Three-dimensional positive cooperative design method and system for highway based on real scene ground model

Abstract

The application provides a highway three-dimensional positive collaborative design method and system based on a real scene ground model, first acquires a mountainous area road survey topographic map, unmanned aerial vehicle oblique photography data and special area information; then the information is time and space registered and fused to generate an enhanced training set; then an initial geographic model is established according to the mountainous area road survey topographic map; then a multi-class support vector machine is trained according to the enhanced training set to calibrate digital fences and grading information in the initial geographic model to obtain an oblique photography geographic model and further determine a highway three-dimensional positive collaborative design scheme. The application establishes an initial geographic model based on a topographic map, then trains a multi-class support vector machine with an enhanced training set, then calibrates digital fences and grading information in the initial model to generate an oblique photography geographic model, forms a closed loop of model building-intelligent optimization-precision improvement and finally outputs a precise design scheme.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional design technology, and in particular to a method and system for three-dimensional forward collaborative design of highways based on real-world terrain models. Background Technology

[0002] Mountainous highway surveying and design is a highly specialized and complex subfield within the field of highway transportation construction. Its core task is to develop highway construction plans that take into account safety, economy, ecology, and constructability in mountainous environments with complex topography and varied geological conditions through scientific surveying and systematic design.

[0003] In the field of mountain highway survey and design, traditional technical methods have long faced multi-dimensional bottlenecks: on the one hand, conventional remote sensing methods, which are relied upon for topographic surveys, are difficult to accurately capture the microscopic features and hidden geological conditions in complex mountain terrain, resulting in incomplete topographic information and an inability to provide sufficient detailed support for the design; on the other hand, manual on-site verification is limited by objective conditions such as rugged mountain terrain and variable climate, which not only makes it difficult to achieve comprehensive coverage, but also easily leads to information deviations due to differences in human judgment, further affecting the reliability of the design basis. Summary of the Invention

[0004] This invention provides a method and system for three-dimensional forward collaborative design of highways based on real-world terrain models, addressing the problem of achieving comprehensive coverage in the survey and design of mountainous highways.

[0005] A first aspect of this invention provides a method for three-dimensional forward collaborative design of highways based on real-world terrain models, comprising: Acquire topographic maps of mountain roads, oblique photography data from drones, and information on special areas; The mountain road survey topographic map, UAV oblique photography data, and special area information are spatiotemporally registered and fused to generate an enhanced training set; the enhanced training set includes a basic feature layer, an engineering feature layer, and a boundary calibration layer; An initial geographical model was established based on the topographic map of the mountain road survey. The multi-class support vector machine is trained based on the enhanced training set, and digital fences and hierarchical information are labeled in the initial geographic model based on the trained multi-class support vector machine to obtain the oblique photogrammetry geographic model. Based on the oblique photogrammetry geographic model, a three-dimensional forward collaborative design scheme for highways is determined.

[0006] In one possible implementation, topographic maps of mountain roads, oblique photogrammetric data from UAVs, and information on special areas are spatiotemporally registered and fused to generate an enhanced training set, including: Aerial triangulation and dense matching are performed on the oblique photography data of UAVs to generate 3D point clouds and digital orthophoto maps. Vectorize the topographic map of the mountain road survey to extract contour lines, elevation points and ground features; Using the coordinate system of the topographic map of the mountain road survey as the reference coordinate system, at least three ground control points are selected, and the three-dimensional point cloud and digital orthophoto map are geometrically corrected and coordinate transformed according to the least squares method to align with the spatial coordinate system of the reference coordinate system. The spatiotemporally registered 3D point cloud, digital orthophoto map, and vectorized topographic map data are overlaid to obtain a fused data volume. Based on the fused data volume, a basic feature layer, an engineering feature layer, and a boundary calibration layer are constructed. A unique identifier is established for each geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer, and the feature data of the three layers are spatially correlated to obtain an enhanced training set.

[0007] In one possible implementation, the spatiotemporally registered 3D point cloud, digital orthophoto map, and vectorized topographic map data are overlaid to obtain a fused data volume, including: Using vectorized topographic map data as the bottom layer, digital orthophoto overlay as the middle layer, and 3D point cloud overlay as the top layer, a three-dimensional overlay structure with topographic, texture, and geometric information is obtained. The system handles elevation conflicts, feature outline conflicts, and element attribute conflicts in the 3D overlay structure to obtain a fused data volume. The accuracy, completeness, and consistency of the generated fused data volume are verified.

[0008] In one possible implementation, based on the fused data volume, a basic feature layer, an engineering feature layer, and a boundary calibration layer are constructed, including: Extract multidimensional basic parameters related to terrain, texture, and geometry from the fused data volume; associate the multidimensional basic parameters according to geographic coordinates to form a basic feature layer containing at least terrain basic parameters, texture feature parameters, and geometric fine parameters; Based on the basic feature layer and the fused data volume, calculate or extract engineering parameters related to engineering design and construction; the engineering parameters include at least engineering topographic parameters, engineering constraint parameters and ecological engineering parameters; associate the engineering parameters with the corresponding geographic units to form the engineering feature layer; According to the preset boundary identification rules, at least the cut and fill boundary, risk control boundary, and engineering constraint boundary are identified and marked in the fused data volume and / or engineering feature layer; the identified boundaries are assigned type, level and associated attribute information to form a boundary marking layer.

[0009] In one possible implementation, a unique identifier is established for each geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer, and the feature data of the three layers are spatially correlated to obtain an enhanced training set, including: The survey area is divided into multiple spatially discrete geographic units; a unique identifier is generated for each geographic unit to uniquely identify its spatial location and the feature layer to which it belongs; Using unique identifiers and / or geographic coordinates as indexes, feature parameters corresponding to the same geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer are associated to establish a mapping relationship between cross-layer feature parameters; Verify the uniqueness of the unique identifier and the logical consistency of cross-layer feature parameters; integrate the verified related data into a structured dataset and form corresponding data description information to generate an enhanced training set.

[0010] In one possible implementation, based on the trained multi-class support vector machine, digital fences and hierarchical information are labeled in the initial geographic model to obtain an oblique photogrammetry geographic model, including: The trained multi-class support vector machine model is integrated with the initial geographic model, and the mapping relationship of the enhanced training set is imported as the classification benchmark. The initial geographic model is spatially discretized, the first feature parameter of each discrete unit is extracted, and the first feature parameter is input into a multi-class support vector machine model. The model outputs the classification results of the boundary type and risk level of each discrete unit. Based on the classification results, discrete units with the same or similar boundary types are spatially aggregated to generate closed digital fences, and each digital fence is assigned corresponding engineering attributes and risk level information. The digital fence and its associated engineering attributes and risk level information are fused with the initial geographic model and oblique photogrammetry data to generate an oblique photogrammetry geographic model.

[0011] In one possible implementation, the digital fence and its associated engineering attributes and risk level information are fused with the initial geographic model and oblique photogrammetry data to generate an oblique photogrammetry geographic model, including: The oblique photogrammetry data is geometrically optimized and visually consistent, and the engineering attributes and risk level information of the digital fence are structured and encoded to generate standardized fusion input data. Using the coordinate system of the initial geographic model as a reference, the preprocessed oblique photogrammetric data is spatially registered with high precision to ensure spatial alignment with the initial geographic model. The structured digital fence is used as a vector feature with attribute information and embedded into the initial geographic model according to its geographic coordinates. The attribute information of the digital fence is then associated with the corresponding terrain unit in the model. The texture and geometric information in the spatially registered oblique photogrammetric data are mapped onto the surface of the initial geographic model to generate a three-dimensional geographic model that integrates real-world terrain, digital fences, and attribute information. Integrate at least one interactive functional module for highway design into the 3D geographic model.

[0012] In one possible implementation, a three-dimensional forward collaborative design scheme for highways is determined based on an oblique photogrammetry geographic model, including: Using oblique photogrammetry geographic models as a unified data source, and configuring differentiated data access and operation permissions for multiple professional teams participating in collaborative design, a collaborative design environment is built. Execute a multi-node collaborative design process, and in the collaborative design process, make collaborative decisions on key design stages based on oblique photogrammetry geographic models; The collaborative design process includes at least one preset collaborative design node, and each collaborative design node corresponds to a design stage.

[0013] In one possible implementation, topographic maps of mountain roads, oblique photogrammetry data from drones, and information on special areas are acquired, including: Select topographic map data sources that meet the project's timeliness requirements, and verify the accuracy of the topographic map data to ensure that its horizontal and vertical accuracy meets the error threshold for subsequent model construction; and convert the topographic map data into a standardized format compatible with subsequent data processing workflows, and extract the core topographic and feature elements. Based on the terrain features of the survey area and the preset model resolution requirements, the flight path and parameters of the UAV are planned; data acquisition is performed during the window period that meets the preset environmental conditions, and optical images and position and attitude data are acquired simultaneously; and the raw data is preprocessed, including image correction, point cloud generation and coordinate system 1, to generate the initial 3D point cloud and digital orthophoto map. Collect background information related to geological hazards, engineering constraints, ecology, and hydrology in the survey area; conduct on-site surveys and supplements in key areas to verify or refine the background information; and standardize and encode all collected and surveyed special area information according to a preset data structure, which includes at least information type, spatial range, attribute description, and geographic coordinates.

[0014] A second aspect of the present invention provides a collaborative design system, including a drone and an electronic device; the method of the first aspect above is used to run on the electronic device.

[0015] Compared to traditional technologies, this invention provides a method for 3D forward collaborative design of highways based on real-world terrain models. First, it acquires topographic maps of mountainous roads, oblique photogrammetry data from drones, and information on special areas. Then, it performs spatiotemporal registration and fusion of these data to generate an enhanced training set, which includes a basic feature layer, an engineering feature layer, and a boundary calibration layer. Next, an initial geographic model is established based on the mountainous road topographic map. Then, a multi-class support vector machine (SVM) is trained using the enhanced training set. Based on the trained SVM, digital fences and hierarchical information are calibrated in the initial geographic model to obtain an oblique photogrammetry geographic model. Finally, a 3D forward collaborative design scheme for highways is determined based on the oblique photogrammetry geographic model. This invention establishes an initial geographic model based on topographic maps, trains a multi-class SVM using an enhanced training set, and then calibrates digital fences and hierarchical information in the initial model to generate an oblique photogrammetry geographic model, forming a closed loop of "model building - intelligent optimization - accuracy improvement," ultimately outputting a precise design scheme. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of the three-dimensional forward collaborative design method for highways based on real-world terrain models provided in this embodiment of the invention. Detailed Implementation

[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart illustrating the implementation of the three-dimensional forward collaborative design method for highways based on real-scene terrain models provided in this embodiment of the invention. Figure 1 As shown, the method includes: Acquire topographic maps of mountain roads, oblique photography data from drones, and information on special areas; The mountain road survey topographic map, UAV oblique photography data, and special area information are spatiotemporally registered and fused to generate an enhanced training set; the enhanced training set includes a basic feature layer, an engineering feature layer, and a boundary calibration layer; An initial geographical model was established based on the topographic map of the mountain road survey. The multi-class support vector machine is trained based on the enhanced training set, and digital fences and hierarchical information are labeled in the initial geographic model based on the trained multi-class support vector machine to obtain the oblique photogrammetry geographic model. Based on the oblique photogrammetry geographic model, a three-dimensional forward collaborative design scheme for highways is determined.

[0019] In this embodiment of the invention, the basic data collection work for mountain road survey is carried out first. The system acquires mountain road survey topographic maps that can reflect the macro topography of the region, UAV oblique photography data that can capture micro-level ground features and three-dimensional morphology, and special area information that includes key constraints such as geological disaster hazards, underground pipeline distribution, and ecologically sensitive areas, and constructs a multi-dimensional and full-element original data system.

[0020] Subsequently, the three types of collected data were technically integrated. Specifically, the coordinate system and time reference of the topographic map of mountain road survey were used as a unified standard. Spatial-temporal registration was used to eliminate spatial location deviations and time dimension differences between data, ensuring that data from different sources corresponded accurately in geographic space and time nodes. On this basis, data fusion processing was carried out. According to information attributes and engineering requirements, the fused data was structured into three layers: a basic feature layer covering basic information such as topographic elevation and image texture; an engineering feature layer containing engineering parameters such as slope, lithology, and soil density; and a boundary calibration layer marking the boundaries of cut and fill and the scope of risk control. Finally, an enhanced training set was formed to support subsequent analysis.

[0021] Based on the obtained topographic maps of mountain roads, core topographic elements such as contour lines, elevation points, and feature outlines are extracted. An initial geographic model that reflects the basic topographic morphology of the region is built using 3D modeling technology, providing a basic framework for subsequent refined optimization.

[0022] By utilizing the three-layer feature data in the enhanced training set, a multi-class support vector machine model is trained, enabling the model to identify different types of engineering areas and determine risk levels. The trained model is then combined with the initial geographic model, which automatically identifies and labels digital fences with clear engineering attributes (such as cut-and-fill balance fences and risk control fences) in the initial geographic model. At the same time, it assigns corresponding hierarchical information (such as fill area priority and risk warning level) to each type of fence. Through this intelligent labeling process, the initial geographic model is upgraded into an oblique photogrammetry geographic model that integrates real-world textures, engineering attributes, and risk levels.

[0023] Finally, using the oblique photogrammetry geographic model as the core design carrier, and relying on the real-world terrain information, digital fence constraints, and hierarchical attributes within the model, a three-dimensional forward collaborative design for highways is carried out. During the design process, engineering parameters in the model can be queried in real time to avoid high-risk areas, optimize route alignment and structure site selection, and accurately calculate cut and fill volumes. Simultaneously, it supports collaborative work among multiple professional teams, including surveying, design, and construction, based on the same model, ensuring that the design schemes at each stage are highly adapted to the actual terrain and engineering constraints, ultimately determining a scientifically sound and implementable three-dimensional forward collaborative design scheme for highways.

[0024] In some embodiments, the topographic map of mountain road survey, the oblique photogrammetry data of UAV and the information of special areas are spatiotemporally registered and fused to generate an enhanced training set, including: performing aerial triangulation and dense matching on the oblique photogrammetry data of UAV to generate a three-dimensional point cloud and a digital orthophoto map. Vectorize the topographic map of the mountain road survey to extract contour lines, elevation points and ground features; Using the coordinate system of the topographic map of the mountain road survey as the reference coordinate system, at least three ground control points are selected, and the three-dimensional point cloud and digital orthophoto map are geometrically corrected and coordinate transformed according to the least squares method to align with the spatial coordinate system of the reference coordinate system. The spatiotemporally registered 3D point cloud, digital orthophoto map, and vectorized topographic map data are overlaid to obtain a fused data volume. Based on the fused data volume, a basic feature layer, an engineering feature layer, and a boundary calibration layer are constructed. A unique identifier is established for each geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer, and the feature data of the three layers are spatially correlated to obtain an enhanced training set.

[0025] Aerial triangulation encryption specifically includes: automatically identifying and matching corresponding feature points between adjacent images using software; combining image position and attitude data recorded by the UAV; and calculating the precise camera position and attitude parameters for each image using bundle adjustment. The dense matching, based on the aerial triangulation results, utilizes a multi-view stereo vision algorithm to perform pixel-by-pixel matching on overlapping image areas, generating a dense 3D point cloud. The geometric correction and coordinate transformation specifically involve: establishing a spatial transformation model including translation, rotation, and scaling parameters based on the known coordinates of selected ground control points in two coordinate systems; solving for the optimal transformation parameters of this model using the least squares method; and then batch-converting all coordinate points of the 3D point cloud and digital orthophoto map according to the solved parameters to achieve spatial alignment with the reference coordinate system.

[0026] In this embodiment of the invention, the oblique photogrammetry data from the UAV is first processed specifically. Specifically, aerial triangulation encryption technology is used to construct an aerial triangulation network containing internal and external orientation elements of the images, utilizing camera parameters, position information, and image overlap relationships recorded during UAV flight. This accurately calculates the spatial position and attitude of each image. Based on this, dense matching is performed, matching each pixel of the encrypted images point-by-point to generate 3D point cloud data that reflects the three-dimensional shape of the terrain. Simultaneously, orthorectification is used to eliminate distortions caused by terrain undulations and image tilt, outputting a digital orthophoto map with clear texture and accurate coordinates. This provides a data source with both geometric accuracy and visual detail for subsequent fusion.

[0027] Next, the topographic maps of the mountain road survey were converted into vector data. Specifically, using professional geographic information software, the topographic maps were converted into vector data format. Through a combination of human-computer interaction and automatic recognition, the core topographic and feature information in the topographic maps was accurately extracted. The topographic information includes contour lines indicating elevation changes and elevation points marking specific elevation values, while the feature information covers the outlines and attributes of existing artificial or natural features such as roads, waterways, buildings, and bridges, forming structured vector topographic map data to ensure that the topographic map information can be efficiently accessed and correlated.

[0028] Subsequently, spatial alignment and calibration of multi-source data were performed. Using the coordinate system adopted for topographic maps of mountain road surveys as a unified reference coordinate system, at least three evenly distributed, topographically significant, and coordinate-known ground control points (such as mountaintops, corners of fixed buildings, and road intersections) were selected within the survey area. Based on the least squares principle, a mathematical transformation model was established between the 3D point cloud, digital orthophoto map, and the reference coordinate system. By calculating the optimal transformation parameters, geometric correction and coordinate transformation were performed on the spatial coordinates of the 3D point cloud and the pixel positions of the digital orthophoto map, enabling precise spatial alignment between the two types of data and the topographic map under the reference coordinate system. This eliminated coordinate deviations between different data sources and ensured spatial consistency for subsequent overlay and fusion.

[0029] After spatial alignment, the registered 3D point cloud, digital orthophoto map, and vectorized topographic map data are layered and integrated: the vectorized topographic map data is used as the bottom layer, retaining its core topographic framework information such as contour lines and elevation points; the digital orthophoto map is superimposed on the bottom layer to ensure that the image texture and the outline of the topographic map features are accurately matched, supplementing the intuitive feature details (such as vegetation cover and surface material differences) that are lacking in the topographic map; finally, the 3D point cloud is superimposed on the middle layer image, and the density and three-dimensionality of the point cloud are used to refine the elevation transition details between the contour lines of the topographic map, forming a three-in-one fused data body of "topographic framework-image texture-three-dimensional geometry", which fully integrates the advantages of the three types of data.

[0030] Based on the comprehensive information of the fused data volume, a three-layer core structure for the enhanced training set is further constructed. Basic information such as terrain elevation, image RGB values, and point cloud geometric coordinates are extracted from the fused data volume to form a basic feature layer reflecting the basic characteristics of the region. Combined with engineering design requirements, engineering parameters such as slope, aspect, and soil type are calculated, and information on geological disaster risks and underground pipeline constraints in special areas is integrated to construct an engineering feature layer supporting engineering decisions. According to preset rules (such as slope thresholds and risk level standards), key boundaries such as cut-and-fill boundaries, risk control areas, and engineering constraint areas are identified and marked in the fused data volume, forming a boundary labeling layer with attribute annotations, thus realizing the transformation of data from "comprehensive fusion" to "classified structuring."

[0031] Finally, a unified association system is established for the three feature layers: For each geographic unit (such as a region or natural feature unit divided by a fixed grid) covered by the basic feature layer, engineering feature layer, and boundary calibration layer, a unique identifier is generated. The identifier contains key information such as layer type, spatial coordinate range, and data generation time to ensure that each geographic unit can be accurately located and distinguished. Using the unique identifier and geographic coordinates as dual indexes, the parameter information (such as the elevation value, slope parameter, and boundary attribute of a certain grid) of the same geographic unit in the three feature layers is associated and bound to establish a cross-layer data mapping relationship, and finally form an enhanced training set with complete structure, information association, and direct support for subsequent model training.

[0032] The specific rules for generating unique identifiers are as follows: A fixed-length character combination is used, with a structure comprising three parts: layer type encoding, grid coordinate encoding, and temporal encoding. The layer type encoding is represented by two letters, such as JC for basic feature layers, GC for engineering feature layers, and QJ for boundary calibration layers. The horizontal and vertical coordinate indices are converted into corresponding numerical sequences based on the row and column positions of the geographic unit in the network partitioning; for example, the index value is obtained by dividing the coordinate value of the lower left corner of the grid by the grid side length and then rounding it down. These three parts are concatenated sequentially to form a string identifier that uniquely represents a geographic unit at a specific spatial location within a specific feature layer.

[0033] In some embodiments, the spatiotemporally registered 3D point cloud, digital orthophoto map, and vectorized topographic map data are overlaid to obtain a fused data volume, including: Using vectorized topographic map data as the bottom layer, digital orthophoto overlay as the middle layer, and 3D point cloud overlay as the top layer, a three-dimensional overlay structure with topographic, texture, and geometric information is obtained. The system handles elevation conflicts, feature outline conflicts, and element attribute conflicts in the 3D overlay structure to obtain a fused data volume. The accuracy, completeness, and consistency of the generated fused data volume are verified.

[0034] In this embodiment of the invention, a layered overlay construction is first performed to build a three-dimensional overlay structure containing terrain, texture, and geometric information. Vectorized topographic map data serves as the bottom layer, which has already undergone preliminary vectorization processing to extract contour lines, elevation points, and ground features (such as existing roads, water systems, and building outlines). This clearly presents the macroscopic topographic framework and key feature distribution of the surveyed area, providing a stable spatial reference and topographic skeleton for the entire overlay structure. A digital orthophoto map is overlaid on top of this bottom layer as the middle layer. This image map has undergone geometric correction and color optimization, allowing for a direct presentation of surface texture details. For example, the density of vegetation cover, the shape of exposed rock areas, and the differences in surface materials (such as the visual distinction between dirt roads and paved roads) can be accurately matched with the outlines of features on the underlying topographic map (for example, the overlap between the road edges in the image and the road lines marked on the topographic map is ≥95%), supplementing the macro-topographic framework with concrete visual information about the surface. Finally, a three-dimensional point cloud is superimposed on the middle layer as the top layer. The three-dimensional point cloud is composed of a large number of spatial points containing X, Y, and Z coordinates, which can accurately restore the three-dimensional geometric shape of the terrain, such as the steepness of slopes, the depth and width of gullies, and the undulating outline of small hills. It can fill the elevation transition gaps between contour lines on the topographic map and the lack of three-dimensional spatial information in the image map, ultimately forming a three-layer three-dimensional superimposed structure of "bottom-layer terrain skeleton - middle-layer texture details - top-layer three-dimensional geometry", realizing the comprehensive integration of the three core information types of terrain, texture, and geometry.

[0035] Next, specific processing was carried out to address three types of data conflicts that may occur in the 3D overlay structure, eliminating information contradictions. For elevation conflicts, i.e., discrepancies between the Z-value (elevation) of the 3D point cloud and the elevation points and contour lines of the vectorized topographic map, a comprehensive judgment was made considering both data accuracy and timeliness: if the discrepancy is small, the average of the two was taken as the final elevation value, balancing data stability and precision; if the discrepancy is large, the elevation data of the UAV 3D point cloud was used first (because UAV data has higher resolution and the shooting time is closer to the current survey node, which can better reflect the latest terrain status), and the location and value of the deviation were marked for subsequent traceability; if the 3D point cloud data contains abnormal elevations caused by local noise points, the noise points were removed by statistical filtering algorithms before elevation comparison. For conflicts in feature outlines, such as inconsistencies between the feature shapes presented in the digital orthophoto map (e.g., newly added temporary buildings, widened gullies) and the feature outlines marked on the vectorized topographic map, verification was required through field sampling. Select 3-5 feature points in the conflict area (such as building corners, gully edges). If the measurement results match the outlines of features in the imagery, the topographic map data is considered outdated and the corresponding feature outlines on the topographic map need to be updated; if the measurement results do not match the topographic map data, the topographic map data is considered lagging and the corresponding feature outlines on the topographic map need to be updated. Figure 1If the misjudgment is determined to be due to perspective deviation or partial occlusion during image capture, local correction of the corresponding area in the image map is required. For conflicts in element attributes, such as overlap between the "geological hazard zone" marked in special area information and the "forest land" attribute marked on the vectorized topographic map, confirmation must be made in conjunction with the geological survey report and on-site investigation. If the area is a "landslide hazard zone under forest cover," it should be assigned a dual attribute of "forest land + landslide hazard zone," preserving the land feature type information while clearly defining engineering risk constraints, avoiding information loss due to a single attribute.

[0036] Finally, the processed fused data volume undergoes three core verifications: accuracy, completeness, and consistency, to ensure data quality meets standards. For accuracy verification, 10-15 evenly distributed ground control points (such as mountain tops, valley bottoms, and road intersections) are selected, and their actual coordinates and elevations are measured and compared with the coordinates and elevations of corresponding points in the fused data volume to ensure the data meets the accuracy requirements for mountain highway surveying and design. For completeness verification, it is checked whether the fused data volume fully covers the entire survey section, whether it includes all key geographic features (such as bridges, tunnel entrances and exits, large gullies, and boundaries of ecologically sensitive areas), and whether there are any missing or blank areas. For consistency verification, using the spatial query function of geographic information software, 20 geographic units (such as 10m×10m grids) are randomly selected to verify whether their information in the three-layer overlay structure is logically consistent. For example, if a geographic unit is marked as "steep slope" (with dense contour lines) on the bottom-level topographic map, the slope calculated in the top-level 3D point cloud should be ≥25°, and the surface should appear as a steep shape in the middle-level image map. If there is a logical contradiction (such as the topographic map marking a steep slope but the 3D point cloud computing slope is only 10°), it is necessary to go back to the conflict handling stage to correct it again until all verification items meet the standards and finally form a qualified fused data body.

[0037] In some embodiments, based on the fused data volume, a basic feature layer, an engineering feature layer, and a boundary calibration layer are constructed, including: Extract multidimensional basic parameters related to terrain, texture, and geometry from the fused data volume; associate the multidimensional basic parameters according to geographic coordinates to form a basic feature layer containing at least terrain basic parameters, texture feature parameters, and geometric fine parameters; Based on the basic feature layer and the fused data volume, calculate or extract engineering parameters related to engineering design and construction; the engineering parameters include at least engineering topographic parameters, engineering constraint parameters and ecological engineering parameters; associate the engineering parameters with the corresponding geographic units to form the engineering feature layer; According to the preset boundary identification rules, at least the cut and fill boundary, risk control boundary, and engineering constraint boundary are identified and marked in the fused data volume and / or engineering feature layer; the identified boundaries are assigned type, level and associated attribute information to form a boundary marking layer.

[0038] In this embodiment of the invention, a basic feature layer is first constructed, focusing on the extraction and association of three core basic information types—terrain, texture, and geometry—from the fused data volume. From the bottom-layer vectorized topographic map of the fused data volume, contour line elevation values ​​reflecting terrain undulations, coordinates of elevation points (X, Y, Z) indicating specific heights, and basic terrain parameters such as existing road widths and water system directions are extracted. From the middle-layer digital orthophoto map, surface texture feature parameters are extracted using image processing techniques. For example, the texture contrast and correlation between vegetation-covered areas and exposed rock areas are calculated using a gray-level co-occurrence matrix, and surface materials are distinguished by RGB three-color channel values ​​(e.g., red clay areas with high red channel values, and forest areas with high green channel values). From the top-layer three-dimensional point cloud, fine geometric parameters reflecting spatial morphology are extracted. These include dense Z-values ​​of ground points (sampled at 0.1m intervals) and point cloud contour coordinates of feature edges (e.g., continuous point cloud sequences at the bottom of gullies, and three-dimensional coordinates of slope inflection points). After extraction, using geographic coordinates as a unified link, the basic terrain parameters, texture feature parameters, and geometric fine parameters of the same geographic unit (such as a 10m×10m grid) are bound one by one to ensure that the multi-dimensional basic information of each unit is complete and traceable, and finally form a basic feature layer that covers the survey area and is structured.

[0039] Next, based on the basic feature layer and the fused data volume, engineering parameters are further transformed and extracted to construct the engineering feature layer. For the engineering terrain parameters, combining the contour elevation values ​​and 3D point cloud Z-values ​​from the basic feature layer, the slope of each geographic unit is calculated using the slope calculation formula (slope = arctan(ΔZ / ΔX)) (accuracy retained to 0.1°). The slope aspect is determined using an azimuth algorithm. Simultaneously, the soil type is correlated with the vectorized terrain icon annotations in the fused data volume to determine the soil density (e.g., 1.6 g / cm³ for sandy soil). 3 1.8 g / cm³ of clay soil 3 For engineering constraint parameters, lithological types (such as granite and shale), groundwater level depths, and the burial depth and diameter of existing pipelines, as indicated in the geological survey report, are extracted from the special regional information contained in the fused data volume and added to the corresponding geographic units. For ecological engineering parameters, vegetation cover is calculated through threshold segmentation based on the RGB values ​​of the imagery in the basic feature layer, while the distribution areas of rare plants, wildlife habitats, and other ecologically sensitive areas recorded in the fused data volume are also marked. The three types of engineering parameters are integrated in the form of "geographic unit-parameter group," and engineering adaptation attributes are marked for each unit (such as "slope 25° + granite + low vegetation cover - suitable for excavation"), forming an engineering feature layer that directly serves design decisions.

[0040] Finally, based on the preset boundary identification rules, key boundaries are identified and calibrated in the fused data volume and engineering feature layer, and a boundary calibration layer is constructed. For cut and fill boundaries, the slope parameters of the engineering feature layer are used as the core judgment basis, combined with the edge contours of the 3D point cloud in the fused data volume (such as the continuous coordinates of slope change points), to generate closed boundary polygons, and the boundary type is labeled. For risk control boundaries, based on the geological hazard areas (such as landslides and debris flows) recorded in the special area information in the fused data volume, areas with abnormal vegetation coverage (such as vegetation fault zones) and areas with sudden slope changes in the engineering feature layer are superimposed, and the three-dimensional contours of the boundaries (such as the slope undulations of landslide bodies) are verified through the 3D point cloud, generating risk boundaries and assigning warning levels (red - prohibition of point selection, yellow - caution in line selection, blue - normal area). For engineering constraint boundaries, the contour coordinates of existing buildings (such as houses and bridges) and cultural relics protection units in the fused data volume are extracted, combined with the prohibited construction area in the special area information, to generate constraint boundaries and label the constraint type. Supplement all boundaries with spatial attributes (such as boundary area, perimeter, and center point coordinates) and associated parameters (such as the basic value of the earthwork volume estimation corresponding to the cut and fill boundary) to ensure that each boundary can be associated with the corresponding geographical unit of the basic feature layer and the engineering feature layer, and finally form a boundary marking layer with clear boundaries, attributes and levels.

[0041] In some embodiments, a unique identifier is established for each geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer, and the feature data of the three layers are spatially correlated to obtain an enhanced training set, including: The survey area is divided into multiple spatially discrete geographic units; a unique identifier is generated for each geographic unit to uniquely identify its spatial location and the feature layer to which it belongs; Using unique identifiers and / or geographic coordinates as indexes, feature parameters corresponding to the same geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer are associated to establish a mapping relationship between cross-layer feature parameters; Verify the uniqueness of the unique identifier and the logical consistency of cross-layer feature parameters; integrate the verified related data into a structured dataset and form corresponding data description information to generate an enhanced training set.

[0042] In this embodiment of the invention, the geographical unit division and unique identifier generation of the survey area are carried out first. Based on the terrain complexity and engineering design accuracy requirements of the survey area, a fixed grid method (such as a 10m×10m or 5m×5m grid) is used to divide the entire survey section into multiple spatially discrete, non-overlapping, and fully covered geographical units. The boundary of each unit is clearly defined by geographical coordinates to ensure that the spatial range of the unit is unique and traceable. Subsequently, a unique identifier is generated for each geographic unit. The identifier adopts a combined structure of "layer type code + grid coordinate code + time sequence code". The layer type code uses 2 letters to distinguish the feature layer ("JC" for the basic feature layer, "GC" for the engineering feature layer, and "QJ" for the boundary calibration layer); the grid coordinate code uses 12 digits to record the coordinate range of the unit (e.g., X300000-X300010 is recorded as "300000", Y200000-Y200010 is recorded as "200000", and the combination is "300000200000"); the time sequence code uses 4 digits to mark the week number generated by the data.

[0043] Next, using unique identifiers and geographic coordinates as dual indexes, cross-layer association of the three-layer feature data is achieved. First, the feature parameters of the same geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer are bound to their corresponding unique identifiers. For example, in the basic feature layer, a unit might have the following characteristics: "elevation value 580m, texture contrast 25, point cloud density 100 points / m²". 2 The engineering feature layer shows that this unit has a slope of 22° and a soil density of 1.7 g / cm³. 3 The groundwater level is 3m deep. The "excavation boundary attribute and risk level (yellow)" of this unit in the boundary marking layer are all associated with the same set of unique identifiers. Then, using geographic coordinates as an auxiliary index, the spatial overlay function of geographic information software is used to check whether the coordinate range of the same unit in the three layers of data is completely matched to ensure that there is no misalignment caused by coordinate offset. At the same time, a mapping relationship table of cross-layer feature parameters is established to clearly mark the corresponding logic of basic feature parameters, engineering feature parameters, and boundary attributes (e.g., "basic feature layer elevation difference > 5m → engineering feature layer is judged as steep slope → boundary marking layer is classified as excavation boundary"), so that the three layers of data form a complete association chain of "parameter-attribute-boundary".

[0044] Subsequently, a dual verification of the uniqueness of the identifier and the logical consistency of cross-layer feature parameters is performed. For the uniqueness of the identifier, the database deduplication function is used to traverse the identifiers of all geographic units to check for duplicate codes (such as the same grid coordinate codes within the same layer, or incorrect coding formats between different layers). If duplicates or errors are found, the code generation process is immediately traced back to correct them, ensuring that each identifier is globally unique across the three feature layers. To ensure logical consistency of parameters across layers, a combination of "sampling verification + rule verification" is adopted: 20% of the geographical units are randomly selected, and the parameters of the three layers are checked for contradictions based on preset engineering logic rules (e.g., when the slope of the basic feature layer is greater than 30°, the engineering feature layer should be marked "unsuitable for filling", and the boundary marking layer should not be classified as a filling boundary). For example, if the slope of the basic feature layer of a certain unit is calculated to be 35°, but the engineering feature layer is marked "suitable for filling" and the boundary marking layer is classified as a "fill boundary", it is determined to be a logical conflict. It is necessary to trace back to the feature layer construction stage to check whether the slope calculation is incorrect and whether the boundary judgment rule is misused, until the parameter logic of all sampled units is consistent. Non-sampled units are batch verified through automated rule scripts to ensure that there are no hidden conflicts.

[0045] Finally, the validated associated data is integrated to generate an enhanced training set with data description information. The associated geographic unit data from the three feature layers is imported into a structured database according to the field format of "unique identifier - basic feature parameters - engineering feature parameters - boundary attributes" to form a standardized data table, facilitating quick retrieval and querying during subsequent model training. Detailed data description information is also added to the dataset, including: data source (e.g., UAV oblique photogrammetry data, field survey report), geographic unit division standard (e.g., 10m×10m grid), definition and unit of each parameter (e.g., "slope: unit is degrees, calculated as arctan(ΔZ / ΔX)"), validation results (e.g., identifier duplication rate 0%, parameter logical consistency rate 99.8%), data generation time and validity period, ensuring the interpretability and reusability of the dataset. The final enhanced training set contains complete parameters from the three feature layers, achieves cross-layer association through unique identifiers, and is quality-validated to ensure reliability, making it directly usable for training multi-class support vector machines.

[0046] In some embodiments, based on a trained multi-class support vector machine, digital fences and hierarchical information are labeled in an initial geographic model to obtain an oblique photogrammetry geographic model, including: The trained multi-class support vector machine model is integrated with the initial geographic model, and the mapping relationship of the enhanced training set is imported as the classification benchmark. The initial geographic model is spatially discretized, the first feature parameter of each discrete unit is extracted, and the first feature parameter is input into a multi-class support vector machine model. The model outputs the classification results of the boundary type and risk level of each discrete unit. Based on the classification results, discrete units with the same or similar boundary types are spatially aggregated to generate closed digital fences, and each digital fence is assigned corresponding engineering attributes and risk level information. The digital fence and its associated engineering attributes and risk level information are fused with the initial geographic model and oblique photogrammetry data to generate an oblique photogrammetry geographic model.

[0047] In some embodiments, digital fences and their associated engineering attributes and risk level information are fused with an initial geographic model and oblique photogrammetry data to generate an oblique photogrammetry geographic model, including: The oblique photogrammetry data is geometrically optimized and visually consistent, and the engineering attributes and risk level information of the digital fence are structured and encoded to generate standardized fusion input data. Using the coordinate system of the initial geographic model as a reference, the preprocessed oblique photogrammetric data is spatially registered with high precision to ensure spatial alignment with the initial geographic model. The structured digital fence is used as a vector feature with attribute information and embedded into the initial geographic model according to its geographic coordinates. The attribute information of the digital fence is then associated with the corresponding terrain unit in the model. The texture and geometric information in the spatially registered oblique photogrammetric data are mapped onto the surface of the initial geographic model to generate a three-dimensional geographic model that integrates real-world terrain, digital fences, and attribute information. The accuracy standard for high-precision spatial registration is as follows: after registration, the deviation of the three-dimensional coordinates of feature points (such as building corners and road intersections) in the oblique photogrammetry data from the corresponding locations in the initial geographic model should not exceed 0.1 meters in the horizontal direction and 0.2 meters in the vertical elevation direction. The verification method is as follows: randomly select no fewer than ten identical feature points that can be clearly identified in both types of data on the registered model, extract their three-dimensional coordinates in the oblique photogrammetry data and the initial geographic model respectively, and calculate the difference between the two. If the difference of all sample points meets the above accuracy standard, the registration is deemed successful.

[0048] After receiving multi-view images acquired by oblique photography from a UAV, the system generates a sparse point cloud through feature point matching, and then performs 3D reconstruction using a structure-of-motion (SOM) algorithm to obtain a dense point cloud. To remove the interference of surface vegetation on roadbed design, the system employs a joint filtering algorithm based on elevation abrupt changes and texture features. The specific steps are as follows: The system divides the point cloud into equally sized spatial grids and calculates the elevation variance of the point cloud within each grid. For grids with elevation variance exceeding the vegetation fluctuation threshold, the system further extracts the gray-level co-occurrence matrix texture features of the point cloud within that grid. If the texture complexity is higher than the road surface texture threshold, it is identified as a vegetation point cloud and removed. Subsequently, the system performs spatial interpolation to densify the bare ground 3D point cloud after vegetation removal, constructing a continuous and seamless triangular mesh patch to generate a high-precision digital model of the real-world surface.

[0049] Integrate at least one interactive functional module for highway design into the 3D geographic model.

[0050] In this embodiment of the invention, the integration and adaptation of the trained model and the initial geographic model are first carried out: the multi-class support vector machine model (with the ability to identify boundary types and determine risk levels), which has been validated through the enhanced training set, is embedded into the computational framework of the initial geographic model through a programming interface, ensuring that the model can call the basic data such as spatial coordinates and terrain elevation of the initial geographic model in real time; at the same time, the mapping relationship of "basic feature parameters - engineering feature parameters - boundary attributes" in the enhanced training set is imported (such as "elevation difference > 8m + soil density < 1.6g / cm³"). 3 →The boundary type is a fill area with a low risk level) as the classification benchmark, providing a unified judgment standard for subsequent unit classification and avoiding the disconnect between classification logic and the training phase.

[0051] Next, the initial geographic model is spatially discretized and its features are classified. Following the same grid division standard as the enhanced training set (e.g., 5m×5m or 10m×10m), the initial geographic model is divided into several spatially independent, coordinate-defined discrete units. For each discrete unit, the first feature parameters corresponding to the model training are extracted. Specifically, this includes basic parameters such as terrain elevation and contour curvature obtained from the initial geographic model, combined with related parameters such as surface texture grayscale values ​​and soil type supplemented from previous survey data, forming a standardized feature vector. This feature vector is then input into a multi-class support vector machine model. By comparing the feature parameters with a preset classification benchmark, the model automatically outputs the classification result for each discrete unit, clearly labeling its boundary type (e.g., cut area, fill area, geological risk area, pipeline constraint area) and risk level (e.g., red high risk, yellow medium risk, blue low risk), achieving refined attribute labeling of the initial geographic model.

[0052] Spatial aggregation is performed based on the classification results to generate a closed digital fence. Specifically, the spatial topology analysis function of the Geographic Information System (GIS) is used to filter and merge the classification results of all discrete units. Discrete units with identical boundary types (e.g., both being "high-risk landslide areas") or highly similar types (e.g., "general fill areas" and "priority fill areas" both belonging to the fill category) and spatially adjacent are outlined and smoothed according to their coordinate boundaries to eliminate jagged edges caused by grid cutting, forming a continuous, closed polygonal region, i.e., a digital fence. For example, 20 adjacent "cut area" discrete units are aggregated to generate a complete cut area digital fence covering the region, with its boundary coordinates connected by the edge points of the outermost unit, ensuring that the fence range matches the actual terrain features.

[0053] Simultaneously, each digital fence is assigned corresponding engineering attributes and risk level information: combining the classification results with the engineering parameters of the enhanced training set, specific attributes are added to the digital fence. For example, the fence in the excavation area is labeled "average excavation depth 3.5m, rock ratio 60%", the fence in the filling area is labeled "recommended fill type, maximum filling height 2m", and the fence in the risk area is labeled "risk triggers (such as landslides) and protection measures recommendations (such as adding anti-slide piles)". The risk level information is directly related to the classification results. For example, the red fence corresponds to "point selection is prohibited, detour is required", the yellow fence corresponds to "careful line selection, enhanced protection is required", and the blue fence corresponds to "normal design, conventional protection". All attribute information is bound to the spatial coordinates of the digital fence, forming a complete data chain of "fence boundary - engineering attributes - risk constraints".

[0054] For oblique photogrammetry data, a 3D point cloud denoising algorithm is used to remove discrete noise points (such as redundant points caused by birds and cloud interference) while preserving the micro-geometric details of ground features (such as rock joints and gully textures). At the same time, the digital orthophoto map is segmented for color equalization to eliminate image color differences caused by uneven lighting in mountainous areas and ensure visual consistency. For digital fence data, its engineering attributes and risk level information are structured in a key-value pair format and a vector file format compatible with the initial geographic model is generated to facilitate subsequent embedding operations.

[0055] Using the coordinate system adopted by the initial geographic model as a reference, 6-8 evenly distributed ground control points (such as mountain tops or corners of fixed buildings) are selected in the survey area to obtain the precise coordinates of the control points. Based on these control points, the coordinate transformation parameters of the oblique photography data (3D point cloud, digital orthophoto map) are calculated using the least squares method, and geometric correction is performed to ensure that the spatial position deviation between the oblique photography data and the initial geographic model is ≤0.1m, laying the coordinate foundation for subsequent fusion.

[0056] Structured digital fences are used as vector elements and precisely embedded into the initial geographic model based on their geographic coordinates. Spatial association technology binds fence attribute information to corresponding terrain units within the model. Designers can click on a fence area to view its engineering attributes and risk level. Subsequently, registered oblique photogrammetric data is overlaid and fused with the initial geographic model. Digital orthophoto maps are used as texture maps and mapped onto the terrain surface of the initial geographic model according to projection matching principles, ensuring that the image texture matches the terrain undulations (e.g., no stretching or distortion in steep slopes). At the same time, dense geometric information from 3D point clouds is integrated into the model to supplement the three-dimensional details of the terrain surface (e.g., protruding rocks, depressions, and gullies), improving the model's 3D visualization accuracy.

[0057] Finally, interactive functional modules for highway design are integrated into the fusion model. At least one practical function is embedded, such as a "dynamic fence attribute query" module (supporting clicking on fences to view cut and fill volumes, risk levels, and protection recommendations), a "design conflict pre-detection" module (automatically popping up warnings when the drawn route crosses a high-risk fence), and a "rapid engineering quantity estimation" module (calculating earthwork volumes based on fence area and average cut and fill depth). This ensures the model not only has the ability to present realistic scenery but also directly supports design decisions. Through these steps, an oblique photogrammetry geographic model is finally generated, integrating real-world terrain, digital fences, and attribute information, and possessing interactive design capabilities.

[0058] In some embodiments, a three-dimensional forward collaborative design scheme for highways is determined based on an oblique photogrammetry geographic model, including: Using oblique photogrammetry geographic models as a unified data source, and configuring differentiated data access and operation permissions for multiple professional teams participating in collaborative design, a collaborative design environment is built. Execute a multi-node collaborative design process, and in the collaborative design process, make collaborative decisions on key design stages based on oblique photogrammetry geographic models; The collaborative design process includes at least one preset collaborative design node, and each collaborative design node corresponds to a design stage.

[0059] Differentiated data access and operation permissions are implemented through a role-based access control mechanism. Specifically, role types corresponding to professional teams are predefined, such as route design roles, geological exploration roles, and ecological assessment roles. For each role, the permitted scope of model data access (e.g., viewing only specified layers or areas) and permitted operation types are defined (e.g., route design roles are allowed to draw and modify route features, geological exploration roles are allowed to edit risk fence attributes, and ecological assessment roles are only allowed to add annotation information). When a user logs into the collaborative design platform with a specific role, the system automatically loads available tools and data based on their role permissions and restricts their access to and operation of unauthorized data.

[0060] In this embodiment of the invention, a collaborative design environment based on an oblique photogrammetry geographic model is first established. The oblique photogrammetry geographic model, which integrates real-world terrain, digital fences, and attribute information, is used as the sole data source and deployed to a cloud-based collaborative platform. This ensures that all participating design teams (such as route design, roadbed design, bridge and culvert design, geological survey, ecological assessment, and cost estimation) access the same version of the model, avoiding data silos and version inconsistencies. Simultaneously, differentiated data permissions are configured for different teams: the route design team has full access to the model and the right to draw route plans, and can directly outline the route direction in the model; the geological survey team has the right to edit risk fence attributes and can update risk levels and protection recommendations based on supplementary survey data; the ecological assessment team only has the right to query and label vegetation cover areas and sensitive areas, and cannot modify core terrain data; the cost estimation team can call the cut and fill boundary data in the model to estimate engineering quantities, but is restricted from changing boundary attributes. Through this permission division, data security is ensured while guaranteeing that each team can obtain the necessary information and carry out targeted work.

[0061] Subsequently, a multi-node collaborative design process is implemented, relying on the model to achieve collaborative decision-making in key aspects. The collaborative design process is divided into several pre-set collaborative design nodes according to the highway design stage. Each node corresponds to a clear design objective and output, and requires the joint participation and confirmation of all relevant teams. For example, the first node is "initial screening of route schemes." Based on the oblique photogrammetry geographic model, combined with the risk zone boundaries (such as red prohibited zones) and ecologically sensitive zone boundaries in the digital fence, the route design team initially proposes 2-3 route corridor schemes that avoid high-risk areas and reduce ecological damage, which are presented in the model in the form of 3D wireframes. The geological, ecological, and cost teams simultaneously check each scheme in the model. The geological team marks the slope stability level of the route, the ecological team calculates the vegetation damage area involved in the scheme, and the cost team estimates the approximate earthwork volume of each scheme. All opinions are fed back to the corresponding position in the model in real time, forming a visual collaborative interface of "scheme wireframe + multi-disciplinary annotation". After collective review, the optimal initial screening scheme is determined to proceed to the next node.

[0062] The second node is "Structure Site Selection Collaboration." For structures such as bridges, tunnels, and culverts in the initial screening route, the bridge and culvert design team makes preliminary site selections in the model (such as the location of a bridge across a ditch or the entrance and exit locations of a tunnel). The model automatically associates the corresponding engineering attributes (such as foundation bearing capacity and rock strata distribution) and risk levels of the area. The geological team assesses the foundation stability at the bridge site based on the 3D point cloud details in the model and proposes suggestions for pile foundation depth. The construction team combines the terrain slope and material transportation path in the model to determine the accessibility of the structure construction. The teams exchange opinions through the model's real-time annotation function. For example, if the construction team marks a tunnel entrance in the model as having a "slope of 35°, making it difficult for large equipment to enter the site," the design team adjusts the entrance location to a 20° slope area accordingly until all disciplines reach a consensus.

[0063] Subsequent stages (such as roadbed slope design, protection engineering optimization, and quantity calculation) all follow a similar logic: using the oblique photogrammetry geographic model as a carrier, each professional team conducts specialized designs based on the model data, accurately links opinions through the model's spatial positioning function, tracks the iterative process of the scheme through version control, and confirms the node deliverables through online review. Ultimately, through iterative optimization of all collaborative nodes, a highly compatible, risk-controllable, and economically feasible 3D forward collaborative design scheme for highways is formed. Key parameters in the scheme, such as route alignment, structure location, and slope gradient, can accurately match the actual terrain in the oblique photogrammetry geographic model, minimizing subsequent construction changes.

[0064] In some embodiments, acquiring topographic maps of mountain roads, oblique photogrammetry data from drones, and information on special areas includes: Select topographic map data sources that meet the project's timeliness requirements, and verify the accuracy of the topographic map data to ensure that its horizontal and vertical accuracy meets the error threshold for subsequent model construction; and convert the topographic map data into a standardized format compatible with subsequent data processing workflows, and extract the core topographic and feature elements. Based on the terrain features of the survey area and the preset model resolution requirements, the flight path and parameters of the UAV are planned; data acquisition is performed during the window period that meets the preset environmental conditions, and optical images and position and attitude data are acquired simultaneously; and the raw data is preprocessed, including image correction, point cloud generation and coordinate system 1, to generate the initial 3D point cloud and digital orthophoto map. Collect background information related to geological hazards, engineering constraints, ecology, and hydrology in the survey area; conduct on-site surveys and supplements in key areas to verify or refine the background information; and standardize and encode all collected and surveyed special area information according to a preset data structure, which includes at least information type, spatial range, attribute description, and geographic coordinates.

[0065] In this embodiment of the invention, the first step is to screen and verify the accuracy of the topographic map data source. Based on the project design cycle and the dynamic characteristics of terrain changes, at least five evenly distributed ground control points (such as mountaintops, corners of fixed buildings, and road intersections) are selected. Their coordinates and elevations are measured in the field and compared with the corresponding point data on the topographic map. The planar position error is verified to meet the standard. If the error exceeds the threshold, the data provider must be contacted for correction or a replacement data source to ensure that the accuracy of the topographic map meets the requirements for subsequent initial geographic model construction.

[0066] Next, the topographic map data format was converted and core elements were extracted. The filtered topographic maps were converted into a standardized format compatible with GIS systems, and redundant information (such as unmarked markers and temporary measurement marks) was removed using professional geographic information software. The core topographic and feature elements were extracted: topographic elements included contour lines (marking elevation values), elevation points (recording precise Z coordinates), and topographic slope aspect markings; feature elements included the center lines and widths of existing roads, the outlines of water systems (rivers, gullies), the locations of bridges and tunnels, and the boundaries of buildings and vegetation cover areas. All extracted elements retained their original coordinate information, forming a structured topographic map base dataset.

[0067] The first step is to plan the UAV flight path. Based on the terrain features of the surveyed area (such as slope and elevation difference) and the preset model resolution requirements (usually requiring an image ground resolution of ≥5cm / pixel), determine the UAV flight parameters: the flight altitude is calculated using the formula "resolution = camera focal length × flight altitude / sensor size" (e.g., for a 20-megapixel camera with a focal length of 16mm, 5cm resolution corresponds to a flight altitude of approximately 80m). The forward overlap is set to 70%-80%, and the lateral overlap is set to 60%-70% to ensure sufficient overlap between images for subsequent matching. For complex terrain sections such as steep slopes and deep canyons, additional intersecting flight paths perpendicular to the main flight path are added to avoid image blind spots. At the same time, no-fly zones (such as under high-voltage lines and military control zones) are marked, and a complete flight path planning document is generated.

[0068] The second step is to select a suitable window for data acquisition. Monitor the weather conditions in the survey area and launch the flight when there is no strong wind (wind speed ≤ 5m / s), no rainfall, and visibility ≥ 5km, avoiding strong midday sunlight (which easily produces strong shadows) and dense fog in the early morning (which affects image clarity). During the flight, the UAV's five-lens tilt camera (front, rear, left, right, and downward view) simultaneously acquires optical images, and the IMU (Inertial Measurement Unit) and GNSS module record the camera position (latitude, longitude, and elevation) and attitude parameters (roll angle, pitch angle, and heading angle) in real time to ensure that each image is associated with accurate spatial position information. After the flight, check the integrity of the images. If there are any missed areas (such as blank areas caused by flight path deviation), a follow-up flight must be carried out immediately.

[0069] The third step involves preprocessing the raw data. Professional photogrammetry software is used to correct distortion in the acquired optical images, eliminating image deformation caused by camera lens errors and flight attitude fluctuations. Based on image overlap and GNSS / IMU data, an aerial triangulation network is constructed using aerial triangulation encryption technology to calculate the precise interior and exterior orientation elements of each image. A dense matching algorithm is then used to perform pixel-by-pixel matching on the encrypted images, generating 3D point cloud data containing a massive number of spatial points (X, Y, Z coordinates). The 3D point cloud is then denoised (removing interference points such as birds and clouds), and orthorectified to generate a digital orthophoto map. Finally, the coordinate system of the 3D point cloud and the digital orthophoto map is aligned with the terrain. Figure 1 A reference coordinate system (such as CGCS2000) is established to form an initial oblique photogrammetry dataset that can be directly used for subsequent registration.

[0070] In the process of acquiring and standardizing information on special areas, the first step is to collect background information through multiple channels. This includes obtaining geological hazard survey reports from local natural resources departments to clarify the approximate scope and risk level of potential hazard areas such as landslides, debris flows, and karst formations; retrieving data on the direction, depth, and diameter of underground pipelines (water supply, gas, and electricity) from housing and construction and transportation departments, as well as design and maintenance records of existing roads, bridges, and tunnels; applying to the ecological and environmental departments for vector maps of the distribution of ecologically sensitive areas such as forest land, wetlands, and habitats of rare plants and animals; and obtaining hydrological data from the hydrological departments for the past 10 years, including river flood levels and gully catchment areas, to ensure coverage of four key categories of information: geological hazards, engineering constraints, ecology, and hydrology.

[0071] Following this, on-site surveys and supplementary verifications were conducted. For areas where the background information was ambiguous or controversial (such as unclear boundaries of geological hazard zones or potential discrepancies between pipeline location markings and actual locations), the survey team conducted on-site verification: ground-penetrating radar was used to detect the distribution of underground karst caves and the actual location of pipelines; rock weathering and soil compaction were analyzed through borehole sampling; total stations were used to measure the boundary coordinates of ecologically sensitive areas; and temporary water level observation points were set up at the bottom of gullies to record flood season water levels. Information discrepancies discovered during the verification (such as the actual pipeline burial depth being 0.5m shallower than the archival record) were corrected, and missing information (such as small areas of unmarked weak interlayers) was supplemented to ensure the accuracy of information in special areas.

[0072] Finally, all information is standardized and coded. Following a pre-defined data structure (containing four main fields: "Information Type - Spatial Range - Attribute Description - Geographic Coordinates"), the collected and surveyed information on special areas is structured: the Information Type field is labeled with categories such as "Geological Hazards," "Underground Pipelines," "Ecologically Sensitive Areas," and "Hydrology"; the Spatial Range field uses coordinate polygons or a center point plus radius to describe the area boundary; the Attribute Description field records specific parameters (e.g., geological hazard areas are labeled "Landslide Risk Level: High, Sliding Surface Depth: 5m," and pipelines are labeled "Type: Gas, Pipeline Diameter: DN300, Burial Depth: 1.8m"); the Geographic Coordinates field uses a coordinate system consistent with topographic maps and oblique photography data; all coded information is imported into the database to form a standardized dataset of special area information, facilitating subsequent integration and retrieval with other data.

[0073] In some embodiments, the collaborative design system is characterized by including a drone and an electronic device; the method of the above embodiments is used to run on the electronic device.

[0074] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0075] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A three-dimensional forward collaborative design method for highways based on real-world terrain models, characterized in that, include: Acquire topographic maps of mountain roads, oblique photography data from drones, and information on special areas; The mountain road survey topographic map, UAV oblique photography data, and special area information are spatiotemporally registered and fused to generate an enhanced training set; wherein, the enhanced training set includes a basic feature layer, an engineering feature layer, and a boundary calibration layer; Based on the topographic map of the mountain roads, an initial geographical model was established; The multi-class support vector machine is trained based on the enhanced training set, and digital fences and hierarchical information are labeled in the initial geographic model based on the trained multi-class support vector machine to obtain the oblique photogrammetry geographic model. Based on the oblique photogrammetry geographic model, a three-dimensional forward collaborative design scheme for highways is determined.

2. The method for three-dimensional forward collaborative design of highways based on real-scene terrain models according to claim 1, characterized in that, The mountain road survey topographic maps, UAV oblique photogrammetry data, and information on special areas are spatiotemporally registered and fused to generate an enhanced training set, including: Aerial triangulation and dense matching are performed on the oblique photography data of the UAV to generate three-dimensional point clouds and digital orthophoto maps. The topographic map of the mountain road survey is vectorized to extract contour lines, elevation points and ground features; Using the coordinate system of the mountain road survey topographic map as the reference coordinate system, at least three ground control points are selected, and the three-dimensional point cloud and the digital orthophoto map are geometrically corrected and coordinate transformed according to the least squares method to align with the spatial coordinate system of the reference coordinate system. The spatiotemporally registered 3D point cloud, digital orthophoto map, and vectorized topographic map data are overlaid to obtain a fused data volume. Based on the fused data volume, a basic feature layer, an engineering feature layer, and a boundary calibration layer are constructed. A unique identifier is established for each geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer, and the feature data of the three layers are associated in spatial location to obtain an enhanced training set.

3. The method for three-dimensional forward collaborative design of highways based on real-scene terrain models according to claim 2, characterized in that, The spatiotemporally registered 3D point cloud, digital orthophoto map, and vectorized topographic map data are overlaid to obtain a fused data volume, including: Using the vectorized topographic map data as the bottom layer, the digital orthophoto map as the middle layer, and the three-dimensional point cloud as the top layer, a three-dimensional overlay structure with topographic, texture, and geometric information is obtained. The elevation conflicts, feature outline conflicts, and element attribute conflicts in the three-dimensional overlay structure are processed to obtain the fused data volume; The accuracy, completeness, and consistency of the generated fused data volume are verified.

4. The method for three-dimensional forward collaborative design of highways based on real-scene terrain models according to claim 3, characterized in that, Based on the fused data volume, a basic feature layer, an engineering feature layer, and a boundary calibration layer are constructed, including: Extract multi-dimensional basic parameters related to terrain, texture, and geometry from the fused data volume; associate the multi-dimensional basic parameters according to geographic coordinates to form a basic feature layer containing at least terrain basic parameters, texture feature parameters, and geometric fine parameters; Based on the basic feature layer and the fused data volume, engineering parameters related to engineering design and construction are calculated or extracted; the engineering parameters include at least engineering terrain parameters, engineering constraint parameters, and ecological engineering parameters; the engineering parameters are associated with corresponding geographic units to form an engineering feature layer; According to the preset boundary identification rules, at least the cut and fill boundary, risk control boundary, and engineering constraint boundary are identified and marked in the fused data body and / or the engineering feature layer; the identified boundaries are assigned type, level, and associated attribute information to form a boundary marking layer.

5. The method for three-dimensional forward collaborative design of highways based on real-scene terrain models according to claim 4, characterized in that, A unique identifier is established for each geographic unit in the basic feature layer, engineering feature layer, and boundary calibration layer, and the feature data of the three layers are spatially correlated to obtain an enhanced training set, including: The survey area is divided into multiple spatially discrete geographic units; a unique identifier is generated for each geographic unit to uniquely identify its spatial location and the feature layer to which it belongs; Using the unique identifier and / or geographic coordinates as indexes, feature parameters corresponding to the same geographic unit in the basic feature layer, engineering feature layer and boundary calibration layer are associated to establish a mapping relationship between cross-layer feature parameters; Verify the uniqueness of the unique identifier and the logical consistency of the cross-layer feature parameters; integrate the verified associated data into a structured dataset and form corresponding data description information to generate the enhanced training set.

6. The method for three-dimensional forward collaborative design of highways based on real-scene terrain models according to claim 5, characterized in that, Based on the trained multi-class support vector machine, digital fences and hierarchical information are labeled in the initial geographic model to obtain the oblique photogrammetry geographic model, including: The trained multi-class support vector machine model is integrated with the initial geographic model, and the mapping relationship of the enhanced training set is imported as the classification benchmark. The initial geographic model is spatially discretized, the first feature parameter of each discrete unit is extracted, and the first feature parameter is input into the multi-class support vector machine model. The model outputs the classification results of the boundary type and risk level of each discrete unit. Based on the classification results, discrete units with the same or similar boundary types are spatially aggregated to generate closed digital fences, and each digital fence is assigned corresponding engineering attributes and risk level information. The digital fence and its associated engineering attributes and risk level information are fused with the initial geographic model and the oblique photogrammetry data to generate an oblique photogrammetry geographic model.

7. The method for three-dimensional forward collaborative design of highways based on real-scene terrain models according to claim 6, characterized in that, The digital fence and its associated engineering attributes and risk level information are fused with the initial geographic model and the oblique photogrammetry data to generate an oblique photogrammetry geographic model, including: The oblique photogrammetry data is subjected to geometric detail optimization and visual consistency processing, and the engineering attributes and risk level information of the digital fence are structured and encoded to generate standardized fusion input data. Using the coordinate system of the initial geographic model as a reference, the preprocessed oblique photogrammetric data is spatially registered with high precision to ensure spatial alignment with the initial geographic model. The structured digital fence is used as a vector feature with attribute information and embedded into the initial geographic model according to its geographic coordinates. The attribute information of the digital fence is then associated with the corresponding terrain unit in the model. The texture and geometric information in the spatially registered oblique photogrammetric data are mapped onto the surface of the initial geographic model to generate a three-dimensional geographic model that integrates real-world terrain, digital fences, and attribute information. At least one interactive functional module for highway design is integrated into the three-dimensional geographic model.

8. The method for three-dimensional forward collaborative design of highways based on real-scene terrain models according to claim 1, characterized in that, Based on the oblique photogrammetry geographic model, a three-dimensional forward collaborative design scheme for highways is determined, including: Using the oblique photogrammetry geographic model as a unified data source, and configuring differentiated data access and operation permissions for multiple professional teams participating in the collaborative design, a collaborative design environment is constructed. A multi-node collaborative design process is executed, and in the collaborative design process, collaborative decisions are made for key design stages based on the oblique photogrammetry geographic model; The collaborative design process includes at least one preset collaborative design node, and each collaborative design node corresponds to a design stage.

9. The method for three-dimensional forward collaborative design of highways based on real-scene terrain models according to claim 1, characterized in that, Acquire topographic maps of mountain roads, oblique photogrammetry data from drones, and information on special areas, including: Select topographic map data sources that meet the project's timeliness requirements, and verify the accuracy of the topographic map data to ensure that its horizontal and vertical accuracy meets the error threshold for subsequent model construction; and convert the topographic map data into a standardized format compatible with subsequent data processing procedures, and extract the core topographic and feature elements. Based on the terrain features of the survey area and the preset model resolution requirements, the flight path and parameters of the UAV are planned; data acquisition is performed during the window period that meets the preset environmental conditions, and optical images and position and attitude data are acquired simultaneously; and the collected raw data is preprocessed, including image correction, point cloud generation and coordinate system one, to generate an initial three-dimensional point cloud and digital orthophoto map. Collect background information related to geological hazards, engineering constraints, ecology, and hydrology in the survey area; conduct on-site surveys and supplements in key areas to verify or refine the background information; and standardize and encode all collected and surveyed special area information according to a preset data structure, which includes at least information type, spatial range, attribute description, and geographic coordinates.

10. A collaborative design system, characterized in that, Including drones and electronic devices; the method described in any one of claims 1-9 is used to operate on the electronic device.

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