Laundry treating apparatus, control method, storage medium, and program product

Abstract

This application provides a garment processing device, control method, storage medium, and program product. The controller of the garment processing device determines at least one target application from multiple applications supported by the garment processing device based on the classification information and usage habit information of the user currently using the garment processing device, and controls the interactive device to display the at least one target application. This reduces the number of applications displayed by the garment processing device and makes them more consistent with the current user's category and usage habits, thereby reducing the number of operations required by the user when processing garments, improving the processing efficiency of the garment processing device, and enhancing the user experience of the garment processing device.

Description

Technical Field

[0001] This application relates to the field of clothing processing technology, and in particular to a clothing processing device, control method, storage medium, and program product. Background Technology

[0002] With the continuous advancement of home appliance technology, garment processing equipment can perform an increasing number of functions on clothing, such as high-temperature washing, rinsing, drying, and sterilization. Some garment processing equipment also provides users with various applications through interactive devices such as displays, allowing users to control the garment processing equipment via these applications.

[0003] However, when there are many applications provided by the garment processing equipment, users of the garment processing equipment need to select the appropriate application to control the garment processing equipment, which reduces the processing efficiency of the garment processing equipment. Summary of the Invention

[0004] This application provides a garment processing device, control method, storage medium, and program product to solve the technical problem of low processing efficiency of garment processing devices in the prior art.

[0005] This application provides a garment processing device, comprising: a housing; an outer cylinder disposed within the housing; an inner cylinder rotatably fitted within the outer cylinder, the interior of the inner cylinder being used to load garments, and the inner cylinder having an inlet for loading garments; and a drying system disposed within the housing, the drying system comprising: a fan for supplying air to the interior of the inner cylinder; and a heating device disposed in the air outlet direction of the fan for heating the air supplied by the fan to the interior of the inner cylinder; the garment processing device further comprising: an interactive device for displaying an application; and a controller for receiving instruction information sent by the user through the application, and controlling the garment processing device to process the garments in the inner cylinder according to the instruction information; the controller is further configured to: determine at least one target application from multiple applications supported by the garment processing device based on the user's classification information and usage habit information, and control the interactive device to display the at least one target application.

[0006] In this embodiment, the controller of the garment processing device displays fewer applications on the interactive device, and these applications are more aligned with the current user's category and usage habits. For users of the garment processing device, they only need to select usable applications from a smaller pool of target applications, which better meets their needs. Therefore, by pre-controlling the selection of applications, the garment processing device reduces the operations required by the user when processing garments. Even if the user does not have access to all the applications on the garment processing device, displaying more suitable applications reduces the difficulty of using the device, thereby improving processing efficiency and enhancing the user experience.

[0007] Furthermore, the controller of the garment processing equipment specifically determines the target application based on two dimensions: user category and usage habits. Considering that different categories of users may have different usage habits, the controller can prevent information from different categories of users from interfering with each other by combining category and usage habits. This improves the effectiveness of the determined target application, makes the target application closer to the user's needs, greatly improves the intelligence level of the garment processing equipment, and further enhances the user experience of the garment processing equipment.

[0008] In one embodiment, the controller is specifically configured to: determine a first usage habit model corresponding to the classification information based on the classification information and the mapping relationship, wherein the mapping relationship includes multiple classification information and a usage habit model corresponding to each classification information; input the usage information into the first usage habit model, and determine the at least one target application based on the output information of the first usage habit model.

[0009] In this embodiment, since the controller can select different usage habit models to determine the target application based on the different classification information of the users currently using the clothing processing equipment, the models and data with similar usage habits are used to identify users of the same category. The usage information of users of the same category is processed by the corresponding usage habit model, which improves the accuracy and effectiveness of the final determination of at least one target application.

[0010] In one embodiment, the controller is specifically configured to: input the user's usage information into a user classification model, and determine the user's classification information based on the output information of the user classification model.

[0011] In this embodiment, by using a user classification model, the controller can more accurately distinguish user classification information, thereby improving the accuracy of the controller in classifying users based on usage information.

[0012] In one embodiment, the controller is specifically configured to: send the user's usage information to a server, so that the server determines the at least one target application based on the usage information; receive indication information of at least one target application sent by the server; and determine the at least one target application based on the indication information.

[0013] In this embodiment, the method of determining at least one target application through a server can reduce the amount of computation required by the controller of the clothing processing device. The server performs more computation on behalf of the clothing processing device, thereby reducing the computational requirements of the clothing processing device, making the software and hardware design of the clothing processing device less complex, and thus reducing the design, manufacturing and use costs of the clothing processing device.

[0014] In one embodiment, the user classification model and the usage habit model are trained through the following steps: collecting multiple usage information of the user; training the user classification model based on the multiple usage information; classifying the multiple usage information into at least one category based on the user classification model; and training the usage habit model corresponding to the at least one category based on the usage information of each category.

[0015] In this embodiment, the controller can train different component models and different usage habit models to determine the target application, thereby using models and data with similar usage habits to identify users of the same category. The usage information of users of the same category is processed by the corresponding usage habit model, which improves the accuracy and effectiveness of the trained model.

[0016] In one embodiment, the user classification model and the usage habit model are trained through the following steps: collecting multiple usage information of the user; determining the effective usage information among the multiple usage information; training the user classification model based on the multiple effective usage information; classifying the multiple usage information into at least one category based on the user classification model, and training the usage habit model corresponding to the at least one category based on the effective usage information of each category.

[0017] In this embodiment, before training the user classification model and usage habit model, the server filters multiple pieces of usage information to determine the valid usage information among them. Then, it trains a user classification model based on this valid usage information and divides the multiple pieces of usage information into at least one category. Finally, based on the valid usage information for each category, it trains the corresponding usage habit information for each category. Thus, by filtering user information, the accuracy of the trained classification model and usage habit model is improved, further ensuring the accuracy of the identified at least one target application.

[0018] In one embodiment, the usage information includes temperature change curves and humidity change curves of the garment processing device during a garment processing process; determining the valid usage information among the plurality of usage information specifically involves: determining a first similarity between the temperature change curve and a standard temperature change curve; determining a second similarity between the humidity change curve and a standard humidity change curve; determining that the usage information is valid usage information when the weighted value of the first similarity and the second similarity is less than a preset value; and determining that the usage information is not valid usage information when the weighted value of the first similarity and the second similarity is greater than or equal to the preset value.

[0019] In this embodiment, the server can use more specific humidity and temperature curves from the user's usage information to remove low-quality data and obtain a more accurate model. This avoids the problem of inputting user behavior information that does not meet the user's expectations into the classification model, which would reduce the model's accuracy.

[0020] In one embodiment, the user classification model includes a DBSCAN model; the usage habit model includes a CNN model and an LSTM model.

[0021] In this embodiment, the use of the DBSCAN model enables more accurate differentiation of user classification information, thereby reducing the data labeling and training costs incurred during DBSCAN model training, and improving the accuracy of the controller in classifying users based on usage information. The use of LSTM and CNN models allows the LSTM model to handle long-term dependencies in the data, enabling the prediction model to associate with user usage data from a longer period. Simultaneously, using CNN for local feature extraction of user behavior improves the accuracy and effectiveness of feature extraction.

[0022] A second aspect of this application provides a control method for a clothing processing device, which can be applied to the clothing processing device provided in the first aspect of this application. The control method includes: determining at least one target application from a plurality of applications supported by the clothing processing device based on the classification information and usage habit information of the user currently using the clothing processing device; and controlling the interactive device to display the at least one target application.

[0023] A third aspect of this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, implement the control method for the clothing processing equipment as described in the second aspect of this application.

[0024] The fourth aspect of this application provides a computer program product, including a computer program that, when executed, implements the control method for the clothing processing equipment as described in the second aspect of this application. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A schematic diagram of a garment processing device provided in this application;

[0027] Figure 2 A schematic diagram of the structure of a garment processing device provided in this application;

[0028] Figure 3 A schematic diagram of an embodiment of the interactive device provided in this application;

[0029] Figure 4 A schematic diagram of another embodiment of the interactive device provided in this application;

[0030] Figure 5 A schematic diagram illustrating the interaction between a garment processing device and a server, as provided in this application;

[0031] Figure 6 A schematic flowchart of an embodiment of the control method for the garment processing equipment provided in this application;

[0032] Figure 7 A schematic diagram of the data processing logic when determining at least one target application for the clothing processing device provided in this application;

[0033] Figure 8 A schematic diagram illustrating a mapping relationship provided for this application;

[0034] Figure 9 A schematic diagram illustrating a specific implementation of a user classification model provided in this application;

[0035] Figure 10 A schematic diagram illustrating a specific implementation of a usage habit model provided in this application;

[0036] Figure 11 This is a schematic diagram of the structure of a control device provided in this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular 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, for example, 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.

[0039] Figure 1 A schematic diagram of a garment processing device provided in this application, such as... Figure 1 The garment processing equipment 1 shown can specifically be a washer-dryer combo, a dryer, etc.

[0040] like Figure 1 As shown, the garment processing device 1 provided in this application embodiment includes: a housing 13, an outer drum 11, an inner drum 12, and a drying system.

[0041] The housing 13 is the outer shell of the entire garment processing equipment 1, used to house and support the overall structure of the garment processing equipment 1.

[0042] The outer cylinder 11 is located inside the box body 13 to accommodate the inner cylinder 12. The outer cylinder 11 is also provided with an opening corresponding to the loading port of the inner cylinder 12.

[0043] The inner tube 12 is disposed inside the outer tube 11, and the inner tube 12 has an inlet for inserting clothes, which matches the opening of the outer tube 11. At the same time, the outer surface of the inner tube 12 is fitted inside the inner surface of the outer tube 11, so that the inner tube 12 can rotate inside the outer tube 11.

[0044] In one embodiment, the garment processing device 1 further includes a motor, which can be used to drive the inner drum 12 to rotate, and the controller of the garment processing device 1 can be used to control the rotation speed of the inner drum 12 through the motor.

[0045] Figure 2 This application provides a schematic diagram of the structure of a garment processing device, such as... Figure 2 The garment processing device 1 shown is in Figure 1 Based on the clothing processing equipment 1 shown, it also includes a drying system, which is installed inside the housing 13, specifically in the space between the housing 13 and the outer cylinder 11.

[0046] In one embodiment, such as Figure 2 The drying system of the garment processing equipment 1 shown includes:

[0047] Fan 21 and heating device 22.

[0048] Fan 21 is used to supply air to inner cylinder 12.

[0049] The heating device 22 is set in the air outlet direction of the fan. When the fan 21 supplies air to the inner drum 12, the heating device 22 can heat the air supplied by the fan 21 to the inner drum 12, so that the air in the inner drum 12 is dry hot air, thereby drying the clothes in the inner drum 12.

[0050] like Figure 2 The illustrated garment processing device 1 also includes an interactive device 30, which is connected to a controller 20. The controller 20 can be used to control the interactive device 30 to display an application and receive instructions sent by the user through the application, thereby controlling the garment processing device 1 to process the garments in the inner drum 12 according to the instructions. In one embodiment, the controller 20 may be a central processing unit (CPU), microcontroller unit (MCU), or system-on-chip (SoC) of the garment processing device 1.

[0051] For example, Figure 3 A schematic diagram of an embodiment of the interactive device provided in this application, as shown below. Figure 3 The interactive device 30 shown can be a touch screen, etc., and the controller 20 can control the interactive device 30 to display multiple applications, such as... Figure 3 The applications supported by the garment processing device 1 are denoted as Application 11, Application 12...Application 33. It is understandable that... Figure 3 Only a portion of the applications supported by the garment processing device 1 are shown in the image; the controller 20 can also display all applications across multiple pages.

[0052] For users of the clothing processing device 1, when using the clothing processing device 1, they can select the application they need to use from the multiple applications displayed on the interactive device 30. They can then send instruction information to the controller 20 through the application by touching the interactive device 30 or other means. After receiving the instruction information sent by the user through the application via the interactive device 30, the controller 20 can control the clothing processing device 1 to process the clothes in the inner drum 12 according to the instruction information.

[0053] For example, after the user operates the clothing processing device 1 to complete the washing process of the clothes, from such... Figure 3 The interactive device 30 shown allows users to select an application for drying clothes and send instructions through the application. The controller 20 of the clothes handling device 1 then controls the device to dry the clothes in the inner drum 12 based on the received instructions. For example, during the rainy season, users typically dry their clothes every evening, which also requires... Figure 3 The user selects an application for drying clothes from the interactive device 30 shown. For example, after exercising on the weekend, if a user needs to remove sweat stains from clothes using a high-temperature wash, they would need to select an application from... Figure 3 The interactive device 30 shown can be used to select an application for high-temperature washing of clothes.

[0054] It can be seen that the garment processing device 1 offers a wide range of applications, supporting the needs of different users in various scenarios and greatly enriching its functionality and intelligence. However, the increasing number of applications also leads to certain drawbacks. Users of the garment processing device 1 must select the appropriate application each time they use it, and only after selecting the correct application can they send instructions to the device. This increases the amount of work required when using the device, especially for users who lack a comprehensive understanding of the applications. This significantly increases the difficulty of using the device, reducing its processing efficiency and ultimately impacting the user experience.

[0055] Based on this, this application provides a garment processing device that enables the controller 20 of the garment processing device 1 to determine at least one target application from among a plurality of supported applications, and controls the interactive device 30 to display the at least one target application, thereby reducing the user's need to perform operations on the application when processing garments 1, and thus improving the user experience of the garment processing device 1. The technical solution of this application will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0056] In one embodiment, this application provides a control method for a garment processing device, which can be applied to, for example... Figure 2 In the clothing processing device 1 shown, specifically, when controlling the interactive device 30, the clothing processing device 1 does not control the interactive device 30 to display all the multiple applications supported by the clothing processing device 1, but rather controls the interactive device 30 to display at least one target application among the multiple applications.

[0057] Specifically, the target application is determined based on the classification information and usage habit information of the users currently using the clothing processing device 1.

[0058] The classification information can be categorized based on the different needs of users using the clothing processing equipment 1, including user age, place of origin, and occupation. For example, users can be divided into at least four categories based on age: children, youth, middle-aged, and elderly. Similarly, users can be divided into different categories based on occupation, such as chefs, sanitation workers, and painters.

[0059] User habit information could include the functions that the user is more accustomed to using when using the garment processing device 1, as well as the specific parameter settings for each function. For example, user habit information could include that the user usually controls the garment processing device 1 to perform a drying task around 8 pm, or that the user controls the garment processing device 1 to perform a deep cleaning task during the daytime on weekends.

[0060] Figure 4 A schematic diagram of another embodiment of the interactive device provided in this application, as shown below. Figure 4 In the illustrated embodiment, after the controller 20 of the clothing processing device 1 determines at least one target application based on the user's classification information and usage habit information, the control interaction device 30 displays the at least one target application. Figure 4 The example uses a target application.

[0061] Combination Figure 4 and Figure 3The comparison shows that the controller 20 of the garment processing device 1 provided in this application embodiment controls the interactive device 30 to display fewer applications, which are more in line with the current user's category and usage habits. For users of the garment processing device 1, they only need to select usable applications from a smaller number of target applications, which is more in line with user needs. Therefore, by pre-controlling the selection of applications, the garment processing device 1 reduces the operations that users need to perform when processing garments. Even if the user does not have access to all the applications in the garment processing device 1, displaying more suitable applications can reduce the difficulty of using the garment processing device 1, thereby improving the processing efficiency of the garment processing device 1 and enhancing the user experience.

[0062] Furthermore, the controller 20 of the clothing processing device 1 provided in this application embodiment specifically determines the target application from two dimensions: user category and usage habits. Considering that the usage habits of different categories of users may have certain differences, the controller 20 can prevent the information of different categories of users from affecting each other by combining categories and usage habits, thereby improving the effectiveness of the determined target application, making the target application closer to the user's needs, greatly improving the intelligence level of the clothing processing device 1, and further enhancing the user experience of the clothing processing device 1.

[0063] More specifically, this application also provides a method for specifically determining at least one target application. In one embodiment, the execution subject of the method can be the clothing processing device 1 itself. For example, after the controller 20 determines at least one target application based on the classification information and usage habit information of the user currently using the clothing processing device 1, it can directly control the interactive device 30 to display the at least one target application.

[0064] In this embodiment, when at least one target application is determined by the controller 20 of the clothing processing device 1, the processing logic is more direct and effective, which can reduce the errors and delays introduced by the interaction between the clothing processing device 1 and other devices, so as to ensure the accuracy and effectiveness of the determined at least one target application.

[0065] In another embodiment, the method can also be executed by a server 3 set up in the cloud by the service provider of the clothing processing equipment 1, for example, Figure 5This application provides a schematic diagram of the interaction between a clothing processing device 1 and a server. The clothing processing device 1 is connected to a server 3 via a network 2. The controller 20 of the clothing processing device 1 can send relevant information to the server 3 via the network 2. The server 3 determines at least one target application based on the user classification information and usage habits of the current user of the clothing processing device 1. The server 3 then sends instruction information for at least one target application to the controller 20 of the clothing processing device 1 via the network 2. After the controller 20 determines at least one target application based on the instruction information, the control interaction device 30 displays the at least one target application.

[0066] In this embodiment, the method of determining at least one target application by server 3 can reduce the amount of computation required by the controller 20 of the clothing processing device 1. Server 3 performs more computation on behalf of clothing processing device 1, thereby reducing the computational requirements of clothing processing device 1, making the software and hardware design of clothing processing device 1 less complex, and thus reducing the design, manufacturing and use costs of clothing processing device 1.

[0067] More specifically, Figure 6 A schematic flowchart of an embodiment of the control method for the garment processing equipment provided in this application is shown below. Figure 6 The method shown can be applied to, for example Figure 2 In the garment processing device 1 shown, and specifically executed by the controller 20; or, as Figure 6 The method shown can be applied to, for example Figure 5 In the scenario shown, it is specifically executed by server 3.

[0068] The following is an example Figure 2 Taking the controller 20 in the garment processing device 1 shown above as an example to illustrate the control method of the garment processing device 1 provided in this embodiment, the control method of the garment processing device 1 provided in this embodiment includes:

[0069] S100: Obtain usage information of the user currently using the clothing processing device 1.

[0070] Specifically, the user's usage information may include personal information such as the age and gender of the user currently using the clothing processing device 1, as well as usage information such as the user's geographical location, frequency of use, time of use, and usage habits when using the clothing processing device 1.

[0071] In one embodiment, the clothing processing device 1 can determine the user's usage information based on the bound information, or the clothing processing device 1 can prompt the user to input their age, usage habits, and other usage information through an application. Alternatively, the clothing processing device 1 can also be equipped with sensors such as cameras to collect the user's usage information.

[0072] S101: The controller 20 determines the classification information of the current user based on the usage information of the current user determined in S100.

[0073] Specifically, Figure 7 A schematic diagram of the data processing logic for determining at least one target application for the garment processing device provided in this application is shown below. Figure 7 As shown, the clothing processing device 1 inputs the acquired usage information into the user classification model, and determines the user's classification information based on the output information of the user classification model. Taking an as an example where there are n user classification information items, the user classification model can classify the user based on the current user's usage information, thereby obtaining the user's classification information.

[0074] In one embodiment, the user classification model provided in this application is specifically the DBSCAN model. The DBSCAN model algorithm is an unsupervised clustering model. This algorithm can more accurately distinguish user classification information without requiring pre-defined parameters, such as the number of clusters, thereby reducing the data labeling and training costs incurred during DBSCAN model training, and improving the accuracy of the controller 20 in classifying users based on usage information.

[0075] S102: The controller 20 determines the first usage habit model based on the classification information determined in S101.

[0076] Specifically, in the clothing processing device 1 provided in this application, the controller 20 pre-stores multiple different usage habit models and determines the corresponding usage habit model based on the quantity information. For example, Figure 8 A schematic diagram of a mapping relationship provided for this application, such as Figure 8 The mapping relationship shown includes multiple classification information: classification information a, classification information b, ..., classification information n, and the correspondence between each classification information and a usage habit model: classification information a corresponds to usage habit model A, classification information b corresponds to usage habit model B, ..., classification information n corresponds to usage habit model N. Then the controller 20 can determine the classification information and the corresponding usage habit model based on the classification information and the corresponding usage habit model. Figure 8 The mapping relationship determines the usage habit model corresponding to the classification information, denoted as the first usage habit model.

[0077] S103: The controller 20 inputs the user's usage information determined in S100 into the first usage habit model determined in S102, and determines at least one target application from the multiple applications supported by the clothing processing device 1 based on the output information of the first usage habit model.

[0078] like Figure 7 As shown, in S102, the controller 20 determines the usage habit model X. Subsequently, the controller 20 inputs usage information into the usage habit model X and determines at least one target application based on the output information of the first usage habit model.

[0079] In one embodiment, the usage habit model provided by this application includes a fusion of a convolutional neural network (CNN) and a long short-term memory network (LSTM). The LSTM model is trained as the basic model of the usage habit model, while the CNN model is used to extract more accurate local features from the user's usage information, thereby ensuring the accuracy and effectiveness of the target application identified by the usage habit model.

[0080] In this embodiment, the controller 20 can select different usage habit models to determine the target application based on the different classification information of the users currently using the clothing processing device 1. This allows the use of models and data with similar usage habits to identify users of the same category. The usage information of users of the same category is processed by the corresponding usage habit model, which improves the accuracy and effectiveness of the final determination of at least one target application.

[0081] S104: Controller 20 controls interactive device 30 to display at least one target application identified in S103.

[0082] The processing flow executed by the controller 20 provided in this embodiment can all be implemented by software-designed algorithms, and user information and other data can be analyzed and processed through big data and other methods. Therefore, no changes need to be made to the existing hardware of the clothing processing equipment 1, which can effectively reduce the development cost and usage cost of the clothing processing equipment 1, and is more conducive to the promotion and application of the clothing processing equipment 1.

[0083] It is understood that S101-S104 above illustrates a complete processing flow of the clothing processing device 1 determining and displaying a target application based on usage information. The clothing processing device 1 may execute the above processing flow each time it is powered on to display at least one target application on the interactive device 30, or the clothing processing device 1 may execute the above processing flow after relevant operations performed by the user, or the clothing processing device 1 may execute the above processing flow once at intervals, etc.

[0084] In one embodiment, the controller 20 can store user classification models and multiple usage habit models in advance, and call the stored user classification models and multiple usage habit models when the above processing flow needs to be executed.

[0085] In one embodiment, the user classification model and usage habit model stored in the controller 20 can be as follows: Figure 5 The data is trained by server 3 as shown. Server 3 can collect multiple usage information from users of clothing processing device 1, train a user classification model based on the collected information, and divide the multiple usage information into at least one category based on the user classification model. Finally, based on the usage information of each category, it trains usage habit information corresponding to each category. In one embodiment, server 3 can, during the use of each clothing processing device 1, train a more suitable user classification model and usage habit model specifically for that clothing processing device 1 in real time based on the user's usage habit information, further ensuring the accuracy and effectiveness of the obtained at least one target application.

[0086] Furthermore, since some users may exhibit unexpected behaviors when the server 3 collects user usage information during the training of the user classification model and usage habit model, if the server 3 cannot distinguish the reliability of the user usage information, it will affect the training process of the user classification model and usage habit model and reduce the accuracy of the model.

[0087] Therefore, in one embodiment, before training the user classification model and the usage habit model, server 3 filters multiple pieces of usage information to determine the valid usage information among them. Then, it trains a user classification model based on the valid usage information and divides the multiple pieces of usage information into at least one category. Finally, based on the valid usage information for each category, it trains the corresponding usage habit information for each category. This filtering of user information improves the accuracy of the trained classification model and usage habit model, further ensuring the accuracy of the identified at least one target application.

[0088] In one embodiment, when the usage information includes temperature and humidity change curves of the clothing processing device 1 during a clothing processing process, the server 3 can specifically determine whether the usage information is valid based on the temperature and humidity change curves. The temperature and humidity change data can be acquired by sensors in the clothing processing device 1, including bundling sensors, etc.

[0089] Specifically, server 3 compares the temperature change curve with a standard temperature change curve to determine the first similarity between them, and compares the humidity change curve with the standard humidity change curve to determine the second similarity between them. In one embodiment, since the humidity curve and temperature curve of the same program may be of unequal length, the Dynamic Warping (DTW) algorithm is used to calculate the similarity of the data during the use of the dryer and washing machine. After performing dynamic time-domain planning processing on the humidity curve and temperature curve and the standard program running curve, the minimum possible distance between them is calculated, and the two curves are combined to obtain the first similarity HumSim = DTWDistance(OperationalData.Humidity, StandardProcess.Humidity) and the second similarity TemSim = DTWDistance(OperationalData.Temperature, StandardProcess.Temperature).

[0090] Subsequently, server 3 weights the first and second similarities to obtain a weighted value: Similarity = αHumSim + βTemSim. Here, α and β are the weights of the humidity curve and temperature curve in calculating similarity, given after evaluation. The smaller the Similarity, the closer the program is to the standard run, and the higher the score. The Similarity2Weight() function is a pre-defined piecewise function that calculates the program weights based on similarity, resulting in data in the format {ProgramID, ProgramWeight}.

[0091] Ultimately, server 3 can determine that when the weighted similarity value is less than a preset value, the current usage information is valid; if the weighted similarity value is greater than or equal to the preset value, the current usage information is not valid. After performing the above processing on multiple pieces of usage information, server 3, based on the above data filtering and evaluation algorithms, can remove low-quality data and obtain a more accurate model, avoiding the problem of inputting usage information that does not meet user expectations into the classification model, thus reducing the model's accuracy.

[0092] Figure 9 A schematic diagram illustrating a specific implementation of a user classification model provided in this application is shown below. Figure 9As shown, taking the DBSCAN model as an example of a user classification model, the DBSCAN model uses Principal Component Analysis (PCA) to extract features from users' past data. The extracted feature vectors are then combined with other user registration data to form new user information data. Finally, a clustering algorithm is used to classify users for different user behavior prediction models, achieving more accurate user behavior analysis. By labeling users into different groups, the model can learn the characteristics of each group, enabling rapid and accurate classification when new users arrive, supporting personalized recommendations, user profile construction, and other applications.

[0093] Figure 10 A schematic diagram illustrating a specific implementation of a usage habit model provided in this application is shown below. Figure 10 As shown, taking the LSTM and CNN models as examples of user behavior prediction models, the user behavior prediction model is trained on previously obtained user data of different categories, resulting in multiple different user behavior prediction models. By classifying new user data and assigning it to different prediction models, the accuracy of intelligent recommendation is improved. Specifically, to provide the prediction model with insights into the user's past behavior, a Long Short-Term Neural Network (LSTM) is used as the base model for training. This model can handle long-term dependencies in the data, allowing the prediction model to associate with the user's usage data from a longer period. Simultaneously, to focus on the user's recent behavior, a CNN is used to extract local features from the user's behavior.

[0094] In the foregoing embodiments of this application, a control method for a garment processing device provided by the embodiments of this application has been described. To achieve the functions of the methods provided by the embodiments of this application, the controller, as the executing entity, may include a hardware structure and / or a software module, implementing the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. Whether a particular function is executed in the form of a hardware structure, a software module, or a hardware structure plus a software module depends on the specific application and design constraints of the technical solution.

[0095] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0096] For example, Figure 11 This application provides a schematic diagram of a control device 1000, which can be used to execute any of the control methods described in the foregoing embodiments. The control device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-executable instructions, and the processor 1001 can execute the computer-executable instructions stored in the memory. When the computer-executable instructions are executed by the processor, the processor implements the control method of any of the clothing processing devices described in the foregoing embodiments of this application. In one embodiment, the control device 100 further includes a communication interface 1003, through which the processor 1001 can transmit data.

[0097] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, can be used to implement the control method of any of the clothing processing devices in the foregoing embodiments of this application.

[0098] This application also provides a chip for executing instructions, the chip being used to execute the control method of any of the clothing processing devices described above.

[0099] This application also provides a computer program product, including a computer program that, when executed, implements a control method for any of the clothing processing devices described above.

[0100] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A garment processing device, characterized in that, include: Box; The outer cylinder is disposed inside the box. The inner tube is rotatably fitted inside the outer tube. The interior of the inner tube is used to hold clothing, and the inner tube is provided with an inlet for dispensing clothing. A drying system is installed inside the chamber, and the drying system includes: A fan is used to supply air to the interior of the inner cylinder; A heating device is provided in the air outlet direction of the fan to heat the air supplied by the fan to the interior of the inner cylinder. The garment processing equipment also includes: Interactive device for displaying applications; The controller is used to receive instruction information sent by the user through the application, and control the clothing processing device to process the clothing in the inner drum according to the instruction information; The controller is also configured to: determine at least one target application from multiple applications supported by the clothing processing device based on the classification information and usage habit information of the user currently using the clothing processing device, and control the interactive device to display the at least one target application.

2. The garment processing equipment according to claim 1, characterized in that, The controller is specifically configured as follows: Based on the classification information and mapping relationship, a first usage habit model corresponding to the classification information is determined, wherein the mapping relationship includes multiple classification information and a usage habit model corresponding to each classification information; The usage information is input into the first usage habit model, and the at least one target application is determined based on the output information of the first usage habit model.

3. The garment processing equipment according to claim 2, characterized in that, The controller is specifically configured as follows: The user's usage information is input into the user classification model, and the user's classification information is determined based on the output information of the user classification model.

4. The garment processing equipment according to claim 1, characterized in that, The controller is specifically configured as follows: The user's usage information is sent to the server, so that the server can determine the at least one target application based on the usage information; Receive at least one instruction message from a target application sent by the server; The at least one target application is determined based on the indicated information.

5. The garment processing equipment according to any one of claims 1-4, characterized in that, The user classification model and the usage habit model are trained through the following steps: Collect multiple usage information of the user; The user classification model is trained based on the aforementioned usage information. The user classification model categorizes the multiple usage information into at least one category, and based on the usage information of each category, a usage habit model corresponding to the at least one category is trained.

6. The garment processing equipment according to any one of claims 1-4, characterized in that, The user classification model and the usage habit model are trained through the following steps: Collect multiple usage information of the user; Determine the valid usage information among the multiple usage information; The user classification model is trained based on the multiple valid usage information. The user classification model categorizes the multiple usage information into at least one category, and based on the effective usage information of each category, a usage habit model corresponding to the at least one category is trained.

7. The garment processing equipment according to claim 6, characterized in that, The usage information includes the temperature change curve and humidity change curve of the clothing processing equipment during a clothing processing process; The specific steps for determining the valid usage information among the plurality of usage information are as follows: Determine the first similarity between the temperature change curve and the standard temperature change curve; Determine the second similarity between the humidity change curve and the standard humidity change curve; When the weighted value of the first similarity and the second similarity is less than a preset value, the usage information is determined to be valid usage information; If the weighted value of the first similarity and the second similarity is greater than or equal to the preset value, it is determined that the usage information is not the valid usage information.

8. The garment processing equipment according to claim 3, characterized in that, The user classification model includes the DBSCAN model; The usage habits models include CNN models and LSTM models.

9. A control method for a garment processing device, characterized in that, The garment processing device includes: a housing; an outer cylinder disposed within the housing; an inner cylinder rotatably fitted inside the outer cylinder, the interior of which is used to load garments, and the inner cylinder has an inlet for loading garments; a drying system disposed within the housing, the drying system including: a fan for supplying air to the interior of the inner cylinder; and a heating device disposed in the air outlet direction of the fan for heating the air supplied by the fan to the interior of the inner cylinder; the garment processing device further includes: an interactive device for displaying an application and receiving instruction information sent by the user through the application; and a controller for controlling the garment processing device to process the garments in the inner cylinder according to the instruction information; the control method includes: Based on the classification information and usage habit information of the users currently using the clothing processing device, at least one target application is determined from multiple applications supported by the clothing processing device; The interactive device is controlled to display at least one target application.

10. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions, which, when executed, implement the control method for the garment processing device as described in claim 9.

11. A computer program product, characterized in that, It includes a computer program, which, when executed, implements the control method for the garment processing equipment as described in claim 9.

Images