CN113223164B - Large-terrain data batch processing method - Google Patents
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Abstract
The invention relates to the technical field of geographic data processing, and discloses a large-terrain data batch processing method, which comprises the following steps: importing elevation data, hydrology, roads, trees and point model vector data, a model base model and appearance data of the large terrain; creating irregular triangles for the elevation data through the processing nodes, building irregular terrain elevation grids, and then setting the altitude of the hydrological vector data and the road vector data with topological relations to obtain an integrated hydrological vector characteristic model and a road vector characteristic model with topological relations; creating a new irregular elevation grid, and cutting the new irregular elevation grid into blocks; the tiled scene graph database, the reference model and the point model with the application script are fused and integrated through a model placement algorithm, model placement on the tiled scene graph is obtained, and therefore the large terrain simulation database is built.
Description
Technical Field
The invention relates to the technical field of geographic data processing, in particular to a large-terrain data batch processing method.
Background
With the development of remote sensing technology, telemetry technology and network communication technology, the acquisition of geospatial data becomes easier. The method is a key point of the current geographic information system modeling research in the face of geographic space data which is huge in data quantity, various in data types and extremely complex in data collection, modeling, organization, expression and analysis. In the aspect of geospatial data modeling, the following problems mainly exist at present:
(1) The traditional terrain modeling software can only carry out merging processing on different data sources through a complicated manual method, and the problems of conflict and error report of surface data and terrain features or incomplete processing results can occur.
(2) The traditional terrain modeling work is a manual intensive database construction process, a large amount of manpower and time cost are needed for constructing a synthetic simulation environment, geospatial source data needs to be modified and updated manually, and the problems of cracks under terrain representation conditions, discontinuity of processing simulation characteristics and poor compatibility in a processing system upgrading process are solved.
(3) Traditional terrain modeling software cannot guarantee that a simulation database accurately expresses a real geospatial relationship, cannot provide accurate sight lines required by real and effective ground simulation and training, and cannot extract material attribute information from source data.
(4) Traditional terrain modeling software takes a lot of time and manpower to manually check and correct the problem of geometric topological relationship inconsistency.
Disclosure of Invention
The invention provides a large-terrain data batch processing method which can rapidly complete large-scale, high-precision and multi-source multi-dimensional heterogeneous data processing, merging and integrated display and improve the precision and efficiency of terrain simulation modeling.
The invention provides a large-terrain data batch processing method, which comprises the following steps:
importing elevation data, hydrological vector data, road vector data, tree vector data, point model vector data, a model base model and appearance data of a large terrain;
performing projection transformation on the hydrological vector data and the road vector data through the characteristic node to obtain the hydrological vector data and the road vector data under a geodetic coordinate system;
performing geometric correction, compression, normalization and matching processing on the hydrological vector data and the road vector data in a geodetic coordinate system through a processing node to obtain hydrological vector characteristic data and road vector characteristic data;
the hydrological vector characteristic data and the road vector characteristic data are spliced and merged through the characteristic node to obtain integrated hydrological vector characteristic data and road vector characteristic data;
generalizing the integrated hydrological vector characteristic data and road vector characteristic data through the characteristic node, extracting geographic entity coordinates, and obtaining hydrological vector data and road vector data structures with topological relations;
creating irregular triangles for the elevation data through the processing nodes, building irregular terrain elevation grids, creating a shade, and then setting the altitude of the hydrological vector data and the road vector data with topological relations to obtain an integrated hydrological vector characteristic model and a road vector characteristic model with topological relations;
removing the nearby points of the elevation grid and the nearby points of the integrated hydrological vector characteristic model and road vector characteristic model through the processing nodes, and cutting, cutting blocks and converting coordinates of the features in the integrated hydrological vector characteristic model and road vector characteristic model through the characteristic nodes;
creating a new irregular elevation grid according to the points in the elevation grid and the hydrological vector characteristics and road vector characteristics after coordinate conversion, and cutting the new irregular elevation grid into blocks;
integrating the imported model base model and the imported appearance data through the model nodes and the appearance nodes to obtain a model with appearance materials;
integrating the new irregular elevation grids after being cut into blocks into a tiled scene graph through the feature nodes, wherein the tiled scene graph comprises terrain feature information;
compiling the model with the appearance material and the tiled scene graph through the processing nodes to obtain a tiled scene graph database;
performing projection transformation on the tree vector data and the point model vector data through the canopy nodes and the model nodes to obtain tree vector data and point model vector data under a geodetic coordinate system;
applying scripts to tree vector data and point model vector data under a geodetic coordinate system through script nodes to obtain trees with application scripts and point models with application scripts;
creating a reference model through model nodes according to the new irregular elevation grid and the tree with the application script, wherein the reference model attributes comprise the position, the direction, the height and the scaling parameters of the model associated with each feature point;
and performing fusion integration on the tiled scene graph database, the reference model and the point model with the application script through a model placement algorithm to obtain model placement on the tiled scene graph, namely completing the construction of the large terrain simulation database.
The specific steps of the model placement algorithm include:
three-dimensional model selection
Searching a three-dimensional model corresponding to the name of the three-dimensional model in a tiled scene graph database, wherein the three-dimensional model comprises geometric and appearance information;
point model attribute information acquisition
Acquiring attribute information of a point model with an application script according to a reference model, wherein the attribute information comprises the position, the direction, the height and the scaling of the point model, and the attribute of the point model corresponds to the attribute of a finally placed three-dimensional model;
three-dimensional model placement position
The three-dimensional model position coordinates comprise an x-axis position, a y-axis position and a z-axis position;
three-dimensional model scaling
The three-dimensional model scaling value comprises an x-axis scaling factor, a y-axis scaling factor and a z-axis scaling factor;
when only one scaling factor exists, it is applied to scale the remaining two dimensions accordingly to preserve the relative length, width and height of the model; when there are two scaling factors on different axes, then the third axis will be scaled according to the geometric mean of the first two scaling factors; when both absolute and relative scaling factors are provided for a particular axis, then the absolute scaling factor is applied;
height of three-dimensional model
Determining a reference point by the elevation of the upper and lower irregular grids of the model, neglecting the z coordinate of the model reference point, and using the reference point when the elevation value of the model is needed;
when the model elevation is set to be a default value of 0, using the grid elevation of the irregular elevation; when set to 1, the elevation value is used to store in the model reference point;
three-dimensional model orientation
The three-dimensional model direction comprises a rolling angle, a pitch angle, a course angle and an orientation;
a roll angle; positive scrolling is counterclockwise positive about the y-axis, corresponding to "scrolling right";
a pitch angle; positive tilt is the positive x-axis of counterclockwise rotation, corresponding to "tilt up";
a course angle; the positive course rotates clockwise to the positive z axis, corresponding to 'turning to the right';
an orientation; when set to 1, results in the model being perpendicular to the surface, covering roll and pitch angles; when set to the default value of 0, no operation is performed;
placed along a linear feature
And according to the three-dimensional model and the attribute information of the point model, placing the three-dimensional model and the point model in a mode of a plurality of reference models which are specified by the linear meta model and are placed along the linear characteristic, and completing the construction of the large terrain simulation database.
The elevation data encodes the elevation using luminance, the higher the elevation, the higher the luminance of the pixel, and the lower the elevation, the lower the luminance of the pixel.
And performing geometric operation and selection operation on the hydrological vector characteristic data and the road vector characteristic data through the characteristic node to realize splicing and merging.
The processing node: performing create, remove, add, and match processing operations on a particular type of input data file to extract features and prepare for later processing, the preparation typically including assignment of particular attributes of the features;
characteristic nodes: performing a geometry and selection operation on the features, selecting features to be processed by looking at feature attributes, feature geometry, or all features in the file;
script nodes: modifying or analyzing feature, appearance, or model library data using scripts;
canopy node: establishing and operating a crown layer aiming at a crown area;
model node: the model is placed and manipulated with reference models containing geometric and appearance information for a particular object and a model library that organizes and assigns a hierarchical set of models in a hierarchical manner.
Compared with the prior art, the invention has the beneficial effects that:
the large-terrain data batch processing method can rapidly complete processing combination and integrated display of large-scale, high-precision and multi-source multi-dimensional heterogeneous data, can complete processing of the large-scale data without deep professional knowledge of geographic information data, can automatically check and diagnose and solve the problem of geographic space data abnormity, and has the greatest flexibility and strong computing and processing capability.
According to the large-terrain data batch processing method, the processing link of the terrain modeling work is simplified, the manpower and time cost required to be invested in a large-terrain modeling project is reduced, and the precision and the efficiency of the terrain simulation modeling are improved.
Drawings
FIG. 1 is a flow chart of a process of constructing a database from drawing source data by a method for batch processing of geodetic data according to the present invention.
FIG. 2 is a shadow relief view of imported elevation data provided by the present invention.
Fig. 3 is a diagram of import vector data provided by the present invention.
Fig. 4 is a diagram of the import road vector provided by the present invention.
FIG. 5 is a diagram of a regular grid and an irregular grid provided by the present invention.
Fig. 6 is a basic block diagram of a representation of feature data provided by the present invention.
The arrows indicate the directions of the edges, the dots indicate nodes, and the asterisks indicate the faces.
FIG. 7 is a graph of the non-linearity of points, distances, and composition provided by the present invention.
FIG. 8 is a graph of linear point, distance, and composition provided by the present invention.
Fig. 9 is a scene diagram of terrain simulation provided in the embodiment of the present invention.
Fig. 10 is a scene diagram of terrain simulation provided in the embodiment of the present invention.
Detailed Description
An embodiment of the present invention will be described in detail below with reference to fig. 1-10, but it should be understood that the scope of the present invention is not limited to the embodiment.
Description of the figures in the drawings, a large-terrain data batch processing method provided in an embodiment of the invention, a platform provides a new method to simulate the database construction process, combines the most advanced terrain generation and function integration algorithms together, uses a set of flexible user interface tools, and interfaces are graphical representations based on the simulated database construction process, i.e., process flow diagrams, showing the various steps involved in constructing the database from drawing source data.
By using tools that can automatically perform various geometric operations, without manually manipulating the geometry, a seamless terrain, video with integrated features is generated, the topology is preserved and a high fidelity simulated restoration of the source data is maintained.
As shown in FIG. 1, in the flow chart of the process of constructing a database from cartographic source data provided in an embodiment of the present invention, each tool is represented by a labeled box called a "node". The feature model library comprises roads, streams, lakes/oceans, crowns, tree lines and integrated land cover algorithms, and can deploy embankments, trenches, fences, independent walls, curbs and sidewalks. Each arrowed line segment in the flow chart represents a data set sent from one node (the generating node) to another node (the next node will process it). Each node executes a processing operation, the nodes are classified according to the executed operation types, and the node types comprise:
importing a node: map data is brought into the platform environment by reading a data file in a standard format and converting it into an internal format.
The processing node: the creation, removal, addition, matching, etc. of processing operations are performed on a particular type of input data file to extract features and prepare them for later processing, the preparation typically including the assignment of particular attributes to the features.
Script nodes: the script node uses the script to modify or analyze the feature, appearance, or model base data.
Characteristic nodes: the feature node performs a geometry and selection operation on the feature, selecting the feature to be processed by looking at the feature attributes, feature geometry (length or area), or all the features in the file.
Elevation node: the elevation node generates or manipulates a digital elevation model.
Irregular elevation grid nodes: and generating an irregular triangular network from the given digital elevation model, and integrating the features into the earth surface material. As shown in FIG. 5, illustrating the advantage of an irregular grid over a regular grid, an irregular grid terrain model in an irregular elevation grid uses few points in the lower right and upper right corners of the graph to represent flat terrain, and more points in the middle and left of the graph to represent rugged mountain terrain. Regular grids are simply placed with uniform points, regardless of the underlying terrain structure. Irregular elevation grids add topographical details, such as sand dune structures.
Canopy node: canopy nodes establish and operate canopies, targeting crown regions.
Model node: the model nodes place models containing geometric and appearance information for specific objects and operate reference models and model libraries that organize and distribute hierarchical sets of models in a hierarchical manner.
Appearance node: the appearance nodes allow editing, combining, and customizing scripts to the appearance files.
Tiling scene graph nodes: and generating a new tiled scene graph or analyzing and operating the current tiled scene graph by the tiled scene graph node.
The steps of the flow chart shown in fig. 1 are as follows:
step 1: the elevation data and the vector data are imported through the elevation nodes and the vector nodes, as shown in fig. 2, the elevation data is used as an image, wherein the elevation is encoded by brightness, the higher the pixel is, and the darker color points to a point with a lower elevation. The import vector includes hydrology, roads, trees, point models, model library models, appearance data, and the like, as shown in fig. 3 and 4.
Step 2: and performing projection transformation on the hydrological vector data and the road vector data through the characteristic node to obtain the hydrological vector data and the road vector data under a geodetic coordinate system.
And step 3: and performing geometric correction, compression, normalization and matching on the hydrological vector data and the road vector data in the geodetic coordinate system through the processing node to obtain hydrological vector characteristic data and road vector characteristic data.
And 4, step 4: and splicing and merging the hydrologic vector characteristic data and the road vector characteristic data through the characteristic node to obtain integrated hydrologic vector characteristic data and road vector characteristic data. As shown in fig. 6, each side of the basic topology of the feature data representation consists of two halves pointing in different directions. A clockwise transverse form of its interior; the counterclockwise direction corresponds to the outer portion of the face. Face B comprises a combination of face C and an edge at node N connected to C. An edge may contain intermediate points that are not nodes. The face C is formed by an edge having two intermediate points, an edge of the face B also includes two intermediate points, and nodes not connected to are all called solid nodes.
And 5: generalizing the integrated hydrological vector characteristic data and road vector characteristic data through the characteristic node, extracting geographic entity coordinates, obtaining hydrological vector data and road vector data structures with topological relations, and deriving linear hydrological, regional hydrological and road vector data.
And 6: an irregular triangle is created for the elevation data through the processing nodes, an irregular terrain elevation grid is built, a shade is created, then the altitude of the hydrological vector data and the road vector data with the topological relation are set, and the integrated hydrological vector feature model and the road vector feature model with the topological relation are obtained.
And 7: and removing the nearby points of the elevation grid and the nearby points of the integrated hydrological vector characteristic model and road vector characteristic model through the processing nodes, and cutting, cutting blocks and converting coordinates of the features in the integrated hydrological vector characteristic model and road vector characteristic model through the characteristic nodes.
And 8: and creating a new irregular elevation grid according to the points in the elevation grid in the S7 and the hydrological vector characteristics and the road vector characteristics after coordinate conversion, and cutting the new irregular elevation grid.
And step 9: and integrating the model base model and the appearance data imported in the S1 through the model nodes and the appearance nodes to obtain the model with the appearance material.
Step 10: and integrating the new irregular elevation grids after being cut into a tiled scene graph through the feature nodes, wherein the tiled scene graph comprises terrain feature information.
Step 11: compiling the model with the appearance material and the tiled scene graph through the processing nodes to obtain the tiled scene graph database.
Step 12: and performing projection transformation on the tree vector data and the point model vector data through the canopy nodes and the model nodes to obtain the tree vector data and the point model vector data in the geodetic coordinate system.
Step 13: and applying the script to the tree vector data and the point model vector data under the geodetic coordinate system through the script nodes to obtain the tree with the application script and the point model with the application script.
Step 14: and according to the new irregular elevation grids and the trees with the application scripts, creating a reference model through model nodes, wherein the reference model attributes comprise the position, the direction, the height and the scaling parameters of the model associated with each feature point.
Step 15: and performing fusion integration on the tiled scene graph database, the reference model and the point model with the application script through a model placement algorithm to obtain model placement on the tiled scene graph, namely completing the construction of the large terrain simulation database.
The model placement algorithm is driven primarily by the attributes of the source data provided. Each model reference point has a set of attributes that specify the three-dimensional model to be used from the model library, as well as the scaling and orientation parameters. A reference model for a particular layout style is automatically generated by assigning attributes to the cartographic source properties. For example, a vegetation polygon may be assigned attributes that will result in the reference model of the tree being randomly dispersed within the polygon.
Step 15.1: three-dimensional model selection
And searching a three-dimensional model corresponding to the name of the three-dimensional model in the tiled scene graph database, wherein the three-dimensional model comprises geometric and appearance information.
Step 15.2: point model attribute information acquisition
And acquiring attribute information of the point model with the application script according to the reference model, wherein the attribute information comprises the position, the direction, the height and the scaling of the point model, and the attribute of the point model corresponds to the attribute of the finally placed three-dimensional model.
Step S15.2.1, three-dimensional model placement position
The three-dimensional model position coordinates include an x-axis position, a y-axis position, and a z-axis position.
Step S15.2.2, three-dimensional model scaling
The three-dimensional model scaling values include an x-axis scaling factor, a y-axis scaling factor, and a z-axis scaling factor.
When only one scaling factor exists, it is applied to scale the remaining two dimensions accordingly to preserve the relative length, width and height of the model; when there are two scaling factors on different axes, then the third axis will be scaled according to the geometric mean of the first two scaling factors; when both absolute and relative scaling factors are provided for a particular axis, then the absolute scaling factor is applied.
Step 15.2.3: height of model
The elevation of the model is determined as a reference point by the elevations of the upper and lower irregular grids of the model, the z coordinate of the model reference point is ignored, but the reference point is used when the elevation value of the model is needed.
When the model elevation is set to be a default value of 0, using the grid elevation of the irregular elevation; when set to 1, the elevation value is used to store in the model reference point.
Step 15.2.4: direction of three-dimensional model
The three-dimensional model direction comprises a rolling angle, a pitch angle, a course angle and an orientation;
a roll angle; positive scrolling is counterclockwise positive about the y-axis, corresponding to "scrolling right";
a pitch angle; positive tilt is the positive x-axis of counterclockwise rotation, corresponding to "tilt up";
a course angle; the positive course rotates clockwise to the positive z axis, corresponding to 'turning to the right';
orientation; when set to 1, results in the model being perpendicular to the surface, covering roll and pitch angles; when set to the default value of 0, no operation is performed.
Step 15.3: placed along a linear feature
And according to the three-dimensional model obtained by the step 15.1 and the point model attribute information obtained by the step 15.2, placing the three-dimensional model and the point model in a mode of a plurality of reference models which are specified by the linear meta model and are placed along the linear characteristic, determining an overall layout style by the model placing mode, referring to the linear characteristic by the determined interval style model and a controlled additional attribute model, and finally completing the construction of a large terrain simulation database.
For a linear meta-model, linking and unlinking may be set, with the difference being whether the three-dimensional model will be placed on linear features on vertices (unlinked), or between vertices (linked). The former is more suitable for placing towers and connected power lines, each tower will be placed on one feature vertex, as shown in fig. 7; the latter, suitable for pipes and fences, consists of a linked chain of three-dimensional models, as shown in fig. 8.
All steps are performed, i.e. the whole flow from the drawing source data to the process of constructing the terrain simulation database is completed, and the final result is shown in fig. 9 and 10.
The large terrain data batch processing method has the following capabilities:
(1) The method can support multi-source geographic data loading, merge different data sources and realize seamless conversion.
(2) And the rapid construction and reuse of the simulation feature database can be supported.
(3) The simulation method can support the reality, accuracy and detail expression construction of the simulation database, has high geometric precision and has the attribute of geographic source data.
(4) The database building task can be monitored and processed using a custom flow chart to automatically check for diagnosis and solve geometry and property placement problems.
According to the method for batch processing of the large-terrain data, a brand new method is adopted to simulate the contents in the ground feature database to carry out rapid batch processing construction of the ground feature model, a complete simulation database construction system is realized, and a high-fidelity simulation database is automatically and rapidly generated from drawing source materials. The method supports the import of multiple types of map data formats and image formats, and automatically constructs high-resolution geographic spatial data. By adopting the method for making the flow architecture diagram, the editing (according to the difference of the size of the target area, the type of the ground object and the ground simulation modeling precision), the previewing and the publishing of the simulation database related to the large terrain can be finished within dozens of minutes.
The platform provides comprehensive integrated terrain generation tools and features, including heterogeneous data importation, fast and incremental database construction, support for realistic and precise geometry, detailed attribute attribution, paging, high-level automation, detailed diagnostics, and batch processing functions.
Is mainly characterized in that
(1) Terrain feature generation and integration function
The method can be used for integrating the topographic features of points, linearity and regions with the earth surface (image data topographic data);
the Ortho and georeference images can be used as picture maps;
scaling of the map and generation of a mini map are supported;
supporting external model import: 3dx Max, openFlight, etc.;
model output supports other modeling platforms: VRML, 3dx Max, etc.
(2) Import function of geospatial basic data
Support TIFF, geotiff and ECW format image data;
geographic space data in formats such as ESRI shape (2D/3D), DLG, DXF, XML and the like are supported;
GRIDASCII, GRIDFLOAT, ASTER and USUG DEM are supported.
(3) Intelligent feature batch generation functionality
Supporting topological geometric relations and attributes of processing features;
and automatically simulating feature data caching and image layer processing.
(4) Streamlined processing tool functionality
Providing a database of simulated feature templates (which may be manually updated) for batch processing of standard data;
providing tool plug-in import data and performing batch processing flow;
a multi-source projection coordinate system is supported, and the conversion error of the geographic space data is reduced as much as possible;
the macro processing mode is introduced for simplifying the batch process flow.
Invoking diagnostic and statistical functions of simulation databases
The formulation of surface data simulation processing indexes is supported;
overload and geometric figure edge number excess alarm are supported;
automatic local compilation error alerts can be implemented.
(6) Optimizing visual simulation feature functions
The display fidelity of the geometric figure can be selectively controlled;
a multiple level of detail (LOD) tile architecture;
and loading modules with different sizes into the elastic memory in different areas.
The invention provides a complete terrain database generation solution, which can automatically process by utilizing geospatial source data and quickly generate a high-fidelity three-dimensional geographic environment simulation database. The method supports various source data formats, can automatically create buildings with interior decoration, provides a batch processing mode function, generates a large-area environment, can automatically generate a plurality of geotechnical blocks or 1 multiplied by 1 degree geographic units in a batch processing mode for large-area simulation, organizes geospatial data into a tree structure by standardizing a series of batch processing mode scripts, constructs each node, processes geospatial source data such as reprojection data of cartography, generates a building model from GIS data, and synthesizes complex vector data, and can be completed through the batch processing mode without constructing complete visual output.
The above disclosure is only for a few specific embodiments of the present invention, however, the present invention is not limited to the above embodiments, and any modifications that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (5)
1. A method for batch processing of topographic data, comprising the steps of:
importing elevation data, hydrological vector data, road vector data, tree vector data, point model vector data, a model base model and appearance data of a large terrain;
performing projection transformation on the hydrological vector data and the road vector data through the characteristic node to obtain the hydrological vector data and the road vector data under a geodetic coordinate system;
performing geometric correction, compression, normalization and matching processing on the hydrological vector data and the road vector data in a geodetic coordinate system through a processing node to obtain hydrological vector characteristic data and road vector characteristic data;
the hydrological vector characteristic data and the road vector characteristic data are spliced and merged through the characteristic node to obtain integrated hydrological vector characteristic data and road vector characteristic data;
generalizing the integrated hydrological vector characteristic data and road vector characteristic data through the characteristic node, extracting geographic entity coordinates, and obtaining hydrological vector data and road vector data structures with topological relations;
creating irregular triangles for the elevation data through the processing nodes, building irregular terrain elevation grids, creating a shade, and then setting the altitude of the hydrological vector data and the road vector data with topological relations to obtain an integrated hydrological vector characteristic model and a road vector characteristic model with topological relations;
removing the nearby points of the elevation grid and the nearby points of the integrated hydrological vector characteristic model and road vector characteristic model through the processing nodes, and cutting, cutting blocks and converting coordinates of the features in the integrated hydrological vector characteristic model and road vector characteristic model through the characteristic nodes;
creating a new irregular elevation grid according to the points in the elevation grid and the hydrological vector characteristics and road vector characteristics after coordinate conversion, and cutting the new irregular elevation grid into blocks;
integrating imported model base model and appearance data through model nodes and appearance nodes ， Obtaining a model with appearance materials;
integrating the new irregular elevation grids after being cut into blocks into a tiled scene graph through the feature nodes, wherein the tiled scene graph comprises terrain feature information;
compiling the model with the appearance material and the tiled scene graph through the processing nodes to obtain a tiled scene graph database;
performing projection transformation on the tree vector data and the point model vector data through the canopy nodes and the model nodes to obtain tree vector data and point model vector data under a geodetic coordinate system;
applying scripts to tree vector data and point model vector data under a geodetic coordinate system through script nodes to obtain trees with application scripts and point models with application scripts;
creating a reference model through model nodes according to the new irregular elevation grid and the tree with the application script, wherein the reference model attributes comprise the position, the direction, the height and the scaling parameters of the model associated with each feature point;
and (3) performing fusion integration on the tiled scene graph database, the reference model and the point model with the application script through a model placement algorithm to obtain model placement on the tiled scene graph, namely completing the construction of the large terrain simulation database.
2. A method of batch processing of topographic data according to claim 1 wherein the detailed steps of the model placement algorithm include:
three-dimensional model selection
Searching a three-dimensional model corresponding to the name of the three-dimensional model in a tiled scene graph database, wherein the three-dimensional model comprises geometric and appearance information;
point model attribute information acquisition
Acquiring attribute information of a point model with an application script according to a reference model, wherein the attribute information comprises the position, the direction, the height and the scaling of the point model, and the attribute of the point model corresponds to the attribute of a finally placed three-dimensional model;
three-dimensional model placement position
The three-dimensional model position coordinates comprise an x-axis position, a y-axis position and a z-axis position;
three-dimensional model scaling
The three-dimensional model scaling value comprises an x-axis scaling factor, a y-axis scaling factor and a z-axis scaling factor;
when only one scaling factor exists, it is applied to scale the remaining two dimensions accordingly to preserve the relative length, width and height of the model; when there are two scaling factors on different axes, then the third axis will be scaled according to the geometric mean of the first two scaling factors; when both absolute and relative scaling factors are provided for a particular axis, then the absolute scaling factor is applied;
height of three-dimensional model
Determining a reference point by the elevation of the upper and lower irregular grids of the model, neglecting the z coordinate of the model reference point, and using the reference point when the elevation value of the model is needed;
when the model elevation is set to be a default value of 0, using the grid elevation of the irregular elevation; when set to 1, the elevation value is used to store in the model reference point;
three-dimensional model orientation
The three-dimensional model direction comprises a rolling angle, a pitch angle, a course angle and an orientation;
a roll angle; positive scrolling is counterclockwise positive about the y-axis, corresponding to "scrolling right";
a pitch angle; positive tilt is the positive x-axis of counterclockwise rotation, corresponding to "tilt up";
a course angle; the positive course rotates clockwise to the positive z axis, corresponding to 'turning to the right';
orientation; when set to 1, results in the model being perpendicular to the surface, covering roll and pitch angles; when set to the default value of 0, no operation is performed;
placed along a linear feature
And according to the three-dimensional model and the attribute information of the point model, placing the three-dimensional model and the point model in a mode of a plurality of reference models which are specified by the linear meta model and are placed along the linear characteristic, and completing the construction of the large terrain simulation database.
3. A method as set forth in claim 1 wherein said elevation data is encoded using luminance to encode elevation, the higher the elevation the higher the luminance of the pixel, and the lower the elevation the lower the luminance of the pixel.
4. A method for batch processing of topographic data according to claim 1, wherein the geometric manipulation and the selection manipulation of the hydrological vector characteristic data and the road vector characteristic data are performed by the characteristic nodes to realize the concatenation and the combination.
5. A method of bulk processing of topographic data according to claim 1 wherein the processing nodes: performing creation, removal, addition and matching processing operations on the input data files of the specific type to extract features and prepare for later processing, wherein the preparation work comprises the allocation of specific attributes of the features;
characteristic nodes: performing a geometry and selection operation on the features, selecting features to be processed by looking at feature attributes, feature geometry, or all features in the file;
script nodes: modifying or analyzing feature, appearance, or model library data using scripts;
canopy node: establishing and operating a crown layer aiming at a crown area;
model node: the model is placed and manipulated with reference models containing geometric and appearance information for a particular object and a model library that organizes and assigns a hierarchical set of models in a hierarchical manner.
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