Garment fabric physical property prediction method and device based on machine learning

Abstract

The invention relates to a garment fabric physical property prediction method and device based on machine learning. The method comprises the steps that to-be-identified category features of a to-be-identified garment fabric are acquired; acquiring a wrinkle feature of the garment fabric corresponding to the to-be-identified category feature; inputting the wrinkle features to a clothing fabric physical property prediction model, and predicting fabric physical properties of the clothing fabric to be identified; wherein the clothing fabric physical property prediction model is obtained by training a preset model on the basis of a training sample, and the training sample comprises wrinkle characteristics faced by clothing and corresponding fabric physical properties. In this way, the problem that the cost is too high due to the fact that a transcoding instrument needs to be used for evaluating the confronting physical properties can be avoided, and the accuracy and efficiency of predicting the physical properties of the garment fabric can be improved to a great extent.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a method and device for predicting physical properties of clothing fabrics based on machine learning. Background Art

[0002] With the development of the clothing industry, clothing fabrics have occupied an important position in the textile field. The performance of clothing fabrics is largely affected by their physical properties. For textile companies, accurately predicting these physical properties is not only helpful for product design and development, but also can improve production efficiency and optimize manufacturing processes. Therefore, how to efficiently and accurately predict the physical properties of clothing fabrics is very interesting.

[0003] In the related art, the prediction of the physical properties of clothing fabrics has long relied on physical tests in the laboratory. These tests usually require the use of specialized instruments, such as thickness gauges, weight analyzers, and tensile testers, to evaluate the physical properties of fabrics by collecting experimental data. Therefore, the related art uses specialized instruments to evaluate the physical properties of fabrics, which will result in high testing costs. Summary of the invention

[0004] The present invention provides a method and device for predicting the physical properties of clothing fabrics based on machine learning.

[0005] According to a first aspect of the present disclosure, a method for predicting physical properties of clothing fabrics based on machine learning is provided, the method comprising:

[0006] Obtaining the features of the category of clothing fabric to be identified;

[0007] Acquire wrinkle features of clothing fabric corresponding to the category features to be identified;

[0008] The wrinkle feature is input into a clothing fabric physical property prediction model to predict the fabric physical properties of the clothing fabric to be identified; wherein the clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle features faced by clothing and the corresponding fabric physical properties.

[0009] According to a second aspect of the present disclosure, a device for predicting physical properties of clothing fabrics based on machine learning is provided, the device comprising:

[0010] A category feature acquisition module is used to acquire the category features to be identified of the clothing fabric to be identified;

[0011] A wrinkle feature acquisition module, used to acquire wrinkle features of clothing fabrics corresponding to the category features to be identified;

[0012] The property prediction module is used to input the wrinkle features into the clothing fabric physical property prediction model to predict the fabric physical properties of the clothing fabric to be identified; wherein the clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle features faced by the clothing and the corresponding fabric physical properties.

[0013] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0014] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0015] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the above method of the present disclosure when executed by a processor.

[0016] The method and device for predicting the physical properties of clothing fabrics based on machine learning provided in the embodiments of the present disclosure predict the physical properties of the clothing fabrics to be identified by obtaining the to-be-identified category features of the clothing fabrics to be identified; obtaining the wrinkle features of the clothing fabrics corresponding to the to-be-identified category features; and inputting the wrinkle features into the clothing fabric physical property prediction model. The clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle features of the clothing and the corresponding fabric physical properties. This can avoid the problem of excessive cost caused by the need to use transcoding instruments to evaluate the physical properties faced, and the embodiments can greatly improve the accuracy and efficiency of the prediction of the physical properties of clothing fabrics. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0018] Figure 1 A schematic diagram of a fabric physical property prediction process for clothing provided by an embodiment of the present disclosure, provided as an exemplary embodiment of the present disclosure;

[0019] Figure 2 A flowchart of a method for predicting physical properties of clothing fabrics based on machine learning provided by an exemplary embodiment of the present disclosure;

[0020] Figure 3 A schematic block diagram of functional modules of a device for predicting physical properties of clothing fabrics based on machine learning provided by an exemplary embodiment of the present disclosure;

[0021] Figure 4 A structural block diagram of an electronic device provided for an exemplary embodiment of the present disclosure;

[0022] Figure 5 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0024] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0025] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0026] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0027] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0028] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0029] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0030] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0031] With the development of the clothing industry, clothing fabrics have occupied an important position in the textile field. The performance of clothing fabrics is largely affected by their physical properties, including quality, thickness, bendability, shearing and density. For textile companies, accurately predicting these physical properties is not only helpful for product design and development, but also can improve production efficiency and optimize manufacturing processes. Therefore, how to efficiently and accurately predict the physical properties of clothing fabrics has become an important research topic in the textile field.

[0032] However, the prediction of the physical properties of clothing fabrics has long relied on physical tests in the laboratory. These tests usually require the use of specialized instruments, such as thickness gauges, weight analyzers, and tensile testers, to evaluate the physical properties of fabrics by collecting experimental data, but this significantly increases the testing cost.

[0033] Moreover, in the related art, the biggest limitation of the traditional method of laboratory physical testing is that samples must be produced before physical property testing. This process is not only time-consuming and labor-intensive, but also accompanied by a large amount of material and energy consumption, which increases costs. Secondly, during the experimental testing process, the requirements for equipment, links and operators are high, and the test results are easily affected by external factors. In addition, the cost of experimental testing is high, which is not conducive to large-scale application in the production process. This method that relies on actual production samples is not only prone to delays in the R&D and production cycles, but also cannot meet today's sustainable development needs. Therefore, the traditional testing methods in the related art seem to be unable to cope with the rapidly changing needs of the modern textile industry.

[0034] Therefore, in order to solve the above technical problems, the embodiments of the present disclosure utilize machine learning algorithms to achieve efficient prediction of the physical properties of fabrics, such as quality, thickness, bending or stretching, to provide guidance for fabric design and production.

[0035] The method for predicting the physical properties of clothing fabrics based on machine learning provided in the embodiments of the present disclosure can improve the prediction accuracy of the physical properties of clothing fabrics and reduce the time and cost of experimental testing. Specifically, the method may include the following steps:

[0036] Step 1: Data collection and preprocessing: Collect a large amount of experimental test data of fabrics, including input parameters and corresponding physical property output parameters. Clean and normalize the data, remove outliers, and build a high-quality data set.

[0037] In an embodiment, the input parameters may be the wrinkle density and wrinkle hardness corresponding to the fabric in a natural draping state, and the corresponding physical property output parameters include at least one of mass, thickness, bending, stretching, stretching linearity, oblique depth and shear linearity.

[0038] Step 2: Model construction and training: Divide the training set and test set, select multiple suitable machine learning algorithms, and build an initial prediction model. Use random search to find the optimal hyperparameter combination, and use cross-validation to evaluate the results.

[0039] In the embodiment, the training set and the test set can be divided into 4:1, and the initial prediction model can be trained by using multiple linear regression, decision tree regression or random forest regression algorithms. Random search is used to find the optimal hyperparameter combination and optimize the model parameters.

[0040] Step 3: Model verification and testing: Use the test set to verify the trained model, evaluate the model's prediction accuracy, robustness, and generalization ability, and select the model with the best performance as the final prediction model.

[0041] In the embodiment, two evaluation indicators, namely, mean square error (MSE) and prediction accuracy, can be selected to evaluate the initial prediction model and select the final prediction model. The calculation formula of mean square error is as follows:

[0042]

[0043] Among them, MSE stands for mean square error, i is the sample number, n is the number of samples, and y i is the actual value of the physical property parameter, ∧

[0044] y i are the predicted physical property parameters of the fabric.

[0045] Step 4: Establish mapping relationships: associate fabric name, tissue structure, and fabric composition with pre-defined wrinkle density and wrinkle hardness, respectively.

[0046] In an embodiment, a data table or database may be constructed, which contains the corresponding relationship between the fabric name, tissue structure, fabric composition and its corresponding wrinkle density and wrinkle hardness value. When the user inputs any of the above parameters, the system searches for the corresponding wrinkle density and wrinkle hardness from the above table or database.

[0047] Step 5: Prediction application: Apply the final prediction model to the prediction of the physical properties of actual fabrics. According to the input fabric parameters, its physical properties can be quickly predicted to provide a basis for fabric design, production and quality control.

[0048] In the embodiment, the final prediction model can input any one of the fabric name, organizational structure and fabric composition, output the physical property parameters of the corresponding fabric, and be applied to fields such as design and production.

[0049] The disclosed embodiment predicts the physical properties of fabrics through machine learning technology, which can not only significantly improve the accuracy of prediction, but also greatly reduce the time and cost of experimental testing, and has broad application prospects.

[0050] like Figure 1 As shown, Figure 1 The schematic diagram of the process flow for predicting the physical properties of fabrics of clothing provided in the embodiment of the present disclosure may specifically include the following steps:

[0051] Step 1: Data collection and preprocessing. A large amount of experimental test data of fabrics can be collected, including input parameters and corresponding physical property output parameters. The data is cleaned and normalized, outliers are removed, and a high-quality data set is constructed.

[0052] In the embodiment, a large amount of experimental test data of fabrics is collected, including category features: fabric name, organizational structure and fabric composition, input features: wrinkle density and wrinkle hardness, and output features: mass, thickness, bending, stretching, stretching linearity, oblique depth and shear linearity; for example, mass size, specific thickness, bending or stretching, etc. Among them, the input feature can be to place the collected fabric data in 3D design software, and make a rating based on its 3D simulated wrinkle effect, and the wrinkle density and wrinkle hardness are respectively taken in the discrete set {1, 1.5, 2, 2.5, 3, 3.5, 4}. The data is cleaned and normalized, outliers are removed, and a high-quality data set is constructed.

[0053] Step 2: Model construction and training. Divide the training set and test set, select multiple suitable machine learning algorithms, and build the initial prediction model. Use random search to find the optimal hyperparameter combination, and use cross-validation to evaluate the results.

[0054] In the embodiment, the training set and the test set can be divided into a ratio of 4:1, and the initial prediction model can be trained by using multiple linear regression, decision tree regression or random forest regression algorithm. During the training process, the mean square error of the multiple linear regression model is too large, and there is a large difference between the prediction results of the model on the test set and the actual value. Therefore, only random search is performed on the decision tree regression model and the random forest regression model to optimize the hyperparameter combination.

[0055] As shown in Table 1, Table 1 is a model parameter setting method provided by an embodiment of the present disclosure. The decision tree regression algorithm in this example includes four parameters, namely, the maximum tree depth max_depth, the minimum number of samples required for re-splitting an internal node min_samples_split, the minimum number of samples required for a leaf node min_samples_leaf, and the number of features for optimal segmentation max_features. In addition to the above parameters, the random forest also has the number of base learners n_estimators. Within the set parameter range, random search and cross-validation are used to optimize the hyperparameter combination, and the values ​​are shown in Table 1.

[0056] Table 1:

[0057]

[0058] Step 3: Model verification and testing: Use the test set to verify the trained model, evaluate the model's prediction accuracy, robustness, and generalization ability, and select the model with the best performance as the final prediction model.

[0059] In an embodiment, a test set can be used to verify the trained model, and the mean square error and prediction accuracy of the test set can be calculated to evaluate the prediction accuracy, robustness and generalization ability of the model, and the model with the best performance can be selected as the final prediction model.

[0060] As shown in Table 2, Table 2 provides model evaluation data for an embodiment of the present disclosure. In this example, the prediction ability of the decision tree regression model is significantly stronger than that of the random forest regression model, and has stronger prediction accuracy and generalization ability.

[0061] Table 2:

[0062] Model MSE Prediction accuracy Decision Tree Regression 0.0874 0.8077 Random Forest Regression 0.0874 0.7308

[0063] Step 4: Establish mapping relationships. Associate fabric name, tissue structure, and fabric composition with predefined wrinkle density and wrinkle hardness.

[0064] In the embodiment, a mapping relationship between the category feature and the input feature may be established, as shown in Table 3, which is a mapping relationship between the category feature and the input feature.

[0065] Table 3:

[0066]

[0067]

[0068] Step 5: Prediction application: Apply the final prediction model to the prediction of the physical properties of actual fabrics. According to the input fabric parameters, its physical properties can be quickly predicted to provide a basis for fabric design, production and quality control.

[0069] In an embodiment, the final prediction model can be applied to the prediction of the physical properties of actual clothing fabrics, so that when the user inputs the category feature, the corresponding wrinkle density and wrinkle hardness values ​​are automatically selected, thereby predicting the corresponding fabric physical property parameters.

[0070] The disclosed embodiments can make high-precision predictions of the physical properties of common clothing fabrics. By applying the above model to newly developed fabric samples and inputting parameters such as the sample's fabric name, organizational structure, and fabric composition, its physical properties such as stretching and bending can be successfully predicted.

[0071] Based on the above embodiments, the present disclosure also provides a method for predicting the physical properties of clothing fabrics based on machine learning, such as Figure 2 As shown, the method may include the following steps:

[0072] In step S210, the to-be-identified category features of the to-be-identified clothing fabric are obtained.

[0073] In an embodiment, a user may input the category features of the fabric of the clothing to be identified, which may include at least one of fabric name, organizational structure and fabric composition, so as to perform predictive identification on the clothing to be identified based on the category features input by the user.

[0074] In an embodiment, an image of a to-be-identified clothing fabric input by a user can also be received, and the category features corresponding to the identification object can be identified by extracting features in the image to be identified. For example, the image to be identified can include a label of clothing, and the category features of the to-be-identified clothing fabric can be identified by extracting characters in the label. The to-be-identified object can also include an image of the to-be-identified clothing fabric, and the image can be preprocessed to extract features such as texture therein to identify the category features of the to-be-identified clothing fabric.

[0075] In step S220, wrinkle features of the clothing fabric corresponding to the category features to be identified are obtained.

[0076] In the embodiment, a mapping relationship between the category feature of the clothing fabric and the wrinkle feature of the clothing fabric is established, and the wrinkle feature of the clothing fabric corresponding to the category feature to be identified is determined based on the mapping relationship. By pre-establishing the corresponding relationship between the category feature and the wrinkle feature, the wrinkle feature includes wrinkle density and wrinkle hardness, and the wrinkle feature belongs to the input feature. For example, as shown in Table 3. In this way, the wrinkle feature of the clothing fabric corresponding to the category feature to be identified can be obtained according to the corresponding relationship.

[0077] In step S230, the wrinkle feature is input into the clothing fabric physical property prediction model to predict the fabric physical properties of the clothing fabric to be identified.

[0078] Among them, the clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle characteristics faced by clothing and the corresponding fabric physical properties.

[0079] In an embodiment, a training set can be constructed in the manner of the above embodiment to obtain training samples. By training a preset model, the preset model can learn the relationship between wrinkle features and physical properties of the fabric. When the wrinkle features are input into the clothing fabric physical property prediction model, the fabric physical properties of the clothing fabric to be identified can be predicted.

[0080] In an embodiment, multiple initial models can be obtained, and the multiple initial models are trained respectively by training samples to obtain multiple trained models. Multiple output results are obtained by inputting test samples to the multiple trained models respectively. In this way, the multiple trained models are evaluated by mean square error and prediction accuracy, and a clothing fabric physical property prediction model is determined from the multiple trained models based on the evaluation results.

[0081] The mean square error can be obtained by the above formula (1). The physical properties of the fabric in the embodiment may include: mass, thickness, bending, stretching, stretching linearity, oblique depth and shear linearity, or a combination of several of them.

[0082] The method for predicting the physical properties of clothing fabrics based on machine learning provided in the embodiments of the present disclosure obtains the category features of the clothing fabrics to be identified; obtains the wrinkle features of the clothing fabrics corresponding to the category features to be identified; inputs the wrinkle features into the clothing fabric physical property prediction model to predict the fabric physical properties of the clothing fabrics to be identified. The clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle features of the clothing and the corresponding fabric physical properties. This can avoid the problem of excessive cost caused by the need to use transcoding instruments to evaluate the physical properties faced, and the embodiments can greatly improve the accuracy and efficiency of the prediction of the physical properties of clothing fabrics.

[0083] In the case of dividing each functional module according to each function, the embodiment of the present disclosure provides a device for predicting the physical properties of clothing fabrics based on machine learning. The device for predicting the physical properties of clothing fabrics can be a server, a terminal, or a chip applied to a server. Figure 3 This is a schematic block diagram of the functional modules of a device for predicting the physical properties of clothing fabrics based on machine learning provided by an exemplary embodiment of the present disclosure. Figure 3 As shown, the device for predicting the physical properties of clothing fabrics based on machine learning includes:

[0084] The category feature acquisition module 10 is used to acquire the category features to be identified of the clothing fabric to be identified;

[0085] A wrinkle feature acquisition module 20 is used to acquire wrinkle features of clothing fabrics corresponding to the category features to be identified;

[0086] The property prediction module 30 is used to input the wrinkle feature into the clothing fabric physical property prediction model to predict the fabric physical properties of the clothing fabric to be identified; wherein the clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle features faced by the clothing and the corresponding fabric physical properties.

[0087] In another embodiment provided by the present disclosure, the wrinkle feature acquisition module is specifically used to:

[0088] Establishing a mapping relationship between the category features of clothing fabrics and the wrinkle features of clothing fabrics;

[0089] The wrinkle feature of the clothing fabric corresponding to the category feature to be identified is determined based on the mapping relationship.

[0090] In another embodiment provided by the present disclosure, the category characteristics of the clothing fabric include: at least one of fabric name, organizational structure and fabric composition; the wrinkle characteristics include: wrinkle density and wrinkle hardness.

[0091] In another embodiment provided by the present disclosure, the device further includes a model selection module, which is used to:

[0092] Acquire multiple initial models, and train the multiple initial models respectively through training samples to obtain multiple trained models;

[0093] Inputting test samples to the multiple trained models respectively to obtain multiple output results;

[0094] The multiple trained models are evaluated by mean square error and prediction accuracy, and a clothing fabric physical property prediction model is determined from the multiple trained models based on the evaluation results.

[0095] In another embodiment provided by the present disclosure, the mean square error is obtained by:

[0096]

[0097] Among them, MSE stands for mean square error, i is the sample number, n is the number of samples, and y i is the actual value of the physical property parameter, are the predicted physical property parameters of the fabric.

[0098] In another embodiment provided by the present disclosure, the physical properties of the fabric include: mass, thickness, bending, stretching, stretching linearity, oblique depth and shear linearity, one or a combination of several of them.

[0099] The device for predicting the physical properties of clothing fabrics based on machine learning provided in the embodiments of the present disclosure predicts the physical properties of the clothing fabrics to be identified by obtaining the category features of the clothing fabrics to be identified; obtaining the wrinkle features of the clothing fabrics corresponding to the category features to be identified; and inputting the wrinkle features into the clothing fabric physical property prediction model. The clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle features of the clothing and the corresponding fabric physical properties. This can avoid the problem of excessive cost caused by the need to use transcoding instruments to evaluate the physical properties faced, and the embodiments can greatly improve the accuracy and efficiency of the prediction of the physical properties of clothing fabrics.

[0100] An embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0101] Figure 4 The structure diagram of an electronic device provided by an exemplary embodiment of the present disclosure is shown in FIG. Figure 4As shown, the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801 , and the processor 1801 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0102] The processor 1801 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 1801 or by instructions in the form of software. The processor 1801 may be a general-purpose processor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a memory 1802, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or other mature storage media in the art. The processor 1801 reads the information in the memory 1802 and completes the steps of the method in combination with its hardware.

[0103] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transmitted from a storage medium or a network to a computer system having a dedicated hardware structure, such as Figure 5 The computer system 1900 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Figure 5 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure.

[0104] Computer system 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0105] like Figure 5 As shown, the computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 to a random access memory (RAM) 1903. In the RAM 1903, various programs and data required for the operation of the computer system 1900 can also be stored. The computing unit 1901, the ROM 1902, and the RAM 1903 are connected to each other via a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0106] A plurality of components in the computer system 1900 are connected to the I / O interface 1905, including: an input unit 1906, an output unit 1907, a storage unit 1908, and a communication unit 1909. The input unit 1906 may be any type of device capable of inputting information to the computer system 1900, and the input unit 1906 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1907 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1908 may include, but is not limited to, a disk, an optical disk. The communication unit 1909 allows the computer system 1900 to exchange information / data with other devices over a network such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0107] The computing unit 1901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1901 performs the various methods and processes described above. For example, in some embodiments, the above methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1908. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 1902 and / or communication unit 1909. In some embodiments, the computing unit 1901 may be configured to perform the above methods disclosed in the embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0108] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0109] The computer-readable storage medium in the disclosed embodiments may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specifically, the computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0110] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0111] The embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor, the above method disclosed in the embodiments of the present disclosure is implemented.

[0112] In embodiments of the present disclosure, computer program codes for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0113] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.

[0115] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0116] The above descriptions are only some embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, a technical solution formed by replacing the above features with the technical features with similar functions disclosed in the present disclosure (but not limited to).

[0117] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for predicting the physical properties of clothing fabrics based on machine learning, characterized in that: The method comprises: Obtaining the features of the category of clothing fabric to be identified; Acquire wrinkle features of clothing fabric corresponding to the category features to be identified; The wrinkle feature is input into a clothing fabric physical property prediction model to predict the fabric physical properties of the clothing fabric to be identified; wherein the clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle features faced by clothing and the corresponding fabric physical properties.

2. The method according to claim 1, characterized in that The step of obtaining the wrinkle feature of the clothing fabric corresponding to the category feature to be identified includes: Establishing a mapping relationship between the category features of clothing fabrics and the wrinkle features of clothing fabrics; The wrinkle feature of the clothing fabric corresponding to the category feature to be identified is determined based on the mapping relationship.

3. The method according to claim 2, characterized in that The category characteristics of the clothing fabric include: at least one of fabric name, fabric structure and fabric composition; the wrinkle characteristics include: wrinkle density and wrinkle hardness.

4. The method according to claim 1, characterized in that: The method further comprises: Acquire multiple initial models, and train the multiple initial models respectively through training samples to obtain multiple trained models; Inputting test samples to the multiple trained models respectively to obtain multiple output results; The multiple trained models are evaluated by mean square error and prediction accuracy, and a clothing fabric physical property prediction model is determined from the multiple trained models based on the evaluation results.

5. The method according to claim 4, characterized in that The mean square error is obtained by: Among them, MSE stands for mean square error, i is the sample number, n is the number of samples, and y i is the actual value of the physical property parameter, are the predicted physical property parameters of the fabric.

6. The method according to any one of claims 1 to 5, characterized in that: The physical properties of the fabric include: mass, thickness, bending, stretching, stretching linearity, oblique depth and shear linearity, or a combination of these properties.

7. A device for predicting the physical properties of clothing fabrics based on machine learning, characterized in that: The device comprises: A category feature acquisition module is used to acquire the category features to be identified of the clothing fabric to be identified; A wrinkle feature acquisition module, used to acquire wrinkle features of clothing fabrics corresponding to the category features to be identified; The property prediction module is used to input the wrinkle features into the clothing fabric physical property prediction model to predict the fabric physical properties of the clothing fabric to be identified; wherein the clothing fabric physical property prediction model is obtained by training a preset model based on training samples, and the training samples include the wrinkle features faced by the clothing and the corresponding fabric physical properties.

8. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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