Automatic pattern overprinting and printing method for textile fabric

Abstract

The invention relates to the field of digital printing machines, and discloses an automatic pattern overprinting and printing system for textile fabrics, which comprises an image acquisition module for controlling a camera to scan and simultaneously acquiring images in real time through the camera, and an image conversion module for performing space coordinate conversion on the acquired images and outputting the converted images. The image conversion module is used for converting an image into a coordinate system and a resolution size of a printer design drawing, and the image recognition module is used for obtaining at least one station image from the current image and is mainly used for carrying out feature recognition to obtain position information of each feature point. According to the method, through cooperation of the image acquisition module and the image recognition module, feature points on the jacquard cloth can be accurately recognized, accurate image correction is conducted through the image correction module, it is ensured that digital printing is perfectly aligned with the pattern contour of the jacquard cloth, and by means of the method, the printing precision is improved; the stereoscopic impression and details of the figured cloth can be kept, and the requirement of a high-end market for digital printing of the figured cloth is met.

Description

Technical Field

[0001] The present invention relates to the field of digital printing machines, and particularly to an automatic overprinting method for printing patterns on textile fabrics. Background Art

[0002] Digital printing is printing using digital technology. Digital printing technology is a high-tech product integrating machinery, computer, and electronic information technology, which has gradually formed with the continuous development of computer technology. The emergence and continuous improvement of this technology have brought a new concept to the textile printing and dyeing industry. Its advanced production principles and means have brought an unprecedented development opportunity to textile printing and dyeing.

[0003] Currently, conventional digital printing equipment can support single-sided printing of ordinary textile fabrics or double-sided printing of ordinary front and back sides.

[0004] With the improvement of people's living standards, there are higher quality requirements for textile fabrics, and two types of product demands have emerged. The first type requires digital printing on jacquard fabrics to provide fabrics with three-dimensional color patterns for people. The second type requires double-sided printing on high-end fabrics, and the patterns on both sides are required to be strictly aligned. For example, for high-end silk fabrics, after searching the existing technology, the current digital printing equipment cannot meet the production requirements of these two types of products. Summary of the Invention

[0005] To make up for the above deficiencies, the present invention provides an automatic overprinting method for printing patterns on textile fabrics, aiming to improve the problem of "being unable to meet the production requirements of products" mentioned in the existing technology.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An automatic overprinting system for printing patterns on textile fabrics, comprising:

[0007] An image acquisition module: used to control the camera to scan and simultaneously collect images in real time through the camera;

[0008] An image transformation module: used to perform spatial coordinate transformation on the collected images and convert the images into the coordinate system and resolution size of the printer design drawing;

[0009] An image recognition module: obtain at least one station image from the current image, mainly for feature recognition to obtain the position information of each feature point;

[0010] An image correction module: mainly used to correct the images;

[0011] A calling module: mainly used to generate a file that can be printed;

[0012] Sending module: mainly sends the files generated by the calling module to the board control software for printing;

[0013] The image acquisition module, image transformation module, image recognition module, image correction module, calling module and sending module are connected through network communication.

[0014] As a further description of the above technical solution:

[0015] The image acquisition module includes at least one camera and a lens matched with the camera, and at least one light source. The camera and lens are used to scan and take pictures of textile fabrics to obtain high-definition photos. The light source is used to fill in the light of the textile fabrics. Through the fill in light, a sufficiently bright picture and a sufficiently clear outline of the jacquard or reverse pattern are obtained. The camera is a high-definition industrial linear array camera.

[0016] As a further description of the above technical solution:

[0017] The image acquisition module includes a camera control unit, an image acquisition unit and an image preprocessing unit. The camera control unit is responsible for initializing and controlling the camera to scan and take pictures. The image acquisition unit mainly acquires image data in real time through the camera. The image preprocessing unit mainly performs denoising and contrast enhancement on the acquired image to improve image quality.

[0018] As a further description of the above technical solution:

[0019] The image transformation module includes a coordinate system conversion unit, a resolution matching unit and an image correction unit. The coordinate system conversion unit converts the captured image from the camera coordinate system to the coordinate system of the printer design drawing. The resolution matching unit adjusts the resolution of the image to the resolution size of the printer design drawing. The image correction unit mainly performs color correction and geometric correction on the converted image to ensure the visual effect of the image when printing.

[0020] As a further description of the above technical solution:

[0021] The image recognition module includes a workstation image acquisition unit, a feature recognition unit, and a feature point position extraction unit. The workstation image acquisition unit mainly acquires a specific workstation image from the collected continuous images. The feature recognition unit mainly uses a deep learning algorithm to identify feature points in the image. The feature point position extraction unit mainly extracts the position information of each feature point from the recognition results.

[0022] As a further description of the above technical solution:

[0023] The image correction module includes a feature point alignment unit, an image correction algorithm unit, and an acceleration unit. The feature point alignment unit mainly aligns the position information of the feature points with the position information of the feature points on the design drawing. The image correction algorithm unit mainly uses an image correction algorithm to correct the deformation of the image according to the alignment result. The acceleration unit mainly calls for acceleration operations during high-speed printing to improve the image processing speed.

[0024] As a further description of the above technical solution:

[0025] The calling module includes an image data conversion unit and a file generation unit. The image data conversion unit mainly converts the corrected image data into the ink dot information data required for printing. The file generation unit completes the data conversion and generates a file by calling the hot folder of the software.

[0026] As a further description of the above technical solution:

[0027] The sending module includes a protocol connection unit, a data sending unit, and a printing control unit. The protocol connection unit mainly establishes a connection with the board control software through a local protocol. The data sending unit mainly sends the generated file data to the board control software through the protocol. The printing control unit mainly controls the printer to perform a printing operation on the data received by the board control software.

[0028] A method for automatic overprinting and printing of patterns on textile fabrics specifically includes the following steps:

[0029] S1: Scan and take a photo of the textile fabric through an image acquisition module to obtain a high-definition photo;

[0030] S2: Control the camera to scan and collect images in real time through the image acquisition module;

[0031] S3: Convert the collected image into the coordinate system and resolution size of the printer design drawing through an image transformation module;

[0032] S4: Use AI technology through an image recognition module to identify the feature points in the image;

[0033] S5: Perform image correction through the image correction module according to the position information of the feature points, and the GPU can be called for acceleration operations;

[0034] S6: Use the RIP calling module to convert the corrected image data into the ink dot information required for printing and generate a PRN file;

[0035] S7: Send the PRN file data to the board control software through the local TCP protocol through the PRN sending module to complete the printing.

[0036] As a further description of the above technical solution: When the image correction module is in high-speed printing, the image size that needs to be corrected and calculated each time is relatively large. The GPU can be called for accelerated operation, which can increase the operation efficiency by more than [X] times. The RIP call module converts the corrected image data into the ink dot information data required for printing by calling the hot folder of the RIP software. The PRN sending module sends the PRN file data to the board control software through the local TCP protocol, and the board control software performs printing after receiving the PRN data.

[0037] The present invention has the following beneficial effects:

[0038] 1. In the present invention, through the cooperation of the image acquisition module and the image recognition module, the feature points on the jacquard fabric can be accurately identified, and the image correction module is used for precise image correction to ensure that the digital printing is perfectly aligned with the pattern contour of the jacquard fabric. This method not only improves the printing accuracy but also retains the three-dimensional sense and details of the jacquard fabric, meeting the needs of the high-end market for digital printing of jacquard fabrics. Compared with the existing digital printing equipment, this system can achieve high-precision color pattern printing on complex jacquard fabrics, providing richer visual effects and higher product quality.

[0039] 2. In the present invention, through the image recognition module and the image correction module, the feature points on the high-end fabric can be accurately identified and aligned, ensuring the strict alignment of the patterns during double-sided printing. The RIP call module generates an accurate PRN file, and the PRN sending module sends the data to the board control software to achieve the consistency of double-sided printing. Compared with the existing digital printing equipment, this system can maintain the high alignment of the patterns during double-sided printing, avoiding the pattern misalignment problem caused by alignment errors, and is particularly suitable for fabrics with extremely high pattern alignment requirements such as high-end silk.

[0040] 3. In the present invention, the high-definition industrial line array camera and the LED light source are adopted in the image acquisition module to ensure the quality of the image and reduce the need for manual intervention. The image recognition module and the image correction module achieve automated processing through AI technology. Compared with the existing digital printing equipment, this system significantly improves the production efficiency and reduces the production cost, especially for the large-scale production of high-end textiles, which has significant economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the complete system framework in the present invention;

[0042] Figure 2 It is a schematic diagram of the detailed framework of the image acquisition module in the present invention;

[0043] Figure 3 It is a schematic diagram of the detailed framework of the image transformation module in the present invention;

[0044] Figure 4 Schematic diagram of the detailed framework of the image recognition module in the present invention;

[0045] Figure 5 Schematic diagram of the detailed framework of the image correction module in the present invention;

[0046] Figure 6 Schematic diagram of the detailed framework of the RIP call module in the present invention;

[0047] Figure 7 Schematic diagram of the detailed framework of the PRN sending module in the present invention;

[0048] Figure 8 Schematic diagram of transformation matrix 1 in the present invention;

[0049] Figure 9 Schematic diagram of transformation matrix 2 in the present invention;

[0050] Figure 10 Schematic diagram of transformation matrix 3 in the present invention;

[0051] Figure 11 Schematic diagram of calibration picture 1 in the present invention;

[0052] Figure 12 Schematic diagram of calibration picture 2 in the present invention;

[0053] Figure 13 Schematic diagram of AI feature selection 1 in the present invention;

[0054] Figure 14 Schematic diagram of AI feature selection 2 in the present invention;

[0055] Figure 15 Schematic diagram before correction in the present invention;

[0056] Figure 16 Schematic diagram after correction in the present invention;

[0057] Figure 17 Schematic diagram of the software parallel operation timing in the present invention;

[0058] Figure 18 Schematic diagram of the software system module in the present invention;

[0059] Figure 19 Schematic diagram of the system process in the present invention.

[0060] Legend:

[0061] 1. Image acquisition module; 2. Image transformation module; 3. Image recognition module; 4. Image correction module; 5. RIP call module; 6. PRN sending module; 101. Camera control unit; 102. Image acquisition unit; 103. Image preprocessing unit; 201. Coordinate system conversion unit; 202. Resolution matching unit; 203. Image correction unit; 301. Station image acquisition unit; 302. AI feature recognition unit; 303. Feature point position extraction unit; 401. Feature point alignment unit; 402. Image correction algorithm unit; 403. GPU acceleration unit; 501. Image data conversion unit; 502. PRN file generation unit; 601. TCP protocol connection unit; 602. Data sending unit; 603. Printing control unit. Detailed implementation manners

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0063] Refer to Figure 1 - Figure 19 , the present invention provides an automatic pattern overprinting and printing system for textile fabrics, including an image acquisition module 1, an image transformation module 2, an image recognition module 3, an image correction module 4, a RIP call module 5 and a PRN sending module 6. The image acquisition module 1 is used to control the camera to scan and, at the same time, collect images in real time through the camera SDK. Specifically, the image acquisition module 1 includes at least one camera, a lens matching the camera, and at least one LED light source. The camera and the lens are used to scan and photograph the textile fabric to obtain high-definition photos, and the LED light source is used to supplement the light of the textile fabric. Through light supplementation, a sufficiently bright picture and sufficiently clear outlines of the jacquard pattern or the reverse pattern are obtained. The camera selects a high-definition industrial line array camera, and the camera uses a gigabit network port. The image acquisition module 1 includes a camera control unit 101, an image acquisition unit 102, and an image preprocessing unit 103. The camera control unit 101 is responsible for initializing and controlling the camera to scan and photograph. The image acquisition unit 102 mainly collects image data in real time through the camera SDK. The image preprocessing unit 103 mainly performs denoising and contrast enhancement processing on the collected images to improve the image quality. The denoising can adopt the following formula:

[0064]

[0065] where I(x) is the image value after denoising, Ω is the search area, ω(x, y) is the weight function, and C(x) is the normalization constant.

[0066] The enhanced contrast can be achieved by the following formula:

[0067] I enhanced (x,y) = CLAHE(I(x,y))

[0068] where I(x,y) is the original image value, and I enhanced (x,y) is the enhanced image value.

[0069] The image transformation module 2 is used to perform spatial coordinate transformation on the acquired image, converting the image into the coordinate system and resolution size of the printer design drawing. Specifically, the image transformation module 2 includes a coordinate system conversion unit 201, a resolution matching unit 202, and an image correction unit 203. The coordinate system conversion unit 201 converts the acquired image from the camera coordinate system to the coordinate system of the printer design drawing. The resolution matching unit 202 adjusts the resolution of the image to the resolution size of the printer design drawing. The image correction unit 203 mainly performs color correction and geometric correction on the converted image to ensure the visual effect during printing.

[0070] The image recognition module 3 obtains at least one station image from the current image, mainly for performing AI feature recognition to obtain the position information of each feature point. Specifically, the image recognition module 3 includes a station image acquisition unit 301, an AI feature recognition unit 302, and a feature point position extraction unit 303. The station image acquisition unit 301 mainly obtains a specific station image from the acquired consecutive images. The AI feature recognition unit 302 mainly uses deep learning algorithms to recognize the feature points in the image. In deep learning feature recognition, assuming the ResNet50 model is used, the algorithm is as follows:

[0071] features = ResNet50(input image)

[0072] The feature point position extraction unit (303) mainly extracts the position information of each feature point from the recognition result. In feature point extraction, assuming a convolutional neural network (CNN) is used to extract feature points, the algorithm is as follows

[0073] feature positions = argmax(features)

[0074] The image correction module 4 is mainly used for correcting images. Specifically, the image correction module 4 includes a feature point alignment unit 401, an image correction algorithm unit 402, and a GPU acceleration unit 403. The feature point alignment unit 401 mainly aligns the position information of feature points with the position information of feature points on the design drawing. The image correction algorithm unit 402 mainly uses the image correction algorithm to correct the deformation of the image according to the alignment result. The GPU acceleration unit 403 mainly calls the GPU for accelerated operation during high-speed printing to improve the image processing speed.

[0075] The RIP call module 5 is mainly used for generating a printable PRN file. Specifically, the RIP call module 5 includes an image data conversion unit 501 and a PRN file generation unit 502. The image data conversion unit 501 mainly converts the corrected image data into the ink dot information data required for printing. The PRN file generation unit 502 completes the data conversion by calling the hot folder of the RIP software to generate a PRN file.

[0076] The PRN sending module 6 mainly sends the file generated by the RIP call module 5 to the board control software for printing. Specifically, the PRN sending module 6 includes a TCP protocol connection unit 601, a data sending unit 602, and a printing control unit 603. The TCP protocol connection unit 601 mainly establishes a connection with the board control software through the local TCP protocol. The data sending unit 602 mainly sends the generated PRN file data to the board control software through the TCP protocol. The printing control unit 603 mainly controls the printer to perform printing operations on the PRN data received by the board control software, where the modbus protocol is used for communication.

[0077] The image acquisition module 1, the image transformation module 2, the image recognition module 3, the image correction module 4, the RIP call module 5, and the PRN sending module 6 are connected through network communication.

[0078] The present invention also provides a method for automatic overprint printing of patterns on textile fabrics, which specifically includes the following steps:

[0079] S1: Scanning and photographing the textile fabric through the image acquisition module 1 to obtain a high-definition photo;

[0080] S2: Controlling the camera to scan and collect images in real time through the image acquisition module 1;

[0081] S3: Converting the collected image into the coordinate system and resolution size of the printer design drawing through the image transformation module 2;

[0082] S4: Using AI technology through the image recognition module 3 to identify the feature points in the image, and for the pattern area to be overprinted, AI feature selection, such as Figure 13 andFigure 14 As shown in the figure, by performing AI feature recognition on the scanned image of the currently transformed textile fabric, the coordinate position of the feature point on the current textile fabric (target coordinate position) is found. Combining with the coordinate position of the feature point on the design drawing (original coordinate position), pattern correction operations are performed on the design drawing to make the design drawing to be printed currently consistent with the pattern outline of the current textile fabric. The algorithm steps are as follows:

[0083] Input: Given n pairs of feature points (p i , q i ); p i , q i ∈R 2 , p i is the coordinate of the feature point in the design drawing, and q i is the coordinate of the feature point in the scanned image: i ∈ 1, …, n: R 2 represents the set of all points in the image, generally a two-dimensional matrix;

[0084] Output: A at least continuous function f: R 2 →R 2 , satisfying f(p i ) = q i ; i = 1, …, n;

[0085] S5: The image correction module 4 performs image correction according to the position information of the feature points, and the GPU can be called for accelerated operation. Specifically, when the image correction module 4 performs high-speed printing, the size of the image that needs to be corrected and calculated each time is relatively large, and the GPU can be called for accelerated operation to increase the operation efficiency by more than 20 times. In the correction calculation, the system uses the AI feature point as the correction particle point, and for the image around the AI feature point, the IDW interpolation algorithm is used for correction operation to achieve the correction operation of the corresponding trend ratio. The IDW interpolation method requires finding a function that satisfies the following form:

[0086]

[0087] (1) p ∈ R 2 , that is, p is a point on the image, and the coordinate is represented as (x, y);

[0088] (2) f i (x) satisfies f i (p i ) = q i , which is the local approximation for the point p i ; i = 1, 2, …, n. For the local approximation f i , generally linear or quadratic polynomials are used, and the polynomial coefficients can be determined by the derivative values of the data points. This algorithm determines each linear function from n pairs of feature points;

[0089] (3) w i : R 2 →R 2 is a weight function and must satisfy the condition and w i (p) ≥ 0, i = 1, …, n.

[0090] Shepard proposed the following simple weight function:

[0091] where IYGHB, d(p, p j ) is the distance between P and p j , and u can take any non - zero constant greater than zero; i

[0092] The comparison diagrams before and after correction are as shown in Figure 15 (before correction) and Figure 16 (after correction). Figure 15 The position difference between the superimposed design drawing and the scanned drawing shown in Figure 16 The position of the superimposed design drawing and the scanned drawing shown in Figure 15 and Figure 16 In, the green dots are the AI feature coordinates on the design drawing, the red dots are the AI feature coordinates on the real - time scanned drawing, and the arrows are the vector distances that need to be deformed.

[0093] S6: Use the RIP call module 5 to convert the corrected image data into the ink dot information required for printing, and generate a PRN file. Specifically, the RIP call module 5 converts the corrected image data into the ink dot information data by calling the hot folder of the RIP software;

[0094] S7: Send the PRN file data to the board control software through the PRN sending module 6 via the local TCP protocol to complete printing. Specifically, the PRN sending module 6 sends the PRN file data to the board control software via the local TCP protocol, and the board control software prints after receiving the PRN data.

[0095] In S3, it is necessary to convert the camera - scanned image, through image transformation, into the coordinate system and resolution size of the design drawing. Move each point (x, y) of the original image to (x + tx, y + ty). The transformation matrix is as shown in Figure 8 and the conversion formula is as follows:

[0096]

[0097] Enlarge (reduce) the abscissa of each point to sx times, and enlarge (reduce) the ordinate to sy times. The transformation matrix is as shown in Figure 9 and the conversion formula is as follows:

[0098] ​

[0099] The rotation matrix is as follows. The 0-degree angle is in the horizontal direction, pointing from left to right. Clockwise is negative and counterclockwise is positive. As Figure 10 shown, the conversion formula is as follows:

[0100]

[0101] Obtain the matrix conversion parameters for rotation, translation, and scaling operations. Calibration work needs to be done before the system runs. The steps are as follows:

[0102] 1. Print the calibration picture file on a blank textile fabric ( Figure 11 is the upper left corner of the calibration picture);

[0103] 2. Use a camera to scan the calibration picture printed on the textile fabric, as Figure 12 shown;

[0104] 3. Extract the center coordinate positions of each dot on the calibration board picture through an algorithm, extract the center coordinate positions of each dot on the scanned picture of the textile fabric, and then obtain the parameter values of the rotation, translation, and scaling transformation matrix between the scanned image of the textile fabric and the printed design drawing through calculation.

[0105] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An automatic overprint printing system for textile fabrics, comprising: An image acquisition module (1): used to control the camera to scan, and at the same time, through the camera SDK, acquire images in real time; An image transformation module (2): used to perform spatial coordinate transformation on the acquired images, and convert the images into the coordinate system and resolution size of the printer design drawing; An image recognition module (3): obtain at least one station image from the current image, mainly for performing AI feature recognition to obtain the position information of each feature point; An image correction module (4): mainly used to correct the images; A RIP call module (5): mainly used to generate a printable PRN file; A PRN sending module (6): mainly send the file generated by the RIP call module (5) to the board control software for printing; It is characterized in that: the image acquisition module (1), the image transformation module (2), the image recognition module (3), the image correction module (4), the RIP call module (5) and the PRN sending module (6) are connected through network communication.

2. The automatic overprinting and printing system for patterns of a textile fabric according to claim 1, wherein: The image acquisition module (1) includes at least one camera, a lens matching the camera, and at least one LED light source. The camera and the lens are used to scan and photograph the textile fabric to obtain high-definition photos. The LED light source is used to supplement light to the textile fabric. Through light supplementation, a sufficiently bright picture and a sufficiently clear jacquard contour or the contour of the reverse pattern are obtained. The camera selects a high-definition industrial line array camera.

3. The automatic overprinting and printing system for the pattern of a textile fabric according to claim 1, wherein: The image acquisition module (1) includes a camera control unit (101), an image acquisition unit (102), and an image preprocessing unit (103). The camera control unit (101) is responsible for initializing and controlling the camera to scan and photograph. The image acquisition unit (102) mainly acquires image data in real time through the camera SDK. The image preprocessing unit (103) mainly performs denoising and contrast enhancement processing on the acquired images to improve the image quality.

4. An automatic overprinting and printing system for patterns of a textile fabric according to claim 1, characterized in that: The image transformation module (2) includes a coordinate system conversion unit (201), a resolution matching unit (202), and an image correction unit (203). The coordinate system conversion unit (201) converts the acquired images from the camera coordinate system to the coordinate system of the printer design drawing. The resolution matching unit (202) adjusts the resolution of the images to the resolution size of the printer design drawing. The image correction unit (203) mainly performs color correction and geometric correction on the converted images to ensure the visual effect during printing.

5. The automatic overprinting and printing system for patterns of a textile fabric according to claim 1, characterized in that: The image recognition module (3) includes a station image acquisition unit (301), an AI feature recognition unit (302), and a feature point position extraction unit (303). The station image acquisition unit (301) mainly obtains a specific station image from the acquired continuous images. The AI feature recognition unit (302) mainly uses deep learning algorithms to recognize feature points in the images. The feature point position extraction unit (303) mainly extracts the position information of each feature point from the recognition results.

6. The automatic overprinting and printing system for the pattern of a textile fabric according to claim 1, characterized in that: The image correction module (4) includes a feature point alignment unit (401), an image correction algorithm unit (402), and a GPU acceleration unit (403). The feature point alignment unit (401) mainly aligns the position information of the feature points with the position information of the feature points on the design drawing. The image correction algorithm unit (402) mainly uses the image correction algorithm to perform deformation correction on the image according to the alignment result. The GPU acceleration unit (403) mainly calls the GPU for accelerated operation during high-speed printing to improve the image processing speed.

7. An automatic overprinting and printing system for patterns of a textile fabric according to claim 1, characterized in that: The RIP call module (5) includes an image data conversion unit (501) and a PRN file generation unit (502). The image data conversion unit (501) mainly converts the corrected image data into the ink dot information data required for printing. The PRN file generation unit (502) completes the data conversion and generates a PRN file by calling the hot folder of the RIP software.

8. The automatic overprinting and printing system for patterns of a textile fabric according to claim 1, characterized in that: The PRN sending module (6) includes a TCP protocol connection unit (601), a data sending unit (602), and a printing control unit (603). The TCP protocol connection unit (601) mainly establishes a connection with the board control software through the local TCP protocol. The data sending unit (602) mainly sends the generated PRN file data to the board control software through the TCP protocol. The printing control unit (603) mainly controls the printer to perform printing operations on the PRN data received by the board control software.

9. A method for automatically overprinting and printing patterns on a textile fabric according to any one of claims 1-8, characterized in that: Specifically, it includes the following steps: S1: Scan and take a photo of the textile fabric through the image acquisition module (1) to obtain a high-definition photo; S2: Control the camera to scan and collect images in real time through the image acquisition module (1); S3: Convert the collected image into the coordinate system and resolution size of the printer design drawing through the image transformation module (2); S4: Use the AI technology of the image recognition module (3) to identify the feature points in the image; S5: Perform image correction according to the position information of the feature points through the image correction module (4), and the GPU can be called for accelerated operation; S6: Use the RIP call module (5) to convert the corrected image data into the ink dot information required for printing and generate a PRN file; S7: Send the PRN file data to the board control software through the local TCP protocol through the PRN sending module (6) to complete printing.

10. A method for automatic overprinting of patterns on a textile fabric according to claim 9, characterized in that: When the image correction module (4) performs high-speed printing, the image size that needs to be corrected and calculated each time is relatively large. The GPU can be called for accelerated operation to increase the operation efficiency by more than 20 times. The RIP call module (5) converts the corrected image data into the ink dot information data required for printing by calling the hot folder of the RIP software. The PRN sending module (6) sends the PRN file data to the board control software through the local TCP protocol, and the board control software performs printing after receiving the PRN data.

Images