Yarn gradient color simulation proofing method
By establishing a physical library and electronic database and performing gradient color simulation, the problems of low gradient color proofing efficiency and difficult to guarantee color accuracy of color are solved, and an efficient proofing process that quickly responds to market demand is achieved.
Patent Information
- Application Number
- CN202411621944.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-14
- Publication Date
- 2025-02-25
AI Technical Summary
In the prior art, the gradient color proofing efficiency of color-spun plush yarns is low, and the color accuracy is difficult to guarantee, resulting in a long production cycle and being unable to respond quickly to market demand.
By establishing a standard physical library and electronic database of loose fiber gradient adjacent colors, analyzing the standard sample parameters, filtering out the specific parameters of gradient adjacent colors and mixing ratios for gradient color simulation until the color difference value reaches the set value, and outputting qualified yarn samples.
It realizes the prediction of the yarn color effect through gradient color simulation after obtaining the standard sample, reduces the number and time of physical proofing, shortens the interception time, improves the proofing efficiency and accuracy, and can quickly respond to market demand.
Smart Images
Figure CN119513946A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of yarn proofing in the textile industry, in particular to a yarn gradient color simulation proofing method, in particular to a color-spun plush yarn gradient color simulation proofing method. Background Art
[0002] At present, manual proofing and computer color matching technology are commonly used for color-spun plush yarn color matching proofing. The yarn gradient color proofing method usually relies on experience and repeated physical proofing. In the production process, it is inevitable to drip, dye, and judge the color many times. Only when the color is qualified can it be sent to customers. The dripping, dyeing, and color matching process takes up a lot of time; Color-spun woolen yarn generally has a long production cycle for proofing and mass production. The best woolen textile companies in China also have a sample delivery cycle of more than 2 days, which makes them unable to respond to the market quickly, putting them at a disadvantage in transactions with big brand customers. In this process, especially for the proofing of gradient color samples, how to improve the proofing efficiency, accurately screen the colors, complete the proofing work as soon as possible and deliver the samples to customers while ensuring the quality of the samples, and reduce the number of corrections and improve the accuracy of the proofing during the proofing process are urgent problems to be solved. To this end, we propose a yarn gradient color simulation proofing method. Summary of the invention
[0003] In order to solve the above technical problems, the present application provides a method for simulating the gradual color change of yarn, comprising the following steps: S1: Establish a standard bulk fiber gradient adjacent color physical library and electronic database; S2: Analyze the parameters of the standard sample, select the specific parameters of the gradient adjacent colors and the mixing ratio to perform gradient color simulation, and the parameters are within 20% of the color difference value of the initial color matching; S3: Set the color difference value and correct the mixing ratio until the color difference value is within the set value; S4: obtaining a suggested list of blending ratios, selecting a blending ratio, converting dyeing parameters into execution instructions, providing reference parameters for execution equipment, and after confirming the reference parameters, performing combing, spinning, weaving, and color determination; S5: If the color is qualified, the color sample will be sent to the customer; If it is unqualified, S3 is repeated until the color difference value reaches within the set value, and the qualified yarn sample is output and sent to the customer for confirmation.
[0004] Further, in step S1, a physical library containing at least 3,000 physical objects of gradient adjacent colors is established, and its color data is entered into the color matching software, and a color database containing at least 10,000 digital color data is established based on the physical library of gradient adjacent colors. The specific steps are as follows: S101: Collect loose fiber samples of yarn, and use a high-precision color measuring device to measure the color of each sample to obtain color data, wherein the color data includes: hue, lightness and saturation parameters; S102: Classify the measured loose fiber samples according to the colors within 10 degrees on the color wheel, establish a physical library, arrange the samples in the physical library in order, and add yarn sample numbers, dyeing parameters and annotation information to the samples; S103: inputting the color data of each sample into a database, the database including the sample number, color data and annotation information; S104: Select an appropriate database management system to manage the color database, update it in real time, and add access permissions for the color database.
[0005] Furthermore, the steps to determine the color scheme are: After obtaining the standard sample provided by the customer, use the color measuring equipment to measure the color of the standard sample; According to the color data of the customer's standard sample, use the color matching software Labprocess and the Stearns-Noechel model to screen out the gradient adjacent colors and their blending ratios that meet the set requirements from the color database; Output the blending ratio suggestion through the color matching software, match and output the corresponding sample information in the physical library according to the suggested ratio, and determine the required yarn sample information, wherein the color selection of the blending ratio is determined by using the color matching algorithm; Taking out corresponding yarn samples from the physical library according to the obtained yarn sample information and the blending ratio, combing, spinning and weaving the yarn samples to prepare yarn samples; Use color measuring equipment to measure the color of the prepared yarn samples and compare the measurement results with the color data of the customer's standard samples; If the color difference exceeds the set value, use the color correction function of the color matching software to adjust the mixing ratio, and repeat the combing, spinning, weaving and color determination steps until the color difference value is within the set value; Once the color difference value is within the set value, the qualified yarn sample is output and sent to the customer for confirmation.
[0006] Furthermore, the color difference DE value and the number of gradient adjacent colors are set, and the Stearns-Noechel model is used to screen out suitable gradient adjacent colors and the mixing ratio between adjacent colors, wherein 2-3 gradient adjacent colors within an angle of 10 degrees are mixed to obtain pure plain colors.
[0007] Furthermore, the steps of gradient color simulation are: Input color requirements: Collect the characteristics of the gradient color of the customer's standard sample, including the starting color, ending color and gradient effect; Color model conversion: convert the input color requirements into a color model that can be processed by the computer; Gradient calculation: using an interpolation algorithm to calculate the gradient color between the starting color and the ending color, wherein linear interpolation is used to calculate the linear gradient, and radial interpolation is used to calculate the radial gradient, wherein the transition points between the gradient colors are determined to achieve a smooth gradient effect; Color output: convert the calculated gradient color to the final color output format; in: The linear interpolation formula is: For linear gradients, the interpolation between two colors is calculated using the following formula:
[0008] in, is the interpolated color, and are the starting and ending colors, is the interpolation parameter (ranging from 0 to 1); The radial interpolation formula is: For radial gradients, radial color interpolation is calculated using the following formula:
[0009] in, is the interpolated color, is the interpolated color, and are the starting and ending colors, is the radial interpolation parameter.
[0010] Further, in S4, the dyeing parameters are converted into execution instructions of the corresponding device, and the steps are as follows: Parameter adjustment result analysis: Analyze dyeing parameters and generate adjustment data, including dye ratio, dye bath temperature, pH value and time.
[0011] Device control command mapping: According to the adjustment data results, it is mapped to the control command of the device; Ensure that the equipment operates according to the expected parameters through PID control algorithms, fuzzy control algorithms or model-based predictive control algorithms; Instruction generation: Generate specific device execution instructions; Device execution: The generated execution instructions are output as execution list text, and the execution instructions and execution list text are sent to the corresponding devices for manual confirmation or modification before the devices execute them.
[0012] Furthermore, in step S5, the step of determining the color is as follows: Color measurement: Use color measurement equipment to measure the color of dyed textiles; Data conversion: convert the measured data into standard color space values; Color difference calculation: Calculate the difference between the measured color and the standard or target color using the color difference formula; Color qualification judgment: According to the predetermined color tolerance standard, judge whether the color difference is within the acceptable range. If the color difference is less than or equal to the tolerance, the color is considered qualified; otherwise, the color is considered unqualified; Result recording and analysis: Record color measurement results and qualified judgment results, and analyze unqualified colors to determine possible causes.
[0013] Further, in step S5, the following steps are also included: after the sample is sent out and confirmed by the customer, the information of the fabric is recorded and analyzed as auxiliary data; Based on the auxiliary data, the yarn interlacing mode is simulated by CAD, and an auxiliary selection model is established, and the yarn selection and interlacing mode results are output through the auxiliary selection model; Among them: The process of the auxiliary selection model outputting the yarn selection results is as follows: Step 1: Input design requirements: including collecting the final use information of the fabric, determining the performance requirements of the fabric, and obtaining the appearance design requirements of the fabric; Step 2: Analyze design requirements: Use data processing algorithms to analyze input design requirements and convert them into quantifiable parameters, such as strength index, softness level, etc. The data processing algorithms are used to analyze input design requirements, such as text mining algorithms, natural language processing algorithms, etc. Step 3, select the appropriate yarn material: according to the performance requirements and appearance design requirements, use the decision tree algorithm to select the appropriate yarn material, wherein the decision tree algorithm is used to select the appropriate yarn material according to the performance requirements and appearance design; Step 4, determining the linear density and twist of the yarn: using an optimization algorithm to determine the linear density and twist of the yarn to meet performance requirements and appearance design, wherein the optimization algorithm is used to determine the linear density and twist of the yarn; Step 5, output results: Output the material, linear density, twist and other characteristic parameters of the selected yarn. If the yarn characteristics do not meet the design requirements, return to step 3 for adjustment.
[0014] The present invention has at least the following beneficial effects: After getting the standard sample, the color effect of the yarn can be predicted before actual production through gradient color simulation, which can reduce the number of physical proofing and shorten the time for sample delivery, so as to respond to the market quickly. Compared with the traditional proofing method which requires a large amount of yarn and dyes for trial and error, it can reduce the time of dripping and dyeing, calculate the required dye ratio, reduce the use of raw materials, and accurately correct the proofing color to ensure the quality of the delivered samples; Through color matching and gradient color simulation, proofing data can be obtained and the same effect can be achieved repeatedly, ensuring that the color of the final product is highly consistent with the design requirements and meets high quality standards; By establishing a color database and physical library, during the proofing process, it is possible to quickly match and locate the physical information that needs to be proofed, quickly start the proofing work, and in this process, the effects of different color combinations can be seen, making it easier to design and modify, and better manage raw material inventory and production plans. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 Schematic diagram of the process of the yarn gradient color simulation proofing method in an embodiment of the present invention; Figure 2 It is a schematic diagram of the flow of the simulation proofing system in an embodiment of the present invention. DETAILED DESCRIPTION
[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1 See also Figure 1 The present invention provides a technical solution: a yarn gradient color simulation proofing method, comprising the following steps: S1: Establish a standard bulk fiber gradient adjacent color physical library and electronic database; S2: Analyze the parameters of the standard sample, select the specific parameters of the gradient adjacent colors and the mixing ratio to perform gradient color simulation, and the parameters are within 20% of the color difference value of the initial color matching; S3: Set the color difference value and correct the mixing ratio until the color difference value is within the set value; S4: obtaining a suggested list of blending ratios, selecting a blending ratio, converting dyeing parameters into execution instructions, providing reference parameters for execution equipment, and after confirming the reference parameters, performing combing, spinning, weaving, and color determination; S5: If the color is qualified, the color sample will be sent to the customer; If it is unqualified, S3 is repeated until the color difference value reaches within the set value, and the qualified yarn sample is output and sent to the customer for confirmation.
[0017] As a preferred embodiment of the present application, a physical library containing at least 3,000 physical objects of gradient adjacent colors is established, and its color data is entered into the color matching software. A color database containing at least 10,000 digital color data is established based on the physical library of gradient adjacent colors. The specific steps are as follows: S101: Collect loose fiber samples of yarn, and use a high-precision color measuring device to measure the color of each sample to obtain color data, wherein the color data includes: hue, lightness and saturation parameters; S102: Classify the measured loose fiber samples according to the colors within 10 degrees on the color wheel, establish a physical library, arrange the samples in the physical library in order, and add yarn sample numbers, dyeing parameters and annotation information to the samples; S103: inputting the color data of each sample into a database, the database including the sample number, color data and annotation information; S104: Select an appropriate database management system to manage the color database, update it in real time, and add access permissions for the color database.
[0018] Example 2 Based on Example 1, the steps of determining the color scheme are as follows: After obtaining the standard sample provided by the customer, use the color measuring equipment to measure the color of the standard sample; According to the color data of the customer's standard sample, use the color matching software Labprocess and the Stearns-Noechel model to screen out the gradient adjacent colors and their blending ratios that meet the set requirements from the color database; Among them, the color matching software Labprocess and the Stearns-Noechel model are provided by Shanghai Dyeing Information Technology Service Co., Ltd. Labprocess is used to screen out the mixing ratio between adjacent colors in the gradient, and the final structure is optimized and corrected to make the color difference value within the set value.
[0019] The Stearns-Noechel model was introduced into Labprocess to explain the color characteristics of color-changing fabrics made of colored fibers.
[0020] Output the blending ratio suggestion through the color matching software, match and output the corresponding sample information in the physical library according to the suggested ratio, and determine the required yarn sample information, wherein the color matching algorithm (such as CIEDE2000 color difference formula) is used to determine the color selection for blending; CIEDE2000 is used to determine the closest color in the color library: ΔE00 = sqrt((L'1 - L'2)^2 + (C'1 - C'2)^2 + (h'1 - h'2)^2 + RT * (C'1 + C'2) * (h'1 - h'2)) Among them, L', C', h' are the values of CIE LCh color space, and RT is a correction term related to hue difference; Taking out corresponding yarn samples from the physical library according to the obtained yarn sample information and the blending ratio, combing, spinning and weaving the yarn samples to prepare yarn samples; Use color measuring equipment to measure the color of the prepared yarn samples and compare the measurement results with the color data of the customer's standard samples; If the color difference exceeds the set value, use the color correction function of the color matching software to adjust the mixing ratio, and repeat the combing, spinning, weaving and color determination steps until the color difference value is within the set value; Once the color difference value is within the set value, the qualified yarn sample is output and sent to the customer for confirmation.
[0021] As a preferred embodiment of the present application, the color difference DE value and the number of gradient adjacent colors are set, and the Stearns-Noechel model is used to screen out suitable gradient adjacent colors and the mixing ratio between adjacent colors, wherein 2-3 gradient adjacent colors within an angle of 10 degrees are mixed to obtain pure plain colors.
[0022] As a preferred embodiment of the present application: the steps of gradient color simulation are: Input color requirements: Collect the characteristics of the gradient color of the customer's standard sample, including the starting color, ending color and gradient effect (such as linear gradient, radial gradient, etc.); Color model conversion: convert the input color requirements into a color model that can be processed by the computer, such as RGB, HSV or Lab color space; Gradient calculation: using an interpolation algorithm to calculate the gradient color between the starting color and the ending color, wherein linear interpolation is used to calculate the linear gradient, and radial interpolation is used to calculate the radial gradient, wherein the transition points between the gradient colors are determined to achieve a smooth gradient effect; Color output: convert the calculated gradient colors into the final color output format, such as RGB values, for display on a computer screen or output on a printer; in: The linear interpolation formula is: For linear gradients, the interpolation between two colors is calculated using the following formula:
[0023] in, is the interpolated color, and are the starting and ending colors, is the interpolation parameter (ranging from 0 to 1); The radial interpolation formula is: For radial gradients, radial color interpolation is calculated using the following formula:
[0024] in, is the interpolated color, is the interpolated color, and are the starting and ending colors, is the radial interpolation parameter (ranges from 0 to 1).
[0025] As a preferred embodiment of the present application, in S4, the dyeing parameters are converted into execution instructions of the corresponding device, and the steps are as follows: Parameter adjustment result analysis: Analyze dyeing parameters and generate adjustment data, including dye ratio, dye bath temperature, pH value and time.
[0026] Equipment control command mapping: Based on the adjustment data results, map them to the control commands of the equipment; for example, the dye ratio may need to be converted to the running time of the dye delivery pump, and the dye bath temperature may need to be converted to the power setting of the heater.
[0027] Ensure that the equipment operates according to the expected parameters through PID control algorithms, fuzzy control algorithms or model-based predictive control algorithms; Instruction generation: Generate specific device execution instructions; wherein the device execution instructions are digital signals that can be directly received and executed by the control system of the device.
[0028] Equipment execution: The generated execution instructions are output as execution list text, and the execution instructions and execution list text are sent to the corresponding equipment (such as dye delivery system, heater, agitator, etc., to execute the dyeing process), and the equipment is executed after manual confirmation or modification.
[0029] Example 3 Based on the above embodiment, in step S5, the step of determining the color is as follows: Color measurement: Color measurement of dyed textiles is done using a color measuring device (spectrophotometer).
[0030] Data conversion: Convert the measured data into standard color space values, such as CIE Lab* or CIE XYZ.
[0031] Color difference calculation: Use the color difference formula to calculate the difference between the measured color and the standard or target color. Common color difference formulas include CIE ΔEab, CIE ΔE00, etc.
[0032] Color qualification judgment: According to the predetermined color tolerance standard, judge whether the color difference is within the acceptable range. If the color difference is less than or equal to the tolerance, the color is considered qualified; otherwise, the color is considered unqualified.
[0033] Result recording and analysis: Record color measurement results and qualified judgment results, and analyze unqualified colors to determine possible causes.
[0034] The color difference formulas are: CIE ΔE*ab color difference formula, CIE ΔE*00 color difference formula As a preferred embodiment of the present application: in step S5, the following steps are also included: after the sample is sent out and confirmed by the customer, the information of the fabric is recorded and analyzed as auxiliary data; (including: Fabric use: for clothing, home textiles or industrial use; Fabric performance requirements: strength, softness, breathability, waterproofness, etc.; Design data for fabrics; including color, pattern, and texture. Based on the auxiliary data, the yarn interlacing mode is simulated by CAD, and an auxiliary selection model is established, and the yarn selection and interlacing mode results are output through the auxiliary selection model; Among them: The process of the auxiliary selection model outputting the yarn selection results is as follows: Step 1. Input design requirements: including collecting information on the final use of the fabric (such as clothing, home textiles or industrial use), determining the performance requirements of the fabric (such as strength, softness, breathability, waterproofness, etc.), and obtaining the appearance design requirements of the fabric (including color, pattern and texture); Step 2: Analyze design requirements: Use data processing algorithms to analyze input design requirements and convert them into quantifiable parameters, such as strength index, softness level, etc. Data processing algorithms are used to analyze input design requirements, such as text mining algorithms, natural language processing algorithms, etc.
[0035] Step 3. Select the appropriate yarn material: According to the performance requirements and appearance design requirements, use the decision tree algorithm to select the appropriate yarn material (such as cotton, wool, synthetic fiber, etc.), among which the decision tree algorithm is used to select the appropriate yarn material according to the performance requirements and appearance design.
[0036] Step 4, determine the linear density and twist of the yarn: use an optimization algorithm (using a genetic algorithm or a simulated annealing algorithm) to determine the linear density (count) and twist of the yarn to meet performance requirements and appearance design, wherein the optimization algorithm is used to determine the linear density and twist of the yarn.
[0037] Step 5, output results: Output the material, linear density, twist and other characteristic parameters of the selected yarn. If the yarn characteristics do not meet the design requirements, return to step 3 for adjustment.
[0038] In this embodiment, the auxiliary data is stored separately by classification and associated with the physical library and the color database, so as to output the yarn selection results of different fabric samples through the auxiliary selection model and the customer design requirements, provide proofing options for users and customers, and eliminate the color differences caused by different interweaving proofing methods.
[0039] It should be noted that the data processing algorithm Steps of use: After the design requirements are entered, data processing algorithms are used to analyze these requirements.
[0040] Result data: Through data processing, design requirements are converted into quantifiable parameters, such as strength index, softness level, etc.
[0041] Decision Tree Algorithm Usage steps: When choosing the appropriate yarn material, the decision tree algorithm can help make choices based on performance requirements and appearance design.
[0042] Result data: The output of the decision tree algorithm is the recommended yarn material, such as cotton, wool, or synthetic fiber.
[0043] Optimization Algorithm Usage steps: When determining the linear density and twist of yarn, optimization algorithms (such as genetic algorithms, simulated annealing algorithms) can find the best parameter combination.
[0044] Result data: The output of the optimization algorithm is the optimal linear density and twist of the yarn to meet the performance requirements and appearance design.
[0045] Example 4 like Figure 1-2 Based on the above embodiment, a yarn gradient color simulation proofing method is provided, which uses a simulation proofing system for implementing the method. The simulation proofing system includes: A data collection unit, used to connect to an adapter device to collect standard sample data and yarn sample physical data; The color database unit is used to manage the color database and physical library data, to increase, decrease and modify the database, to store the physical library sample data set, to associate and bind the color database with the physical library samples, and to enable the color database and the physical library to mutually index and query data; A color matching unit is used to set a target color and determine whether one or more selected colors can be combined into the target color; Gradient simulation unit, used to integrate gradient color simulation algorithm, simulate gradient effect to generate and output gradient effect simulation graph; The proofing instruction generation unit is used to output the parameters required for proofing yarn samples according to the gradient simulation structure, including color matching ratio, blending ratio, and dyeing process equipment related parameters, and output readable files; Quality control unit, used to set verification conditions and requirements and determine whether the color meets the requirements; A report generation unit, used to convert the calculation result data transmitted by the module and output a readable file to the connected device; The data acquisition unit is connected to the report generation unit through the color database unit, the color matching unit, the gradient simulation unit, the proofing instruction generation unit and the quality control unit.
[0046] In an example scenario: A textile company received a new order to produce fabrics made of blue series gradient yarns. The fabrics will be used to make fashion clothing and require color consistency and gradient effects. The company provided standard samples and information on color range and gradient type.
[0047] The specific process is as follows: Analyze the standard samples and related information provided by the customer to obtain the parameters that need to be input, including gradient type and color range.
[0048] According to the customer's design requirements, the system queries the color library and finds the closest color or multiple color combinations (the color library is pre-established based on the textile company's physical library, and the color library stores a large amount of gradient color yarn data).
[0049] Two colors are found, light blue and dark blue, and the color difference value is within the acceptable range. The CIEDE2000 color difference formula is used to determine whether the selected colors can be combined into the target color.
[0050] A linear interpolation algorithm is used to simulate the gradient effect, and a gradient effect simulation image is generated, showing the gradient effect from light blue to dark blue; According to the simulation results, relevant proofing data and proofing execution instructions are generated, the instructions are transmitted and the data is output to a readable file. The proofing data and proofing execution instructions include the color ratio of the yarn, the parameters of the dyeing process equipment, etc.; Automatically execute the sample execution instructions or adjust the parameters according to the output readable file to perform the carding, spinning and weaving steps to obtain the sample; After the sample color is judged to be qualified, it will be mailed to the customer, and the customer confirms the color and the cooperation is completed.
[0051] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0052] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. .
[0053] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0054] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.
Claims
1. A yarn gradient color simulation proofing method, characterized in that: The following steps are involved: S1: Establish a standard bulk fiber gradient adjacent color physical library and electronic database; S2: Analyze the parameters of the standard sample, select the specific parameters of the gradient adjacent colors and the mixing ratio to perform gradient color simulation, and the parameters are within 20% of the color difference value of the initial color matching; S3: Set the color difference value and correct the mixing ratio until the color difference value is within the set value; S4: obtaining a suggested list of blending ratios, selecting a blending ratio, converting dyeing parameters into execution instructions, providing reference parameters for execution equipment, and after confirming the reference parameters, performing combing, spinning, weaving, and color determination; S5: If the color is qualified, the color sample will be sent to the customer; If it is unqualified, S3 is repeated until the color difference value reaches within the set value, and the qualified yarn sample is output and sent to the customer for confirmation.
2. The yarn gradient color simulation proofing method according to claim 1, characterized in that: In step S1, a physical library containing at least 3,000 physical objects of gradient adjacent colors is established, and its color data is entered into the color matching software. A color database containing at least 10,000 digital color data is established based on the physical library of gradient adjacent colors. The specific steps are as follows: S101: Collect loose fiber samples of yarn, and use a high-precision color measuring device to measure the color of each sample to obtain color data, wherein the color data includes: hue, lightness and saturation parameters; S102: Classify the measured loose fiber samples according to the colors within 10 degrees on the color wheel, establish a physical library, arrange the samples in the physical library in order, and add yarn sample numbers, dyeing parameters and annotation information to the samples; S103: inputting the color data of each sample into a database, the database including the sample number, color data and annotation information; S104: Select an appropriate database management system to manage the color database, update it in real time, and add access permissions for the color database.
3. The yarn gradient color simulation proofing method according to claim 2, characterized in that: The steps to determine the color scheme are: After obtaining the standard sample provided by the customer, use the color measuring equipment to measure the color of the standard sample; According to the color data of the customer's standard sample, use the color matching software Labprocess and the Stearns-Noechel model to screen out the gradient adjacent colors and their blending ratios that meet the set requirements from the color database; Output the blending ratio suggestion through the color matching software, match and output the corresponding sample information in the physical library according to the suggested ratio, and determine the required yarn sample information, wherein the color selection of the blending ratio is determined by using the color matching algorithm; Taking out corresponding yarn samples from the physical library according to the obtained yarn sample information and the blending ratio, combing, spinning and weaving the yarn samples to prepare yarn samples; Use color measuring equipment to measure the color of the prepared yarn samples and compare the measurement results with the color data of the customer's standard samples; If the color difference exceeds the set value, use the color correction function of the color matching software to adjust the mixing ratio, and repeat the combing, spinning, weaving and color determination steps until the color difference value is within the set value; Once the color difference value is within the set value, the qualified yarn sample is output and sent to the customer for confirmation.
4. The yarn gradient color simulation proofing method according to claim 3, characterized in that: The color difference DE value and the number of gradient adjacent colors are set, and the appropriate gradient adjacent colors and the mixing ratio between adjacent colors are screened out through the Stearns-Noechel model. Among them, 2-3 gradient adjacent colors within an angle of 10 degrees are mixed to obtain pure plain colors.
5. The yarn gradient color simulation proofing method according to claim 4, characterized in that: The steps of gradient color simulation are: Input color requirements: Collect the characteristics of the gradient color of the customer's standard sample, including the starting color, ending color and gradient effect; Color model conversion: convert the input color requirements into a color model that can be processed by the computer; Gradient calculation: using an interpolation algorithm to calculate the gradient color between the starting color and the ending color, wherein linear interpolation is used to calculate the linear gradient, and radial interpolation is used to calculate the radial gradient, wherein the transition points between the gradient colors are determined to achieve a smooth gradient effect; Color output: convert the calculated gradient color to the final color output format; in: The linear interpolation formula is: For linear gradients, the interpolation between two colors is calculated using the following formula: in, is the interpolated color, and are the starting and ending colors, is the interpolation parameter; The radial interpolation formula is: For radial gradients, radial color interpolation is calculated using the following formula: in, is the interpolated color, is the interpolated color, and are the starting and ending colors, is the radial interpolation parameter.
6. The yarn gradient color simulation proofing method according to any one of claims 1 to 5, characterized in that: In S4, the dyeing parameters are converted into execution instructions of the corresponding equipment. The steps are as follows: Parameter adjustment result analysis: Analyze dyeing parameters and generate adjustment data, including dye ratio, dye bath temperature, pH value and time; Device control command mapping: According to the adjustment data results, it is mapped to the control command of the device; Ensure that the equipment operates according to the expected parameters through PID control algorithms, fuzzy control algorithms or model-based predictive control algorithms; Instruction generation: Generate specific device execution instructions; Device execution: The generated execution instructions are output as execution list text, and the execution instructions and execution list text are sent to the corresponding devices for manual confirmation or modification before the devices execute them.
7. The yarn gradient color simulation proofing method according to claim 6, characterized in that: In step S5, the steps of determining the color are: Color measurement: Use color measurement equipment to measure the color of dyed textiles; Data conversion: convert the measured data into standard color space values; Color difference calculation: Calculate the difference between the measured color and the standard or target color using the color difference formula; Color qualification judgment: According to the predetermined color tolerance standard, judge whether the color difference is within the acceptable range. If the color difference is less than or equal to the tolerance, the color is considered qualified; otherwise, the color is considered unqualified; Result recording and analysis: Record color measurement results and qualification judgment results.
8. The yarn gradient color simulation proofing method according to claim 7, characterized in that: In step S5, the following steps are also included: after the sample is sent out and confirmed by the customer, the information of the fabric is recorded and analyzed as auxiliary data; Based on the auxiliary data, the yarn interlacing mode is simulated by CAD, and an auxiliary selection model is established, and the yarn selection and interlacing mode results are output through the auxiliary selection model; Among them: The process of the auxiliary selection model outputting the yarn selection results is as follows: Step 1: Input design requirements: including collecting the final use information of the fabric, determining the performance requirements of the fabric, and obtaining the appearance design requirements of the fabric; Step 2: Analyze the design requirements: Use a data processing algorithm to analyze the input design requirements and convert the design requirements into quantifiable parameters, wherein the data processing algorithm is used to analyze the input design requirements; Step 3, select the appropriate yarn material: according to the performance requirements and appearance design requirements, use the decision tree algorithm to select the appropriate yarn material, wherein the decision tree algorithm is used to select the appropriate yarn material according to the performance requirements and appearance design; Step 4, determining the linear density and twist of the yarn: using an optimization algorithm to determine the linear density and twist of the yarn to meet performance requirements and appearance design, wherein the optimization algorithm is used to determine the linear density and twist of the yarn; Step 5, output results: Output the material, linear density, and twist characteristic parameters of the selected yarn. If the yarn characteristics do not meet the design requirements, return to step 3 for adjustment.