Coated paper surface treatment method and application of coated paper surface treatment method in digital printing machine

Abstract

The invention provides a coated paper surface treatment method and application thereof to a digital printer, and the coated paper surface treatment method comprises the following steps: collecting coated paper surface image data through scanning equipment, and processing the image data by adopting an edge detection algorithm to obtain surface texture distribution characteristics; if the binding force predicted value is lower than a preset threshold value, querying matched ink component data through a chemical agent formula database, and determining the type of a chemical connection enhancer; after the type of the chemical connection enhancer is obtained, generating a multi-layer processing sequence in combination with surface texture distribution characteristics, and adjusting the processing sequence by adopting a sequence optimization algorithm to obtain a final preprocessing protocol; aiming at the final preprocessing protocol, simulating application scenes under different ink components, judging whether the protocol stably outputs a printing quality index or not, and obtaining an effect verification result; and according to an effect verification result, iteratively updating surface modification model parameters, judging whether the updated model improves the interaction mechanism simulation precision or not, and determining an optimized preprocessing method.

Description

Technical Field

[0001] This invention relates to the field of high-speed digital printing presses, and more particularly to a method for surface treatment of coated paper and its application in digital printing presses. Background Technology

[0002] Digital printing technology, as a crucial pillar of the modern printing industry, directly impacts print quality and efficiency, especially in its application on high-quality papers such as coated paper. Coated paper, with its smooth surface and excellent texture, is often used for high-end printed materials; however, in digital printing, insufficient ink adhesion is a key issue limiting its application. Research in this area not only concerns improving print quality but also has undeniable value in driving technological innovation within the industry.

[0003] Currently, although some pretreatment methods for coated paper exist on the market, these methods often struggle to adapt to the complex requirements of different printing environments and ink types. Many solutions, when addressing paper surface characteristics, neglect the deep-seated interaction mechanisms between the surface and the ink, leading to unstable treatment results, especially under high humidity or different ink compositions, where the effect of improving adhesion is significantly reduced. This limitation causes printing companies to face quality fluctuations in actual production, necessitating a more comprehensive technological breakthrough.

[0004] Against this backdrop, the core technical challenges of coated paper pretreatment have become increasingly apparent. The primary issue lies in effectively altering the physical properties of the paper surface to better accommodate ink, a problem that extends to establishing a robust chemical bond between the paper and the ink. Adjusting surface physical properties requires reducing tension and removing impurities, but without a subsequent chemical bonding mechanism, ink adhesion may still be weak. For example, in actual printing processes, after simple treatment, some coated papers exhibit uneven ink distribution, even peeling off with slight friction, directly impacting the quality and durability of the printed materials.

[0005] Therefore, addressing both insufficient physical adsorption and unstable chemical bonding on coated paper during pretreatment has become a key issue in improving digital printing quality. Solving this problem requires not only focusing on basic paper surface treatment but also exploring multi-layered processing methods to ensure a lasting bond between ink and paper, providing more reliable technical support for the printing industry. Summary of the Invention

[0006] This invention provides a method for surface treatment of coated paper, mainly including: The surface image data of coated paper is acquired by a scanning device, and the image data is processed by an edge detection algorithm to obtain the surface texture distribution characteristics. Based on the surface texture distribution characteristics, it is determined whether the characteristics meet the preset smoothness standard. If not, the surface modification model is applied to calculate and adjust the parameters to determine the physical property optimization scheme. Key adjustment parameters are extracted from the physical property optimization scheme. These parameters are then input into the high humidity environment simulation conditions. Finite element analysis is used to simulate the ink distribution behavior and obtain the predicted value of the bonding force. If the predicted bonding strength is lower than the preset threshold, the chemical bonding enhancer type is determined by querying the matching ink composition data through the chemical agent formulation database. After obtaining the type of chemical linker, a multi-layer processing sequence is generated by combining the surface texture distribution characteristics. The processing order is adjusted by a sequence optimization algorithm to obtain the final preprocessing protocol. For the final preprocessing protocol, we simulated application scenarios under different ink compositions to determine whether the protocol stably outputs printing quality indicators and obtain effect verification results. Based on the results of the effect verification, the surface modification model parameters are iteratively updated to determine whether the updated model improves the simulation accuracy of the interaction mechanism and to determine the optimized preprocessing method.

[0007] An application of a surface treatment method for coated paper, which is applied to a large-format high-speed digital printing press.

[0008] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a method and application for surface treatment of coated paper, aiming to solve problems such as insufficient surface smoothness, low ink adhesion, and unstable print quality in high-humidity environments. By collecting paper surface image data and using an edge detection algorithm to extract texture distribution features, this invention determines whether the surface smoothness meets the standard. If it does not meet the standard, a surface modification model is used to calculate and adjust parameters and formulate an optimization scheme. Subsequently, finite element analysis is used to simulate ink distribution behavior and predict adhesion. If the adhesion is insufficient, a chemical bonding enhancer is matched and a multi-layer processing sequence is generated. Finally, the preprocessing protocol is optimized and the print quality stability is verified. This invention improves simulation accuracy by iteratively updating model parameters, ensuring the adaptability and effectiveness of the processing method. Overall, this invention achieves full-process control from surface characteristic analysis to chemical enhancement and process optimization, significantly improving the print quality stability in high-humidity environments. Attached Figure Description

[0009] Figure 1 This is a flowchart of a method for surface treatment of coated paper according to the present invention.

[0010] Figure 2 This is a schematic diagram of a surface treatment method for coated paper according to the present invention.

[0011] Figure 3This is another schematic diagram of a method for surface treatment of coated paper according to the present invention.

[0012] Figure 4 This is a structural diagram of a large-format high-speed digital printing press, which is an application of the surface treatment method for coated paper of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0014] like Figure 1-3 This embodiment of a method for surface treatment of coated paper may specifically include: Step S101: Acquire image data of the surface of the coated paper using a scanning device, and process the image data using an edge detection algorithm to obtain the surface texture distribution features; the specific calculation formula is as follows: This formula is used to evaluate the uniformity characteristics of surface texture, where U represents the uniformity index, σ² represents the variance of texture density, μ² represents the square of the mean texture density, and n represents the total number of texture regions analyzed. This represents the texture density value of the i-th region. This represents the average texture density across all regions.

[0015] The surface image of coated paper is acquired using a scanning device to obtain initial image data. An edge detection algorithm is then used to process the initial image data, extracting the boundary information of the surface texture to obtain texture boundary data. Region segmentation is performed on the texture boundary data to divide it into different texture regions and determine the distribution range of each region. Based on the distribution range of each region, the density difference of the texture regions is calculated to obtain density distribution characteristics. If significant differences exist in the density distribution characteristics, the regions with differences are locally magnified to obtain refined texture detail data. The texture detail data is then used to analyze the texture change trend in local areas to determine the uniformity of the surface texture. If the uniformity characteristic is below a preset threshold, a second edge detection process is performed on the texture detail data of the local area to obtain more accurate texture distribution information.

[0016] Specifically, image data of the coated paper surface is acquired using a scanning device, such as a high-resolution industrial camera (e.g., a CCD camera with a resolution of 5000x4000 pixels). Under fixed lighting conditions, the paper surface is continuously scanned at a rate of 10 frames per second. The acquired image data is stored in grayscale format, with each pixel having a grayscale value ranging from 0 to 255, ensuring sufficient capture of image details. Subsequently, an edge detection algorithm is used to process the image data, such as the Canny edge detection algorithm. First, the image is smoothed using Gaussian filtering with a Gaussian kernel size of 5x5 and a standard deviation of 1.4 to reduce noise interference. Then, the image gradient intensity and direction are calculated, and non-maximum suppression is used to suppress non-edge pixels. Finally, dual threshold detection is set, assuming a low threshold of 50 and a high threshold of 150, to extract strong edges and connect weak edges, forming a complete edge contour map and obtaining the texture edge features of the paper surface. Next, based on the edge detection results, the surface texture distribution characteristics are analyzed, and the edge density, i.e., the number of edge pixels per square centimeter, is calculated. Assuming a region has a total of 2000 edge pixels and an area of ​​10 square centimeters, the edge density is 200 pixels / square centimeter. Simultaneously, the edge direction distribution is statistically analyzed using histogram analysis, dividing the direction into eight intervals (each 45 degrees). If the proportion of edge pixels in the 0-45 degree interval is 30%, it can be inferred that the texture in this region has a significant directional tendency. Through the above data analysis, it can be further linked to paper production process parameters. For example, high texture density may be related to rolling pressure. Assuming the density is 200 pixels / square centimeter at a pressure of 5 MPa, if adjusted to 6 MPa, the predicted density may increase to 220 pixels / square centimeter, forming a process optimization feedback logic and ensuring a complete thought chain from image acquisition to feature analysis and then to process correlation.

[0017] Step S102: Based on the surface texture distribution characteristics, determine whether the characteristics meet the preset smoothness standard. If not, apply the surface modification model to calculate and adjust the parameters to determine the physical property optimization scheme. To determine the surface texture and distribution characteristics, image acquisition tools are used to obtain detailed surface data, yielding preliminary texture distribution information. Based on this preliminary information, a standard comparison method is used to compare the surface with a preset standard to determine if it meets the smoothness standard, generating comparison result data. If the comparison result data indicates that the smoothness standard is not met, a pre-established modification model is used to analyze the surface texture, calculate adjustment parameters, and determine the parameter output content. Based on the parameter output content, physical properties are simulated and adjusted, and numerical mapping tools are used to generate corresponding optimization scheme data. By combining the optimization scheme data with the surface texture distribution characteristics, specific improvement directions are obtained, and the improved surface treatment scheme is determined. Based on the improved surface treatment scheme, final physical property adjustment instructions are generated, resulting in executable processing flow data.

[0018] Specifically, the analysis and optimization of the surface texture distribution characteristics of coated paper begins with a smoothness assessment based on the extracted texture data. Assuming the surface roughness parameter calculated by the algorithm is 2.5 micrometers, while the preset smoothness standard requires a roughness of less than 2.0 micrometers, the system automatically determines that the current feature does not meet the standard, triggering subsequent adjustment procedures. Next, the system calls the surface modification model, employing a machine learning-based regression analysis algorithm. Inputting the current roughness value of 2.5 micrometers and relevant environmental parameters such as humidity of 40% and temperature of 25 degrees Celsius, the model predicts that the surface coating thickness needs adjustment. The calculation shows that the coating thickness should be increased by 0.3 micrometers to reduce roughness. Furthermore, combined with historical data analysis, when the coating thickness increases from 1.2 micrometers to 1.5 micrometers, the roughness can be reduced from 2.5 micrometers to 1.8 micrometers, meeting the standard requirements. Subsequently, the system further generates a physical property optimization scheme, determines the adjustment parameters for the coating material ratio, and, assuming the adhesive accounts for 20% of the original ratio, suggests increasing it to 25% based on model calculations. Combining this with the correlation curve between the ratio and smoothness in the database, it predicts that the surface friction coefficient will decrease from 0.35 to 0.28 after the adjustment, improving paper smoothness. To form a complete logical chain, the system links the optimization scheme with subsequent production batches, automatically generating adjustment instructions and transmitting them to the production control module to ensure real-time parameter updates. If the adjusted parameters still do not meet the standards, the system iteratively calculates a new ratio scheme until it conforms to the standard, constructing a closed-loop process from feature judgment to parameter optimization.

[0019] Step S103: Extract key adjustment parameters from the physical property optimization scheme, input the parameters for high humidity environment simulation conditions, and use finite element analysis to simulate ink distribution behavior to obtain the predicted bonding force value; the specific calculation formula is as follows:

[0020] This indicates the predicted bonding strength between the ink and the substrate. Represents the stress tensor components. Let Ω represent the strain tensor components, φ(h,t) represent the computational domain of the finite element analysis, and φ(h,t) represent the coupling function of humidity and temperature. This formula predicts the magnitude of the binding force by integrating the stress-strain product over the entire computational domain. Where φ(h,t) represents the coupling function of humidity and temperature. , Let γ represent the humidity-temperature coupling potential function, γ represent the coupling strength coefficient, and T represent the current temperature. The formula represents the critical temperature, ΔT represents the temperature characteristic scale, δ represents the humidity fluctuation amplitude, ρ represents the humidity density parameter, and L represents the characteristic length; this formula describes the nonlinear coupling potential relationship between humidity and temperature. Core adjustment parameters are extracted from the physical property optimization scheme. These parameters are then organized for high-humidity environmental simulation conditions and categorized and archived using data processing tools, resulting in a categorized parameter set. Based on this set, parameter matching is performed for the high-humidity environmental simulation conditions. The environmental simulation module loads the parameter set to obtain the matched environmental input data. Using this matched input data, a preliminary model of ink distribution behavior is built using finite element analysis (FEM) tools, determining the simulated boundary conditions for ink distribution. Based on these boundary conditions, multi-dimensional simulation processing of ink distribution behavior is conducted. The FEM tools iteratively calculate the simulation process to obtain dynamic behavior data for ink distribution. Based on this dynamic behavior data, predictive analysis of the bonding force is performed. If the dynamic behavior data exceeds a preset threshold, the simulation parameters are fine-tuned to obtain adjusted predictive behavior data. Finally, using this adjusted predictive behavior data, a data mapping is performed to establish the correlation between physical properties and bonding force. The data processing module generates corresponding property correlation data to determine the final prediction result.

[0021] Specifically, in optimizing the physical properties of the paper surface, the system first extracts key adjustment parameters from the generated optimization schemes. For example, to improve ink adhesion performance, the surface tension adjustment value is set to 42.5 mN / m, combined with the coating viscosity parameter set to 3.8 Pa·s. These parameters will serve as the basis for subsequent simulations. Next, the system inputs these parameters into a high humidity environment simulation module. Assuming an ambient humidity of 85% and a temperature of 28 degrees Celsius, the environmental simulation algorithm calculates the influence coefficient of humidity on surface tension to be 0.92. Combining this with historical humidity and adhesion performance curves from the database, the system predicts that the surface tension will decrease to 39.1 mN / m under high humidity, forming a preliminary analysis result of the interaction between parameters and the environment. Subsequently, the system uses the finite element analysis method to simulate ink distribution behavior, constructing a three-dimensional mesh model of the paper surface with a mesh unit size of 0.1 micrometers and an ink droplet volume of 2.5 nanoliters. By iteratively calculating the ink diffusion rate and contact angle change on the surface, the system obtains an ink distribution uniformity index of 0.87, and analyzes the ink penetration depth to be 1.2 micrometers, forming detailed distribution behavior data. Finally, based on the finite element analysis results and combined with the correlation model between surface tension and ink distribution uniformity, the system calculated a predicted adhesion force between the ink and paper of 3.4 N / m. By comparing this with the standard adhesion force value of 3.0 N / m, the system determined the effect of the current parameter adjustment on the adhesion performance, providing data support for subsequent process optimization. To ensure a closed-loop logic, the system correlates the predicted value with the production environment database. If the predicted adhesion force value is too low, it automatically triggers a secondary optimization calculation of the surface tension parameter, forming a complete process from parameter extraction to predictive analysis.

[0022] Step S104: If the predicted bonding strength is lower than the preset threshold, the chemical bonding enhancer type is determined by querying the matching ink component data through the chemical agent formulation database.

[0023] A systematic search of ink composition data is conducted using a chemical formula database to obtain chemical formula records related to adhesion strength, establishing a preliminary formula matching list. Based on this list, data processing tools are used to categorize and organize the compatibility of ink components and reinforcing agents, resulting in a set of categorized compatibility categories. For this set, a pre-established component analysis module is used to obtain recommended reinforcing agent data related to adhesion strength improvement, determining a priority ranking. If the highest priority reinforcing agent in the priority ranking does not meet the preset threshold criteria, alternative chemical formulas are searched again from the database to obtain new reinforcing agent selection data. Based on this new selection data, a data mapping tool is used to simulate and match combinations of ink components and reinforcing agents, obtaining compatibility assessment data. Using this assessment data, a secondary verification of the adhesion strength is performed to determine if the preset threshold conditions are met, thus determining the final reinforcing agent application scheme. If the final reinforcing agent application scheme still does not reach the preset threshold, the component analysis module is used to perform in-depth analysis of the ink composition data to obtain alternative reinforcing agent selection schemes, determining the adjusted compatibility category.

[0024] Specifically, when the system detects that the predicted bonding strength is lower than the preset threshold of 2.8 N / m, it automatically initiates a query process in the chemical agent formulation database. Using a built-in matching algorithm, it filters reinforcing agent data compatible with the current ink composition. Assuming the main component of the current ink is water-based acrylic resin, the system selects a polyurethane-based bonding reinforcing agent based on the chemical compatibility index recorded in the database. The initial addition ratio is set at 1.5% of the total ink volume. Based on historical data analysis, the system finds that the chemical bonding strength enhancement coefficient of this reinforcing agent at a concentration of 0.02 g / ml is 1.18, forming a preliminary formulation adjustment plan. Subsequently, the system uses a chemical reaction simulation algorithm to calculate the intermolecular forces between the reinforcing agent and the ink resin. The reaction temperature is set at 25 degrees Celsius, and the reaction time at 120 minutes. The interfacial bonding energy after mixing the reinforcing agent and ink is found to be 5.6 kJ / mol. Based on the correlation model between bonding energy and actual bonding strength in the database, the system predicts that the adjusted bonding strength can be increased to 3.2 N / m, providing data-supported optimization. To ensure the feasibility of the formula adjustment, the system further accessed the production process database to analyze the impact of reinforcing agent addition on ink flowability. The initial ink viscosity was set at 4.2 Pa·s. Using a rheological simulation algorithm, the viscosity change rate after adding the reinforcing agent was calculated to be +8.3%, resulting in a new viscosity of 4.55 Pa·s. Considering the production equipment's viscosity tolerance range of 3.5 to 5.0 Pa·s, the adjustment plan was confirmed to meet process requirements. Finally, the system correlated the optimized formula data with production environment parameters, automatically generating a formula adjustment command, which was transmitted to the ink mixing module. Simultaneously, the system recorded the adjusted predicted adhesion data to the historical database, forming a closed-loop logic from detection to adjustment, ensuring seamless integration of subsequent production stages.

[0025] Step S105: After obtaining the type of chemical bonding enhancer, a multi-layer processing sequence is generated based on the surface texture distribution characteristics. A sequence optimization algorithm is then used to adjust the processing order to obtain the final preprocessing protocol. The specific optimization algorithm is as follows:

[0026] Let represent the optimized processing sequence, s represent the sequence variable, and N represent the total number of processing steps. This represents the priority probability of the nth processing step. This represents the processing time for the nth step. λ represents the target processing time, and λ represents the weight parameter of the time constraint.

[0027] By using a pre-established chemical bonding database, data records related to reinforcing agent types are acquired to determine the initial chemical bonding configuration scheme. Based on the initial chemical bonding configuration scheme and surface texture data, a data processing tool is used to generate a basic framework for multi-layer processing, resulting in a preliminary multi-layer processing structure. For the preliminary multi-layer processing structure, a texture data analysis module is used to obtain processing layer distribution information matching the surface texture and determine the priority order of the layer distribution. If the priority order of the layer distribution does not match the preset matching conditions, the database is queried again for alternative reinforcing agent type data to obtain an adjusted chemical bonding configuration. Based on the adjusted chemical bonding configuration, a sequence optimization tool is used to rearrange the order of multi-layer processing to determine the optimized processing sequence. Using the optimized processing sequence and distribution data, a final preprocessing scheme is generated, and it is determined whether it meets the preset layer distribution requirements. For the final preprocessing scheme, a data storage tool is used to save it to a preset scheme library, obtaining scheme identifiers that can be used for subsequent calls.

[0028] Specifically, after obtaining the type of chemical bonding enhancer, the system first extracts the surface texture distribution characteristics of the target substrate through the material surface analysis module. Assuming the detected average surface roughness is 3.2 micrometers and the texture depth distribution ranges from 1.5 to 4.8 micrometers, the system inputs this data into the texture feature database. Using a feature matching algorithm, the influence coefficient of surface texture on the permeability of the chemical enhancer is calculated to be 0.85, indicating that surface texture has a certain limitation on the processing effect. Next, the system combines the enhancer type and texture characteristics, calls the multi-layer processing sequence generation model, and initially generates a three-layer processing sequence, including surface cleaning, enhancer coating, and curing. The cleaning layer is set to a processing time of 30 seconds, the enhancer coating layer thickness is 0.05 mm, and the curing layer temperature is 80 degrees Celsius. Subsequently, the system employs a sequence optimization algorithm to adjust the processing order. Utilizing a genetic algorithm-based optimization model, with 100 iterations and an objective function balancing processing time and effectiveness, the optimized sequence is calculated to be: first, reinforcer coating; then, cleaning; and finally, curing. This adjustment reduces the total processing time to 2.5 minutes, with a predicted improvement coefficient of 1.12. To further refine the protocol, the system performs correlation analysis between the optimized sequence and a substrate thermal conductivity database. Assuming a substrate thermal conductivity of 0.23 W / m·K, the impact of the curing layer temperature on the substrate is calculated to be 0.9, confirming no overheating risk. Finally, a pretreatment protocol is generated, including parameters such as a coating thickness of 0.05 mm, a cleaning time of 30 seconds, and a curing temperature of 80 degrees Celsius. This protocol data is stored in the system cloud for direct use by subsequent production modules, forming a complete logical chain from feature extraction to protocol generation.

[0029] Step S106: For the final preprocessing protocol, simulate application scenarios under different ink compositions, determine whether the protocol can stably output printing quality indicators, and obtain effect verification results.

[0030] By using a pre-defined ink component database, classification data of various ink components are obtained. This classification data is then preliminarily filtered to obtain a set of ink components suitable for simulation analysis. Based on this set, data processing tools are used to generate simulation parameters for various application scenarios, determining the compatibility information between the ink components and the preprocessing protocol for each scenario. For this compatibility information, the operating environment data for the scenario simulation is acquired. If the compatibility information does not match the pre-defined compatibility threshold, the ink component set is re-filtered to obtain an updated compatibility combination. Using this updated compatibility combination, a support vector machine algorithm is employed to predict and analyze printing quality indicators, assessing the stability of these indicators in each scenario. Based on the predictive analysis results, stable output judgment data is obtained. If scenarios exhibit unstable performance within the judgment data, their simulation parameters are recorded, identifying a set of scenarios requiring focused attention. For this set of focused scenarios, the simulation parameters and judgment data are saved using data storage tools, creating a scenario archive for subsequent analysis. Based on the scenario archive, historical data related to printing quality is obtained to determine if there are any potential factors causing changes in conditions, ultimately determining the final verification results.

[0031] Specifically, for the verification of the final preprocessing protocol, the system automatically analyzes whether the protocol can stably output printing quality indicators by simulating application scenarios under different ink compositions, forming a complete effect verification process. First, the system extracts three typical ink formulation data from the ink composition database: water-based ink (viscosity 2.3 mPa·s, surface tension 28.5 mN / m), oil-based ink (viscosity 5.1 mPa·s, surface tension 32.7 mN / m), and mixed ink (viscosity 3.8 mPa·s, surface tension 30.2 mN / m). This data is then input into the printing simulation model and combined with the preprocessing protocol parameters to conduct virtual application tests. Next, the system invokes a quality assessment algorithm, setting printing quality indicators including ink adhesion (target value greater than 4.5 N / m), color uniformity (deviation less than 0.03), and drying time (less than 120 seconds). Using finite element analysis, the system simulates the ink deposition process on the substrate, calculating the following results: water-based ink adhesion is 4.7 N / m, color uniformity deviation is 0.02, and drying time is 98 seconds; oil-based ink adhesion is 4.6 N / m, color uniformity deviation is 0.025, and drying time is 105 seconds; and mixed ink adhesion is 4.8 N / m, color uniformity deviation is 0.018, and drying time is 95 seconds. The system then performs a stability analysis on the simulation results, using a statistical variance calculation model. The variance values ​​for the quality indicators under the three ink types are 0.012, 0.015, and 0.01, respectively, all less than the preset threshold of 0.02, indicating that the protocol outputs stably under different ink compositions. To further verify the results, the system compared and analyzed the results with historical printing data, invoked data mining algorithms to extract quality records from the past 500 printing tasks from the cloud, calculated the average adhesion to be 4.4 N / m, and found that the current protocol output values ​​were all higher than the historical averages, confirming the applicability of the protocol. Finally, the system generated a verification report, automatically stored it in the cloud database, and linked it with the subsequent printing process optimization module to ensure that the verification results can be used to dynamically adjust process parameters, forming a closed-loop logical chain from simulation to verification.

[0032] Step S107: Based on the effect verification results, iteratively update the surface modification model parameters, determine whether the updated model improves the simulation accuracy of the interaction mechanism, and determine the optimized preprocessing method.

[0033] For the surface modification process, historical experimental data related to surface modification are obtained from a pre-established database. Data filtering tools are used to classify and process the data, resulting in a core dataset related to the interaction mechanism. Based on this core dataset and the characteristic information of the model parameters, a parameter adjustment framework is constructed. Data processing tools are used to perform preliminary simulations of the parameter update scheme to determine the initially adjusted parameter combination. For the initially adjusted parameter combination, basic simulation environment data related to the interaction mechanism are obtained. If the simulation environment data does not match the preset threshold range, a data correction tool is used to perform a secondary adjustment of the parameter combination, resulting in a corrected parameter set. Based on the corrected parameter set, a support vector machine algorithm is used to predict and analyze the accuracy performance of the mechanism simulation. Key indicator data related to accuracy performance in the prediction results are obtained to assess the stability of the accuracy performance. For the stability data of the accuracy performance, reference information related to the preprocessing scheme is extracted from historical experimental data. Data comparison tools are used to analyze the matching degree between the stability data and the reference information to determine the optimized preprocessing scheme framework. Based on the optimized preprocessing scheme framework, application scenario data related to surface modification is obtained. Data storage tools are used to archive the framework and scenario data, resulting in a scheme archive available for subsequent analysis.

[0034] Specifically, based on the effect verification results, the system automatically initiates an iterative update process for the surface modification model parameters. It optimizes the preprocessing method through a data-driven approach and evaluates the performance of the updated model in simulating the interaction mechanism. First, the system extracts key data from the verification result database, such as the initial value of the substrate surface energy (35.2 mN / m) and the ink-substrate contact angle (42.5 degrees). Then, it calls the parameter optimization algorithm, using gradient descent to adjust the surface modification coefficients in the model. The initial coefficient is set to 0.75, with an iteration step size of 0.02. After 10 rounds of calculation, it is updated to 0.82. Simultaneously, the error value for each iteration is recorded, showing that the error decreased from the initial 0.15 to 0.09, indicating that the parameter adjustment direction is reasonable. Next, the system resimulated the interaction between the ink and the substrate using the updated model. A molecular dynamics algorithm was employed to calculate the contact angle change, yielding a new value of 40.1 degrees. This value deviated from the target value of 39.8 degrees by only 0.3 degrees, a significant reduction compared to the previous deviation of 2.7 degrees. Simultaneously, energy fluctuations during the simulation were recorded, with a variance of 0.005, below the preset threshold of 0.01, indicating a significant improvement in model accuracy. Subsequently, the system compared the output results of the models before and after the update using a data analysis module. The calculation accuracy improvement was 38.5%, and this was validated using historical data. The average contact angle deviation from the past 100 simulations retrieved from the cloud was 2.4 degrees, confirming that the current optimization result is superior to historical levels. Finally, the system automatically adjusts the pretreatment method parameters based on the improved accuracy data, reducing the surface modification time from the initial 15 seconds to 12 seconds and increasing the modifier concentration from 0.5% to 0.6%. Simulation verification shows that the surface energy under the new parameters is stable at 36.1 mN / m, meeting the process requirements. At the same time, the optimized parameters are linked with the subsequent process design module, automatically updating the database records to form a complete closed-loop logic from parameter iteration to method optimization, ensuring continuous improvement.

[0035] like Figure 4 As shown: The application of the surface treatment method for coated paper on a large-format high-speed digital printing press. The large-format high-speed digital printing press includes a paper feeding module 1, a pre-treatment module 2, an inkjet module 3, a paper delivery module 4, and a drying module 5.

[0036] The paper feeding module 1 is equipped with a coated paper unwinding roller 11 for outputting coated paper; Both the pre-processing module 2 and the inkjet module 3 are housed in a single enclosure. The pre-processing module 2 includes a front conveyor roller 21 and a pre-processing roller 22. The front conveyor roller 21 receives the coated paper output from the paper feeding module 1, and the pre-processing roller 22 processes the surface of the coated paper. The inkjet module 3 is located on one side of the pre-processing roller 22 and includes a middle conveyor roller 31 and an inkjet roller 32. The middle conveyor roller 31 receives the coated paper output from the pre-processing roller 22, and the inkjet roller 32 carries the coated paper for inkjet processing. The paper receiving module 4 and the drying module 5 are both housed in a frame, and the frame is connected to the output end of the inkjet module 4. The paper receiving module 4 includes a paper receiving roller 41 and a reversing roller 42, and coated paper is wound on the paper receiving roller 41 and the reversing roller 42. The drying module 5 is located between the paper receiving roller 41 and the reversing roller 42 and is used to dry the ink on the coated paper that has passed through the reversing roller 42.

[0037] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for surface treatment of coated paper, characterized in that: The method includes: The surface image data of coated paper is acquired by a scanning device, and the image data is processed by an edge detection algorithm to obtain the surface texture distribution characteristics. Based on the surface texture distribution characteristics, it is determined whether the characteristics meet the preset smoothness standard. If not, the surface modification model is applied to calculate and adjust the parameters to determine the physical property optimization scheme. Key adjustment parameters are extracted from the physical property optimization scheme. These parameters are then input into the high humidity environment simulation conditions. Finite element analysis is used to simulate the ink distribution behavior and obtain the predicted value of the bonding force. If the predicted bonding strength is lower than the preset threshold, the chemical bonding enhancer type is determined by querying the matching ink composition data through the chemical agent formulation database. After obtaining the type of chemical linker, a multi-layer processing sequence is generated by combining the surface texture distribution characteristics. The processing order is adjusted by a sequence optimization algorithm to obtain the final preprocessing protocol. For the final preprocessing protocol, we simulated application scenarios under different ink compositions to determine whether the protocol stably outputs printing quality indicators and obtain effect verification results. Based on the results of the effect verification, the surface modification model parameters are iteratively updated to determine whether the updated model improves the simulation accuracy of the interaction mechanism and to determine the optimized preprocessing method.

2. The method for surface treatment of coated paper according to claim 1, characterized in that: The scanning device acquires image data of the coated paper surface, and the image data is processed using an edge detection algorithm to obtain surface texture distribution features. The specific calculation formula is as follows: ； This formula is used to evaluate the uniformity characteristics of surface texture, where U represents the uniformity index, σ² represents the variance of texture density, μ² represents the square of the mean texture density, and n represents the total number of texture regions analyzed. This represents the texture density value of the i-th region. This represents the average texture density across all regions. This includes: acquiring surface images of coated paper using a scanning device to obtain initial image data; An edge detection algorithm is used to process the initial image data to extract the boundary information of the surface texture, thus obtaining the texture boundary data; Region segmentation processing is performed on the texture boundary data to divide it into different texture regions and determine the distribution range of each region; Based on the distribution range of each region, calculate the density difference of the texture region to obtain the density distribution characteristics; If there are significant differences in the density distribution characteristics, the difference areas are magnified locally to obtain refined texture detail data. By analyzing the refined texture detail data, the texture change trend in local areas is analyzed to determine the uniformity characteristics of the surface texture; If the uniformity feature is lower than the preset threshold, a secondary edge detection process is performed on the texture detail data of the local area to obtain more accurate texture distribution information.

3. The method for surface treatment of coated paper according to claim 1, characterized in that: The step involves determining whether the surface texture distribution characteristics meet a preset smoothness standard. If not, a surface modification model is applied to calculate and adjust parameters to determine a physical property optimization scheme, including: To determine the surface texture and distribution characteristics, image acquisition tools are used to obtain detailed data of the surface and obtain preliminary texture distribution information. Based on the preliminary texture distribution information, the data is compared with the preset standard using a standard comparison method to determine whether it meets the smoothness scale and obtain the comparison result data. If the comparison results show that the smoothness standard has not been met, the surface texture is analyzed by the pre-established modification model, the adjustment parameters are calculated, and the parameter output content is determined. Based on the parameter output, the physical properties are simulated and adjusted, and the corresponding optimization scheme data is generated using numerical mapping tools; By optimizing the scheme data and combining it with the distribution characteristics of surface texture, specific improvement directions are obtained, and the improved surface treatment scheme is determined. Based on the improved surface treatment scheme, the final physical property adjustment instructions are generated, resulting in executable processing flow data.

4. The surface treatment method for coated paper according to claim 1, characterized in that: The key adjustment parameters are extracted from the physical property optimization scheme, and these parameters are input under high humidity environment simulation conditions. Finite element analysis is used to simulate the ink distribution behavior to obtain the predicted bonding force value. The specific calculation formula is as follows: ； This indicates the predicted bonding strength between the ink and the substrate. Represents the stress tensor components. Let Ω represent the strain tensor components, φ(h,t) represent the computational domain of the finite element analysis, and φ(h,t) represent the coupling function of humidity and temperature. This formula predicts the magnitude of the binding force by integrating the stress-strain product over the entire computational domain. Where φ(h,t) represents the coupling function of humidity and temperature. , Let γ represent the humidity-temperature coupling potential function, γ represent the coupling strength coefficient, and T represent the current temperature. The formula represents the critical temperature, ΔT represents the temperature characteristic scale, δ represents the humidity fluctuation amplitude, ρ represents the humidity density parameter, and L represents the characteristic length; this formula describes the nonlinear coupling potential relationship between humidity and temperature. This includes: extracting core adjustment parameters from physical property optimization schemes, organizing parameters for high humidity environment simulation conditions, classifying and archiving parameters using data processing tools, and obtaining a set of classified parameters; Based on the classified parameter set, parameter matching is performed for high humidity environment simulation conditions. The environmental simulation module loads the parameter set and obtains the matched environmental input data. By using the matched environmental input data and combining it with finite element analysis tools, a preliminary model of ink distribution behavior is created to determine the simulated boundary condition data for ink distribution. Based on the simulated boundary condition data, multi-dimensional simulation processing is performed on the ink distribution behavior. The simulation process is iteratively calculated using finite element analysis tools to obtain dynamic behavior data of ink distribution. Based on dynamic behavior data, predictive analysis is performed on the bonding force. If the dynamic behavior data exceeds the preset threshold range, the simulation parameters are fine-tuned to obtain the adjusted behavior prediction data. By using the adjusted behavioral prediction data, data mapping is performed on the correlation between physical properties and bonding forces. The data processing module generates corresponding characteristic correlation data to determine the final prediction result data.

5. The method for surface treatment of coated paper according to claim 1, characterized in that: If the predicted bonding strength value is lower than a preset threshold, the chemical bonding enhancer type is determined by querying the chemical agent formulation database to match the ink component data, including: By systematically searching the ink composition data through the chemical formula database, we can obtain chemical formula records related to the bonding strength value and determine a preliminary formula matching list. Based on the formula matching list, data processing tools are used to classify and organize the compatibility of ink components and reinforcing agents, resulting in a set of classified compatibility categories. For the set of suitable categories after classification, the recommended data of reinforcing agents related to the improvement of binding force value are obtained through the pre-established component analysis module, and the priority ranking results are determined. If the highest priority enhancing agent in the priority ranking results does not match the preset threshold judgment condition, the backup chemical formula will be retrieved again from the database source to obtain new enhancement selection data; Based on the new enhancement selection data, a data mapping tool was used to simulate and match the combination of ink components and enhancement agents to obtain compatibility evaluation data; By using the compatibility assessment data, a secondary verification of the bonding force value is performed to determine whether it meets the preset threshold conditions, and the final application scheme of the reinforcing agent is determined. If the final application scheme of the reinforcing agent still fails to reach the preset threshold, the ink composition data will be deeply analyzed through the component analysis module to obtain alternative reinforcing options and determine the adjusted adaptation category.

6. The surface treatment method for coated paper according to claim 1, characterized in that: After obtaining the type of chemical bonding enhancer, a multi-layer processing sequence is generated based on the surface texture distribution characteristics. A sequence optimization algorithm is then used to adjust the processing order to obtain the final preprocessing protocol. The specific optimization algorithm is as follows: ； Let represent the optimized processing sequence, s represent the sequence variable, and N represent the total number of processing steps. This represents the priority probability of the nth processing step. This represents the processing time for the nth step. λ represents the target processing time, and λ represents the weight parameter of the time constraint. This includes: obtaining data records related to the type of reinforcing agent through a pre-established chemical linkage database to determine the initial chemical linkage configuration scheme; Based on the initial chemical bonding configuration scheme and combined with surface texture data, a basic framework for multi-layer processing is generated using data processing tools, resulting in a preliminary multi-layer processing structure. For the initial multi-layer processing structure, the texture data analysis module is used to obtain the processing layer distribution information that matches the surface texture and determine the priority order of the layer distribution. If the priority order of the hierarchical distribution does not match the preset matching conditions, the database is queried again for the backup reinforcing agent type data to obtain the adjusted chemical connection configuration. Based on the adjusted chemical linkage configuration, the sequence optimization tool was used to rearrange the order of the multilayer processing to determine the optimized processing sequence; By combining the optimized processing sequence with the distributed data, the final preprocessing scheme is generated, and it is determined whether it meets the preset hierarchical distribution requirements. For the final preprocessing scheme, a data storage tool is used to save it to a preset scheme library to obtain the scheme identifier that can be called later.

7. The method for surface treatment of coated paper according to claim 1, characterized in that: The process involves simulating application scenarios with different ink compositions for the final preprocessing protocol, determining whether the protocol stably outputs printing quality indicators, and obtaining effect verification results, including: By using a pre-set ink component database, classification data of various ink components are obtained. The classification data is then preliminarily screened to obtain a set of ink components suitable for simulation analysis. Based on the selected ink component set, data processing tools are used to generate simulation parameters for various application scenarios, and the compatibility information between the ink components and the preprocessing protocol in each scenario is determined. For the adaptation information, obtain the running environment data of the scenario simulation. If the adaptation information does not match the preset adaptation threshold, the ink component set is re-filtered to obtain the updated adaptation combination. By using the updated adaptation combination, the support vector machine algorithm is used to predict and analyze the printing quality indicators and determine the stability of the quality indicators in various scenarios. Based on the results of the predictive analysis, obtain the judgment data for stable output. If there are scenarios with unstable performance in the judgment data, record the simulation parameters of the scenario and determine the set of scenarios that need to be focused on. For the set of key scenarios, the simulation parameters and judgment data are saved through data storage tools to obtain scenario files that can be used for subsequent analysis; Based on the scene archive, obtain historical data related to print quality, determine whether there are potential factors that may change conditions, and determine the final verification result.

8. The method for surface treatment of coated paper according to claim 1, characterized in that: Based on the effect verification results, the surface modification model parameters are iteratively updated to determine whether the updated model improves the simulation accuracy of the interaction mechanism, and the optimized preprocessing method is determined, including: For the surface modification process, historical experimental data related to surface modification were obtained from a pre-established database, and data filtering tools were used to classify and process the data to obtain a core dataset related to the interaction mechanism. Based on the core dataset and the feature information of the model parameters, a parameter adjustment framework is constructed. The parameter update scheme is initially simulated using data processing tools to determine the parameter combination after the initial adjustment. For the initially adjusted parameter combination, basic simulation environment data related to the interaction mechanism are obtained. If the simulation environment data does not match the preset threshold range, the parameter combination is adjusted a second time using a data correction tool to obtain the corrected parameter set. Based on the corrected parameter set, the support vector machine algorithm is used to predict and analyze the accuracy performance of the mechanism simulation, obtain key indicator data related to accuracy performance in the prediction results, and judge the stability of accuracy performance. For the stability data of accuracy performance, reference information related to the preprocessing scheme is extracted from historical experimental data. The matching degree between the stability data and the reference information is analyzed by data comparison tools to determine the optimized preprocessing scheme framework. Based on the optimized preprocessing scheme framework, application scenario data related to surface modification are obtained. The framework and scenario data are archived using data storage tools to obtain a scheme archive that can be used for subsequent analysis.

9. The application of the surface treatment method for coated paper according to any one of claims 1-8, characterized in that: It is used in large-format high-speed digital printing presses.

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