Platemaking optimization method for silk-screen printing

Abstract

The invention provides a plate-making optimization method for silk-screen printing, which relates to the technical field of printing and comprises the following steps of: extracting image features of a printing image needing silk-screen printing to obtain a first image feature; based on the first image feature and the printing parameter of the target equipment, combining with a machine learning model to carry out platemaking prediction so as to determine a first platemaking parameter of the target equipment; performing plate making based on the first plate making parameter to obtain a first plate making result, and performing quality detection on the first plate making result based on preset scanning equipment to obtain a first detection result; and carrying out platemaking optimization on the first platemaking parameter of the target equipment based on the first detection result. According to the method, the printing image is subjected to feature extraction and plate making prediction, so that more accurate plate making parameters are obtained, plate making is performed, the plate making result is detected in real time, the plate making parameters are adjusted in time, and plate making of target equipment based on the printing image is more accurate and efficient.

Description

Technical Field

[0001] The invention relates to the technical field of printing, and in particular to a screen printing plate making optimization method. Background Art

[0002] At present, screen printing, as a printing process with a long history, has shown wide application value in many fields such as artistic creation and industrial production. However, the traditional plate-making process relies on manual operation, which is time-consuming, error-prone, and costly. Therefore, in recent years, some automated and semi-automated equipment has been introduced into the plate-making process.

[0003] However, the existing plate-making process still lacks a systematic intelligent optimization solution, resulting in low overall efficiency of screen printing.

[0004] Therefore, the present invention provides a method for optimizing plate making for screen printing. Summary of the invention

[0005] The present invention provides a screen printing platemaking optimization method, which is used to extract features of printed images and perform platemaking predictions to obtain more accurate platemaking parameters, thereby performing platemaking, and performing real-time detection of the platemaking results, thereby adjusting the platemaking parameters in time, so that the target device can make platemaking based on the printed image more accurate and efficient.

[0006] The present invention provides a screen printing plate making optimization method, comprising: Step 1: extracting image features of a printed image to be screen printed to obtain a first image feature; Step 2: Combining the first image feature and the printing parameters of the target device with the machine learning model to perform platemaking prediction, thereby determining the first platemaking parameters of the target device; Step 3: performing plate making based on the first plate making parameters to obtain a first plate making result, and performing quality inspection on the first plate making result to obtain a first inspection result; Step 4: Optimize the first platemaking parameters of the target device based on the first detection result.

[0007] According to the present invention, image feature extraction is performed on a printing image to be screen-printed to obtain a first image feature, including: Step 11: Capturing a printing image to be screen-printed based on a preset high-definition camera to obtain a first printing image; Step 12: performing image processing on the first printed image based on an image preprocessing technology to obtain a second printed image; Step 13: Perform feature extraction and feature recognition on the second printed image based on a deep learning algorithm to obtain the first image features of the second printed image.

[0008] According to the present invention, the first image feature and the printing parameter of the target device are combined with the machine learning model to perform platemaking prediction, thereby determining the first platemaking parameter of the target device, including: Step 21: determining an initial platemaking prediction model of the target device based on the first image feature and the real-time printing parameters of the target device; Step 22: training the initial platemaking prediction model based on historical printing parameters of the historical printing process of the target device and corresponding historical image features to obtain a first platemaking prediction model; Step 23: extract each model parameter corresponding to the first plate-making prediction model to obtain the first plate-making parameter of the target device.

[0009] According to the present invention, plate making is performed based on the first plate making parameter to obtain a first plate making result, including: Step 01: inputting the first plate-making parameters into the target plate-making device, thereby realizing the image plate-making of the second printing image and obtaining the first plate-making result; Step 02: Acquire the real-time plate-making status in the image plate-making process based on the preset integrated sensor; Step 03: The real-time platemaking status at each platemaking moment of the image platemaking is judged to determine whether the real-time platemaking status at each platemaking moment belongs to the standard platemaking status range of the target device, thereby adjusting the first platemaking parameter of the target device.

[0010] The image platemaking for the second printing image provided by the present invention includes: completing the steps of screen cleaning, photosensitive resin coating, exposure, and development based on the second printing image.

[0011] According to the present invention, judging whether the real-time plate-making state at each plate-making moment belongs to the standard plate-making state range of the target device, thereby adjusting the first plate-making parameter of the target device, includes: Step 031: Determine the real-time plate-making state S at each plate-making moment; ; Where S is the real-time plate-making status at the current plate-making moment, is the gray value of the i-th plate-making detection point at the current plate-making moment, is the standard grayscale value of the i-th plate-making detection point at the current plate-making moment, To preset the data transmission error of the integrated sensor, is the plate-making speed of the ith plate-making detection point at the current plate-making moment, is the plate-making speed at the previous plate-making moment of the i-th plate-making detection point, is the speed conversion coefficient, is the weight of the gray value of the plate-making detection point on the plate-making state. is the weight of the plate-making speed on the plate-making state, and n is the number of plate-making detection points in the image plate-making process of the target device; Step 032: comparing the real-time plate-making state at the current plate-making moment with the standard plate-making state range of the target device at the current plate-making moment; If the real-time plate-making state belongs to the standard plate-making state range of the target device at the current plate-making moment, then the real-time plate-making state at the current plate-making moment is determined to be qualified; Otherwise, it is determined that the real-time plate-making state at the current plate-making moment is unqualified, and the first plate-making parameter of the target device is adjusted in real time.

[0012] According to the present invention, the first plate-making result is subjected to quality inspection to obtain a first inspection result, including: Step 31: extracting the plate-making image in the first plate-making result to obtain the first plate-making image; Step 32: performing image processing on the first plate-making image to obtain a second plate-making image, and extracting image features of the second plate-making image to obtain second image features; The second plate-making image and the second image feature are the first plate-making result of the target device for screen printing; Step 33: obtaining first image parameters of the second printing image, and obtaining second image parameters of the second plate-making image; Step 34: Compare the parameter difference between the first image parameter and the corresponding sub-parameter in the second image parameter to obtain a first parameter difference value; Step 35: combining the first parameter difference value with the corresponding image parameter type to determine the parameter consistency between the second image parameter and each sub-parameter in the first image parameter; If the parameter matching degree corresponding to each sub-parameter is greater than the preset standard parameter matching degree, a first comparison is performed between the second plate-making image and the second printing image to obtain a first comparison result; If there is a sub-parameter whose parameter matching degree is not greater than the preset standard parameter matching degree among the sub-parameters, extracting a first sub-parameter corresponding to the first image parameter and a second sub-parameter corresponding to the second image parameter; Comparing the parameter values ​​of the first sub-parameter and the second sub-parameter, and adjusting the sub-parameter with the larger parameter value, thereby obtaining an adjusted third image parameter, and using the image parameter that has not been adjusted as the fourth image parameter, so that the sub-parameter of each parameter type in the third image parameter is consistent with the sub-parameter of the corresponding parameter type in the fourth image parameter; Adjusting the corresponding image based on the third image parameter to obtain an adjusted third plate-making image, and using the image corresponding to the fourth image parameter as a fourth plate-making image; Performing a first comparison on the third plate-making image and the fourth plate-making image to obtain a first comparison result; Step 36: Compare the first image feature with the second image feature to obtain a second comparison result; Step 37: Based on the first comparison result and the second comparison result, a first detection result of the target device performing platemaking based on the first platemaking parameter is obtained.

[0013] According to the present invention, the first plate-making parameter of the target device is optimized based on the first detection result, including: Step 41: obtaining the printing accuracy of the target device for screen printing, thereby obtaining the maximum error result of the screen printing corresponding to the current printing accuracy; Step 42: Compare the maximum screen printing error result with the first detection result, thereby determining the first plate making defect of the target device based on the comparison result; Step 43: Compare the first plate-making defect with a preset defect database to determine a defect optimization solution corresponding to the first plate-making defect; Step 44: adjusting the first plate-making parameters of the target device accordingly based on the defect optimization solution to obtain second plate-making parameters; Step 45: Determine a second platemaking prediction model of the target device based on the second platemaking parameters, and perform platemaking optimization based on the second platemaking prediction model.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a screen printing platemaking optimization method, which obtains more accurate platemaking parameters by extracting features of printed images and performing platemaking predictions, thereby performing platemaking, and performing real-time detection of the platemaking results, thereby adjusting the platemaking parameters in time, which can make the target device's platemaking based on printed images more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a screen printing plate making optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a screen printing plate making optimization method, such as Figure 1 As shown, including: Step 1: extracting image features of a printed image to be screen printed to obtain a first image feature; Step 2: Combining the first image feature and the printing parameters of the target device with the machine learning model to perform platemaking prediction, thereby determining the first platemaking parameters of the target device; Step 3: performing plate making based on the first plate making parameters to obtain a first plate making result, and performing quality inspection on the first plate making result to obtain a first inspection result; Step 4: Optimize the first platemaking parameters of the target device based on the first detection result.

[0019] In this embodiment, screen printing refers to a printing method that uses a silk screen as a base and transfers ink to the substrate through the mesh holes of the graphic part through the extrusion of a scraper. It is suitable for various materials, such as paper, plastic, metal, glass, etc., and can produce fine patterns and texts.

[0020] In this embodiment, the printed image refers to the printed image that the target device needs to screen print, wherein the printed image is captured based on a preset high-precision camera. Image capture based on the high-precision camera can improve the platemaking efficiency and platemaking accuracy of the platemaking process.

[0021] In this embodiment, the first image feature refers to feature information that can represent the image content extracted from the printed image. For example, the first image feature includes the color, texture, shape, spatial relationship, etc. of the image.

[0022] In this embodiment, printing parameters refer to various parameters that affect printing quality and effect, such as the type of ink, pressure of the squeegee, printing speed, etc. The selection and setting of printing parameters are crucial to the printing quality of screen printing on the target device.

[0023] In this embodiment, the machine learning model is an algorithm or system that learns and predicts new data through training data. In plate making prediction, the machine learning model can predict the optimal plate making parameters based on image features and printing parameters.

[0024] In this embodiment, platemaking prediction refers to the process of predicting the optimal platemaking parameters based on image features and printing parameters using technical means such as machine learning models. Platemaking prediction helps reduce the trial and error costs in the platemaking process and improve platemaking efficiency and quality.

[0025] In this embodiment, the first platemaking parameter is an optimal platemaking parameter suitable for the target device and is predicted based on a machine learning model.

[0026] In this embodiment, the first platemaking result is a printing plate material obtained after platemaking based on the first platemaking parameters.

[0027] In this embodiment, quality detection refers to extracting the platemaking image from the first platemaking result, and comparing the extracted platemaking result with the standard platemaking result (ie, the second printing image), thereby determining the platemaking quality based on the comparison result.

[0028] In this embodiment, the first detection result refers to the result obtained after quality detection of the first plate-making result.

[0029] In this embodiment, platemaking optimization refers to the process of adjusting and improving platemaking parameters according to quality inspection results. Through platemaking optimization, the platemaking quality and printing effect can be further improved.

[0030] The beneficial effect of the above technical solution is: by extracting features from the printed image and making platemaking predictions, more accurate platemaking parameters are obtained, thereby making plates, and the platemaking results are detected in real time, so that the platemaking parameters can be adjusted in time, so that the target device can make platemaking based on printed images more accurate and efficient.

[0031] Embodiment 2: Based on Example 1, image features are extracted from the printed image to be screen-printed to obtain the first image features, including: Step 11: Capturing a printing image to be screen-printed based on a preset high-definition camera to obtain a first printing image; Step 12: performing image processing on the first printed image based on an image preprocessing technology to obtain a second printed image; Step 13: Perform feature extraction and feature recognition on the second printed image based on a deep learning algorithm to obtain the first image features of the second printed image.

[0032] In this embodiment, screen printing refers to a printing method that uses a silk screen as a base and transfers ink to the substrate through the mesh holes of the graphic part through the extrusion of a scraper. It is suitable for various materials, such as paper, plastic, metal, glass, etc., and can produce fine patterns and texts.

[0033] In this embodiment, the first printed image refers to a printed image that needs to be screen-printed by the target device, wherein the first printed image is captured based on a preset high-precision camera. Image capture based on a high-precision camera can improve the platemaking efficiency and platemaking accuracy of the platemaking process.

[0034] In this embodiment, the second printed image is a printed image obtained by performing image preprocessing on the first printed image in a manner such as denoising and contrast enhancement. The image quality of the printed image can be improved by image preprocessing.

[0035] In this embodiment, the deep learning algorithm is an important algorithm in the field of machine learning. It establishes a deep neural network model to perform multi-layer abstraction and representation of input data, thereby realizing effective processing and analysis of complex data. In the field of image processing, the deep learning algorithm can extract deep features in the image, providing strong support for subsequent image recognition, classification and other tasks.

[0036] In this embodiment, feature extraction refers to the process of extracting information that can represent the content or characteristics of the image from an image. In a deep learning algorithm, feature extraction is usually achieved by training a neural network model that can learn the key features in the image and represent them as a high-dimensional vector or feature map.

[0037] In this embodiment, feature recognition is to further analyze and identify the extracted features based on feature extraction to determine whether there is specific content or pattern in the image. For example, in a deep learning algorithm, feature recognition is usually achieved by training a classifier or regressor, which can use the extracted features to predict the category, location or other attributes of the image.

[0038] In this embodiment, the first image feature refers to feature information that can represent the image content extracted from the second printed image. For example, the first image feature includes the color, texture, shape, spatial relationship, etc. of the image.

[0039] The beneficial effect of the above technical solution is: by performing image processing on the printed image and extracting image features, the image parameters of the printed image can be determined more accurately, thereby obtaining a more accurate platemaking prediction model for platemaking prediction, making the platemaking of the target device based on the printed image more accurate and efficient.

[0040] Embodiment 3: Based on Example 2, the first image feature and the printing parameters of the target device are combined with the machine learning model to perform platemaking prediction, thereby determining the first platemaking parameters of the target device, including: Step 21: determining an initial platemaking prediction model of the target device based on the first image feature and the real-time printing parameters of the target device; Step 22: training the initial platemaking prediction model based on historical printing parameters of the historical printing process of the target device and corresponding historical image features to obtain a first platemaking prediction model; Step 23: extract each model parameter corresponding to the first plate-making prediction model to obtain the first plate-making parameter of the target device.

[0041] In this embodiment, the real-time printing parameters refer to various parameters that affect the printing quality and effect, such as the type of ink, the pressure of the squeegee, the printing speed, etc. The selection and setting of the printing parameters are crucial to the printing quality of screen printing on the target device.

[0042] In this embodiment, the initial plate-making prediction model is a simulation prediction model that is determined based on the first image feature and the real-time printing parameters of the target device and can be used for simulation.

[0043] In this embodiment, historical printing parameters and historical image features refer to the historical printing parameters and corresponding historical image features of the target device during the historical platemaking process, wherein the parameter type of the historical printing parameters is consistent with the parameter type of the real-time printing parameters, and the feature type of the historical image features is consistent with the feature type of the first image features.

[0044] In this embodiment, the first platemaking prediction model is a prediction model that is more suitable for the prediction of platemaking parameters of the target device after optimizing the initial platemaking prediction model according to the historical printing parameters and historical image features of the target device.

[0045] In this embodiment, the first platemaking parameters refer to the best platemaking parameters suitable for the target device obtained by performing platemaking prediction based on the first platemaking prediction model.

[0046] The beneficial effect of the above technical solution is: by training the machine learning model, a first platemaking prediction model is obtained, so as to determine the first platemaking parameters of the target device for platemaking, which helps to reduce the trial and error cost in the platemaking process and improve the platemaking efficiency and quality.

[0047] Embodiment 4: Based on the third embodiment, plate making is performed based on the first plate making parameters to obtain a first plate making result, including: Step 01: inputting the first plate-making parameters into the target plate-making device, thereby realizing the image plate-making of the second printing image and obtaining the first plate-making result; Step 02: Acquire the real-time plate-making status in the image plate-making process based on the preset integrated sensor; Step 03: The real-time platemaking status at each platemaking moment of the image platemaking is judged to determine whether the real-time platemaking status at each platemaking moment belongs to the standard platemaking status range of the target device, thereby adjusting the first platemaking parameter of the target device.

[0048] In this embodiment, the first platemaking result is a printing plate material obtained after platemaking based on the first platemaking parameters.

[0049] In this embodiment, the real-time platemaking status is the platemaking status of each platemaking moment in the image platemaking process of the target device captured based on a preset integrated sensor, wherein the real-time platemaking status is related to the platemaking speed of the target device and the difference between the grayscale value of the platemaking detection point and the standard grayscale value.

[0050] In this embodiment, the standard plate-making state range is predetermined based on the plate-making accuracy of the target device, wherein the value of the standard plate-making state range is (0.9, 1).

[0051] The beneficial effect of the above technical solution is that by using the plate-making parameters determined by the plate-making prediction model to make plates for the target device, the plate-making efficiency can be improved and the trial-and-error cost of the plate-making process can be reduced.

[0052] Embodiment 5: Based on the fourth embodiment, the second printing image is subjected to image plate making, including: completing the steps of screen cleaning, photosensitive resin coating, exposure, and development based on the second printing image.

[0053] The beneficial effect of the above technical solution is: by using the prediction model to predict platemaking and determine the platemaking parameters, image platemaking can be performed on the target device, which helps to reduce the trial and error costs in the platemaking process and improve platemaking efficiency and quality.

[0054] Embodiment 6: Based on Example 4, adjusting the first plate-making parameters of the target device includes: Step 031: Determine the real-time plate-making state S at each plate-making moment; ; Where S is the real-time plate-making status at the current plate-making moment, is the gray value of the i-th plate-making detection point at the current plate-making moment, is the standard grayscale value of the i-th plate-making detection point at the current plate-making moment, To preset the data transmission error of the integrated sensor, is the plate-making speed of the ith plate-making detection point at the current plate-making moment, is the plate-making speed at the previous plate-making moment of the i-th plate-making detection point, is the speed conversion coefficient, is the weight of the gray value of the plate-making detection point on the plate-making state. is the weight of the plate-making speed on the plate-making state, and n is the number of plate-making detection points in the image plate-making process of the target device; Step 032: comparing the real-time plate-making state at the current plate-making moment with the standard plate-making state range of the target device at the current plate-making moment; If the real-time plate-making state belongs to the standard plate-making state range of the target device at the current plate-making moment, then the real-time plate-making state at the current plate-making moment is determined to be qualified; Otherwise, it is determined that the real-time plate-making state at the current plate-making moment is unqualified, and the first plate-making parameter of the target device is adjusted in real time.

[0055] In this embodiment, the real-time platemaking status is the platemaking status of each platemaking moment in the image platemaking process of the target device captured based on a preset integrated sensor, wherein the real-time platemaking status is related to the platemaking speed of the target device and the difference between the grayscale value of the platemaking detection point and the standard grayscale value.

[0056] In this embodiment, the standard plate-making state range is predetermined based on the plate-making accuracy of the target device, wherein the value of the standard plate-making state range is (0.9, 1).

[0057] The beneficial effect of the above technical solution is that by using the plate-making parameters determined by the plate-making prediction model to make plates for the target device, the plate-making efficiency can be improved and the trial-and-error cost of the plate-making process can be reduced.

[0058] Embodiment 7: Based on Example 4, the first plate-making result is subjected to quality inspection to obtain a first inspection result, including: Step 31: extracting the plate-making image in the first plate-making result to obtain the first plate-making image; Step 32: performing image processing on the first plate-making image to obtain a second plate-making image, and extracting image features of the second plate-making image to obtain second image features; The second plate-making image and the second image feature are the first plate-making result of the target device for screen printing; Step 33: obtaining first image parameters of the second printing image, and obtaining second image parameters of the second plate-making image; Step 34: Compare the parameter difference between the first image parameter and the corresponding sub-parameter in the second image parameter to obtain a first parameter difference value; Step 35: combining the first parameter difference value with the corresponding image parameter type to determine the parameter consistency between the second image parameter and each sub-parameter in the first image parameter; If the parameter matching degree corresponding to each sub-parameter is greater than the preset standard parameter matching degree, a first comparison is performed between the second plate-making image and the second printing image to obtain a first comparison result; If there is a sub-parameter whose parameter matching degree is not greater than the preset standard parameter matching degree among the sub-parameters, extracting a first sub-parameter corresponding to the first image parameter and a second sub-parameter corresponding to the second image parameter; Comparing the parameter values ​​of the first sub-parameter and the second sub-parameter, and adjusting the sub-parameter with the larger parameter value, thereby obtaining an adjusted third image parameter, and using the image parameter that has not been adjusted as the fourth image parameter, so that the sub-parameter of each parameter type in the third image parameter is consistent with the sub-parameter of the corresponding parameter type in the fourth image parameter; Adjusting the corresponding image based on the third image parameter to obtain an adjusted third plate-making image, and using the image corresponding to the fourth image parameter as a fourth plate-making image; Performing a first comparison on the third plate-making image and the fourth plate-making image to obtain a first comparison result; Step 36: Compare the first image feature with the second image feature to obtain a second comparison result; Step 37: Based on the first comparison result and the second comparison result, a first detection result of the target device performing platemaking based on the first platemaking parameter is obtained.

[0059] In this embodiment, the first plate-making image refers to the plate-making image included in the first plate-making result.

[0060] In this embodiment, the second plate-making image is a plate-making image obtained by performing image processing on the first plate-making image based on an image preprocessing technology.

[0061] In this embodiment, the second image feature is feature information extracted from the second plate-making image that can represent the image content. For example, the second image feature includes the color, texture, shape, spatial relationship, etc. of the image.

[0062] In this embodiment, the second plate-making image and the second image feature are the first plate-making result of the target device for screen printing.

[0063] In this embodiment, the first image parameter refers to the image parameter included in the second printing image, and the second image parameter refers to the image parameter included in the second platemaking image, wherein the image parameters include image resolution, image size, image color, etc.

[0064] In this embodiment, the first parameter difference value refers to the parameter difference value of the corresponding sub-parameter in the first image parameter and the second image parameter. For example, the image resolution in the first image parameter is 1920x1080, and the image resolution in the second image parameter is 1080x1080, then the corresponding first parameter difference value is (840,0).

[0065] In this embodiment, the parameter consistency is related to the first parameter difference value and the corresponding image parameter type, wherein the more important the image parameter type is to the image and the smaller the first parameter difference value is, the higher the corresponding parameter consistency is.

[0066] In this embodiment, the first comparison result refers to comparing the image parameters of the second plate-making image with the image parameters of the second printing image.

[0067] In this embodiment, the third image parameter means that when there is a sub-parameter whose parameter matching degree of the sub-parameter corresponding to the image parameter is not greater than the preset standard parameter matching degree, the first sub-parameter corresponding to the first image parameter and the second sub-parameter corresponding to the second image parameter are extracted, and the parameter values ​​of the first sub-parameter and the second sub-parameter are compared, so as to adjust the sub-parameter with the larger parameter value, and the image parameter corresponding to the sub-parameter with the smaller parameter value is obtained after the adjustment, and the image parameter corresponding to the sub-parameter with the smaller parameter value is the fourth image parameter.

[0068] In this embodiment, the image corresponding to the third image parameter is the third plate-making image, and the image corresponding to the fourth image parameter is the fourth plate-making image.

[0069] In this embodiment, the second comparison result refers to a comparison result obtained by comparing the first image feature and the second image feature.

[0070] In this embodiment, the first detection result is a result obtained by detecting and determining the image platemaking quality of the target device based on a comprehensive determination of the image comparison result and the image feature comparison result.

[0071] The beneficial effect of the above technical solution is: by comparing the platemaking image of the platemaking result with the second printing image, the platemaking result can be detected in real time based on the comparison result, so that the platemaking parameters can be adjusted in time, which can make the platemaking of the target device based on the printing image more accurate and efficient.

[0072] Embodiment 8: Based on Example 7, the first plate-making parameter of the target device is optimized based on the first detection result, including: Step 41: obtaining the printing accuracy of the target device for screen printing, thereby obtaining the maximum error result of the screen printing corresponding to the current printing accuracy; Step 42: Compare the maximum screen printing error result with the first detection result, thereby determining the first plate making defect of the target device based on the comparison result; Step 43: Compare the first plate-making defect with a preset defect database to determine a defect optimization solution corresponding to the first plate-making defect; Step 44: adjusting the first plate-making parameters of the target device accordingly based on the defect optimization solution to obtain second plate-making parameters; Step 45: Determine a second platemaking prediction model of the target device based on the second platemaking parameters, and perform platemaking optimization based on the second platemaking prediction model.

[0073] In this embodiment, the first plate-making defects include line breakage, color deviation, and the like.

[0074] In this embodiment, the defect optimization solution refers to a solution such as performing defect correction on the first platemaking defect or performing re-platemaking.

[0075] In this embodiment, the second platemaking parameters are platemaking parameters obtained by adjusting the first platemaking parameters of the target device according to the defect optimization solution.

[0076] In this embodiment, the second platemaking prediction model is a platemaking prediction model obtained by adjusting the corresponding model parameters of the first platemaking prediction model based on the second platemaking parameters.

[0077] In this embodiment, platemaking optimization refers to the process of adjusting and improving platemaking parameters according to quality inspection results. Through platemaking optimization, the platemaking quality and printing effect can be further improved.

[0078] The beneficial effect of the above technical solution is: by comparing and analyzing the first detection results, the platemaking defects of the target device are determined, and a defect optimization plan is determined to optimize the platemaking, which can make the platemaking optimization of the target device more timely and accurate.

[0079] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these 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 invention.

Claims

1. A screen printing plate making optimization method, characterized in that: include: Step 1: extracting image features of a printed image to be screen printed to obtain a first image feature; Step 2: Combining the first image feature and the printing parameters of the target device with the machine learning model to perform platemaking prediction, thereby determining the first platemaking parameters of the target device; Step 3: performing plate making based on the first plate making parameters to obtain a first plate making result, and performing quality inspection on the first plate making result to obtain a first inspection result; Step 4: Optimize the first platemaking parameters of the target device based on the first detection result.

2. A screen printing plate making optimization method according to claim 1, characterized in that: Performing image feature extraction on a printing image to be screen printed to obtain a first image feature includes: Step 11: Capturing a printing image to be screen-printed based on a preset high-definition camera to obtain a first printing image; Step 12: performing image processing on the first printed image based on an image preprocessing technology to obtain a second printed image; Step 13: Perform feature extraction and feature recognition on the second printed image based on a deep learning algorithm to obtain the first image features of the second printed image.

3. A screen printing plate making optimization method according to claim 2, characterized in that: The first image feature and the printing parameters of the target device are combined with the machine learning model to perform platemaking prediction, thereby determining the first platemaking parameters of the target device, including: Step 21: determining an initial platemaking prediction model of the target device based on the first image feature and the real-time printing parameters of the target device; Step 22: training the initial platemaking prediction model based on historical printing parameters of the historical printing process of the target device and corresponding historical image features to obtain a first platemaking prediction model; Step 23: extract each model parameter corresponding to the first plate-making prediction model to obtain the first plate-making parameter of the target device.

4. A screen printing plate making optimization method according to claim 3, characterized in that: Plate making is performed based on the first plate making parameters to obtain a first plate making result, including: Step 01: inputting the first plate-making parameters into the target plate-making device, thereby realizing the image plate-making of the second printing image and obtaining the first plate-making result; Step 02: Acquire the real-time plate-making status in the image plate-making process based on the preset integrated sensor; Step 03: The real-time platemaking status at each platemaking moment of the image platemaking is judged to determine whether the real-time platemaking status at each platemaking moment belongs to the standard platemaking status range of the target device, thereby adjusting the first platemaking parameter of the target device.

5. A screen printing plate making optimization method according to claim 4, characterized in that: The second printing image is subjected to image plate making, including: completing the steps of screen cleaning, photosensitive resin coating, exposure, and development based on the second printing image.

6. A screen printing plate making optimization method according to claim 4, characterized in that: Determining whether the real-time plate-making state at each plate-making moment belongs to the standard plate-making state range of the target device, thereby adjusting the first plate-making parameter of the target device, including: Step 031: Determine the real-time plate-making state S at each plate-making moment; ; Where S is the real-time plate-making status at the current plate-making moment, is the gray value of the i-th plate-making detection point at the current plate-making moment, is the standard grayscale value of the i-th plate-making detection point at the current plate-making moment, To preset the data transmission error of the integrated sensor, is the plate-making speed of the ith plate-making detection point at the current plate-making moment, is the plate-making speed at the previous plate-making moment of the i-th plate-making detection point, is the speed conversion coefficient, is the weight of the gray value of the plate-making detection point on the plate-making state. is the weight of the plate-making speed on the plate-making state, and n is the number of plate-making detection points in the image plate-making process of the target device; Step 032: comparing the real-time plate-making state at the current plate-making moment with the standard plate-making state range of the target device at the current plate-making moment; If the real-time plate-making state belongs to the standard plate-making state range of the target device at the current plate-making moment, then the real-time plate-making state at the current plate-making moment is determined to be qualified; Otherwise, it is determined that the real-time plate-making state at the current plate-making moment is unqualified, and the first plate-making parameter of the target device is adjusted in real time.

7. The screen printing plate making optimization method according to claim 4, characterized in that: Performing quality inspection on the first plate-making result to obtain a first inspection result, including: Step 31: extracting the plate-making image in the first plate-making result to obtain the first plate-making image; Step 32: performing image processing on the first plate-making image to obtain a second plate-making image, and extracting image features of the second plate-making image to obtain second image features; The second plate-making image and the second image feature are the first plate-making result of the target device for screen printing; Step 33: obtaining first image parameters of the second printing image, and obtaining second image parameters of the second plate-making image; Step 34: Compare the parameter difference between the first image parameter and the corresponding sub-parameter in the second image parameter to obtain a first parameter difference value; Step 35: combining the first parameter difference value with the corresponding image parameter type to determine the parameter consistency between the second image parameter and each sub-parameter in the first image parameter; If the parameter matching degree corresponding to each sub-parameter is greater than the preset standard parameter matching degree, a first comparison is performed between the second plate-making image and the second printing image to obtain a first comparison result; If there is a sub-parameter whose parameter matching degree is not greater than the preset standard parameter matching degree among the sub-parameters, extracting a first sub-parameter corresponding to the first image parameter and a second sub-parameter corresponding to the second image parameter; Comparing the parameter values ​​of the first sub-parameter and the second sub-parameter, and adjusting the sub-parameter with the larger parameter value, thereby obtaining an adjusted third image parameter, and using the image parameter that has not been adjusted as the fourth image parameter, so that the sub-parameter of each parameter type in the third image parameter is consistent with the sub-parameter of the corresponding parameter type in the fourth image parameter; Adjusting the corresponding image based on the third image parameter to obtain an adjusted third plate-making image, and using the image corresponding to the fourth image parameter as a fourth plate-making image; Performing a first comparison on the third plate-making image and the fourth plate-making image to obtain a first comparison result; Step 36: Compare the first image feature with the second image feature to obtain a second comparison result; Step 37: Based on the first comparison result and the second comparison result, a first detection result of the target device performing platemaking based on the first platemaking parameter is obtained.

8. A screen printing plate making optimization method according to claim 7, characterized in that: Optimizing the first plate-making parameters of the target device based on the first detection result includes: Step 41: obtaining the printing accuracy of the target device for screen printing, thereby obtaining the maximum error result of the screen printing corresponding to the current printing accuracy; Step 42: Compare the maximum screen printing error result with the first detection result, thereby determining the first plate making defect of the target device based on the comparison result; Step 43: Compare the first plate-making defect with a preset defect database to determine a defect optimization solution corresponding to the first plate-making defect; Step 44: adjusting the first plate-making parameters of the target device accordingly based on the defect optimization solution to obtain second plate-making parameters; Step 45: Determine a second platemaking prediction model of the target device based on the second platemaking parameters, and perform platemaking optimization based on the second platemaking prediction model.

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