PCB Immersion Copper Plating Quality Monitoring Method and System Based on Video Image Analysis

Abstract

The present invention discloses a method and system for monitoring the quality of PCB immersion copper electroplating based on video image analysis, which relates to the technical field of quality monitoring. Through multi-perspective shooting and the use of image stitching technology, this method can obtain the surface images of the PCB before and after electroplating from multiple aspects, ensuring that the quality monitoring of each area is covered, and further avoiding the blind area problem caused by the perspective limitation of traditional detection methods. By using image processing technology to accurately extract the gray difference value, it can efficiently and accurately analyze the possible quality defects in the electroplating process, especially for minor electroplating unevenness or local electroplating defects. This method combines the PCB design drawing to automatically identify the non-conductive area and conductive area on the PCB surface, and based on this, sets the threshold W for the area division of the PCB surface, so as to effectively identify the pseudo-conductive area after electroplating and avoid the error of manual intervention.

Description

Technical Field

[0001] The present invention relates to the technical field of quality monitoring, and in particular to a method and system for monitoring the quality of PCB electroless copper plating based on video image analysis. Background Art

[0002] As an important basic component of modern electronic products, PCB (Printed Circuit Board) is widely used in industries such as consumer electronics, communication equipment, and automotive electronics. With the continuous development of electronic technology, the performance and quality requirements for PCB are gradually increasing. Especially in the manufacturing process of complex circuit boards, the electroless copper plating process (usually used to fill the conductive channels on the PCB) plays a crucial role. The electroless copper plating process aims to fill the conductive areas on the PCB surface through copper plating and keep the non-conductive areas clean to ensure the reliability and conductivity of the circuit. Therefore, the real-time monitoring and evaluation of the PCB surface quality have become an indispensable part of the modern PCB manufacturing process.

[0003] Although there are many existing detection methods for the PCB surface quality, such as X-ray, microscope detection, etc., most of these methods have some obvious deficiencies. First, during the electroless copper plating process, due to different factors such as the distribution of the plating solution, immersion time, and current density, local over-plating or under-plating is likely to occur. These tiny quality differences are often difficult to detect and correct in a timely manner by traditional manual detection means. Second, on the PCB surface, the tiny differences between conductive and non-conductive areas in different regions are often difficult to be captured by existing detection technologies. These deficiencies will lead to the omission of unqualified plating areas, thereby affecting the conductivity and reliability of the entire circuit board. If the quality defects in the electroless copper plating cannot be discovered in time, it will directly affect the quality of the final product and even lead to the failure of the product during subsequent use. In addition, misjudging non-conductive areas as conductive areas or misidentifying under-plated conductive areas will result in inaccurate quality sorting during the production process, affecting the efficiency of the entire production line. Seriously, it may even cause the outflow of unqualified products, affecting the company's reputation and market competitiveness. Therefore, there is an urgent need for a more accurate and automated method for monitoring the quality of PCB electroplating. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method and system for monitoring the quality of PCB electroless copper plating based on video image analysis, which solves the problems in the above background art.

[0005] To achieve the above object, the present invention is realized through the following technical solutions: A method for monitoring the quality of PCB electroless copper plating based on video image analysis, including the following steps,

[0006] S1. Use a CMOS camera to take multi - perspective pictures of the PCB surface before and after electroplating to obtain a video stream. After pre - processing the video stream through image processing, extract consecutive image frames, and through stitching, obtain a set of PCB surface image frames before and after electroplating;

[0007] S2. According to the PCB design drawing, identify the non - conductive areas and conductive areas on the PCB surface, and combine the set of PCB surface image frames before and after electroplating to obtain the gray - level difference at each pixel on the PCB before and after electroplating. Based on the gray - level difference at each pixel on the PCB before and after electroplating, set the PCB surface area division threshold W; Based on the gray - level difference at each pixel on the PCB before and after electroplating set the PCB surface area division threshold W;

[0008] S3. According to the PCB surface area division threshold W set in S2, identify whether there are pseudo - conductive areas on the PCB surface after electroplating. If not, monitor the relevant gloss data information on the PCB surface, and based on the relevant gloss data information, analyze the electroplating state within the conductive areas on the PCB surface to construct an electroplating quality evaluation index Dpzs;

[0009] S4. Preset an evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality state of the current PCB surface after electroless copper plating, and based on the judgment result, take corresponding sorting measures.

[0010] Preferably, the specific steps of S1 include:

[0011] S11. Use a CMOS camera to take multi - perspective pictures of the PCB surface before and after electroplating, and during the shooting process, use an LED array to evenly irradiate the PCB surface to obtain a video stream;

[0012] S12. Use the median filtering method to denoise the video stream, and use the open - source computer vision library OpenCV to read each frame of the image in the video stream to form consecutive image frames. Use the scale - invariant feature transform algorithm to find the same feature points in two adjacent sets of image frames for the consecutive image frames, and use the homography matrix to perform perspective transformation on the consecutive image frames to align the image of the next frame to the image of the current frame to form a set of PCB surface image frames before and after electroplating, where the set of PCB surface image frames before and after electroplating includes a set of PCB surface image frames before electroplating and a set of PCB surface image frames after electroplating.

[0013] Preferably, the specific steps of S2 include:

[0014] S21. First, convert each frame of the image in the set of PCB surface image frames before and after electroplating into a grayscale image type;

[0015] S22. Pre-obtain the PCB design diagram from the PCB manufacturer, and based on the PCB design diagram, identify the non-conductive areas and conductive areas on the PCB surface and make marking processing. Combine the PCB surface image frame sets before and after electroplating, and calculate the gray difference value at each pixel of the PCB before and after electroplating. , where the gray value before electroplating at each pixel on the PCB surface is obtained by performing feature extraction on the PCB surface image frame sets before and after electroplating. And the gray value after electroplating at each pixel on the PCB surface. ; The gray difference value at each pixel of the PCB before and after electroplating Is obtained through the following formula: .

[0016] Preferably, the specific steps of S2 further include:

[0017] S23. Based on the gray image types converted from each frame of the image, record the number of pixels at each gray level in the gray image to calculate the gray histogram H within the PCB surface image frame sets before and after electroplating, where the gray value range within the gray image type is: , and the gray histogram Is expressed as: Where, , ,..., Are respectively the number of pixels with gray level 0, the number of pixels with gray level 1,..., the number of pixels with gray level 255;

[0018] S24. Based on the content of S23, analyze the differences between the non-conductive areas and the conductive areas under different gray levels of the PCB after electroplating in sequence to obtain the regional difference factor , which is specifically obtained according to the following formula:

[0019] ;

[0020] In the formula, Represents the regional difference factor when the gray level is T; Represents the proportion of the number of pixels before the gray level T in the total number of pixels; Represents the proportion of the number of pixels after the gray level T in the total number of pixels; Represents the average gray value of the pixels before the gray level T; Represents the average gray value of the pixels after the gray level T.

[0021] Preferably, the specific steps of S2 further include:

[0022] S25. Statistically analyze the regional difference factors under different gray level conditions obtained in S24 to generate a condition group, and set the PCB surface area division threshold W in the following manner:

[0023] S251. According to the condition group, extract the gray level condition that maximizes the regional difference factor and denote it as the final gray level , and the final gray level is represented in the following manner: where, represents the gray level condition that maximizes the regional difference factor ;

[0024] S252. Use the final gray level as the PCB surface area division threshold W.

[0025] Preferably, the specific steps of S3 include:

[0026] S31. Based on the PCB surface area division threshold W set in S25 and combined with the gray level difference at each pixel of the PCB before and after electroplating , identify whether the non-conductive area on the surface of the electroplated PCB is mis-electroplated. The specific identification content is as follows:

[0027] S311. If the gray level difference at the corresponding pixel of the PCB before and after electroplating exceeds the PCB surface area division threshold W, it indicates that the corresponding pixel on the PCB surface has been subjected to copper deposition electroplating operation;

[0028] S312. If the gray level difference at the corresponding pixel of the PCB before and after electroplating exceeds the PCB surface area division threshold W, it indicates that the corresponding pixel on the PCB surface has not been subjected to copper deposition electroplating operation;

[0029] S313. Based on the content of S311 and S312, and in combination with the non-conductive areas and conductive areas on the PCB surface identified in S22, judge the content of S311 and S312 again. If the corresponding pixel in S311 has been subjected to electroless copper plating operation and belongs to the conductive area on the PCB surface identified in S22, it is judged that the conductive area at the corresponding pixel on the PCB surface after electroplating has been subjected to electroless copper plating operation; if the corresponding pixel in S311 has been subjected to electroless copper plating operation and belongs to the non-conductive area on the PCB surface identified in S22, it is judged that the non-conductive area at the corresponding pixel on the PCB surface after electroplating has been wrongly subjected to electroless copper plating operation; if the corresponding pixel in S312 has not been subjected to electroless copper plating operation and belongs to the conductive area on the PCB surface identified in S22, it is judged that the conductive area at the corresponding pixel on the PCB surface after electroplating has not been subjected to electroless copper plating operation; if the corresponding pixel in S312 has not been subjected to electroless copper plating operation and belongs to the non-conductive area on the PCB surface identified in S22, it is judged that the non-conductive area at the corresponding pixel on the PCB surface after electroplating has not been subjected to electroless copper plating operation;

[0030] S314. If it is judged that the non-conductive area at the corresponding pixel on the PCB surface after electroplating has been wrongly subjected to electroless copper plating operation, at this time, regard the corresponding pixel as a pseudo-conductive area and trigger an end instruction for the first quality monitoring outward;

[0031] S315. If it is judged that the conductive area at the corresponding pixel on the PCB surface after electroplating has not been subjected to electroless copper plating operation, at this time, trigger an end instruction for the first quality monitoring outward.

[0032] Preferably, the specific steps of S3 further include:

[0033] S32. If it is judged that the conductive area on the PCB surface after electroplating has been subjected to electroless copper plating operation, and it is judged that the non-conductive area on the PCB surface after electroplating has not been subjected to electroless copper plating operation, at this time, continue to monitor the relevant gloss data information on the PCB surface after electroplating, wherein the relevant gloss data information includes specular reflection light intensity , diffuse reflection light intensity , incident light intensity and transmitted light intensity ;

[0034] S33. Based on the relevant gloss data information, respectively obtain the reflection coefficient Fsxs and optical density Gxmd in the conductive area on the PCB surface, and specifically obtain them in the following way:

[0035] ;

[0036] In the formula, Denoted as the specular reflection light intensity, Denoted as the diffuse reflection light intensity;

[0037] ;

[0038] In the formula, Denoted as the incident light intensity, Denoted as the transmitted light intensity;

[0039] S34. Based on the gray - scale difference at each pixel of the PCB before and after electroplating , analyze the differences between pixels within the conductive area on the PCB surface to calculate the difference coefficient Cyxs, which is obtained specifically in the following manner:

[0040] ;

[0041] In the formula, Denoted as the standard deviation of the gray - scale difference within the conductive area, Denoted as the average gray - scale difference within the conductive area.

[0042] Preferably, the specific steps of S3 further include:

[0043] S35. By correlating the reflection coefficient Fsxs, the optical density Gxmd, and the difference coefficient Cyxs, and after linear normalization processing, analyze the electroplating state within the conductive area on the PCB surface to construct an electroplating quality evaluation index Dpzs. The electroplating quality evaluation index Dpzs is obtained through the following formula:

[0044] ;

[0045] In the formula, , and are the weight values of the optical density Gxmd, the difference coefficient Cyxs, and the reflection coefficient Fsxs respectively, is a correction constant, where , and The specific values are set by the user according to the situation.

[0046] Preferably, the specific steps of S4 include:

[0047] S41. By comparing and analyzing the electroplating quality evaluation index Dpzs with the evaluation threshold K, comprehensively judge the quality state of the current PCB surface after electroless copper plating. The specific content is as follows:

[0048] If the electroplating quality evaluation index Dpzs exceeds the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after electroless copper plating is unqualified, and at this time, the first quality monitoring end instruction will be triggered;

[0049] If the electroplating quality evaluation index Dpzs does not exceed the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after electroless copper plating is qualified, and at this time, the second quality monitoring end instruction will be triggered;

[0050] S42. Based on the corresponding instructions triggered in S41 and S31, corresponding sorting means are adopted, specifically as follows:

[0051] If the first quality monitoring end instruction is triggered, the corresponding PCB will be conveyed to the manual intervention sorting area;

[0052] If the second quality monitoring end instruction is triggered, the corresponding PCB will be conveyed to the next monitoring process.

[0053] The PCB electroless copper plating quality monitoring system based on video image analysis includes an image capture module, a pseudo-conductive area recognition module, an electroplating quality inspection module, and a sorting feedback module;

[0054] The image capture module is used to use a CMOS camera to take multi-angle pictures of the PCB surface before and after electroplating, obtain a video stream, and after preprocessing the video stream, extract continuous image frames, and after splicing, obtain a set of PCB surface image frames before and after electroplating;

[0055] The pseudo-conductive area recognition module is used to identify the non-conductive area and conductive area on the PCB surface according to the PCB design drawing, and combine the set of PCB surface image frames before and after electroplating to obtain the gray difference value at each pixel of the PCB before and after electroplating , based on the gray difference value at each pixel of the PCB before and after electroplating , set the PCB surface area division threshold W;

[0056] The electroplating quality inspection module is used to identify whether there is a pseudo-conductive area on the PCB surface after electroplating according to the set PCB surface area division threshold W. If not, monitor the relevant gloss data information on the PCB surface, and analyze the electroplating state in the conductive area of the PCB surface according to the relevant gloss data information to construct the electroplating quality evaluation index Dpzs;

[0057] The sorting feedback module is used to preset the evaluation threshold K, compare and analyze it with the electroplating quality evaluation index Dpzs, comprehensively judge the quality state of the current PCB surface after electroless copper plating, and adopt corresponding sorting means based on the judgment result.

[0058] The present invention provides a method and system for monitoring the quality of PCB immersion copper electroplating based on video image analysis, which has the following beneficial effects:

[0059] (1) By using a CMOS camera to take multi-view images of the PCB surface and combining image stitching technology to obtain a set of continuous image frames before and after electroplating, the entire PCB surface can be comprehensively covered, and minute quality differences that may occur during the immersion copper electroplating process can be accurately captured. By analyzing the gray-scale differences of each pixel point, the detection accuracy of electroplating quality is effectively improved, local electroplating problems are discovered, and the missed detection or misjudgment of local areas by traditional detection methods is further avoided. Combining with the PCB design drawing, the conductive and non-conductive areas before and after electroplating are automatically identified, and the surface area division threshold is set through gray-scale difference analysis, and whether there are pseudo-conductive areas can be automatically identified, so as to realize fully automated quality detection, reduce manual intervention and judgment errors. This method effectively reduces the errors of manual detection, improves the reliability and consistency of the production process. This method can not only obtain the glossiness and electroplating status information during the electroplating process in real time, but also make a comprehensive judgment based on the electroplating quality evaluation index Dpzs, and timely discover the quality problems existing in the electroplating process. By comparing and analyzing the set evaluation threshold K with the electroplating quality evaluation index Dpzs, the electroplating quality of the PCB surface can be quickly evaluated, and a decision on automatic sorting or manual intervention can be triggered according to the evaluation result to ensure that unqualified products do not flow into the subsequent production links. In short, this method can comprehensively and accurately detect the quality problems of PCB electroplating, reduce the risk of defective products flowing out due to misjudgment or missed judgment in traditional detection means, and improve the overall efficiency and quality control ability of the production line. By reducing manual intervention and improving detection accuracy, production costs can be reduced, and the production cycle can be shortened while improving product quality. Monitoring the quality of the PCB surface through video image analysis avoids any physical damage or unnecessary contact with the circuit board, ensuring the integrity of the material and the accurate detection of the electroplated surface. Compared with traditional contact detection methods, this non-destructive detection method can better meet the quality control requirements of modern precision electronic products.

[0060] (2) Through the calculation and analysis of the regional difference factor, the system can more accurately control the quality of the copper layer during the electroplating process, accurately divide the conductive and non-conductive areas, ensure that only the areas that need to be electroplated obtain the copper layer, and accurately judge whether each area is correctly electroplated through the analysis of gray-scale differences and regional difference factors, ensuring that there are no errors in the electroplating quality between the conductive area and the non-conductive area, thereby reducing mis-electroplating or leakage electroplating problems during the electroplating process, ensuring the consistency and quality of the product, and reducing rework and cost waste caused by quality problems;

[0061] (3) After the system determines that there is no pseudo-conductive area on the PCB surface, for the electroplated area, it monitors the gloss data closely related to the electroplating quality, so as to obtain more detailed electroplated surface quality information. These gloss data provide an objective reflection of the change in surface optical characteristics and are important parameters for evaluating electroplating quality. Through step S34, the system calculates the difference coefficient according to the standard deviation of the gray difference value and the average gray difference value of the conductive area. This coefficient can accurately reflect the gray change of different pixels within the conductive area. A larger difference coefficient may mean uneven electroplating quality or defects. By calculating the reflection coefficient and optical density and combining the gray difference coefficient, a more comprehensive quality assessment of the electroplated surface can be obtained. Brief Description of the Drawings

[0062] Figure 1 It is a schematic flow chart of the method for monitoring the quality of PCB electroless copper plating based on video image analysis according to the present invention;

[0063] Figure 2 It is a block diagram of the system for monitoring the quality of PCB electroless copper plating based on video image analysis according to the present invention. Detailed Embodiments

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0065] Embodiment 1

[0066] Please refer to Figure 1 , the present invention provides a method for monitoring the quality of PCB electroless copper plating based on video image analysis, including the following steps

[0067] S1. Use a CMOS camera to take multi-angle photos of the PCB surface before and after electroplating to obtain a video stream. After preprocessing the video stream, extract continuous image frames, and through splicing, obtain a set of PCB surface image frames before and after electroplating;

[0068] S2. According to the PCB design drawing, identify the non-conductive area and conductive area on the PCB surface, and combine the set of PCB surface image frames before and after electroplating to obtain the gray difference value at each pixel of the PCB before and after electroplating , based on the gray difference value at each pixel of the PCB before and after electroplating , set the threshold W for dividing the PCB surface area;

[0069] S3. According to the PCB surface area division threshold W set in S2, identify whether there is a pseudo-conductive area on the surface of the PCB after electroplating. If not, monitor the relevant gloss data information on the PCB surface, and analyze the electroplating status in the conductive area of the PCB surface based on the relevant gloss data information to construct an electroplating quality evaluation index Dpzs;

[0070] S4. Preset an evaluation threshold K in advance, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after electroless copper plating. Based on the judgment result, take corresponding sorting measures.

[0071] In this embodiment, by taking multi-angle photos and using the image stitching technology, this method can obtain the PCB surface images before and after electroplating from multiple aspects, ensuring that the quality monitoring of each area is covered, and further avoiding the blind area problem caused by the perspective limitation of the traditional detection method. Using image processing technology to accurately extract the gray difference value can efficiently and accurately analyze the possible quality defects in the electroplating process, especially for tiny electroplating unevenness or local electroplating defects. By combining with the PCB design drawing, this method automatically identifies the non-conductive area and the conductive area on the PCB surface, and based on this, sets the PCB surface area division threshold W, so as to effectively identify the pseudo-conductive area after electroplating and avoid the error of manual intervention. By automatically judging the electroplating status and the electroplating accuracy of the non-conductive area, the defective electroplating area can be found in time, ensuring the efficient implementation of the electroplating process.

[0072] Embodiment 2

[0073] Please refer to Figure 1 , specifically: The specific steps of S1 include:

[0074] S11. Use a CMOS camera to take multi-angle photos of the PCB surface before and after electroplating, and during the shooting process, use an LED array to evenly irradiate the PCB surface to obtain a video stream;

[0075] S12. Use the median filtering method to denoise the video stream, and use the open-source computer vision library OpenCV to read each frame image in the video stream to form a continuous image frame. Use the scale-invariant feature transform algorithm (SIFT) to find the same feature points in two adjacent groups of image frames for the continuous image frames, and use the homography matrix to perform perspective transformation on the continuous image frames to align the image of the next frame to the image of the current frame (for example, stitch the transformed second image with the first image) to form a set of PCB surface image frames before and after electroplating, where the set of PCB surface image frames before and after electroplating includes a set of PCB surface image frames before electroplating and a set of PCB surface image frames after electroplating.

[0076] The specific steps of S2 include:

[0077] S21. First, convert each frame image in the set of PCB surface image frames before and after electroplating into grayscale image type;

[0078] S22. Pre-obtain the PCB design drawing from the PCB manufacturer, and based on the PCB design drawing, identify the non-conductive area and conductive area on the PCB surface, and make marking processing. Combine the set of PCB surface image frames before and after electroplating, and calculate the grayscale difference at each pixel of the PCB before and after electroplating , where the pre-electroplating grayscale value at each pixel (position) on the PCB surface is obtained by performing feature extraction on the set of PCB surface image frames before and after electroplating and the post-electroplating grayscale value at each pixel on the PCB surface ; the grayscale difference at each pixel of the PCB before and after electroplating is obtained through the following formula: .

[0079] In this embodiment, by using a CMOS camera and an LED array to uniformly irradiate the PCB surface and taking pictures from different perspectives, the image data of the PCB surface before and after electroplating can be comprehensively collected. Due to the use of multi-perspective shooting, it is possible to better capture the changes in surface features at different angles, further avoiding the defect that the details of the PCB surface cannot be fully reflected by a single perspective alone. Uniform irradiation helps to eliminate the influence of uneven illumination on the image quality, ensuring that the image brightness and contrast are consistent in all areas and increasing the reliability of subsequent analysis. Using the median filtering method to denoise the video stream can effectively remove the noise and interference information in the image, ensuring that subsequent image processing (such as feature extraction and registration) is not affected by noise. This is a key step in image processing, which helps to improve the accuracy of image analysis and ensure that only effective surface information is collected and processed. Using the Scale-Invariant Feature Transform (SIFT) algorithm and the Homography Matrix to perform feature point matching and perspective transformation on consecutive image frames, thereby ensuring that the image frames before and after electroplating are correctly aligned in space. This step ensures that the images from different perspectives can be seamlessly stitched together to generate a unified set of PCB surface images before and after electroplating, which is very important for subsequent comparison and analysis of the electroplating process at different time points. The information obtained from the PCB design drawing and combined with the set of image frames before and after electroplating can accurately mark the conductive and non-conductive areas. This marking provides an important reference framework for subsequent analysis, helps to distinguish the electroplating status of different types of areas, and reduces errors and interference. By calculating the gray-scale difference before and after electroplating, the change in electroplating quality can be quantitatively analyzed, indicating the gray-scale difference of each pixel before and after electroplating, and then evaluating the electroplating thickness, uniformity, and whether there are electroplating defects (such as local non-electroplating or over-electroplating) in each area. This quantitative gray-scale difference analysis can not only reveal the overall electroplating quality but also be specific to each pixel level, accurately pointing out the potential problems existing in the electroplating process. Combining the gray-scale difference data before and after electroplating can further evaluate the uniformity of the electroplating process and the stability of electroplating quality. If it is found that the gray-scale difference in some areas is too large, it may mean that the electroplating layer is too thick or too thin, which will affect the electrical or mechanical properties of the PCB. Through this method, not only can electroplating defects be detected in a timely manner, but also the electroplating process can be optimized according to the statistical results of the gray-scale difference, reducing the variation in the production process and improving the overall product quality. By using image processing technology and automated gray-scale difference calculation, automated detection and real-time monitoring can be achieved, which means that the electroplating quality of each PCB can be immediately evaluated during the production process without manual intervention, saving labor costs and increasing production efficiency. In addition, the system can automatically generate inspection reports, feedback the electroplating quality of each PCB, and ensure that each product meets the predetermined quality standards.

[0080] Embodiment 3

[0081] Please refer to Figure 1 , specifically: The specific steps of S2 also include:

[0082] S23. Based on the grayscale image types converted from each frame of the image, record the number of pixels at each gray level in the grayscale image to calculate the grayscale histogram H within the set of PCB surface image frames before and after electroplating. Among them, the range of gray values within the grayscale image type is: , and the grayscale histogram is expressed as: wherein, , ,..., are respectively the number of pixels with a gray level of 0, the number of pixels with a gray level of 1,..., and the number of pixels with a gray level of 255;

[0083] S24. Based on the content of S23, analyze the differences between the non-conductive area and the conductive area under different gray levels of the PCB after electroplating in sequence to obtain the regional difference factor under different gray levels , which is specifically obtained according to the following formula:

[0084] ;

[0085] In the formula, represents the regional difference factor when the gray level is T; represents the proportion of the number of pixels before the gray level of T in the total number of pixels; represents the proportion of the number of pixels after the gray level of T in the total number of pixels; represents the average gray value of the pixels before the gray level of T; represents the average gray value of the pixels after the gray level of T; represents the mean difference between the non-conductive area and the conductive area. If the mean difference between the two areas is large, then even if the difference in the number of pixels between the two areas is large, the regional difference factor will also be large;

[0086] S241. The proportion of the number of pixels before the gray level of T in the total number of pixels is obtained through the following formula:

[0087] ;

[0088] In the formula, represents the height of the grayscale image, represents the length of the grayscale image, is the total number of pixels of the grayscale image, represents the number of pixels when the gray level is i, T is the corresponding gray level condition, and i = 0, 1,..., T;

[0089] S242. The ratio of the number of pixels after the gray level T to the total number of pixels is obtained by the following formula:

[0090] ;

[0091] where i = T + 1, T + 2,..., 255;

[0092] S243. The average gray value of the pixels before the gray level T is obtained by the following formula:

[0093] ;

[0094] S245. The average gray value of the pixels after the gray level T is obtained by the following formula:

[0095] ;

[0096] where represents the total brightness contribution of the gray level i in the region.

[0097] The specific steps of S2 further include:

[0098] S25. Statistically analyze the regional difference factors under different gray level conditions obtained in S24 to generate a condition group, and set the PCB surface area division threshold W in the following way:

[0099] S251. According to the condition group, extract the gray level condition that makes the regional difference factor the largest, and denote it as the final gray level , and the final gray level is represented in the following way: where represents the gray level condition that makes the regional difference factor the largest;

[0100] S252. Take the final gray level as the PCB surface area division threshold W.

[0101] In this embodiment, the calculation of the grayscale histogram: By recording the number of pixels at each grayscale level, the system can comprehensively analyze the grayscale distribution of the PCB surface image frames before and after electroplating. The grayscale histogram H provides the grayscale level distribution information of the surface images before and after electroplating, providing basic data for subsequent analysis of regional differences. Grayscale value range: In a grayscale image, the grayscale value range is usually from 0 to 255, representing pixel values from the darkest (black) to the brightest (white). Through the analysis of the grayscale histogram, the system can identify the grayscale differences between different regions (such as conductive regions and non-conductive regions), and then evaluate the electroplating quality. By calculating the difference between the conductive region and the non-conductive region under different grayscale conditions, the regional difference factor can be obtained. This factor helps to quantify the grayscale difference between the two regions, thus effectively reflecting the non-electroplated and electroplated regions that may occur during the electroplating process. A larger regional difference factor indicates a greater difference in the electroplating layer quality of these regions during the electroplating process, which is beneficial for dividing the electroplated region and the non-electroplated region on the PCB surface; By calculating the proportion of the number of pixels before and after the grayscale level T to the total number of pixels, it helps to quantify the change in pixel distribution, which can reflect the change in the number of pixels in the conductive and non-conductive regions between different grayscale levels, especially before and after electroplating. By calculating the average grayscale values of the pixels before and after the grayscale level T respectively, the grayscale change trend within different regions can be obtained. If the mean difference between the two regions is large, the regional difference factor will increase. According to the above statistical results, the grayscale level condition with the largest regional difference factor is selected as the final grayscale level, which marks the change critical point of the electroplating quality at this grayscale level. This threshold helps to accurately separate the non-conductive regions that do not need to be electroplated from the conductive regions that need to be electroplated during the electroplating process, thereby improving the accuracy of electroplating quality control. After determining the threshold W, the conductive region and the non-conductive region can be accurately divided, so as to ensure that the copper layer is electroplated within the conductive region, while avoiding electroplating the copper layer in the non-conductive region. This provides strong data support for quality control during the electroplating process and helps to detect possible excessive electroplating or leakage phenomena during the electroplating process.

[0102] Example 4

[0103] Please refer to Figure 1 , specifically: The specific steps of S3 include:

[0104] S31. Based on the PCB surface area division threshold W set in S25 and combined with the grayscale difference at each pixel of the PCB before and after electroplating , to identify whether the non-conductive region on the surface of the electroplated PCB is mis-electroplated. The specific identification content is as follows:

[0105] S311. If the grayscale difference at the corresponding pixels of the PCB before and after electroplating When it exceeds the PCB surface area division threshold W, it indicates that the copper deposition electroplating operation has been performed at the corresponding pixel on the PCB surface;

[0106] S312. If the gray level difference at the corresponding pixel of the PCB before and after electroplating exceeds the PCB surface area division threshold W, it indicates that the copper deposition electroplating operation has not been performed at the corresponding pixel on the PCB surface;

[0107] S313. According to the content of S311 and S312, and combined with the non-conductive area and conductive area on the PCB surface identified in S22, judge the content of S311 and S312 again. If the copper deposition electroplating operation has been performed at the corresponding pixel in S311 and it belongs to the conductive area on the PCB surface identified in S22, then judge that the copper deposition electroplating operation has been performed on the conductive area at the corresponding pixel on the PCB surface after electroplating; if the copper deposition electroplating operation has been performed at the corresponding pixel in S311 and it belongs to the non-conductive area on the PCB surface identified in S22, then judge that the non-conductive area at the corresponding pixel on the PCB surface after electroplating has been wrongly performed with the copper deposition electroplating operation; if the copper deposition electroplating operation has not been performed at the corresponding pixel in S312 and it belongs to the conductive area on the PCB surface identified in S22, then judge that the copper deposition electroplating operation has not been performed on the conductive area at the corresponding pixel on the PCB surface after electroplating; if the copper deposition electroplating operation has not been performed at the corresponding pixel in S312 and it belongs to the non-conductive area on the PCB surface identified in S22, then judge that the copper deposition electroplating operation has not been performed on the non-conductive area at the corresponding pixel on the PCB surface after electroplating;

[0108] S314. If it is judged that the non-conductive area at the corresponding pixel on the PCB surface after electroplating has been wrongly performed with the copper deposition electroplating operation, at this time, regard the corresponding pixel as a pseudo-conductive area and trigger a first quality monitoring end instruction outward;

[0109] S315. If it is judged that the copper deposition electroplating operation has not been performed on the conductive area at the corresponding pixel on the PCB surface after electroplating, at this time, trigger a first quality monitoring end instruction outward.

[0110] In this embodiment, by combining and analyzing the gray-scale difference at each pixel before and after electroplating with the PCB surface area division threshold W, it is possible to accurately identify whether the conductive and non-conductive areas after electroplating are correctly electroplated. This method helps to detect incorrect electroplating, that is, the non-conductive area is incorrectly electroplated as a conductive area or the conductive area is missed in electroplating, thus ensuring the accuracy and quality of the electroplating process. S311 and S312: By comparing whether the gray-scale difference before and after electroplating exceeds the set threshold W, the system can determine whether each pixel has been subjected to copper deposition electroplating operation. This mechanism can timely detect quality problems during the production process and provide timely feedback to reduce waste and non-conforming products in production; when the system finds that the conductive area has not been electroplated, it will also trigger the first quality monitoring end instruction, indicating that there are defects in the production process, avoiding the occurrence of unplated areas, and ensuring the quality and performance of the PCB. And through automated gray-scale difference calculation and area division analysis, the system can reduce the manual inspection burden, thereby reducing labor costs. Workers only need to intervene when serious problems occur, and the system can complete quality judgment through automated monitoring at other times.

[0111] Embodiment 5

[0112] Please refer to Figure 1 , specifically: The specific steps of S3 also include:

[0113] S32. If it is determined that the conductive area on the surface of the PCB after electroplating has been subjected to copper deposition electroplating operation and it is determined that the non-conductive area on the surface of the PCB after electroplating has not been subjected to copper deposition electroplating operation, at this time, the relevant gloss data information on the surface of the PCB after electroplating will be continuously monitored, where the relevant gloss data information includes specular reflection light intensity , diffuse reflection light intensity , incident light intensity and transmitted light intensity ;

[0114] S33. Based on the relevant gloss data information, the reflection coefficient Fsxs and optical density Gxmd within the conductive area on the PCB surface are respectively obtained. The specific method for obtaining is as follows:

[0115] ;

[0116] In the formula, represents the specular reflection light intensity, represents the diffuse reflection light intensity;

[0117] ;

[0118] In the formula, represents the incident light intensity, represents the transmitted light intensity;

[0119] S34. Based on the gray-scale difference at each pixel of the PCB before and after electroplating , analyze the differences between pixels within the conductive area on the PCB surface to calculate the difference coefficient Cyxs, which is obtained specifically in the following manner:

[0120] ;

[0121] In the formula, represents the standard deviation of the gray-scale difference within the conductive area, represents the average gray-scale difference within the conductive area.

[0122] The above-mentioned specular reflection light intensity refers to the intensity of light reflected from a smooth surface according to the law of reflection, and is usually measured using a spectral radiometer or a reflection photometer;

[0123] Diffuse reflection light intensity refers to the intensity of light randomly reflected when light irradiates an irregular surface. Usually, a diffuse reflection photometer is used to measure the diffuse reflection light intensity. Such an instrument scatters the light emitted by the light source over the entire surface and measures the intensity of the light reflected at different angles;

[0124] Incident light intensity refers to the intensity of light irradiating the target surface, and is usually measured using an illuminometer or a radiometer. An illuminometer can measure the light intensity per unit area at a certain position;

[0125] Transmitted light intensity refers to the intensity of light transmitted after passing through a translucent material. Measuring the transmitted light intensity usually uses a transmission photometer or a spectral transmittance meter, and these instruments can accurately measure the transmitted light intensity through the PCB or other materials.

[0126] In this embodiment, for gloss monitoring: by monitoring relevant gloss data information such as incident light intensity, transmitted light intensity, specular reflection light intensity, and diffuse reflection light intensity, the surface gloss of the PCB after electroplating can be analyzed in depth. Such data support can effectively judge the optical characteristics during the electroplating process, especially for comparing the gloss differences between conductive regions and non-conductive regions, thereby further evaluating the electroplating quality; through calculations based on the gloss data information, the reflection coefficient and optical density are obtained, which can accurately evaluate the changes in surface reflection and transmitted light in the conductive region, and then judge the uniformity and quality of the surface electroplating. Through in-depth analysis of the optical characteristic data and gray-scale differences, continuous improvement of the electroplating quality can be promoted. After each electroplating, the system can provide a detailed optical analysis report, providing directions for improvement and specific operation suggestions for subsequent production. This series of steps can accurately judge the changes in optical characteristics during the electroplating process through gloss data information and gray-scale difference analysis, and further evaluate the electroplating uniformity of the conductive region.

[0127] Embodiment 6

[0128] Please refer to Figure 1 , specifically: The specific steps of S3 also include:

[0129] S35. By correlating the reflection coefficient Fsxs, the optical density Gxmd, and the difference coefficient Cyxs, and after linear normalization processing, analyze the electroplating state within the conductive region of the PCB surface to construct an electroplating quality evaluation index Dpzs. The electroplating quality evaluation index Dpzs is obtained through the following formula:

[0130] ;

[0131] In the formula, , and are the weight values of the optical density Gxmd, the difference coefficient Cyxs, and the reflection coefficient Fsxs respectively, is a correction constant, where , and The specific values are set by the user according to the situation.

[0132] Among them, the larger the optical density Gxmd, the stronger the optical absorption characteristics of the PCB surface deposition layer, usually indicating that the surface is relatively smooth and uniform, the electroplating layer is thicker, and the light scattering is smaller. This may be because the electroless copper plating layer is uniform and dense, resulting in increased light absorption. A higher optical density is usually associated with a better quality electroplating layer, indicating that the copper layer on the PCB surface is denser and has no obvious defects. Optical density is a parameter that measures the light absorption ability of materials. Usually, it refers to the attenuation degree of light intensity after light passes through the material. In the evaluation of PCB electroplating quality, optical density can reflect the uniformity and optical characteristics of the electroplating layer.

[0133] The reflection coefficient Fsxs is a parameter that describes the light reflection ability of the surface, reflecting the smoothness of the surface and the uniformity of the copper layer. The larger the reflection coefficient, the stronger the intensity of the reflected light, usually indicating that the PCB surface is smooth, the electroless copper plating layer is uniform and flat. A smooth surface can reflect more incident light, which also implies that the uniformity and thickness of the electroplating layer are appropriate and suitable for the manufacture of high-precision electronic products.

[0134] The specific steps of S4 include:

[0135] S41. By comparing and analyzing the electroplating quality evaluation index Dpzs with the evaluation threshold K, comprehensively judge the quality status of the current PCB surface after electroless copper plating. The specific content is as follows:

[0136] If the electroplating quality evaluation index Dpzs exceeds the evaluation threshold K, at this time, it will be comprehensively judged that the quality status of the current PCB surface after electroless copper plating is unqualified, and at this time, a first quality monitoring end instruction will be triggered;

[0137] If the electroplating quality evaluation index Dpzs does not exceed the evaluation threshold K, at this time, it will be comprehensively judged that the quality status of the current PCB surface after electroless copper plating is qualified, and at this time, a second quality monitoring end instruction will be triggered;

[0138] S42. Based on the corresponding instructions triggered in S41 and S31, adopt corresponding sorting means, specifically as follows:

[0139] If the first quality monitoring end instruction is triggered, at this time, the corresponding PCB will be conveyed to the area waiting for manual intervention and sorting;

[0140] If the second quality monitoring end instruction is triggered, at this time, the corresponding PCB will be conveyed to the next monitoring process.

[0141] In this embodiment, through three key indicators of reflection coefficient, optical density, and coefficient of variation, combined with linear normalization processing, an electroplating quality evaluation index Dpzs is constructed. This evaluation index integrates different physical characteristic data and provides a more comprehensive electroplating quality evaluation model, which can accurately analyze different regions (conductive regions and non-conductive regions) on the PCB surface. Flexibility of weights and correction constants: By setting weight values and correction constants, the evaluation model has strong flexibility and can be adjusted according to different production conditions and user requirements, thereby ensuring effective evaluation of electroplating quality in various situations. Optical density: Reflects the thickness and optical properties of the deposited layer on the surface and affects electroplating uniformity. Coefficient of variation: Reflects the uniformity during the electroplating process. A larger value may indicate non-uniform electroplating or defects. Reflection coefficient: Evaluates the surface smoothness and optical properties of the electroplating layer. A high reflection coefficient usually means a smooth and uniform electroplating layer. Electroplating quality evaluation index comparison threshold: By comparing with a preset evaluation threshold, the system can automatically determine whether the current electroplating quality is qualified. If the electroplating quality evaluation index exceeds the threshold, it means that the electroplating quality does not meet the standard, and the system will automatically trigger the end instruction of the first quality monitoring; if it does not exceed the threshold, it is determined to be qualified, and the end instruction of the second quality monitoring is triggered. This automated quality judgment process reduces the errors and delays of manual judgment, improves the intelligence level of the production line, and makes the production process more efficient and reliable. Automated sorting system: By triggering the end instructions of the first and second quality monitoring, the system can quickly respond and automatically convey the unqualified PCBs to the manual intervention sorting area, while the qualified PCBs continue to enter the next process. This not only improves the sorting efficiency but also ensures that unqualified products do not continue to enter the production process, reducing quality problems at the source. Combination of manual intervention and automation: By sending unqualified PCBs to the manual intervention sorting area, a more flexible quality intervention method is provided for the production line, ensuring the timely resolution of quality problems. Reduction of unqualified product production: The automated quality evaluation and sorting system further reduces human errors and missed quality judgments, ensuring that the electroplating quality of each PCB is accurately evaluated.

[0142] Embodiment 7

[0143] Please refer to Figure 2 , specifically: A PCB copper deposition electroplating quality monitoring system based on video image analysis, including an image capture module, a pseudo-conductive region identification module, an electroplating quality inspection module, and a sorting feedback module;

[0144] The image capture module is used to take multi-angle photos of the PCB surface before and after electroplating using a CMOS camera, obtain a video stream, and after image preprocessing of the video stream, extract continuous image frames, and through stitching, obtain a set of PCB surface image frames before and after electroplating;

[0145] The pseudo-conductive area identification module is used to identify the non-conductive areas and conductive areas on the PCB surface according to the PCB design drawing, and combine the set of PCB surface image frames before and after electroplating to obtain the gray difference at each pixel of the PCB before and after electroplating. , based on the gray difference at each pixel of the PCB before and after electroplating , set the threshold W for dividing the PCB surface area;

[0146] The electroplating quality inspection module is used to identify whether there are pseudo-conductive areas on the surface of the electroplated PCB according to the set threshold W for dividing the PCB surface area. If not, it monitors the relevant gloss data information on the PCB surface, and analyzes the electroplating state in the conductive areas on the PCB surface based on the relevant gloss data information to construct the electroplating quality evaluation index Dpzs.

[0147] The sorting feedback module is used to preset the evaluation threshold K and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality state of the current PCB surface after electroless copper plating, and take corresponding sorting measures based on the judgment result.

[0148] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A PCB copper plating quality monitoring method based on video image analysis, characterized in that: The following steps are included: S1. Use a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles to obtain a video stream, and extract continuous image frames after image preprocessing the video stream, and obtain a set of PCB surface image frames before and after electroplating by splicing; S2. According to the PCB design drawing, identify the non-conductive area and the conductive area on the PCB surface, and combine the PCB surface image frame set before and after electroplating to obtain the grayscale difference of each pixel of the PCB before and after electroplating. , based on the grayscale difference of each pixel before and after electroplating , set the PCB surface area division threshold W; S3, according to the PCB surface area division threshold W set in S2, identifying whether there is a pseudo conductive area on the surface of the PCB after electroplating, if not, monitoring the relevant gloss data information of the PCB surface, and analyzing the electroplating state in the conductive area of ​​the PCB surface according to the relevant gloss data information, so as to construct an electroplating quality evaluation index Dpzs; S4. Preset an evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after copper electroplating, and take corresponding sorting measures based on the judgment result.

2. The PCB copper plating quality monitoring method based on video image analysis according to claim 1 is characterized in that: The specific steps of S1 include: S11, using a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles, and during the shooting process, using an LED array to evenly illuminate the PCB surface to obtain a video stream; S12. Use median filtering to denoise the video stream, and use the open source computer vision library OpenCV to read each frame of the video stream to form continuous image frames. Use a scale-invariant feature transformation algorithm to find the same feature points in two adjacent groups of image frames in the continuous image frames, and use a homography matrix to perform perspective transformation on the continuous image frames, align the image of the next frame to the image of the current frame to form a set of PCB surface image frames before and after electroplating, wherein the set of PCB surface image frames before and after electroplating includes a set of PCB surface image frames before electroplating and a set of PCB surface image frames after electroplating.

3. The PCB copper plating quality monitoring method based on video image analysis according to claim 2 is characterized in that: The specific steps of S2 include: S21, first converting each frame image in the PCB surface image frame set before and after electroplating into a grayscale image type; S22, obtaining the PCB design drawing from the PCB manufacturer in advance, and identifying the non-conductive area and the conductive area on the PCB surface according to the PCB design drawing, and marking them, and calculating the grayscale difference of each pixel of the PCB before and after the electroplating by combining the PCB surface image frame set before and after the electroplating , wherein the grayscale value before electroplating at each pixel on the PCB surface is obtained by extracting features from the PCB surface image frame set before and after electroplating. And the gray value after electroplating at each pixel on the PCB surface ; The grayscale difference of each pixel of the PCB before and after electroplating Obtained by the following formula: .

4. The PCB copper plating quality monitoring method based on video image analysis according to claim 3 is characterized in that: The specific steps of S2 also include: S23, based on the grayscale image type converted from each frame image, record the number of pixels of each grayscale level in the grayscale image to calculate the grayscale histogram H in the PCB surface image frame set before and after electroplating, wherein the grayscale value range in the grayscale image type is: , the grayscale histogram It is expressed as: in, , ,..., are the number of pixels with gray level 0, the number of pixels with gray level 1, ..., the number of pixels with gray level 255; S24. Based on the content of S23, analyze the differences between the non-conductive area and the conductive area of ​​the PCB under different gray levels after electroplating to obtain the regional difference factors under different gray levels. , which is obtained according to the following formula: ； In the formula, It is expressed as the regional difference factor when the gray level is T; It is expressed as the ratio of the number of pixels before the gray level is T to the total number of pixels; It is expressed as the ratio of the number of pixels after the gray level is T to the total number of pixels; It is expressed as the average gray value of the pixels before the gray level is T; Expressed as the average gray value of pixels after the gray level is T.

5. The PCB copper plating quality monitoring method based on video image analysis according to claim 4 is characterized in that: The specific steps of S2 also include: S25, the regional difference factors under different gray levels obtained in S24 Statistics are performed to generate condition groups and the PCB surface area division threshold W is set in the following way: S251. Extract the regional difference factors according to the condition group The maximum gray level condition is recorded as the final gray level , the final gray level This is expressed in the following way: in, Represents the regional difference factor Maximum gray level condition; S252, the final gray level As the PCB surface area division threshold W.

6. The PCB copper electroplating quality monitoring method based on video image analysis according to claim 5 is characterized in that: The specific steps of S3 include: S31, based on the PCB surface area division threshold W set in S25, and combined with the grayscale difference of each pixel of the PCB before and after electroplating , in order to identify whether the non-conductive area on the surface of the electroplated PCB is incorrectly electroplated. The specific identification content is as follows: S311, if the grayscale difference of the corresponding pixel of the PCB before and after electroplating When the PCB surface area division threshold W is exceeded, it means that copper electroplating has been performed at the corresponding pixel on the PCB surface; S312, if the grayscale difference of the corresponding pixel of the PCB before and after electroplating When the PCB surface area division threshold W is exceeded, it means that the copper plating operation has not been performed at the corresponding pixel on the PCB surface; S313, according to the contents of S311 and S312, and in combination with the non-conductive area and the conductive area on the PCB surface identified in S22, the contents of S311 and S312 are judged again. If the corresponding pixel in S311 has been subjected to copper plating and belongs to the conductive area on the PCB surface identified in S22, it is judged that the conductive area on the corresponding pixel on the PCB surface after electroplating has been subjected to copper plating; if the corresponding pixel in S311 has been subjected to copper plating and belongs to the non-conductive area on the PCB surface identified in S22, it is judged that the conductive area on the corresponding pixel on the PCB surface after electroplating has been subjected to copper plating. The copper plating operation is mistakenly performed on the non-conductive area at the corresponding pixel of the PCB surface after electroplating; if the copper plating operation is not performed on the corresponding pixel in S312 and it belongs to the conductive area of ​​the PCB surface identified in S22, it is judged that the copper plating operation is not performed on the conductive area at the corresponding pixel of the PCB surface after electroplating; if the copper plating operation is not performed on the corresponding pixel in S312 and it belongs to the non-conductive area of ​​the PCB surface identified in S22, it is judged that the copper plating operation is not performed on the non-conductive area at the corresponding pixel of the PCB surface after electroplating; S314, if it is determined that the copper plating operation is mistakenly performed on the non-conductive area at the corresponding pixel on the surface of the electroplated PCB, the corresponding pixel is regarded as a pseudo-conductive area, and a first quality monitoring end instruction is triggered externally; S315. If it is determined that the conductive area at the corresponding pixel on the surface of the PCB after electroplating has not been subjected to the copper plating operation, a No. 1 quality monitoring end instruction will be triggered externally.

7. The PCB copper electroplating quality monitoring method based on video image analysis according to claim 6 is characterized in that: The specific steps of S3 also include: S32: If it is determined that the conductive area of ​​the PCB surface after electroplating has been subjected to copper plating, and it is determined that the non-conductive area of ​​the PCB surface after electroplating has not been subjected to copper plating, then the gloss data information of the PCB surface after electroplating will continue to be monitored, wherein the gloss data information includes the intensity of the specular reflection light. , diffuse light intensity , incident light intensity and transmitted light intensity ; S33, based on the relevant gloss data information, respectively obtain the reflection coefficient Fsxs and the optical density Gxmd in the conductive area of ​​the PCB surface, specifically in the following manner: ； In the formula, Expressed as the specular light intensity, Expressed as diffuse light intensity; ； In the formula, Expressed as the incident light intensity, Expressed as transmitted light intensity; S34, based on the grayscale difference of each pixel before and after electroplating , analyze the differences between pixels in the conductive area of ​​the PCB surface to calculate the difference coefficient Cyxs, which can be obtained in the following way: ； In the formula, Expressed as the standard deviation of the grayscale difference within the conductive area, Expressed as the average grayscale difference within the conductive area.

8. The PCB copper electroplating quality monitoring method based on video image analysis according to claim 7 is characterized in that: The specific steps of S3 also include: S35, by correlating the reflection coefficient Fsxs, the optical density Gxmd and the difference coefficient Cyxs, and after linear normalization, analyzing the electroplating state in the conductive area of ​​the PCB surface to construct an electroplating quality evaluation index Dpzs, the electroplating quality evaluation index Dpzs is obtained by the following formula: ； In the formula, , and are the weight values ​​of optical density Gxmd, difference coefficient Cyxs and reflection coefficient Fsxs, is the correction constant, where , and The specific value is set by the user according to the situation.

9. The PCB copper electroplating quality monitoring method based on video image analysis according to claim 6 is characterized in that: The specific steps of S4 include: S41, by comparing and analyzing the electroplating quality evaluation index Dpzs with the evaluation threshold K, the quality state of the current PCB surface after copper electroplating is comprehensively judged, and the specific contents are as follows: If the electroplating quality evaluation index Dpzs exceeds the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after copper electroplating is unqualified, and the first quality monitoring end instruction will be triggered; If the electroplating quality evaluation index Dpzs does not exceed the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after copper electroplating is in a qualified state, and the second quality monitoring end instruction will be triggered; S42: Based on the corresponding instructions triggered in S41 and S31, corresponding sorting means are adopted, as follows: If the No. 1 quality monitoring end instruction is triggered, the corresponding PCB will be transported to the sorting area for manual intervention; If the second quality monitoring end instruction is triggered, the corresponding PCB will be transported to the next monitoring process.

10. A PCB copper electroplating quality monitoring system based on video image analysis, used to implement the PCB copper electroplating quality monitoring method based on video image analysis as described in any one of claims 1 to 9, characterized in that: It includes image capture module, pseudo-conductive area recognition module, electroplating quality inspection module and sorting feedback module; The image capture module is used to use a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles to obtain a video stream, and after image preprocessing the video stream, extract continuous image frames, and obtain a set of PCB surface image frames before and after electroplating by splicing; The pseudo-conductive area recognition module is used to identify the non-conductive area and the conductive area on the PCB surface according to the PCB design drawing, and to obtain the grayscale difference of each pixel of the PCB before and after the electroplating by combining the PCB surface image frame set before and after the electroplating. , based on the grayscale difference of each pixel before and after electroplating , set the PCB surface area division threshold W; The electroplating quality inspection module is used to identify whether there is a pseudo-conductive area on the surface of the electroplated PCB according to the set PCB surface area division threshold W. If not, the relevant gloss data information of the PCB surface is monitored, and the electroplating state in the conductive area of ​​the PCB surface is analyzed according to the relevant gloss data information to construct the electroplating quality evaluation index Dpzs; The sorting feedback module is used to pre-set the evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after copper electroplating, and take corresponding sorting measures based on the judgment result.

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