A wrinkle-resistant treatment method for coat fabrics

Abstract

The present invention discloses a method for anti-wrinkle treatment of coat fabrics, which relates to the field of clothing manufacturing. The method comprises: obtaining usage scenario information of a target coat fabric, performing test status analysis, and obtaining a coat fabric test status parameter table; performing an anti-wrinkle test, capturing images of the test process using a high-resolution camera device, and obtaining a coat fabric image dataset; preprocessing the coat fabric image dataset based on an image data preprocessing program to obtain a standard coat fabric image dataset; performing wrinkle area detection and feature quantification analysis to obtain quantitative results of coat fabric wrinkle features; performing statistical analysis based on an anti-wrinkle performance index set to generate a coat fabric anti-wrinkle performance report, and performing anti-wrinkle optimization treatment on the target coat fabric. The method solves the technical problem of inaccurate anti-wrinkle performance evaluation and limited optimization effect in existing coat fabric anti-wrinkle treatments, thereby achieving the technical effect of improving the accuracy of anti-wrinkle performance evaluation and targeted optimization.

Description

Technical Field

[0001] The present application relates to the field of clothing manufacturing, and in particular to a method for anti-wrinkle treatment of coat fabrics. Background Art

[0002] In the field of clothing manufacturing, coats are important outerwear garments, and the selection and processing of their fabrics are directly related to the appearance quality, wearing comfort, and durability of the garments. As consumers' demands for clothing quality continue to increase, the wrinkle resistance of coat fabrics has become one of the important indicators for measuring clothing quality. Fabrics with good wrinkle resistance can effectively resist deformation under external forces, maintain the flatness and aesthetics of the garment, and extend the service life of the garment. Existing wrinkle resistance treatment methods for coat fabrics rely on the experience of fabric manufacturers or clothing designers to judge the wrinkle resistance of the fabric. They lack objective and accurate evaluation standards and are unable to accurately quantify the degree and distribution characteristics of the fabric wrinkles. Due to the inaccuracy of the evaluation method, the anti-wrinkle optimization treatment of coat fabrics lacks specificity and effectiveness, and the optimization effect is limited.

[0003] In the current related technologies, the anti-wrinkle treatment of coat fabrics has the technical problem of inaccurate anti-wrinkle performance evaluation, resulting in limited optimization effects. Summary of the Invention

[0004] The present application provides an anti-wrinkle treatment method for coat fabrics, obtains usage scenario information of a target coat fabric, performs test status analysis, performs anti-wrinkle testing according to a coat fabric test status parameter table, uses a high-resolution camera to capture images of the test process, obtains a coat fabric image dataset, pre-processes the image dataset through an image data pre-processing program to obtain a standard coat fabric image dataset, further performs wrinkle area detection and feature quantification analysis on the standard image dataset to obtain a quantitative result of the wrinkle feature of the coat fabric, performs statistical analysis on the quantitative result of the wrinkle feature according to an anti-wrinkle performance index set, generates an anti-wrinkle performance report for the coat fabric, and performs anti-wrinkle optimization treatment on the target coat fabric based on the report, thereby achieving the technical effect of improving the accuracy of anti-wrinkle performance evaluation and optimizing the pertinence.

[0005] The present application provides a method for anti-wrinkle treatment of coat fabrics, comprising:

[0006] Obtain usage scenario information of a target coat fabric, perform a test status analysis on the usage scenario information, and obtain a coat fabric test status parameter table; perform an anti-wrinkle test on the target coat fabric according to the coat fabric test status parameter table, and simultaneously capture images of the test process through a high-resolution camera device to obtain a coat fabric image data set; obtain an image data preprocessing program, and preprocess the coat fabric image data set based on the image data preprocessing program to obtain a standard coat fabric image data set; perform wrinkle area detection and feature quantification analysis on the standard coat fabric image data set to obtain a coat fabric wrinkle feature quantification result; perform statistical analysis on the coat fabric wrinkle feature quantification result according to an anti-wrinkle performance index set to generate a coat fabric anti-wrinkle performance report, and perform anti-wrinkle optimization processing on the target coat fabric based on the coat fabric anti-wrinkle performance report.

[0007] In a possible implementation, the coat fabric test state parameter table is obtained and the following processing is performed:

[0008] Obtain coat usage scenario factor information, wherein the coat usage scenario factor information includes usage environment, usage status, and usage behavior; perform usage status analysis on the usage scenario information based on the coat usage scenario factor information to obtain a usage scenario factor status set; perform test parameter analysis on the usage scenario factor status set to obtain a usage scenario factor test parameter set; perform parameter orthogonal arrangement on the usage scenario factor test parameter set to obtain a coat fabric test status parameter table.

[0009] In a possible implementation, the standard coat fabric image dataset is obtained and the following processing is performed:

[0010] According to the image data preprocessing program, an image denoising processing program and an image enhancement processing program are determined; the coat fabric image dataset is converted into a fabric grayscale image dataset, and noise identification and classification are performed on the fabric grayscale image dataset to obtain multi-scale image data noise; according to the image denoising processing program, a multi-scale noise filtering rule is obtained, and the multi-scale image data noise is filtered and preprocessed based on the multi-scale noise filtering rule to obtain a denoised coat fabric image dataset; based on the image enhancement processing program, image enhancement processing is performed on the denoised coat fabric image dataset to obtain the standard coat fabric image dataset.

[0011] In a possible implementation, the standard coat fabric image dataset is obtained and the following processing is performed:

[0012] According to the denoised coat fabric image dataset, an image grayscale distribution histogram is generated; based on the image enhancement processing program, the image grayscale distribution histogram is equalized to obtain a fabric balanced image dataset; edge detection is performed on the fabric balanced image dataset using a Canny edge detector to obtain fabric edge detection information; edge contour screening is performed on the fabric edge detection information using a preset edge threshold to obtain fabric edge contour information; image enhancement processing is performed on the denoised coat fabric image dataset based on the fabric edge contour information to obtain the standard coat fabric image dataset.

[0013] In a possible implementation, the coat fabric wrinkle feature quantification result is obtained by performing the following processing:

[0014] A convolutional neural network is used to perform morphological annotation training on a historical coat fabric wrinkle dataset to generate a fabric image wrinkle recognition network; wrinkle detection is performed on the standard coat fabric image dataset based on the fabric image wrinkle recognition network to determine a coat fabric wrinkle morphology detection set; wrinkle areas of the standard coat fabric image dataset are marked according to the coat fabric wrinkle morphology detection set to obtain a coat fabric wrinkle area anchor frame set; feature clustering and quantitative analysis are performed on the coat fabric wrinkle area anchor frame set in sequence to obtain the coat fabric wrinkle feature quantification results.

[0015] In a possible implementation, the process of obtaining the quantitative result of the wrinkle feature of the coat fabric is as follows:

[0016] According to the coat fabric wrinkle morphology detection set, the coat fabric wrinkle area anchor frame set is clustered by morphological features to obtain a fabric wrinkle morphology cluster area set; a wrinkle feature evaluation index set is obtained, and the wrinkle feature evaluation index set includes wrinkle area, wrinkle depth and wrinkle distribution; based on the wrinkle feature evaluation index set, the fabric wrinkle morphology cluster area set is evaluated and calculated respectively to obtain a fabric cluster area wrinkle feature set; and statistical quantitative analysis is performed on the fabric cluster area wrinkle feature set in turn to obtain a quantitative result of the coat fabric wrinkle feature.

[0017] In a possible implementation, the anti-wrinkle optimization process is performed on the target coat fabric based on the coat fabric anti-wrinkle performance report, and the following processes are performed:

[0018] A coat fabric treatment process database is constructed, wherein the coat fabric treatment process database includes fabric treatment process data of different fabric attributes and corresponding wrinkle treatment effect data; the coat fabric treatment process database is matched and divided based on the attribute information of the target coat fabric to obtain a coat fabric treatment process selection space; the coat fabric anti-wrinkle performance report is used as a constraint parameter, and a global optimization is performed in the coat fabric treatment process selection space to obtain coat fabric anti-wrinkle process parameters; and anti-wrinkle optimization treatment is performed on the target coat fabric based on the coat fabric anti-wrinkle process parameters.

[0019] In a possible implementation, the anti-wrinkle optimization process is performed on the target coat fabric based on the coat fabric anti-wrinkle process parameters, and the following processes are performed:

[0020] The target coat fabric is subjected to anti-wrinkle treatment based on the anti-wrinkle process parameters of the coat fabric to obtain feedback parameters of the anti-wrinkle effect of the fabric; the feedback parameters of the anti-wrinkle effect of the fabric are optimized and analyzed to obtain parameter variation rules; the anti-wrinkle process parameters of the coat fabric are mutated and fine-tuned based on the parameter variation rules to obtain optimized parameters of the anti-wrinkle process of the coat fabric.

[0021] The present application proposes an anti-wrinkle treatment method for coat fabrics. First, usage scenario information of the target coat fabric is obtained, a test status analysis is performed on the usage scenario information, and a coat fabric test status parameter table is obtained. Then, an anti-wrinkle test is performed on the target coat fabric according to the coat fabric test status parameter table. At the same time, images of the test process are captured by a high-resolution camera to obtain a coat fabric image data set. Then, an image data preprocessing program is obtained, and the coat fabric image data set is preprocessed based on the image data preprocessing program to obtain a standard coat fabric image data set. Wrinkle area detection and feature quantification analysis are performed on the standard coat fabric image data set to obtain a coat fabric wrinkle feature quantification result. Finally, the coat fabric wrinkle feature quantification result is statistically analyzed according to an anti-wrinkle performance index set to generate a coat fabric anti-wrinkle performance report. Based on the coat fabric anti-wrinkle performance report, the target coat fabric is subjected to anti-wrinkle optimization treatment, thereby achieving the technical effect of improving the accuracy of anti-wrinkle performance evaluation and optimizing targetedness. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the methods according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, the various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0023] Figure 1 A schematic flow chart of a method for anti-wrinkle treatment of coat fabrics provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of a process for obtaining quantitative results of wrinkle characteristics of a coat fabric in an anti-wrinkle treatment method for a coat fabric provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0026] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0027] In the following description, reference is made to “some embodiments” which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0028] The present application provides a method for anti-wrinkle treatment of coat fabrics, such as Figure 1 As shown, the method includes:

[0029] Step S100 obtains usage scenario information for the target coat fabric, performs a test status analysis on this usage scenario information, and generates a coat fabric test status parameter table. Specifically, information is collected regarding the target coat fabric's expected usage scenarios, namely, the various environments and conditions the coat fabric may encounter in actual use, including environmental conditions (such as temperature and humidity), the wearer's activity level, and washing and maintenance methods. This usage scenario information is analyzed to determine how it affects the coat fabric's performance. Based on the analysis results, a coat fabric test status parameter table is developed. This table describes how to test the coat fabric's wrinkle resistance in different usage scenarios, specifically, the parameters and conditions required to test the coat fabric's wrinkle resistance in different usage scenarios.

[0030] In one possible implementation, obtaining the coat fabric test state parameter table in step S100 further includes step S110 of obtaining coat usage scenario factor information. The coat usage scenario factor information includes usage environment, usage state, and usage behavior. Specifically, through various methods such as market research, user interviews, and experimental testing, information is collected regarding various scenario factors that coat fabrics may encounter in actual use, including but not limited to the coat's usage environment (the environmental conditions encountered by the coat fabric during actual use, such as temperature, humidity, and lighting conditions), the coat's usage state (the specific form or state of the coat fabric during use, such as hanging, washing, and wearing), and usage behavior (the specific operations or behaviors performed by the user or wearer on the coat fabric, such as the wearer's activity habits). Step S120 then performs a usage state analysis on the usage scenario information based on the coat usage scenario factor information to obtain a usage scenario factor state set. Specifically, after obtaining information on coat usage scenario factors, we analyze how they affect the performance and appearance of the coat fabric, including analyzing the impact of different environmental factors on the fabric (such as high temperature and high humidity environment may cause fabric deformation), and the impact of different usage conditions and usage behaviors on the fabric's wrinkle resistance (such as rubbing and washing may cause fabric wrinkles).

[0031] In step S130, test parameter analysis is performed on each of the usage scenario factor state sets to obtain a usage scenario factor test parameter set. Specifically, after performing usage scenario analysis on the usage scenario information, the analysis results are converted into specific test parameters, including physical parameters such as temperature, humidity, and pressure required for the test, as well as test conditions such as test time and number of tests. This test parameter analysis is performed for each usage scenario factor state set. In step S140, the parameters of the usage scenario factor test parameter set are orthogonally arranged to obtain the coat fabric test state parameter table. Specifically, after obtaining all usage scenario factor test parameters, these parameters are orthogonally arranged to form a comprehensive coat fabric test state parameter table. This orthogonal arrangement ensures that different levels (or values) of each factor have a chance to appear in the test, thereby providing a more comprehensive assessment of the coat fabric's wrinkle resistance. Ultimately, this coat fabric test state parameter table serves as the basis for subsequent wrinkle resistance testing. This implementation method ensures that the test fully reflects the anti-wrinkle performance of coat fabrics in actual use by considering multiple usage scenario factors, thereby improving the comprehensiveness of the test. By analyzing the test parameters and arranging them orthogonally, it ensures that the test conditions are closer to actual usage and improves the accuracy of the test.

[0032] Step S200 involves performing a wrinkle resistance test on the target coat fabric according to the coat fabric test state parameter table. High-resolution imaging equipment is used to capture images of the test process, thereby obtaining a coat fabric image dataset. Specifically, the target coat fabric undergoes a wrinkle resistance test (a test that assesses a fabric's ability to recover its original shape after being subjected to an external force) using the specified coat fabric test state parameter table. During the test, a high-resolution imaging device (capable of capturing detailed images) is used to record the fabric's reactions and changes under different test conditions. This image data is then collected to form the coat fabric image dataset.

[0033] Step S300: Obtain an image data preprocessing program. Based on this program, the coat fabric image dataset is preprocessed to obtain a standard coat fabric image dataset. Specifically, a program for processing image data is obtained. This program is used to improve image quality and make it more suitable for further analysis, including steps such as denoising, enhancement, and correction. This program is used to process the coat fabric image dataset to remove noise and improve image quality. The processed image dataset becomes the standard coat fabric image dataset.

[0034] In one possible implementation, the standard coat fabric image dataset is obtained, and step S300 further includes step S310, determining an image denoising processing program and an image enhancement processing program based on the image data preprocessing program. Specifically, based on the preset image data preprocessing program, two key processing subroutines are defined: an image denoising processing program and an image enhancement processing program. The image denoising processing program is used to identify and remove noise in the image and improve the clarity of the image. The image enhancement processing program is used to enhance the contrast, brightness and other attributes of the image to make the details in the image clearer and facilitate subsequent analysis and processing. Step S320, converting the coat fabric image dataset into a fabric grayscale image dataset, performing noise identification and classification on the fabric grayscale image dataset, and obtaining multi-scale image data noise. Specifically, the color coat fabric image dataset is converted into a grayscale image dataset. Grayscale images only contain brightness information, not color information, which helps to simplify subsequent processing steps. On the fabric grayscale image dataset, noise is identified and classified according to its characteristics (such as frequency, amplitude, etc.) so that noise can be removed in a targeted manner in subsequent steps.

[0035] In step S330, multiscale noise filtering rules are obtained according to the image denoising program. The noise in the multiscale image data is filtered and pre-processed based on the multiscale noise filtering rules to obtain a denoised coat fabric image dataset. Specifically, based on the noise identification and classification results, multiscale noise filtering rules are obtained. These rules take into account the behavior of noise at different scales and can more effectively remove noise. The images are filtered using the multiscale noise filtering rules to remove noise from the images, obtaining a denoised coat fabric image dataset. In step S340, image enhancement processing is performed on the denoised coat fabric image dataset based on the image enhancement program to obtain the standard coat fabric image dataset. Specifically, after noise removal, the images are enhanced to improve image properties such as contrast and brightness, making details clearer and facilitating subsequent wrinkle region detection and feature quantification analysis. This implementation improves image quality through denoising and enhancement, making details clearer. High-quality images can more accurately reflect the wrinkles in the coat fabric, thereby improving analysis accuracy.

[0036] In one possible implementation, the step S340 of obtaining the standard coat fabric image dataset further includes step S341: generating an image grayscale distribution histogram based on the denoised coat fabric image dataset. Specifically, the grayscale values ​​of each image in the denoised coat fabric image dataset are extracted, and then the frequency or number of pixels at each grayscale value is counted. These frequencies or numbers are plotted into an image grayscale distribution histogram. This histogram displays the image grayscale distribution and serves as a basis for subsequent image enhancement processing. Step S342: performing equalization processing on the image grayscale distribution histogram based on the image enhancement processing program to obtain a balanced fabric image dataset. Specifically, the image grayscale distribution histogram generated in step S341 is equalized by redistributing grayscale values. The equalization process aims to adjust the image grayscale distribution so that the grayscale value distribution of the output image is more uniform, thereby enhancing the image contrast and improving the image visual effect. Step S343: performing edge detection on the balanced fabric image dataset using a Canny edge detector to obtain fabric edge detection information. Specifically, a Canny edge detector is used to perform edge detection on the fabric equalization image dataset that has undergone equalization processing. The Canny edge detector is a multi-stage algorithm that includes steps such as noise removal, image gradient calculation, non-maximum suppression, and dual threshold detection, ultimately generating an edge image. In step S344, the fabric edge detection information is subjected to edge contour screening using a preset edge threshold to obtain fabric edge contour information. Specifically, the edge image generated in step S343 is screened based on the preset edge threshold to remove pseudo-edges caused by noise and retain the true fabric edge contour information. In step S345, image enhancement processing is performed on the denoised coat fabric image dataset based on the fabric edge contour information to obtain the standard coat fabric image dataset. Specifically, the fabric edge contour information obtained in step S344 is used to perform further image enhancement processing on the denoised coat fabric image dataset, including contrast enhancement, sharpening, and other processing, to highlight the edge contour of the fabric and make the image clearer. This implementation method improves the contrast and clarity of the image through grayscale distribution histogram equalization and edge detection, making the edge contour of the fabric more prominent. It provides high-quality image data for subsequent fabric wrinkle area detection and feature quantification analysis, and improves the accuracy and reliability of the anti-wrinkle performance evaluation of coat fabrics.

[0037] Step S400 performs wrinkle region detection and feature quantification analysis on the standard coat fabric image dataset to obtain coat fabric wrinkle feature quantification results. Specifically, image processing techniques (such as morphological analysis) are used to detect wrinkle regions in the standard coat fabric image dataset. Feature quantification analysis is performed on these wrinkle regions, such as by measuring the area, depth, and distribution of wrinkles. This quantified data is then compiled to generate coat fabric wrinkle feature quantification results, which are documents or datasets containing coat fabric wrinkle feature quantification data.

[0038] In one possible implementation, obtaining quantitative results of coat fabric wrinkle features in step S400 further includes step S410: using a convolutional neural network to perform morphological annotation training on a historical coat fabric wrinkle dataset to generate a fabric image wrinkle recognition network. Specifically, a large number of historical coat fabric wrinkle datasets are collected, containing images of various wrinkle morphologies and varying degrees of wrinkle. The collected data is annotated to clearly identify the morphology and characteristics of the wrinkles in each image, thereby forming an annotated dataset. A convolutional neural network (CNN) is used to construct a fabric image wrinkle recognition network model. The annotated dataset is input into the CNN model, and model parameters are adjusted through forward and backpropagation algorithms to enable the model to accurately identify wrinkle morphologies in fabric images. The model is then validated and tested using a validation dataset that was not used in training to ensure its accuracy and generalization ability. Step S420: Using the fabric image wrinkle recognition network, wrinkle detection is performed on the standard coat fabric image dataset to determine a coat fabric wrinkle morphology detection set. Specifically, the standard coat fabric image dataset is input into the trained fabric image wrinkle recognition network. The network extracts and classifies the input images, identifies the wrinkle morphology in the images, and outputs the identified wrinkle morphology to form a coat fabric wrinkle morphology detection set.

[0039] In step S430, wrinkle regions are marked on the standard coat fabric image dataset according to the coat fabric wrinkle morphology detection set to obtain a coat fabric wrinkle region anchor frame set. Specifically, the specific location of each wrinkle in the image is determined based on the coat fabric wrinkle morphology detection set. One or more anchor frames (i.e., rectangular frames) are generated around each wrinkle location to mark the wrinkle region. All generated anchor frames are output to form a coat fabric wrinkle region anchor frame set. In step S440, feature clustering and quantitative analysis is performed on the coat fabric wrinkle region anchor frame set in sequence to obtain coat fabric wrinkle feature quantification results. Specifically, features are extracted from the wrinkle region within each anchor frame, including features such as fold area, depth, and shape. Cluster analysis is performed on the extracted features using a clustering algorithm (e.g., K-means clustering), grouping similar wrinkle features together. Quantitative analysis is then performed on the clustered features to calculate quantitative metrics such as the average wrinkle area and maximum wrinkle depth for each category. The quantitative analysis results are output to form a coat fabric wrinkle feature quantification result. This approach uses a convolutional neural network for wrinkle recognition training, learning the complex characteristics of wrinkles in fabric images and improving recognition accuracy and robustness. By performing clustering and quantitative analysis of wrinkle regions, the fabric's wrinkle resistance is comprehensively evaluated, including indicators such as wrinkle area, depth, and distribution uniformity, providing strong data support for fabric optimization.

[0040] like Figure 2 As shown, in a possible implementation, the step of obtaining the quantitative results of the coat fabric wrinkle features in step S440 further includes step S441, clustering the morphological features of the coat fabric wrinkle region anchor frame set according to the coat fabric wrinkle morphology detection set to obtain a fabric wrinkle morphology clustered region set. Specifically, the wrinkle morphology in each anchor frame is determined based on the coat fabric wrinkle morphology detection set. The morphological features of the wrinkles, including the shape, direction, curvature, etc. of the wrinkles, are extracted from each anchor frame. A clustering algorithm is used to cluster the anchor frames based on the extracted morphological features, and anchor frames with similar morphological features are grouped together to form a fabric wrinkle morphology clustered region set. Step S442, obtaining a set of wrinkle feature evaluation indicators, wherein the set of wrinkle feature evaluation indicators includes wrinkle area, wrinkle depth, and wrinkle distribution. Specifically, the set of wrinkle feature evaluation indicators is determined based on the needs of anti-wrinkle performance evaluation, and these indicators include wrinkle area, wrinkle depth, and wrinkle distribution. The wrinkle area refers to the area of ​​the wrinkle region in the image (which can be the number of pixels in the wrinkle region in the image); the wrinkle depth refers to the vertical depth of the wrinkle (the distance from the highest point to the lowest point of the wrinkle), which reflects the severity of the wrinkle; the wrinkle distribution refers to the distribution of wrinkles on the fabric, such as uniform distribution or local concentration.

[0041] In step S443, the fabric fold morphology cluster region sets are evaluated and calculated based on the wrinkle feature evaluation index set to obtain a fabric cluster region wrinkle feature set. Specifically, the corresponding wrinkle feature data is extracted from the anchor frames in each cluster region set, and a separate evaluation calculation is performed for each cluster region based on the wrinkle feature evaluation index set. For example, the average wrinkle area and maximum wrinkle depth of each cluster region are calculated. The evaluation calculation results are recorded to form a fabric cluster region wrinkle feature set. In step S444, statistical quantitative analysis is performed on the fabric cluster region wrinkle feature sets in sequence to obtain quantitative results of the coat fabric wrinkle features. Specifically, the data for each type of wrinkle morphology in the fabric cluster region wrinkle feature set is aggregated to form an overall fabric wrinkle feature dataset. Statistical analysis is performed on the overall fabric wrinkle feature dataset, such as calculating the average wrinkle area, the average value of the maximum wrinkle depth, and the degree of dispersion of the wrinkle distribution. The statistical analysis results are output to form a quantitative result of the coat fabric wrinkle features. This implementation method uses a clear set of wrinkle feature evaluation indicators for quantitative analysis. The wrinkle feature evaluation indicator set covers multiple aspects such as wrinkle area, wrinkle depth, and wrinkle distribution, and comprehensively reflects the anti-wrinkle performance of the fabric. By statistically analyzing these indicators, the comprehensiveness of the fabric anti-wrinkle performance evaluation is improved, providing strong data support for fabric optimization and quality control.

[0042] Step S500 performs a statistical analysis on the quantitative results of the coat fabric's wrinkle characteristics according to a set of anti-wrinkle performance indicators, generates a coat fabric anti-wrinkle performance report, and performs anti-wrinkle optimization on the target coat fabric based on the anti-wrinkle performance report. Specifically, a statistical analysis is performed on the quantitative results of the coat fabric's wrinkle characteristics according to a preset set of anti-wrinkle performance indicators (a set of indicators used to evaluate fabric anti-wrinkle performance, such as average wrinkle area, maximum wrinkle depth, and wrinkle distribution uniformity). Based on the statistical analysis results, a coat fabric anti-wrinkle performance report is generated, which details the fabric's anti-wrinkle performance. Based on the data in the report, anti-wrinkle optimization is performed on the target coat fabric, such as by adjusting the fabric's composition and production process. The embodiment of the present application obtains usage scenario information of the target coat fabric, performs test status analysis, and performs anti-wrinkle testing according to the coat fabric test status parameter table. A high-resolution camera is used to capture images of the test process to obtain a coat fabric image dataset. The image dataset is preprocessed by an image data preprocessing program to obtain a standard coat fabric image dataset. The standard image dataset is further subjected to wrinkle area detection and feature quantification analysis to obtain quantitative results of coat fabric wrinkle features. The wrinkle feature quantification results are statistically analyzed according to an anti-wrinkle performance index set to generate an anti-wrinkle performance report for the coat fabric. Anti-wrinkle optimization processing is performed on the target coat fabric based on the report, and other technical means are used to achieve the technical effect of improving the accuracy of anti-wrinkle performance evaluation and optimizing the pertinence.

[0043] In one possible implementation, the anti-wrinkle optimization treatment of the target coat fabric based on the anti-wrinkle performance report of the coat fabric is performed. Step S500 further includes step S510, which constructs a coat fabric treatment process database. The coat fabric treatment process database includes fabric treatment process data for different fabric attributes and corresponding wrinkle treatment effect data. Specifically, a large amount of coat fabric treatment process data (such as temperature, time, pressure, and chemical reagent type) for different fabric attributes (such as fabric material, fiber type, weaving method, and dye type) is collected. For each coat fabric with each fabric attribute, wrinkle treatment effect data after different treatment processes is recorded, such as average wrinkle area and maximum wrinkle depth after treatment. The collected fabric attribute information, treatment process data, and wrinkle treatment effect data are integrated to construct a coat fabric treatment process database, which is used for querying, matching, and data analysis.

[0044] Step S520 involves matching and partitioning the coat fabric treatment process database based on the target coat fabric's attribute information to obtain a coat fabric treatment process selection space. Specifically, attribute information, such as material and fiber type, is extracted from the target coat fabric. This extracted attribute information is then matched with fabric attribute information in the coat fabric treatment process database to select fabric treatment process data with attributes similar to those of the target coat fabric. Based on the matching results, the selected fabric treatment process data is integrated into a coat fabric treatment process selection space, which contains a range of treatment processes potentially applicable to the target coat fabric.

[0045] In step S530, a global optimization algorithm is performed within the selected coat fabric treatment process space, using the coat fabric anti-wrinkle performance report as a constraint parameter to obtain anti-wrinkle process parameters. Specifically, the quantitative results (such as average wrinkle area and maximum wrinkle depth) in the coat fabric anti-wrinkle performance report are used as the constraint parameters. Within the selected coat fabric treatment process space, a global optimization algorithm is applied to search for the optimal combination of treatment process parameters that meets the constraint parameters. The optimal combination of treatment process parameters is determined based on the output of the global optimization algorithm. In step S540, anti-wrinkle optimization treatment is performed on the target coat fabric based on the coat fabric anti-wrinkle process parameters. Specifically, the parameters of the treatment equipment are set based on the optimal combination of treatment process parameters determined in step S530. The target coat fabric is placed in the treatment equipment and treated according to the set parameters. After treatment, the target coat fabric is subjected to a quantitative wrinkle feature analysis to evaluate whether the treatment effect meets the expected requirements. This implementation method, through database construction and matching partitioning, can quickly screen for treatment processes that may be suitable for the target coat fabric, reducing trial-and-error costs and time. The global optimization algorithm can search for the optimal solution in the space of coat fabric treatment process selection, ensuring that the treatment process parameters found can maximize the improvement of the fabric's anti-wrinkle performance and optimize the treatment effect.

[0046] In one possible implementation, the anti-wrinkle optimization treatment of the target coat fabric based on the anti-wrinkle process parameters for the coat fabric, step S540, further includes step S541: performing anti-wrinkle treatment on the target coat fabric based on the anti-wrinkle process parameters for the coat fabric, and obtaining feedback parameters of the fabric's anti-wrinkle effect. Specifically, various parameters of the anti-wrinkle treatment equipment (e.g., a steam iron, a press, etc.), such as temperature, time, pressure, and amount of chemical reagents, are adjusted based on the anti-wrinkle process parameters for the coat fabric obtained in step S530. The target coat fabric is placed in the anti-wrinkle treatment equipment and anti-wrinkle treated according to the set parameters. After the treatment is completed, the anti-wrinkle effect of the fabric is quantitatively evaluated using a high-resolution camera to obtain feedback parameters of the fabric's anti-wrinkle effect, such as the average wrinkle area and maximum wrinkle depth after treatment. Step S542: Optimizing and analyzing the feedback parameters of the fabric's anti-wrinkle effect to obtain parameter variation rules. Specifically, statistical analysis is performed on the feedback parameters of the fabric's anti-wrinkle effect obtained in step S541 to compare the treatment effects under different parameter combinations. Based on the statistical analysis results, key parameters influencing the fabric's wrinkle-resistant performance and their variation patterns, i.e., parameter variation patterns, are extracted. These patterns include correlations between parameters and trends in the impact of parameter changes on the fabric's wrinkle-resistant performance. In step S543, the anti-wrinkle process parameters for the coat fabric are subjected to variational fine-tuning and optimization based on the parameter variation patterns to obtain optimized anti-wrinkle process parameters for the coat fabric. Specifically, based on the parameter variation patterns obtained in step S542, the anti-wrinkle process parameters for the coat fabric are fine-tuned to further improve the fabric's wrinkle-resistant performance. By repeatedly fine-tuning and evaluating the anti-wrinkle feedback parameters, the optimal anti-wrinkle process parameter combination is iteratively sought. When the fabric's wrinkle-resistant performance reaches a preset standard or cannot be significantly improved, the final optimized anti-wrinkle process parameters for the coat fabric are determined. This implementation method, through optimization analysis of the anti-wrinkle feedback parameters, extracts key parameters influencing the fabric's wrinkle-resistant performance and their variation patterns, enabling precise adjustment of process parameters and improving processing accuracy. Furthermore, through variational fine-tuning and optimization, the anti-wrinkle performance of the fabric is further improved, resulting in a smoother and more evenly treated fabric that meets higher quality requirements.

[0047] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for anti-wrinkle treatment of coat fabrics, characterized in that: The method comprises: Obtaining usage scenario information of the target coat fabric, performing test status analysis on the usage scenario information, and obtaining a coat fabric test status parameter table; performing an anti-wrinkle test on the target coat fabric according to the coat fabric test state parameter table, and simultaneously capturing images of the test process using a high-resolution camera device to obtain a coat fabric image dataset; obtaining an image data preprocessing program, and preprocessing the coat fabric image dataset based on the image data preprocessing program to obtain a standard coat fabric image dataset; Performing wrinkle region detection and feature quantification analysis on the standard coat fabric image dataset to obtain coat fabric wrinkle feature quantification results; performing statistical analysis on the quantitative results of wrinkle characteristics of the coat fabric according to the anti-wrinkle performance index set, generating a coat fabric anti-wrinkle performance report, and performing anti-wrinkle optimization processing on the target coat fabric based on the coat fabric anti-wrinkle performance report; The method of obtaining the quantitative results of the wrinkle characteristics of the coat fabric includes: Use convolutional neural networks to perform morphological annotation training on a historical coat fabric wrinkle dataset and generate a fabric image wrinkle recognition network; Performing wrinkle detection on the standard coat fabric image dataset based on the fabric image wrinkle recognition network to determine a coat fabric wrinkle morphology detection set; Marking wrinkle regions of the standard coat fabric image dataset according to the coat fabric wrinkle morphology detection set to obtain a coat fabric wrinkle region anchor frame set; Performing feature clustering and quantitative analysis on the anchor frame set of the coat fabric wrinkle region in sequence to obtain a quantitative result of the coat fabric wrinkle feature; The obtaining of the quantitative result of the wrinkle feature of the coat fabric includes: performing morphological feature clustering on the coat fabric wrinkle region anchor frame set according to the coat fabric wrinkle morphology detection set to obtain a fabric wrinkle morphology clustered region set; Obtaining a set of wrinkle feature evaluation indicators, wherein the set of wrinkle feature evaluation indicators includes wrinkle area, wrinkle depth, and wrinkle distribution; Based on the wrinkle feature evaluation index set, the fabric wrinkle morphology cluster region set is evaluated and calculated respectively to obtain a fabric cluster region wrinkle feature set; Statistical quantitative analysis is performed on the wrinkle feature sets of the fabric clustering regions in sequence to obtain quantitative results of the wrinkle features of the coat fabric.

2. The method for anti-wrinkle treatment of coat fabrics according to claim 1, characterized in that: The method of obtaining the coat fabric test state parameter table includes: Acquire coat usage scenario factor information, wherein the coat usage scenario factor information includes usage environment, usage status, and usage behavior; Performing a usage status analysis on the usage scenario information based on the coat usage scenario factor information to obtain a usage scenario factor state set; Performing test parameter analysis on each of the usage scenario factor state sets to obtain a usage scenario factor test parameter set; The parameter set of the usage scenario factor test parameters is orthogonally arranged to obtain the coat fabric test state parameter table.

3. The method for anti-wrinkle treatment of coat fabrics according to claim 1, characterized in that: The obtained standard coat fabric image dataset includes: Determining an image denoising processing program and an image enhancement processing program according to the image data preprocessing program; Converting the coat fabric image dataset into a fabric grayscale image dataset, performing noise identification and classification on the fabric grayscale image dataset to obtain multi-scale image data noise; Obtaining a multi-scale noise filtering rule according to the image denoising processing program, and performing filtering preprocessing on the multi-scale image data noise based on the multi-scale noise filtering rule to obtain a denoised coat fabric image dataset; Image enhancement processing is performed on the denoised coat fabric image dataset based on the image enhancement processing program to obtain the standard coat fabric image dataset.

4. The method for anti-wrinkle treatment of coat fabrics according to claim 3, characterized in that: The step of obtaining the standard coat fabric image dataset includes: Generating an image grayscale distribution histogram according to the denoised coat fabric image dataset; Performing equalization processing on the image grayscale distribution histogram based on the image enhancement processing program to obtain a fabric balanced image data set; Performing edge detection on the fabric balanced image dataset using a Canny edge detector to obtain fabric edge detection information; Preset edge threshold to perform edge contour screening on the fabric edge detection information to obtain fabric edge contour information; Image enhancement processing is performed on the denoised coat fabric image dataset based on the fabric edge contour information to obtain the standard coat fabric image dataset.

5. The method for anti-wrinkle treatment of coat fabrics according to claim 1, characterized in that: The performing anti-wrinkle optimization processing on the target coat fabric based on the coat fabric anti-wrinkle performance report includes: Constructing a coat fabric treatment process database, wherein the coat fabric treatment process database includes fabric treatment process data of different fabric properties and corresponding wrinkle treatment effect data; Matching and dividing the coat fabric treatment process database based on the attribute information of the target coat fabric to obtain a coat fabric treatment process selection space; Using the anti-wrinkle performance report of the coat fabric as a constraint parameter, a global optimization is performed in the coat fabric treatment process selection space to obtain the anti-wrinkle process parameters of the coat fabric; The target coat fabric is subjected to anti-wrinkle optimization treatment based on the coat fabric anti-wrinkle process parameters.

6. The method for anti-wrinkle treatment of coat fabrics according to claim 5, characterized in that: The anti-wrinkle optimization treatment of the target coat fabric based on the coat fabric anti-wrinkle process parameters includes: performing anti-wrinkle treatment on the target coat fabric based on the anti-wrinkle process parameters of the coat fabric, and obtaining feedback parameters of the fabric anti-wrinkle effect; Optimizing and analyzing the feedback parameters of the anti-wrinkle effect of the fabric to obtain parameter variation rules; Based on the parameter variation rules, the anti-wrinkle process parameters of the coat fabric are mutated and fine-tuned to obtain the optimized parameters of the anti-wrinkle process of the coat fabric.

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