Control method and device of clothes treatment equipment, equipment and medium

Abstract

The invention belongs to the technical field of electric appliances, and particularly relates to a control method and device of clothes processing equipment, the equipment and a medium, and the method comprises the steps: obtaining historical operation data of the clothes processing equipment in a target period, the target period is used for indicating a time interval between the last cleaning time of the clothes processing equipment and the current time; obtaining feature data according to the historical operation data; and inputting the feature data into a pre-trained prediction model, and according to a prediction result output by the prediction model, determining whether to start a drum self-cleaning program for the clothes processing equipment. According to the method, whether the equipment needs to be self-cleaned or not is intelligently judged and predicted according to the historical operation condition of the equipment, it is effectively guaranteed that the clothes processing equipment can be cleaned and nursed in time, the equipment operation efficiency is improved, and the user experience is enhanced.

Description

Technical Field

[0001] The present application belongs to the field of electrical appliance technology, and specifically relates to a control method, device, equipment and medium for a clothing processing device. Background Art

[0002] After a period of use, the inner drum of a clothing treatment device needs to be cleaned regularly due to the accumulation of dirt, so as to ensure the operating efficiency of the device and effectively wash and care for the clothing.

[0003] However, the cleaning of the inner drum currently requires manual cleaning by the user, which is not only cumbersome but also easy to cause secondary pollution. At the same time, the user cannot accurately judge when the inner drum needs to be cleaned according to the usage, and even disassembles the machine. This can easily lead to the clothes handling equipment not being cleaned in time, thus affecting the washing and care of the clothes and affecting the user experience. Summary of the invention

[0001] In order to solve the problem that existing clothing processing equipment is not cleaned in time, thereby affecting the operating efficiency, the present application provides a control method, device, equipment and medium for clothing processing equipment.

[0002] In a first aspect, the present application provides a control method for a clothes processing device, the method comprising:

[0003] Acquiring historical operation data of the clothes processing device within a target period, where the target period is used to indicate the time interval between the last time the clothes processing device was cleaned and the current time;

[0004] Acquiring characteristic data according to the historical operation data;

[0005] The characteristic data is input into a pre-trained prediction model, and based on the prediction result output by the prediction model, it is determined whether to enable the drum self-cleaning program for the clothes processing device.

[0006] In a possible implementation, acquiring characteristic data according to the historical operation data includes:

[0007] According to the historical operation data, the accumulated operation cycle of the clothing treatment device and parameter information corresponding to each operation are obtained, wherein the parameter information includes at least one of clothing material, operation program, clothing weight or foam amount;

[0008] The characteristic data is obtained by performing cumulative characteristic calculation on the parameter information corresponding to each operation.

[0009] In a possible implementation, the accumulating characteristic calculation of the parameter information corresponding to each operation to obtain the characteristic data includes:

[0010] The corresponding laundry weight and foam volume during each operation are summed up to obtain the cumulative laundry weight and cumulative foam volume;

[0011] According to the corresponding clothing materials and operation programs during each operation, obtaining the clothing material distribution and the operation program distribution within the target period;

[0012] The characteristic data is obtained according to the accumulated operation cycles, the accumulated laundry weight, the accumulated foam volume, the clothing material distribution and the operation program distribution.

[0013] In a possible implementation, before inputting the feature data into a pre-trained prediction model, the method further includes:

[0014] Acquiring historical reference data of the laundry processing device within a cleaning cycle, the historical reference data including the accumulated operation cycle, operation parameters and whether a drum self-cleaning program is used corresponding to each operation, the cleaning cycle indicating the time interval between two adjacent cleaning times;

[0015] According to the historical reference data, sample data and corresponding labels are obtained;

[0016] Acquire the prediction model according to the sample data and the corresponding labels;

[0017] The sample data includes the corresponding accumulated operation cycles, accumulated laundry weight, accumulated foam volume, clothing material distribution and operation program distribution for each operation, and the label is used to indicate whether the drum self-cleaning program is enabled.

[0018] In a possible implementation, acquiring the prediction model according to the sample data and the corresponding label includes:

[0019] Preprocessing the sample data to obtain standardized sample data, wherein the preprocessing includes removing missing values, outliers or duplicate values;

[0020] Dividing the standardized sample data and corresponding labels according to a preset ratio to obtain a training set and a test set;

[0021] According to the training set, training is performed based on a regression logic algorithm to obtain the trained prediction model;

[0022] The trained prediction model is adjusted based on the training set.

[0023] In a possible implementation manner, adjusting the trained prediction model based on the training set includes:

[0024] Inputting the sample data in the test set into the prediction model to obtain the test results output by the prediction model;

[0025] Obtaining an evaluation result of the prediction model according to the test result and the label corresponding to the sample data, wherein the evaluation result includes at least one of accuracy, precision, recall rate, or F1 value;

[0026] According to the evaluation results, the hyperparameters of the prediction model are adjusted.

[0027] In a possible implementation, if the prediction result is to enable the drum self-cleaning program, the method further includes:

[0028] After the drum self-cleaning program is executed, the accumulated number of times the laundry processing device executes the drum self-cleaning program within a preset period is obtained, where the preset period represents the time interval between the current time and the last time the laundry processing device was disassembled for cleaning;

[0029] If the accumulated number of times is greater than a preset threshold, a prompt message is pushed to the user terminal to remind the user to disassemble the clothing processing device for cleaning.

[0030] In a second aspect, the present application provides a control device for a clothes processing device, the device comprising:

[0031] an acquisition module, used for acquiring historical operation data of the clothes processing device within a target period, wherein the target period is used for indicating the time interval between the last time the clothes processing device was cleaned and the current time;

[0032] The acquisition module is also used to acquire characteristic data according to the historical operation data;

[0033] The processing module is used to input the characteristic data into a pre-trained prediction model, and confirm whether to enable the drum self-cleaning program for the clothing processing device according to the prediction result output by the prediction model.

[0034] In a third aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0035] In a fourth aspect, the present application provides a clothes processing device, comprising: at least one processor and a memory; wherein:

[0036] The memory stores computer-executable instructions;

[0037] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method as described in any one of the first aspects.

[0038] The control method, device, equipment and medium of the clothing processing equipment provided in this embodiment obtains the historical operation data of the clothing processing equipment in the target cycle, confirms the parameter data required by the prediction model, obtains the pre-processed feature data, inputs the feature data into the prediction model, obtains the prediction result, and determines whether to enable the drum self-cleaning program. The method intelligently judges whether the predicted equipment needs to be self-cleaned based on the historical operation of the equipment, effectively ensures that the clothing processing equipment can be cleaned and cared for in a timely manner, improves the operation efficiency of the equipment, and provides convenience for users and enhances the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] Figure 1 The process of the control method of the clothing processing device provided in the embodiment of the present application Figure 1 ;

[0041] Figure 2 The process of the control method of the clothing processing device provided in the embodiment of the present application Figure 2 ;

[0042] Figure 3 The process of the control method of the clothing processing device provided in the embodiment of the present application Figure 3 ;

[0043] Figure 4 A diagram of a control device for a clothes processing device provided by an embodiment of the present invention;

[0044] Figure 5 A hardware schematic diagram of a clothes processing device provided in an embodiment of the present invention.

[0045] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein, for example.

[0048] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0049] As the clothes treatment equipment is used for a long time, the internal part is relatively humid, and the detergent components in the water, the oily stains in the clothes, or the shed hair will adhere to the inner and outer drum walls, inevitably accumulating a lot of dirt, which will pollute the clothes, resulting in a decrease in the washing effect, and even the bacteria growing inside may cause cross infection and affect the health of users. However, due to the complex internal structure of the equipment, it is easy for users to be unable to perceive the dirtiness of the equipment, and manual cleaning is also difficult.

[0050] In the prior art, some methods are to regularly remind users to clean manually, but the method of self-cleaning by users is not only cumbersome to operate, but also easy to cause secondary pollution; other methods are to pre-configure the self-cleaning program for the device, and when the device operation cycle reaches the threshold, the user is reminded to enable the self-cleaning program to clean the device. Although this method realizes the function of self-cleaning of the device, it is not accurate to judge whether the device needs to be cleaned only by the cleaning interval. Each user has different usage habits, or the same user uses the device differently in different time periods. Therefore, in the same time period, the accumulation of dirt on the device may also be different, which may easily lead to untimely or excessive cleaning of the device. At the same time, the user needs to manually enable the program, and the user is prone to forget the operation, resulting in inadequate cleaning.

[0051] Based on the above problems, the present application proposes a control method for clothing processing equipment, which obtains the historical operation data of the clothing processing equipment at the time interval between the current time and the last time it was cleaned, accumulates the historical operation data to form feature data, and inputs it into a prediction model to determine whether the cumulative operation of the equipment by the end of this operation requires the activation of the drum self-cleaning program to clean the equipment. At the same time, the prediction model is trained based on historical reference data, and the prediction model is trained through each operation data and the corresponding known label of whether the drum self-cleaning program is used, and the prediction model is further evaluated and adjusted to improve the accuracy of the prediction. Based on the historical operation of the equipment, this method intelligently judges whether the predicted equipment needs to be cleaned, thereby automatically performing the self-cleaning program, effectively ensuring that the clothing processing equipment can be cleaned in a timely manner, avoiding the problem of untimely cleaning or excessive cleaning, and enhancing the user experience.

[0052] The following specific embodiments are used to describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0053] Figure 1 The process of the control method of the clothing processing device provided in the embodiment of the present application Figure 1 .like Figure 1 As shown, the method includes:

[0054] S101. Acquire historical operation data of the clothes processing device within a target period, where the target period is used to indicate the time interval between the last time the clothes processing device was cleaned and the current time.

[0055] In this step, the historical operation data can represent the operation record and status of the clothing treatment equipment within the target cycle. Based on the historical operation data, parameter items useful for analyzing the dirtiness of the equipment can be extracted, including but not limited to the cumulative operation cycle, clothing material, clothing weight, operation program, and foam volume. For example, the key parameter items can be selected according to the actual situation, or based on a large amount of experimental data, the parameters that have a more obvious impact on the dirtiness of the equipment can be confirmed, which is not limited here. The time for obtaining historical operation data can be at the end of each operation, and the historical operation data within the time interval from the last cleaning time to the current time is collected to analyze and confirm the dirtiness of the current equipment.

[0056] S102: Acquire characteristic data according to the historical operation data.

[0057] In this step, since the dirtiness of the equipment is the result of cumulative operation within a preset period, after obtaining the historical operation data, it is necessary to perform pre-processing to obtain the cumulative data of various parameters, that is, the characteristic data that can be input into the prediction model. Since the types of parameters are different, including numerical types, name types, etc., different cumulative calculation methods can be selected for different types of parameters. For example, numerical parameters can be summed or averaged, and distribution diagrams or other intuitive analysis methods can be obtained for name parameters, which have been adapted to the analysis logic of dirtiness. Furthermore, the raw data stored by the equipment is usually chaotic and incomplete, and the model often cannot effectively identify and extract information from it. Therefore, before entering the prediction model, the data can be pre-processed, including but not limited to cleaning, deduplication, missing value processing, etc. The pre-processing of the data can be performed after obtaining the key parameter items, or when the data is accumulated and calculated, and there is no limitation here.

[0058] S103: Input the characteristic data into a pre-trained prediction model, and confirm whether to enable a drum self-cleaning program for the clothes processing device according to a prediction result output by the prediction model.

[0059] In this step, the feature data is input into the prediction model, and the prediction result output by the prediction model can be obtained, which is equivalent to the prediction model quantifying the input parameters into numbers, and then calculating the probability percentage of needing cleaning based on the calculation logic of the model. If the probability exceeds a certain level, it can be determined that cleaning is required, otherwise no cleaning is required. Furthermore, the prediction model of this solution meets the characteristics of a binary classification problem, so the prediction result can be represented by outputting 1 or 0: 1 means that cleaning is required this time, and 0 means that cleaning is not required. According to the predicted probability, if the probability is greater than 0.5, 1 can be output, and if the probability is less than or equal to 0.5, 0 can be output.

[0060] It should be noted that the prediction of the device is generally performed before shutting down when the current operation is completed, and the device is in a normal startup state. When the prediction is completed, if the result is that the self-cleaning program needs to be enabled, the device can be set to automatically perform self-cleaning; in other implementation methods, it is also possible to confirm with the user whether to execute the self-cleaning program by sending a confirmation message to the user end, so as to avoid the extra operation of the device causing trouble to the user's rest.

[0061] Exemplarily, after the drum self-cleaning program is executed, the cumulative number of times the laundry processing device executes the drum self-cleaning program within a preset period is obtained, and the preset period represents the time interval between the current time and the last disassembly and cleaning of the laundry processing device;

[0062] If the accumulated number of times is greater than a preset threshold, a prompt message is pushed to the user terminal to remind the user to disassemble the clothing processing device for cleaning.

[0063] It should be noted that the self-cleaning ability of the equipment is actually limited. For equipment with a long service life and high frequency of use, deep dirt may accumulate. The self-cleaning program cannot thoroughly clean this type of dirt. Therefore, manual disassembly and cleaning is also an important method to ensure the reliability of equipment use. The deep dirt of the equipment can be monitored by setting a threshold for the number of times the self-cleaning program runs. In the time interval from the current time to the last disassembly and cleaning, if the number of times the equipment's self-cleaning program runs is relatively frequent and reaches the threshold, it means that the amount of deep dirt accumulated in the equipment may be large. At this time, a prompt message can be sent to the user to prompt the user to disassemble the equipment for cleaning. In other implementations, in order to increase the convenience of the user, the inquiry information on whether to apply for door-to-door service can be directly pushed for the user to choose.

[0064] The control method of the clothing processing device provided in this embodiment obtains the historical operation data of the clothing processing device within the target cycle, confirms the parameter data required by the prediction model, obtains the pre-processed feature data, inputs the feature data into the prediction model, obtains the prediction result, and determines whether to enable the drum self-cleaning program. The method intelligently judges whether the device needs to be self-cleaned based on the historical operation of the device, effectively ensures that the clothing processing device can be cleaned and cared for in a timely manner, improves the operation efficiency of the device, and provides convenience for users and enhances the user experience.

[0065] Figure 2 The process of the control method of the clothing processing device provided in the embodiment of the present application Figure 2 .like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, the process of obtaining characteristic data based on historical operation data is described in detail. The method includes:

[0066] S201. Acquire, based on the historical operation data, the accumulated operation cycle of the clothing processing device and parameter information corresponding to each operation, wherein the parameter information includes at least one of clothing material, operation program, clothing weight or foam amount.

[0067] In this step, based on historical operating data, parameter data that plays a key role in model analysis can be extracted, including but not limited to cumulative operating cycles, clothing material, operating program, clothing weight or foam volume, etc. It should be understood that such parameters have the most obvious impact on the dirtiness of the equipment. For example, the material and weight of the clothing can reflect the amount of impurities such as hair scraps attached; excessive foam will cause clothing treatment agents to be deposited on the drum wall; different operating programs have different corresponding temperatures, washing and care times, and flushing processes, resulting in large differences in the impact on the dirtiness of the drum wall. The above-mentioned influencing factors also comprehensively affect the degree of dirtiness of the equipment, making the macro effect more obvious.

[0068] S202: summing up the corresponding laundry weight and foam volume during each operation to obtain a cumulative laundry weight and a cumulative foam volume.

[0069] S203, obtaining the clothing material distribution and the running program distribution within the target period according to the clothing material and the running program corresponding to each running.

[0070] It should be noted that due to the different types of parameters, including numerical and name types, different calculation methods need to be used for different types of parameters in order to perform cumulative calculations and quantify features. For numerical parameters, you can add up the individual parameters to obtain the cumulative value; for text parameters such as clothing materials and running programs, you can confirm the parameter performance by selecting the distribution acquisition method and analyze the parameters based on the characteristics of the distribution.

[0071] S204, obtaining the characteristic data according to the accumulated operation cycles, the accumulated laundry weight, the accumulated foam volume, the clothing material distribution, and the operation program distribution.

[0072] In this step, based on the accumulated characteristic values ​​obtained, characteristic data that can be input into the prediction model is obtained. It should be understood that each set of characteristic data contains at least one selected parameter item. When a set of characteristic data is input into the prediction model, each set of input data can be mapped into a number between 0 and 1 according to the calculation logic of the prediction model. When the function value is greater than 0.5, it is judged to be 1, otherwise it is 0.

[0073] The control method of the clothing treatment device provided in this embodiment obtains key parameter items based on historical operation data, and performs cumulative characteristic calculation on the key parameter items to obtain the cumulative performance of each parameter, forming characteristic data for the model to perform analysis and calculation. By performing cumulative calculation on key parameters, the method accurately expresses the cumulative operation of the equipment, thereby improving the analysis accuracy of the model.

[0074] Figure 3 The process of the control method of the clothing processing device provided in the embodiment of the present application Figure 3 .like Figure 3 As shown, in this embodiment Figure 2 Based on the embodiment, the training process of the prediction model is described in detail. The method includes:

[0075] S301. Obtain historical reference data of the clothing processing device within a cleaning cycle, wherein the historical reference data includes a cumulative operating cycle corresponding to each operation, operating parameters, and whether a drum self-cleaning program is used, and the cleaning cycle represents a time interval between two adjacent cleaning times.

[0076] In this step, the prediction model needs to be pre-trained, that is, the model is trained using sample data and corresponding labels. It should be noted that the historical reference data includes a large amount of collected operation data of clothing processing equipment, as well as the results of whether cleaning is required after each operation. Usually, a large number of repetitive experiments are carried out before the equipment is produced, so the existing experimental data can be used for analysis and processing.

[0077] S302: Obtain sample data and corresponding labels according to the historical reference data.

[0078] The sample data includes the corresponding accumulated operation cycles, accumulated laundry weight, accumulated foam volume, clothing material distribution and operation program distribution for each operation, and the label is used to indicate whether the drum self-cleaning program is enabled.

[0079] In this step, based on the large amount of historical reference data collected, the parameter items analyzed therein can also be cumulatively calculated to obtain the corresponding cumulative feature data, that is, sample data, and the labels corresponding to each group of sample data when each run is completed.

[0080] S303: preprocess the sample data to obtain standardized sample data, wherein the preprocessing includes removing missing values, abnormal values ​​or duplicate values.

[0081] In this step, before inputting the data into the prediction model, in order to clean and organize the source data, data preprocessing is required, including but not limited to removing missing values, abnormal values ​​or duplicate values, etc. For the case of more data sources, a more comprehensive data cleaning is required to avoid semantic ambiguity, instance representation ambiguity, inconsistency, redundancy and other problems. Data preprocessing can also be performed first when the source data is obtained to reduce the complexity of subsequent data calculations.

[0082] S304: Divide the standardized sample data and corresponding labels according to a preset ratio to obtain a training set and a test set.

[0083] In this step, both the training and evaluation of the model require a large amount of sample data for processing, so the data can be divided into two categories, including training set and test set. The training set is used to train the model, that is, to fit the parameters, and the classifier is established by training the fitting parameters; the test set is used to evaluate the performance of the final model. The test set does not participate in the training of the model, and is mainly used to test the accuracy of the trained model. The preset ratio of sample data division can be determined according to demand. For example, it can be divided according to the ratio of 70% of the sample data for training and 30% of the sample data for testing.

[0084] S305: Perform training based on the regression logic algorithm according to the training set to obtain the trained prediction model.

[0085] In this step, the divided training set is input into the regression logic algorithm to fit the parameters in the model. Logistic regression is a supervised learning in machine learning. The derivation process and calculation method are similar to the regression process. It is suitable for solving some binary classification problems. The model is trained by a given N group of training data, and the model parameters are determined based on the maximum likelihood estimation or loss function. Model training based on logistic regression can achieve a linear relationship with a good fitting effect, and the calculation speed is fast and the noise resistance is strong. In other implementations, for the realization of the prediction process, more intelligent models such as deep learning can also be selected to increase the accuracy of the prediction, which is not limited here.

[0086] S306: Adjust the trained prediction model based on the training set.

[0087] Exemplarily, the sample data in the test set is input into the prediction model to obtain the test result output by the prediction model;

[0088] Obtaining an evaluation result of the prediction model according to the test result and the label corresponding to the sample data, wherein the evaluation result includes at least one of accuracy, precision, recall rate, or F1 value;

[0089] According to the evaluation results, the hyperparameters of the prediction model are adjusted.

[0090] Exemplarily, in addition to the above-mentioned adjustment of hyperparameters, adjustments may be made based on the evaluation results. The model may also be optimized by adding more training data, selecting other feature selection methods, etc.

[0091] The control method of the clothing processing device provided in this embodiment obtains sample data and corresponding labels by acquiring historical reference data, and divides the sample data to obtain a training set and a test set, and inputs the training set into a logistic regression algorithm for training, determines the model parameters, and obtains a trained prediction model. Furthermore, the test set is input into the prediction model, the prediction model is further evaluated, and the model is adjusted according to the evaluation results. Based on the logistic regression algorithm, the accuracy of the classification prediction results is effectively improved, and the model prediction accuracy is improved through model evaluation to ensure the reliability of the prediction.

[0092] Figure 4 A control device diagram of a clothes processing device provided by an embodiment of the present invention, such as Figure 4 As shown, the control device 40 includes: an acquisition module 401 and a processing module 402 .

[0093] An acquisition module 401 is used to acquire historical operation data of the clothes processing device within a target period, where the target period is used to indicate the time interval between the last time the clothes processing device was cleaned and the current time;

[0094] The acquisition module 402 is also used to acquire characteristic data according to the historical operation data;

[0095] The processing module 403 is used to input the characteristic data into a pre-trained prediction model, and confirm whether to enable the drum self-cleaning program for the clothing processing device according to the prediction result output by the prediction model.

[0096] Figure 5 Schematic diagram of the hardware of the clothes processing device provided by the embodiment of the present invention. Figure 5 As shown, the laundry processing device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. The device 50 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0097] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0098] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0099] In the above Figure 5 In the illustrated embodiment, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0100] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0101] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0102] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method described above is implemented.

[0103] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0104] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0105] The division of the units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0108] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0109] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0110] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above embodiments are only used to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for a clothes processing device, characterized in that: The method comprises: Acquiring historical operation data of the clothes processing device within a target period, where the target period is used to indicate the time interval between the last time the clothes processing device was cleaned and the current time; Acquiring characteristic data according to the historical operation data; The characteristic data is input into a pre-trained prediction model, and based on the prediction result output by the prediction model, it is determined whether to enable the drum self-cleaning program for the clothes processing device.

2. The method according to claim 1, characterized in that The acquiring characteristic data according to the historical operation data includes: According to the historical operation data, the accumulated operation cycle of the clothing treatment device and parameter information corresponding to each operation are obtained, wherein the parameter information includes at least one of clothing material, operation program, clothing weight or foam amount; The characteristic data is obtained by performing cumulative characteristic calculation on the parameter information corresponding to each operation.

3. The method according to claim 2, characterized in that The accumulating characteristic calculation of the parameter information corresponding to each operation to obtain the characteristic data includes: The corresponding laundry weight and foam volume during each operation are summed up to obtain the cumulative laundry weight and cumulative foam volume; According to the corresponding clothing materials and operation programs during each operation, obtaining the clothing material distribution and the operation program distribution within the target period; The characteristic data is obtained according to the accumulated operation cycles, the accumulated laundry weight, the accumulated foam volume, the clothing material distribution and the operation program distribution.

4. The method according to claim 1, characterized in that Before inputting the feature data into the pre-trained prediction model, the method further includes: Acquiring historical reference data of the laundry processing device within a cleaning cycle, the historical reference data including the accumulated operation cycle, operation parameters and whether a drum self-cleaning program is used corresponding to each operation, the cleaning cycle indicating the time interval between two adjacent cleaning times; According to the historical reference data, sample data and corresponding labels are obtained; Acquire the prediction model according to the sample data and the corresponding labels; The sample data includes the corresponding accumulated operation cycles, accumulated laundry weight, accumulated foam volume, clothing material distribution and operation program distribution for each operation, and the label is used to indicate whether the drum self-cleaning program is enabled.

5. The method according to claim 4, characterized in that The step of obtaining the prediction model according to the sample data and the corresponding labels includes: Preprocessing the sample data to obtain standardized sample data, wherein the preprocessing includes removing missing values, outliers or duplicate values; Dividing the standardized sample data and corresponding labels according to a preset ratio to obtain a training set and a test set; According to the training set, training is performed based on a regression logic algorithm to obtain the trained prediction model; The trained prediction model is adjusted based on the training set.

6. The method according to claim 5, characterized in that The adjusting the trained prediction model based on the training set includes: Inputting the sample data in the test set into the prediction model to obtain the test results output by the prediction model; Obtaining an evaluation result of the prediction model according to the test result and the label corresponding to the sample data, wherein the evaluation result includes at least one of accuracy, precision, recall rate, or F1 value; According to the evaluation results, the hyperparameters of the prediction model are adjusted.

7. The method according to claim 1, characterized in that If the prediction result is to activate the drum self-cleaning program, the method further includes: After the drum self-cleaning program is executed, the accumulated number of times the laundry processing device executes the drum self-cleaning program within a preset period is obtained, where the preset period represents the time interval between the current time and the last time the laundry processing device was disassembled for cleaning; If the accumulated number of times is greater than a preset threshold, a prompt message is pushed to the user terminal to remind the user to disassemble the clothing processing device for cleaning.

8. A control device for a clothes processing device, characterized in that: The device comprises: an acquisition module, used for acquiring historical operation data of the clothes processing device within a target period, wherein the target period is used for indicating the time interval between the last time the clothes processing device was cleaned and the current time; The acquisition module is also used to acquire characteristic data according to the historical operation data; The processing module is used to input the characteristic data into a pre-trained prediction model, and confirm whether to enable the drum self-cleaning program for the clothing processing device according to the prediction result output by the prediction model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1 to 7.

10. A clothes processing device, characterized in that: include: at least one processor and memory; wherein, The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method according to any one of claims 1 to 7.

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