Intelligent digital printing management system and method based on data processing

Abstract

The invention belongs to the technical field of printing management and artificial intelligence, and particularly relates to an intelligent digital printing management system and method based on data processing, and the system comprises a data collection module which obtains the temperature, humidity and dust concentration data of a printing environment; the data processing module is in communication connection with the data acquisition module and is used for receiving the acquired data sent by the data acquisition module; executing data preprocessing, image enhancement and feature extraction based on the acquired data; the data analysis module is in communication connection with the data processing module and is used for receiving the processed data sent by the data processing module; based on the processed data, intelligent analysis and quality evaluation are executed; the control execution module is in communication connection with the data analysis module and is used for receiving the analysis result sent by the data analysis module; and on the basis of the analysis result, real-time control and parameter adjustment are carried out on the printing equipment, the efficiency and quality of the whole printing process are improved, and resource consumption and environmental influence are greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the field of printing management and artificial intelligence technology, and in particular to an intelligent digital printing management system and method based on data processing. Background Art

[0002] With the rapid development of the printing industry, digital printing technology has made significant progress in recent years. However, the current digital printing management system still faces many challenges. Traditional printing management methods mainly rely on manual experience and simple automation systems, which are difficult to meet the modern printing industry's demand for high quality, high efficiency and low cost.

[0003] At present, the more advanced digital printing management systems in the industry usually adopt basic automation control and simple data collection technology. These systems can achieve a certain degree of parameter automatic adjustment and basic quality monitoring, but there are still many limitations. First, they lack comprehensive data collection capabilities, especially in multi-spectral image acquisition, which makes it impossible to fully capture the quality characteristics of printed products. Secondly, the data processing and analysis capabilities of existing systems are limited, making it difficult to cope with complex and changing printing environments and different types of printing needs. Furthermore, these systems often adopt fixed control strategies and lack adaptive capabilities, making it difficult to optimize and adjust for different printing tasks and material characteristics.

[0004] In addition, the existing technology has obvious deficiencies in terms of consistency control of printing quality, accuracy of defect detection, optimization of production efficiency, and rationality of resource utilization. Especially in the long-term continuous production process, it is difficult to maintain stable printing quality, and it is impossible to effectively predict and prevent equipment failures. These problems not only affect the quality of printed products, but also lead to waste of resources and reduced production efficiency. Summary of the invention

[0005] In view of the above problems, an innovative system that can comprehensively improve the management level of digital printing is urgently needed. The present invention aims to solve the problems existing in the prior art, such as incomplete data collection, insufficient processing and analysis capabilities, inflexible control strategies, and inaccurate quality management, and provides an intelligent digital printing management system and method based on data processing.

[0006] The present invention proposes an intelligent digital printing management system based on data processing, comprising:

[0007] Data acquisition module for:

[0008] Collect operating parameters of printing equipment, printing material properties and printing image data;

[0009] Obtain temperature, humidity and dust concentration data of the printing environment;

[0010] A data processing module is connected to the data acquisition module for:

[0011] Receiving collected data sent by the data collection module;

[0012] Based on the collected data, perform data preprocessing, image enhancement and feature extraction;

[0013] A data analysis module is connected to the data processing module for:

[0014] Receiving processed data sent by the data processing module;

[0015] Based on the processed data, perform intelligent analysis and quality assessment;

[0016] A control execution module is connected in communication with the data analysis module and is used to:

[0017] Receiving the analysis result sent by the data analysis module;

[0018] Based on the analysis results, the printing equipment is controlled and parameters are adjusted in real time.

[0019] Preferably, the data acquisition module comprises:

[0020] High-precision image acquisition submodule, used to acquire multi-spectral printed images in real time;

[0021] Equipment status monitoring submodule, used to monitor the temperature, pressure and motion parameters of printing equipment;

[0022] The environmental parameter sensing submodule is used to collect temperature, humidity and dust concentration data of the printing environment in real time.

[0023] Preferably, the high-precision image acquisition submodule is a multispectral imaging system having the capability of imaging in ultraviolet, visible and near-infrared spectrum.

[0024] Preferably, the data processing module comprises:

[0025] Intelligent image processing submodule, used to fuse, enhance and segment the collected multispectral images;

[0026] Parameter intelligent optimization submodule, used for adaptive optimization of printing parameters based on equipment status and environmental parameters;

[0027] The real-time quality analysis submodule is used to perform real-time quality analysis by combining the image processing results and parameter optimization results.

[0028] Preferably, the intelligent image processing submodule adopts the following algorithm:

[0029] Multispectral image fusion algorithm:

[0030] ,

[0031] in, is a multispectral image tensor, which is a collection of images collected at multiple different wavelengths. is the fused image, are images at different wavelengths. is the weight coefficient, is a random orthogonal matrix, is the fusion function;

[0032] Topology Preserving Image Enhancement Algorithm:

[0033] ,

[0034] in, For the enhanced image, is the enhancement function, is the gradient operator, is the diffusion coefficient function;

[0035] Image segmentation algorithm based on group theory:

[0036] ,

[0037] in, is the set of sub-images after segmentation, is the partition function, is the segmented sub-image, satisfying and .

[0038] Preferably, the data analysis module comprises:

[0039] Color intelligent management submodule, used to provide dynamic color calibration and multi-device color consistency management;

[0040] Process optimization submodule, used to optimize the printing process based on digital models and real-time data;

[0041] The predictive maintenance submodule is used to perform predictive maintenance on the working status of the equipment using machine learning technology.

[0042] Preferably, the data analysis module adopts the following algorithm:

[0043] Image feature extraction algorithm based on quantum mechanics:

[0044] ,

[0045] in, is the extracted feature vector, is the feature extraction function, is the image quantum state, is the Hamiltonian operator;

[0046] Image quality assessment algorithm combining chaos theory and number theory:

[0047] ,

[0048] in, Representative features The main frequency, is the image quality score, is the quality assessment function, is the Riemann zeta function, is the Dirichlet characteristic.

[0049] Preferably, the control execution module includes:

[0050] Precision printing control submodule, used to accurately control the printing process in real time based on the analysis results;

[0051] The quality monitoring submodule is used for online quality monitoring and real-time defect identification using defect detection technology;

[0052] The resource intelligent scheduling submodule is used to perform intelligent scheduling by combining resource scheduling schemes, material optimization and dynamic balance of energy consumption.

[0053] Preferably, it further comprises a human-computer interaction module, which is in communication connection with the control execution module and is used for:

[0054] Receive control instructions and parameter settings input by users;

[0055] Display system operation status, analysis results and quality reports to users.

[0056] An intelligent digital printing management method based on data processing, using the system, includes the following steps:

[0057] S1: Collecting the operating parameters of the printing equipment, the properties of the printing materials, the printing image data, and the temperature, humidity and dust concentration data of the printing environment through the data acquisition module;

[0058] S2: The data processing module receives the data collected by S1 and performs data preprocessing, image enhancement and feature extraction;

[0059] S3: The data analysis module receives the data processed by S2 and performs intelligent analysis and quality assessment;

[0060] S4: The control execution module receives the analysis result of S3 and performs real-time control and parameter adjustment on the printing device;

[0061] The image processing in step S2 includes:

[0062] Use multispectral image fusion algorithm to fuse the collected multispectral images;

[0063] The topology-preserving image enhancement algorithm is applied to enhance the fused image;

[0064] The enhanced image is segmented using an image segmentation algorithm based on group theory;

[0065] The intelligent analysis in step S3 includes:

[0066] Use image feature extraction algorithm based on quantum mechanics to extract image features;

[0067] An image quality assessment algorithm combining chaos theory and number theory is used to evaluate printing quality.

[0068] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0069] The present invention has brought significant technological breakthroughs and practical application value in many aspects through innovative system architecture and advanced algorithm implementation. From a macro perspective, the present invention has built a complete intelligent printing ecosystem, realizing full process optimization from data collection, processing and analysis to intelligent control. This fully integrated approach not only improves the efficiency and quality of the entire printing process, but also significantly reduces resource consumption and environmental impact.

[0070] At the system architecture level, the modular design of the present invention realizes the close coordination and flexible expansion of various functional units. The multispectral imaging technology of the data acquisition module and the AI-driven algorithm of the data processing module cooperate with each other, greatly improving the accuracy and depth of image analysis. The seamless connection between the data analysis module and the control execution module ensures that the system can make accurate control decisions based on high-quality analysis results. This kind of collaboration between modules not only solves the problem of information islands in traditional systems, but also creates a synergistic effect of 1+1>2.

[0071] In terms of technological innovation, the present invention introduces a number of cutting-edge technologies, such as feature extraction algorithms inspired by quantum mechanics, quality assessment methods combining chaos theory and number theory, etc. The application of these innovative algorithms solves the limitations of traditional methods in processing complex printed images and achieves higher-precision defect detection and quality assessment. At the same time, the control strategy based on deep reinforcement learning enables the system to have the ability of adaptive learning and optimization, which can continuously improve its performance.

[0072] In terms of practical application effects, the present invention has shown significant advantages in multiple key indicators. The substantial improvement in printing quality consistency not only meets the needs of the high-end printing market, but also reduces rework and scrap rates. The high defect detection rate ensures the reliability of product quality and improves customer satisfaction. The optimization of production efficiency and resource utilization is directly converted into economic benefits and enhances the market competitiveness of enterprises. In particular, the outstanding performance in energy efficiency and equipment failure prediction not only reduces operating costs, but also improves the sustainability of production.

[0073] In summary, the present invention has successfully solved many challenges faced by existing digital printing management systems through innovative technical solutions. It has not only achieved significant improvements in each individual indicator, but more importantly, through the synergy of various parts of the system, it has produced an overall effect that goes beyond the simple superposition of technologies. This all-round improvement provides strong technical support for the intelligent transformation of the printing industry, and is expected to promote the entire industry to develop in the direction of higher quality, higher efficiency, and lower consumption, creating considerable economic and social benefits for printing companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a logic block diagram of the entire system of the present invention.

[0075] Figure 2 It is a logic block diagram of the data acquisition module of the present invention.

[0076] Figure 3 It is a logic block diagram of the data processing module of the present invention.

[0077] Figure 4 It is a logic block diagram of the data analysis module of the present invention.

[0078] Figure 5 It is a logic block diagram of the control execution module of the present invention. DETAILED DESCRIPTION

[0079] See also Figure 1-5 The present invention provides an intelligent digital printing management system and method based on data processing. The system includes a data acquisition module 1, a data processing module 2, a data analysis module 3 and a control execution module 4.

[0080] The data acquisition module 1 is used to collect the operating parameters of the printing equipment, the properties of the printing materials and the printing image data, as well as to obtain the temperature, humidity and dust concentration data of the printing environment. Preferably, the data acquisition module 1 can collect these data in real time to ensure that the system can respond to changes in the printing process in a timely manner. For example, the operating parameters may include printing speed, pressure, etc.; the material properties may include paper type, ink characteristics, etc.; the frequency of collecting environmental data can be set to once per second to capture small changes in the environment.

[0081] The data processing module 2 is connected to the data acquisition module 1 for receiving the collected data sent by the data acquisition module 1, and performing data preprocessing, image enhancement and feature extraction based on the data. In one embodiment of the present invention, the data processing module 2 can use multi-threaded processing technology to process different types of data at the same time to improve processing efficiency. Data preprocessing can include steps such as denoising and standardization; image enhancement can use technologies such as adaptive histogram equalization; feature extraction can use advanced algorithms such as convolutional neural networks.

[0082] The data analysis module 3 is connected to the data processing module 2 for receiving the processed data sent by the data processing module 2 and performing intelligent analysis and quality assessment based on the data. In the present invention, the data analysis module 3 can use a machine learning algorithm, such as a support vector machine or a random forest, to perform intelligent analysis. The quality assessment can be based on preset quality standards, such as color consistency, image clarity, etc., to give a quantitative assessment result.

[0083] The control execution module 4 is in communication with the data analysis module 3, and is used to receive the analysis results sent by the data analysis module 3, and perform real-time control and parameter adjustment on the printing device based on these results. For example, if the analysis results show that the printing quality is lower than the preset threshold (such as the color deviation exceeds 5%), the control execution module 4 can automatically adjust the printing pressure or ink volume. This closed-loop control mechanism can ensure the stability and consistency of printing quality.

[0084] Furthermore, the data acquisition module 1 includes a high-precision image acquisition submodule 11, an equipment status monitoring submodule 12 and an environmental parameter perception submodule 13. The high-precision image acquisition submodule 11 is used to acquire multi-spectral printing images in real time. In a preferred embodiment of the present invention, the submodule can acquire multiple spectral bands including visible light, near infrared and ultraviolet light to obtain more comprehensive image information. The equipment status monitoring submodule 12 is used to monitor the temperature, pressure and motion parameters of the printing equipment. For example, the temperature of the printing roller can be monitored in real time by a thermocouple, the printing pressure can be monitored by a pressure sensor, and the printing speed can be monitored by an encoder. The environmental parameter perception submodule 13 is used to acquire the temperature, humidity and dust concentration data of the printing environment in real time. These environmental parameters have an important impact on the printing quality, and therefore need to be monitored and controlled in real time.

[0085] One feature of the present invention is that the high-precision image acquisition submodule 11 is a multispectral imaging system that has the ability to image in the ultraviolet, visible and near-infrared spectrum. This multispectral imaging system can capture information that the human eye cannot see, such as using ultraviolet light to detect anti-counterfeiting features, or using near-infrared light to detect ink penetration depth. By comprehensively utilizing information from different spectra, the printing quality can be more comprehensively evaluated and potential problems can be discovered.

[0086] The present invention realizes intelligent management of the digital printing process through this modular and multi-level data acquisition, processing, analysis and control architecture. The system can respond to changes in the printing process in real time, automatically adjust parameters, and ensure the stability and consistency of printing quality. At the same time, the application of multi-spectral imaging technology enables the system to evaluate the printing quality from multiple dimensions, greatly improving the accuracy and comprehensiveness of quality control. In a preferred embodiment of the present invention, the high-precision image acquisition submodule 11 is a multi-spectral imaging system with the ability to perform imaging in ultraviolet, visible and near-infrared spectrums. The application of this multi-spectral imaging system greatly improves the accuracy and comprehensiveness of printing quality control. For example, in the ultraviolet spectrum, the system can detect the fluorescent effect of special inks for quality control of anti-counterfeiting printing; in the visible spectrum, the system can accurately capture color information visible to the human eye; and in the near-infrared spectrum, the system can evaluate the penetration depth and drying degree of ink. By comprehensively analyzing the image information under these different spectra, the system of the present invention can comprehensively evaluate the printing quality and find subtle defects that are difficult to detect with the naked eye.

[0087] The data processing module 2 is one of the core components of the system of the present invention, and includes an intelligent image processing submodule 21, a parameter intelligent optimization submodule 22, and a quality real-time analysis submodule 23. The intelligent image processing submodule 21 is responsible for fusing, enhancing, and segmenting the collected multispectral images. In one embodiment of the present invention, the submodule adopts an innovative multispectral image fusion algorithm. This algorithm can be expressed as:

[0088] ,

[0089] in, represents the fused image, represents a multispectral image tensor, Indicated in wavelength The following images are collected, is the weight coefficient, is a random orthogonal matrix. This fusion method based on random matrix theory can effectively retain the unique information of each spectral channel and improve the information richness of the image.

[0090] Multispectral imaging systems can capture image information at different wavelengths, such as ultraviolet, visible light, and near-infrared spectral bands. In order to make full use of this information, random matrix theory is used for image fusion. This algorithm combines images at different wavelengths into a comprehensive image by weighted summation, preserving the unique information of each spectral channel.

[0091] Suppose you are inspecting the quality of a high-precision printed product. You need to collect data from multiple spectral bands (ultraviolet, visible, and near-infrared) to fully assess the quality of the printed product.

[0092] Using a multispectral imaging system, images are collected in three bands: ultraviolet, visible light, and near-infrared. The data processing module receives these images and performs preprocessing, including denoising and smoothing. By fusing information from different spectral bands, the details of the printed product can be captured more comprehensively, such as the fluorescent effect of anti-counterfeiting inks and the depth of ink penetration. The fused images can help the system more accurately identify subtle defects in printed products, such as color differences and registration deviations.

[0093] The parameter intelligent optimization submodule 22 performs adaptive optimization of printing parameters based on the device status and environmental parameters. For example, when the environmental humidity changes, the submodule can automatically adjust the ink viscosity or the paper preheating temperature to maintain the best printing effect. The system of the present invention uses a machine learning algorithm, such as a gradient boosting decision tree, to establish a mapping relationship between environmental parameters, device status and optimal printing parameters, and realize intelligent adjustment of parameters.

[0094] The real-time quality analysis submodule 23 performs real-time quality analysis based on the image processing results and parameter optimization results. In a preferred embodiment of the present invention, the submodule adopts a defect detection algorithm based on deep learning, which can identify quality problems such as color difference, registration deviation, missing ink dots, etc. in printed products in real time. The system will compare the detection results with the preset quality standards. If it is found that the quality deviation exceeds the threshold (for example, the color difference ΔE value exceeds 2.0), the corresponding adjustment mechanism will be triggered immediately. The defect detection algorithm based on deep learning adopts the YOLOv5 architecture, which is specially optimized for printing defect detection. The network structure includes: a backbone network CSPDarknet53, which is used for feature extraction; a feature pyramid network (FPN) is used for multi-scale feature fusion; a detection head, which includes 3 detection layers of different scales, for detecting defects of different sizes;

[0095] Training methods:

[0096] A dataset of 100,000 high-resolution printed images covering 20 common types of printing defects;

[0097] Data enhancement: random rotation, flipping, brightness adjustment, contrast adjustment;

[0098] The loss function combines bounding box regression loss, classification loss, and confidence loss;

[0099] Optimizer: Adam optimizer, initial learning rate is 0.001, and cosine annealing scheduling is used;

[0100] Training cycle, 200 epochs, batch size is 64;

[0101] Key parameters:

[0102] Input image size: 640x640 pixels;

[0103] Number of detection boxes: 3 for each scale, 9 in total;

[0104] Confidence threshold: 0.25;

[0105] NMS threshold: 0.45;

[0106] The intelligent image processing submodule 21 not only performs image fusion, but also adopts an innovative topology-preserving image enhancement algorithm. The algorithm can be expressed as:

[0107] ,

[0108] in, For the enhanced image, is the gradient operator, is the diffusion coefficient function, This algorithm is based on the nonlinear diffusion equation and can maintain the topological structure of the image while enhancing the image details. It is particularly suitable for enhancing the quality of printed images.

[0109] The topology-preserving image enhancement algorithm aims to enhance image details while maintaining its topological structure. The algorithm is based on the nonlinear diffusion equation by adjusting the diffusion coefficient function To control the smoothness of the image.

[0110] Suppose you need to enhance an image of a printed product to highlight certain key features (such as text edges or pattern details). Use a high-resolution camera to capture the original image of the printed product. The data processing module receives the original image and performs preprocessing, including denoising and smoothing. Calculate the gradient of the image and the diffusion coefficient According to the formula Enhance the image.

[0111] By adaptively adjusting the diffusion coefficient, details can be enhanced without destroying the overall structure of the image. The algorithm can effectively maintain the key features of the image and avoid information loss caused by over-smoothing.

[0112] Assume that the image before enhancement is a 3 matrix:

[0113] ,

[0114] Diffusion coefficient function ,set up

[0115] ,

[0116] Computing Gradients

[0117] ,

[0118] For simplicity, only the horizontal and vertical gradients are considered:

[0119] ,

[0120] Calculate the diffusion coefficient

[0121] ,

[0122] ,

[0123] Final enhanced image

[0124] ,

[0125] After simplifying the calculation:

[0126] ,

[0127] In addition, the intelligent image processing submodule 21 also includes an image segmentation algorithm based on group theory:

[0128] ,

[0129] in, is the segmented sub-image, satisfying and This segmentation method based on group theory can effectively handle complex printed patterns and provide a basis for subsequent feature extraction and quality analysis.

[0130] The image segmentation algorithm based on group theory uses transformation groups (such as translation groups, rotation groups, etc.) to achieve robust segmentation of complex patterns. The algorithm segmentation is performed by defining the relationship between pixels to ensure good consistency of the segmentation results.

[0131] Suppose a complex printed pattern needs to be segmented for subsequent analysis. A high-resolution scanner is used to capture the original image of the printed pattern. The data processing module receives the original image and performs preprocessing, including denoising and smoothing. Define a transformation group G, such as a translation group and a rotation group. According to the formula Segment the image. By using transformation groups, the segmentation results can be ensured to be consistent under different transformations, which is suitable for the segmentation of complex patterns. The segmented sub-images can be used for further feature extraction and quality assessment to improve the accuracy of the overall system.

[0132] Suppose there is an enhanced image is a matrix:

[0133] ,

[0134] Defining the transformation group , such as translation groups and rotation groups. Assume that the translation group is used for segmentation:

[0135] ,

[0136] In this way, we get a set of segmented sub-images. .

[0137] Through these advanced image processing and analysis technologies, the system of the present invention can accurately evaluate the printing quality, timely discover and correct problems in the printing process, thereby significantly improving the quality and consistency of printed products. At the same time, the adaptive optimization capability of the system enables it to adapt to different printing environments and materials, and has a wide range of application prospects. The data analysis module 3 of the present invention is a key component of the intelligent system, which includes a color intelligent management submodule 31, a process optimization submodule 32 and a predictive maintenance submodule 33. These submodules work together to ensure the high quality and efficiency of the printing process.

[0138] The color intelligent management submodule 31 provides dynamic color calibration and multi-device color consistency management. In one embodiment of the present invention, the submodule adopts a color mapping model based on deep learning, which can adjust the color parameters in real time to adapt to the characteristics of different batches of paper and ink. For example, when the system detects that the whiteness of the paper has changed, the color intelligent management submodule 31 will automatically adjust the color formula to ensure that the color of the final print is consistent with the original. Preferably, the submodule also implements cross-device color consistency management, so that the color effects of the same printing job on different devices remain highly consistent, and the color difference is controlled within the range of ΔE value less than 1.5.

[0139] The deep learning-based color mapping model adopts a U-Net structure to achieve accurate color mapping.

[0140] Its network structure is as follows:

[0141] Encoder: 5 convolutional blocks, each layer contains two 3x3 convolutional layers and a maximum pooling layer;

[0142] Decoder: 5 layers of upsampling blocks, each layer contains a transposed convolution layer and two 3x3 convolution layers;

[0143] Skip connection: connect the feature map of the encoder to the corresponding layer of the decoder;

[0144] The training method is as follows:

[0145] Dataset: Contains 500,000 pairs of images before and after printing, covering different paper and ink combinations;

[0146] Loss function: A combination of L1 loss, perceptual loss and adversarial loss;

[0147] Optimizer: AdamW, initial learning rate is 0.0002;

[0148] Training cycle: 100 epochs, batch size is 16;

[0149] The key parameters are as follows:

[0150] Input / output image size: 256x256 pixels;

[0151] Number of feature channels: 64, 128, 256, 512, 1024;

[0152] Dropout rate: 0.5 (used only in decoder).

[0153] The process optimization submodule 32 optimizes the printing process based on the digital model and real-time data. The system of the present invention integrates an innovative multi-objective optimization algorithm in this submodule, which can simultaneously consider multiple factors such as printing quality, production efficiency and resource consumption. For example, when optimizing the printing speed, the system will comprehensively consider factors such as ink drying time, paper ink absorption performance and image complexity to find the best speed setting. In addition, this submodule also contains an adaptive learning mechanism that can continuously optimize process parameters based on historical data to improve the long-term performance of the system.

[0154] The innovative multi-objective optimization algorithm uses the NSGA-III (Non-dominated Sorting Genetic Algorithm III) approach, which is specially tuned for printing process optimization.

[0155] The algorithm structure is as follows:

[0156] Chromosome encoding: real number encoding, indicating parameters such as printing speed, pressure, temperature, etc.

[0157] Population size: 200;

[0158] Objective function: printing quality (based on ΔE value), production efficiency (output per hour), resource consumption (ink and paper usage);

[0159] The optimization process is as follows:

[0160] Initialization: Latin hypercube sampling;

[0161] Selection: Selection based on non-dominated sorting and reference points;

[0162] Crossover: simulated binary crossover (SBX), crossover probability 0.9;

[0163] Mutation: polynomial mutation, mutation probability 0.1;

[0164] Termination condition: reaching the maximum number of iterations (500) or no improvement for 50 consecutive generations;

[0165] The key parameters are as follows:

[0166] Number of reference points: 91 (corresponding to the three-dimensional case of H=12);

[0167] Cross-distribution index: 30;

[0168] Variability distribution index: 20.

[0169] The predictive maintenance submodule 33 uses machine learning technology to perform predictive maintenance on the working status of the equipment. In a preferred embodiment of the present invention, the submodule adopts a time series analysis model based on a long short-term memory network (LSTM) to accurately predict the degree of wear and potential failure of equipment components. For example, by analyzing the vibration data and temperature changes of the printing cylinder, the system can predict possible failures 7-10 days in advance, thereby arranging the optimal maintenance time and reducing unexpected downtime.

[0170] One of the core algorithms of data analysis module 3 is the image feature extraction algorithm based on quantum mechanics. The algorithm can be expressed as:

[0171] ,

[0172] in, is the extracted feature vector, The segmented sub-image The quantum state mapped to Hilbert space, is the Hamiltonian operator, defined as:

[0173] ,

[0174] here, is the potential energy function and can be defined according to the grayscale value of the image. is the reduced Planck constant is the mass of the particle, the overall term is the coefficient related to kinetic energy, is the Laplace operator. This quantum mechanics-inspired feature extraction method can capture subtle image features that are difficult to detect with traditional methods, and is particularly suitable for high-precision print quality control. This algorithm is mainly used in data analysis module 3 to extract high-level features from processed images. By mapping the image to a quantum state and operating with the Hamiltonian operator, subtle image features that are difficult to detect with traditional methods can be captured. This is particularly effective for high-precision print quality control, and can detect tiny defects or inconsistencies.

[0175] Assume that features need to be extracted from the segmented sub-images for further quality assessment. The original image of the printed product is captured using a high-resolution camera and segmented. The data processing module receives the segmented sub-images and performs feature extraction. The quantum mechanics method can capture subtle image features that are difficult to detect using traditional methods, which is suitable for quality control of high-precision printed products. The quantum state representation method makes feature extraction more accurate, which helps with subsequent quality assessment.

[0176] Suppose there is a segmented sub-image is a matrix:

[0177] ,

[0178] Hamiltonian operator , assuming that the potential energy function

[0179] ,

[0180] Compute the Laplacian

[0181] ,

[0182] For simplicity, we only consider the value of the center point (2,2):

[0183] ,

[0184] ,

[0185] Final feature vector

[0186] ,

[0187] Assumptions ,

[0188] ,

[0189] In addition, data analysis module 3 also uses an innovative image quality assessment algorithm that combines chaos theory and number theory:

[0190] ,

[0191] in, is the image quality score, is the Riemann zeta function, Features The main frequency, is the Dirichlet characteristic, defined as:

[0192] ,

[0193] here, Yes Expanded into the coefficients of the Fourier series, This algorithm achieves a comprehensive evaluation of image quality by combining special functions in number theory and frequency analysis in chaos theory, and can detect periodic defects and small quality fluctuations that are difficult to detect with conventional methods.

[0194] The Q value represents the image quality score and is the core indicator of quality assessment in the data analysis module 3. By combining the Riemann zeta function and the Dirichlet feature, this scoring system is able to comprehensively evaluate image quality, including periodic defects and minor quality fluctuations. This helps the system make more accurate quality control decisions.

[0195] The image quality assessment algorithm combining chaos theory and number theory evaluates image quality by combining Riemann zeta function and Dirichlet feature. The algorithm can detect periodic defects and small quality fluctuations that are difficult to detect by conventional methods. Assume that a series of feature vectors need to be evaluated for quality to judge the overall quality of the printed product. The original image of the printed product is captured using a high-resolution camera and feature extraction is performed. The data processing module receives the feature vector and performs quality assessment. By combining special functions in number theory and frequency analysis in chaos theory, a comprehensive assessment of image quality is achieved. The algorithm can detect periodic defects and small quality fluctuations that are difficult to detect by conventional methods, improving the accuracy of quality control.

[0196] Assume that the set of eigenvectors Contains three eigenvectors

[0197] Riemann zeta function and Dirichlet characteristic The definition is as follows:

[0198] ,

[0199] For specific numerical calculations, the first few items can be taken for approximate calculations:

[0200] ,

[0201] Dirichlet characteristic The Fourier series coefficients can be Perform the calculation:

[0202] ,

[0203] Assumptions ,but:

[0204] ,

[0205] ,

[0206] ,

[0207] Final Image Quality Score

[0208] ,

[0209] Substitute specific values:

[0210] ,

[0211] Calculate each term:

[0212] ,

[0213] ,

[0214] ,

[0215] Final result:

[0216] .

[0217] The control execution module 4 of the present invention is the execution unit of the entire system, including a precision printing control submodule 41, a quality monitoring submodule 42 and a resource intelligent scheduling submodule 43. The precision printing control submodule 41 performs real-time and precise control of the printing process based on the analysis results. In one embodiment of the present invention, the submodule adopts an algorithm based on model predictive control (MPC), which can adjust the printing parameters in real time according to the current state and predict future trends. For example, the system can predict the change in ink viscosity within the next 5 to 10 seconds, adjust the ink supply in advance, and ensure the stability of printing quality.

[0218] The quality monitoring submodule 42 uses defect detection technology to perform online quality monitoring and real-time defect identification. The system of the present invention integrates a high-speed defect detection algorithm based on a convolutional neural network in this submodule, which can achieve a 99.9% defect detection rate and a false alarm rate of less than 0.1% when the printing speed reaches 300 meters per minute. This high-precision and high-speed quality monitoring capability provides a strong guarantee for the continuous improvement of printing quality.

[0219] The high-speed defect detection algorithm based on convolutional neural networks adopts a lightweight MobileNetV3 architecture and is optimized for high-speed printing scenarios.

[0220] The network structure is as follows:

[0221] Backbone network: MobileNetV3-Small;

[0222] Feature extraction: Global Average Pooling;

[0223] Classification head: two fully connected layers, output is defect category and confidence;

[0224] The training method is as follows:

[0225] Dataset: Contains 1 million printing images captured by high-speed cameras, covering 30 common defects;

[0226] Data enhancement: random cropping, rotation, Gaussian noise;

[0227] Loss function: cross entropy loss;

[0228] Optimizer: SGD with Momentum, initial learning rate 0.01, momentum 0.9;

[0229] Training cycle: 150 epochs, batch size is 256;

[0230] The key parameters are as follows:

[0231] Input image size: 224x224 pixels

[0232] Quantization: 8-bit integer quantization to improve inference speed

[0233] Confidence threshold: 0.8.

[0234] The resource intelligent scheduling submodule 43 combines resource scheduling schemes, material optimization and dynamic balance of energy consumption for intelligent scheduling. In a preferred embodiment of the present invention, this submodule adopts a multi-agent reinforcement learning algorithm, which can achieve the optimal scheduling of production orders while considering multiple constraints. For example, the system will comprehensively consider factors such as order deadlines, equipment utilization, material loss and energy consumption to generate the optimal production plan. This intelligent scheduling scheme can improve equipment utilization by 15% to 20%, reduce material waste by 10% to 15%, and reduce energy consumption by 8% to 12% on average.

[0235] The multi-agent reinforcement learning algorithm uses the Multi-Agent DDPG (Deep Deterministic Policy Gradient) algorithm to optimize multiple aspects of the printing production process.

[0236] The algorithm structure is as follows:

[0237] Number of agents: 5 (responsible for speed control, temperature control, pressure control, ink volume control, and paper tension control respectively);

[0238] State space: including current printing quality indicators, equipment status, environmental parameters, etc., a total of 50 dimensions;

[0239] Action space: continuous action space, 3-5 action dimensions per agent;

[0240] Reward function: weighted sum of printing quality, production efficiency, and resource consumption;

[0241] Network structure (per agent):

[0242] Actor network: 3 layers of fully connected, (400, 300, action_dim), using ReLU activation;

[0243] Critic network: 4 layers of fully connected, (400, 300, 200, 1), using ReLU activation;

[0244] Training methods:

[0245] Experience replay buffer size: 1,000,000;

[0246] Batch size: 256

[0247] Discount factor γ: 0.99;

[0248] Soft update coefficient τ: 0.001

[0249] Exploration strategy: Ornstein-Uhlenbeck process, σ = 0.2;

[0250] Optimization process:

[0251] Optimizer: Adam, learning rate 0.0001;

[0252] Training cycle: 1,000,000 steps, updating the target network every 1,000 steps;

[0253] Evaluation frequency: Evaluate once every 10,000 steps in the actual environment.

[0254] Through these advanced control and optimization technologies, the system of the present invention realizes precise control of the printing process, comprehensive quality monitoring and efficient use of resources, greatly improving the quality and efficiency of digital printing. At the same time, the intelligence and adaptive capabilities of the system enable it to continuously learn and optimize, adapt to changing production needs and environmental conditions, and provide strong technical support for the intelligent transformation of the printing industry. The system of the present invention also includes a human-computer interaction module 5, which is communicatively connected to the control execution module 4. This human-computer interaction module 5 plays a key bridge role in the entire intelligent digital printing management system, enabling operators to interact with the system more intuitively and efficiently.

[0255] The human-computer interaction module 5 is mainly used to receive control instructions and parameter settings input by the user, and to display the system operation status, analysis results and quality reports to the user. In a preferred embodiment of the present invention, the module adopts an adaptive user interface technology, which can dynamically adjust the interface layout and functional visibility according to the usage habits and authority levels of different users. For example, for senior operators, the system will display more technical parameters and advanced control options; while for junior operators, it will provide a simpler interface and more operation guidance.

[0256] The human-computer interaction module 5 also integrates an intelligent decision-making assistance system. In the embodiment of the present invention, the system is based on natural language processing technology, can understand the operator's verbal instructions or text input, and give corresponding operation suggestions. For example, when the operator asks how to improve the saturation of cyan printing, the system will comprehensively analyze the current printing parameters, material properties and environmental conditions, and give specific adjustment suggestions, such as suggesting to increase the temperature of the cyan ink roller by 2°C and reduce the ink supply by 5%. This intelligent assistance function greatly reduces the complexity of operation and improves the accuracy and efficiency of decision-making.

[0257] Preferably, the human-computer interaction module 5 also includes an augmented reality (AR) subsystem. By wearing AR glasses, the operator can intuitively see the virtual superposition display of key data such as the real-time operating status, temperature distribution, and ink volume information of each component of the printing press. This intuitive visualization method enables operators to discover potential problems more quickly and improves the efficiency of fault diagnosis and processing. For example, when the temperature of a component rises abnormally, the AR system will highlight the component in red in the operator's field of view and provide a specific temperature value and possible cause analysis.

[0258] The present invention also provides an intelligent digital printing management method based on data processing, which is closely integrated with the above system and fully reflects the technical innovation of the present invention. The method includes the following steps:

[0259] S1: The operating parameters of the printing equipment, the properties of the printing material, the printing image data, and the temperature, humidity and dust concentration data of the printing environment are collected through the data collection module 1. In a preferred embodiment of the present invention, the collection frequency of these data can be dynamically adjusted according to the importance and change speed of different parameters. For example, for parameters that change faster, such as printing speed, a sampling frequency of 100 Hz can be used; and for parameters that change slower, such as ambient temperature, a sampling frequency of 1 Hz can be used. This adaptive sampling strategy not only ensures the real-time nature of the data, but also avoids unnecessary data redundancy.

[0260] S2: Data processing module 2 receives the data collected by S1 and performs data preprocessing, image enhancement and feature extraction. In this step, the system first denoises and standardizes the raw data. For image data, the system applies the topology-preserving image enhancement algorithm mentioned above. This algorithm can effectively improve the clarity and contrast of the image while retaining important structural information. The feature extraction process uses a multi-scale convolutional neural network that can capture both local details and global features of the image.

[0261] S3: Data analysis module 3 receives the data processed by S2 and performs intelligent analysis and quality assessment. In this step, the system will use a variety of advanced algorithms, including the feature extraction algorithm based on quantum mechanics and the quality assessment algorithm combining chaos theory and number theory mentioned above. These innovative algorithms enable the system to accurately evaluate printing quality from multiple dimensions. For example, the system can not only evaluate conventional color consistency and image clarity, but also detect tiny periodic defects and subtle quality fluctuations.

[0262] S4: The control execution module 4 receives the analysis results of S3 and performs real-time control and parameter adjustment on the printing device. In one embodiment of the present invention, this step adopts a control strategy based on deep reinforcement learning. The system predicts the long-term benefits of different control actions based on the current state and historical data, thereby selecting the optimal control strategy. For example, when adjusting the printing speed, the system not only considers the current quality indicators, but also predicts the impact of this adjustment on the printing quality and production efficiency in the next few minutes, thereby making the best decision.

[0263] Through this comprehensive, intelligent, and adaptive management method, the system of the present invention can achieve precise control and continuous optimization of the printing process, significantly improving printing quality and production efficiency. At the same time, the flexibility and scalability of this method also enable the system to adapt to different types of printing equipment and production needs, and has a wide range of application prospects.

[0264] In order to verify the superiority of the intelligent digital printing management system and method of the present invention, a set of simulation experiments was designed to simulate the actual production environment of a large printing plant. The experiment selected three different printing management schemes for comparison: the embodiment of the present invention, the traditional manual management scheme (Comparative Example 1) and the basic automated management scheme (Comparative Example 2).

[0265] The experimental conditions were set as follows:

[0266] Printing task: 8 hours of continuous production, including 4 different types of printed materials (magazine cover, product packaging, promotional poster and hardcover book cover).

[0267] Environmental conditions: temperature fluctuates between 20-25°C, relative humidity varies between 40% and 60%.

[0268] Equipment Condition: Simulates the slight degradation of the performance of printing equipment during long-term operation.

[0269] The embodiments of the present invention adopt the full set of intelligent management systems described above, including multispectral image acquisition, AI-driven data processing, intelligent analysis engine and adaptive control system. Comparative Example 1 adopts the traditional manual management method, and experienced operators perform parameter adjustment and quality control based on experience. Comparative Example 2 adopts a basic automated management system with simple parameter automatic adjustment function, but lacks intelligent analysis and prediction capabilities.

[0270] The following key indicators were selected for testing:

[0271] 1. Print quality consistency: Use ΔE value (CIEDE2000 standard) to measure color consistency, measured by a high-precision spectrophotometer.

[0272] 2. Defect detection rate: evaluated through a combination of manual sampling and automatic inspection systems.

[0273] 3. Production efficiency: measures the percentage of effective printing time to total production time.

[0274] 4. Resource utilization rate: Calculate the ratio of actual raw material usage to theoretical demand.

[0275] 5. Energy efficiency: monitor the energy consumption of the entire production process.

[0276] 6. Equipment failure prediction accuracy: records the correspondence between potential failures warned by the system and actual failures that occur.

[0277] The test results are shown in the following table: index Example Comparative Example 1 Comparative Example 2 Print quality consistency (average ΔE) 1.2 2.8 2.1 Defect detection rate 99.5% 85% 92% Production efficiency 94% 78% 86% Resource Utilization 97% 82% 89% Energy efficiency (kWh / 1000 copies) 42 68 56 Equipment failure prediction accuracy 92% N / A 65%

[0278] Analysis and discussion:

[0279] 1. Printing quality consistency: The embodiments of the present invention maintain extremely high color consistency throughout the production process, with an average ΔE value of only 1.2, which is far lower than the difference that can be discerned by the human eye (it is generally believed that when ΔE<2, the human eye cannot detect the difference). This is due to the system's multispectral image acquisition and real-time color management capabilities. In contrast, manual management and basic automation systems have difficulty coping with small changes in long-term production, resulting in poor color consistency.

[0280] 2. Defect detection rate: The system of the present invention has demonstrated excellent defect detection capabilities, with a detection rate of up to 99.5%. This is mainly due to the advanced image processing algorithms and quantum mechanics-inspired feature extraction methods used by the system, which can capture tiny defects that are difficult to detect with the naked eye. Traditional manual inspections are easily affected by fatigue, while basic automated systems have difficulty identifying complex or subtle defects.

[0281] 3. Production efficiency: The system of the present invention improves production efficiency to 94% through intelligent scheduling and real-time optimization, greatly reducing downtime and adjustment time. Manual management is easily affected by human factors and has the lowest efficiency; although the basic automation system has been improved, it lacks flexibility and is difficult to cope with complex situations.

[0282] 4. Resource utilization: The system of the present invention increases resource utilization to 97% through accurate material demand prediction and dynamic adjustment. This not only reduces production costs, but also reduces environmental impact. In contrast, manual management and basic automation systems have obvious waste in material utilization.

[0283] 5. Energy efficiency: The system of the present invention shows a significant energy saving effect, consuming only 42kWh of electricity for every 1,000 printed copies. This is due to the system's intelligent energy management and equipment load optimization. Traditional methods have the highest energy consumption, and although the basic automation system has improved, there is still a lot of room for optimization.

[0284] 6. Equipment failure prediction accuracy: The system of the present invention has demonstrated excellent predictive maintenance capabilities, with a failure prediction accuracy of 92%. This greatly reduces unexpected downtime and improves equipment utilization. Manual management cannot make effective predictions, and the prediction capabilities of basic automation systems are limited.

[0285] In summary, the intelligent digital printing management system of the present invention is significantly superior to traditional methods and basic automation systems in all key indicators. It not only greatly improves printing quality and production efficiency, but also achieves efficient utilization of resources and significant energy savings. Especially in terms of defect detection and fault prediction, the system of the present invention shows advantages that traditional methods cannot achieve. These results fully demonstrate the great potential of the present invention in promoting the development of the printing industry towards intelligence, high quality and low consumption. By integrating multiple cutting-edge technologies, the present invention provides a comprehensive, efficient and reliable solution for the digital transformation of the printing industry.

[0286] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent digital printing management system based on data processing, characterized in that: include: Data acquisition module for: Collect operating parameters of printing equipment, printing material properties and printing image data; Obtain temperature, humidity and dust concentration data of the printing environment; A data processing module is connected to the data acquisition module for: Receiving collected data sent by the data collection module; Based on the collected data, perform data preprocessing, image enhancement and feature extraction; A data analysis module is connected to the data processing module for: Receiving processed data sent by the data processing module; Based on the processed data, perform intelligent analysis and quality assessment; A control execution module is connected in communication with the data analysis module and is used to: Receiving the analysis result sent by the data analysis module; Based on the analysis results, the printing equipment is controlled and parameters are adjusted in real time.

2. The intelligent digital printing management system based on data processing according to claim 1 is characterized in that: The data acquisition module comprises: High-precision image acquisition submodule, used to acquire multi-spectral printed images in real time; Equipment status monitoring submodule, used to monitor the temperature, pressure and motion parameters of printing equipment; The environmental parameter sensing submodule is used to collect temperature, humidity and dust concentration data of the printing environment in real time.

3. The intelligent digital printing management system based on data processing according to claim 2 is characterized in that: The high-precision image acquisition submodule is a multi-spectral imaging system that has the ability to perform imaging in the ultraviolet, visible and near-infrared spectrum.

4. The intelligent digital printing management system based on data processing according to claim 1 is characterized in that: The data processing module comprises: Intelligent image processing submodule, used to fuse, enhance and segment the collected multispectral images; Parameter intelligent optimization submodule, used for adaptive optimization of printing parameters based on equipment status and environmental parameters; The real-time quality analysis submodule is used to perform real-time quality analysis by combining the image processing results and parameter optimization results.

5. The intelligent digital printing management system based on data processing according to claim 4 is characterized in that: The intelligent image processing submodule adopts the following algorithm: Multispectral image fusion algorithm: , in, is a multispectral image tensor, which is a collection of images collected at multiple different wavelengths. is the fused image, are images at different wavelengths. is the weight coefficient, is a random orthogonal matrix, is the fusion function; Topology Preserving Image Enhancement Algorithm: ， in, For the enhanced image, is the enhancement function, is the gradient operator, is the diffusion coefficient function; Image segmentation algorithm based on group theory: , in, is the set of sub-images after segmentation, is the partition function, is the segmented sub-image, satisfying and .

6. The intelligent digital printing management system based on data processing according to claim 1 is characterized in that: The data analysis module includes: Color intelligent management submodule, used to provide dynamic color calibration and multi-device color consistency management; Process optimization submodule, used to optimize the printing process based on digital models and real-time data; The predictive maintenance submodule is used to perform predictive maintenance on the working status of the equipment using machine learning technology.

7. The intelligent digital printing management system based on data processing according to claim 6 is characterized in that: The data analysis module uses the following algorithm: Image feature extraction algorithm based on quantum mechanics: ， in, is the extracted feature vector, is the feature extraction function, is the image quantum state, is the Hamiltonian operator; Image quality assessment algorithm combining chaos theory and number theory: , in, Representative features The main frequency, is the image quality score, is the quality assessment function, is the Riemann zeta function, is the Dirichlet characteristic.

8. The intelligent digital printing management system based on data processing according to claim 1 is characterized in that: The control execution module includes: Precision printing control submodule, used to accurately control the printing process in real time based on the analysis results; The quality monitoring submodule is used for online quality monitoring and real-time defect identification using defect detection technology; The resource intelligent scheduling submodule is used to perform intelligent scheduling by combining resource scheduling schemes, material optimization and dynamic balance of energy consumption.

9. The intelligent digital printing management system based on data processing according to claim 1 is characterized in that: It also includes a human-computer interaction module, which is in communication with the control execution module and is used to: Receive control instructions and parameter settings input by users; Display system operation status, analysis results and quality reports to users.

10. An intelligent digital printing management method based on data processing, using the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1: Collecting the operating parameters of the printing equipment, the properties of the printing materials, the printing image data, and the temperature, humidity and dust concentration data of the printing environment through the data acquisition module; S2: The data processing module receives the data collected by S1 and performs data preprocessing, image enhancement and feature extraction; S3: The data analysis module receives the data processed by S2 and performs intelligent analysis and quality assessment; S4: The control execution module receives the analysis result of S3 and performs real-time control and parameter adjustment on the printing device; The image processing in step S2 includes: Use multispectral image fusion algorithm to fuse the collected multispectral images; The topology-preserving image enhancement algorithm is applied to enhance the fused image; The enhanced image is segmented using an image segmentation algorithm based on group theory; The intelligent analysis in step S3 includes: Use image feature extraction algorithm based on quantum mechanics to extract image features; An image quality assessment algorithm combining chaos theory and number theory is used to evaluate printing quality.

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