A method and system for automatically producing hardcover boxes

Abstract

The present application discloses an automated production method for hardcover boxes and a system thereof, the method comprising presetting a reference positioning point and a calibration reference point on a production line for hardcover boxes; obtaining the paper processing parameters of the hardcover box paper currently being pre-processed in each processing step; obtaining the paper position image and paper position coordinate data of the hardcover box paper according to a preset visual positioning component; calculating deviation data to be adjusted based on the reference positioning point image, the calibration reference point image and the positioning mark points of the visual positioning component; obtaining the corresponding adjustment threshold interval; judging whether the deviation data to be adjusted is within the corresponding adjustment threshold interval, and obtaining an adjustment deviation judgment result; the robot obtains the current alignment adjustment data based on the paper position coordinate data and the corresponding adjustment deviation judgment result, and performs an alignment adjustment operation when the alignment adjustment threshold is met, and the robot positions the current hardcover box blank on the hardcover box paper; the present application improves the positioning accuracy of automated production of hardcover boxes.

Description

Technical Field

[0001] The present application relates to the field of automated production technology, and in particular to an automated production method and system for hardcover boxes. Background Art

[0002] In the hardcover box production line, due to the high requirements for product positioning accuracy, traditional manual production or simple mechanized production can no longer meet the efficiency and quality requirements of modern industrial production; with the development of industrial automation, more and more production lines have begun to introduce robots and visual positioning systems to improve production efficiency.

[0003] In the automated production process of hardcover boxes, precise positioning is the key to achieving efficient production; however, in the actual production process, due to the influence of various factors such as equipment problems and operator problems, the positioning data is often inaccurate, especially in production lines that use semi-automatic methods to produce and package hardcover boxes. This production method is not only inefficient, but also has low positioning accuracy, which can easily lead to unstable product quality. Therefore, there is room for improvement. Summary of the invention

[0004] In order to improve the positioning accuracy of the automated production of hardcover boxes and effectively ensure the stability of product quality, the present application provides a hardcover box automated production method and system.

[0005] In the first aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0006] An automated production method for hardcover boxes, comprising:

[0007] Preset the benchmark positioning points and calibration reference points on the production line of hardcover boxes;

[0008] In each processing step, the paper processing parameters of the hardcover box paper currently being processed are obtained; the paper position image of the hardcover box paper currently being processed is obtained according to a preset visual positioning component; the visual positioning component is provided with positioning mark points, and the paper position image includes a reference positioning point image and a calibration reference point image;

[0009] Acquire the paper position coordinate data based on the paper position image; calculate the deviation data to be adjusted of the hardcover box paper in each processing step based on the reference positioning point image and the calibration reference point image and taking the positioning mark point of the preset visual positioning component as the reference;

[0010] Based on the paper processing parameters, the adjustment threshold interval corresponding to the hardcover box paper currently being processed is obtained; judging whether the deviation data to be adjusted is within the corresponding adjustment threshold interval, and obtaining an adjustment deviation judgment result;

[0011] The surface paper position coordinate data and the corresponding adjustment deviation judgment result are sent to a robot used to perform packaging. The robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs an alignment adjustment operation when the alignment adjustment data meets a preset alignment adjustment threshold. The robot is used to position the current hardcover box blank on the hardcover box surface paper.

[0012] By adopting the above technical solution, the reference positioning point and the calibration reference point are positioning effect identification points pre-fixed at fixed positions of the hardcover box automatic production line, and the distance between the reference positioning point and the calibration reference point is fixed and unchanged, so as to detect and calculate whether the hardcover box face paper is accurately placed in each processing step; specifically, in each processing step of the hardcover box, the image of the currently pre-processed hardcover box face paper in the processing area is obtained through the visual positioning component, and the image acquisition range includes the reference positioning point and the calibration reference point. The two positioning effect identification points (referring to the reference positioning point and the calibration reference point) are applicable to hardcover box face papers of various shapes, which is conducive to improving the applicability of the hardcover box production line; Then, the tissue paper position coordinate data is obtained through the tissue paper position image. In order to determine the deviation of the tissue paper coordinate data, the reference positioning point image, the calibration reference point image and the preset positioning mark point of the preset visual positioning component are used as a reference to calculate and determine the deviation data to be adjusted of the currently pre-processed hardcover box tissue paper. Then, based on the adjustment threshold interval corresponding to the currently pre-processed hardcover box tissue paper, it is determined whether the deviation data to be adjusted is within the corresponding adjustment threshold interval. When the deviation data to be adjusted is greater than the corresponding adjustment threshold interval, it indicates that the deviation of the currently pre-processed hardcover box tissue paper is large. The tissue paper position coordinate data and the corresponding adjustment deviation judgment result are sent to the robot used to perform packaging.

[0013] Since the robot is used to position the hardcover box blank on the hardcover box surface paper, and the robot will have a preset hardcover box blank placement coordinate position data in the processing and packaging process of the same batch of hardcover boxes, before placing the hardcover box blank, in order to improve the placement accuracy of the hardcover box blank and improve the packaging effect of the entire hardcover box, the present application will obtain the alignment adjustment data of the hardcover box blank based on the current surface paper position coordinate data and the corresponding adjustment deviation judgment result, and compare the alignment adjustment data with the preset alignment adjustment threshold to determine whether the robot needs to adjust the placement position of the hardcover box blank, and perform the alignment adjustment operation when it is determined that the alignment adjustment data of the current pre-processed hardcover box blank meets the preset alignment adjustment threshold. After performing the alignment adjustment operation, the hardcover box blank is positioned and placed on the hardcover box surface paper, so as to improve the positioning accuracy of the automated production of hardcover boxes and effectively ensure the stability of product quality.

[0014] In a preferred example of the present application: the visual alignment component includes a first visual alignment component and a second visual alignment component; the first visual alignment component is aligned with the reference positioning point, and the second visual alignment component is aligned with the calibration reference point;

[0015] The method of calculating the deviation data to be adjusted of the hardcover box surface paper of each processing step based on the reference positioning point image and the calibration reference point image and taking the positioning mark point of the preset visual positioning component as a reference includes:

[0016] In each processing step, based on the reference positioning point image collected by the first visual alignment component, combined with the positioning mark points of the first visual alignment component, the actual reference position data of the hardcover box surface paper is calculated; based on the calibration reference point image collected by the second visual alignment component, combined with the positioning mark points of the second visual alignment component, the actual reference position data of the hardcover box surface paper is calculated;

[0017] According to the actual benchmark position data, the actual reference position data, and the position distance between the benchmark identification point and the calibration reference point, the deviation data to be adjusted of the hardcover box surface paper in each processing step is calculated.

[0018] By adopting the above-mentioned technical scheme, the present application provides a dual-vision positioning system, in which the reference positioning point is the positioning identification point of the first vision positioning component for positioning calibration, and is used for the precise position of the positioning calibration, that is, the reference positioning point is used to provide the first vision positioning component with the positioning coordinate data calibration reference of the hardcover box surface paper. Similarly, the calibration reference point is the positioning identification point of the second vision positioning component for positioning calibration, and is used for the precise position of the positioning calibration, that is, the calibration reference point is used to provide the second vision positioning component with the positioning coordinate data calibration reference of the hardcover box surface paper. Since the position distance between the reference positioning point and the calibration reference point is fixed, the method of judging the positioning effect of the hardcover box surface paper in each processing step based on the reference positioning point and the calibration reference point is conducive to improving the accuracy of judging the positioning effect of the hardcover and surface paper, and is suitable for the detection needs of image positioning effects of various types of hardcover box surface paper with different shapes.

[0019] Furthermore, in order to conveniently and efficiently calculate the deviation data to be adjusted of the hardcover box paper in each processing step, the actual reference position data of the hardcover box paper is calculated based on the reference positioning point image of the first visual alignment component in combination with the positioning mark points of the first visual alignment component, and the actual reference position data of the hardcover box paper is calculated based on the calibration reference point image collected by the second visual alignment component in combination with the positioning mark points of the second visual alignment component. Then, the position distance between the reference identification point of the ideal target position and the calibration reference point is used as the positioning reference to calculate the deviation data to be adjusted of the hardcover box paper in each processing step. The calculation efficiency is high and the calculated deviation data to be adjusted has high accuracy.

[0020] In a preferred example of the present application: the adjustment deviation judgment result includes a first adjustment deviation judgment result and a second adjustment deviation judgment result; the adjustment threshold interval corresponding to the hardcover box paper currently being pre-processed is obtained based on the paper processing parameters; and whether the deviation data to be adjusted is within the corresponding adjustment threshold interval is determined to obtain the adjustment deviation judgment result, including:

[0021] Acquire the facial tissue processing mark corresponding to the currently pre-processed hardcover box facial tissue based on the facial tissue processing parameter;

[0022] Based on the paper processing mark, in each processing step, the deviation data to be adjusted of the hardcover box paper currently being processed is sent to a preset threshold judgment model to identify and obtain the adjustment threshold interval corresponding to the deviation data to be adjusted of the hardcover box paper currently being processed;

[0023] Determining whether the deviation data to be adjusted is within a corresponding adjustment threshold range;

[0024] If the deviation data to be adjusted is less than or equal to the adjustment threshold interval, obtaining the first adjustment deviation judgment result;

[0025] If the deviation data to be adjusted is greater than the adjustment threshold interval, the second adjustment deviation judgment result is obtained.

[0026] By adopting the above technical scheme, the facial paper processing mark is the unique processing information code of the pre-processed hardcover box facial paper. Based on the facial paper processing mark, the packaging processing requirements of various types of hardcover boxes when the processing standards are met can be obtained, so as to facilitate the judgment of the adjustment deviation effect of the hardcover box facial paper; the present application identifies and obtains the corresponding adjustment threshold interval based on the facial paper processing mark of the currently processed hardcover box in each processing step, and then compares the deviation data to be adjusted of the hardcover box facial paper with the corresponding adjustment threshold interval to determine whether the deviation data to be adjusted is within the corresponding adjustment threshold interval, and obtains the corresponding adjustment deviation judgment result. The deviation state and deviation degree (referring to a lighter deviation or a more serious deviation) of the positioning placement of the currently pre-processed hardcover box facial paper are obtained through the adjustment deviation judgment result, so as to facilitate real-time monitoring of the processing state of the hardcover box during the processing process.

[0027] In a preferred example of the present application: the robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs the alignment adjustment operation when the alignment adjustment data meets the preset alignment adjustment threshold value, and the robot is used to position the current hardcover box blank on the hardcover box surface paper, including:

[0028] When the adjustment deviation judgment result is the first adjustment deviation judgment result, the robot judges that the current hardcover box blank does not meet the preset alignment adjustment threshold based on the first adjustment deviation judgment result, and positions the current hardcover box blank on the hardcover box surface paper;

[0029] When the adjustment deviation judgment result is the second adjustment deviation judgment result, the robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the second adjustment deviation judgment result; based on the surface paper processing mark, obtains the alignment adjustment threshold corresponding to the alignment adjustment data, compares the alignment adjustment data with the corresponding alignment adjustment threshold, and when the alignment adjustment data is greater than or equal to the alignment adjustment threshold, triggers the alignment adjustment instruction to execute the alignment adjustment operation to position the current hardcover box blank on the hardcover box surface paper; otherwise, directly position the current hardcover box blank on the hardcover box surface paper.

[0030] By adopting the above technical solution, the robot pre-stores the positioning and placement coordinate data of the hardcover box blank. In each processing step of the robot, the current hardcover box blank will be positioned on the hardcover box surface paper based on the preset positioning and placement coordinate data. Since the adjustment deviation judgment result corresponding to the surface paper position coordinate data will have three situations in actual applications: the first situation is that the surface paper position coordinate data is accurately placed (corresponding to the first adjustment deviation judgment result in this application), and the positioning of the hardcover box surface paper meets the high-precision processing standard requirements; the second situation is that the alignment adjustment data in the second adjustment deviation judgment result is less than the corresponding alignment adjustment threshold value. At this time, the placement deviation of the surface paper position coordinate data is small, but it still meets the processing standard requirements of the hardcover box surface paper. At this time, there is still no need to adjust the hardcover box surface paper. The robot in the next processing step can still directly position the current hardcover box blank on the hardcover box surface paper based on the preset positioning and placement coordinate data.

[0031] The third situation is when the placement deviation of the surface paper position coordinate data is large (referring to when the adjustment deviation judgment result is the second adjustment deviation judgment result, and the alignment adjustment data is greater than or equal to the corresponding alignment adjustment threshold value), if the robot directly places the hardcover box blank according to the preset positioning placement coordinate data, it will reduce the product packaging quality of the hardcover box. At this time, by triggering the alignment adjustment instruction, the alignment adjustment operation is performed first, and then the current hardcover box blank is positioned on the hardcover box surface paper, so that the entire hardcover box automated production system has a high degree of automation, and the corresponding packaging processing procedures are executed through different adjustment deviation judgment results, which can not only meet high production efficiency and effectively ensure the operation speed of the production line, but also ensure that the positioning accuracy of the robot and the production line meets the standard requirements and ensures the positioning accuracy.

[0032] In a preferred example of the present application, when the pattern of the currently pre-processed hardcover box paper is fixed, after the paper position image of the currently pre-processed hardcover box paper is obtained according to the preset visual positioning component, the method further includes:

[0033] In a preset defect detection model, the tissue paper position image is subjected to tissue paper defect detection based on a preset defect target detection algorithm and a preset first monitoring frequency to obtain a corresponding tissue paper defect detection result; the tissue paper position image is associated with a tissue paper image identifier;

[0034] When the facial paper defect detection result includes a facial paper defect detection target, a target detection frame is matched and generated in the corresponding facial paper position image, the facial paper defect detection result is matched with the corresponding facial paper image identifier, and an information prompt instruction is generated and sent to a preset data monitoring terminal.

[0035] By adopting the above technical scheme, the present application also has the function of detecting defects of hardcover box paper. Since the hardcover box paper may be too thin and may be folded when grabbed and moved, or the paper may be wrinkled or damaged, etc., the hardcover box paper may have defects due to uncontrollable factors, thereby affecting the quality of the hardcover box packaging product. In order to automatically and intelligently monitor the paper quality and defect status of the hardcover box paper, the present application performs real-time defect detection on the hardcover box paper being packaged on the production line through a preset defect detection model and a first monitoring frequency, and obtains corresponding paper defect detection results. The facial paper position and the facial paper of the hardcover box with drawing defects are located in real time and accurately, and the defective hardcover box products are screened out in the subsequent process. The application also associates the facial paper image identification, and generates a target detection frame by matching the facial paper position image. By matching the facial paper defect detection result with the corresponding facial paper image identification, and generating an information prompt instruction and sending it to the preset data monitoring terminal, the defect status and position of the hardcover box facial paper can be prompted in real time, and the relevant staff can be prompted to take treatment measures in time, which effectively ensures the high product quality of the hardcover box and has a high economic promotion value.

[0036] In a preferred example of the present application, when the pattern of the currently pre-processed hardcover box paper is randomly generated, after the paper position image of the currently pre-processed hardcover box paper is obtained according to the preset visual positioning component, the method further includes:

[0037] Perform image preprocessing and feature extraction on the paper position image of the current frame to obtain image feature information, wherein the image feature information includes image color information, image pixel distribution, and image brightness information; and obtain the current image feature position coordinates based on the image feature information;

[0038] Obtaining image prediction and matching information of the current frame, and associating and matching the image prediction and matching information of the current frame with the current image feature position coordinates to obtain an image feature judgment result; the image prediction and matching information of the current frame is obtained by prediction and matching based on the paper position image of the previous frame and the current processing time node;

[0039] The image feature judgment result is output to the preset defect detection model.

[0040] By adopting the above technical solution, in actual production, there may be situations where the hardcover box face paper has a fixed face paper pattern or a randomly generated face paper pattern. For the production scenario where the face paper pattern is randomly generated, the present application also has the functions of extracting image feature information and comparing image feature information. By comparing the image feature information, it is helpful to quickly determine whether there is a printing abnormality or a drawing defect abnormality in the hardcover box face paper. Specifically, the present application associates and matches the image feature position coordinates corresponding to the image color information, image pixel distribution and image brightness information in the image feature information with the corresponding image prediction matching information, and then performs image feature comparison and feature information coordinate comparison, wherein the image prediction matching information is the target image information of the face paper position image of the current frame. When the image prediction matching information corresponds to the image feature information of the face paper position image, it indicates that the face paper pattern of the currently pre-processed hardcover box face paper is correct and the face paper is accurately placed and positioned. The judgment method of associating and matching the image feature position coordinates is helpful to improve the efficiency of defect judgment on the hardcover box drawing, indirectly improves production efficiency, and reduces the model calculation amount of the defect detection model.

[0041] In a preferred example of the present application: the image feature judgment result includes an abnormal image and a normal image, and the method further includes:

[0042] When the image feature judgment result is an image abnormality, an information prompt instruction is generated and sent to a preset data monitoring terminal; based on the information prompt instruction, the image feature judgment result and the time data when the information acquisition instruction is issued are stored in a preset record list;

[0043] When the image feature judgment result is that the image is normal, in the preset defect detection model, the surface paper position image is subjected to surface paper defect detection based on the preset defect target detection algorithm and the preset second monitoring frequency to obtain the corresponding surface paper defect detection result.

[0044] By adopting the above technical scheme, the next processing method can be determined based on different image feature judgment results. When the image is abnormal, information prompt instructions can be generated in time to prompt relevant staff to take subsequent processing measures. When the image is normal, the defect detection operation of the surface paper can be performed through the defect detection model. The defect detection method of the hardcover box surface paper has a high degree of applicability and a more intelligent detection method. It can not only effectively ensure the high production efficiency of the hardcover box surface paper, but also achieve high-precision and precise measurement of the hardcover box surface paper; further, the method of recording information prompt instructions and image feature judgment results in a record list is conducive to recording abnormal data and abnormal conditions in the automated production process of hardcover boxes during high-speed production, and the entire hardcover box automated production system is more intelligent.

[0045] In the second aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0046] An automated production system for hardcover boxes, the system comprising:

[0047] Production line equipment for hardcover boxes, the production line equipment for hardcover boxes is preset with reference positioning points, calibration reference points and a visual positioning component provided with positioning mark points, the visual positioning component is aligned and adapted with the reference positioning points and the calibration reference points; the visual positioning component is used to obtain a paper position image of the hardcover box paper currently being processed;

[0048] Robots for picking, positioning and packaging hardcover box blanks;

[0049] The automated control terminal is used to obtain the position coordinate data of the tissue paper based on the tissue paper position image; based on the reference positioning point image and the calibration reference point image, and taking the positioning mark point of the preset visual positioning component as a reference, calculate the deviation data to be adjusted of the hardcover box tissue paper of each processing step; based on the tissue paper processing parameters, obtain the adjustment threshold interval corresponding to the currently pre-processed hardcover box tissue paper; determine whether the deviation data to be adjusted is within the corresponding adjustment threshold interval, and obtain the adjustment deviation judgment result; and send the tissue paper position coordinate data and the corresponding adjustment deviation judgment result to a robot for performing packaging;

[0050] The robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs the alignment adjustment operation when the alignment adjustment data meets the preset alignment adjustment threshold. The robot is used to position the current hardcover box blank on the hardcover box surface paper.

[0051] By adopting the above technical solution, the reference positioning point and the calibration reference point are positioning effect identification points pre-fixed at fixed positions of the hardcover box automatic production line, and the distance between the reference positioning point and the calibration reference point is fixed and unchanged, so as to detect and calculate whether the hardcover box face paper is accurately placed in each processing step; specifically, in each processing step of the hardcover box, the image of the currently pre-processed hardcover box face paper in the processing area is obtained through the visual positioning component, and the image acquisition range includes the reference positioning point and the calibration reference point. The two positioning effect identification points (referring to the reference positioning point and the calibration reference point) are suitable for hardcover box face papers of various shapes, which is conducive to improving the production of hardcover boxes. The applicability of the line; then the tissue paper position coordinate data is obtained through the tissue paper position image. In order to determine the deviation of the tissue paper coordinate data, based on the reference positioning point image, the calibration reference point image and the preset positioning mark point of the preset visual positioning component, the deviation data to be adjusted of the currently pre-processed hardcover box tissue paper is calculated and determined. Then, based on the adjustment threshold interval corresponding to the currently pre-processed hardcover box tissue paper, it is determined whether the deviation data to be adjusted is within the corresponding adjustment threshold interval. When the deviation data to be adjusted is greater than the corresponding adjustment threshold interval, it indicates that the deviation of the currently pre-processed hardcover box tissue paper is large. The tissue paper position coordinate data and the corresponding adjustment deviation judgment result are sent to the robot used to perform packaging.

[0052] Since the robot is used to position the hardcover box blank on the hardcover box surface paper, and the robot will have a preset hardcover box blank placement coordinate position data in the processing and packaging process of the same batch of hardcover boxes, before placing the hardcover box blank, in order to improve the placement accuracy of the hardcover box blank and improve the packaging effect of the entire hardcover box, the present application will obtain the alignment adjustment data of the hardcover box blank based on the current surface paper position coordinate data and the corresponding adjustment deviation judgment result, and compare the alignment adjustment data with the preset alignment adjustment threshold to determine whether the robot needs to adjust the placement position of the hardcover box blank, and perform the alignment adjustment operation when it is determined that the alignment adjustment data of the current pre-processed hardcover box blank meets the preset alignment adjustment threshold. After performing the alignment adjustment operation, the hardcover box blank is positioned and placed on the hardcover box surface paper, so as to improve the positioning accuracy of the automated production of hardcover boxes and effectively ensure the stability of product quality.

[0053] In the third aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0054] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for automated production of hardcover boxes are implemented.

[0055] In a fourth aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0056] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for automated production of hardcover boxes are implemented.

[0057] In summary, the present application includes at least one of the following beneficial technical effects:

[0058] 1. In each processing step of the hardcover box, the image of the hardcover box face paper currently being processed in the processing area is obtained through the visual positioning component. The image acquisition range includes the reference positioning point and the calibration reference point. The two positioning effect identification points (referring to the reference positioning point and the calibration reference point) are applicable to hardcover box face papers of various shapes, which is beneficial to improving the applicability of the hardcover box production line; then the face paper position coordinate data is obtained through the face paper position image. In order to determine the deviation of the face paper coordinate data, the deviation data to be adjusted of the hardcover box face paper currently being processed is calculated based on the reference positioning point image, the calibration reference point image and the preset positioning mark point of the preset visual positioning component. Then, based on the adjustment threshold interval corresponding to the hardcover box face paper currently being processed, it is determined whether the deviation data to be adjusted is within the corresponding adjustment threshold interval. When the deviation data to be adjusted is greater than the corresponding adjustment threshold interval, it indicates that the deviation of the hardcover box face paper currently being processed is large. The robot is used to position the hardcover box blank on the hardcover box surface paper, and the robot has a preset hardcover box blank placement coordinate position data in the processing and packaging process of the same batch of hardcover boxes. Before placing the hardcover box blank, in order to improve the placement accuracy of the hardcover box blank and improve the packaging effect of the entire hardcover box, the present application will obtain the alignment adjustment data of the hardcover box blank based on the current surface paper position coordinate data and the corresponding adjustment deviation judgment result, and compare the alignment adjustment data with the preset alignment adjustment threshold to determine whether the robot needs to adjust the placement position of the hardcover box blank, and perform the alignment adjustment operation when it is determined that the alignment adjustment data of the current pre-processed hardcover box blank meets the preset alignment adjustment threshold. After performing the alignment adjustment operation, the hardcover box blank is positioned and placed on the hardcover box surface paper, so as to improve the positioning accuracy of the automated production of hardcover boxes and effectively ensure the stability of product quality.

[0059] 2. The present application provides a dual-vision positioning system, wherein the reference positioning point is the precise position of the positioning identification point of the first vision positioning component for positioning calibration, that is, the reference positioning point is used to provide the first vision positioning component with a calibration reference for the positioning coordinate data of the hardcover box surface paper. Similarly, the calibration reference point is the precise position of the positioning identification point of the second vision positioning component for positioning calibration, that is, the calibration reference point is used to provide the second vision positioning component with a calibration reference for the positioning coordinate data of the hardcover box surface paper. Since the position distance between the reference positioning point and the calibration reference point is fixed, the method for judging the positioning effect of the hardcover box surface paper in each processing step based on the reference positioning point and the calibration reference point is conducive to improving the accuracy of judging the positioning effect of the hardcover and surface paper. It is also suitable for detecting the image positioning effects of various types of hardcover box paper of different shapes; further, in order to conveniently and efficiently calculate the deviation data to be adjusted of the hardcover box paper in each processing step, based on the reference positioning point image of the first visual alignment component, combined with the positioning mark point of the first visual alignment component, the actual reference position data of the hardcover box paper is calculated, and based on the calibration reference point image collected by the second visual alignment component, combined with the positioning mark point of the second visual alignment component, the actual reference position data of the hardcover box paper is calculated, and then the position distance between the reference identification point of the ideal target position and the calibration reference point is used as the positioning reference to calculate the deviation data to be adjusted of the hardcover box paper in each processing step, with high calculation efficiency and high accuracy of the calculated deviation data to be adjusted. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of an automated production method for hardcover boxes in one embodiment of the present application. DETAILED DESCRIPTION

[0061] The present application is further described in detail below in conjunction with the accompanying drawings.

[0062] In one embodiment, if Figure 1 As shown, the present application discloses an automated production method for hardcover boxes, which specifically includes the following steps:

[0063] S1: Preset the benchmark positioning points and calibration reference points on the production line of hardcover boxes.

[0064] In this embodiment, the reference positioning points and the calibration reference points are spaced and fixedly arranged in the peripheral area of ​​the hardcover box production line close to the hardcover box processing platform. In order to improve the accuracy of the reference positioning points and the calibration reference points, the reference reference points and the calibration reference points need to be fixed on the ground of the production line with a fixing device to reduce the influence of external environmental vibration or other factors on the reference reference points and the calibration reference points; the reference positioning points and the calibration reference points are positioning effect identification points pre-fixed at fixed positions of the hardcover box automated production line, and the distance between the reference positioning points and the calibration reference points is fixed.

[0065] S2: In each processing step, the paper processing parameters of the hardcover box paper currently being pre-processed are obtained; the paper position image of the hardcover box paper currently being pre-processed is obtained according to a preset visual positioning component; the visual positioning component is provided with positioning mark points, and the paper position image includes a reference positioning point image and a calibration reference point image.

[0066] In this embodiment, the facial paper processing parameters include facial paper materials, facial paper processing standards and parameters to be processed, etc.; a pair of visual positioning components are provided, and the two visual positioning components are respectively adapted to the reference positioning points and the calibration reference points in one-to-one correspondence. The field of view of the first visual positioning component includes the reference positioning points and the processing area of ​​the hardcover box facial paper, and the field of view of the second visual positioning component includes the calibration reference points and the processing area of ​​the hardcover box facial paper; in actual application, the visual positioning component is a high-pixel CCD camera, which performs visual positioning with high precision; the positioning mark point is marked with a cross-shaped scale line pre-stored in the high-pixel CCD camera, and the intersection of the cross-shaped scale lines is the positioning mark point, which is a pre-calculated coordinate mark point for alignment calculation.

[0067] S3: Acquire the surface paper position coordinate data based on the surface paper position image; calculate the deviation data to be adjusted of the hardcover box surface paper in each processing step based on the reference positioning point image and the calibration reference point image and the positioning mark point of the preset visual positioning component.

[0068] In this embodiment, the face paper position coordinate data includes the coordinate data of each drawing edge of the face paper; the face paper position coordinate data is obtained through the face paper position image, and in order to determine the deviation of the face paper coordinate data, based on the reference positioning point image, the calibration reference point image and the preset positioning mark point of the preset visual positioning component, the deviation data to be adjusted of the currently pre-processed hardcover box face paper is calculated and determined.

[0069] S4: based on the paper processing parameters, obtaining the adjustment threshold interval corresponding to the hardcover box paper currently being processed; judging whether the deviation data to be adjusted is within the corresponding adjustment threshold interval, and obtaining the adjustment deviation judgment result.

[0070] In this embodiment, different packaging precision standards of hardcover boxes are different, so the adjustment threshold intervals of different paper processing parameters are not the same; adjustment threshold intervals are pre-set corresponding to the paper processing parameters of different hardcover boxes.

[0071] Specifically, when the deviation data to be adjusted is greater than the corresponding adjustment threshold interval, it indicates that the deviation of the currently pre-processed hardcover box paper is large. At this time, the paper position coordinate data and the corresponding adjustment deviation judgment result are sent to the robot used to perform packaging.

[0072] S5: Send the surface paper position coordinate data and the corresponding adjustment deviation judgment result to the robot used to perform packaging; the robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs the alignment adjustment operation when the alignment adjustment data meets the preset alignment adjustment threshold. The robot is used to position the current hardcover box blank on the hardcover box surface paper.

[0073] In this embodiment, the robot pre-stores the positioning and placement coordinate data of the hardcover box blank. In each processing step, the robot will position the current hardcover box blank on the hardcover box surface paper based on the preset positioning and placement coordinate data.

[0074] Specifically, the alignment adjustment data of the hardcover box blank is obtained based on the current surface paper position coordinate data and the corresponding adjustment deviation judgment result, and the alignment adjustment data is compared with the preset alignment adjustment threshold to determine whether the robot needs to adjust the placement position of the hardcover box blank. When it is determined that the alignment adjustment data of the current pre-processed hardcover box blank meets the preset alignment adjustment threshold, the alignment adjustment operation is performed. After the alignment adjustment operation is performed, the hardcover box blank is positioned and placed on the hardcover box surface paper, so as to improve the positioning accuracy of the automated production of hardcover boxes and effectively ensure the stability of product quality.

[0075] In one embodiment, the visual alignment component includes a first visual alignment component and a second visual alignment component; the first visual alignment component is aligned with the reference positioning point, and the second visual alignment component is aligned with the calibration reference point; in step S3, based on the reference positioning point image and the calibration reference point image, the positioning mark point of the preset visual positioning component is used as a reference to calculate the deviation data to be adjusted of the hardcover box surface paper of each processing step, including:

[0076] S31: In each processing step, based on the reference positioning point image collected by the first vision alignment component and combined with the positioning mark points of the first vision alignment component, the actual reference position data of the hardcover box surface paper is calculated; based on the calibration reference point image collected by the second vision alignment component and combined with the positioning mark points of the second vision alignment component, the actual reference position data of the hardcover box surface paper is calculated.

[0077] In this embodiment, the reference positioning point is the positioning identification point of the first visual positioning component for positioning calibration, and is used for the precise position of the alignment calibration, that is, the reference positioning point is used to provide the first visual positioning component with a positioning coordinate data calibration reference for the hardcover box surface paper. Similarly, the calibration reference point is the positioning identification point of the second visual positioning component for positioning calibration, and is used for the precise position of the alignment calibration, that is, the calibration reference point is used to provide the second visual positioning component with a positioning coordinate data calibration reference for the hardcover box surface paper.

[0078] Specifically, the position distance between the reference positioning point and the calibration reference point is fixed. Therefore, the method of judging the positioning effect of the hardcover box surface paper in each processing step based on the reference positioning point and the calibration reference point is conducive to improving the accuracy of judging the positioning effect of the hardcover and surface paper, and is suitable for the detection needs of image positioning effects of various types of hardcover box surface paper of different shapes.

[0079] S32: Calculate the deviation data to be adjusted of the hardcover box paper in each processing step according to the actual benchmark position data, the actual reference position data, and the position distance between the benchmark identification point and the calibration reference point.

[0080] In this embodiment, based on the reference positioning point image of the first visual alignment component, combined with the positioning mark points of the first visual alignment component, the actual reference position data of the hardcover box surface paper is calculated, and based on the calibration reference point image collected by the second visual alignment component, combined with the positioning mark points of the second visual alignment component, the actual reference position data of the hardcover box surface paper is calculated, and then the position distance between the reference identification point of the ideal target position and the calibration reference point is used as the positioning reference to calculate the deviation data to be adjusted for the hardcover box surface paper in each processing step, with high calculation efficiency and high accuracy of the calculated deviation data to be adjusted.

[0081] In one embodiment, the adjustment deviation judgment result includes a first adjustment deviation judgment result and a second adjustment deviation judgment result. In step S4, an adjustment threshold interval corresponding to the hardcover box paper currently being pre-processed is obtained based on the paper processing parameters; and it is determined whether the deviation data to be adjusted is within the corresponding adjustment threshold interval, and the adjustment deviation judgment result is obtained, including:

[0082] S41: Obtaining a paper processing identifier corresponding to the hardcover box paper currently being processed based on the paper processing parameters.

[0083] In this embodiment, the tissue paper processing mark is a unique processing information code of the pre-processed hardcover box tissue paper. Based on the tissue paper processing mark, the packaging processing requirements of various types of hardcover boxes when they meet the processing standards can be obtained, so as to facilitate the judgment of the adjustment deviation effect of the hardcover box tissue paper.

[0084] S42: Based on the paper processing mark, in each processing step, the deviation data to be adjusted of the currently pre-processed hardcover box paper is sent to a preset threshold judgment model to identify and obtain the adjustment threshold interval corresponding to the deviation data to be adjusted of the currently pre-processed hardcover box paper.

[0085] S43: Determine whether the deviation data to be adjusted is within the corresponding adjustment threshold range.

[0086] Specifically, the deviation data to be adjusted of the hardcover box paper is compared with the corresponding adjustment threshold interval to determine whether the deviation data to be adjusted is within the corresponding adjustment threshold interval.

[0087] S44: If the deviation data to be adjusted is less than or equal to the adjustment threshold interval, a first adjustment deviation judgment result is obtained.

[0088] Specifically, the first adjustment deviation judgment result indicates that the deviation to be adjusted of the hardcover box surface paper is small.

[0089] S45: If the deviation data to be adjusted is greater than the adjustment threshold range, a second adjustment deviation judgment result is obtained.

[0090] In this embodiment, the second adjustment deviation judgment result indicates that the deviation amount to be adjusted of the hardcover box surface paper is large, so that the deviation state and degree of deviation (referring to a lighter deviation or a more serious deviation) of the positioning and placement of the currently pre-processed hardcover box surface paper can be obtained by adjusting the deviation judgment result, which is convenient for real-time monitoring of the processing state of the hardcover box during the processing.

[0091] In one embodiment, in step S5, the robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs the alignment adjustment operation when the alignment adjustment data meets the preset alignment adjustment threshold value. The robot is used to position the current hardcover box blank on the hardcover box surface paper, including:

[0092] S51: When the adjustment deviation judgment result is the first adjustment deviation judgment result, the robot judges that the current hardcover box blank does not meet the preset alignment adjustment threshold based on the first adjustment deviation judgment result, and positions the current hardcover box blank on the hardcover box surface paper.

[0093] In this embodiment, the robot pre-stores the positioning and placement coordinate data of the hardcover box blank. In each processing step, the robot will position the current hardcover box blank on the hardcover box surface paper based on the preset positioning and placement coordinate data. Since the adjustment deviation judgment result corresponding to the surface paper position coordinate data may exist in three situations in actual applications: the first situation is that the surface paper position coordinate data is accurately placed (corresponding to the first adjustment deviation judgment result in this application), and the positioning of the hardcover box surface paper meets the high-precision processing standard requirements.

[0094] S52: When the adjustment deviation judgment result is the second adjustment deviation judgment result, the robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the second adjustment deviation judgment result; based on the surface paper processing mark, obtains the alignment adjustment threshold corresponding to the alignment adjustment data, compares the alignment adjustment data with the corresponding alignment adjustment threshold, and when the alignment adjustment data is greater than or equal to the alignment adjustment threshold, triggers the alignment adjustment instruction to execute the alignment adjustment operation to position the current hardcover box blank on the hardcover box surface paper; otherwise, directly position the current hardcover box blank on the hardcover box surface paper.

[0095] In this embodiment, the second situation is when the alignment adjustment data in the second adjustment deviation judgment result is less than the corresponding alignment adjustment threshold value. At this time, the placement deviation of the facial paper position coordinate data is small, but it still meets the processing standard requirements of the hardcover box facial paper. At this time, there is no need to adjust the hardcover box facial paper. The robot of the next processing step can still directly position the current hardcover box blank on the hardcover box facial paper based on the preset positioning placement coordinate data; the third situation is when the placement deviation of the facial paper position coordinate data is large (referring to when the adjustment deviation judgment result is the second adjustment deviation judgment result, at this time, the alignment adjustment data is greater than or equal to the alignment adjustment threshold value. Corresponding alignment adjustment threshold), if the robot directly places the hardcover box blank according to the preset positioning placement coordinate data, the product packaging quality of the hardcover box will be reduced. At this time, by triggering the alignment adjustment instruction, the alignment adjustment operation is performed first, and then the current hardcover box blank is positioned on the hardcover box surface paper. Therefore, the entire hardcover box automated production system has a high degree of automation, and the corresponding packaging processing procedures are executed through different adjustment deviation judgment results, which can not only meet high production efficiency and effectively guarantee the operating speed of the production line, but also ensure that the positioning accuracy of the robot and the production line meets the standard requirements and guarantees the positioning accuracy.

[0096] In one embodiment, when the pattern of the currently pre-processed hardcover box surface paper is fixed, after step S2, a hardcover box automated production method further includes:

[0097] S201: In a preset defect detection model, a surface paper defect detection is performed on a surface paper position image based on a preset defect target detection algorithm and a preset first monitoring frequency to obtain a corresponding surface paper defect detection result; and the surface paper position image is associated with a surface paper image identifier.

[0098] Specifically, the defect detection model is a YOLOV8 defect detection model based on the Pytorch version of the deep learning software framework, and the preset defect target detection algorithm is a YOLOV8+SE defect target detection algorithm that integrates the SE attention mechanism. This application also has the defect detection function of hardcover box paper. Because the hardcover box paper may be too thin, so that there will be folding when grabbing and moving, the paper may be wrinkled or damaged, and the hardcover box paper may be defective due to uncontrollable factors, which may affect the quality of the hardcover box packaging product. In order to automatically and intelligently monitor the paper quality and defect status of the hardcover box paper, this application uses a preset defect detection model and a first monitoring frequency to perform real-time defect detection on the hardcover box paper that is being packaged and processed on the production line, and obtain the corresponding paper defect detection results, so as to perform real-time and accurate positioning of the paper position and the hardcover box paper with drawing defects, and screen out defective hardcover box products in subsequent processes.

[0099] S202: When the paper defect detection result includes a paper defect detection target, a target detection frame is generated by matching in the corresponding paper position image, the paper defect detection result is matched with the corresponding paper image identifier, and an information prompt instruction is generated and sent to a preset data monitoring terminal.

[0100] Specifically, each tissue paper position image of the present application is also associated with a tissue paper image identifier, and a target detection frame is generated by matching the corresponding tissue paper position image. By matching the tissue paper defect detection result with the corresponding tissue paper image identifier, an information prompt instruction is generated and sent to a preset data monitoring terminal, so as to provide real-time prompts for the defective status and position of the tissue paper of the hardcover box, and promptly prompt relevant staff to take processing measures, thereby effectively ensuring the high product quality of the hardcover box and having a high economic promotion value.

[0101] In one embodiment, when the pattern of the currently pre-processed hardcover box surface paper is randomly generated, after step S2, a hardcover box automatic production method further includes:

[0102] S211: performing image preprocessing and feature extraction on the paper position image of the current frame to obtain image feature information, where the image feature information includes image color information, image pixel distribution and image brightness information; and obtaining the current image feature position coordinates based on the image feature information.

[0103] In this embodiment, for the production scenario where the pattern of the hardcover box surface paper is randomly generated, the present application also has the functions of image feature information extraction and image feature information comparison. Through image feature information comparison, it is helpful to quickly determine whether there is a printing anomaly or a drawing defect anomaly on the hardcover box surface paper.

[0104] S212: Obtain the image prediction matching information of the current frame, associate and match the image prediction matching information of the current frame with the current image feature position coordinates to obtain the image feature judgment result; the image prediction matching information of the current frame is obtained by prediction matching based on the surface paper position image of the previous frame and the current processing time node.

[0105] S213: Outputting the image feature judgment result to a preset defect detection model.

[0106] Specifically, the present application associates and matches the image feature position coordinates corresponding to the image color information, image pixel distribution and image brightness information in the image feature information with the corresponding image prediction matching information, and then performs image feature comparison and feature information coordinate comparison, wherein the image prediction matching information is the target image information of the tissue position image of the current frame. When the image prediction matching information corresponds to the image feature information of the tissue position image, it indicates that the tissue pattern of the currently pre-processed hardcover box tissue is correct and the tissue placement is accurate. The judgment method of associating and matching the image feature position coordinates is conducive to improving the efficiency of defect judgment on the hardcover box drawings, indirectly improving production efficiency and reducing the model calculation amount of the defect detection model.

[0107] In one embodiment, a method for automated production of hardcover boxes further includes:

[0108] S10: When the image feature judgment result is that the image is abnormal, an information prompt instruction is generated and sent to a preset data monitoring terminal; based on the information prompt instruction, the image feature judgment result and the time data when the information acquisition instruction is issued are stored in a preset record list.

[0109] S20: When the image feature judgment result is that the image is normal, in a preset defect detection model, surface paper defect detection is performed on the surface paper position image based on a preset defect target detection algorithm and a preset second monitoring frequency to obtain a corresponding surface paper defect detection result.

[0110] Specifically, based on the results of different image feature judgments, the next processing method is determined. When the image is abnormal, information prompt instructions can be generated in time to prompt relevant staff to take subsequent processing measures. When the image is normal, the defect detection model can be used to perform surface paper defect detection operations. The defect detection method for hardcover box surface paper has a high applicability and a more intelligent detection method. It can not only effectively ensure the high production efficiency of hardcover box surface paper, but also achieve high-precision and precise measurement of hardcover box surface paper. Furthermore, by recording information prompt instructions and image feature judgment results in a record list, it is beneficial to record abnormal data and abnormal conditions in the automated production process of hardcover boxes during high-speed production, and the entire hardcover box automated production system is more intelligent.

[0111] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0112] In one embodiment, a system for automatically producing hardcover boxes is provided, and the system for automatically producing hardcover boxes corresponds to an automatic method for producing hardcover boxes in the above embodiment.

[0113] An automated production system for hardcover boxes includes production line equipment, robots and automated control terminals for hardcover boxes. The detailed description of each functional module is as follows:

[0114] The production line equipment of the hardcover box is preset with a reference positioning point, a calibration reference point and a visual positioning component with a positioning mark point. The visual positioning component is aligned with the reference positioning point and the calibration reference point; the visual positioning component is used to obtain the surface paper position image of the hardcover box surface paper currently being processed;

[0115] Robots for picking, positioning and packaging hardcover box blanks;

[0116] The automated control terminal is used to obtain the position coordinate data of the paper based on the paper position image; based on the reference positioning point image and the calibration reference point image, and taking the positioning mark point of the preset visual positioning component as the reference, calculate the deviation data to be adjusted of the hardcover box paper in each processing step; based on the paper processing parameters, obtain the adjustment threshold interval corresponding to the hardcover box paper currently being processed; determine whether the deviation data to be adjusted is within the corresponding adjustment threshold interval, and obtain the adjustment deviation judgment result; and send the paper position coordinate data and the corresponding adjustment deviation judgment result to the robot for performing packaging;

[0117] The robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs the alignment adjustment operation when the alignment adjustment data meets the preset alignment adjustment threshold. The robot is used to position the current hardcover box blank on the hardcover box surface paper.

[0118] For the specific definition of a hardcover box automated production system, please refer to the definition of a hardcover box automated production method above, which will not be repeated here; each module in the above-mentioned hardcover box automated production system can be implemented in whole or in part through software, hardware and their combination; each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0119] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0120] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for automated production of hardcover boxes, characterized in that: include: Preset the benchmark positioning points and calibration reference points on the production line of hardcover boxes; In each processing step, the paper processing parameters of the hardcover box paper currently being processed are obtained; the paper position image of the hardcover box paper currently being processed is obtained according to a preset visual alignment component; the visual alignment component is provided with positioning mark points, and the paper position image includes a reference positioning point image and a calibration reference point image; Acquire the paper position coordinate data based on the paper position image; calculate the deviation data to be adjusted of the hardcover box paper in each processing step based on the reference positioning point image and the calibration reference point image and taking the positioning mark point of the preset visual alignment component as the reference; Based on the paper processing parameters, the adjustment threshold interval corresponding to the hardcover box paper currently being processed is obtained; judging whether the deviation data to be adjusted is within the corresponding adjustment threshold interval, and obtaining an adjustment deviation judgment result; The surface paper position coordinate data and the corresponding adjustment deviation judgment result are sent to a robot used to perform packaging; the robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs an alignment adjustment operation when the alignment adjustment data meets a preset alignment adjustment threshold, and the robot is used to position the current hardcover box blank on the surface paper of the hardcover box; The visual alignment component includes a first visual alignment component and a second visual alignment component; the first visual alignment component is aligned with the reference positioning point, and the second visual alignment component is aligned with the calibration reference point; The method of calculating the deviation data to be adjusted of the hardcover box surface paper of each processing step based on the reference positioning point image and the calibration reference point image and taking the positioning mark point of the preset visual alignment component as a reference includes: In each processing step, based on the reference positioning point image collected by the first visual alignment component, combined with the positioning mark points of the first visual alignment component, the actual reference position data of the hardcover box surface paper is calculated; based on the calibration reference point image collected by the second visual alignment component, combined with the positioning mark points of the second visual alignment component, the actual reference position data of the hardcover box surface paper is calculated; According to the actual benchmark position data, the actual reference position data, and the position distance between the benchmark positioning point and the calibration reference point, the deviation data to be adjusted of the hardcover box surface paper in each processing step is calculated.

2. The method for automatically producing a hardcover box according to claim 1, characterized in that: The adjustment deviation judgment result includes a first adjustment deviation judgment result and a second adjustment deviation judgment result; the adjustment threshold interval corresponding to the hardcover box surface paper currently being pre-processed is obtained based on the surface paper processing parameters; Determining whether the deviation data to be adjusted is within a corresponding adjustment threshold interval to obtain an adjustment deviation determination result includes: Acquire the tissue processing mark corresponding to the currently pre-processed hardcover box tissue based on the tissue processing parameter; Based on the paper processing mark, in each processing step, the deviation data to be adjusted of the hardcover box paper currently being processed is sent to a preset threshold judgment model to identify and obtain the adjustment threshold interval corresponding to the deviation data to be adjusted of the hardcover box paper currently being processed; Determining whether the deviation data to be adjusted is within a corresponding adjustment threshold range; If the deviation data to be adjusted is less than or equal to the adjustment threshold interval, obtaining the first adjustment deviation judgment result; If the deviation data to be adjusted is greater than the adjustment threshold interval, the second adjustment deviation judgment result is obtained.

3. The method for automated production of hardcover boxes according to claim 2, characterized in that: The robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs the alignment adjustment operation when the alignment adjustment data meets the preset alignment adjustment threshold value. The robot is used to position the current hardcover box blank on the hardcover box surface paper, including: When the adjustment deviation judgment result is the first adjustment deviation judgment result, the robot judges that the current hardcover box blank does not meet the preset alignment adjustment threshold based on the first adjustment deviation judgment result, and positions the current hardcover box blank on the hardcover box surface paper; When the adjustment deviation judgment result is the second adjustment deviation judgment result, the robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the second adjustment deviation judgment result; based on the surface paper processing mark, obtains the alignment adjustment threshold corresponding to the alignment adjustment data, compares the alignment adjustment data with the corresponding alignment adjustment threshold, and when the alignment adjustment data is greater than or equal to the alignment adjustment threshold, triggers the alignment adjustment instruction to execute the alignment adjustment operation to position the current hardcover box blank on the hardcover box surface paper; otherwise, directly position the current hardcover box blank on the hardcover box surface paper.

4. The method for automated production of hardcover boxes according to claim 1, characterized in that: When the pattern of the currently pre-processed hardcover box paper is fixed, after obtaining the paper position image of the currently pre-processed hardcover box paper according to the preset visual alignment component, the method further includes: In a preset defect detection model, the tissue paper position image is subjected to tissue paper defect detection based on a preset defect target detection algorithm and a preset first monitoring frequency to obtain a corresponding tissue paper defect detection result; the tissue paper position image is associated with a tissue paper image identifier; When the facial paper defect detection result includes a facial paper defect detection target, a target detection frame is matched and generated in the corresponding facial paper position image, the facial paper defect detection result is matched with the corresponding facial paper image identifier, and an information prompt instruction is generated and sent to a preset data monitoring terminal.

5. The method for automatically producing a hardcover box according to claim 4, characterized in that: When the pattern of the currently pre-processed hardcover box paper is randomly generated, after obtaining the paper position image of the currently pre-processed hardcover box paper according to the preset visual alignment component, the method further includes: Perform image preprocessing and feature extraction on the paper position image of the current frame to obtain image feature information, wherein the image feature information includes image color information, image pixel distribution, and image brightness information; and obtain the current image feature position coordinates based on the image feature information; Obtaining image prediction and matching information of the current frame, and associating and matching the image prediction and matching information of the current frame with the current image feature position coordinates to obtain an image feature judgment result; the image prediction and matching information of the current frame is obtained by prediction and matching based on the paper position image of the previous frame and the current processing time node; The image feature judgment result is output to the preset defect detection model.

6. The method for automatically producing a hardcover box according to claim 5, characterized in that: The image feature judgment result includes an abnormal image and a normal image, and the method further includes: When the image feature judgment result is an image abnormality, an information prompt instruction is generated and sent to a preset data monitoring terminal; based on the information prompt instruction, the image feature judgment result and the time data when the information acquisition instruction is issued are stored in a preset record list; When the image feature judgment result is that the image is normal, in the preset defect detection model, the surface paper position image is subjected to surface paper defect detection based on the preset defect target detection algorithm and the preset second monitoring frequency to obtain the corresponding surface paper defect detection result.

7. An automated production system for hardcover boxes, characterized in that: The system comprises: Production line equipment for hardcover boxes, the production line equipment for hardcover boxes is preset with reference positioning points, calibration reference points and a visual alignment component provided with positioning mark points, the visual alignment component is aligned with the reference positioning points and the calibration reference points; the visual alignment component is used to obtain a paper position image of the hardcover box paper currently being processed; Robots for picking, positioning and packaging hardcover box blanks; The automated control terminal is used to obtain the position coordinate data of the paper based on the paper position image; based on the reference positioning point image and the calibration reference point image, the positioning mark point of the preset visual alignment component is used as the reference to calculate the deviation data to be adjusted of the hardcover box paper in each processing step; based on the paper processing parameters, the adjustment threshold interval corresponding to the hardcover box paper currently being processed is obtained; it is determined whether the deviation data to be adjusted is within the corresponding adjustment threshold interval to obtain the adjustment deviation judgment result; and the paper position coordinate data and the corresponding adjustment deviation judgment result are sent to a robot for performing packaging; The robot obtains the alignment adjustment data of the current hardcover box blank based on the surface paper position coordinate data and the corresponding adjustment deviation judgment result, and performs the alignment adjustment operation when the alignment adjustment data meets the preset alignment adjustment threshold value, and the robot is used to position the current hardcover box blank on the hardcover box surface paper; The visual alignment component includes a first visual alignment component and a second visual alignment component; the first visual alignment component is aligned with the reference positioning point, and the second visual alignment component is aligned with the calibration reference point; The method of calculating the deviation data to be adjusted of the hardcover box surface paper of each processing step based on the reference positioning point image and the calibration reference point image and taking the positioning mark point of the preset visual alignment component as a reference includes: In each processing step, based on the reference positioning point image collected by the first visual alignment component, combined with the positioning mark points of the first visual alignment component, the actual reference position data of the hardcover box surface paper is calculated; based on the calibration reference point image collected by the second visual alignment component, combined with the positioning mark points of the second visual alignment component, the actual reference position data of the hardcover box surface paper is calculated; According to the actual benchmark position data, the actual reference position data, and the position distance between the benchmark positioning point and the calibration reference point, the deviation data to be adjusted of the hardcover box surface paper in each processing step is calculated.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for automated production of hardcover boxes as claimed in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for automatically producing hardcover boxes as claimed in any one of claims 1 to 6 are implemented.

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