Intelligent color separation method, application method and system

Abstract

The application discloses an intelligent color separation method, application method and system, avoids instability caused by human factors, reduces error probability, improves color separation efficiency, accuracy and color uniformity, is simple, easy to learn, high in efficiency and good in accuracy in actual application, can help users improve color control ability, screen high-quality suppliers and improve product quality. The technical scheme is as follows: firstly, taking colors to be separated as standards, corresponding color difference values and related parameters are calculated respectively; secondly, taking the colors to be separated as standards, ranges of related parameters meeting color difference threshold requirements are calculated respectively; and then, colors meeting the related parameter ranges and meeting the color difference threshold requirements are screened out as the same group, and final color separation results are obtained after optimization. The color separation method can be applied to intelligent color separation and evaluation of suppliers of production colors.

Description

Technical Field

[0001] This invention relates to a color separation technology, specifically to an intelligent color separation method, application method, and system. Background Technology

[0002] As people's living standards improve, consumers are demanding higher and higher standards for the appearance, quality, and brand recognition of products. Color is one of the key attributes of a product, attracting consumers' attention first, stimulating their desire to consume, and is also an important indicator of product quality.

[0003] In today's fast-paced market environment, traditional stock preparation and order systems are gradually becoming inadequate to meet market demands. To enhance competitiveness, many brands are striving to shorten delivery cycles, thus requiring them to place orders with multiple factories simultaneously. Taking apparel brands as an example, before garment production, they need to obtain blank fabrics, process them through dyeing and printing plants to obtain fabrics of different colors, and then have garment factories manufacture the finished garments. In the dyeing and printing process, brands place orders with multiple dyeing and printing factories. Although the standard colors and color difference requirements are the same, it's impossible to achieve perfect consistency with the standard sample. Therefore, while the colors delivered by each factory are within the color difference requirements, they are not identical, and there are also differences between different batches from the same factory. Consequently, a situation arises where "while the differences between each batch delivered by each factory and the standard sample are small, the differences between factories and between batches are significant." If color separation is not performed and garments are randomly assigned to different downstream garment factories, noticeable color differences will occur in different parts of the same garment, or between garments within the same batch, severely impacting product quality and significantly increasing the defect rate. In addition, due to the pressure of delivery deadlines, brands are sometimes forced to accept some substandard colored fabrics that do not meet the color difference requirements. The color difference between these fabrics and the standard samples is greater than that of the qualified colored fabrics. In this case, color separation is even more necessary before garment production.

[0004] Color separation involves grouping similar colors in a batch together, ensuring that any color within that category has an acceptable color difference from the others. Currently, there is no unified color separation standard in actual production. Experienced workers typically perform manual color separation based on their experience, sometimes incorporating colorimeter measurements. This method lacks a unified standard, meaning that different workers may produce different results for the same batch of colors. Even within the same batch, the same worker may yield different results at different times (days or months). Therefore, existing color separation methods are highly susceptible to human error, lack stability, and are time-consuming when there are many colors to be separated, increasing the probability of errors in manual color separation.

[0005] Furthermore, to ensure product quality and minimize the possibility of accepting substandard colors that do not meet color difference requirements, suppliers need effective methods to assess their production capabilities. This allows for the selection of high-quality suppliers and improved color control. The color separation results of supplier deliveries can effectively evaluate the supplier's production stability.

[0006] In summary, obtaining an effective intelligent color separation method and application, unifying color separation methods and evaluation standards, minimizing human influence, improving color separation efficiency and stability, and enhancing brand owners' ability to control colors, select high-quality suppliers, and improve product quality are of great significance. Summary of the Invention

[0007] 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.

[0008] The purpose of this invention is to solve the above-mentioned problems by providing an intelligent color separation method, application method, and system. It establishes a unified color separation method and standard, considering color difference factors while also incorporating relevant parameters affecting the color difference threshold. Utilizing a computer for intelligent color separation avoids instability caused by human factors, reduces the probability of errors, and improves the efficiency and accuracy of color separation. In practical applications, it is simple to learn, highly efficient, and accurate, helping users improve their color control capabilities, select high-quality suppliers, and improve product quality.

[0009] The technical solution of this invention is as follows: This invention discloses an intelligent color separation method, the method comprising:

[0010] Step 1: Obtain the color to be separated, light source type, color difference standard type, and color difference threshold;

[0011] Step 2: Using the colors to be separated as standard samples, calculate the corresponding color difference values ​​and parameters according to the color difference standard type and color difference threshold;

[0012] Step 3: Using the colors to be separated as standard samples, calculate the parameter range that meets the color difference threshold requirements for each color, and mark the colors that meet and do not meet the requirements.

[0013] Step 4: First, find the standard sample with the most non-compliant colors and its corresponding non-compliant colors, then group the standard sample and the compliant colors into the same group and number it K1;

[0014] Step 5: For the remaining unsatisfied colors from Step 4, continue the loop using the methods from Steps 3 to 4 until the number of remaining unsatisfied colors is 0, thus obtaining K2 to K. m combination;

[0015] Step 6: First, try adding each color from combination K1 to combination K2, and update the corresponding combination; then, try adding each color from the updated K1 to combination K3, and update the corresponding combination; repeat this process until you can try adding colors to combination K1. m The loop ends after updating the corresponding combination.

[0016] Step 7: Following the method in Step 6, try adding each color from combination K2 to combinations K3 through K1 one by one. m Then, following the method in step 6, try adding the colors from combination K3 to combinations K4 through K1 one by one. m In the middle, update the corresponding combination; repeat this process until combination K is reached. m This yields the final color separation result.

[0017] According to an embodiment of the intelligent color separation method of the present invention, in step 1, the light source type includes one of: D65, CWF, A, TL84, U3000, UL35, TL83; the color difference standard type includes: CIE DE, DE CMC(l：c) One of them.

[0018] According to an embodiment of the intelligent color separation method of the present invention, in step 2, when the color difference standard type is CIE DE, the corresponding parameters are: lightness difference, red-green difference, and yellow-blue difference; the color difference standard type is DE. CMC(l：c) When the corresponding parameters are: weighted brightness difference, weighted saturation difference, and weighted hue difference.

[0019] According to an embodiment of the intelligent color separation method of the present invention, in step 3, a matrix is ​​first established, and then the parameter range is calculated based on the matrix. When the color difference standard type is CIE DE, the ranges of the parameters lightness difference and chromaticity difference are calculated; when the color difference standard type is DE... CMC(l：c) When calculating the range of weighted brightness difference, weighted saturation difference, and weighted hue difference, the parameters are determined.

[0020] According to an embodiment of the intelligent color separation method of the present invention, step 6, the method of attempting to add a combination, further includes: judging according to the method of step 3; if all colors meet the requirements, a combination can be added, the combination name remains unchanged, and the combination content is updated to a new batch sample color name combination.

[0021] This invention also discloses an application method for intelligent color separation, the application method including:

[0022] Step 1: Input the standard color, the colors of the different batches to be separated, the light source type, the color difference standard type, and the color difference threshold;

[0023] Step 2: Based on the input, calculate the color difference and parameters between the different batches of colors to be separated and the standard color, and determine whether the color difference threshold requirement is met;

[0024] Step 3: Based on the pass / fail status of different batches of colors to be separated, divide the different batches of colors to be separated into two categories: pass and fail.

[0025] Step 4: According to the intelligent color separation method as described in any one of claims 1 to 5, first separate the colors of the qualified class, and classify the color separation results. Classification case 1 is: the number of colors in all combinations is greater than 1, and classification case 2 is: the number of colors in a combination is 1.

[0026] Step 5: Based on the classification in Step 4, perform color separation according to the intelligent color separation method as described in any one of claims 1 to 5 to obtain the color separation result.

[0027] According to an embodiment of the intelligent color separation application method of the present invention, step five of the color separation process further includes:

[0028] Case 1: In the color separation results of the qualified color category, the number of colors in all combinations is greater than 1;

[0029] The corresponding processing methods include:

[0030] 1) According to any one of the intelligent color separation methods as described in claims 1 to 5, the colors of the unqualified categories are separated to obtain color separation combinations;

[0031] 2) Integrate the color separation results of the qualified colors in step 1) and step 4 to obtain the final overall color separation result combination;

[0032] Case 2: In the color separation results of the qualified color category, there is a case where the number of colors in the combination is 1;

[0033] The corresponding processing methods include:

[0034] 1) Filter out combinations with 1 color in the qualified color separation results, and renumber the remaining combinations;

[0035] 2) Combine the filtered colors and unqualified colors from 1) and perform color separation according to the intelligent color separation method as described in any one of claims 1 to 5 to obtain a color separation combination;

[0036] 3) Integrate the color separation results from 1) and 2) to obtain the final overall color separation result combination.

[0037] This invention also discloses an application method for intelligent color separation, the application method including:

[0038] Step 1: Enter supplier information, corresponding standard color, colors of different batches to be separated, light source type, color difference standard type, and color difference threshold;

[0039] Step 2: Based on the input, calculate the color difference values ​​and parameters between the different batches of colors to be separated and the standard color, and determine whether the color difference threshold requirements are met.

[0040] Step 3: Calculate the product qualification rate for the colors produced by the supplier;

[0041] Step 4: First calculate the color bias consistency rate, and then obtain the color separation result according to the intelligent color separation method as described in any one of claims 1 to 5.

[0042] Step 5: Output the following obtained from steps 2 to 4: color difference values ​​and parameters, product qualification rate, color deviation consistency rate, and color separation results.

[0043] According to an embodiment of the intelligent color separation application method of the present invention, the method for determining color bias consistency in the fourth step further includes:

[0044] 1) Calculate the color difference parameters (brightness difference, red-green difference, and yellow-blue difference) between the different batches of colors to be separated and the standard color;

[0045] 2) Determine the relationship between the color difference parameters (brightness difference, red-green difference, yellow-blue difference) and 0 for different batches of colors to be separated and the standard color;

[0046] 3) Based on the results in 2), determine the consistency of color bias between different batches to be separated: if the color difference parameters of the two colors with respect to the standard color, namely the difference in brightness, the difference in red-green hue, and the difference in yellow-blue hue, are all the same with 0, then the color bias is consistent.

[0047] If the color difference parameters of the two colors relative to the standard color—lightness difference, red-green difference, and yellow-blue difference—have different relationships with 0, then the color bias is inconsistent.

[0048] This invention also discloses an intelligent color separation system, the system comprising:

[0049] The input module is used to obtain the color to be separated, the light source type, the color difference standard type, and the color difference threshold.

[0050] The parameter calculation module is used to calculate the corresponding color difference value and parameters according to the color difference standard type and color difference threshold, using the color to be separated as the standard sample in turn;

[0051] The marking module is used to calculate the parameter range that meets the color difference threshold requirement for each color to be separated, using the color to be separated as a standard sample, and to mark the colors that meet and do not meet the requirement.

[0052] The initial module for combination partitioning is used to first find the standard sample with the most non-compliant colors and its corresponding non-compliant colors, and then group the standard sample and the compliant colors into the same combination, numbered K1;

[0053] The combination partitioning loop processing module is used to continue looping according to the methods configured in the marking module and the initial combination partitioning module for the remaining non-satisfied colors after the initial combination partitioning module, until the number of remaining non-satisfied colors is 0, thus obtaining K2 to K. m combination;

[0054] The combination update module first attempts to add each color from combination K1 to combination K2, updating the corresponding combination; then, it attempts to add each color from the updated K1 to combination K3, updating the corresponding combination; this process is repeated until all colors in combination K1 are attempted to be added. m The loop ends after updating the corresponding combination.

[0055] The color separation result acquisition module, following the method of the combination update module, first attempts to add each color in combination K2 to combinations K3 through K4 one by one. m In the middle, update the corresponding combination; then, following the method of the combination update module, try adding the colors in combination K3 to combinations K4 through K1 one by one. m In the middle, update the corresponding combination; repeat this process until combination K is reached. m This yields the final color separation result.

[0056] Compared with the prior art, the present invention has the following beneficial effects: The intelligent color separation method of the present invention establishes a unified color separation method and standard, and uses a computer for intelligent color separation, which avoids the instability caused by human factors, reduces the probability of errors, and improves the efficiency and accuracy of color separation.

[0057] Furthermore, the intelligent color separation method of the present invention does not simply consider the color difference threshold, but also incorporates relevant parameters that affect the color difference threshold, thereby further improving the accuracy of the color separation results and the color balance.

[0058] Finally, in terms of the application of the intelligent color separation method of the present invention, it is simple to operate and easy to learn, fast, efficient and accurate, which can improve users' ability to control colors, help users select suppliers and improve product quality. Attached Figure Description

[0059] 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.

[0060] Figure 1 A flowchart of an embodiment of the intelligent color separation method of the present invention is shown.

[0061] Figure 2 A flowchart illustrating an embodiment of the intelligent color separation application method of the present invention is shown.

[0062] Figure 3 A flowchart illustrating another embodiment of the intelligent color separation application method of the present invention is shown.

[0063] Figure 4 A schematic diagram of an embodiment of the intelligent color separation system of the present invention is shown. Detailed Implementation

[0064] 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.

[0065] Figure 1 The flowchart of an embodiment of the intelligent color separation method of the present invention is shown. Please refer to... Figure 1 The implementation steps of the method in this embodiment are described in detail below.

[0066] Step S1: Obtain the color to be separated, light source type, color difference standard type, and color difference threshold.

[0067] In this step, the light source type includes one of the following: D65, CWF, A, TL84, U3000, UL35, and TL83;

[0068] In this step, the color difference standard types include: CIE DE, DE CMC(l：c) One of them.

[0069] Step S2: Using the color to be separated as the standard sample, calculate the corresponding color difference value and parameters according to the color difference standard type and color difference threshold.

[0070] In this step, when the color difference standard type is CIE DE, the corresponding parameters are: lightness difference DL, chromaticity difference Da, and Db. In the CIE Lab color system, there are three factors in the three-dimensional space structure: L, a, and b. L represents lightness, with the positive direction representing brightness and the negative direction representing darkness. a and b both represent colored light, with the positive direction of a representing red and the negative direction representing green. The positive direction of b represents yellow and the negative direction representing blue. In this embodiment, DL is the lightness difference, Da is the red-green light difference, and Db is the yellow-blue light difference.

[0071] In this step, the color difference standard type is DE. CMC(l：c) When the corresponding parameter is: weighted brightness difference Weighted saturation difference Weighted hue difference Where S L S C S H These are the weighting coefficients for brightness difference, saturation difference, and hue difference, respectively, while l and c are the coefficients for adjusting the relative tolerance of brightness and saturation, respectively.

[0072] Step S3: Using the colors to be separated as standard samples, calculate the parameter ranges that meet the color difference threshold requirements, and mark the colors that meet and do not meet the requirements.

[0073] The specific calculation method is as follows.

[0074] First, based on the color difference standard type and color difference threshold, calculate the parameter range that meets the color difference threshold requirements;

[0075] When the color difference standard type is CIE DE

[0076] The ranges for lightness difference (DL), chromaticity difference (Da), and chromaticity difference (Db) are all:

[0077] When the color difference standard type is DE CMC(l：c) hour,

[0078] The weighted brightness difference, weighted saturation difference, and weighted hue difference ranges are all:

[0079] Where: DE represents the color difference threshold when the color difference standard type is CIE DE;

[0080] DE CMC(l：c) Indicates: Color difference standard type is DE CMC(l：c) The color difference threshold at that time.

[0081] Next, using the colors to be separated as standard samples, determine whether the remaining colors to be separated meet the parameter range based on the corresponding parameter values ​​calculated in step S2.

[0082] Finally, based on the judgment results, the colors of those that satisfy and those that do not are marked, and the matrix is ​​set up as follows:

[0083]

[0084] Where: C i Indicates: using color i as the standard sample;

[0085] C ij Indicates: When color i is used as the standard sample, whether color j among the remaining colors to be separated satisfies the following condition; if it does, C... ij =1, C is not satisfied ij =0.

[0086] Step S4: First, find the standard sample with the most non-satisfied colors and its corresponding non-satisfied colors, then group the standard sample and the satisfying colors into the same group, numbered K1.

[0087] Step S5: For the remaining unsatisfactory colors from step S4, continue the loop using the method from steps S3 to S4 until the number of remaining unsatisfactory colors is 0, thus obtaining K2 to K. m combination.

[0088] Step S6: First, try adding each color from combination K1 to combination K2 and update the corresponding combination. Then, try adding each color from the updated K1 to combination K3 and update the corresponding combination. Repeat this process until you can try adding colors to combination K1. m The loop ends after updating the corresponding combination.

[0089] Step S7: Following the method in step S6, try adding each color from combination K2 to combinations K3 through K1 one by one. m Then, following the method in step S6, try adding the colors from combination K3 to combinations K4 through K1 one by one. m In the middle, update the corresponding combination; repeat this process until combination K is reached. m This yields the final color separation result.

[0090] One scenario where the method described in the above embodiments is applied is when it is necessary to separate colors from different batches. Please refer to... Figure 2 The steps shown are detailed below.

[0091] Step 1: Input the standard color, the colors of different batches to be separated, the light source type, the color difference standard type, and the color difference threshold.

[0092] Step 2: Based on the input, calculate the color difference and parameters between the different batches of colors to be separated and the standard color, and determine whether the color difference threshold requirement is met.

[0093] Step 3: Based on the pass / fail status of different batches of colors to be separated, divide the different batches of colors to be separated into two categories: pass and fail.

[0094] Step 4: Following the intelligent color separation method described above, first separate the qualified colors and then classify the color separation results. Classification 1 is: the number of colors in all combinations is greater than 1. Classification 2 is: the number of colors in a combination is 1.

[0095] Step 5: Based on the classification results in Step 4, perform color separation using the intelligent color separation method described above to obtain the color separation results.

[0096] Step five, the color separation process, further includes the following steps.

[0097] Case 1: In the color separation results of the qualified color category, the number of colors in all combinations is greater than 1;

[0098] The corresponding processing methods include:

[0099] 1) According to Figure 1 The intelligent color separation method in the illustrated embodiment separates unqualified colors to obtain color separation combinations;

[0100] 2) Integrate the color separation results of the qualified colors in step 1) and step 4 to obtain the final overall color separation result combination;

[0101] Case 2: In the color separation results of the qualified color category, there is a case where the number of colors in the combination is 1;

[0102] The corresponding processing methods include:

[0103] 1) Filter out combinations with 1 color in the qualified color separation results, and renumber the remaining combinations;

[0104] 2) Combine the filtered colors and unacceptable colors from 1) and process them as follows: Figure 1 The intelligent color separation method in the illustrated embodiment performs color separation to obtain color-separated combinations;

[0105] 3) Integrate the color separation results from 1) and 2) to obtain the final overall color separation result combination.

[0106] One scenario in which the method described in the above embodiments is applied is when it is necessary to evaluate suppliers of the produced colors. Please refer to... Figure 3 The steps shown are detailed below.

[0107] Step 1: Enter supplier information, corresponding standard color, colors of different batches to be separated, light source type, color difference standard type, and color difference threshold.

[0108] Step 2: Based on the input, calculate the color difference values ​​and parameters between the different batches of colors to be separated and the standard color, and determine whether the color difference threshold requirements are met.

[0109] Step 3: Calculate the product qualification rate of the supplier's produced colors according to the following formula. The higher the qualification rate, the better.

[0110]

[0111] Step 4: First, calculate the color deviation consistency rate using the following formula, then follow... Figure 1 The intelligent color separation method shown obtains the color separation results.

[0112] The higher the color consistency rate, the fewer combinations there are in the color separation results, the more colors in a single combination, and the better the production stability.

[0113]

[0114] In step four, the method for determining whether the colors are consistent is as follows:

[0115] 1) Calculate the relevant color difference parameters DL, Da, and Db between the colors of different batches to be separated and the standard color;

[0116] 2) Determine the relationship between DL, Da, Db and 0 for different batches of colors to be separated;

[0117] 3) Based on the results in 2), determine the consistency of color bias between different batches to be separated: if the relationships between DL, Da, Db and 0 of the two colors are the same, then the color bias is consistent; if the relationships between DL, Da, Db and 0 of the two colors are different, then the color bias is inconsistent.

[0118] Step 5: Output the following obtained from steps 2 to 4: color difference values ​​and parameters, product qualification rate, color deviation consistency rate, and color separation results.

[0119] Figure 4 The principle of an embodiment of the intelligent color separation system of the present invention is illustrated. Please refer to [link / reference]. Figure 4 The principle of the system in this embodiment is described in detail below.

[0120] The system in this embodiment includes the following modules: input module, parameter calculation module, marking module, combination partitioning initial module, combination partitioning loop processing module, combination update module, and color separation result acquisition module.

[0121] The input module is used to obtain the color to be separated, the light source type, the color difference standard type, and the color difference threshold.

[0122] The light source types include one of the following: D65, CWF, A, TL84, U3000, UL35, and TL83; the color difference standard types include: CIE DE and DE. CMC(l：c) One of them.

[0123] The parameter calculation module is used to calculate the corresponding color difference value and parameters, one by one, using the color to be separated as the standard sample, according to the color difference standard type and color difference threshold.

[0124] The parameter calculation module is further configured to perform the following processing:

[0125] When the color difference standard type is CIE DE, the corresponding parameters are: lightness difference DL, chromaticity difference Da, and Db.

[0126] In this step, the color difference standard type is DE. CMC(l：c) When the corresponding parameter is: weighted brightness difference Weighted saturation difference Weighted hue difference

[0127] The marking module is used to sequentially calculate the parameter range that meets the color difference threshold requirement for each color to be separated, using the colors to be separated as standard samples, and then marking the colors that meet and do not meet the requirement.

[0128] The tagging module is further configured to perform the following processing:

[0129] First, based on the color difference standard type and color difference threshold, calculate the parameter range that meets the color difference threshold requirements;

[0130] When the color difference standard type is CIE DE

[0131] The ranges for lightness difference (DL), chromaticity difference (Da), and chromaticity difference (Db) are all:

[0132] When the color difference standard type is DE CMC(l：c) hour,

[0133] The weighted brightness difference, weighted saturation difference, and weighted hue difference ranges are all:

[0134] Where: DE represents the color difference threshold when the color difference standard type is CIE DE;

[0135] DE CMC(l：c) Indicates: Color difference standard type is DE CMC(l：c) The color difference threshold at that time.

[0136] Secondly, using the colors to be separated as standard samples, the remaining colors to be separated are judged according to the corresponding parameter values ​​calculated by the parameter calculation module to determine whether they meet the parameter range.

[0137] Finally, based on the judgment results, the colors of those that satisfy and those that do not are marked, and the matrix is ​​set up as follows:

[0138]

[0139] Where: C i Indicates: using color i as the standard sample;

[0140] C ij Indicates whether color j satisfies the following condition when color i is used as the standard sample;

[0141] When C is satisfied ij =1, C is not satisfied ij =0.

[0142] The initial module for combination partitioning is used to first find the standard sample with the most non-satisfied colors and its corresponding non-satisfied colors, and then classify the standard sample and the satisfying colors into the same combination, numbered K1.

[0143] The combined partitioning loop processing module is used to process the remaining non-satisfied colors after the initial combined partitioning module, continuing the loop according to the methods configured in the marking module and the initial combined partitioning module, until the number of remaining non-satisfied colors is 0, thus obtaining K2 to K. m combination.

[0144] The combination update module first attempts to add each color from combination K1 to combination K2, updating the corresponding combination; then, it attempts to add each color from the updated K1 to combination K3, updating the corresponding combination; this process is repeated until all colors in combination K1 are attempted to be added. m The loop ends after updating the corresponding combination.

[0145] The color separation result acquisition module, following the method of the combination update module, first attempts to add each color in combination K2 to combinations K3 through K4 one by one. m In the middle, update the corresponding combination; then, following the method of the combination update module, try adding the colors in combination K3 to combinations K4 through K1 one by one. m In the middle, update the corresponding combination; repeat this process until combination K is reached. m This yields the final color separation result.

[0146] Although the methods described above are illustrated and depicted as a series of actions for the sake of simplicity, it should be understood and appreciated that these methods are not limited by the order of the actions, as some actions may occur in a different order and / or concurrently with other actions from the illustrations and descriptions herein or not illustrated and described herein but which may be understood by those skilled in the art, according to one or more embodiments.

[0147] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0148] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0149] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0150] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0151] 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. An intelligent color separation method, characterized in that the method include: Step 1: Obtain the color to be separated, light source type, color difference standard type, and color difference threshold; Step 2: Using the colors to be separated as standard samples, calculate the corresponding color difference values ​​and parameters according to the color difference standard type and color difference threshold; Step 3: Using the colors to be separated as standard samples, calculate the parameter range that meets the color difference threshold requirements for each color, and mark the colors that meet and do not meet the requirements. Step 4: First, find the standard sample with the most non-compliant colors and its corresponding non-compliant colors, then group the standard sample and the compliant colors into the same group and number it K1; Step 5: For the remaining unsatisfied colors from Step 4, continue the loop using the methods from Steps 3 to 4 until the number of remaining unsatisfied colors is 0, thus obtaining K2 to K. m combination; Step 6: First, try adding each color from combination K1 to combination K2 and update the corresponding combination. Then, try adding each color from the updated K1 to combination K3 and update the corresponding combination. Repeat this process until you can try adding colors to combination K1. m The loop ends after updating the corresponding combination. Step 7: Following the method in Step 6, try adding each color from combination K2 to combinations K3 through K1 one by one. m Then, following the method in step 6, try adding the colors from combination K3 to combinations K4 through K1 one by one. m In the middle, update the corresponding combination; repeat this process until combination K is reached. m This yields the final color separation result.

2. The intelligent color separation method according to claim 1, characterized in that, In step 1, the light source type includes one of the following: D65, CWF, A, TL84, U3000, UL35, TL83; the color difference standard type includes: CIE DE, DE CMC(l：c) One of them.

3. The intelligent color separation method according to claim 1, characterized in that, In step 2, when the color difference standard type is CIEDE, the corresponding parameters are: lightness difference, red-green difference, and yellow-blue difference; when the color difference standard type is DE... CMC(l：c) When the corresponding parameters are: weighted brightness difference, weighted saturation difference, and weighted hue difference.

4. The intelligent color separation method according to claim 1, characterized in that, In step 3, a matrix is ​​first established, and then the parameter range is calculated based on the matrix. When the color difference standard type is CIE DE, the ranges of the lightness difference and chromaticity difference parameters are calculated; when the color difference standard type is DE... CMC(l：c) When calculating the range of weighted brightness difference, weighted saturation difference, and weighted hue difference, the parameters are determined.

5. The intelligent color separation method according to claim 1, characterized in that, In step 6, the method of trying to add a combination further includes: judging according to the method in step 3. If all colors meet the requirements, a combination can be added. The combination name remains unchanged, and the combination content is updated to the new batch sample color name combination.

6. A method for intelligent color separation, characterized in that, Application methods include: Step 1: Input the standard color, the colors of different batches to be separated, the light source type, the color difference standard type, and the color difference threshold; Step 2: Based on the input, calculate the color difference and parameters between the different batches of colors to be separated and the standard color, and determine whether the color difference threshold requirement is met; Step 3: Based on the pass / fail status of different batches of colors to be separated, divide the different batches of colors to be separated into two categories: pass and fail. Step 4: According to the intelligent color separation method as described in any one of claims 1 to 5, first separate the colors of the qualified class, and classify the color separation results. Classification case 1 is: the number of colors in all combinations is greater than 1, and classification case 2 is: the number of colors in a combination is 1. Step 5: Based on the classification in Step 4, perform color separation according to the intelligent color separation method as described in any one of claims 1 to 5 to obtain the color separation result.

7. The application method of intelligent color separation according to claim 6, characterized in that, Step five, the color separation process, further includes: Case 1: In the color separation results of the qualified color category, the number of colors in all combinations is greater than 1; The corresponding processing methods include: 1) According to any one of the intelligent color separation methods as described in claims 1 to 5, the colors of the unqualified categories are separated to obtain color separation combinations; 2) Integrate the color separation results of the qualified colors in step 1) and step 4 to obtain the final overall color separation result combination; Case 2: In the color separation results of the qualified color category, there is a case where the number of colors in the combination is 1; The corresponding processing methods include: 1) Filter out combinations with 1 color in the qualified color separation results, and renumber the remaining combinations; 2) Combine the filtered colors and unqualified colors from 1) and perform color separation according to the intelligent color separation method as described in any one of claims 1 to 5 to obtain a color separation combination; 3) Integrate the color separation results from 1) and 2) to obtain the final overall color separation result combination.

8. A method for intelligent color separation, characterized in that, Application methods include: Step 1: Enter supplier information, corresponding standard color, colors of different batches to be separated, light source type, color difference standard type, and color difference threshold; Step 2: Based on the input, calculate the color difference values ​​and parameters between the different batches of colors to be separated and the standard color, and determine whether the color difference threshold requirements are met. Step 3: Calculate the product qualification rate for the colors produced by the supplier; Step 4: First, calculate the color bias consistency rate, and then obtain the color separation result according to the intelligent color separation method as described in any one of claims 1 to 5; Step 5: Output the following obtained from steps 2 to 4: color difference values ​​and parameters, product qualification rate, color deviation consistency rate, and color separation results.

9. The application method of intelligent color separation according to claim 8, characterized in that, The method for determining color consistency in step four further includes: 1) Calculate the color difference parameters (brightness difference, red-green difference, and yellow-blue difference) between the different batches of colors to be separated and the standard color; 2) Determine the relationship between the color difference parameters (brightness difference, red-green difference, yellow-blue difference) and 0 for different batches of colors to be separated and the standard color; 3) Based on the results in 2), determine the consistency of color bias between different batches to be separated: if the color difference parameters of the two colors with respect to the standard color, namely the difference in brightness, the difference in red-green hue, and the difference in yellow-blue hue, are all the same with 0, then the color bias is consistent. If the color difference parameters of the two colors relative to the standard color—lightness difference, red-green difference, and yellow-blue difference—have different relationships with 0, then the color bias is inconsistent.

10. An intelligent color separation system, characterized in that, The system includes: The input module is used to obtain the color to be separated, the light source type, the color difference standard type, and the color difference threshold. The parameter calculation module is used to calculate the corresponding color difference value and parameters according to the color difference standard type and color difference threshold, using the color to be separated as the standard sample in turn; The marking module is used to calculate the parameter range that meets the color difference threshold requirement for each color to be separated, using the color to be separated as a standard sample, and to mark the colors that meet and do not meet the requirement. The initial module for combination partitioning is used to first find the standard sample with the most non-compliant colors and its corresponding non-compliant colors, and then group the standard sample and the compliant colors into the same combination, numbered K1; The combination partitioning loop processing module is used to continue looping according to the methods configured in the marking module and the initial combination partitioning module for the remaining non-satisfied colors after the initial combination partitioning module, until the number of remaining non-satisfied colors is 0, thus obtaining K2 to K. m combination; The combination update module first attempts to add each color from combination K1 to combination K2, updating the corresponding combination; then, it attempts to add each color from the updated K1 to combination K3, updating the corresponding combination; this process is repeated until all colors in combination K1 are attempted to be added. m The loop ends after updating the corresponding combination. The color separation result acquisition module, following the method of the combination update module, first attempts to add each color in combination K2 to combinations K3 through K4 one by one. m In the middle, update the corresponding combination; then, following the method of the combination update module, try adding the colors in combination K3 to combinations K4 through K1 one by one. m In the middle, update the corresponding combination; repeat this process until combination K is reached. m This yields the final color separation result.

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