Circuit board processing control method and system applied to industrial Internet of Things

Abstract

The invention provides a circuit board processing control method and system applied to the industrial Internet of Things, and relates to the field of the industrial Internet of Things. A multi-view visual acquisition device deployed in the industrial Internet of Things is utilized to acquire a real-time visual image set of a circuit board processing area, and visual feature extraction processing is performed on the real-time visual image set; and obtaining circuit outline of the circuit board, welding spot pre-positioning and processing material posture characteristics. The features are matched with preset standard features to generate visual feature deviation information, and equipment dynamic adjustment parameters including the machining head position, the machining force and the material supply angle adjustment parameters are deduced in combination with machining process requirements. And generating a processing control instruction guided by computer vision according to the dynamic adjustment parameters, transmitting the processing control instruction to corresponding equipment, monitoring a processing vision state after the instruction is executed in real time through a vision acquisition device, continuously optimizing a subsequent control instruction based on feedback, realizing dynamic control of the circuit board processing process, and improving the processing precision and quality.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) technology, and more specifically, to a circuit board processing control method and system applied to industrial IoT. Background Technology

[0002] Currently, the PCB manufacturing industry is moving towards intelligent and high-precision manufacturing. As a key component of electronic devices, the quality of PCB manufacturing directly affects the performance and reliability of electronic products.

[0003] Traditional circuit board (PCB) manufacturing control methods have many limitations. On the one hand, they rely heavily on manual inspection and experience-based judgment to adjust processing equipment and material supply devices. Manual inspection is not only inefficient but also easily influenced by subjective factors, leading to inaccurate results and failing to detect subtle deviations in the PCB manufacturing process in a timely manner. For example, when inspecting the outline of PCB traces and the location of solder joints, manual visual inspection may not accurately identify minute dimensional deviations or positional shifts, thus affecting the quality of the PCB manufacturing.

[0004] On the other hand, while existing automated processing control methods improve processing efficiency to some extent, they often lack real-time, precise visual feedback mechanisms. These methods typically process according to preset fixed parameters, making it difficult to dynamically adjust based on the actual state of the circuit board during processing. When the posture of the processed material changes or the processing equipment experiences minor deviations, they cannot respond in time, leading to quality problems in the processed circuit boards, such as short circuits and poor solder joints, increasing the defect rate and raising production costs. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a circuit board processing control method applied to the Industrial Internet of Things, the method comprising: A multi-view vision acquisition device deployed through the Industrial Internet of Things (IIoT) acquires a set of real-time visual images of the circuit board processing area. The set of real-time visual images includes images of the circuit board area to be processed, images of the circuit board status during processing, and images of the material supply posture. Visual feature extraction processing is performed on the real-time visual image set to obtain the circuit board outline features, circuit board solder joint pre-position features, and processing material posture features; The circuit board outline features, circuit board solder joint pre-positioning features, and processing material posture features are matched with preset circuit board processing visual standard features to generate visual feature deviation information. Based on the visual feature deviation information and combined with the circuit board processing technology requirements, dynamic adjustment parameters of the circuit board processing equipment are derived. The dynamic adjustment parameters include equipment processing head position adjustment parameters, equipment processing force adjustment parameters, and material supply angle adjustment parameters. Based on the dynamically adjusted parameters, computer vision-guided circuit board processing control instructions are generated and transmitted to the corresponding circuit board processing equipment and material supply device. At the same time, the visual status of the circuit board processing after the instructions are executed is monitored in real time through a vision acquisition device. Based on the visual status feedback, subsequent control instructions are continuously optimized to achieve dynamic control of the circuit board processing process.

[0006] Furthermore, embodiments of the present invention also provide a circuit board processing control system applied to the Industrial Internet of Things, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to perform the above-described circuit board fabrication control method for the Industrial Internet of Things by executing the machine-executable instructions.

[0007] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, a processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-described circuit board processing control method applied to the Industrial Internet of Things.

[0008] Based on the above, a multi-view vision acquisition device deployed through the Industrial Internet of Things (IIoT) acquires a real-time visual image set of the circuit board processing area, covering information such as the area to be processed, the processing status, and the material supply posture. Then, visual feature extraction processing is performed on the real-time visual image set to accurately obtain key features such as the circuit board outline, solder joint pre-positioning, and the posture of the processed materials. Furthermore, by matching the extracted features with preset standard features to generate visual feature deviation information, the difference between the actual and ideal states during circuit board processing can be intuitively reflected. Based on this, dynamic adjustment parameters derived from the visual feature deviation information and processing requirements can be precisely adjusted according to real-time changes during circuit board processing, including the position of the processing head, processing force, and material supply angle, achieving dynamic optimization of the processing process. Based on dynamically adjusted parameters, computer vision-guided circuit board processing control instructions are generated and transmitted to the corresponding equipment. At the same time, the processing visual status after the instructions are executed is monitored in real time through a vision acquisition device, and subsequent control instructions are continuously optimized. This enables timely response to various changes in the processing process, effectively improving the accuracy and quality of circuit board processing, reducing the defect rate, increasing production efficiency, and enhancing the stability and reliability of the circuit board processing process. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the circuit board processing control method for industrial Internet of Things provided in an embodiment of the present invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of a circuit board processing control system for industrial Internet of Things provided in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a circuit board processing control method for industrial Internet of Things (IoT) according to an embodiment of the present invention. The following is a detailed description of this circuit board processing control method for industrial IoT.

[0012] Step S110: Obtain a set of real-time visual images of the circuit board processing area through a multi-view vision acquisition device deployed by the Industrial Internet of Things. The set of real-time visual images includes images of the circuit board area to be processed, images of the circuit board status during processing, and images of the material supply posture.

[0013] In this embodiment, taking an automated circuit board (PCB) production line of an electronics manufacturing company as an example, the production line deploys multi-view vision acquisition devices around the processing station of the PCB processing equipment. The top-view vision module of the multi-view vision acquisition device is installed directly above the processing equipment, with its shooting angle vertically downwards, covering the entire area of ​​the PCB to be processed. The side-view vision module is installed on the side of the processing equipment at a certain angle to the processing station, capturing the contact process between the processing head and the PCB. The material supply end vision module is installed near the discharge port of the material supply device to monitor the posture of the material to be processed. When processing a multilayer printed circuit board, the top-view vision module continuously captures the overall layout of the PCB, the side-view vision module records the drilling and soldering operations of the processing head, and the material supply end vision module monitors the transmission posture of materials such as the copper-clad laminate and solder mask to be processed. Through the above-mentioned vision acquisition devices, image information of the PCB processing area can be obtained in real time.

[0014] Step S111: For the top-down view module, adjust the shooting angle and focal length of the top-down view module so that the shooting field of view covers the entire area of ​​the circuit board to be processed. Set the shooting frame rate to be synchronized with the processing rhythm of the circuit board processing equipment so that one frame of the circuit board to be processed area image is captured synchronously when processing each circuit unit.

[0015] In this embodiment, the area of ​​the circuit board to be processed has a specific size, and the shooting angle of the top-down vision module needs to be adjusted according to the installation position and size of the circuit board. First, by adjusting the horizontal and vertical angles of the top-down vision module, the lens's field of view can completely cover the four corners of the area of ​​the circuit board to be processed. Then, the focal length is adjusted so that the details of the circuit board in the image are clearly discernible, such as the width and spacing of the lines, which can be accurately identified. The shooting frame rate needs to be matched with the processing rhythm of the circuit board processing equipment. If the processing equipment takes a certain amount of time to complete the processing of each line unit, then the shooting frame rate is set to the frequency corresponding to that time, ensuring that the top-down vision module captures one frame of image for each line unit processed. For example, if the processing equipment processes one line unit per second, the shooting frame rate is set to 1 frame / second. This ensures that image acquisition is synchronized with the processing process, facilitating subsequent analysis of the circuit board's processing progress and quality.

[0016] Step S112: For the side-view vision module, adjust the shooting angle and light source intensity of the side-view vision module so that the shooting image can effectively present the contact state between the processing head and the circuit board during the circuit board processing process. Set the shooting trigger mechanism to automatically trigger shooting when the processing head starts processing action to generate a circuit board status image during processing.

[0017] In this embodiment, the shooting angle of the side-view vision module must be able to clearly capture the contact area between the processing head and the circuit board. By adjusting the horizontal and vertical angles of the side-view vision module, the lens's line of sight is made perpendicular to the movement direction of the processing head to obtain the best shooting effect. Simultaneously, the light source intensity is adjusted according to the lighting conditions of the processing environment to ensure moderate brightness of the captured image, avoiding excessive brightness or darkness that could lead to loss of image detail. The shooting trigger mechanism is implemented by installing a sensor on the processing head of the processing equipment. When the processing head starts processing, the sensor sends a signal to trigger the side-view vision module to capture an image. For example, when the processing head begins drilling, the sensor detects the downward movement of the processing head, triggering the side-view vision module to capture and record information such as the moment of contact between the processing head and the circuit board, the drilling depth, etc., generating an image of the circuit board status during processing.

[0018] Step S113: For the material supply end vision module, adjust the shooting position and image resolution of the material supply end vision module so that the shooting field of view covers the end area of ​​the material being transported to the processing station. Set the shooting interval to match the material transport speed so that a frame of material supply posture image is captured synchronously when each piece of processed material is transported.

[0019] In this embodiment, the shooting position of the material supply end vision module needs to be adjusted according to the structure of the material conveying device and the size of the material to ensure that the lens can cover the end area of ​​the material conveyed to the processing station, that is, the area where the material is about to enter the processing station. By adjusting the horizontal and vertical positions of the material supply end vision module, the material to be processed is positioned appropriately in the image, facilitating subsequent analysis of its posture. At the same time, the image resolution is adjusted to obtain a sufficiently clear image of the material, such as the material's outline and markings, so that these features can be accurately identified. The shooting interval needs to be matched with the material conveying speed. If the material conveying device conveys one material per second, the shooting interval is set to 1 second. In this way, the material supply end vision module will capture one frame of image for each material conveyed, recording the material's deflection angle, position offset, and other posture information.

[0020] Step S114: Construct a visual image synchronous transmission mechanism to transmit the image data collected by the top overhead vision module, the side side vision module, and the material supply end vision module to the image processing unit through a dedicated industrial IoT communication link.

[0021] In this embodiment, the visual image synchronization transmission mechanism is built based on the communication protocol of the Industrial Internet of Things (IIoT). First, a unique identifier is assigned to each visual acquisition module to distinguish image data from different modules during transmission. Then, the image data is packaged using the TCP / IP protocol, with each data packet containing pixel information, a timestamp, and module identification. These data packets are transmitted to the image processing unit via Industrial Ethernet to ensure the real-time performance and accuracy of the image data. For example, when transmitting an image of a circuit board's processing area, the data packet contains the image's grayscale matrix, acquisition time, and the identifier of the top-down visual module. After receiving the data packet, the image processing unit can synchronize the image data from different modules based on the timestamp.

[0022] Step S115: Perform timestamp alignment processing on the image data transmitted to the image processing unit, classify the circuit board area images to be processed, the circuit board status images during processing, and the material supply posture images collected at the same processing time into a group, integrate the image data after multiple timestamp alignment, and form a real-time visual image set containing the circuit board area images to be processed, the circuit board status images during processing, and the material supply posture images.

[0023] In this embodiment, after receiving image data from three vision modules, the image processing unit first reads the timestamp of each image data. Then, based on the timestamp, images acquired at the same time are grouped together. For example, image data with timestamp T1 includes an image of the circuit board area to be processed, an image of the circuit board in process, and an image of the material supply posture; these three images are grouped together. Next, the image data from multiple groups with aligned timestamps are integrated to form a real-time visual image set. During the integration process, it is necessary to ensure that the order of the image data is consistent with the chronological order of the processing time to facilitate subsequent feature extraction and analysis. For example, when processing a double-sided circuit board, the real-time visual image set contains images of the circuit board area to be processed, images of the circuit board in process, and images of the material supply posture at different time points. These images are arranged in chronological order, clearly showing the processing of the circuit board.

[0024] Step S120: Perform visual feature extraction processing on the real-time visual image set to obtain the circuit board outline features, circuit board solder joint pre-positioning features, and processing material posture features.

[0025] In this embodiment, the real-time visual image set includes images of the circuit board's processing area, images of the circuit board's status during processing, and images of the material supply posture. By extracting visual features from these images, the key features of the circuit board can be obtained. The visual feature extraction process includes processing different types of images to extract corresponding features. For example, circuit contour features are extracted from the image of the circuit board's processing area, solder joint pre-positioning features are extracted from the image of the circuit board's status during processing, and the processing material posture features are extracted from the image of the material supply posture.

[0026] Step S121: Extract the image of the circuit board to be processed area from the real-time visual image set, perform image preprocessing operation on the image of the circuit board to be processed area to obtain the preprocessed image of the circuit board to be processed area. The image preprocessing operation includes grayscale conversion, Gaussian filtering for noise reduction and edge enhancement processing to enhance the clarity of the outline of the circuit board lines in the image of the circuit board to be processed area.

[0027] In this embodiment, taking the image of the area to be processed on the circuit board as an example, the color image is first converted into a grayscale image. The grayscale conversion method involves calculating the corresponding grayscale value for each pixel based on its RGB value, using the formula: Grayscale value = 0.299 × R + 0.587 × G + 0.114 × B (this is only for illustrative purposes; in actual processing, this calculation is implemented through programming). The converted grayscale image retains the main features of the image and has low processing complexity. Next, Gaussian filtering is performed for noise reduction. By constructing a Gaussian filter and performing a convolution operation on the grayscale image, noise in the image is removed. The template size and standard deviation of the Gaussian filter are adjusted according to the noise level of the image; for example, for images with more noise, the template size and standard deviation can be increased. Finally, edge enhancement processing is performed using edge enhancement algorithms such as the Sobel operator to highlight edge information in the image, making the outline of the circuit board lines clearer. For example, for an image of a circuit board area to be processed that contains noise, after grayscale conversion, the image becomes black and white; then, Gaussian filtering removes speckle noise from the image; finally, edge enhancement processing makes the edges of the circuit more obvious, which facilitates subsequent feature extraction.

[0028] Step S122: Perform image segmentation processing on the preprocessed circuit board area image to be processed, and use a threshold segmentation algorithm to separate the circuit board substrate area and the circuit pattern area to obtain a binary image of the circuit pattern.

[0029] In this embodiment, the preprocessed image of the circuit board's processing area undergoes image segmentation processing, which can separate the substrate area and the circuit pattern area of ​​the circuit board. Threshold segmentation algorithm is a commonly used image segmentation method. By determining a suitable threshold, the pixels in the image are divided into two categories, corresponding to the substrate area and the circuit pattern area, respectively.

[0030] Step S1221: Perform grayscale histogram statistics on the preprocessed circuit board image to be processed, obtain the distribution frequency of grayscale values ​​in the image to be processed, and determine the two peak regions of grayscale value distribution, which correspond to the grayscale range of the circuit board substrate area and the grayscale range of the circuit pattern area, respectively.

[0031] In this embodiment, a grayscale histogram is plotted on the preprocessed image of the circuit board's processing area to determine the frequency of each grayscale value. By plotting the grayscale histogram, the distribution of grayscale values ​​in the image can be observed. Typically, the grayscale value distribution in the circuit board substrate area and the circuit pattern area forms two peak regions: the substrate area has lower grayscale values, while the circuit pattern area has higher grayscale values. For example, in the grayscale histogram, the grayscale values ​​in the substrate area are concentrated between 0 and 50, while the grayscale values ​​in the circuit pattern area are concentrated between 150 and 200; these two regions are the two peak regions of the grayscale value distribution.

[0032] Step S1222: Calculate the optimal threshold for image segmentation using the maximum inter-class variance algorithm. The optimal threshold is the gray value that maximizes the inter-class variance between the substrate region and the circuit pattern region.

[0033] In this embodiment, the principle of the maximum inter-class variance algorithm is to calculate the inter-class variance under different gray-level thresholds and find the threshold that maximizes the inter-class variance. The formula for calculating the inter-class variance is: σ²=w0×(μ0-μ)²+w1×(μ1-μ)², where w0 and w1 are the pixel ratios of the substrate region and the circuit pattern region, respectively, μ0 and μ1 are the gray-level averages of the substrate region and the circuit pattern region, respectively, and μ is the gray-level average of the entire image. By traversing all possible gray-level thresholds, calculating the corresponding inter-class variances, and finding the threshold corresponding to the maximum value, the optimal threshold is obtained.

[0034] Step S1223: Compare the gray value of each pixel in the preprocessed circuit board area image with the optimal threshold. If the pixel gray value is greater than the optimal threshold, mark the pixel as a circuit pattern area pixel and assign it a preset foreground pixel value. If the pixel gray value is less than or equal to the optimal threshold, mark the pixel as a circuit board substrate area pixel and assign it a preset background pixel value.

[0035] In this embodiment, for each pixel in the preprocessed image of the circuit board's processing area, its grayscale value is compared with an optimal threshold. For example, if the optimal threshold is 100, a pixel with a grayscale value of 120 is marked as a pixel in the circuit pattern area and assigned a value of 255 (foreground pixel value); a pixel with a grayscale value of 80 is marked as a pixel in the circuit board substrate area and assigned a value of 0 (background pixel value). In this way, the image is segmented into a black-and-white binary image, with the circuit pattern area being white and the substrate area being black.

[0036] Step S1224: Perform noise removal processing on the marked circuit board area image to be processed, perform edge smoothing processing on the processed circuit board area image to be processed, and save the circuit board area image to be processed as a binary image of the circuit pattern.

[0037] In this embodiment, the marked image of the circuit board area to be processed may contain some noise points, such as isolated white or black pixels. Morphological operations, such as opening and closing operations, are used to remove these noise points. Opening operations first perform erosion, then dilation, to remove small white noise points; closing operations first perform dilation, then erosion, to remove small black noise points. Then, the processed image undergoes edge smoothing using methods such as median filtering to make the edges smoother, facilitating subsequent contour extraction. Finally, the processed image is saved as a binary image of the circuit pattern for subsequent circuit contour feature extraction.

[0038] Step S123: In the binary image of the circuit pattern, the continuous edge pixels of the circuit board are extracted using a contour extraction algorithm, the coordinate sequence of the edge pixels is calculated, and the geometric contour parameters of the circuit board are generated based on the coordinate sequence. The geometric contour parameters include the line width, line spacing and line corner curvature. The line width, line spacing and line corner curvature are integrated to form the circuit board contour features.

[0039] In this embodiment, the circuit pattern area in the binary image of the circuit pattern is white, and the substrate area is black. A contour extraction algorithm, such as a contour tracking algorithm based on pixel connectivity, is used to extract continuous edge pixels of the circuit board. For example, starting from the top left corner of the image, pixels are scanned row by row. When a white pixel is encountered, its coordinates are recorded, and its adjacent white pixels are tracked to form continuous edges. Then, the coordinate sequence of these edge pixels is calculated, and the geometric contour parameters of the circuit are calculated by analyzing the coordinate sequence. The circuit width is calculated by measuring the distance between adjacent edge pixels in the direction perpendicular to the circuit direction; the circuit spacing is calculated by measuring the distance between edge pixels of two adjacent circuits; the circuit corner curvature is calculated by curve fitting to the edge pixels at the circuit corner and calculating the curvature of the fitted curve. These parameters are integrated to form the circuit board contour features, which are stored in the form of an array or vector, containing information on the width, spacing, and corner curvature of multiple circuits.

[0040] Step S124: Extract the image of the circuit board to be processed area from the real-time visual image set, perform region of interest extraction processing on the image of the circuit board to be processed area, and determine the pre-positioning area of ​​solder joints in the image of the circuit board to be processed area based on the preset solder joint layout rules.

[0041] In this embodiment, the preset solder joint layout rules are determined according to the design requirements of the circuit board, such as the position, spacing, and arrangement of the solder joints. First, the image of the area to be processed on the circuit board is analyzed to identify key locations such as the intersections and endpoints of the lines; these locations are usually candidate areas for solder joints. Then, according to the solder joint layout rules, regions of interest are determined around these candidate areas. The size and shape of the regions of interest are adjusted according to the size and distribution of the solder joints. For example, for a circular solder joint, the region of interest is a square with a side length slightly larger than the diameter of the solder joint, and its center coincides with the center of the candidate area for the solder joint.

[0042] Step S125: Perform feature enhancement processing on the pre-positioning area of ​​the solder joint, use the morphological dilation algorithm to enhance the texture features of the pre-positioning area of ​​the solder joint, and use the Harris corner detection algorithm to identify the feature corners in the pre-positioning area of ​​the solder joint.

[0043] In this embodiment, the morphological dilation algorithm enhances the texture features of the pre-positioned solder joint region by expanding its pixel range. The size and shape of the structuring element in the dilation operation are adjusted according to the size of the solder joint; for example, a 3×3 square structuring element is used for small solder joints. The dilated pre-positioned solder joint region is more prominent, facilitating subsequent corner detection. Then, the Harris corner detection algorithm is used to detect corners in the pre-positioned solder joint region. The Harris corner detection algorithm determines the corner positions by calculating the grayscale change matrix of the pixels. For example, for a pre-positioned solder joint region, the corner detection algorithm identifies the corners at its edges, and the coordinates of these corners are used for subsequent spatial position parameter calculations.

[0044] Step S126: Calculate the distance and angle relationship between feature corner points to generate spatial position parameters of the solder joint pre-positioning area. The spatial position parameters include the solder joint center coordinates, solder joint distribution density, and connection deviation between the solder joint and the circuit. Integrate the solder joint center coordinates, solder joint distribution density, and connection deviation between the solder joint and the circuit to form the circuit board solder joint pre-positioning feature.

[0045] In this embodiment, firstly, the center coordinates of the solder joints are calculated based on the coordinates of the feature corner points. For example, for a square solder joint pre-location area, the center coordinates are the average of the coordinates of the four corner points. Then, the distribution density of the solder joints, i.e., the number of solder joints per unit area, is calculated by statistically analyzing the number of solder joints within the region of interest and the area of ​​the region of interest. Finally, the connection deviation between the solder joints and the circuit is calculated, i.e., the distance between the center coordinates of the solder joint and the intersection coordinates of the circuit line. Integrating these parameters forms the circuit board solder joint pre-location feature, which includes information on the position, density, and connection quality of the solder joints.

[0046] Step S127: Extract the material supply posture image from the real-time visual image set, perform posture feature extraction processing on the material supply posture image, and use the background difference algorithm to separate the processing material area and the background area in the material supply posture image to obtain the processing material foreground image.

[0047] In this embodiment, the background difference algorithm separates the material processing area by differentiating the material supply posture image and the background image. The background image is an image captured when there is no material and stored in the image processing unit. First, the current material supply posture image and background image are read and converted into grayscale images. Then, a difference operation is performed on the two images to obtain a difference image. In the difference image, the pixel values ​​of the material area and the background area differ significantly. By setting a threshold, the material area and the background area are separated to obtain the foreground image of the processed material. For example, when the pixel value of the difference image is greater than the threshold, it is marked as a pixel of the material area; otherwise, it is marked as a pixel of the background area.

[0048] Step S128: Perform contour fitting processing on the foreground image of the processed material to generate the outer rectangle of the processed material, calculate the angle between the major axis direction of the outer rectangle and the preset reference direction, and determine the deflection angle parameter of the processed material.

[0049] Step S1281: Perform contour detection processing on the foreground image of the processed material, use the chain code tracking algorithm to extract the sequence of outer contour pixels of the foreground region of the processed material, and record the coordinate information of each contour pixel.

[0050] In this embodiment, taking the material supply posture image of processing a small patch element as an example, the foreground image of the processing material shows the shape of the patch element. The chain code tracking algorithm starts from the top left corner of the image and scans the pixels in a clockwise or counterclockwise direction. When it encounters a pixel belonging to the foreground area of ​​the processing material, it records its coordinate information and continues to track adjacent foreground pixels until it returns to the starting point, forming a closed outer contour pixel sequence. For example, for a rectangular patch element, the outer contour pixel sequence will contain the pixel coordinates of its four vertices and four sides. This coordinate information will be used for subsequent outer bounding box generation.

[0051] Step S1282: Based on the contour pixel sequence, the minimum bounding rectangle algorithm is used to generate the minimum bounding rectangle of the processed material. The minimum bounding rectangle is the rectangle that can completely surround the foreground area of ​​the processed material and has the smallest area.

[0052] In this embodiment, based on the extracted sequence of outer contour pixels, the extreme values ​​of these pixels' coordinates are calculated, namely the maximum and minimum values ​​of the x-coordinate and the y-coordinate. Then, by rotating the rectangles formed by these extreme points, the rectangle with the smallest area is found; this rectangle is the minimum bounding rectangle. For example, for a tilted rectangular patch element, the original extreme point rectangle may have a large area. By rotating it by a certain angle, the resulting minimum bounding rectangle can more tightly surround the patch element and has the smallest area.

[0053] Step S1283: Extract the coordinates of the four vertices of the minimum bounding rectangle, calculate the distance between adjacent vertices, and determine the length of the long side and the length of the short side of the minimum bounding rectangle. The side corresponding to the length of the long side is the major axis of the bounding rectangle.

[0054] In this embodiment, the coordinates of the four vertices of the minimum bounding rectangle are known. The lengths of the four sides are obtained by calculating the Euclidean distance between adjacent vertices. Comparing the lengths of these four sides, the side corresponding to the longer of the two longest sides is the major axis of the bounding rectangle, and the other is the minor axis. For example, for a rectangular patch element, the length of the major axis of its minimum bounding rectangle is significantly greater than the length of the minor axis. The major and minor axes can be accurately distinguished by calculating the distance between adjacent vertices.

[0055] Step S1284: Set a preset reference direction in the image coordinate system. The preset reference direction is a direction parallel to the feed direction of the circuit board processing equipment. Record the angle value of the preset reference direction.

[0056] In this embodiment, the feed direction of the circuit board processing equipment is typically horizontal (i.e., the positive x-axis direction in the image coordinate system). Therefore, the preset reference direction angle is 0 degrees (assuming the positive x-axis direction is 0 degrees, the counterclockwise rotation angle increases). This angle value will serve as the reference for subsequently calculating the angle between the major axis of the circumscribed rectangle and the preset reference direction.

[0057] Step S1285: Calculate the angle between the major axis of the circumscribed rectangle and the preset reference direction. If the major axis deviates clockwise relative to the preset reference direction, the angle is recorded as a positive value; if the major axis deviates counterclockwise relative to the preset reference direction, the angle is recorded as a negative value.

[0058] In this embodiment, the direction vector of the major axis is calculated based on the coordinates of the two endpoints of the major axis of the minimum bounding rectangle. Then, the angle between this direction vector and the preset reference direction (positive x-axis direction) is calculated. For example, if the direction vector of the major axis is (x2-x1, y2-y1) and the vector of the preset reference direction is (1, 0), the cosine value of the angle is calculated using the dot product formula of vectors, and then the magnitude of the angle is obtained using the arcsine or arctangent function. If the major axis direction deviates clockwise relative to the preset reference direction, the angle is recorded as a positive value; if it deviates counterclockwise, it is recorded as a negative value.

[0059] Step S1286: Perform precision calibration on the calculated included angle value, use a sub-pixel level edge detection algorithm to reposition the contour pixels, correct the vertex coordinates of the circumscribed rectangle, and then recalculate the included angle value to ensure that the accuracy of the deflection angle parameter meets the requirements of circuit board processing.

[0060] In this embodiment, to improve the accuracy of the included angle value, a sub-pixel-level edge detection algorithm is used to reposition the contour pixels. The sub-pixel-level edge detection algorithm can more accurately determine the position of pixels, achieving sub-pixel-level accuracy. By correcting the vertex coordinates of the circumscribed rectangle and recalculating the angle between the major axis and the preset reference direction, the accuracy of the deflection angle parameter meets the requirements of circuit board processing, for example, achieving an accuracy within 0.1 degrees.

[0061] Step S1287: Determine the calibrated included angle value as the deflection angle parameter of the processed material.

[0062] In this embodiment, the included angle value after precision calibration is the deflection angle parameter of the processed material, which will be used for subsequent material supply angle adjustment. For example, if the calibrated included angle value is 3 degrees, it means that the processed material has deflected 3 degrees clockwise relative to the preset reference direction, and the subsequent material conveying roller deflection angle adjustment will be based on this parameter.

[0063] Step S129: Obtain the centroid coordinates of the foreground image of the processed material using the image moment calculation method, compare the deviation between the centroid coordinates and the preset material supply reference coordinates, determine the position offset parameters of the processed material, and integrate the deflection angle parameters and the position offset parameters to form the attitude characteristics of the processed material.

[0064] In this embodiment, the image moment calculation method determines the centroid coordinates by calculating the pixel moments of the foreground image of the processed material. The pixel moment calculation includes the zero-order moment, the first-order moment, etc. The formula for calculating the centroid coordinates is: centroid x-coordinate = first-order moment M10 / zero-order moment M00, centroid y-coordinate = first-order moment M01 / zero-order moment M00. The preset material supply reference coordinates are determined based on the outlet position of the material supply device and the processing position of the circuit board. For example, the reference coordinates are (x0, y0). The calculated centroid coordinates (x, y) are compared with the reference coordinates (x0, y0) to obtain the position offset parameters: Δx = x - x0, Δy = y - y0. The deflection angle parameter and the position offset parameter are integrated to form the processing material posture feature. This processing material posture feature is stored in the form of a vector, containing deflection angle, x-direction offset, and y-direction offset information.

[0065] Step S130: Match the circuit board outline features, circuit board solder joint pre-positioning features, and processing material posture features with the preset circuit board processing visual standard features to generate visual feature deviation information.

[0066] In this embodiment, the preset visual standard features for circuit board processing are formulated based on the design requirements and processing technology of the circuit board, and include standard features for circuit board outlines, standard features for circuit board solder joint pre-positioning, and standard features for the posture of processed materials. The standard features for circuit board outlines include standard line width, standard line spacing, and standard line corner curvature; the standard features for circuit board solder joint pre-positioning include standard solder joint center coordinates, standard solder joint distribution density, and standard solder joint-to-line connection deviation; the standard features for the posture of processed materials include standard material deflection angle and standard material position offset. The extracted circuit board outline features, circuit board solder joint pre-positioning features, and processed material posture features are compared with their corresponding standard features, and the deviations between them are calculated to generate visual feature deviation information.

[0067] Step S131: Retrieve preset visual standard features for circuit board processing. The preset visual standard features for circuit board processing include standard features for circuit board outline, standard features for circuit board solder joint pre-positioning, and standard features for the posture of processed materials.

[0068] In this embodiment, preset visual standard features for circuit board processing are stored in the database of the image processing unit. The standard features for circuit board line contours are determined based on the circuit board design drawings; for example, the standard line width is a specific value, the standard line spacing is a specific value, and the standard line corner radius is a specific value. The standard features for circuit board solder joint pre-positioning are determined based on the solder joint layout requirements; for example, the standard solder joint center coordinates are specific coordinates, the standard solder joint distribution density is a specific value, and the standard solder joint connection deviation from the line is a specific range. The standard features for the processing material posture are determined based on the material supply device's discharge requirements and the circuit board's processing position; for example, the standard material deflection angle is 0 degrees, and the standard material position offset is (0, 0).

[0069] Step S132: Compare the line width in the extracted circuit board line contour features with the standard line width in the circuit board line contour standard features, calculate the difference between the two, and obtain the line width deviation value.

[0070] In this embodiment, the line width in the circuit board's line outline features is an array containing the width values ​​of multiple lines. The standard line width is also an array, corresponding one-to-one with the line width array. For each line's width value, the difference between it and the corresponding standard line width value is calculated to obtain the line width deviation value. For example, if a line's width value is w1 and the standard line width value is w0, the line width deviation value is Δw = w1 - w0.

[0071] Step S133: Compare the line spacing in the extracted circuit board line contour features with the standard line spacing in the circuit board line contour standard features, calculate the difference between the two, and obtain the line spacing deviation value.

[0072] In this embodiment, the line spacing in the circuit board's line outline features is an array containing the spacing values ​​between multiple lines. The standard line spacing is also an array, corresponding one-to-one with the line spacing array. For each line spacing value, the difference between it and the corresponding standard line spacing value is calculated to obtain the line spacing deviation value. For example, if the spacing between two lines is d1 and the standard line spacing is d0, the line spacing deviation value is Δd = d1 - d0.

[0073] Step S134: Compare the arc of the line corner in the extracted circuit board line contour features with the arc of the standard line corner in the standard features of the circuit board line contour, calculate the difference between the two, and obtain the line corner arc deviation value.

[0074] In this embodiment, the arc radius of the circuit board's circuit outline features is an array containing multiple arc radius values ​​for circuit corners. The standard arc radius of the circuit corner is also an array, corresponding one-to-one with the array of arc radius values. For each arc radius value, the difference between it and the corresponding standard arc radius value is calculated to obtain the arc radius deviation value. For example, if the arc radius value of a certain circuit corner is θ1, and the standard arc radius value is θ0, the arc radius deviation value is Δθ = θ1 - θ0.

[0075] Step S135: Compare the center coordinates of the solder joints in the extracted circuit board solder joint pre-positioning features with the center coordinates of the standard solder joints in the circuit board solder joint pre-positioning standard features, calculate the coordinate difference between the two in the X-axis and Y-axis directions, and obtain the center coordinate deviation value of the solder joints.

[0076] In this embodiment, the solder joint center coordinates in the circuit board solder joint pre-positioning feature are an array containing the center coordinates (x1, y1) of multiple solder joints. The standard solder joint center coordinates are also an array containing the standard center coordinates (x0, y0) of multiple solder joints. For each solder joint center coordinate, the difference between its center coordinate and the corresponding standard center coordinate in the X and Y axes is calculated to obtain the solder joint center coordinate deviation value (Δx, Δy). For example, if the center coordinates of a solder joint are (x1, y1) and the standard center coordinates are (x0, y0), then the solder joint center coordinate deviation value is (Δx = x1 - x0, Δy = y1 - y0).

[0077] Step S136: Compare the solder joint distribution density in the extracted circuit board solder joint pre-positioning features with the standard solder joint distribution density in the circuit board solder joint pre-positioning standard features, calculate the difference between the two, and obtain the solder joint distribution density deviation value.

[0078] In this embodiment, the solder joint distribution density in the circuit board solder joint pre-positioning feature is a numerical value representing the number of solder joints per unit area. The standard solder joint distribution density is also a numerical value representing the number of standard solder joints per unit area. The difference between the two is calculated to obtain the solder joint distribution density deviation value. For example, if the solder joint distribution density value is ρ1 and the standard solder joint distribution density value is ρ0, the solder joint distribution density deviation value is Δρ = ρ1 - ρ0.

[0079] Step S137: Compare the solder joint and circuit connection deviation in the extracted circuit board solder joint pre-positioning features with the standard solder joint and circuit connection deviation in the circuit board solder joint pre-positioning standard features, calculate the difference between the two, and obtain the solder joint connection deviation value.

[0080] In this embodiment, the solder joint connection deviation in the circuit board solder joint pre-positioning feature is a numerical value, representing the deviation in the connection quality between the solder joint and the circuit. The standard solder joint connection deviation is also a numerical value, representing the standard connection quality deviation. The difference between the two is calculated to obtain the solder joint connection deviation value. For example, if the solder joint connection deviation value is ε1 and the standard solder joint connection deviation value is ε0, the solder joint connection deviation value is Δε = ε1 - ε0.

[0081] Step S138: Compare the material deflection angle in the extracted processing material posture features with the standard material deflection angle in the processing material posture standard features, calculate the difference between the two, and obtain the material deflection angle deviation value.

[0082] In this embodiment, the material deflection angle in the processed material posture characteristics is a numerical value representing the deflection angle of the processed material. The standard material deflection angle is also a numerical value representing the standard deflection angle. The difference between the two is calculated to obtain the material deflection angle deviation value. For example, if the material deflection angle value is α1 and the standard material deflection angle value is α0, the material deflection angle deviation value is Δα = α1 - α0.

[0083] Step S139: Compare the material position offset in the extracted processing material posture features with the standard material position offset in the processing material posture standard features, calculate the offset difference between the two in the X-axis and Y-axis directions, and obtain the material position offset deviation value.

[0084] In this embodiment, the material position offset in the processed material posture characteristics is a vector containing offset values ​​(Δx1, Δy1) in the X-axis and Y-axis directions. The standard material position offset is also a vector containing standard offset values ​​(Δx0, Δy0) in the X-axis and Y-axis directions. The difference between the two in the X-axis and Y-axis directions is calculated to obtain the material position offset deviation value (Δx'=Δx1-Δx0, Δy'=Δy1-Δy0).

[0085] Step S1310: Integrate the deviation values ​​of line width, line spacing, line corner curvature, weld center coordinates, weld distribution density, weld connection, material deflection angle, and material position offset to form visual feature deviation information.

[0086] In this embodiment, the various deviation values ​​calculated above are integrated together to form visual feature deviation information. The visual feature deviation information is stored in the form of a structure or array, including line width deviation value, line spacing deviation value, line corner curvature deviation value, solder joint center coordinate deviation value, solder joint distribution density deviation value, solder joint connection deviation value, material deflection angle deviation value, and material position offset deviation value. The above deviation values ​​will be used for subsequent dynamic adjustment parameter derivation.

[0087] Step S140: Based on the visual feature deviation information and combined with the circuit board processing requirements, deduce the dynamic adjustment parameters of the circuit board processing equipment. The dynamic adjustment parameters include the equipment processing head position adjustment parameters, the equipment processing force adjustment parameters, and the material supply angle adjustment parameters.

[0088] In this embodiment, the circuit board processing requirements specify the processing accuracy, processing force, and material supply posture of the circuit board. Based on visual feature deviation information, the impact of each deviation on processing quality is analyzed, and dynamic adjustment parameters of the circuit board processing equipment are derived. For example, when the line width deviation is large, the position of the processing head needs to be adjusted; when the solder joint connection deviation is large, the processing force needs to be adjusted; when the material position offset deviation is large, the material supply angle needs to be adjusted.

[0089] Step S141: Retrieve the circuit board processing technology requirements document and extract the precision requirements, processing force requirements, and material supply posture requirements for circuit board processing from the circuit board processing technology requirements document.

[0090] In this embodiment, the circuit board processing requirements document is stored in the control system of the processing equipment. Accuracy requirements include tolerances for line width, spacing, and corner curvature; processing force requirements include the downward pressure of the processing head and dwell time; material supply posture requirements include the deflection angle tolerance and position offset tolerance of the material. For example, the accuracy requirements specify a line width tolerance of ±0.1mm, the processing force requirements specify a downward pressure of 5N-10N for the processing head, and the material supply posture requirements specify a deflection angle tolerance of ±1 degree.

[0091] Step S142: Analyze the impact of line width deviation, line spacing deviation, and line corner curvature deviation values ​​in the visual feature deviation information on the circuit board processing accuracy, and determine the type of equipment processing head position parameters that need to be adjusted. The equipment processing head position parameter types include the X-axis position, Y-axis position, and Z-axis position of the processing head.

[0092] In this embodiment, the line width deviation, line spacing deviation, and line corner curvature deviation reflect the machining accuracy deviation of the circuit board lines. The line width deviation mainly affects the position of the machining head in the X-axis direction, the line spacing deviation mainly affects the position of the machining head in the Y-axis direction, and the line corner curvature deviation mainly affects the position (machining depth) of the machining head in the Z-axis direction. Therefore, the types of machining head position parameters that need to be adjusted include the machining head X-axis position, machining head Y-axis position, and machining head Z-axis position.

[0093] Step S143: Based on the preset pixel equivalent conversion coefficient, convert the line width deviation value from pixel units to physical length units to obtain the physical line width deviation value; calculate the X-axis position adjustment amount of the processing head according to the physical line width deviation value. If the physical line width deviation value is positive, the processing head needs to be adjusted in the negative X-axis direction, and the adjustment amount is positively correlated with the physical line width deviation value; if the physical line width deviation value is negative, the processing head needs to be adjusted in the positive X-axis direction, and the adjustment amount is positively correlated with the absolute value of the physical line width deviation value.

[0094] Step S1431: Retrieve the position adjustment accuracy document of the processing head of the circuit board processing equipment, determine the minimum unit value of the X-axis position adjustment of the processing head, and make the calculated adjustment amount an integer multiple of the minimum unit value.

[0095] In this embodiment, the document specifying the minimum unit value for the X-axis position adjustment of the processing head in the PCB processing equipment is 0.01mm. This means that the calculated X-axis position adjustment amount of the processing head must be an integer multiple of 0.01mm to ensure that the position adjustment of the processing head can be executed accurately.

[0096] Step S1432: Establish a correlation model between the physical line width deviation value and the X-axis position adjustment amount of the processing head. The correlation model takes the physical line width deviation value as input and the X-axis position adjustment amount of the processing head as output.

[0097] In this embodiment, the correlation model is established based on a large amount of experimental data and theoretical analysis. By testing and recording the adjustment amount of the processing head's X-axis position under different physical line width deviation values, regression analysis and other methods are used to establish the mathematical relationship between the two. For example, the correlation model can be expressed as: Processing head X-axis position adjustment amount = k × physical line width deviation value + b, where k is the proportionality coefficient and b is the intercept. These parameters are obtained by fitting experimental data.

[0098] Step S1433: If the physical line width deviation value is positive, substitute the physical line width deviation value into the correlation model. The correlation model determines the adjustment coefficient based on the magnitude of the physical line width deviation value. The larger the physical line width deviation value, the larger the adjustment coefficient. The adjustment amount is determined by the product relationship between the physical line width deviation value and the adjustment coefficient.

[0099] In this embodiment, when the physical line width deviation is positive, it indicates that the actual line width is greater than the standard value, and the processing head needs to be adjusted in the negative X-axis direction. The adjustment coefficient in the correlation model is adjusted according to the magnitude of the physical line width deviation; the larger the deviation, the larger the adjustment coefficient, to ensure that the adjustment amount can effectively correct the line width deviation. For example, if the physical line width deviation is 0.2mm and the adjustment coefficient is 0.8, then the X-axis position adjustment of the processing head is 0.2mm × 0.8 = 0.16mm.

[0100] Step S1434: Perform precision calibration on the calculated adjustment amount. If the adjustment amount is not an integer multiple of the minimum unit value, round it up or down. The rounded value will maximize the reduction of the physical line width deviation value after adjustment.

[0101] In this embodiment, the calculated adjustment amount of the machining head's X-axis position needs to be calibrated for accuracy. For example, if the calculated adjustment amount is 0.165mm, and the minimum unit value is 0.01mm, then 0.165mm needs to be rounded up to 0.17mm or down to 0.16mm. By analyzing the change in the physical line width deviation value after adjustment, the rounding method that maximizes the reduction in deviation value is selected. If rounding up results in a greater reduction in the adjusted physical line width deviation value, then rounding up is selected.

[0102] Step S1435: If the physical line width deviation value is a certain physical length unit value, the adjustment coefficient is a preset adjustment coefficient value, and the minimum unit value is the equipment coordinate value corresponding to the preset physical length unit, then the adjustment amount is determined by the product relationship between the physical length unit value and the preset adjustment coefficient value. If the result is an integer multiple of the minimum unit value, then no rounding is required.

[0103] In this embodiment, it is assumed that the physical line width deviation is 0.2mm (physical length unit value), the preset adjustment coefficient is 0.8, and the minimum unit value is 0.01mm (the device coordinate value corresponding to the preset physical length unit). The adjustment amount = 0.2mm × 0.8 = 0.16mm. This result is an integer multiple (16 times) of 0.01mm, so there is no need to perform a rounding operation, and 0.16mm is directly used as the adjustment amount of the X-axis position of the processing head.

[0104] Step S1436: If the physical line width deviation value is a certain physical length unit value, the adjustment coefficient is a preset adjustment coefficient value, the minimum unit value is the equipment coordinate value corresponding to the preset physical length unit, and the calculated adjustment amount is not an integer multiple of the minimum unit value, then round up to maximize the reduction of the physical line width deviation value after adjustment.

[0105] In this embodiment, assuming the physical line width deviation is 0.21mm, the preset adjustment coefficient is 0.8, and the minimum unit is 0.01mm, the adjustment amount is 0.21mm × 0.8 = 0.168mm, which is not an integer multiple of 0.01mm. Rounding up to 0.17mm, the adjusted physical line width deviation is 0.21mm - 0.17mm = 0.04mm; rounding down to 0.16mm, the adjusted physical line width deviation is 0.21mm - 0.16mm = 0.05mm. Clearly, rounding up results in a greater reduction in deviation; therefore, rounding up to 0.17mm is chosen.

[0106] Step S1437: If the physical line width deviation value is negative, take the absolute value of the physical line width deviation value and substitute it into the correlation model. The calculation method is the same as for positive deviation, and the adjustment direction is changed to the positive X-axis direction.

[0107] In this embodiment, when the physical line width deviation is negative, it indicates that the actual line width is less than the standard value, and the processing head needs to be adjusted in the positive X-axis direction. In this case, the absolute value of the physical line width deviation is substituted into the correlation model, and the calculated adjustment amount is the same as that calculated for positive deviations, but the adjustment direction is changed to the positive X-axis direction. For example, if the physical line width deviation is -0.2mm, its absolute value is 0.2mm. Substituting this into the correlation model yields an adjustment amount of 0.16mm, and the adjustment direction is the positive X-axis direction, meaning the processing head is adjusted 0.16mm in the positive X-axis direction.

[0108] Step S1438: Verify the calibrated adjustment amount, substitute the adjustment amount into the line width deviation prediction formula, predict the adjusted physical line width deviation value, and if the predicted adjusted physical line width deviation value is within the allowable range of the process requirements, then determine the adjustment amount as the final X-axis position adjustment amount of the processing head.

[0109] In this embodiment, the formula for predicting the physical line width deviation is: Adjusted physical line width deviation = Original physical line width deviation - Machining head X-axis position adjustment (when the adjustment direction is the negative X-axis direction) or Adjusted physical line width deviation = Original physical line width deviation + Machining head X-axis position adjustment (when the adjustment direction is the positive X-axis direction). Substituting the calibrated adjustment amount into this formula, the adjusted physical line width deviation is predicted. If the predicted value is within the allowable range of the process requirements, for example, ±0.05mm, then this adjustment amount is determined as the final machining head X-axis position adjustment amount; if it is not within the allowable range, the adjustment coefficient needs to be readjusted and the adjustment amount recalculated until the predicted value meets the process requirements.

[0110] Step S1439: If the predicted deviation value of the adjusted physical line width exceeds the allowable range of the process requirements, increase the adjustment coefficient and recalculate the adjustment amount until the predicted deviation value of the adjusted physical line width meets the process requirements.

[0111] In this embodiment, if the predicted deviation of the adjusted physical line width exceeds the allowable range of the process requirements, it indicates that the adjustment coefficient is too small and needs to be increased. For example, if the original adjustment coefficient is 0.8 and the predicted deviation is 0.06mm, exceeding the allowable range of ±0.05mm, then the adjustment coefficient is increased to 0.9, and the adjustment amount is recalculated as 0.2mm × 0.9 = 0.18mm. The adjusted deviation is 0.2mm - 0.18mm = 0.02mm, which is within the allowable range. Therefore, the adjustment coefficient is determined to be 0.9, and the adjustment amount is 0.18mm.

[0112] Step S144: Based on the preset pixel equivalent conversion coefficient, convert the line spacing deviation value from pixel units to physical length units to obtain the physical line spacing deviation value; calculate the Y-axis position adjustment amount of the processing head according to the physical line spacing deviation value. If the physical line spacing deviation value is positive, the processing head needs to be adjusted in the negative Y-axis direction, and the adjustment amount is positively correlated with the physical line spacing deviation value; if the physical line spacing deviation value is negative, the processing head needs to be adjusted in the positive Y-axis direction, and the adjustment amount is positively correlated with the absolute value of the physical line spacing deviation value.

[0113] In this embodiment, the conversion and adjustment method for the line spacing deviation value is similar to that for the line width deviation value. The line spacing deviation value (in pixels) is multiplied by a pixel equivalent conversion factor to obtain the physical line spacing deviation value (in mm). Then, based on the sign and magnitude of the physical line spacing deviation value, the adjustment amount for the processing head's Y-axis position is calculated. For example, if the physical line spacing deviation value is +0.3mm, the processing head needs to be adjusted in the negative Y-axis direction by an adjustment amount of 0.3mm; if the physical line spacing deviation value is -0.2mm, the processing head needs to be adjusted in the positive Y-axis direction by an adjustment amount of 0.2mm.

[0114] Step S145: Calculate the Z-axis position adjustment amount of the processing head based on the line corner curvature deviation value. If the line corner curvature deviation value is positive, the processing head needs to be adjusted in the positive Z-axis direction to reduce the processing depth. The adjustment amount is positively correlated with the line corner curvature deviation value. If the line corner curvature deviation value is negative, the processing head needs to be adjusted in the negative Z-axis direction to increase the processing depth. The adjustment amount is positively correlated with the absolute value of the line corner curvature deviation value.

[0115] In this embodiment, the line corner radii deviation value reflects the curvature deviation of the line corner. A positive value indicates that the curvature of the corner is too large, requiring a reduction in machining depth. Therefore, the machining head is adjusted in the positive Z-axis direction, and the adjustment amount is positively correlated with the line corner radii deviation value. Conversely, a negative value indicates that the curvature of the corner is too small, requiring an increase in machining depth. Therefore, the machining head is adjusted in the negative Z-axis direction, and the adjustment amount is positively correlated with the absolute value of the line corner radii deviation value. For example, a line corner radii deviation of +0.1 rad corresponds to an adjustment of 0.1 mm (assuming 1 rad corresponds to 1 mm); a line corner radii deviation of -0.05 rad corresponds to an adjustment of 0.05 mm.

[0116] Step S146: Integrate the X-axis position adjustment amount, Y-axis position adjustment amount, and Z-axis position adjustment amount of the machining head to form the machining head position adjustment parameters.

[0117] In this embodiment, the X-axis, Y-axis, and Z-axis position adjustment amounts of the machining head are integrated to form the machining head position adjustment parameters. These parameters are stored as a vector, containing the X-axis, Y-axis, and Z-axis adjustment amounts. For example, if the X-axis adjustment is -0.2mm, the Y-axis adjustment is +0.3mm, and the Z-axis adjustment is +0.1mm, the machining head position adjustment parameters are (-0.2, +0.3, +0.1).

[0118] Step S147: Analyze the impact of the weld center coordinate deviation value, weld distribution density deviation value, and weld connection deviation value in the visual feature deviation information on the weld processing quality. Combined with the processing force requirements in the processing process requirements, determine the type of equipment processing force parameter that needs to be adjusted. The type of equipment processing force parameter includes the processing head downward pressure and the processing head dwell time.

[0119] In this embodiment, the deviation values ​​of the weld point center coordinates, weld point distribution density, and weld point connection reflect the machining quality deviations of the weld points. When the weld point center coordinate deviation is large, the downward pressure of the machining head needs to be adjusted to ensure a firm weld connection; when the weld point distribution density deviation is large, the dwell time of the machining head needs to be adjusted to improve machining efficiency or ensure weld quality; when the weld point connection deviation is large, the machining force also needs to be adjusted. Therefore, the types of machining force parameters that need to be adjusted include the downward pressure of the machining head and the dwell time of the machining head.

[0120] Step S148: Based on the preset pixel equivalent conversion coefficient, convert the solder joint center coordinate deviation value from pixel units to physical length units to obtain the physical solder joint center coordinate deviation value; calculate the adjustment amount of the processing head's downward pressure based on the physical solder joint center coordinate deviation value. If the physical solder joint center coordinate deviation value exceeds the preset solder joint coordinate deviation threshold, the processing head's downward pressure needs to be increased to ensure a firm solder joint connection. The adjustment amount is positively correlated with the absolute value of the physical solder joint center coordinate deviation value. If the physical solder joint center coordinate deviation value does not exceed the preset solder joint coordinate deviation threshold, the processing head's downward pressure needs to be maintained or decreased to avoid damage to the circuit board substrate. The adjustment amount is negatively correlated with the absolute value of the physical solder joint center coordinate deviation value.

[0121] In this embodiment, the physical solder joint center coordinate deviation value (in pixels) is obtained by multiplying the solder joint center coordinate deviation value by the pixel equivalent conversion factor. The preset solder joint coordinate deviation threshold is determined based on the processing requirements, for example, 0.1 mm. If the physical solder joint center coordinate deviation value exceeds this threshold, it indicates a large solder joint position deviation, requiring an increase in the processing head's downward pressure to ensure a firm solder joint connection. The adjustment amount is positively correlated with the absolute value of the physical solder joint center coordinate deviation value. If it does not exceed this threshold, it indicates a small solder joint position deviation, requiring maintaining or reducing the processing head's downward pressure to avoid damage to the substrate. The adjustment amount is negatively correlated with the absolute value of the physical solder joint center coordinate deviation value. For example, if the physical solder joint center coordinate deviation value is 0.2 mm (exceeding the threshold), the adjustment amount is +0.5 N (positively correlated with the absolute value); if the physical solder joint center coordinate deviation value is 0.05 mm (not exceeding the threshold), the adjustment amount is -0.2 N (negatively correlated with the absolute value).

[0122] Step S149: Calculate the adjustment amount of the processing head dwell time based on the solder joint distribution density deviation value. If the solder joint distribution density deviation value is positive, the dwell time of the processing head at each solder joint needs to be shortened to improve processing efficiency. The adjustment amount is positively correlated with the solder joint distribution density deviation value. If the solder joint distribution density deviation value is negative, the dwell time of the processing head at each solder joint needs to be extended to ensure solder joint quality. The adjustment amount is positively correlated with the absolute value of the solder joint distribution density deviation value.

[0123] In this embodiment, the solder joint distribution density deviation reflects the deviation between the number of solder joints and the standard number. A positive value indicates that there are too many solder joints, requiring a shorter dwell time of the processing head at each solder joint to improve processing efficiency; the adjustment amount is positively correlated with the solder joint distribution density deviation value. A negative value indicates that there are too few solder joints, requiring a longer dwell time of the processing head at each solder joint to ensure solder joint quality; the adjustment amount is positively correlated with the absolute value of the solder joint distribution density deviation value. For example, a solder joint distribution density deviation of +0.2 (too many solder joints) requires an adjustment of -0.1s (shortening the dwell time); a deviation of -0.1 (too few solder joints) requires an adjustment of +0.05s (extending the dwell time).

[0124] Step S1410: Integrate the adjustment amount of the processing head downward pressure and the adjustment amount of the processing head dwell time to form the equipment processing force adjustment parameters.

[0125] In this embodiment, the adjustment amount of the machining head downward pressure and the adjustment amount of the machining head dwell time are integrated to form the equipment machining force adjustment parameter. The equipment machining force adjustment parameter is stored in the form of a vector, which includes the adjustment amount of downward pressure and the adjustment amount of dwell time. For example, if the adjustment amount of the machining head downward pressure is +0.5N and the adjustment amount of dwell time is -0.1s, the equipment machining force adjustment parameter is (+0.5, -0.1).

[0126] Step S1411: Analyze the influence of the material deflection angle deviation value and the material position offset deviation value in the visual feature deviation information on the material supply posture. Combined with the material supply posture requirements in the processing technology requirements, determine the type of material supply angle parameter that needs to be adjusted. The material supply angle parameter type includes the material conveying roller deflection angle and the material pushing device tilt angle.

[0127] In this embodiment, the material deflection angle deviation and the material position offset deviation reflect the deviation of the material supply posture. When the material deflection angle deviation is large, the deflection angle of the material conveying roller needs to be adjusted; when the material position offset deviation is large, the tilt angle of the material pushing device needs to be adjusted. Therefore, the types of material supply angle parameters that need to be adjusted include the material conveying roller deflection angle and the tilt angle of the material pushing device.

[0128] Step S1412: Calculate the adjustment amount of the material conveying roller deflection angle based on the material deflection angle deviation value. If the material deflection angle deviation value is positive, the material conveying roller needs to be adjusted counterclockwise. The adjustment amount is positively correlated with the material deflection angle deviation value. If the material deflection angle deviation value is negative, the material conveying roller needs to be adjusted clockwise. The adjustment amount is positively correlated with the absolute value of the material deflection angle deviation value.

[0129] In this embodiment, the sign of the material deflection angle deviation determines the adjustment direction of the material conveying roller. A positive deviation indicates a large deflection angle (relative to a preset reference direction), requiring the material conveying roller to be adjusted counter-clockwise; the adjustment amount is positively correlated with the deviation value. A negative deviation indicates a small deflection angle, requiring the material conveying roller to be adjusted clockwise; the adjustment amount is positively correlated with the absolute value of the deviation. For example, a deviation of +1 degree requires an adjustment of +1 degree (1 degree counter-clockwise); a deviation of -0.5 degrees requires an adjustment of +0.5 degrees (0.5 degrees clockwise, since the absolute value is 0.5 degrees).

[0130] Step S1413: Based on the preset pixel equivalent conversion coefficient, convert the material position offset deviation value from pixel units to physical length units to obtain the physical material position offset deviation value; calculate the tilt angle adjustment amount of the material pushing device according to the physical material position offset deviation value. If the physical material position offset deviation value is positive in the X-axis direction, the material pushing device needs to be tilted in the negative X-axis direction, and the adjustment amount is positively correlated with the offset deviation value in the X-axis direction; if the physical material position offset deviation value is positive in the Y-axis direction, the material pushing device needs to be tilted in the negative Y-axis direction, and the adjustment amount is positively correlated with the offset deviation value in the Y-axis direction.

[0131] In this embodiment, the material position offset deviation value (in pixels) is multiplied by the pixel equivalent conversion factor to obtain the physical material position offset deviation value (in mm). Based on the components of the physical material position offset deviation value in the X and Y axes, the tilt angle adjustment amount of the material pushing device is calculated. For example, if the physical material position offset deviation value in the X-axis direction is +0.2 mm, it indicates that the material is biased to the right in the X-axis direction, and the material pushing device needs to be tilted in the negative X-axis direction, with an adjustment amount of +0.2 degrees (positively correlated with the X-axis offset deviation value); if it is +0.3 mm in the Y-axis direction, it indicates that the material is biased upwards in the Y-axis direction, and the material pushing device needs to be tilted in the negative Y-axis direction, with an adjustment amount of +0.3 degrees (positively correlated with the Y-axis offset deviation value).

[0132] Step S1414: Integrate the material conveying roller deflection angle adjustment amount and the material pushing device tilt angle adjustment amount to form the material supply angle adjustment parameter.

[0133] In this embodiment, the adjustment amount of the material conveyor roller deflection angle and the adjustment amount of the material pushing device tilt angle are integrated to form the material supply angle adjustment parameter. The material supply angle adjustment parameter is stored as a vector, containing the adjustment amount of the material conveyor roller deflection angle and the adjustment amount of the material pushing device tilt angle (in the X and Y axes). For example, if the material conveyor roller deflection angle adjustment is +1 degree, the material pushing device tilt angle adjustment is (+0.2 degrees, +0.3 degrees), and the material supply angle adjustment parameter is (+1, +0.2, +0.3).

[0134] Step S150: Generate computer vision-guided circuit board processing control instructions based on the dynamically adjusted parameters, transmit the circuit board processing control instructions to the corresponding circuit board processing equipment and material supply device, and simultaneously monitor the visual status of the circuit board processing after the instructions are executed in real time through a vision acquisition device. Based on the visual status feedback, continuously optimize subsequent control instructions to achieve dynamic control of the circuit board processing process.

[0135] In this embodiment, the dynamically adjusted parameters include equipment processing head position adjustment parameters, equipment processing force adjustment parameters, and material supply angle adjustment parameters. Based on these parameters, circuit board processing control commands are generated, which include information such as processing head position adjustment, processing force adjustment, and material supply angle adjustment. These circuit board processing control commands are transmitted to the corresponding circuit board processing equipment and material supply device, causing them to adjust according to the commands. Simultaneously, a vision acquisition device monitors the visual state of the circuit board processing after the commands are executed in real time, analyzes new visual feature deviation information, and continuously optimizes subsequent control commands, achieving dynamic control of the circuit board processing process.

[0136] Step S151: Analyze the equipment processing head position adjustment parameter, equipment processing force adjustment parameter, and material supply angle adjustment parameter in the dynamic adjustment parameters, and extract the specific value and adjustment direction of each adjustment parameter in the equipment processing head position adjustment parameter, equipment processing force adjustment parameter, and material supply angle adjustment parameter.

[0137] In this embodiment, the dynamic adjustment parameters are stored in the form of a structure, including equipment processing head position adjustment parameters, equipment processing force adjustment parameters, and material supply angle adjustment parameters. First, the equipment processing head position adjustment parameters are analyzed to extract the specific values ​​and adjustment directions (positive or negative) of the X-axis, Y-axis, and Z-axis position adjustment amounts of the processing head. Then, the equipment processing force adjustment parameters are analyzed to extract the specific values ​​and adjustment directions of the processing head downward pressure adjustment amount and dwell time adjustment amount. Finally, the material supply angle adjustment parameters are analyzed to extract the specific values ​​and adjustment directions of the material conveyor roller deflection angle adjustment amount and the material pushing device tilt angle adjustment amount (X-axis and Y-axis directions). For example, the equipment processing head position adjustment parameters are (-0.2, +0.3, +0.1), indicating that the processing head X-axis position adjustment amount is -0.2mm (negative direction adjustment), the Y-axis position adjustment amount is +0.3mm (positive direction adjustment), and the Z-axis position adjustment amount is +0.1mm (positive direction adjustment).

[0138] Step S152: Generate a processing head control command fragment for the circuit board processing equipment. The processing head control command fragment includes equipment identification, processing head X-axis position adjustment value, processing head Y-axis position adjustment value, processing head Z-axis position adjustment value, processing head downward pressure adjustment value, and processing head dwell time adjustment value.

[0139] In this embodiment, the equipment identifier of the circuit board processing equipment is unique and used to distinguish different processing equipment. Based on the parsed equipment processing head position adjustment parameters and equipment processing force adjustment parameters, a processing head control command fragment is generated. The format of the processing head control command fragment is determined according to the communication protocol of the processing equipment, for example, using G-code or a custom command format. The processing head control command fragment includes the equipment identifier, the processing head X-axis position adjustment value, Y-axis position adjustment value, Z-axis position adjustment value, downward pressure adjustment value, and dwell time adjustment value. For example, if the equipment identifier is "Machine001", the processing head X-axis position adjustment value is -0.2mm, the Y-axis position adjustment value is +0.3mm, the Z-axis position adjustment value is +0.1mm, the downward pressure adjustment value is +0.5N, and the dwell time adjustment value is -0.1s, the processing head control command fragment would be "Machine001, X=-0.2, Y=+0.3, Z=+0.1, Pressure=+0.5, Time=-0.1".

[0140] Step S153: Generate a material attitude control instruction fragment for the material supply device. The material attitude control instruction fragment includes the device identifier, the material conveying roller deflection angle adjustment value, and the material pushing device tilt angle adjustment value.

[0141] In this embodiment, the device identifier of the material supply device is unique and used to distinguish different material supply devices. Based on the parsed material supply angle adjustment parameters, a material attitude control command fragment is generated. The format of the material attitude control command fragment is determined according to the communication protocol of the material supply device and includes the device identifier, the material conveyor roller deflection angle adjustment value, and the material pusher tilt angle adjustment value (X-axis and Y-axis directions). For example, if the device identifier is "Feeder001", the material conveyor roller deflection angle adjustment value is +1 degree, and the material pusher tilt angle adjustment value is (+0.2 degrees, +0.3 degrees), the material attitude control command fragment would be "Feeder001, Roller=+1, PushX=+0.2, PushY=+0.3".

[0142] Step S154: Synchronously integrate the processing head control instruction segment and the material posture control instruction segment according to the execution time, so that the processing head adjustment and the material posture adjustment are executed within the same processing cycle, forming a computer vision-guided circuit board processing control instruction.

[0143] In this embodiment, the processing cycle time is the time required for the circuit board processing equipment to complete one processing unit. To ensure that the processing head adjustment and material posture adjustment are executed within the same processing cycle time, the execution times of the processing head control command segment and the material posture control command segment need to be synchronized. For example, if the processing cycle time is 10 seconds, the execution times of both the processing head control command segment and the material posture control command segment are set to the current time plus 10 seconds, so that they will start executing at the same time. The integrated circuit board processing control command includes the processing head control command segment, the material posture control command segment, and the execution time information.

[0144] Step S155: Convert the circuit board processing control command to an industrial IoT communication protocol and transmit the converted circuit board processing control command to the corresponding circuit board processing equipment and material supply device. This allows the circuit board processing equipment and material supply device to receive the circuit board processing control command and execute the corresponding operation according to the adjustment parameters in the circuit board processing control command. At the same time, the multi-view vision acquisition device deployed in the industrial IoT will collect the visual status image of the circuit board processing after the command is executed in real time.

[0145] In this embodiment, the industrial IoT communication protocol is selected based on the communication interface of the processing equipment and the material supply device, such as Modbus, Profinet, etc. The circuit board processing control commands are converted into a format conforming to the communication protocol and then transmitted to the corresponding circuit board processing equipment and material supply device via the industrial IoT network. After receiving the commands, the circuit board processing equipment and material supply device parse the adjustment parameters in the commands and execute the corresponding operations. Simultaneously, a multi-view vision acquisition device acquires real-time visual images of the circuit board processing status after the commands are executed.

[0146] Step S156: Perform real-time visual feature extraction on the acquired circuit board processing visual state image to obtain the circuit board outline features after instruction execution, the circuit board solder joint pre-positioning features after instruction execution, and the processing material posture features after instruction execution.

[0147] In this embodiment, the acquired visual state images of the circuit board processing include images of the circuit board area to be processed after the instruction is executed, images of the circuit board status during processing, and images of the material supply posture. Visual feature extraction processing, similar to step S120, is performed on these images to obtain the circuit board outline features, circuit board solder joint pre-positioning features, and processing material posture features after the instruction is executed. For example, the circuit outline features are extracted from the image of the circuit board area to be processed after the instruction is executed, compared with previous features, and the effect of the adjustment is analyzed.

[0148] For example, step S1561: Extract the circuit board outline features from the image of the circuit board area to be processed after the command is executed, and obtain the circuit board outline features after the command is executed.

[0149] In this embodiment, the image of the circuit board area to be processed after the command execution is processed in a manner similar to steps S121-S123, including image preprocessing, image segmentation, and contour extraction, to obtain the circuit board contour features after the command execution. For example, after adjustment, the width, spacing, and corner curvature of the lines are closer to the standard values, and the deviation of the circuit contour features is reduced.

[0150] Step S1562: If the brightness of the circuit board area to be processed after the instruction is executed differs from the brightness of the previously acquired circuit board area to be processed image, the standard deviation of the Gaussian filter and the intensity parameters of the edge enhancement need to be readjusted.

[0151] In this embodiment, changes in image brightness may affect the image preprocessing effect. If the image brightness after the instruction is executed differs from the previous image brightness, it is necessary to readjust the standard deviation of the Gaussian filter and the intensity parameter of the edge enhancement. For example, if the image brightness increases, the standard deviation of the Gaussian filter can be appropriately reduced to avoid over-filtering; the intensity parameter of the edge enhancement can be appropriately reduced to avoid overly strong edges.

[0152] Step S1563: In the image segmentation step, if the grayscale contrast between the line pattern area and the substrate area changes, the maximum inter-class variance threshold needs to be recalculated.

[0153] In this embodiment, changes in the grayscale contrast between the circuit pattern region and the substrate region may affect the image segmentation results. If the grayscale contrast changes, the maximum inter-class variance threshold needs to be recalculated to ensure the accuracy of image segmentation. For example, if the grayscale value of the circuit pattern region increases while the grayscale value of the substrate region decreases, the grayscale contrast is enhanced, and the maximum inter-class variance threshold needs to be recalculated to adapt to the new grayscale distribution.

[0154] Step S1564: Extract the pre-positioning features of the solder joints of the circuit board from the image of the area to be processed after the execution of the command, and obtain the pre-positioning features of the solder joints of the circuit board after the execution of the command.

[0155] In this embodiment, the image of the circuit board area to be processed after the command execution is processed in a manner similar to steps S124-S126, including region of interest extraction, feature enhancement, corner detection, etc., to obtain the pre-positioning features of the circuit board solder joints after the command execution. For example, after adjustment, the center coordinates, distribution density, connection deviation, and other features of the solder joints are closer to the standard values, and the deviation value of the pre-positioning features of the solder joints is reduced.

[0156] Step S1565: In the region of interest extraction step, if the position of the pre-positioned area of ​​the solder joint changes due to the adjustment of the processing head, a new region of interest needs to be determined based on the preset solder joint layout rules.

[0157] In this embodiment, adjusting the processing head may cause a change in the position of the solder joint pre-positioning area. If the position of the solder joint pre-positioning area changes, a new region of interest needs to be redefined based on the preset solder joint layout rules. For example, after the processing head position is adjusted, the intersection position of the lines changes, the center of the solder joint pre-positioning area also needs to be adjusted accordingly, and the size and shape of the region of interest also need to be redefined.

[0158] Step S1566: In the feature enhancement step, if the texture features of the solder joint area change due to the adjustment of the processing force, the size of the structuring element of the morphological dilation algorithm needs to be readjusted to ensure accurate identification of feature corner points.

[0159] In this embodiment, adjusting the processing intensity may cause changes in the texture features of the solder joint area. If the texture features change, the size of the structuring element of the morphological dilation algorithm needs to be readjusted to ensure accurate identification of feature corner points. For example, if the processing intensity increases and the texture of the solder joint area becomes clearer, the size of the structuring element of the morphological dilation algorithm can be appropriately reduced to avoid over-dilation.

[0160] Step S1567: Extract the processing material posture features from the material supply posture image after the instruction is executed to obtain the processing material posture features after the instruction is executed.

[0161] In this embodiment, the material supply posture image after the command execution is processed in a manner similar to steps S127-S129, including background subtraction, contour fitting, and centroid calculation, to obtain the processed material posture characteristics after command execution. For example, after adjustment, the deflection angle, position offset, and other characteristics of the material are closer to the standard values, and the deviation value of the processed material posture characteristics is reduced.

[0162] Step S1568: In the background differentiation step, if the material supply background changes due to the adjustment of the conveyor roller, the background image needs to be re-acquired and the background model updated.

[0163] In this embodiment, adjusting the conveyor rollers may cause changes in the material supply background. If the background changes, it is necessary to re-acquire the background image and update the background model to ensure the accuracy of the background difference. For example, after adjusting the conveyor rollers, the position of the material conveying device in the background changes, requiring the re-acquisition of the background image to reflect the new background conditions.

[0164] Step S1569: In the contour fitting step, if the material posture changes due to the adjustment of the pushing device, the fitting parameters of the minimum bounding rectangle algorithm need to be re-optimized to ensure that the deflection angle and position offset are calculated accurately.

[0165] In this embodiment, adjustments to the pushing device may cause changes in the material's posture. If the material's posture changes, the fitting parameters of the minimum bounding rectangle algorithm need to be re-optimized to ensure accurate calculation of the deflection angle and position offset. For example, if the shape of the material changes, the fitting parameters of the minimum bounding rectangle need to be readjusted to adapt to the new material posture.

[0166] Step S15610: Integrate the extracted circuit board outline features after instruction execution, circuit board solder joint pre-positioning features after instruction execution, and processed material posture features after instruction execution.

[0167] In this embodiment, the circuit board outline features, circuit board solder joint pre-positioning features, and processed material posture features after instruction execution are integrated into a complete feature set for subsequent deviation calculation and instruction optimization. For example, the integrated feature set includes various feature deviation values ​​for circuits, solder joints, and materials.

[0168] Step S157: Compare the circuit board outline features, circuit board solder joint pre-position features, and processed material posture features after instruction execution with the preset circuit board processing visual standard features, and calculate new visual feature deviation information.

[0169] In this embodiment, the features after the instruction is executed are compared with preset standard features to calculate new visual feature deviation information. The comparison method is similar to step S130, calculating the deviation value of each feature and integrating them to form new visual feature deviation information. For example, the line width deviation value after the instruction is executed is +0.05mm, which is smaller than the previous +0.2mm, indicating that the adjustment has a certain effect.

[0170] Step S158: If the deviation values ​​of line width, line spacing, line corner curvature, solder joint center coordinates, solder joint distribution density, solder joint connection, material deflection angle, and material position offset in the new visual feature deviation information are all within the allowable range of the process requirements, then the subsequent circuit board processing control instructions remain unchanged.

[0171] In this embodiment, the allowable range of process requirements is determined based on the quality standards of the circuit board. If all deviation values ​​in the new visual feature deviation information are within the allowable range, it indicates that the current adjustment parameters can meet the processing requirements. Subsequent circuit board processing control instructions remain unchanged, and processing continues according to the current parameters.

[0172] Step S159: If there are deviation values ​​in the new visual feature deviation information that exceed the allowable range, then the dynamic adjustment parameters are re-derived based on the deviation values ​​that exceed the allowable range in the new visual feature deviation information, and optimized circuit board processing control instructions are generated.

[0173] In this embodiment, if the new visual feature deviation information contains deviation values ​​that exceed the allowable range, it indicates that the current adjustment parameters need further optimization. Based on the deviation values ​​that exceed the allowable range, their impact on processing quality is re-analyzed, and new dynamic adjustment parameters are derived. For example, if the line width deviation value still exceeds the allowable range, the position parameters of the processing head need to be readjusted to generate optimized circuit board processing control commands.

[0174] Step S1510: Transmit the optimized circuit board processing control command to the corresponding circuit board processing equipment and material supply device, repeat the above steps of visual monitoring, feature comparison, deviation calculation and command optimization, and continuously and dynamically adjust the circuit board processing process.

[0175] In this embodiment, the optimized circuit board processing control instructions are transmitted to the corresponding equipment, causing it to adjust according to the new parameters. Simultaneously, the steps of visual monitoring, feature comparison, deviation calculation, and instruction optimization are repeatedly executed to continuously optimize and adjust the parameters, ensuring that the circuit board processing quality meets the requirements. For example, after multiple adjustments, the deviation values ​​of various characteristics of the circuit board are all within the allowable range, and the processing tends to stabilize.

[0176] Based on the same inventive concept, please refer to Figure 2 This paper shows a schematic block diagram of a circuit board processing control system 100 for implementing the above-described circuit board processing control method for industrial Internet of Things (IoT) provided in an embodiment of this application. The circuit board processing control system 100 for industrial IoT may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0177] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located within the PCB manufacturing control system 100 applied to the Industrial Internet of Things (IIoT) and are separately configured. However, it should be understood that the machine-readable storage medium 120 may also be independent of the PCB manufacturing control system 100 applied to the IIoT and may be accessed by the processor 130 via a bus interface. Alternatively, the machine-readable storage medium 120 may also be integrated into the processor 130 and may communicate and interact with external systems through the communication unit 110.

[0178] The processor 130 is the control center of the PCB manufacturing control system 100 applied to the Industrial Internet of Things (IIoT). It connects various parts of the IIoT-based PCB manufacturing control system 100 via various interfaces and lines. By running or executing software programs and / or modules stored in the machine-readable storage medium 120, and by calling data stored in the machine-readable storage medium 120, it performs various functions and processes data of the IIoT-based PCB manufacturing control system 100, thereby providing overall monitoring of the IIoT-based PCB manufacturing control system 100. Optionally, the processor 130 may include one or more main processors; for example, the processor 130 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to implement the PCB manufacturing control method for the Industrial Internet of Things provided in the foregoing method embodiments.

[0179] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A circuit board processing control method applied to the Industrial Internet of Things, characterized in that, The method includes: A multi-view vision acquisition device deployed through the Industrial Internet of Things (IIoT) acquires a set of real-time visual images of the circuit board processing area. The set of real-time visual images includes images of the circuit board area to be processed, images of the circuit board status during processing, and images of the material supply posture. Visual feature extraction processing is performed on the real-time visual image set to obtain the circuit board outline features, circuit board solder joint pre-position features, and processing material posture features; The circuit board outline features, circuit board solder joint pre-positioning features, and processing material posture features are matched with preset circuit board processing visual standard features to generate visual feature deviation information. Based on the visual feature deviation information and combined with the circuit board processing technology requirements, dynamic adjustment parameters of the circuit board processing equipment are derived. The dynamic adjustment parameters include equipment processing head position adjustment parameters, equipment processing force adjustment parameters, and material supply angle adjustment parameters. Based on the dynamically adjusted parameters, computer vision-guided circuit board processing control instructions are generated and transmitted to the corresponding circuit board processing equipment and material supply device. At the same time, the visual status of the circuit board processing after the instructions are executed is monitored in real time through a vision acquisition device. Based on the visual status feedback, subsequent control instructions are continuously optimized to achieve dynamic control of the circuit board processing process.

2. The circuit board processing control method for industrial Internet of Things according to claim 1, characterized in that, In the industrial IoT circuit board processing scenario, a multi-view vision acquisition device is deployed around the processing station of the circuit board processing equipment. The multi-view vision acquisition device includes a top-down vision module, a side-view vision module, and a material supply end vision module. The multi-view vision acquisition device deployed through the Industrial Internet of Things acquires a set of real-time visual images of the circuit board processing area, including: For the top-down view module, adjust the shooting angle and focal length of the top-down view module so that the shooting field of view covers the entire area of ​​the circuit board to be processed. Set the shooting frame rate to be synchronized with the processing rhythm of the circuit board processing equipment so that one frame of the circuit board to be processed area is captured simultaneously when processing each circuit unit. For the side-view vision module, the shooting angle and light source intensity of the side-view vision module are adjusted so that the shooting image can effectively present the contact state between the processing head and the circuit board during the circuit board processing process. The shooting trigger mechanism is set to automatically trigger shooting when the processing head starts processing action, and generate a circuit board status image during processing. For the material supply end vision module, adjust the shooting position and image resolution of the material supply end vision module so that the shooting field of view covers the end area of ​​the material being transported to the processing station. Set the shooting interval to match the material transport speed so that a frame of material supply posture image is captured synchronously when each piece of processed material is transported. A visual image synchronous transmission mechanism is constructed to transmit image data collected by the top overhead vision module, the side side vision module, and the material supply end vision module to the image processing unit through a dedicated industrial IoT communication link. The image data transmitted to the image processing unit is time-stamp aligned. Images of the circuit board area to be processed, the circuit board status image during processing, and the material supply posture image collected at the same processing time are grouped together. Multiple groups of time-stamp aligned image data are integrated to form a real-time visual image set containing images of the circuit board area to be processed, images of the circuit board status during processing, and images of the material supply posture.

3. The circuit board processing control method for industrial Internet of Things according to claim 1, characterized in that, The process of performing visual feature extraction on the real-time visual image set yields circuit board outline features, circuit board solder joint pre-positioning features, and processed material posture features, including: Extract the image of the circuit board to be processed area from the real-time visual image set, perform image preprocessing operation on the image of the circuit board to be processed area to obtain the preprocessed image of the circuit board to be processed area. The image preprocessing operation includes grayscale conversion, Gaussian filtering for noise reduction and edge enhancement processing to enhance the clarity of the outline of the circuit board lines in the image of the circuit board to be processed area. Image segmentation processing is performed on the preprocessed image of the circuit board to be processed area. A threshold segmentation algorithm is used to separate the circuit board substrate area and the circuit pattern area to obtain a binary image of the circuit pattern. In the binary image of the circuit pattern, a contour extraction algorithm is used to extract continuous edge pixels of the circuit board, calculate the coordinate sequence of the edge pixels, and generate geometric contour parameters of the circuit board based on the coordinate sequence. The geometric contour parameters include the line width, line spacing and line corner curvature. The line width, line spacing and line corner curvature are integrated to form the circuit board contour features. Extract the image of the circuit board to be processed area from the real-time visual image set, perform region of interest extraction processing on the image of the circuit board to be processed area, and determine the pre-positioning area of ​​solder joints in the image of the circuit board to be processed area based on the preset solder joint layout rules. Feature enhancement processing is performed on the pre-positioning region of the solder joint. The morphological dilation algorithm is used to enhance the texture features of the pre-positioning region of the solder joint, and the Harris corner detection algorithm is used to identify the feature corners in the pre-positioning region of the solder joint. Calculate the distance and angle relationship between feature corner points to generate spatial position parameters of the solder joint pre-positioning area. The spatial position parameters include the solder joint center coordinates, solder joint distribution density and the connection deviation between the solder joint and the circuit. Integrate the solder joint center coordinates, solder joint distribution density and the connection deviation between the solder joint and the circuit to form the circuit board solder joint pre-positioning feature. Extract material supply posture images from the real-time visual image set, perform posture feature extraction processing on the material supply posture images, and use the background difference algorithm to separate the processed material region and the background region in the material supply posture images to obtain the foreground image of the processed material. Perform contour fitting processing on the foreground image of the processed material to generate the bounding rectangle of the processed material. Calculate the angle between the major axis of the bounding rectangle and the preset reference direction to determine the deflection angle parameter of the processed material. The centroid coordinates of the foreground image of the processed material are obtained by image moment calculation method. The deviation between the centroid coordinates and the preset material supply reference coordinates is compared to determine the position offset parameters of the processed material. The deflection angle parameters and position offset parameters are integrated to form the attitude characteristics of the processed material.

4. The circuit board processing control method for industrial Internet of Things according to claim 3, characterized in that, The process of performing image segmentation on the preprocessed circuit board area image, using a threshold segmentation algorithm to separate the circuit board substrate area from the circuit pattern area, yields a binary image of the circuit pattern, including: The grayscale histogram statistics are performed on the preprocessed image of the circuit board to be processed area to obtain the distribution frequency of grayscale values ​​in the image of the circuit board to be processed area, and the two peak areas of grayscale value distribution are determined, which correspond to the grayscale range of the circuit board substrate area and the grayscale range of the circuit pattern area, respectively. The optimal threshold for image segmentation is calculated using the maximum inter-class variance algorithm. The optimal threshold is the gray value that maximizes the inter-class variance between the substrate region and the circuit pattern region. The grayscale value of each pixel in the preprocessed image of the circuit board area to be processed is compared with the optimal threshold. If the pixel grayscale value is greater than the optimal threshold, the pixel is marked as a pixel in the circuit pattern area and assigned a preset foreground pixel value. If the pixel grayscale value is less than or equal to the optimal threshold, the pixel is marked as a pixel in the circuit board substrate area and assigned a preset background pixel value. Noise removal is performed on the marked image of the circuit board area to be processed. After edge smoothing is performed on the processed image of the circuit board area to be processed, the image of the circuit board area to be processed is saved as a binary image of the circuit pattern.

5. The circuit board processing control method for industrial Internet of Things according to claim 3, characterized in that, The process of performing contour fitting on the foreground image of the processed material to generate the circumscribed rectangle of the processed material, calculating the angle between the major axis of the circumscribed rectangle and the preset reference direction, and determining the deflection angle parameters of the processed material includes: Contour detection processing is performed on the foreground image of the processed material. The chain code tracking algorithm is used to extract the sequence of outer contour pixels of the foreground region of the processed material and record the coordinate information of each contour pixel. Based on the contour pixel sequence, the minimum bounding rectangle algorithm is used to generate the minimum bounding rectangle of the processed material. The minimum bounding rectangle is a rectangle that can completely enclose the foreground area of ​​the processed material and has the smallest area. Extract the coordinates of the four vertices of the minimum bounding rectangle, calculate the distance between adjacent vertices, and determine the length of the long side and the length of the short side of the minimum bounding rectangle. The side corresponding to the length of the long side is the major axis of the bounding rectangle. In the image coordinate system, a preset reference direction is set. The preset reference direction is a direction parallel to the feed direction of the circuit board processing equipment. The angle value of the preset reference direction is recorded. Calculate the angle between the major axis of the circumscribed rectangle and the preset reference direction. If the major axis deviates clockwise relative to the preset reference direction, the angle is recorded as a positive value; if the major axis deviates counterclockwise relative to the preset reference direction, the angle is recorded as a negative value. The calculated included angle value is calibrated for accuracy. A sub-pixel level edge detection algorithm is used to relocate the contour pixels, correct the vertex coordinates of the outer rectangle, and then recalculate the included angle value so that the accuracy of the deflection angle parameter meets the requirements of circuit board processing. The calibrated included angle value is determined as the deflection angle parameter of the processed material.

6. The circuit board processing control method for industrial Internet of Things according to claim 1, characterized in that, The step of matching the circuit board outline features, circuit board solder joint pre-positioning features, and processed material posture features with preset circuit board processing visual standard features to generate visual feature deviation information includes: Retrieve preset visual standard features for circuit board processing, which include standard features for circuit board outline, standard features for circuit board solder joint pre-positioning, and standard features for the posture of processed materials. The standard features of the circuit board outline include standard line width, standard line spacing, and standard line corner curvature; the standard features of the circuit board solder joint pre-positioning include standard solder joint center coordinates, standard solder joint distribution density, and standard solder joint connection deviation with the line; the standard features of the processed material posture include standard material deflection angle and standard material position offset. The line width in the extracted circuit board line contour features is compared with the standard line width in the standard circuit board line contour features, and the difference between the two is calculated to obtain the line width deviation value. The line spacing in the extracted circuit board line contour features is compared with the standard line spacing in the standard circuit board line contour features, and the difference between the two is calculated to obtain the line spacing deviation value. The radii of the line corners in the extracted line contour features of the circuit board are compared with the radii of the standard line corners in the standard features of the line contour of the circuit board. The difference between the two is calculated to obtain the deviation value of the line corner radii. The center coordinates of the solder joints in the pre-positioning features of the circuit board solder joints are compared with the center coordinates of the standard solder joints in the pre-positioning standard features of the circuit board solder joints. The coordinate difference between the two in the X-axis and Y-axis directions is calculated to obtain the center coordinate deviation value of the solder joints. The solder joint distribution density in the extracted circuit board solder joint pre-positioning features is compared with the standard solder joint distribution density in the circuit board solder joint pre-positioning standard features. The difference between the two is calculated to obtain the solder joint distribution density deviation value. The solder joint connection deviation in the extracted circuit board solder joint pre-positioning features is compared with the standard solder joint connection deviation in the circuit board solder joint pre-positioning standard features. The difference between the two is calculated to obtain the solder joint connection deviation value. The material deflection angle in the extracted processing material posture features is compared with the standard material deflection angle in the processing material posture standard features. The difference between the two is calculated to obtain the material deflection angle deviation value. The material position offset in the extracted processing material posture features is compared with the standard material position offset in the processing material posture standard features. The offset difference between the two in the X-axis and Y-axis directions is calculated to obtain the material position offset deviation value. The deviation values ​​of line width, line spacing, line corner curvature, weld center coordinates, weld distribution density, weld connection, material deflection angle, and material position offset are integrated to form visual feature deviation information.

7. The circuit board processing control method for industrial Internet of Things according to claim 1, characterized in that, The process of deriving dynamic adjustment parameters for circuit board processing equipment based on the visual feature deviation information and in conjunction with circuit board processing technology requirements includes: Retrieve the circuit board processing technology requirements document and extract the precision requirements, processing force requirements, and material supply posture requirements for circuit board processing from the document. For the line width deviation value, line spacing deviation value, and line corner curvature deviation value in the visual feature deviation information, analyze the impact of the line width deviation value, line spacing deviation value, and line corner curvature deviation value on the circuit board processing accuracy, and determine the type of equipment processing head position parameter that needs to be adjusted. The equipment processing head position parameter type includes the processing head X-axis position, processing head Y-axis position, and processing head Z-axis position. Based on a preset pixel equivalent conversion coefficient, the line width deviation value is converted from pixel units to physical length units to obtain the physical line width deviation value. The X-axis position adjustment amount of the processing head is calculated based on the physical line width deviation value. If the physical line width deviation value is positive, the processing head needs to be adjusted in the negative X-axis direction, and the adjustment amount is positively correlated with the physical line width deviation value. If the physical line width deviation value is negative, the processing head needs to be adjusted in the positive X-axis direction, and the adjustment amount is positively correlated with the absolute value of the physical line width deviation value. Based on a preset pixel equivalent conversion coefficient, the line spacing deviation value is converted from pixel units to physical length units to obtain the physical line spacing deviation value. The adjustment amount of the processing head's Y-axis position is calculated based on the physical line spacing deviation value. If the physical line spacing deviation value is positive, the processing head needs to be adjusted in the negative Y-axis direction, and the adjustment amount is positively correlated with the physical line spacing deviation value. If the physical line spacing deviation value is negative, the processing head needs to be adjusted in the positive Y-axis direction, and the adjustment amount is positively correlated with the absolute value of the physical line spacing deviation value. The adjustment amount of the machining head's Z-axis position is calculated based on the deviation value of the line corner curvature. If the deviation value of the line corner curvature is positive, the machining head needs to be adjusted in the positive direction of the Z-axis to reduce the machining depth. The adjustment amount is positively correlated with the deviation value of the line corner curvature. If the deviation value of the line corner curvature is negative, the machining head needs to be adjusted in the negative direction of the Z-axis to increase the machining depth. The adjustment amount is positively correlated with the absolute value of the deviation value of the line corner curvature. The X-axis position adjustment amount, Y-axis position adjustment amount, and Z-axis position adjustment amount of the machining head are integrated to form the machining head position adjustment parameters of the equipment. For the deviation values ​​of weld center coordinates, weld distribution density, and weld connection in the visual feature deviation information, the influence of these values ​​on the weld processing quality is analyzed. Combined with the processing force requirements in the processing technology requirements, the types of equipment processing force parameters that need to be adjusted are determined. These equipment processing force parameter types include the processing head downward pressure and the processing head dwell time. Based on a preset pixel equivalent conversion coefficient, the solder joint center coordinate deviation value is converted from pixel units to physical length units to obtain the physical solder joint center coordinate deviation value. The adjustment amount of the processing head's downward pressure is calculated based on this value. If the physical solder joint center coordinate deviation value exceeds a preset solder joint coordinate deviation threshold, the processing head's downward pressure needs to be increased to ensure a firm solder joint connection; the adjustment amount is positively correlated with the absolute value of the physical solder joint center coordinate deviation value. If the physical solder joint center coordinate deviation value does not exceed the preset threshold, the processing head's downward pressure needs to be maintained or decreased to avoid damage to the circuit board substrate; the adjustment amount is negatively correlated with the absolute value of the physical solder joint center coordinate deviation value. The adjustment amount of the processing head dwell time is calculated based on the deviation value of the solder joint distribution density. If the deviation value of the solder joint distribution density is positive, the dwell time of the processing head at each solder joint needs to be shortened to improve processing efficiency. The adjustment amount is positively correlated with the deviation value of the solder joint distribution density. If the deviation value of the solder joint distribution density is negative, the dwell time of the processing head at each solder joint needs to be extended to ensure solder joint quality. The adjustment amount is positively correlated with the absolute value of the deviation value of the solder joint distribution density. The adjustment parameters for the processing head's downward pressure and dwell time are integrated to form the equipment's processing force adjustment parameters. For the material deflection angle deviation value and material position offset deviation value in the visual feature deviation information, the influence of the material deflection angle deviation value and material position offset deviation value on the material supply posture is analyzed. Combined with the material supply posture requirements in the processing technology requirements, the types of material supply angle parameters that need to be adjusted are determined. The types of material supply angle parameters include the material conveying roller deflection angle and the material pushing device tilt angle. The adjustment amount of the material conveying roller deflection angle is calculated based on the material deflection angle deviation value. If the material deflection angle deviation value is positive, the material conveying roller needs to be adjusted counterclockwise. The adjustment amount is positively correlated with the material deflection angle deviation value. If the material deflection angle deviation value is negative, the material conveying roller needs to be adjusted clockwise. The adjustment amount is positively correlated with the absolute value of the material deflection angle deviation value. Based on a preset pixel equivalent conversion coefficient, the material position offset deviation value is converted from pixel units to physical length units to obtain the physical material position offset deviation value. The tilt angle adjustment of the material pushing device is calculated based on this value. If the physical material position offset deviation value is positive in the X-axis direction, the material pushing device needs to be tilted in the negative X-axis direction, with the adjustment amount positively correlated with the X-axis offset deviation value. If the physical material position offset deviation value is positive in the Y-axis direction, the material pushing device needs to be tilted in the negative Y-axis direction, with the adjustment amount positively correlated with the Y-axis offset deviation value. The material supply angle adjustment parameters are formed by integrating the material conveying roller deflection angle adjustment and the material pushing device tilt angle adjustment.

8. The circuit board processing control method for industrial Internet of Things according to claim 7, characterized in that, The process involves converting the line width deviation value from pixel units to physical length units based on a preset pixel equivalent conversion coefficient, resulting in a physical line width deviation value. The X-axis position adjustment amount of the processing head is calculated based on this physical line width deviation value. If the physical line width deviation value is positive, the processing head needs to be adjusted in the negative X-axis direction, with the adjustment amount positively correlated with the physical line width deviation value. If the physical line width deviation value is negative, the processing head needs to be adjusted in the positive X-axis direction, with the adjustment amount positively correlated with the absolute value of the physical line width deviation value. This includes: Retrieve the position adjustment accuracy document of the processing head of the circuit board processing equipment, determine the minimum unit value of the X-axis position adjustment of the processing head, and ensure that the calculated adjustment amount is an integer multiple of the minimum unit value; Establish a correlation model between the physical line width deviation and the X-axis position adjustment of the processing head. The correlation model takes the physical line width deviation as input and the X-axis position adjustment of the processing head as output. If the physical line width deviation is positive, the physical line width deviation is substituted into the correlation model. The correlation model determines the adjustment coefficient based on the magnitude of the physical line width deviation. The larger the physical line width deviation, the larger the adjustment coefficient. The adjustment amount is determined by the product relationship between the physical line width deviation and the adjustment coefficient. The calculated adjustment amount is calibrated for accuracy. If the adjustment amount is not an integer multiple of the minimum unit value, it is rounded up or down. The rounded value maximizes the reduction of the physical line width deviation value after adjustment. If the deviation value of the physical line width is a certain physical length unit value, the adjustment coefficient is a preset adjustment coefficient value, and the minimum unit value is the equipment coordinate value corresponding to the preset physical length unit, then the adjustment amount is determined by the product relationship between the physical length unit value and the preset adjustment coefficient value. If the result is an integer multiple of the minimum unit value, then no rounding is required. If the deviation value of the physical line width is a certain physical length unit value, the adjustment coefficient is a preset adjustment coefficient value, the minimum unit value is the equipment coordinate value corresponding to the preset physical length unit, and the calculated adjustment amount is not an integer multiple of the minimum unit value, then it is rounded up to maximize the reduction of the physical line width deviation value after adjustment. If the physical line width deviation is negative, take the absolute value of the physical line width deviation and substitute it into the correlation model. The calculation method is the same as for positive deviation, and the adjustment direction is changed to the positive X-axis direction. The calibrated adjustment amount is verified, and the adjustment amount is substituted into the line width deviation prediction formula to predict the adjusted physical line width deviation value. If the predicted adjusted physical line width deviation value is within the allowable range of the process requirements, then the adjustment amount is determined as the final X-axis position adjustment amount of the machining head. If the predicted deviation of the physical line width exceeds the allowable range of the process requirements, the adjustment coefficient is increased and the adjustment amount is recalculated until the predicted deviation of the physical line width meets the process requirements.

9. The circuit board processing control method for industrial Internet of Things according to claim 1, characterized in that, The process involves generating computer vision-guided circuit board processing control instructions based on the dynamically adjusted parameters, transmitting these instructions to the corresponding circuit board processing equipment and material supply device, and simultaneously monitoring the visual status of the circuit board processing after instruction execution in real time using a vision acquisition device. Based on the visual status feedback, subsequent control instructions are continuously optimized to achieve dynamic control of the circuit board processing process, including: Analyze the dynamic adjustment parameters, including the equipment processing head position adjustment parameter, equipment processing force adjustment parameter, and material supply angle adjustment parameter, and extract the specific value and adjustment direction of each adjustment parameter. Generate a processing head control command fragment for the circuit board processing equipment. This processing head control command fragment includes equipment identification, processing head X-axis position adjustment value, processing head Y-axis position adjustment value, processing head Z-axis position adjustment value, processing head downward pressure adjustment value, and processing head dwell time adjustment value. Generate a material attitude control instruction fragment for the material supply device. The material attitude control instruction fragment includes device identification, material conveying roller deflection angle adjustment value and material pushing device tilt angle adjustment value. The processing head control command segment and the material posture control command segment are synchronously integrated according to the execution time, so that the processing head adjustment and the material posture adjustment are executed within the same processing cycle, forming a computer vision-guided circuit board processing control command. The circuit board processing control commands are converted using the Industrial Internet of Things (IIoT) communication protocol. The converted circuit board processing control commands are then transmitted to the corresponding circuit board processing equipment and material supply device. This enables the circuit board processing equipment and material supply device to receive the circuit board processing control commands and execute corresponding operations according to the adjustment parameters in the circuit board processing control commands. At the same time, the multi-view vision acquisition device deployed in the Industrial Internet of Things acquires the visual status image of the circuit board processing after the command execution in real time. Real-time visual feature extraction is performed on the collected visual state images of the circuit board processing to obtain the circuit board outline features, circuit board solder joint pre-position features, and processing material posture features after the command is executed. The circuit board outline features, circuit board solder joint pre-position features, and processed material posture features after instruction execution are compared with the preset circuit board processing visual standard features to calculate new visual feature deviation information. If the deviation values ​​of line width, line spacing, line corner radius, solder joint center coordinates, solder joint distribution density, solder joint connection, material deflection angle, and material position offset in the new visual feature deviation information are all within the allowable range of the process requirements, then the subsequent circuit board processing control instructions remain unchanged. If there are deviation values ​​in the new visual feature deviation information that exceed the allowable range, the dynamic adjustment parameters are re-derived based on the deviation values ​​that exceed the allowable range in the new visual feature deviation information, and optimized circuit board processing control instructions are generated. The optimized circuit board processing control instructions are transmitted to the corresponding circuit board processing equipment and material supply device. The above steps of visual monitoring, feature comparison, deviation calculation and instruction optimization are repeated to continuously and dynamically adjust the circuit board processing process.

10. A circuit board processing control system applied to the Industrial Internet of Things, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the circuit board processing control method for industrial Internet of Things as described in any one of claims 1 to 9 by executing the machine-executable instructions.

Images