Three-dimensional modeling method and system for cliff based on structural plane constraint

Abstract

The application discloses a cliff three-dimensional modeling method and system based on structure surface constraint, relates to the technical field of three-dimensional geographic information systems, collects cliff regional geological data including cliff multi-view images and cliff surface dense point cloud data, and constructs a geological prior knowledge base; image recognition technology is used to process the cliff multi-view images, combined with point cloud geometry analysis, to identify the spatial orientation of structure surfaces including rock layer interfaces and joint surfaces. The application effectively solves the geometric distortion problem of traditional digital elevation models in cliff regions by fusing geological prior knowledge, uses image recognition technology to extract the spatial orientation of rock layer interfaces and joint surfaces, and uses the spatial orientation as a hard constraint condition to construct a control triangular net framework, thereby avoiding the forced smoothing error of a regular grid DEM and the overhanging surface problem of an irregular triangular net, and through an adaptive triangular division algorithm, the rock layer structure, crack distribution and dangerous rock body shape of the cliff can be accurately restored.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional geographic information system technology, specifically to a method and system for three-dimensional modeling of steep cliffs based on structural surface constraints. Background Technology

[0002] Steep cliffs are high-risk areas for geological disasters such as landslides, collapses, and rockfalls. 3D modeling can accurately restore the geometry, rock structure, and crack distribution of steep cliffs. Combined with geomechanical analysis, it not only helps in the planning of complex mountain engineering projects, but also in the prevention and control of geological disasters. By simulating the disaster occurrence process, it can provide data support for the early warning system. Furthermore, after a disaster occurs, the 3D model can quickly assess the scope of damage, assist in the formulation of repair plans, and reduce the risk of secondary disasters.

[0003] Existing technologies often employ Digital Elevation Models (DEMs) to process the 3D modeling process of steep cliffs. However, DEMs suffer from severe geometric distortion in areas with steep cliffs >60°. Specifically, forced smoothing of regular grid DEMs leads to errors in the calculation of the volume of unstable rock masses, while traditional irregular triangular mesh models generate a large number of suspended triangular faces, disrupting the continuity of the terrain. Furthermore, commercial software (such as ContextCapture) does not incorporate geological constraints and cannot represent the spatial distribution of rock strata joint surfaces. Therefore, how to solve the problem of geometric distortion of cliffs based on 3D modeling technology that integrates prior geological knowledge, and how to combine image recognition technology to ensure the accuracy of 3D modeling of steep cliffs, is the problem that this invention aims to solve. To this end, a 3D modeling method and system for steep cliffs based on structural surface constraints is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for three-dimensional modeling of steep cliffs based on structural surface constraints, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] The first aspect is a 3D modeling method for steep cliffs based on structural surface constraints, which includes the following steps:

[0007] Step 1: Collect geological data of the steep cliff area, including multi-view images of the steep cliff and dense point cloud data of the steep cliff surface, and construct a geological prior knowledge base;

[0008] Step 2: Use image recognition technology to process multi-view images of steep cliffs, and combine point cloud geometric analysis to identify the spatial orientation of structural surfaces, including rock layer interfaces and joint surfaces.

[0009] Step 3: Using the identified structural surfaces as hard constraints, construct a control triangular network framework to ensure that the rock strata attitude and the spatial distribution of joint surfaces conform to geological laws;

[0010] Step 4: Combine point cloud density and structural surface constraints, use an adaptive triangulation algorithm to generate terrain surfaces, eliminate suspended triangular surfaces and forced smoothing distortion, and construct a digital elevation model.

[0011] Step 5: Check the consistency of the triangulation network based on the topological relationship of the structural surface, remove abnormal patches in the digital elevation model, repair discontinuous areas of terrain, and ensure geometric integrity.

[0012] Step 6: By comparing the model error with the measured profile lines and point cloud data, the structural surface constraint parameters are iteratively optimized.

[0013] Step 7: Integrate rock strata texture and joint surface properties to output a 3D model of the steep cliff that meets accuracy requirements, accurately represents the cliff features, and incorporates geological constraints.

[0014] A further improvement to the technical solution of this invention lies in the following: In step 1, the process of constructing the geological prior knowledge base is as follows:

[0015] Based on the area of ​​the steep cliff and the required modeling accuracy, a data acquisition plan was formulated. At the same time, a UAV for acquiring multi-view images of the steep cliff and a ground laser scanner for acquiring point cloud data were selected and debugged and calibrated to ensure that the equipment was operating normally, so as to acquire geological data of the steep cliff area, including multi-view images of the steep cliff and dense point cloud data of the steep cliff surface.

[0016] According to the predetermined route and stations, drones were used to take oblique photos of the steep cliffs from different perspectives to ensure comprehensive coverage and no serious obstruction, and multi-view image data of the steep cliffs were obtained. Ground laser scanners were used to obtain dense point cloud data of the steep cliff surface and record accurate spatial coordinate information.

[0017] The collected multi-view images of steep cliffs and dense point cloud data on the cliff surface were preprocessed to remove noise and outliers. The preprocessed data were then classified and stored. Combined with geological literature, a geological prior knowledge base was constructed through integrated analysis.

[0018] A further improvement to the technical solution of this invention lies in the following: In step 2, the process of identifying the spatial orientation of structural surfaces, including rock strata interfaces and joint surfaces, is as follows:

[0019] Semantic segmentation was performed on the multi-view image data of the steep cliff to identify the rock layer interface and joint linear features. An image recognition algorithm with edge detection was used to enhance the geometric contour of the structural surface. The fracture information was enhanced by combining high-contrast images. The SfM sparse point cloud of the image was calculated simultaneously to obtain the three-dimensional spatial distribution of feature points.

[0020] Based on dense point cloud data of steep cliff surface, rock layer interfaces and joint surfaces are divided by normal vector estimation and clustering algorithm. Principal component analysis is used to calculate the rock layer attitude, i.e. dip or dip angle. Combined with two-dimensional features extracted from images, the linear joint features are mapped to three-dimensional point cloud through spatial registration to verify the geometric consistency of structural surfaces and remove abnormal interpretation results.

[0021] By integrating the structural surface parameters of geological data in steep cliff areas, the dominant orientation and spacing distribution of joint surfaces are analyzed using extreme density maps. The spatial topological relationship of structural surfaces is established, a controlling geometric network is generated, and a three-dimensional vector model of rock strata attitude and joint distribution is output.

[0022] A further improvement to the technical solution of this invention lies in the following: In step 3, the process of constructing the control triangular mesh framework is as follows:

[0023] The extracted rock strata interfaces and joint surfaces are converted into three-dimensional polygon boundaries as hard constraints for Delaunay triangulation, which forces the edges of the triangulation mesh to be consistent with the direction of the structural surfaces, preserves the sharp features of the geological structure, and inserts control points through the constraint boundaries to ensure that the triangulation mesh forms topologically correct nodes at the joint intersections, avoiding geometric conflicts.

[0024] Based on the dynamic adjustment of mesh size according to the density of structural surfaces and the distribution of point clouds, the triangular mesh is densified in the dense areas of joint surfaces and appropriately widened in the smooth areas. The Ruppert algorithm is used to optimize the quality of triangles and eliminate narrow triangles with a minimum angle ≥25°. At the same time, the Laplacian smoothing weight is constrained to prevent distortion of rock strata attitude. The dense areas of joint surfaces are areas with a spacing <0.5m, and the triangular mesh is densified to make the side length ≤0.1m.

[0025] Verify the continuity of structural surface intersection lines, eliminate self-intersecting or suspended triangular faces, identify holes and anomalous facets using the Alpha Shape algorithm, repair discontinuous areas using radial basis function interpolation, and output a control triangular mesh skeleton that satisfies geological laws and preserves structural details.

[0026] A further improvement to the technical solution of this invention lies in the following: In step 4, the process of constructing the digital elevation model is as follows:

[0027] Integrate dense point cloud data on steep cliff surfaces, analyze point cloud density distribution characteristics, clarify the density of data in different areas, and extract structural surface information and convert it into constraints.

[0028] Based on point cloud density and structural surface constraints, an adaptive triangulation algorithm is used to dynamically adjust the mesh size. The constraints are followed at the structural surfaces to generate triangular meshes that fit the terrain and initially eliminate overhanging surfaces, thus constructing the terrain surface.

[0029] The forced smoothing distortion problem in the terrain surface is checked and handled. Through local adjustment and optimization algorithms, the terrain features are accurately represented and a high-fidelity digital elevation model that meets the requirements is output.

[0030] A further improvement to the technical solution of this invention lies in the following: In step 5, the process of removing abnormal patches from the digital elevation model and repairing discontinuous terrain areas is as follows:

[0031] A spatial topology network is constructed based on the intersection lines of structural surfaces. The topological relationships of structural surfaces are extracted, and the intersecting, parallel, or adjacent relationships between structural surfaces are clarified. The triangular network is then mapped to the topological relationships of structural surfaces, and the structural surfaces associated with each triangular facet are marked.

[0032] Based on the topological relationship of the structural surfaces, check whether the facets in the triangulation conform to the consistency rules, mark and remove abnormal facets that do not conform to the rules, and ensure the local rationality of the triangulation.

[0033] After analyzing the location and characteristics of terrain discontinuities after removing abnormal patches, and combining the topological relationships of surrounding structural surfaces and triangulation information, interpolation and smoothing methods are used to repair terrain discontinuities, ensuring the geometric integrity of the digital elevation model.

[0034] A further improvement to the technical solution of this invention is that the process of marking and removing abnormal patches that do not conform to the rules is as follows:

[0035] Load the structural surface topology network data, including structural surface nodes, intersection nodes, and the topology graph structure. Also load the triangulation data, including vertex coordinates, edge connectivity, and facet indices. Traverse all faces in the triangulation, checking the matching of their geometric properties with geological constraints, and verifying the deviation between the facet normal vector and the attitude of the associated rock strata (dip / dip tolerance ≤ 5). o ), the degree of fit between the boundary edge and the intersection line of the structural surface (distance threshold ≤ 0.02m), and whether there are self-intersecting or suspended surfaces;

[0036] By comparing spatial location with normal vectors, it is determined whether the requirements are met. Then, attribute flags are set to mark abnormal facets that deviate from the constraints, ensuring that the triangular mesh strictly follows the spatial distribution rules of the structural surfaces. The location of the abnormal facets, the associated structural surface information, and the specific non-compliance items are recorded in detail.

[0037] Using the adjacency matrix data structure, all faces marked as abnormal are found based on attribute identifiers and removed from the triangulation. While removing abnormal faces, the topological connectivity between the remaining faces is maintained. After removing abnormal faces, the connectivity of the triangulation is analyzed to find isolated faces or missing regions, identify the resulting local incomplete regions, and perform geometric adjustments and optimizations to ensure that the triangulation has a reasonable structure and smooth connections within a local range, meeting the needs of subsequent analysis and applications.

[0038] A further improvement to the technical solution of the present invention is that step 6 specifically includes:

[0039] The target steep cliff area was measured in the field to obtain profile data, and point cloud data was collected simultaneously. The two types of data were preprocessed by denoising and filtering, and the coordinate system was unified to ensure data format compatibility.

[0040] The measured profile line is compared point by point with the corresponding elevation value in the digital elevation model. The root mean square error and maximum deviation are calculated. The error distribution pattern is identified through spatial statistical analysis. Areas with insufficient or excessive structural surface constraints are located. An error heat map is generated to guide parameter adjustment. Based on the error analysis results, the structural surface constraint parameters are iteratively optimized in a targeted manner, the model shape is adjusted, and the deviation between the model and the measured data is gradually reduced.

[0041] The optimized model was compared with the measured data again to verify whether the error was within the allowable range.

[0042] A further improvement to the technical solution of the present invention is that step 7 specifically includes:

[0043] Based on the rock layer interface and joint linear features obtained from the geological data of the steep cliff area, the rock layer texture and joint surface properties are analyzed. The rock layer texture is mapped to the digital elevation model that iteratively optimizes the structural surface constraint parameters to ensure color and geometry alignment and construct an initial geological constraint framework.

[0044] Based on the structural surface identification results, joint surface attributes, including spacing, continuity and roughness, are assigned to the linear features of joints. The joint surface attributes are then transformed into geometric constraints. The mesh topology is adjusted through parametric modeling to make the structural surfaces continuous in the model and conform to geological laws, thus forming a 3D model of a steep cliff with semantic labels.

[0045] Export a 3D model of a steep cliff, compatible with the model format of geological analysis software, including rock texture maps, joint attribute tables, and structural surface topological relationships, ensuring that the 3D model of the steep cliff has high-fidelity terrain representation and geological semantic information, and can be directly used for stability analysis or visualization applications.

[0046] Secondly, a 3D modeling system for steep cliffs based on structural surface constraints is provided to implement the aforementioned 3D modeling method for steep cliffs based on structural surface constraints, including:

[0047] The geological data acquisition and preprocessing module is used to acquire multi-view images and ground laser scanning point cloud data of steep cliff areas, and to build a geological prior knowledge base in combination with geological literature to provide high-precision, unobstructed geological data of steep cliff areas.

[0048] The structural surface recognition and analysis module is used to identify the spatial orientation of structural surfaces of rock layer interfaces and joint surfaces by using image recognition technology and point cloud geometric analysis, accurately identify the spatial distribution of structural surfaces, provide hard constraints for modeling, and avoid geometric distortion.

[0049] The constrained Delaunay triangulation module is used to construct a control triangulation framework by taking the identified structural planes as hard constraints, ensuring that the rock strata attitude and the spatial distribution of joint surfaces conform to geological laws, and generating a control triangulation framework that conforms to geological structural constraints.

[0050] The digital elevation model generation and optimization module is used to combine point cloud density and structural surface constraints, and adopts an adaptive triangulation algorithm to generate terrain surfaces, construct digital elevation models, eliminate the geometric distortion problem of traditional DEM in steep cliff areas, and improve the elevation accuracy of the model.

[0051] The model iteration and optimization module is used to compare the model error with the measured profile lines and point cloud data, iteratively optimize the structural surface constraint parameters, and generate a steep cliff 3D model with both geometric accuracy and semantic labels. The iterative optimization ensures the model accuracy and reduces the deviation from the measured data.

[0052] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0053] 1. This invention provides a method and system for three-dimensional modeling of steep cliffs based on structural surface constraints. By integrating prior geological knowledge, it effectively solves the geometric distortion problem of traditional digital elevation models in steep cliff areas. It uses image recognition technology to extract the spatial orientation of rock layer interfaces and joint surfaces, and uses them as hard constraints to construct a control triangular mesh skeleton. This avoids the forced smoothing error of regular grid DEMs and the problem of suspended surfaces in irregular triangular meshes. Through an adaptive triangulation algorithm, it can accurately restore the rock layer structure, crack distribution, and unstable rock mass morphology of steep cliffs, providing reliable three-dimensional terrain data support for complex mountain engineering planning.

[0054] 2. This invention provides a method and system for 3D modeling of steep cliffs based on structural surface constraints. The modeling technology based on structural surface constraints eliminates suspended triangular faces and forces smooth distortion to construct a high-fidelity digital elevation model that meets the accuracy requirements of engineering design. It can dynamically adjust the mesh size, densify the triangular mesh in densely jointed areas, and widen the mesh in gentle areas, balancing computational efficiency and modeling accuracy. This allows the model to be directly used for site selection and stability analysis in engineering projects, reducing engineering rework caused by terrain errors and optimizing resource utilization efficiency. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0056] Figure 1 This is a schematic diagram of the workflow of the steep cliff 3D modeling method and system based on structural surface constraints of the present invention.

[0057] Figure 2 This is a schematic diagram of the method flow for the 3D modeling method of steep cliffs based on structural surface constraints according to the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1, as Figure 1 , Figure 2 As shown, this invention provides a 3D modeling method for steep cliffs based on structural surface constraints, comprising the following steps:

[0060] Step 1: Collect geological data of the steep cliff area, including multi-view images and dense point cloud data of the steep cliff surface, and construct a geological prior knowledge base. Based on the scope of the steep cliff area and the modeling accuracy requirements, formulate a data collection plan. At the same time, select a UAV for collecting multi-view images of the steep cliff and a ground laser scanner for collecting point cloud data, and complete debugging and calibration to ensure that the equipment is operating normally. Collect geological data of the steep cliff area, including multi-view images and dense point cloud data of the steep cliff surface. According to the predetermined route and station, use UAV oblique photography to take steep cliff images from different perspectives to ensure comprehensive coverage and no serious obstruction, and obtain multi-view image data of the steep cliff. Use a ground laser scanner to obtain dense point cloud data of the steep cliff surface, record accurate spatial coordinate information, preprocess the collected multi-view images and dense point cloud data of the steep cliff surface to remove noise and outliers, and classify and store the preprocessed data. Combine with geological literature, integrate and analyze to construct a geological prior knowledge base.

[0061] The specific work content includes: Based on the specific scope of the steep cliff area and the required accuracy standards for modeling, developing a data acquisition plan, clarifying the data acquisition tasks, time nodes, and personnel division of labor at different stages to ensure the orderly progress of the acquisition work; simultaneously, conducting equipment selection work, choosing suitable UAVs for the multi-view image acquisition needs of the steep cliff, considering factors such as flight stability and image capture quality; for ground laser scanning point cloud data acquisition, selecting a high-precision ground laser scanner, focusing on its scanning range, accuracy, and speed; after completing the equipment selection, conducting comprehensive debugging and calibration, precisely setting and calibrating the UAV's flight parameters, image capture parameters, and the ground laser scanner's scanning mode and coordinate system to ensure the equipment can operate normally and acquire accurate data in subsequent acquisition work; and planning the UAV flight route and ground laser scanning stations according to the pre-established data acquisition plan, with operators... Unmanned aerial vehicles (UAVs) were used to conduct oblique photography along a predetermined route, capturing images of the steep cliff from multiple different perspectives to ensure comprehensive coverage of the cliff area without severe obstruction. This yielded multi-view image data of the cliff. Simultaneously, ground-based scanning stations were deployed, and ground-based laser scanners were used to scan the cliff surface, acquiring dense point cloud data containing accurate spatial coordinates of each point on the cliff surface. Preprocessing was performed on the acquired multi-view images and dense point cloud data. For the multi-view images, image processing algorithms were used to remove noise and outliers, improving image clarity and quality. For the dense point cloud data, point cloud filtering methods were used to remove noise and outliers, ensuring the accuracy of the point cloud data. The preprocessed image and point cloud data were categorized and stored, and combined with geological literature, the stored data was integrated and analyzed to construct a geological prior knowledge base.

[0062] Step 2: Process multi-view images of the steep cliff using image recognition technology. Combine point cloud geometric analysis to identify the spatial orientation of structural surfaces, including rock strata interfaces and joint surfaces. Perform semantic segmentation on the multi-view image data of the steep cliff to identify the linear features of rock strata interfaces and joints. Use image recognition algorithms with edge detection to enhance the geometric contours of the structural surfaces. Combine high-contrast images to enhance fracture information. Simultaneously calculate the SfM sparse point cloud of the images to obtain the three-dimensional spatial distribution of feature points. Based on the dense point cloud data of the steep cliff surface, divide the rock strata interfaces and joint surfaces through normal vector estimation and clustering algorithms. Use principal component analysis to calculate the rock strata attitude, i.e., dip or dip angle. Combine the two-dimensional features extracted from the images and map the linear features of joints to the three-dimensional point cloud through spatial registration. Verify the geometric consistency of the structural surfaces and remove anomalies in the interpretation results. Integrate the structural surface parameters of the geological data of the steep cliff area. Use pole density maps to analyze the dominant orientation and spacing distribution of joint surfaces. Establish the spatial topological relationship of the structural surfaces, generate a control geometric network, and output a three-dimensional vector model of rock strata attitude and joint distribution.

[0063] The specific work involves: semantic segmentation of multi-view image data of steep cliffs to identify rock strata interfaces and joint lines; edge detection image recognition algorithms to enhance the geometric contours of structural surfaces, making the features clearer; combining high-contrast images to enhance fracture information and improve the ability to identify fine fractures; calculating sparse point clouds of the images based on Structure of Motion (SfM) technology to preliminarily obtain the three-dimensional spatial distribution of feature points; and calculating local geometric features based on the acquired dense point cloud data of the steep cliff surface using Random Normal Vector Estimation (RANSAC) and using a K-means clustering algorithm to divide rock strata interfaces and joint surfaces, clarifying the distribution range of different structural surfaces, and utilizing Principal Component Analysis (PCA) to further refine the analysis. The process involves calculating the attitude (dip and dip angle) of rock strata to determine their spatial orientation. Combined with two-dimensional joint line features extracted from images, spatial registration (ICP algorithm) maps these features to a three-dimensional point cloud. This verifies the geometric consistency of the structural surfaces, eliminates anomalous interpretation results (false joints caused by noise or vegetation interference), and ensures the accuracy of structural surface identification. Furthermore, it integrates the structural surface parameters from geological data of steep cliff areas, analyzes the dominant orientation and spacing distribution of joint surfaces using extreme density maps, understands the spatial distribution patterns of joint surfaces, establishes spatial topological relationships between structural surfaces, constructs a controlling geometric network, presents the interrelationships between structural surfaces, and finally outputs a three-dimensional vector model of rock strata attitude and joint distribution, intuitively and accurately displaying the geological structural characteristics of the steep cliff area.

[0064] Step 3: Using the identified structural surfaces as hard constraints, a control triangular mesh framework is constructed to ensure that the rock strata attitude and joint surface spatial distribution conform to geological laws. The extracted rock strata interfaces and joint surfaces are converted into 3D polygon boundaries as hard constraints for Delaunay triangulation, forcing the triangular mesh edges to align with the structural surfaces, preserving the sharp features of the geological structure. Control points are inserted through the constraint boundaries to ensure that the triangular mesh forms topologically correct nodes at joint intersections, avoiding geometric conflicts. The mesh size is dynamically adjusted based on the structural surface density and point cloud distribution. The triangular mesh is densified in dense joint surface areas and appropriately widened in smooth areas. The Ruppert algorithm is used to optimize the triangle quality, eliminating narrow triangles with a minimum angle ≥25°. At the same time, Laplacian smoothing weights are constrained to prevent distortion of the rock strata attitude. The dense joint surface areas are those with a spacing <0.5m. The triangular mesh is densified to make the side length ≤0.1m. The continuity of the structural surface intersection lines is verified, and self-intersecting or overhanging triangles are eliminated. Alpha is used to further refine the mesh. The Shape algorithm identifies holes and anomalous patches, and combines radial basis function interpolation to repair discontinuous areas, outputting a control triangular mesh skeleton that satisfies geological laws and preserves structural details;

[0065] The specific work involves: converting the extracted rock strata interfaces and joint surfaces into 3D closed polygon boundaries as hard constraints for Delaunay triangulation, ensuring that the triangulation strictly conforms to the spatial distribution of geological structural surfaces. Through the insertion of these forced constraint boundaries, the triangulation generates nodes conforming to topological rules at joint intersections, avoiding geometric conflicts or overlapping surfaces. Furthermore, to preserve the sharp features of steep cliff areas (rock strata faulting, joint cutting, etc.), the constraint boundaries do not participate in smoothing optimization, maintaining the original geometric shape. Simultaneously, control points are inserted in areas of varying structural surface density to dynamically adjust the local mesh resolution, giving the triangulation higher geometric representation capabilities in complex tectonic zones. Based on the spatial distribution density of structural surfaces and point cloud data... To assess the structural features, an adaptive size field-controlled triangulation mesh generation method was employed. In densely populated joint areas (spacing < 0.5m), the mesh was densified (side length ≤ 0.1m), while in smoother areas, the mesh width was increased to 0.5m, balancing computational efficiency and modeling accuracy. The Ruppert algorithm was used to optimize the quality of triangular elements, eliminating elongated triangles (minimum angle ≥ 25°) to ensure numerical stability. During the smoothing stage, the weight coefficients of the Laplacian operator were constrained, and only non-structural areas were moderately smoothed to prevent distortion of the rock strata's attitude (dip / dip angle) due to excessive smoothing, thus maintaining the original geometric features of the geological structure. The spatial continuity of structural surface intersections was verified by eliminating self-intersecting or overhanging triangular patches through geometric collision detection, and Alpha was used to further refine the mesh. The Shape algorithm identifies holes and anomalous patches (isolated triangular faces with abrupt changes in area), and combines geological patterns (the extensibility of joint sets) to determine whether they are real features or noise. For discontinuous areas, local interpolation reconstruction is performed based on radial basis functions (RBF). While maintaining the sharpness of structural surfaces, topographic fractures are repaired. Finally, a control triangular mesh skeleton that follows geological structural constraints and ensures the geometric and topological correctness of rock layer interfaces, joint surfaces and their intersection areas is output.

[0066] Step 4: Combining point cloud density and structural surface constraints, an adaptive triangulation algorithm is used to generate a terrain surface, eliminating overhanging triangular faces and forced smoothing distortion, constructing a digital elevation model, integrating dense point cloud data on steep cliff surfaces, analyzing point cloud density distribution characteristics, clarifying the data density in different areas, extracting structural surface information and converting it into constraint conditions, dynamically adjusting the mesh size based on point cloud density and structural surface constraints, following constraints at structural surfaces, generating triangular meshes that fit the terrain and initially eliminate overhanging faces, constructing a terrain surface, checking and handling forced smoothing distortion problems in the terrain surface, and accurately representing terrain features through local adjustment and optimization algorithms, outputting a high-fidelity digital elevation model that meets the requirements;

[0067] The specific work involves: analyzing the spatial distribution characteristics of point cloud density based on dense point cloud data from steep cliff surfaces, quantifying the density of different regions, and identifying sparse and dense point cloud regions by calculating the number of points per unit area. For sparse point cloud regions, issues such as insufficient data collection or occlusion exist; for dense point cloud regions, issues may arise due to repeated data collection or complex terrain. After completing the point cloud density analysis, structural surface information is extracted from the dense point cloud regions. Cluster analysis and normal vector estimation methods are used to identify sets of structural surface points with similar characteristics, which are then transformed into constraints to guide terrain surface construction. Based on the point cloud density and structural surface constraints, an adaptive triangulation algorithm is used to dynamically adjust the mesh size. In areas with dense point clouds and complex structural surfaces, a smaller mesh size is used. To accurately represent terrain details, in areas with sparse point clouds and gentle terrain, the mesh size is appropriately increased to improve computational efficiency. Constraints are strictly followed at structural surfaces to ensure that the generated triangular mesh fits the terrain, initially eliminating overhanging surfaces and constructing a preliminary terrain surface. The geometric quality of the initially generated terrain surface is checked to identify and handle local distortions caused by data sparsity or constraint conflicts. Local adjustment and optimization algorithms are used to refine the distorted areas. By adjusting node positions and optimizing triangle shapes, the terrain features are accurately represented. After repeated checks and optimizations, the terrain surface is ensured to meet the actual terrain features and accuracy requirements. Finally, a high-fidelity digital elevation model that meets the requirements is output, ensuring that it not only meets the elevation accuracy of the point cloud data but also follows the spatial distribution rules of the structural surfaces.

[0068] Step 5: Check the consistency of the triangulation network based on the topological relationship of the structural surface, remove abnormal patches in the digital elevation model, repair discontinuous areas of terrain, and ensure geometric integrity.

[0069] Step 6: By comparing the model error with the measured profile lines and point cloud data, the structural surface constraint parameters are iteratively optimized.

[0070] Step 7: Integrate rock strata texture and joint surface properties to output a 3D model of the steep cliff that meets accuracy requirements, accurately represents the cliff features, and incorporates geological constraints.

[0071] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, in step 5, the process of removing abnormal patches from the digital elevation model and repairing discontinuous terrain areas is as follows:

[0072] A spatial topological network is constructed based on the intersection lines of structural surfaces. The topological relationships of structural surfaces are extracted, and the intersecting, parallel, or adjacent relationships between structural surfaces are clarified. The triangular network is mapped to the topological relationships of structural surfaces, and the structural surfaces associated with each triangular facet are marked. Based on the topological relationships of structural surfaces, it is checked whether the faces in the triangular network conform to the consistency rules. Abnormal faces that do not conform to the rules are marked and removed to ensure the local rationality of the triangular network. The location and characteristics of the terrain discontinuity areas after removing abnormal faces are analyzed. Combining the topological relationships of surrounding structural surfaces and triangular network information, interpolation and smoothing methods are used to repair the terrain discontinuity areas and ensure the geometric integrity of the digital elevation model.

[0073] Furthermore, the process of marking and removing abnormal patches that do not conform to the rules is as follows:

[0074] Load the structural surface topology network data, including structural surface nodes, intersection nodes, and the topology graph structure. Also load the triangulation data, including vertex coordinates, edge connectivity, and facet indices. Traverse all faces in the triangulation, checking the matching of their geometric properties with geological constraints, and verifying the deviation between the facet normal vector and the attitude of the associated rock strata (dip / dip tolerance ≤ 5). oThe checks include the degree of fit between the boundary edge and the intersection line of the structural surface (distance threshold ≤ 0.02m) and the presence of self-intersecting or suspended facets. Specifically, for the deviation check of the facet normal vector from the attitude of the associated rock strata, the normal vector is obtained by calculating the cross product of the vectors formed by the three vertices of each facet in the triangular network, and then normalized. Based on the coordinates of the facet center point, the nearest structural surface node is searched in the structural surface topology network to determine the associated structural surface. The dip and dip angle deviations between the facet normal vector and the attitude of the associated rock strata are calculated, and the facet normal vector is converted into dip and dip angle representations. This is compared with the attitude of the associated rock strata, and the deviation value is calculated. If the deviation exceeds the tolerance range, the facet is marked as having an abnormal normal vector deviation. For the degree of fit between the boundary edge and the intersection line of the structural surface, the distance to the intersection line of the structural surface is obtained by calculating the shortest distance from a point on the boundary edge to the intersection line of the structural surface. If the distance exceeds the set threshold (0.02m), the deviation is not considered.If the boundary alignment is 0.2m, the facet is marked as having an abnormal boundary fit. For the inspection of self-intersecting or suspended facets, in the self-intersecting inspection, spatial indexing is used to accelerate the search process, traversing the edges and facets of the triangulation network to check for intersections between edges and facets. If a self-intersecting situation is found, the relevant facet is marked as a self-intersecting anomaly. In the suspended facet inspection, it is checked whether the facet has a connection relationship with structural surfaces or other facets. If a facet is not connected to any structural surface or adjacent facets, or the connection relationship is unreasonable, the facet is marked as a suspended anomaly. By comparing the spatial position association with the normal vector, it is determined whether the requirements are met, and then an attribute flag is set to mark the abnormal facets that deviate from the constraint conditions. This ensures that the triangulation network strictly follows the spatial distribution law of the structural surface, and records the position of the abnormal facets, the associated structural surface information, and the specific non-compliance items in detail. Among them, an attribute flag is set for each triangular facet to mark whether the facet meets the requirements of geometric properties and geological constraints. For facets that do not meet the requirements, the corresponding attribute flag is set, and the position information of the abnormal facets is recorded in detail, including the coordinates of the facet center point and the abnormal facets. The system records structural information associated with each facet, including its number, plane equation, and stratum attitude. It also records specific non-conformities, such as normal vector deviation, distance between boundary edges and structural surface intersections, and details of self-intersections or overhangs. Using an adjacency matrix data structure, it identifies all faces marked as anomalous based on attribute identifiers and removes them from the triangulation. While removing anomalous faces, it maintains the topological connections between the remaining faces. After removing anomalous faces, it analyzes the connectivity of the triangulation to find isolated faces or missing regions, identifying locally incomplete areas. These areas are then geometrically adjusted and optimized to ensure the triangulation is structurally sound and smoothly connected within its local area, meeting the needs of subsequent analysis and applications. Locally incomplete areas include regions with incomplete boundaries and missing facet regions. For regions with incomplete boundaries, the position and direction of the boundary edges are adjusted based on the extension trend of surrounding structural surfaces and the geometric characteristics of adjacent faces to ensure a natural transition with the structural surfaces and adjacent faces. For missing facet regions, interpolation methods are used to generate new vertices, reconstruct the facet, and fill in the missing areas.

[0075] The specific work involves: constructing a spatial topology network based on the intersection lines of structural surfaces; extracting the spatial connection relationships between structural surfaces, including intersecting, parallel, or adjacent geometric relationships; establishing a topology graph structure by calculating the continuity of the intersection lines and the endpoint connection status, where nodes represent structural surfaces or intersection lines, and edges represent their connection relationships; mapping the triangular mesh to the topology network; and labeling the structural surface type of each triangular facet by spatial location association, ensuring that the vertices and edges of the triangular mesh strictly match the boundaries of the structural surfaces to avoid geometric misalignment. The resulting data structure should simultaneously contain the geometric information of the triangular mesh and the attribute labels of the structural surfaces. After the topology mapping is completed, checking whether the triangular mesh faces conform to the geometric consistency rules of the structural surface constraints, specifically including: whether the facet normal vector is consistent with the attitude of the associated rock strata (dip / dip tolerance ≤ 5°), whether the boundary strictly fits the intersection lines of the structural surfaces, and whether there are any invalid geometric shapes such as overhangs or self-intersections. An automated algorithm is used to traverse all faces and label them. Record non-compliant anomalous areas, such as isolated patches, sharp and distorted triangles, or units that deviate excessively from structural surfaces. Remove the marked anomalous patches and create data holes in the corresponding areas to ensure that the triangulation network conforms to the geological structure within a local range and avoids geometric distortion caused by data noise or constraint conflicts. After removing anomalous patches, repair local discontinuities in the terrain surface by combining the topological relationship of the structural surfaces and the information of the surrounding triangulation network. Identify the boundary features of discontinuous areas, such as fracture lines, steep slopes, or holes, and infer reasonable terrain trends based on the extension trend of adjacent structural surfaces. Use interpolation methods (radial basis functions) to generate elevation points in the hole areas to ensure that the repaired surface transitions naturally with the surrounding structural surfaces. At the same time, perform restricted smoothing on the repaired area, allowing only moderate adjustment of the vertices of non-structural surface areas to maintain the sharp features of rock strata and joints. The output digital elevation model must pass geometric integrity verification to ensure that the terrain is continuous and strictly follows the spatial distribution law of structural surfaces.

[0076] Step 6 specifically includes:

[0077] In the target steep cliff area, profile data was obtained through field measurements, and point cloud data was collected simultaneously. Both types of data underwent denoising and filtering preprocessing. A unified coordinate system was implemented to ensure data format compatibility. The measured profile lines were compared point-by-point with the corresponding elevation values ​​in the digital elevation model. The root mean square error and maximum deviation were calculated. Spatial statistical analysis was used to identify error distribution patterns, pinpointing areas with insufficient or excessive structural surface constraints. An error heatmap was generated to guide parameter adjustments. Based on the error analysis results, the structural surface constraint parameters were iteratively optimized to adjust the model shape and gradually reduce the deviation between the model and the measured data. The optimized model was then compared with the measured data again to verify whether the error was within the allowable range.

[0078] The specific work involves: using a total station to conduct on-site measurements of the target steep cliff area to obtain high-precision profile data, while simultaneously collecting point cloud data of the area. Both types of data undergo denoising and filtering processes to remove outliers and smooth noise, ensuring data quality. A unified coordinate system is implemented, converting the profile data and point cloud data to the same spatial reference frame to ensure data format compatibility. The measured profile data is compared point-by-point with the corresponding elevation values ​​in the digital elevation model, calculating key indicators including root mean square error and maximum deviation to quantify the difference between the model and the measured data. Spatial statistical analysis is used to generate an error heatmap, identifying error distribution patterns and pinpointing areas with insufficient or excessive structural surface constraints. Based on the error analysis results, the structural surface constraint parameters are adjusted accordingly, iteratively optimizing the model morphology to gradually reduce the deviation between the model and the measured data until the error converges to within acceptable limits, ensuring the model accurately reflects the true geometric characteristics of the steep cliff. After parameter optimization, the optimized model is compared again with the measured data to verify whether the error meets accuracy requirements. The model must follow the spatial distribution patterns of the structural surfaces to ensure geometric consistency and geological rationality.

[0079] Step 7 specifically includes:

[0080] Based on the rock strata interface and joint linear features obtained from geological data of steep cliff areas, the rock strata texture and joint surface attributes are analyzed. The rock strata texture is mapped to a digital elevation model with iteratively optimized structural surface constraint parameters to ensure color and geometry alignment. An initial geological constraint framework is constructed. Based on the structural surface identification results, joint surface attributes, including spacing, continuity, and roughness, are assigned to the joint linear features. The joint surface attributes are then converted into geometric constraints. The mesh topology is adjusted through parametric modeling to ensure that the structural surfaces are continuous in the model and conform to geological laws, forming a steep cliff 3D model with semantic labels. The steep cliff 3D model is exported and is compatible with the model format of geological analysis software. It includes rock strata texture maps, joint attribute tables, and structural surface topological relationships, ensuring that the steep cliff 3D model has high-fidelity terrain representation and geological semantic information, and can be directly used for stability analysis or visualization applications.

[0081] The specific work involves: analyzing the rock strata texture based on the rock interface and joint linear features extracted from geological data of steep cliff areas, determining its color distribution, bedding direction, and thickness variation patterns; mapping the rock strata texture to a digital elevation model using image registration technology, ensuring that the texture coordinates are aligned with the geometric vertices to avoid stretching or misalignment; simultaneously, constructing an initial geological constraint framework by combining iteratively optimized structural surface constraint parameters, so that the model retains topographic details while the rock strata interface presents continuous distribution characteristics in three-dimensional space, ensuring that the texture-mapped model is both visually realistic and reflects the original geometric morphology of the rock strata; and based on the structural surface recognition results, providing joint... Linear features are assigned geological attributes, including parameters such as spacing, continuity, and roughness, which are then transformed into geometric constraints. A parametric modeling method is used to adjust the triangular mesh topology, densifying nodes at joint intersections to ensure spatial continuity of structural surfaces within the model while conforming to geological statistical laws. A constrained Delaunay triangulation algorithm is employed, forcing joint boundaries as hard constraint edges embedded in the mesh to avoid geometric distortion caused by smoothing processes. This generates a steep cliff 3D model containing high-precision terrain surfaces and semantic labels for joint surfaces. The optimized steep cliff 3D model is exported to a format compatible with geological analysis software (OBJ, LAS, or Gocad TSuf), including rock texture maps, joint attribute tables, and structural surface topological relationships. The model retains the integrity of geometric accuracy and semantic information, ensuring that the geometric representation of rock interfaces, joint surfaces, and their intersections conforms to geological laws. The attribute table records parameters such as the attitude and spacing of joint groups, while the topological relationships are stored in a graph structure, describing spatial associations such as intersection and parallelism between structural surfaces. The steep cliff 3D model can be directly used for stability calculations, fracture network analysis, or 3D visualization applications.

[0082] Example 3, as Figure 1 As shown, based on Examples 1-2, the present invention also provides a 3D modeling system for steep cliffs based on structural surface constraints, used to implement the above-mentioned 3D modeling method for steep cliffs based on structural surface constraints, including:

[0083] The geological data acquisition and preprocessing module is used to acquire multi-view images and ground laser scanning point cloud data of steep cliff areas, and to build a geological prior knowledge base in combination with geological literature to provide high-precision, unobstructed geological data of steep cliff areas.

[0084] The structural surface identification and analysis module is used to identify the spatial orientation of structural surfaces of rock strata interfaces and joint surfaces using image recognition technology and point cloud geometric analysis, accurately identify the spatial distribution of structural surfaces, provide hard constraints for modeling, avoid geometric distortion, and ensure that the rock strata attitude and joint surface distribution conform to geological laws through spatial topological relationship analysis.

[0085] The constrained Delaunay triangulation module is used to construct a control triangular mesh skeleton by taking the identified structural surfaces as hard constraints. This ensures that the rock strata attitude and the spatial distribution of joint surfaces conform to geological laws, generates a control triangular mesh skeleton that conforms to geological structural constraints, preserves the sharp features of rock strata and joints, avoids the suspended triangular surfaces and forced smoothing distortion in traditional methods, and improves the geometric accuracy of the model.

[0086] The digital elevation model generation and optimization module is used to combine point cloud density and structural surface constraints, and uses an adaptive triangulation algorithm to generate terrain surfaces and construct digital elevation models. This eliminates the geometric distortion problem of traditional DEMs in steep cliff areas, improves the elevation accuracy of the model, and ensures that the terrain surfaces conform to geological laws through structural surface constraints.

[0087] The model iteration and optimization module is used to compare the model error with the measured profile lines and point cloud data, iteratively optimize the structural surface constraint parameters, and generate a steep cliff 3D model with both geometric accuracy and semantic labels. The iterative optimization ensures the model accuracy and reduces the deviation from the measured data.

[0088] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A 3D modeling method for steep cliffs based on structural surface constraints, characterized in that, Includes the following steps: Step 1: Collect geological data of the steep cliff area, including multi-view images of the steep cliff and dense point cloud data of the steep cliff surface, and construct a geological prior knowledge base; Step 2: Process multi-view images of steep cliffs using image recognition technology, and combine them with point cloud geometric analysis to identify the spatial orientation of structural surfaces, including rock strata interfaces and joint surfaces. The specific process is as follows: Semantic segmentation was performed on the multi-view image data of the steep cliff to identify the rock layer interface and joint linear features. An image recognition algorithm with edge detection was used to enhance the geometric contour of the structural surface. The fracture information was enhanced by combining high-contrast images. The SfM sparse point cloud of the image was calculated simultaneously to obtain the three-dimensional spatial distribution of feature points. Based on dense point cloud data of steep cliff surface, rock layer interfaces and joint surfaces are divided by normal vector estimation and clustering algorithm. Principal component analysis is used to calculate the rock layer attitude, i.e. dip or dip angle. Combined with two-dimensional features extracted from images, the linear joint features are mapped to three-dimensional point cloud through spatial registration to verify the geometric consistency of structural surfaces and remove abnormal interpretation results. By integrating the structural surface parameters of geological data in steep cliff areas, using extreme density maps to analyze the dominant orientation and spacing distribution of joint surfaces, establishing spatial topological relationships of structural surfaces, generating a controlling geometric network, and outputting a three-dimensional vector model of rock strata attitude and joint distribution; Step 3: Using the identified structural surfaces as hard constraints, construct the controlling triangular mesh framework. The specific process is as follows: The extracted rock strata interfaces and joint surfaces are converted into three-dimensional polygon boundaries, which serve as hard constraints for Delaunay triangulation, forcing the edges of the triangular mesh to align with the structural surfaces, and control points are inserted through the constraint boundaries. The mesh size is dynamically adjusted based on the density of the structural surface and the distribution of the point cloud. The triangular mesh is densified in the dense area of ​​the joint surface. The Ruppert algorithm is used to optimize the quality of the triangles and eliminate narrow triangles with a minimum angle ≥ 25°. The dense area of ​​the joint surface is the region with a spacing < 0.5m. The triangular mesh is densified to make the side length ≤ 0.1m. Verify the continuity of the intersection lines of the structural surfaces, eliminate self-intersecting or overhanging triangular surfaces, identify holes and abnormal patches using the Alpha Shape algorithm, repair discontinuous regions by combining radial basis function interpolation, and output the control triangular mesh skeleton. Step 4: Combining point cloud density and structural surface constraints, an adaptive triangulation algorithm is used to generate terrain surfaces and construct a digital elevation model. The specific process is as follows: Integrate dense point cloud data on steep cliff surfaces, analyze point cloud density distribution characteristics, clarify the density of data in different areas, and extract structural surface information and convert it into constraints. Based on point cloud density and structural surface constraints, an adaptive triangulation algorithm is used to dynamically adjust the mesh size, generate a triangular mesh that fits the terrain and initially eliminates overhanging surfaces, and construct the terrain surface. The forced smoothing distortion problem in the terrain surface is checked and handled. Through local adjustment and optimization algorithms, a high-fidelity digital elevation model that meets the requirements is output. Step 5: Check the consistency of the triangulation network based on the topological relationship of the structural surface, remove abnormal patches in the digital elevation model, and repair discontinuous areas of the terrain. Step 6: By comparing the model error with the measured profile lines and point cloud data, the structural surface constraint parameters are iteratively optimized. Step 7: Integrate rock strata texture and joint surface properties to output a 3D model of the steep cliff that incorporates geological constraints.

2. The method for three-dimensional modeling of steep cliffs based on structural surface constraints according to claim 1, characterized in that: In step 1, the process of constructing the geological prior knowledge base is as follows: Based on the area of ​​the steep cliff and the required modeling accuracy, a data acquisition plan was formulated. At the same time, a UAV for acquiring multi-view images of the steep cliff and a ground laser scanner for acquiring point cloud data were selected and debugged and calibrated to acquire geological data of the steep cliff area, including multi-view images of the steep cliff and dense point cloud data of the steep cliff surface. According to the predetermined route and stations, drones were used to take oblique photos of the steep cliffs from different perspectives to obtain multi-view image data of the steep cliffs. Ground laser scanners were used to obtain dense point cloud data of the steep cliff surface and record accurate spatial coordinate information. The collected multi-view images of steep cliffs and dense point cloud data on the cliff surface were preprocessed to remove noise and outliers. The preprocessed data were then classified and stored. Combined with geological literature, a geological prior knowledge base was constructed through integrated analysis.

3. The method for three-dimensional modeling of steep cliffs based on structural surface constraints according to claim 1, characterized in that: In step 5, the process of removing abnormal patches from the digital elevation model and repairing discontinuous terrain areas is as follows: A spatial topology network is constructed based on the intersection lines of structural surfaces. The topological relationships of structural surfaces are extracted, and the intersecting, parallel, or adjacent relationships between structural surfaces are clarified. The triangular network is then mapped to the topological relationships of structural surfaces, and the structural surfaces associated with each triangular facet are marked. Based on the topological relationship of the structural surfaces, check whether the facets in the triangular mesh conform to the consistency rules, and mark and remove abnormal facets that do not conform to the rules. After removing abnormal patches, the location and characteristics of the terrain discontinuities are analyzed. Based on the topological relationships of the surrounding structural surfaces and triangular network information, interpolation and smoothing methods are used to repair the terrain discontinuities.

4. The method for three-dimensional modeling of steep cliffs based on structural surface constraints according to claim 3, characterized in that: The process of marking and removing abnormal patches that do not conform to the rules is as follows: Load the structural surface topology network data, including structural surface nodes, intersection nodes, and topology graph structure, and load the triangular mesh data. Traverse all the facets in the triangular mesh, check the matching of their geometric properties with geological constraints, verify the deviation of the facet normal vector from the attitude of the associated rock strata, the fit of the boundary edge with the intersection line of the structural surface, and whether there are self-intersecting or suspended facets. By comparing spatial location with normal vectors, it is determined whether the requirements are met. Then, attribute flags are set to mark abnormal surfaces that deviate from the constraints, and the location, associated structural surface information, and specific non-compliance items of the abnormal surfaces are recorded in detail. Using the adjacency matrix data structure, based on attribute identifier bits, all faces marked as abnormal are found and removed from the triangulation. While removing abnormal faces, the topological connectivity between the remaining faces is maintained. After removing abnormal faces, the connectivity of the triangulation is analyzed to find isolated faces or missing regions, identify the resulting local incomplete regions, and perform geometric adjustments and optimizations.

5. The method for three-dimensional modeling of steep cliffs based on structural surface constraints according to claim 4, characterized in that: Step 6 specifically includes: The target steep cliff area was measured in the field to obtain profile data, and point cloud data was collected simultaneously. The two types of data were preprocessed by denoising and filtering, and the coordinate system was unified. The measured profile line is compared point by point with the corresponding elevation value in the digital elevation model. The root mean square error and maximum deviation are calculated. The error distribution pattern is identified through spatial statistical analysis. Areas with insufficient or excessive structural surface constraints are located. An error heat map is generated to guide parameter adjustment. Based on the error analysis results, the structural surface constraint parameters are iteratively optimized in a targeted manner, the model shape is adjusted, and the deviation between the model and the measured data is gradually reduced. The optimized model was compared with the measured data again to verify whether the error was within the allowable range.

6. The method for three-dimensional modeling of steep cliffs based on structural surface constraints according to claim 1, characterized in that: Step 7 specifically includes: Based on the rock layer interface and joint linear features obtained from the geological data of the steep cliff area, the rock layer texture and joint surface properties are analyzed, and the rock layer texture is mapped to the digital elevation model that iteratively optimizes the structural surface constraint parameters to construct an initial geological constraint framework. Based on the structural surface identification results, joint surface attributes, including spacing, continuity and roughness, are assigned to the linear features of joints. The joint surface attributes are then transformed into geometric constraints. The mesh topology is adjusted through parametric modeling to make the structural surfaces continuous in the model and conform to geological laws, thus forming a 3D model of a steep cliff with semantic labels. Export a 3D model of a steep cliff, compatible with the model format of geological analysis software, including rock texture maps, joint attribute tables, and structural surface topological relationships.

7. A 3D modeling system for steep cliffs based on structural surface constraints, used to implement the 3D modeling method for steep cliffs based on structural surface constraints as described in any one of claims 1-6, characterized in that, include: The geological data acquisition and preprocessing module is used to acquire multi-view images and ground laser scanning point cloud data of steep cliff areas, and to build a geological prior knowledge base in combination with geological literature. The structural surface identification and analysis module is used to identify the spatial orientation of structural surfaces of rock strata interfaces and joint surfaces by using image recognition technology and point cloud geometric analysis. The constrained Delaunay triangulation module is used to construct a control triangulation framework by using the identified structural surfaces as hard constraints. The digital elevation model generation and optimization module is used to combine point cloud density and structural surface constraints, and to generate terrain surfaces using an adaptive triangulation algorithm to construct a digital elevation model. The model iteration and optimization module is used to compare the model error with the measured profile lines and point cloud data, iteratively optimize the structural surface constraint parameters, and generate a steep cliff 3D model with both geometric accuracy and semantic labels.

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