A three-dimensional surface matrix fine modeling method based on multi-source data

Abstract

The present invention relates to the field of natural resource survey and monitoring technology, and specifically to a three-dimensional surface matrix fine modeling method based on multi-source data. It includes a three-dimensional modeling method system that integrates source data processing, surface dimensionality upgrade to establish a structural model, establishes a voxel model, and assigns structured semantic processing to model entity attributes to establish an attribute grid model, so as to achieve orderly and multi-dimensional management of geological data, and reproduce the spatial distribution state of different types of surface matrices in a layer-controlled and fine manner. This technical solution provides a three-dimensional modeling technology for the surface matrix layer between the surface cover layer and the underground resource layer, which realizes the visual expression and analysis application of the internal structural relationship, spatiotemporal evolution and its mechanism with other natural elements of the surface matrix layer, which can meet the needs of special investigations and the needs of ecological governance in typical landform areas.

Description

Technical Field

[0001] The invention relates to the technical field of surface matrix monitoring, and in particular to a three-dimensional surface matrix fine modeling method based on multi-source data. Background Art

[0002] The surface matrix refers to the penetrable area located above the underground resource layer and below the surface cover. Its spatial range starts from the ground, passes through the soil, and extends to the top of the bedrock or the bottom of the aeration zone. It is the main area where the five major spheres of the earth interact with each other and is the basis for the action of various natural elements. While the surface cover layer will change due to human use and management, the surface matrix layer is relatively stable. Therefore, the surface matrix is ​​not only the basis for various special surveys (such as land surveys and forestry surveys), but also determines the difficulty of ecological restoration and environmental improvement.

[0003] At present, the research on surface matrix is ​​still at the stage of classification and construction of surface matrix model, which cannot meet the needs of various special investigations and ecological governance in typical geomorphic areas. There are still very few modeling methods for three-dimensional models of surface matrix. The existing modeling methods are limited to modeling resources above the surface and underground. Since the data sources and internal spatial distribution of surface matrix are often different from general modeling, the modeling methods of conventional geological bodies are difficult to directly apply to the modeling of surface matrix. Therefore, it is necessary to provide a suitable three-dimensional modeling method to accurately restore the internal structural relationship of the surface matrix. Summary of the invention

[0004] The purpose of the present invention is to provide a three-dimensional modeling method for the problem of fine modeling application of actual surface matrix, so as to meet the needs of various special investigations and the needs of ecological management in typical landform areas.

[0005] In order to achieve the above object, the present invention provides a three-dimensional surface matrix fine modeling method based on multi-source data, and the specific steps include:

[0006] Step 1: Data extraction and fusion: The geological data, soil data, surface matrix survey data and vector geometry data are integrated for the selected work area where surface matrix modeling is required; the soil data must include the effective soil layer thickness attribute, and the vector geometry data is the third national land survey data as the land geometry boundary constraint;

[0007] Step 2: Establish a mapping relationship between the fused data according to the land type and surface matrix classification standards, define and supplement the surface matrix classification attributes, and then establish the data identification code field;

[0008] Step 3: Establish the structural model: modify the elevation of the latest DEM data of the selected area according to the effective soil layer thickness as the underlying data for model construction; use the effective soil layer thickness of each data in the vector database and the three-level class of the surface matrix as the geometric boundary constraints, clip the original DEM and the modified DEM respectively, use the identification code as the name of each DEM, and store them in the set database, including the original DEM library and the modified DEM library;

[0009] The original DEM library and the modified DEM library data are used to create upper and lower layered TIN models of the three-level surface matrix, and the coverage of the three-level layered TIN model of the surface matrix is ​​created;

[0010] By stretching adjacent TINs, a geological body between two rock layers is created. The solid models of the three-level classes of each surface matrix are established in turn. The established models are merged to form surface matrix volume data.

[0011] Step 4: Establishing volume element model: The surface matrix volume data is converted into a fine surface matrix volume element model of CPG structure through the algorithm tool of converting TIN model into CPG model;

[0012] Step 5: Data storage: various data generated in the process of building the entity model are stored according to the set data structure, and the data structure at least includes vector data, DEM data, TIN model data, structural model data, and voxel data;

[0013] Step 6: Symbolize the fine surface matrix volume model according to the color definition of each category of the three-level surface matrix, and perform attribute supplementation and semantic definition, and realize model visualization by establishing a surface matrix model database.

[0014] As a preferred solution, the structural model in step 3 is a multi-layer DEM cut monomer modeling, using the third national land survey data as geometric boundary constraints.

[0015] As a preferred solution, in step 3, the elevation is modified according to the effective soil layer thickness, and the specific formula is:

[0016] Modified DEM elevation = original DEM elevation - effective soil layer thickness

[0017] When clipping DEM, the seamless connection problem after the model is established is considered. A threshold with an accuracy not less than 1 times of DEM and not more than 2 times of DEM is set to establish a buffer for each data in the vector database, so as to clip the DEM for each data in the vector database after the buffer is established.

[0018] As a preferred solution, step 3 can process data by establishing batch rules to improve efficiency and optimize the modeling process.

[0019] As a preferred solution, the CPG in step 4 is a voxel model based on an irregular hexahedral grid data structure, and the cell coordinates are calculated according to the data structure characteristics of the CPG, that is, the stored cell coordinates include the x and y coordinates of each grid point on the top and bottom surfaces of the model and the z coordinate of each point in each cell of the model, and the x and y coordinates of the cells in the middle layer of the model are obtained by calculating the x and y values ​​of the top and bottom surface grid points and the z value of each point in each cell.

[0020] x C =x A +(z C -z A )*(x B -x A ) / (z B -z A )

[0021] y C =y A +(z C -z A )*(y B -y A ) / (z B -z A )

[0022] x D =x A +(z D -z A )*(x B -x A ) / (z B -z A )

[0023] y D =y A +(z D -z A )*(y B -y A ) / (z B -z A )

[0024] Line CD is a side of a cell. The two points C and D on this side correspond to points A and B on the top and bottom surfaces respectively. The coordinates of points C and D are (x C ,y C )(x D ,y D), by solving the above formula, we can use the coordinates of points A and B and the z values ​​of points C and D to find the plane coordinates of points C and D. According to the following conversion steps: grid boundary determination, direction determination, grid division, grid coordinate determination, an algorithm tool for converting TIN model to CPG model is established. The surface matrix volume element model finally established belongs to the TIN-CPG hybrid data structure 3D model.

[0025] The beneficial effects of the present invention are as follows:

[0026] This three-dimensional surface matrix fine modeling method makes full use of the original data, integrates the fineness of the data and the comprehensiveness of the attributes, as well as the currentness of the data, ensures the reliability and timeliness of the data, selects the appropriate modeling method, batch processes the data, and converts the surface model to the volume model, which is convenient for data analysis and application. It reflects the underground physical and chemical properties and the attributes of the spots, and visually expresses them, and obtains the spatial distribution of different types of surface matrices, realizing the visual expression and analytical application of the internal structural relationship of the surface matrix layer, its spatiotemporal evolution, and its interaction mechanism with other natural elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic diagram of the upper and lower layered TIN model of the three-level surface matrix;

[0028] Figure 2 This is a schematic diagram of the surface matrix structure model;

[0029] Figure 3 It is a schematic diagram of the corner network model;

[0030] Figure 4 Schematic diagram of cell coordinate calculation in CPG model;

[0031] Figure 5 It is a partially enlarged schematic diagram of the surface matrix volume element model;

[0032] Figure 6 This is a schematic diagram of the surface matrix visualization database;

[0033] Figure 7 Flowchart for the technical implementation of the present invention. DETAILED DESCRIPTION

[0034] The following is further described in detail through specific implementation methods:

[0035] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same technical meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. It should also be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, components and / or combinations thereof.

[0037] like Figure 1-7 As shown, the present invention provides a three-dimensional surface matrix fine modeling method based on multi-source data, which specifically includes the following steps:

[0038] Step S1: Data extraction and fusion: Setting a unified coordinate system and elevation benchmark to fuse geological data, soil data, surface matrix survey data and vector geometry data for the selected work area where surface matrix modeling is required; the soil data must include effective soil layer thickness attributes, and the vector geometry data is the third national land survey data as land geometry boundary constraints;

[0039] The specific methods of data extraction and fusion in this step are as follows:

[0040] First, a unified coordinate system and elevation benchmark are set, and data from different data sources are cut out according to the selected work area, including the updated evaluation data of cultivated land quality, the second soil census data, and the third national land survey data; then key attributes are extracted, such as the effective soil thickness (YXTCHD dataset), surface soil texture (BCTRZD dataset), soil organic matter content (TRYJZHL dataset), soil acidity (TRSJD dataset) and other basic soil properties and physical and chemical properties datasets in the updated evaluation data of cultivated land quality, the soil thickness (Chongqing soil map dataset) of the second soil census data, and the land type patches (DLTB dataset) of the third national land survey data. The scale of the second soil census data is small, and it is used as supplementary data for the updated evaluation data of cultivated land quality; the attributes of the third national land survey data are mainly extracted from land type and ownership. After data processing, the three data are fused, and the fragmented patches are processed after fusion.

[0041] Step S2: Establish a mapping relationship between the fused data according to the land type and surface matrix classification standards, define and supplement the surface matrix classification attributes, and then establish the data identification code field;

[0042] The specific method of adding surface matrix category attributes in this step is as follows:

[0043] According to the definition of natural resource surface matrix layer classification in the technical document "Technical Requirements for Natural Resource Surface Matrix Layer Survey (1:50000)", the surface matrix layer classification is refined into three levels, and the attribute fields of the first-level, second-level and third-level surface matrix are added to the fused data; combined with the "Detailed Rules for the Identification of Classification Land Types in the Third National Land Survey", a mapping relationship between the third-level surface matrix layer and the land types of the third national land survey is established. For example, the surface matrix layer attributes corresponding to arid land are loam and sand. According to this mapping relationship, the surface matrix attribute fields of the fused data are completed.

[0044] In addition, an identification code attribute field is added. The field value of the identification code can be randomly generated by the system or customized. The example uses the customized method, which consists of "district and county ownership code + element map ID".

[0045] Step S3: Establishing a structural model: The latest DEM data of the selected area is modified in elevation according to the effective soil layer thickness as the underlying data for model construction; the effective soil layer thickness of each data in the vector database and the three-level class of the surface matrix are used as geometric boundary constraints, and the original DEM and the modified DEM are respectively clipped, and the identification code is used as the name of each DEM, and they are stored in the set database, including the original DEM library and the modified DEM library;

[0046] The original DEM library and the modified DEM library data are used to create upper and lower layered TIN models of the three-level surface matrix, and the coverage of the three-level layered TIN model of the surface matrix is ​​created;

[0047] By stretching adjacent TINs, a geological body between two rock layers is created. The solid models of the three-level classes of each surface matrix are established in turn. The established models are merged to form surface matrix volume data.

[0048] In this step S3, the specific method of establishing the structural model is as follows:

[0049] S31. First, the latest DEM data of the selected area is cropped.

[0050] S32. Modify the elevation according to the effective soil layer thickness as the bottom layer data for model construction. The specific formula is:

[0051] Modified DEM elevation = original DEM elevation - effective soil layer thickness

[0052] When clipping DEM, the seamless connection problem after the model is established is considered. A threshold with an accuracy not less than 1 times of DEM and not more than 2 times of DEM is set to establish a buffer for each data in the vector database, so as to clip the DEM for each data in the vector database after the buffer is established.

[0053] S33, using the effective soil layer thickness and the three-level class of the surface matrix of each data in the vector database as geometric boundary constraints, establishing batch processing rules to clip the original DEM and the modified DEM respectively, and using the identification code as the name of each DEM, and storing them in the set database respectively, including the original DEM library and the modified DEM library;

[0054] S34. Use the original DEM library and the modified DEM library data as input data sources, establish processing rules, and create upper and lower layered TIN models of the three-level surface matrix respectively (the original DEM corresponds to the upper layer TIN model, and the modified DEM corresponds to the lower layer TIN model). The naming is also based on the identification code. The upper layer TIN model is added with a "top" prefix for distinction, and they are stored in the set database respectively.

[0055] The coverage of the layered TIN model is generated using the created TIN model (either the upper layer TIN model or the lower layer TIN model, select one of the libraries) as the boundary constraint of the model, and the name is also named after the identification code.

[0056] S35, creating a geological body between two rock layers by stretching adjacent TIN models with the same identification code at different elevations at the same location, establishing respective entity models in turn, merging the established models, and after merging, each data is also identified with an identification code to form surface matrix body data.

[0057] Step S4: Establishing a volume element model: converting the surface matrix volume data into a fine surface matrix volume element model of a CPG structure through an algorithm tool for converting a TIN model into a CPG model;

[0058] In step S4, the specific method of establishing the voxel model is as follows:

[0059] CPG is an unstructured grid that is currently widely used. CPG is essentially a series of irregular hexahedral grid units. The grid position is determined by the row and column layer numbers i, j, and k. Compared with the rectangular grid, the length and width of the corner grid model unit cell network are variable, and the grid surface vertically connecting the top and bottom grid points can also be inclined, so it can well express the morphology of complex underground structures.

[0060] CPG is a I*J*K regular topological structure model in terms of logical structure. The I*J*K regular topological structure means that there are I+1 line segments in the X direction, dividing the grid into I cells; there are J+1 lines in the Y direction, dividing the grid into J cells; there are K+1 lines in the Z direction, dividing the grid into K cells, and the total number of cells is I*J*K, and each unit grid belongs to a corner grid unit body.

[0061] In the process of converting the structural model to the voxel model, the grid boundary must be determined first. The top and bottom grid boundaries can be determined based on the top and bottom surface models of the structural model established based on the TIN data structure. Since the model is basically consistent on the side boundary after the research range boundary processing, and the shape of the model on the top and bottom surfaces is not horizontal, it needs to be determined through bounding box calculation. During the calculation, the maximum bounding box of the top and bottom surfaces of a stratigraphic unit in the structural-stratigraphic grid model in the horizontal direction is projected onto the xoy plane respectively, and the result is the maximum rectangular boundary of the projection plane, which is the top and bottom surface boundary of the stratigraphic unit.

[0062] Secondly, determine the number, direction and direction of the plane grid and vertical grid division; the grid direction is the direction of the grid on the plane (I, J direction). The grid direction is different from the coordinate axis direction. It represents the direction of each layer of grid arrangement. The determination of the grid direction is very important for the entire CPG model, which can affect the direction of the entire model. Parallel grid division is when the plane grid division of the structural-stratigraphic framework model is performed. First, according to the grid boundary, the number of cell rows and columns I*J in the cell arrangement direction (I, J direction) is determined, and then the model is divided into equal intervals in the XOY plane to generate the plane top and bottom control grids of the model. The vertical grid division is divided into a single layer, k=1, that is, I*J*1.

[0063] Then the plane control grids are projected into the construction model, and the plane coordinates after projection (i.e., x, y coordinates) are calculated. The z coordinates of all grid points inside each layer are calculated by interpolation, as follows:

[0064] The cell coordinates are calculated according to the data structure characteristics of CPG, that is, the stored cell coordinates include the x and y coordinates of each grid point on the top and bottom surfaces of the model and the z coordinate of each point in each cell of the model. The x and y coordinates of the cells in the middle layer of the model are calculated through the x and y values ​​of the top and bottom surface grid points and the z value of each point in each cell.

[0065] x C =x A +(z C -z A )*(x B -x A ) / (z B -z A )

[0066] y C =y A +(z C -z A )*(y B -y A ) / (z B -zA )

[0067] x D =x A +(z D -z A )*(x B -x A ) / (z B -z A )

[0068] y D =y A +(z D -z A )*(y B -y A ) / (z B -z A )

[0069] Line CD is a side of a cell. The two points C and D on this side correspond to points A and B on the top and bottom surfaces respectively. The coordinates of points C and D are (x C ,y C )(x D ,y D ), by solving the above formula, we can use the coordinates of points A and B and the z values ​​of points C and D to find the plane coordinates of points C and D. Establish an algorithm tool for converting TIN model to CPG model. The surface matrix volume element model finally established belongs to the TIN-CPG hybrid data structure 3D model.

[0070] Finally, the x, y, and z coordinates are stored according to the CPG data organization method of the model.

[0071] Step S5: Data storage: various data generated in the process of building the entity model are stored according to a set data structure, wherein the data structure at least includes vector data, DEM data, TIN model data, structural model data, and voxel data;

[0072] The specific method of data storage in this step is as follows:

[0073] The organizational model of vector data is organized in the form of feature collection and feature class of Geodatabase. The spatial objects of vector data are stored in Geometry model; the attribute content of vector data is stored in Oracle relational table to realize the integrated storage of spatial attributes. JSON is used to build database storage for stereo models. All data IDs (i.e. association relationships) associated with each model are stored in the metadata table, and data relationships are recorded in JSON fields.

[0074] Comprehensively utilize relational databases and file databases. Oracle is used as the relational database to solve the problems of consistent writing, retrieval performance of complex queries, and backup. MongoDB is used as the file database to focus on solving the problem of high-concurrency read response.

[0075] Step S6: symbolize the fine surface matrix volume element model according to the color definition of each category of the three-level surface matrix, and perform attribute supplementation and semantic definition, and realize model visualization by establishing a surface matrix model database.

[0076] The specific method of model visualization in this step is as follows:

[0077] The vector data after data fusion is matched with attributes according to the identification code and the voxel model, and attributes are supplemented and semantically defined.

[0078] Refer to the "Geological Map Color Standards and Color Principles" (DZ / T 0179-1997) to determine the location of the selected area and the rock strata involved, define the surface matrix layer division and color scheme table, and realize the color symbolization of each category of the three-level surface matrix of the volume element model.

[0079] Based on the Three.js front-end 3D rendering framework, combined with the characteristics of 3D rendering mechanism and various data models, collaborative customization of service interface standards, compatible with and integrated with a variety of WebGIS services in different GIS formats, online calls to basic geographic information data, builds a surface matrix database, realizes model visualization, and takes into account surface matrix distribution, structure, geological disasters and other data services.

[0080] The present invention provides a three-dimensional surface matrix modeling method, which takes into account the currentness and precision of data, and realizes the unification of data formats by fusion processing of different data sources. In order to improve the modeling efficiency, a rule for batch processing of data is established. In order to realize the purpose of fine expression and multiple spatial analysis and attribute analysis of the three-dimensional model according to actual application needs, the structural model is converted into a voxel model, so that the constructed model can not only finely express the surface state of the surface matrix model, but also accurately and intuitively represent the part below the surface, so as to realize a comprehensive, practical, professional and fine expression of the surface matrix layer.

[0081] The above is only an embodiment of the present invention and is not intended to limit the present application. For those skilled in the art, the present application may be modified and optimized in various ways. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application. The protection scope required by the present application shall be based on the content of its claims, and the specific implementation methods and other records in the specification may be used to interpret the content of the claims.

Claims

1. A three-dimensional surface matrix fine modeling method based on multi-source data, characterized in that: include Step 1: Data extraction and fusion: The geological data, soil data, surface matrix survey data and vector geometry data are integrated for the selected work area where surface matrix modeling is required; the soil data must include the effective soil layer thickness attribute, and the vector geometry data is the land class patch data as the land class geometry boundary constraint; Step 2: Establish a mapping relationship between the fused data according to the land type and surface matrix classification standards, define and supplement the surface matrix classification attributes, and then establish the data identification code field; Step 3: Establish the structural model: modify the elevation of the DEM data of the selected area according to the effective soil layer thickness as the underlying data for model construction; use the effective soil layer thickness of each data in the vector database and the three-level class of the surface matrix as the geometric boundary constraints, clip the original DEM and the modified DEM respectively, use the identification code as the name of each DEM, and store them in the set database, including the original DEM library and the modified DEM library; The original DEM library and the modified DEM library data are used to create upper and lower layered TIN models of the three-level surface matrix, and the coverage of the three-level layered TIN model of the surface matrix is ​​created; By stretching adjacent TINs, a geological body between two rock layers is created. The solid models of the three-level classes of each surface matrix are established in turn. The established models are merged to form surface matrix volume data. Step 4: Establishing volume element model: The surface matrix volume data is converted into a fine surface matrix volume element model of CPG structure through the algorithm tool of converting TIN model into CPG model; Step 5: Data storage: various data generated in the process of building the entity model are stored according to the set data structure, and the data structure at least includes vector data, DEM data, TIN model data, structural model data, and voxel data; Step 6: Symbolize the fine surface matrix volume model according to the color definition of each category of the three-level surface matrix, and perform attribute supplementation and semantic definition, and realize model visualization by establishing a surface matrix model database.

2. A three-dimensional surface matrix fine modeling method based on multi-source data according to claim 1, characterized in that: The structural model in step 3 is a multi-layer DEM cut monomer modeling.

3. The method for fine modeling of three-dimensional surface matrix based on multi-source data according to claim 1, characterized in that: In step 3, the elevation is modified according to the effective soil layer thickness, and the specific formula is: Modified DEM elevation = original DEM elevation - effective soil layer thickness A threshold of not less than 1 times the accuracy of DEM and not more than 2 times the accuracy of DEM is set to establish a buffer for each data in the vector database, so as to clip the DEM for each data in the vector database after the buffer is established.

4. The method for fine modeling of three-dimensional surface matrix based on multi-source data according to claim 1, characterized in that: The step 3 processes the data by establishing batch rules.

5. The method for fine modeling of three-dimensional surface matrix based on multi-source data according to claim 1, characterized in that: The CPG in step 4 is a voxel model based on an irregular hexahedral grid data structure. The cell coordinates are calculated according to the data structure characteristics of the CPG, that is, the stored cell coordinates include the x and y coordinates of each grid point on the top and bottom surfaces of the model and the z coordinate of each point in each cell of the model, and the x and y coordinates of the cells in the middle layer of the model are obtained by calculating the x and y values ​​of the grid points on the top and bottom surfaces and the z value of each point in each cell. Line CD is a side of a cell. The two points C and D on this side correspond to points A and B on the top and bottom surfaces respectively. The coordinates of points C and D are (x C ,y C )(x D ,y D ), by solving the above formula, we can use the coordinates of points A and B and the z values ​​of points C and D to find the plane coordinates of points C and D. According to the following conversion steps: grid boundary determination, direction determination, grid division, grid coordinate determination, an algorithm tool for converting TIN model to CPG model is established. The surface matrix volume element model finally established belongs to the TIN-CPG hybrid data structure three-dimensional model.

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