Section diagram real-time synchronous display method and system based on GIS (Geographic Information System)

Abstract

The invention discloses a GIS-based cross-section diagram real-time synchronous display method and system, and relates to the technical field of geological modeling, and the method comprises the steps: importing multi-source heterogeneous geographic data, carrying out the standardized preprocessing, and generating a geographic information data set in a unified format; carrying out multi-dimensional feature extraction on the geographic information data set by adopting a feature level fusion algorithm, and constructing a multi-dimensional geographic feature model fusing geological structure, terrain elevation and earth surface coverage information; performing data optimization by adopting a spatial interpolation algorithm based on the multi-dimensional geographic feature model to generate a continuous geographic surface model; and performing parallel contour tracking and topological sorting on the discrete section line segment set, and generating a user-defined section diagram supporting interactive parameter adjustment in real time. According to the method, the multi-dimensional geographic feature model is constructed through the feature level fusion algorithm, semantic level association of the geologic structure, the terrain elevation and the earth surface coverage information is realized, the problem of pixel level fusion is solved, and the data space consistency error is reduced.

Description

Technical Field

[0001] The present invention relates to the field of geological modeling technology, and in particular to a GIS-based real-time synchronous display method and system for cross-section diagrams. Background Art

[0002] In recent years, GIS and three-dimensional geological modeling have been widely used in geological exploration and engineering surveys. Existing methods primarily rely on surface modeling from a single data source and constructing a basic grid structure through Delaunay triangulation. With the growing demand for integrating multi-source heterogeneous geographic data, modeling methods based on data fusion have been proposed. For example, weighted averaging is used to fuse multi-scale terrain features, or regular grid interpolation is used to generate continuous surface models. However, existing methods face significant bottlenecks in cross-dimensional feature correlation and dynamic interaction analysis.

[0003] The current technology is deficient in two aspects: on the one hand, multi-source data fusion mostly stays at the pixel level or data level, lacking feature-level semantic association, which makes it difficult to ensure the spatial consistency of geological structure, topography and surface cover information; on the other hand, the existing section generation method relies on static topological networks and cannot respond to parameter adjustments in real time. In addition, the section annotation 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 scientific nature of the section analysis and the efficiency of engineering decision-making. Summary of the Invention

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

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

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for real-time synchronous display of cross-section diagrams based on GIS, which comprises importing multi-source heterogeneous geographic data, performing standardized preprocessing, and generating a geographic information dataset in a unified format; A feature-level fusion algorithm is used to extract multi-dimensional features from geographic information datasets, and a multi-dimensional geographic feature model is constructed that integrates geological structure, terrain elevation, and surface cover information. Based on the multi-dimensional geographic feature model, spatial interpolation algorithm is used to optimize data and generate a continuous geographic surface model; Construct a triangulated network topology based on a continuous geographic surface model, and establish a topological network containing spatial entity geometry data and geological-elevation attribute mapping relationships; The section parameters input by the user in real time are converted into spatial plane equations. Through the intersection detection between the plane equation and the triangular facets, a discrete section segment set with geological attribute annotations is generated. Perform parallel contour tracing and topological sorting on discrete cross-section line segments, and generate custom cross-section graphs in real time with interactive parameter adjustment support.

[0007] As a preferred solution of the GIS-based real-time synchronous display method of cross-section diagrams of the present invention, wherein: the multi-source heterogeneous geographic data includes CAD design files, GIS topographic maps and on-site collected point cloud data; The specific steps of importing multi-source heterogeneous geographic data, performing standardization preprocessing, and generating a unified format geographic information dataset are as follows: Clean abnormal data from multi-source heterogeneous data to generate denoised intermediate data sets; Convert the spatial coordinate system of the intermediate dataset to the preset geographic coordinate system; Perform format standardization on the data after geographic coordinate system conversion to generate structured geographic information datasets.

[0008] As a preferred solution of the GIS-based real-time synchronous display method of cross-section diagrams of the present invention, the following is described: The geographic information dataset includes geological drilling data, terrain elevation point cloud and surface coverage raster image; The method uses a feature-level fusion algorithm to extract multi-dimensional features from geographic information datasets and construct a multi-dimensional geographic feature model that integrates geological structure, terrain elevation, and surface cover information. The specific steps are as follows: Extract stratigraphic structural features from geological drilling data and generate geological stratification feature vectors; Extract terrain curvature and slope features from terrain elevation point cloud to generate terrain morphological feature matrix; Extract ground feature classification features from the surface cover raster image to generate a surface cover feature map; The geological stratification feature vectors, terrain morphology feature matrix and surface cover feature map are aligned in time and space to form a multi-dimensional joint feature space; Based on the deep learning fusion network, cross-modal correlation analysis of the joint feature space is performed to construct a multidimensional geographic feature model that integrates geological, topographic and surface cover information.

[0009] As a preferred solution of the GIS-based cross-section diagram real-time synchronous display method of the present invention, wherein: the multi-dimensional geographic feature model includes fused feature data of geological structure, terrain elevation and surface cover; The specific steps of generating a continuous geographic surface model are as follows: Perform spatial data partitioning on the fused feature data to generate regional data sets with similar geological and topographic features; Dynamically select the spatial interpolation algorithm based on the data characteristics of each region. Inverse distance weighted interpolation is used in flat areas, and Kriging interpolation is used in complex terrain areas. The parameters of regional data are optimized according to the selected interpolation algorithm to generate an interpolation weight matrix, and the partition data are seamlessly spliced ​​to generate a continuous geographic surface model.

[0010] As a preferred solution of the GIS-based real-time synchronous display method of cross-section diagrams of the present invention, wherein: the continuous geographic surface model includes geological structure layering data and terrain elevation gradient information; The triangulated network topology structure is constructed based on the continuous geographic surface model, and a topological network containing spatial entity geometric data and geological-elevation attribute mapping relationships is established. The specific steps are as follows: Grid the continuous geographic surface model based on the Delaunay triangulation algorithm to generate an initial set of triangle faces; Analyze the topological relationship of the triangular face set, establish a three-level topological association structure of nodes, arc segments and face regions, and form a spatial adjacency relationship between face regions and nodes through arc segments; Map the geological structure type as the domain attribute and the terrain elevation value as the node attribute to generate a geological-elevation dual-dimensional attribute mapping table; The spatial index structure is constructed by combining the spatial adjacency relationship to form a topological network for intersection detection.

[0011] As a preferred solution of the GIS-based real-time synchronous display method of the cross-section diagram of the present invention, the steps of generating a discrete cross-section line segment set with geological attribute annotations are as follows: Based on the cut plane normal vector and offset input by the user, the spatial plane equation is generated through vector normalization calculation; Traverse the topological network of the triangulated network and filter out the intersecting faces whose signed distances from the vertices to the plane equations are different in sign; Perform linear interpolation calculations on the edges of intersecting patches to generate local section segments and query the geological type and stratigraphic age of the corresponding area; Conflicting types are merged according to stratigraphic age priority rules, and a discrete section segment set with geological annotations is output.

[0012] As a preferred solution of the GIS-based real-time synchronous display method of cross-section diagrams of the present invention, the following specific steps are used to perform parallel contour tracing and topological sorting on a set of discrete cross-section line segments to generate a custom cross-section diagram supporting interactive parameter adjustment in real time: Parallel contour tracing and topological sorting are performed on discrete cross-section line segments to generate ordered cross-section contour chains; Adjust the geometric shape and attribute annotation rules of the ordered cross-section contour chain according to the parameters input by the user; Render and output custom cross-sections in real time based on the adjusted contour chain.

[0013] In a second aspect, the present invention provides a GIS-based real-time synchronous display system for cross-section diagrams, comprising a data regularization module, a feature fusion module, an interpolation optimization module, a topology 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 data set 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, terrain elevation, and surface cover information; The interpolation optimization module is used to optimize data using a spatial interpolation algorithm based on a multidimensional geographic feature model to generate a continuous geographic surface model; The topology modeling module is used to construct a triangulated network topology structure based on a continuous geographic surface model, and to establish a topological network containing spatial entity geometric data and geological-elevation attribute mapping relationships; The intersection detection module is used to convert the section parameters input by the user in real time into a spatial plane equation, and generate a discrete section line segment set with geological attribute annotations by performing intersection detection between the plane equation and the triangular facets; The dynamic rendering module is used to perform parallel contour tracing and topological sorting on a discrete cross-section line segment set, and to generate a custom cross-section diagram supporting interactive parameter adjustment in real time.

[0014] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the GIS-based real-time synchronous display method of cross-sectional views as described in the first aspect of the present invention.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the GIS-based real-time synchronous display method of cross-sectional views as described in the first aspect of the present invention.

[0016] The beneficial effects of the present invention are as follows: the present invention constructs a multi-dimensional geographic feature model through a feature-level fusion algorithm, realizes the semantic-level association of geological structure, terrain elevation and surface cover information, solves the problem of pixel-level fusion, and reduces the spatial consistency error of data. The continuous surface model generated based on spatial interpolation optimization, combined with the triangulated topological network of two-dimensional attribute mapping (geology-elevation), significantly enhances the efficiency of spatial entity attribute query and improves the speed of topological relationship analysis. In interactive section generation, parallel contour tracking and dynamic priority fusion support the automatic resolution of stratigraphic age coverage rules and lithology conflicts. In addition, the real-time rendering pipeline based on GPU acceleration compresses the parameter adjustment response time to milliseconds, meeting the engineering-level interaction requirements of large-scale geological models. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of the method for real-time synchronous display of cross-section diagrams based on GIS in Example 1.

[0019] Figure 2 This is a module diagram of the GIS-based real-time synchronous display system of cross-section diagrams in Example 1.

[0020] Figure 3 A flowchart for constructing a multidimensional geographic feature model in Example 1.

[0021] Figure 4 A flow chart for generating a continuous geographic surface model in Example 1. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0025] Example 1, with reference to Figures 1 to 4 , which is the first embodiment of the present invention, provides a method for real-time synchronous display of cross-section diagrams based on GIS, comprising the following steps: S1. Import multi-source heterogeneous geographic data, perform standardized preprocessing, and generate geographic information datasets in a unified format.

[0026] Furthermore, multi-source heterogeneous geographic data includes CAD design files, GIS topographic maps, and field-collected point cloud data; It should be noted that CAD design files refer to computer-aided design files that contain vector graphics of geological structure boundaries and drilling locations in engineering design. Their rationale lies in providing high-precision manual design data; GIS topographic maps refer to raster / vector maps containing contour lines, elevation points, and land cover classifications. Their rationale lies in providing a macro-topographic framework; On-site point cloud data refers to dense three-dimensional point clouds (density ≥ 500 points / ㎡) obtained through photogrammetry. Its rationality lies in reflecting the actual terrain and micro-topography.

[0027] Clean abnormal data from multi-source heterogeneous data to generate denoised intermediate data sets; Specifically, when cleaning abnormal data from multi-source heterogeneous data, we first design differentiated cleaning methods for different data types. For field-collected point cloud data, we use an adaptive denoising algorithm based on density clustering. By setting a neighborhood radius and a minimum point count threshold, we identify and remove outliers while retaining micro-topographic features such as steep slopes and fault lines. For CAD design files, we use geometric rule verification to remove isolated line segments or broken polygons caused by drawing errors, reducing the rate of topological contradictions. For GIS topographic maps, we use topological relationship verification to correct data logic errors. Finally, through multi-level cleaning, we generate a denoised intermediate dataset with lower spatial consistency errors than the original data, providing a high-confidence data base for subsequent coordinate transformation and fusion.

[0028] Convert the spatial coordinate system of the intermediate dataset to the preset geographic coordinate system; It should be noted that in the process of spatial coordinate system conversion, the intermediate dataset metadata is first parsed to identify the original coordinate system, and the conversion rules are determined by dynamically matching the EPSG code. Subsequently, the vertex coordinates of the vector data and the three-dimensional point set of the point cloud data are batch converted using the seven-parameter Helmert transformation. At the same time, a topological consistency check is performed on the converted data, and the projection information field in the metadata is updated. Finally, a unified dataset that is strictly aligned with the preset geographic coordinate system is generated.

[0029] Perform format standardization on the data after geographic coordinate system conversion to generate structured geographic information datasets.

[0030] Specifically, when formatting data after geographic coordinate system conversion, data type conversion and field mapping are first performed according to a preset structured data template. CAD vector features are converted to the lightweight GeoJSON format, while attribute fields and topological relationship metadata are retained. Point cloud data is compressed into the LAS format, and classification labels and intensity value fields are defined. Simultaneously, a semantic mapping algorithm is used to uniformly map heterogeneous attribute names to standardized fields to eliminate semantic ambiguity. A schema validator is used to verify data integrity, remove invalid records, and ultimately generate a structured geographic information dataset.

[0031] S2. Use feature-level fusion algorithm to extract multi-dimensional features from geographic information datasets and construct a multi-dimensional geographic feature model that integrates geological structure, terrain elevation, and surface cover information.

[0032] Furthermore, geographic information datasets include geological drill hole data, terrain elevation point clouds, and surface cover raster images; It should be noted that geological drilling data refers to structured data containing core sampling depth, lithology description and stratigraphic age labels. Its rationale lies in providing vertical geological structure information, but it needs to be associated with horizontal spatial distribution through feature extraction; Terrain elevation point cloud refers to a dense three-dimensional point set (density ≥ 20 points / ㎡) obtained through photogrammetry. Its rationality lies in reflecting the micro-topography, but curvature and slope need to be extracted to quantify morphological features; Surface cover raster images refer to land use / cover rasters (resolution ≤ 10m) generated by classification of multispectral remote sensing images (such as Sentinel-2). Their rationale lies in providing surface material properties, but the problem of mixed pixels needs to be resolved.

[0033] Extract stratigraphic structural features from geological drilling data and generate geological stratification feature vectors; Specifically, when extracting stratigraphic structural features from geological borehole data, the vertical lithologic sequence of the borehole is first decomposed into continuous observation units. The probabilistic relationship between lithologic types and stratigraphic units is analyzed through hidden Markov modeling to identify stratigraphic mutation interfaces. Subsequently, the physical property differences between adjacent stratigraphic units are quantified based on the lithologic difference metric, and a characteristic vector representing the stratigraphic layered structure is constructed in combination with the interface depth information.

[0034] Extract terrain curvature and slope features from terrain elevation point cloud to generate terrain morphological feature matrix; It should be noted that when extracting terrain curvature and slope features from terrain elevation point clouds, the spatial distribution of the point clouds is first analyzed based on the local surface fitting method. The surface curvature features are quantified by calculating the normal vector and curvature type (such as plane curvature and profile curvature) of each point. At the same time, the slope value is calculated based on the elevation change rate of the neighborhood points. Subsequently, the curvature and slope features are spatially aggregated according to a regular grid (such as 1m×1m) to generate a three-dimensional morphological feature matrix containing curvature intensity, slope classification and terrain undulation direction.

[0035] Extract ground feature classification features from the surface cover raster image to generate a surface cover feature map; Specifically, when extracting ground feature classification features from surface cover raster images, a semantic segmentation network is constructed based on a deep learning framework. By fusing texture and spectral features from multispectral bands (such as visible light, near-infrared, and red edge), surface cover types such as vegetation, water bodies, bare rock, and artificial structures are identified. The semantic segmentation network uses an encoder-decoder structure, fusing shallow details with deep semantic information through skip connections to resolve the problem of mixed pixel classification ambiguity. Transfer learning is also introduced to improve the model's generalization ability in small sample scenarios. The final output is pixel-level classification results, generating a surface cover feature map that includes ground feature type codes, cover density, and boundary clarity.

[0036] The geological stratification feature vectors, terrain morphology feature matrix and surface cover feature map are aligned in time and space to form a multi-dimensional joint feature space; It should be noted that in the process of spatiotemporal alignment, a unified spatial grid coordinate system is first established, and the geological stratification feature vectors are mapped to the surface projection grid through Kriging interpolation to align them with the grid cells of the terrain morphological feature matrix. At the same time, the surface cover feature map is resampled to ensure that the spatial resolution is consistent with the terrain grid. Subsequently, a spatiotemporal index structure is constructed to associate vertical geological units, surface morphological attributes and cover types according to spatial positions, and dynamic data versions are matched by timestamps. In the final generated multi-dimensional joint feature space, each grid cell integrates the three-dimensional attributes of geological lithology, terrain curvature slope and surface cover type, realizing the spatial consistency expression of cross-modal data and the association with time evolution, supporting the subsequent fusion network to accurately explore the fault-landform-ecological coupling relationship.

[0037] Based on the deep learning fusion network, cross-modal correlation analysis of the joint feature space is performed to construct a multidimensional geographic feature model that integrates geological, topographic and surface cover information.

[0038] It should be noted that when performing cross-modal correlation analysis on the joint feature space based on a deep learning fusion network, a multimodal attention mechanism is constructed to capture the nonlinear coupling relationship between geological stratification, terrain curvature, and land cover through adaptive weight allocation. The semantic segmentation network adopts an encoder-decoder architecture. During the encoding phase, a cross-modal feature cross-attention layer is used to extract the controlling effect of geological structure on terrain evolution and the response of land cover to lithology. During the decoding phase, global context information is integrated to generate a joint feature embedding vector. This constructs a multidimensional geographic feature model of geology, terrain, and land cover through end-to-end.

[0039] Specifically, the multidimensional geographic feature model of geology-topography-surface cover information is expressed as: ; in, Expressed as three-dimensional space coordinates The multidimensional geographic feature model output at Expressed as a geological stratification characteristic function, the range is ∈[0,1], Expressed as a terrain morphological characteristic function, the range is ∈[0,1], Expressed as a surface cover characteristic function, the range is ∈[0,1], It is expressed as a cross-modal feature fusion function.

[0040] The better one can deeply integrate the spatiotemporal correlation of underground geological structure (such as faults, rock formations), surface undulation (such as slope, elevation belt) and surface cover type (such as vegetation, artificial buildings), and realize the collaborative analysis of geological process deduction (such as landslide risk prediction), surface resource distribution simulation (such as groundwater enrichment area identification) and human activity impact assessment (such as engineering construction disturbance of ecologically sensitive areas) through dynamic coupling of multi-source data (such as remote sensing images, geological profiles, three-dimensional point clouds).

[0041] Geological stratification characteristic function, expression:

[0042] in, Represented as a variable From 1 to All integer values ​​of are accumulated, Represented as an index variable, Expressed as the total number of stratigraphic units, ranging from ≥1, Indicated as at position Belongs to stratigraphic unit The posterior probability of P ∈[0,1], Expressed as stratigraphic units, ranging from ∈{1,2,...,N}, Expressed as stratigraphic units The feature encoding vector of .

[0043] Terrain morphological characteristic function, expression:

[0044] in, Represented as a region in space Integrate on Represents a terrain analysis window, in units of , Expressed as curvature, Expressed as slope, Expressed as a spatial consistency kernel function, Represents the terrain unit identifier, the unit is ∈{1,2,...,K}, Represented as position The curvature value at , in units of , Represented as position The slope value at is in the range of , Represented as filter and Belong to the same terrain unit Neighborhood points of Represented as position The terrain category, Expressed as the integral area differential, the unit is .

[0045] The surface cover characteristic function is expressed as: ; in, It is represented by the cumulative sum of all categories. Expressed as the total number of feature categories, Represented as an index variable, Expressed as feature categories, ranging from ∈{1,2,..., }, Indicated as at position Belongs to the feature category The probability of , the range is Q∈[0,1], Represented as feature category The feature encoding vector of .

[0046] The cross-modal fusion function is expressed as: ; in, Represented as cross-modal feature fusion output, Expressed as a nonlinear mapping function, Expressed as a feature interaction operator, Expressed as geological stratification characteristic function, Expressed as a terrain morphological characteristic function, Expressed as the surface cover characteristic function, Represented as a spatial correlation operator.

[0047] S3. Based on the multidimensional geographic feature model, spatial interpolation algorithm is used to optimize data and generate a continuous geographic surface model.

[0048] Furthermore, the multidimensional geographic feature model includes fused feature data of geological structure, terrain elevation, and land cover; It should be noted that geological structural characteristic data refers to the three-dimensional spatial data of underground and surface rock strata distribution, structural morphology, and physical parameters. This includes the geometric morphology (e.g., dip, fault throw) and rock physical properties (e.g., density, porosity) of geological elements such as stratigraphic units (e.g., sandstone layers, shale layers), fault lines, and fold axes, and is used to quantify the spatial distribution patterns of geological structures. Terrain elevation characteristic data refers to the quantitative indicators that characterize the surface morphology and terrain evolution process, including curvature (such as the negative curvature of -0.12 in the valley area). ), slope (e.g. steep slope 25°) and terrain unit classification (e.g. ridge, slope), extracted through LiDAR point cloud or DEM data to reflect micro-geomorphological characteristics and their correlation with geological activities; Surface cover characteristic data refers to multidimensional parameters of surface material type, ecological attributes, and boundary characteristics, including land feature classification (such as vegetation, bare rock, and buildings), vegetation coverage (NDVI 0.65), and permeability coefficient (0.3 m / day). These data are derived from remote sensing image classification and physical property inversion, revealing the response mechanism of surface processes to geological and topographic conditions.

[0049] Perform spatial data partitioning on the fused feature data to generate regional data sets with similar geological and topographic features; It should be noted that when spatial data partitioning is performed on the fused feature data, the geographical area is first divided into several subunits based on the coupling relationship between geological structure (e.g., lithology type, fault distribution) and topographic characteristics (e.g., slope, curvature, and elevation variation coefficient); Specifically, a multidimensional clustering algorithm (such as density-based DBSCAN) is used to comprehensively calculate the geological-topographic similarity of each spatial grid point. For example, sandstone-low slope areas and shale-high curvature areas are classified separately. Then, a boundary optimization algorithm (such as morphological erosion and expansion) is used to smooth the partition outline, eliminate scattered noise points, ensure a natural transition between the attributes of adjacent units, and finally generate a partitioned data set.

[0050] Dynamically select the spatial interpolation algorithm based on the data characteristics of each region. Inverse distance weighted interpolation is used in flat areas, and Kriging interpolation is used in complex terrain areas. It should be noted that when dynamically selecting the spatial interpolation algorithm, the region is first divided into flat areas and complex terrain areas according to the terrain complexity indicators (such as terrain curvature, slope and elevation variation coefficient). For flat areas (curvature < 0.05 And the slope is less than 5°, the inverse distance weighted interpolation is used. Because it relies on the distance attenuation effect to quickly generate a uniform surface, the computational efficiency is 2-3 times higher than that of the Kriging method and it is suitable for scenes with uniform data distribution. For complex terrain areas (curvature ≥ 0.05 or slope ≥ 5°), Kriging interpolation is used to model spatial autocorrelation through the variogram, which effectively suppresses interpolation oscillations caused by sudden changes in terrain (for example, the error on steep slopes is reduced by 60% compared with IDW).

[0051] The parameters of regional data are optimized according to the selected interpolation algorithm to generate an interpolation weight matrix, and the partition data are seamlessly spliced ​​to generate a continuous geographic surface model.

[0052] It should be noted that in the parameter optimization stage, the interpolation algorithms for different partitions are adaptively adjusted separately. For the inverse distance weighted interpolation in flat areas, the optimal distance decay power (such as power = 2) is determined through cross-validation to balance the weight ratio of neighboring points and avoid "bull's eye" artifacts. For the Kriging interpolation in complex terrain, the range and sill value are optimized based on the semivariogram fitting (such as the spherical model) to capture the spatial autocorrelation of the terrain mutation zone. Subsequently, the interpolation weight matrix is ​​generated according to the optimized parameters to ensure that the weights at the boundaries of adjacent partitions vary linearly with distance in the overlapping zone (such as a 5-meter-wide transition zone). Finally, the interpolation results of each partition are weighted and fused to eliminate the attribute jumps at the seams (such as elevation difference ≤ 0.2 meters), so as to achieve seamless splicing of the continuous geographic surface model.

[0053] Specifically, the continuous geographic surface model is expressed as: ; The distance-weighted interpolation in flat areas is expressed as: ; The Kriging interpolation expression for complex areas is: ; in, Expressed as coordinates The continuous geographic surface model output value at represents the total number of spatial partitions divided into geological-topographic clusters, It is represented by the cumulative summation of all spatial partitions. Represented as partitions, Represented as partitions In position The interpolation result on , Expressed as the indicator function of the flat area, Expressed as the indicator function of the complex region, Expressed as the inverse distance weighted interpolation result, Represented as the Kriging interpolation result, Represented as sample points The measured attribute value of Represented as the points to be interpolated and the sample points distance, Expressed as a power parameter, Represented as the set of all sample points of the partition, Represented as interpolation points degree of contribution.

[0054] Preferably, it can express the spatial gradient characteristics of surface morphology (such as elevation, temperature or soil properties) seamlessly and with high precision through regular grids, eliminate data breaks and interpolation errors caused by traditional discrete sampling points or vector boundaries, and support refined modeling such as terrain curvature analysis, hydrological runoff simulation, and spatial diffusion of climate elements.

[0055] S4. Construct a triangulated network topology structure based on the continuous geographic surface model, and establish a topological network containing spatial entity geometric data and geological-elevation attribute mapping relationships.

[0056] Furthermore, the continuous geographic surface model contains geological structure layering data and terrain elevation gradient information; It should be noted that geological structure layering data is three-dimensional spatial information that describes the distribution and structural characteristics of underground rock strata. It includes the classification identification of different lithologic types (such as sandstone, shale, limestone, etc.), the depth position of stratum interfaces, and their physical property parameters (such as density and porosity). It is obtained through geological exploration, drill core analysis, or geophysical inversion, and records the geometric morphology and physical property differences of vertical stratigraphic units in a structured form. Terrain elevation gradient information is a spatial vector field that quantifies changes in surface morphology. It covers derivative indicators such as slope, aspect, and curvature, and reflects the dynamic response of the surface under external forces such as erosion and tectonic activity. It is usually generated based on DEM calculations, stored in the form of high-resolution grids, and associated with the geometric features of terrain units (such as ridge lines and valley lines).

[0057] Grid the continuous geographic surface model based on the Delaunay triangulation algorithm to generate an initial set of triangle faces; It should be noted that when gridding a continuous geographic surface model 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 adjacent points are connected according to the Delaunay criterion to generate triangular facets. This criterion requires that the circumcircle of any triangle does not contain other points, thereby avoiding the generation of narrow or distorted triangles and ensuring the optimal mesh geometry quality. The generated triangular facet set covers the entire geographic surface, and its topological structure strictly follows the spatial proximity relationship. It can adapt to complex changes in surface morphology (such as faults and valleys) while retaining the spatial continuity of the original data.

[0058] Analyze the topological relationship of the triangular face set, establish a three-level topological association structure of nodes, arc segments and face regions, and form a spatial adjacency relationship between face regions and nodes through arc segments; Specifically, when performing topological relationship analysis on a set of triangular facets, we first traverse each triangular facet, extract its three vertices as nodes and three edges as arcs, and define the triangular facet itself as a domain. Subsequently, by recording the subordinate relationship between arcs and nodes and the association relationship between arcs and domains, we construct a hierarchical topological chain of "node → arc → domain". On this basis, we identify adjacent domains that share the same arc, establish spatial adjacency between domains, and transfer node attributes (such as elevation values) to the associated domains through arcs.

[0059] Map the geological structure type as the domain attribute and the terrain elevation value as the node attribute to generate a geological-elevation dual-dimensional attribute mapping table; It should be noted that when constructing the geology-elevation dual-dimensional attribute mapping table, each triangular patch (domain) is first associated with the geological structure type (e.g., fault, sedimentary layer) it covers, which serves as the domain's classification attribute. Simultaneously, the elevation values ​​of the three vertices (nodes) of the triangular patch are extracted as continuous numerical attributes, and the statistical characteristics of the node elevations within the domain (e.g., mean and range) are calculated. By associating the domain's geological type with the node elevation distribution, a structured mapping table is generated, indexed by the domain and containing the geological type code and the associated node elevation characteristics.

[0060] The spatial index structure is constructed by combining the spatial adjacency relationship to form a topological network for intersection detection.

[0061] Specifically, when constructing a spatial index structure based on spatial adjacency relationships, a topological connectivity map is first established based on the adjacent arcs between face domains, and the face domains sharing common boundaries are clustered into spatial units. The geometric range of each unit is defined by the minimum enclosing rectangle. Subsequently, a hierarchical indexing strategy is used to divide the spatial units into multiple levels, and the MBRs of adjacent units are organized into a tree-like hierarchical structure. The low-level index describes the local adjacency relationship, and the high-level index covers the global spatial range. In intersection detection, potential intersection units are quickly located by traversing the hierarchical relationship of the index tree, and precise geometric calculations are performed only on adjacent or overlapping units, thereby reducing the exponential complexity of traditional full-domain traversal to a logarithmic level.

[0062] Preferably, a spatial index structure is constructed by combining spatial adjacency relationships and a topological network for intersection detection is formed. This can improve the efficiency of intersection relationship queries for massive spatial objects by dynamically pruning redundant computing units and quickly locating spatially adjacent entities. At the same time, the topological network is used to explicitly express the connectivity between entities, supporting parallel processing of complex spatial analyses such as conflict detection and path optimization.

[0063] S5. Convert the section parameters input by the user in real time into a spatial plane equation, and generate a discrete section line segment set with geological attribute annotations through intersection detection between the plane equation and the triangular facet.

[0064] Furthermore, based on the cut plane normal vector and offset input by the user, the spatial plane equation is generated through vector normalization calculation; It should be noted that when generating the spatial plane equation, the section plane direction vector input by the user is first standardized to ensure that the plane direction is determined only by the direction rather than the vector length. For example, if the input direction vector is long, it is proportionally shortened to unit length to avoid plane position deviation caused by vector scaling. Subsequently, the plane equation is constructed through the principle of geometric projection based on the adjusted unit direction vector and the user-specified offset distance (i.e., the vertical distance from the plane to the origin).

[0065] Traverse the topological network of the triangulated network and filter out the intersecting faces whose signed distances from the vertices to the plane equations are different in sign; Specifically, when traversing the triangulated network, the signed distances between the three vertices of each triangle and the cutting plane are calculated. If the signs of the three vertices are not identical, the triangle is considered to intersect the cutting plane. For example, if two vertices of a triangle are above the plane and one vertex is below the plane, the sign condition is met and the triangle is selected as an intersecting facet.

[0066] Perform linear interpolation calculations on the edges of intersecting patches to generate local section segments and query the geological type and stratigraphic age of the corresponding area; It should be noted that when linearly interpolating the edges of intersecting triangular facets, the intersection point is determined based on the signed distance ratio from the vertices at both ends of the edge to the section plane. If the signs of vertices A and B are opposite, the intersection point is generated by interpolating along the edge according to the distance ratio, and adjacent intersection points are connected to form a local section line segment. Subsequently, the geological type (e.g., sandstone, fault) and stratigraphic age (e.g., Jurassic, Cretaceous) of the intersecting facets are queried through the topological association of the face domains. If the section segment spans multiple face domains, conflicting attributes are merged based on stratigraphic age priority (e.g., "new strata overlay old strata"). For example, when a section crosses both a fault (high priority) and a sedimentary layer, the fault line is prioritized to ensure that the geological profile conforms to the laws of tectonic activity and supports exploration drilling trajectory design or geological hazard surface identification.

[0067] Conflicting types are merged according to stratigraphic age priority rules, and a discrete section segment set with geological annotations is output.

[0068] Specifically, when resolving conflicts in geological attributes of section segments, a rule table is first established based on the stratigraphic sedimentary sequence and tectonic activity. When a segment spans multiple geological units, the type with the highest priority is automatically selected as the label according to the rule. For example, if a segment passes through a Cretaceous sandstone layer (newer) and a Jurassic fault (higher structural priority) at the same time, it is labeled as a fault line first. If a segment passes through a sedimentary layer and an intrusive rock body of the same age, it is labeled as a rock body boundary based on the intrusive relationship (rock body cutting the sedimentary layer). Driven by this rule, all local section segments are traversed, and the attribute conflict points are merged according to the preset logic. Finally, a set of labeled segments with consistent geological attributes and in line with the law of stratigraphic superposition is output, which supports the accurate expression of structural lines and stratigraphic lines in geological profiles and avoids logical inconsistencies caused by manual corrections.

[0069] S6. 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.

[0070] Parallel contour tracing and topological sorting are performed on discrete cross-section line segments to generate ordered cross-section contour chains; It should be noted that when performing parallel contour tracing on a discrete section segment set, the segments are first divided into multiple subsets according to spatial proximity, and each subset is assigned to an independent thread for processing. The path is extended from a random starting point along the segment adjacency relationship to generate a local contour chain; then, a global sorting is performed based on the segment topological dependency, the results of each thread are merged and the circular dependency is eliminated, and finally an ordered contour chain arranged according to geological structure priority is generated.

[0071] Adjust the geometric shape and attribute annotation rules of the ordered cross-section contour chain according to the parameters input by the user; Specifically, when adjusting the ordered section contour chain according to the parameters input by the user, the geometric parameters and attribute labeling rules set by the user are first parsed, and the contour chain is optimized in real time through a dynamic adjustment algorithm. For the geometry, the curvature of the contour chain is optimized based on the smoothing level set by the user. For the attribute labeling, the color and line type are remapped according to the geological type priority defined by the user to ensure that the key structural features are visualized first.

[0072] Render and output custom cross-sections in real time based on the adjusted contour chain.

[0073] It should be noted that when rendering a custom cross-section diagram in real time based on the adjusted contour chain, graphics hardware acceleration is used to dynamically bind the geometric data of the ordered contour chain to the attribute rules, and the vertex coordinates, attribute labels and rendering instructions are synchronously transmitted to the GPU through a streaming pipeline to achieve millisecond-level image generation. Vectorized anti-aliasing and layered rendering are combined to ensure clear expression of geological boundaries. The final output cross-section diagram supports multi-format export and retains attribute metadata.

[0074] This embodiment also provides a GIS-based real-time synchronous display system for cross-section diagrams, including: a data regularization module, a feature fusion module, an interpolation optimization module, a topology 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 standardized preprocessing, and generate a geographic information dataset in a unified format; the feature fusion module is used to use a feature-level fusion algorithm to extract multi-dimensional features from the geographic information dataset and construct a multi-dimensional geographic feature model that integrates geological structure, terrain elevation, and surface cover information; the interpolation optimization module is used to use a spatial interpolation algorithm to optimize data based on the multi-dimensional geographic feature model and generate a continuous geographic surface model; the topology modeling module is used to construct a triangulated network topology structure based on the continuous geographic surface model and establish a topological network containing spatial entity geometry data and geological-elevation attribute mapping relationships; the intersection detection module is used to convert the section parameters input by the user in real time into spatial plane equations, and generate a discrete cross-section line segment set with geological attribute annotations through intersection detection between the plane equations and triangular facets; the dynamic rendering module is used to perform parallel contour tracing and topological sorting on the discrete cross-section line segment set to generate a custom cross-section diagram in real time that supports interactive parameter adjustment.

[0075] This embodiment also provides a computer device suitable for the case of a real-time synchronous display method of cross-section diagrams based on GIS, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the real-time synchronous display method of cross-section diagrams based on GIS proposed in the above embodiment.

[0076] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0077] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for realizing the real-time synchronous display of cross-section diagrams based on GIS as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0078] In summary, the present invention uses a feature-level fusion algorithm to construct a multi-dimensional geographic feature model, achieves semantic-level association between geological structure, terrain elevation and surface cover information, solves the problem of pixel-level fusion, and reduces data spatial consistency errors. The continuous surface model generated based on spatial interpolation optimization, combined with the triangulated topological network of two-dimensional attribute mapping (geology-elevation), significantly enhances the efficiency of spatial entity attribute query and improves the speed of topological relationship analysis. In interactive section generation, parallel contour tracking and dynamic priority fusion support the automatic resolution of stratigraphic age coverage rules and lithology conflicts. In addition, the real-time rendering pipeline based on GPU acceleration compresses the parameter adjustment response time to milliseconds, meeting the engineering-level interaction requirements of large-scale geological models.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for real-time synchronous display of cross-section diagrams based on GIS, characterized by: include, Import multi-source heterogeneous geographic data, perform standardized preprocessing, and generate geographic information datasets in a unified format; A feature-level fusion algorithm is used to extract multi-dimensional features from geographic information datasets, and a multi-dimensional geographic feature model is constructed that integrates geological structure, terrain elevation, and surface cover information. Based on the multi-dimensional geographic feature model, spatial interpolation algorithm is used to optimize data and generate a continuous geographic surface model; Construct a triangulated network topology based on a continuous geographic surface model, and establish a topological network containing spatial entity geometry data and geological-elevation attribute mapping relationships; The section parameters input by the user in real time are converted into spatial plane equations. Through the intersection detection between the plane equation and the triangular facets, a discrete section segment set with geological attribute annotations is generated. Perform parallel contour tracing and topological sorting on discrete cross-section line segments, and generate custom cross-section graphs in real time with interactive parameter adjustment support.

2. The method for real-time synchronous display of cross-section diagrams based on GIS according to claim 1, characterized in that: The multi-source heterogeneous geographic data includes CAD design files, GIS topographic maps and on-site collected point cloud data; The specific steps of importing multi-source heterogeneous geographic data, performing standardization preprocessing, and generating a unified format geographic information dataset are as follows: Clean abnormal data from multi-source heterogeneous data to generate denoised intermediate data sets; Convert the spatial coordinate system of the intermediate dataset to the preset geographic coordinate system; Perform format standardization on the data after geographic coordinate system conversion to generate structured geographic information datasets.

3. The method for real-time synchronous display of cross-section diagrams based on GIS according to claim 2, characterized in that: The geographic information dataset includes geological drilling data, terrain elevation point cloud and surface coverage raster image; The method uses a feature-level fusion algorithm to extract multi-dimensional features from geographic information datasets and construct a multi-dimensional geographic feature model that integrates geological structure, terrain elevation, and surface cover information. The specific steps are as follows: Extract stratigraphic structural features from geological drilling data and generate geological stratification feature vectors; Extract terrain curvature and slope features from terrain elevation point cloud to generate terrain morphological feature matrix; Extract ground feature classification features from the surface cover raster image to generate a surface cover feature map; The geological stratification feature vectors, terrain morphology feature matrix and surface cover feature map are aligned in time and space to form a multi-dimensional joint feature space; Based on the deep learning fusion network, cross-modal correlation analysis of the joint feature space is performed to construct a multidimensional geographic feature model that integrates geological, topographic and surface cover information.

4. The method for real-time synchronous display of cross-section diagrams based on GIS according to claim 3, characterized in that: The multidimensional geographic feature model includes fused feature data of geological structure, terrain elevation and surface cover; The specific steps of generating a continuous geographic surface model are as follows: Perform spatial data partitioning on the fused feature data to generate regional data sets with similar geological and topographic features; Dynamically select the spatial interpolation algorithm based on the data characteristics of each region. Inverse distance weighted interpolation is used in flat areas, and Kriging interpolation is used in complex terrain areas. The parameters of regional data are optimized according to the selected interpolation algorithm to generate an interpolation weight matrix, and the partition data are seamlessly spliced ​​to generate a continuous geographic surface model.

5. The method for real-time synchronous display of cross-section diagrams based on GIS according to claim 4, characterized in that: The continuous geographic surface model includes geological structure layering data and terrain elevation gradient information; The triangulated network topology structure is constructed based on the continuous geographic surface model, and a topological network containing spatial entity geometric data and geological-elevation attribute mapping relationships is established. The specific steps are as follows: Grid the continuous geographic surface model based on the Delaunay triangulation algorithm to generate an initial set of triangle faces; Analyze the topological relationship of the triangular face set, establish a three-level topological association structure of nodes, arc segments and face regions, and form a spatial adjacency relationship between face regions and nodes through arc segments; Map the geological structure type as the domain attribute and the terrain elevation value as the node attribute to generate a geological-elevation dual-dimensional attribute mapping table; The spatial index structure is constructed by combining the spatial adjacency relationship to form a topological network for intersection detection.

6. The method for real-time synchronous display of cross-section diagrams based on GIS according to claim 5, characterized in that: The specific steps of generating a discrete cross-section line segment set with geological attribute annotations are as follows: Based on the cut plane normal vector and offset input by the user, the spatial plane equation is generated through vector normalization calculation; Traverse the topological network of the triangulated network and filter out the intersecting faces whose signed distances from the vertices to the plane equations are different in sign; Perform linear interpolation calculations on the edges of intersecting patches to generate local section segments and query the geological type and stratigraphic age of the corresponding area; Conflicting types are merged according to stratigraphic age priority rules, and a discrete section segment set with geological annotations is output.

7. The method for real-time synchronous display of cross-sections based on GIS according to claim 6, characterized in that: The steps of performing parallel contour tracing and topological sorting on a discrete cross-section segment set and generating a custom cross-section graph supporting interactive parameter adjustment in real time are as follows: Parallel contour tracing and topological sorting are performed on discrete cross-section line segments to generate ordered cross-section contour chains; Adjust the geometric shape and attribute annotation rules of the ordered cross-section contour chain according to the parameters input by the user; Render and output custom cross-sections in real time based on the adjusted contour chain.

8. A GIS-based real-time synchronous display system for cross-sections, based on the GIS-based real-time synchronous display method for cross-sections according to any one of claims 1 to 7, characterized in that: Including data regularization module, feature fusion module, interpolation optimization module, topology modeling module, intersection detection module and dynamic rendering module; The data regularization module is used to import multi-source heterogeneous geographic data, perform standardization preprocessing, and generate a geographic information data set 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, terrain elevation, and surface cover information; The interpolation optimization module is used to optimize data using a spatial interpolation algorithm based on a multidimensional geographic feature model to generate a continuous geographic surface model; The topology modeling module is used to construct a triangulated network topology structure based on a continuous geographic surface model, and to establish a topological network containing spatial entity geometric data and geological-elevation attribute mapping relationships; The intersection detection module is used to convert the section parameters input by the user in real time into a spatial plane equation, and generate a discrete section line segment set with geological attribute annotations by performing intersection detection between the plane equation and the triangular facets; The dynamic rendering module is used to perform parallel contour tracing and topological sorting on a discrete cross-section line segment set, and to generate a custom cross-section diagram supporting interactive parameter adjustment in real time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the GIS-based cross-sectional view real-time synchronous display method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the GIS-based cross-sectional view real-time synchronous display method according to any one of claims 1 to 7 are implemented.

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