Control method and device of clothes treatment equipment, equipment and storage medium
By analyzing the sound information of the clothing processing equipment to determine the target working parameters, the problem that the equipment cannot match the operating parameters based on the clothing information is solved, and intelligent operation and higher processing quality are achieved.
Patent Information
- Application Number
- CN202311744080.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-18
- Publication Date
- 2025-06-20
AI Technical Summary
The clothing processing equipment cannot match the operating parameters based on the clothing information, resulting in poor processing results.
By obtaining the sound information of the clothing processing equipment, analyzing and determining the target working parameters, the equipment is then controlled to operate with the target parameters.
It realizes the intelligent operation of clothing processing equipment, improves the quality of clothing processing, and solves the problem that the equipment cannot match operation parameters based on clothing information.
Smart Images
Figure CN120174581A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of computer technology, and in particular, to a control method, device, equipment, and storage medium for a clothing treatment device. Background Art
[0002] Clothing treatment devices are household appliances used by many families, such as washing machines and dryers. In the related art, the operating parameters of clothing treatment devices are usually set by users and cannot be matched according to clothing information, resulting in poor clothing treatment effects. Summary of the Invention
[0003] Embodiments of the present application provide a control method, device, equipment, and storage medium for a clothing treatment device, which solve the problem that in the related art, the clothing treatment device cannot match the operating parameters according to clothing information, resulting in poor clothing treatment effects.
[0004] To achieve the above object, the technical solution of the present application is realized as follows:
[0005] A control method for a clothing treatment device, the method includes:
[0006] Controlling the clothing treatment device to operate and obtaining the sound information of the clothing treatment device;
[0007] Determining the target working parameters of the clothing treatment device based on the sound information;
[0008] Controlling the clothing treatment device to operate with the target working parameters.
[0009] A control device for a clothing treatment device, the control device for the clothing treatment device includes:
[0010] An obtaining unit, configured to control the clothing treatment device to operate and obtain the sound information of the clothing treatment device;
[0011] A determining unit, configured to determine the target working parameters of the clothing treatment device based on the sound information;
[0012] A control unit, configured to control the clothing treatment device to operate with the target working parameters.
[0013] A clothing treatment device, characterized in that the clothing treatment device includes:
[0014] A memory, configured to store executable instructions;
[0015] A processor, configured to implement the steps in the control method for the clothing treatment device as described in any one of the above when executing the executable instructions stored in the memory.
[0016] A storage medium stores computer-executable instructions configured to execute the control method of the clothing treatment device provided in any one of the above.
[0017] The control method, device, equipment, and storage medium of the clothing treatment device provided by the embodiments of the present application control the operation of the clothing treatment device, obtain the sound information of the clothing treatment device; determine the target working parameters of the clothing treatment device based on the sound information; and control the clothing treatment device to operate with the target working parameters. That is to say, in the embodiments of the present application, after controlling the operation of the clothing treatment device, the sound information of the clothing treatment device is obtained to determine the target working parameters of the clothing treatment device, and the clothing treatment device is controlled to operate with the target working parameters. This solves the problem in the related art that the clothing treatment device cannot match the operation parameters according to the clothing information, resulting in poor clothing treatment effect, realizes the intelligent operation of the clothing treatment device, and improves the treatment quality of the clothing. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 It is a schematic flow chart of a control method for a clothing treatment device provided by an embodiment of the present application;
[0019] Figure 2 It is a schematic flow chart of another control method for a clothing treatment device provided by an embodiment of the present application;
[0020] Figure 3 It is a schematic flow chart of another control method for a clothing treatment device provided by an embodiment of the present application;
[0021] Figure 4 It is a schematic structural diagram of a control device for a clothing treatment device provided by an embodiment of the present invention;
[0022] Figure 5 It is a schematic hardware structure diagram of a clothing treatment device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0023] In order to make the purpose, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be further described in detail below in conjunction with the drawings and embodiments. The described embodiments should not be regarded as limitations on the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.
[0024] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.
[0025] The terms "first / second / third" involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.
[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.
[0027] The present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0028] An embodiment of the present application provides a control method for a clothing treatment device. Referring to Figure 1 as shown, the method includes the following steps:
[0029] Step S101: Control the clothing treatment device to operate and obtain the sound information of the clothing treatment device.
[0030] It can be understood that the clothing treatment device may include, but is not limited to, a washing machine, a dryer, a washing and drying integrated machine, and a device with clothing treatment functions. In the embodiments of the present application, an inner drum is usually provided inside the clothing treatment device for placing the clothing to be treated. In real life, clothing includes a variety of different materials, and different materials of clothing need to be matched with corresponding treatment programs during the treatment process to avoid damaging the clothing; for example: Woolen clothing is relatively thick and takes a long time to dry, but at the same time, it is necessary to avoid too high a temperature causing the clothing to shrink or pill.
[0031] The sound information of the clothing treatment device is the sound information generated when the clothing treatment device is operating. The sound information includes, but is not limited to, voiceprint information, and the present application does not make specific limitations on this. A voiceprint is the sound wave spectrum carrying speech information displayed by electroacoustic instruments, including various voiceprint characteristics such as wavelength, frequency, and intensity. The sound information may include the characteristic information of the sound wave spectrum collected when the clothing treatment device is operating. When there is no clothing to be treated placed in the inner drum or different numbers and different types of clothing to be treated are placed, the sound information of the clothing treatment device collected is different.
[0032] In practical applications, after the clothing treatment device has been operating for a period of time, the sound information can be obtained through a voice collection module inside the clothing treatment device, and the present application does not make specific limitations on this. Exemplarily, a microphone can be set inside the clothing treatment device to collect the sound information.
[0033] In some embodiments, controlling the operation of a laundry treatment device includes the laundry treatment device operating according to working parameters set by a user, or pre-operating operations of the laundry treatment device before operating according to the working parameters. This application does not make specific limitations thereto.
[0034] Step S102: Determine the target working parameters of the laundry treatment device based on the sound information.
[0035] It can be understood that the target working parameters may include working parameters related to the operation of the laundry treatment device. The target working parameters may be working parameters obtained by adjusting the working parameters set by the user, or working parameters obtained after pre-operation. This application does not make specific limitations thereto.
[0036] In practical applications, the target working parameters may include a target working frequency and a target working duration. Taking the laundry treatment device as a dryer as an example, the target working frequency can be understood as the working frequency of the compressor of the dryer, and the target working duration can be understood as the working duration of the motor.
[0037] By analyzing the sound information, the clothing information of the clothing to be processed can be obtained. Exemplarily, by using a neural network model to identify the sound information, the clothing information of the clothing to be processed can be obtained. The clothing information may include the number of clothing items and / or the type of clothing. Determining the target working parameters of the laundry treatment device based on the sound information can be understood as matching the working frequency and working duration for the laundry treatment device according to the number of clothing items and / or the type of clothing. Exemplarily, taking the laundry treatment device as a dryer as an example, when there are more woolen clothing items in the clothing to be processed, the working duration of the motor of the dryer can be longer, and the compressor operates at a lower frequency to avoid excessive temperature. The specific working frequency of the compressor and the working duration of the motor can be determined based on a drying mode table constructed from the drying data of different clothing items.
[0038] Step S103: Control the laundry treatment device to operate with the target working parameters.
[0039] After determining the target working parameters, control the laundry treatment device to operate according to the target working parameters.
[0040] In the embodiments of this application, by obtaining the sound information of the laundry treatment device, analyzing the sound information, and determining the target working parameters of the laundry treatment device based on the sound information, different clothing items are matched with target working parameters, solving the problem in the related art that the laundry treatment device cannot match the operating parameters according to the clothing information and the clothing treatment effect is poor, realizing the intelligent operation of the laundry treatment device and improving the quality of clothing treatment.
[0041] In some embodiments of the present application, step S102 of determining the target operating parameters of the laundry treatment device based on the sound information can be implemented in the following manner:
[0042] Input the sound information into the trained neural network model for recognition to obtain the clothing information of the clothing to be processed in the laundry treatment device; the clothing information indicates the type of the clothing to be processed and / or the number of pieces of the clothing to be processed; determine the target operating parameters of the laundry treatment device based on the clothing information.
[0043] It can be understood that the neural network model can be understood as a model constructed based on a neural network. The types of neural networks include, but are not limited to, Deep Neural Networks (DNN), Recurrent Neural Network (RNN), Convolutional Neural Networks (CNN), Feed Forward (FF), and Generative Adversarial Network (GAN). Exemplarily, the trained neural network model in the embodiments of the present application is described by taking CNN as an example.
[0044] The clothing information can at least include two different types of information, namely clothing type information and clothing quantity information. In practical applications, the trained neural network model can be obtained by training the CNN with a large amount of sound information. The trained CNN can analyze the sound information and determine the number of clothing pieces and the clothing type according to the sound characteristics of different clothing. The laundry treatment device can match the target operating frequency and target operating duration for the clothing to be processed according to the number of clothing pieces, the clothing type, and the preset mode table.
[0045] The clothing information can also include clothing weight information. Input the collected sound information into the trained CNN for recognition, and the clothing weight information of the clothing to be processed in the laundry treatment device can be obtained.
[0046] In some embodiments of the present application, inputting the sound information into the trained neural network model for recognition to obtain the clothing information of the clothing to be processed includes:
[0047] Input the sound information into the first branch of the trained neural network model to obtain the first clothing information; the first clothing information indicates the type of the clothing to be processed.
[0048] Input the sound information into the second branch of the trained neural network model to obtain the second clothing information; the second clothing information indicates the number of pieces of the clothing to be processed.
[0049] Input the voice information into the third branch of the trained neural network model to obtain the third clothing information; the third clothing information indicates the weight of the clothing to be processed.
[0050] It can be understood that a CNN can include multiple branches. Different branches can perform different data analyses on the input data and then output results at the output layer. The first branch can be understood as the branch for analyzing the clothing type based on the voice information, the second branch can be understood as the branch for analyzing the number of clothing pieces based on the voice information; the third branch can be understood as the branch for analyzing the weight of the clothing based on the voice information.
[0051] In practical applications, different branches of the CNN can perform different feature extractions on the same data. After a large amount of data training, the classification processing of the information is completed. A large amount of voice information during the operation of the clothing processing device can be obtained to train the first branch, the second branch, and the third branch of the CNN. After the training is completed, the voice information to be recognized can be input into the first branch, the second branch, and the third branch respectively to obtain the clothing type information, the number of clothing pieces information, and the clothing weight information.
[0052] In some embodiments of the present application, the method further includes:
[0053] Obtain training sample data; the training sample data includes sample voice information, first sample clothing information, and second sample clothing information;
[0054] Input each group of sample voice information and the first sample clothing information into the neural network model for training to obtain the first branch of the trained neural network model;
[0055] Input each group of sample voice information and the second sample clothing information into the neural network model for training to obtain the second branch of the trained neural network model.
[0056] It can be understood that the first sample clothing information, the second sample clothing information, and the sample voice information in each group of sample clothing data can be correspondingly marked, and then each group of sample voice information and the first sample clothing information are input into the first branch of the CNN for training. The first branch performs feature extraction on the voice information to complete the classification of the clothing type; each group of sample voice information and the second sample clothing information are input into the second branch of the CNN for training. The second branch performs feature extraction on the voice information to complete the classification of the number of clothing pieces. And so on.
[0057] In practical applications, for combinations of different clothing types, numbers of clothing items, and clothing weights, the sound of the clothing processing device when processing the combined clothing can be collected, and the collected sound data can be segmented. For example, sound data of 2 - 3s can be used as a sample for annotation to form training set data. The number of samples in the training set data can be set according to actual needs. Exemplarily, at least 2000 samples can be set to form the training set data. For the training process of the neural network model, multiple different neural network models can be selected for supervised training. During this process, the training data and parameters of each model are adjusted to optimize the performance of the model, and the best model is selected for use to determine the number of clothing items, clothing type, and clothing weight of the clothing to be processed by analyzing the sound information.
[0058] As can be seen from the above, in the embodiments of the present application, by obtaining the sound information of the clothing processing device and analyzing the sound information, at least one of the clothing type, number of clothing items, and clothing weight of the clothing to be processed in the clothing processing device is obtained; based on at least one of the clothing type, number of clothing items, and clothing weight of the clothing to be processed, the target working parameters of the clothing processing device are determined, so as to match the target working duration and target working frequency for different clothing, solving the problem in the related art that the clothing processing device cannot match the operating parameters according to the clothing information and the clothing processing effect is poor, realizing the intelligent operation of the clothing processing device and improving the processing quality of the clothing.
[0059] In some embodiments of the present application, determining the target working parameters of the clothing processing device based on the clothing information includes:
[0060] Based on the clothing information, query the target working duration and target working frequency corresponding to the clothing to be processed in the preset mode table.
[0061] It can be understood that the preset mode table can be at least one of a washing mode table, a drying mode table, and a care mode table.
[0062] The washing mode table can be constructed by marking and screening historical washing data, or by inputting a large amount of clothing washing data into a neural network model, training the neural network model, and predicting the target working duration and target working frequency of different combined clothing by the model. The present application does not make specific limitations on this. The target working duration can be the target washing duration, and the target working frequency can be the frequency of the motor operation.
[0063] The drying mode table can be constructed by marking and screening historical drying data, or by inputting a large amount of clothing drying data into a neural network model, training the neural network model, and predicting the target drying duration and target drying frequency of different combined clothing by the model. Table 1 is an example of the drying mode table.
[0064] Number of clothing items Clothing type Compressor frequency Drying time 5 Jeans + cotton coat 3000Hz 100min 3 Shirt 2000Hz 50min … … … …
[0065] Table 1
[0066] When the clothes to be processed include 5 pairs of jeans and a cotton coat, the matching drying frequency is 3000 Hz and the drying duration is 100 minutes; when the clothes to be processed include a combined clothing of 3 shirts, the matching drying frequency is 2000 Hz and the drying duration is 50 minutes. The above is only an example of this application and is not exhaustive here.
[0067] In some embodiments of the present application, the method further includes:
[0068] Obtaining historical working data; the historical working data includes the type of historical processed clothes, the number of historical processed clothes, the historical working duration, and the historical working frequency;
[0069] Based on the type of historical processed clothes, the number of historical processed clothes, the historical working duration, and the historical working frequency, constructing a preset mode table.
[0070] It can be understood that the preset mode table can be constructed for the historical working data of different combinations of clothing types, the number of clothes, and the weight of clothes. For the same type of combined clothes, different working durations and working frequencies can be adopted, and the optimal working duration and working frequency of each type of combined clothes can be determined according to the humidity and softness of the clothes.
[0071] In practical applications, at least several thousand groups of clothing combinations can be set to complete the work and recorded to construct a preset mode table. When determining the clothing type, the number of clothes, and the weight of the combined clothes, the working duration and working frequency corresponding to different combined clothes can be determined by querying the preset mode table.
[0072] The washing mode table is constructed based on the clothing type of historical washed clothes, the number of historical washed clothes, the historical washing duration, and the historical washing frequency.
[0073] The drying mode table is constructed based on the clothing type of historical dried clothes, the number of historical dried clothes, the historical drying duration, and the historical drying frequency.
[0074] The care mode table is constructed based on the clothing type of historical cared clothes, the number of historical cared clothes, the historical care duration, and the historical care frequency.
[0075] As can be seen from the above, in the embodiments of the present application, by obtaining the sound information of the clothing treatment device, analyzing the sound information, at least one of the clothing type, the number of clothing pieces, and the clothing weight of the clothing to be treated in the clothing treatment device is obtained; according to at least one of the clothing type, the number of clothing pieces, and the clothing weight of the clothing to be treated, the target working parameters of the clothing treatment device are queried in the preset mode table, so as to realize matching the target working duration and the target working frequency for different clothes, solve the problem in the related art that the clothing treatment device cannot match the operating parameters according to the clothing information and the clothing treatment effect is not good, realize the intelligent operation of the clothing treatment device, and improve the treatment quality of the clothes.
[0076] In an implementable scenario, as shown in Figure 2 a control method of a clothing treatment device can be implemented in the following manner:
[0077] Step S201: Install a microphone in the washing machine or dryer barrel, and obtain a sound signal through the microphone.
[0078] Step S202: Construct a clothing type and number recognition network, and determine the clothing type and number in the barrel by judging the sound signal through this network.
[0079] Step S203: Match a washing or drying program for the user.
[0080] In some embodiments of the present application, the method further includes:
[0081] Determine the operating information of the clothing treatment device based on the sound information;
[0082] If the operating information indicates that the operating state of the clothing treatment device is the first state, control the clothing treatment device to operate with the target working parameters;
[0083] If the operating information indicates that the operating state of the clothing treatment device is the second state, control the clothing treatment device to stop operating.
[0084] It can be understood that the operating information can be understood as the operating state information of different components in the clothing treatment device. The clothing treatment device mainly includes three components: a motor, a compressor, and an inner barrel. The operating information can include the motor operating information of the clothing treatment device, the compressor operating information of the clothing treatment device, and the inner barrel operating information. The sound information can be input into the trained neural network model to determine the operating information of the clothing treatment device. A large amount of training data is collected to train the neural network model, so that the neural network model can extract the operating information related to the clothing treatment device according to the sound information of the clothing treatment device. The present application does not make specific limitations on this.
[0085] In practical applications, if the motor operation information indicates that the operating state of the motor is the first state, it can be understood that the operating state of the motor is normal; if the motor operation information indicates that the operating state of the motor is the second state, it can be understood that the operating state of the motor is abnormal. If the compressor operation information indicates that the operating state of the compressor is the first state, it can be understood that the operating state of the compressor is normal; if the compressor operation information indicates that the operating state of the compressor is the second state, it can be understood that the operating state of the compressor is abnormal. If the inner drum operation information indicates that the operating state of the inner drum is the first state, it can be understood that the operating state of the inner drum is normal; if the inner drum operation information indicates that the operating state of the inner drum is the second state, it can be understood that the operating state of the inner drum is abnormal. If any component of the motor, compressor, and inner drum is abnormal, the laundry treatment device cannot operate normally to complete the treatment of the laundry. At this time, it is necessary to control the laundry treatment device to stop operating.
[0086] In some embodiments of the present application, the operation information includes at least one of the following:
[0087] The motor operation information of the laundry treatment device;
[0088] The compressor operation information of the laundry treatment device;
[0089] The inner drum operation information of the laundry treatment device.
[0090] In practical applications, the laundry treatment device mainly includes three components: a motor, a compressor, and an inner drum. Detecting the operating states of the motor, compressor, and inner drum can prevent the laundry treatment device from being in an abnormal state and being unable to complete the treatment of the laundry or being unable to treat the laundry according to the target operating parameters, resulting in damage to the laundry.
[0091] In a realizable scenario, taking the laundry treatment device as a dryer as an example, referring to Figure 3 as shown, a control method for a laundry treatment device can also be implemented in the following manner:
[0092] Step S301: The dryer starts to operate.
[0093] Step S302: The microphone collects the sound of the dryer.
[0094] Step S303: The algorithm analyzes the state of the dryer and the weight of the laundry to be dried.
[0095] Step S304: Determine whether the dryer is in a normal working state; if so, execute Step S305; if not, execute Step S306.
[0096] Step S305: Match the best program according to the predicted weight.
[0097] Step S306: Prompt the user that the dryer is abnormal.
[0098] Based on the same inventive concept as described above, Figure 4 FIG. 4 is a schematic structural diagram of a control device of a laundry treatment apparatus provided by an embodiment of the present invention. The control device 400 of the laundry treatment apparatus includes:
[0099] An acquisition unit 401, configured to control the operation of the laundry treatment apparatus and acquire sound information of the laundry treatment apparatus;
[0100] A determination unit 402, configured to determine a target operating parameter of the laundry treatment apparatus based on the sound information;
[0101] A control unit 403, configured to control the laundry treatment apparatus to operate with the target operating parameter.
[0102] In some embodiments of the present application, the determination unit 402 is further configured to input the sound information into a trained neural network model for recognition to obtain laundry information of laundry to be processed in the laundry treatment apparatus; the laundry information indicates the type of the laundry to be processed and / or the number of pieces of the laundry to be processed; and determine the target operating parameter of the laundry treatment apparatus based on the laundry information.
[0103] In some embodiments of the present application, the determination unit 402 is further configured to input the sound information into a first branch of the trained neural network model to obtain first laundry information; the first laundry information indicates the type of the laundry to be processed; input the sound information into a second branch of the trained neural network model to obtain second laundry information; the second laundry information indicates the number of pieces of the laundry to be processed.
[0104] In some embodiments of the present application, the acquisition unit 401 is further configured to acquire training sample data; the training sample data includes sample sound information, first sample laundry information, and second sample laundry information; input each group of sample sound information and first sample laundry information into the neural network model for training to obtain the first branch of the trained neural network model; input each group of sample sound information and second sample laundry information into the neural network model for training to obtain the second branch of the trained neural network model.
[0105] In some embodiments of the present application, the determination unit 402 is further configured to query a target working duration and a target working frequency corresponding to the laundry to be processed in a preset mode table based on the laundry information.
[0106] In some embodiments of the present application, the acquisition unit 401 is further configured to acquire historical working data; the historical working data includes the type of the laundry processed historically, the number of pieces of the laundry processed historically, the historical working duration, and the historical working frequency; and construct a preset mode table based on the type of the laundry processed historically, the number of pieces of the laundry processed historically, the historical working duration, and the historical working frequency.
[0107] In some embodiments of the present application, the control unit 403 is further configured to determine the operation information of the laundry treatment device based on the sound information; if the operation information indicates that the operation state of the laundry treatment device is the first state, control the laundry treatment device to operate with target working parameters; if the operation information indicates that the operation state of the laundry treatment device is the second state, control the laundry treatment device to stop operating.
[0108] In some embodiments of the present application, the operation information includes at least one of the following:
[0109] The motor operation information of the laundry treatment device;
[0110] The compressor operation information of the laundry treatment device;
[0111] The inner drum operation information of the laundry treatment device.
[0112] Based on the foregoing embodiments, an embodiment of the present application provides a laundry treatment device, Figure 5 FIG. is a schematic hardware structure diagram of the laundry treatment device according to an embodiment of the present invention. The laundry treatment device 500 includes: at least one processor 501, a memory 502. Optionally, the laundry treatment device 500 may further include at least one communication interface 503. Each component in the laundry treatment device 500 is coupled together through a bus system 504. It can be understood that the bus system 504 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 504 further includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, in Figure 5 all kinds of buses are labeled as the bus system 504.
[0113] It can be understood that the memory 502 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), sync link dynamic random access memory (SLDRAM), direct rambus random access memory (DRRAM).The memory 502 described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memories.
[0114] The memory 502 in the embodiments of the present invention is used to store various types of data to support the operation of the laundry treatment device 500. Examples of such data include: any computer programs for operating on the laundry treatment device 500, and the programs implementing the methods of the embodiments of the present invention may be included in the memory 502.
[0115] The methods disclosed in the embodiments of the present invention above can be applied to the processor 501 or implemented by the processor 501. The processor may be an integrated circuit chip with the ability to process signals. In the implementation process, the steps of the above methods can be completed by the integrated logic circuit in hardware or instructions in software form in the processor. The above-mentioned processor may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. Combining the steps of the methods disclosed in the embodiments of the present invention, it can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in the storage medium, and this storage medium is located in the memory. The processor reads the information in the memory and combines its hardware to complete the steps of the foregoing methods.
[0116] In an exemplary embodiment, the laundry treatment device 500 can be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontroller units (MCUs), microprocessors, or other electronic components, for executing the above methods.
[0117] Based on the foregoing embodiments, an embodiment of the present application provides a storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are configured to execute Figure 1 the control method of the laundry treatment device provided in the corresponding embodiment.
[0118] It should be noted that the above computer storage medium may be a memory such as ROM, PROM, EPROM, EEPROM, FRAM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM; or it may be various electronic devices including one or any combination of the above memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.
[0119] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without more limitations, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including that element.
[0120] The serial numbers of the above embodiments of the present application are only for description and do not represent the superiority or inferiority of the embodiments.
[0121] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.
[0122] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.
[0123] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the function specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the block or blocks.
[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the block or blocks.
[0125] The foregoing are only preferred embodiments of the present application and do not limit the scope of patents of the present application. Any equivalent structural or equivalent process transformations made by using the contents of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, are equally included in the scope of patent protection of the present application.
Claims
1. A control method for a clothing treatment device, characterized in that, The method includes: Controlling the operation of the laundry treatment device and obtaining the sound information of the operation of the laundry treatment device; Determining the target working parameters of the laundry treatment device based on the sound information; Controlling the laundry treatment device to operate with the target working parameters.
2. The method according to claim 1, characterized in that, The determining the target working parameters of the laundry treatment device based on the sound information includes: Inputting the sound information into a trained neural network model for recognition to obtain the clothing information of the laundry to be processed in the laundry treatment device; the clothing information indicates the type of the laundry to be processed and / or the number of pieces of the laundry to be processed; Determining the target working parameters of the laundry treatment device based on the clothing information.
3. The method according to claim 2, characterized in that, The inputting the sound information into a trained neural network model for recognition to obtain the clothing information of the laundry to be processed includes: Inputting the sound information into the first branch of the trained neural network model to obtain the first clothing information; the first clothing information indicates the type of the laundry to be processed; Inputting the sound information into the second branch of the trained neural network model to obtain the second clothing information; the second clothing information indicates the number of pieces of the laundry to be processed.
4. The method according to claim 3, characterized in that, The method further includes: Obtaining training sample data; the training sample data includes sample sound information, first sample clothing information, and second sample clothing information; Inputting each group of the sample sound information and the first sample clothing information into the neural network model for training to obtain the first branch of the trained neural network model; Inputting each group of the sample sound information and the second sample clothing information into the neural network model for training to obtain the second branch of the trained neural network model.
5. The method according to claim 1, characterized in that, The determining the target working parameters of the laundry treatment device based on the clothing information includes: Querying the target working duration and target working frequency corresponding to the laundry to be processed in a preset mode table based on the clothing information.
6. The method according to claim 5, characterized in that, The method further includes: Obtaining historical working data; the historical working data includes the type of the historical processed laundry, the number of pieces of the historical processed laundry, the historical working duration, and the historical working frequency; Constructing the preset mode table based on the type of the historical processed laundry, the number of pieces of the historical processed laundry, the historical working duration, and the historical working frequency.
7. The method according to claim 1, characterized in that, The method further includes: Determining the operation information of the laundry treatment device based on the sound information; If the operation information characterizes that the operation state of the laundry treatment device is the first state, controlling the laundry treatment device to operate with the target working parameters; If the operation information characterizes that the operation state of the laundry treatment device is the second state, controlling the laundry treatment device to stop operating.
8. The method according to claim 7, characterized in that, The operation information includes at least one of the following: The motor operation information of the laundry treatment device; The compressor operation information of the laundry treatment device; The inner drum operation information of the laundry treatment device.
9. A control device for a clothing treatment device, characterized in that, The control device of the laundry treatment device includes: An obtaining unit, configured to control the operation of the laundry treatment device and obtain the sound information of the laundry treatment device; A determination unit, configured to determine a target operating parameter of the laundry treating device based on the sound information; A control unit, configured to control the laundry treating device to operate with the target operating parameter.
10. A clothing treatment device, characterized in that, The laundry treating device includes: A memory, configured to store executable instructions; A processor, configured to implement the steps of the control method of the laundry treating device according to any one of claims 1 to 8 when executing the executable instructions stored in the memory.
11. A storage medium storing computer-executable instructions configured to execute the control method of the clothing treatment device provided in any one of claims 1 to 8 above.