A method and system for detecting color fastness of clothing fabrics

Abstract

The invention discloses a method and system for detecting color fastness of clothing fabrics, and relates to the technical field of fabric detection. The method comprises: collecting initial color information and initial texture information of clothing fabrics to be detected; simulating the influencing factors of the clothing fabrics to be detected in multiple preset scenes, so as to perform color fastness tests on the clothing fabrics to be detected in multiple preset scenes; wherein the influencing factors in the multiple preset scenes include friction, washing, rain, exposure to the sun, light, seawater immersion, saliva immersion, water stains and sweat stains; collecting reference color information sets and reference texture information sets of the clothing fabrics to be detected that have undergone color fastness tests in multiple preset scenes; analyzing the initial color information and the reference color information sets after the color fastness tests in multiple preset scenes; and evaluating the color fastness of the clothing fabrics to be detected according to the calculated color difference and the change of the surface structure of the clothing fabrics to be detected.

Description

Technical Field

[0001] The present application relates to the technical field of fabric detection, and in particular to a method and system for detecting color fastness of clothing fabrics. Background Art

[0002] Color fastness refers to the degree of fading of printed and dyed textiles under the influence of external factors during use or processing, and is an important indicator of clothing fabrics. During their use, printed and dyed textiles will be affected by various external factors such as light and washing. Some printed and dyed textiles also undergo special finishing and processing, which requires that the color of printed and dyed textiles maintain a certain fastness. When it is necessary to test the color fastness of clothing fabrics, visual rating is usually used. However, visual rating is easily affected by objective and subjective factors, resulting in low accuracy of the evaluation results.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method and system for detecting color fastness of clothing fabrics to solve the above-mentioned technical problems.

[0005] The present application provides a method for testing color fastness of clothing fabrics, comprising: collecting initial color information and initial texture information of the clothing fabric to be tested;

[0006] The factors acting on the garment fabric to be tested in a plurality of preset scenarios are simulated to perform color fastness tests on the garment fabric to be tested in a plurality of preset scenarios; wherein the factors acting on the plurality of preset scenarios include friction, washing, rain, exposure to the sun, light, seawater immersion, saliva immersion, water stains and sweat stains;

[0007] Collecting a reference color information set and a reference texture information set of the garment fabric to be tested that has undergone color fastness tests under a variety of preset scenarios;

[0008] Analyzing the initial color information and a set of reference color information after color fastness tests under various preset scenarios, and calculating color differences based on a color difference formula;

[0009] Analyzing the initial texture information and a reference texture information set after color fastness tests under a variety of preset scenarios to evaluate changes in the surface structure of the garment fabric to be tested;

[0010] The color fastness of the clothing fabric to be tested is evaluated based on the calculated color difference and the change in the surface structure of the clothing fabric to be tested.

[0011] Further, the initial color information and the reference color information set after color fastness tests under various preset scenes are analyzed, and the color difference is calculated based on the color difference formula, and is configured as follows:

[0012] The initial color information And a reference color information set after color fastness testing in a variety of preset scenarios To conduct analysis; among them, Indicates the indexes in different preset scenarios; Each element in Corresponding to Reference color information for a preset scene, Including this scenario value, value, value;

[0013] Calculate the color difference in each preset scene based on the following color difference formula;

[0014] ;

[0015] in, Used to indicate the Color differences under different preset scenes; , , Indicated in The coordinates of the reference color information tested for color fastness in a preset scene in the CIELAB color space; , , The coordinates of the initial color information in the CIELAB color space are used to represent the reference color of the clothing fabric to be detected before any processing is performed.

[0016] Further, the change in the surface structure of the clothing fabric to be detected includes the surface roughness, fiber arrangement, texture density and color distribution uniformity of the fabric; the color fastness of the clothing fabric to be detected is evaluated according to the calculated color difference and the change in the surface structure of the clothing fabric to be detected, and is configured as follows:

[0017] Using an adaptive neural fuzzy inference algorithm to determine a first weight of the color difference and a second weight of the change in the surface structure of the clothing fabric to be tested, so as to calculate a color fastness characteristic index of the clothing fabric to be tested;

[0018] ;

[0019] in, is the color fastness characteristic index; and are first weights and second weights determined by the adaptive neuro-fuzzy inference algorithm; Used to indicate the Color differences under different preset scenes; Used to indicate the Changes in the surface structure of the garment fabric to be tested under a preset scenario;

[0020] Calculate the overall color fastness index of the clothing fabric to be tested according to the color fastness characteristic index of the clothing fabric to be tested combined with fuzzy reasoning logic;

[0021] ;

[0022] in, is the overall color fastness index of the garment fabric to be tested; Represents the "OR" operation in fuzzy logic; is the number of preset scenes; It is Fuzzy membership function under a preset scenario, Color fastness index Converted into a membership value, the membership value indicates that the clothing fabric to be tested is in the The color fastness level under a preset scenario, the membership value ranges from 0 to 1, where 1 means that the color fastness requirements are fully met and 0 means that they are completely unsatisfactory;

[0023] The color fastness of the clothing fabric to be tested is evaluated according to the overall color fastness index.

[0024] The present application provides a clothing fabric color fastness detection system, comprising:

[0025] The initial image acquisition module is used to acquire the initial color information and initial texture information of the clothing fabric to be detected;

[0026] A fabric processing module is used to simulate the factors affecting the garment fabric to be tested in a variety of preset scenarios, so as to perform color fastness tests on the garment fabric to be tested in a variety of preset scenarios; wherein the factors affecting the garment fabric in the various preset scenarios include friction, washing, rain, exposure to the sun, light, seawater immersion, saliva immersion, water stains and sweat stains;

[0027] A reference image acquisition module, used for acquiring a reference color information set and a reference texture information set of the garment fabric to be tested after the color fastness test under a plurality of preset scenes;

[0028] A color analysis module, used to analyze the initial color information and a reference color information set after color fastness tests under various preset scenarios, and calculate color differences based on a color difference formula;

[0029] A texture analysis module, used to analyze the initial texture information and a reference texture information set after color fastness tests under various preset scenarios, so as to evaluate changes in the surface structure of the garment fabric to be tested;

[0030] The comprehensive processing module is used to evaluate the color fastness of the clothing fabric to be tested according to the calculated color difference and the change of the surface structure of the clothing fabric to be tested.

[0031] Furthermore, the color analysis module analyzes the initial color information and a reference color information set after color fastness tests under a plurality of preset scenes, and calculates the color difference based on a color difference formula, and is configured as follows:

[0032] The initial color information And a reference color information set after color fastness testing in a variety of preset scenarios To conduct analysis; among them, Indicates the indexes in different preset scenarios; Each element in Corresponding to Reference color information for a preset scene, Including this scenario value, value, value;

[0033] Calculate the color difference in each preset scene based on the following color difference formula;

[0034] ;

[0035] in, Used to indicate the Color differences under different preset scenes; , , Indicated in The coordinates of the reference color information tested for color fastness in a preset scene in the CIELAB color space; , , The coordinates of the initial color information in the CIELAB color space are used to represent the reference color of the clothing fabric to be detected before any processing is performed.

[0036] Furthermore, the change in the surface structure of the clothing fabric to be detected includes the surface roughness, fiber arrangement, texture density and color distribution uniformity of the fabric; the comprehensive processing module evaluates the color fastness of the clothing fabric to be detected according to the calculated color difference and the change in the surface structure of the clothing fabric to be detected, and is configured as follows:

[0037] Using an adaptive neural fuzzy inference algorithm to determine a first weight of the color difference and a second weight of the change in the surface structure of the clothing fabric to be tested, so as to calculate a color fastness characteristic index of the clothing fabric to be tested;

[0038] ;

[0039] in, is the color fastness characteristic index; and are first weights and second weights determined by the adaptive neuro-fuzzy inference algorithm; Used to indicate the Color differences under different preset scenes; Used to indicate the Changes in the surface structure of the garment fabric to be tested under a preset scenario;

[0040] Calculate the overall color fastness index of the clothing fabric to be tested according to the color fastness characteristic index of the clothing fabric to be tested combined with fuzzy reasoning logic;

[0041] ;

[0042] in, is the overall color fastness index of the garment fabric to be tested; Represents the "OR" operation in fuzzy logic; is the number of preset scenes; It is Fuzzy membership function under a preset scenario, Color fastness index Converted into a membership value, the membership value indicates that the clothing fabric to be tested is in the The color fastness level under a preset scenario, the membership value ranges from 0 to 1, where 1 means that the color fastness requirements are fully met and 0 means that they are completely unsatisfactory;

[0043] The color fastness of the clothing fabric to be tested is evaluated according to the overall color fastness index.

[0044] Further, based on the following formula, the first weight is adjusted according to the fabric properties of the garment fabric to be tested and the environmental factors: and the second weight ;

[0045] ;

[0046] ;

[0047] in, and It is Environmental factors under a preset scenario, including temperature, humidity, light and gas concentration; , , , The parameters are obtained through training of the fabric properties of the garment fabric to be tested and historical test data; and It is a coefficient adjusted according to the fabric properties of the clothing fabric to be tested.

[0048] Furthermore, the fuzzy membership function Defined as:

[0049] ;

[0050] in, is the color fastness characteristic index; represents the center point of the membership function, that is, the point at which the membership function reaches its maximum value; Represents the width of the membership function, that is, from the center point The distance to the point where the membership drops to zero.

[0051] Further, in the process of performing color fastness tests on the clothing fabric to be tested under multiple preset scenarios, the test items of the color fastness test include acid perspiration color fastness test, alkali perspiration color fastness test, water color fastness test, friction color fastness test and saliva color fastness test;

[0052] The fabric processing module comprises a fabric adaptive adjustment unit, and the fabric adaptive adjustment unit is used to adjust the parameters and conditions of the test items of the color fastness test according to the fabric properties of the clothing fabric to be tested.

[0053] Furthermore, the clothing fabric color fastness detection system also includes:

[0054] A data analysis and processing unit, used to generate a color fastness rating report based on the evaluation result of the color fastness of the clothing fabric to be tested;

[0055] The user interaction interface is used to display the color fastness rating report and receive parameter values ​​of influencing factors under various preset scenarios input by the user.

[0056] Based on the embodiment provided by the present application, the initial color information and initial texture information of the clothing fabric to be tested are collected; the factors acting on the clothing fabric to be tested in multiple preset scenes are simulated to test the color fastness of the clothing fabric to be tested in multiple preset scenes; wherein the factors acting in multiple preset scenes include friction, washing, rain, exposure, light, seawater immersion, saliva immersion, water stains and sweat stains; the reference color information set and the reference texture information set of the clothing fabric to be tested that have been tested for color fastness in multiple preset scenes are collected; the initial color information and the reference color information set after the color fastness test in multiple preset scenes are analyzed, and the color difference is calculated based on the color difference formula; the initial texture information and the reference texture information set after the color fastness test in multiple preset scenes are analyzed to evaluate the change of the surface structure of the clothing fabric to be tested; according to the calculated color difference and the change of the surface structure of the clothing fabric to be tested, the color fastness of the clothing fabric to be tested is evaluated. Thus, the color fastness of the clothing fabric to be tested is automatically detected, and the efficiency and accuracy of the color fastness of the clothing fabric are improved.

[0057] Specifically, it has the following beneficial effects: Comprehensive color fastness evaluation: by collecting the initial color information and initial texture information of the clothing fabric to be tested, the system can comprehensively record the original state of the fabric and provide benchmark data for subsequent color fastness tests; the system can simulate the factors in a variety of preset scenarios, such as friction, washing, rain, etc. These factors cover various environmental conditions that clothing fabrics may encounter in actual use, ensuring the practicality and reliability of the test results; by conducting color fastness tests on the clothing fabrics to be tested in a variety of preset scenarios, the system can comprehensively consider the impact of various external factors on the color fastness of the fabric and provide a more comprehensive color fastness analysis; using the color difference formula to calculate the color difference, the system can accurately quantify the color changes of the fabric in different test scenarios, It provides a scientific basis for color fastness evaluation; by analyzing the initial texture information and the reference texture information set after color fastness tests under various preset scenarios, the system can evaluate the changes in the surface structure of the fabric, which has an important impact on the appearance and performance of the fabric; through automated image acquisition and data analysis, the system reduces errors caused by manual operations, improves the efficiency and accuracy of the test, and makes color fastness evaluation more reliable; the system conducts a comprehensive evaluation of the color fastness of the tested clothing fabrics based on the calculation results of color differences and surface structure changes, providing data-driven decision support for manufacturers and quality inspectors; by accurately evaluating the color fastness of the fabric, the system helps to promptly discover and resolve quality problems, thereby improving the quality control level of clothing fabrics and enhancing the market competitiveness of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0059] Figure 1 It is a flow chart of an optional method for detecting color fastness of clothing fabrics according to an embodiment of the present application;

[0060] Figure 2 The figure is a structural diagram of an optional clothing fabric color fastness detection system according to an embodiment of the present application.

[0061] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0063] Alternatively, if Figure 1 As shown, the present application provides a method for detecting color fastness of clothing fabrics, comprising:

[0064] S101, collecting initial color information and initial texture information of the clothing fabric to be tested;

[0065] S102, simulating the factors acting on the garment fabric to be tested in a plurality of preset scenarios, so as to perform color fastness tests on the garment fabric to be tested in a plurality of preset scenarios; wherein the factors acting in the plurality of preset scenarios include friction, washing, rain, exposure to the sun, light, seawater immersion, saliva immersion, water stains and sweat stains; S103, collecting a reference color information set and a reference texture information set of the garment fabric to be tested that has undergone the color fastness tests in a plurality of preset scenarios;

[0066] S104, analyzing the initial color information and the reference color information set after color fastness tests under various preset scenes, and calculating the color difference based on a color difference formula;

[0067] S105, analyzing the initial texture information and the reference texture information set after the color fastness test under various preset scenes to evaluate the change of the surface structure of the garment fabric to be tested;

[0068] S106, evaluate the color fastness of the clothing fabric to be tested according to the calculated color difference and the change of the surface structure of the clothing fabric to be tested. Based on the embodiment provided by the present application, the initial color information and initial texture information of the clothing fabric to be tested are collected; the factors acting on the clothing fabric to be tested in multiple preset scenes are simulated to test the color fastness of the clothing fabric to be tested in multiple preset scenes; wherein the factors acting in multiple preset scenes include friction, washing, rain, exposure, light, seawater immersion, saliva immersion, water stains and sweat stains; the reference color information set and the reference texture information set of the clothing fabric to be tested that have been tested for color fastness in multiple preset scenes are collected; the initial color information and the reference color information set after the color fastness test in multiple preset scenes are analyzed, and the color difference is calculated based on the color difference formula; the initial texture information and the reference texture information set after the color fastness test in multiple preset scenes are analyzed to evaluate the change of the surface structure of the clothing fabric to be tested; the color fastness of the clothing fabric to be tested is evaluated according to the calculated color difference and the change of the surface structure of the clothing fabric to be tested. Thereby, the automatic detection of color fastness of clothing fabrics is realized, and the efficiency and accuracy of the detection of color fastness of clothing fabrics are improved.

[0069] Specifically, it has the following beneficial effects: Comprehensive color fastness evaluation: by collecting the initial color information and initial texture information of the clothing fabric to be tested, the system can comprehensively record the original state of the fabric and provide benchmark data for subsequent color fastness tests; the system can simulate the factors in a variety of preset scenarios, such as friction, washing, rain, etc. These factors cover various environmental conditions that clothing fabrics may encounter in actual use, ensuring the practicality and reliability of the test results; by conducting color fastness tests on the clothing fabrics to be tested in a variety of preset scenarios, the system can comprehensively consider the impact of various external factors on the color fastness of the fabric and provide a more comprehensive color fastness analysis; using the color difference formula to calculate the color difference, the system can accurately quantify the color changes of the fabric in different test scenarios, It provides a scientific basis for color fastness evaluation; by analyzing the initial texture information and the reference texture information set after color fastness tests under various preset scenarios, the system can evaluate the changes in the surface structure of the fabric, which has an important impact on the appearance and performance of the fabric; through automated image acquisition and data analysis, the system reduces errors caused by manual operations, improves the efficiency and accuracy of the test, and makes color fastness evaluation more reliable; the system conducts a comprehensive evaluation of the color fastness of the tested clothing fabrics based on the calculation results of color differences and surface structure changes, providing data-driven decision support for manufacturers and quality inspectors; by accurately evaluating the color fastness of the fabric, the system helps to promptly discover and resolve quality problems, thereby improving the quality control level of clothing fabrics and enhancing the market competitiveness of products.

[0070] Furthermore, the initial color information and the reference color information set after the color fastness test under various preset scenes are analyzed, and the color difference is calculated based on the color difference formula, which is configured as follows:

[0071] Initial color information And a reference color information set after color fastness testing in a variety of preset scenarios To conduct analysis; among them, Indicates the indexes in different preset scenarios; Each element in Corresponding to Reference color information for a preset scene, Including this scenario value, value, value;

[0072] It should be noted that in the CIELAB color space, Included Preset scenes value, value, The values ​​represent the characteristics of three different dimensions of color.

[0073] Specifically, The value is the brightness component, which indicates how light or dark the color is. The values ​​usually range from 0 to 100, where 0 represents black and 100 represents white. Value is a measure of the change in brightness of a color, that is, how much lighter or darker the color becomes. The value is a chromaticity component, representing the position of the color on the red-green axis. Positive values ​​indicate a color that is more red, and negative values ​​indicate a color that is more green. Values ​​describe the red or green saturation of a color. Value is another chromaticity component that represents the color's position on the yellow-blue axis. Positive values ​​indicate a yellowish color, while negative values ​​indicate a bluish color. The value is used to describe the yellow or blue saturation of the color. In the process of testing the color fastness of the garment fabric under various preset scenarios, the initial color information can be analyzed. And a reference color information set after color fastness testing in a variety of preset scenarios , to evaluate the color difference of the tested clothing fabric before and after various color fastness tests. The color difference in each preset scenario can be used to indicate the color stability, i.e., color fastness, of the tested clothing fabric after the color fastness tests in different preset scenarios. For example, after washing, if The value decreases, indicating that the color becomes darker; if The value becomes negative, indicating that the color changes from red to green; if The value increases, indicating that the color changes from blue to yellow. Through the changes in these parameters, we can evaluate the color retention ability of the fabric in actual use.

[0074] Calculate the color difference in each preset scene based on the following color difference formula;

[0075] ;

[0076] in, Used to indicate the Color differences under different preset scenes; , , Indicated in The coordinates of the reference color information tested for color fastness in a preset scene in the CIELAB color space; , , Represents the coordinates of the initial color information in the CIELAB color space, and is used to represent the reference color of the garment fabric to be tested before any processing is performed.

[0077] Furthermore, the changes in the surface structure of the clothing fabric to be tested include the surface roughness, fiber arrangement, texture density and color distribution uniformity of the fabric; based on the calculated color difference and the changes in the surface structure of the clothing fabric to be tested, the color fastness of the clothing fabric to be tested is evaluated, and is configured as follows:

[0078] Using an adaptive neural fuzzy inference algorithm to determine a first weight of the color difference and a second weight of the change of the surface structure of the clothing fabric to be tested, so as to calculate a color fastness characteristic index of the clothing fabric to be tested;

[0079] ;

[0080] in, is the characteristic index of color fastness; and are first weights and second weights determined by an adaptive neuro-fuzzy inference algorithm; Used to indicate the Color differences under different preset scenes; Used to indicate the Changes in the surface structure of the garment fabric to be tested under a preset scenario;

[0081] According to the color fastness characteristic index of the clothing fabric to be tested and combined with fuzzy reasoning logic, the overall color fastness index of the clothing fabric to be tested is calculated;

[0082] ;

[0083] in, It is the overall color fastness index of the garment fabric to be tested; Represents the "OR" operation in fuzzy logic; is the number of preset scenes; It is Fuzzy membership function under a preset scenario, Color fastness index Converted into membership value, which indicates the membership of the clothing fabric to be tested in the The color fastness level under a preset scenario, the membership value ranges from 0 to 1, where 1 means that the color fastness requirements are fully met and 0 means that they are completely unsatisfactory;

[0084] The color fastness of the tested clothing fabrics is evaluated based on the overall color fastness index.

[0085] Alternatively, if Figure 2 As shown, the present application provides a clothing fabric color fastness detection system, comprising:

[0086] The initial image acquisition module 201 is used to acquire the initial color information and initial texture information of the garment fabric to be detected;

[0087] The fabric processing module 202 is used to simulate the factors affecting the garment fabric to be tested in various preset scenarios, so as to perform color fastness tests on the garment fabric to be tested in various preset scenarios; wherein the factors affecting the various preset scenarios include friction, washing, rain, exposure to the sun, light, seawater immersion, saliva immersion, water stains and sweat stains;

[0088] The reference image acquisition module 203 is used to acquire a reference color information set and a reference texture information set of the garment fabric to be tested that has undergone color fastness tests under a variety of preset scenes;

[0089] A color analysis module 204 is used to analyze the initial color information and the reference color information set after the color fastness test under various preset scenes, and calculate the color difference based on the color difference formula;

[0090] Based on the calculated color differences and changes in the surface structure of the clothing fabric to be tested, the color differences in color fastness ratings can be digitized, standardized and concretized.

[0091] The texture analysis module 205 is used to analyze the initial texture information and the reference texture information set after the color fastness test under various preset scenes to evaluate the change of the surface structure of the garment fabric to be tested;

[0092] The comprehensive processing module 206 is used to evaluate the color fastness of the clothing fabric to be tested based on the calculated color difference and the change of the surface structure of the clothing fabric to be tested.

[0093] It should be noted that in the digital image rating of this application, the relevant standards for color difference grade assessment include "GB / T 39648-2020 Digital Image Technology Rating of Textile Color Fastness Test", which is rated based on the CIEDE2000 color difference formula.

[0094] In the embodiment of the present application, according to "GB / T 39648-2020 Textile Color Fastness Test Digital Image Technical Rating", the position of color information in its color space is converted into a gray scale grade for staining.

[0095] Furthermore, the color analysis module analyzes the initial color information and the reference color information set after the color fastness test under various preset scenes, and calculates the color difference based on the color difference formula, and is configured as follows:

[0096] Initial color information And a reference color information set after color fastness testing in a variety of preset scenarios To conduct analysis; among them, Indicates the indexes in different preset scenarios; Each element in Corresponding to Reference color information for a preset scene, Including this scenario value, value, value;

[0097] Calculate the color difference in each preset scene based on the following color difference formula;

[0098] ;

[0099] in, Used to indicate the Color differences under different preset scenes; , , Indicated in The coordinates of the reference color information tested for color fastness in a preset scene in the CIELAB color space; , , Represents the coordinates of the initial color information in the CIELAB color space, and is used to represent the reference color of the garment fabric to be tested before any processing is performed.

[0100] After calculating the color difference in each preset scene, the calculated Compare with the standard gray card. Gray card is a series of standardized gray cards used to visually assess the severity of color change. Each pair of gray cards is divided into 5 fastness grades, namely 5, 4, 3, 2, 1, and half a grade is added in every two grades, namely 4-5, 3-4, 2-3, 1-2, collectively known as the five-grade nine-grade gray card.

[0101] according to The color fastness grade of the fabric to be tested is determined by comparing the results with the gray card. The values ​​are compared to the color difference values ​​on the gray card to determine the closest match.

[0102] Specifically, we first established a digital image technology rating system based on the GB / T 39648-2020 Textile Color Fastness Test Digital Image Technology Rating standard. Correspondence with gray card level. This correspondence matches the color difference in various preset scenes with the color difference value on the gray card to determine the closest matching level.

[0103] Then, the gray card grades are digitized. For example, grade 5 can be set to 0, grade 4 to 1, grade 3 to 2, and so on, until grade 1 is set to 4. Half grades can be expressed as decimals, such as 4-5 grades are set to 0.5, 3-4 grades are set to 1.5, etc.

[0104] Next, the calculated The value is compared to the digitized gray card level. This can be done by lookup or interpolation to find the closest According to the comparison results, determine the color fastness grade of the garment fabric to be tested. The smaller the value, the less the color change and the higher the color fastness grade; if A larger value indicates that the color changes significantly and the color fastness grade is lower.

[0105] Finally, the determined color fastness grade is output as part of the color fastness rating report. This grade can intuitively reflect the ability of the clothing fabric to be tested to resist color changes in a specific preset scenario. Furthermore, the changes in the surface structure of the clothing fabric to be tested include the surface roughness, fiber arrangement, texture density and color distribution uniformity of the fabric; the comprehensive processing module evaluates the color fastness of the clothing fabric to be tested based on the calculated color differences and the changes in the surface structure of the clothing fabric to be tested, and is configured as follows:

[0106] Using an adaptive neural fuzzy inference algorithm to determine a first weight of the color difference and a second weight of the change of the surface structure of the clothing fabric to be tested, so as to calculate a color fastness characteristic index of the clothing fabric to be tested;

[0107] ;

[0108] in, is the characteristic index of color fastness; and are first weights and second weights determined by an adaptive neuro-fuzzy inference algorithm; Used to indicate the Color differences under different preset scenes; Used to indicate the Changes in the surface structure of the garment fabric to be tested under a preset scenario;

[0109] According to the color fastness characteristic index of the clothing fabric to be tested and combined with fuzzy reasoning logic, the overall color fastness index of the clothing fabric to be tested is calculated;

[0110] ;

[0111] in, It is the overall color fastness index of the garment fabric to be tested; Represents the "OR" operation in fuzzy logic; is the number of preset scenes; It is Fuzzy membership function under a preset scenario, Color fastness index Converted into membership value, which indicates the membership of the clothing fabric to be tested in the The color fastness level under a preset scenario, the membership value ranges from 0 to 1, where 1 means that the color fastness requirements are fully met and 0 means that they are completely unsatisfactory;

[0112] The color fastness of the tested clothing fabric is evaluated according to the overall color fastness index. Further, based on the following formula, the first weight is adjusted according to the fabric properties and environmental factors of the tested clothing fabric: and the second weight ;

[0113] ;

[0114] ;

[0115] in, and It is Environmental factors under a preset scenario, including temperature, humidity, light and gas concentration; , , , It is a parameter obtained by training the fabric properties of the garment fabric to be tested and historical test data; and It is a coefficient adjusted according to the fabric properties of the clothing fabric to be tested.

[0116] Fabric properties refer to the physical, chemical and sensory attributes of a fabric that determine its appearance, feel, durability and suitability for use.

[0117] The following are some fabric properties and their corresponding adjustment factors and Example:

[0118] Cotton fabric: Features: natural fiber, good breathability, but easy to fade; = 1.2 (color differences are weighted more because cotton fades easily); = 0.8 (the weight of surface structure changes is low because the cotton fabric structure is relatively stable).

[0119] Nylon fabric: Features: synthetic fiber, wear-resistant and color-fast, but may deform at high temperatures; = 0.9 (moderate weighting of color difference because nylon is more durable); = 1.1 (the weight of surface structural changes is higher because high temperature may affect the morphology of nylon).

[0120] Silk fabric: Features: natural protein fiber, good luster, but easily affected by light; = 1.0 (weighted standard for color difference, because silk is more durable); = 0.9 (changes in surface structure are weighted less because silk is structurally stable, but lighting may affect its appearance).

[0121] Polyester fabric: Features: synthetic fiber, good heat resistance, color fastness, but may pilling under friction; = 0.8 (the weight of color difference is low because polyester color is very strong); = 1.2 (the weight of surface structure changes is higher because friction may cause pilling of the surface);

[0122] Wool fabric: Features: natural fiber, good warmth retention, but easily affected by humidity; = 1.1 (color differences are weighted higher because humidity can affect wool color); = 1.0 (weighting criterion for surface structural changes, since wool structure may change under humidity).

[0123] Furthermore, the fuzzy membership function Defined as:

[0124] ;

[0125] in, is the characteristic index of color fastness; represents the center point of the membership function, that is, the point at which the membership function reaches its maximum value; Represents the width of the membership function, that is, from the center point The distance to the point where the membership drops to zero.

[0126] Furthermore, in the process of color fastness testing the garment fabrics to be tested under various preset scenarios, the color fastness test items include acid perspiration color fastness test, alkali perspiration color fastness test, water color fastness test, friction color fastness test and saliva color fastness test;

[0127] The fabric processing module comprises a fabric self-adapting adjustment unit, which is used to adjust the parameters and conditions of the test items of the color fastness test according to the fabric properties of the garment fabric to be tested.

[0128] Furthermore, the clothing fabric color fastness testing system also includes:

[0129] A data analysis and processing unit, used to generate a color fastness rating report based on the evaluation results of the color fastness of the clothing fabric to be tested;

[0130] The user interaction interface is used to display the color fastness rating report and receive the parameter values ​​of the influencing factors under various preset scenarios input by the user.

[0131] It should be noted that, in the present application, the embodiments implemented by the clothing fabric color fastness detection system side can be referenced with the embodiments implemented by the clothing fabric color fastness detection method side, and the present application will not repeat them one by one.

[0132] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for testing color fastness of clothing fabrics, characterized in that: include: Collect the initial color information and initial texture information of the clothing fabric to be tested; The factors acting on the garment fabric to be tested in a plurality of preset scenarios are simulated to perform color fastness tests on the garment fabric to be tested in a plurality of preset scenarios; wherein the factors acting on the plurality of preset scenarios include friction, washing, rain, exposure to the sun, light, seawater immersion, saliva immersion, water stains and sweat stains; Collecting a reference color information set and a reference texture information set of the garment fabric to be tested that has undergone color fastness tests under a variety of preset scenarios; Analyzing the initial color information and a set of reference color information after color fastness tests under various preset scenarios, and calculating color differences based on a color difference formula; Analyzing the initial texture information and a reference texture information set after color fastness tests under a variety of preset scenarios to evaluate changes in the surface structure of the garment fabric to be tested; According to the calculated color difference and the change of the surface structure of the clothing fabric to be tested, the color fastness of the clothing fabric to be tested is evaluated; The analyzing the initial color information and the reference color information set after the color fastness test under various preset scenes, and calculating the color difference based on the color difference formula, is configured as follows: The initial color information And a reference color information set after color fastness testing in a variety of preset scenarios To conduct analysis; among them, Indicates the indexes in different preset scenarios; Each element in Corresponding to Reference color information for a preset scene, Including this scenario value, value, value; Calculate the color difference in each preset scene based on the following color difference formula; ； in, Used to indicate the Color differences under different preset scenes; , , Indicated in The coordinates of the reference color information tested for color fastness in a preset scene in the CIELAB color space; , , The coordinates of the initial color information in the CIELAB color space are used to represent the reference color of the clothing fabric to be tested before any processing is performed; The change in the surface structure of the clothing fabric to be detected includes the surface roughness, fiber arrangement, texture density and color distribution uniformity of the fabric; the color fastness of the clothing fabric to be detected is evaluated according to the calculated color difference and the change in the surface structure of the clothing fabric to be detected, and is configured as follows: Using an adaptive neural fuzzy inference algorithm to determine a first weight of the color difference and a second weight of the change in the surface structure of the clothing fabric to be tested, so as to calculate a color fastness characteristic index of the clothing fabric to be tested; ； in, is the color fastness characteristic index; and are first weights and second weights determined by the adaptive neuro-fuzzy inference algorithm; Used to indicate the Color differences under different preset scenes; Used to indicate the Changes in the surface structure of the garment fabric to be tested under a preset scenario; Calculate the overall color fastness index of the clothing fabric to be tested according to the color fastness characteristic index of the clothing fabric to be tested combined with fuzzy reasoning logic; ； in, is the overall color fastness index of the garment fabric to be tested; Represents the "or" operation in fuzzy logic; is the number of preset scenes; It is Fuzzy membership function under a preset scenario, Color fastness index Converted into a membership value, the membership value indicates that the clothing fabric to be tested is in the The color fastness level under a preset scenario, the membership value ranges from 0 to 1, where 1 means that the color fastness requirements are fully met and 0 means that they are completely unsatisfactory; The color fastness of the clothing fabric to be tested is evaluated according to the overall color fastness index.

2. A clothing fabric color fastness detection system, the system implements the clothing fabric color fastness detection method according to claim 1, characterized in that: include: The initial image acquisition module is used to acquire the initial color information and initial texture information of the clothing fabric to be detected; A fabric processing module is used to simulate the factors affecting the garment fabric to be tested in a variety of preset scenarios, so as to perform color fastness tests on the garment fabric to be tested in a variety of preset scenarios; wherein the factors affecting the garment fabric in the various preset scenarios include friction, washing, rain, exposure to the sun, light, seawater immersion, saliva immersion, water stains and sweat stains; A reference image acquisition module, used for acquiring a reference color information set and a reference texture information set of the garment fabric to be tested after the color fastness test under a plurality of preset scenes; A color analysis module, used to analyze the initial color information and a reference color information set after color fastness tests under various preset scenarios, and calculate color differences based on a color difference formula; A texture analysis module, used to analyze the initial texture information and a reference texture information set after color fastness tests under various preset scenarios, so as to evaluate changes in the surface structure of the garment fabric to be tested; A comprehensive processing module, used for evaluating the color fastness of the clothing fabric to be tested according to the calculated color difference and the change of the surface structure of the clothing fabric to be tested; The color analysis module analyzes the initial color information and a reference color information set after color fastness tests under various preset scenarios, and calculates the color difference based on a color difference formula, and is configured as follows: The initial color information And a reference color information set after color fastness testing in a variety of preset scenarios To conduct analysis; among them, Indicates the indexes in different preset scenarios; Each element in Corresponding to Reference color information for a preset scene, Including this scenario value, value, value; Calculate the color difference in each preset scene based on the following color difference formula; ； in, Used to indicate the Color differences under different preset scenes; , , Indicated in The coordinates of the reference color information tested for color fastness in a preset scene in the CIELAB color space; , , The coordinates of the initial color information in the CIELAB color space are used to represent the reference color of the clothing fabric to be tested before any processing is performed; The changes in the surface structure of the clothing fabric to be tested include the surface roughness, fiber arrangement, texture density and color distribution uniformity of the fabric; the comprehensive processing module evaluates the color fastness of the clothing fabric to be tested according to the calculated color difference and the changes in the surface structure of the clothing fabric to be tested, and is configured as follows: Using an adaptive neural fuzzy inference algorithm to determine a first weight of the color difference and a second weight of the change in the surface structure of the clothing fabric to be tested, so as to calculate a color fastness characteristic index of the clothing fabric to be tested; ； in, is the color fastness characteristic index; and are first weights and second weights determined by the adaptive neuro-fuzzy inference algorithm; Used to indicate the Color differences under different preset scenes; In the expression Changes in the surface structure of the garment fabric to be tested under a preset scenario; Calculate the overall color fastness index of the clothing fabric to be tested according to the color fastness characteristic index of the clothing fabric to be tested combined with fuzzy reasoning logic; ； in, is the overall color fastness index of the garment fabric to be tested; Represents the "or" operation in fuzzy logic; is the number of preset scenes; It is Fuzzy membership function under a preset scenario, Color fastness index Converted into a membership value, the membership value indicates that the clothing fabric to be tested is in the The color fastness level under a preset scenario, the membership value ranges from 0 to 1, where 1 means that the color fastness requirements are fully met and 0 means that they are completely unsatisfactory; The color fastness of the clothing fabric to be tested is evaluated according to the overall color fastness index.

3. The clothing fabric color fastness detection system according to claim 2, characterized in that: Based on the following formula, the first weight is adjusted according to the fabric properties of the garment fabric to be tested and the environmental factors: and the second weight ; ； ； in, and It is Environmental factors under a preset scenario, including temperature, humidity, light and gas concentration; , , , The parameters are obtained through training of the fabric properties of the garment fabric to be tested and historical test data; and It is a coefficient adjusted according to the fabric properties of the clothing fabric to be tested.

4. The clothing fabric color fastness detection system according to claim 3, characterized in that: The fuzzy membership function Defined as: ； in, is the color fastness characteristic index; represents the center point of the membership function, that is, the point at which the membership function reaches its maximum value; Represents the width of the membership function, that is, from the center point The distance to the point where the membership drops to zero.

5. The clothing fabric color fastness detection system according to claim 4, characterized in that: In the process of performing color fastness tests on the clothing fabric to be tested under multiple preset scenarios, the test items of the color fastness test include acid perspiration color fastness test, alkali perspiration color fastness test, water color fastness test, friction color fastness test and saliva color fastness test; The fabric processing module comprises a fabric adaptive adjustment unit, and the fabric adaptive adjustment unit is used to adjust the parameters and conditions of the test items of the color fastness test according to the fabric properties of the clothing fabric to be tested.

6. The clothing fabric color fastness detection system according to claim 2, characterized in that: The clothing fabric color fastness detection system also includes: A data analysis and processing unit, used to generate a color fastness rating report based on the evaluation result of the color fastness of the clothing fabric to be tested; The user interaction interface is used to display the color fastness rating report and receive parameter values ​​of influencing factors under various preset scenarios input by the user.

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