Circuit board processing abnormality detection system

Abstract

The present application relates to the technical field of anomaly detection, in particular to a circuit board processing anomaly detection system, the system comprising an image anchoring module, a gray scale contrast module, a candidate region screening module, a gradient closure verification module and a behavior trajectory evolution module. In the present application, by setting gray scale anchor points in the image frame and collecting standard gray scale values, deviation accumulation caused by overall image illumination changes can be effectively avoided, response lag areas are identified through inter-frame gray scale contrast, early intervention in the etching abnormal process is realized, low-amplitude gray scale fluctuation sections in the image boundary area are extracted to identify texture missing areas, the identification accuracy of structure blur is enhanced, a gray scale gradient chain construction and closure error judgment mechanism are combined to realize fine extraction of abnormal structure morphology, and region response change trajectory tracking in the frame sequence is superimposed, so that the judgment is not limited to single-frame anomaly detection and has time sequence evolution identification capability, and abnormal symptom areas can be stably captured and reliable detection results can be generated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of anomaly detection, in particular to a circuit board processing anomaly detection system. BACKGROUND

[0002] The technical field of anomaly detection involves identifying, analyzing and responding to abnormal states in systems, devices or production processes. The core issues include fault information collection methods, fault mode identification paths and system response strategy settings. It is widely used in industrial manufacturing, device maintenance and automation control. Among them, the circuit board processing anomaly detection system refers to a system that detects abnormal conditions caused by equipment errors, process deviations or material defects during drilling, etching, mounting and other processing steps of printed circuit boards. It usually collects images through optical camera components installed on processing equipment and uses edge feature matching methods to judge processing position deviation or pattern missing anomalies. Some systems use threshold comparison methods based on statistical rules to continuously monitor specific process parameters such as temperature, voltage and pressure to identify abnormal states that exceed the set range.

[0003] In the existing circuit board processing anomaly detection process, edge feature matching is mainly used, which is easily affected by pattern complexity and edge clarity. In uneven lighting or incomplete pattern scenarios, the matching accuracy decreases significantly. The threshold comparison-based process parameter monitoring method cannot effectively respond to image-level structural anomalies. For example, when the overall pattern structure is blurred or etching is insufficient but does not exceed the parameter threshold, the system often cannot timely identify processing deviations, resulting in defective circuit board products not being screened out and continuing to flow into the next process, reducing overall process quality and stability. SUMMARY

[0004] To solve the technical problems existing in the prior art, the present application provides a circuit board processing anomaly detection system. The technical solution is as follows:

[0005] On the one hand, a circuit board processing anomaly detection system is provided, which includes:

[0006] The image anchoring module acquires continuous process image frames in the etching stage of the circuit board, sets positioning anchors in each image frame, collects standard gray scale values of each anchor as first frame reference data, and generates gray scale anchor reference data by statistics;

[0007] The gray scale comparison module obtains gray scale data at the same position in subsequent image frames according to the coordinates of each anchor in the gray scale anchor reference data, performs inter-frame gray scale value comparison operations, identifies anchor sequences in consecutive frames with differences below the etching process monitoring threshold, and obtains etching response lag areas;

[0008] The candidate region screening module extracts a pixel gray scale sequence of a corresponding image boundary region according to a position of the etching response lagging region in the image frame, identifies a continuous pixel segment with a stable gray scale value change amplitude in a low dynamic range interval as a structural fuzzy segment region, and obtains a fuzzy structure candidate segment.

[0009] The gradient closure verification module extracts a gray scale difference value between adjacent pixels based on the pixel gray scale sequence in the fuzzy structure candidate segment, performs absolute value extraction on a head-tail gray scale difference of a gradient chain and error judgment with a gradient closure tolerance, records a segment region with an error exceeding the gradient closure tolerance as an abnormal structure region, and generates a structure chain closure abnormal region.

[0010] As a further scheme of the present application, the gray scale anchor point reference data includes an anchor point position index, a standard gray scale level, and a pattern boundary mapping relationship, the etching response lagging region includes an inter-frame gray scale change sequence, a low-variation anchor point set, and a threshold trigger label, the fuzzy structure candidate segment includes a low dynamic range gray scale sequence, a continuous pixel segment index, and an image boundary region identifier, and the structure chain closure abnormal region includes a gradient chain head-tail gray scale difference abnormal segment, a gray scale closure error record, and an abnormal structure segment label.

[0011] As a further scheme of the present application, the standard gray scale value is specifically a gray scale level defined by an international standard for acceptance of electronic circuit boards, the etching process monitoring threshold value is specifically a gray scale change allowed range defined by a rigid printed board qualification and performance specification, and the low dynamic range interval is specifically a gray scale fluctuation range defined by an international standard for image encoding.

[0012] As a further scheme of the present application, the image anchoring module includes:

[0013] A gray scale acquisition submodule acquires continuous process image frames in a circuit board etching stage, sets a plurality of pixel points within a pattern boundary in each image as positioning anchor points, acquires standard gray scale values of each anchor point, eliminates image edge interference pixels, and generates a set of image anchor point gray scale values;

[0014] A reference gray scale setting submodule selects a first frame data as a reference image frame in a time sequence based on the gray scale values of the anchor points in each frame in the set of image anchor point gray scale values, extracts standard gray scale values of all anchor points in the first frame, calls a standard color card gray scale level reference value for comparison and calibration, screens effective reference anchor points, and obtains effective anchor point gray scale reference values;

[0015] A gray scale reference generation submodule calculates a gray scale change interval in subsequent continuous image frames according to the gray scale values of each anchor point in the effective anchor point gray scale reference values, counts a gray scale mean value, a maximum deviation value, and a change trend value of each anchor point in all image frames, classifies and groups the overall anchor points, and generates gray scale anchor point reference data.

[0016] As a further scheme of the present application, the gray scale contrast module comprises:

[0017] The image gray scale extraction submodule obtains the coordinate information of all anchor points in the gray scale anchor point reference data, collects the gray scale values of the corresponding coordinate positions in the subsequent image frames, selects continuous frame images with the same frame sequence length as the reference data, positions and extracts the gray scale values of all anchor points in each frame image, and generates anchor point gray scale sequence values;

[0018] The anchor point frame difference judgment submodule calculates the gray scale difference between each anchor point based on the gray scale difference values of each anchor point in the adjacent frames in the anchor point gray scale sequence value, and if the absolute value does not exceed the etching process monitoring threshold, the anchor point is marked as a continuous consistent frame point. The anchor point set that meets the continuous consistent condition is extracted, and a gray scale consistent anchor point sequence is obtained;

[0019] The response anomaly recognition submodule performs spatial clustering on the corresponding coordinates according to the anchor point area in the gray scale consistent anchor point sequence that appears with a continuous frame difference value lower than the etching process monitoring threshold, identifies a continuously consistent anchor point group that repeatedly appears in multiple frames, judges whether the proportion of the anchor point group in the total anchor points is higher than the etching uniformity threshold, and if it is higher, marks it as a response lag area, establishes a lag area index map under the corresponding frame sequence, and obtains an etching response lag area.

[0020] As a further scheme of the present application, the candidate region screening module comprises:

[0021] The image region extraction submodule obtains the region index and coordinate information of all corresponding image frames in the etching response lag area, reads the image boundary range corresponding to the region marked by the index in each frame image, constructs a boundary pixel extraction band by extending outward according to the coordinates, traverses all pixel points in the boundary band and records the gray scale values, arranges the extracted gray scale values in row and column order to form a one-dimensional gray scale sequence, establishes a gray scale boundary record set of all lag regions in the corresponding image frames, and generates a boundary gray scale sequence set;

[0022] The gray scale fluctuation detection submodule calculates the difference between the maximum gray scale value and the minimum gray scale value of each sequence based on each pixel sequence in the boundary gray scale sequence set, screens out sequence segments with a gray scale fluctuation less than or equal to a gray scale fluctuation judgment threshold, and records the starting position and length of the pixels, to generate a low dynamic gray scale interval segment;

[0023] The fuzzy structure recognition submodule marks a sequence as a candidate segment if the length of the continuous pixel segment is greater than the gray scale fluctuation judgment threshold based on the starting coordinate position and the length of the continuous pixels in all gray scale sequences in the low dynamic gray scale interval segment, counts the spatial position index of each corresponding region in the image frame, and matches it with the initial lag region coordinates to obtain a fuzzy structure candidate segment.

[0024] As a further scheme of the present application, the gradient closure verification module comprises:

[0025] The gray scale difference extraction submodule obtains the gray scale value sequence of all pixel points in the fuzzy structure candidate segment in the image frame, extracts the gray scale difference between adjacent two pixels according to the pixel arrangement order in each segment, records each difference value and the corresponding pixel position in each gradient chain, and generates a continuous gray scale gradient chain;

[0026] The gradient error calculation submodule extracts the gray scale value difference between the first and last points of each chain according to the continuous gray scale gradient chain, calculates the absolute value and records it as a closure difference value, screens the gradient chains with a closure difference value greater than the gradient closure tolerance in all segments, records the segment number, the first and last pixel coordinates and the error value, and obtains the first and last gradient error value;

[0027] The abnormal area determination submodule performs area index extraction operation on all segments with an error value greater than the gradient closure tolerance based on the error data corresponding to each segment in the first and last gradient error value, counts the coordinate range, pixel number and gradient chain length of the segment in the image frame, marks the abnormal area number and archives the corresponding frame number, and obtains the structure chain closure abnormal area.

[0028] As a further scheme of the present application, the system further comprises:

[0029] The behavior trajectory evolution module marks whether the area appears or not in different frames according to the corresponding position of the structure chain closure abnormal area in the continuous image frame, traces the response change in the image frame index order, determines whether the corresponding area continuously shows stable abnormal signs, and generates a circuit board processing abnormality detection result.

[0030] As a further scheme of the present application, the circuit board processing abnormality detection result comprises an abnormal area trajectory index result, an abnormal response continuous marking result and a stable abnormality determination identification record.

[0031] As a further scheme of the present application, the behavior trajectory evolution module comprises:

[0032] The inter-frame marking recording submodule obtains the frame number and coordinate index information of each abnormal segment in the structure chain closure abnormal area, traverses the continuous image frame sequence, marks whether there is an abnormal area with the same number and the same position in each frame of image, constructs a binary sequence according to the frame order, and generates an abnormal area frame sequence record value;

[0033] The continuous response tracking sub-module calculates the length of the continuous existence section of each abnormal area in the frame sequence according to the interframe change of each record in the abnormal area frame sequence record value, and if the length of the continuous existence section is greater than or equal to the stable section judgment reference length, the continuous response section is determined, and the abnormal area number and the start and end frame index that meet the condition are extracted to obtain the response continuous frame section interval;

[0034] The abnormal state judgment sub-module filters the area number that meets the stable abnormal judgment threshold based on the area number and the continuous frame section length in the response continuous frame section interval, integrates and counts the corresponding coordinate range and the response frequency in the image frame, and archives according to the frame index to obtain the circuit board processing abnormality detection result.

[0035] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0036] By setting the gray scale anchor point in the image frame and collecting the standard gray value, the deviation accumulation caused by the overall illumination change of the image can be effectively avoided, the response lag area is identified through the interframe gray scale comparison, the early intervention of the etching abnormality process is realized, the texture missing area is identified by extracting the low-amplitude section of the gray scale fluctuation in the image boundary area, the identification accuracy of the structure blur is enhanced, the abnormal structure form is finely extracted by combining the gray scale gradient chain construction and the closed error judgment mechanism, the response change trajectory tracking of the area in the frame sequence is superimposed, so that the judgment is not limited to single-frame abnormality detection and has the time sequence evolution identification capability, and thus under the conditions of image noise interference and etching process floating, the abnormal sign area can still be stably captured and the reliable detection result can be generated. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical scheme in the embodiment of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 is a schematic diagram of a circuit board processing abnormality detection system provided by the embodiment of the present application;

[0039] Figure 2 is a system framework schematic diagram of the present application;

[0040] Figure 3 is a flowchart of the image anchoring module of the present application;

[0041] Figure 4 is a flowchart of the gray scale comparison module of the present application;

[0042] Figure 5A flow chart of a candidate region screening module of the present application;

[0043] Figure 6 A flow chart of a gradient closure verification module of the present application;

[0044] Figure 7 A flow chart of a behavior trajectory evolution module of the present application. DETAILED DESCRIPTION

[0045] The technical solutions in the present application will be described below with reference to the drawings.

[0046] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0047] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent. The words "of", "corresponding" and "relevant" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent.

[0048] In the embodiments of the present application, the subscript such as W1 can be written in the form of non-subscript such as W1 at times. When the distinction is not emphasized, the meanings expressed are consistent.

[0049] To make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail with reference to the drawings and specific embodiments.

[0050] The embodiments of the present application provide a circuit board processing abnormality detection system, as shown in Figures 1-2 The circuit board processing abnormality detection system schematic diagram, the system comprises:

[0051] The image anchoring module acquires the continuous process image frame in the circuit board etching stage, sets multiple pixel points in the pattern boundary in each image as positioning anchor points, collects the standard gray scale value (the gray scale level (0-255 level) defined by the international standard for circuit board acceptance, used to quantify the etching area image features. The standard is calibrated by a standard color card, and the measurement value of the color difference meter is ΔE≤1.5) of each anchor point as the first frame reference data, and generates gray scale anchor point reference data by statistics;

[0052] The gray scale contrast module obtains the gray scale data of the same position in the subsequent image frame according to the corresponding coordinates of each anchor point in the gray scale anchor point reference data, sequentially performs the inter-frame gray scale value contrast operation, identifies the anchor point sequence with a difference lower than the etching process monitoring threshold (the gray scale change allowed range (the gray scale difference between adjacent frames ≤ 15) defined by the rigid printed board qualification and performance specification, and the value exceeding the threshold is regarded as an abnormal response. The threshold is determined based on the copper foil etching rate experiment, and the measurement standard is etching uniformity ≥ 95%), and obtains the etching response lag area;

[0053] The candidate region screening module extracts the pixel gray scale sequence of the corresponding image boundary region according to the position of the etching response lag area in the image frame, identifies the continuous pixel segment with a stable gray scale value change amplitude in the low dynamic range interval (the gray scale fluctuation range (maximum-minimum gray scale value ≤ 20) defined by the international standard for image coding, which is used to identify the texture feature missing area), determines it as a structure fuzzy segment area, and obtains the fuzzy structure candidate segment.

[0054] The gradient closure verification module extracts the gray scale difference between adjacent pixels based on the pixel gray scale sequence in the fuzzy structure candidate segment, forms a continuous gray scale gradient chain, performs absolute value extraction on the gray scale difference at the beginning and end of the gradient chain, and judges the error with the gradient closure tolerance (the allowed error (default ± 5 gray scale levels) of gradient consistency defined by the specification standard, which is used to determine the quality of contour closure), records the segment area with an error exceeding the gradient closure tolerance as an abnormal structure area, and generates a structure chain closure abnormal area.

[0055] The behavior trajectory evolution module marks whether the area appears in different frames according to the corresponding position of the structure chain closure abnormal area in the continuous image frame, traces the response change in the image frame index order, determines whether the corresponding area continuously shows stable abnormal signs, and generates a circuit board processing abnormality detection result.

[0056] The gray scale anchor point reference data includes anchor point position index, standard gray scale level, and pattern boundary mapping relationship, the etching response lag area includes inter-frame gray scale change sequence, low-variation anchor point set, and threshold trigger mark, the fuzzy structure candidate segment includes low dynamic range gray scale sequence, continuous pixel segment index, and image boundary region identification, the structure chain closure abnormal area includes gradient chain beginning and end gray scale difference abnormal segment, gray scale closure error record, and abnormal structure segment label, and the circuit board processing abnormality detection result includes abnormal area trajectory index result, abnormal response continuous marking result, and stable abnormality determination identification record.

[0057] Specifically, as shown in Figure 2 , Figure 3 , the image anchoring module includes:

[0058] The gray scale acquisition submodule acquires continuous process image frames in the circuit board etching stage, sets multiple pixel points within the pattern boundary in each image as anchor points, acquires standard gray scale values of each anchor point, removes edge interference pixels, and generates an image anchor gray scale value set;

[0059] To acquire continuous process image frames in the circuit board etching stage, an automatic image acquisition device is first called to capture image frame sequences on the circuit board process line in real time during etching, the image acquisition frequency is set to 10 frames per second, the total acquisition time is set to 20 seconds, 200 frames of images are acquired, and the time intervals of the image frames need to be uniform during the acquisition process. The size of each image is set to 2048 pixels x 2048 pixels, and the image format is set to grayscale mode for subsequent gray scale value extraction processing. Then, the pattern boundary range is set in each image. According to the standard circuit diagram size specification, the boundary can be set to the area within a radius of 500 pixels from the center of the circuit diagram. Thirty pixel points are uniformly selected as anchor points in this area. Specifically, anchor points can be distributed by selecting every 100 pixels, for example, in the 10th image, anchor points are set at coordinates (100, 100), (200, 100), (300, 100), and so on. The corresponding gray scale values of each anchor point in the grayscale image are acquired. The gray scale value is an unsigned integer between 0 and 255. Then, edge interference removal processing is performed on all acquired gray scale value data. By removing pixel point values within 30 pixels from the boundary of the image edge, interference data is excluded. The gray scale values of the remaining anchor points are then summarized. For example, after removing 5 edge points from the 10th image, the remaining 25 anchor point gray scale values are 220, 218, 215, 219, etc. By comparing these gray scale values with the reference gray scale range under the standard color card gray scale, the ΔE color difference value calibration limit is set to ΔE ≤ 1.5. The color difference between the acquired anchor point gray scale and the standard gray scale is measured using a portable color difference meter. If the ΔE value exceeds 1.5, the anchor point value is removed. Finally, the set of anchor point gray scale values that meet the ΔE value condition are retained as the valid data set for the anchor points in the image. wherein , , is the three stimulus values of the measured color value, , , is the reference value under the standard color card. This formula can be used to verify the color difference of each anchor point gray scale data, and finally generate an image anchor gray scale value set.

[0060] The reference gray scale setting submodule selects the first frame data as the reference image frame according to the time sequence based on the gray scale values of each frame anchor point in the image anchor gray scale value set, extracts the standard gray scale values of all anchor points in the first frame, calls the standard color card gray scale reference value for comparison and calibration, selects valid reference anchor points, and obtains valid anchor point gray scale reference values.

[0061] Based on the image anchor gray scale value set of each frame image anchor data, all image frames need to be arranged in time sequence first, for example, the first frame to the 200th frame image, taking the first frame collected earliest as the reference image frame, reading the standard gray scale value data of all 30 anchors in this frame, at this time, assuming that the anchor gray scale values in the first frame image are 210, 212, 215, 213, 211, etc. in turn, calling the Lab* gray scale standard value in the standard color card to compare the gray scale value of this frame to determine whether each anchor belongs to the reference range, measuring the ΔE value of each anchor gray scale in this frame by the color difference meter, for example, the measured ΔE value of anchor 1 is 1.2, anchor 2 is 1.6, anchor 3 is 1.3, etc., selecting all anchors with ΔE value falling within 1.0 to 1.5, if the anchor numbers in the first frame image meeting the requirements of this interval are 1, 3, 4, 5, 8, 10, etc. a total of 18 anchors, then the gray scale values of these 18 anchors are extracted to construct the reference anchor set, and the judgment and selection threshold interval is set as ΔE range 1.0 to 1.5, which is derived from the international electronic image acceptance standard IEC61747, and is formulated according to the standard requirement of medium deviation level, based on the circuit board color card data, finally the corresponding anchor gray scale data is extracted, and a reference gray scale value sequence is formed, for example, the gray scale values of anchors 1, 3 and 4 are 210, 215 and 213 respectively, forming an effective anchor reference value matrix for subsequent reference operation, obtaining the effective anchor gray scale reference value.

[0062] The gray scale reference generation submodule calculates the gray scale change interval in the subsequent continuous image frames according to the gray scale values of each anchor in the effective anchor gray scale reference value, calculates the gray scale mean value, maximum deviation value and change trend value of each anchor in all image frames, classifies and groups the whole anchors, and generates gray scale anchor reference data;

[0063] According to the 18 reference anchor data in the effective anchor gray scale reference value, the gray scale values of the corresponding anchor positions in the 2nd to 200th frames of images are called to detect and record the gray scale change range, the maximum value, minimum value and average value of each anchor in the whole frame sequence are calculated, for example, the gray scale values of anchor 1 in the 2nd to 200th frames are 209, 208, 210, 211, etc. the maximum value is 213, the minimum value is 207, and the average value is 210.2 in the 198th frame, and the gray scale change trend is calculated as: For anchor 1, it is In this way, the gray scale trend values of all 18 anchor points are calculated, and then the trend values are grouped, and the trend value grouping threshold is set as: 0-0.02, 0.02-0.05, and 0.05 or more, which are divided into three categories, namely stable, slight fluctuation, and significant fluctuation, wherein the stable anchor points are numbered as 2, 5, and 9, and the significant fluctuation anchor points are numbered as 1, 7, and 10, etc. Finally, the gray scale standards of the anchor points in different groups are constructed, the gray scale mean value, standard deviation and median value of the trend value of each group are calculated to form the gray scale standard template of the group, which is used as the reference basis for the overall gray scale anchor point data, and a mapping relationship table between all anchor points and the gray scale trend and standard is constructed, and finally the gray scale anchor point reference data is generated.

[0064] Specifically, as shown in Figure 2 , Figure 4 , the gray scale contrast module includes:

[0065] The image gray scale extraction submodule obtains the coordinate information of all anchor points in the gray scale anchor point reference data, collects the gray scale values of the corresponding coordinate positions in the subsequent image frames, selects continuous frame images with the same sequence length as the reference data, positions and extracts the gray scale values of all anchor points in each image to generate the anchor point gray scale sequence value;

[0066] To obtain the coordinate information of all anchor points in the gray scale anchor point reference data, the anchor point index table stored in the reference data needs to be read first, which includes image frame number, anchor point number and corresponding coordinate position. In a specific implementation, for example, the first frame of image includes anchor point numbers 001 to 030, which correspond to coordinate positions (150, 200), (250, 300), etc. Then, the gray scale values of the corresponding coordinate positions in the subsequent image frames are collected. The image frames are recorded continuously at a rate of 10 frames per second for 20 seconds, a total of 200 gray scale images, and the gray scale range is set to integer values between 0 and 255. The frame sequence length consistent with the reference data is selected, i.e. image frames 2 to 200, and the gray scale values of the 30 anchor point coordinate points in each image are extracted. The gray scale extraction process is based on pixel gray scale reading, and the pixel values of the anchor point positions in each frame are read out to form a list. For example, in the 10th frame of image, the gray scale value of anchor point 001 is 210, and the gray scale value of anchor point 002 is 215. In this way, a two-dimensional gray scale recording table is established with frame number as horizontal axis and anchor point number as vertical axis, and the elements in the table are the gray scale values under the corresponding frame and anchor point. After the table is established, each row represents the complete time sequence gray scale change value of a certain anchor point, and each column represents the gray scale distribution of all anchor points under the same frame. For example, the gray scale values of anchor point 005 in frame numbers 2 to 200 are 208, 210, 209, 211, etc. to form a group of records representing the change of anchor point gray scale with time. At the same time, the image frame number and the collection time stamp are bound one by one to establish a complete gray scale recording system of all anchor points in the frame sequence, and the anchor point gray scale sequence value is obtained.

[0067] The anchor point frame difference judgment sub-module calculates the gray scale difference between each anchor point based on the gray scale difference values of each anchor point in the adjacent frames in the anchor point gray scale sequence value, and if the absolute value does not exceed the etching process monitoring threshold, the anchor point is marked as a continuous consistent frame point. The anchor point set meeting the continuous consistency condition is extracted to obtain the gray scale consistent anchor point sequence.

[0068] To call the gray scale difference values of each anchor point in the adjacent frames in the anchor point gray scale sequence value, the anchor point gray scale sequence needs to be read line by line from the above record table and the adjacent frame difference value is calculated. For example, the gray scale value of anchor point 008 between frame 5 and frame 6 is 212 and 198, and the difference value is 14. The difference value calculation method is , wherein is the gray scale value of the nth frame. It is judged whether each difference value exceeds the etching process monitoring threshold 15. If the result is less than or equal to 15, the anchor point is marked as a gray scale consistent frame point. Repeat this calculation to judge all anchor points in the full frame range, establish a gray scale consistency marking matrix, and set the matrix elements to 1 for consistency and 0 for inconsistency. For example, anchor point 003 has 8 pairs of adjacent frame gray scale difference values less than 15 within frames 10 to 20, so its corresponding position is 1, and the rest is 0. Then, the total number of consistent frame points of each anchor point in all frames is counted. If an anchor point remains consistent in more than 80% of the frame pairs, the anchor point is included in the consistent anchor point set. For example, anchor point 012 has 182 difference values less than or equal to 15 in 198 adjacent frame pairs, with a corresponding proportion of 91.9%, meeting the consistency condition. The final anchor point number and coordinate information that meet the standard are extracted to form a data set to obtain the gray scale consistent anchor point sequence.

[0069] The response abnormality identification sub-module performs spatial clustering on the corresponding coordinates according to the anchor point region in the gray scale consistent anchor point sequence that has a continuous frame difference value below the etching process monitoring threshold, identifies a continuous consistent anchor point group that repeatedly appears in multiple frames, and judges whether the anchor point group accounts for more than the etching uniformity threshold in the total anchor points. If it is higher, it is marked as a response lag area, an index map of the lag area under the corresponding frame sequence is established, and the etching response lag area is obtained.

[0070] According to the anchor point region in which the continuous frame difference value is less than 15 in the gray-scale consistent anchor point sequence, the coordinate information of the consistent anchor point set is extracted first, and the positions corresponding to the coordinate information in the circuit board image are subjected to spatial clustering processing. The Euclidean distance is used as the spatial division basis, the maximum clustering radius between anchor points is set to 100 pixels, and multiple consistent anchor points within the range are classified into the same region. For example, anchor points 001, 002, 003, and 004 are located between the coordinate regions (100, 200) to (190, 210), and the distance between any two of them does not exceed 100 pixels, so they are classified into the same region. Then, the number of anchor points in each clustering region is counted, and the etching uniformity threshold is set to 95%. The threshold is derived from the image response judgment standard in the copper foil etching rate experiment. If the number of anchor points in a region divided by the total number of anchor points is greater than 0.95, that is, more than 28.5 anchor points are gray-scale consistent points, it is judged that the region is a response lag region. For example, if a region contains 29 consistent anchor points and the total number of anchor points is 30, the proportion is 96.7%, which meets the set standard. The region index information and frame segment number are retained, and a lag region index map is established. The index map takes the image frame number as the main index and the region number as the secondary index. The corresponding frame segment and anchor point spatial clustering result are recorded to obtain the etching response lag region.

[0071] Specifically, as shown in Figure 2 、 Figure 5 , the candidate region screening module includes:

[0072] The image region extraction submodule obtains the region index and coordinate information of all corresponding image frames in the etching response lag region. The image boundary range corresponding to the region marked by the index is read in each frame image. The boundary pixel extraction band is constructed by extending outward according to the coordinates. All pixel points in the boundary band are traversed and the gray scale value is recorded. The extracted gray scale value is arranged in row and column order to form a one-dimensional gray scale sequence. The gray scale boundary record set of all lag regions in the corresponding image frame is established, and the boundary gray scale sequence set is generated.

[0073] The region index and coordinate information of all corresponding image frames in the etching response lag area are obtained. First, the frame number marked as response lag in the last module and its corresponding region number and coordinate range information are extracted, for example, the lag region number in the 18th frame is Z01, and the boundary coordinate range is the upper left corner (200, 150) and the lower right corner (300, 250). Then, based on the coordinate range, a boundary pixel extraction band is constructed by extending 50 pixels outward along the four boundaries in each frame image. The new boundary range after extension is (150, 100) to (350, 300). Then, the gray scale values of all pixel points in this range are read row by row, and the gray scale values are spliced into a one-dimensional gray scale sequence according to the image scanning order. The sequence length is equal to the region width multiplied by the boundary band width, for example, the extraction band width is 50 pixels, and the region width is 100 pixels. Therefore, the length of each sequence is 5000 pixel gray scale values, and the gray scale value is an integer between 0 and 255. Each pixel gray scale value in the sequence corresponds to a specific position in the image. A record set is established with four fields of frame number, region number, pixel position and gray scale value. Multiple regions correspond to multiple sequences, for example, the boundary range of lag region Z03 in frame 20 is (400, 300) to (500, 400), the gray scale extraction band range is (350, 250) to (550, 450), and the corresponding gray scale sequence length is 10000. Finally, all region gray scale sequences are aggregated according to the frame number to obtain the boundary gray scale sequence set.

[0074] The gray scale fluctuation detection submodule calculates the difference between the maximum gray scale value and the minimum gray scale value of each pixel sequence in the boundary gray scale sequence set, filters out the sequence segment with a gray scale fluctuation less than or equal to the gray scale fluctuation judgment threshold, and records the starting position and length of the pixel to generate a low dynamic gray scale interval segment.

[0075] According to each pixel sequence in the boundary gray scale sequence set, the difference between the maximum gray scale value and the minimum gray scale value in the sequence is calculated by reading the sequence one by one and extracting the maximum value and minimum value inside each sequence. The gray scale fluctuation calculation formula is This difference is an important indicator for judging the fluctuation range of texture structure. The threshold value is set to 20, which is derived from the international standard for image encoding ITU-T T.81. The definition standard for texture missing area is used to define all difference values ​The sequence is determined as a low dynamic interval sequence, and the starting pixel position and sequence length of the sequence in the original image are recorded for subsequent matching. For example, if the length of a sequence is 5000 pixels, the maximum value is 198, and the minimum value is 182, the difference is 16, which meets the set threshold condition, and the starting pixel position is recorded as (350, 250), and the sequence is marked as a low dynamic sequence. The detection operation is repeated, the difference value is calculated for each sequence, and a data set containing the starting position, length, and gray level fluctuation value of all sequences meeting the condition is established to obtain the low dynamic gray level interval segment.

[0076] The fuzzy structure identification submodule marks the sequences with a continuous pixel length greater than the gray level fluctuation judgment threshold as candidate segments based on the starting coordinate position and continuous pixel length of all gray level sequences in the low dynamic gray level interval segment. The spatial position index of each segment in the image frame is counted and matched with the initial buffer region coordinates to obtain the fuzzy structure candidate segment.

[0077] The starting coordinate point (x, y) and sequence length L are extracted from each record based on the starting coordinate position and continuous pixel length of all gray level sequences in the low dynamic gray level interval segment. The L value is filtered, and the effective candidate segment judgment threshold is set to 20, i.e. the continuous length of the sequence is greater than 20 pixels to be considered as a candidate segment. The interval comparison method is used to compare the L value of each record with the constant 20. If , the sequence enters the candidate set. Then, the spatial position of each candidate segment in the image frame is marked and matched with the buffer region coordinates under the corresponding frame. The overlap area ratio calculation method is used, and the spatial overlap threshold is set to 90%. The calculation formula is: , where represents the overlap area of the candidate segment and the buffer region, represents the area of the candidate segment itself. If the R value is greater than or equal to 90%, the segment is retained as a fuzzy structure region. For example, the area of a candidate segment in frame 28 is 1200 pixels, and the overlap area with the buffer region is 1120 pixels. Therefore, R = 93.3%, which meets the condition and is marked as a fuzzy segment. Finally, the segments meeting the spatial overlap condition are extracted from all candidates to establish a region number, frame number, and coordinate index table to obtain the fuzzy structure candidate segment.

[0078] Specifically, as shown in Figure 2 , Figure 6 , the gradient closure verification module includes:

[0079] The gray scale difference extraction submodule obtains the gray scale value sequence of all pixel points in the image frame in the fuzzy structure candidate segment, extracts the gray scale difference between adjacent two pixels according to the pixel arrangement order in each segment, records each difference value and the corresponding pixel position in each gradient chain, and generates a continuous gray scale gradient chain;

[0080] To obtain the gray scale value sequence of all pixel points in the image frame in the fuzzy structure candidate segment, the pixel coordinate index of each segment in the image needs to be read one by one, and the corresponding gray scale value sequence needs to be extracted according to the horizontal or vertical pixel arrangement. Assuming that there is a segment P07 in frame No. 28, the gray scale pixel distribution coordinates are from (150, 200) to (170, 200), a total of 21 continuous pixel points, and the corresponding gray scale values are 190, 192, 193, 194, 192, 191, 190, and so on. The difference between the gray scale values of adjacent two pixels is extracted according to the original pixel arrangement order. Assuming that the gray scale value of the first pixel is , the gradient difference is . If the gray scale values of the first and second pixels are 190 and 192 respectively, then . In this way, a complete gradient chain sequence is formed: {ΔG_1, ΔG_2, ΔG_3, …, ΔG_{n-1}}. Here, is the number of pixel points in the segment. Each gradient difference value is paired to record its sequence number in the gradient chain and the coordinates of the previous and next pixels, forming a one-dimensional gray scale gradient chain data structure. The corresponding frame number and segment number are also marked for archiving, and the continuous gray scale gradient chain is obtained.

[0081] The gradient error calculation submodule extracts the gray scale value difference between the first and last points of each chain according to the continuous gray scale gradient chain, calculates the absolute value and records it as the closing difference value, selects the gradient chains with a closing difference value greater than the gradient closing tolerance in all segments, records the segment number, the first and last pixel coordinates, and the error value, and obtains the first and last gradient error value.

[0082] According to the continuous gray scale gradient chain, the gray scale value difference between the first and last points of each chain is extracted. First, the gray scale value of the first pixel and the gray scale value of the last pixel are extracted from each gradient chain, the first and last gray scale difference is calculated as , and the error threshold is set to 5. This threshold is the maximum gray scale error value specified in the gradient closing tolerance standard. If the actual calculated , it is considered that the gradient closing condition is not met, and the error selection operation is performed. For example, if the first pixel gray scale value of the segment P12 is 188 and the last pixel is 196, the closing difference value is ​greater than a set threshold 5, it is determined to be an abnormal closed chain, if the first and last gray scales of another segment P13 are 210 and 206 respectively, the closed difference value is 4, which belongs to a closed normal chain, all abnormal gradient chain segment numbers, frame numbers, first and last coordinate positions and error values with a closed difference value greater than 5 are recorded, a temporary abnormal gradient chain marker list is established, and first and last gradient error values are obtained.

[0083] The abnormal region determination submodule performs region index extraction operations on all segments with error values greater than the gradient closure tolerance based on the error data corresponding to each segment in the first and last gradient error values, counts the coordinate range, pixel number and gradient chain length of the segment in the image frame, marks the abnormal region number and archives the corresponding frame number, and obtains the structural chain closure abnormal region;

[0084] The error data corresponding to each segment in the first and last gradient error values is called, region index extraction operations are performed on all segments with error values greater than 5, the abnormal gradient chain segment number and the image frame number in which it is located are read, the starting pixel coordinate and the ending coordinate of the segment are located, for example, segment P07 is located in frame 36, the starting coordinate is (400, 210), the ending coordinate is (430, 210), the total pixel number is 31, the gray scale difference value chain length of each abnormal segment is counted wherein is the number of segment pixels, corresponding to the number of difference values in the gradient chain, if multiple abnormal segments are concentrated in the same region or adjacent frames, they are merged and classified as an abnormal structure region, an abnormal region index table is constructed, the table fields include region number, frame number, segment number set, first and last coordinates and error value interval range, and the region numbers are sorted from large to small according to the number of segments, finally all segment regions that meet the gradient error threshold condition are merged and registered, and the structural chain closure abnormal region is obtained.

[0085] Specifically, as shown in Figure 2 , Figure 7 , the behavior trajectory evolution module includes:

[0086] The inter-frame marker recording submodule obtains the frame number and coordinate index information of each abnormal segment in the structural chain closure abnormal region, traverses the continuous image frame sequence, and marks whether there is an abnormal region with the same number and the same position in each frame of image, constructs a binary sequence according to the frame order, and generates an abnormal region frame sequence record value;

[0087] To obtain the frame number and coordinate index information of each abnormal segment within a structural chain closure anomaly region, the region number, image frame number, and start and end coordinate information of each segment need to be extracted from the anomaly region label table obtained from the preceding module. An anomaly region tracking matrix is ​​established for each segment. Each frame of image data is traversed in the frame sequence, checking if a region with the same region number or a coordinate range overlap rate greater than 90% exists in each frame. If it exists, it is marked as 1; otherwise, it is marked as 0. The generated results are stored in a binary label vector, arranged in frame number order to form a binary sequence. For example, if anomaly region number Z07 actually appears in frames 31, 32, 33, 35, and 38 in frames 31 to 40, the constructed binary sequence is {1, 1, 1, 0, 0, 1, 0, 0, 1, 0}, with a sequence length equal to the number of image frames (10 frames). A single label indicates whether the region appears within the corresponding frame. The region matching method uses a pixel coordinate interval similarity judgment mechanism, based on the region coordinate set in the frame image. With the original coordinate set of the anomaly region The ratio of the intersection area to the original area is used as the criterion for judgment. A valid match is considered when the frame is checked frame by frame. After each frame is checked, a binary existence sequence with a length equal to the number of image frames is generated for each abnormal segment, and the frame sequence record value of the abnormal region is obtained.

[0088] The continuous response tracking submodule calculates the length of the continuous existence segment of each abnormal region in the frame sequence based on the inter-frame changes of each record in the abnormal region frame sequence record value. If the length of the continuous existence segment is greater than or equal to the benchmark length of the stable segment, it is determined to be a continuous response segment. The module extracts the abnormal region numbers and start and end frame indices that meet the conditions to obtain the continuous response frame segment interval.

[0089] Based on the inter-frame changes of each record in the abnormal region frame sequence, a sliding window operation needs to be performed on the binary sequence to determine whether there is a segment with consecutive 1 values. Assuming the sliding window size is 3 frames, if a region's sequence contains three or more consecutive subsequences of 1 values, then that segment is defined as an abnormal response continuous frame segment. For example, if a region's sequence is {0, 1, 1, 1, 0, 1, 0, 1, 1, 0}, then frames 2 to 4 constitute a valid continuous frame segment with start and end indices [2, 4]. The sliding window method involves continuously scanning 3 frames of data starting from frame 1. If the condition is met, the frame index and region number are recorded. This operation is repeated until the end of the sequence. If multiple segments meet the condition, all start and end frame indices are recorded. The final result is a list of abnormal regions and their corresponding one or more response segments, with the length of each segment recorded. ,in To establish the start and end frame indices, this tracing process is performed across all regions to obtain the corresponding continuous frame segment intervals.

[0090] The abnormal state determination submodule filters the region numbers that meet the stable abnormal judgment threshold based on the abnormal region numbers and the length of the continuous frame segment in the response, integrates and statistically analyzes the corresponding coordinate range and response frequency in the image frame, and archives them by frame index to obtain the circuit board processing abnormal detection results.

[0091] The call retrieves the index of the abnormal region and the duration of the continuous frame segment within the response frame interval. First, a stable anomaly detection threshold is set to a duration greater than or equal to 5 frames. That is, if an abnormal region has a response length of [missing information] within a continuous image frame interval... If it is a stable abnormal region, then during the filtering process, each record will be selected from the stable abnormal regions. The value is compared with the constant 5. If the condition is met, the region number is included in the result region. The corresponding frame index range and original coordinate interval are extracted. This type of region is grouped and integrated according to frame number, and the response frequency in the corresponding image is counted. ,in, This indicates the number of frames where the abnormal region occurred. Given the total number of frames, if a region numbered Z12 appears 9 times in 30 frames, the response frequency is 30%, and it is classified as a stable abnormal region. All regions that meet the conditions are uniformly numbered and a data table is established. The fields in the table include region number, start and end frames, duration, response frequency in the frame, and spatial coordinate index, to obtain the circuit board processing abnormality detection results.

[0092] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A circuit board processing anomaly detection system, characterized in that, The system includes: The image anchoring module acquires continuous process image frames during the circuit board etching stage, sets positioning anchor points in each frame, collects the standard grayscale values ​​of each anchor point as the first frame reference data, and statistically generates grayscale anchor point reference data. The grayscale comparison module obtains grayscale data at the same position in subsequent image frames based on the coordinates of each anchor point in the grayscale anchor point reference data, performs inter-frame grayscale value comparison operation, identifies anchor point sequences in consecutive frames with differences lower than the etching process monitoring threshold, and obtains the etching response sluggish region. The candidate region filtering module extracts the pixel grayscale sequence of the corresponding image boundary region based on the position of the etch response sluggish region in the image frame, identifies continuous pixel segments whose grayscale value changes are stable in the low dynamic range range, determines them as structurally blurred fragment regions, and obtains blurred structural candidate fragments. The gradient closure verification module extracts the grayscale difference between adjacent pixels based on the pixel grayscale sequence in the fuzzy structure candidate segment, performs absolute value extraction on the grayscale difference between the beginning and end of the gradient chain and performs error judgment with the gradient closure tolerance, records the segment region whose error exceeds the gradient closure tolerance as an abnormal structure region, and generates an abnormal region of structure chain closure.

2. The circuit board processing anomaly detection system according to claim 1, characterized in that: The grayscale anchor reference data includes anchor position index, standard grayscale level, and pattern boundary mapping relationship. The etch response lag region includes inter-frame grayscale change sequence, low-variation anchor point set, and threshold trigger marker. The blurred structure candidate fragment includes low dynamic range grayscale sequence, continuous pixel segment index, and image boundary region identifier. The structural chain closure anomalous region includes gradient chain start and end grayscale difference anomalous segments, grayscale closure error record, and anomalous structural fragment label.

3. The circuit board processing anomaly detection system according to claim 1, characterized in that: The standard grayscale value is specifically the grayscale level defined by the international standard for electronic circuit board acceptance; the etching process monitoring threshold is specifically the allowable range of grayscale changes defined by the rigid printed circuit board qualification and performance specifications; and the low dynamic range is specifically the grayscale fluctuation range defined by the international standard for image coding.

4. The circuit board processing anomaly detection system according to claim 1, characterized in that: The image anchoring module includes: The grayscale acquisition submodule acquires image frames of continuous processes during the circuit board etching stage. In each image frame, multiple pixels within the pattern boundary are set as positioning anchor points. The standard grayscale value of each anchor point is acquired, and interference pixels at the image edge are removed to generate a set of image anchor point grayscale values. The reference grayscale setting submodule selects the first frame data as the reference image frame according to the time series based on the grayscale values ​​of each frame anchor point in the image anchor grayscale value set, extracts the standard grayscale values ​​of all anchor points in the first frame, calls the standard color card grayscale level reference value for comparison and calibration, filters the effective reference anchor points, and obtains the effective anchor point grayscale reference value. The grayscale reference generation submodule calculates the grayscale change range in subsequent consecutive image frames based on the grayscale values ​​of each anchor point in the effective anchor point grayscale reference values, and statistically analyzes the grayscale mean, maximum deviation, and trend value of each anchor point in all image frames. It then classifies and groups the overall anchor points to generate grayscale anchor point reference data.

5. The circuit board processing anomaly detection system according to claim 1, characterized in that: The grayscale contrast module includes: The image grayscale extraction submodule obtains the coordinate information of all anchor points in the grayscale anchor point reference data, collects the grayscale values ​​of the corresponding coordinate positions in subsequent image frames, selects continuous frame images with the same frame sequence length as the reference data, locates and extracts the grayscale values ​​of all anchor point coordinates in each frame image, and generates anchor point grayscale sequence values. The anchor point frame difference determination submodule calculates the gray level difference between each anchor point in the gray level sequence value of the anchor point in the adjacent frames. If the absolute value does not exceed the etching process monitoring threshold, the anchor point is marked as a continuous and consistent frame point. The set of anchor points that meet the continuous and consistent condition is extracted to obtain the gray level consistent anchor point sequence. The response anomaly identification submodule performs spatial clustering based on the coordinates of anchor point regions in the grayscale consistent anchor point sequence where the difference between consecutive frames is lower than the etching process monitoring threshold. It identifies continuous consistent anchor point groups that appear repeatedly in multiple frames, determines whether the proportion of the anchor point group in the total anchor points is higher than the etching uniformity threshold, and marks it as a response sluggish region if it is higher. It then establishes a sluggish region index map under the corresponding frame sequence to obtain the etching response sluggish region.

6. The circuit board processing anomaly detection system according to claim 1, characterized in that: The candidate region filtering module includes: The image region extraction submodule obtains the region index and coordinate information of all corresponding image frames in the etch response sluggish region. In each frame image, it reads the image boundary range corresponding to the region marked by the index, extends outward according to the coordinates to construct the boundary pixel extraction band, traverses all pixels in the boundary band and records the gray level value, arranges the extracted gray level value in row and column order to form a one-dimensional gray level sequence, establishes the gray level boundary record set of all sluggish regions in the corresponding image frame, and generates the boundary gray level sequence set. The grayscale fluctuation detection submodule calculates the difference between the maximum and minimum grayscale values ​​of each pixel sequence in the boundary grayscale sequence set, filters out sequence segments with grayscale fluctuation less than or equal to the grayscale fluctuation judgment threshold, and records the pixel start position and length to generate low dynamic grayscale interval segments. The fuzzy structure recognition submodule, based on the starting coordinates and continuous pixel lengths of all grayscale sequences in the low dynamic grayscale interval, marks sequences with continuous pixel lengths greater than the grayscale fluctuation judgment threshold as candidate segments, calculates the spatial position index of each corresponding region in the image frame, and matches it with the coordinates of the initial stagnant region to obtain fuzzy structure candidate segments.

7. The circuit board processing anomaly detection system according to claim 1, characterized in that: The gradient closure verification module includes: The grayscale difference extraction submodule obtains the grayscale value sequence of all pixels in the image frame of the candidate segment of the fuzzy structure, extracts the grayscale difference between two adjacent pixels according to the pixel arrangement order in each segment, records each difference in each gradient chain and the corresponding pixel position, and generates a continuous grayscale gradient chain. The gradient error calculation submodule extracts the gray level difference between the first and last points of each chain based on the continuous gray level gradient chain, calculates the absolute value and records it as the closure difference, filters all segments with closure differences greater than the gradient closure tolerance, records the segment number, the first and last pixel coordinates and the error value, and obtains the first and last gradient error values. The abnormal region determination submodule performs a region index extraction operation on all segments with error values ​​greater than the gradient closure tolerance based on the error data corresponding to each segment in the first and last gradient error values. It counts the coordinate range, number of pixels and gradient chain length of the segment in the image frame, marks the abnormal region number and archives it according to the corresponding frame number, and obtains the structural chain closure abnormal region.

8. The circuit board processing anomaly detection system according to claim 1, characterized in that, The system also includes: The behavior trajectory evolution module marks whether the structural chain closure abnormal region appears in different frames according to the corresponding position of the abnormal region in consecutive image frames, tracks the response changes in the image frame index order, determines whether the corresponding region continues to show stable abnormal signs, and generates circuit board processing abnormality detection results.

9. The circuit board processing anomaly detection system according to claim 8, characterized in that: The circuit board processing anomaly detection results include anomaly area trajectory index results, anomaly response continuous marking results, and stable anomaly judgment identification records.

10. The circuit board processing anomaly detection system according to claim 8, characterized in that: The behavior trajectory evolution module includes: The inter-frame marking and recording submodule obtains the frame number and coordinate index information of each abnormal segment in the structural chain closure abnormal region, traverses the continuous image frame sequence, marks whether there are abnormal regions with the same number and the same position in each frame image, constructs a binary sequence according to the frame order, and generates abnormal region frame sequence recording values. The continuous response tracking submodule calculates the length of the continuous existence segment of each abnormal region in the frame sequence based on the inter-frame changes of each record in the abnormal region frame sequence record value. If the length of the continuous existence segment is greater than or equal to the stable segment judgment benchmark length, it is determined to be a response continuous segment. All abnormal region numbers and start and end frame indices that meet the conditions are extracted to obtain the response continuous frame segment interval. The abnormal state determination submodule filters out the region numbers that meet the stable abnormal judgment threshold based on the abnormal region numbers and the length of the continuous frame segment in the response, integrates and statistically analyzes the corresponding coordinate range and response frequency in the image frame, and archives them by frame index to obtain the circuit board processing abnormality detection results.

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