Deep learning self-calibration system for high-precision color management

Abstract

The application provides a deep learning self-calibration system for high-precision color management, relates to the technical field of color calibration, and the system architecture comprises an algorithm layer, a system architecture layer and a data processing and optimization layer. Through the fusion of various advanced algorithms in the algorithm layer, the precision of color correction is effectively improved, the high consistency of color of printed products in the whole production process is ensured, the quality stability of printed products is greatly improved, data processing is performed by using each module of the data processing and optimization layer, efficient processing of real-time data in the printing production process and dynamic updating of the model are performed, the model can timely adapt to the printing data distribution and color change trend, the design of the system architecture layer provides a comprehensive adaptation scheme for large-scale production, multiple links and various equipment materials in the printing industry, the universality and flexibility of the system are enhanced, the time and labor cost required for readjusting the system due to equipment material change are reduced, and the production response capability of enterprises is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of color calibration, in particular to a deep learning self-calibration system for high-precision color management. BACKGROUND

[0002] According to the method for improving the printing precision of a color printing system disclosed in Chinese Patent No. "CN112918116B", the method comprises the following steps: 1, printing an overprint optical identifier; 2, digitally collecting the overprint optical identifier; 3, performing CMYK color channel segmentation on the collected overprint optical identifier; 4, performing edge feature extraction on the images of each channel of CMYK; 5, positioning the geometric center of the edge features of each channel of CMYK image, and calculating to obtain the nozzle calibration value. The overprint optical identifier positioning algorithm has high precision, which can effectively improve the quality of overprint printed products. The overprint optical identifier is small in size, which can avoid waste of the printing material. The method has high automation degree, which reduces the labor cost and the debugging time of the equipment.

[0003] According to the printing color detection control system applied to a printing production line disclosed in Chinese Patent No. "CN118306108B", the color of the current printed product is detected and analyzed after the printing is completed, the color difference estimate value of the current printed product is obtained, the parameters of the printing equipment are controlled and adjusted based on the comparison result of the color difference estimate value of the current printed product, the time point when the search signaling is triggered is marked as the abnormal start time, the time point when the related working parameters of the printing equipment are adjusted is marked as the calibration completion time, all the printed products in the time range are extracted, the color difference estimate values of the printed products in the time range are analyzed, the change trend index of the printing equipment in the time range is obtained, and if the change trend index is large, it indicates that the printing equipment needs to be checked and maintained, the existing hidden dangers and problems are timely treated, and the production quality and intelligent degree of the whole printing production line are improved.

[0004] The above patent documents and prior art have the following technical problems in use:

[0005] Problem one, the traditional printing color management technology often relies on simple color matching algorithms or manual adjustment based on experience, which is difficult to comprehensively and deeply analyze the complex color characteristics of the printed image. In the face of different batches of printing raw materials differences, such as paper whiteness, ink color phase subtle changes, printing equipment characteristics fluctuations and printing process changes, such as multi-color printing overprint sequence adjustment, it is difficult to accurately predict and correct color deviation, which leads to poor color consistency of the printed product, and obvious color difference in different batches of printing for the same design. For high-quality printing needs, such as high-end commercial printing and artistic printing, it is difficult to meet the requirements, the waste rate is high, and the production cost and resource waste are increased;

[0006] The second problem is that the printing industry is characterized by large-scale production, many stages, and the involvement of various equipment and materials. Existing technology lacks an effective system architecture to integrate these complex factors. On the one hand, it is inefficient in large-scale data processing. Traditional centralized computing architectures are unable to cope with the rapid processing and model training of massive printing color data, resulting in extended production cycles. On the other hand, each production stage is relatively isolated. There is a lack of effective information feedback and collaboration mechanisms in pre-press, printing, and post-press, which makes it impossible to detect and correct the transmission and accumulation of color deviations between different stages in a timely manner. At the same time, when printing equipment or materials are changed, a lot of time and manpower are required to readjust color management parameters. There is a lack of adaptive adjustment capabilities, which seriously affects production efficiency and enterprise competitiveness.

[0007] Thirdly, existing data processing methods for printing color management are relatively simple, with limited training data and a lack of diversity. This makes it difficult to cover various color deviations that may occur in actual printing, resulting in weak generalization ability of deep learning models. Traditional model training methods are mostly static training, which cannot adapt to changes in data distribution and color fluctuations in the printing production process in real time. Once production conditions change, the model performance drops sharply, requiring frequent retraining. This not only interrupts production but also increases the model training cost and time cost, which is not conducive to the intelligent and efficient management of printing production. Summary of the Invention

[0008] Technical problems to be solved

[0009] To address the shortcomings of existing technologies, this invention provides a deep learning self-calibration system for high-precision color management, solving the following problems:

[0010] 1. Addressing the problem of high scrap rates due to insufficient accuracy and consistency in printing color correction, which in turn increases consumable costs;

[0011] 2. To address the problem of low production efficiency caused by the inability to adapt to the complex and ever-changing production environment and processes in the printing industry;

[0012] 3. To address the problem of inconvenient production management caused by limited image data processing capabilities and poor model adaptability during printing.

[0013] Technical solution

[0014] To achieve the above objectives, the present invention provides the following technical solution: a deep learning self-calibration system for high-precision color management, wherein the system architecture includes an algorithm layer, a system architecture layer, and a data processing and optimization layer, wherein:

[0015] The algorithm layer is used to perform deep feature extraction, deviation prediction and correction strategy generation for the color of printed images. The algorithm layer includes a multi-algorithm fusion model, which includes a combination unit of convolutional neural network (CNN) and recurrent neural network (RNN), a combination unit of generative adversarial network (GAN) and autoencoder, and a combination unit of deep belief network (DBN) and restricted Boltzmann machine (RBM).

[0016] The system architecture layer is used to construct an architecture system adapted to the large-scale production, multiple stages, and various equipment and materials in the printing industry. It includes a distributed deep learning architecture, a multi-level feedback self-calibration architecture, and an adaptive color calibration architecture. The distributed deep learning architecture distributes the training task of the deep learning model to multiple computing nodes for parallel processing. Different nodes focus on data processing for different printing product lines or different color spaces. The central coordinator integrates the results of each node. The multi-level feedback self-calibration architecture performs preliminary color correction on the image in the pre-press stage and then inputs the correction results back into the system for secondary detection and analysis in the printing stage. It adjusts the parameters of the deep learning model or the correction strategy according to the feedback information at different levels and establishes a feedback channel from the post-press quality inspection stage to the pre-press design and printing stages. The adaptive color calibration architecture uses a pre-established feature library of printing equipment and materials to quickly identify and match the corresponding model configuration during printing production and automatically adjust the structure and parameters of the deep learning model.

[0017] The data processing and optimization layer is used to preprocess, expand, and dynamically update printing color data. This layer includes a data augmentation and synthesis module, a semantic segmentation data processing module, and a real-time data streaming and incremental learning module. The data augmentation and synthesis module expands the training dataset by performing transformations such as rotation, scaling, or contrast adjustment on existing printing color images and by artificially synthesizing image data with specific color deviation characteristics. The semantic segmentation data processing module uses semantic segmentation technology to divide different regions in the printed image according to color category, object category, or functional region, and performs color deviation analysis and correction accordingly. The real-time data streaming and incremental learning module rapidly processes and extracts features from the color data generated in real time during the printing production process, and uses incremental learning technology to adapt the model to new printing data distributions and color change trends.

[0018] Preferably, the combined unit of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is used to deeply extract features such as color pixel distribution and pattern texture of printed images, and analyze the variation law of color deviation across the entire printed page and different printing batches based on the spatial and temporal dependencies between printed image sequences. In the combined unit of Generative Adversarial Network (GAN) and Autoencoder, the generator of GAN generates simulated printed image data similar to the target printed color standard, and the discriminator distinguishes between real printed images and generated images. The two are trained adversarially to improve the quality of generated images. The autoencoder compresses and reconstructs the input printed color image and learns the latent color representation of the image. The two work together to correct printed color deviation and ensure color consistency. The combined unit of Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM) uses RBM to perform unsupervised pre-training on printed color data, learns the basic feature distribution and structure of printed color data, and then stacks them to build DBN, mining high-level abstract features hidden in complex printed color data, providing accurate basis for subsequent automatic correction.

[0019] Preferably, each computing node in the distributed deep learning architecture has an independent data storage unit and a computing unit. The data storage unit is used to temporarily store a specific subset of printing color data allocated to it, and the computing unit performs local training operations on the deep learning model based on the stored data. The computing nodes are connected through a high-speed data transmission channel to ensure the efficiency of data interaction and model integration.

[0020] Preferably, the feedback information in the multi-level feedback self-calibration architecture includes color deviation values, deviation distribution areas, color space coordinate differences, and corresponding printing stage identification information. The color deviation values ​​are accurate to three decimal places, the deviation distribution areas are represented by the range of image pixel coordinates or the physical size area of ​​the printed matter, the color space coordinate differences are quantitatively described according to the color spaces commonly used in the printing industry, such as CMYK or LAB, and the printing stage identification information clearly distinguishes the source of the pre-press, printing, and post-press stages.

[0021] Preferably, the data stored in the printing equipment and material feature library in the adaptive color calibration architecture covers printing press brand and model, printing press mechanical parameters such as pressure, speed, dot gain curve, paper type such as coated paper, offset paper, cardstock, including its basis weight, roughness, whiteness, etc., ink brand and model and characteristics such as hue, saturation, drying speed, viscosity, etc., and the data is stored in the form of a structured database, which facilitates quick retrieval and model configuration matching.

[0022] Preferably, the color data synthesis algorithm in the data enhancement and synthesis module is built based on the physical model and mathematical statistical model of printing color. When synthesizing image data with specific color deviation characteristics, it can accurately generate simulated images according to the deviation parameters set by the user, such as the degree of deviation, the direction of deviation, and the distribution pattern of deviation. Moreover, the generated image data format is consistent with the real printed image data format and can be directly used for deep learning model training.

[0023] Preferably, the semantic segmentation technology in the semantic segmentation data processing module adopts a deep learning semantic segmentation model. The model structure is based on the U-Net architecture and improves upon it by adding a multi-scale feature fusion module. It can accurately identify text, image subject, background and other regions in printed images at different resolutions, with segmentation accuracy reaching the pixel level. It also has good adaptability to different types of printed images, such as posters, book pages, and packaging boxes.

[0024] Preferably, the data streaming processing algorithm in the real-time data streaming processing and incremental learning module adopts sliding window technology and distributed caching mechanism. The sliding window size is dynamically adjusted according to the printing data flow and processing speed to ensure that no data is lost and that data can be processed in a timely manner during peak data flow. The distributed caching mechanism caches recently processed data on multiple nodes, which facilitates the incremental learning module to quickly obtain data for model updates. The validity period of the cached data is dynamically set according to the continuity of the printing task and the timeliness of the data.

[0025] Preferably, the DBN model training in the combined Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM) unit adopts a combination of layer-by-layer pre-training and overall fine-tuning. In the layer-by-layer pre-training stage, the number of training iterations for each layer is set according to the complexity of the printing color data, and the number of iterations is gradually reduced from the bottom layer to the top layer. In the overall fine-tuning stage, an optimization algorithm based on gradient descent is used to adjust the learning rate and momentum parameters, with the accuracy of printing color deviation correction as the optimization objective function.

[0026] Preferably, the autoencoder in the fusion generative adversarial network (GAN) and autoencoder combination unit adopts a symmetrical encoder-decoder structure. The encoder consists of multiple convolutional layers and pooling layers, which are used to compress the printed color image into a low-dimensional feature vector. The decoder consists of multiple deconvolutional layers and upsampling layers, which are used to reconstruct the low-dimensional feature vector into an image of the original image size. Skip connections are set between the encoder and decoder to preserve the original feature information of the image and improve the quality of the reconstructed image.

[0027] Preferably, the RNN model in the combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) unit adopts a Long Short-Term Memory (LSTM) structure. The LSTM unit contains a forget gate, an input gate, a cell state update gate, and an output gate. By controlling the flow and update of information through a gating mechanism, it can effectively handle long-distance dependencies in printed image sequences. Furthermore, the number of layers in the LSTM network is dynamically adjusted according to the complexity of the printed image and the accuracy requirements of color deviation analysis. The initial weights of the network are set using the Xavier initialization method.

[0028] Beneficial effects

[0029] This invention provides a deep learning self-calibration system for high-precision color management. It offers the following advantages:

[0030] 1. This invention effectively ensures high-precision color correction and consistency. Through the fusion of multiple advanced algorithms in the algorithm layer, such as the combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), it can accurately analyze the color pixel distribution, texture features, and color deviation patterns between different printing batches of printed images. The fusion of Generative Adversarial Network (GAN) and Autoencoder (Autoencoder) can generate high-quality simulated images and collaboratively correct color deviations. The combination of Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM) mines deep-level features to provide accurate correction basis. These algorithms work together to effectively improve the accuracy of color correction, ensuring high color consistency of printed materials throughout the entire production process. Whether it's the same batch or different batches, the printed materials can achieve extremely close color standards, greatly improving the quality stability of printed products. This meets the demanding requirements of high-end printing businesses, such as the production of art albums and brand promotional posters, reducing the scrap rate caused by color deviations, lowering production costs, and enhancing corporate reputation.

[0031] 2. The system of this invention can efficiently adapt to the complex production environment and processes of the printing industry. Utilizing the system architecture layer design, it provides a comprehensive adaptation solution for large-scale production, multiple stages, and various equipment and materials in the printing industry. The distributed deep learning architecture uses multiple computing nodes to process massive amounts of printing color data in parallel, accelerating the model training process and improving production efficiency. The multi-level feedback self-calibration architecture runs through the pre-press, printing, and post-press stages, forming a closed-loop feedback control system that can promptly detect and correct color deviations, optimizing the entire printing process. The adaptive color calibration architecture, based on a rich feature library of printing equipment and materials, can quickly adjust the model configuration according to different printing presses, papers, inks, and other conditions. This allows the system to easily cope with the complexities of frequent equipment and material changes and different production tasks in the printing industry, enhancing the system's versatility and flexibility, reducing the time and manpower costs of readjusting the system due to changes in equipment and materials, and improving the enterprise's production responsiveness.

[0032] 3. This invention optimizes data processing and improves model performance. It utilizes various modules in the data processing and optimization layer for data processing. The data augmentation and synthesis module expands the training dataset based on the physical and mathematical models of printing color. This rich data diversity allows the deep learning model to better learn various color deviations, improving its generalization ability. This enables accurate prediction and correction of deviations even when faced with complex and variable color data in actual printing. The semantic segmentation data processing module uses an improved deep learning semantic segmentation model to perform refined region division and color analysis of printed images, correcting for different region characteristics to further improve the accuracy and visual effect of color management. The real-time data streaming processing and incremental learning module, leveraging sliding window technology and a distributed caching mechanism, achieves efficient processing of real-time data and dynamic model updates during printing production. This ensures the model can adapt to the distribution of printing data and color change trends in a timely manner, maintaining good correction performance without frequent production interruptions for large-scale model retraining. This improves production continuity and equipment utilization, helping enterprises achieve intelligent and efficient printing production management. Attached Figure Description

[0033] Fig. 1 This is a system architecture diagram of the present invention;

[0034] Fig. 2 This is a flowchart of the system operation steps of the present invention;

[0035] Fig. 3 This is a dynamic data flow diagram of the system according to the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0038] like Figs. 1-3 As shown, the deep learning self-calibration system for high-precision color management has an architecture comprising an algorithm layer, a system architecture layer, and a data processing and optimization layer, wherein:

[0039] The algorithm layer is used for deep feature extraction, deviation prediction, and correction strategy generation of printed image colors. This layer includes a multi-algorithm fusion model, comprising a combination unit of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), a combination unit of Generative Adversarial Network (GAN) and Autoencoder, and a combination unit of Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM). The CNN and RNN combination unit is used to deeply extract features such as color pixel distribution and pattern texture of printed images. Based on the spatial and temporal dependencies between printed image sequences, it analyzes the variation of color deviation across the entire printed page and between different printing batches. It can accurately analyze the color pixel distribution, texture features, and color deviation patterns between different printing batches of printed images. In the GAN and autoencoder combination unit, the GAN generator generates simulated printed image data similar to the target printing color standard, the discriminator distinguishes between real printed images and generated images, and the two train adversarially to improve the quality of the generated images. The autoencoder processes the input printed images... The process involves compressing and reconstructing printed color images, learning the latent color representation of the images, and working together to correct printing color deviations and ensure color consistency. This generates high-quality simulated images and collaboratively corrects color deviations. A Deep Belief Network (DBN) and a Restricted Boltzmann Machine (RBM) combined unit perform unsupervised pre-training on the printed color data through the RBM, learning the basic feature distribution and structure of the printed color data. These are then stacked to construct the DBN, uncovering high-level abstract features hidden in complex printed color data. This provides a precise basis for subsequent automatic correction. The algorithms work together to effectively improve the accuracy of color correction, ensuring high color consistency throughout the entire production process. Whether it's the same batch or different batches of printed materials, they achieve extremely close color standards, greatly improving the quality stability of printed products. This meets the demanding needs of high-end printing businesses with stringent color requirements, such as the production of art albums and brand promotional posters, reducing scrap rates caused by color deviations, lowering production costs, and enhancing corporate reputation.

[0040] The system architecture layer is used to build an architecture system adapted to the large-scale production, multiple stages, and various equipment and materials in the printing industry. It provides a comprehensive adaptation solution for large-scale production, multiple stages, and various equipment and materials in the printing industry, including a distributed deep learning architecture, a multi-level feedback self-calibration architecture, and an adaptive color calibration architecture. The distributed deep learning architecture distributes the training task of the deep learning model to multiple computing nodes for parallel processing. Different nodes focus on data processing for different printing product lines or different color spaces, and a central coordinator integrates the results from each node. The multi-level feedback self-calibration architecture performs preliminary color correction on the image in the pre-press stage, and then inputs the correction results back into the system for secondary detection and analysis in the printing stage. It adjusts the parameters of the deep learning model or the correction strategy based on feedback information from different levels, and establishes a feedback channel from the post-press quality inspection stage to the pre-press design and printing stages. The adaptive color calibration architecture... The pre-established feature library of printing equipment and materials enables rapid identification and matching of corresponding model configurations during printing production. It automatically adjusts the structure and parameters of the deep learning model. The distributed deep learning architecture utilizes multiple computing nodes to process massive amounts of printing color data in parallel, accelerating model training and improving production efficiency. A multi-layered feedback self-calibration architecture spans pre-press, printing, and post-press stages, forming a closed-loop feedback control system that can promptly detect and correct color deviations, optimizing the entire printing process. The adaptive color calibration architecture, based on a rich feature library of printing equipment and materials, can quickly adjust model configurations according to different printing presses, papers, inks, and other conditions. This allows the system to easily cope with the complexities of frequent equipment and material changes and different production tasks in the printing industry, enhancing the system's versatility and flexibility. It reduces the time and manpower costs of readjusting the system due to equipment and material changes, improving the company's production responsiveness.

[0041] The data processing and optimization layer is used for preprocessing, expanding, and dynamically updating printing color data. This layer includes a data augmentation and synthesis module, a semantic segmentation data processing module, and a real-time data streaming and incremental learning module. The data augmentation and synthesis module expands the training dataset by performing transformations such as rotation, scaling, or contrast adjustment on existing printing color images, and by artificially synthesizing image data with specific color deviation characteristics. The semantic segmentation data processing module uses semantic segmentation technology to divide different regions in the printed image according to color category, object category, or functional region, and performs color deviation analysis and correction accordingly. The real-time data streaming and incremental learning module rapidly processes and extracts features from the color data generated in real time during the printing production process, and uses incremental learning technology to adapt the model to new printing data distributions and color change trends. The data augmentation and synthesis module expands the data based on the physical and mathematical models of printing color. The training dataset provides a rich and diverse dataset, enabling the deep learning model to better learn various color deviations and improve its generalization ability. This allows it to accurately predict and correct deviations when faced with complex and ever-changing color data in actual printing. The semantic segmentation data processing module uses an improved deep learning semantic segmentation model to perform refined region division and color analysis on printed images, and corrects for different region characteristics, further improving the accuracy and visual effect of color management. The real-time data streaming processing and incremental learning module utilizes sliding window technology and a distributed caching mechanism to achieve efficient processing of real-time data and dynamic model updates during the printing production process. This ensures that the model can adapt to the distribution of printing data and color change trends in a timely manner, maintaining good correction performance at all times. It eliminates the need for frequent production interruptions for large-scale model retraining, improving production continuity and equipment utilization, and helping enterprises achieve intelligent and efficient printing production management.

[0042] Each sub-architecture within the overall system architecture layer, when used, further includes the following:

[0043] In a distributed deep learning architecture, each computing node has an independent data storage unit and a computing unit. The data storage unit is used to temporarily store a specific subset of printing color data allocated to it, and the computing unit performs local training operations on the deep learning model based on the stored data. The computing nodes are connected through a high-speed data transmission channel to ensure the efficiency of data interaction and model integration.

[0044] The feedback information in the multi-level feedback self-calibration architecture includes color deviation values, deviation distribution areas, color space coordinate differences, and corresponding printing stage identification information. The color deviation values ​​are accurate to three decimal places, the deviation distribution areas are represented by the range of image pixel coordinates or the physical size area of ​​the printed matter, the color space coordinate differences are quantitatively described according to the color spaces commonly used in the printing industry, such as CMYK or LAB, and the printing stage identification information clearly distinguishes the source of the pre-press, printing, and post-press stages.

[0045] The data stored in the printing equipment and material feature library in the adaptive color calibration architecture covers printing press brand and model, printing press mechanical parameters such as pressure, speed, dot gain curve, paper type such as coated paper, offset paper, and cardstock, including its basis weight, roughness, whiteness, etc., ink brand and model and characteristics such as hue, saturation, drying speed, viscosity, etc., and the data is stored in the form of a structured database, which facilitates quick retrieval and model configuration matching.

[0046] When using any module within the entire data processing and optimization layer, the following further applies:

[0047] The color data synthesis algorithm in the data augmentation and synthesis module is built on the physical model and mathematical statistical model of printing color. When synthesizing image data with specific color deviation characteristics, it can accurately generate simulated images according to the deviation parameters set by the user, such as the degree of deviation, the direction of deviation, and the distribution pattern of deviation. Moreover, the generated image data format is consistent with the real printed image data format and can be directly used for deep learning model training.

[0048] The semantic segmentation technology in the semantic segmentation data processing module adopts a deep learning semantic segmentation model. The model structure is based on the U-Net architecture and improves upon it by adding a multi-scale feature fusion module. It can accurately identify text, image subjects, backgrounds and other regions in printed images at different resolutions, with segmentation accuracy reaching the pixel level. It also has good adaptability to different types of printed images, such as posters, book pages, and packaging boxes.

[0049] The data streaming processing algorithm in the real-time data streaming and incremental learning module adopts sliding window technology and distributed caching mechanism. The sliding window size is dynamically adjusted according to the printing data flow and processing speed to ensure that no data is lost and that data can be processed in a timely manner during peak data flow. The distributed caching mechanism caches recently processed data on multiple nodes, which facilitates the incremental learning module to quickly obtain data for model updates. The validity period of the cached data is dynamically set according to the continuity of printing tasks and the timeliness of data. Specific Implementation Example 2:

[0051] like Figs. 1-3 As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0052] The specific algorithm details for each unit in the algorithm layer within the overall system architecture described above are as follows:

[0053] The DBN model training in the combined Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM) unit adopts a combination of layer-by-layer pre-training and global fine-tuning. In the layer-by-layer pre-training stage, the number of training iterations for each layer is set according to the complexity of the printing color data, and the number of iterations is gradually reduced from the bottom layer to the top layer. In the global fine-tuning stage, an optimization algorithm based on gradient descent is used to adjust the learning rate and momentum parameters, with the accuracy of printing color deviation correction as the optimization objective function.

[0054] The combined unit of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) adopts a Long Short-Term Memory (LSTM) structure.

[0055] Forgotten Gate:

[0056] in:

[0057] : Indicates the time. The forget gate output determines how much past information should be forgotten from the cell state;

[0058] : is the activation function, usually the sigmoid function, which maps the input value to the interval between 0 and 1 to control the proportion of information transmission. Here, the sigmoid function will process the linear combination result within the parentheses so that the output value is between 0 (complete forgetting) and 1 (complete retention).

[0059] : is the weight matrix of the forget gate, which is concatenated with the input vector. Matrix multiplication is performed to weight the input information in order to determine the importance of different parts of the information.

[0060] : is the hidden state vector of the previous time step, which contains some state information of the network processing information in the previous time step, and can be used as a reference for the forget gate decision in the current time step;

[0061] : is the input vector at the current moment. When processing a sequence of printed images, it may be the pixel feature vector at the current position in the image sequence, etc.

[0062] : is the bias vector of the forget gate, used to adjust the offset of the linear combination to better adapt to the data characteristics;

[0063] Input Gate:

[0064] The explanation is similar to the forget gate, except that here it determines how much new information to update the cell state;

[0065] Cell state update gate:

[0066] in:

[0067] : Represents the processed state of candidate cells, which is achieved by weighting the input information (by...). and bias After that, it is processed by the tanh activation function. The tanh function maps the input value to the interval between -1 and 1, which can better represent the update range of cell state.

[0068] The interpretations of other symbols are similar to those above, involving different weighting and processing of the input information to obtain appropriate candidate states for updating.

[0069] Cell status update:

[0070] in:

[0071] : This is the cell state updated at the current moment, which is determined by the forget gate. The cell state at the previous moment that determines the part to be forgotten. In addition to the input gate Determine the candidate cell state of the updated part As a result, the cell state can be updated reasonably based on new input information and past states.

[0072] Output gate:

[0073] Similar to the forget gate, it determines how much information is output from the current cell state as the hidden state at the current moment;

[0074] Hidden status update:

[0075] in:

[0076] : This is the hidden state at the current moment. It is a cell-like state processed by the tanh function, where the output ratio is determined by the output gate. The hidden state obtained will serve as part of the input for the network processing in the next moment, and can also be used for subsequent analysis or output tasks. For example, when analyzing the color deviation pattern of a printed image sequence, it may serve as a basis for judging the color change trend.

[0077] Implementation steps:

[0078] Initialize the weight matrix , , , and bias vector , , , Typically, random initialization or specific initialization methods can be used, such as the Xavier initialization method mentioned above.

[0079] For each moment Calculate the forget gate in sequence Input gate Cell state update gate Cell state update Output gate and hidden state update Perform the corresponding matrix multiplication, vector addition, and activation function processing operations according to the above formula;

[0080] The obtained hidden state It can be used for subsequent tasks, such as combining with CNN to deeply extract features such as color pixel distribution and pattern texture of printed images, and to analyze the variation of color deviation across the entire printed page and between different printing batches.

[0081] Operating Logic: LSTM controls the flow and updating of information through four gating mechanisms: forget gate, input gate, cell state update gate, and output gate. When processing printed image sequences, for each input time step (such as the pixel feature vector at a certain position in the image sequence), the forget gate determines how much past cell state information to forget, the input gate determines how much new information to update the cell state, the cell state update gate generates candidate cell state update values, and then updates the cell state using the cell state update formula. Finally, the output gate determines how much information to output from the updated cell state as the hidden state at the current time step. In this way, the network can effectively handle long-distance dependencies in printed image sequences, that is, it can take into account the interrelationships between different positions in the image sequence, thereby better analyzing features such as color deviation.

[0082] Beneficial effects: It can effectively handle long-distance dependencies in printed image sequences, which is crucial for analyzing color deviation patterns across the entire page and between different batches. This is because color deviation in printed images may not only be related to pixels at the current position but also to pixels at previous or subsequent positions. LSTM can capture these complex relationships through gating mechanisms, thereby more accurately analyzing and predicting color deviation. By deeply extracting features such as color pixel distribution and pattern texture from printed images and combining them with CNNs, it can more comprehensively and accurately analyze printed images, improve the accuracy of color correction, and ensure high color consistency of printed products throughout the entire production process.

[0083] The autoencoder in the combined unit of Generative Adversarial Network (GAN) and autoencoder adopts a symmetrical encoder-decoder structure. The encoder consists of multiple convolutional layers and pooling layers, which are used to compress the printed color image into a low-dimensional feature vector. The decoder consists of multiple deconvolutional layers and upsampling layers, which are used to reconstruct the low-dimensional feature vector into an image of the original image size. Skip connections are set between the encoder and decoder to preserve the original feature information of the image and improve the quality of the reconstructed image.

[0084] The fusion unit combining generative adversarial networks (GANs) and autoencoders further includes the following:

[0085] Convolutional Neural Network-based Generators:

[0086]

[0087] in:

[0088] : This is the output of the generator, i.e., the generated simulated printed image data;

[0089] Activation functions, such as the sigmoid function, are used to map the results processed internally by the generator to a suitable range in order to generate image data that meets the requirements. Here, the results after multiple processing layers are mapped to the range between 0 and 1 (if processing image pixel values, etc.).

[0090] , : is the weight matrix of different layers in the generator, used to weight the input to determine the importance of different parts of information and gradually shape the features of the image during the image generation process;

[0091] , : These are the bias vectors for different layers in the generator, used to adjust the offset of the linear combination to better adapt to the data characteristics;

[0092] : is the random noise vector input to the generator. Based on this random noise, the generator generates simulated printed image data through multiple internal processing layers (such as processing through and first, then processing through the tanh activation function, and finally processing through and ).

[0093] Discriminator based on convolutional neural networks:

[0094]

[0095] in:

[0096] : This is the output of the discriminator, and its value is between 0 and 1, representing the input image. The probability of whether it is a real printed image or a simulated image generated by the generator is calculated. If the output is close to 1, it is considered a real image; if the output is close to 0, it is considered a generated image.

[0097] Similar to generators, , It is the weight matrix of the discriminator. , It is the bias vector of the discriminator, and it is the image input to the discriminator, which can be a real printed image or a simulated image generated by the generator.

[0098] Self-encoder:

[0099] Encoder:

[0100] in:

[0101] : This is the output of the encoder, i.e., the compressed low-dimensional feature vector;

[0102] : Represents the encoding function of the encoder for the input image;

[0103] : This is the encoder's weight matrix, used to weight the input image to achieve compression.

[0104] : is the encoder's bias vector, used to adjust the offset of the linear combination to better adapt to the data characteristics;

[0105] Decoder:

[0106] in:

[0107] : This is the output of the decoder, i.e., the reconstructed image, which is intended to be as close as possible to the original input image;

[0108] : Represents the decoding function of the decoder for low-dimensional feature vectors;

[0109] : This is the weight matrix of the decoder, used to weight the low-dimensional feature vectors to achieve reconstruction.

[0110] : is the bias vector of the decoder, used to adjust the offset of the linear combination to better adapt to the data characteristics.

[0111] Implementation steps:

[0112] Initialize the weight matrices of the generator and discriminator. , , , and bias vector , , , Simultaneously initialize the weight matrix of the autoencoder. , and bias vector , Random initialization or other suitable initialization methods are usually used;

[0113] During the training phase:

[0114] For the generator: a random noise vector is obtained by sampling from a random noise distribution. ,Will Input to generator The simulated printing image data is obtained by processing the data according to the above formula. ;

[0115] For the discriminator:

[0116] Select real printed images from the real printed image dataset. Simultaneously, the simulated printing image data generated by the generator... Also used as input, real printed images can be selected alternately or at a certain ratio. and simulated printed image data The inputs are respectively fed into the discriminator. By processing according to the above formula, the discriminator's discrimination results for the real image and the generated image are obtained. ;

[0117] For autoencoders:

[0118] Input images are selected from a dataset of real printed images. ,Will Input to encoder Process according to the above formula This yields low-dimensional feature vectors. ,Will Input to decoder The reconstructed image is obtained by processing it according to the above formula. ;

[0119] Based on the discrimination results of the discriminator and the reconstruction results of the autoencoder, the weight matrices and bias vectors of the generator, discriminator and autoencoder are updated. Gradient descent-based optimization algorithms, such as stochastic gradient descent (SGD) or its variants (such as Adam), are usually used to generate high-quality simulated images, accurately distinguish between real and fake images and reconstruct images well.

[0120] Operating logic: The generator attempts to generate simulated printing image data that closely resembles the target printing color standard from random noise vectors. The discriminator, on the other hand, must distinguish between real printed images and generated images. Through continuous adversarial training, the generator continuously generates better simulated images to "deceive" the discriminator, while the discriminator continuously improves its discrimination ability to "detect" the generator's "deception." In this process, the performance of both the generator and the discriminator is improved, and ultimately the generator is able to generate high-quality simulated images.

[0121] An autoencoder compresses and reconstructs the input printed color image. The encoder compresses the image into a low-dimensional feature vector, and the decoder reconstructs this vector back to the original image size. During this process, the autoencoder learns the latent color representation of the image and preserves the original feature information by establishing skip connections between the encoder and decoder, thus improving the quality of the reconstructed image. Then, the generator and autoencoder work together to correct printing color deviations and ensure color consistency. For example, the simulated image generated by the generator can be used as a reference to correct color deviations in the real image, and the latent color representation learned by the autoencoder helps to better understand and process the color features of the image.

[0122] Beneficial effects: The generator and discriminator improve the quality of generated images through adversarial training, making the generated simulated printed images closer to the target printing color standard. This is very helpful for analyzing and correcting printing color deviations, because by comparing with real images, the color deviation can be identified more accurately. The autoencoder learns the latent color representation of the image and can reconstruct the image better. By working in collaboration with the generator, it can more comprehensively ensure the correction of printing color deviations and color consistency, improve the high color consistency of printed materials throughout the entire production process, and meet the needs of high-end printing businesses with stringent color requirements.

[0123] The RNN model in the combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) unit adopts the Long Short-Term Memory (LSTM) structure. The LSTM unit contains a forget gate, an input gate, a cell state update gate, and an output gate. Through the gating mechanism, it controls the flow and update of information, which can effectively handle long-distance dependencies in printed image sequences. The number of layers in the LSTM network is dynamically adjusted according to the complexity of the printed image and the accuracy requirements of color deviation analysis. The initial weights of the network are set using the Xavier initialization method.

[0124] Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM) Combination Unit

[0125] Energy function of a Restricted Boltzmann Machine (RBM):

[0126]

[0127] in:

[0128] : is the energy function of the RBM, which is related to the visible cell state vector. and hidden unit state A function of a vector, training aims to reduce its value;

[0129] : The bias vector elements of the visible cells, adjusting the energy contribution of the visible cells;

[0130] : The bias vector element of the hidden unit, which adjusts the energy contribution of the hidden unit;

[0131] The weight matrix elements connecting visible and hidden units determine the degree of mutual influence.

[0132] : The visible unit state vector of the first Individual elements, such as the feature values ​​of printing color data;

[0133] : The first hidden unit state vector Each element is generated by interacting with visible units.

[0134] Conditional probability distribution:

[0135] The conditional probability of a visible cell given a hidden cell:

[0136]

[0137] in:

[0138] Given the hidden unit state vector At that time, the visible unit state vector is the first element The conditional probability;

[0139] The sigmoid function maps the input to a probability between 0 and 1.

[0140] The conditional probability of a hidden cell given a visible cell:

[0141]

[0142] in:

[0143] Given the visible cell state vector At that time, the hidden unit state vector of the th element The conditional probability;

[0144] Deep Belief Networks (DBNs) are composed of multiple Religious Belief Machines (RBMs) stacked together.

[0145] Assuming the first one has been trained Each RBM has a hidden unit state vector. As the first Input of one RBM;

[0146] The training employs a combination of layer-by-layer pre-training and overall fine-tuning:

[0147] Layer-by-layer pre-training: For each RBM (from bottom to top), pre-train according to the RBM training method (training by minimizing the energy function and using the conditional probability distribution), and set the number of training iterations for each layer to gradually decrease from bottom to top;

[0148] Overall fine-tuning: Using gradient descent-based optimization algorithms (such as Adam), the learning rate and momentum parameters are adjusted. The accuracy of printing color deviation correction is used as the optimization objective function to fine-tune the entire DBN, mining high-level abstract features to provide accurate basis for correction.

[0149] Implementation steps:

[0150] Initialize the weight matrix of the RBM Visible element bias vector and hidden unit bias vector Random initialization or other suitable methods;

[0151] Training-limited Boltzmann machine (RBM):

[0152] Randomly initialize the visible cell state vector and the hidden cell state vector. ,

[0153] Multiple iterations of training: Calculating using conditional probability distributions Update the hidden unit state vector by sampling according to probability. (e.g., Gibbs sampling), using conditional probability distribution to calculate Update the visible cell state vector based on probability sampling. According to the new and The gradient of the energy function with respect to the parameters is calculated based on gradient descent, and the weight matrix is ​​updated accordingly. Visible element bias vector and hidden unit bias vector Continue until the training stopping condition is met, such as reaching the preset number of iterations or the energy function converges;

[0154] Training a Deep Belief Network (DBN):

[0155] Layer-by-layer pre-training: The bottom layer uses the original printed color data as the visible unit state vector of the first RBM. The hidden unit state vector is obtained through training. The following is the first RBM The hidden unit state vector of the above RBM The visible cell state vector Training follows the principle of decreasing iterations from the bottom layer to the top layer.

[0156] Overall fine-tuning: After completing layer-by-layer pre-training, an optimization algorithm based on gradient descent (such as Adam) is used. With appropriate learning rate and momentum parameters, and the printing color deviation correction accuracy as the objective function, the gradient of the algorithm with respect to all parameters of DBN is calculated. The parameters are adjusted to mine high-level abstract features, adapt to the characteristics of printing color data, and provide a basis for correction.

[0157] Operating logic: In a Restricted Boltzmann Machine (RBM), the system operates based on an energy function and a conditional probability distribution. By adjusting parameters such as weights, the energy function value is reduced so that the conditional probability distribution of the hidden units when the visible unit is in a given state, and vice versa, conforms to the actual data distribution. This is to capture the inherent features and correlations of the color data. For example, the visible and hidden unit states are updated through Gibbs sampling to achieve weight training.

[0158] Deep Belief Networks (DBNs) are based on stacked Restricted Models (RBMs). During layer-by-layer pre-training, each RBM performs feature transformation on the input data (starting from the original color data and then the state vectors of the hidden units in the upper layer) to mine more abstract features. The features become more abstract with the number of layers. In the overall fine-tuning stage, the accuracy of printing color deviation correction is used as the objective function to adjust all parameters of the network so that the network can adapt to the needs of printing color data and mine high-level abstract features to provide accurate basis for correction. These features help to reveal the deep-seated causes of printing color deviation and the internal relationship of regional color features.

[0159] Beneficial effects: RBMs provide unsupervised pre-training for printing color data, automatically learning the distribution and structure of basic features without requiring extensive manual data annotation. This lays the foundation for DBN construction and training, facilitating the acquisition of preliminary data features by the printing color management system. After multiple RBMs are stacked to form a DBN, high-level abstract features in complex printing color data can be mined, more accurately reflecting the essence of the data, such as the underlying causes of color deviations between different batches and regional color correlations. This provides a precise basis for subsequent automatic correction, improves the color consistency of printed products, reduces the scrap rate, and meets the needs of high-end printing businesses. Furthermore, by combining layer-by-layer pre-training with overall fine-tuning, the network's generalization ability is improved, enabling it to cope with complex and variable color data in actual printing and accurately predict and correct deviations. Specific Implementation Example 3:

[0161] like Figs. 1-3 As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0162] The entire operation process in the above system architecture is as follows:

[0163] Sp1: Data Acquisition and Input: Acquire raw printed image data from image acquisition devices on the printing production line, such as scanners and cameras. This data includes color information of the printed matter and pixel distribution of the image. At the same time, the system reads relevant data from the printing equipment and material feature library, including printing press brand and model, mechanical parameters, paper type and characteristics, ink brand, model and characteristics, etc. This data serves as the basic configuration information for system operation.

[0164] SP2: Data Processing and Optimization Layer Operations:

[0165] SP2.1: The data augmentation and synthesis module processes the original printed image data. First, it expands the original dataset by performing transformation operations such as rotation, scaling, or contrast adjustment on the existing printed color images. Then, based on the deviation parameters set by the user, such as the degree of deviation, the direction of deviation, and the distribution pattern of deviation, it uses a color data synthesis algorithm built on the physical model and mathematical statistical model of printing color to artificially synthesize image data with specific color deviation characteristics. The generated image data format is consistent with the format of the real printed image data. The processed data is sent to the algorithm layer for subsequent analysis.

[0166] SP2.2: The semantic segmentation data processing module adopts a deep learning semantic segmentation model based on the U-Net architecture with an improved multi-scale feature fusion module. It divides printed images according to color category, object category, or functional region, such as dividing the image into text regions, main image regions, and background regions. Color features are extracted for different regions, and these region feature data, along with the corresponding color deviation analysis data, are transmitted to the algorithm layer.

[0167] SP2.3: The real-time data streaming and incremental learning module uses sliding window technology and a distributed caching mechanism to process the color data generated in real time during the printing production process. The size of the sliding window is dynamically adjusted according to the printing data flow and processing speed to ensure that no data is lost and that data can be processed in a timely manner during peak data flow periods. The distributed caching mechanism caches recently processed data on multiple nodes, then extracts data features and transmits them to the algorithm layer. At the same time, the incremental learning module adjusts the model's learning rate and momentum parameters based on the newly incoming data and cached data using a gradient descent-based optimization algorithm. The model is dynamically updated with the accuracy of printing color deviation correction as the optimization objective function, so that the model can adapt to new printing data distributions and color change trends.

[0168] Sp3: Algorithm layer operations:

[0169] SP3.1: The combined unit of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) receives image data from the data processing and optimization layer. The CNN performs in-depth extraction of features such as color pixel distribution and pattern texture of the printed image. The RNN analyzes the variation law of color deviation across the entire printed page and between different printing batches based on the spatial and temporal dependencies between printed image sequences, and generates color deviation analysis results and preliminary correction strategy suggestions, which are then transmitted to the system architecture layer.

[0170] Sp3.2: The combined unit of Generative Adversarial Network (GAN) and Autoencoder receives image data. The generator of GAN generates simulated printing image data that is similar to the target printing color standard. The discriminator distinguishes between real printing images and generated images. The two train against each other to improve the quality of generated images. The autoencoder compresses and reconstructs the input printing color image and learns the latent color representation of the image. The two work together to generate color correction data and color consistency evaluation data, which are transmitted to the system architecture layer.

[0171] SP3.3: After receiving data, the Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM) combined unit performs unsupervised pre-training on the printing color data through the RBM, learns the basic feature distribution and structure of the printing color data, and then stacks them to build a DBN. This process uncovers high-level abstract features hidden in the complex printing color data, generates accurate color deviation feature data and correction parameter data, and transmits them to the system architecture layer.

[0172] SP4: System Architecture Layer Operations

[0173] SP4.1: The distributed deep learning architecture integrates various data from the algorithm layer and the data processed by its own computing nodes. Each computing node has an independent data storage unit and a computing unit. The data storage unit temporarily stores a specific subset of printing color data allocated to it. The computing unit performs local training operations on the deep learning model based on the stored data. The computing nodes are connected through a high-speed data transmission channel to ensure the efficiency of data interaction and model integration. Finally, the overall color correction model data is generated and transmitted to the multi-layered feedback self-calibration architecture.

[0174] SP4.2: The multi-level feedback self-calibration architecture performs preliminary color correction on the image based on the received color correction model data in the prepress stage, and records relevant correction information and color deviation data, including color deviation values ​​accurate to three decimal places, deviation distribution areas represented by image pixel coordinate ranges or physical size areas of printed matter, color space coordinate differences quantified according to commonly used color spaces in the printing industry such as CMYK or LAB, and corresponding printing stage identification information to clearly distinguish the prepress stage. In the printing stage, the correction results are input into the system again for secondary detection and analysis. Based on feedback information at different levels, the deep learning model parameters or correction strategies are adjusted, and relevant data from the printing stage is transmitted to the post-press quality inspection stage, as well as the algorithm layer and data processing and optimization layer for further optimization and adjustment. The post-press quality inspection stage inspects the printed products and transmits the detected color deviation data and corresponding printed matter information (including printing stage identification information to clearly distinguish the postpress stage) back to the prepress design and printing stages, as well as the algorithm layer and data processing and optimization layer, forming a closed-loop feedback control system to optimize the entire printing process.

[0175] SP4.3: The adaptive color calibration architecture uses a pre-established library of printing equipment and material features to quickly identify the current printing equipment and material information during printing production, match the corresponding model configuration, and automatically adjust the structure and parameters of the deep learning model to ensure that the system can adapt to the color management needs under different printing presses, paper, inks and other conditions. The adjusted model data is then transmitted to a distributed deep learning architecture for integrated processing.

[0176] SP5: Output and Application: After a series of processing and corrections by the system, the final output is high-precision color-corrected printed image data or printed color control parameter data. This data is applied to the printing production process to directly control the color output of the printing equipment, ensuring that the printed products meet high-precision color standards, satisfying the needs of high-end printing business, improving the quality stability of printed products, reducing scrap rate, lowering production costs, and enhancing corporate reputation. Specific Implementation Example 4:

[0178] like Figs. 1-3As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0179] In the above system architecture, the overall data processing flow is as follows:

[0180] Data sources: The raw data mainly comes from printing image data acquired by image acquisition equipment on the printing production line, as well as equipment and material related data in the printing equipment and material feature library.

[0181] Data processing and optimization layer:

[0182] Input: Original printed image data, printing equipment and material feature library data.

[0183] Output: Expanded image dataset, regional feature data and color deviation analysis requirements data, real-time data features and dynamically updated model parameter data.

[0184] Algorithm layer:

[0185] Input: Data output from the data processing and optimization layer.

[0186] Output: Color deviation analysis results and preliminary correction strategy suggestions, color correction data and color consistency assessment data, precise color deviation characteristic data and correction parameter data.

[0187] System architecture layer:

[0188] Input: Data output from the algorithm layer and data transferred between its various architectural modules.

[0189] Output: Overall color correction model data, adjusted model data, and closed-loop feedback information data.

[0190] Data flow: Raw data flows from the printing image acquisition equipment and feature library to the data processing and optimization layer. The data processed by the data processing and optimization layer flows to the algorithm layer. The data processed by the algorithm layer flows to the system architecture layer. Data is transferred between the various architecture modules within the system architecture layer, and the final processing result data flows to the printing production equipment to control color output or is fed back to other related links for optimization and adjustment.

[0191] Final result: Obtain high-precision color-corrected printed image data or printed color control parameter data, achieve high color consistency of printed products throughout the entire production process, improve the quality stability of printed products, reduce scrap rate, enhance the enterprise's production responsiveness, and realize intelligent and efficient printing production management. Specific Implementation Example 5:

[0193] like Figs. 1-3 As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0194] In the above system architecture, the corresponding hardware structure in actual use is as follows:

[0195] The hardware architecture of the algorithm layer is shown below:

[0196] The hardware for the combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) unit is shown below:

[0197] Computing Core: Employs high-performance graphics processing units (GPUs), such as the NVIDIA A100 series GPUs, which have a large number of CUDA cores and can process convolution operations in CNNs and sequence calculations in RNNs in parallel, accelerating feature extraction and deviation pattern analysis. For example, when processing high-resolution printed images, the powerful computing capabilities of the A100 can quickly complete the tasks of color pixel distribution and pattern texture feature extraction, ensuring that the color deviation variation patterns between different printing batches can be analyzed in a short time.

[0198] Memory configuration: Equipped with large-capacity high-speed video memory, such as 32GB or higher GDDR6 video memory, which helps to store intermediate data during CNN and RNN operations, including image feature matrices, hidden states of RNNs, and other information, avoiding data transmission delays and reduced computational efficiency due to insufficient memory.

[0199] Data transfer interface: It uses a PCIe 4.0 or higher interface to connect to the host system, ensuring high-speed data transfer between the host memory and the GPU memory. For example, when transferring printed image data from the host to the GPU for computation, the PCIe 4.0 interface can achieve a data transfer rate of tens of GB per second, quickly loading the image data into the GPU for processing.

[0200] The hardware for the fusion unit of Generative Adversarial Network (GAN) and autoencoder is shown below:

[0201] Dual-GPU architecture: This architecture uses two GPUs working together. One GPU is dedicated to training the generator and discriminator of the GAN, while the other is used for compression and reconstruction operations of the autoencoder. For example, for GAN training that generates high-quality printed image simulation data, the GPU can use its powerful parallel computing capabilities to accelerate the adversarial training process between the generator and discriminator. Meanwhile, the autoencoder runs independently on the other GPU to ensure the efficiency of image compression and reconstruction. The two GPUs interact with each other through a high-speed data bus (such as NVLink) to achieve synergy.

[0202] High-speed storage system: Equipped with a solid-state drive array based on the NVMe protocol, used to store a large amount of image data, generator and discriminator model parameters, and autoencoder feature vector data during GAN training. The NVMe solid-state drive has a read and write speed of up to several GB per second, which can quickly load and save data, reduce data reading waiting time, and improve the operating efficiency of the entire combined unit.

[0203] Network Interface Card: Install a high-performance network interface card that supports network transmission rates of 10Gbps or higher. This helps to achieve fast data transmission when synchronizing data and sharing model parameters between GAN and autoencoder, ensuring that the two can work closely together and improving the effect of color correction and consistency assurance.

[0204] The hardware architecture of the combined Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM) unit is as follows:

[0205] Cluster computing architecture: Construct a cluster consisting of multiple servers, each equipped with a multi-core CPU, such as an Intel Xeon series processor, and a certain number of GPU accelerator cards. During the RBM pre-training stage, the multi-core advantage of the CPU is used to perform parallel processing of large-scale data, accelerating the learning process of data feature distribution and structure. During DBN stack construction and overall training, the parallel computing capabilities of GPU accelerator cards are used to perform deep feature extraction and model optimization. For example, a cluster containing 10 servers, each equipped with 2 24-core CPUs and 4 GPU accelerator cards, can efficiently process massive amounts of printing color data and mine the high-level abstract features hidden within it.

[0206] Distributed storage system: Employs a distributed file system, such as Ceph or GlusterFS, to store printing color data as well as DBN and RBM model parameters. The distributed file system can distribute data across multiple storage nodes, providing high reliability and high scalability, and ensuring secure storage and fast access to data during large-scale data processing.

[0207] High-speed interconnection network: Deploy a high-speed InfiniBand network within the cluster to achieve low-latency, high-bandwidth data transmission between servers. During DBN model training, servers need to frequently exchange data and model parameters. The InfiniBand network can provide transmission rates of up to 100Gbps or even higher, reducing data transmission time and improving the training efficiency of the entire combined unit.

[0208] The system architecture layer hardware architecture is shown below:

[0209] The hardware for a distributed deep learning architecture is shown below:

[0210] Computing node cluster: Composed of multiple computing nodes, each computing node uses an industry-standard server, configured with dual-socket multi-core CPUs (such as Intel Xeon Gold series), large-capacity memory (such as 128GB or higher), and multiple GPU accelerator cards (such as NVIDIA T4 or higher models). Different computing nodes are connected through high-speed Ethernet switches to form an internal high-speed network, realizing data communication and task collaboration between nodes. For example, a distributed deep learning architecture may contain 50 computing nodes. The GPU accelerator card of each node can process data from different printing product lines or color spaces in parallel, accelerating the model training process and improving the overall system's ability to process large-scale printing data.

[0211] Central Coordination Server: Equipped with a high-performance server, featuring a multi-core CPU and large-capacity memory, and running distributed task scheduling software and data integration programs, the central coordination server is responsible for allocating deep learning model training tasks to various computing nodes, monitoring the running status of the nodes, collecting and integrating the computing results of each node, and ensuring the stable operation and efficient collaboration of the entire distributed system. For example, the central coordination server rationally allocates printing color data processing tasks based on the load and task priority of each computing node, ensuring full utilization of system resources.

[0212] Storage Area Network (SAN): A SAN based on Fibre Channel or iSCSI protocol is built to connect all computing nodes and a central coordination server. The SAN is used to centrally store massive amounts of printing color data, model training data, and parameter files of deep learning models. Its high-speed data storage and access capabilities can meet the high requirements of distributed systems for data reading and writing, and ensure the rapid sharing and transmission of data between nodes. For example, the storage capacity of a SAN can reach tens of TB or even higher, and can store a large amount of color data and model files accumulated by printing companies over many years.

[0213] The hardware architecture of the multi-level feedback self-calibration architecture is shown below:

[0214] Online inspection equipment: High-precision color inspection equipment, such as spectrophotometers or colorimeters, is deployed in the pre-press, press, and post-press stages of the printing production line. These devices can collect color data of printed materials in real time, including information such as spectral reflectance and chromaticity coordinates, and transmit the data to the back-end processing system. For example, the inspection equipment in the pre-press stage can perform color detection on the input images or films to ensure the color accuracy of the materials entering the printing process; the inspection equipment in the press stage can monitor color changes during the printing process and detect deviations in a timely manner; the inspection equipment in the post-press stage performs final color quality inspection on the finished product, providing a basis for feedback and adjustment.

[0215] Data transmission network: An industrial Ethernet network is used to build a stable data transmission network from online detection equipment to the back-end processing system. The network has a redundancy design to ensure the reliability of data transmission and prevent data loss or transmission delay due to color deviation caused by network failure. For example, the network adopts a ring topology structure. When a link fails, the data can be automatically switched to the backup link to continue transmission, ensuring the continuous operation of the system.

[0216] Feedback Control Server: A dedicated feedback control server receives color deviation data from detection devices at each stage and analyzes and processes it according to preset algorithms and strategies. Running feedback control software, the server determines whether to adjust deep learning model parameters or correction strategies based on color deviation values, deviation distribution areas, color space coordinate differences, and printing stage identification information. It then sends adjustment instructions to the appropriate stages, such as prepress design software and printing press control systems. For example, when post-press inspection detects excessive color deviation, the feedback control server analyzes the cause based on the deviation information and then sends instructions to the prepress design software to modify image color parameters, or to the printing press control system to adjust parameters such as ink supply and printing pressure, thus achieving closed-loop feedback control.

[0217] The hardware for the adaptive color calibration architecture is shown below:

[0218] Equipment and Material Identification System: Sensors and identification devices are installed on the printing equipment to read the brand and model of the printing press, mechanical parameters (such as pressure sensors, speed sensors, dot gain measuring instruments, etc.), and relevant information about paper and ink (such as paper type identification sensors, ink viscosity measuring instruments, etc.). These sensors and identification devices transmit the collected data to the adaptive control unit. For example, by installing an ink viscosity measuring instrument in the ink supply system of the printing press, the viscosity change of the ink can be monitored in real time, providing important parameter basis for adaptive color calibration.

[0219] Adaptive Control Unit: Using an industrial control computer or embedded controller as the hardware platform for the adaptive control unit, running adaptive color calibration algorithm software, this unit receives data from the equipment and material identification system, quickly matches the corresponding color calibration model configuration by querying a pre-established feature library of printing equipment and materials, and generates control commands to send to the color control system or data processing system of the printing press. For example, when the paper type is detected to be changed, the adaptive control unit selects a suitable color calibration model from the feature library according to the paper's basis weight, roughness, whiteness and other parameters, and adjusts the color output parameters of the printing press to ensure the accuracy of printed colors under different paper conditions;

[0220] Feature Library Storage Server: Equipped with a high-performance server as the storage medium for the feature library of printing equipment and materials, the server adopts redundant disk array (RAID) technology to ensure data security and reliability. The feature library storage server stores a large amount of detailed information on printing presses, paper, inks and other equipment and materials, as well as corresponding color calibration model parameters, providing fast data retrieval and model matching services for the adaptive control unit. For example, the feature library stores dot gain curve data for hundreds of different brands and models of printing presses, optical property data for thousands of types of paper, and color property data for various inks, which can meet the diverse production needs of printing companies.

[0221] The hardware architecture for the data processing and optimization layer is shown below:

[0222] The hardware for the data augmentation and synthesis module is shown below:

[0223] Workstation host: A high-performance workstation is used as the operating platform for the data enhancement and compositing module. The workstation is equipped with a multi-core CPU (such as Intel Core i9 series), a large capacity of memory (such as 64GB or higher), and a mid-to-high-end GPU (such as NVIDIA GeForce RTX series). The CPU is responsible for executing the logic control and data preprocessing tasks in the data enhancement and compositing algorithm, while the GPU is mainly used to accelerate image transformation operations and color data compositing calculations. For example, when performing transformation operations such as rotation and scaling on printed images, the CPU first reads the image and sets the basic parameters, and then the GPU uses its parallel computing capabilities to quickly complete the transformation calculation of image pixels, thereby improving the efficiency of data enhancement.

[0224] Data storage devices: Connect to high-capacity external storage devices, such as Network Attached Storage (NAS) or Direct Attached Storage (DAS). These storage devices are used to store original printed image data, enhanced image datasets, and synthesized image data with specific color deviation characteristics. NAS devices can share data over a network, facilitating access by multiple workstations or system modules. DAS devices, on the other hand, provide higher local data read and write speeds to meet the needs of rapid data storage and retrieval during data enhancement and synthesis. For example, a 10TB NAS device can store a large amount of image data resources from a printing company, which can be accessed and processed by the data enhancement and synthesis modules at any time.

[0225] The hardware for the semantic segmentation data processing module is shown below:

[0226] Deep learning server: A dedicated deep learning server is used to run the semantic segmentation model. The server is equipped with multiple high-performance GPUs (such as NVIDIA A100 or V100 series) and large-capacity memory. The GPU is used to accelerate the training and inference process of the semantic segmentation model, and can quickly process pixel-level semantic segmentation tasks of printed images. For example, when performing semantic segmentation on high-resolution printed poster images, multiple A100 GPUs can process different regions of the image in parallel, accurately identify text, image subject, background and other regions, and the segmentation accuracy can reach the pixel level.

[0227] Image acquisition card: If image data needs to be acquired directly from the printing production line for semantic segmentation, a high-speed image acquisition card is installed in the server. The image acquisition card can quickly convert the analog or digital signals acquired by the printing image sensor and transmit them to the server memory, ensuring the real-time performance and accuracy of the data. For example, an image acquisition card that supports 4K resolution and 60 frames per second can meet the requirements of high-speed printing production lines for image data acquisition and provide timely data input for semantic segmentation.

[0228] The hardware for the real-time data streaming and incremental learning module is shown below:

[0229] Edge computing devices: Deploy edge computing devices, such as industrial-grade micro servers or high-performance embedded computers, near the printing production equipment. These edge computing devices are responsible for the preliminary processing and feature extraction of color data generated in real time during the printing production process. They use low-power, high-performance processors (such as multi-core processors based on ARM architecture) and a certain amount of memory. For example, edge computing devices can perform real-time filtering, data compression and other preprocessing operations on color data collected by the online inspection equipment of the printing press, reducing the amount of data transmission and improving the real-time performance of data processing.

[0230] Cloud computing platform: Incremental learning and model update tasks are deployed on the cloud computing platform. The cloud computing platform consists of a large-scale data center server cluster with powerful computing capabilities and massive storage resources. Edge computing devices upload processed feature data to the cloud computing platform through the network. The cloud computing platform uses its abundant computing resources to run incremental learning algorithms and update deep learning models. For example, the cloud computing platform can dynamically allocate computing resources according to printing data traffic and processing needs, and automatically expand server instances during peak data traffic to ensure the timely completion of incremental learning tasks, enabling the model to quickly adapt to the distribution of printing data and color change trends.

[0231] Network communication equipment: Establishes a high-speed, stable network communication link between edge computing devices and cloud computing platforms, such as 5G wireless networks or high-speed fiber optic networks. Network communication equipment ensures that real-time data can be transmitted quickly and reliably between the two, meeting the timeliness and accuracy requirements of data streaming processing and incremental learning for data transmission. For example, 5G wireless networks can provide low-latency, high-bandwidth data transmission services, enabling edge computing devices to upload data to the cloud computing platform in a timely manner. At the same time, the cloud computing platform can also quickly download updated model parameters to the edge computing devices, realizing real-time updates and applications of the model. Specific Implementation Example Six:

[0233] like Figs. 1-3 As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0234] In addition to the printing industry, the above system architecture can also be used in other industries for color management and automatic calibration. Specific examples are as follows:

[0235] Textile printing and dyeing industry

[0236] Application Scenarios and Industry Needs: In the textile printing and dyeing industry, color management is crucial, from fabric pattern design to the final dyeing process. Different batches of dyes, different fabric materials, and different printing and dyeing processes can all lead to color deviations. A high-precision color management system can ensure the consistency of fabric colors, meet the stringent requirements of fashion brands for product color standardization, reduce the defect rate caused by color differences, and improve production efficiency and product quality.

[0237] System architecture changes:

[0238] Algorithm layer:

[0239] In terms of data input, data collection and processing are required for textile fabric images and dyeing process parameters. For example, the fabric texture, fiber material information, and parameters such as temperature, time, and dye concentration during the dyeing process should be included in the data.

[0240] In terms of algorithm adjustments, the CNN-RNN combined unit can focus on analyzing the relationship between fabric texture and color penetration patterns, as well as the color distribution changes on the fabric during different batches of dyeing and printing. When generating simulated dyeing and printing effect images, the GAN-Autoencoder combined unit needs to consider factors such as the absorption characteristics of textile materials for dyes in order to generate color correction references that are more in line with actual production. The DBN-RBM combined unit is pre-trained and feature mining for the data feature distribution unique to the textile dyeing and printing industry, such as learning the basic color presentation patterns of different dye combinations on different fabrics.

[0241] System architecture layer:

[0242] In a distributed deep learning architecture, the task allocation of computing nodes is divided according to the type of textile product (such as cotton, linen, silk, chemical fiber, etc.) and the type of dyeing and printing process (such as dip dyeing, pad dyeing, printing, etc.).

[0243] In addition to conventional information such as color deviation values, the feedback information of the multi-level feedback self-calibration architecture should also include fabric material information, dyeing process identification, and dye batch information, so as to more accurately trace and adjust the root cause of color deviation.

[0244] The feature library of the adaptive color calibration architecture is expanded to include textile fabric type characteristics (such as water absorption and color fastness), printing and dyeing equipment parameters (such as printing machine precision and dyeing vat capacity), and dye brand, model and characteristics (such as solubility and dyeing rate).

[0245] Data processing and optimization layer:

[0246] When compositing image data with specific color deviation characteristics, the data augmentation and compositing module needs to build a model based on the physicochemical principles of textile printing and dyeing, such as considering the color change pattern during the dye diffusion process.

[0247] The semantic segmentation data processing module focuses on dividing textile fabric images into pattern areas, background areas, and areas where different materials are spliced ​​together, so as to analyze and correct the color management needs of different areas.

[0248] The real-time data streaming and incremental learning module needs to adapt to the relatively slow data generation speed and long production cycle in the textile printing and dyeing process, and adjust the calculation logic of the sliding window size and cached data validity period.

[0249] The steps are as follows:

[0250] Sp1: Data Acquisition and Input: Acquire color data from textile fabric design images and online detection equipment during the dyeing and printing process, while simultaneously reading information from textile fabric, dyeing and printing equipment, and dye feature libraries;

[0251] SP2: Data Processing and Optimization Layer Operations:

[0252] SP2.1: The data enhancement and compositing module expands and composites the original image data by combining it with dyeing and printing process parameters to generate diverse simulated dyeing and printing data;

[0253] Sp2.2: The semantic segmentation data processing module divides the textile fabric image into regions, extracts the color features of each region, and transmits them to the algorithm layer;

[0254] SP2.3: The real-time data streaming and incremental learning module processes real-time data during the dyeing and printing process, extracts features, and performs incremental learning and updating of the model based on the characteristics of the production cycle.

[0255] SP3: Algorithm Layer Operations

[0256] Sp3.1: CNN-RNN combined units analyze fabric images and dyeing process data to predict color deviation patterns and generate correction strategy suggestions;

[0257] Sp3.2: The GAN-Autoencoder combined unit generates simulated images of printing and dyeing effects and collaboratively corrects color deviations to evaluate color consistency;

[0258] Sp3.3: DBN-RBM combined unit mines deep features of dyeing and printing data and provides accurate correction parameters.

[0259] SP4: System Architecture Layer Operations

[0260] SP4.1: A distributed deep learning architecture integrates data from various nodes, generates an overall color correction model, and allocates resources among different textile printing and dyeing tasks;

[0261] Sp4.2: The multi-level feedback self-calibration architecture performs color detection and feedback in the pre-, mid-, and post-printing stages, and adjusts model parameters and printing process parameters.

[0262] SP4.3: The adaptive color calibration architecture matches the model configuration based on fabric, equipment, and dye information, and adjusts the model structure and parameters.

[0263] SP5: Output and Application: Outputs high-precision color-corrected printing and dyeing process parameters or fabric color control instructions, which are applied to the textile printing and dyeing production process to ensure product color consistency.

[0264] Digital advertising and display manufacturing industry

[0265] Application Scenarios and Industry Needs: In the digital advertising field, whether it's a large outdoor display screen or an indoor high-definition advertising screen, the accuracy and consistency of color directly affect advertising effectiveness and brand image. During the display manufacturing process, different panel materials, backlight technologies, and driver chips all influence the displayed color. This system can be used to ensure the color calibration accuracy of the display screen during the production process, as well as the consistent color presentation of advertising content under different ambient light conditions.

[0266] System architecture changes:

[0267] Algorithm layer:

[0268] The data input includes information such as the physical parameters of the display panel (e.g., resolution, color gamut, contrast ratio), backlight spectral characteristics, and color conversion functions of the driver chip;

[0269] The CNN-RNN ensemble unit focuses on analyzing the color display variation patterns of video image sequences on different display devices, considering the impact of factors such as panel pixel arrangement and refresh rate on color deviation. The GAN-Autoencoder ensemble unit generates simulated images that conform to the target advertising color standards and are adapted to the characteristics of specific display screens. The autoencoder learns the latent representation of the colors displayed on the screen to optimize the color correction effect. The DBN-RBM ensemble unit mines the features behind the complex data during the manufacturing and use of the display screen, such as the influence of different backlight aging levels on color.

[0270] System architecture layer:

[0271] The distributed deep learning architecture divides tasks among computing nodes based on display type (such as LCD screen, OLED screen, etc.) and application scenario (such as outdoor large screen, indoor small screen, etc.).

[0272] The multi-level feedback self-calibration architecture provides feedback information including the display device number, ambient light sensor data, and the distribution of color deviation in different areas of the display (such as edge areas and center areas) to accurately adjust the display's color calibration parameters.

[0273] The adaptive color calibration architecture feature library stores information on display panel suppliers, backlight models and characteristics, driver chip parameters, and color correction models under different ambient light conditions.

[0274] Data processing and optimization layer:

[0275] The data enhancement and compositing module synthesizes simulated display data with different color deviations based on the display principle and the characteristics of the advertising image, such as data simulating color saturation changes in a low-brightness environment;

[0276] The semantic segmentation data processing module divides the advertising image into regions, such as the main character region, background region, and text region, and performs color deviation analysis in combination with the display characteristics of the display screen, for example, focusing on the clarity and color contrast of the text region.

[0277] The real-time data streaming and incremental learning module needs to adapt to the high-speed data update requirements when the display screen plays videos or dynamic advertisements, optimize the data processing algorithm and model update mechanism, and ensure that a large amount of image frame data is processed in a short time and the model is updated in a timely manner.

[0278] Operating steps:

[0279] Sp1: Data Acquisition and Input: Acquire data from advertising image materials, display production test data, and ambient light sensors, and read relevant feature library information of the display.

[0280] SP2: Data Processing and Optimization Layer Operations

[0281] SP2.1: The data augmentation and synthesis module processes advertising image data in conjunction with display screen characteristics, expands the dataset, and synthesizes simulated display deviation data.

[0282] SP2.2: The semantic segmentation data processing module divides the advertising image into regions, extracts the color features of the regions, and transmits them to the algorithm layer.

[0283] SP2.3: The real-time data streaming and incremental learning module processes real-time image data during the display playback process, extracts features, and quickly updates the model.

[0284] SP3: Algorithm Layer Operations

[0285] Sp3.1: The CNN-RNN combined unit analyzes the color display patterns of advertising images on the display screen, predicts deviations, and generates correction strategies.

[0286] Sp3.2: The GAN-Autoencoder ensemble unit generates high-quality analog display images, collaboratively corrects color deviations, and evaluates consistency.

[0287] Sp3.3: The DBN-RBM combined unit mines deep features of the display screen data and provides accurate correction parameters.

[0288] SP4: System Architecture Layer Operations

[0289] SP4.1: A distributed deep learning architecture integrates data from various nodes to generate color correction models suitable for different displays and assigns tasks.

[0290] SP4.2: The multi-level feedback self-calibration architecture performs color detection and feedback during display production testing, installation, commissioning, and use, and adjusts model and display parameters.

[0291] SP4.3: The adaptive color calibration architecture adjusts the model structure and parameters based on the display device information and ambient light conditions to match the model configuration.

[0292] SP5: Output and Application: Outputs high-precision color-corrected display drive parameters or advertising image color adjustment instructions, which are applied in the display manufacturing and advertising process to ensure color accuracy and consistency.

[0293] In practical use, the entire system architecture described above can be applied to other industries that require color management and self-calibration, in addition to the printing, textile, and advertising printing industries mentioned above. When in use, the data source of the system can be adjusted according to the industry, while the overall architecture remains unchanged. Based on the source data, the corresponding model and calibration parameters are output, making the entire system adaptable to multiple industry fields and increasing its adaptability during use.

[0294] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0295] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A deep learning self-calibration system for high-precision color management, characterized in that: The system comprises an algorithm layer, a system architecture layer, and a data processing and optimization layer, wherein: The algorithm layer is used to perform deep feature extraction, deviation prediction and correction strategy generation for the color of printed images. The algorithm layer includes a multi-algorithm fusion model, which includes a combination unit of convolutional neural network (CNN) and recurrent neural network (RNN), a combination unit of generative adversarial network (GAN) and autoencoder, and a combination unit of deep belief network (DBN) and restricted Boltzmann machine (RBM). The combined unit of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is used to deeply extract the color pixel distribution and pattern texture features of printed images. Based on the spatial and temporal dependencies between printed image sequences, it analyzes the variation law of color deviation across the entire printed page and between different printing batches. In the combined unit of Generative Adversarial Network (GAN) and Autoencoder, the generator of GAN generates simulated printed image data that is similar to the target printed color standard, and the discriminator distinguishes between real printed images and generated images. The two work together to improve the quality of generated images through adversarial training. The autoencoder compresses and reconstructs the input printed color image and learns the latent color representation of the image. The two work together to correct printed color deviation and ensure color consistency. The combined unit of Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM) uses RBM to perform unsupervised pre-training on printed color data, learns the basic feature distribution and structure of printed color data, and then stacks them to build DBN, mining the high-level abstract features hidden in complex printed color data, providing accurate basis for subsequent calibration. The system architecture layer is used to construct an architecture system adapted to the large-scale production, multiple stages, and various equipment and materials in the printing industry. It includes a distributed deep learning architecture, a multi-level feedback self-calibration architecture, and an adaptive color calibration architecture. The distributed deep learning architecture distributes the training task of the deep learning model to multiple computing nodes for parallel processing. Different nodes focus on data processing for different printing product lines or different color spaces. The central coordinator integrates the results of each node. The multi-level feedback self-calibration architecture performs preliminary color correction on the image in the pre-press stage and then inputs the correction results back into the system for secondary detection and analysis in the printing stage. It adjusts the parameters of the deep learning model or the correction strategy according to the feedback information at different levels and establishes a feedback channel from the post-press quality inspection stage to the pre-press design and printing stages. The adaptive color calibration architecture uses a pre-established feature library of printing equipment and materials to quickly identify and match the corresponding model configuration during printing production and automatically adjust the structure and parameters of the deep learning model. The data processing and optimization layer is used to preprocess, expand, and dynamically update printing color data. This layer includes a data augmentation and synthesis module, a semantic segmentation data processing module, and a real-time data streaming and incremental learning module. The data augmentation and synthesis module expands the training dataset by rotating, scaling, or adjusting the contrast of existing printing color images, and by artificially synthesizing image data with specific color deviation characteristics. The semantic segmentation data processing module uses semantic segmentation technology to divide different regions in the printed image according to color category, object category, or functional region, and performs color deviation analysis and correction accordingly. The real-time data streaming and incremental learning module rapidly processes and extracts features from the color data generated in real time during the printing production process, and uses incremental learning technology to adapt the model to new printing data distributions and color change trends.

2. The deep learning self-calibration system for high-precision color management according to claim 1, characterized in that: Each computing node in the distributed deep learning architecture has an independent data storage unit and a computing unit. The data storage unit is used to temporarily store a specific subset of printing color data allocated to it. The computing unit performs local training operations on the deep learning model based on the stored data. The computing nodes are connected through a high-speed data transmission channel to ensure the efficiency of data interaction and model integration.

3. The deep learning self-calibration system for high-precision color management according to claim 1, characterized in that: The feedback information in the multi-level feedback self-calibration architecture includes color deviation values, deviation distribution areas, color space coordinate differences, and corresponding printing stage identification information. The color deviation values ​​are accurate to three decimal places, the deviation distribution areas are represented by the range of image pixel coordinates or the physical size area of ​​the printed matter, the color space coordinate differences are quantitatively described according to the color spaces commonly used in the printing industry, and the printing stage identification information clearly distinguishes the source of the pre-press, printing, and post-press stages.

4. The deep learning self-calibration system for high-precision color management according to claim 1, characterized in that: The adaptive color calibration architecture stores data in its printing equipment and material feature library that includes printing press brand and model, printing press mechanical parameters, paper type, ink brand and model, and characteristic information. The data is stored in a structured database format, which facilitates quick retrieval and model configuration matching.

5. The deep learning self-calibration system for high-precision color management according to claim 1, characterized in that: The color data synthesis algorithm in the data enhancement and synthesis module is built based on the physical model and mathematical statistical model of printing color. When synthesizing image data with specific color deviation characteristics, rotation, scaling or contrast is accurately generated according to the deviation parameters set by the user to generate simulated images. The generated image data format is consistent with the real printed image data format. Rotation, scaling or contrast is directly used for deep learning model training.

6. The deep learning self-calibration system for high-precision color management according to claim 1, characterized in that: The semantic segmentation technology in the semantic segmentation data processing module adopts a deep learning semantic segmentation model. The model structure is based on the improved U-Net architecture and adds a multi-scale feature fusion module, which can accurately identify text, image subjects or background areas in printed images at different resolutions, with segmentation accuracy reaching the pixel level.

7. The deep learning self-calibration system for high-precision color management according to claim 1, characterized in that: The data streaming processing algorithm in the real-time data streaming and incremental learning module adopts sliding window technology and distributed caching mechanism. The size of the sliding window is dynamically adjusted according to the printing data flow and processing speed to ensure that no data is lost and that data can be processed in a timely manner during peak data flow. The distributed caching mechanism caches recently processed data on multiple nodes, which facilitates the incremental learning module to quickly obtain data for model updates. The validity period of the cached data is dynamically set according to the continuity of the printing task and the timeliness of the data.

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