A surface color difference control system for plastic products

Abstract

This invention discloses a surface color difference control system for plastic products. It relates to the field of plastic product technology and solves the problems of excessive data processing and low efficiency in existing color difference analysis methods. This invention constructs a three-dimensional coordinate system, converting the Lab value of each pixel within a feature region into three-dimensional coordinates, and calculates the mean coordinates as feature coordinates. These coordinates are then compared with the standard coordinates of a standard region to accurately quantify the color difference difference (CY) between the feature region and the standard region. This quantitative analysis method not only accurately determines whether the color difference meets the standard but also provides a clear quantitative basis for subsequent parameter adjustments. By comparing the feature coordinates of the region to be confirmed with the standard coordinates, the coordinate difference values ​​(CLq, Caq, Cbq) are calculated, and the changing features that need adjustment are accurately identified based on a preset threshold (Y3).

Description

Technical Field

[0001] This invention relates to the field of plastic products technology, specifically to a surface color difference control system for plastic products. Background Technology

[0002] In the plastics manufacturing industry, the consistency and accuracy of product surface color are crucial to product quality and market competitiveness. Due to various factors involved in the production process, such as fluctuations in raw material properties, changes in processing parameters, and differences in equipment performance, color differences are highly likely to occur on the surface of plastic products. Traditional color difference detection methods primarily rely on manual visual inspection, which is not only inefficient but also susceptible to the subjective influence of the inspectors, making it difficult to guarantee accuracy and consistency. With the development of machine vision technology, its application in industrial inspection is becoming increasingly widespread.

[0003] Early machine vision inspection systems, while capable of acquiring images of plastic product surfaces, suffered from numerous shortcomings in image processing and color difference analysis. In image processing, most systems employed simple grayscale methods, failing to accurately reflect color information and resulting in low precision in subsequent contour recognition and color difference analysis. For example, directly averaging RGB values ​​for grayscale conversion ignored the differences in human eye sensitivity to different colors, causing the image to lose crucial information during grayscale conversion.

[0004] In the color difference analysis stage, early systems typically only performed simple color comparisons on the overall image, failing to conduct detailed analysis of different color areas and easily overlooking local color difference issues. Moreover, these systems lacked effective parameter adjustment mechanisms, making it difficult to accurately provide corresponding adjustment solutions even when color difference problems were detected, resulting in color difference issues in the production process being difficult to resolve in a timely manner.

[0005] In addition, existing color difference detection systems are also inadequate in terms of data management and analysis, and cannot make full use of historical data to guide parameter adjustments in the production process, resulting in a lack of data support for optimizing the production process. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a surface color difference control system for plastic products, which solves the problems of excessive data processing and low efficiency in existing color difference analysis methods.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a surface color difference control system for plastic products, comprising:

[0008] The image feature processing unit acquires the surface image of the processed plastic product, assigns different weights to the different RGB values ​​associated with different points, confirms the grayscale values ​​associated with each point, converts the surface image into a grayscale image, and then, based on the gradient features existing in the grayscale image, sequentially labels several contour regions existing in the grayscale image. Specifically:

[0009] Based on the different gray values ​​associated with different pixels in a grayscale image (HD) i The Sobel algorithm is used to determine the vertical gradient T associated with the corresponding pixel. i and vertical gradient D i ;

[0010] Re-adopted: Confirm the overall gradient ZH associated with the corresponding pixel. i , will satisfy: ZH i Pixels with a value greater than or equal to Y1 are labeled as gradient pixels; otherwise, no labeling is performed and Y1 is a preset value.

[0011] Based on several gradient pixels identified in the grayscale image, the gradient contour is identified, and the regions included by different gradient contours are recorded as different contour regions. The grayscale image with the completed contour region labeling is then transmitted to the partition verification and checking end.

[0012] The partitioned verification end compares the grayscale image with the pre-stored template image after the contour area is calibrated, identifies the Lab values ​​associated with the same point in different images, and, based on the overall difference in Lab values ​​within the same area, determines whether the surface image of the plastic product meets the color difference standard. The specific method is as follows:

[0013] Based on the outline regions marked in the grayscale image, region marking is performed in the original surface image. The regions marked with the outline regions are recorded as feature regions, and different outline regions correspond to different feature regions.

[0014] The center points of the surface image and the template image are aligned. After center point alignment, the standard regions associated with the feature regions within the template image are confirmed. The standard regions associated with each feature region are confirmed sequentially, and color difference verification is performed.

[0015] The Lab values ​​associated with each different pixel within the feature region are identified, and a three-dimensional coordinate system is constructed. The x-axis is associated with luminance data, the y-axis with red-green values, and the z-axis with yellow-blue values. Based on the different Lab values ​​associated with different pixels within the feature region, the corresponding three-dimensional coordinates (L...) of each pixel are determined within the three-dimensional coordinate system. k a k b k), where k represents the different pixels associated with this feature region, and k is set to 1, 2, ..., n, where n is the total number of pixels in the feature region;

[0016] use: Identify the mean coordinates (JL, Ja, Jb) associated with the three-dimensional coordinates of all pixels within this feature region, and record them as the feature coordinates of this feature region.

[0017] Then, by using the same method of determining the same feature coordinates within the feature region, the standard coordinates of the standard region associated with this feature region are confirmed, and the standard coordinates are labeled as (BL, Ba, Bb).

[0018] Based on feature coordinates and standard coordinates, the color difference between the feature region and the standard region is determined: using... Confirm the color difference value CY and assess whether the color difference value CY meets the following condition: CY≥Y2, where Y2 is a preset value. If it meets the condition, mark this feature area as the feature to be confirmed area and perform subsequent change feature confirmation for re-analysis to determine the specific parameters that need to be controlled and adjusted. If it does not meet the condition, no calibration is required.

[0019] Preferred options also include:

[0020] The standard image database contains pre-stored standard template images, which are prepared and stored in advance by relevant personnel according to the standards.

[0021] Preferred options also include:

[0022] The change feature confirmation end extracts and confirms the feature coordinates and standard coordinates associated with the region to be confirmed, and then confirms the change features based on the coordinate differences between the feature coordinates and the standard coordinates. The specific method is as follows:

[0023] Mark the feature coordinates associated with the region to be confirmed as (JL). q Ja q Jb q ), where q represents different feature regions to be identified, and the standard coordinates associated with these feature regions to be identified are denoted as (BL). q Ba q Bb q );

[0024] Adopted by: CL q =(BL) q -JL q ), Ca q =(Ba q -Ja q ) and Cb q =(Bb q -Jbq Confirm the change difference CL associated with the corresponding coordinate parameters. q Ca q and Cb q If there exists a change difference of |change difference| ≥ Y3, then the corresponding change difference will be labeled as a change feature; otherwise, no labeling will be performed. Y3 is a preset value.

[0025] Transmit several changing features identified in the region to be confirmed to the output of the correction parameters;

[0026] The correction parameter output terminal, based on several changing features determined in the corresponding feature-to-be-confirmed region, combines historical data to confirm and output the correction parameters. Specifically, the method is as follows:

[0027] Identify the item to which the change feature belongs: If the change feature is CL q CL confirmed q If the value is positive, then confirm the heating temperature associated with increasing the L value by one unit from historical data, and use: CL q × Heating temperature = Increased temperature. Confirm the increased temperature that needs to be corrected. If it is negative, confirm the heating temperature associated with a decrease of one unit in L value from historical data, and use the same processing method to confirm the decreased temperature that needs to be corrected.

[0028] If the characteristic of change is Ca q Confirm Ca q If the value is positive, then confirm the proportion of red pigment associated with an increase of one unit in the 'a' value from historical data, and use: Ca q × Red pigment ratio = Red enhancement ratio. Confirm the red enhancement ratio that needs correction. If it's negative, determine the green pigment ratio associated with a one-unit decrease in the 'a' value from historical data, and use: Ca q × Green pigment ratio = Green enhancement ratio, confirm the green enhancement ratio that needs to be corrected;

[0029] If the change characteristic is Cb q Confirm Cb q If the value is positive, then confirm the proportion of yellow pigment associated with a one-unit increase in the b-value from historical data, and use: Ca q × Yellow pigment ratio = Yellow enhancement ratio. Confirm the yellow enhancement ratio that needs correction. If it's negative, determine the blue pigment ratio associated with a one-unit decrease in the 'a' value from historical data, and use: Ca q × Blue pigment ratio = Blue enhancement ratio, confirm the blue enhancement ratio that needs to be corrected.

[0030] This invention provides a surface color difference control system for plastic products. Compared with the prior art, it has the following advantages:

[0031] This invention constructs a three-dimensional coordinate system, transforms the Lab value of each pixel within a feature region into three-dimensional coordinates, and calculates the mean coordinates as feature coordinates. These coordinates are then compared with the standard coordinates of the standard region to accurately quantify the color difference (CY) between the feature region and the standard region. This quantitative analysis method not only accurately determines whether the color difference meets the standard but also provides a clear quantitative basis for subsequent parameter adjustments.

[0032] By comparing the feature coordinates of the region to be identified with the standard coordinates, the coordinate difference values ​​(CLq, Caq, Cbq) are calculated, and the changing features that need adjustment are accurately identified based on a preset threshold (Y3). This method can clearly determine the specific changes in color in terms of brightness, red-green value, yellow-blue value, etc., providing direction for subsequent parameter adjustments. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the principle framework of the present invention;

[0034] Figure 2 This is a schematic diagram illustrating the confirmation of the feature to be confirmed area of ​​the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] First Embodiment

[0037] Please see Figure 1 This application provides a surface color difference control system for plastic products, including an image feature processing terminal, a standard image database, a partition verification terminal, a change feature confirmation terminal, and a correction parameter output terminal. The image feature processing terminal and the standard image database are electrically connected to the input node of the partition verification terminal, the partition verification terminal is electrically connected to the input node of the change feature confirmation terminal, and the change feature confirmation terminal is electrically connected to the input node of the correction parameter output terminal.

[0038] In the image feature processing stage, machine vision equipment is used to acquire images of the processed plastic product surface. Different weights are assigned to the different RGB values ​​associated with different points in the surface image to confirm the grayscale values ​​associated with those points. The surface image is then converted into a grayscale image. Based on the gradient features present in the grayscale image, several contour regions within the grayscale image are sequentially calibrated. Specifically, the RGB values, R, G, and B, can be directly confirmed during the image acquisition process. When the corresponding image is grayscaled, different weights are assigned to different values ​​to lock the grayscale values ​​associated with the corresponding points. The surface image is then grayscaled according to the different grayscale values ​​associated with different points to lock the grayscale image associated with the corresponding surface image. The grayscale value range is 0-255. By confirming the grayscale features within the converted grayscale image and using the Sobel algorithm to confirm the gradient features associated with different pixels, the contour regions associated with the grayscale image are sequentially confirmed and calibrated.

[0039] The specific method for converting a surface image into a grayscale image is as follows:

[0040] Based on surface images of plastic products acquired by machine vision equipment (during image acquisition, the plastic products must be placed in a fixed position, and the machine vision equipment acquires images of the surface of the plastic products to be inspected; the fixed positions of the machine vision equipment and the plastic products are pre-set by relevant personnel), the RGB values ​​associated with different pixels in the surface image are sequentially confirmed, and HD is used. i =0.299R+0.587G+0.114B confirms the grayscale value HD associated with the corresponding pixel. i , where i represents different pixels;

[0041] Based on the different gray values ​​associated with different pixels, the surface image is converted to grayscale to confirm the grayscale image associated with the surface image (converting the acquired image to grayscale is common in existing technologies, so it will not be elaborated on here).

[0042] The specific method for labeling different contour regions within a grayscale image is as follows:

[0043] Based on the different gray values ​​associated with different pixels in a grayscale image (HD) i The Sobel algorithm is used to determine the vertical gradient T associated with the corresponding pixel. i and vertical gradient D iSpecifically, when confirming gradient data, the Sobel algorithm generally uses the corresponding pixel and multiple sets of surrounding pixels as a basis. Based on the different pixel values ​​associated with different pixels, different weights are assigned to different pixel values. Then, the weighted parameters are summed to confirm the corresponding vertical gradient and vertical gradient. Since the method of confirming gradient features is quite common in existing technologies, it will not be elaborated on here.

[0044] Re-adopted: Confirm the overall gradient ZH associated with the corresponding pixel. i , will satisfy: ZH i Pixels with a value greater than or equal to Y1 are labeled as gradient pixels; otherwise, no labeling is performed and Y1 is a preset value.

[0045] Based on several gradient pixels identified within the grayscale image, gradient contours are confirmed, and the regions included by different gradient contours are recorded as different contour regions (when the color difference of the corresponding region is obvious, the contour around it belongs to the confirmed gradient contour; confirmation can be made quickly and effectively based on the specific pixel values ​​associated with the pixels; based on the corresponding gradient features, different contour regions can be identified from the grayscale image, and different contour regions correspond to different color regions), and the grayscale image with the completed contour region labeling is transmitted to the partition verification and checking end.

[0046] Its standard image database contains pre-stored standard template images, which are prepared and stored in advance by relevant personnel according to the standards, so as to facilitate subsequent color difference verification and comparison.

[0047] Its partition verification and checking end compares and verifies the grayscale image of the completed contour area with the pre-stored template image, identifies the Lab value associated with the same point in different images, and identifies whether the color difference of the surface image of the plastic product meets the standard. Specifically, the grayscale image and the pre-stored template image are the same size. Unless there is an error in the size inspection of the corresponding plastic product, before performing the color difference inspection, it is necessary to ensure that the size of the corresponding plastic product is correct, and that the arrangement of the corresponding grayscale image and the template image is in the same state, which facilitates the specific image comparison process in the later stage.

[0048] The specific method for comparison and verification is as follows:

[0049] Based on the outline regions marked in the grayscale image, region marking is performed in the original surface image. The regions marked with the outline regions are recorded as feature regions, and different outline regions correspond to different feature regions.

[0050] Combination Figure 2The surface image and the template image are aligned at their center points. A preset center point exists within the template image. The center point position is marked during image acquisition of the surface image, or it can be confirmed based on the overall image edge of the surface image. The image edge is combined with a two-dimensional coordinate system to determine the internal center point of the surface image. After center point alignment, the standard regions associated with each feature region within the template image are confirmed. The standard regions associated with each feature region are then sequentially confirmed, and color difference verification is performed.

[0051] The Lab values ​​associated with each different pixel within the feature region are confirmed (where L is the brightness data of the corresponding point, a is the red-green value of the corresponding point, positive values ​​represent red, negative values ​​represent green, and b is the yellow-blue value of the corresponding point, positive values ​​represent yellow, and negative values ​​represent blue). A three-dimensional coordinate system is constructed, with the x-axis associated with brightness data, the y-axis with red-green value data, and the z-axis with yellow-blue value data. Based on the different Lab values ​​associated with different pixels within the feature region, the three-dimensional coordinates (L...) associated with the corresponding pixel are confirmed within the three-dimensional coordinate system. k a k b k ), where k represents the different pixels associated with this feature region, and k is set to 1, 2, ..., n, where n is the total number of pixels in the feature region;

[0052] use: Identify the mean coordinates (JL, Ja, Jb) associated with the three-dimensional coordinates of all pixels within this feature region, and record them as the feature coordinates of this feature region.

[0053] Then, by using the same method of determining the same feature coordinates within the feature region, the standard coordinates of the standard region associated with this feature region are confirmed, and the standard coordinates are labeled as (BL, Ba, Bb).

[0054] Based on feature coordinates and standard coordinates, the color difference between the feature region and the standard region is determined: using... Confirm the color difference value CY and assess whether the color difference value CY meets the following condition: CY≥Y2, where Y2 is a preset value, and its specific value is determined by the operator based on experience. If it meets the condition, it means that the color difference in this feature area is relatively obvious. This feature area is marked as the feature to be confirmed area, and subsequent change feature confirmation is performed for further analysis to determine the specific parameters that need to be controlled and adjusted. If it does not meet the condition, no calibration is required.

[0055] Specifically, based on multiple points associated with the corresponding partition within the image, the 3D coordinates of each point are confirmed using its associated Lab value. Then, from the confirmed 3D coordinates of these points, the mean coordinates are locked, and the corresponding feature points (i.e., feature coordinates) are confirmed within the 3D coordinate system using these mean coordinates. Subsequently, a standard region with a comparison relationship is identified based on this feature region, and the numerical points associated with the standard region are simultaneously confirmed. The difference between the two sets of numerical points is verified through actual comparison. If the difference is large, it indicates a significant difference between the color characteristics of this feature region and the standard region, necessitating specific verification of the feature values ​​to identify whether the corresponding color difference length or value exceeds the standard. This color difference verification method allows for rapid and effective verification of regions with the same characteristic color, providing timely verification results and detailed display.

[0056] The change feature confirmation end extracts and confirms the feature coordinates and standard coordinates associated with the feature to be confirmed region. Then, based on the coordinate difference between the feature coordinates and the standard coordinates, it confirms the change features and transmits the confirmed change features to the correction parameter output end. The specific method of confirmation is as follows:

[0057] Mark the feature coordinates associated with the region to be confirmed as (JL). q Ja q Jb q ), where q represents different feature regions to be identified, and the standard coordinates associated with these feature regions to be identified are denoted as (BL). q Ba q Bb q );

[0058] Adopted by: CL q =(BL) q -JL q ), Ca q =(Ba q -Ja q ) and Cb q =(Bb q -Jb q Confirm the change difference CL associated with the corresponding coordinate parameters. q Ca q and Cb q If there exists a change difference |≥Y3, then the corresponding change difference will be marked as a change feature; otherwise, no marking will be performed. Y3 is a preset value, and its specific value is determined by the operator based on experience. Y3 is generally five change units (that is, five scales on the corresponding coordinates).

[0059] Several change features identified in the region to be confirmed are transmitted to the correction parameter output. Specifically, these change features are the relevant parameters that need to be adjusted later. When the corresponding change feature is negative, the parameter needs to be lowered when processing the same region later. When the corresponding change feature is positive, the parameter needs to be higher when processing the same region later.

[0060] Its correction parameter output terminal, based on several changing features determined by the corresponding feature to be confirmed region, combines historical data to confirm and output the correction parameters. The specific method for confirming the correction parameters is as follows:

[0061] Identify the item to which the change feature belongs: If the change feature is CL q CL confirmed q If the value is positive, then confirm the heating temperature associated with increasing the L value by one unit from historical data, and use: CL q × Heating temperature = Increased temperature. Confirm the increased temperature that needs to be corrected. If it is negative, confirm the heating temperature associated with a decrease of one unit in L value from historical data, and use the same processing method to confirm the decreased temperature that needs to be corrected.

[0062] If the characteristic of change is Ca q Confirm Ca q If the value is positive, then confirm the proportion of red pigment associated with an increase of one unit in the 'a' value from historical data, and use: Ca q × Red pigment ratio = Red enhancement ratio. Confirm the red enhancement ratio that needs correction. If it's negative, determine the green pigment ratio associated with a one-unit decrease in the 'a' value from historical data, and use: Ca q × Green pigment ratio = Green enhancement ratio, confirm the green enhancement ratio that needs to be corrected;

[0063] If the change characteristic is Cb q Confirm Cb q If the value is positive, then confirm the proportion of yellow pigment associated with a one-unit increase in the b-value from historical data, and use: Ca q × Yellow pigment ratio = Yellow enhancement ratio. Confirm the yellow enhancement ratio that needs correction. If it's negative, determine the blue pigment ratio associated with a one-unit decrease in the 'a' value from historical data, and use: Ca q × Blue pigment ratio = Blue enhancement ratio, confirm the blue enhancement ratio that needs to be corrected;

[0064] The confirmed correction parameters are output for external personnel to review, and the associated parameters are controlled and adjusted in a timely manner.

[0065] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0066] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A surface color difference control system for plastic products, characterized in that, include: The image feature processing end acquires the surface image of the processed plastic product, assigns different weights to the different RGB values ​​associated with different points, confirms the gray values ​​associated with the points, converts the surface image into a grayscale image, and then, based on the gradient features existing in the grayscale image, sequentially calibrates several contour regions existing in the grayscale image. The partitioned verification end compares the grayscale image with the pre-stored template image after the contour area is calibrated, identifies the Lab values ​​associated with the same point in different images, and, based on the overall difference in Lab values ​​within the same area, determines whether the surface image of the plastic product meets the color difference standard. The specific method is as follows: Based on the outline regions marked in the grayscale image, region marking is performed in the original surface image. The regions marked with the outline regions are recorded as feature regions, and different outline regions correspond to different feature regions. The center points of the surface image and the template image are aligned. After center point alignment, the standard regions associated with the feature regions within the template image are confirmed. The standard regions associated with each feature region are confirmed sequentially, and color difference verification is performed. The Lab values ​​associated with each different pixel within the feature region are identified, and a three-dimensional coordinate system is constructed. The x-axis is associated with luminance data, the y-axis with red-green values, and the z-axis with yellow-blue values. Based on the different Lab values ​​associated with different pixels within the feature region, the corresponding three-dimensional coordinates (L...) of each pixel are determined within the three-dimensional coordinate system. k a k b k ), where k represents the different pixels associated with this feature region, and k is set to 1, 2, ..., n, where n is the total number of pixels in the feature region; use: Identify the mean coordinates (JL, Ja, Jb) associated with the three-dimensional coordinates of all pixels within this feature region, and record them as the feature coordinates of this feature region. Then, by using the same method of determining the same feature coordinates within the feature region, the standard coordinates of the standard region associated with this feature region are confirmed, and the standard coordinates are labeled as (BL, Ba, Bb). Based on feature coordinates and standard coordinates, the color difference between the feature region and the standard region is determined: using... Confirm the color difference value CY, and assess whether the color difference value CY meets the following condition: CY≥Y2, where Y2 is a preset value. If it meets the condition, mark this feature area as the feature to be confirmed area, and analyze and process it through the change feature confirmation terminal to determine the specific parameters that need to be controlled and adjusted. The change feature confirmation end extracts and confirms the feature coordinates and standard coordinates associated with the region to be confirmed. Then, based on the coordinate difference between the feature coordinates and the standard coordinates, it confirms the change features and transmits the confirmed change features to the correction parameter output end. The specific method is as follows: Mark the feature coordinates associated with the region to be confirmed as (JL). q Ja q Jb q ), where q represents different feature regions to be identified, and the standard coordinates associated with these feature regions to be identified are denoted as (BL). q Ba q Bb q ); Adopted by: CL q =(BL) q -JL q ), Ca q =(Ba q -Ja q ) and Cb q =(Bb q -Jb q Confirm the change difference CL associated with the corresponding coordinate parameters. q Ca q and Cb q If there exists a change difference of |change difference| ≥ Y3, then the corresponding change difference will be labeled as a change feature; otherwise, no labeling will be performed. Y3 is a preset value. Transmit several changing features identified in the region to be confirmed to the output of the correction parameters; The correction parameter output terminal determines the correction parameters based on several changing features identified in the corresponding feature-to-be-confirmed region, combined with historical data.

2. The surface color difference control system for plastic products according to claim 1, characterized in that, The image feature processing terminal converts the surface image into a grayscale image in the following specific way: Based on surface images of plastic products acquired by machine vision equipment, the RGB values ​​associated with different pixels within the surface image are sequentially identified, and then HD is used. i =0.299R+0.587G+0.114B confirms the grayscale value HD associated with the corresponding pixel. i , where i represents different pixels; Based on the different gray values ​​associated with different pixels, the surface image is converted to grayscale to confirm the grayscale image associated with the surface image.

3. The surface color difference control system for plastic products according to claim 2, characterized in that, The image feature processing terminal uses the following method to label different contour regions within the grayscale image: Based on the different gray values ​​associated with different pixels in a grayscale image (HD) i The Sobel algorithm is used to determine the horizontal gradient T associated with the corresponding pixel. i and vertical gradient D i ; Re-adopted: Confirm the overall gradient ZH associated with the corresponding pixel. i , will satisfy: ZH i Pixels with a value greater than or equal to Y1 are labeled as gradient pixels; otherwise, no labeling is performed and Y1 is a preset value. Based on several gradient pixels identified within the grayscale image, gradient contours are confirmed, and the regions included by different gradient contours are recorded as different contour regions. The grayscale image with the completed contour region labeling is then transmitted to the partition verification and checking terminal.

4. The surface color difference control system for plastic products according to claim 1, characterized in that, Also includes: The standard image database contains pre-stored standard template images, which are prepared and stored in advance by relevant personnel according to the standards.

5. A surface color difference control system for plastic products according to claim 1, characterized in that, If CY does not satisfy CY≥Y2, then no calibration is required.

6. A surface color difference control system for plastic products according to claim 1, characterized in that, The specific method for confirming the correction parameter output is as follows: Identify the item to which the change feature belongs: If the change feature is CL q CL confirmed q If the value is positive, then confirm the heating temperature associated with increasing the L value by one unit from historical data, and use: CL q × Heating temperature = Increased temperature. Confirm the increased temperature that needs to be corrected. If it is negative, confirm the heating temperature associated with a decrease of one unit in L value from historical data, and use the same processing method to confirm the decreased temperature that needs to be corrected. If the characteristic of change is Ca q Confirm Ca q If the value is positive, then confirm the proportion of red pigment associated with an increase of one unit in the 'a' value from historical data, and use: Ca q × Red pigment ratio = Red enhancement ratio. Confirm the red enhancement ratio that needs correction. If it's negative, determine the green pigment ratio associated with a one-unit decrease in the 'a' value from historical data, and use: Ca q × Green pigment ratio = Green enhancement ratio, confirm the green enhancement ratio that needs to be corrected; If the change characteristic is Cb q Confirm Cb q If the value is positive, then determine the proportion of yellow pigment associated with a one-unit increase in the b-value from historical data, and use: Cb q × Yellow pigment ratio = Yellow enhancement ratio. Confirm the yellow enhancement ratio that needs correction. If it's negative, determine the blue pigment ratio associated with a one-unit decrease in the b-value from historical data, and use: Cb q × Blue pigment ratio = Blue enhancement ratio, confirm the blue enhancement ratio that needs to be corrected.

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