A cross-section map real-time synchronous display method and system based on GIS

Abstract

The application discloses a cross-section map real-time synchronous display method and system based on GIS, relates to the technical field of geological modeling, and comprises the following steps: importing multi-source heterogeneous geographic data, performing standardized pretreatment, and generating a unified format geographic information dataset; performing multi-dimensional feature extraction on the geographic information dataset by using a feature-level fusion algorithm to construct a multi-dimensional geographic feature model fusing geological structure, terrain elevation and surface cover information; performing data optimization on the multi-dimensional geographic feature model based on a spatial interpolation algorithm to generate a continuous geographic surface model; and performing parallel contour tracing and topological sorting on a discrete cross-section line segment set to generate a self-defined cross-section map supporting interactive parameter adjustment in real time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological modeling, and in particular to a cross-section map real-time synchronous display method and system based on GIS. BACKGROUND

[0002] In recent years, GIS and three-dimensional geological modeling have been widely used in geological exploration and engineering investigation fields. The existing method mainly relies on surface modeling of a single data source, and constructs a basic grid structure through Delaunay triangulation. With the increasing demand for integration of multi-source heterogeneous geographic data, a modeling method based on data fusion is proposed, such as using a weighted average method to fuse multi-scale terrain features, or generating a continuous surface model through regular grid interpolation. However, the existing method has significant bottlenecks in cross-dimension feature correlation and dynamic interactive analysis.

[0003] The main deficiencies of the current technology are as follows: on the one hand, multi-source data fusion is mostly at the pixel level or data level, lacking feature-level semantic correlation, which makes it difficult to guarantee the spatial consistency of geological structure, terrain and surface cover information; on the other hand, the existing cross-section generation method relies on a static topological network, cannot respond to parameter adjustment in real time, and the cross-section labeling is limited to a single attribute dimension (such as only elevation or lithology), which makes it difficult to support complex geological logic such as stratigraphic age priority and lithology conflict fusion, affecting the scientificity of section analysis and the efficiency of engineering decision-making. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a GIS-based cross-section map real-time synchronous display method to solve the problems of insufficient feature-level fusion of multi-source geographic data, weak topological network attribute correlation, and poor real-time performance of interactive cross-section generation.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In the first aspect, the present application provides a GIS-based cross-section map real-time synchronous display method, which includes importing multi-source heterogeneous geographic data, performing standardized preprocessing, and generating a unified format geographic information dataset;

[0008] A feature-level fusion algorithm is used to extract multi-dimensional features from the geographic information dataset, and a multi-dimensional geographic feature model is constructed by fusing geological structure, terrain elevation and surface cover information;

[0009] A spatial interpolation algorithm is used to optimize the data based on the multi-dimensional geographic feature model, and a continuous geographic surface model is generated;

[0010] According to the continuous geographic surface model, a triangulation network topology is constructed, and a topological network containing spatial entity geometric data and geological-elevation attribute mapping relationship is established;

[0011] The user's real-time inputted sectioning surface parameters are converted into a spatial plane equation, a discrete section line segment set with geological attribute labels is generated through intersection detection of the plane equation and the triangular facets;

[0012] Parallel contour tracing and topological sorting are performed on the discrete section line segment set, and a self-defined section drawing supporting interactive parameter adjustment is generated in real time.

[0013] As a preferred scheme of the GIS-based section drawing real-time synchronous display method, the multi-source heterogeneous geographic data includes CAD design files, GIS topographic maps and field collected point cloud data;

[0014] The multi-source heterogeneous geographic data is imported, standardized pretreated and unified into a geographic information data set in a uniform format, and the specific steps are as follows,

[0015] Abnormal data cleaning is performed on the multi-source heterogeneous data to generate an intermediate data set after denoising;

[0016] The intermediate data set is converted into a spatial coordinate system and unified into a preset geographic coordinate system;

[0017] The data after the geographic coordinate system is standardized and processed to generate a structured geographic information data set.

[0018] As a preferred scheme of the GIS-based section drawing real-time synchronous display method, the multi-source heterogeneous geographic data includes CAD design files, GIS topographic maps and field collected point cloud data;

[0019] The geographic information data set contains geological drilling data, terrain elevation point cloud and surface coverage raster image;

[0020] The geographic information data set is subjected to multi-dimensional feature extraction by using a feature-level fusion algorithm to construct a multi-dimensional geographic feature model fusing geological structure, terrain elevation and surface coverage information, and the specific steps are as follows,

[0021] The geological drilling data is subjected to stratum structure feature extraction to generate a geological layering feature vector;

[0022] The terrain elevation point cloud is subjected to terrain curvature and slope feature extraction to generate a terrain morphology feature matrix;

[0023] The surface coverage raster image is subjected to ground object classification feature extraction to generate a surface coverage feature atlas;

[0024] The geological layering feature vector, the terrain morphology feature matrix and the surface coverage feature atlas are subjected to space-time alignment to form a multi-dimensional joint feature space;

[0025] The multi-dimensional geographic feature model of fused geological-terrain-surface cover information is constructed by performing cross-modal correlation analysis on the joint feature space based on the deep learning fusion network.

[0026] As a preferred scheme of the cross-section map real-time synchronous display method based on GIS, the multi-dimensional geographic feature model comprises fused feature data of geological structure, terrain elevation and surface cover.

[0027] The continuous geographic surface model is generated by the following specific steps,

[0028] The fused feature data is spatially partitioned to generate regional data sets of similar geological-terrain features.

[0029] Based on the dynamic selection of spatial interpolation algorithms according to the characteristics of the regional data, inverse distance weighted interpolation is used for flat regions, and Kriging interpolation is used for complex terrain regions.

[0030] According to the selected interpolation algorithm, the regional data is parameter-optimized to generate an interpolation weight matrix, and the partitioned data is seamlessly spliced to generate a continuous geographic surface model.

[0031] As a preferred scheme of the cross-section map real-time synchronous display method based on GIS, the continuous geographic surface model comprises geological structure layered data and terrain elevation gradient information.

[0032] The triangular net topology structure is constructed according to the continuous geographic surface model, and a topological network comprising spatial entity geometric data and geological-elevation attribute mapping relationship is established by the following specific steps,

[0033] The continuous geographic surface model is gridded based on the Delaunay triangulation algorithm to generate an initial triangular facet set.

[0034] The topological relationship of the triangular facet set is analyzed to establish a three-level topological association structure of nodes, arc segments and surface domains, and the surface domains and nodes form spatial adjacency relationships through arc segments.

[0035] The geological structure type is mapped as a surface domain attribute, and the terrain elevation value is mapped as a node attribute to generate a geological-elevation two-dimensional attribute mapping table.

[0036] The spatial index structure is constructed in combination with the spatial adjacency relationship to form a topological network for intersection detection.

[0037] As a preferred scheme of the cross-section map real-time synchronous display method based on GIS, the discrete cross-section line segment set with geological attribute labeling is generated by the following specific steps,

[0038] Based on the user inputted section plane normal vector and offset, the spatial plane equation is calculated by vector normalization;

[0039] The intersecting facets with different signs of the signed distance from the vertex to the plane equation are screened out by traversing the topological network of the triangular mesh;

[0040] The linear interpolation calculation is performed on the edges of the intersecting facets to generate the local section line segments, and the geological type and stratigraphic age of the corresponding surface domain are queried;

[0041] The conflict type is fused according to the stratigraphic age priority rule, and the discrete section line segment set with geological annotation is output.

[0042] As a preferred scheme of the GIS-based section map real-time synchronous display method, wherein: the discrete section line segment set is subjected to parallel contour tracing and topological sorting to generate a custom section map supporting interactive parameter adjustment, and the specific steps are as follows,

[0043] The discrete section line segment set is subjected to parallel contour tracing and topological sorting to generate an ordered section contour chain;

[0044] The geometric shape and attribute annotation rule of the ordered section contour chain are adjusted according to the user inputted parameters;

[0045] The custom section map is rendered and output in real time based on the adjusted contour chain.

[0046] In a second aspect, the application provides a GIS-based section map real-time synchronous display system, comprising a data regularization module, a feature fusion module, an interpolation optimization module, a topological modeling module, an intersection detection module and a dynamic rendering module;

[0047] The data regularization module is used to import multi-source heterogeneous geographic data, perform standardization preprocessing, and generate a unified format geographic information data set;

[0048] The feature fusion module is used to perform multi-dimensional feature extraction on the geographic information data set by using a feature-level fusion algorithm, and construct a multi-dimensional geographic feature model fusing geological structure, terrain elevation and surface cover information;

[0049] The interpolation optimization module is used to perform data optimization based on the multi-dimensional geographic feature model by using a spatial interpolation algorithm to generate a continuous geographic surface model;

[0050] The topological modeling module is used to construct a triangular mesh topological structure according to the continuous geographic surface model, and establish a topological network containing spatial entity geometric data and geological-elevation attribute mapping relationship;

[0051] The intersection detection module is configured to convert the sectioning surface parameters input by the user in real time into a spatial plane equation, and generate a discrete section line segment set with geological attribute labels by detecting the intersection of the plane equation and the triangular facets.

[0052] The dynamic rendering module is configured to perform parallel contour tracing and topological sorting on the discrete section line segment set, and generate a custom section graph that supports interactive parameter adjustment in real time.

[0053] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the GIS-based real-time synchronous display method of the section graph according to the first aspect of the present application.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the GIS-based real-time synchronous display method of the section graph according to the first aspect of the present application.

[0055] The present application has the following beneficial effects: The present application constructs a multi-dimensional geographic feature model through a feature-level fusion algorithm, realizes semantic-level association of geological structures, terrain elevations and surface cover information, solves the problem of pixel-level fusion, and reduces data spatial consistency error. Based on a continuous surface model generated by spatial interpolation optimization, combined with a triangular net topological network of double-dimensional attribute mapping (geology-elevation), spatial entity attribute query efficiency is significantly enhanced, and topological relationship analysis speed is improved. In interactive section generation, through parallel contour tracing and dynamic priority fusion, stratigraphic age coverage rules and lithology conflicts are automatically resolved. In addition, a real-time rendering pipeline based on GPU acceleration compresses parameter adjustment response time to the millisecond level, meeting the engineering-level interactive requirements of large-scale geological models. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0057] Fig. 1 The flowchart of the GIS-based real-time synchronous display method of the section graph in embodiment 1.

[0058] Fig. 2 The module diagram of the GIS-based real-time synchronous display system of the section graph in embodiment 1.

[0059] Fig. 3A flowchart is constructed for the multi-dimensional geographic feature model in Example 1.

[0060] Fig. 4 A flowchart is generated for the continuous geographic surface model in Example 1. DETAILED DESCRIPTION

[0061] In order to make the above objectives, features and advantages of the present application more apparent, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0062] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details given herein, that the present application can be practiced with other than the described embodiments, and that variations from the particular embodiments described herein can be made and still be within the scope of the present application.

[0063] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments.

[0064] Example 1, Reference Figs. 1-4 For the first embodiment of the present application, the embodiment provides a GIS-based cross-section map real-time synchronous display method, including the following steps:

[0065] S1, import multi-source heterogeneous geographic data, perform standardization preprocessing, and generate a unified format geographic information dataset.

[0066] Further, the multi-source heterogeneous geographic data includes CAD design files, GIS topographic maps, and field collected point cloud data.

[0067] It should be noted that the CAD design file refers to a computer-aided design file containing geological structure boundaries and drilling position vector graphics in engineering design, and its rationality lies in providing high-precision artificial design data.

[0068] The GIS topographic map refers to a raster / vector map containing contour lines, elevation points, and surface coverage classification, and its rationality lies in providing a macroscopic topographic framework.

[0069] The field collected point cloud data refers to dense three-dimensional point cloud (density ≥ 500 points / ㎡) obtained through photogrammetry, and its rationality lies in reflecting the actual topographic microrelief.

[0070] The multi-source heterogeneous data is subjected to abnormal data cleaning to generate a denoised intermediate dataset.

[0071] Specifically, when cleaning abnormal data from multi-source heterogeneous data, first, design differential cleaning for different data types. For on-site collected point cloud data, use an adaptive denoising algorithm based on density clustering to identify and remove outliers by setting the neighborhood radius and minimum point threshold, while preserving micro-terrain features such as cliffs and fault lines. For CAD design files, remove isolated line segments or broken polygons caused by drawing errors through geometric rule checking to reduce topological contradiction rate. For GIS topographic maps, correct data logical errors by verifying topological relationships. Finally, generate denoised intermediate data sets through multi-level cleaning, which reduces spatial consistency error compared to original data and provides high-confidence data foundation for subsequent coordinate conversion and fusion.

[0072] Convert the intermediate data set to a spatial coordinate system to a preset geographic coordinate system.

[0073] It should be noted that during the spatial coordinate system conversion process, first analyze the intermediate data set metadata to identify the original coordinate system, determine the conversion rule by dynamically matching the EPSG code, then use seven-parameter Helmert transformation to batch convert the vertex coordinates of vector data and the three-dimensional point set of point cloud data. At the same time, perform topological consistency checking on the converted data and update the projection information field in the metadata, and finally generate a unified data set strictly aligned with the preset geographic coordinate system.

[0074] Perform format standardization processing on the data after the geographic coordinate system is unified to generate a structured geographic information data set.

[0075] Specifically, when performing format standardization processing on the data after the geographic coordinate system is unified, first perform data type conversion and field mapping according to the preset structured data template, convert CAD vector elements to lightweight GeoJSON format, retain attribute fields and topological relationship metadata, compress point cloud data to LAS format, define classification tags and intensity value fields, and use semantic mapping algorithm to unify heterogeneous attribute names to standardized fields to eliminate semantic ambiguity. Verify data integrity through pattern checker and remove invalid records to finally generate a structured geographic information data set.

[0076] S2, use feature-level fusion algorithm to extract multi-dimensional features from geographic information data set, and construct multi-dimensional geographic feature model fused with geological structure, terrain elevation, and surface coverage information.

[0077] Further, the geographic information data set includes geological drilling data, terrain elevation point cloud, and surface coverage raster image.

[0078] It should be noted that the geological drilling data refers to structured data containing core sampling depth, lithology description and stratigraphic age label, which is reasonable to provide vertical geological structure information, but needs to be associated with horizontal spatial distribution through feature extraction;

[0079] The terrain elevation point cloud refers to a dense three-dimensional point set (density ≥ 20 points / ㎡) obtained by photogrammetry, which is reasonable to reflect micro-topographic undulations, but needs to extract curvature and slope to quantify morphological features;

[0080] The land cover raster image refers to a land use / cover raster (resolution ≤ 10m) generated by classifying multi-spectral remote sensing images (such as Sentinel-2), which is reasonable to provide surface material properties, but needs to solve the mixed pixel problem.

[0081] Stratigraphic structure feature extraction is performed on the geological drilling data to generate a geological stratigraphic feature vector;

[0082] Specifically, when performing stratigraphic structure feature extraction on the geological drilling data, first, the vertical lithology sequence of the drilling is decomposed into continuous observation units, the probability relationship between lithology types and stratigraphic units is analyzed through hidden Markov analysis, the stratigraphic mutation interface is identified, then the physical property difference of adjacent stratigraphic units is quantified based on lithology difference, and the feature vector representing the stratigraphic layered structure is constructed combining with the interface depth information.

[0083] Topographic curvature and slope feature extraction is performed on the terrain elevation point cloud to generate a topographic morphological feature matrix;

[0084] It should be noted that when performing topographic curvature and slope feature extraction on the terrain elevation point cloud, first, the spatial distribution of the point cloud is analyzed based on local surface fitting method, the normal vector and curvature type (such as plane curvature, profile curvature) of each point are calculated to quantify the surface bending features, and the slope value is calculated based on the elevation change rate of the neighborhood points, then the curvature and slope features are spatially aggregated according to the regular grid (such as 1m×1m) to generate a three-dimensional morphological feature matrix containing curvature intensity, slope classification and terrain undulation direction.

[0085] Feature extraction is performed on the ground feature classification of the land cover raster image to generate a land cover feature map;

[0086] Specifically, when performing feature extraction of ground object classification on the surface coverage grid image, a semantic segmentation network is constructed based on a deep learning framework, texture and spectral features of multi-spectral bands (such as visible light, near-infrared, and red edge) are fused to identify surface coverage types such as vegetation, water, bare rock, and artificial buildings, the semantic segmentation network adopts an encoder-decoder structure, shallow details and deep semantic information are fused through a jump connection to solve the mixed pixel classification ambiguity problem, and transfer learning is introduced in a small sample scenario to improve the model generalization ability. Finally, the pixel-level classification result is output to generate a surface coverage feature atlas containing ground object type code, coverage density, and boundary clarity.

[0087] The geological stratification feature vector, the terrain morphology feature matrix, and the surface coverage feature atlas are spatio-temporally aligned to form a multi-dimensional joint feature space.

[0088] It should be noted that in the spatio-temporal alignment process, first, a unified spatial grid coordinate system is established, the geological stratification feature vector is mapped to the surface projection grid through Kriging interpolation to align with the grid cells of the terrain morphology feature matrix, and at the same time, the surface coverage feature atlas is resampled to ensure that the spatial resolution is consistent with the terrain grid. Subsequently, a spatio-temporal index structure is constructed to associate the vertical geological unit, the surface morphology attribute, and the coverage type according to the spatial position, and the dynamic data version is matched through the time stamp. In the finally generated multi-dimensional joint feature space, each grid cell integrates the three-dimensional attributes of geological lithology, terrain curvature and slope, and surface coverage type, realizing the spatial consistency expression and time evolution association of cross-modal data, and supporting the subsequent fusion network to accurately mine the fault-landform-ecological coupling relationship.

[0089] A multi-dimensional geographic feature model integrating geological, terrain, and surface coverage information is constructed based on the deep learning fusion network for cross-modal correlation analysis of the joint feature space.

[0090] It should be noted that when the deep learning fusion network is used for cross-modal correlation analysis of the joint feature space, a multi-modal attention mechanism is constructed to capture the nonlinear coupling relationship between geological stratification, terrain curvature, and surface coverage through adaptive weight distribution. The semantic segmentation network adopts an encoder-decoder architecture, extracts the control effect of geological structure on terrain evolution and the response law of surface coverage on lithology through a cross-modal feature cross-attention layer in the encoding stage, and generates a joint feature embedding vector by fusing global context information in the decoding stage. A multi-dimensional geographic feature model of geological-terrain-surface coverage is constructed through end-to-end.

[0091] Specifically, the multi-dimensional geographic feature model of geological-terrain-surface coverage information has the expression:

[0092] ;

[0093] wherein, a multi-dimensional geographical feature model output at a three-dimensional spatial coordinate , a geological layer feature function, with a range of ∈ [0, 1], a topographic form feature function, with a range of ∈ [0, 1], a land cover feature function, with a range of ∈ [0, 1], a cross-modal feature fusion function.

[0094] More preferably, the spatio-temporal correlation of underground geological structures (such as faults, rock layer occurrence), surface relief form (such as slope, elevation zone), and surface cover type (such as vegetation, artificial construction) can be deeply fused, and through dynamic coupling of multi-source data (such as remote sensing images, geological profiles, three-dimensional point clouds), the co-analysis of geological process deduction (such as landslide risk prediction), surface resource distribution simulation (such as identification of groundwater enrichment area), and human activity impact assessment (such as disturbance of engineering construction on ecological sensitive area) can be realized.

[0095] a geological layer feature function, with an expression of

[0096] ;

[0097] wherein, is an accumulation of all integer values of the variable from 1 to , is an index variable, is the total number of stratigraphic units, with a range of ≥ 1, is the posterior probability of the stratigraphic unit at the location , with a range of P ∈ [0, 1], is the th stratigraphic unit, with a range of ∈ {1, 2,..., N}, is the feature encoding vector of the stratigraphic unit , with a range of ∈ [-1, 1].

[0098] a topographic form feature function, with an expression of

[0099] ;

[0100] wherein, is an integral over the spatial region , This is displayed as a terrain analysis window, with units of... , Represented as curvature, Expressed as slope, Represented as a spatial consistency kernel function, Represented as terrain unit identifier, unit is ∈{1,2,...,K}, Represented as position The curvature value at that point, in units of , Represented as position The slope value at the location is within the range of , Represented as filtering and Belonging to the same terrain unit neighborhood points, Represented as position The terrain category to which it belongs. Represented as an integral area infinitesimal, with units of 1000 ppm. .

[0101] The land cover characteristic function is expressed as follows:

[0102] ;

[0103] in, This is expressed as a cumulative summation over all categories. Represented as the total number of land cover categories, Represented as an index variable, Represented as the first Each land cover category, ranging from ∈{1,2,..., }, Indicated as in position It belongs to the category of land features The probability of Q ∈ [0,1] is given by the following value. Represented as land cover category The feature encoding vector, ranging from ∈[-1,1].

[0104] The cross-modal fusion function is expressed as follows:

[0105] ;

[0106] in, This is represented as the cross-modal feature fusion output. Represented as a nonlinear mapping function, Represented as a feature interaction operator, Represented as a geological stratification characteristic function, denoted as a topographic feature function, denoted as a land cover feature function, denoted as a spatial correlation operator.

[0107] S3, data optimization is performed based on the multi-dimensional geological feature model using a spatial interpolation algorithm to generate a continuous geographic surface model.

[0108] Further, the multi-dimensional geological feature model includes fused feature data of geological structure, topographic elevation, and land cover;

[0109] It should be noted that the geological structure feature data refers to three-dimensional spatial data of underground and surface rock layer distribution, structural morphology, and physical property parameters, including the geometric morphology (such as dip angle, fault throw) and rock physical properties (such as density, porosity) of geological elements such as stratigraphic units (such as sandstone layer, shale layer), fault line, and fold axis, which are used to quantify the spatial distribution of geological structures;

[0110] The topographic elevation feature data refers to quantitative indicators representing the fluctuation of the surface morphology and the evolution process of the terrain, covering curvature (such as negative curvature -0.12 m -1 in valley areas, slope (such as steep slope 25°), and land unit classification (such as ridge, slope), which are extracted from LiDAR point cloud or DEM data, reflecting the micro-geomorphic features and their correlation with geological activities;

[0111] The land cover feature data refers to multi-dimensional parameters of surface material types, ecological properties, and boundary features, including feature classification (such as vegetation, bare rock, building), vegetation coverage (NDVI 0.65), and permeability coefficient (0.3 m / day), which are derived from remote sensing image classification and physical property inversion, revealing the response mechanism of surface processes to geological-terrain conditions.

[0112] The fused feature data is spatially partitioned to generate regional data sets of similar geological-terrain features;

[0113] It should be noted that when spatially partitioning the fused feature data, first, based on the coupling relationship between geological structure (such as lithology type, fault distribution) and topographic features (such as slope, curvature, elevation variation coefficient), the geographic area is divided into several sub-units;

[0114] Specifically, the geological-terrain similarity of each spatial grid point is calculated comprehensively through a multi-dimensional clustering algorithm (such as DBSCAN based on density), for example, sandstone-low slope areas and shale-high curvature areas are classified separately, and then the boundary optimization algorithm (such as morphological erosion and expansion) is used to smooth the partition profile, eliminate scattered noise points, ensure the natural transition of adjacent unit properties, and finally generate the partition data set.

[0115] Based on the data characteristics of each region, the spatial interpolation algorithm is dynamically selected. Inverse distance weighted interpolation is used in flat areas, and Kriging interpolation is used in complex terrain areas.

[0116] It should be noted that when dynamically selecting the spatial interpolation algorithm, the region is first divided into flat areas and complex terrain areas based on geomorphic complexity indicators (such as terrain curvature, slope, and elevation variation coefficient). For flat areas (curvature < 0.05m)... -1 For slopes < 5°, inverse distance-weighted interpolation is used. Because it relies on distance attenuation to quickly generate a uniform surface, its computational efficiency is 2-3 times higher than the kriging method, and it is suitable for scenarios with uniform data distribution. For complex terrain areas (curvature ≥ 0.05 m)... -1 For slopes ≥ 5°, Kriging interpolation is used, and spatial autocorrelation is modeled through variograms to effectively suppress interpolation oscillations caused by abrupt changes in terrain (e.g., the error is reduced by 60% compared to IDW on steep slopes).

[0117] The parameters of the regional data are optimized according to the selected interpolation algorithm to generate an interpolation weight matrix. The partitioned data are then seamlessly stitched together to generate a continuous geographic surface model.

[0118] It should be noted that during the parameter optimization stage, the interpolation algorithms for different zones are adaptively tuned. For inverse distance weighted interpolation in flat areas, the optimal distance decay power (e.g., power = 2) is determined through cross-validation to balance the weight ratio of nearest neighbors and avoid "bull's-eye" artifacts. For Kriging interpolation in complex terrain, the range and sill values ​​are optimized based on semi-variogram fitting (e.g., spherical model) to capture the spatial autocorrelation of terrain abrupt change areas. Subsequently, an interpolation weight matrix is ​​generated based on the optimized parameters to ensure that the weights at the boundaries of adjacent zones gradually change linearly with distance in the overlapping zone (e.g., a 5-meter-wide transition zone). Finally, the interpolation results of each zone are weighted and fused to eliminate attribute jumps at the seams (e.g., elevation differences ≤ 0.2 meters), achieving seamless stitching of continuous geographic surface models.

[0119] Specifically, the expression for a continuous geographic surface model is:

[0120] ;

[0121] The distance-weighted interpolation for flat regions is expressed as follows:

[0122] ;

[0123] The expression for Kriging interpolation in complex regions is:

[0124] ;

[0125] in, Represented as coordinates continuous geosurface model output values, total number of spatial partitions represented as geology-terrain cluster divisions, cumulative summation over all spatial partitions, partition, partition interpolation results at locations , indicator function represented as flat areas, indicator function represented as complex areas, inverse distance weighted interpolation results, Kriging interpolation results, measured attribute values of sample points , distances of interpolation points to sample points , power parameter, all sample points set of a partition, contribution degree of interpolation points .

[0126] More preferably, the spatial gradual change characteristics of the earth surface morphology (such as elevation, temperature or soil attribute) are expressed by a regular grid without gaps and high precision, which can eliminate the data discontinuity and interpolation error caused by traditional discrete sampling points or vector boundaries, and support fine modeling such as terrain curvature analysis, hydrological runoff simulation, spatial diffusion of climate elements, etc.

[0127] S4, constructing a triangular mesh topology according to the continuous geosurface model, and establishing a topological network containing spatial entity geometry data and geology-elevation attribute mapping relationship.

[0128] Further, the continuous geosurface model contains geology structure layering data and terrain elevation gradient information;

[0129] It should be noted that the geology structure layering data is three-dimensional spatial information describing the distribution and structural characteristics of underground rock layers, including classification marks of different lithology types (such as sandstone, shale, limestone, etc.), depth positions of stratigraphic interfaces and their physical attribute parameters (such as density, porosity), which are obtained through geological exploration, drilling core analysis or geophysical inversion, and recorded in a structured form to describe the geometric shape and physical property differences of vertical stratigraphic units;

[0130] The terrain elevation gradient information is a spatial vector field quantifying the change of earth surface morphology, covering derived indicators such as slope, aspect, curvature, reflecting the dynamic response of the earth surface under the action of external forces such as erosion and tectonic activity, and is usually calculated based on DEM to generate and stored in high-resolution raster form, and is associated with the geometric characteristics of the terrain unit (such as ridge line, valley line).

[0131] The continuous geographic surface model is meshed based on a Delaunay triangulation algorithm to generate an initial triangular facet set;

[0132] It should be noted that when the continuous geographic surface model is meshed based on the Delaunay triangulation algorithm, the geographic surface is first discretized into a series of spatial point sets (such as elevation points, geological sampling points), and then triangular facets are generated by connecting adjacent points according to the Delaunay criterion, which requires that no other points are contained in the circumcircle of any triangle, thereby avoiding the generation of narrow or distorted triangles, ensuring the optimal geometric quality of the mesh, and the generated triangular facet set covers the entire geographic surface, and its topological structure strictly follows the spatial proximity relationship, and can adaptively change the complexity of the surface form (such as faults and valleys), while preserving the spatial continuity of the original data.

[0133] The triangular facet set is analyzed for topological relationship to establish a three-level topological association structure of nodes, arc segments and surface domains, and the surface domains and nodes form spatial adjacency relationships through arc segments;

[0134] Specifically, when the triangular facet set is analyzed for topological relationship, first, each triangular facet is traversed, its three vertices are extracted as nodes, and its three edges are extracted as arc segments, and the triangular facet itself is defined as a surface domain, then the hierarchical topological chain of “node→arc segment→surface domain” is constructed by recording the affiliation relationship of arc segments and nodes and the association relationship of arc segments and surface domains, on the basis of which, adjacent surface domains sharing the same arc segment are identified, the spatial adjacency relationship between surface domains is established, and the node attributes (such as elevation values) are transmitted to the associated surface domains through the arc segments.

[0135] The geological structure type is mapped as a surface domain attribute, and the terrain elevation value is mapped as a node attribute to generate a geological-elevation two-dimensional attribute mapping table;

[0136] It should be noted that when the geological-elevation two-dimensional attribute mapping table is constructed, first, each triangular facet (surface domain) is bound with its covered geological structure type (such as fault, sedimentary layer) as a classification attribute of the surface domain, and the elevation values of the three vertices (nodes) of the triangular facet are extracted as continuous numerical attributes, and the statistical characteristics (such as mean, range) of the node elevations in the surface domain are calculated. By associating the geological type of the surface domain with the node elevation distribution, a structured mapping table is generated, which is indexed by the surface domain and contains the geological type code and the associated node elevation characteristics.

[0137] A spatial index structure is constructed in combination with the spatial adjacency relationship to form a topological network for intersection detection.

[0138] Specifically, in the construction of a spatial index structure combined with spatial adjacency relations, firstly, a topological connectivity graph is established based on the adjacency arcs between surface domains, surface domains sharing a common boundary are clustered into a spatial unit, and the geometric range of each unit is defined by the minimum bounding rectangle, then, a hierarchical index strategy is adopted to divide the spatial unit into multiple levels, the MBRs of adjacent units are organized into a tree-like hierarchical structure, the low-level index describes the local adjacency relation, and the high-level index covers the global spatial range, in the intersection detection, the hierarchical relation of the index tree is traversed to quickly locate the potential intersecting units, and only the adjacent or overlapping units are subjected to accurate geometric calculation, thereby reducing the exponential complexity of the traditional global traversal to logarithmic level.

[0139] Preferably, the construction of a spatial index structure combined with spatial adjacency relations and the formation of a topological network for intersection detection can dynamically prune redundant calculation units and quickly locate spatial adjacent entities, thereby improving the efficiency of intersection relation query of massive spatial objects, and the connectivity between entities is explicitly expressed by the topological network, thereby supporting parallel processing of complex spatial analysis such as conflict detection and path optimization.

[0140] S5. Converting the sectioning surface parameters input by the user in real time into a spatial plane equation, and generating a discrete section line segment set with geological attribute annotations through intersection detection of the plane equation and the triangular patches.

[0141] Further, based on the sectioning surface normal vector and the offset input by the user, a spatial plane equation is generated through vector normalization calculation;

[0142] It should be noted that when generating the spatial plane equation, the direction vector input by the user is first standardized to ensure that the plane direction is determined only by the direction and not by the vector length, for example, if the input direction vector is too long, it is shortened to a unit length in proportion, thereby avoiding the position deviation of the plane caused by vector scaling, and then, based on the adjusted unit direction vector and the offset distance (i.e., the vertical distance from the plane to the origin) specified by the user, the plane equation is constructed through geometric projection principle.

[0143] Traversing the topological network of the triangular mesh, and screening out intersecting patches with different signs of the signed distance from the vertices to the plane equation;

[0144] Specifically, when traversing the topological network of the triangular mesh, for each triangular patch, the signed distances of its three vertices to the sectioning surface are calculated, and if the signs of the three vertices are not all the same, it is determined that the patch intersects the sectioning surface. For example, if two vertices of a triangular patch are above the plane and one vertex is below the plane, the hetero sign condition is met, and the patch is screened out as an intersecting patch.

[0145] Performing linear interpolation calculation on the edges of the intersecting patch to generate a local section line segment, and querying the geological type and stratigraphic age of the corresponding surface domain;

[0146] It should be noted that when linearly interpolating the edges of intersecting triangular facets, the intersection point position is determined according to the proportional signed distance of the two end vertices to the section plane. If the signs of the vertices A and B are opposite, the intersection point is generated by distance proportional interpolation along the edge. The adjacent intersection points are connected to form a local section line segment. Then, the geological type (such as sandstone, fault) and the stratum age (such as Jurassic system, Cretaceous system) of the intersecting facet are queried through the topological association relationship of the facet domain. If the section line segment crosses multiple facet domains, the conflicting attributes are fused according to the stratum age priority (such as "new stratum covers old stratum"). For example, when the section simultaneously passes through a fault (high priority) and a sedimentary layer, the fault line is preferentially marked to ensure that the geological section conforms to the tectonic activity law and supports the design of exploration drilling trajectory or the identification of geological disaster surface.

[0147] The conflicting types are fused according to the stratum age priority rule, and a discrete section line segment set with geological annotations is output.

[0148] Specifically, in solving the geological attribute conflict of the section line segment, first, a rule table is established according to the stratum deposition sequence and tectonic activity. When the line segment crosses multiple geological units, the highest priority type is automatically selected as the annotation according to the rule. For example, if the line segment simultaneously passes through the Cretaceous sandstone layer (newer age) and the Jurassic fault (high tectonic priority), the fault line is preferentially marked. If the line segment passes through sedimentary layers and intrusive rock bodies of the same age, the rock body boundary is marked according to the intrusive relationship (sedimentary layer cut by rock body). Under the driving of this rule, all local section line segments are traversed, and the attribute conflict points are fused according to the preset logic. Finally, a set of annotated line segments with no contradictions in geological attributes and conforming to the stratum superposition law is output, supporting the accurate expression of structural lines and stratigraphic lines in the geological section map and avoiding logical inconsistency caused by manual correction.

[0149] S6, parallel contour tracing and topological sorting are performed on the discrete section line segment set to generate a custom section map supporting interactive parameter adjustment.

[0150] Parallel contour tracing and topological sorting are performed on the discrete section line segment set to generate an ordered section contour chain.

[0151] It should be noted that when performing parallel contour tracing on the discrete section line segment set, first, the line segments are divided into multiple subsets according to spatial proximity, and each subset is assigned an independent thread for processing. From a random starting point, the path is expanded along the line segment adjacency relationship to generate a local contour chain. Then, based on the line segment topological dependency relationship, the global order is sorted, the thread results are merged, and the circular dependency is eliminated. Finally, an ordered contour chain arranged according to the geological structure priority is generated.

[0152] The geometric shape and attribute annotation rules of the ordered section contour chain are adjusted according to the user input parameters.

[0153] Specifically, in adjusting the ordered section profile chain according to the parameters input by the user, first, the geometric shape parameters set by the user and the attribute labeling rules are analyzed, and the profile chain is optimized in real time through a dynamic adjustment algorithm. For geometric shape, the curvature of the profile chain is optimized based on the smooth level set by the user. For attribute labeling, the color and line type are remapped according to the priority of the geological types defined by the user, to ensure that the key structural features are visualized preferentially.

[0154] The custom section view is rendered and output in real time based on the adjusted profile chain.

[0155] It should be noted that when the custom section view is rendered in real time based on the adjusted profile chain, the geometric data of the ordered profile chain and the attribute rules are dynamically bound by using graphic hardware acceleration, the vertex coordinates, attribute labels and rendering instructions are synchronously transmitted to the GPU through the streaming pipeline, millisecond-level image generation is realized, vectorized anti-aliasing and layered rendering are combined to ensure clear expression of the geological boundary, and finally the output section view supports multiple format export and retains attribute metadata.

[0156] The embodiment also provides a GIS-based real-time synchronous display system for section views, which comprises a data regularization module, a feature fusion module, an interpolation optimization module, a topological modeling module, an intersection detection module and a dynamic rendering module. The data regularization module is used to import multi-source heterogeneous geographic data, perform standardization preprocessing, and generate a geographic information dataset in a unified format. The feature fusion module is used to perform multi-dimensional feature extraction on the geographic information dataset by using a feature-level fusion algorithm, and construct a multi-dimensional geographic feature model fusing geological structure, terrain elevation and surface coverage information. The interpolation optimization module is used to perform data optimization on the multi-dimensional geographic feature model by using a spatial interpolation algorithm, and generate a continuous geographic surface model. The topological modeling module is used to construct a triangular mesh topological structure according to the continuous geographic surface model, and establish a topological network containing spatial entity geometric data and geological-elevation attribute mapping relationship. The intersection detection module is used to convert the sectioning plane parameters input by the user in real time into a spatial plane equation, and generate a discrete section line segment set with geological attribute labeling by performing intersection detection between the plane equation and triangular facets. The dynamic rendering module is used to perform parallel profile tracking and topological sorting on the discrete section line segment set, and generate a custom section view supporting interactive parameter adjustment in real time.

[0157] The embodiment also provides a computer device suitable for the GIS-based real-time synchronous display method for section views, which comprises a memory and a processor. The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the GIS-based real-time synchronous display method for section views proposed in the above embodiment.

[0158] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved by WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0159] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the method for real-time synchronous display of a section view based on GIS proposed in the above embodiment; and the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0160] In summary, the present application achieves semantic-level association of geological structure, terrain elevation and surface cover information by constructing a multi-dimensional geographic feature model through a feature-level fusion algorithm, solves the problem of pixel-level fusion, and reduces data spatial consistency error. Based on the continuous surface model generated by spatial interpolation optimization, combined with the three-dimensional network topology of the double-dimensional attribute mapping (geology-elevation) triangular network, the spatial entity attribute query efficiency is significantly enhanced, and the topology relationship analysis speed is improved. In the interactive section generation, through parallel contour tracking and dynamic priority fusion, the stratigraphic age coverage rules and lithology conflicts are supported for automatic resolution. In addition, the GPU-accelerated real-time rendering pipeline compresses the parameter adjustment response time to the millisecond level, meeting the engineering-level interactive requirements of large-scale geological models.

[0161] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application, and all modifications and equivalents should be included in the scope of the claims of the present application.

Claims

1. A method for real-time synchronous display of cross-sectional maps based on GIS, characterized in that: include, Import heterogeneous geographic data from multiple sources, perform standardized preprocessing, and generate a geographic information dataset in a unified format; A feature-level fusion algorithm is used to extract multi-dimensional features from the geographic information dataset, and a multi-dimensional geographic feature model is constructed that integrates geological structure, topographic elevation, and land cover information. The multidimensional geographic feature model includes fused feature data of geological structure, topographic elevation, and land cover; Based on a multidimensional geographic feature model, a spatial interpolation algorithm is used to optimize the data and generate a continuous geographic surface model. The specific steps are as follows. Spatial data partitioning is performed on the fused feature data to generate regional datasets with similar geological and topographic features; Based on the data characteristics of each region, the spatial interpolation algorithm is dynamically selected. Inverse distance weighted interpolation is used in flat areas, and Kriging interpolation is used in complex terrain areas. The parameters of the regional data are optimized according to the selected interpolation algorithm to generate an interpolation weight matrix. The partitioned data are then seamlessly stitched together to generate a continuous geographic surface model. The continuous geographic surface model includes geological structure layering data and topographic elevation gradient information; Based on the continuous geographic surface model, a triangular network topology is constructed, establishing a topological network that includes spatial entity geometric data and geological-elevation attribute mapping relationships. The specific steps are as follows. The continuous geographic surface model is meshed based on the Delaunay triangulation algorithm to generate an initial set of triangular patches. Perform topological relation analysis on the triangular facet set to establish a three-level topological association structure of nodes, arcs, and regions, and form spatial adjacency relationships between regions and nodes through arcs; By mapping geological structure type as area attribute and terrain elevation value as node attribute, a geological-elevation dual-dimensional attribute mapping table is generated. A spatial index structure is constructed by combining spatial adjacency relationships to form a topological network for intersection detection; The cross-sectional parameters input by the user in real time are converted into spatial plane equations. Through the intersection detection of the plane equations and triangular patches, a set of discrete cross-sectional line segments with geological attribute annotations is generated. Parallel contour tracing and topological sorting are performed on discrete cross-section line segment sets to generate custom cross-section diagrams that support interactive parameter adjustment in real time.

2. The method for real-time synchronous display of GIS-based cross-sectional maps as described in claim 1, characterized in that: The multi-source heterogeneous geographic data includes CAD design files, GIS topographic maps, and point cloud data collected on-site. The process of importing multi-source heterogeneous geographic data, performing standardized preprocessing, and generating a geographic information dataset in a unified format involves the following steps: Perform outlier cleaning on multi-source heterogeneous data to generate a denoised intermediate dataset; Transform the intermediate dataset to a preset geographic coordinate system; The data after the geographic coordinate system is standardized to generate a structured geographic information dataset.

3. The method for real-time synchronous display of GIS-based cross-sectional maps as described in claim 2, characterized in that: The geographic information dataset includes geological borehole data, topographic elevation point cloud, and surface cover raster imagery. The method involves using a feature-level fusion algorithm to extract multi-dimensional features from the geographic information dataset, constructing a multi-dimensional geographic feature model that integrates geological structure, topographic elevation, and land cover information. The specific steps are as follows. Geological borehole data is used to extract stratigraphic structural features and generate geological stratification feature vectors. Topographic curvature and slope features are extracted from the topographic elevation point cloud to generate a topographic morphology feature matrix. Land cover classification features are extracted from land cover raster images to generate land cover feature maps; Geological stratification feature vectors, topographic morphology feature matrices, and land cover feature maps are spatiotemporally aligned to form a multi-dimensional joint feature space. Based on deep learning fusion networks, cross-modal correlation analysis is performed on the joint feature space to construct a multidimensional geographic feature model that integrates geological, topographic, and land cover information.

4. The method for real-time synchronous display of GIS-based cross-sectional maps as described in claim 1, characterized in that: The specific steps for generating the set of discrete cross-section line segments labeled with geological attributes are as follows: Based on the user-input cutting plane normal vector and offset, the spatial plane equation is generated through vector normalization. Traverse the topology of the triangular network and filter out intersecting faces whose vertices have opposite signs of distances from the plane equations; Linear interpolation is performed on the edges of intersecting surfaces to generate local cross-sectional segments, and the geological type and stratigraphic age of the corresponding surface region are queried. The conflict types are merged according to the priority rule of stratigraphic age, and the set of discrete cross-section segments with geological annotations is output.

5. The method for real-time synchronous display of GIS-based cross-sectional maps as described in claim 1, characterized in that: The specific steps for performing parallel contour tracing and topological sorting on the discrete cross-section line segment set to generate custom cross-section diagrams that support interactive parameter adjustment in real time are as follows. Parallel contour tracing and topological sorting are performed on the discrete cross-section line segment set to generate an ordered cross-section contour chain. Adjust the geometry and attribute annotation rules of the ordered cross-sectional contour chain according to the parameters input by the user; Based on the adjusted contour chain, render and output a custom cross-section diagram in real time.

6. A GIS-based real-time synchronous display system for cross-sectional maps, based on the GIS-based real-time synchronous display method for cross-sectional maps according to any one of claims 1 to 5, characterized in that: It includes a data normalization module, a feature fusion module, an interpolation optimization module, a topology modeling module, an intersection detection module, and a dynamic rendering module; The data normalization module is used to import multi-source heterogeneous geographic data, perform standardized preprocessing, and generate a geographic information dataset in a unified format. The feature fusion module is used to extract multi-dimensional features from the geographic information dataset using a feature-level fusion algorithm, and to construct a multi-dimensional geographic feature model that integrates geological structure, topographic elevation, and land cover information. The interpolation optimization module is used to optimize data based on a multidimensional geographic feature model using a spatial interpolation algorithm to generate a continuous geographic surface model. The topology modeling module is used to construct a triangular network topology based on a continuous geographic surface model, and to establish a topology network that includes spatial entity geometric data and geological-elevation attribute mapping relationships. The intersection detection module is used to convert the cutting surface parameters input by the user in real time into spatial plane equations, and generate a set of discrete cross-section line segments with geological attribute annotations through the intersection detection of the plane equations and triangular facets. The dynamic rendering module is used to perform parallel contour tracking and topological sorting on the discrete cross-section line segment set, and generate a custom cross-section diagram that supports interactive parameter adjustment in real time.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the GIS-based real-time synchronous display method for cross-sectional maps as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the GIS-based real-time synchronous display method for cross-sectional maps as described in any one of claims 1 to 5.

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