A method and system for electronic nautical chart drawing based on data fusion technology

Abstract

This application relates to the field of digital fusion technology, specifically an electronic nautical chart drawing method and system based on data fusion technology, including: S10: constructing and dynamically updating a hybrid index structure based on a preset electronic nautical chart database and real-time acquired incremental data packets; S20: matching elements with the same name and fusing identified spatially overlapping areas to output a deduplicated global nautical chart dataset; S30: dividing into several sub-regions and constructing a quadtree-R-tree index to output a quadtree-R-tree two-layer hybrid index structure; S40: converting screen coordinates to geographic coordinates and filtering visible elements to output a set of visible area elements; S50: based on the set of visible area elements and preset style rules, allocating rendering tasks and optimizing the matching GPU instruction set to output a real-time nautical chart image at a preset frame rate. This application significantly improves the real-time processing efficiency and visualization smoothness of electronic nautical charts and optimizes the user interaction experience.

Description

Technical Field

[0001] This application relates to the field of digital fusion technology, specifically to an electronic nautical chart drawing method and system based on data fusion technology. Background Technology

[0002] Electronic nautical charts (ENCs) are digital products used to describe maritime geography and navigation information. Due to issues with the ENC data format, researchers designed the System Electronic Chart (SENC) storage format to improve efficiency and operational performance. Existing electronic chart rendering and display solutions suffer from the following problems: time-consuming data merging, low efficiency in map sheet and layer traversal, time-consuming data cropping process, and difficulty in meeting the needs of high-performance equipment.

[0003] Therefore, improvements are needed. Summary of the Invention

[0004] To address the above technical issues, this application provides an electronic nautical chart drawing method and system based on data fusion technology.

[0005] A method for electronic nautical chart drawing based on data fusion technology includes:

[0006] S10: Based on the preset electronic chart database and real-time collected incremental data packets, construct and dynamically update the hybrid index structure;

[0007] S20: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, it matches elements with the same name and merges the identified spatial overlapping areas to output a deduplicated global nautical chart dataset.

[0008] S30: Based on the global nautical chart dataset, divide it into several sub-regions and construct a quadtree-R-tree index, outputting a quadtree-R-tree dual-layer hybrid index structure;

[0009] S40: Based on the real-time collected user screen coordinate data and the quadtree-R-tree dual-layer hybrid index structure, convert the screen coordinates to geographic coordinates, filter visible elements, and output the set of visible area elements.

[0010] S50: Based on the set of visible area elements and preset style rules, allocate rendering tasks and optimize the matching of GPU instruction sets to output real-time nautical chart images at a preset frame rate.

[0011] In a preferred embodiment, step S10: constructing and dynamically updating a hybrid index structure based on a preset electronic chart database and real-time acquired incremental data packets, includes:

[0012] Based on a pre-set electronic nautical chart database, a basic nautical chart dataset is extracted and a hybrid index structure is constructed.

[0013] The basic nautical chart dataset includes nautical chart sheets, nautical chart layers, and nautical chart elements of global electronic nautical charts. The hybrid index structure includes a quadtree index structure and a corresponding R-tree index structure.

[0014] When an incremental data packet is received, preprocessing is performed and the preprocessing result is output. The preprocessing result includes the content of the incremental data, the incremental operation type, and the associated spatial range. The incremental operation type includes addition, deletion, and modification.

[0015] The preprocessing includes:

[0016] Extract the content of incremental data based on preset content parsing rules;

[0017] Based on preset operation type encoding rules, parse incremental operation types;

[0018] Based on preset spatial range encoding rules, the associated spatial range is parsed;

[0019] Based on the incremental operation type and associated spatial range, the constructed quadtree index structure is located and the affected quadtree units are output.

[0020] Based on the affected quadtree units, update the constructed R-tree index structure and output the adjusted R-tree nodes;

[0021] Based on the content of the incremental data, perform incremental operations on the chart elements and output an updated set of chart elements.

[0022] Based on the updated set of nautical chart elements, the quadtree index structure and R-tree index structure are dynamically adjusted, and the maintained hybrid index structure is output.

[0023] In a preferred embodiment, step S20, which involves matching identically named features and fusing identified spatially overlapping areas based on a preset electronic nautical chart database, collected multi-source layer data, and a dynamically updated hybrid index structure, and outputting a deduplicated global nautical chart dataset, includes:

[0024] Based on the preset electronic nautical chart database, the collected multi-source layer data and the dynamically updated hybrid index structure, the multi-source nautical chart data is integrated and processed to output a global nautical chart dataset in a unified format.

[0025] Based on the global nautical chart dataset, filter and match nautical chart elements with the same name, and output a list of successfully matched nautical chart elements.

[0026] The filtering and matching of nautical chart elements with the same name includes spatial matching and attribute matching;

[0027] When the spatial extents of nautical chart elements overlap, it is determined to be a spatial match;

[0028] When the attribute information of nautical chart elements is completely consistent, it is determined to be an attribute match;

[0029] Based on the nine-intersection model, the topological relationships between chart elements in the global nautical chart dataset are calculated, the overlap types are identified and labeled, and a spatial dataset labeled with the overlap types is output.

[0030] The overlap types include complete overlap, partial overlap, and containment relationship;

[0031] When the geometry of the elements on two nautical charts is exactly the same, they are considered to be completely overlapping.

[0032] When the geometry of two nautical chart elements partially overlaps, it is determined that they are partially overlapping.

[0033] When the geometry of one nautical chart element completely encompasses that of another nautical chart element... Figure 1 When considering elements, determine if there is an inclusion relationship.

[0034] In a preferred embodiment, step S20, which involves matching identically named features and fusing identified spatially overlapping areas based on a preset electronic nautical chart database, collected multi-source layer data, and a dynamically updated hybrid index structure, and outputting a deduplicated global nautical chart dataset, further includes:

[0035] Based on the list of nautical chart elements and the spatial dataset, unique identifier elements are retained for completely overlapping nautical chart elements, geometric merging is performed on partially overlapping nautical chart elements, and parent elements are retained for nautical chart elements with inclusion relationships.

[0036] Based on a preset spatial conflict detection algorithm, overlapping regions are fused, and a fused spatial dataset is output.

[0037] Based on the IHO S-100 standard, redundant elements of the fused spatial dataset are removed, and a deduplicated global nautical chart dataset is output.

[0038] Based on the deduplicated global nautical chart dataset and the preset geometric simplification algorithm, the complexity of nautical chart elements is optimized, and the final optimized global nautical chart dataset is output.

[0039] In a preferred embodiment, step S30: based on the global nautical chart dataset, dividing it into several sub-regions and constructing a quadtree-R-tree index, and outputting a quadtree-R-tree dual-layer hybrid index structure, includes:

[0040] Based on the final optimized global nautical chart dataset, the geographical scope is divided, the boundaries of sub-regions are determined, and the sub-region division results are output.

[0041] Based on the nautical chart elements in each sub-region, a local quadtree index is constructed, and the local quadtree index structure is output.

[0042] Based on a preset spatial range forced alignment algorithm and a preset node-level pointer binding algorithm, an associated local R-tree index is constructed for each local quadtree index structure, and a direct mapping relationship is established between the quadtree node and the R-tree root node, forming a quadtree-R-tree two-layer hybrid index structure.

[0043] In a preferred embodiment, step S40, which involves converting screen coordinates to geographic coordinates based on real-time collected user screen coordinate data and the quadtree-R-tree hybrid index structure, filtering visible features, and outputting a set of visible area features, includes:

[0044] The scaling factor is calculated based on real-time collected user screen coordinate data, and the scaling factor is output.

[0045] Based on the scaling factor and preset inverse Mercator projection parameters, the user screen coordinate data is transformed to output the geographic latitude and longitude range and the geographic boundary of the user viewport.

[0046] Based on the user viewport geographic boundary and the quadtree-R tree dual-layer hybrid index structure, recursive traversal is performed on the quadtree root node. Based on the preset spatial range matching algorithm, the quadtree sub-regions covering the viewport are filtered, and the list of matching quadtree sub-regions is output.

[0047] Based on the matched quadtree sub-region list and the user viewport geographic boundary, perform a range query on the local R-tree index and output a set of candidate nautical chart features;

[0048] Based on the candidate chart feature set, preset visibility rules, chart feature hierarchy priority, and preset type rules, the candidate chart features are filtered by hierarchy and type, and a preliminary set of visible features is output.

[0049] In a preferred embodiment, S50: Based on the set of visible area elements and preset style rules, the step of allocating rendering tasks and optimizing the matching of GPU instruction sets to output real-time nautical chart images at a preset frame rate includes:

[0050] Based on the initially visible set of elements and the preset rendering engine, the rendering tasks are assigned to the nautical chart elements, and a list of nautical chart elements to be rendered is output.

[0051] Based on the list of nautical chart elements to be rendered and preset style rules, optimize the matching of GPU instruction sets and output the optimized GPU rendering instruction set;

[0052] Based on the optimized GPU rendering instruction set, GPU rendering instructions are executed to generate real-time nautical chart images and output real-time nautical chart images at a preset frame rate.

[0053] The second objective of this invention is achieved through the following technical solution:

[0054] An electronic nautical chart drawing system based on data fusion technology includes:

[0055] Module 1: Based on a pre-set electronic nautical chart database and real-time incremental data packets, construct and dynamically update a hybrid index structure;

[0056] The second module: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, it matches elements with the same name and merges the identified spatial overlapping areas to output a deduplicated global nautical chart dataset.

[0057] The third module: Based on the global nautical chart dataset, divide it into several sub-regions and construct a quadtree-R-tree index, outputting a quadtree-R-tree dual-layer hybrid index structure;

[0058] The fourth module: Based on the real-time collected user screen coordinate data and the quadtree-R-tree dual-layer hybrid index structure, the screen coordinates are converted to geographic coordinates, and visible elements are filtered to output a set of visible area elements;

[0059] The fifth module: Based on the set of visible area elements and preset style rules, it allocates rendering tasks and optimizes the matching of GPU instruction sets to output real-time nautical chart images at a preset frame rate.

[0060] The above-mentioned objective three of this application is achieved through the following technical solution:

[0061] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described electronic chart drawing method based on data fusion technology.

[0062] The fourth objective of this application is achieved through the following technical solution:

[0063] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described electronic nautical chart drawing method based on data fusion technology.

[0064] In summary, this application includes at least one of the following beneficial technical effects:

[0065] Adaptive region segmentation is performed based on the geographical distribution characteristics of the global nautical chart dataset. Boundary constraints ensure the spatial continuity of sub-regions, providing a structured foundation for local index construction. At the local index layer, a quadtree structure is built for each sub-region to capture the spatial distribution patterns of elements, leveraging the hierarchical recursive characteristics of the quadtree for rapid region localization. At the hybrid optimization layer, a spatial range forced alignment algorithm ensures the geometric consistency between the quadtree segmentation boundary and the R-tree node coverage. A node-level pointer binding mechanism establishes a direct association between the quadtree terminal node and the R-tree root node, forming a two-layer index linkage system. This scheme, through a progressive processing of spatial divide-and-conquer, local indexing, and hybrid optimization, maintains the advantages of quadtree spatial proximity queries while fully leveraging the optimization capabilities of R-trees for complex range queries. This significantly improves the spatial query efficiency and dynamic adaptability of the hybrid index structure, providing high-performance data access support for real-time nautical chart rendering and spatial analysis. Attached Figure Description

[0066] Figure 1 This is a flowchart of an embodiment of an electronic nautical chart drawing method based on data fusion technology according to this application;

[0067] Figure 2 This is a flowchart of step S10 in an embodiment of an electronic nautical chart drawing method based on data fusion technology according to this application;

[0068] Figure 3 This is a flowchart of step S20 in an embodiment of an electronic nautical chart drawing method based on data fusion technology according to this application;

[0069] Figure 4 This is another implementation flowchart of step S20 in an embodiment of an electronic nautical chart drawing method based on data fusion technology in this application;

[0070] Figure 5 This is a flowchart of step S30 in an embodiment of an electronic nautical chart drawing method based on data fusion technology according to this application;

[0071] Figure 6 This is a schematic block diagram of a computer device according to this application. Detailed Implementation

[0072] The following is in conjunction with the appendix Figure 1-6 This application will be described in further detail.

[0073] In one embodiment, such as Figure 1 As shown, this application discloses an electronic nautical chart drawing method based on data fusion technology, which specifically includes the following steps:

[0074] S10: Based on the preset electronic chart database and real-time collected incremental data packets, construct and dynamically update the hybrid index structure;

[0075] S20: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, it matches elements with the same name and merges the identified spatial overlapping areas to output a deduplicated global nautical chart dataset.

[0076] S30: Based on the global nautical chart dataset, divide it into several sub-regions and construct a quadtree-R-tree index, outputting a quadtree-R-tree dual-layer hybrid index structure;

[0077] S40: Based on the real-time collected user screen coordinate data and the quadtree-R-tree dual-layer hybrid index structure, convert the screen coordinates to geographic coordinates, filter visible elements, and output the set of visible area elements.

[0078] S50: Based on the set of visible area elements and preset style rules, allocate rendering tasks and optimize the matching of GPU instruction sets to output real-time nautical chart images at a preset frame rate.

[0079] In this embodiment, a quadtree-R-tree hybrid index structure is constructed to achieve dynamic fusion of the preset nautical chart database and real-time incremental data, ensuring that the spatial index structure is updated in real time as the data changes, providing an efficient access interface for upper-level processing. Based on spatial topology analysis and standardized deduplication, multi-source heterogeneous layer data is integrated to eliminate element redundancy and spatial conflicts, generating a logically consistent global nautical chart dataset, significantly improving data availability. A quadtree-R-tree two-layer hybrid architecture with geographical range division is adopted, and through spatial range alignment and bidirectional mapping mechanisms, efficient organization and rapid retrieval of massive elements are achieved, resulting in an order-of-magnitude improvement in regional query efficiency. Combined with viewport-driven coordinate transformation and element filtering mechanisms, the user's observation range is accurately matched, and the rendering load is greatly reduced through hierarchical filtering and type control. Relying on style-driven rendering task allocation and GPU instruction set optimization technology, high-performance rendering of nautical chart elements is achieved, maintaining smooth frame rate output even in complex scenes. Through deep coupling of data flow and control flow, each technical module forms a complete optimized link from raw data acquisition to terminal screen rendering, ensuring the timeliness and accuracy of nautical charts while providing users with a highly responsive and low-latency interactive navigation experience.

[0080] Figure 2 Step S10 includes:

[0081] S101: Based on the preset electronic nautical chart database, extract the basic nautical chart dataset and construct a hybrid index structure;

[0082] S102: The basic nautical chart dataset includes nautical chart sheets, nautical chart layers and nautical chart elements of global electronic nautical charts, and the hybrid index structure includes a quadtree index structure and a corresponding R-tree index structure;

[0083] S103: When an incremental data packet is received, preprocessing is performed and the preprocessing result is output. The preprocessing result includes the content of the incremental data, the incremental operation type, and the associated spatial range. The incremental operation type includes addition type, deletion type, and modification type.

[0084] S104: The preprocessing includes: extracting the content of incremental data based on preset content parsing rules;

[0085] S105: Parse incremental operation types based on preset operation type encoding rules;

[0086] S106: Based on the preset spatial range encoding rules, parse the associated spatial range;

[0087] S107: Based on the incremental operation type and associated spatial range, locate the constructed quadtree index structure and output the affected quadtree units;

[0088] S108: Based on the affected quadtree units, update the constructed R-tree index structure and output the adjusted R-tree nodes;

[0089] S109: Based on the content of the incremental data, perform incremental operations on the chart elements and output the updated set of chart elements.

[0090] S110: Based on the updated set of nautical chart elements, dynamically adjust the quadtree index structure and R-tree index structure, and output the maintained hybrid index structure.

[0091] In this embodiment, a quadtree-R-tree hybrid index structure is constructed based on a pre-defined nautical chart database. Spatial hierarchical structure and feature aggregation form an efficient access channel. When an incremental data packet is received, multi-dimensional parsing rules are used to structurally decompose the incremental content. Operation type encoding and spatial range encoding are used to accurately locate data changes. During the index update phase, a dynamic mapping relationship between incremental operations and index nodes is established. By locating quadtree units in affected areas and adjusting R-tree nodes, localized updates to the index structure are achieved, avoiding global reconstruction overhead. Finally, through the coordinated adjustment of feature-level incremental operations and the index structure, data consistency is ensured while significantly improving the dynamic maintenance efficiency of the hybrid index. This solution, through a pipelined parsing-location-update process, forms a complete closed loop from data change awareness to index structure optimization, enabling the nautical chart data update process to have both real-time response capabilities and storage access performance, providing continuously optimized spatial indexing services for upper-layer applications.

[0092] Figure 3 Step S20 includes:

[0093] S201: Based on the preset electronic chart database, the collected multi-source layer data and the dynamically updated hybrid index structure, the multi-source chart data is integrated and processed to output a global chart dataset in a unified format.

[0094] S202: Based on the global nautical chart dataset, filter and match nautical chart elements with the same name, and output a list of successfully matched nautical chart elements;

[0095] S203: The filtering and matching of nautical chart elements with the same name includes spatial matching and attribute matching;

[0096] S204: When the spatial extents of nautical chart elements overlap, it is determined to be a spatial match;

[0097] S205: When the attribute information of nautical chart elements is completely consistent, it is determined to be attribute matching;

[0098] S206: Based on the nine-intersection model, calculate the topological relationships between chart elements in the global nautical chart dataset, identify and label the overlap types, and output a spatial dataset labeled with the overlap types;

[0099] S207: The overlap types include complete overlap, partial overlap, and inclusion relationship;

[0100] S208: When the geometry of two chart elements is exactly the same, they are judged to be completely overlapping;

[0101] S209: When there is partial geometric overlap between two nautical chart elements, it is determined to be a partial overlap;

[0102] S210: When the geometry of one chart element completely encompasses that of another... Figure 1 When considering elements, determine if there is an inclusion relationship.

[0103] In this embodiment, standardized conversion rules are used to unify the format and align the semantics of multi-source nautical chart layers. A dynamically updated hybrid index structure enables spatial association across data sources, constructing a logically consistent global nautical chart data base. At the feature matching layer, a dual verification mechanism of spatial range intersection detection and attribute information comparison is employed. Through a combination of geometric topology analysis and metadata verification, identically named features across layers are accurately identified, effectively resolving the feature duplication problem caused by the heterogeneity of multi-source data. At the topology analysis layer, a nine-intersection model is used to formally describe the spatial relationships between features. Fine-grained distinctions between complete overlap, partial overlap, and inclusion relationships provide topological constraints for subsequent data fusion. Finally, at the redundancy processing layer, overlapping features are hierarchically labeled and logically merged based on the topology analysis results, eliminating data redundancy while preserving key spatial features. This solution, through a chain of data integration, feature matching, topology analysis, and redundancy processing, forms a complete processing flow from multi-source heterogeneous data access to unified nautical chart dataset output, significantly improving the integrity and spatial consistency of nautical chart data and providing high-quality, redundancy-free global nautical chart services for upper-layer applications.

[0104] Figure 4 Step S20 also includes:

[0105] SB1: Based on the list of nautical chart elements and the spatial dataset, retain unique identifier elements for completely overlapping nautical chart elements, perform geometric merging for partially overlapping nautical chart elements, and retain parent elements for nautical chart elements with inclusion relationships.

[0106] SB2: Based on a preset spatial conflict detection algorithm, overlapping regions are fused, and the fused spatial dataset is output.

[0107] SB3: Based on the IHO S-100 standard, remove redundant elements from the fused spatial dataset and output the deduplicated global nautical chart dataset;

[0108] SB4: Based on the deduplicated global nautical chart dataset and a preset geometric simplification algorithm, optimize the complexity of nautical chart elements and output the final optimized global nautical chart dataset.

[0109] In this embodiment, a uniqueness preservation strategy is adopted for completely overlapping elements, geometric merging is performed on partially overlapping elements, hierarchical simplification is performed on elements with inclusion relationships, and data redundancy is eliminated through intelligent spatial relationship analysis. In the spatial conflict fusion layer, topological correction is performed on overlapping areas based on a preset conflict detection algorithm, and spatial continuity is ensured through boundary smoothing and gap filling. In the standardization processing layer, the quality of the fused data is strictly verified according to the IHO S-100 international standard, and data standardization is achieved through element uniqueness verification and attribute consistency checks. In the geometric optimization layer, simplification strategies such as the Douglas-Puk algorithm are used to appropriately abstract complex elements, reducing geometric complexity while maintaining key spatial features. This scheme forms a complete processing link from raw data integration to optimized data output through a chain of processes: redundancy elimination, conflict fusion, standard verification, and geometric optimization. This significantly improves the spatial consistency and display efficiency of nautical chart data, providing a lightweight, high-performance data foundation for real-time rendering and interactive analysis while ensuring compliance with international standards.

[0110] Figure 5 Step S30 includes:

[0111] S301: Based on the final optimized global nautical chart dataset, divide the geographical area, determine the boundaries of sub-regions, and output the sub-region division results;

[0112] S302: Based on the chart elements in each sub-region, construct a local quadtree index and output the local quadtree index structure;

[0113] S303: Based on the preset spatial range forced alignment algorithm and the preset node-level pointer binding algorithm, an associated local R-tree index is constructed for each local quadtree index structure, and a direct mapping relationship between the quadtree node and the R-tree root node is established to form a quadtree-R-tree two-layer hybrid index structure.

[0114] In this embodiment, adaptive region segmentation is performed based on the geographical distribution characteristics of the global nautical chart dataset. Boundary constraints ensure the spatial continuity of sub-regions, providing a structured foundation for local index construction. At the local index layer, a quadtree structure is constructed for each sub-region to capture the spatial distribution patterns of elements, utilizing the hierarchical recursive characteristics of the quadtree for rapid region localization. At the hybrid optimization layer, a spatial range forced alignment algorithm ensures the geometric consistency between the quadtree segmentation boundary and the R-tree node coverage. A node-level pointer binding mechanism establishes a direct association between the quadtree terminal node and the R-tree root node, forming a two-layer index linkage system. This scheme, through a progressive processing of spatial divide-and-conquer, local indexing, and hybrid optimization, maintains the advantages of quadtree spatial proximity queries while fully leveraging the optimization capabilities of R-trees for complex range queries. This significantly improves the spatial query efficiency and dynamic adaptability of the hybrid index structure, providing high-performance data access support for real-time nautical chart rendering and spatial analysis.

[0115] Step S40 includes:

[0116] S401: Calculates the scaling factor based on real-time collected user screen coordinate data and outputs the scaling factor.

[0117] S402: Based on the scaling factor and preset inverse Mercator projection parameters, perform transformation on the user's screen coordinate data and output the geographic latitude and longitude range and the geographic boundary of the user's viewport.

[0118] S403: Based on the user viewport geographic boundary and the quadtree-R tree dual-layer hybrid index structure, recursively traverse the quadtree root node, and based on the preset spatial range matching algorithm, filter the quadtree sub-regions covering the viewport and output the list of matching quadtree sub-regions.

[0119] S404: Based on the matched quadtree sub-region list and the user viewport geographic boundary, perform a range query on the local R-tree index and output a set of candidate nautical chart elements;

[0120] S405: Based on the candidate chart feature set, preset visibility rules, chart feature hierarchy priority, and preset type rules, perform hierarchy and type filtering on the candidate chart features and output a preliminary set of visible features.

[0121] In this embodiment, based on real-time screen coordinates and dynamic scaling factors, an inverse Mercator projection algorithm is used to achieve accurate mapping from pixel space to geographic space, generating the user viewport geographic boundary as a spatial query benchmark. In the index query layer, a quadtree-R-tree hybrid index structure is used. Viewport-aware quadtree recursive traversal quickly locates the coverage area, and the range query capability of the R-tree efficiently retrieves candidate elements, significantly improving the response speed of spatial queries. In the element filtering layer, a hierarchical priority-driven visibility rule, combined with an element type whitelist mechanism, performs dual filtering on candidate elements, retaining key navigation information while eliminating redundant display content. This solution, through a pipelined processing of coordinate transformation, index query, and element filtering, forms a complete optimized link from user interaction input to visible element output. While ensuring the accuracy of nautical chart display, it effectively reduces rendering load and improves interaction smoothness, providing an efficient solution for real-time visualization of complex nautical chart scenarios.

[0122] The S50 steps include:

[0123] S501: Based on the initially visible set of elements and the preset rendering engine, the rendering task is assigned to the nautical chart elements, and a list of nautical chart elements to be rendered is output.

[0124] S502: Based on the task list of nautical chart elements to be rendered and preset style rules, optimize the matching of GPU instruction sets and output the optimized GPU rendering instruction set;

[0125] S503: Based on the optimized GPU rendering instruction set, it executes GPU rendering instructions to generate real-time nautical chart images and outputs real-time nautical chart images at a preset frame rate.

[0126] In this embodiment, based on the set of visible elements and rendering priority rules, the spatial locality principle is used to allocate rendering tasks, ensuring that key navigation elements are loaded first. At the instruction optimization layer, a style rule-driven GPU instruction set matching mechanism maps the geometric features and shading requirements of nautical chart elements to the optimal GPU execution path, utilizing the parallel processing capabilities of vertex and fragment shaders to reduce rendering pipeline idle time. At the image generation layer, the optimized instruction set is executed based on the GPU's parallel computing architecture, and batch rendering and state caching techniques reduce rendering call overhead, ensuring the accuracy of nautical chart symbolization while achieving stable rendering frame rate output. This solution, through pipeline optimization of task scheduling, instruction optimization, and image generation, forms a complete acceleration chain from rendering task preparation to final image output, significantly improving the rendering efficiency and interactive response speed of complex nautical chart scenes, providing users with a smooth, high-quality real-time navigation visual experience.

[0127] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0128] In one embodiment, an electronic nautical chart drawing system based on data fusion technology is provided, which corresponds to the electronic nautical chart drawing method based on data fusion technology described in the above embodiment. The electronic nautical chart drawing system based on data fusion technology includes:

[0129] Module 1: Based on a pre-set electronic nautical chart database and real-time incremental data packets, construct and dynamically update a hybrid index structure;

[0130] The second module: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, it matches elements with the same name and merges the identified spatial overlapping areas to output a deduplicated global nautical chart dataset.

[0131] The third module: Based on the global nautical chart dataset, divide it into several sub-regions and construct a quadtree-R-tree index, outputting a quadtree-R-tree dual-layer hybrid index structure;

[0132] The fourth module: Based on the real-time collected user screen coordinate data and the quadtree-R-tree dual-layer hybrid index structure, the screen coordinates are converted to geographic coordinates, and visible elements are filtered to output a set of visible area elements;

[0133] The fifth module: Based on the set of visible area elements and preset style rules, it allocates rendering tasks and optimizes the matching of GPU instruction sets to output real-time nautical chart images at a preset frame rate.

[0134] Optional, also includes:

[0135] Module 6: Based on the pre-set electronic nautical chart database, extract the basic nautical chart dataset and construct a hybrid index structure;

[0136] Module 7: The basic nautical chart dataset includes the nautical chart sheets, nautical chart layers and nautical chart elements of global electronic nautical charts, and the hybrid index structure includes a quadtree index structure and a corresponding R-tree index structure;

[0137] Module 8: When an incremental data packet is received, preprocessing is performed and the preprocessing result is output. The preprocessing result includes the content of the incremental data, the incremental operation type, and the associated spatial range. The incremental operation type includes addition, deletion, and modification.

[0138] Module 9: The preprocessing includes: extracting the content of incremental data based on preset content parsing rules;

[0139] Module 10: Parse incremental operation types based on preset operation type encoding rules;

[0140] Module 11: Based on preset spatial range encoding rules, parse the associated spatial range;

[0141] Module 12: Based on the incremental operation type and associated spatial range, locate the constructed quadtree index structure and output the affected quadtree units;

[0142] Module 13: Based on the affected quadtree units, update the constructed R-tree index structure and output the adjusted R-tree nodes;

[0143] Module 14: Based on the content of the incremental data, perform incremental operations on the chart elements and output the updated set of chart elements;

[0144] Module 15: Based on the updated set of nautical chart elements, dynamically adjust the quadtree index structure and R-tree index structure, and output the maintained hybrid index structure.

[0145] Optional, also includes:

[0146] Module 16: Based on the preset electronic chart database, the collected multi-source layer data and the dynamically updated hybrid index structure, the multi-source chart data is integrated and processed to output a global chart dataset in a unified format;

[0147] Module 17: Based on the global nautical chart dataset, filter and match nautical chart elements with the same name, and output a list of successfully matched nautical chart elements;

[0148] Module 18: The filtering and matching of nautical chart elements with the same name includes spatial matching and attribute matching;

[0149] Module 19: When the spatial extent of nautical chart elements overlaps, it is determined to be a spatial match;

[0150] Module 20: When the attribute information of nautical chart elements is completely consistent, it is determined to be an attribute match;

[0151] Module 21: Based on the nine-intersection model, calculate the topological relationships between chart elements in the global nautical chart dataset, identify and label the overlap types, and output a spatial dataset labeled with the overlap types;

[0152] Module 22: The overlap types include complete overlap, partial overlap, and containment relationships;

[0153] Module 23: When the geometry of two nautical chart elements is exactly the same, it is determined that they are completely overlapping;

[0154] Module 24: When the geometry of two nautical chart elements partially overlaps, it is determined that they are partially overlapping;

[0155] Module 25: When the geometry of one nautical chart element completely encompasses that of another nautical chart element. Figure 1 When considering elements, determine if there is an inclusion relationship.

[0156] Optional, also includes:

[0157] Module 26: Based on the list of nautical chart elements and the spatial dataset, retain unique identifiers for completely overlapping nautical chart elements, perform geometric merging for partially overlapping nautical chart elements, and retain parent elements for nautical chart elements with inclusion relationships.

[0158] Module 27: Based on a preset spatial conflict detection algorithm, overlapping regions are fused, and the fused spatial dataset is output.

[0159] Module 28: Based on the IHO S-100 standard, remove redundant elements from the fused spatial dataset and output the deduplicated global nautical chart dataset.

[0160] Module 29: Based on the deduplicated global nautical chart dataset and a preset geometric simplification algorithm, optimizes the complexity of nautical chart elements and outputs the final optimized global nautical chart dataset.

[0161] Optional, also includes:

[0162] Module 30: Based on the final optimized global nautical chart dataset, the geographic scope is divided, the boundaries of sub-regions are determined, and the sub-region division results are output.

[0163] Module 31: Based on the chart elements in each sub-region, construct a local quadtree index and output the local quadtree index structure;

[0164] Module 32: Based on the preset spatial range forced alignment algorithm and the preset node-level pointer binding algorithm, it constructs an associated local R-tree index for each local quadtree index structure, and establishes a direct mapping relationship between quadtree nodes and R-tree root nodes, forming a quadtree-R-tree two-layer hybrid index structure.

[0165] Optional, also includes:

[0166] Module 33: Calculates the scaling factor based on real-time collected user screen coordinate data and outputs the scaling factor;

[0167] Module 33: Based on the scaling factor and preset inverse Mercator projection parameters, it performs a transformation on the user's screen coordinate data and outputs the geographical latitude and longitude range and the geographical boundary of the user's viewport.

[0168] Module 34: Based on the user viewport geographic boundary and the quadtree-R tree dual-layer hybrid index structure, recursively traverses the quadtree root node, filters the quadtree sub-regions covering the viewport based on the preset spatial range matching algorithm, and outputs a list of matching quadtree sub-regions.

[0169] Module 35: Based on the matched quadtree sub-region list and the user viewport geographic boundary, perform range queries on the local R-tree index and output a set of candidate nautical chart elements;

[0170] Module 36: Based on the candidate chart element set, preset visibility rules, chart element hierarchy priority, and preset type rules, perform hierarchy and type filtering on the candidate chart elements and output a preliminary set of visible elements.

[0171] Optional, also includes:

[0172] Module 37: Based on the initially visible set of elements and the preset rendering engine, it performs rendering task allocation on the nautical chart elements and outputs a list of nautical chart elements to be rendered.

[0173] Module 39: Based on the task list of nautical chart elements to be rendered and preset style rules, optimize the matching of GPU instruction sets and output the optimized GPU rendering instruction set;

[0174] Module 40: Based on the optimized GPU rendering instruction set, it executes GPU rendering instructions to generate real-time nautical chart images and outputs real-time nautical chart images at a preset frame rate.

[0175] For specific limitations regarding an electronic nautical chart drawing system based on data fusion technology, please refer to the limitations of an electronic nautical chart drawing method based on data fusion technology mentioned above, which will not be repeated here. Each module in the aforementioned electronic nautical chart drawing system based on data fusion technology can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0176] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores nautical chart elements. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements an electronic nautical chart drawing method based on data fusion technology.

[0177] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements an electronic chart drawing method based on data fusion technology.

[0178] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being executed by a processor as an electronic chart drawing method based on data fusion technology.

[0179] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0180] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for drawing electronic nautical charts based on data fusion technology, characterized in that, include: S10: Based on the preset electronic chart database and real-time collected incremental data packets, construct and dynamically update the hybrid index structure; S20: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, it matches elements with the same name and merges the identified spatial overlapping areas to output a deduplicated global nautical chart dataset. Based on the preset electronic nautical chart database, the collected multi-source layer data and the dynamically updated hybrid index structure, the multi-source nautical chart data is integrated and processed to output a global nautical chart dataset in a unified format. Based on the global nautical chart dataset, filter and match nautical chart elements with the same name, and output a list of successfully matched nautical chart elements. The filtering and matching of nautical chart elements with the same name includes spatial matching and attribute matching; When the spatial extents of nautical chart elements overlap, it is determined to be a spatial match; When the attribute information of nautical chart elements is completely consistent, it is determined to be an attribute match; Based on the nine-intersection model, the topological relationships between chart elements in the global nautical chart dataset are calculated, the overlap types are identified and labeled, and a spatial dataset labeled with the overlap types is output. The overlap types include complete overlap, partial overlap, and containment relationship; When the geometry of the elements on two nautical charts is exactly the same, they are considered to be completely overlapping. When the geometry of two nautical chart elements partially overlaps, it is determined that they are partially overlapping. When the geometry of one chart element completely contains another chart element, it is determined to be an inclusion relationship. S30: Based on the global nautical chart dataset, divide it into several sub-regions and construct a quadtree-R-tree index, outputting a quadtree-R-tree dual-layer hybrid index structure; S40: Based on the real-time collected user screen coordinate data and the quadtree-R-tree dual-layer hybrid index structure, convert the screen coordinates to geographic coordinates, filter visible elements, and output the set of visible area elements. The scaling factor is calculated based on real-time collected user screen coordinate data, and the scaling factor is output. Based on the scaling factor and preset inverse Mercator projection parameters, the user screen coordinate data is transformed to output the geographic latitude and longitude range and the geographic boundary of the user viewport. Based on the user viewport geographic boundary and the quadtree-R tree dual-layer hybrid index structure, recursive traversal is performed on the quadtree root node. Based on the preset spatial range matching algorithm, the quadtree sub-regions covering the viewport are filtered, and the list of matching quadtree sub-regions is output. Based on the matched quadtree sub-region list and the user viewport geographic boundary, perform a range query on the local R-tree index and output a set of candidate nautical chart features; Based on the candidate chart feature set, preset visibility rules, chart feature hierarchy priority, and preset type rules, the candidate chart features are filtered by hierarchy and type, and a preliminary set of visible features is output. S50: Based on the set of visible area elements and preset style rules, allocate rendering tasks and optimize the matching of GPU instruction sets to output real-time nautical chart images at a preset frame rate.

2. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that, S10: The step of constructing and dynamically updating the hybrid index structure based on the preset electronic chart database and real-time collected incremental data packets includes: Based on a pre-set electronic nautical chart database, a basic nautical chart dataset is extracted and a hybrid index structure is constructed. The basic nautical chart dataset includes nautical chart sheets, nautical chart layers, and nautical chart elements of global electronic nautical charts. The hybrid index structure includes a quadtree index structure and a corresponding R-tree index structure. When an incremental data packet is received, preprocessing is performed and the preprocessing result is output. The preprocessing result includes the content of the incremental data, the incremental operation type, and the associated spatial range. The incremental operation type includes addition, deletion, and modification. The preprocessing includes: Extract the content of incremental data based on preset content parsing rules; Based on preset operation type encoding rules, parse incremental operation types; Based on preset spatial range encoding rules, the associated spatial range is parsed; Based on the incremental operation type and associated spatial range, the constructed quadtree index structure is located and the affected quadtree units are output. Based on the affected quadtree units, update the constructed R-tree index structure and output the adjusted R-tree nodes; Based on the content of the incremental data, perform incremental operations on the chart elements and output an updated set of chart elements. Based on the updated set of nautical chart elements, the quadtree index structure and R-tree index structure are dynamically adjusted, and the maintained hybrid index structure is output.

3. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that, S20: Based on the preset electronic nautical chart database, collected multi-source layer data, and dynamically updated hybrid index structure, the step of matching elements with the same name and fusing the identified spatial overlapping areas to output a deduplicated global nautical chart dataset further includes: Based on the list of nautical chart elements and the spatial dataset, unique identifier elements are retained for completely overlapping nautical chart elements, geometric merging is performed on partially overlapping nautical chart elements, and parent elements are retained for nautical chart elements with inclusion relationships. Based on a preset spatial conflict detection algorithm, overlapping regions are fused, and a fused spatial dataset is output. Based on the IHO S-100 standard, redundant elements of the fused spatial dataset are removed, and a deduplicated global nautical chart dataset is output. Based on the deduplicated global nautical chart dataset and the preset geometric simplification algorithm, the complexity of nautical chart elements is optimized, and the final optimized global nautical chart dataset is output.

4. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that, Step S30: Based on the global nautical chart dataset, the steps of dividing the data into several sub-regions and constructing a quadtree-R-tree index, and outputting a quadtree-R-tree dual-layer hybrid index structure include: Based on the final optimized global nautical chart dataset, the geographical scope is divided, the boundaries of sub-regions are determined, and the sub-region division results are output. Based on the nautical chart elements in each sub-region, a local quadtree index is constructed, and the local quadtree index structure is output. Based on a preset spatial range forced alignment algorithm and a preset node-level pointer binding algorithm, an associated local R-tree index is constructed for each local quadtree index structure, and a direct mapping relationship is established between the quadtree node and the R-tree root node, forming a quadtree-R-tree two-layer hybrid index structure.

5. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that, S50: Based on the set of visible area elements and preset style rules, the steps of allocating rendering tasks and optimizing the matching of GPU instruction sets to output real-time nautical chart images at a preset frame rate include: Based on the initially visible set of elements and the preset rendering engine, the rendering tasks are assigned to the nautical chart elements, and a list of nautical chart elements to be rendered is output. Based on the list of nautical chart elements to be rendered and preset style rules, optimize the matching of GPU instruction sets and output the optimized GPU rendering instruction set; Based on the optimized GPU rendering instruction set, GPU rendering instructions are executed to generate real-time nautical chart images and output real-time nautical chart images at a preset frame rate.

6. An electronic nautical chart drawing system based on data fusion technology, characterized in that, include: Module 1: Based on a pre-set electronic nautical chart database and real-time incremental data packets, construct and dynamically update a hybrid index structure; The second module: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, it matches elements with the same name and merges the identified spatial overlapping areas to output a deduplicated global nautical chart dataset. Module 16: Based on the preset electronic chart database, the collected multi-source layer data and the dynamically updated hybrid index structure, the multi-source chart data is integrated and processed to output a global chart dataset in a unified format; Module 17: Based on the global nautical chart dataset, filter and match nautical chart elements with the same name, and output a list of successfully matched nautical chart elements; Module 18: The filtering and matching of nautical chart elements with the same name includes spatial matching and attribute matching; Module 19: When the spatial extent of nautical chart elements overlaps, it is determined to be a spatial match; Module 20: When the attribute information of nautical chart elements is completely consistent, it is determined to be an attribute match; Module 21: Based on the nine-intersection model, calculate the topological relationships between chart elements in the global nautical chart dataset, identify and label the overlap types, and output a spatial dataset labeled with the overlap types; Module 22: The overlap types include complete overlap, partial overlap, and containment relationships; Module 23: When the geometry of two nautical chart elements is exactly the same, it is determined that they are completely overlapping; Module 24: When the geometry of two nautical chart elements partially overlaps, it is determined that they are partially overlapping; Module 25: When the geometry of one chart element completely contains another chart element, determine if there is an inclusion relationship; The third module: Based on the global nautical chart dataset, divide it into several sub-regions and construct a quadtree-R-tree index, outputting a quadtree-R-tree dual-layer hybrid index structure; The fourth module: Based on the real-time collected user screen coordinate data and the quadtree-R-tree dual-layer hybrid index structure, the screen coordinates are converted to geographic coordinates, and visible elements are filtered to output a set of visible area elements; Module 33: Calculates the scaling factor based on real-time collected user screen coordinate data and outputs the scaling factor; Module 33: Based on the scaling factor and preset inverse Mercator projection parameters, it performs a transformation on the user's screen coordinate data and outputs the geographical latitude and longitude range and the geographical boundary of the user's viewport. Module 34: Based on the user viewport geographic boundary and the quadtree-R tree dual-layer hybrid index structure, recursively traverses the quadtree root node, filters the quadtree sub-regions covering the viewport based on the preset spatial range matching algorithm, and outputs a list of matching quadtree sub-regions. Module 35: Based on the matched quadtree sub-region list and the user viewport geographic boundary, perform range queries on the local R-tree index and output a set of candidate nautical chart elements; Module 36: Based on the candidate chart element set, preset visibility rules, chart element hierarchy priority, and preset type rules, perform hierarchy and type filtering on the candidate chart elements and output a preliminary set of visible elements; The fifth module: Based on the set of visible area elements and preset style rules, it allocates rendering tasks and optimizes the matching of GPU instruction sets to output real-time nautical chart images at a preset frame rate.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the electronic chart drawing method based on data fusion technology as described in claims 1-5.

8. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of an electronic nautical chart drawing method based on data fusion technology as described in claims 1-5.

Images