CN112687006A - Rapid building three-dimensional grid data graph generation method - Google Patents
Rapid building three-dimensional grid data graph generation method Download PDFInfo
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Abstract
The invention discloses a method for quickly generating a three-dimensional grid data map of a building, which belongs to the technical field of information and can quickly and accurately determine the position of each user in the building and service data information. The technical scheme of the invention comprises the following steps: and selecting a proper mesh level to subdivide the minimum outsourcing rectangle of the polygonal area of the outer contour of the building, and randomly selecting m sample meshes. And dividing the m sample grids into n classes by using a clustering algorithm, and taking n clustering center points as control points of the Voronoi diagram to generate the Voronoi diagram. And (4) segmenting the polygonal area of the outer contour of the building by using the voronoi diagram to obtain a 3D grid diagram of the interior of the building. And forming a spatial index code of each household by using Beidou grid codes corresponding to control points on the Voronoi diagram. And performing associated storage on the spatial index codes and corresponding business data of each household in the building business data, and simultaneously associating the spatial index codes with a 3D grid map in the building to obtain and display a three-dimensional grid data map of the building.
Description
Technical Field
The invention relates to the technical field of information, in particular to a method for quickly generating a three-dimensional grid data graph of a building.
Background
A stereoscopic grid data map of a building, typically includes two parts: the grid diagram of the building is drawn on a front-end page according to the outline of the building, and each grid in the page is provided with a Beidou grid code corresponding to the grid diagram; and an index is established for each piece of service data in the background database and the service database of the building according to the Beidou grid code. And each grid on the front-end page inquires corresponding service data from the background database according to the Beidou grid code of the grid, and then the corresponding service data is displayed according to actual requirements, so that the three-dimensional grid data graph system based on the Beidou grid code is formed.
When constructing an actual building dimensional grid data map system, the grid on the front-end page and the business data in the back-end database are typically associated in units of users. This requires the location (longitude, latitude and elevation) of each household, which is encoded as a beidou trellis code, which is used as an index to the database.
However, the existing data basically has no position information of each household. A small number of buildings have BIM models or CAD plans from which location information for each household can be calculated. However, a large number of buildings do not have BIM models or CAD plan views, and the above-mentioned building three-dimensional grid data map system needs to be implemented to acquire position information in the field again. In the face of mass stock data, the cost is not imaginable, and the project construction period is greatly prolonged, so that the popularization and the application of the building three-dimensional grid data graph system are greatly limited.
Therefore, a technical scheme capable of quickly and accurately determining the position information of each user in the building is lacked at present.
Disclosure of Invention
In view of this, the present invention provides a method for generating a three-dimensional grid data map of a building, where the generated three-dimensional grid data map of a building can quickly and accurately determine the locations of each user and the service data information in the building.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
step 1, extracting an outer contour of a building from a map, projecting a plane of the outer contour of the building into a polygonal area, determining a minimum outsourcing rectangle according to the polygonal area of the outer contour of the building, selecting a proper mesh level to subdivide the minimum outsourcing rectangle into individual meshes, and randomly selecting m meshes positioned in the polygonal area of the outer contour of the building from the subdivided meshes to be used as sample meshes to obtain a sample mesh set.
And 2, extracting the number n of the users of each floor from a service database of the building, and then dividing the m sample grids into n classes by using a clustering algorithm to obtain n sub-classes in total.
And 3, taking the clustering center points of the n subclasses as control points of the Voronoi diagram, and generating the Voronoi diagram corresponding to the minimum outsourcing rectangle.
And 4, segmenting the polygonal area of the outer contour of the building by using the generated Voronoi diagram to obtain the polygonal area of the outer contour of each family of the building, and forming a 3D grid diagram in the building.
Step 5, the control point of the polygonal area of the outline of each user corresponding to the Voronoi diagram is the position of the current user; and forming a spatial index code of the current user by using the Beidou grid code corresponding to the control point on the Voronoi diagram.
And 6, performing associated storage on the spatial index code of each household in the building and the corresponding service data of each household in the service data of the building, and simultaneously associating the spatial index code with the 3D grid map in the building to obtain and display the three-dimensional grid data map of the building.
Further, the suitable grid level is selected by comparing the actual size of each house in the building according to the size of the grid used.
Furthermore, the value of m is 20-30% of the number of all the meshes obtained by subdivision.
Further, the sample grid set is used to represent a polygonal area of the outline of the entire building.
Further, the clustering algorithm is a k-means algorithm.
Further, in the clustering algorithm, the distance between the sample grids is the distance between the center points of the sample grids, and the clustering center point of each subclass obtained by the clustering algorithm is the control point of the polygonal area corresponding to each family.
Further, the spatial index code of the current user also contains information indicating the number of layers where the current user is located.
Has the advantages that:
according to the rapid building three-dimensional grid data map generation method provided by the invention, the position information of each household in the building can be rapidly determined by means of the public third-party map platform and the building service data provided by the user, Beidou grid coding is realized, and the spatial index of the building service data is established. Compared with the field measurement and calibration of the position of each household, the method has the advantages that the cost and the time are greatly saved, and therefore a stereoscopic grid data graph system can be constructed by mass building big data.
Drawings
Fig. 1 is a building Beidou grid code generation and three-dimensional grid map establishment process provided by the embodiment of the invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
In the building three-dimensional grid data graph system, the Beidou grid code of each family is unique, has the advantage of a position index code, and is very suitable for retrieval and query based on positions. It reflects the position accuracy rather second.
As before, in-field measurements are required to obtain an accurate location for each household within the building. But for massive data, neither cost nor time is practical to do so. If the approximate position of each household can be obtained quickly, the fact that the general positions of the households are within the range of a building, the spatial position relation between the households in the same floor is approximately reasonable, and the coded Beidou grid code is unique and suitable for position retrieval and query is guaranteed, the method is reasonable. The invention solves the problem of Beidou grid coding of each family based on the thought.
First, the outer contour vector data of the building can be extracted on a public third-party map. Second, the number of households per floor of the building can be extracted from the service database of the building. And finally, calculating the polygonal area of each household according to the outer contour data of the building and the number of the households on each floor. Although this area is only of schematic effect, each household area is within a polygonal area of the building, and their spatial location is relatively reasonable. The polygon area control points of each household are extracted to serve as the position of each household, so that the Beidou grid code generated by coding is unique, and the characteristics that the grid code facilitates space retrieval and query are achieved.
The method does not need field operation, only needs to use the public third-party map and the data provided by the client, and automatically processes the data by a computer after manually extracting and cleaning the necessary data, which is feasible in cost and time. Based on the method, it is possible to establish a building three-dimensional grid data system based on mass data, rather than only establishing an experimental verification system based on small-scale data volume.
As shown in fig. 1, the method for generating a three-dimensional grid data map of a building according to the present invention comprises the following steps:
step 1, extracting the outer contour of a building from a map, determining the minimum outsourcing rectangle of the building according to the polygonal area (plane projection) of the building, and then selecting a proper grid level to divide the minimum outsourcing rectangle into grids one by one. M grids located in the outline polygonal area of the building are randomly selected as sample grids, and represent the outline area of the whole building.
When the minimum outer wrapping rectangle is split, a Beidou grid is used, and the Beidou grid is established according to a global subdivision grid GeoSOT. The global subdivision grid (GeoSOT) is an important research result of 'research on global aerospace information subdivision organization mechanism and application method' in the national 973 project. The technology divides the earth space with 5.7 ten thousand KM from the center of the earth to the periphery of the earth into 32-level multi-level discrete grids with similar volume shapes and no gaps or no overlap. The minimum three-dimensional grid can reach 1.5 cm. The mesh after subdivision has unique codes, and the codes have the advantages of multi-scale, identification, positioning, indexing, calculation, automatic spatial correlation and the like, so that a spatial mesh framework for big data management and application is formed.
When the Beidou grids are used for dividing the minimum outsourcing rectangle corresponding to the outer contour of the building, the selected proper division level is equal to the size of each household in the building, each household in the building is related to the use type of the building, for example, if the building is a residential building, grids of about 4 meters can be selected according to the corresponding size of each household, and therefore the proper division level is selected correspondingly; if the building is an office building, where the size of each resident may be larger, for example, about 8 or 16 meters of grids may be selected to correspond to a suitable subdivision level.
In the embodiment of the invention, the value of m should be selected to reflect the mesh distribution in the polygonal area of the outline of the building, and generally 20% -30% of the number of all meshes obtained by subdivision can be taken as the value selection range of m.
And 2, extracting the number n of the users on each floor from a service database of the building, and then dividing the m sample grids into n classes by using a clustering algorithm (such as k-means, and other clustering algorithms can be adopted), wherein each subclass corresponds to one user. The distance between grids in the clustering algorithm is represented by the distance between the center points of the grids. The cluster center point of each subclass is the control point of each family multilateral region.
And 3, taking the n clustering center points as control points of a Voronoi Diagram (Voronoi Diagram), and generating the Voronoi Diagram with the minimum outsourcing rectangle. The voronoi diagram (also called the taisen polygon) is a subdivision of a spatial plane, and is characterized in that any point in a polygon is closest to a control point of the polygon and is further away from the control point in an adjacent polygon, and there is only one control point in each polygon.
And 4, dividing the outer contour polygonal area of the building by using the generated Voronoi diagram to obtain the outer contour polygonal area of each household. And in each divided area, the control point is the position of each household, and the Beidou grid code can be generated based on the control point, so that the spatial index of the background building business data is established. And the outer contour polygonal area of each household can generate a 3D grid map in the building, which can schematically present the space relative position of each household in the building, associate the service attribute data of each household, and realize the display requirement of the three-dimensional grid data map.
The invention has the advantages that the position information of each household in the building can be quickly determined by means of the public third-party map platform and the building service data provided by the user, the Beidou grid coding is realized, and the spatial index of the building service data is established. Compared with the field measurement and calibration of the position of each household, the method has the advantages that the cost and the time are greatly saved, and therefore a stereoscopic grid data graph system can be constructed by mass building big data.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A method for rapidly generating a three-dimensional grid data map of a building is characterized by comprising the following steps:
step 1, extracting an outer contour of a building from a map, wherein a plane projection of the outer contour of the building is a polygonal area, determining a minimum outsourcing rectangle according to the polygonal area of the outer contour of the building, selecting a proper mesh level to divide the minimum outsourcing rectangle into meshes, and randomly selecting m meshes positioned in the polygonal area of the outer contour of the building from the divided meshes to be used as sample meshes to obtain a sample mesh set;
step 2, extracting the number n of the users of each floor from a service database of the building, and then dividing the m sample grids into n classes by using a clustering algorithm to obtain n sub-classes in total;
step 3, taking the clustering center points of the n subclasses as control points of the Voronoi diagram, and generating the Voronoi diagram corresponding to the minimum outsourcing rectangle;
step 4, dividing the polygonal area of the outer contour of the building by using the generated Voronoi diagram to obtain the polygonal area of the outer contour of each family of the building to form a 3D grid diagram inside the building;
step 5, the control point of the polygonal area of the outline of each user corresponding to the Voronoi diagram is the position of the current user; forming a spatial index code of the current user by using Beidou grid codes corresponding to control points on the Voronoi diagram;
and 6, performing associated storage on the spatial index code of each household in the building and the corresponding service data of each household in the service data of the building, and simultaneously associating the spatial index code with the 3D grid map in the building to obtain and display the three-dimensional grid data map of the building.
2. The method of claim 1, wherein the suitable grid level is selected based on the size of the grid used, compared to the actual size of each individual household in the building.
3. The method of claim 2, wherein the value of m is between 20% and 30% of the number of all meshes obtained by dividing.
4. The method of claim 3, wherein the set of sample meshes are used to represent polygonal areas of the outline of the entire building exterior.
5. The method of claim 4, wherein the clustering algorithm is a k-means algorithm.
6. The method of claim 5, wherein in the clustering algorithm, the distance between the sample grids is the distance between the center points of the sample grids, and the clustering center point of each subclass obtained by the clustering algorithm is the control point of the polygonal area corresponding to each family.
7. The method of claim 6, wherein the spatial index code of the current user further comprises information indicating the number of layers the current user is located in.
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