A Method and System for Optimizing Batch Nautical Chart Printing Tasks Based on Intelligent Merging

Abstract

This invention discloses a method and system for optimizing batch nautical chart printing tasks based on intelligent merging. The method analyzes nautical chart task data packets in real time and constructs an element distribution matrix. It then dynamically calculates regional color gradation compensation parameters based on the reflectivity of the printing medium, generating color gradation compensation vectors. A hierarchical clustering algorithm is used to cluster the compensation vectors, achieving adaptive merging of nautical chart printing tasks and generating color gradation calibration curves that match the characteristics of the medium. Through a closed-loop process of task merging decision-making and parameter optimization, the method solves the color deviation problem caused by medium differences in batch nautical chart printing, reducing the frequency of repeated equipment calibration. This invention, through intelligent task merging and dynamic calibration technology, significantly improves printing efficiency and color consistency, reduces printing costs, and is suitable for the large-volume, high-precision nautical chart printing needs in fields such as navigation and surveying.

Description

Technical Field

[0001] This invention relates to the field of printing optimization technology, and in particular to a method and system for optimizing batch nautical chart printing tasks based on intelligent merging. Background Technology

[0002] As an indispensable information carrier in navigation, the printing quality and production efficiency of nautical charts directly affect maritime safety and navigation accuracy. Traditional nautical chart printing typically employs a print-on-demand (POD) model, enabling personalized printing based on user needs. However, existing nautical chart printing processes still face numerous challenges when handling batch processing.

[0003] First, nautical charts are complex, containing multiple layers including vector graphics, raster images, nautical symbols, and text annotations. Traditional printing methods struggle to achieve precise color gradation control for different areas, resulting in low color fidelity across various media and impacting the final navigational effect. Second, current nautical chart printing tasks are often processed independently, failing to fully leverage the similarities between tasks for optimization, leading to inefficient task allocation and color management during production.

[0004] Furthermore, because nautical charts cover different geographical regions, the density of content varies significantly from chart to chart. Traditional printing methods fail to intelligently compensate for the element distribution characteristics of the charts, easily leading to problems such as color cast, loss of detail, or overexposure. At the same time, existing printing tasks are relatively scattered and lack a systematic consolidation strategy, resulting in low utilization of printing equipment and increased production costs and time expenditure.

[0005] With the development of intelligent technologies, optimizing the nautical chart printing process using image processing, machine learning, and intelligent scheduling methods has become an industry trend. In particular, intelligently merging batch nautical chart printing tasks can not only improve print quality but also optimize task scheduling, increase production efficiency, and reduce costs. Therefore, there is an urgent need for a batch nautical chart printing task optimization method and system based on intelligent merging to achieve precise color control, intelligent task allocation, and efficient printing management. Summary of the Invention

[0006] To address at least one of the aforementioned technical problems, this invention proposes a method and system for optimizing batch nautical chart printing tasks based on intelligent merging.

[0007] The first aspect of this invention provides a method for optimizing batch nautical chart printing tasks based on intelligent merging, comprising:

[0008] The system acquires user chart printing task data packages in real time, performs layer parsing on each data package, extracts chart elements, and constructs a chart element distribution matrix.

[0009] The distribution matrix of the nautical chart elements is analyzed to identify the density of nautical chart elements in each region of the nautical chart. The printing color level compensation parameters for each region of the nautical chart are calculated by combining the reflectance detection value of the printing medium, and a color level compensation vector is generated for each nautical chart printing task.

[0010] The color compensation vector of each nautical chart printing task is clustered based on the hierarchical clustering algorithm to obtain the clustering results. The nautical chart printing tasks are then merged based on the clustering results to construct a nautical chart printing task combination.

[0011] A color calibration curve is generated for the nautical chart printing task combination, and the nautical chart printing task is optimized based on the color calibration curve.

[0012] In this solution, the real-time acquisition of the user's nautical chart printing task data packets, layer parsing of each data packet, extraction of nautical chart elements, and construction of a nautical chart element distribution matrix are specifically as follows:

[0013] A chart printing management system based on a B / S architecture is constructed. Based on the chart printing order data of users placed within a preset time period received in real time by the chart printing management system, the chart PDF files of the chart printing order data are extracted and a chart printing task data package is constructed.

[0014] Each nautical chart in the nautical chart printing task data package is converted into a grayscale image. The contour features of the grayscale image are extracted based on the Canny edge detection algorithm to generate a binarized edge image. The Hough parameter space and target geometric feature equation are constructed for the target geometric features of the nautical chart. The binarized edge image is accumulated and voted in the Hough parameter space according to the target geometric feature equation.

[0015] When the cumulative voting in the Hough parameter space is completed, the local peak points in the cumulative voting results are filtered out, the local peak points are mapped to the original image space and the corresponding geometric feature parameters are determined, the geometric feature parameters are converted into vector graphic coordinate data, and a vector layer containing straight lines, polylines or polygonal nautical chart elements is generated.

[0016] The grayscale image is traversed for each pixel and its neighboring pixels with preset directions and spacing. The frequency of the occurrence of preset grayscale level combinations is counted to generate a grayscale co-occurrence matrix. Texture feature values ​​are calculated based on the grayscale co-occurrence matrix. The texture feature values ​​include contrast, energy, and entropy. The texture feature values ​​are mapped to the original image space to generate a raster layer containing nautical chart texture features.

[0017] A standard nautical symbol database is constructed, and a nautical symbol recognition model is built based on a convolutional neural network. The nautical symbols in the standard nautical symbol database are imported into the recognition model for training. Based on the trained nautical symbol recognition model and a preset text recognition model, nautical symbols and text are extracted from each nautical chart in the nautical chart printing task data package to generate a labeling layer for the nautical chart.

[0018] Based on the vector layer, raster layer, and annotation layer of the nautical chart, nautical chart elements are extracted, and the location information of the nautical chart elements is determined. A nautical chart element distribution matrix is ​​constructed based on the location information of the nautical chart elements and sea area elements.

[0019] In this solution, the analysis of the chart element distribution matrix identifies the density of chart elements in each area of ​​the chart. Combined with the reflectance detection value of the printing medium, printing color level compensation parameters for each area of ​​the chart are calculated to generate a color level compensation vector for each chart printing task. Specifically:

[0020] The nautical chart element distribution matrix is ​​uniformly divided into grid cells of a preset size. Based on the nautical chart element distribution matrix, the coverage ratio of vector graphic coordinates and nautical chart texture features, the number of nautical symbols and the text density in each grid cell are statistically analyzed to obtain the initial density information of nautical chart elements in each grid cell.

[0021] The Gaussian kernel function is selected as the kernel function of the kernel density estimation algorithm. The smoothing bandwidth parameter of the kernel density estimation algorithm is set. The initial density information of the chart elements of each grid cell is weighted and summed according to the kernel density estimation algorithm to construct a continuous density distribution model and calculate the density value of each grid cell.

[0022] The density value of each grid cell is normalized to a preset range. Based on the normalized density value, a threshold for dividing the density of nautical elements is preset. The density of nautical elements in each grid cell of the nautical chart is divided according to the preset threshold for dividing the density of nautical elements, and the density of nautical elements in each grid cell is determined.

[0023] Obtain the reflectance detection value of the nautical chart printing medium, construct a preset reflectance-compensation coefficient mapping table, and match the corresponding basic compensation coefficient according to the interval of the reflectance detection value, wherein the reflectance detection value and the basic compensation coefficient are positively correlated;

[0024] Based on the density division of nautical chart elements, regions of different density levels are mapped to preset compensation weight gradient intervals. For each grid cell, the color level compensation correction amount is calculated using a linear superposition formula based on the basic compensation coefficient of its density level and the matched gradient weight. Based on the color level compensation correction amount of each grid cell, a compensation parameter matrix with the same dimension as the nautical chart element distribution matrix is ​​generated. The compensation parameter matrix is ​​expanded into a one-dimensional sequence by rows and normalized to output a color level compensation vector.

[0025] In this scheme, the hierarchical clustering algorithm is used to cluster the color compensation vectors of each nautical chart printing task to obtain clustering results. Based on these clustering results, nautical chart printing tasks are merged to construct a nautical chart printing task combination. Specifically:

[0026] The Euclidean distance between the color compensation vectors of each nautical chart printing task is calculated based on the hierarchical clustering algorithm. An initial clustering distance threshold is set, and the color compensation vectors are initially clustered according to the Euclidean distance and the initial clustering distance threshold.

[0027] Calculate the mean vector of all color level compensation vectors within each initial cluster group, and use it as the color level compensation reference vector for that group;

[0028] To obtain the maximum chromaticity compensation processing volume of the POD printer, the chromaticity compensation baseline vector of each initial cluster group is added to the chromaticity compensation vector of all chart printing tasks within the group to obtain the total chromaticity compensation of the current group.

[0029] Determine whether the total chromaticity compensation exceeds the maximum chromaticity compensation processing amount. If the total chromaticity compensation of the current group exceeds the maximum chromaticity compensation processing amount, reduce the initial clustering distance threshold, recalculate the Euclidean distance and iteratively perform clustering grouping until the total chromaticity compensation of all groups is not greater than the maximum chromaticity compensation processing amount.

[0030] If there are still groups exceeding the maximum color level compensation processing amount when the number of iterations reaches the preset upper limit, then the color level compensation vector of the group exceeding the limit is subjected to dispersion analysis, and the standard deviation of the color level compensation vector within the group is calculated. If the standard deviation is greater than the preset dispersion threshold, then the group exceeding the limit is split into multiple subgroups, and each subgroup is assigned an independent printing batch number.

[0031] Based on the final clustering results and subgroups, hierarchical chart printing task combinations are generated, with each task group containing chart printing tasks with the same batch number.

[0032] In this solution, the step of generating a color calibration curve for the nautical chart printing task combination and optimizing the nautical chart printing task based on the color calibration curve specifically involves:

[0033] An initial calibration curve is generated based on the color level compensation vector of each group in the nautical chart printing task combination. After a trial printing is performed by the printing equipment, the reflectivity distribution data and actual color level values ​​of the printing medium are collected.

[0034] Cross-analysis was performed on reflectance distribution data and nautical chart element density data to calculate the compensation correlation index between reflectance deviation and density in each region;

[0035] The surface reflectance-density correction coefficient of the printing medium is generated based on the compensation correlation index, and the compensation parameters in the initial calibration curve are then reassigned based on this coefficient.

[0036] The compensation parameters after secondary allocation are input into the color level drift prediction model, and the color level drift coefficient is calculated by combining the color difference between the actual color level value and the target value.

[0037] When the color level drift coefficient exceeds the preset threshold, an inverse compensation matrix is ​​constructed based on the reflectance-density correction coefficient, and the compensation parameters in the color level calibration curve are corrected by the inverse compensation matrix.

[0038] If the corrected color level drift coefficient still exceeds the threshold, then the unused redundant compensation parameters in the nautical chart printing task combination are extracted, a compensation parameter buffer is established, and the buffer parameters are called to perform superimposed compensation according to the gradient change of the color level drift coefficient to generate the final color level calibration curve.

[0039] The user's nautical chart printing task is printed according to the final color calibration curve.

[0040] In this solution, the step of performing the printing operation on the user's nautical chart printing task based on the final color calibration curve specifically includes:

[0041] Based on the final color calibration curve, generate color conversion parameters that match the POD printing device, input the parameters into the color management module of the third-party print management software, and drive the printer to perform color calibration.

[0042] Extract the corresponding nautical chart PDF file from the nautical chart information and PDF file library, call the rasterization engine to perform layer-by-layer parsing of the PDF file, and rasterize the nautical chart content in combination with the calibrated color level parameters.

[0043] Select the printing media type according to the printing task type. If it is a central task type, use the preset long paper roll or short paper roll for printing. If it is a user order type, automatically adapt the printing parameters according to the printer type configured by the user.

[0044] When performing POD printing, the nautical chart PDF file is rotated and white borders are added based on the no-cut standard. After printing, the printed products of the central task are compared with the standard symbols in the nautical chart information database through the system's automatic verification module, and an inspection report is generated. If the inspection passes, the version information is stored in the database and marked as releasable.

[0045] For user order tasks, if the user does not have a POD printer, a logistics tracking code is generated and bound to the order information, and the finished product is printed and shipped by express courier. If the user has a printer, the calibrated nautical chart PDF file is pushed to the user's printer for direct output via the network.

[0046] A second aspect of the present invention also provides a batch nautical chart printing task optimization system based on intelligent merging. The system includes a memory and a processor. The memory includes a batch nautical chart printing task optimization method program based on intelligent merging. When the processor executes the batch nautical chart printing task optimization method program based on intelligent merging, it performs the following steps:

[0047] The system acquires user chart printing task data packages in real time, performs layer parsing on each data package, extracts chart elements, and constructs a chart element distribution matrix.

[0048] The distribution matrix of the nautical chart elements is analyzed to identify the density of nautical chart elements in each region of the nautical chart. The printing color level compensation parameters for each region of the nautical chart are calculated by combining the reflectance detection value of the printing medium, and a color level compensation vector is generated for each nautical chart printing task.

[0049] The color compensation vector of each nautical chart printing task is clustered based on the hierarchical clustering algorithm to obtain the clustering results. The nautical chart printing tasks are then merged based on the clustering results to construct a nautical chart printing task combination.

[0050] A color calibration curve is generated for the nautical chart printing task combination, and the nautical chart printing task is optimized based on the color calibration curve.

[0051] This invention discloses a method and system for optimizing batch nautical chart printing tasks based on intelligent merging. The method analyzes nautical chart task data packets in real time and constructs an element distribution matrix. It then dynamically calculates regional color gradation compensation parameters based on the reflectivity of the printing medium, generating color gradation compensation vectors. A hierarchical clustering algorithm is used to cluster the compensation vectors, achieving adaptive merging of nautical chart printing tasks and generating color gradation calibration curves that match the characteristics of the medium. Through a closed-loop process of task merging decision-making and parameter optimization, the method solves the color deviation problem caused by medium differences in batch nautical chart printing, reducing the frequency of repeated equipment calibration. This invention, through intelligent task merging and dynamic calibration technology, significantly improves printing efficiency and color consistency, reduces printing costs, and is suitable for the large-volume, high-precision nautical chart printing needs in fields such as navigation and surveying. Attached Figure Description

[0052] Figure 1 A flowchart of a batch chart printing task optimization method based on intelligent merging according to the present invention is shown;

[0053] Figure 2 The flowchart illustrating the generation of the color level compensation vector for each nautical chart printing task according to the present invention is shown.

[0054] Figure 3 The flowchart illustrating the printing operation of a user's nautical chart printing task according to the present invention is shown.

[0055] Figure 4 A block diagram of a batch nautical chart printing task optimization system based on intelligent merging according to the present invention is shown. Detailed Implementation

[0056] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0058] Figure 1 The flowchart of a batch chart printing task optimization method based on intelligent merging according to the present invention is shown.

[0059] like Figure 1 As shown, the first aspect of the present invention provides a method for optimizing batch nautical chart printing tasks based on intelligent merging, comprising:

[0060] S102: Real-time acquisition of user's nautical chart printing task data packets, layer parsing of each data packet, extraction of nautical chart elements, and construction of nautical chart element distribution matrix;

[0061] S104, Analyze the distribution matrix of the nautical chart elements, identify the density of nautical chart elements in each area of ​​the nautical chart, calculate the printing color level compensation parameters for each area of ​​the nautical chart by combining the reflectance detection value of the printing medium, and generate the color level compensation vector for each nautical chart printing task.

[0062] S106, perform clustering operation on the color compensation vector of each nautical chart printing task based on hierarchical clustering algorithm to obtain clustering results, and merge nautical chart printing tasks according to the clustering results to construct a nautical chart printing task combination.

[0063] S108, generate a color calibration curve for the nautical chart printing task combination, and optimize the nautical chart printing task according to the color calibration curve.

[0064] It should be noted that by acquiring users' nautical chart printing task data packages in real time and performing layer analysis on each data package, nautical chart elements can be accurately extracted, and a nautical chart element distribution matrix can be constructed, thereby comprehensively understanding the content structure and layout characteristics of the nautical chart. Analysis of the nautical chart element distribution matrix can identify the element density in different areas of the nautical chart, and by combining the reflectivity detection value of the printing medium, printing color gradation compensation parameters can be calculated, effectively optimizing the color performance of different areas and reducing color shift problems caused by media differences. Based on a hierarchical clustering algorithm, the color gradation compensation vectors of each nautical chart printing task are clustered, allowing tasks with similar color gradation compensation requirements to be reasonably merged, thereby optimizing printing resource allocation and improving the efficiency and consistency of batch printing. Constructing nautical chart printing task combinations based on the clustering results not only reduces the frequency of color adjustments on the printing press but also reduces the waste of printing consumables, thereby improving production efficiency. Finally, by generating a color gradation calibration curve and optimizing the nautical chart printing tasks based on this calibration curve, the color reproduction of the printed product is more accurate, improving the adaptability and navigation readability of the nautical chart on different printing media, ensuring the actual application effect and service life of the nautical chart.

[0065] According to an embodiment of the present invention, the real-time acquisition of the user's nautical chart printing task data package, performing layer parsing on each data package, extracting nautical chart elements, and constructing a nautical chart element distribution matrix specifically includes:

[0066] A chart printing management system based on a B / S architecture is constructed. Based on the chart printing order data of users placed within a preset time period received in real time by the chart printing management system, the chart PDF files of the chart printing order data are extracted and a chart printing task data package is constructed.

[0067] Each nautical chart in the nautical chart printing task data package is converted into a grayscale image. The contour features of the grayscale image are extracted based on the Canny edge detection algorithm to generate a binarized edge image. The Hough parameter space and target geometric feature equation are constructed for the target geometric features of the nautical chart. The binarized edge image is accumulated and voted in the Hough parameter space according to the target geometric feature equation.

[0068] When the cumulative voting in the Hough parameter space is completed, the local peak points in the cumulative voting results are filtered out, the local peak points are mapped to the original image space and the corresponding geometric feature parameters are determined, the geometric feature parameters are converted into vector graphic coordinate data, and a vector layer containing straight lines, polylines or polygonal nautical chart elements is generated.

[0069] The grayscale image is traversed for each pixel and its neighboring pixels with preset directions and spacing. The frequency of the occurrence of preset grayscale level combinations is counted to generate a grayscale co-occurrence matrix. Texture feature values ​​are calculated based on the grayscale co-occurrence matrix. The texture feature values ​​include contrast, energy, and entropy. The texture feature values ​​are mapped to the original image space to generate a raster layer containing nautical chart texture features.

[0070] A standard nautical symbol database is constructed, and a nautical symbol recognition model is built based on a convolutional neural network. The nautical symbols in the standard nautical symbol database are imported into the recognition model for training. Based on the trained nautical symbol recognition model and a preset text recognition model, nautical symbols and text are extracted from each nautical chart in the nautical chart printing task data package to generate a labeling layer for the nautical chart.

[0071] Based on the vector layer, raster layer, and annotation layer of the nautical chart, nautical chart elements are extracted, and the location information of the nautical chart elements is determined. A nautical chart element distribution matrix is ​​constructed based on the location information of the nautical chart elements and sea area elements.

[0072] It should be noted that target geometric features mainly refer to common geometric structures on nautical charts, such as straight lines, polygons, and arcs. These geometric features are typically used to represent channels, boundaries, isobaths, and other navigational information. The Hough parameter space is a space that transforms geometric features in an image into parametric representations. This space transforms the problem of detecting straight lines or curves in an image into a point detection problem in the parameter space. For example, a straight line can usually be represented by the polar coordinate equation ρ = xcosθ + ysinθ. Target geometric feature equations are mathematical expressions used to describe these target geometric features, such as equations for straight lines, circles, and polygon boundaries. By accumulating votes on the binary edge image in the Hough parameter space, the position and shape of geometric features in the image can be detected. The original image space refers to the original pixel coordinate system of the nautical chart, i.e., the two-dimensional image representation of the nautical chart in a computer, where each pixel has specific coordinate values. The accumulated voting, for example, in straight line detection, involves calculating all possible combinations of ρ and θ using the polar coordinate parametric equation and updating the accumulator matrix. The feature parameters include straight line parameters (length, direction angle, coordinates of the starting and ending points, distance from the origin), polyline parameters (vertex coordinate sequence, number of line segments, trend of change of inflection point angle), polygon parameters (vertex coordinates, number of sides, area, perimeter), and arc parameters (center coordinates, radius, starting angle, ending angle, arc length); the chart elements include vector layer elements (coastline, channel boundary, isobath, boundary of restricted / dangerous area, wharf / anchorage, lighthouse / buoy location), raster layer elements (terrain shadow, water depth level, seabed type), and annotation layer elements (nautical symbols, place name annotations, channel number, navigation warnings).

[0073] Figure 2 The flowchart illustrating the generation of the color level compensation vector for each nautical chart printing task according to the present invention is shown.

[0074] According to an embodiment of the present invention, the step of analyzing the distribution matrix of nautical chart elements, identifying the density of nautical chart elements in each region of the chart, calculating the printing color level compensation parameters for each region of the chart in combination with the reflectance detection value of the printing medium, and generating a color level compensation vector for each nautical chart printing task, specifically involves:

[0075] S202, the nautical chart element distribution matrix is ​​evenly divided into grid units of a preset size, and the coverage ratio of vector graphic coordinates and nautical chart texture features, the number of nautical symbols and the text density in each grid unit are statistically analyzed according to the nautical chart element distribution matrix to obtain the initial density information of nautical chart elements in each grid unit;

[0076] S204, Select the Gaussian kernel function as the kernel function of the kernel density estimation algorithm, set the smoothing bandwidth parameter of the kernel density estimation algorithm, perform weighted summation calculation on the initial density information of the chart elements of each grid cell according to the kernel density estimation algorithm, construct a continuous density distribution model, and calculate the density value of each grid cell.

[0077] S206, normalize the density value of each grid cell to a preset value range, preset a threshold for dividing the density of chart elements based on the normalized density value, divide the density of chart elements in each grid cell of the chart according to the preset threshold for dividing the density of chart elements, and determine the density of chart elements in each grid cell.

[0078] It should be noted that the visual representation of printed color gradations is influenced by both the density of elements on the nautical chart and the reflectivity of the printing medium. Therefore, color gradation compensation parameters need to be dynamically adjusted during the printing process to ensure consistent print quality. Sparse areas: Due to fewer elements and more blank areas on the nautical chart, the printing process is easily affected by the reflectivity of the printing medium. High reflectivity can lead to overly light colors and loss of detail. Therefore, the color gradation compensation value needs to be appropriately increased, i.e., increasing the color gradation density to enhance contrast. Medium-dense areas: The elements in these areas are relatively evenly distributed and less affected by reflectivity. Therefore, the color gradation compensation value is calculated according to standards to ensure balanced print colors. High-dense areas: Due to the high density of elements on the nautical chart and strong black-and-white contrast, if the reflectivity of the printing medium is low, the colors may be too dark and details may be blurred. Therefore, the color gradation compensation value needs to be appropriately reduced to ensure clear color gradations. Very high-dense areas: These areas typically contain a large number of nautical symbols, geographical boundaries, or dense topographic features. If the printed color gradation is too high, the symbols and lines may become blurred. Therefore, the color gradation density needs to be further reduced, and non-linear adjustment strategies can be applied to maintain print details.

[0079] S208, obtain the reflectance detection value of the nautical chart printing medium, construct a preset reflectance-compensation coefficient mapping table, and match the corresponding basic compensation coefficient according to the interval of the reflectance detection value, wherein the reflectance detection value and the basic compensation coefficient are positively correlated;

[0080] S210: Based on the density division results of nautical chart elements, different density level regions are mapped to preset compensation weight gradient intervals. For each grid cell, the color level compensation correction amount is calculated by using a linear superposition formula based on the basic compensation coefficient of its density level and the matched gradient weight. Based on the color level compensation correction amount of each grid cell, a compensation parameter matrix with the same dimension as the nautical chart element distribution matrix is ​​generated. The compensation parameter matrix is ​​expanded into a one-dimensional sequence by rows and normalized to output a color level compensation vector.

[0081] It's important to note that different printing media (such as different types of paper or special coating materials) have varying light reflection characteristics, and the color reproduction in printing is closely related to the reflectivity of the medium. For example, some colors may appear lighter on a high-reflectivity medium, while a low-reflectivity medium may absorb more color, resulting in a darker print. Therefore, by measuring the reflectivity of the medium and using a preset reflectivity-compensation coefficient mapping table, a base compensation coefficient can be matched for each medium. Since higher reflectivity requires higher compensation, the two are positively correlated, providing a global adjustment benchmark for gradation compensation. Different areas of a nautical chart contain different element densities. For example, port areas may contain numerous nautical symbols, markings, and complex lines, while open sea areas may contain only a few isobaths. Dense areas contain more color information and are more susceptible to color buildup, blurred details, or color distortion during printing. Therefore, different levels of gradation compensation need to be applied to areas of different densities based on the density of elements on the nautical chart. The density levels of each region are mapped to a preset compensation weight gradient range: high-density regions correspond to larger compensation weights, while low-density regions have smaller compensation weights. The color gradation compensation correction for each grid cell is calculated: combining the region's base compensation coefficient (obtained by matching the medium's reflectivity) and the corresponding compensation weight (determined by density), a linear superposition formula is used to calculate the color gradation compensation correction, ensuring appropriate color adjustments for different regions. A compensation parameter matrix is ​​generated: the color gradation compensation corrections for all grid cells form a matrix with the same dimension as the chart element distribution matrix, allowing for targeted color adjustments for each region. Since the numerical range of the color gradation compensation correction may fluctuate significantly, to ensure comparability of compensation parameters between different chart tasks, the compensation parameter matrix is ​​ultimately expanded into a one-dimensional sequence and normalized (e.g., scaled to the [0,1] or [-1,1] range). The resulting color gradation compensation vector comprehensively reflects the color gradation compensation requirements of the chart under different media and regions, providing a standardized data foundation for subsequent printing task optimization and task merging.

[0082] According to an embodiment of the present invention, the hierarchical clustering algorithm is used to cluster the color compensation vectors of each nautical chart printing task to obtain clustering results. Based on these clustering results, nautical chart printing tasks are merged to construct a nautical chart printing task combination. Specifically:

[0083] The Euclidean distance between the color compensation vectors of each nautical chart printing task is calculated based on the hierarchical clustering algorithm. An initial clustering distance threshold is set, and the color compensation vectors are initially clustered according to the Euclidean distance and the initial clustering distance threshold.

[0084] Calculate the mean vector of all color level compensation vectors within each initial cluster group, and use it as the color level compensation reference vector for that group;

[0085] To obtain the maximum chromaticity compensation processing volume of the POD printer, the chromaticity compensation baseline vector of each initial cluster group is added to the chromaticity compensation vector of all chart printing tasks within the group to obtain the total chromaticity compensation of the current group.

[0086] Determine whether the total chromaticity compensation exceeds the maximum chromaticity compensation processing amount. If the total chromaticity compensation of the current group exceeds the maximum chromaticity compensation processing amount, reduce the initial clustering distance threshold, recalculate the Euclidean distance and iteratively perform clustering grouping until the total chromaticity compensation of all groups is not greater than the maximum chromaticity compensation processing amount.

[0087] If there are still groups exceeding the maximum color level compensation processing amount when the number of iterations reaches the preset upper limit, then the color level compensation vector of the group exceeding the limit is subjected to dispersion analysis, and the standard deviation of the color level compensation vector within the group is calculated. If the standard deviation is greater than the preset dispersion threshold, then the group exceeding the limit is split into multiple subgroups, and each subgroup is assigned an independent printing batch number.

[0088] Based on the final clustering results and subgroups, hierarchical chart printing task combinations are generated, with each task group containing chart printing tasks with the same batch number.

[0089] It should be noted that in batch nautical chart printing tasks, the varying color compensation requirements of different charts can lead to uneven distribution of printing tasks, thus affecting printing efficiency and quality. For example, some chart tasks may have excessively high color compensation requirements, potentially exceeding the processing capacity of the POD (Print on Demand) printer, resulting in color overload, uneven printing, or decreased equipment performance. Conversely, some chart tasks may have lower color compensation requirements, potentially wasting printing resources. Therefore, a hierarchical clustering algorithm is used to calculate the Euclidean distance between the color compensation vectors of each chart printing task, and an initial clustering distance threshold is set to ensure that similar tasks are clustered into the same group. This ensures that chart printing tasks within the same group have similar color compensation requirements. Next, the mean vector of all color compensation vectors within each initial cluster group is calculated as the color compensation baseline vector for that group, and the total color compensation is further calculated. This step aims to measure the overall color compensation requirement of each task group, ensuring that it does not exceed the maximum color compensation processing capacity of the POD printer. If the total chromaticity compensation for a task group exceeds the equipment's processing capacity, the clustering parameters need to be adjusted to reduce the clustering distance threshold until the total chromaticity compensation for all task groups is within the equipment's processing range. This ensures that the equipment does not degrade print quality due to overload. When clustering adjustments still cannot resolve the overload issue, i.e., some task groups still exceed the maximum chromaticity compensation processing capacity, further analysis of the dispersion of the chromaticity compensation vector within the group is required. If the standard deviation of the chromaticity compensation vector within the group is large, it indicates that the chromaticity compensation requirements for nautical chart tasks within that task group are relatively dispersed and unsuitable for processing in the same printing batch. The group needs to be split into multiple subgroups, and each subgroup should be assigned an independent printing batch number to ensure that tasks with different chromaticity requirements receive accurate compensation and printing. This not only optimizes the printing process and improves production efficiency but also ensures color consistency of nautical charts within the same batch, avoiding inconsistent print quality due to different chromaticity compensation.

[0090] According to an embodiment of the present invention, the step of generating a color calibration curve for the nautical chart printing task combination and optimizing the nautical chart printing task based on the color calibration curve specifically includes:

[0091] An initial calibration curve is generated based on the color level compensation vector of each group in the nautical chart printing task combination. After a trial printing is performed by the printing equipment, the reflectivity distribution data of the printing medium and the actual color level value of the printed nautical chart are collected.

[0092] Cross-analysis was performed on reflectance distribution data and nautical chart element density data to calculate the compensation correlation index between reflectance deviation and density in each region;

[0093] The surface reflectance-density correction coefficient of the printing medium is generated based on the compensation correlation index, and the compensation parameters in the initial calibration curve are then reassigned based on this coefficient.

[0094] The compensation parameters after secondary allocation are input into the color level drift prediction model, and the color level drift coefficient is calculated by combining the color difference between the actual color level value and the target value.

[0095] When the color level drift coefficient exceeds the preset threshold, an inverse compensation matrix is ​​constructed based on the reflectance-density correction coefficient, and the compensation parameters in the color level calibration curve are corrected by the inverse compensation matrix.

[0096] If the corrected color level drift coefficient still exceeds the threshold, then the unused redundant compensation parameters in the nautical chart printing task combination are extracted, a compensation parameter buffer is established, and the buffer parameters are called to perform superimposed compensation according to the gradient change of the color level drift coefficient to generate the final color level calibration curve.

[0097] The user's nautical chart printing task is printed according to the final color calibration curve.

[0098] It should be noted that in the field of marine surveying, nautical charts need to accurately represent the color gradation differences of different elements (such as water depth gradients, reef markings, etc.). During the printing process, the reflectivity differences of the printing medium (due to paper ink absorption characteristics or changes in environmental temperature and humidity) interact with the density distribution of chart elements. If fixed compensation parameters are applied directly, color gradation distortion may occur due to deviations between the medium characteristics and the theoretical model, affecting the accuracy of nautical information interpretation. This claim achieves dynamic optimization through the following steps: First, an initial calibration curve is generated as the basic compensation scheme, but the actual effect needs to be verified through trial printing (by acquiring reflectivity distribution data of the printed sample using an optical scanning device). Spatial correlation analysis is then performed between the measured data and the density distribution of chart elements to calculate the compensation correlation index (using statistical correlation analysis to quantify the correlation strength between reflectivity deviation and element density). Based on this, a reflectivity-density correction coefficient is generated (by establishing a dynamic mapping model between medium characteristics and compensation parameters), and the initial compensation parameters are reassigned to adapt to differences in medium characteristics. A color gradation drift prediction model (a time-series prediction model trained on historical printing data) is used to predict the color gradation shift trend after compensation. When the predicted value exceeds the safety threshold, the inverse compensation matrix (a set of inverse adjustment parameters generated through matrix operations) dynamically corrects the compensation direction. A redundant compensation buffer mechanism (a dynamic backup library of pre-set unused compensation parameters) uses a gradient matching strategy to call the backup parameters for superimposed compensation, ultimately generating an adaptive calibration curve that takes into account both media characteristics and element distribution. The redundant compensation parameters refer to the set of backup adjustment parameters pre-reserved during the printing color gradation calibration process; essentially, they provide additional correction margin for dynamic color gradation deviations through over-design. In printing task optimization scenarios, due to the superposition effect of densely distributed chart elements (such as the cumulative effect of ink diffusion when multiple high-density areas are printed adjacently) or local abrupt changes in the reflectivity of the printing medium (such as color gradation jumps caused by uneven ink absorption on paper), relying solely on the initial calibration parameters may not fully cover the nonlinear color differences in actual printing. Therefore, when generating initial compensation parameters, the system will reserve a portion of compensation parameters that are not directly assigned as redundancy reserves by using parameter decomposition techniques (such as decomposing the compensation vector into basic components and high-frequency detail components according to frequency domain characteristics) or by using historical data prediction models.

[0099] Figure 3 The flowchart illustrating the printing operation of a user's nautical chart printing task according to the present invention is shown.

[0100] According to an embodiment of the present invention, the step of performing the printing operation on the user's nautical chart printing task based on the final color calibration curve specifically includes:

[0101] S302, Generate color conversion parameters that match the POD printing device based on the final color calibration curve, input the parameters into the color management module of the third-party printing management software, and drive the printer to perform color calibration;

[0102] S304: Extract the corresponding nautical chart PDF file from the nautical chart information and PDF file library, call the rasterization engine to perform layer-by-layer parsing of the PDF file, and perform rasterization processing of the nautical chart content in combination with the calibrated color level parameters.

[0103] S306: Select the printing media type according to the printing task type. If it is a central task type, use the preset long paper roll or short paper roll for printing. If it is a user order type, automatically adapt the printing parameters according to the printer type configured by the user.

[0104] S308, when performing POD printing, rotates and adds white borders to the nautical chart PDF file based on the no-cut standard. After printing, for the printed products of the central task, the system's automatic verification module compares the printed products with the standard symbols in the nautical chart information database and generates an inspection report. If the inspection passes, the version information is stored in the database and marked as releasable.

[0105] For user order tasks, if the user does not have a POD printer, the S310 generates a logistics tracking code and binds it to the order information, prints the finished product, and executes express delivery. If the user has a printer, the calibrated nautical chart PDF file is pushed to the user's printer for direct output via the network.

[0106] It should be noted that third-party print management software refers to a professional print control software platform independent of the nautical chart printing optimization system. It typically includes a color management module, a print job queue management module, and a device driver interface, used to implement color parameter parsing, print job scheduling, and hardware control functions. The rasterization engine is the core processing module that converts vector-format nautical chart PDF files into pixelated raster images (such as TIFF or JPG formats) recognizable by printing devices. Its technical process includes: parsing the mixed structure of vector graphics layers (such as isobaths described by Bézier curves), text layers (nautical markings), and raster layers (seabed topography maps) in the PDF file; performing geometric transformations and rasterization reconstruction on each layer element according to the target printer's resolution parameters (such as 1200×1200dpi); and mapping color calibration parameters to the CMYK / Lab color value calculation of each pixel (such as adjusting the ink jet volume of a specific ink channel according to the calibration curve).

[0107] Figure 4 A block diagram of a batch nautical chart printing task optimization system based on intelligent merging according to the present invention is shown.

[0108] A second aspect of the present invention also provides a batch nautical chart printing task optimization system 4 based on intelligent merging. The system includes a memory 41 and a processor 42. The memory includes a batch nautical chart printing task optimization method program based on intelligent merging. When the processor executes the batch nautical chart printing task optimization method program based on intelligent merging, it performs the following steps:

[0109] The system acquires user chart printing task data packages in real time, performs layer parsing on each data package, extracts chart elements, and constructs a chart element distribution matrix.

[0110] The distribution matrix of the nautical chart elements is analyzed to identify the density of nautical chart elements in each region of the nautical chart. The printing color level compensation parameters for each region of the nautical chart are calculated by combining the reflectance detection value of the printing medium, and a color level compensation vector is generated for each nautical chart printing task.

[0111] The color compensation vector of each nautical chart printing task is clustered based on the hierarchical clustering algorithm to obtain the clustering results. The nautical chart printing tasks are then merged based on the clustering results to construct a nautical chart printing task combination.

[0112] A color calibration curve is generated for the nautical chart printing task combination, and the nautical chart printing task is optimized based on the color calibration curve.

[0113] This invention discloses a method and system for optimizing batch nautical chart printing tasks based on intelligent merging. The method analyzes nautical chart task data packets in real time and constructs an element distribution matrix. It then dynamically calculates regional color gradation compensation parameters based on the reflectivity of the printing medium, generating color gradation compensation vectors. A hierarchical clustering algorithm is used to cluster the compensation vectors, achieving adaptive merging of nautical chart printing tasks and generating color gradation calibration curves that match the characteristics of the medium. Through a closed-loop process of task merging decision-making and parameter optimization, the method solves the color deviation problem caused by medium differences in batch nautical chart printing, reducing the frequency of repeated equipment calibration. This invention, through intelligent task merging and dynamic calibration technology, significantly improves printing efficiency and color consistency, reduces printing costs, and is suitable for the large-volume, high-precision nautical chart printing needs in fields such as navigation and surveying.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0115] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0117] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0119] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing batch nautical chart printing tasks based on intelligent merging, characterized in that, Includes the following steps: The system acquires user chart printing task data packages in real time, performs layer parsing on each data package, extracts chart elements, and constructs a chart element distribution matrix. The distribution matrix of the nautical chart elements is analyzed to identify the density of chart elements in each region of the chart. Based on the reflectivity detection values ​​of the printing medium, printing color gradation compensation parameters for each region of the chart are calculated, generating a color gradation compensation vector for each chart printing task. Specifically: The nautical chart element distribution matrix is ​​uniformly divided into grid cells of a preset size. Based on the nautical chart element distribution matrix, the coverage ratio of vector graphic coordinates and nautical chart texture features, the number of nautical symbols and the text density in each grid cell are statistically analyzed to obtain the initial density information of nautical chart elements in each grid cell. The Gaussian kernel function is selected as the kernel function of the kernel density estimation algorithm. The smoothing bandwidth parameter of the kernel density estimation algorithm is set. The initial density information of the chart elements of each grid cell is weighted and summed according to the kernel density estimation algorithm to construct a continuous density distribution model and calculate the density value of each grid cell. The density value of each grid cell is normalized to a preset range. Based on the normalized density value, a threshold for dividing the density of nautical elements is preset. The density of nautical elements in each grid cell of the nautical chart is divided according to the preset threshold for dividing the density of nautical elements, and the density of nautical elements in each grid cell is determined. Obtain the reflectance detection value of the nautical chart printing medium, construct a preset reflectance-compensation coefficient mapping table, and match the corresponding basic compensation coefficient according to the interval of the reflectance detection value, wherein the reflectance detection value and the basic compensation coefficient are positively correlated; Based on the density division of nautical chart elements, regions of different density levels are mapped to preset compensation weight gradient intervals. For each grid cell, the color level compensation correction amount is calculated using a linear superposition formula based on the basic compensation coefficient of its density level and the matched gradient weight. Based on the color level compensation correction amount of each grid cell, a compensation parameter matrix with the same dimension as the nautical chart element distribution matrix is ​​generated. The compensation parameter matrix is ​​expanded into a one-dimensional sequence by rows and normalized to output a color level compensation vector. The color compensation vector of each nautical chart printing task is clustered based on the hierarchical clustering algorithm to obtain the clustering results. The nautical chart printing tasks are then merged based on the clustering results to construct a nautical chart printing task combination. A color calibration curve is generated for the nautical chart printing task combination, and the nautical chart printing task is optimized based on the color calibration curve.

2. The method for optimizing batch nautical chart printing tasks based on intelligent merging according to claim 1, characterized in that, The process of acquiring user chart printing task data packets in real time, performing layer parsing on each data packet, extracting chart elements, and constructing a chart element distribution matrix specifically involves: A chart printing management system based on a B / S architecture is constructed. Based on the chart printing order data of users placed within a preset time period received in real time by the chart printing management system, the chart PDF files of the chart printing order data are extracted and a chart printing task data package is constructed. Each nautical chart in the nautical chart printing task data package is converted into a grayscale image. The contour features of the grayscale image are extracted based on the Canny edge detection algorithm to generate a binarized edge image. The Hough parameter space and target geometric feature equation are constructed for the target geometric features of the nautical chart. The binarized edge image is accumulated and voted in the Hough parameter space according to the target geometric feature equation. When the cumulative voting in the Hough parameter space is completed, the local peak points in the cumulative voting results are filtered out, the local peak points are mapped to the original image space and the corresponding geometric feature parameters are determined, the geometric feature parameters are converted into vector graphic coordinate data, and a vector layer containing straight lines, polylines or polygonal nautical chart elements is generated. The grayscale image is traversed for each pixel and its neighboring pixels with preset directions and spacing. The frequency of the occurrence of preset grayscale level combinations is counted to generate a grayscale co-occurrence matrix. Texture feature values ​​are calculated based on the grayscale co-occurrence matrix. The texture feature values ​​include contrast, energy, and entropy. The texture feature values ​​are mapped to the original image space to generate a raster layer containing nautical chart texture features. A standard nautical symbol database is constructed, and a nautical symbol recognition model is built based on a convolutional neural network. The nautical symbols in the standard nautical symbol database are imported into the recognition model for training. Based on the trained nautical symbol recognition model and a preset text recognition model, nautical symbols and text are extracted from each nautical chart in the nautical chart printing task data package to generate a labeling layer for the nautical chart. Based on the vector layer, raster layer, and annotation layer of the nautical chart, nautical chart elements are extracted, and the location information of the nautical chart elements is determined. A nautical chart element distribution matrix is ​​constructed based on the location information of the nautical chart elements and sea area elements.

3. The method for optimizing batch nautical chart printing tasks based on intelligent merging according to claim 1, characterized in that, The hierarchical clustering algorithm is used to cluster the color compensation vector of each nautical chart printing task to obtain clustering results. Based on the clustering results, nautical chart printing tasks are merged to construct a nautical chart printing task combination, specifically as follows: The Euclidean distance between the color compensation vectors of each nautical chart printing task is calculated based on the hierarchical clustering algorithm. An initial clustering distance threshold is set, and the color compensation vectors are initially clustered according to the Euclidean distance and the initial clustering distance threshold. Calculate the mean vector of all color level compensation vectors within each initial cluster group, and use it as the color level compensation reference vector for that group; To obtain the maximum chromaticity compensation processing volume of the POD printer, the chromaticity compensation baseline vector of each initial cluster group is added to the chromaticity compensation vector of all chart printing tasks within the group to obtain the total chromaticity compensation of the current group. Determine whether the total chromaticity compensation exceeds the maximum chromaticity compensation processing amount. If the total chromaticity compensation of the current group exceeds the maximum chromaticity compensation processing amount, reduce the initial clustering distance threshold, recalculate the Euclidean distance and iteratively perform clustering grouping until the total chromaticity compensation of all groups is not greater than the maximum chromaticity compensation processing amount. If there are still groups exceeding the maximum color level compensation processing amount when the number of iterations reaches the preset upper limit, then the color level compensation vector of the group exceeding the limit is subjected to dispersion analysis, and the standard deviation of the color level compensation vector within the group is calculated. If the standard deviation is greater than the preset dispersion threshold, then the group exceeding the limit is split into multiple subgroups, and each subgroup is assigned an independent printing batch number. Based on the final clustering results and subgroups, hierarchical chart printing task combinations are generated, with each task group containing chart printing tasks with the same batch number.

4. The method for optimizing batch nautical chart printing tasks based on intelligent merging according to claim 1, characterized in that, The process of generating a color calibration curve for the chart printing task combination and optimizing the chart printing task based on the color calibration curve specifically involves: An initial calibration curve is generated based on the color level compensation vector of each group in the nautical chart printing task combination. After a trial printing is performed by the printing equipment, the reflectivity distribution data and actual color level values ​​of the printing medium are collected. Cross-analysis was performed on reflectance distribution data and nautical chart element density data to calculate the compensation correlation index between reflectance deviation and density in each region; The surface reflectance-density correction coefficient of the printing medium is generated based on the compensation correlation index, and the compensation parameters in the initial calibration curve are then reassigned based on this coefficient. The compensation parameters after secondary allocation are input into the color level drift prediction model, and the color level drift coefficient is calculated by combining the color difference between the actual color level value and the target value. When the color level drift coefficient exceeds the preset threshold, an inverse compensation matrix is ​​constructed based on the reflectance-density correction coefficient, and the compensation parameters in the color level calibration curve are corrected by the inverse compensation matrix. If the corrected color level drift coefficient still exceeds the threshold, then the unused redundant compensation parameters in the nautical chart printing task combination are extracted, a compensation parameter buffer is established, and the buffer parameters are called to perform superimposed compensation according to the gradient change of the color level drift coefficient to generate the final color level calibration curve. The user's nautical chart printing task is printed according to the final color calibration curve.

5. The method for optimizing batch nautical chart printing tasks based on intelligent merging according to claim 4, characterized in that, The specific steps for performing the printing operation on the user's nautical chart printing task based on the final color calibration curve are as follows: Based on the final color calibration curve, generate color conversion parameters that match the POD printing device, input the parameters into the color management module of the third-party print management software, and drive the printer to perform color calibration. Extract the corresponding nautical chart PDF file from the nautical chart information and PDF file library, call the rasterization engine to perform layer-by-layer parsing of the PDF file, and rasterize the nautical chart content in combination with the calibrated color level parameters. Select the printing media type according to the printing task type. If it is a central task type, use the preset long paper roll or short paper roll for printing. If it is a user order type, automatically adapt the printing parameters according to the printer type configured by the user. When performing POD printing, the nautical chart PDF file is rotated and white borders are added based on the no-cut standard. After printing, the printed products of the central task are compared with the standard symbols in the nautical chart information database through the system's automatic verification module, and an inspection report is generated. If the inspection passes, the version information is stored in the database and marked as releasable. For user order tasks, if the user does not have a POD printer, a logistics tracking code is generated and bound to the order information, and the finished product is printed and shipped by express courier. If the user has a printer, the calibrated nautical chart PDF file is pushed to the user's printer for direct output via the network.

6. A batch nautical chart printing task optimization system based on intelligent merging, characterized in that, The intelligent merging-based batch nautical chart printing task optimization system includes a storage unit and a processor. The storage unit includes a method program for optimizing batch nautical chart printing tasks based on intelligent merging. When the processor executes the method program for optimizing batch nautical chart printing tasks based on intelligent merging, it performs the following steps: The system acquires user chart printing task data packages in real time, performs layer parsing on each data package, extracts chart elements, and constructs a chart element distribution matrix. The distribution matrix of the nautical chart elements is analyzed to identify the density of chart elements in each region of the chart. Based on the reflectivity detection values ​​of the printing medium, printing color gradation compensation parameters for each region of the chart are calculated, generating a color gradation compensation vector for each chart printing task. Specifically: The nautical chart element distribution matrix is ​​uniformly divided into grid cells of a preset size. Based on the nautical chart element distribution matrix, the coverage ratio of vector graphic coordinates and nautical chart texture features, the number of nautical symbols and the text density in each grid cell are statistically analyzed to obtain the initial density information of nautical chart elements in each grid cell. The Gaussian kernel function is selected as the kernel function of the kernel density estimation algorithm. The smoothing bandwidth parameter of the kernel density estimation algorithm is set. The initial density information of the chart elements of each grid cell is weighted and summed according to the kernel density estimation algorithm to construct a continuous density distribution model and calculate the density value of each grid cell. The density value of each grid cell is normalized to a preset range. Based on the normalized density value, a threshold for dividing the density of nautical elements is preset. The density of nautical elements in each grid cell of the nautical chart is divided according to the preset threshold for dividing the density of nautical elements, and the density of nautical elements in each grid cell is determined. Obtain the reflectance detection value of the nautical chart printing medium, construct a preset reflectance-compensation coefficient mapping table, and match the corresponding basic compensation coefficient according to the interval of the reflectance detection value, wherein the reflectance detection value and the basic compensation coefficient are positively correlated; Based on the density division of nautical chart elements, regions of different density levels are mapped to preset compensation weight gradient intervals. For each grid cell, the color level compensation correction amount is calculated using a linear superposition formula based on the basic compensation coefficient of its density level and the matched gradient weight. Based on the color level compensation correction amount of each grid cell, a compensation parameter matrix with the same dimension as the nautical chart element distribution matrix is ​​generated. The compensation parameter matrix is ​​expanded into a one-dimensional sequence by rows and normalized to output a color level compensation vector. The color compensation vector of each nautical chart printing task is clustered based on the hierarchical clustering algorithm to obtain the clustering results. The nautical chart printing tasks are then merged based on the clustering results to construct a nautical chart printing task combination. A color calibration curve is generated for the nautical chart printing task combination, and the nautical chart printing task is optimized based on the color calibration curve.

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