Electronic chart drawing method and system based on data fusion technology

Abstract

The invention relates to the technical field of digital fusion, in particular to an electronic chart drawing method and system based on a data fusion technology, and the method comprises the steps: S10, building and dynamically updating a mixed index structure based on a preset electronic chart database and an incremental data packet collected in real time; s20, matching the same-name elements, fusing the identified space overlapping regions, and outputting a de-duplicated global sea chart data set; s30, dividing a plurality of sub-regions, constructing a quadtree-R tree index, and outputting a quadtree-R tree double-layer mixed index structure; s40, converting the screen coordinates into geographic coordinates, screening visible elements, and outputting a visible region element set; and S50, based on the visible area element set and a preset style rule, allocating a rendering task, optimizing and matching a GPU instruction set, and outputting a real-time sea chart picture with a preset frame rate. According to the method, the real-time processing efficiency and the visualization fluency of the electronic chart are remarkably improved, and the user interaction experience is optimized.

Description

Technical Field

[0001] The present application relates to the field of digital fusion technology, and specifically to a method and system for drawing electronic nautical charts based on data fusion technology. Background Art

[0002] Electronic Navigational Charts (ENCs) are digital products used to describe maritime geography and navigational information. Due to issues with the ENC data format, researchers designed the System Electronic Navigational Chart (SENC) storage format to improve efficiency and operational performance. Existing ENC rendering and display solutions suffer from the following issues: time-consuming data merging, inefficient chart and layer traversal, and time-consuming data cropping, making them difficult to meet the requirements of high-performance equipment.

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

[0004] In order to solve the above technical problems, the present application provides an electronic nautical chart drawing method and system based on data fusion technology.

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

[0006] S10: Based on the preset electronic chart database and the incremental data packets collected in real time, a hybrid index structure is constructed and dynamically updated;

[0007] S20: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, the elements with the same name are matched and the identified spatially overlapping areas are merged to output a deduplicated global nautical chart dataset;

[0008] S30: Based on the global nautical chart dataset, divide the data into several sub-areas and construct a quadtree-R-tree index, and output a quadtree-R-tree double-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 a visible area element set;

[0010] S50: Based on the visible area element set and preset style rules, the rendering tasks are allocated and the GPU instruction set is optimized and matched to output a real-time nautical chart image with a preset frame rate.

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

[0012] Based on the preset electronic chart database, basic chart data sets are extracted and a hybrid index structure is constructed;

[0013] The basic nautical chart dataset includes chart tiles, chart layers, and chart elements of global electronic nautical charts, and 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 a preprocessing result is output, wherein the preprocessing result includes the content of the incremental data, the incremental operation type and the associated spatial range, wherein the incremental operation type includes the addition type, the deletion type and the modification type;

[0015] The pretreatment includes:

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

[0017] Parse the incremental operation type based on the preset operation type encoding rules;

[0018] Analyze the associated spatial range based on the preset spatial range coding rules;

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

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

[0021] performing incremental operations on the chart elements based on the content of the incremental data, and outputting an updated set of chart elements;

[0022] Based on the updated chart feature set, the quadtree index structure and the R-tree index structure are dynamically adjusted to output a maintained hybrid index structure.

[0023] In a preferred embodiment, the step S20: matching elements with the same name 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 to output a deduplicated global nautical chart dataset includes:

[0024] Based on the preset electronic chart database, 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;

[0025] Based on the global nautical chart dataset, screening and matching nautical chart elements with the same name, and outputting a list of successfully matched nautical chart elements;

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

[0027] When the spatial ranges of chart elements intersect, it is judged as spatial matching;

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

[0029] Based on the nine-intersection model, the topological relationship between the chart elements of the global chart dataset is calculated, the overlapping types are identified and marked, and a spatial dataset marked with the overlapping types is output;

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

[0031] When the geometry of two chart elements is exactly the same, they are judged to be completely overlapping;

[0032] When the geometries of two chart elements partially intersect, they are judged as partially overlapping;

[0033] When the geometry of one chart feature completely contains the geometry of another Figure 1 When there are elements, it is judged to be an inclusion relationship.

[0034] In a preferred embodiment, the step S20: matching elements with the same name 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 to output a deduplicated global nautical chart dataset, further includes:

[0035] Based on the list of nautical chart elements and the spatial dataset, retaining unique identification elements for completely overlapping nautical chart elements, performing geometric merging on partially overlapping nautical chart elements, and retaining parent elements for nautical chart elements with inclusion relationships;

[0036] Based on the preset spatial conflict detection algorithm, overlapping areas are fused and the fused spatial dataset is output;

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

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

[0039] In a preferred embodiment, the step S30: dividing the global nautical chart dataset into a plurality of 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 chart dataset, the geographical range is divided, the sub-region boundaries are determined, and the sub-region division results are output;

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

[0042] 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 double-layer hybrid index structure.

[0043] In a preferred embodiment, the step S40: converting screen coordinates to geographic coordinates based on the real-time collected user screen coordinate data and the quadtree-R-tree dual-layer hybrid index structure, screening visible elements, and outputting a visible area element set includes:

[0044] Calculating a scaling factor based on the user screen coordinate data collected in real time, and outputting the scaling factor;

[0045] Based on the scaling factor and the preset inverse Mercator projection parameters, the user screen coordinate data is converted to output the geographic latitude and longitude range and the geographic boundaries of the user's viewport;

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

[0047] Based on the matching quadtree sub-region list and the user viewport geographic boundaries, a range query is performed on the local R-tree index to output a set of candidate chart features;

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

[0049] In a preferred embodiment, S50: Based on the visible area element set and the 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 includes:

[0050] Based on the initially visible feature set and the preset rendering engine, the rendering task is assigned to the chart features, and a task list of the chart features to be rendered is output;

[0051] Based on the task list of chart elements to be rendered and preset style rules, the GPU instruction set is optimized and matched, and the optimized GPU rendering instruction set is output;

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

[0053] The second object of the invention of this application is achieved through the following technical solutions:

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

[0055] The first module: Based on the preset electronic chart database and the incremental data packets collected in real time, a hybrid index structure is constructed and dynamically updated;

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

[0057] The third module: based on the global chart dataset, divides the data into several sub-areas and constructs a quadtree-R-tree index, and outputs a quadtree-R-tree double-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, converts the screen coordinates to geographic coordinates, filters visible elements, and outputs a visible area element set;

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

[0060] The third objective of this application is achieved through the following technical solutions:

[0061] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned electronic nautical chart drawing method based on data fusion technology are implemented.

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

[0063] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned 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 regional segmentation is performed based on the geographic distribution characteristics of the global nautical chart dataset, and the spatial continuity of sub-regions is ensured through boundary constraints, 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 features, and the hierarchical recursive characteristics of the quadtree are utilized to achieve rapid regional positioning. At the hybrid optimization layer, a spatial range forced alignment algorithm is used to ensure the geometric consistency of the quadtree segmentation boundary and the R-tree node coverage range. A node-level pointer binding mechanism is used to establish a direct association between the quadtree terminal node and the R-tree root node, forming a two-layer index linkage system. Through the progressive processing of spatial divide-and-conquer-local indexing-hybrid optimization, this solution fully utilizes the R-tree's optimization capabilities for complex range queries while maintaining the advantages of the quadtree's spatial proximity query. 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. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0067] Figure 2 This is a flowchart for implementing step S10 in an embodiment of an electronic nautical chart drawing method based on data fusion technology of the present application;

[0068] Figure 3 This is a flowchart for implementing step S20 in an embodiment of an electronic nautical chart drawing method based on data fusion technology of the present 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 of the present application;

[0070] Figure 5 This is a flowchart for implementing step S30 in an embodiment of an electronic nautical chart drawing method based on data fusion technology of the present application;

[0071] Figure 6 This is a principle block diagram of a computer device of the present application. DETAILED DESCRIPTION

[0072] The following is combined with Figure 1-6 This application is described in further detail.

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

[0074] S10: Based on the preset electronic chart database and the incremental data packets collected in real time, a hybrid index structure is constructed and dynamically updated;

[0075] S20: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, the elements with the same name are matched and the identified spatially overlapping areas are merged to output a deduplicated global nautical chart dataset;

[0076] S30: Based on the global nautical chart dataset, divide the data into several sub-areas and construct a quadtree-R-tree index, and output a quadtree-R-tree double-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 a visible area element set;

[0078] S50: Based on the visible area element set and preset style rules, the rendering tasks are allocated and the GPU instruction set is optimized and matched to output a real-time nautical chart image with a preset frame rate.

[0079] In this embodiment, by constructing a quadtree-R-tree hybrid index structure, dynamic fusion of the preset nautical chart database and real-time incremental data is achieved, ensuring that the spatial index structure is updated in real time with data changes, providing an efficient access interface for upper-level processing; based on spatial topology analysis and standardized deduplication processing, multi-source heterogeneous layer data is integrated, element redundancy and spatial conflicts are eliminated, and a logically consistent global nautical chart dataset is generated, 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, which increases the efficiency of regional queries by orders of magnitude; combined with the coordinate conversion and element screening mechanism of viewport dynamic perception, 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 drawing of nautical chart elements is achieved, and smooth frame rate output can be maintained in complex scenarios. 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, while ensuring the timeliness and accuracy of nautical charts, providing users with a highly responsive, low-latency interactive navigation experience.

[0080] Figure 2 , step S10 includes:

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

[0082] S102: The basic nautical chart dataset includes chart tiles, chart layers, and 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 a 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 the addition type, the deletion type, and the modification type.

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

[0085] S105: Parsing the incremental operation type based on the preset operation type encoding rules;

[0086] S106: parsing the associated spatial range based on a preset spatial range coding rule;

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

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

[0089] S109: performing incremental operations on the nautical chart elements based on the content of the incremental data, and outputting an updated set of nautical chart elements;

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

[0091] In this embodiment, a quadtree-R-tree hybrid index structure is constructed based on a preset nautical chart database, and an efficient access channel is formed through spatial stratification and feature aggregation. When an incremental data packet is received, multi-dimensional parsing rules are used to structurally decompose the incremental content, and accurate positioning of data changes is achieved through operation type encoding and spatial range encoding. During the index update phase, a dynamic mapping relationship between incremental operations and index nodes is established, and local updates of the index structure are achieved through positioning of quadtree units and adjustment of R-tree nodes in the affected area, avoiding global reconstruction overhead. Finally, through the coordinated adjustment of feature-level incremental operations and index structures, the dynamic maintenance efficiency of the hybrid index is significantly improved while ensuring data consistency. This solution forms a complete closed loop from data change perception to index structure optimization through the pipeline processing of parsing-positioning-update, so that the chart data update process has both real-time response capabilities and storage access performance, providing continuously optimized spatial indexing services for upper-level applications.

[0092] Figure 3 , step S20 includes:

[0093] S201: 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;

[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: screening and matching chart elements with the same name includes spatial matching and attribute matching;

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

[0097] S205: When the attribute information of the chart elements is completely consistent, it is determined that the attributes match;

[0098] S206: Based on the nine-intersection model, calculating the topological relationship between the chart elements of the global chart dataset, identifying and marking the overlapping types, and outputting the spatial dataset marked with the overlapping types;

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

[0100] S208: When the geometry of two chart elements is exactly the same, it is determined that they completely overlap;

[0101] S209: When the geometries of two nautical chart elements partially intersect, it is determined that they are partially overlapping;

[0102] S210: When the geometry of one chart element completely contains the geometry of another chart element Figure 1 When there are elements, it is judged to be an inclusion relationship.

[0103] In this embodiment, standardized conversion rules are used to unify the formats and semantically align multi-source nautical chart layers. A dynamically updated hybrid index structure is then used to achieve spatial association across data sources, building 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. By combining geometric topological analysis with metadata validation, identical features across layers are accurately identified, effectively resolving feature duplication issues caused by heterogeneous multi-source data. At the topological analysis layer, a nine-intersection model is used to formally describe the spatial relationships between features. Fine-grained distinctions between full overlap, partial overlap, and inclusion relationships are used to provide topological constraints for subsequent data fusion. Finally, at the redundancy processing layer, overlapping features are hierarchically labeled and logically merged based on the topological analysis results, eliminating data redundancy while preserving key spatial features. This solution, through a chained process of data integration, feature matching, topological analysis, and redundancy processing, forms a complete processing flow from multi-source heterogeneous data access to unified chart dataset output. This significantly improves the integrity and spatial consistency of chart data, providing high-quality, redundancy-free global nautical chart services for upper-layer applications.

[0104] Figure 4 , step S20 further includes:

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

[0106] SB2: Fuse overlapping areas based on a preset spatial conflict detection algorithm and output the fused spatial dataset;

[0107] SB3: Based on the IHO S-100 standard, redundant elements of the fused spatial dataset are deleted, and a deduplicated global chart dataset is output;

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

[0109] In this embodiment, a uniqueness-preserving strategy is adopted for fully overlapping features, a geometric merge operation is performed on partially overlapping features, and hierarchical simplification is performed on containment-related features. Data redundancy is eliminated through intelligent spatial relationship analysis. At 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. At the standardization processing layer, the fused data is quality-verified in strict accordance with the IHO S-100 international standard, achieving data normalization through feature uniqueness verification and attribute consistency checks. At the geometric optimization layer, simplification strategies such as the Douglas-Peucker algorithm are used to moderately abstract complex features, reducing geometric complexity while preserving key spatial features. This solution, through a chained process of redundancy elimination, conflict fusion, standard verification, and geometric optimization, forms a complete processing chain from raw data integration to optimized data output. 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 range, determine the sub-region boundaries, and output the sub-region division results;

[0112] S302: constructing a local quadtree index based on the nautical chart elements in each sub-area and outputting 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 double-layer hybrid index structure.

[0114] In this embodiment, adaptive regional segmentation is performed based on the geographic distribution characteristics of the global nautical chart dataset. Boundary constraints are used to 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 features, and the hierarchical recursive characteristics of the quadtree are used to achieve rapid regional positioning. At the hybrid optimization layer, a spatial range forced alignment algorithm is used to ensure the geometric consistency of the quadtree segmentation boundary and the R-tree node coverage range. A node-level pointer binding mechanism is used to establish a direct association between the quadtree terminal node and the R-tree root node, forming a two-layer index linkage system. Through the progressive processing of spatial divide-and-conquer-local indexing-hybrid optimization, this solution fully utilizes the R-tree's optimization capabilities for complex range queries while maintaining the advantages of the quadtree's spatial proximity query. 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: Calculating a scaling factor based on user screen coordinate data collected in real time, and outputting the scaling factor;

[0117] S402: Based on the scaling factor and the preset inverse Mercator projection parameters, the user screen coordinate data is converted to output the geographic latitude and longitude range and the geographic boundary of the user 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, filter the quadtree sub-regions covering the viewport based on a preset spatial range matching algorithm, and output a list of matching quadtree sub-regions;

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

[0120] S405: Based on the candidate chart element set, preset visibility rules, chart element level priority, and preset type rules, perform level and type filtering on the candidate chart elements and output a preliminary visible element set.

[0121] In this embodiment, based on real-time screen coordinates and a dynamic scaling factor, an inverse Mercator projection algorithm is used to accurately map pixel space to geographic space, generating the user's viewport's geographic boundaries as a reference for spatial queries. At the index query layer, a quadtree-R-tree dual-layer hybrid index structure is utilized to rapidly locate coverage areas through viewport-aware recursive quadtree traversal. This, combined with the R-tree's range query capabilities, allows for efficient retrieval of candidate features, significantly improving spatial query response speed. At the feature filtering layer, a hierarchical priority-driven visibility rule, combined with a feature type whitelist mechanism, performs dual filtering of candidate features, preserving critical navigation information while eliminating redundant display content. This solution, through a pipelined process of coordinate conversion, index query, and feature filtering, forms a complete, optimized chain from user interaction input to visible feature output. While ensuring chart display accuracy, it effectively reduces rendering load and improves interactive fluency, providing an efficient solution for real-time visualization of complex chart scenes.

[0122] Step S50 includes:

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

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

[0125] S503: Based on the optimized GPU rendering instruction set, execute the GPU rendering instruction to generate a real-time chart image and output the real-time chart image at a preset frame rate.

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

[0127] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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. The electronic nautical chart drawing system based on data fusion technology corresponds to the electronic nautical chart drawing method based on data fusion technology in the above embodiment. The electronic nautical chart drawing system based on data fusion technology includes:

[0129] The first module: Based on the preset electronic chart database and the incremental data packets collected in real time, a hybrid index structure is constructed and dynamically updated;

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

[0131] The third module: based on the global chart dataset, divides the data into several sub-areas and constructs a quadtree-R-tree index, and outputs a quadtree-R-tree double-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, converts the screen coordinates to geographic coordinates, filters visible elements, and outputs a visible area element set;

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

[0134] Optionally, also include:

[0135] Module 6: Based on the preset electronic chart database, extract the basic chart data set and build a hybrid index structure;

[0136] Module 7: The basic nautical chart dataset includes chart tiles, chart layers, and 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 receiving an incremental data packet, preprocess it and output the preprocessing result, which includes the content of the incremental data, the incremental operation type and the associated spatial range. The incremental operation type includes the addition type, deletion type and modification type.

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

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

[0140] Module 11: Analyze the associated spatial range based on the preset spatial range coding rules;

[0141] Module 12: Based on the incremental operation type and the 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: performing incremental operations on the chart elements based on the content of the incremental data and outputting an updated set of chart elements;

[0144] Module 15: Based on the updated chart feature set, the quadtree index structure and the R-tree index structure are dynamically adjusted to output the maintained hybrid index structure.

[0145] Optionally, also include:

[0146] Module 16: Based on the preset electronic chart database, collected multi-source layer data and the dynamically updated hybrid index structure, it integrates and processes multi-source chart data and outputs 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 screening and matching of chart elements with the same name includes spatial matching and attribute matching;

[0149] Module 19: When the spatial ranges of chart elements intersect, it is judged as spatial matching;

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

[0151] Module 21: Based on the nine-intersection model, calculate the topological relationship between the chart elements of the global chart dataset, identify and mark the overlapping types, and output the spatial dataset marked with the overlapping types;

[0152] Module 22: The overlapping types include complete overlap, partial overlap and inclusion relationship;

[0153] Module 23: When the geometry of two chart elements is exactly the same, it is judged to be completely overlapping;

[0154] Module 24: When the geometries of two chart elements partially intersect, it is judged as partial overlap;

[0155] Module 25: When the geometry of one chart element completely contains another Figure 1 When there are elements, it is judged to be an inclusion relationship.

[0156] Optionally, also include:

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

[0158] Module 27: Fusion of overlapping areas based on a preset spatial conflict detection algorithm, outputting the fused spatial dataset;

[0159] Module 28: Based on the IHO S-100 standard, redundant elements of the fused spatial dataset are deleted, and a deduplicated global chart dataset is output;

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

[0161] Optionally, also include:

[0162] Module 30: Based on the final optimized global chart dataset, divide the geographical range, determine the sub-region boundaries, and output the sub-region division results;

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

[0164] Module 32: 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 double-layer hybrid index structure.

[0165] Optionally, also include:

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

[0167] Module 33: Based on the scaling factor and the preset inverse Mercator projection parameters, the user screen coordinate data is converted to output the geographic latitude and longitude range and the geographic boundary of the user's viewport;

[0168] Module 34: Based on the user's viewport geographic boundaries and the quadtree-R-tree dual-layer hybrid index structure, recursively traverse the quadtree root node, screen the quadtree sub-regions covering the viewport based on a preset spatial range matching algorithm, and output a list of matching quadtree sub-regions;

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

[0170] Module 36: 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 visible feature set.

[0171] Optionally, also include:

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

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

[0174] Module 40: Based on the optimized GPU rendering instruction set, execute GPU rendering instructions, generate real-time chart images, and output real-time chart images with a preset frame rate.

[0175] The specific definition of an electronic nautical chart drawing system based on data fusion technology can be found in the definition of an electronic nautical chart drawing method based on data fusion technology above and will not be repeated here. Each module in the aforementioned electronic nautical chart drawing system based on data fusion technology can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory 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. The computer device 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 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store nautical chart elements. The network interface of the computer device is used to communicate with an external terminal 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, an electronic nautical chart drawing method based on data fusion technology is implemented.

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

[0179] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database 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), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0180] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by 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 an electronic nautical chart based on data fusion technology, characterized in that: include: S10: Based on the preset electronic chart database and the incremental data packets collected in real time, a hybrid index structure is constructed and dynamically updated; S20: Based on the preset electronic nautical chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, the elements with the same name are matched and the identified spatially overlapping areas are merged to output a deduplicated global nautical chart dataset; S30: Based on the global nautical chart dataset, divide the data into several sub-areas and construct a quadtree-R-tree index, and output a quadtree-R-tree double-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 a visible area element set; S50: Based on the visible area element set and preset style rules, the rendering tasks are allocated and the GPU instruction set is optimized and matched to output a real-time nautical chart image with a preset frame rate.

2. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that: The step S10: constructing and dynamically updating a hybrid index structure based on a preset electronic chart database and incremental data packets collected in real time, includes: Based on the preset electronic chart database, extract the basic chart data set and build a hybrid index structure; The basic nautical chart dataset includes chart tiles, chart layers, and chart elements of global electronic nautical charts, and 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 a preprocessing result is output, wherein the preprocessing result includes the content of the incremental data, the incremental operation type and the associated spatial range, wherein the incremental operation type includes the addition type, the deletion type and the modification type; The pretreatment includes: Extract the content of incremental data based on preset content parsing rules; Parse the incremental operation type based on the preset operation type encoding rules; Analyze the associated spatial range based on the preset spatial range coding rules; Based on the incremental operation type and the associated spatial range, the constructed quadtree index structure is located and the affected quadtree units are output; Based on the affected quadtree units, the constructed R-tree index structure is updated and the adjusted R-tree nodes are output; performing incremental operations on the chart elements based on the content of the incremental data, and outputting an updated set of chart elements; Based on the updated chart feature set, the quadtree index structure and the R-tree index structure are dynamically adjusted to output a maintained hybrid index structure.

3. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that: The step S20: matching elements with the same name 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 to output a deduplicated global nautical chart dataset includes: Based on the preset electronic chart database, 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; Based on the global nautical chart dataset, screening and matching nautical chart elements with the same name, and outputting a list of successfully matched nautical chart elements; The screening and matching of chart elements with the same name includes spatial matching and attribute matching; When the spatial ranges of chart elements intersect, it is judged as spatial matching; When the attribute information of the chart elements is completely consistent, it is judged to be an attribute match; Based on the nine-intersection model, the topological relationship between the chart elements of the global chart dataset is calculated, the overlapping types are identified and marked, and a spatial dataset marked with the overlapping types is output; The overlapping types include complete overlap, partial overlap and inclusion relationship; When the geometry of two chart elements is exactly the same, they are judged to be completely overlapping; When the geometries of two chart elements partially intersect, they are judged as partially overlapping; When the geometry of one chart element completely contains another chart element, it is judged to be a containment relationship.

4. The electronic nautical chart drawing method based on data fusion technology according to claim 3 is characterized in that: The step S20: matching elements with the same name 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 to output a deduplicated global nautical chart dataset, further includes: Based on the list of nautical chart elements and the spatial dataset, retaining unique identification elements for completely overlapping nautical chart elements, performing geometric merging on partially overlapping nautical chart elements, and retaining parent elements for nautical chart elements with inclusion relationships; Based on the preset spatial conflict detection algorithm, overlapping areas are fused and the fused spatial dataset is output; Based on the IHO S-100 standard, redundant elements of the fused spatial dataset are deleted, and a deduplicated global chart dataset is output; Based on the deduplicated global chart dataset and the preset geometric simplification algorithm, the complexity of chart elements is optimized and the final optimized global chart dataset is output.

5. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that: The step S30: dividing the global nautical chart dataset into a plurality of sub-areas and constructing a quadtree-R-tree index, and outputting a quadtree-R-tree double-layer hybrid index structure includes: Based on the final optimized global chart dataset, the geographical range is divided, the sub-region boundaries are determined, and the sub-region division results are output; Based on the chart elements in each sub-area, a local quadtree index is constructed and the local quadtree index structure is output; 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 double-layer hybrid index structure.

6. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that: The step S40: based on the real-time collected user screen coordinate data and the quadtree-R-tree dual-layer hybrid index structure, converting the screen coordinates to geographic coordinates, screening visible elements, and outputting a visible area element set includes: Calculating a scaling factor based on the user screen coordinate data collected in real time, and outputting the scaling factor; Based on the scaling factor and the preset inverse Mercator projection parameters, the user screen coordinate data is converted to output the geographic latitude and longitude range and the geographic boundaries of the user's viewport; Based on the user's viewport geographic boundaries and the quadtree-R-tree dual-layer hybrid index structure, a recursive traversal is performed on the quadtree root node. Based on a preset spatial range matching algorithm, the quadtree sub-regions covering the viewport are screened and a list of matching quadtree sub-regions is output. Based on the matching quadtree sub-region list and the user viewport geographic boundaries, a range query is performed on the local R-tree index to output a set of candidate chart features; Based on the candidate chart feature set, preset visibility rules, chart feature level priority, and preset type rules, the candidate chart features are filtered by level and type, and a preliminary visible feature set is output.

7. The electronic nautical chart drawing method based on data fusion technology according to claim 1, characterized in that: S50: Based on the visible area element set and the preset style rules, the rendering task is assigned and the GPU instruction set is optimized and matched to output a real-time nautical chart image with a preset frame rate, including: Based on the initially visible feature set and the preset rendering engine, the rendering task is assigned to the chart features, and a task list of the chart features to be rendered is output; Based on the task list of chart elements to be rendered and preset style rules, the GPU instruction set is optimized and matched, and the optimized GPU rendering instruction set is output; Based on the optimized GPU rendering instruction set, the GPU rendering instructions are executed to generate real-time chart images and output real-time chart images with a preset frame rate.

8. An electronic nautical chart drawing system based on data fusion technology, characterized in that: include: The first module: Based on the preset electronic chart database and the incremental data packets collected in real time, a hybrid index structure is constructed and dynamically updated; The second module: Based on the preset electronic chart database, the collected multi-source layer data, and the dynamically updated hybrid index structure, it matches the elements with the same name and fuses the identified spatially overlapping areas to output a deduplicated global chart dataset; The third module: based on the global chart dataset, divides the data into several sub-areas and constructs a quadtree-R-tree index, and outputs a quadtree-R-tree double-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, converts the screen coordinates to geographic coordinates, filters visible elements, and outputs a visible area element set; The fifth module: Based on the visible area element set and preset style rules, it allocates rendering tasks and optimizes the matching GPU instruction set to output a real-time nautical chart image with a preset frame rate.

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

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of the electronic nautical chart drawing method based on data fusion technology according to claims 1 to 7.

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