Method and device for controlling drum speed of laundry equipment and laundry equipment

Abstract

The present application discloses a method, device, and laundry device for controlling the drum speed of a laundry appliance. The method includes acquiring multiple first images of clothing in a drum in real time when the drum rotates one circle; processing each of the first clothing images using a trained neural network model to identify at least one first position information of the clothing in the drum when the drum rotates one circle, wherein the neural network model is used to identify the position information of the clothing in the drum based on the clothing images; and controlling the drum speed based on each first position information and a preset height threshold in the drum. The technical solution of the present application achieves the purpose of determining the first position information of the clothing in the drum using the first clothing images in the drum, and then controlling the drum speed based on the first position information and the preset height threshold in the drum, thereby avoiding problems such as clothing sticking to the wall or poor clothing tumbling effect, and improving the effect of clothing washing.

Description

Technical Field

[0001] This application relates to the field of washing machines, and more specifically, to a method, apparatus, and washing machine for controlling the drum speed of a washing machine. Background Technology

[0002] A drum washing machine is a household appliance used to clean clothes by means of friction between the rotating drum and the clothes. It mainly works by rotating the drum so that the clothes fall and tumble after reaching a high point.

[0003] Existing front-loading washing machines simply execute pre-set programs when controlling the spin speed. At the same drum speed, different types of clothing experience varying degrees of tumbling; for example, heavy garments may not be lifted. Fixed-program washing methods cannot accommodate the characteristics of different clothes, potentially resulting in clothes sticking to the walls without proper tumbling or failing to reach the highest point of tumbling before being tumbled, thus affecting washing performance. Summary of the Invention

[0004] This application provides a method, device, and washing equipment for controlling the drum speed of a washing machine, so as to at least solve the technical problem that improper control of the drum speed of current washing equipment causes clothes to stick to the wall or the tumbling effect to be poor.

[0005] According to a first aspect of the embodiments of this application, a method for controlling the drum speed of a washing machine is provided, the control method comprising:

[0006] Multiple images of the first garment inside the drum are captured in real time as the drum rotates one revolution;

[0007] The trained neural network model processes each of the first garment images to identify at least one first position information of the garment inside the drum when the drum rotates once. The neural network model is used to identify the position information of the garment inside the drum based on the garment images.

[0008] The rotational speed of the drum is controlled based on the first position information and the preset height threshold within the drum.

[0009] Optionally, controlling the drum rotation speed based on each first position information and a preset height threshold within the drum includes:

[0010] If the height of the clothing position in any of the first location information reaches a preset height threshold, it is determined whether the clothing is stuck to the wall;

[0011] If it is determined that the clothing is stuck to the wall, the speed of the roller is reduced, and the step of acquiring multiple images of the first clothing inside the roller in real time after one rotation of the roller is executed.

[0012] If you determine that the clothes are not sticking to the sides, maintain the drum speed at the same rate.

[0013] If the height of the clothing position in all the first location information is less than the preset height threshold, then the rotation speed of the roller is increased, and when the rotation speed of the roller is increased, the step of acquiring multiple images of the first clothing inside the roller in real time after one rotation of the roller is executed.

[0014] Optionally, determining whether the clothing is clinging to the wall includes:

[0015] Acquire multiple images of the second garment for each full rotation of the drum, at least twice;

[0016] The presence or absence of clothing clinging to the wall is determined by examining each of the second clothing images.

[0017] Optionally, determining whether the clothing is clinging to the wall based on each of the second clothing images includes:

[0018] The trained neural network model processes each of the second garment images to identify the second position information of the garment inside the drum during one rotation. Based on the second position information, it is determined whether the garment is moving in a circular motion against the wall.

[0019] Optionally, if it is determined that the clothing is not adhering to the wall, after maintaining the current rotation speed, the method further includes:

[0020] Check if the current washing process has ended;

[0021] If the current washing cycle is completed, the drum rotation will stop;

[0022] If the current washing process has not ended, then execute the step of acquiring multiple images of the first garment inside the drum as it rotates once in real time.

[0023] Optionally, the real-time acquisition of multiple images of the first garment inside the drum during one rotation includes:

[0024] When the washing machine is in the washing state, multiple images of the first clothes inside the drum at different times are acquired by at least one camera device installed in the washing machine as the drum rotates once, wherein the shooting range of the camera device covers the drum.

[0025] Optionally, the training process of the neural network model includes:

[0026] Obtain a training dataset, wherein the training dataset includes multiple image samples of different clothes in each rotation cycle of the drum during the washing process of clothes washing at different speeds and in different directions, and each image sample of clothes is labeled with a position label for marking the position of the clothes in the drum;

[0027] The initial neural network model is trained a predetermined number of times using the training dataset. After each training session, the training results are compared with the location labels.

[0028] If the comparison results do not meet the preset training conditions, adjust the model parameters of the initial neural network model to obtain an updated initial neural network model, and then train the initial neural network model again using the training dataset for a preset number of times.

[0029] When the comparison results meet the preset training conditions or the initial neural network model has been trained a preset number of times, the training of the initial neural network model is completed, and a neural network model that can learn from the input image and output the location information of the clothing in the input image is obtained.

[0030] According to a second aspect of the present application, a control device for the drum speed of a washing machine is provided, the control device comprising:

[0031] The first clothing image acquisition module is used to acquire multiple images of the first clothing inside the drum in real time as the drum rotates one revolution;

[0032] The first position information determination module is used to process each of the first garment images through a trained neural network model to identify at least one first position information of the garment in the drum when the drum rotates once. The neural network model is used to identify the position information of the garment in the drum based on the garment image.

[0033] The drum speed adjustment module is used to control the drum speed based on the first position information and the preset height threshold inside the drum.

[0034] According to a third aspect of the embodiments of this application, a laundry apparatus is provided, the laundry apparatus comprising:

[0035] A device for controlling the rotational speed of drums and washing machines;

[0036] The control device for the drum speed of the washing machine achieves control of the drum speed of the washing machine through the control method for the drum speed of the washing machine as described in any embodiment of this application.

[0037] According to a fourth aspect of the embodiments of this application, an electronic device is provided, the electronic device comprising:

[0038] Storage device for storing one or more programs.

[0039] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for controlling the drum speed of the washing machine as described in any of the embodiments of this application.

[0040] According to a fifth aspect of the present application, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements a method for controlling the drum speed of a washing machine as described in any of the embodiments of the present application.

[0041] In this embodiment, multiple images of the first garment inside the drum are acquired in real time as the drum rotates once. A trained neural network model processes each image to identify at least one first position of the garment within the drum during one rotation. Based on this first position information and a preset height threshold within the drum, the drum speed is controlled. This technical solution achieves the goal of determining the first position of the garment within the drum using images of the first garment inside, and then controlling the drum speed based on this first position information and a preset height threshold. This avoids problems such as garments sticking to the drum walls or poor tumbling effect, thus improving the washing effect. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating a method for controlling the drum speed of a washing machine according to one embodiment of this application;

[0043] Figure 2 This is an example of a preset height threshold in one embodiment of this application;

[0044] Figure 3 This is a flowchart illustrating a method for controlling the drum speed of a washing machine according to one embodiment of this application;

[0045] Figure 4 This is a schematic diagram of the structure of a drum speed control device for a washing machine according to one embodiment of this application;

[0046] Figure 5 This is a schematic diagram of the structure of an electronic device according to one embodiment of this application. Detailed Implementation

[0047] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0048] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application 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 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.

[0049] This application provides an embodiment of a method for controlling the drum speed of a washing machine. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0050] like Figure 1 As shown, the method for controlling the drum speed of this washing machine includes the following steps:

[0051] Step S110: Acquire multiple images of the first garment inside the drum in real time as the drum rotates one revolution.

[0052] Specifically, during the washing process, multiple first images of the clothes inside the drum are captured in real time as the drum rotates once, which facilitates subsequent processing of the first images to determine the position of the clothes inside the drum.

[0053] Step S120: Process each of the first garment images using the trained neural network model to identify at least one first position information of the garment inside the drum when the drum rotates once.

[0054] The neural network model is used to identify the position of the clothing within the drum based on the clothing image. The neural network model can refer to the single-stage object detection model YOLOv5, or other types of object detection models. The first position information can include the position of the clothing within the drum. For example, by establishing a two-dimensional coordinate system based on the drum, a plane perpendicular to the drum's rotation axis can be determined, and the first position information can be the horizontal and vertical coordinates in the two-dimensional coordinate system.

[0055] Specifically, the pre-trained neural network model processes each first garment image to identify at least one first position information of the garment inside the drum when the drum rotates once. Since the first garment images are acquired at different times when the drum rotates once, the positions of the garments in the first garment images inside the drum are different. Therefore, at least one first position information can describe the different position information of the garments during the process of the drum rotating once.

[0056] Step S130: Control the rotational speed of the drum according to the first position information and the preset height threshold inside the drum.

[0057] The preset height threshold can be set based on the roller model, roller size, etc., for example... Figure 2 As shown, Figure 2 The circle in the diagram represents the cross-section of the roller, with a preset height of H, which is the horizontal line H.

[0058] Specifically, based on the information of each first position and the preset height threshold inside the drum, the rotation and washing status of the clothes inside the drum can be determined, thereby controlling the drum speed and adjusting the washing status of the clothes to improve the washing effect.

[0059] The technical solution of this application involves acquiring multiple images of the first garment inside the drum during one rotation in real time. A trained neural network model processes each image to identify at least one first position of the garment within the drum during one rotation. Based on this first position information and a preset height threshold within the drum, the drum speed is controlled. This technical solution achieves the goal of determining the first position of the garment within the drum using images of the first garment inside, and then controlling the drum speed based on this first position information and a preset height threshold. This avoids problems such as garments sticking to the drum walls or poor tumbling effect, thus improving the washing effect.

[0060] In another embodiment of the application, step S130: controlling the rotation speed of the drum according to each first position information and a preset height threshold within the drum includes: determining whether the clothing is stuck to the wall when the height of the clothing position in any of the first position information reaches the preset height threshold; if the clothing is stuck to the wall, controlling the drum rotation speed to decrease, and performing the step of acquiring multiple images of the first clothing inside the drum in real time after one rotation of the drum; if the clothing is not stuck to the wall, maintaining the drum rotation speed unchanged; if the height of the clothing position in all the first position information is less than the preset height threshold, controlling the drum rotation speed to increase, and performing the step of acquiring multiple images of the first clothing inside the drum in real time after one rotation of the drum while increasing the drum rotation speed.

[0061] In this embodiment, "attaching to the wall" specifically refers to the clothing moving in a circular motion along or attached to the roller.

[0062] In this embodiment, when the height of the clothing at any given first position reaches a preset height threshold, confirming that the clothing has been lifted, it can be further determined whether the clothing is stuck to the wall. If the clothing is stuck, it indicates that the drum speed is too high. Therefore, the drum speed is reduced, and multiple images of the first clothing inside the drum are acquired in real time during one rotation. Subsequent steps involve processing each first clothing image using a neural network model to obtain updated first position information of the clothing within the drum. The clothing height in the first position information is then compared with the preset height threshold, and the drum speed is controlled based on the comparison result. This process is repeated cyclically. When the clothing is not stuck to the wall, the drum speed is maintained constant, thus preventing clothing from sticking to the wall and affecting the washing effect. The solution of this application can improve the washing effect of clothing.

[0063] If the trained neural network model processes each first garment image and identifies at least one first position information of the garment within the drum during one rotation, and the garment height in all first position information is less than a preset height threshold, it indicates that the garment has not been lifted. In this case, the drum speed is increased to lift the garment, and the process continues to acquire multiple first garment images within the drum during one rotation. Subsequent processing of these images using the neural network model yields updated first position information of the garment within the drum. The garment height in the first position information is then compared to the preset height threshold, and the drum speed is controlled based on the comparison result. This process is repeated cyclically. During drum rotation, first garment images are acquired in real-time, and the relationship between the garment height in the corresponding first position information and the preset height threshold is determined in real-time, thereby controlling the drum speed to achieve optimal washing results.

[0064] In another embodiment of the application, determining whether the clothing is stuck to the wall includes: acquiring multiple images of the second clothing for each rotation of the roller at least twice; and determining whether the clothing is stuck to the wall based on each of the second clothing images.

[0065] Specifically, this embodiment acquires multiple images of the second garment during each of the first two rotations of the drum, and determines whether the garment is stuck to the wall based on each of these images. If the garment is stuck to the wall in every rotation, it indicates that the garment is stuck to the wall. This method, rather than relying solely on the first garment image during the first rotation, results in a smaller error and a more accurate determination of whether the garment is stuck to the wall.

[0066] In another embodiment of the application, determining whether the clothing is stuck to the wall based on each of the second clothing images includes: processing each of the second clothing images using a trained neural network model to identify each of the second positions of the clothing in the drum during one rotation, and determining whether the clothing is stuck to the wall and making circular motion based on each of the second position information.

[0067] Specifically, the trained neural network model processes each image of the second garment to identify its second position within the drum during one rotation. Based on the second position information at each rotation, if the garment is moving in a circular motion against the drum wall, it indicates that the garment is attached to the wall. Specifically, the order of the second position information for each garment image acquired during one rotation is determined. Then, based on the second position information and its order, the movement trajectory of the garment during one rotation can be determined, and this trajectory can be used to determine whether the garment is attached to the wall. This application's solution achieves accurate second position information for the garment based on the neural network model, thus improving the accuracy of determining whether the garment is attached to the wall.

[0068] In another embodiment of the application, if it is determined that the clothes are not stuck to the wall, after maintaining the current rotation speed, the method for controlling the drum rotation speed of the washing equipment further includes: detecting whether the current washing process has ended; if the current washing process has ended, stopping the rotation of the drum; if the current washing process has not ended, performing the step of acquiring multiple images of the first clothes inside the drum in real time after one rotation of the drum.

[0069] In this embodiment, if it is determined that the clothes are not sticking to the wall and the current rotation speed is kept constant, it is detected whether the current washing process has ended. If it has ended, the rotation of the drum is stopped. If the current process is still in progress, step S110 and subsequent steps are executed. In this way, the drum rotation speed is controlled in real time throughout the washing process, which improves the washing effect of the clothes.

[0070] In another embodiment of the application, step S110: real-time acquisition of multiple images of the first garment inside the drum during one rotation of the drum includes: acquiring multiple images of the first garment inside the drum at different times during one rotation of the drum using at least one photographic device set in the washing equipment while the washing equipment is in the washing state, wherein the shooting range of the photographic device covers the drum.

[0071] The camera can also capture images of a second garment.

[0072] Specifically, when the washing equipment is in the washing state, multiple images of the clothes inside the drum at different times are acquired by at least one camera device installed in the washing equipment as the drum rotates once. The camera device installed in the washing equipment in this application takes pictures of the clothes, which is convenient, fast and has strong real-time performance.

[0073] In this application, there may be one camera, in which case the shooting range of the camera covers the drum. There may be multiple camera devices, in which case the sum of the shooting ranges of each camera covers the drum. The position of the camera devices can be set according to the actual situation, and this application does not limit this.

[0074] In another embodiment of the application, the training process of the neural network model includes: acquiring a training dataset, wherein the training dataset includes multiple images of different clothes in each rotation cycle of the drum during the washing process of clothes washing at different speeds and in different directions, and each image of clothes has a position label for marking the position of the clothes in the drum; training the initial neural network model a preset number of times using the training dataset, and comparing the training results with the position labels after each training; if the comparison results do not meet the preset training conditions, adjusting the model parameters of the initial neural network model to obtain an updated initial neural network model, and training the initial neural network model again a preset number of times using the training dataset; when the comparison results meet the preset training conditions or the number of training times of the initial neural network model reaches the preset number, completing the training of the initial neural network model, and obtaining a neural network model that can learn from the input image and output the position information of the clothes in the input image.

[0075] The comparison result can refer to the error between the training result and the location label, and the training condition can refer to the error being within a preset error range or the error being less than a preset error threshold.

[0076] Specifically, a pre-set training dataset is obtained. For any given training iteration: the initial neural network model is trained a preset number of times using the training dataset. After training, the training results are compared with the location labels. If the comparison results do not meet the preset training conditions, the model parameters of the initial neural network model are adjusted based on the comparison results to obtain an updated initial neural network model. At this point, one training iteration is complete, and the training iteration count is incremented by one.

[0077] If the comparison results meet the preset training conditions or the training times reach the preset number, the initial neural network model is trained, resulting in a neural network model that can learn from the input image and output the position information of the clothing in the input image. Embodiments of this application provide a training dataset and a training process for the initial neural network model to obtain the neural network model. The training method of this application achieves the training of the initial neural network model, ensuring the generalization ability of the trained neural network model. This allows the trained neural network model to process each of the first clothing images and more accurately identify the first position information of the clothing within the drum.

[0078] In another embodiment of this application, a method for controlling the drum speed of a washing machine is provided. This method is an alternative to the drum speed control method described in the foregoing embodiments. Technical terms that are the same as or similar to those in the foregoing embodiments will not be repeated. Figure 3 As shown, the steps of the method for controlling the drum speed of the washing machine in this application include:

[0079] Step S301: Detect the washing machine's start-up status.

[0080] Step S302: Take a first image of the clothes inside the drum using a camera device installed on the washing machine, and upload the image to the trained neural network model, namely the YOLOv5 model.

[0081] In this embodiment, the position information of the clothes inside the washing machine drum is labeled using computer vision image target detection, thereby obtaining a clothing image with position tags. The initial YOLOv5 model is then trained using this clothing image to obtain a trained YOLOv5 model capable of recognizing the input clothing image and obtaining the position information of the clothes inside the drum. The training process is the same as the initial neural network model training process in the aforementioned embodiment, and will not be repeated here.

[0082] Step S303: Identify the first position information of the clothes inside the drum.

[0083] Step S304: Determine whether the height of the clothing position in the first position information reaches the preset height threshold. If the height of the clothing position reaches the preset height threshold, proceed to step S305. If the height of the clothing position does not reach the preset height threshold, proceed to step S306.

[0084] Step S305: Determine whether the clothing is attached to the wall. If the clothing is attached to the wall, proceed to step S307. If the clothing is not attached to the wall, proceed to step S308.

[0085] The method for determining whether clothing is clinging to the wall is the same as that in the previous embodiments, and will not be repeated here. For example, as shown... Figure 2 As shown, if the height of the clothing position in the second position information of the clothing passes through 1, 2, 3 or 3, 2, 1 in sequence, then it is determined that the clothing is attached to the wall.

[0086] Step S306: Increase the drum speed by R1 per second, and then proceed to step 302.

[0087] Step S307: Reduce the rotation speed by R2 per second, and then proceed to step S302.

[0088] The specific rotational speeds of R1 and R2 can be set according to actual conditions, and are not limited here.

[0089] Step S308: Maintain the current drum speed unchanged.

[0090] Step S309: Check if the washing process has ended. If the washing process has ended, proceed to step S310. If the washing process has not ended, proceed to step S302.

[0091] Step S310, End.

[0092] The technical solution of this application enables real-time tracking of the position of clothes within the drum during the washing process using a YOLOv5 model. It determines whether the clothes are too low or sticking to the drum wall, and then controls the drum speed accordingly. This solution solves the problem of clothes falling before reaching a set height threshold or sticking to the wall without being properly dislodged during the washing process. The drum speed control method described in this application allows for real-time control of the drum speed during the washing process, ensuring that the drum speed matches the current washing status and ultimately optimizing the washing effect.

[0093] In another embodiment of this application, a washing machine is provided, comprising: a drum and a control device for the drum speed of the washing machine; the control device for the drum speed of the washing machine controls the drum speed of the washing machine through the control method for the drum speed of the washing machine in any of the foregoing embodiments of this application.

[0094] In another embodiment of this application, a device for controlling the drum speed of a washing machine is provided. Figure 4This is a schematic diagram of a drum speed control device for a washing machine provided in an embodiment of this application. The drum speed control device for a washing machine provided in this embodiment can execute the drum speed control method for a washing machine provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method. The device includes: a first garment image acquisition module 410, a first position information determination module 420, and a drum speed adjustment module 430; wherein:

[0095] The first clothing image acquisition module 410 is used to acquire multiple images of the first clothing inside the drum in real time as the drum rotates once; the first position information determination module 420 is used to process each of the first clothing images through a trained neural network model to identify at least one first position information of the clothing inside the drum as the drum rotates once, and the neural network model is used to identify the position information of the clothing inside the drum based on the clothing images; the drum speed adjustment module 430 is used to control the drum speed based on each first position information and a preset height threshold inside the drum.

[0096] Furthermore, the drum speed adjustment module 430 is also used for:

[0097] If the height of the clothing position in any of the first location information reaches a preset height threshold, it is determined whether the clothing is stuck to the wall; if it is determined that the clothing is stuck to the wall, the roller speed is reduced, and the step of acquiring multiple images of the first clothing inside the roller in real time after one rotation of the roller is executed; if it is determined that the clothing is not stuck to the wall, the roller speed is kept constant; if the height of the clothing position in all the first location information is less than the preset height threshold, the roller speed is increased, and the step of acquiring multiple images of the first clothing inside the roller in real time after one rotation of the roller is executed when the roller speed is increased.

[0098] Furthermore, the drum speed adjustment module 430 is also used for:

[0099] Acquire multiple images of the second garment for each full rotation of the drum, at least twice;

[0100] The presence or absence of clothing clinging to the wall is determined by examining each of the second clothing images.

[0101] Furthermore, the drum speed adjustment module 430 is also used for:

[0102] The trained neural network model processes each of the second garment images to identify the second position information of the garment inside the drum during one rotation. Based on the second position information, it is determined whether the garment is moving in a circular motion against the wall.

[0103] Furthermore, the device also includes:

[0104] The washing process detection module is used to detect whether the current washing process has ended; if the current washing process has ended, the rotation of the drum is stopped; if the current washing process has not ended, the step of acquiring multiple images of the first garment inside the drum in real time after one rotation of the drum is executed.

[0105] Furthermore, the first clothing image acquisition module 410 is also used for:

[0106] When the washing machine is in the washing state, multiple images of the first clothes inside the drum at different times are acquired by at least one camera device installed in the washing machine as the drum rotates once, wherein the shooting range of the camera device covers the drum.

[0107] Furthermore, the training module of the neural network model is used for:

[0108] A training dataset is obtained, comprising multiple image samples of different garments during each rotation cycle of the drum in a washing process where the drum rotates at different speeds and in different directions. Each image sample of the garments is labeled with a location tag indicating the position of the garments within the drum. An initial neural network model is trained a predetermined number of times using the training dataset. After each training iteration, the training results are compared with the location tags. If the comparison results do not meet the predetermined training conditions, the model parameters of the initial neural network model are adjusted to obtain an updated initial neural network model, which is then trained again a predetermined number of times using the training dataset. When the comparison results meet the predetermined training conditions or the predetermined number of training iterations of the initial neural network model is reached, the training of the initial neural network model is completed, resulting in a neural network model that can learn from the input image and output the location information of the garments in the input image.

[0109] The technical solution of this application involves acquiring multiple images of the first garment inside the drum during one rotation in real time. A trained neural network model processes each image to identify at least one first position of the garment within the drum during one rotation. Based on this first position information and a preset height threshold within the drum, the drum speed is controlled. This technical solution achieves the goal of determining the first position of the garment within the drum using images of the first garment inside, and then controlling the drum speed based on this first position information and a preset height threshold. This avoids problems such as garments sticking to the drum walls or poor tumbling effect, thus improving the washing effect.

[0110] It is worth noting that the modules included in the above-mentioned device are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional module are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0111] In another embodiment of the application, an electronic device is also provided. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 A block diagram is shown of an exemplary electronic device 50 suitable for implementing embodiments of the present application. Figure 5 The electronic device 50 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0112] like Figure 5 As shown, the electronic device 50 is represented in the form of a general-purpose computing device. The components of the electronic device 50 may include, but are not limited to: one or more processors or processing units 501, system memory 502, and bus 503 connecting different system components (including system memory 502 and processing unit 501).

[0113] Bus 503 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0114] Electronic device 50 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 50, including volatile and non-volatile media, removable and non-removable media.

[0115] System memory 502 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 504 and / or cache memory 505. Electronic device 50 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 506 may be used to read and write non-removable, non-volatile magnetic media (… Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 503 via one or more data media interfaces. Memory 502 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0116] A program / utility 508 having a set (at least one) of program modules 507 may be stored, for example, in memory 502. Such program modules 507 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 507 typically perform the functions and / or methods described in the embodiments of this application.

[0117] Electronic device 50 can also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), and with one or more devices that enable a user to interact with the electronic device 50, and / or with any device that enables the electronic device 50 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 511. Furthermore, electronic device 50 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 512. As shown, network adapter 512 communicates with other modules of electronic device 50 via bus 503. It should be understood that, although... Figure 5 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0118] The processing unit 501 executes various functional applications and data processing by running programs stored in the system memory 502, such as implementing the drum speed control method of the washing equipment provided in the embodiments of this application.

[0119] In another embodiment of this application, a computer-readable storage medium is also provided, on which a computer program is stored. When executed by a processor, the program implements a method for controlling the drum speed of a washing machine. The method for controlling the drum speed of the washing machine includes:

[0120] Multiple images of the first garment inside the drum are acquired in real time as the drum rotates once. Each first garment image is processed by a trained neural network model to identify at least one first position information of the garment inside the drum as the drum rotates once. The neural network model is used to identify the position information of the garment inside the drum based on the garment images. The drum rotation speed is controlled based on each first position information and a preset height threshold inside the drum.

[0121] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0122] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0123] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0124] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0126] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0127] 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.

[0128] 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.

[0129] Furthermore, the functional units in the various embodiments of this application 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.

[0130] 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 this application, 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 this application.

[0131] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling the drum speed of a washing machine, characterized in that, The control method includes: Multiple images of the first garment inside the drum are captured in real time as the drum rotates one revolution; The trained neural network model processes each of the first garment images to identify at least one first position information of the garment inside the drum when the drum rotates once. The neural network model is used to identify the position information of the garment inside the drum based on the garment images. The rotational speed of the drum is controlled based on the first position information and the preset height threshold within the drum. The step of controlling the drum rotation speed based on each first position information and a preset height threshold within the drum includes: If the height of the clothing position in any of the first location information reaches a preset height threshold, it is determined whether the clothing is stuck to the wall; The determination of whether clothing is clinging to the wall includes: Acquire multiple images of the second garment for each full rotation of the drum, at least twice; The trained neural network model processes each of the second garment images to identify the second position information of the garment in the drum during each rotation, and determines whether the garment is sticking to the wall and making circular motion based on the second position information. If it is determined that the clothing is stuck to the wall, the speed of the roller is reduced, and the step of acquiring multiple images of the first clothing inside the roller in real time after one rotation of the roller is executed. If you determine that the clothes are not sticking to the sides, keep the drum speed constant.

2. The method for controlling the drum speed of a washing machine according to claim 1, characterized in that, The step of determining whether the clothing is sticking to the wall and making circular motion based on various second position information includes: Based on the order of the second clothing images acquired after one rotation, determine the order of the second position information of each second clothing image; Based on the second position information and the sequence, determine the movement trajectory of the clothes during one rotation of the drum; Determine whether the clothing is clinging to the wall based on its movement trajectory.

3. The method for controlling the drum speed of a washing machine according to claim 1, characterized in that, If it is determined that the clothing is not adhering to the wall, after maintaining the current rotation speed, the method further includes: Check if the current washing process has ended; If the current washing cycle is completed, the drum rotation will stop; If the current washing process has not ended, then execute the step of acquiring multiple images of the first garment inside the drum as it rotates once in real time.

4. The method for controlling the drum speed of a washing machine according to claim 1, characterized in that, The control method further includes: If the height of the clothing position in all the first location information is less than the preset height threshold, then the rotation speed of the roller is increased, and when the rotation speed of the roller is increased, the step of acquiring multiple images of the first clothing inside the roller in real time after one rotation of the roller is executed.

5. The method for controlling the drum speed of a washing machine according to any one of claims 1-4, characterized in that, The real-time acquisition of multiple images of the first garment inside the drum during one revolution includes: When the washing machine is in the washing state, multiple images of the first clothes inside the drum at different times are acquired by at least one camera device installed in the washing machine as the drum rotates once, wherein the shooting range of the camera device covers the drum.

6. The method for controlling the drum speed of a washing machine according to any one of claims 1-4, characterized in that, The training process of the neural network model includes: Obtain a training dataset, wherein the training dataset includes multiple image samples of different clothes in each rotation cycle of the drum during the washing process of clothes washing at different speeds and in different directions, and each image sample of clothes is labeled with a position label for marking the position of the clothes in the drum; The initial neural network model is trained a predetermined number of times using the training dataset. After each training session, the training results are compared with the location labels. If the comparison results do not meet the preset training conditions, adjust the model parameters of the initial neural network model to obtain an updated initial neural network model, and then train the initial neural network model again using the training dataset for a preset number of times. When the comparison results meet the preset training conditions or the initial neural network model has been trained a preset number of times, the training of the initial neural network model is completed, and a neural network model that can learn from the input image and output the location information of the clothing in the input image is obtained.

7. A device for controlling the drum speed of a washing machine, characterized in that, The control device includes: The first clothing image acquisition module is used to acquire multiple images of the first clothing inside the drum in real time as the drum rotates one revolution; The first position information determination module is used to process each of the first garment images through a trained neural network model to identify at least one first position information of the garment in the drum when the drum rotates once. The neural network model is used to identify the position information of the garment in the drum based on the garment image. The drum speed adjustment module is used to control the drum speed based on the first position information and the preset height threshold inside the drum; The step of controlling the drum rotation speed based on each first position information and a preset height threshold within the drum includes: If the height of the clothing position in any of the first location information reaches a preset height threshold, it is determined whether the clothing is stuck to the wall; The determination of whether clothing is clinging to the wall includes: Acquire multiple images of the second garment for each full rotation of the drum, at least twice; The trained neural network model processes each of the second garment images to identify the second position information of the garment in the drum during each rotation, and determines whether the garment is sticking to the wall and making circular motion based on the second position information. If it is determined that the clothing is stuck to the wall, the speed of the roller is reduced, and the step of acquiring multiple images of the first clothing inside the roller in real time after one rotation of the roller is executed. If you determine that the clothes are not sticking to the sides, keep the drum speed constant.

8. A laundry appliance, characterized in that, The laundry equipment includes: A device for controlling the rotational speed of drums and washing machines; The control device for the drum speed of the washing machine achieves control of the drum speed of the washing machine through the control method for the drum speed of the washing machine as described in any one of claims 1-6.

9. An electronic device, characterized in that, The electronic device includes: Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for controlling the drum speed of the washing machine as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for controlling the drum speed of the washing machine as described in any one of claims 1-7.

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