a preset speed and to count the cumulative time of the spin-drying operation at the preset speed; and a third control module, configured to control the washing machine to stop performing the spin-drying operation at the preset speed when the cumulative time reaches a predetermined time, and to acquire image data and the weight of the clothes.

[0084] In an optional embodiment of the present invention, the image data includes first image data and second image data. The first acquisition unit includes: an acquisition module, used to acquire the first image data of the inner drum using a first image acquisition device and to acquire the second image data of the inner drum using a second image device, wherein the first image acquisition device is an image acquisition device installed directly in front of the inner drum, and the second image acquisition device is an image acquisition device installed above the front or rear of the inner drum; and the first acquisition module is used to weigh the clothes to be spun using a weighing module built into the washing machine to obtain the weight of the clothes to be spun.

[0085] In an optional embodiment of the present invention, the second acquisition unit includes: a second acquisition module, configured to input first image data into a parameter determination model to process the first image data using the parameter determination model to obtain the stacking height of the clothes to be dehydrated in the inner drum; a third acquisition module, configured to input second image data into a parameter determination model to process the second image data using the parameter determination model to obtain the unfolded area and distribution state of the clothes to be dehydrated in the inner drum, wherein the distribution state includes the position and uniformity of the clothes to be dehydrated in the inner drum; and a fourth acquisition module, configured to integrate the stacking height, unfolded area, and distribution state to obtain the clothing state parameters of the clothes to be dehydrated in the inner drum.

[0086] In an optional embodiment of the present invention, the dehydration operation includes a pre-dehydration operation and a main dehydration operation. The control unit includes: a fifth acquisition module, used to divide the dehydration control strategy to obtain a pre-dehydration control strategy and a main dehydration control strategy, wherein the pre-dehydration control strategy is used to control the pre-dehydration stage in the washing machine's dehydration process, and the main dehydration control strategy is used to control the main dehydration stage in the washing machine's dehydration process, and the pre-dehydration stage is the dehydration stage preceding the main dehydration stage; and a second determination module, used to determine the pre-dehydration acceleration curve of the inner drum's rotation speed according to the pre-dehydration control strategy, wherein the pre-dehydration acceleration curve refers to the predicted acceleration curve of the rotation speed in the pre-dehydration stage. The system comprises five modules: a first module (line); a second module (a fourth control module), a third module (a fifth control module), and a fourth module (a sixth acquisition module), which controls the washing machine to perform a pre-spinning operation according to the pre-spinning control strategy, and obtains the actual pre-spinning speed-up curve of the spin speed. The sixth module (a sixth acquisition module) is used to compare the actual pre-spinning speed-up curve with the first pre-spinning speed-up curve to obtain the comparison result. The third module (a third determination module) is used to determine the target main spin-drying control strategy for controlling the main spin-drying stage based on the comparison result. The fifth module (a sixth control module) controls the washing machine to perform a main spin-drying operation according to the target main spin-drying control strategy, so that the water content in the inner drum is lower than the water content threshold. The main spin-drying operation refers to the spin-drying operation performed in the main spin-drying stage.

[0087] In an optional embodiment of the present invention, the third determining module includes: a first determining submodule, configured to determine the main dehydration control strategy as the target main dehydration control strategy when the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve; a first acquiring submodule, configured to acquire the current image data of the inner drum and the current weight of the clothes to be dehydrated when the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve, wherein the current image data refers to the data of the inner drum after the washing machine has completed the pre-dehydration operation, and the current weight of the clothes refers to the weight of the clothes to be dehydrated after the washing machine has completed the pre-dehydration operation; and a second acquiring submodule, configured to update the image data input to the parameter determining model to the current image data and update the weight of the clothes input to the dehydration strategy determining model to the current weight of the clothes, and then sequentially execute the second acquiring step and the third acquiring step to obtain the target main dehydration control strategy for controlling the main dehydration stage.

[0088] In an optional embodiment of the present invention, the spin-drying control device of the washing machine further includes: a first optimization unit, configured to optimize the parameter determination model based on the image data and the clothing state parameters after each time the clothing state parameters corresponding to the image data are obtained using the parameter determination model, to obtain an optimized parameter determination model; and a second optimization unit, configured to optimize the spin-drying strategy determination model based on the clothing state parameters, clothing weight, and spin-drying control strategy after each time the spin-drying strategy determination model is obtained using the spin-drying strategy determination model, to obtain an optimized spin-drying strategy determination model.

[0089] According to another aspect of the present invention, a spin-drying control system for a washing machine is also provided, wherein the spin-drying control system for the washing machine uses any of the spin-drying control methods described above.

[0090] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any of the above-described washing machine spin-drying control methods.

[0091] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any communication device in a group of communication devices.

[0092] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: a first acquisition step, in which, upon determining that the dehydration process for the clothes to be spun in the inner drum of the washing machine is to begin, acquires image data of the inner drum and the weight of the clothes to be spun; a second acquisition step, in which the image data is input into a parameter determination model to process the image data using the parameter determination model to obtain the state parameters of the clothes to be spun in the inner drum, wherein the parameter determination model is trained using multiple sets of first training data through a deep learning method, and each set of the multiple sets of first training data includes: sample image data, a sample image corresponding to the sample image data, and a sample image corresponding to the sample image data. The third step involves obtaining the garment state parameters and weight by inputting them into the dehydration strategy determination model. This model processes the garment state parameters and weight to obtain a dehydration control strategy for the washing machine. The dehydration strategy determination model is trained using multiple sets of second training data via deep learning. Each set of second training data includes: sample garment state parameters, sample garment weight, and a sample dehydration control strategy corresponding to the sample garment state parameters and weight. The control step involves controlling the washing machine to perform the dehydration operation according to the dehydration control strategy, so that the water content in the inner drum is lower than the water content threshold.

[0093] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: upon determining that the draining operation of the washing machine has ended, determining to start the spin-drying process on the clothes to be spun in the inner drum; controlling the washing machine to perform a shaking operation to shake the clothes to be spun in the inner drum, wherein the shaking operation is used to make the looseness of the clothes to be spun higher than a looseness threshold; controlling the washing machine to perform the spin-drying operation at a preset speed, and counting the cumulative time of the washing machine performing the spin-drying operation at the preset speed; when the cumulative time reaches a predetermined time, controlling the washing machine to stop performing the spin-drying operation at the preset speed, and acquiring image data and the weight of the clothes.

[0094] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: acquiring first image data of the inner drum using a first image acquisition device, and acquiring second image data of the inner drum using a second image device, wherein the first image acquisition device is an image acquisition device installed directly in front of the inner drum, and the second image acquisition device is an image acquisition device installed above the front or rear of the inner drum; weighing the clothes to be spun using a weighing module built into the washing machine to obtain the weight of the clothes to be spun.

[0095] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: inputting first image data into a parameter determination model to process the first image data using the parameter determination model to obtain the stacking height of the clothes to be dehydrated in the inner drum; inputting second image data into a parameter determination model to process the second image data using the parameter determination model to obtain the unfolded area and distribution state of the clothes to be dehydrated in the inner drum, wherein the distribution state includes the position and uniformity of the clothes to be dehydrated in the inner drum; and integrating the stacking height, unfolded area, and distribution state to obtain the clothing state parameters of the clothes to be dehydrated in the inner drum.

[0096] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: dividing the spin-drying control strategy to obtain a pre-spin-drying control strategy and a main spin-drying control strategy, wherein the pre-spin-drying control strategy is used to control the pre-spin-drying stage in the washing machine spin-drying process, and the main spin-drying control strategy is used to control the main spin-drying stage in the washing machine spin-drying process, and the pre-spin-drying stage is the spin-drying stage preceding the main spin-drying stage; determining the pre-spin-drying acceleration curve of the inner drum's rotation speed according to the pre-spin-drying control strategy, wherein the pre-spin-drying acceleration curve refers to the rotation speed at the pre-spin-drying stage. The predicted speed-up curve for the water stage; controlling the washing machine to perform pre-spinning operation according to the pre-spinning control strategy to obtain the actual pre-spinning speed-up curve, where the pre-spinning operation refers to the spin-drying operation performed in the pre-spinning stage; comparing the actual pre-spinning speed-up curve with the pre-spinning speed-up curve to obtain the comparison result; determining the target main spin-drying control strategy for controlling the main spin-drying stage based on the comparison result; controlling the washing machine to perform the main spin-drying operation according to the target main spin-drying control strategy to make the water content in the inner drum lower than the water content threshold, where the main spin-drying operation refers to the spin-drying operation performed in the main spin-drying stage.

[0097] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: if the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve, determine the main dehydration control strategy as the target main dehydration control strategy; if the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve, acquire the current image data of the inner drum and the current weight of the clothes to be dehydrated, wherein the current image data refers to the data of the inner drum after the washing machine has completed the pre-dehydration operation, and the current weight of the clothes refers to the weight of the clothes to be dehydrated after the washing machine has completed the pre-dehydration operation; after updating the image data input to the parameter determination model to the current image data and updating the weight of the clothes input to the dehydration strategy determination model to the current weight of the clothes, execute the second acquisition step and the third acquisition step in sequence to obtain the target main dehydration control strategy for controlling the main dehydration stage.

[0098] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: after obtaining the clothing state parameters corresponding to the image data using the parameter determination model each time, optimizing the parameter determination model based on the image data and the clothing state parameters to obtain an optimized parameter determination model; after obtaining the dehydration control strategy corresponding to the clothing state parameters and clothing weight using the dehydration strategy determination model each time, optimizing the dehydration strategy determination model based on the clothing state parameters, clothing weight, and dehydration control strategy to obtain an optimized dehydration strategy determination model.

[0099] According to another aspect of the present invention, a processor is also provided, which is used to run a program, wherein the program executes any of the above-described washing machine spin-drying control methods.

[0100] According to another aspect of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, perform any of the above-described spin-drying control methods for a washing machine.

[0101] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0102] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0104] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0107] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling the spin-drying process of a washing machine, characterized in that, include: The first acquisition step involves acquiring image data of the inner drum and the weight of the clothes to be spun in the washing machine's inner drum, once it is determined that the spun-drying process will begin. The second acquisition step involves inputting the image data into a parameter determination model to process the image data using the parameter determination model, thereby obtaining the garment state parameters of the garments to be dehydrated in the inner drum. The parameter determination model is trained using multiple sets of first training data through deep learning. Each set of the multiple sets of first training data includes: sample image data and sample garment state parameters corresponding to the sample image data. The third acquisition step involves inputting the garment state parameters and the garment weight into the dehydration strategy determination model. The dehydration strategy determination model is then used to process the garment state parameters and the garment weight to obtain a dehydration control strategy for the washing machine. The dehydration strategy determination model is trained using multiple sets of second training data through deep learning. Each set of second training data includes: sample garment state parameters, sample garment weight, and a sample dehydration control strategy corresponding to the sample garment state parameters and the sample garment weight. The control step involves controlling the washing machine to perform a spin-drying operation according to the spin-drying control strategy, so that the water content in the inner drum is lower than the water content threshold. The dehydration operation includes a pre-dehydration operation and a main dehydration operation. Controlling the washing machine to perform the dehydration operation according to the dehydration control strategy to reduce the water content in the inner drum below a water level threshold includes: The dehydration control strategy is divided into a pre-dehydration control strategy and a main dehydration control strategy. The pre-dehydration control strategy is used to control the pre-dehydration stage in the dehydration process of the washing machine, and the main dehydration control strategy is used to control the main dehydration stage in the dehydration process of the washing machine. The pre-dehydration stage is the dehydration stage preceding the main dehydration stage. The pre-dehydration acceleration curve of the inner cylinder's rotational speed is determined according to the pre-dehydration control strategy, wherein the pre-dehydration acceleration curve refers to the predicted acceleration curve of the rotational speed in the pre-dehydration stage. The washing machine is controlled to perform the pre-spinning operation according to the pre-spinning control strategy to obtain the actual pre-spinning speed-up curve of the rotation speed, wherein the pre-spinning operation refers to the spin-drying operation performed in the pre-spinning stage; The actual pre-dehydration rate increase curve is compared with the pre-dehydration rate increase curve to obtain the comparison result; Based on the comparison results, a target main dehydration control strategy for controlling the main dehydration stage is determined. The washing machine is controlled to perform a main spin-drying operation according to the target main spin-drying control strategy, so that the water content in the inner drum is lower than the water content threshold, wherein the main spin-drying operation refers to the spin-drying operation performed in the main spin-drying stage.

2. The spin-drying control method for a washing machine according to claim 1, characterized in that, Upon determining that the spin-drying process for the clothes to be spun in the inner drum of the washing machine has begun, image data of the inner drum and the weight of the clothes to be spun are acquired, including: Once it is determined that the draining operation of the washing machine has been completed, it is determined that the dehydration process of the clothes to be spun in the inner drum will begin. The washing machine is controlled to perform a shaking operation to shake the clothes to be spun in the inner drum, wherein the shaking operation is used to make the looseness of the clothes to be spun higher than a looseness threshold. The washing machine is controlled to perform the spin-drying operation at a preset speed, and the cumulative duration of the spin-drying operation at the preset speed is counted. When the accumulated time reaches the predetermined time, the washing machine is controlled to stop performing the spin-drying operation at the preset speed, and the image data and the weight of the clothes are acquired.

3. The spin-drying control method for a washing machine according to claim 1, characterized in that, The image data includes first image data and second image data. Acquiring the image data of the inner drum and the weight of the clothes to be dehydrated includes: The first image data of the inner cylinder is acquired using a first image acquisition device, and the second image data of the inner cylinder is acquired using a second image acquisition device, wherein the first image acquisition device is an image acquisition device installed directly in front of the inner cylinder, and the second image acquisition device is an image acquisition device installed above the front or rear of the inner cylinder. The weight of the clothes to be spun is obtained by using the weighing module built into the washing machine.

4. The spin-drying control method for a washing machine according to claim 3, characterized in that, The image data is input into a parameter determination model to process the image data and obtain the garment state parameters of the garments to be dehydrated in the inner drum, including: The first image data is input into the parameter determination model to process the first image data using the parameter determination model, thereby obtaining the stacking height of the clothes to be dehydrated in the inner drum; The second image data is input into the parameter determination model to process the second image data using the parameter determination model, thereby obtaining the unfolded area and distribution state of the clothes to be dehydrated in the inner drum, wherein the distribution state includes the position and uniformity of the clothes to be dehydrated in the inner drum; By integrating the stacking height, the unfolded area, and the distribution state, the garment state parameters of the garments to be dehydrated in the inner drum are obtained.

5. The spin-drying control method for a washing machine according to claim 1, characterized in that, Based on the comparison results, a target primary dehydration control strategy for controlling the primary dehydration stage is determined, including: If the comparison result indicates that the actual pre-dehydration rate-up curve is the same as the pre-dehydration rate-up curve, then the main dehydration control strategy is determined to be the target main dehydration control strategy. If the comparison result indicates that the actual pre-dehydration acceleration curve is the same as the pre-dehydration acceleration curve, the current image data of the inner drum and the current weight of the clothes to be dehydrated are obtained. The current image data refers to the data of the inner drum after the washing machine has completed the pre-dehydration operation, and the current weight of the clothes refers to the weight of the clothes to be dehydrated after the washing machine has completed the pre-dehydration operation. After updating the image data input to the parameter determination model to the current image data and updating the clothing weight input to the dehydration strategy determination model to the current clothing weight, the second acquisition step and the third acquisition step are executed sequentially to obtain the target main dehydration control strategy for controlling the main dehydration stage.

6. The spin-drying control method for a washing machine according to claim 1, characterized in that, Also includes: After each time the parameter determination model is used to obtain the clothing state parameters corresponding to the image data, the parameter determination model is optimized based on the image data and the clothing state parameters to obtain the optimized parameter determination model. After obtaining the dehydration control strategy corresponding to the garment state parameters and the garment weight each time using the dehydration strategy determination model, the dehydration strategy determination model is optimized based on the garment state parameters, garment weight, and the dehydration control strategy to obtain the optimized dehydration strategy determination model.

7. A spin-drying control device for a washing machine, characterized in that, include: The first acquisition unit is used to perform the first acquisition step, which, when it is determined that the dehydration process of the clothes to be dehydrated in the inner drum of the washing machine is to begin, acquires the image data of the inner drum and the weight of the clothes to be dehydrated. The second acquisition unit is used to perform the second acquisition step, inputting the image data into the parameter determination model, so as to process the image data using the parameter determination model to obtain the garment state parameters of the garment to be dehydrated in the inner drum. The parameter determination model is trained using multiple sets of first training data through deep learning. Each set of the multiple sets of first training data includes: sample image data and sample garment state parameters corresponding to the sample image data. The third acquisition unit is used to perform the third acquisition step, inputting the clothing state parameters and the clothing weight into the dehydration strategy determination model, so as to use the dehydration strategy determination model to process the clothing state parameters and the clothing weight to obtain a dehydration control strategy for the washing machine to dehydrate. The dehydration strategy determination model is trained using multiple sets of second training data through deep learning. Each set of the multiple sets of second training data includes: sample clothing state parameters, sample clothing weight, and sample dehydration control strategy corresponding to the sample clothing state parameters and the sample clothing weight. The control unit is used to execute control steps, controlling the washing machine to perform a spin-drying operation according to the spin-drying control strategy, so that the water content in the inner drum is lower than the water content threshold. The dehydration operation includes a pre-dehydration operation and a main dehydration operation. The control unit includes: a fifth acquisition module, used to divide the dehydration control strategy to obtain a pre-dehydration control strategy and a main dehydration control strategy, wherein the pre-dehydration control strategy is used to control the pre-dehydration stage in the washing machine's dehydration process, and the main dehydration control strategy is used to control the main dehydration stage in the washing machine's dehydration process, and the pre-dehydration stage is the dehydration stage preceding the main dehydration stage; a second determination module, used to determine the pre-dehydration acceleration curve of the inner drum's rotation speed according to the pre-dehydration control strategy, wherein the pre-dehydration acceleration curve refers to the predicted acceleration curve of the rotation speed in the pre-dehydration stage; and a fourth control module. The system is configured to: control the washing machine to perform the pre-spinning operation according to the pre-spinning control strategy, and obtain the actual pre-spinning speed-up curve of the rotation speed, wherein the pre-spinning operation refers to the spin-drying operation performed in the pre-spinning stage; a sixth acquisition module is configured to compare the actual pre-spinning speed-up curve with the pre-spinning speed-up curve to obtain a comparison result; a third determination module is configured to determine the target main spin-drying control strategy for controlling the main spin-drying stage based on the comparison result; and a fifth control module is configured to control the washing machine to perform the main spin-drying operation according to the target main spin-drying control strategy, so that the water content in the inner drum is lower than the water content threshold, wherein the main spin-drying operation refers to the spin-drying operation performed in the main spin-drying stage.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program executes the spin-drying control method of the washing machine according to any one of claims 1 to 6.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, the spin-drying control method of the washing machine according to any one of claims 1 to 6 is performed.

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