A batch house segmentation processing method based on semantic information

Abstract

The present invention discloses a batch house household division processing method based on semantic information, and relates to the field of geographic information data processing technology. The method comprises: simplifying graphics based on building data, obtaining the outer contour of the building, extracting the central axis, optimizing the central axis, generating a dividing line, generating a hierarchical household division map based on the dividing line, and storing the map attribute information of the generated hierarchical household division map in a geographic space database; performing stretching processing using three-dimensional software according to the existing hierarchical household division data to generate a building block model; spatially matching the building block model with the vector contour data and the corresponding height information in the geographic space database to realize the hierarchical household division management function of the building block model. The present invention significantly improves the usability of the building three-dimensional model in the three-dimensional system on the basis of ensuring the accuracy of household division.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information data processing, and more specifically, to a method for batch house segmentation processing based on semantic information. Background Art

[0002] As the core component of the urban geographical environment, the construction of the 3D semantic model of buildings is of great significance to the construction of 3D urban geographic information systems. The 3D model of buildings is monomerized so that each floor and each household become independent individuals, which can be selected and have corresponding attribute information, so as to better perform browsing, query, analysis and other functions.

[0003] At present, the realization of layering and household division of building tilt models is mainly through matching geographical location information of house layering and household division maps in DWG format with three-dimensional models to complete the binding of attributes. Since the layering and household division maps lack spatial information, manual matching requires a lot of work, and in the actual production process, many buildings cannot obtain layering and household division map data. Therefore, this work has problems such as large labor volume and low efficiency, which hinders the efficient construction and widespread application of three-dimensional urban geographic information systems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a batch house segmentation processing method based on semantic information, which automatically generates layered and household-by-household vector graphics of buildings according to the floor and number of households attribute information of house vectors, thereby simplifying the operation of individual layered and household-by-household segmentation of large-scale building three-dimensional models, and significantly improving the availability of building three-dimensional models in three-dimensional systems while ensuring the accuracy of household segmentation.

[0005] The present invention adopts the following technical solution:

[0006] The present invention provides a method for batch house segmentation based on semantic information, the method comprising:

[0007] Acquire building vector graphics data; wherein the building vector graphics data includes building graphics and attribute tables, the building graphics are polygon data representing the actual boundaries of the building, and the attribute table contains two fields: number of floors and number of households;

[0008] Based on the polygon data, a tolerance threshold is set to eliminate redundant nodes to obtain the outer contour line of the building;

[0009] Based on the outer contour of the building, extract the central axis;

[0010] Optimizing the central axis according to the differences in the directions of the starting point, the midpoint, and the end point of the central axis to obtain an optimized central axis;

[0011] According to the actual number of households on each floor of the building, the optimized central axis is equally divided to generate a dividing line to achieve the division of the apartment type;

[0012] Based on the segmentation lines, the building graphics are segmented in batches to form a plurality of independent sub-areas, each of which represents the spatial layout of a household;

[0013] According to the total number of floors of the building, combined with the floor plan of each floor that has been segmented, the layered household map of each floor is automatically generated, and a unique household number is assigned to each household space;

[0014] The map attribute information of the generated hierarchical household map is stored in the geographic space database;

[0015] According to the existing stratified household data, the building block model is generated by stretching using 3D software; wherein the geometric shape and position of each floor and each room in the building block model are consistent with the 2D stratified household data. Figure 1 To;

[0016] The building block model is spatially matched with the vector outline data and corresponding height information in the geographic space database to realize the hierarchical and household-based management function of the building block model.

[0017] Furthermore, based on the polygon data, a tolerance threshold is set to remove redundant nodes to obtain the outer contour line of the building, including:

[0018] Determine the starting point P1 and end point P of the polygon data n , arrange all points in order, and calculate the distance from the starting point P1 to the end point P n The error is determined by the distance between the straight line and each intermediate point, and the point with the largest error is selected as the next separation point until all errors are less than the tolerance threshold , obtain multiple contour point sets, select the external contour point set to form the outer contour line of the building; where:

[0019] For each intermediate point, calculate the distance from the intermediate point to the straight line P1P n Vertical distance ,when , delete the intermediate point.

[0020] Further, based on the outer contour of the building, a central axis is extracted, including:

[0021] Based on the vector surface where the outer contour line of the building is located, the coordinates of the centroid of the vector surface are calculated by the following formula: :

[0022] ;

[0023] In the formula, is the horizontal coordinate of the center of mass, is the vertical coordinate of the center of mass, is the area of ​​the polygon, M is the number of vertices of the polygon, m Number the vertices of the polygon, and Respectively m and m +1 vertex's horizontal coordinate, and Respectively m and m +1 vertical coordinate of the vertex;

[0024] Use Thiessen polygon method to generate the central axis of the figure through the centroid position and contour features;

[0025] Starting from the centroid, the Thiessen polygon map is created using the building outline boundary. The Thiessen polygon map divides the building space into several areas, with the distance between the centroid and the outline as the boundary;

[0026] In the Thiessen polygon graph, select the boundary segment closest to the centroid as the centerline;

[0027] The center line that meets the screening conditions is used as the central axis; wherein the screening conditions are expressed as:

[0028] ;

[0029] In the formula, and are the areas to the left and right of the center line, respectively. is the total area of ​​the figure, To set the tolerance value.

[0030] Further, the central axis is optimized according to the direction differences of the starting point, the midpoint and the end point of the central axis to obtain an optimized central axis, including:

[0031] Determine the coordinates of the starting point, midpoint, and end point of the central axis. , , , the direction vector of the central axis is calculated by the following formula:

[0032] ;

[0033] In the formula, is the direction vector of the central axis, , and are the horizontal coordinates of the starting point, midpoint, and end point of the central axis, respectively. , and They are the ordinates of the starting point, midpoint, and end point of the central axis respectively;

[0034] Based on the set threshold:

[0035] like If it is less than the set threshold, the central axis is determined to be a close straight line, and the straight line path closest to the central axis is used as the optimized central axis;

[0036] like If the value is greater than or equal to the set threshold and the central axis presents a broken line or curve feature, the central axis is determined to be a complex shape central axis, and the optimized central axis is determined by the following method:

[0037] If two adjacent nodes j and j +1 direction vector If the set conditions are met, the node j +1 as the key inflection point, wherein the setting condition is expressed as:

[0038] ;

[0039] In the formula, is the threshold angle for determining the key inflection point, and Node j The horizontal and vertical coordinates of and Node j +1 for the horizontal and vertical coordinates, and Node j +2 for the horizontal and vertical coordinates;

[0040] Select the key turning points on the central axis as segment nodes, and determine the node positions by detecting the direction changes of adjacent line segments;

[0041] Each segment node is fitted to obtain the optimized central axis.

[0042] Furthermore, according to the actual number of households on each floor of the building, the optimized central axis is equally divided to generate a dividing line to achieve the division of the apartment type, including:

[0043] Based on the attribute table, obtaining the number of households in each building;

[0044] Divide the central axis into n Equal parts; among them, n is the number of households on each floor of the building;

[0045] For optimizing the starting point of the central axis and end point , the coordinate calculation formula of the equally divided points is:

[0046] ;

[0047] In the formula, Indicates i Equal points, and The horizontal and vertical coordinates represent the starting point of the optimized central axis. and The abscissa and ordinate representing the end point of the optimized central axis;

[0048] At each equal point A dividing line perpendicular to the optimized central axis is generated at , then the vertical direction vector is ;

[0049] Extend the dividing line. If the extension range of the dividing line is , then The endpoint coordinates of the dividing line are:

[0050] ;

[0051] In the formula, is the starting point of the dividing line, is the end point of the dividing line, is the x-axis coordinate of the equally divided point, is the y-axis coordinate of the equal division point.

[0052] Furthermore, based on the segmentation lines, the building graphics are segmented in batches to form a plurality of independent sub-regions, including:

[0053] Calculate the intersection of each segmentation line and the building outline to ensure that the segmentation line can divide the building into multiple sub-areas;

[0054] Every two adjacent segmentation lines and the building outline together enclose a sub-area, each area represents a resident space in the building, and each sub-area is a closed polygon;

[0055] Starting from the first segmentation line, process to the last one in sequence, and take the intersection of each adjacent segmentation line and the building outline as the boundary point of the sub-area;

[0056] For each sub-region, a closed polygon is generated and marked, and the geometric information of each region is stored in the graphics file.

[0057] Furthermore, according to the total number of floors of the building and the floor plan of each floor that has been segmented, the layered household map of each floor is automatically generated, and a unique household number is assigned to each household space, including:

[0058] Get the total number of floors N of the building, determine the floor plan and number of partitioned areas of each floor;

[0059] Generate the upper-level division graphics based on the copied ground-level division plan to keep the apartment structure of each floor consistent;

[0060] During the process of copying layer by layer, modify the height value of each layer of graphics, and the height value increases layer by layer, and the increment value is a fixed building height;

[0061] Assign a unique household number to each household according to the coordinate system, and generate the household number according to the floor number and the household type location, wherein the household number is unique and identifiable;

[0062] Assign household numbers to the divided floor plan of each floor and fill in the corresponding attribute data, and ensure data consistency.

[0063] Furthermore, the map attribute information of the generated hierarchical household map is stored in the geographic spatial database, including:

[0064] A table structure for storing building hierarchical and household information is created in a geospatial database; wherein the table structure includes the following fields:

[0065] Unique ID: A globally unique identifier consisting of 32 digits and encoded in hexadecimal to ensure the uniqueness of each room;

[0066] Household number: a unique number for each household, used for quick location and query;

[0067] Area: The area of ​​each household is automatically calculated based on the graph;

[0068] Floor number: the floor where each household is located;

[0069] Floor height: the height of the floor where each household is located;

[0070] Use the batch data import tool of the geospatial database to import the hierarchical household maps and their attribute information into the database at one time.

[0071] Furthermore, based on the existing stratified and household-based data, three-dimensional software is used for stretching to generate a building block model, including:

[0072] Import the two-dimensional vector layered household map of each layer into the three-dimensional model processing software, and ensure that the coordinate system and size ratio of the two-dimensional plan are accurate.

[0073] Set the building's floor height, total number of floors and other building dimensions in the modeling software to ensure that the height of each floor is consistent with the floor height recorded in the database;

[0074] According to the plane outline of each floor, each room area is stretched, and the two-dimensional polygonal area is stretched vertically to the preset floor height to generate a building block model.

[0075] Furthermore, spatially matching the building block model with the vector outline data and corresponding height information in the geographic spatial database to realize the hierarchical and household management function of the building block model includes:

[0076] Spatial matching: Based on the building block model and two-dimensional vector data, spatial matching operations are performed layer by layer according to vector graphics and height attributes to ensure that all levels of data are accurately matched;

[0077] Information association: automatically bind each room block to the unique ID code of the corresponding household in the database, and associate and bind the corresponding household number and area to achieve automatic association between the building block model and database attributes;

[0078] Query and management: Based on the query function of the geospatial database, it responds to the input floor or apartment information and highlights the corresponding rooms in the building block model to achieve intuitive hierarchical and household management.

[0079] The present invention has at least the following beneficial effects:

[0080] (1) In the process of building figure segmentation, the present invention applies differentiated solution algorithms for vector shapes of different shapes to ensure that the segmentation results are accurate and reliable and basically conform to the actual situation.

[0081] (2) The present invention supports parallel computing and has a fast segmentation speed. Tests have shown that even millions of house graphs can be quickly segmented and divided into different layers, significantly shortening the data processing time.

[0082] (3) During the processing, the present invention can automatically bind the generated vector graphics of each household at each layer with the corresponding ownership information, thus avoiding manual secondary operations and improving the efficiency of data processing.

[0083] (4) Compared with the currently popular method of using machine fuzzy matching to achieve spatial data household location, the present invention uses the data of natural buildings to perform stratification and household division, which significantly improves the matching accuracy and ensures the consistency and reliability of the data.

[0084] (5) The present invention effectively solves the problem that many buildings in cities currently lack data on stratification and household division. By simply obtaining the number of households and floors of the natural building, batch automatic stratification and household division can be achieved, which greatly reduces the workload of manual drawing and saves time and cost.

[0085] (6) The present invention provides solid technical support for realizing the three-dimensional modeling of architectural objects with integrated graphics and attributes, better meets the data production and application requirements of three-dimensional building models under the geographic information system, and provides a scientific basis for smart city management, planning and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A flowchart of a method for batch house segmentation based on semantic information according to an embodiment of the present invention is shown;

[0087] Figure 2 A schematic diagram of building graphic preparation according to an embodiment of the present invention is shown;

[0088] Figure 3 A comparison diagram of contour simplification according to an embodiment of the present invention is shown, wherein (a) is the contour before simplification, and (b) is the contour after simplification;

[0089] Figure 4 A schematic diagram of a central axis according to an embodiment of the present invention is shown;

[0090] Figure 5 A schematic diagram of a dividing line according to an embodiment of the present invention is shown;

[0091] Figure 6 shows a building segmentation diagram according to an embodiment of the present invention;

[0092] Figure 7 A hierarchical household diagram according to an embodiment of the present invention is shown;

[0093] Figure 8 A schematic diagram of data storage according to an embodiment of the present invention is shown;

[0094] Fig. 9 A schematic diagram of a building block model according to an embodiment of the present invention is shown;

[0095] Fig.10 A schematic diagram of implementing hierarchical household division in a system according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0096] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and specific embodiments, but are not intended to limit the present invention. For the various steps described herein, if there is no necessity for a causal relationship between each other, the order in which they are described as examples herein should not be regarded as a limitation, and those skilled in the art should know that they can be adjusted in order, as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0097] The embodiment of the present invention provides a method for batch house segmentation based on semantic information. Figure 1 As shown, the method includes steps S101 to S110, which are described in detail as follows.

[0098] S101: Acquire building vector graphic data; wherein the building vector graphic data includes building graphics and an attribute table, the building graphics are polygon data representing the actual boundary of the building, and the attribute table includes two fields: number of floors and number of households.

[0099] Exemplarily, the input building vector graphics data adopts the shp format, and the building graphics are polygon data, representing the actual boundary of the building; its attribute table must contain two fields: the number of floors and the number of households, and they cannot be empty.

[0100] S102: Based on the polygon data, a tolerance threshold is set to eliminate redundant nodes to obtain the outer contour line of the building.

[0101] In some embodiments, the polygon data is simplified using the Douglas-Peucker algorithm, which sets a tolerance threshold ϵ to remove redundant nodes so that the simplified polygon shape is as close to the original polygon outline as possible.

[0102] Algorithm: Assume the starting point is P1 and the end point is P n , arrange all points in order. Determine the error by calculating the distance between the straight line from the starting point to the end point and each intermediate point, and select the point with the largest error as the next segmentation point until all errors are less than the threshold .

[0103] For each point P1 calculate its distance to the line P1P n Vertical distance ,when , the point is a redundant point and is deleted.

[0104] For each point, calculate its perpendicular distance to the line. If the point is redundant, delete it.

[0105] The simplified graph processed by Douglas-Peucker algorithm contains multiple contour point sets. The external contour point set is selected to form the outer contour line of the building for subsequent processing of central axis extraction.

[0106] S103: Extracting a central axis based on the outer contour of the building.

[0107] In some embodiments, step S103 specifically includes:

[0108] S1031: Centroid calculation

[0109] For the simplified vector surface, calculate its centroid coordinates :

[0110] ;

[0111] In the formula, is the horizontal coordinate of the center of mass, is the vertical coordinate of the center of mass, is the area of ​​the polygon, M is the number of vertices of the polygon, m Number the vertices of the polygon, and Respectively m and m +1 vertex's horizontal coordinate, and Respectively m and m +1 vertical coordinate of the vertex;

[0112] S1033: Extraction of central axis based on Thiessen polygon method.

[0113] Using the Thiessen polygon method, the central axis of the graph is generated through the centroid position and contour features, and implemented through the following steps:

[0114] S10331: Generate Thiessen polygon graph.

[0115] Starting from the centroid, a Thiessen polygon map is created using the building outline boundary. The Thiessen polygon map divides the building space into several areas, with the distance between the centroid and the outline as the boundary.

[0116] S10332: Centerline construction.

[0117] The boundary segments closest to the centroid are selected in the Thiessen polygon graph to ensure balanced segmentation of the centerline.

[0118] S10333: Symmetry test.

[0119] The center line should meet the requirement that the building is symmetrically distributed about this axis. The criterion for checking this condition is the symmetry of the long side being equally divided, and it is screened by the distance formula:

[0120] ;

[0121] In the formula, and are the areas to the left and right of the center line, respectively. is the total area of ​​the figure, To set the tolerance value.

[0122] S104: Optimizing the central axis according to the differences in directions of the starting point, midpoint and end point of the central axis to obtain an optimized central axis.

[0123] Since the graphics of actual buildings often present irregular geometric shapes, the extracted central axis may appear as a complex curve rather than a simple straight line. To ensure that the central axis adapts to the diverse shapes of buildings, this embodiment proposes the following strategy to optimize the processing according to the differences in the directions of the starting point, midpoint, and end point of the central axis.

[0124] Specifically, step S104 includes:

[0125] S1041: Processing of the central axis that is close to a straight line.

[0126] Judgment condition: If the starting point, midpoint and end point of the central axis are almost collinear (that is, the deviation of the three points is within a given threshold), the central axis is considered to be close to a straight line.

[0127] Reference formula: Assume that the coordinates of the starting point, midpoint, and end point of the central axis are , , , the direction vector of the central axis is calculated by the following formula:

[0128] ;

[0129] In the formula, is the direction vector of the central axis, , and are the horizontal coordinates of the starting point, midpoint, and end point of the central axis, respectively. , and They are the vertical coordinates of the starting point, midpoint and end point of the central axis respectively.

[0130] In this embodiment, 1 is the set threshold. Less than When 1, it is judged to be close to a straight line.

[0131] Processing method: Select the straight line path closest to the curve as the optimized center axis to simplify the processing process and ensure higher accuracy.

[0132] S1042: Segmentation of the central axis of a complex shape (L-shaped or U-shaped).

[0133] Judgment conditions: If the central axis presents obvious broken line or curve characteristics, such as L-shape or U-shape, it is judged to be a complex shape.

[0134] Processing method: The central axis is segmented through step-by-step node analysis, and the curve is approximated by straight line segments to reduce complexity. The segmentation processing steps include:

[0135] S10421: Node identification: Select key turning points on the optimized central axis as segment nodes, and determine the node positions by detecting the direction changes of adjacent line segments.

[0136] S10422: Segment-by-segment approximation: Fit each segment to the closest straight line and ensure that each segment is connected end to end, thereby forming a segment-by-segment approximation central axis as the optimized central axis.

[0137] Formula: If two adjacent nodes j and j +1 direction vector If the following conditions are met, the node j +1 for the key inflection point:

[0138] ;

[0139] In the formula, is the threshold angle for determining the key inflection point, and Node j The horizontal and vertical coordinates of and Node j +1 for the horizontal and vertical coordinates, and Node j +2 for the horizontal and vertical coordinates.

[0140] S105: According to the actual number of households on each floor of the building, the building is divided equally along the optimized central axis to generate a dividing line to achieve the division of the apartment types.

[0141] In some embodiments, step S105 specifically includes:

[0142] S1051: Obtain household number information.

[0143] Read the attribute information of the building vector graphics and obtain the number of households in each building.

[0144] S1052: Calculation of equal division points.

[0145] Assume that the number of households on each floor of the building is n, and divide the optimized central axis into n equal parts, ensuring that each part can accommodate one household. and end point , the coordinate calculation formula of the equally divided points is:

[0146] ;

[0147] In the formula, Indicates i Equal points, and The horizontal and vertical coordinates represent the starting point of the optimized central axis. and The horizontal and vertical coordinates represent the end points of the optimized centerline.

[0148] These equally divided points serve as the base points for subsequent dividing lines to meet the distribution requirements of building units.

[0149] S1053: Generate a dividing line, including:

[0150] S10531: Vertical calculation

[0151] At each equal point Generate a segmentation line perpendicular to the optimized central axis. Assume that the direction vector of the optimized central axis is , then the vertical direction vector is .

[0152] S10532: Extend the dividing line

[0153] To ensure that the dividing line can completely cover the outline of the building, the vertical line is appropriately extended. The length is calculated based on the size of the building's outline to ensure that both ends exceed the outline.

[0154] The extension range is as follows: The extension range of the dividing line is set to , then The endpoint coordinates of the dividing line are:

[0155] ;

[0156] In the formula, is the starting point of the dividing line, is the end point of the dividing line, is the x-axis coordinate of the equally divided point, is the y-axis coordinate of the equal division point.

[0157] S106: Based on the segmentation lines, batch segment the building graphics to form multiple independent sub-areas, each of which represents the spatial layout of a household.

[0158] In some embodiments, step S106 specifically includes:

[0159] S1061: Calculate the intersection of the segmentation line and the building outline.

[0160] For each segmentation line, calculate its intersection with the building outline to ensure that the segmentation line can accurately divide the building into multiple sub-areas. The specific calculation method is as follows: Use the vector intersection formula or geometric method to detect the intersection of the segmentation line and the building outline. If the outline is a polygon, the intersection can be calculated segment by segment to ensure effective cutting of the segmentation line and the outline.

[0161] S1062: forming an independent sub-region, including:

[0162] S10621: Regional division.

[0163] Every two adjacent segmentation lines and the building outline together form a sub-area. These sub-areas are saved independently, and each area represents a resident space in the building.

[0164] S10623: Spatial structure inspection.

[0165] Make sure each sub-region is a closed polygon to avoid incomplete or overlapping regions during segmentation.

[0166] S1063: Batch segmentation, including:

[0167] S10631: Traverse the dividing line.

[0168] Starting from the first segmentation line, the process is processed sequentially to the last one, and the intersection of each adjacent segmentation line and the building outline is taken as the boundary point of the sub-area.

[0169] S10632: Generate a polygonal area.

[0170] For each sub-region, a closed polygon is generated and marked. The geometric information of each region will be stored in a graphics file for subsequent processing and query.

[0171] S107: According to the total number of floors of the building and the floor plan of each floor that has been divided, a layered household map of each floor is automatically generated, and a unique household number is assigned to each household space.

[0172] In some embodiments, step S107 specifically includes:

[0173] S1071: Generation of hierarchical household maps, including:

[0174] S10711: Read floor information: Input the total number of floors N of the building, and determine the floor plan and number of divided areas of each floor.

[0175] S10712: Copy layer by layer: For the partition plan of the ground floor, copy it and generate the partition plan of the upper floor to ensure that the apartment structure of each floor remains consistent.

[0176] S10713: Height assignment: During the layer-by-layer copying process, the height value of each layer of graphics is modified. The height value increases layer by layer, and the incremental value is a fixed building height.

[0177] S1072: Automatic household number allocation: Bind each household number as attribute information to the corresponding segmented area to associate it with the graphic data, including:

[0178] S10721: Determine household number generation rules: Assign a unique household number to each household based on the coordinate system, and generate household numbers based on the floor number and unit type location to ensure that the household number is unique and identifiable. The household number format uses the floor number + unit type number format, for example, "501" represents the first household on the 5th floor.

[0179] S10722: Assign values ​​layer by layer: Assign household numbers to the split floor plan of each layer and fill in the corresponding attribute data, and ensure data consistency.

[0180] S108: Store the map attribute information of the generated hierarchical household map into the geographic spatial database.

[0181] In some embodiments, step S108 specifically includes:

[0182] S1081: Database structure design.

[0183] Create a table in the geospatial database to store building hierarchical and household information. The table structure contains the following main fields:

[0184] Unique ID code: A globally unique identifier consisting of 32 digits and encoded in hexadecimal to ensure the uniqueness of each room.

[0185] Household number: A unique number for each household, used for quick positioning and query.

[0186] Area: The area of ​​each household is automatically calculated based on the graph.

[0187] Number of floors: The floor where each household is located.

[0188] Floor height: The height of the floor where each household is located.

[0189] S1082: Batch import of data.

[0190] Use the batch data import tool of the geospatial database (such as the COPY command in PostGIS) to import the hierarchical household maps and their attribute information into the database at one time.

[0191] S1083: Data verification.

[0192] After the import is completed, a data consistency check is performed to ensure that the information such as the household number, area and floor height of each apartment type is correct, and that there is no omission or overlap in the spatial data.

[0193] S109: Based on the existing stratified household data, three-dimensional software is used to perform stretching processing to generate a building block model; wherein the geometric shape and position of each floor and each room in the building block model are consistent with the two-dimensional stratified household data. Figure 1 To.

[0194] In some embodiments, step S109 specifically includes:

[0195] S1091: 3D modeling preparation, including:

[0196] S10911: Import two-dimensional layered household map: Import the two-dimensional vector layered household map of each layer into the three-dimensional model processing software, and ensure that the coordinate system and size ratio of the two-dimensional plan are accurate.

[0197] S10912: Parameter setting: Set the building's floor height, total number of floors and other building dimensions in the modeling software to ensure that the height of each floor is consistent with the floor height recorded in the database.

[0198] S1092: Stretching to generate a three-dimensional model. According to the plane outline of each floor, each room area is stretched, and the two-dimensional polygonal area is stretched in the vertical direction to a preset floor height to generate a three-dimensional block.

[0199] S110: spatially matching the building block model with the vector outline data and corresponding height information in the geographic spatial database to achieve the hierarchical and household-based management function of the building block model.

[0200] In some embodiments, step S110 specifically includes:

[0201] S1101: Spatial matching.

[0202] Based on the three-dimensional model (i.e. the building block model, the same below) and two-dimensional vector data, spatial matching operations are performed layer by layer according to vector graphics and height attributes to ensure accurate matching of data at all levels.

[0203] S1102: Information association.

[0204] The system automatically binds each room block to the unique identity code of the corresponding household in the database, and associates and binds the corresponding household number, area and other attribute information, thereby realizing the automatic association of the three-dimensional model with the database attributes.

[0205] S1103: Query and management.

[0206] Through the query function of the spatial database, users can select floors or apartment types and highlight the corresponding rooms in the three-dimensional model to achieve intuitive hierarchical and household management.

[0207] The following embodiment of the present invention will further illustrate the feasibility and advancement of the present invention in conjunction with a specific case. The specific case is implemented by the following steps:

[0208] Step 1: Prepare the building vector (SHP) file to be segmented and ensure that the attribute table contains two attributes: number of floors and number of households. Figure 2 shown.

[0209] Step 2: Use the algorithm to calculate the minimum boundary geometry of the building graph, reduce the redundant nodes in the graph, and extract the outer contour of the building. Figure 3 To simplify the comparison chart, Figure 3 (a) and (b) show the outlines before and after simplification, respectively.

[0210] Step 3: Extract the central axis based on the simplified vector surface to ensure that the shape is symmetrically distributed about this axis. Figure 4 shown.

[0211] Step 4: Generate a segmentation line based on the number of households on each floor of the building, ensuring that the segmentation line can completely cover the building graphics. Figure 5 shown.

[0212] Step 5: Based on the segmentation lines, batch segment the buildings, such as Figure 6 shown.

[0213] Step 6: According to the total number of floors of the building, combined with the floor plans of each floor that have been segmented, a detailed layered household map of each floor is automatically generated. Figure 7 shown.

[0214] Step 7: Store the segmented data in batches into the database, such as Figure 8 shown.

[0215] Step 8: Based on the existing stratified household data, use 3D software to perform stretching processing to generate a building block model. Ensure that the geometric shape and position of each floor and each room in the building block model are consistent with the 2D stratified household data. Figure 1The obtained building block model is as follows Fig. 9 shown.

[0216] Step 9: spatially match the generated building block model with the vector outline data and corresponding height information stored in the spatial database. Through the matching results, hierarchical and household management of building block models in the system is achieved, such as Fig.10 shown.

[0217] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. The elements in the claims are to be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in this specification or during the prosecution of this application, which examples are to be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0218] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) may be used in combination with each other. For example, a person of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features may be grouped together to simplify the present invention. This should not be interpreted as an intention that a feature of an invention that is not claimed for protection is necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of an embodiment of a particular invention. Thus, the following claims are incorporated herein as examples or embodiments in the specific embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments may be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalent forms granted by these claims.

Claims

1. A method for batch house segmentation based on semantic information, characterized in that: The method comprises: Acquire building vector graphics data; wherein the building vector graphics data includes building graphics and attribute tables, the building graphics are polygon data representing the actual boundaries of the building, and the attribute table contains two fields: number of floors and number of households; Based on the polygon data, a tolerance threshold is set to eliminate redundant nodes to obtain the outer contour line of the building; Based on the outer contour of the building, extract the central axis; Optimizing the central axis according to the differences in the directions of the starting point, the midpoint, and the end point of the central axis to obtain an optimized central axis; According to the actual number of households on each floor of the building, the optimized central axis is equally divided to generate a dividing line to achieve the division of the apartment type; Based on the segmentation lines, the building graphics are segmented in batches to form a plurality of independent sub-areas, each of which represents the spatial layout of a household; According to the total number of floors of the building, combined with the floor plan of each floor that has been segmented, the layered household map of each floor is automatically generated, and a unique household number is assigned to each household space; The map attribute information of the generated hierarchical household map is stored in the geographic space database; According to the existing stratified household data, three-dimensional software is used to perform stretching processing to generate a building block model; wherein the geometric shape and position of each floor and each room of the building block model are consistent with the two-dimensional stratified household map; The building block model is spatially matched with the vector outline data and corresponding height information in the geographic space database to realize the hierarchical and household-based management function of the building block model.

2. The method according to claim 1, characterized in that Based on the polygon data, a tolerance threshold is set to remove redundant nodes to obtain the outer contour of the building, including: Determine the starting point P1 and end point P of the polygon data n , arrange all points in order, and calculate the distance from the starting point P1 to the end point P n The error is determined by the distance between the straight line and each intermediate point, and the point with the largest error is selected as the next separation point until all errors are less than the tolerance threshold , obtain multiple contour point sets, select the external contour point set to form the outer contour line of the building; where: For each intermediate point, calculate the distance from the intermediate point to the straight line P1P n Vertical distance ,when , delete the intermediate point.

3. The method according to claim 1, characterized in that: Based on the outer contour of the building, the central axis is extracted, including: Based on the vector surface where the outer contour line of the building is located, the coordinates of the centroid of the vector surface are calculated by the following formula: : ； In the formula, is the horizontal coordinate of the center of mass, is the vertical coordinate of the center of mass, is the area of ​​the polygon, M is the number of vertices of the polygon, m Number the vertices of the polygon, and Respectively m and m +1 vertex's horizontal coordinate, and Respectively m and m +1 vertical coordinate of the vertex; Use Thiessen polygon method to generate the central axis of the figure through the centroid position and contour features; Starting from the centroid, the Thiessen polygon map is created using the building outline boundary. The Thiessen polygon map divides the building space into several areas, with the distance between the centroid and the outline as the boundary; In the Thiessen polygon graph, select the boundary segment closest to the centroid as the centerline; The center line that meets the screening conditions is used as the central axis; wherein the screening conditions are expressed as: ； In the formula, and are the areas to the left and right of the center line, respectively. is the total area of ​​the figure, To set the tolerance value.

4. The method according to claim 1, characterized in that The central axis is optimized according to the direction differences of the starting point, the midpoint and the end point of the central axis to obtain an optimized central axis, including: Determine the coordinates of the starting point, midpoint, and end point of the central axis. , , , the direction vector of the central axis is calculated by the following formula: ； In the formula, is the direction vector of the central axis, , and are the horizontal coordinates of the starting point, midpoint, and end point of the central axis, respectively. , and They are the ordinates of the starting point, midpoint, and end point of the central axis respectively; Based on the set threshold: like If it is less than the set threshold, the central axis is determined to be a close straight line, and the straight line path closest to the central axis is used as the optimized central axis; like If the value is greater than or equal to the set threshold and the central axis presents a broken line or curve feature, the central axis is determined to be a complex shape central axis, and the optimized central axis is determined by the following method: If two adjacent nodes j and j +1 direction vector If the set conditions are met, the node j +1 as the key inflection point, wherein the setting condition is expressed as: ； In the formula, is the threshold angle for determining the key inflection point, and Node j The horizontal and vertical coordinates of and Node j +1 for the horizontal and vertical coordinates, and Node j +2 for the horizontal and vertical coordinates; Select the key turning points on the central axis as segment nodes, and determine the node positions by detecting the direction changes of adjacent line segments; Each segment node is fitted to obtain the optimized central axis.

5. The method according to claim 1, characterized in that According to the actual number of households on each floor of the building, the optimized central axis is equally divided to generate a dividing line to achieve the division of the apartment type, including: Based on the attribute table, obtaining the number of households in each building; Divide the central axis into n Equal parts; among them, n is the number of households on each floor of the building; For optimizing the starting point of the central axis and end point , the coordinate calculation formula of the equally divided points is: ； In the formula, Indicates i Equal points, and The horizontal and vertical coordinates represent the starting point of the optimized central axis. and The abscissa and ordinate representing the end point of the optimized central axis; At each equal point A dividing line perpendicular to the optimized central axis is generated at , then the vertical direction vector is ; Extend the dividing line. If the extension range of the dividing line is , then The endpoint coordinates of the dividing line are: ； In the formula, is the starting point of the dividing line, is the end point of the dividing line, is the x-axis coordinate of the equally divided point, is the y-axis coordinate of the equal division point.

6. The method according to claim 1, characterized in that Based on the segmentation lines, the building graphics are segmented in batches to form multiple independent sub-areas, including: Calculate the intersection of each segmentation line and the building outline to ensure that the segmentation line can divide the building into multiple sub-areas; Every two adjacent segmentation lines and the building outline together enclose a sub-area, each area represents a resident space in the building, and each sub-area is a closed polygon; Starting from the first segmentation line, process to the last one in sequence, and take the intersection of each adjacent segmentation line and the building outline as the boundary point of the sub-area; For each sub-region, a closed polygon is generated and marked, and the geometric information of each region is stored in the graphics file.

7. The method according to claim 1, characterized in that According to the total number of floors of the building, combined with the floor plan of each floor that has been segmented, the layered household map of each floor is automatically generated, and a unique household number is assigned to each household space, including: Get the total number of floors N of the building, determine the floor plan and number of partitioned areas of each floor; Generate the upper-level division graphics based on the copied ground-level division plan to keep the apartment structure of each floor consistent; During the process of copying layer by layer, modify the height value of each layer of graphics, and the height value increases layer by layer, and the increment value is a fixed building height; Assign a unique household number to each household according to the coordinate system, and generate the household number according to the floor number and the household type location, wherein the household number is unique and identifiable; Assign household numbers to the divided floor plan of each floor and fill in the corresponding attribute data, and ensure data consistency.

8. The method according to claim 1, characterized in that The map attribute information of the generated hierarchical household map is stored in the geospatial database, including: A table structure for storing building hierarchical and household information is created in a geospatial database; wherein the table structure includes the following fields: Unique ID: A globally unique identifier consisting of 32 digits and encoded in hexadecimal to ensure the uniqueness of each room; Household number: a unique number for each household, used for quick location and query; Area: The area of ​​each household is automatically calculated based on the graph; Floor number: the floor where each household is located; Floor height: the height of the floor where each household is located; Use the batch data import tool of the geospatial database to import the hierarchical household maps and their attribute information into the database at one time.

9. The method according to claim 1, characterized in that: Based on the existing stratified and household data, 3D software is used for stretching to generate a building block model, including: Import the 2D vector layered household map of each floor into the 3D model processing software, and ensure that the coordinate system and size ratio of the 2D plan are accurate; Set the building's floor height, total number of floors and other building dimensions in the modeling software to ensure that the height of each floor is consistent with the floor height recorded in the database; According to the plane outline of each floor, each room area is stretched, and the two-dimensional polygonal area is stretched vertically to the preset floor height to generate a building block model.

10. The method according to claim 1, characterized in that The building block model is spatially matched with the vector outline data and corresponding height information in the geographic space database to realize the hierarchical and household management function of the building block model, including: Spatial matching: Based on the building block model and two-dimensional vector data, spatial matching operations are performed layer by layer according to vector graphics and height attributes to ensure that all levels of data are accurately matched; Information association: automatically bind each room block to the unique ID code of the corresponding household in the database, and associate and bind the corresponding household number and area to achieve automatic association between the building block model and database attributes; Query and management: Based on the query function of the geospatial database, it responds to the input floor or apartment information and highlights the corresponding rooms in the building block model to achieve intuitive hierarchical and household management.

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