Operation method of clothes treatment equipment, control device and storage medium

Abstract

The invention relates to an operation method of clothes treatment equipment, a control device and a storage medium. The method comprises the following steps: acquiring first state parameter information of to-be-processed clothes; based on the first state parameter information, state parameter information matching is conducted on cases in a case library, at least one piece of first operation strategy information is obtained, and the cases comprise the state parameter information and operation strategy information corresponding to the state parameter information; and controlling the current equipment to operate based on the first operation strategy information. According to the technical scheme, the first state parameter information of the to-be-processed clothes is obtained and is matched with the cases in the case library in detail, so that personalized adjustment of the operation strategy of the clothes processing equipment is successfully realized. The process ensures that the most suitable operation strategy can be automatically selected according to the state parameter information of the clothes in each clothes treatment process. Therefore, the technology obviously improves the clothes treatment efficiency and effect.

Description

Technical Field

[0001] The present invention relates to the technical field of clothing processing, and specifically provides an operating method, a control device and a storage medium for clothing processing equipment. Background Art

[0002] In the prior art, clothing processing equipment such as washing machines usually rely on preset programs to run various processing stages, but these programs do not take into account the clothing conditions of the clothing to be processed placed in the equipment, such as the specific number of clothes and material differences.

[0003] For example, during the dehydration stage of a washing machine, the quantity and material of the clothes directly affect the efficiency of water removal. Simply using the preset program may result in insufficient dehydration or over-dehydration of the clothes, resulting in poor clothing treatment results.

[0004] Accordingly, the art requires a new operation solution for clothes processing equipment to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects, the present invention is proposed to provide a solution or at least partially solve the technical problem in the prior art of poor clothing treatment effect caused by relying solely on preset programs to control the operation of clothing treatment equipment.

[0006] In a first aspect, the present invention provides a method for operating a clothing processing device, the method comprising: obtaining first state parameter information of the clothing to be processed; matching state parameter information of cases in a case library based on the first state parameter information to obtain at least one first operation strategy information, wherein the case includes state parameter information and operation strategy information corresponding to the state parameter information; and controlling the operation of the current device based on the first operation strategy information.

[0007] As an alternative or supplement to the above scheme, in a method according to an embodiment of the present invention, the "controlling the operation of the current device based on the first operation strategy information" includes: obtaining first relevant parameters of the current device and second relevant parameters of the clothing processing device corresponding to the first operation strategy information; correcting the first operation strategy information based on the first relevant parameters and the second relevant parameters to obtain second operation strategy information; and controlling the operation of the current device based on the second operation strategy information.

[0008] As an alternative or supplement to the above scheme, in a method according to an embodiment of the present invention, the method further includes: obtaining the mode in which the current device is ready to run; the "matching the state parameter information of the cases in the case library based on the first state parameter information to obtain at least one operation strategy information" includes: matching the state parameter information of the cases in the case library based on the operation mode and the first state parameter information to obtain at least one operation strategy information.

[0009] As an alternative or supplement to the above scheme, in a method according to an embodiment of the present invention, the "matching state parameter information of cases in the case library based on the first state parameter information to obtain at least one operation strategy information" includes: preprocessing the first state parameter information to obtain second state parameter information; obtaining corresponding similarities based on the second state parameter information and the state parameter information of the cases in the case library; comparing the various similarities to obtain cases that meet preset requirements; and obtaining at least one first operation strategy information based on the cases that meet the preset requirements.

[0010] As an alternative to or supplement to the above scheme, in a method according to an embodiment of the present invention, the “preprocessing the first state parameter information to obtain the second state parameter information” includes: performing a normalization operation on the first state parameter information to obtain the second state parameter information; and the “obtaining the corresponding similarity based on the second state parameter information and the state parameter information of the cases in the case library” includes: obtaining the corresponding similarity by calculating the weighted Euclidean distance between the second state parameter information and the state parameter information of the cases in the case library.

[0011] As an alternative or supplement to the above solution, in a method according to an embodiment of the present invention, the "preprocessing the first state parameter information to obtain the second state parameter information" includes: performing a PCA operation on the first state parameter information to obtain the second state parameter information.

[0012] As an alternative or supplement to the above solution, in a method according to an embodiment of the present invention, the state parameter information of the case is data processed by a preprocessing method consistent with the second state parameter information.

[0013] As an alternative or supplement to the above solution, in a method according to an embodiment of the present invention, the first state parameter information includes state parameters, and the state parameters in the first state parameter information are set based on the mode in which the current device is ready to run.

[0014] In a second aspect, a control device is provided, which includes a processor and a storage device, wherein the storage device is suitable for storing multiple computer programs, and the computer programs are suitable for being loaded and run by the processor to execute the operating method of the clothing processing device described in any one of the technical solutions of the operating method of the above-mentioned clothing processing device.

[0015] In a third aspect, a computer-readable storage medium is provided, which stores a plurality of computer programs, wherein the computer programs are suitable for being loaded and run by a processor to execute the method for operating a clothing processing device described in any one of the technical solutions for the method for operating a clothing processing device.

[0016] The above one or more technical solutions of the present invention have at least one or more of the following Beneficial effects:

[0017] In implementing the technical solution of this invention, by acquiring the first state parameter information of the clothing to be treated and matching it with the cases in the case library, the operating strategy of the clothing treatment equipment is successfully adjusted individually. This process ensures that the most appropriate operating strategy is automatically selected for each clothing treatment process based on the clothing state parameter information. As a result, this technology significantly improves the efficiency and effectiveness of clothing treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The disclosure of the present invention will be more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Furthermore, similar numbers in the drawings represent similar components, wherein:

[0019] Figure 1 is a flow chart of main steps of an operating method of a clothes treating apparatus according to one embodiment of the present invention;

[0020] Figure 2 is a flowchart of the secondary steps of an operating method of a clothes processing apparatus according to one embodiment of the present invention;

[0021] Figure 3 1 is a flow chart of the secondary steps of an operating method of a clothes processing apparatus according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] Some embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] In the description of the present invention, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as computer programs, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing computer programs, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, and the like. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "a" and "the" may also include the plural forms.

[0024] Example 1:

[0025] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of the method for operating a clothes processing device according to an embodiment of the present invention. Figure 1 As shown, the operating method of the clothes processing device in the embodiment of the present invention mainly includes the following steps S10 to S30.

[0026] Step S10: Acquire first state parameter information of the clothes to be processed.

[0027] In this embodiment, the state parameter information is a set of data or features describing the current state of the laundry to be processed. The first state parameter information specifically refers to the acquired state parameter information of the laundry to be processed. The state parameter information is composed of one or more state parameters.

[0028] In one embodiment, the status parameter information may include one of the status parameters of the type, weight, material, color, or degree of stain of the laundry to be processed, or a combination of multiple status parameters.

[0029] In this embodiment, clothes with different state parameter information are processed in different ways to achieve the best washing effect. In this embodiment, by obtaining and analyzing the first state parameter information, the subsequent clothes processing equipment can adjust its operation strategy based on the first state parameter information to adapt to the specific needs of various clothes, thereby improving washing efficiency, saving energy, and extending the service life of clothes.

[0030] In another embodiment, the first state parameter information is related to the mode in which the current device is ready to run. Specifically, the state parameters in the first state parameter information are set based on the mode in which the current device is ready to run.

[0031] For example, in the laundry mode, important status parameters may include the degree of soiling and the type of clothing material, as these status parameters will affect the selected operating strategy. On the contrary, in the dehydration mode, the degree of soiling is not a factor that should be considered under normal conditions, so it is not necessary information, while the weight of the clothing and the type of clothing material may become key factors in determining the dehydration speed. Therefore, in the example provided in this embodiment, if the mode in which the current device is ready to operate is the laundry mode, then the first status parameter information includes the degree of soiling and may also include information such as the type of clothing material. On the contrary, if the mode in which the current device is ready to operate is the dehydration mode, then the first status parameter information does not include the degree of soiling, but may include information such as the weight of the clothing and the type of clothing material.

[0032] In this embodiment, the specific state parameters in the first state parameter information are closely associated with the current operating mode of the clothing treatment device, making the collection of the first state parameter information more targeted and ensuring that the device can obtain key information to optimize its operating strategy. Furthermore, by associating the specific state information in the first state parameter information with the current operating mode of the clothing treatment device, the state parameters can be collected more specifically, avoiding the collection of meaningless state parameters, reducing the amount of subsequent calculations, and making it easier and more convenient for the current device to find the most appropriate strategy.

[0033] Furthermore, an example is given to illustrate a method for obtaining the first state parameter information, as follows:

[0034] In the example given in this embodiment, the first state parameter information includes the weight, material, color depth, degree of stain, etc. of the clothes to be processed.

[0035] In one embodiment, a weight sensor can be used to measure the total weight of the laundry to be treated placed in the laundry treating apparatus.

[0036] In one embodiment, material identification can be achieved through infrared sensors or image recognition technology, and the material of the clothing, such as cotton, wool, or synthetic fiber, can be determined by analyzing the texture and spectral characteristics of the clothing surface.

[0037] In one embodiment, the color depth of the clothes can be achieved through spectral analysis or color sensor to distinguish dark and light clothes, thereby affecting subsequent clothes processing operations.

[0038] In one embodiment, stain detection may utilize a combination of image recognition technology and chemical sensors to identify the type and severity of the stain (e.g., oil, blood, or red wine). The stain level and type can be used to adjust the duration and intensity of the wash cycle, or even selectively use a specific detergent.

[0039] Step S20: Matching the state parameter information of cases in the case library based on the first state parameter information to obtain at least one first operation strategy information.

[0040] In this embodiment, the case includes state parameter information and operation strategy information corresponding to the state parameter information.

[0041] In one embodiment, the case library is a pre-established database that stores a large amount of historical laundry processing data, including state parameter information for each instance and its corresponding operational strategy information. In one embodiment, the operational strategy information includes one of water usage, detergent usage, washing or drying time, and a washing or drying curve. In this embodiment, the operational strategy information is used to directly control the operation of the laundry processing device.

[0042] Furthermore, in one embodiment, the case library collects appropriate historical data from various clothing processing processes. In one embodiment, suitability can be determined by human judgment, whereby the data is compared with expected results to determine suitability and then stored in the case library. In another embodiment, the data in the case library can be derived from user experience with various operating strategies, with user satisfaction scores or positive reviews.

[0043] Furthermore, in one embodiment, at least one first operation strategy information is obtained through steps S201-S204, such as Figure 2 As shown, the details are as follows:

[0044] Step S201: pre-process the first state parameter information to obtain second state parameter information.

[0045] In this embodiment, selecting different pre-processing methods will have different effects on subsequent steps.

[0046] In one embodiment, the preprocessing is performed by a normalization operation, specifically, implemented through step S201 - 1 .

[0047] Step S201 - 1 : normalize the first state parameter information to obtain second state parameter information.

[0048] In one embodiment, normalization is the process of converting data of different dimensions to the same scale to eliminate dimensionality effects and enable comparison of the same state parameter across different state parameter information. In one embodiment, normalization can be achieved by subtracting the mean and dividing by the standard deviation, ensuring that the mean value of each parameter is 0 and the standard deviation is 1. This ensures that when matching state parameters, each state parameter, regardless of its original magnitude, has a balanced impact on the matching result.

[0049] Step S202: Based on the second state parameter information and the state parameter information of the cases in the case library, obtain the corresponding similarity.

[0050] In one embodiment, the similarity is obtained through step S202 - 1 .

[0051] Step S202-1: Calculate the weighted Euclidean distance between the second state parameter information and the state parameter information of the cases in the case library to obtain the corresponding similarity.

[0052] In this embodiment, the similarity is the weighted Euclidean distance between the second state parameter information and the state parameter information of the cases in the case library.

[0053] In one embodiment, the weighted Euclidean distance is a method for measuring the actual distance between two points in a multidimensional space. The weighted Euclidean distance takes into account the differences in the importance of different state parameters on the final matching result. In this embodiment, certain state parameters are given higher weights because they have a greater impact on the clothing processing behavior. Since the weights of the state parameters are different, they will ultimately affect the similarity result. For example, it may happen that due to the different weights of the state parameters, there may be a case in which many state parameters in the state parameter information of this case are similar to the state parameters of the second state parameter information, but because the weights of these state parameters are low, the overall calculated similarity will be low.

[0054] In one embodiment, the calculation formula of the weighted Euclidean distance is:

[0055]

[0056] Among them, d(x,y) is the similarity, x and y are the state parameters in the second state parameter information and the state parameters in the state parameter information of the case, respectively, w i is the weight of the i-th state parameter, and n is the total number of state parameters.

[0057] In this embodiment, the above method can quantitatively evaluate the similarity between the second state parameter information of the garment to be treated and each case in the case library. For example, if the garment to be treated is made of cotton and contains oil stains, and there is a case in the case library that also treats cotton and oil stains, then the state parameters for the material and stain type of the two cases will be very similar, and therefore the weighted Euclidean distance between them will be relatively small. If all other state parameters are the same, then the two scenarios are relatively similar.

[0058] In this embodiment, through the technical solution of this step, the clothing processing equipment can accurately identify and select the solution that best suits the current clothing state from many possible washing solutions, ensuring that the washing process is more in line with the current state of the clothing to be processed and achieving better results.

[0059] Step S203: Compare the similarities to obtain cases that meet preset requirements.

[0060] In one embodiment, the purpose of comparing similarities is to identify the case that best matches the state of the clothing to be processed. A smaller weighted Euclidean distance means a higher similarity, indicating that the state of the clothing to be processed is closer to the state of a case in the case library.

[0061] In one embodiment, cases that meet a preset requirement are screened based on similarity. In this embodiment, the preset requirement is a preset number of cases with the highest similarity that need to be obtained.

[0062] In one implementation, if the preset requirement is to select the three cases with the highest similarity, a specific implementation is provided here:

[0063] First, all cases are sorted in ascending order based on similarity. Then, the top three cases with the highest similarity, i.e., the top three cases with the shortest distance, are selected from the sorted results as cases that meet the preset requirements.

[0064] Preferably, using a quick sort or heap sort algorithm can complete the sorting task of large-scale data in a shorter time.

[0065] Step S204: obtaining at least one first operation strategy information based on the case meeting the preset requirements.

[0066] In one embodiment, based on the screened cases that meet the preset requirements, the system will extract the first operation strategy information recorded in these cases.

[0067] Through the precise execution of these two steps, the clothing treatment equipment can automatically select the operating strategy (operation strategy) that best matches the clothing to be treated, thereby improving the intelligence level of the equipment and enabling it to provide personalized washing and care solutions based on the specific needs of different clothing.

[0068] Step S30: Control the current device operation based on the first operation strategy information.

[0069] In one embodiment, the first operation strategy information is directly used to control the operation of the current device. In this embodiment, based on the amount of the first operation strategy information, there will be different subsequent processing solutions. Specifically as follows:

[0070] When there is only one piece of first operating strategy information, that operating strategy is directly used to set the current device's operating mode. The current device will operate according to the control information in the first operating strategy information. In one embodiment, the first operating strategy information includes: water volume, detergent dosage, washing time, or drying temperature. For example, if the first operating strategy information matched from the case library recommends an operating strategy with a water volume of 2 liters, the device will automatically set the water volume to 2 liters and perform subsequent operations, such as starting a washing program, based on this parameter.

[0071] When there is more than one first operating strategy information, each case provides a set of operating strategies. In this case, a method is needed to integrate these operating strategies to determine the final operating settings for the device. In one embodiment, the average of all recommended operating strategies is calculated and used as the device's operating strategy. For example, if the three most matching cases recommend water volumes of 2 liters, 2.1 liters, and 1.9 liters, respectively, the device's water volume setting will be adjusted to the average of these three recommended values, i.e., 2 liters.

[0072] In addition to calculating the average, other methods can be considered to combine multiple operational strategies, such as using a weighted average method. In this method, different weights are assigned to each case based on their similarity, so that cases with higher similarity have a greater impact on the final operational strategy. Alternatively, the median method can be considered.

[0073] After the final operation strategy is determined using the above method, the device control system will adjust various settings accordingly to start the clothing treatment program.

[0074] Example 2:

[0075] Most of the techniques in this embodiment are the same as those in Example 1. The difference is that step S30 in this embodiment: controlling the operation of the current device based on the first operation strategy information. Other than that, the remaining techniques are the same as those in Example 1 and are not described in detail here.

[0076] In this embodiment, step S30 is implemented through steps S301-S303. Figure 3 As shown, the details are as follows:

[0077] Step S301: obtaining first relevant parameters of the current device and second relevant parameters of the clothes processing device corresponding to the first operation strategy information.

[0078] In one embodiment, the relevant parameters refer to device-specific parameters that affect the device's operating efficiency and laundry treatment results, such as the drum radius, the motor's speed limit, the heating element's maximum power output, etc. The relevant parameters are directly related to the device's laundry treatment capabilities.

[0079] In one embodiment, the first relevant parameter is preset in the system. The first relevant parameter can be obtained from a product manual or measured and set by a technician before the device leaves the factory. For example, the current device may have a preset roller radius of 35 cm, which is a fixed value determined based on actual measurements.

[0080] In one embodiment, the second relevant parameter is obtained from the case library, and the second relevant parameter records the relevant parameters of the clothes processing device corresponding to the case. By comparing the first relevant parameter and the second relevant parameter, the difference between the current device and the clothes processing devices in the case library can be identified.

[0081] In this embodiment, by setting and comparing relevant parameters, the impact of structural and performance differences between different device models can be reduced or eliminated, allowing the first operating strategy information extracted from the case library to be more accurately applied to the current device after further processing. By obtaining and analyzing the first and second relevant parameters, the system can make necessary adjustments and corrections to the first operating strategy information from the case library to ensure its applicability to the current device configuration and performance range, thereby achieving more optimized and personalized clothing treatment results.

[0082] For example, if a case in the case library is based on a device with a motor speed limit of 1200 rpm, while the current device has a motor speed limit of 1000 rpm, then directly applying the operation strategy of this case may cause the device to overload. By obtaining this difference information in step S301, the system can adjust other operation strategies accordingly, such as increasing the time of the spin stage, thereby achieving better clothing treatment results than directly using the first operation strategy.

[0083] Step S302: modifying the first operation strategy information based on the first relevant parameter and the second relevant parameter to obtain second operation strategy information.

[0084] In one embodiment, the first operating strategy information extracted from the case library is fine-tuned based on the specific capabilities and characteristics of the current device to form second operating strategy information suitable for the current device. This process is achieved by comparing and analyzing the first relevant parameters (i.e., the inherent properties of the current device) with the second relevant parameters (i.e., the inherent properties of clothing treatment devices in similar situations in the case library).

[0085] In one embodiment, the second operation strategy information is obtained by a performance curve matching method, as follows:

[0086] The performance curve matching method optimizes device performance by precisely adjusting operating strategies while taking into account device performance differences. Specifically, a mathematical model is used to describe and compare the performance curves of the current device (characterized by its first relevant parameter) with those of clothing treatment devices in a case library (described by a second relevant parameter).

[0087] The specific methods are as follows:

[0088] Mathematical models are used to describe the relationship between the first and second relevant parameters and the equipment operation strategy. These models can be built based on physical principles or learned from large amounts of operating data through data-driven methods such as regression analysis.

[0089] By comparing the performance curve of the current device with that of devices in the case library, the model can identify the direction and magnitude of adjustments to the operating strategy. For example, if the heating element power of the current device is lower than that of the case device, and the wash temperature in the case has been shown to be particularly effective for removing a certain type of stain, the model will indicate that the heating time should be increased to compensate for the power shortfall in order to achieve a similar wash temperature.

[0090] Then, according to the result of the performance curve matching, the first operation strategy information is adjusted to form the second operation strategy information adapted to the first relevant parameter of the current device.

[0091] In this embodiment, the performance curve matching method is used to achieve accurate operation strategy adjustment, ensuring that even when there are significant performance differences between devices, the operation strategy can be adjusted to enable the current device to achieve the best processing effect.

[0092] In another embodiment, a neural network model is used to adjust and optimize the first operating strategy information to obtain second operating strategy information suitable for the current device. Specifically, a neural network model is constructed that takes the first relevant parameter and the second relevant parameter as input and outputs the adjusted operating strategy. The neural network training process relies on historical operating data, including relevant parameters of different devices and the corresponding optimal operating strategies.

[0093] Specifically, the first step is to select an appropriate neural network architecture, such as a multi-layer perceptron (MLP), convolutional neural network (CNN), or recurrent neural network (RNN). Next, a large amount of device operation data is collected, including relevant parameters of different device models and operation strategies from successful cases. This data needs to be preprocessed, such as normalized, to make it suitable for the neural network model.

[0094] The neural network model is then trained using the sample data, and the network weights are adjusted to minimize the difference between the predicted and actual operating strategies. Finally, the first and second relevant parameters are input into the trained neural network model to predict the adjusted operating strategy.

[0095] By using neural network technology to adjust operating strategies, current equipment can achieve more intelligent and precise operation control, significantly improving clothing processing effects.

[0096] Step S303: Control the current device operation based on the second operation strategy information.

[0097] In one implementation, the technology here is basically the same as that in Example 1, except that the second operation strategy information is used, which will not be described again here.

[0098] Furthermore, the second operation strategy and the first related parameters are uploaded as a case to the case library.

[0099] Example 3:

[0100] Most of the techniques in this embodiment are the same as those in Example 1. The difference lies in step S20 in this embodiment: matching the state parameter information of the cases in the case library based on the first state parameter information to obtain at least one operation strategy information. Other than that, the remaining techniques are the same as those in Example 1 and are not described in detail here.

[0101] In this embodiment, unlike the first embodiment in which only the first state parameter information is used to match the state parameter information of the cases in the case library, the current mode of the device ready to run is also considered, as follows:

[0102] Step S101: Obtain the current mode in which the device is ready to run.

[0103] In one embodiment, the predetermined operating mode of the laundry treatment device is determined by responding to a user's instruction to the current device, wherein the user can select a different operating mode. The software system embedded in the current device then reads the user instruction and uses it as the mode in which the current device is to operate.

[0104] Similarly, the user can control the operation of the device through the UI interface or remote commands, so that the current device obtains the mode in which the current device is ready to run.

[0105] Step S205: matching the state parameter information of the cases in the case library based on the operation mode and the first state parameter information to obtain at least one operation strategy information.

[0106] In one implementation, the system accurately matches cases in the case library by considering the current device's intended operating mode and first-state parameter information. This implementation not only considers the clothing's first-state parameter information, but also incorporates the operating mode as a key matching dimension. For example, if the user selects the "heavily soiled wash" mode, the system will prioritize matching cases with proven success in handling heavily soiled clothing.

[0107] In this embodiment, through the above steps, the current device can combine the operating mode to more accurately select the optimal operating strategy that is suitable for the current clothing state.

[0108] Example 4:

[0109] Most of the techniques in this embodiment are the same as those in Example 1. The difference is that in step S201 of this embodiment: preprocessing the first state parameter information to obtain the second state parameter information. Other than that, the remaining techniques are the same as those in Example 1 and are not described in detail here.

[0110] In this embodiment, the preprocessing is done by PCA.

[0111] Step S201 - 2 : performing a PCA operation on the first state parameter information to obtain second state parameter information.

[0112] In this embodiment, the state parameter information of the cases in the case library is also PCA-ized.

[0113] In one implementation, PCA transforms a set of potentially correlated variables into a set of linearly independent variables, called principal components, through an orthogonal transformation. These principal components sequentially capture the largest variance in the data, with the first principal component capturing the most variance, and each subsequent component capturing the largest portion of the remaining variance. The goal of PCA is to reduce the dimensionality of the data while preserving as much of the variability in the original data as possible.

[0114] In this embodiment, an example is given, where the state parameter information of a case in the case library may include:

[0115] Garment Weight: The weight of the garment quantified in grams.

[0116] Clothing type: Indicates the type of clothing material, such as cotton, silk, or synthetic fiber.

[0117] Water absorption before dehydration: The amount of water absorbed by the clothes before dehydration, reflecting the wet weight of the clothes.

[0118] Then, for non-numeric data (such as clothing types), appropriate encoding methods (such as one-hot encoding or label encoding) are used to convert them into numerical data to ensure that each type can be fairly considered in the PCA process.

[0119] All data were then standardized before PCA, i.e., the mean of the feature was subtracted from each state parameter and divided by its standard deviation to ensure that all state parameters were on the same scale.

[0120] Furthermore, PCA is applied to the standardized state parameter set. For example, assuming the original feature space is three-dimensional (clothing weight, clothing type code, water absorption before dehydration), PCA can reduce the dimensionality to two or one dimension, and the number of principal components retained is determined based on the cumulative explained variance ratio.

[0121] Here is an example to illustrate the status parameter information before it is passed as follows: Clothing weight Clothing type code Water absorption before dehydration 500 0 300 750 1 450 600 0 360

[0122] After PCB, assume the data is transformed into: Heavy ink on clothes Clothing type code Absorb ink before dehydration -1 -1 -1 1 1 1 0 -1 0

[0123] After applying PCA, we found that the first principal component was primarily related to "clothing weight" and "water absorption before dehydration," while the second principal component was likely related to "clothing type code." This suggests that clothing weight and water absorption may have a more direct impact on dehydration efficiency than the specific clothing type when optimizing the dehydration process.

[0124] Unlike normalization or conventional weighting, PCA reduces the dimensionality of state parameter information, reducing the computational complexity of subsequent matching and significantly improving matching efficiency. Furthermore, by retaining the most important sources of variance, PCA can help identify the primary factors influencing clothing treatment results, enabling more accurate and appropriate matching.

[0125] Since the state parameter information has many dimensions, PCA processing will greatly reduce the amount of calculation during subsequent matching.

[0126] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0127] It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment of the present invention may also be completed by instructing the relevant hardware through a computer program, and the computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each of the above method embodiments may be implemented. The computer program includes a computer program, and the computer program may be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0128] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the operating method of the clothing processing device according to the above method embodiment, and the processor can be configured to execute the program in the storage device, which includes but is not limited to a program for executing the operating method of the clothing processing device according to the above method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device can be a control device device formed by various electronic devices.

[0129] Furthermore, the present invention also provides a computer-readable storage medium. In one embodiment of a computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program for executing the operating method of the clothing processing device of the above-mentioned method embodiment. The program can be loaded and executed by the processor to implement the operating method of the above-mentioned clothing processing device. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-transitory computer-readable storage medium.

[0130] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present invention, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0131] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules does not cause the technical solution to deviate from the principles of the present invention. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of the present invention.

[0132] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for operating a clothes processing device, characterized in that: include: Acquiring first state parameter information of the clothes to be processed; Matching state parameter information of cases in a case library based on the first state parameter information to obtain at least one first operation strategy information, wherein the case includes state parameter information and operation strategy information corresponding to the state parameter information; The current device operation is controlled based on the first operation strategy information.

2. The operating method of the clothes processing device according to claim 1, characterized in that: The “controlling the current device operation based on the first operation strategy information” includes: Acquire a first relevant parameter of the current device and a second relevant parameter of the clothes processing device corresponding to the first operation strategy information; Modifying the first operation strategy information based on the first relevant parameter and the second relevant parameter to obtain second operation strategy information; The current device operation is controlled based on the second operation strategy information.

3. The operating method of the clothes processing device according to claim 1 or 2, characterized in that: The method further comprises: Get the current mode in which the device is ready to run; The "matching the state parameter information of the cases in the case library based on the first state parameter information to obtain at least one operation strategy information" includes: Based on the operation mode and the first state parameter information, state parameter information of cases in the case library is matched to obtain at least one operation strategy information.

4. The operating method of the clothes processing device according to claim 1 or 2, characterized in that: The "matching the state parameter information of the cases in the case library based on the first state parameter information to obtain at least one operation strategy information" includes: Preprocessing the first state parameter information to obtain second state parameter information; Obtaining corresponding similarity based on the second state parameter information and the state parameter information of the cases in the case library; Comparing the similarities to obtain cases that meet preset requirements; At least one first operation strategy information is obtained based on the case that meets the preset requirements.

5. The operating method of the clothes processing device according to claim 4, characterized in that: The “preprocessing the first state parameter information to obtain second state parameter information” includes: Normalizing the first state parameter information to obtain second state parameter information; The “obtaining corresponding similarity based on the second state parameter information and the state parameter information of the cases in the case library” includes: The corresponding similarity is obtained by calculating the weighted Euclidean distance between the second state parameter information and the state parameter information of the cases in the case library.

6. The operating method of the clothes processing device according to claim 4, characterized in that: The “preprocessing the first state parameter information to obtain second state parameter information” includes: Perform a PCA operation on the first state parameter information to obtain second state parameter information.

7. The operating method of the clothes processing device according to claim 4, characterized in that: The state parameter information of the case is data processed by a preprocessing method consistent with the second state parameter information.

8. The operating method of the clothes processing device according to claim 1, characterized in that: The first state parameter information includes state parameters, and the state parameters in the first state parameter information are set based on the mode in which the current device is ready to run.

9. A control device comprising a processor and a storage device, wherein the storage device is adapted to store a plurality of computer programs, wherein: The computer program is suitable for being loaded and run by the processor to execute the operating method of the laundry treating apparatus according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of computer programs, characterized in that: The computer program is suitable for being loaded and run by a processor to execute the operating method of the laundry treating apparatus according to any one of claims 1 to 8.

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