Automatic association and adjustment method for ground marking lines based on block division

Abstract

This invention belongs to the field of image processing, specifically involving an automatic correlation and adjustment method for ground marking lines based on block partitioning. It aims to solve problems such as long update cycles, potential errors and omissions, high resource consumption, and low efficiency. The invention includes: parsing airport AIP vector data to generate a structured dataset; constructing topological sub-regions within each functional area based on the connection relationships of marking endpoints; subdividing grid cells when marking density exceeds a threshold to output a layered regional map; using regions / grid cells as detection units to detect changes in AIP areas and location markers, generating new changed area data; recursively searching for N-degree correlation lines starting from the changed areas, updating only correlation lines with changes in geometry, type, or topological connection; and finally, stitching the changed and unchanged data together, generating a complete airport model through adaptive boundary extension and cropping and global topological reconstruction. This method achieves efficient and accurate updating of airport ground markings.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, and specifically relates to a method for automatic association and adjustment of ground marking lines based on block division. Background Technology

[0002] With the rapid development of the civil aviation transportation industry, airports around the world are constantly expanding and adjusting to meet the growing demand for air traffic. These changes typically involve extending runways, adding taxiways, expanding aprons, and upgrading terminal facilities. To ensure flight safety, pilots must undergo simulator training to familiarize themselves with the operating procedures and environments of different airports. Therefore, the airport models in simulator training systems must be consistent with the latest layouts of actual airports.

[0003] Currently, after physical structural changes are made to an airport, the airport model in the simulator training system also needs to be updated accordingly. This process is typically carried out by professional developers based on official change documents provided by the airport and other relevant materials. Specifically, this includes the following steps:

[0004] Data collection: Developers need to obtain all relevant change documents from the airport management, including airport layout maps, runway data, parking stand configurations, taxiways, etc.

[0005] Analysis and Planning: Conduct a detailed analysis of all received change documents to determine which parts need to be modified in the simulator training system.

[0006] Manual adjustments: Based on the analysis results, developers need to manually modify the airport model in the simulator training system, which may involve adjusting the position, shape, and size of elements such as runways, taxiways, and parking positions in the simulation environment.

[0007] Verification Testing: After the model adjustment is completed, a series of verification tests are required to ensure that the updated airport model can accurately reflect the actual situation without any omissions or errors.

[0008] While this manual approach can update airport models, it suffers from several significant problems:

[0009] Long update cycle: Due to the need to collect and review a large number of change documents and conduct detailed analysis and planning, the time interval between airport changes and simulator model updates is often long.

[0010] Potential errors and omissions: When dealing with a large amount of change information, even experienced developers cannot avoid omissions or errors, especially when there are large-scale changes to the airport, this problem is even more prominent.

[0011] High resource consumption: Each update requires a large investment of human and material resources, which not only increases operating costs, but may also increase the pressure on resource allocation due to frequent changes.

[0012] Inefficiency: The manual update process is inefficient, especially when dealing with changes to multiple airports simultaneously, which may lead to update delays and affect the quality of pilot training.

[0013] Based on this, the present invention proposes an automatic association and adjustment method for ground marking lines based on block division. Summary of the Invention

[0014] To address the aforementioned problems in existing technologies, namely long update cycles, potential errors and omissions, high resource consumption, and low efficiency, this invention provides an automatic correlation and adjustment method for ground marking lines based on block division. This method includes:

[0015] Analyze the vector graphics data in the latest airport regulations AIP diagram, extract the geometric coordinates, line types, and topological connections of all ground marking lines, and generate a structured marking dataset;

[0016] The airport is divided into multiple functional areas based on the preset functional area type. Within each functional area, a topological sub-region is constructed according to the connection relationship of the endpoints of the markings. When the marking density of the sub-region exceeds the threshold, it is subdivided into grid unit regions with dynamic grid size, and a layered regional map is output, recording the regional boundaries, hierarchical relationships and the set of marking identifiers contained therein.

[0017] The sub-regions and / or grid cell regions in the layered region map are used as detection units. For each detection unit, the change areas between the old and new versions of AIP and their location identifiers in the layered region map are detected by comparing the coverage of the markings and matching the feature points. New marking data is generated for the markings of the change areas based on the structured marking dataset.

[0018] Based on the topological connectivity recorded in the hierarchical regional map, N-degree correlation lines are recursively searched starting from the changed region, and only correlation lines with changes in geometric attributes or connectivity are updated.

[0019] The changed area data is stitched together with the unchanged area data, and a complete airport model is generated through boundary consistency verification and global topology reconstruction.

[0020] Furthermore, within each functional area, a topological sub-region is constructed based on the connection relationship of the endpoints of the markings, as follows:

[0021] When the Euclidean distance between the endpoints of two marking lines is less than the spatial tolerance threshold, the endpoints are determined to coincide, and the two marking lines are assigned to the same topological sub-region.

[0022] Furthermore, the step of subdividing the sub-region into grid cell regions with dynamic grid size when the sub-region marking density exceeds a threshold specifically includes:

[0023] When the total length of the markings per unit area in a sub-region exceeds a preset density threshold, the side length of the grid cells is adjusted according to the spatial dispersion of the marking endpoints within the sub-region; where the more dispersed the endpoints are, the larger the grid size is, and the more concentrated the distribution is, the smaller the grid size is.

[0024] The subregion is divided into equally sized rectangular grid cell regions based on the calculated grid size.

[0025] Furthermore, the grid cell side length is adjusted according to the spatial dispersion of the endpoints of the markings within the sub-region. The method is as follows:

[0026] The longest continuous line within a sub-region is identified as a reference line, and the length of the reference line is used as the grid reference size.

[0027] If there are parallel marking groups within a sub-region, then the grid cells are extended along the direction of the marking groups to form a rectangular grid with the long side parallel to the marking direction and the length being twice the grid reference size, and the short side being equal to the grid reference size; wherein, the parallel marking groups are a set of markings with three or more markings and the angle between any two markings being less than a preset degree.

[0028] If the marking direction within a sub-region has no dominant direction, a square grid with a side length equal to the grid reference size is generated.

[0029] Furthermore, for each detection unit, the changed areas between the old and new versions of AIP and their location markers in the layered region map are detected by comparing the coverage of the datum lines and matching feature points. The method is as follows:

[0030] Calculate the absolute difference in the total length of the old and new versions of the markings within the detection unit. If the difference exceeds the length tolerance threshold, it is marked as a preliminary screening change area.

[0031] Extract the endpoints and intersections of all markings within the initial screening change area as feature points;

[0032] The coordinates of feature points in the old and new versions are matched bidirectionally. If the proportion of unmatched feature points exceeds the feature point tolerance threshold, the area is output as the final changed area and its hierarchical position identifier in the layered area map.

[0033] Furthermore, the bidirectional matching includes forward matching and reverse matching:

[0034] The forward matching includes searching for the nearest neighbor feature point within a preset radius in the new version for each feature point in the old version;

[0035] The reverse matching includes searching for the nearest neighbor feature point within a preset radius in the old version for each feature point in the new version;

[0036] A match is considered successful only if the feature points are the nearest neighbors of each other in both forward and reverse matching.

[0037] Furthermore, based on the topological connectivity recorded in the hierarchical regional map, N-degree correlation lines are recursively searched starting from the changed region, and only correlation lines with changes in geometric attributes or connectivity are updated. The method is as follows:

[0038] Starting with each line marking within the changed area, recursively search for all lines markings that have a direct endpoint connection to it as 1-degree association lines;

[0039] Continue searching for lines that have direct endpoint connections to the 1-degree correlation line as 2-degree correlation lines, until the N-degree correlation line search is completed, where N is a preset recursion depth, pre-set based on the airport area type; the direct endpoint connection means that the endpoint coordinates of two lines coincide within the spatial tolerance range, and their topological connection relationship is recorded in the layered area map.

[0040] For each line in the set of associated lines, perform the following:

[0041] Compare the geometric coordinate sequences, line types, and topology connection object identifiers in the old and new versions;

[0042] If the geometric coordinate deviation exceeds the coordinate tolerance, the line type changes, or the topology connection object identifiers are inconsistent, then data reconstruction is performed.

[0043] Furthermore, the data reconstruction includes:

[0044] When the geometric coordinate deviation exceeds the coordinate tolerance, the new coordinate system will overwrite the old coordinate system.

[0045] When the line type changes, update the type identifier and record the change version number;

[0046] When the topology connection object identifiers are inconsistent, the topology connection relationships are reconstructed and the set of boundary line identifiers of the associated regions is updated.

[0047] Furthermore, the changed area data is concatenated with the unchanged area data, and a complete airport model is generated through boundary consistency verification and global topology reconstruction. The method is as follows:

[0048] The new version of the changed area data is spatially overlaid with the old version of the unchanged area data according to the geographic coordinate system, and the endpoints of the marking lines of the adjacent areas are aligned with the boundary of the changed area as the reference.

[0049] The system detects markings that cross the boundary of a changed area. If the endpoint coordinates of a marking that crosses the boundary of a changed area deviate in adjacent areas, it automatically extends or trims the markings in adjacent areas to match the coordinates, using the changed area data as a reference. The determination criterion for the endpoint coordinate deviation is as follows:

[0050] The Euclidean distance between the endpoints of the same identifier is greater than the coordinate matching threshold;

[0051] Or, unclosed line endpoints appear at the boundary of adjacent areas;

[0052] A global topology connection table is generated based on the endpoint connection relationships of all markings. The topology connection table is verified to have no isolated marking segments or unclosed regions. The global topology connection table includes at least: a unique identifier for each marking, coordinates of the starting and ending points of the marking, a set of identifiers of adjacent markings connected to it, and the codes of the functional area and sub-area to which it belongs.

[0053] Furthermore, when the endpoint of the changed area marking exceeds the boundary of the adjacent area, the adjacent area marking is extended to the coordinates of that endpoint;

[0054] When the adjacent area markings extend and overlap with the changed area markings, the overlapping part is trimmed and the topology connection is updated.

[0055] Recalculate the geometric coordinate sequence for extended or cropped markings.

[0056] The beneficial effects of this invention are:

[0057] This invention replaces manual data collection and analysis by directly parsing AIP vector graphics data and generating a structured map dataset. It dynamically constructs gridded sub-regions based on functional area types and records topological relationships, providing a structured map for change detection. This eliminates the time bottleneck of manual document processing, enabling rapid response from airport changes to model updates and ensuring real-time synchronization between the training system and the real-world environment.

[0058] This invention employs a combination of line coverage comparison and feature point matching to collaboratively verify changed regions, improving detection accuracy. Layered region maps clearly record the connectivity between lines, avoiding topological breaks that can occur during manual updates. The invention automatically identifies changed regions and associated lines through algorithms, mitigating model biases caused by human oversight and ensuring the geometric and topological integrity of the updated results.

[0059] This invention starts with the changed region and recursively updates the N-degree correlation lines according to the topological connection relationship, avoiding full model reconstruction. A splicing mechanism preserves data from unchanged regions, reducing redundant calculations. This significantly saves computing resources and manpower, making it particularly suitable for scenarios involving parallel updates of multiple airports, and significantly reducing long-term operation and maintenance costs.

[0060] This invention automatically refines the mesh cells in high-density marking areas to ensure the update accuracy of critical areas (such as runway intersections). It automatically verifies boundary continuity during stitching and repairs potential gaps. It is adaptable to large-scale airport expansion scenarios (such as adding taxiways and aprons), ensuring the accuracy of the model in highly complex areas.

[0061] This invention automatically reconstructs the overall airport topology network after the update, verifying the logical consistency of taxi routes. Automated processing based on authoritative AIP data eliminates errors from manual transcription. It ensures that the simulator environment strictly matches the geometry, topology, and operating rules of a real airport, providing pilots with a highly reliable training environment. Attached Figure Description

[0062] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0063] Figure 1 This is a flowchart illustrating an automatic association and adjustment method for ground marking lines based on block division according to the present invention. Detailed Implementation

[0064] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0065] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0066] This invention provides a method for automatically associating and adjusting ground marking lines based on block division, the method comprising the following steps:

[0067] Step S10: Analyze the vector graphics data in the latest airport detailed rules AIP diagram, extract the geometric coordinates, line types and topological connections of all ground marking lines, and generate a structured marking dataset;

[0068] Step S20: Divide the airport into multiple functional areas based on the preset functional area type. Within each functional area, construct a topological sub-region according to the connection relationship of the datum endpoints. When the datum density of the sub-region exceeds the threshold, subdivide it into grid unit regions with dynamic grid size, and output a layered region map, recording the region boundary, hierarchical relationship and the set of datum identifiers contained therein.

[0069] Step S30: Using the sub-regions and / or grid cell regions in the layered region map as detection units, for each detection unit, the change regions between the old and new versions of AIP and their location identifiers in the layered region map are detected by comparing the coverage of the markings and matching the feature points. New marking data is generated for the markings of the change regions based on the structured marking dataset.

[0070] Step S40: Based on the topological connection relationships recorded in the layered regional map, recursively search for N-degree connection lines starting from the changed region, and only update the connection lines with changes in geometric attributes or connection relationships.

[0071] Step S50: The changed area data is spliced ​​with the unchanged area data, and a complete airport model is generated through boundary consistency verification and global topology reconstruction.

[0072] To more clearly explain the automatic association and adjustment method for ground marking lines based on block division according to the present invention, the following is in conjunction with... Figure 1 The steps in the embodiments of the present invention will be described in detail below.

[0073] The first embodiment of the present invention provides an automatic association and adjustment method for ground marking lines based on block division, comprising steps S10-S50, each step of which is described in detail below:

[0074] Step S10: Analyze the vector graphics data in the latest airport detailed rules AIP diagram, extract the geometric coordinates, line types and topological connections of all ground marking lines, and generate a structured marking dataset;

[0075] In step S10 of this embodiment, when parsing the vector graphics data in the latest airport rules AIP diagram, a structured grading dataset is generated using a processing method based on complete line segments.

[0076] The specific implementation process is as follows: First, use a GIS processing engine to read vector graphics files in DXF or SHP format and filter out all layer elements that represent ground marking lines; for these line elements, process them as a whole as original line segments rather than splitting them into discrete points, and preserve the complete geometric shape of each marking line.

[0077] When extracting geometric coordinates, the ordered vertex sequence of each line segment is directly obtained as a continuous coordinate string, while the corresponding line type attributes (such as runway edge lines, taxiway center lines, etc.) are recorded. The construction of topological connections is achieved by automatically identifying the spatial proximity of line segment endpoints: when the distance between the endpoints of different line segments is less than a preset tolerance threshold (usually 0.1 meters), they are merged into the same topological node, and a unique identifier is assigned to each node;

[0078] The final result is a structured dataset comprising three core components: a set of line features recording the complete geometric coordinates and types of all line segments; a topology node table storing node locations and connection relationships; and a connection mapping table that clearly defines the correspondence between the start and end nodes of each line segment. The entire process ensures that each ground marker line is stored as a complete line segment entity, maintaining its original geometric continuity, while explicitly maintaining the topological connections between lines.

[0079] Step S20: Divide the airport into multiple functional areas based on the preset functional area type. Within each functional area, construct a topological sub-region according to the connection relationship of the datum endpoints. When the datum density of the sub-region exceeds the threshold, subdivide it into grid unit regions with dynamic grid size, and output a layered region map, recording the region boundary, hierarchical relationship and the set of datum identifiers contained therein.

[0080] In this embodiment, within each functional area, a topological sub-region is constructed based on the connection relationship of the endpoints of the grading lines. The method is as follows:

[0081] When the Euclidean distance between the endpoints of two marking lines is less than the spatial tolerance threshold, the endpoints are determined to coincide, and the two marking lines are assigned to the same topological sub-region.

[0082] Specifically, the airport is first divided into multiple functional zones based on the preset types of airport functional zones. Within each functional zone, a topological sub-region is constructed based on the connection relationship of the endpoints of ground marking lines: when the Euclidean distance between the endpoints of two marking lines is less than a preset spatial tolerance threshold, the endpoints are determined to overlap and the two marking lines are assigned to the same topological sub-region, until all connected marking lines are assigned to independent sub-regions.

[0083] In this embodiment, the preset airport functional area types are based on the standard airport planning practices of the International Civil Aviation Organization (ICAO) and the International Air Transport Association (IATA), and typically include core functional areas such as the Runway Area (RWY), Taxiway Area (TWY), Apron Area (APN), Stand Area (STD), Maintenance Area (MTN), and Vehicle Service Area (VSA). The system automatically divides the entire airport's digitized vector graphic data into these different functional area categories according to preset rules (such as geographical scope and main functional characteristics).

[0084] Within each defined functional area, the system begins constructing a topological sub-region based on the connection relationships between the endpoints of the marking lines. The specific method is as follows: the system calculates the Euclidean distance between the endpoints of any two ground marking lines within the functional area and compares this distance with a preset spatial tolerance threshold. This tolerance threshold is typically set based on mapping accuracy and actual engineering requirements, with a typical value of 0.1 meters or 0.3 feet; in this embodiment, 0.1 feet is preferred.

[0085] When the distance between the endpoints of two markings is less than or equal to this threshold, the system determines that the two endpoints coincide in spatial location and assigns the two markings to the same topological sub-region. By traversing all markings and their endpoints within this functional area, based on this endpoint coincidence determination principle, the system gradually aggregates all spatially connected markings together, ultimately forming several topological sub-regions that do not overlap in space but are topologically connected internally.

[0086] The step of subdividing a sub-region into grid cell regions with dynamic grid size when the sub-region marking density exceeds a threshold specifically includes:

[0087] When the total length of the markings per unit area in a sub-region exceeds a preset density threshold, the side length of the grid cells is adjusted according to the spatial dispersion of the marking endpoints within the sub-region; where the more dispersed the endpoints are, the larger the grid size is, and the more concentrated the distribution is, the smaller the grid size is.

[0088] The subregion is divided into equally sized rectangular grid cell regions based on the calculated grid size.

[0089] In this embodiment, after the sub-region division is completed, the system will evaluate the grading density of each topological sub-region. The evaluation is based on calculating the total grading length per unit area within the sub-region, for example, the total grading length divided by the sub-region area, with units of meters per square meter or feet per square foot. The system then compares the calculated density value with a preset density threshold.

[0090] The density threshold is set according to the type of functional area and the actual scenario. For example, in a taxi waiting area with dense markings or a complex apron area, the typical threshold is set to 5 meters / square meter, while in an open runway area or maintenance area it may be set to 3 meters / square meter.

[0091] If the marking density of a certain sub-region exceeds the preset density threshold of its corresponding functional area, it indicates that the markings in that area are too dense and require more detailed grid division in order to detect changes more accurately in the future.

[0092] For overly dense sub-regions requiring finer grid subdivision, the system employs a dynamic grid size strategy. The actual size of the grid cell, i.e., its side length, is not fixed but dynamically determined by the spatial dispersion of all marking endpoints within the sub-region (usually calculated based on the standard deviation of endpoint coordinates). Specifically, the more dispersed the endpoints (i.e., the greater the spatial variability), the larger the grid cell size; conversely, the more concentrated the endpoints (i.e., the smaller the spatial variability), the smaller the grid cell size. The dynamic adjustment formula can be designed as: Grid size reference value = Base size + K × Endpoint position standard deviation, where the base size is the initial reference value (set to 1 meter in this embodiment), and K is a preset adjustment coefficient. This is done to improve accuracy by using smaller grids in areas with dense and concentrated markings (such as complex intersections), while using slightly larger grids to improve efficiency in areas with relatively sparse or scattered markings.

[0093] When calculating the edge length of a grid cell, the system first identifies the longest continuous line within the current overdense subregion. This line is usually the longest taxiway centerline or guide line, and the actual length of this reference line is used as the basic grid reference size for mesh generation.

[0094] The method for adjusting the grid cell side length based on the spatial dispersion of the endpoints of the markings within a sub-region is as follows:

[0095] The longest continuous line within a sub-region is identified as a reference line, and the length of the reference line is used as the grid reference size.

[0096] If there are parallel marking groups within a sub-region, then the grid cells are extended along the direction of the marking groups to form a rectangular grid with the long side parallel to the marking direction and the length being twice the grid reference size, and the short side being equal to the grid reference size; wherein, the parallel marking groups are a set of markings with three or more markings and the angle between any two markings being less than a preset degree.

[0097] If the marking direction within a sub-region has no dominant direction, a square grid with a side length equal to the grid reference size is generated.

[0098] In this embodiment, the specific shape of the grid cell is determined based on the overall characteristics of the marking direction within the sub-region, including the following two cases:

[0099] Scenario 1: Dominant parallel direction exists: The system will analyze whether there is a group of three or more markings in the sub-region, where the angle between any two markings is less than the preset parallel judgment angle threshold, i.e. a dominant set of parallel markings, such as a row of parking position guide lines or parallel taxiway edge lines.

[0100] If such a dominant parallel line group exists, the system will automatically determine the main direction of the line markings in that group. Then, the system will extend the grid cells along this dominant direction. The generated grid cells will no longer be squares, but rectangles. Their long sides will be strictly parallel to the direction of the dominant line marking group, with a length set to twice the reference dimension; their short sides will remain equal to the reference dimension. This results in elongated grid cells that better conform to the geometry of taxiway or apron parallel line marking bundles.

[0101] In this embodiment, the parallel determination angle threshold is preferably 15 degrees.

[0102] Scenario 2, No Dominant Direction (Disorderly Directions): If the directions of the markings within a sub-region are highly disordered, and it is impossible to find a group that meets the parallelism criteria, i.e., there is no dominant direction, the system directly uses a square grid for subdivision. Each generated grid cell is a square, and its side length is uniformly set to the reference dimension value.

[0103] In this embodiment, regardless of the shape used (rectangular or square), the system ultimately divides the original overly dense topological sub-region into multiple equally sized grid cell regions based on the calculated dynamic grid size (long and short sides for rectangles, and side length for squares). All grid cells collectively cover the range of the atomic region. The system constructs and outputs a hierarchical region map containing information about these functional areas, topological sub-regions, and grid cells (if any). This map explicitly records the geometric boundaries of the regions, the hierarchical inclusion relationships between regions (e.g., a functional area contains multiple sub-regions, and sub-regions are further subdivided into multiple grid cells), and the unique identifier set of ground marking lines contained within each region (functional area, sub-region, grid cell). This hierarchical structure forms the basis for subsequent change detection, correlation adjustments, and model reconstruction.

[0104] This implementation also proposes a third scenario: when there are more than three marking endpoints within the radius R of a certain endpoint, the corresponding endpoint is marked as the intersection core; a sub-mesh cell with a side length of half the grid reference size is created with the intersection core as the center; where the radius R is taken as the minimum turning radius in the airport design specifications.

[0105] If two grid cells contain consecutive line segments of the same marking line, they are merged into a single grid cell.

[0106] If the boundaries of the sub-grid cells overlap with those of the main grid cells, they are integrated into a continuous polygon detection region.

[0107] In this embodiment, if the system detects that an endpoint has three or more other tracing endpoints within a circular neighborhood of its radius R, i.e., the spatial clustering is high, then the endpoint is determined to be a key geometric feature point—the core of the intersection.

[0108] The value of radius R here is strictly determined according to the minimum turning radius of aircraft in airport design specifications. The specific value is selected based on the airport class and applicable aircraft type. For example, according to ICAO specifications, the minimum turning radius of runway connecting taxiways for Category E aircraft such as the B747 is typically 60 meters, while it is 25 meters for Category C aircraft such as the A320. This definition of radius R ensures that the handling of intersection areas meets the actual ground operation requirements of aircraft.

[0109] After identifying the core intersection point, the system immediately creates a higher-precision local mesh in the surrounding area. The creation rule is as follows: a new sub-mesh cell is constructed centered on the coordinates of the core intersection point. This sub-mesh cell is square in shape, and its side length is explicitly defined as half the mesh reference size used when meshing the atomic region. This sub-mesh design provides finer spatial division near high-density intersection points.

[0110] In the mesh merging optimization phase, the system analyzes the geometric relationship between mesh cells and marking lines: if two adjacent or neighboring mesh cells (whether main or sub-mesh) are found to contain a continuous segment of the same ground marking line (i.e., the marking line passes through these two cells without interruption), it is determined that this separation may disrupt the continuity of the marking line. To maintain topological integrity, the system automatically merges these two mesh cells into a single, larger mesh cell. This merging operation ensures that a single continuous marking line exists intact within the detection cell, avoiding artificial segmentation caused by mesh boundaries.

[0111] After generating all levels of meshes (main mesh and sub-mesh), the system detects the spatial relationships between mesh cell boundaries. If it finds that the physical boundary of a sub-mesh cell created to handle intersections overlaps or is adjacent to the boundary of its parent main mesh cell (i.e., spatially connected or partially overlapping), the system will not retain these multiple independent small cells. Instead, the system will integrate them into a coherent, non-rectangular, complex, continuous polygonal region. This integrated region serves as a new, unified "detection region" unit in subsequent change detection steps, thereby eliminating unnecessary artificial boundaries between tiny mesh cells and ensuring the consistency and integrity of identifying adjacent change regions.

[0112] Step S30: Using the sub-regions and / or grid cell regions in the layered region map as detection units, for each detection unit, the change regions between the old and new versions of AIP and their location identifiers in the layered region map are detected by comparing the coverage of the markings and matching the feature points. New marking data is generated for the markings of the change regions based on the structured marking dataset.

[0113] In this embodiment, for each detection unit, the changed areas between the old and new versions of AIP and their location markers in the layered area map are detected by comparing the coverage of the datum lines and matching feature points. The method is as follows:

[0114] Calculate the absolute difference in the total length of the old and new versions of the markings within the detection unit. If the difference exceeds the length tolerance threshold, it is marked as a preliminary screening change area.

[0115] Extract the endpoints and intersections of all markings within the initial screening change area as feature points;

[0116] The coordinates of feature points in the old and new versions are matched bidirectionally. If the proportion of unmatched feature points exceeds the feature point tolerance threshold, the area is output as the final changed area and its hierarchical position identifier in the layered area map.

[0117] In this embodiment, the system uses the topological sub-regions (when not too dense), subdivided grid cell regions (after dividing overly dense sub-regions), or continuous polygon detection regions for intersections (after sub-grid integration) recorded in the layered area map as basic detection units. For each detection unit in the new and old versions of the Airport Instructions (AIP) map, a two-step rigorous comparison is performed to identify the changed location.

[0118] The first step is to perform an initial screening of road marking coverage: Calculate the absolute difference in the total length of all ground marking lines in the old and new versions of AIP for the same detection unit. The unit of length must be consistent in meters or feet. Compare this difference with a preset length tolerance threshold. The length tolerance threshold is determined based on the airport's actual maintenance standards and the AIP mapping accuracy, typically set to 1.5 to 2 times the road marking design accuracy, for example, 0.45 meters. If the absolute difference exceeds this threshold, the detection unit is considered to have a significant possibility of line type change and is marked as a preliminary changed area. This step is used to quickly eliminate a large number of unchanged areas, improving the efficiency of subsequent processing.

[0119] For each marked area in the initial screening of changed regions, the system performs a more refined geometric feature matching analysis. The system extracts the endpoints (line start / end points) and intersections (geometric points formed by intersecting lines) of all lines in the old version of the AIP within that region, forming the old version feature point set. Simultaneously, it extracts the endpoints and intersections of the corresponding positions in the new version of the AIP within the same region, forming the new version feature point set. The feature point type (endpoint / intersection) and its unique coordinates in space are the key criteria for matching.

[0120] The bidirectional matching includes forward matching and reverse matching:

[0121] The forward matching includes searching for the nearest neighbor feature point within a preset radius in the new version for each feature point in the old version;

[0122] The reverse matching includes searching for the nearest neighbor feature point within a preset radius in the old version for each feature point in the new version;

[0123] A match is considered successful only if the feature points are the nearest neighbors of each other in both forward and reverse matching.

[0124] In this embodiment, forward matching specifically involves traversing every point in the old feature point set and searching for the nearest neighbor feature point in the new feature point set, centered on the original point's coordinates and within a circular neighborhood of a preset search radius (usually set to 0.15 meters). If a unique nearest neighbor exists and its distance is within the search radius, this point pair is recorded as a "forward candidate matching point pair".

[0125] Reverse matching, specifically, involves iterating through each point in the new feature point set and searching for the nearest neighbor feature point in the old feature point set, centered on the new point's coordinates and within a preset search radius (0.15 meters). If a unique nearest neighbor exists and its distance is within the search radius, this point pair is recorded as a "reverse candidate matching point pair".

[0126] The matching determination uses the reverse nearest neighbor principle: a pair of points is considered a successful match only if both of the following conditions are met:

[0127] This pair of points is identified as a candidate matching pair in the forward matching (i.e., old version point A points to new version point B);

[0128] Furthermore, this pair of points is also identified as a candidate matching pair in the reverse matching (i.e., the new version point B points to the old version point A, and the two are each other's unique nearest neighbors).

[0129] After all feature point matching operations are completed, the system calculates the proportion of feature points that failed to match in the initial screening change area to the total number of feature points (both old and new versions). If this proportion exceeds the preset feature point tolerance threshold (usually set to 15% of the total number of feature points, but adjusted according to data quality; this invention does not impose specific limitations), then it is determined that the initial screening change area does indeed have a non-negligible geometric change. The area is then output as the "final change area," and its location identifier is accurately recorded in the hierarchical regional map structure, including: the type of functional area it belongs to, the atomic region or grid cell ID, and its row and column index in the hierarchical map.

[0130] For the final changed area confirmed as a change, the system directly extracts all grading information corresponding to the new version of AIP vector data within that area based on the structured grading dataset. This grading information includes, but is not limited to, geometric coordinates, line type, width, color, and attributes. This generates a new grading data set for that area, which is then used for subsequent correlation updates. Simultaneously, the area's location identifier is written to the change record list.

[0131] Step S40: Based on the topological connection relationships recorded in the layered regional map, recursively search for N-degree connection lines starting from the changed region, and only update the connection lines with changes in geometric attributes or connection relationships.

[0132] In this embodiment, based on the topological connectivity recorded in the layered regional map, N-degree connectivity lines are recursively searched starting from the changed region, and only connectivity lines with changes in geometric attributes or connectivity relationships are updated. The method is as follows:

[0133] Starting with each line marking within the changed area, recursively search for all lines markings that have a direct endpoint connection to it as 1-degree association lines;

[0134] Continue searching for lines that have direct endpoint connections to the 1-degree correlation line as 2-degree correlation lines, until the N-degree correlation line search is completed, where N is a preset recursion depth, pre-set based on the airport area type; the direct endpoint connection means that the endpoint coordinates of two lines coincide within the spatial tolerance range, and their topological connection relationship is recorded in the layered area map.

[0135] For each line in the set of associated lines, perform the following:

[0136] Compare the geometric coordinate sequences, line types, and topology connection object identifiers in the old and new versions;

[0137] If the geometric coordinate deviation exceeds the coordinate tolerance, the line type changes, or the topology connection object identifiers are inconsistent, then data reconstruction is performed.

[0138] In this embodiment, the system starts with the final changed area marked in the layered area map and initiates a recursive search process for N-degree correlation lines based on the topological connection relationship of the markings recorded in the map, that is, the network relationship formed by the connection of the markings through the endpoints.

[0139] First, for each modified line within the changed area, the system treats it as an initial search node. The rule for finding the first-degree correlation line is: recursively search all other lines that have direct endpoint connections to the modified line, i.e., lines connected through endpoints with coincident coordinates. The specific connection determination follows the standard in step S20: if the Euclidean distance between the endpoints of two lines is ≤ a preset spatial tolerance threshold (standard value 0.1 meters) and their topological relationship has been marked in the layered map, then they are considered to be directly connected endpoints.

[0140] After completing the first degree of correlation line search, the system continues to perform the Kth degree (K=2 to N) correlation line search: using the correlation lines found in the previous order (K-1 degree) as a new starting point, it recursively searches for all other markings that have direct endpoint connections with these lines, excluding already marked lines. This process is repeated until the preset recursion depth N is reached. The value of the recursion depth parameter N is dynamically determined by the airport functional area type. For example, in this embodiment, the complex taxiway area (TWY) requires depth detection, so N=3 is set; the relatively simple runway area (RWY) or apron (APN) is set to N=2; and open areas can be N=1. The system finally outputs a set of correlation markings consisting of lines within the changed area and all their N-degree correlation lines. This set contains all K=1 to N degree correlation lines.

[0141] After obtaining the complete set of associated lines, the system performs a data comparison analysis between the old and new versions for each associated line in the set, updating only the lines that have actually changed. Specific comparison dimensions include:

[0142] Geometric coordinate sequence: Compare the node coordinate sequence of the marking line in the new version and the old version. If the coordinate displacement deviation between any corresponding nodes exceeds the geometric coordinate tolerance threshold (this threshold is stricter than the spatial tolerance and is usually set to 0.05 meters), it is considered a geometric change.

[0143] Line type: Check whether the type attributes (such as taxiway centerline, parking stand guide line, runway edge line, etc.) of the lines are consistent between the old and new versions;

[0144] Topology connection object identifier: Verifies whether the set of adjacent line IDs connected to this line in the old version completely matches the new version (i.e., the connected line objects have not been added, removed, or replaced).

[0145] The data reconstruction includes:

[0146] When the geometric coordinate deviation exceeds the coordinate tolerance, the new coordinate system will overwrite the old coordinate system.

[0147] When the line type changes, update the type identifier and record the change version number;

[0148] When the topology connection object identifiers are inconsistent, the topology connection relationships are reconstructed and the set of boundary line identifiers of the associated regions is updated.

[0149] Specifically, if the geometric coordinate deviation exceeds the standard: the system directly overwrites the old coordinate data with the accurate coordinate sequence in the new version of AIP data, while retaining the historical version number mark;

[0150] If the line type changes: the system updates the type identifier attribute of the line and records the change version number and timestamp (e.g., TYPE_UPDATE:AIP_2025) in the metadata.

[0151] If the identifiers of the topology connection objects are inconsistent (e.g., connection points disappear, new branches are added, or reconnections are made): the system reconstructs the topology connection relationship of the line and its associated lines (e.g., deletes old connection relationships, establishes new connection edges), and synchronously updates the set of boundary line identifiers of all affected areas in the layered area map (e.g., the list of boundary line IDs associated with grid cells) to ensure that the area boundary description is consistent with the actual geometry.

[0152] This invention only updates associated lines whose attributes have changed (the three categories mentioned above). Associated lines without detected changes (i.e., those with consistent geometry, type, and connection) remain unchanged, minimizing unnecessary processing. The reconstructed line data and topological relationships are then passed to the subsequent stitching process.

[0153] Step S50: The changed area data is spliced ​​with the unchanged area data, and a complete airport model is generated through boundary consistency verification and global topology reconstruction.

[0154] In this embodiment, the changed area data is concatenated with the unchanged area data, and a complete airport model is generated through boundary consistency verification and global topology reconstruction. The method is as follows:

[0155] The new version of the changed area data is spatially overlaid with the old version of the unchanged area data according to the geographic coordinate system, and the endpoints of the marking lines of the adjacent areas are aligned with the boundary of the changed area as the reference.

[0156] The system detects markings that cross the boundary of a changed area. If the endpoint coordinates of a marking that crosses the boundary of a changed area deviate in adjacent areas, it automatically extends or trims the markings in adjacent areas to match the coordinates, using the changed area data as a reference. The determination criterion for the endpoint coordinate deviation is as follows:

[0157] The Euclidean distance between the endpoints of the same identifier is greater than the coordinate matching threshold;

[0158] Or, unclosed line endpoints appear at the boundary of adjacent areas;

[0159] A global topology connection table is generated based on the endpoint connection relationships of all markings. The topology connection table is verified to have no isolated marking segments or unclosed regions. The global topology connection table includes at least: a unique identifier for each marking, coordinates of the starting and ending points of the marking, a set of identifiers of adjacent markings connected to it, and the codes of the functional area and sub-area to which it belongs.

[0160] In this embodiment, the new version of the changed area data (including the updated geometric and topological attributes of the markings) output in step S40 is spatially overlaid with the old version of the data for other unchanged areas of the airport, using a unified geographic coordinate system (such as WGS-84UTM). During this process, the system uses the polygonal boundary of the changed area as a baseline to perform coordinate calibration on the markings on the boundaries of the adjacent unchanged areas. Specifically, this involves identifying all cross-boundary markings (i.e., the same marking is distributed in both the changed area and adjacent areas) and forcibly aligning the endpoint coordinates of the markings on the adjacent area side to the endpoint coordinates on the changed area side.

[0161] The system then detects boundary consistency conflicts. For each line crossing the boundary of the changed area (i.e., partly within the changed area and partly within the adjacent unchanged area), if any of the following situations are detected, it is determined to be a boundary mismatch and adaptive processing is triggered:

[0162] Coordinate tolerance: The Euclidean distance between the endpoints of the same marker on both sides of the boundary is greater than the coordinate matching threshold (this threshold is more lenient than the geometric tolerance, usually set to 0.2 meters, to tolerate splicing errors).

[0163] Unclosed endpoints: At the boundary of an area, there are marking endpoints that are not connected to objects (such as the adjacent area lacking markings that should connect to the endpoints of the changed area), resulting in holes or discontinuities in the boundary.

[0164] Specifically, when the endpoint of the changed area marking exceeds the boundary of the adjacent area, the adjacent area marking is extended to the coordinates of that endpoint.

[0165] When the adjacent area markings extend and overlap with the changed area markings, the overlapping part is trimmed and the topology connection is updated.

[0166] Recalculate the geometric coordinate sequence for extended or cropped markings.

[0167] Specifically, the extension operation works as follows: if the endpoint coordinates of the changed area's side marker exceed the boundary of the adjacent unchanged area (i.e., the point is inside the adjacent area), the system will automatically extend the nearest associated marker in the adjacent area, extending its geometric path to the endpoint coordinates of the changed area. The extension uses a linear interpolation algorithm to generate new nodes, ensuring a smooth path.

[0168] Pruning operation: If adjacent area markings partially overlap with the markings of the changed area after extension (overlap distance > tolerance), the system will prune the overlapping segment (usually retaining the changed area data) and generate a new endpoint at the pruning point. After pruning, the geometric coordinate sequence of the markings is recalculated, and redundant nodes are removed.

[0169] Topology update: For lines that have undergone geometric deformation after extension or clipping, the system synchronously updates their topological connectivity: deletes or adds connections with adjacent lines, and writes the changes to the boundary line identifier set of the region to which the line belongs in the layered area map (e.g., adding a newly generated extension segment ID to the boundary of the taxiway sub-region).

[0170] After completing all boundary consistency processing, the system initiates global topology reconstruction: scanning the endpoint coordinates of all markings (including changed and unchanged areas) across the entire airport model, reconstructing the topology connection network based on a unified spatial tolerance threshold (0.1 meters), and generating a complete global topology connection table. The core fields of this table include:

[0171] Fields describe Line_ID unique identifier for gradation Start_Point <![CDATA[Starting point coordinates (x1, y1)]]> End_Point <![CDATA[End coordinates (x2, y2)]]> Connected_Lines Set of adjacent line IDs connected by direct endpoints Function_Zone Functional area code (e.g., RWY_01) Sub_Region_ID Sub-region or grid cell code

[0172] Perform topology integrity checks using this table to ensure that:

[0173] No isolated lines: Each set of lines contains at least one adjacent ID (except for special endpoints such as runway end lines);

[0174] No unclosed areas: The boundary markings of functional areas must form closed polygons (e.g., the boundary of the apron area must be completely closed).

[0175] Full network connectivity: There is a marked connection path between any two points in the taxiway system.

[0176] If the verification fails (e.g., a breakpoint or isolated point is found), the system automatically marks the topology conflict location and issues an alarm. Upon successful verification, a full-element model of the airport ground markings with complete spatial coordinates, attributes, and topological relationships is output.

[0177] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0178] A second embodiment of the present invention provides an automatic association and adjustment system for ground marking lines based on block division, which is based on a first embodiment of an automatic association and adjustment method for ground marking lines based on block division. The system includes:

[0179] The structured marking dataset parsing module is configured to parse the vector graphics data in the latest airport detailed AIP map, extract the geometric coordinates, line types and topological connections of all ground marking lines, and generate a structured marking dataset.

[0180] The airport is divided into multiple functional areas based on the preset functional area type. Within each functional area, a topological sub-region is constructed according to the connection relationship of the endpoints of the markings. When the marking density of the sub-region exceeds the threshold, it is subdivided into grid unit regions with dynamic grid size, and a layered regional map is output, recording the regional boundaries, hierarchical relationships and the set of marking identifiers contained therein.

[0181] The sub-regions and / or grid cell regions in the layered region map are used as detection units. For each detection unit, the change areas between the old and new versions of AIP and their location identifiers in the layered region map are detected by comparing the coverage of the markings and matching the feature points. New marking data is generated for the markings of the change areas based on the structured marking dataset.

[0182] Based on the topological connectivity recorded in the hierarchical regional map, N-degree correlation lines are recursively searched starting from the changed region, and only correlation lines with changes in geometric attributes or connectivity are updated.

[0183] The changed area data is concatenated with the unchanged area data, and a complete airport model is generated through boundary consistency verification and global topology reconstruction. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related explanations of the system described above can be found in the corresponding processes of the foregoing method embodiments, and will not be repeated here.

[0184] It should be noted that the above embodiment of the automatic correlation and adjustment system for ground marking lines based on block division is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0185] An electronic device according to a third embodiment of the present invention includes:

[0186] At least one processor; and

[0187] A memory communicatively connected to at least one of the processors; wherein,

[0188] The memory stores instructions that can be executed by the processor to implement the above-described method for automatic association and adjustment of ground marking lines based on block division.

[0189] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described method for automatic association and adjustment of ground marking lines based on block division.

[0190] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the storage device and processing device described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0191] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0192] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0193] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0194] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for automatic association and adjustment of ground marking lines based on block division, characterized in that, The method includes: Analyze the vector graphics data in the latest airport regulations AIP diagram, extract the geometric coordinates, line types, and topological connections of all ground marking lines, and generate a structured marking dataset; The airport is divided into multiple functional areas based on the preset functional area type. Within each functional area, a topological sub-region is constructed according to the connection relationship of the endpoints of the markings. When the marking density of the sub-region exceeds the threshold, it is subdivided into grid unit regions with dynamic grid size, and a layered regional map is output, recording the regional boundaries, hierarchical relationships and the set of marking identifiers contained therein. The sub-regions and / or grid cell regions in the layered region map are used as detection units. For each detection unit, the change areas between the old and new versions of AIP and their location identifiers in the layered region map are detected by comparing the coverage of the markings and matching the feature points. New marking data is generated for the markings of the change areas based on the structured marking dataset. Based on the topological connectivity recorded in the hierarchical regional map, N-degree correlation lines are recursively searched starting from the changed region, and only correlation lines with changes in geometric attributes or connectivity are updated. The changed area data is stitched together with the unchanged area data, and a complete airport model is generated through boundary consistency verification and global topology reconstruction.

2. The method for automatic association and adjustment of ground marking lines based on block division according to claim 1, characterized in that, Within each functional area, a topological sub-region is constructed based on the connection relationship of the endpoints of the markings. The method is as follows: When the Euclidean distance between the endpoints of two marking lines is less than the spatial tolerance threshold, the endpoints are determined to coincide, and the two marking lines are assigned to the same topological sub-region.

3. The method for automatic association and adjustment of ground marking lines based on block division according to claim 1, characterized in that, The step of subdividing a sub-region into grid cell regions with dynamic grid size when the sub-region marking density exceeds a threshold specifically includes: When the total length of the markings per unit area in a sub-region exceeds a preset density threshold, the side length of the grid cells is adjusted according to the spatial dispersion of the marking endpoints within the sub-region; where the more dispersed the endpoints are, the larger the grid size is, and the more concentrated the distribution is, the smaller the grid size is. The subregion is divided into equally sized rectangular grid cell regions based on the calculated grid size.

4. The method for automatic association and adjustment of ground marking lines based on block division according to claim 3, characterized in that, The method for adjusting the grid cell side length based on the spatial dispersion of the endpoints of the markings within a sub-region is as follows: The longest continuous line within a sub-region is identified as a reference line, and the length of the reference line is used as the grid reference size. If there are parallel marking groups within a sub-region, then the grid cells are extended along the direction of the marking groups to form a rectangular grid with the long side parallel to the marking direction and the length being twice the grid reference size, and the short side being equal to the grid reference size; wherein, the parallel marking groups are a set of markings with three or more markings and the angle between any two markings being less than a preset degree. If the marking direction within a sub-region has no dominant direction, a square grid with a side length equal to the grid reference size is generated.

5. The method for automatic association and adjustment of ground marking lines based on block division according to claim 1, characterized in that, For each detection unit, the change areas between the old and new versions of AIP and their location markers in the layered region map are detected by comparing the coverage of the datum lines and matching feature points. The method is as follows: Calculate the absolute difference in the total length of the old and new versions of the markings within the detection unit. If the difference exceeds the length tolerance threshold, it is marked as a preliminary screening change area. Extract the endpoints and intersections of all markings within the initial screening change area as feature points; The coordinates of feature points in the old and new versions are matched bidirectionally. If the proportion of unmatched feature points exceeds the feature point tolerance threshold, the area is output as the final changed area and its hierarchical position identifier in the layered area map.

6. The method for automatic association and adjustment of ground marking lines based on block division according to claim 5, characterized in that, The bidirectional matching includes forward matching and reverse matching: The forward matching includes searching for the nearest neighbor feature point within a preset radius in the new version for each feature point in the old version; The reverse matching includes searching for the nearest neighbor feature point within a preset radius in the old version for each feature point in the new version; A match is considered successful only if the feature points are the nearest neighbors of each other in both forward and reverse matching.

7. The method for automatic association and adjustment of ground marking lines based on block division according to claim 1, characterized in that, Based on the topological connectivity recorded in the hierarchical regional map, the method recursively searches for N-degree connectivity lines starting from the changed region, and only updates connectivity lines whose geometric attributes or connectivity relationships have changed. Starting with each line marking within the changed area, recursively search for all lines markings that have a direct endpoint connection to it as 1-degree association lines; Continue searching for lines that have direct endpoint connections to the 1-degree correlation line as 2-degree correlation lines, until the N-degree correlation line search is completed, where N is a preset recursion depth, pre-set based on the airport area type; the direct endpoint connection means that the endpoint coordinates of two lines coincide within the spatial tolerance range, and their topological connection relationship is recorded in the layered area map. For each line in the set of associated lines, perform the following: Compare the geometric coordinate sequences, line types, and topology connection object identifiers in the old and new versions; If the geometric coordinate deviation exceeds the coordinate tolerance, the line type changes, or the topology connection object identifiers are inconsistent, then data reconstruction is performed.

8. The method for automatic association and adjustment of ground marking lines based on block division according to claim 7, characterized in that, The data reconstruction includes: When the geometric coordinate deviation exceeds the coordinate tolerance, the new coordinate system will overwrite the old coordinate system. When the line type changes, update the type identifier and record the change version number; When the topology connection object identifiers are inconsistent, the topology connection relationships are reconstructed and the set of boundary line identifiers of the associated regions is updated.

9. The method for automatic association and adjustment of ground marking lines based on block division according to claim 1, characterized in that, The modified area data is concatenated with the unchanged area data, and a complete airport model is generated through boundary consistency verification and global topology reconstruction. The method is as follows: The new version of the changed area data is spatially overlaid with the old version of the unchanged area data according to the geographic coordinate system, and the endpoints of the marking lines of the adjacent areas are aligned with the boundary of the changed area as the reference. The system detects markings that cross the boundary of a changed area. If the endpoint coordinates of a marking that crosses the boundary of a changed area deviate in adjacent areas, it automatically extends or trims the markings in adjacent areas to match the coordinates, using the changed area data as a reference. The determination criterion for the endpoint coordinate deviation is as follows: The Euclidean distance between the endpoints of the same identifier is greater than the coordinate matching threshold; Or, unclosed line endpoints appear at the boundary of adjacent areas; A global topology connection table is generated based on the endpoint connection relationships of all markings. The topology connection table is verified to have no isolated marking segments or unclosed regions. The global topology connection table includes at least: a unique identifier for each marking, coordinates of the starting and ending points of the marking, a set of identifiers of adjacent markings connected to it, and the codes of the functional area and sub-area to which it belongs.

10. The method for automatic association and adjustment of ground marking lines based on block division according to claim 9, characterized in that, When the endpoint of the change zone line exceeds the boundary of the adjacent zone, extend the adjacent zone line to the coordinates of that endpoint. When the adjacent area markings extend and overlap with the changed area markings, the overlapping part is trimmed and the topology connection is updated. Recalculate the geometric coordinate sequence for extended or cropped markings.

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