A green printing plate making method and device

Abstract

The application discloses a kind of green printing plate making method and device, comprising: collecting after exposure offset plate image and dividing detection block, through edge detection algorithm and block gray value calculation image definition;If image definition does not meet the requirement, construct image definition and exposure time sequence, through correlation analysis, determine like definition and exposure time sequence correlation situation;If there is association, mark the offset plate with image definition lower than minimum value as exposure optimization plate, carry out fitting analysis to image definition and exposure time, obtain compensation time, based on exposure compensation time to exposure optimization plate compensation exposure.The device contains image acquisition, analysis, correlation calculation and compensation optimization module.The present application can optimize exposure time, improve offset plate definition, reduce material waste, and help to realize green and environmental protection plate making.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of printing plate making, in particular to a green and environmentally friendly printing plate making method and device. BACKGROUND

[0002] In the field of printing plate making, the quality of the offset plate directly affects the final effect of the printed matter. With the increasing awareness of environmental protection and the increasing demand for high-quality printed matter in the market, traditional printing plate making technology gradually exposes some problems.

[0003] In the prior art, due to inaccurate exposure parameter adjustment, the printing plate is discarded due to non-compliance of the sharpness, causing waste of photosensitive materials, chemical agents and other materials. At the same time, repeated production of offset plates also increases energy consumption, which does not conform to the concept of green and environmental protection.

[0004] In the prior art, the prior art can rarely monitor the exposure effect of the offset plate in real time and timely feedback adjustment. This means that if there is a problem during the exposure process, it cannot be discovered and corrected in time, and the problem is discovered only after the offset plate is completed. At this time, only the offset plate can be discarded or re-produced, which increases the cost and time consumption.

[0005] Therefore, the present application provides a green and environmentally friendly printing plate making method and device. SUMMARY

[0006] The purpose of the present application is to provide a green and environmentally friendly printing plate making method and device to solve at least one of the above-mentioned problems of the prior art.

[0007] In a first aspect, the present application provides a green and environmentally friendly printing plate making method, comprising:

[0008] Step one, using an image sensor to collect an exposed offset plate image, dividing the image into a plurality of detection blocks, and obtaining the gray value of each pixel point in the detection block;

[0009] Step two, analyzing the exposure result of each detection block, using an edge detection algorithm to obtain the edge strength of each monitoring block, and combining the block gray value for numerical analysis to obtain the image sharpness Tx, and based on the image sharpness Tx, judging whether the image sharpness meets the requirements;

[0010] Step three, if the sharpness of the image does not meet the requirements, obtaining the historical image sharpness and historical exposure time, constructing an image sharpness sequence and an exposure time sequence, and performing correlation analysis on the image sharpness sequence and the exposure time sequence to determine whether the exposure time of the offset plate is correlated with the image sharpness;

[0011] Step four, if the exposure time of the offset plate is associated with the image sharpness Tx, the offset plate with the image sharpness Tx lower than the minimum value of the image sharpness range is marked as an exposure optimization plate, the image sharpness and the exposure time are analyzed by fitting, the exposure compensation time is obtained, and the exposure optimization plate is compensated based on the exposure compensation time.

[0012] In a second aspect, the present application provides a green and environmentally friendly printing plate making device, comprising: an image acquisition module: using an image sensor to acquire an exposed offset plate image, dividing the image into multiple detection blocks, and obtaining the gray value of each pixel point in the detection block;

[0013] An image analysis module: analyzes the exposure result of each detection block, uses an edge detection algorithm to obtain the edge strength of each monitoring block, and performs numerical analysis combined with the block gray value to obtain the image sharpness Tx, and judges whether the image sharpness meets the requirements based on the image sharpness Tx;

[0014] An association calculation module: if the image sharpness does not meet the requirements, the historical image sharpness and the historical exposure time are obtained, the image sharpness sequence and the exposure time sequence are constructed, and the correlation analysis is performed on the image sharpness sequence and the exposure time sequence to determine whether the exposure time of the offset plate is associated with the image sharpness;

[0015] A compensation optimization module: if the exposure time of the offset plate is associated with the image sharpness Tx, the offset plate with the image sharpness Tx lower than the minimum value of the image sharpness range is marked as an exposure optimization plate, the image sharpness and the exposure time are analyzed by fitting, the exposure compensation time is obtained, and the exposure optimization plate is compensated based on the exposure compensation time.

[0016] The beneficial effects of the present application are:

[0017] 1. By acquiring the offset plate image and dividing the detection block, the image sharpness Tx is accurately calculated by using the edge detection algorithm combined with the block gray value to judge the exposure effect. For the offset plate that does not meet the sharpness requirements, the historical image sharpness and exposure time data are further obtained for correlation analysis to determine the relationship between the exposure time and the image sharpness.

[0018] 2. For the exposure optimization plate with the image sharpness Tx lower than the minimum value of the range, the exposure compensation time is obtained by fitting analysis and compensation. This data-driven precise optimization method can make the exposure time more accurately adapt to the characteristics of each offset plate, effectively improve the image sharpness, reduce the problems of edge blur and abnormal contrast caused by insufficient or excessive exposure, reduce the number of repeated production due to offset plate quality problems, and thus realize green and environmentally friendly printing plate making. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on these drawings.

[0020] Figure 1 is a flow chart of image acquisition and analysis provided by the first embodiment of the present application;

[0021] Figure 2 is a flow chart of correlation analysis and exposure optimization provided by the second embodiment of the present application;

[0022] Figure 3 is a module diagram of a green and environmentally friendly printing plate making device provided by the present application;

[0023] Figure 4 is a structural schematic diagram of a computer analysis device provided by the fourth embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should belong to the scope of protection of the present application.

[0025] Embodiment one

[0026] Figure 1 The flow chart of the image acquisition and analysis method provided by the first embodiment of the present application, the present application can be applicable to the case of offset plate image acquisition and image analysis. The image acquisition and analysis method can be executed by a green and environmentally friendly printing plate making system, which can be realized by software and / or hardware. The green and environmentally friendly printing plate making system can be configured in a green and environmentally friendly printing plate making device. Optionally, the green and environmentally friendly printing plate making device can be an electronic device, which can be a notebook, a desktop computer, a smart tablet and the like. The present application does not limit this.

[0027] As shown in Figure 1 The method for image acquisition and analysis provided by the present embodiment specifically includes the following steps:

[0028] Step one, using an image sensor to collect images in real time, and the image sensor is installed at the top end of the exposure device, the image is divided into multiple detection blocks, and the gray value of each pixel point in the detection block is obtained;

[0029] In some embodiments, an image sensor is installed at the top end of the exposure device, and the image sensor is used to obtain the divided image of the offset plate, and the image is divided into multiple detection blocks;

[0030] It should be noted that in the process of offset printing, the photosensitive material will undergo photochemical reaction under the action of ultraviolet light, and the exposed image part of the offset plate after exposure will be removed in the developing process, and the exposed image part will be retained;

[0031] In some embodiments, the image of the offset plate in each monitoring block after exposure is obtained, and the color image is converted into a gray scale image;

[0032] Specifically, the gray value of each pixel point in each monitoring block of the image is obtained through the gray value calculation formula: Gray=0.299*R+0.587*G+0.114*B, and the gray value of each pixel point is adjusted according to the gray value, wherein R, G and B are respectively the pixel values of the red, green and blue channels of the offset plate image, and the pixel values of the red, green and blue channels are obtained by the image sensor;

[0033] For example, if the pixel values of R, G and B channels of a pixel point are 100, 120 and 80 respectively, the gray value Gray is obtained through the gray value calculation formula as 109.46;

[0034] Based on the gray scale calculation formula, the gray value of each pixel point is extracted;

[0035] Step two, based on the gray value of each pixel point, the exposure result of each detection block is analyzed, the edge strength G(i,j) of each monitoring block is obtained by using an edge detection algorithm, the numerical analysis is carried out combined with the block gray value, the image definition Tx is obtained, and whether the image definition meets the requirements is judged based on the image definition Tx;

[0036] In some embodiments, the edge strength G(i,j) of each detection block is obtained by using an edge detection algorithm;

[0037] Specifically, two 3x3 convolution kernels are constructed by using Sobel operator of the edge detection algorithm, the upper left vertex of the image is taken as the coordinate origin, and the coordinate system is established, and the coordinates of each pixel point are (i,j);

[0038] Based on the gray value of each pixel point in each monitoring block, the gray scale change gradient of each detection block in the horizontal direction and the vertical direction is calculated respectively, the gradient in the horizontal direction is marked as Gx (i,j), the gradient value in the vertical direction is G y (i,j);

[0039] For example, the convolution kernel in the vertical direction of the pixel point (1, 1) coordinate is: The convolution kernel in the horizontal direction is:

[0040] The coordinates (i, j) of the pixel points in the monitoring block and the convolution kernel corresponding to the pixel point coordinates are obtained, and the gradient value G x (i,j) in the horizontal direction of each pixel point in the monitoring block is calculated.

[0041] The gradient value in the horizontal direction is obtained through the formula:

[0042] Where I(i+m, j+n) is the pixel gray value of the pixel point with coordinates (i+m, j+n) in the detection block, K x (m, n) is the corresponding element in the horizontal direction convolution kernel, and m and n are index variables for traversing the element positions of the convolution kernel.

[0043] It should be noted that for the Sobel convolution kernel in the horizontal direction, it is a 3x3 matrix, the matrix has 9 elements, the value range of m and n is [0, 2], when m=0 and n=0, it represents the element at the top left corner of the convolution kernel, when m=0 and n=1, it represents the element at the first row and the second column of the convolution kernel, based on the increasing order of the element serial number, it is ensured that each element can be multiplied with the corresponding pixel in the image pixel region.

[0044] Based on the gradient value in the horizontal direction of the pixel point in the monitoring block, the gradient value G y (i,j) in the vertical direction of the pixel point is obtained.

[0045] Based on the gradient value G x (i,j) in the horizontal direction, the gradient value G y (i,j) in the vertical direction of each pixel point in the detection block is obtained.

[0046] The edge strength G(i,j) of each pixel point in the detection block is obtained through the formula

[0047] For example, taking the pixel point (1, 1) as an example, when m=0 and n=0, 1, 2, G x (1,1)=-20, -40, -20, when m=1 and n=0, 1, 2, G x (1,1)=20, 0, 0, when m=2 and n=0, 1, 2, G x ​​(1,1) = -20, -40, -20, the gradient value G in the horizontal direction is obtained by summation x (1,1) = -140, the gradient value G in the vertical direction is obtained based on the vertical gradient calculation method in the horizontal direction y (1,1) = 0, the edge strength G(1,1) = 140;

[0048] The edge strength G(i,j) of all pixel points in the monitoring block is obtained, and the edge strength G(i,j) of all pixel points is summed and averaged to obtain the block edge strength;

[0049] The block edge strength of all monitoring blocks is obtained, and the edge strength of all monitoring blocks is summed and averaged to obtain the image edge strength, which is marked as By;

[0050] The gray value of each pixel point in the monitoring block is obtained, and the gray value of each pixel point is put into the gray value data group to obtain the maximum and minimum values of the gray value in the gray value data group;

[0051] The maximum and minimum values of the gray value in the gray value data group are subtracted to obtain the block contrast;

[0052] The block contrast of all monitoring blocks is obtained, and the block contrast of all monitoring blocks is summed and averaged to obtain the image contrast, which is marked as Db;

[0053] The image edge strength By and the image contrast Db are dimensionless processed to calculate the image sharpness Tx;

[0054] Through the formula: The image sharpness Tx is obtained, where a = 0.561 and b = 0.439;

[0055] The image sharpness Tx is compared with the image sharpness range to determine whether the sharpness of the exposed offset plate image meets the requirements;

[0056] If the image sharpness Tx is in the image sharpness range, it indicates that the sharpness of the exposed offset plate image meets the requirements;

[0057] If the image sharpness Tx is not in the image sharpness range, it indicates that the image sharpness Tx in the exposed offset plate image does not meet the requirements;

[0058] For example, if the block edge strength is 140 and the block contrast is 70, the image sharpness Tx = 0.713 is obtained through the image sharpness calculation formula, and the image sharpness range is [1, 2.5], which indicates that the image sharpness of the offset plate is not in the image sharpness range, and the image sharpness does not meet the requirements;

[0059] It should be noted that the edge strength reflects the sharpness of the edges in the image, and the higher the edge strength of the profile of each detection block of the image, the clearer the image is;

[0060] The block contrast reflects the difference between the bright and dark parts of the image, and the high-contrast block has high clarity and large gray difference, that is, the block contrast is high, which can make the outline and details of the object clearer;

[0061] The technical scheme of the embodiment is: using an image sensor to collect the exposed offset plate image in real time, dividing the image into a plurality of detection blocks, obtaining the gray value of each pixel point in the detection block, analyzing the exposure result of each detection block based on the gray value of each pixel point, using an edge detection algorithm to obtain the edge strength of each monitoring block, combining the block gray value for numerical analysis to obtain the image clarity Tx, judging whether the image clarity meets the requirements based on the image clarity Tx, realizing the analysis and judgment of the offset plate clarity, and laying a data foundation for subsequent optimization analysis.

[0062] Embodiment two

[0063] As shown in Figure 2 The method for correlation analysis and exposure optimization provided by the embodiment of the application specifically includes the following steps:

[0064] Step three, if the image clarity does not meet the requirements, the historical image clarity and the historical exposure time are obtained, the image clarity sequence and the exposure time sequence are constructed, and the correlation degree analysis is performed on the image clarity sequence and the exposure time sequence to determine whether the exposure time of the offset plate is correlated with the image clarity;

[0065] If the image clarity does not meet the requirements, the data of the historical image clarity is obtained, and the image clarity sequence X is constructed;

[0066] Wherein X=[Tx1 Tx2... Tx z ], z is the total number of historical images, and Tx1 represents the image clarity of the first image;

[0067] In some embodiments, a timer is installed in the exposure device to obtain the exposure time of each exposure offset plate, and the exposure time is marked as Pg;

[0068] The exposure time of each historical image is obtained, and the exposure time sequence Y is constructed;

[0069] Wherein Y=[Pg1 Pg2... Pg z ], z is the total number of images, and Pg1 is the exposure time of the first image;

[0070] The image sharpness sequence X and the exposure time sequence Y are normalized;

[0071] Specifically, by the formula: The average value of the image sharpness sequence X is obtained The sharpness of each image in the image sharpness sequence is divided by the average value of the image sharpness sequence X to obtain the image sharpness sequence X';

[0072] Based on the acquisition method of the image sharpness sequence X', the exposure time sequence Y' is obtained;

[0073] Based on the image sharpness sequence X' and the exposure time sequence Y', the correlation coefficient ξ i (k) is calculated;

[0074] The correlation coefficient ξ i (k) is obtained by the formula: , wherein k represents the kth data point in the image sharpness sequence X' or the exposure time sequence Y', k = 1, 2,..., z, ρ is a resolution coefficient, ρ = 0.5, min i min k |X i ′(k)-Y i ′(k)| represents the absolute value of the minimum difference between two levels, and max i max k |X i ′(k)-Y i ′(k)| represents the absolute value of the maximum difference between two levels;

[0075] It should be noted that the minimum difference between two levels and the maximum difference between two levels reflect the difference degree in the sequence;

[0076] Based on the correlation coefficient ξ i (k), the correlation coefficient ξ i (k) of the image sharpness sequence X' and the exposure time sequence Y' is calculated;

[0077] The grey correlation degree r is obtained by the formula: , wherein r takes a value range (0, 1);

[0078] It should be noted that the closer r is to 1, the higher the correlation degree of the exposure time and the image sharpness Tx, and the closer r is to 0, the lower the correlation degree;

[0079] The correlation difference is obtained by difference processing the grey correlation degree r and the maximum endpoint value of the value range (0, 1) of r;

[0080] The correlation difference is compared with the length of the r value range to obtain a correlation proximity ratio;

[0081] The correlation proximity ratio is compared with a proximity ratio threshold to determine whether the exposure time of the offset plate and the image sharpness Tx present a correlation;

[0082] If the correlation proximity ratio is higher than the proximity ratio threshold, it indicates that the exposure time of the offset plate and the image sharpness Tx present a correlation;

[0083] If the correlation proximity ratio is lower than the proximity ratio threshold, it indicates that the exposure time of the offset plate and the image sharpness Tx do not present a correlation;

[0084] Step four, if the exposure time of the offset plate and the image sharpness Tx present a correlation, mark the offset plate with an image sharpness lower than the minimum value of the image sharpness range as an exposure optimization plate, perform fitting analysis on the image sharpness and the exposure time to obtain an exposure compensation time, and compensate the exposure time of the exposure optimization plate based on the exposure compensation time;

[0085] If the image sharpness Tx is lower than the minimum value of the image sharpness range, mark the offset plate corresponding to the image as an exposure optimization plate;

[0086] If the image sharpness Tx is higher than the maximum value of the image sharpness range, do not process it;

[0087] It should be noted that when the image sharpness Tx is higher than the maximum value of the image sharpness range, the overexposure caused by the high edge intensity or high contrast cannot be repaired by adjusting the exposure time, that is, the image sharpness cannot be compensated and repaired by adjusting the exposure time;

[0088] Obtain the image sharpness Tx under the historical exposure time, and draw a scatter plot of exposure time-image sharpness Tx in a two-dimensional rectangular coordinate system;

[0089] Take the minimum and maximum values of the image sharpness range as reference values, and draw reference lines corresponding to the reference values in the two-dimensional rectangular coordinate system, respectively;

[0090] Use the polofit function in the numpy library in Python to perform quadratic polynomial fitting on the image sharpness and the exposure time to obtain a fitting curve;

[0091] Obtain the intersection point of the fitting curve and the minimum value of the image sharpness range, and take the intersection point of the fitting curve and the minimum value of the image sharpness range as a compensation time point;

[0092] Obtain the exposure time corresponding to the exposure optimization plate, and mark the exposure time corresponding to the exposure optimization plate as a time point to be compensated;

[0093] The time corresponding to the to-be-compensated calculation point and the compensation time point is processed by difference to obtain an exposure compensation time;

[0094] The exposure time of the exposure optimization plate is compensated based on the exposure compensation time, that is, the exposure optimization plate is exposed by an exposure device according to the exposure compensation time;

[0095] It should be noted that the exposure compensation of the exposure optimization plate can improve the offset plate with a low sharpness, thereby improving the quality of the offset plate, reducing the material waste of the offset plate, reducing the number of repeatedly produced offset plates, and realizing green and environmentally-friendly printing plate making;

[0096] The technical scheme of the embodiment is as follows: if the sharpness of the image does not meet the requirements, the historical image sharpness and the historical exposure time are obtained, the image sharpness sequence and the exposure time sequence are constructed, and the correlation degree analysis is performed on the image sharpness sequence and the exposure time sequence to determine whether the exposure time of the offset plate is correlated with the image sharpness; if the exposure time of the offset plate is correlated with the image sharpness Tx, the offset plate with the image sharpness Tx lower than the minimum value of the image sharpness range is marked as an exposure optimization plate, the image sharpness and the exposure time are fitted and analyzed to obtain an exposure compensation time, and the exposure time of the exposure optimization plate is compensated based on the exposure compensation time, so as to reduce the edge blur and abnormal contrast caused by insufficient or excessive exposure, reduce the number of repeatedly produced offset plates due to the quality problem of the offset plate, and further realize green and environmentally-friendly printing plate making.

[0097] Embodiment three

[0098] As shown in Figure 3 , the green and environmentally-friendly printing plate making and device provided by the embodiment of the application comprises:

[0099] The image acquisition module: an image sensor is used to acquire the image of the exposed offset plate, the image is divided into a plurality of detection blocks, and the gray value of each pixel point in the detection block is obtained;

[0100] The image analysis module: the exposure result of each detection block is analyzed, the edge strength of each monitoring block is obtained by using an edge detection algorithm, the numerical analysis is performed in combination with the block gray value, the image sharpness Tx is obtained, and whether the image sharpness meets the requirements is determined based on the image sharpness Tx;

[0101] The correlation calculation module: if the sharpness of the image does not meet the requirements, the historical image sharpness and the historical exposure time are obtained, the image sharpness sequence and the exposure time sequence are constructed, and the correlation degree analysis is performed on the image sharpness sequence and the exposure time sequence to determine whether the exposure time of the offset plate is correlated with the image sharpness;

[0102] The compensation optimization module: if the exposure time of the offset plate is related to the image definition Tx, the offset plate with the image definition Tx lower than the minimum value of the image definition range is marked as an exposure optimization plate, fitting analysis is performed on the image definition and the exposure time, the exposure compensation time is obtained, and exposure time compensation is performed on the exposure optimization plate based on the exposure compensation time;

[0103] Embodiment four

[0104] With reference to Figure 3 The embodiment of the present application also provides a computer device 3, which comprises a memory 302, a processor 301 and a computer program 303 stored in the memory 302, and when the computer program 303 is executed on the processor 301, a green printing plate making method is realized.

[0105] The computer device 3 can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The computer device 3 can comprise, but is not limited to, the processor 301 and the memory 302. Those skilled in the art can understand that,

[0106] Figure 3 The computer device 3 is only an example and does not constitute a limitation on the computer device 3, and can comprise more or fewer components than those shown, or combine certain components, or different components, for example, can also comprise an input / output device, a network access device and the like.

[0107] The processor 301 can be a central processing unit (CPU), and the processor 301 can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or can also be any conventional processor.

[0108] The memory 302 may, in some embodiments, be an internal storage unit of the computer device 3, such as a hard disk or a memory of the computer device 3. The memory 302 may, in other embodiments, also be an external storage device of the computer device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, and the like equipped on the computer device 3. Further, the memory 302 may, in addition, include both an internal storage unit and an external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, a Boot Loader, data, and other programs, such as program codes of the computer program, and the like. The memory 302 may, in addition, be used to temporarily store data that has been output or is to be output.

[0109] Embodiment Five

[0110] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. The computer program is run by a processor to implement the green printing plate making method according to any one of the above methods.

[0111] In the embodiment, the integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the processes of the above-mentioned embodiment methods, which can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program is executed by a processor to implement the steps of the above-mentioned method embodiments. The computer program includes computer program codes, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium may not be an electrical carrier signal and a telecommunications signal.

[0112] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0113] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0114] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely schematic, for example, the division of the modules or units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0115] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0116] The above formulas are dimensionless values calculated, and the formulas are obtained by collecting a large amount of data to simulate the most recent real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0117] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made within the scope of the present application are still within the scope of the present application.

Claims

1. A green and environmentally friendly printing plate making method, characterized by, The method comprises the following steps: Step 1: using an image sensor to collect an exposed offset plate image, dividing the image into multiple detection blocks, and obtaining the gray value of each pixel point in the detection block; Step 2: analyzing the exposure result of each detection block, using an edge detection algorithm to obtain the edge strength of each monitoring block, and combining the block gray value for numerical analysis to obtain the image sharpness Tx, and judging whether the image sharpness meets the requirements based on the image sharpness Tx; The way of judging whether the image sharpness meets the requirements is: Based on the image edge strength By and the image contrast Db, the image sharpness Tx is calculated; The image definition Tx is obtained by the formula: wherein a and b are preset proportion coefficients. If the image sharpness Tx is within the image sharpness range, it indicates that the sharpness of the exposed offset plate image meets the requirements; Step 3: If the image sharpness does not meet the requirements, the historical image sharpness and historical exposure time are obtained, the image sharpness sequence and exposure time sequence are constructed, and the correlation degree analysis is performed on the image sharpness sequence and exposure time sequence to determine whether the exposure time of the offset plate is correlated with the image sharpness; Step 4: If the exposure time of the offset plate is correlated with the image sharpness Tx, the offset plate with an image sharpness Tx lower than the minimum value of the image sharpness range is marked as an exposure optimization plate, the image sharpness and exposure time are fitted and analyzed to obtain an exposure compensation time, and the exposure time of the exposure optimization plate is compensated based on the exposure compensation time.

2. The green and environmentally friendly printing plate making method according to claim 1, wherein The way of obtaining the image edge strength By is: Using the Sobel operator of the edge detection algorithm to construct two 3x3 convolution kernels, taking the top-left corner of the image as the coordinate origin, and establishing a coordinate system, the coordinates of each pixel point are (i, j); Based on the gray value of each pixel point in each monitoring block, the gray change gradient of each detection block in the horizontal direction and the vertical direction is calculated respectively, the gradient in the horizontal direction is marked as , and the gradient value in the vertical direction is marked as ; Obtaining the coordinate (i, j) of the pixel point of the monitoring block and the convolution kernel corresponding to the pixel point coordinate, calculating the gradient value of each pixel point in the horizontal direction of the monitoring block ; The gradient value in the horizontal direction is obtained by the formula: wherein Gx(i, j) is the gradient value in the horizontal direction at the pixel point with coordinates (i, j) in the detection block, is the pixel gray value of the pixel point with coordinates (i+m, j+n) in the detection block, is the corresponding element in the horizontal direction convolution kernel, and m and n are index variables for traversing the element positions of the convolution kernel. Based on the gradient value calculation mode of the horizontal direction of the monitoring block pixel point, the gradient value of the vertical direction of the pixel point is obtained ; based on gradient values in the horizontal direction , gradient values in the vertical direction , obtain edge strength of each detection block : Through formula Obtain the edge intensity of each pixel in the detection block. ; Obtain the edge intensity of all pixels within the monitoring area. The edge intensity of all pixels Perform summation and averaging to obtain the block edge strength, and mark the block edge strength as By; Obtain the block edge strength of all monitoring blocks, sum all the edge strengths of the monitoring blocks and take the average to obtain the image edge strength, and mark the image edge strength as By.

3. The green and environmentally friendly printing plate making method according to claim 1, wherein The way of obtaining the image contrast Db is: Obtain the gray value of each pixel point in the monitoring block, put the gray value of each pixel point into the gray value data group, and obtain the maximum and minimum values of the gray value in the gray value data group; Difference processing is performed on the maximum and minimum values of the gray value in the gray value data group to obtain the block contrast; Obtain the block contrast of all monitoring blocks, sum all the block contrasts of the monitoring blocks and take the average to obtain the image contrast, and mark the image contrast as Db.

4. The green and environmentally friendly printing plate making method according to claim 1, wherein The way of judging whether the exposure time of the offset plate is correlated with the image sharpness is: Difference processing is performed on the gray correlation degree r and the maximum endpoint value of the r value range (0, 1) to obtain the correlation difference; The correlation difference and the length of the r value range are compared to obtain the correlation proximity ratio; The correlation proximity ratio is compared with the proximity ratio threshold to determine whether the exposure time of the offset plate is correlated with the image sharpness Tx. If the correlation proximity ratio is higher than the proximity ratio threshold, it indicates that the exposure time of the offset plate is correlated with the image sharpness Tx.

5. The green printing plate making method according to claim 4, wherein, The gray correlation degree r is obtained in the following manner: Based on the correlation coefficient Computing an image sharpness sequence , an exposure time sequence The grey correlation degree r; The grey correlation degree r is obtained by the formula: ​ 6. The green printing plate making method according to claim 5, wherein, The correlation coefficient The acquisition mode is: If the image sharpness does not meet the requirements, the data of historical image sharpness is obtained, and a sequence of image sharpness X is constructed; wherein , z is the total number of historical images, and Tx1represents the image definition of the first image; A timer is installed in the exposure device to obtain the exposure time of each exposure offset plate, and the exposure time is marked as Pg; The exposure time of each historical image is obtained, and a sequence of exposure time Y is constructed; wherein , z is the total number of images, Pg1 is the exposure time of the first image; The sequence of image sharpness X and the sequence of exposure time Y are normalized; By formula: The average value of the image sharpness sequence X is obtained The sharpness of each image in the image sharpness sequence is divided by the average value of the image sharpness sequence X The ratio processing is performed to obtain the image sharpness sequence ; Based on the acquisition mode of the image sharpness sequence , the exposure time sequence is obtained; Based on image sharpness sequence , exposure time sequence Computing correlation coefficient ; Through the formula: Obtain the correlation coefficient , where k represents the image sharpness sequence or exposure time series The k-th data point, k=1,2,...,z, where ρ is the resolution coefficient. This represents the absolute value of the minimum difference between two levels. It represents the absolute value of the maximum difference between the two levels.

7. The green printing plate making method according to claim 1, wherein, The exposure compensation time is obtained in the following manner: The intersection point of the fitting curve and the minimum value of the image sharpness range is obtained, and the intersection point is taken as the compensation time point; The exposure time corresponding to the exposure optimized plate is obtained, and the exposure time corresponding to the exposure optimized plate is marked as the time point to be compensated; The time corresponding to the time point to be compensated and the compensation time point is difference processed to obtain the exposure compensation time; Based on the exposure compensation time, the exposure time of the exposure optimized plate is compensated, that is, the exposure device is used to expose the exposure optimized plate according to the exposure compensation time.

8. The green printing plate making method according to claim 7, wherein, The fitting curve is obtained in the following manner: If the image sharpness Tx is lower than the minimum value of the image sharpness range, the offset plate corresponding to the image is marked as the exposure optimized plate; The image sharpness Tx under the historical exposure time is obtained, and a scatter plot of exposure time-image sharpness Tx is drawn in a two-dimensional rectangular coordinate system. The minimum value and the maximum value of the image sharpness range are taken as reference values, and reference lines corresponding to the reference values are drawn in a two-dimensional rectangular coordinate system, respectively. The polofit function in the numpy library of Python is used to perform quadratic polynomial fitting on the image sharpness and the exposure time to obtain the fitting curve.

9. A green printing plate making device for implementing the green printing plate making method according to any one of claims 1-8, characterized in that, It comprises: An image acquisition module: an image sensor is used to acquire the image of the exposed offset plate, the image is divided into multiple detection blocks, and the gray value of each pixel point in the detection block is obtained; An image analysis module: the exposure result of each detection block is analyzed, the edge strength of each monitoring block is obtained using an edge detection algorithm, and numerical analysis is performed combining the block gray value to obtain the image sharpness Tx, and whether the image sharpness meets the requirements is judged based on the image sharpness Tx; A correlation calculation module: if the image sharpness does not meet the requirements, the historical image sharpness and the historical exposure time are obtained, the sequence of image sharpness and the sequence of exposure time are constructed, and the correlation degree analysis is performed on the sequence of image sharpness and the sequence of exposure time to determine whether the exposure time of the offset plate is correlated with the image sharpness. The compensation optimization module: if the exposure time of the offset plate is associated with the image definition Tx, the offset plate with the image definition Tx lower than the minimum value of the image definition range is marked as an exposure optimization plate, the image definition and the exposure time are analyzed by fitting, the exposure compensation time is obtained, and the exposure time compensation is performed on the exposure optimization plate based on the exposure compensation time.

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