High-precision earth surface Xinjiang transient identification method in global large-scale scene

Abstract

The invention discloses a high-precision earth surface Xinjiang transient identification method in a global large-scale scene. The method comprises the following steps: step 1, extracting a sea surface geometric space from an authoritative data source; step 2, constructing a ray grid and identifying a sea-land area; wherein in the ray grid construction process, firstly, the initial longitude of each ray is determined, and then according to the resolution requirement, each ray extends at different levels and different latitudes to gradually divide the ray grid of the whole map area; in the sea-land area identification stage, intersection points of rays and land boundaries are detected through intersection operation of the rays and sea surface geometric space, so that the rays are divided into a plurality of ray segments, and then each ray segment is marked as land or ocean based on the entry and exit attributes of the sea surface; and 3, when a target point needs to be identified, calculating the exact position of the target point in the ray grid, and directly judging the sea-land category of the target point. According to the invention, efficient and accurate determination of sea and land attributes of any position in the world is realized.

Description

Technical Field

[0001] The present invention relates to the field of aviation, and in particular to a high-precision surface sea border instant recognition method in a global large-scale scenario. Background Art

[0002] In the aviation field, with the rapid development of geographic information systems and remote sensing technology, accurate and efficient determination of the land and sea properties of the earth's surface is of great significance for flight navigation, target positioning and risk assessment. Traditional land and sea classification methods are limited by data accuracy and processing efficiency, and are mostly based on static maps or simple algorithms. They cannot adapt to the complex and changeable land and sea boundary environment at the global scale, and cannot meet the high requirements of accuracy and real-time performance in the modern aviation field. Summary of the invention

[0003] The purpose of the invention is to provide a high-precision surface sea boundary instant recognition method in a global large-scale scenario. Through the three core links of basic data extraction, ray grid construction and sea and land area identification, and efficient judgment of sea and land attributes of target points, efficient and accurate judgment of sea and land attributes at any location in the world is achieved, providing important technical support for the development of the aviation field.

[0004] The object of the present invention is achieved through the following technical solutions:

[0005] A high-precision surface sea border instant recognition method in a global large scene includes the following steps:

[0006] Step 1: Extract the ocean surface geometry space from the authoritative data source;

[0007] Step 2: Ray grid construction and sea and land area identification;

[0008] Among them, in the process of ray grid construction, the starting longitude of each ray is first determined, and then the ray grid of the entire map area is gradually divided by extending each ray at different levels and latitudes according to the resolution requirements; in the sea and land area identification stage, the intersection of the ray and the land boundary is detected through the intersection operation of the ray and the sea surface geometric space, so as to divide the ray into multiple ray segments, and then each ray segment is marked as land or sea based on the entry and exit properties of the sea surface;

[0009] Step 3: When there is a target point that needs to be identified, calculate the exact position of the target point in the ray grid and directly determine the sea and land category of the target point.

[0010] Preferably, step one comprises the following steps:

[0011] Step 1.1) Extract the sea surface geometry from the authoritative data source;

[0012] Step 1.2) performing geometric topology correction on the sea surface geometry;

[0013] Step 1.3) Perform spatial analysis and union operations on the sea surface geometry so that the sea surface data in each map sheet is a unified sea surface geometry space.

[0014] Preferably, step 1.1) comprises the following steps:

[0015] Step 1.1.1) Convert the authoritative data source into a dataset-layer-feature database style;

[0016] Step 1.1.2) performing attribute retrieval in the feature database based on the specific code of the sea surface feature to obtain the sea surface geometric features of interest;

[0017] Step 1.1.3) For the retrieved sea surface geometric elements, first remove duplication and correct errors, then merge these sea surface geometric elements into a complete sea surface geometric body; finally, verify and optimize the merged geometric body.

[0018] Preferably, step 1.2) comprises the following steps:

[0019] Step 1.2.1) Format conversion, projection unification and data cleaning of sea surface geometry;

[0020] Step 1.2.2) Check the topological errors in the data and complete the calibration of all geometric topological errors;

[0021] Step 1.2.3) Topology correction: For node position errors or missing, the topology relationship is corrected by adding, deleting or moving nodes; for boundary errors or inconsistencies, the topology relationship is repaired by adjusting the boundary;

[0022] Step 1.2.4) After completing the topology correction, perform post-processing verification on the sea surface geometry to ensure that all topological errors have been corrected.

[0023] Preferably, step 1.3) comprises the following steps:

[0024] Step 1.3.1) Collect all sea surface geometric bodies to be merged in the map sheet to form an initial sea surface geometric data set to be processed;

[0025] Step 1.3.2) In the process of merging sea surface geometries, spatial relationship analysis is used to determine which sea surface geometries should be merged together;

[0026] Step 1.3.3) After the merging process is completed, verify and clean the merged geometric data;

[0027] Step 1.3.4) Output the merged sea surface geometry data into an overall unified sea surface geometry space.

[0028] Preferably, step three comprises the following steps:

[0029] First, determine the ray number in the ray grid according to the latitude information of the target point, so as to quickly locate the corresponding ray in the ray grid;

[0030] Then locate the ray segment where the target point is located through binary search;

[0031] Finally, the target point is determined to be ocean or land based on the markings of the ray segments.

[0032] The beneficial effects of the present invention are:

[0033] The present invention realizes the rapid and accurate determination of the sea and land attributes of any location in the world through high-precision basic data extraction, ray grid construction and sea and land area identification, and efficient determination of the sea and land attributes of target points. This not only improves the accuracy of flight navigation and target positioning, but also provides strong technical support for the fields of marine resource management, military strategic deployment, and meteorological disaster warning. The efficiency and accuracy of the algorithm ensure the real-time nature of data processing and meet the high requirements of accuracy and real-time nature in the field of modern aviation.

[0034] The present invention reduces storage and retrieval costs through the data compression technology of ray grid, which makes the method show extraordinary practical value in large-scale applications. At the same time, the present invention also provides a processing strategy for overlapping areas, adjacent areas and inclusion relationships, as well as a data verification and cleaning process to ensure the accuracy and consistency of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram for identifying sea and land segmentation areas.

[0036] Figure 2 This is a schematic diagram for determining the sea and land attributes of the target point. DETAILED DESCRIPTION

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Ma thin p[3] illustrate.

[0038] A high-precision surface sea border instant recognition method in a global large scene includes the following steps:

[0039] Step 1: Extract basic data

[0040] This step extracts the sea surface geometric space for sea and land classification from the authoritative data source. As an example, the authoritative data source can be GJB100W data and GJB1400W data, which have been used to extract high-precision land and sea boundaries for domestic and global scales respectively. The basic data extraction process ensures the accuracy and completeness of the data, laying a solid foundation for subsequent sea and land classification work. In the basic data extraction stage, advanced algorithms are used to filter noise, remove noise and irrelevant information, and ensure the purity of the data. At the same time, the extracted sea surface elements are subjected to geometric topological correction and geometric data merging to further improve the accuracy and consistency of the data. Specifically, the following steps are included:

[0041] Step 1.1) Extract the ocean surface geometry from the authoritative data source. This includes the following steps:

[0042] Step 1.1.1) Layer preparation: In this step, we first convert the GJB (National Military Standard) data into a dataset-layer-feature database style. This conversion process is to unify the data format for subsequent processing and analysis. The specific operations are:

[0043] a. Read GJB data and understand its data structure and content;

[0044] b. Create a new dataset and divide it into different layers as needed, such as coastline layer, island layer, etc.;

[0045] c. Import the relevant information in the GJB data into the corresponding layers to form a feature database.

[0046] Step 1.1.2) Feature acquisition: After completing the layer preparation, perform attribute retrieval in the feature database based on the specific coding of the sea surface feature to obtain the sea surface geometric features we are interested in. The specific operations are as follows:

[0047] a. Determine the type of sea surface features to be extracted, such as coastlines, islands, etc., and find the corresponding feature codes;

[0048] b. In the feature database, perform attribute retrieval based on feature codes to filter out the required sea surface geometric features;

[0049] c. Extract and store these sea surface geometric elements for subsequent processing.

[0050] Step 1.1.3) Geometric element conversion: In this step, the retrieved sea surface geometric elements are merged. Since the sea surface geometric elements may be fragmented, direct processing will increase the computational complexity and processing time. Therefore, we merge these fragmented sea surface geometric elements into a complete sea surface geometry to facilitate subsequent processing. The specific operations are as follows:

[0051] a. Preprocess the retrieved sea surface geometric elements, such as removing duplications and correcting errors;

[0052] b. Use professional geometry processing tools or methods to merge these sea surface geometry elements into a complete sea surface geometry;

[0053] c. Verify and optimize the merged geometry to ensure its accuracy and completeness.

[0054] Step 1.2) Perform geometric topology correction on the sea surface geometry

[0055] Geometric topological correction is an important part of geographic information system (GIS) and remote sensing image processing, which aims to detect and correct topological errors in vector or raster data to ensure the accuracy and consistency of data. Topological relationships define the spatial relationships between geographic features, such as adjacency, inclusion, etc. When extracting sea surface data, topological errors such as gaps, overlaps, and self-intersections may occur due to large data volume, imperfections in the extraction algorithm, or errors in the data source itself. Geometric data with topological errors cannot be used in the subsequent spatial geometric relationship solution process. Therefore, before use, the spatial geometric data must be corrected for geometric topological errors.

[0056] Step 1.2.1) Data preprocessing

[0057] a. Before performing geometric topology correction, the sea surface geometry needs to be preprocessed, including format conversion, projection unification, data cleaning, etc., to ensure data quality and consistency.

[0058] Step 1.2.2) Topology check

[0059] a. Use the two-dimensional geospatial analysis tool GEOS to automatically check the topological errors in the data and complete the calibration of all geometric topological errors.

[0060] Step 1.2.3) Topology Correction

[0061] a. Node correction: In case of incorrect or missing node positions, the topological relationship can be corrected by adding, deleting or moving nodes;

[0062] b. Boundary correction: For errors or inconsistencies in boundaries, such as gaps or overlaps, the topological relationship is repaired by adjusting the boundaries. This may involve operations such as merging, splitting, or redefining boundaries.

[0063] Step 1.2.4) Post-processing verification:

[0064] a. After completing the topology correction, perform post-processing verification on the sea surface geometry to ensure that all topological errors have been corrected. This can be achieved by running the automatic topology check tool again or performing manual verification.

[0065] Step 1.3) Merge geometry data

[0066] After completing the geometric topology correction of the sea surface geometry of each map sheet, the sea surface geometry is subjected to the union operation in spatial analysis, so that the sea surface data in each map sheet is a unified sea surface geometric space, laying the foundation for the subsequent spatial relationship intersection. The specific implementation process is as follows:

[0067] Step 1.3.1) Collecting geometric data volumes to be merged: Collect all sea surface geometric volumes to be merged in the map sheet to form an initial sea surface geometric data set to be processed;

[0068] Step 1.3.2) Processing of spatial relations of sea surface geometry: In the process of merging sea surface geometries, spatial relation analysis is used to determine which sea surface geometries should be merged together. Spatial relations between sea surface geometries include: overlap (two sea surface geometries share the same spatial area), adjacency (two sea surface geometries are spatially adjacent), inclusion (one sea surface geometry completely contains another sea surface geometry), etc. For overlapping areas, the overlapping parts need to be removed, for adjacent areas, they are directly merged into a whole geometric object, and for inclusion areas, the included sea surface geometry is directly discarded;

[0069] Step 1.3.3) Data verification and cleaning: After the merging process is completed, the merged geometric data needs to be verified and cleaned. This includes checking whether the merged geometric objects meet the expected spatial relationship, repairing topological errors, and removing duplicate geometric objects;

[0070] Step 1.3.4) Result output: Finally, the merged sea surface geometry data is output as an overall unified sea surface geometry space.

[0071] Step 2: Ray grid construction and sea and land area identification

[0072] On the basis of the geometric space of the sea surface, the present invention adopts innovative ray grid technology to construct a ray grid suitable for different spatial resolution requirements. This technology realizes efficient and accurate sea and land classification of the target area by constructing a pyramid subdivision structure. In the process of ray grid construction, the starting longitude of each ray is first determined, and then the ray grid of the entire map area is gradually divided according to the resolution requirements by extending each ray at different levels and latitudes. In the sea and land segmentation area identification stage, the intersection of the ray and the land boundary is accurately detected by the intersection operation of the ray and the sea surface geometric space, thereby dividing a plurality of ray segments, each ray segment represents a continuous sea area or land area. Furthermore, dynamic solution is performed based on the entry and exit attributes of the sea surface to clarify the attributes of each ray segment (i.e. land or ocean). Specifically, the following steps are included:

[0073] Step 2.1) Construct the ray grid

[0074] 1) Determine the starting point: select a suitable starting point. In the present invention, 180 degrees west longitude is selected as the starting longitude of the ray;

[0075] 2) Setting grid specifications: According to the required accuracy and computing resources, set the specifications of the ray grid, including the grid level, the size of each grid, etc. The data resolution mapping table corresponding to the grid level is shown in Table 1:

[0076] Table 1 Mapping table of ray grid level and data resolution

[0077]

[0078] 3) Generate ray grid: Starting from the starting point, the ray grid of the entire map area is gradually divided according to the set grid specifications. Each level of grid is further subdivided into smaller grids until the required finest level is reached, and each ray is numbered by latitude.

[0079] Step 2.2) Identification of land and sea segmentation areas

[0080] 1) Intersection between rays and sea surface: In this process, a series of rays are first selected from the ray grid. These rays will start from a fixed longitude and extend to different latitudes. The goal is to find the intersection of these rays and the sea surface geometric space. The specific implementation process is as follows:

[0081] a. Start and end position establishment: First, select a starting point on the -180 degree longitude line. Since most of the earth's surface is covered by oceans, the starting point is set to the sea by default. Send a ray from the selected starting point along a specific latitude line to prepare for the next scanning process;

[0082] b. Scan and obtain intersection points: Starting from -180 degrees, scan gradually eastward along the ray until it reaches the 180-degree longitude line. During the entire scanning process, the topological intersection algorithm based on spatial analysis will be used to accurately detect the intersection points between the ray and the geometric space boundary of the sea surface. Whenever the ray intersects with the geometric space boundary of the sea surface, the system will accurately record the longitude and latitude positions of the intersection points. These recorded intersection points divide the ray into several segments, each of which clearly represents a continuous sea or land area, providing basic data for subsequent sea and land attribute determination.

[0083] 2) Determine the properties of the ray segments: After finding the intersection points between the ray and the sea surface, these intersection points are further used to divide the ray segments. Each ray is divided into multiple segments by the intersection points, and each segment is either completely on land or completely in the sea. In order to clarify the properties of each ray segment (i.e. land or sea), the algorithm will perform dynamic calculations based on the in and out properties of the sea surface. This process is as follows Figure 2 The specific implementation process is as follows:

[0084] a. Segment marking: The starting segment (from -180 degrees to the first intersection) is marked as a sea segment because the starting point is set to be on the sea by default. Subsequently, each time an intersection is passed, the segment attribute changes. That is, if the previous segment is a sea segment, then the next segment (between two intersections) is marked as a land segment, and vice versa;

[0085] b. Mapping completed: When the ray scans to the 180-degree longitude line, the scanning process ends. At this point, we have established a complete land-sea alternating segment map, in which each segment is clearly marked as a sea segment or a land segment;

[0086] 3) Labeling land and sea attributes: After determining the attributes of each ray segment, labeling is performed. Specifically, the corresponding attribute label is recorded according to whether the ray segment is land or sea. This step is crucial for subsequent data analysis and application because it provides us with a clear and unambiguous land and sea boundary dataset. Ultimately, the entire ray grid will be assigned land and sea attributes.

[0087] Step 3: Efficient determination of the sea and land attributes of the target point

[0088] The present invention develops an efficient mechanism for determining the sea and land attributes of target points. Through an intelligent algorithm, the exact position of the target point in the ray grid is quickly calculated, including the latitude number and longitude segment position of the target point in the hierarchical ray. With the help of a pre-calculated and highly compressed sea and land entry and exit identification data set, the sea and land category of the target point can be directly determined without time-consuming point-by-point comparison. This efficient determination mechanism not only greatly improves the determination speed, but also reduces storage and retrieval costs through data compression technology, making this method show extraordinary practical value in large-scale applications.

[0089] Step 3.1) Target point ray segment positioning

[0090] 1) Latitude positioning: In the latitude direction, we first need to determine the ray number in the ray grid based on the latitude information of the target point. This process usually relies on the ray grid pre-defined in step 2. The ray grid divides the earth's surface into multiple latitude bands according to different geographical latitudes, and assigns a unique ray number ID to each latitude band, as shown in the figure Figure 2 (Lat). The specific process is:

[0091] a. Calculate the ray ID: First, calculate the corresponding ray ID based on the latitude value (Lat) of the target point. The ID can identify a unique ray in the ray grid at a specific level;

[0092] b. Locate a unique ray: Once we have the ray ID, we can quickly locate the corresponding ray in the ray grid. This ray is unique and represents the exact location of the target point in the latitude direction.

[0093] 2) Longitude positioning: After determining the ray in the latitude direction, we need to further locate the specific position of the target point in the longitude direction, as shown in the figure Figure 2 (Lon) is shown. The specific implementation steps are:

[0094] a. Establish a segmented area mapping table: On the ray, divide the ray into multiple segmented areas according to the longitude. The starting and ending longitudes of each segmented area are recorded in the mapping table, and the mapping table is sorted according to the longitude value. In this way, the ray segmented area where the target point is located can be efficiently located through binary search, as shown in the figure below: Figure 2 (Map[i]);

[0095] b. Find the segment index: By comparing the specific position of the target point on the ray with the boundaries of each segment, the corresponding segment is found, and each segment position is uniquely identified by the segment index. In this way, the segment identification of the target point in the ray is completed.

[0096] Step 3.2) Determine the sea and land attributes of the target point

[0097] Once the segment where the target point is located is determined, the sea and land attributes of the segment can be obtained from the previously established sea-land alternating segment mapping. This mapping table has clearly marked whether each segment is a sea segment or a land segment. Finally, the sea and land attributes of the segment where the target point is located are directly determined as the sea and land attributes of the target point. If the segment where the target point is located is marked as a sea segment, then the target point is determined to be the sea; if the segment where the target point is located is marked as a land segment, then the target point is determined to be land.

[0098] It is understandable that those skilled in the art can make equivalent substitutions or changes based on the technical solution and inventive concept of the present invention, and all these changes or substitutions should fall within the protection scope of the claims attached to the present invention.

Claims

1. A high-precision surface sea border instant recognition method in a global large-scale scenario, characterized by The following steps are involved: Step 1: Extract the ocean surface geometry space from the authoritative data source; Step 2: Ray grid construction and sea and land area identification; Among them, in the process of ray grid construction, the starting longitude of each ray is first determined, and then the ray grid of the entire map area is gradually divided by extending each ray at different levels and latitudes according to the resolution requirements; in the sea and land area identification stage, the intersection of the ray and the land boundary is detected through the intersection operation of the ray and the sea surface geometric space, so as to divide the ray into multiple ray segments, and then each ray segment is marked as land or sea based on the entry and exit properties of the sea surface; Step 3: When there is a target point that needs to be identified, calculate the exact position of the target point in the ray grid and directly determine the sea and land category of the target point.

2. According to the method of claim 1, the method is characterized in that Step 1 includes the following steps: Step 1.1) Extract the sea surface geometry from the authoritative data source; Step 1.2) performing geometric topology correction on the sea surface geometry; Step 1.3) Perform spatial analysis and union operations on the sea surface geometry so that the sea surface data in each map sheet is a unified sea surface geometry space.

3. According to claim 2, a high-precision surface sea border instant recognition method in a global large-scale scene is characterized in that Step 1.1) includes the following steps: Step 1.1.1) Convert the authoritative data source into a dataset-layer-feature database style; Step 1.1.2) performing attribute retrieval in the feature database based on the specific code of the sea surface feature to obtain the sea surface geometric features of interest; Step 1.1.3) For the retrieved sea surface geometric elements, first remove duplication and correct errors, then merge these sea surface geometric elements into a complete sea surface geometric body; finally, verify and optimize the merged geometric body.

4. According to claim 2, a high-precision surface sea border instant recognition method in a global large-scale scene is characterized in that Step 1.2) includes the following steps: Step 1.2.1) Format conversion, projection unification and data cleaning of sea surface geometry; Step 1.2.2) Check the topological errors in the data and complete the calibration of all geometric topological errors; Step 1.2.3) Topology correction: For node position errors or missing situations, the topology relationship is corrected by adding, deleting or moving nodes; for boundary errors or inconsistencies, the topology relationship is repaired by adjusting the boundary; Step 1.2.4) After completing the topology correction, perform post-processing verification on the sea surface geometry to ensure that all topological errors have been corrected.

5. According to claim 2, a high-precision surface sea border instant recognition method in a global large-scale scene is characterized in that Step 1.3) includes the following steps: Step 1.3.1) Collect all sea surface geometric bodies to be merged in the map sheet to form an initial sea surface geometric data set to be processed; Step 1.3.2) In the process of merging sea surface geometries, spatial relationship analysis is used to determine which sea surface geometries should be merged together; Step 1.3.3) After the merging process is completed, verify and clean the merged geometric data; Step 1.3.4) Output the merged sea surface geometry data into an overall unified sea surface geometry space.

6. According to the method of claim 1, the method is characterized in that Step 3 includes the following steps: First, determine the ray number in the ray grid according to the latitude information of the target point, so as to quickly locate the corresponding ray in the ray grid; Then locate the ray segment where the target point is located through binary search; Finally, the target point is determined to be ocean or land based on the markings of the ray segments.

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