Express carton printing and packaging system and method

Abstract

The invention relates to the technical field of express carton printing, and discloses an express carton printing and packaging system and method.The method comprises the following steps that the size and shape parameters of an express carton and vertex coordinates of a surface printing area are obtained based on 3D scanning; a logistics system API is docked in real time, and logistics information including a receiving address, a two-dimensional code and a tracking number is generated; according to the size and the shape of the express carton and the vertex coordinates of the surface printing area, the layout and the proportion of a printing pattern are adjusted, and pattern filling is optimized through a pattern adaptation model; and querying a preset express carton recovery grade database, and automatically switching the water-based ink and the UV curing ink according to the recovery grade. Based on the visual inspection optimization model, the printing defect condition coefficient is quantified through the difference degree formula, the unqualified products are automatically sorted to the rework line in combination with the preset threshold value, the omission ratio is effectively reduced, and the manual reinspection cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of express delivery carton printing, and particularly to an express delivery carton printing and packaging system and method. Background Art

[0002] With the rapid development of the e-commerce industry, express delivery cartons, as the main carriers for transporting goods, have seen an explosive growth in their printing and packaging demands. Traditional express delivery carton printing and packaging technologies mostly rely on manual operations or semi-automatic equipment, suffering from problems such as low efficiency, poor adaptability, and resource waste, and are difficult to meet the growing demands for high efficiency, environmental protection, and personalization.

[0003] In the prior art, carton printing adaptation is mostly based on fixed templates or simple size adjustments, and it is unable to accurately adapt to special-shaped cartons (such as rounded rectangles, hexagons, etc.), resulting in visual distortions such as stretching and misalignment of printed patterns at curved surfaces or corners. For example, Patent CN111814790A discloses a two-dimensional image identification method for automatic identification of aquaculture cages, including the following steps: S1: Divide the two-dimensional image space into X×Y parts at equal intervals along the X and Y directions in a rectangular coordinate system to form X×Y color blocks, and confirm the color value range of the color blocks.

[0004] For example, Patent CN115723422A discloses a continuous printing method for plastic packaging bags, which relates to the field of plastic packaging bags. It includes obtaining plastic bag information, where the plastic bag information includes the plastic bag model and the printing position corresponding to the plastic bag model; querying the printing pattern corresponding to the plastic bag model from a preset database, where the printing pattern includes a printed graphic and a printed color; generating and executing a pattern update instruction according to the printing pattern corresponding to the plastic bag model, and the pattern update instruction is used to update the printed graphic and the printed color. In the technology disclosed in this patent, there is a lack of judgment of defects through pixel difference thresholds, and the comprehensive influence of inkjet area and image structure difference is not considered, which is prone to misjudgment or missed detection. Summary of the Invention

[0005] The present invention provides an express delivery carton printing and packaging system and method to solve the existing technical problems and addresses the problems in the above background art.

[0006] To solve the above technical problems, according to one aspect of the present invention, more specifically, an express delivery carton printing and packaging method includes the following steps: S1. Obtain the size, shape parameters, and vertex coordinates of the surface printing area of the express delivery carton based on 3D scanning; S2. Real-time dock with the express delivery query interface of the logistics system to generate logistics information including the recipient address, QR code, and tracking number; S3. Adjust the layout and proportion of the printed pattern according to the size, shape, and vertex coordinates of the surface printing area of the express delivery carton, and optimize the pattern filling through the pattern adaptation model; S4. Query the preset express delivery carton recycling grade database and automatically switch between water-based ink and UV-curable ink according to the recycling grade; S5. After printing is completed, trigger the automatic folding device to fold the express delivery carton into shape and seal the edges; S6. Compare the printed finished product with the preset standard image based on the visual inspection optimization model, and sort the express delivery cartons with printing defects to the rework line. The visual inspection optimization model determines whether the express delivery carton has printing defects based on the inkjet area on the express delivery carton and the difference degree between the image after inkjet and the preset standard image, where: ; In the formula, represents the conditional coefficient for the printed express delivery carton to have printing defects. When it means that there are printing defects on the printed express delivery carton, and reprinting is required at this time; represents the difference degree between the inkjet area on the printed express delivery carton and the preset standard image; represents the difference degree between the image after inkjet on the printed express delivery carton and the preset standard image.

[0007] Furthermore, the specific steps in step S1 are as follows: S101. Locate and perform full-coverage 3D scanning on the express delivery carton, and reconstruct the three-dimensional data of the express delivery carton obtained by scanning; S102. Extract size and shape parameters; S103. Calibrate the vertex coordinates of the surface printing area.

[0008] Furthermore, for reconstructing the three-dimensional data of the express delivery carton obtained by scanning, calculate the phase difference of the grating image based on the phase unwrapping algorithm to generate the three-dimensional data of the carton surface.

[0009] Furthermore, the extraction of the size and shape parameters also includes identifying the angular curvature and surface flatness of the carton through the curvature analysis algorithm, and then determining whether the express delivery carton is a special-shaped box body.

[0010] Furthermore, the calibration of the vertex coordinates of the printing area also includes using the U-Net image segmentation algorithm to segment the printable area on the carton surface, excluding the folding lines, seams, and existing printed content.

[0011] Furthermore, the specific steps for the pattern adaptation model to optimize the pattern filling are as follows: 1), Receive the three-dimensional data of the carton obtained from 3D scanning, including dimensions, shape parameters, and the vertex coordinates of the printing area; 2), Identify the high-density filling area and low-density area in the pattern based on the U-Net image segmentation algorithm; 3), Apply the dot screening algorithm to the low-density area to convert continuous tones into discrete dots; use a spiral filling inkjet path for the high-density filling area; 4), Calculate the minimum ink coverage rate of each express carton, and set a separate target threshold for each express carton according to this minimum ink coverage rate.

[0012] Furthermore, the calculation formula for the minimum ink coverage rate is: ; In the formula, represents the individual minimum ink coverage rate of each express carton; represents the area of the i-th rectangular grid that actually needs to be sprayed with ink after dividing the printable area into rectangular grids; represents the total area of the printable area.

[0013] Furthermore, in step S2, the TLS 1.3 protocol can be used to encrypt the transmission of logistics information during the real-time docking of the express query interface of the logistics system.

[0014] Furthermore, the calculation formula for the difference degree between the inkjet image on the printed express carton and the preset standard image is: ; In the above formula, represents the total number of pixels in the image; represents the gray value of the i-th pixel in the actual printed image; represents the gray value of the i-th pixel in the preset standard image.

[0015] The express carton printing and packaging system includes: A 3D scanning module for real-time capturing of the three-dimensional data of the express carton; A logistics integration module for docking with the logistics system API to dynamically generate logistics labels; A pattern adaptation model for optimizing the pattern layout based on the carton size and calculating the minimum ink coverage rate; An ink switching device for calling different ink storage tanks according to the recycling grade; An automatic folding device integrating a pneumatic robotic arm and a hot melt adhesive machine to achieve automatic forming of the express carton; A vision inspection optimization model for identifying printing defects and sorting out unqualified products through a convolutional neural network.

[0016] The express delivery carton printing and packaging system and method provided by the present invention, compared with the prior art, have the following effects: 1. By combining 3D scanning technology with the phase unwrapping algorithm, the present invention can obtain accurate three-dimensional data of the express delivery carton in real time, and use the U-Net image segmentation algorithm to accurately calibrate the printable area, excluding folding lines, seams and existing printed content. Combining the thin plate spline interpolation algorithm to perform non-rigid deformation of the pattern to adapt to the curved surface carton, effectively avoiding visual distortion and ensuring the accurate matching of the printed pattern with the carton surface, significantly improving the printing quality and adaptability.

[0017] 2. By querying the recycling grade database, the present invention can automatically switch between water-based ink and UV-curable ink according to the carton material and recycling grade, and adopt a wastewater recycling mechanism during the switching process to reduce pollution. At the same time, by optimizing the ink spraying strategy through the halftone algorithm and spiral filling path, and combining the minimum ink coverage calculation model, the ink usage threshold is dynamically set, greatly reducing the ink consumption, and taking into account environmental protection and cost control.

[0018] 3. From the real-time generation of logistics information, automatic adaptation of printed patterns, to ink switching, automatic folding and encapsulation, and visual inspection and sorting, the whole process of the present invention realizes a high degree of automation. Among them, the automatic folding device generates the motion trajectory of the robotic arm in real time according to the carton size, and dynamically adjusts the glue spraying parameters to ensure the folding efficiency and bonding strength, and the overall production efficiency is significantly improved compared with the traditional manual operation.

[0019] 4. Based on the visual inspection optimization model, the present invention quantifies the printing defect condition coefficient through the difference formula, and automatically sorts unqualified products to the rework line in combination with the preset threshold, effectively reducing the missed inspection rate and reducing the cost of manual re-inspection.

[0020] 5. The present invention identifies special-shaped box bodies through the curvature analysis algorithm, and combines the thin plate spline interpolation algorithm and masking technology to ensure that key areas such as two-dimensional codes are not deformed, meeting the requirements of diverse carton shapes and high-precision printing. Description of the Drawings

[0021] Figure 1 is the flow chart of the present invention; Figure 2 is the relationship diagram between the condition coefficient g and the difference degree s in the present invention; Figure 3 is the relationship diagram between the condition coefficient g and the difference degree m in the present invention; Figure 4 is the relationship diagram between the condition coefficient g and the difference degrees s and m in the present invention. Detailed Embodiments

[0022] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Example 1, as Figure 1 shown, according to one aspect of the present invention, a printing and packaging method for express delivery cartons is provided, which obtains the size, shape parameters of the express delivery carton and the vertex coordinates of the surface printing area based on 3D scanning; 3D scanning captures the three-dimensional geometric data of the express delivery carton in real time through technologies such as structured light projection, laser ranging, and binocular cameras. Its function is to obtain the size (length, width, height), shape (curvature, radian of edges and corners) of the carton and the vertex coordinates of the surface printing area, and to provide basic data for subsequent pattern adaptation to ensure that the printed pattern precisely matches the carton surface.

[0024] The specific steps for obtaining the size, shape parameters of the express delivery carton and the vertex coordinates of the surface printing area based on 3D scanning are as follows: 1). The carton enters the scanning area through the conveyor belt, and the infrared sensor triggers the clamping device to fix the position of the carton after detecting that the carton is in place. Among them, this step requires using a cleaning device (such as an air gun) to remove dust or foreign objects on the surface of the carton to ensure the scanning accuracy.

[0025] 2). The structured light projector projects a high-density grating pattern onto the surface of the carton, and at the same time, the binocular camera array synchronously collects the deformed grating images. And the laser ranging module assists in obtaining the depth information of the edge contour of the carton to compensate for the data loss of the structured light at complex curved surfaces.

[0026] 3). Based on the phase unwrapping algorithm, calculate the phase difference of the grating images to generate the three-dimensional point cloud data of the carton surface. Integrate the laser ranging data and the structured light point cloud, and optimize the accuracy of the three-dimensional model through the ICP (Iterative Closest Point) algorithm, with the error controlled within ±0.1 mm.

[0027] The phase unwrapping algorithm is a calculation method for processing the phase difference of grating images, which is used to restore the three-dimensional shape of an object from the deformed grating images. Its function is to convert the phase difference of the scanned grating images into the three-dimensional point cloud data of the carton surface, and can combine the laser ranging data to optimize the model accuracy (error ±0.1 mm) through the ICP algorithm to ensure the reliability of the scanning results.

[0028] 4). Extract the length, width, and height information of the carton from the three-dimensional model. And identify the radian of the edges and corners and the surface flatness of the carton through the curvature analysis algorithm to determine whether it is a special-shaped box body (such as a hexagon, a rounded rectangle).

[0029] 5), Use the U-Net image segmentation algorithm to segment the printable area on the surface of the paper box, excluding the folding lines, seams, and existing printed content. And establish a coordinate system with the lower left corner of the paper box as the origin, and output the vertex coordinates and center point coordinates of the printed area.

[0030] The U-Net image segmentation algorithm is a deep learning-based image segmentation model, which is good at processing high-precision region segmentation under small sample data. Its purpose is to segment the printable area on the surface of the paper box, excluding the folding lines, seams, and existing printed content; and protect key elements such as LOGO and QR codes from deformation during pattern adaptation.

[0031] Example 2, Real-time dock the express query interface of the logistics system to generate logistics information including the recipient address, QR code, and tracking number; in this process, the TLS 1.3 protocol can also be used to encrypt the transmission of logistics information to prevent man-in-the-middle attacks. It can also support seamless docking of the APIs of mainstream logistics systems (such as SF Express and JD.com) through standardized RESTful interface design, and provide a parameter mapping template to adapt to the data format differences of different platforms.

[0032] Example 3, According to the size, shape of the express paper box and the vertex coordinates of the surface printed area, adjust the layout and proportion of the printed pattern, and optimize the pattern filling through the pattern adaptation model; among them, the specific steps of the pattern adaptation model to optimize the pattern filling are: 1), Receive the three-dimensional data of the paper box from the 3D scanning module, including the size (length, width, height), shape parameters (curvature, edge radian), and vertex coordinates of the printed area. And load the preset printed pattern (vector graphic or bitmap), parse its original resolution, color mode, and key elements (such as LOGO, text area); at the same time, perform grid segmentation on the printed area to generate a rasterized coordinate matrix covering the printed area.

[0033] 2), Based on the actual size of the paper box and the area of the printed area, scale the pattern to the appropriate size through the bilinear interpolation algorithm to ensure that the edge blanking ≤ 2mm. And if the surface of the paper box is a curved surface (such as a rounded rectangle), use the Thin Plate Spline algorithm to perform non-rigid deformation on the pattern to compensate for the visual distortion caused by the curved surface. At the same time, use the masking technology to lock the key areas such as LOGO and QR codes, prohibiting scaling or deformation to ensure their recognizability.

[0034] The Thin Plate Spline interpolation algorithm is a non-rigid deformation algorithm used to correct visual distortion when adapting the pattern to the curved surface. It is used for the surface of rounded and irregular paper boxes to adjust the geometric shape of the pattern, avoid stretching or twisting, and combine with the curvature parameters of the paper box to generate an adapted printing path in real time.

[0035] 3), Identify high-density filled areas (such as solid color blocks) and low-density areas (such as gradient backgrounds) in the pattern through an image segmentation algorithm (such as U-Net). For low-density areas, use the halftone algorithm to convert continuous tones into discrete dots, reducing ink usage; for high-density areas, optimize the inkjet path and use a spiral filling pattern to avoid overlapping spraying.

[0036] The halftone algorithm is a technique for converting continuous-tone images into discrete dots, simulating gray-scale changes through dot density. This algorithm reduces ink usage by using dot filling for low-density areas (such as gradient backgrounds) and reduces the coverage rate while ensuring pattern clarity.

[0037] The spiral filling pattern is an inkjet path planning strategy that covers high-density filled areas (such as solid color blocks) in a spiral trajectory. This method can optimize the movement path of the inkjet head, reduce ink waste, ensure color consistency in high-density areas, and prevent local over-thickness or missed printing.

[0038] 4), Calculate the minimum ink coverage rate for each express delivery carton, and set a separate target threshold for each express delivery carton based on this minimum ink coverage rate. The calculation formula for the minimum ink coverage rate is: ; In the formula, represents the individual minimum ink coverage rate for each express delivery carton; represents the area of the i-th rectangular grid that actually needs to be sprayed with ink after dividing the printable area into rectangular grids; represents the total area of the printable area.

[0039] Example 4, Query the preset express delivery carton recycling grade database, and automatically switch between water-based ink and UV-curable ink according to the recycling grade; the specific decision logic and pollution control during the switching process are as follows: 1), Based on the carton material (corrugated paper / kraft paper), recycling grade (A / B / C), and printing pattern complexity, construct a decision tree to select the optimal ink type.

[0040] 2), When switching inks, enable a flushing procedure to clean the residual ink in the print head, and the waste water is recycled after sedimentation and filtration.

[0041] Example 5, After printing is completed, trigger an automatic folding device to fold the express delivery carton into shape and seal the edges; this process also requires optimization of the robotic arm path and folding quality inspection, including: 1), Generate the robotic arm movement trajectory in real time based on the carton size; 2), Dynamically adjust the glue spraying speed and temperature according to the environmental temperature and humidity to ensure the bonding strength; 3). After the folding is completed, the edge fit of the paper box is detected by a pressure sensor. If the pressure value is lower than the threshold (such as 5 N), secondary folding or an alarm is triggered.

[0042] Example 6: Based on the optimized model of visual inspection, the printed finished product is compared with the preset standard image, and the express paper boxes with printing defects are sorted to the rework line. The optimized model of visual inspection determines whether the express paper box has printing defects according to the inkjet area on the express paper box and the difference degree between the image after inkjet and the preset standard image. There is: ; In the formula, represents the conditional coefficient for the printed express paper box to have printing defects. When , it means that there are printing defects on the printed express paper box, and at this time, reprinting is required; represents the difference degree between the inkjet area on the printed express paper box and the preset standard image; represents the difference degree between the image after inkjet on the printed express paper box and the preset standard image.

[0043] The above formula is the analysis carried out to solve the actual problems of the enterprise, that is, empirical analysis. The data obtained in sequence and the characteristic relationships between the data are the external manifestations of the empirical formula. Then the reasoning process is as follows: 1). Fit the formula for the conditional coefficient g of the printed express paper box to have printing defects.

[0044] Among them, the conditional coefficient g of the printed express paper box to have printing defects can be represented by the number of defects in the sample express paper boxes.

[0045] For example, collect the data of 100 express paper box samples. If, after manual evaluation, the number of defects in a certain express paper box exceeds the data in other 50 samples, then it means that the conditional coefficient g of the printed express paper box to have printing defects = 50%.

[0046] All other data are collected by the equipment itself for subsequent calculation and fitting statistics.

[0047] 2). Establish a mathematical model for the relationship between the conditional coefficient g and the difference degree s (as shown in Figure 2 ), where the red dots in the figure are the distributions of the 100 samples collected. Then there is: (Formula 1) In the above Formula 1, k represents the empirical constant for adjusting the sensitivity of the above model.

[0048] 3). Establish a mathematical model for the relationship between the conditional coefficient g and the difference degree m (as shown in Figure 3As shown, the blue dots in the figure are the distributions of 100 samples collected), then there is: (Formula 2) In the above Formula 2, k represents an empirical constant for adjusting the sensitivity of the above model.

[0049] 4), establish a mathematical model for the relationship between the conditional coefficient g and the difference degrees s and m (as Figure 4 shown, it can be known from this model that the difference degrees s and m are positively correlated), and combined with the characteristic relationship between the above Formula 1 and Formula 2, then there is: g = (Formula 1) × (Formula 2) Then according to the above derivation, it can be known that the conditional coefficient g for the printed express delivery carton to have printing defects is: ; Among them, the calculation formula for the difference between the inkjet area on the printed express delivery carton and the preset standard image is: ; In the above formula, represents the actual inkjet area; represents the inkjet area of the preset standard image.

[0050] Among them, the calculation formula for the difference between the image after inkjet on the printed express delivery carton and the preset standard image is: ; In the above formula, represents the total number of pixels of the image; represents the gray value of the i-th pixel in the actual printed image; represents the gray value of the i-th pixel in the preset standard image.

[0051] Then, when data sampling and quality inspection are carried out on a certain express delivery carton, the difference between the inkjet area on the printed express delivery carton and the preset standard image takes ; the difference between the image after inkjet on the printed express delivery carton and the preset standard image takes , then there is:

[0052] According to the above calculation, it can be known that the conditional coefficient for the printed express delivery carton to have printing defects takes , then it means that there are printing defects on the printed express delivery carton, and reprinting is required at this time. And by comparing multiple groups of data, there is: Table 1 Partial implementation data and the actual printing status of the express delivery carton

[0053] Based on the data in Table 1 above, it can be known that when the sample data tends to infinity, the value of the conditional coefficient g is used as the dividing line to divide the printing state of the express delivery carton. As can be known from the data in Table 1 above, this dividing line is 68.2%. Then when this is the case, after manual evaluation, the number and size of its defects are both relatively obvious. At this time, it means that there are printing defects on the printed express delivery carton and reprinting is required.

[0054] Embodiment 7, an express delivery carton printing and packaging system, including: A 3D scanning module for capturing the three-dimensional data of the express delivery carton in real time; A logistics integration module for docking with the logistics system API to dynamically generate logistics labels; A pattern adaptation model for optimizing the pattern layout based on the carton size and calculating the minimum ink coverage rate; An ink switching device for calling different ink storage tanks according to the recycling grade; An automatic folding device integrating a pneumatic robotic arm and a hot melt adhesive machine to realize the automatic forming of the express delivery carton; A visual inspection optimization model for identifying printing defects through a convolutional neural network and sorting out unqualified products. A convolutional neural network is a deep learning model that is good at extracting local features of images and classifying them, and can be used to identify blurring, jaggedness or color deviation in printed finished products, pushing unqualified products to the rework line to reduce manual intervention.

[0055] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it cannot be understood as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for printing and packaging express delivery paper boxes, characterized in that, It includes the following steps: S1. Obtain the size, shape parameters of the express delivery carton and the vertex coordinates of the surface printing area based on 3D scanning; S2. Real-time dock with the express delivery query interface of the logistics system to generate logistics information including the recipient address, QR code, and tracking number; S3. Adjust the layout and proportion of the printed pattern according to the size, shape of the express delivery carton and the vertex coordinates of the surface printing area, and optimize the pattern filling through the pattern adaptation model; S4. Query the preset express delivery carton recycling grade database and automatically switch between water-based ink and UV-curing ink according to the recycling grade; S5. After printing is completed, trigger the automatic folding device to fold the express delivery carton into shape and seal the edges; S6. Based on the visual inspection optimization model, compare the printed finished product with the preset standard image, and sort the express delivery cartons with printing defects to the rework line. The visual inspection optimization model determines whether the express delivery carton has printing defects according to the inkjet area on the express delivery carton and the difference degree between the image after inkjet and the preset standard image. The calculation formula is: ； In the formula, represents the conditional coefficient for the printed express delivery carton having printing defects. When , it indicates that there are printing defects on the printed express delivery carton, and at this time, reprinting is required; represents the difference degree between the inkjet area on the printed express delivery carton and the preset standard image; represents the difference degree between the image after inkjet on the printed express delivery carton and the preset standard image.

2. The express delivery carton printing and packaging method according to claim 1, wherein: The specific steps in step S1 are: S101. Position and conduct full-coverage 3D scanning on the express delivery carton, and reconstruct the three-dimensional data of the express delivery carton obtained by scanning; S102. Extract size and shape parameters; S103. Calibrate the vertex coordinates of the surface printing area.

3. The express delivery carton printing and packaging method according to claim 2, wherein: Reconstruct the three-dimensional data of the express delivery carton obtained by scanning. Based on the phase unwrapping algorithm, calculate the phase difference of the grating image to generate the three-dimensional data of the carton surface.

4. The express delivery carton printing and packaging method according to claim 2, wherein: The extraction of the size and shape parameters also includes identifying the edge radian and surface flatness of the carton through the curvature analysis algorithm, and then determining whether the express delivery carton is a special-shaped box body.

5. The express delivery carton printing and packaging method according to claim 2, wherein: The calibration of the vertex coordinates of the printing area also includes using the U-Net image segmentation algorithm to segment the printable area on the carton surface, excluding the folding lines, seams, and existing printed content.

6. The express delivery carton printing and packaging method according to claim 1, wherein: The specific steps for the pattern adaptation model to optimize pattern filling are: 1). Receive the three-dimensional data of the carton obtained from 3D scanning, including size, shape parameters, and vertex coordinates of the printing area; 2). Based on the U-Net image segmentation algorithm, identify the high-density filling area and low-density area in the pattern; 3). Use the halftone dot algorithm for the low-density area to convert the continuous tone into discrete dots; use a spiral filling inkjet path for the high-density filling area; 4). Calculate the minimum ink coverage rate of each express delivery carton, and set a separate target threshold for each express delivery carton according to the minimum ink coverage rate.

7. The express delivery carton printing and packaging method according to claim 6, wherein: The calculation formula for the minimum ink coverage rate is: ； In the formula, represents the minimum ink coverage rate of each express delivery carton individually; represents that after dividing the printable area into rectangular grids, the area of the i-th rectangular grid that actually needs to be sprayed with ink; represents the total area of the printable area.

8. The express delivery carton printing and packaging method according to claim 1, characterized in that: In step S2, during the process of real-time docking with the express delivery query interface of the logistics system, the TLS 1.3 protocol can be used to encrypt and transmit the logistics information.

9. The express delivery carton printing and packaging method according to claim 1, wherein: The calculation formula for the difference degree between the image after inkjet on the printed express delivery carton and the preset standard image is: ； In the above formula, represents the total number of pixels of the image; represents the gray value of the i-th pixel in the actual printed image; represents the gray value of the i-th pixel in the preset standard image.

10. Express delivery carton printing and packaging system, characterized in that, Applied to the express delivery carton printing and packaging method described in any one of claims 1-9, the express delivery carton printing and packaging system includes: A 3D scanning module for real-time capturing the three-dimensional data of the express delivery carton; A logistics integration module for docking with the logistics system API to dynamically generate logistics labels; Pattern adaptation model, which optimizes the pattern layout based on the carton size and calculates the minimum ink coverage rate; Ink switching device, which is used to call different ink storage tanks according to the recycling grade; Automatic folding equipment, which integrates a pneumatic robotic arm and a hot melt adhesive machine to realize the automatic forming of express cartons; Visual inspection optimization model, which identifies printing defects and sorts out unqualified products through a convolutional neural network.

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