A method and system for optimizing textile dyeing recipes

Abstract

The application discloses a method and system for optimizing a textile dyeing formula, and optimizes a computer theoretical formula by establishing a smart matching point, and improves a one-time matching success rate.The technical scheme is as follows: by establishing a mathematical model, on the basis of realizing computer system fast matching, by taking actual dyeing results in a laboratory or on a production line as the smart matching point, the relationship between the theoretical formula and the actual formula is optimized, and the one-time matching success rate is improved.Furthermore, in combination with an actual sample printing environment, repeated color correction caused by differences in different dyes, dyeing processes and the like can be effectively avoided, and the color matching efficiency and the color matching success rate are obviously improved.

Description

Technical Field

[0001] This invention relates to the processing technology of textile dyeing formulations, and more specifically to a method and system for optimizing textile dyeing formulations. Background Technology

[0002] With technological advancements, my country's textile industry has entered a stage of transformation and upgrading, and an increasing number of dyeing and printing enterprises are beginning to use computer-based color matching systems. Compared with traditional manual color matching, computer-based color matching offers advantages such as saving on dye and chemical usage, shortening production cycles, and improving production efficiency.

[0003] However, since traditional computer color matching only provides theoretically matched dyeing formulas without considering actual differences in dyes and dyeing processes, in actual factory applications, if the dyeing formula differs from the pre-dyeing process conditions and substrates, there will be significant color differences between the computer formula and the actual dyeing, including severe color differences. This results in the inability to effectively improve the first-time dyeing success rate and a low color matching success rate.

[0004] Smart color matching algorithms, through the accumulation of data from computer-generated color matching outputs and actual dyeing results in the laboratory or production line, can quickly correct deviations caused by various factors, such as deviations in basic parameter inputs, changes in the substrate used during dyeing, changes in dyeing processes, and changes in the production environment. Smart color matching not only includes traditional color matching calculations but also correction methods that can eliminate deviations caused by inconsistencies between actual and theoretical conditions before the first dyeing.

[0005] How to apply sophisticated color matching techniques to the optimization of textile dyeing formulations is a problem that the industry urgently needs to solve. Summary of the Invention

[0006] The following provides a brief overview of one or more aspects to offer a basic understanding of them. This overview is not an exhaustive summary of all conceived aspects, nor is it intended to identify key or decisive elements of all aspects, nor to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form to prepare for the more detailed descriptions that follow.

[0007] The purpose of this invention is to solve the above-mentioned problems and provide a method and system for optimizing textile dyeing formulas. By establishing a smart color matching point, the computer theoretical formula is optimized, and the success rate of color matching in one attempt is improved.

[0008] The technical solution of this invention is as follows: This invention discloses a method for optimizing textile dyeing formulations, the method comprising:

[0009] Step 1: Establish basic dye data;

[0010] Step 2: Measure the reflectance Rs of the standard color sample within the preset range, calculate the K / S value of the standard color sample and record it as Fs, where K is the absorption coefficient of the opaque object and S is the scattering coefficient of the opaque object.

[0011] Step 3: Measure the reflectance of the blank white sample within the preset range and record it as p; calculate the K / S value of the blank white sample and record it as Ft.

[0012] Step 4: Using the reflectance of the standard color sample measured in Step 2, calculate the rate of change of the reflectance of the standard color sample with the K / S value, and then obtain the initial concentration C0 of the standard color sample.

[0013] Step 5: Establish a smart color scheme database containing basic data on smart color scheme points;

[0014] Step 6: Using the initial concentration C0 of the standard color sample obtained in Step 4 and the smart color matching database established in Step 5, calculate the smart color matching point C with the smallest color difference between C0 and the smart color matching database using the color difference formula. M ;

[0015] Step 7: Calculate the smart color matching point C with the smallest concentration difference between C0 and the smart color matching data using the concentration difference formula. N ;

[0016] Step 8: Based on the color difference and concentration points obtained in Steps 6 and 7, determine the smart color matching point closest to C0 according to the actual conditions and record it as C. s And C s The difference coefficients between the initial concentration and the concentration of agile are denoted as f. a f b f c ;

[0017] Step 9: Based on the difference coefficient f from step 8 a f b f c The clever formula C for the standard color sample is calculated;

[0018] Step 10: Dye the fabric according to the Smart Formula C from Step 9, and then measure the color. When the color difference between Smart Formula C and the standard color sample exceeds the preset tolerance range, repeat Steps 6 to 8 to obtain the corrected formula, and then dye the fabric again until the dyed fabric sample matches the standard color sample.

[0019] According to one embodiment of the method for optimizing textile dyeing formulations of the present invention, in step 2, the K / S value of the standard color sample is calculated using the KUBELKA-MUNK equation. Where R is the reflectance of the standard color sample under visible light, and the 31-point matrix represented by this reflectance is denoted as Fs; in step 3, the K / S value of the blank white sample is calculated using the KUBELKA-MUNK equation: This 31-point matrix is ​​denoted as Ft.

[0020] According to an embodiment of the method for optimizing textile dyeing formulations of the present invention, step 5 further includes: first selecting a dye combination that is the same as the standard color sample formulation, then dyeing the dye combination according to different concentration levels and measuring the color, and then inputting the results into a computer as a smart color matching database, wherein the basic data of smart color matching points comes from the smart color matching database.

[0021] In one embodiment of the method for optimizing textile dyeing formulations according to the present invention, the color difference formula in step 6 is:

[0022]

[0023] From the above calculations, we obtain

[0024] In the formula, Indicates the color difference between C0 and the smart color matching point; △L * This represents the difference in lightness between C0 and the clever color matching point; △a * This indicates the red-green light bias between C0 and the smart color matching point; △b * This indicates the yellow-blue light bias between C0 and the smart color matching point. △L represents the minimum color difference between C0 and the smart color matching point. * , △a * , △b * The subscripts 1, 2, n, and M represent 1 to M smart color matching points contained in the smart color matching database.

[0025] In one embodiment of the method for optimizing textile dyeing formulations according to the present invention, the concentration difference formula in step 7 is:

[0026]

[0027] From the above calculations, we obtain Where dcc represents the concentration difference between C0 and each smart color point, dcc min This represents the smart color matching point in the smart color matching database with the smallest difference from the CO concentration. C0 represents the concentration of each dye contained within it. a C b C c The numbers 1, 2, ..., n below represent 1 to n smart color matching points contained in the smart color matching database. C represents the Nth smart color point in the smart color matching database. N The concentrations of the three dyes included.

[0028] The present invention also discloses a system for optimizing textile dyeing formulations, the system comprising:

[0029] The dye basic data module is configured to establish dye basic data.

[0030] The standard color sample module is configured to measure the reflectance Rs of the standard color sample within a preset range, calculate the K / S value of the standard color sample and record it as Fs, where K is the absorption coefficient of the opaque object and S is the scattering coefficient of the opaque object.

[0031] The blank color sample module is configured to measure the reflectance of the blank white sample within a preset range and record it as p, and calculate the K / S value of the blank white sample and record it as Ft;

[0032] The standard color sample initial concentration module is configured to use the reflectance of the standard color sample measured by the standard color sample module to calculate the rate of change of the reflectance of the standard color sample with the K / S value, and then obtain the initial concentration C0 of the standard color sample.

[0033] The Smart Color Matching Database module is configured to create a smart color matching database containing basic data on smart color matching points.

[0034] The smart color matching point search module based on color difference is configured to use the initial concentration C0 of the standard color sample obtained from the initial concentration module of the standard color sample and the smart color matching database established in the smart color matching database module to calculate the smart color matching point C with the smallest color difference between C0 and the smart color matching database using the color difference formula. M ;

[0035] The concentration-based smart color matching point search module is configured to calculate, based on the concentration difference formula, the smart color matching point C with the smallest concentration difference between C0 and the smart color matching data. N ;

[0036] The difference coefficient calculation module is configured to find smart color matching points with similar color difference and concentration based on the color difference-based smart color matching point search module and the concentration-based smart color matching point search module. It then determines the smart color matching point closest to C0 based on actual conditions and records it as C. s And C s The difference coefficients between the initial concentration and the concentration of agile are denoted as f. a f b f c ;

[0037] The smart recipe calculation module is configured to calculate the difference coefficient f based on the difference coefficient calculation module. a f b fc The clever formula C for the standard color sample is calculated;

[0038] The matching dyeing module is configured to dye the fabric sample according to the smart formula C obtained by the smart formula calculation module, and then measure the color. When the color difference between smart formula C and the standard color sample exceeds the preset tolerance range, the corrected formula is obtained again and dyeing is performed again until the dyed fabric sample matches the standard color sample.

[0039] According to one embodiment of the system for optimizing textile dyeing formulations of the present invention, the standard color sample module is further configured to: calculate the K / S value of the standard color sample using the KUBELKA-MUNK equation. Where R is the reflectance of the standard color sample under visible light, and the 31-point matrix represented by this reflectance is denoted as Fs; the blank white sample module is further configured as follows: the K / S value of the blank white sample is calculated using the KUBELKA-MUNK equation: This 31-point matrix is ​​denoted as Ft.

[0040] According to one embodiment of the system for optimizing textile dyeing formulations of the present invention, the smart color matching database module is further configured as follows: first, select a dye combination that is the same as the standard color sample formulation, then dye the dye combination according to different concentration levels and measure the color before inputting it into a computer as a smart color matching database, wherein the basic data of smart color matching points comes from the smart color matching database.

[0041] According to one embodiment of the system for optimizing textile dyeing formulations of the present invention, the color difference formula in the smart color matching point search module based on color difference is:

[0042]

[0043] From the above calculations, we obtain

[0044] In the formula, Indicates the color difference between C0 and the smart color matching point; △L * This represents the difference in lightness between C0 and the clever color matching point; △a * This indicates the red-green light bias between C0 and the smart color matching point; △b * This indicates the yellow-blue light bias between C0 and the smart color matching point. △L represents the minimum color difference between C0 and the smart color matching point. * , △a * , △b * The subscripts 1, 2, n, and M represent 1 to M smart color matching points contained in the smart color matching database.

[0045] According to one embodiment of the system for optimizing textile dyeing formulations of the present invention, the color difference formula in the concentration-based smart color matching point search module is as follows:

[0046]

[0047] From the above calculations, we obtain Where dcc represents the concentration difference between C0 and each smart color point, dcc min This represents the smart color matching point in the smart color matching database with the smallest difference from the CO concentration. C0 represents the concentration of each dye contained within it. a C b C c The numbers 1, 2, ..., n below represent 1 to n smart color matching points contained in the smart color matching database. C represents the Nth smart color point in the smart color matching database. N The concentrations of the three dyes included.

[0048] Compared with existing technologies, this invention offers the following advantages: The method for optimizing textile dyeing formulations, by establishing a mathematical model and enabling rapid color matching via a computer system, utilizes actual dyeing results from the laboratory or production line as precise color matching points to optimize the relationship between theoretical and practical formulations, thereby improving the success rate of first-time color matching. Furthermore, when combined with actual sampling environments, it effectively avoids repeated color adjustments due to differences in dyes and dyeing processes, significantly improving color matching efficiency and success rate. In detail, the advantages of this invention include:

[0049] (1) This invention can not only improve work efficiency, but also improve color matching accuracy. Generally, the color of the standard sample can be achieved through 1-3 rounds of sampling.

[0050] (2) This invention has a precise color correction function, which is based on actual production data and has wide applicability and high accuracy.

[0051] (3) This invention helps to reduce the number of color matching times, improve sampling efficiency, shorten the production cycle, and reduce costs. Attached Figure Description

[0052] The above-described features and advantages of the present invention will be better understood after reading the following detailed description of embodiments of the present disclosure in conjunction with the accompanying drawings. In the drawings, components are not necessarily drawn to scale, and components having similar related characteristics or features may have the same or similar reference numerals.

[0053] Figure 1 A flowchart of an embodiment of the method for optimizing textile dyeing formulations according to the present invention is shown.

[0054] Figure 2A schematic diagram of an embodiment of the system for optimizing textile dyeing formulations according to the present invention is shown. Detailed Implementation

[0055] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should be noted that the aspects described below with reference to the accompanying drawings and specific embodiments are merely exemplary and should not be construed as limiting the scope of protection of the present invention in any way.

[0056] Figure 1 A flowchart illustrating an embodiment of the method for optimizing textile dyeing formulations according to the present invention is provided. Please refer to... Figure 1 The implementation steps of the method in this embodiment are described in detail below.

[0057] Step S1: Establish basic dye data. Dye the dye products to be estimated according to different concentration levels to create basic color samples, measure the color and input the data into the computer to establish basic dye data (referred to as database 1).

[0058] The actual concentration range used in this step is generally 0.001% to 10%, and a basic color sample is prepared after dyeing.

[0059] In this embodiment, the preset range is set to 400-700nm.

[0060] In this step, the reflectance at different concentrations in the 400-700nm range is measured using an instrument, and the K / S value of the basic color sample is obtained using the KUBELKA-MUNK equation. Within a certain dyeing concentration range, the amount of dye applied to the fiber is directly proportional to the dye concentration C used in the dye bath, thus obtaining the K / S value for each estimated dye unit concentration in the 400-700nm range.

[0061] Where k is the unit concentration Value, where c is the concentration.

[0062] Step S2: Measure the reflectance Rs of the standard color sample within the preset range, calculate the K / S value of the standard color sample and record it as Fs, where K is the absorption coefficient of the opaque object and S is the scattering coefficient of the opaque object.

[0063] In this step, the reflectance spectrum Rs of the standard color sample in the visible spectrum range of 400-700 nm is measured by an instrument, and the K / S value of the standard color sample is calculated using the KUBELKA-MUNK equation. Where R is the reflectance of the standard color sample under visible light. The 31-point matrix represented by this reflectance (hereinafter referred to as the 31-point matrix) is denoted as Fs.

[0064] Step S3: Measure the reflectance of the blank white sample within the preset range and record it as p. Calculate the K / S value of the blank white sample and record it as Ft.

[0065] In this step, the blank white sample is, for example, a white greige fabric. The K / S value of the blank white sample is calculated using the KUBELKA-MUNK equation: This 31-point matrix is ​​denoted as Ft.

[0066] Step S4: Using the reflectance Rs of the standard color sample measured in step S2, calculate the rate of change of the reflectance of the standard color sample with respect to the K / S value, and thus obtain the initial concentration C0 of the standard color sample. The concentrations of the three dyes corresponding to C0 (here, "three dyes" is just an example; normally, three dyes are used to create a color, but sometimes two dyes are used) are denoted as follows:

[0067] The method for calculating the initial concentration using the reflectance of a color sample is described in "Methods for Predicting Textile Dyeing Formulations" (CN103645142 B).

[0068] In this step, we first calculate the D matrix:

[0069]

[0070] Obtain matrix D:

[0071]

[0072] Then, the initial concentration C0 is calculated using the matrix formula:

[0073] C0 = (MpDT) - 1·MpD[Fs - Ft]

[0074] Where Mp is the spectral energy of a specific light source, D is the change in reflectance of the standard color sample with respect to the K / S value, T is the unit concentration K / S value of the dye combination to be estimated, Fs is the K / S value of the standard color sample, and Ft is the K / S value of the substrate (i.e., the blank white sample in step S3).

[0075] Step S5: Establish basic data for smart color matching. First, select a dye combination that is the same as the standard color sample formula. Then, dye the dye combination according to different concentration levels, measure the color, and input the data into the computer to create a smart color matching database (referred to as Database 2).

[0076] In this step, the reflectance at different concentrations in the 400-700 nm range was measured using instruments, along with different concentrations (C1C2…C) of the dye combination. n With the corresponding colors L1a1b1L2a2b2…L n a n b n Save them together to the computer.

[0077] In this step, the basic data for the smart color matching point can also be selected from the historical database. It is important to note that the smart formula is calculated based on the results of pre-dyeing with different concentrations of the selected dye. The accuracy of this data determines the quality of the smart formula. Therefore, a high-precision, reproducible dyeing process is required.

[0078] Step S6: Using the initial concentration C0 of the standard color sample obtained in step S4 and the database 2 established in step S5, calculate the smart color matching point C with the smallest color difference between C0 and the smart color matching data (database 2) using the color difference formula. M .

[0079] In this step, the color difference formula is introduced:

[0080]

[0081] From the above calculations, we obtain

[0082] In the formula, This represents the color difference value between C0 and each smart color matching point; △L * This represents the difference in lightness between C0 and the clever color matching point; △a * This indicates the red-green light bias between C0 and the smart color matching point; △b * This indicates the yellow-blue light bias between C0 and the smart color matching point. △L * , △a * , △b * The subscripts 1, 2, n, and M represent 1 to M smart color matching points contained in the smart color matching database.

[0083] Step S7: Calculate the smart color matching point C with the smallest concentration difference between C0 and the smart color matching data according to the concentration difference formula. N .

[0084] In this step, the concentration difference formula is introduced:

[0085]

[0086] From the above calculations, we obtain

[0087] In the formula, dcc represents the concentration difference between C0 and each smart color matching point; C0 represents the initial concentration of the standard color sample. C0 represents the concentration of each dye contained in the sample (assuming it contains 3 dyes). a C b C c The numbers 1, 2, ..., n below represent 1 to n smart color matching points contained in the smart color matching database, dcc minThis represents the smart color matching point in the smart color matching database with the smallest difference from the CO concentration. C represents the Nth smart color point in the smart color matching database. N The concentrations of the three dyes included.

[0088] Step S8: Based on the color difference and concentration points obtained in steps S6 and S7, determine the smart color matching point closest to C0 according to the actual conditions, and denot it as C. s C s The difference coefficient between the initial concentration and the concentration of alumina is denoted as f. a f b f c .

[0089] In this step, C is derived from database 1 and database 2 respectively. s initial concentration The concentration of shrewdness is: but:

[0090]

[0091] Step S9: Based on the difference coefficient f from step S8 a f b f c The smart formula C for the standard color sample is calculated as follows:

[0092]

[0093]

[0094]

[0095] In this step, C a C b C c This refers to the concentration of C in the formula.

[0096] Step S10: Dye the fabric according to the smart formula C from step S9, and measure the color. When the color difference between smart formula C and the standard color sample exceeds the preset tolerance range, repeat steps S6 to S8 to obtain a corrected formula, and then dye the fabric again until the dyed fabric sample matches the standard color sample. The output result at this time is the corrected concentration and the corresponding color difference value.

[0097] In this step, the smart formula and results of each dyeing process will be used as smart data in smart color matching calculations.

[0098] Figure 2 The principle of one embodiment of the system for optimizing textile dyeing formulations according to the present invention is illustrated. Please see [link to documentation]. Figure 2The system in this embodiment includes the following modules connected in sequence: dye basic data module, standard color sample module, blank white sample module, standard color sample initial concentration module, smart color matching database module, smart color matching point search module based on color difference, smart color matching point search module based on concentration, difference coefficient calculation module, smart formula calculation module, and matching dyeing module.

[0099] The dye basic data module is configured to establish basic dye data. The specific processing of the dye basic data module and... Figure 1 Step S1 in the illustrated embodiment is the same and will not be repeated here.

[0100] The standard color sample module is configured to measure the reflectance spectrum Rs of the standard color sample within a preset range, calculate the K / S value of the standard color sample and record it as Fs, where K is the absorption coefficient of the opaque object and S is the scattering coefficient of the opaque object.

[0101] The standard color sample module is further configured to calculate the K / S value of the standard color sample using the KUBELKA-MUNK equation. Where R is the reflectance of the standard color sample under visible light, and the 31-point matrix represented by this reflectance is denoted as Fs.

[0102] The specific processing of the standard color sample module and Figure 1 Step S2 in the illustrated embodiment is the same and will not be repeated here.

[0103] The blank color sample module is configured to measure the reflectance of the blank white sample within a preset range and record it as p, and calculate the K / S value of the blank white sample and record it as Ft.

[0104] The blank color sample module is further configured to: calculate the K / S value of the blank white sample using the KUBELKA-MUNK equation: This 31-point matrix is ​​denoted as Ft.

[0105] Specific processing of the blank color swatch module and Figure 1 Step S3 in the illustrated embodiment is the same and will not be repeated here.

[0106] The standard color sample initial concentration module is configured to use the reflectance of the standard color sample measured by the standard color sample module to calculate the rate of change of the reflectance of the standard color sample with the K / S value, and then obtain the initial concentration C0 of the standard color sample.

[0107] Specific processing of the initial concentration module of standard color samples and Figure 1 Step S4 in the illustrated embodiment is the same and will not be repeated here.

[0108] The Smart Color Matching Database module is configured to create a smart color matching database containing basic data on smart color matching points.

[0109] The Smart Color Matching Database module is further configured as follows: First, select a dye combination that is the same as the standard color sample formula. Then, dye the dye combination according to different concentration levels, measure the color, and input it into the computer as the Smart Color Matching Database. The basic data of the Smart Color Matching Points comes from the Smart Color Matching Database.

[0110] The specific processing of the Smart Color Matching Database module and Figure 1 Step S5 in the illustrated embodiment is the same and will not be repeated here.

[0111] The smart color matching point search module based on color difference is configured to use the initial concentration C0 of the standard color sample obtained from the initial concentration module of the standard color sample and the smart color matching database established in the smart color matching database module to calculate the smart color matching point C with the smallest color difference between C0 and the smart color matching database using the color difference formula. M .

[0112] The color difference formula in the color difference-based smart color matching search module is:

[0113]

[0114] From the above calculations, we obtain

[0115] In the formula, Indicates the color difference between C0 and the smart color matching point; △L * This represents the difference in lightness between C0 and the clever color matching point; △a * This indicates the red-green light bias between C0 and the smart color matching point; △b * Indicates the yellow-blue light bias between C0 and the smart color matching point, △L * , △a * , △b * The subscripts 1, 2, n, and M represent 1 to M smart color matching points contained in the smart color matching database.

[0116] The specific processing of the color difference-based smart color matching search module and Figure 1 Step S6 in the illustrated embodiment is the same and will not be repeated here.

[0117] The concentration-based smart color matching point search module is configured to calculate, based on the concentration difference formula, the smart color matching point C with the smallest concentration difference between C0 and the smart color matching data. N .

[0118] The color difference formula in the concentration-based smart color matching search module is:

[0119]

[0120] From the above calculations, we obtain Where dcc represents the concentration difference between C0 and each smart color point, dcc min This represents the smart color matching point in the smart color matching database with the smallest difference from the CO concentration. C0 represents the concentration of each dye contained within it. a C b C c The numbers 1, 2, ..., n below represent 1 to n smart color matching points contained in the smart color matching database. C represents the Nth smart color point in the smart color matching database. N The concentrations of the three dyes included.

[0121] The specific processing of the concentration-based smart color matching search module and Figure 1 Step S7 in the illustrated embodiment is the same and will not be repeated here.

[0122] The difference coefficient calculation module is configured to find smart color matching points with similar color difference and concentration based on the color difference-based smart color matching point search module and the concentration-based smart color matching point search module. It then determines the smart color matching point closest to C0 based on actual conditions and records it as C. s And C s The difference coefficients between the initial concentration and the concentration of agile are denoted as f. a f b f c .

[0123] The specific processing of the difference coefficient calculation module and Figure 1 Step S8 in the illustrated embodiment is the same and will not be repeated here.

[0124] The smart recipe calculation module is configured to calculate the difference coefficient f based on the difference coefficient calculation module. a f b f c The clever formula C for the standard color sample was calculated.

[0125] The specific processing of the smart recipe calculation module and Figure 1 Step S9 in the illustrated embodiment is the same and will not be repeated here.

[0126] The matching dyeing module is configured to dye the fabric sample according to the smart formula C obtained by the smart formula calculation module, and then measure the color. When the color difference between smart formula C and the standard color sample exceeds the preset tolerance range, the corrected formula is obtained again and dyeing is performed again until the dyed fabric sample matches the standard color sample.

[0127] The specific processing of the matching staining module and Figure 1 Step S10 in the illustrated embodiment is the same and will not be repeated here.

[0128] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing textile dyeing formulations, characterized in that, The methods include: Step 1: Establish basic dye data; Step 2: Measure the reflectance Rs of the standard color sample within the preset range, calculate the K / S value of the standard color sample and record it as Fs, where K is the absorption coefficient of the opaque object and S is the scattering coefficient of the opaque object. Step 3: Measure the reflectance of the blank white sample within the preset range and record it as p; calculate the K / S value of the blank white sample and record it as Ft. Step 4: Using the reflectance Rs of the standard color sample measured in Step 2, calculate the rate of change of the reflectance of the standard color sample with the K / S value, and then obtain the initial concentration C0 of the standard color sample. Step 5: Establish a smart color scheme database containing basic data on smart color scheme points; Step 6: Using the initial concentration C0 of the standard color sample obtained in Step 4 and the smart color matching database established in Step 5, calculate the smart color matching point C with the smallest color difference between C0 and the smart color matching database using the color difference formula. M ; Step 7: Calculate the smart color matching point C with the smallest concentration difference between C0 and the smart color matching data using the concentration difference formula. N ; Step 8: Based on the color difference and concentration points obtained in Steps 6 and 7, determine the smart color matching point closest to C0 according to the actual conditions and record it as C. s And C s The difference coefficients between the initial concentration and the concentration of agile are denoted as f. a f b f c ; Step 9: Based on the difference coefficient f from step 8 a f b f c The clever formula C for the standard color sample is calculated; Step 10: Dye the fabric according to the Smart Formula C from Step 9, and then measure the color. When the color difference between Smart Formula C and the standard color sample exceeds the preset tolerance range, repeat Steps 6 to 8 to obtain the corrected formula, and then dye the fabric again until the dyed fabric sample matches the standard color sample.

2. The method for optimizing textile dyeing formulations according to claim 1, characterized in that, In step 2, the K / S value of the standard color sample is calculated using the KUBELKA-MUNK equation. Where R is the reflectance of the standard color sample under visible light, and the 31-point matrix represented by this reflectance is denoted as Fs; in step 3, the K / S value of the blank white sample is calculated using the KUBELKA-MUNK equation: This 31-point matrix is ​​denoted as Ft.

3. The method for optimizing textile dyeing formulations according to claim 1, characterized in that, Step 5 further includes: first, selecting a dye combination that is the same as the standard color sample formula, then dyeing the dye combination according to different concentration levels and measuring the color before inputting it into the computer as a smart color matching database, wherein the basic data of smart color matching points comes from the smart color matching database.

4. The method for optimizing textile dyeing formulations according to claim 1, characterized in that, The color difference formula in step 6 is: From the above calculations, we obtain In the formula, Indicates the color difference between C0 and the smart color matching point; △L * This represents the difference in lightness between C0 and the clever color matching point; △a * This indicates the red-green light bias between C0 and the smart color matching point; △b * This indicates the yellow-blue light bias between C0 and the smart color matching point. △L represents the minimum color difference between C0 and the smart color matching point. * , △a * , △b * The subscripts 1, 2, n, and M represent 1 to M smart color matching points contained in the smart color matching database.

5. The method for optimizing textile dyeing formulations according to claim 1, characterized in that, The concentration difference formula in step 7 is: From the above calculations, we obtain Where dcc represents the concentration difference between C0 and each smart color point, dcc min This represents the smart color matching point in the smart color matching database with the smallest difference from the CO concentration. C0 represents the concentration of each dye contained within it. a C b C c The numbers 1, 2, ..., n below represent 1 to n smart color matching points contained in the smart color matching database. C represents the Nth smart color point in the smart color matching database. N The concentrations corresponding to the three dyes included.

6. A system for optimizing textile dyeing formulations, characterized in that the system... include: The dye basic data module is configured to establish dye basic data. The standard color sample module is configured to measure the reflectance Rs of the standard color sample within a preset range, calculate the K / S value of the standard color sample and record it as Fs, where K is the absorption coefficient of the opaque object and S is the scattering coefficient of the opaque object. The blank color sample module is configured to measure the reflectance of the blank white sample within a preset range and record it as p, and calculate the K / S value of the blank white sample and record it as Ft; The standard color sample initial concentration module is configured to use the reflectance Rs of the standard color sample measured by the standard color sample module to calculate the rate of change of the reflectance of the standard color sample with the K / S value, and then obtain the initial concentration C0 of the standard color sample. The Smart Color Matching Database module is configured to create a smart color matching database containing basic data on smart color matching points. The smart color matching point search module based on color difference is configured to use the initial concentration C0 of the standard color sample obtained from the initial concentration module of the standard color sample and the smart color matching database established in the smart color matching database module to calculate the smart color matching point C with the smallest color difference between C0 and the smart color matching database using the color difference formula. M ; The concentration-based smart color matching point search module is configured to calculate, based on the concentration difference formula, the smart color matching point C with the smallest concentration difference between C0 and the smart color matching data. N ; The difference coefficient calculation module is configured to find smart color matching points with similar color difference and concentration based on the color difference-based smart color matching point search module and the concentration-based smart color matching point search module. It then determines the smart color matching point closest to C0 based on actual conditions and records it as C. s And C s The difference coefficients between the initial concentration and the concentration of agile are denoted as f. a f b f c ; The smart recipe calculation module is configured to calculate the difference coefficient f based on the difference coefficient calculation module. a f b f c The clever formula C for the standard color sample is calculated; The matching dyeing module is configured to dye the fabric sample according to the smart formula C obtained by the smart formula calculation module, and then measure the color. When the color difference between smart formula C and the standard color sample exceeds the preset tolerance range, the corrected formula is obtained again and dyeing is performed again until the dyed fabric sample matches the standard color sample.

7. The system for optimizing textile dyeing formulations according to claim 6, characterized in that, The standard color sample module is further configured to calculate the K / S value of the standard color sample using the KUBELKA-MUNK equation. Where R is the reflectance of the standard color sample under visible light, and the 31-point matrix represented by this reflectance is denoted as Fs; the blank white sample module is further configured as follows: the K / S value of the blank white sample is calculated using the KUBELKA-MUNK equation: This 31-point matrix is ​​denoted as Ft.

8. The system for optimizing textile dyeing formulations according to claim 6, characterized in that, The Smart Color Matching Database module is further configured as follows: First, select a dye combination that is the same as the standard color sample formula. Then, dye the dye combination according to different concentration levels, measure the color, and input it into the computer as the Smart Color Matching Database. The basic data of the Smart Color Matching Points comes from the Smart Color Matching Database.

9. The system for optimizing textile dyeing formulations according to claim 6, characterized in that, The color difference formula in the color difference-based smart color matching search module is: From the above calculations, we obtain In the formula, Indicates the color difference between C0 and the smart color matching point; △L * This represents the difference in lightness between C0 and the clever color matching point; △a * This indicates the red-green light bias between C0 and the smart color matching point; △b * This indicates the yellow-blue light bias between C0 and the smart color matching point. △L represents the minimum color difference between C0 and the smart color matching point. * , △a * , △b * The subscripts 1, 2, n, and M represent 1 to M smart color matching points contained in the smart color matching database.

10. The system for optimizing textile dyeing formulations according to claim 6, characterized in that, The color difference formula in the concentration-based smart color matching search module is: From the above calculations, we obtain Where dcc represents the concentration difference between C0 and each smart color point, dcc min This represents the smart color matching point in the smart color matching database with the smallest difference from the CO concentration. C0 represents the concentration of each dye contained within it. a C b C c The numbers 1, 2, ..., n below represent 1 to n smart color matching points contained in the smart color matching database. C represents the Nth smart color point in the smart color matching database. N The concentrations corresponding to the three dyes included.

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