Road engineering design modeling system and method based on BIM three-dimensional visualization technology

Abstract

The invention relates to a road engineering design modeling system and method based on a BIM three-dimensional visualization technology, and relates to the technical field of 3D modeling, and the system comprises a data obtaining module which is used for obtaining basic data of a road design area; the terrain modeling module is used for modeling a terrain surface and a geological layer of the road design area according to the basic data of the road design area based on a BIM three-dimensional visualization technology, and generating a three-dimensional terrain model of the road design area; the design acquisition module is used for acquiring various road engineering design schemes of the road design area; the design modeling module is used for generating a road three-dimensional model corresponding to each road engineering design scheme according to the three-dimensional terrain model of the road design area and the road engineering design scheme; the design optimization module is used for generating an optimal road engineering design scheme based on the road three-dimensional model corresponding to each road engineering design scheme, and the intelligent level of road engineering design is improved.

Description

Technical Field

[0001] This invention relates to the field of 3D modeling technology, and in particular to a road engineering design modeling system and method based on BIM 3D visualization technology. Background Technology

[0002] As a core area of ​​infrastructure construction, road engineering's design level directly impacts project safety, economy, and environmental adaptability. With increasing traffic demand and accelerated urbanization, traditional road design methods are no longer sufficient to meet the demands for efficient and precise design in complex scenarios. Currently, the industry's technological development exhibits the following characteristics: 1. Digital transformation of design methods: Computer-aided design technology has been widely used in road engineering, but two-dimensional drawings (such as plan views, longitudinal profile views, and cross-section views) still have limitations in spatial expression, making it difficult to intuitively present topographic undulations, geological changes, and surrounding environmental constraints, leading to frequent design errors and construction conflicts.

[0003] 2. The urgent need for multi-source data fusion: Road design requires the integration of multi-dimensional data. However, in traditional methods, data collection is scattered and formats are incompatible, leading to the need for manual integration during the design phase. This is inefficient and prone to information loss, becoming a key bottleneck restricting design quality.

[0004] Therefore, there is a need to provide a road engineering design modeling system and method based on BIM 3D visualization technology to improve the intelligence level of road engineering design. Summary of the Invention

[0005] This invention provides a road engineering design and modeling system based on BIM 3D visualization technology, comprising: a data acquisition module for acquiring basic data of a road design area, wherein the basic data includes at least topographic data, geological data, existing facility data, and meteorological data; a terrain modeling module for modeling the terrain surface and geological layers of the road design area based on the basic data of the road design area using BIM 3D visualization technology, thereby generating a 3D terrain model of the road design area; a design acquisition module for acquiring multiple road engineering design schemes for the road design area, wherein the road engineering design schemes include at least horizontal alignment design, longitudinal profile design, and cross-sectional design; a design modeling module for generating a road 3D model corresponding to each road engineering design scheme based on the 3D terrain model of the road design area and the road engineering design schemes using BIM 3D visualization technology; and a design optimization module for generating an optimal road engineering design scheme based on the road 3D model corresponding to each road engineering design scheme.

[0006] Furthermore, the data acquisition module acquires terrain data of the road design area, including: acquiring satellite imagery of the road design area; dividing the road design area into multiple first sub-regions based on the satellite imagery; determining the collaborative acquisition route of the UAV swarm based on the multiple first sub-regions; and acquiring terrain data of the road design area based on the collaborative acquisition route of the UAV swarm and the multiple first sub-regions.

[0007] Furthermore, the data acquisition module acquires geological data of the road design area, including: dividing the road design area into multiple second sub-regions based on meteorological data of the road design area; determining the correlation coefficient of geological differences between any two second sub-regions; acquiring geological data of multiple initial sampling points in each second sub-region; calculating the geological difference value of each second sub-region based on the geological data of the multiple initial sampling points in the second sub-region; determining the density of geological sampling points in each second sub-region based on the geological difference value of each second sub-region and the correlation coefficient of geological differences between any two second sub-regions; and acquiring the geological data of the road design area based on the density of geological sampling points in each second sub-region.

[0008] Furthermore, the terrain modeling module, based on BIM 3D visualization technology, models the terrain surface and geological layers of the road design area according to the basic data of the road design area, generating a 3D terrain model of the road design area. This includes: identifying similar historical road design areas based on the terrain and geological data of the road design area; retrieving the 3D terrain models of similar historical road design areas; and adjusting the 3D terrain models of similar historical road design areas based on the terrain and geological data of the road design area and the terrain and geological data of similar historical road design areas to generate a 3D terrain model of the road design area.

[0009] Furthermore, the design modeling module, based on BIM 3D visualization technology, generates a road 3D model corresponding to each road engineering design scheme according to the 3D terrain model of the road design area and the road engineering design scheme. This includes: determining similar historical road engineering design schemes based on the road engineering design schemes of the road design area and similar historical road design areas; obtaining initial road 3D models of similar historical road engineering design schemes; and fusing the 3D terrain model of the road design area and the initial road 3D models of similar historical road engineering design schemes to generate a road 3D model corresponding to each road engineering design scheme.

[0010] Furthermore, the design optimization module generates the optimal road engineering design scheme based on the road 3D model corresponding to each road engineering design scheme, including: obtaining the road connectivity topology map corresponding to the road to be designed; predicting the traffic flow data of the road to be designed based on the road connectivity topology map corresponding to the road to be designed; and generating the optimal road engineering design scheme based on the traffic flow data of the road to be designed and the road 3D model corresponding to each road engineering design scheme.

[0011] Furthermore, the design optimization module predicts traffic flow data for the road to be designed based on the road connectivity topology graph corresponding to the road to be designed, including: extracting graph features from the road connectivity topology graph corresponding to the road to be designed; determining similar historical roads to be designed based on the graph features from the road connectivity topology graph corresponding to the road to be designed; obtaining a traffic flow correlation matrix of similar historical roads to be designed; determining traffic flow-related roads for the road to be designed based on the traffic flow correlation matrix of similar historical roads to be designed; and predicting traffic flow data for the road to be designed based on the historical traffic flow data of the traffic flow-related roads for the road to be designed.

[0012] Furthermore, the design optimization module predicts the traffic flow data of the road to be designed based on the historical traffic flow data of the traffic flow associated roads, including: determining the traffic flow association matrix of the road to be designed based on the traffic flow association matrix of similar historical roads to be designed; and predicting the traffic flow data of the road to be designed based on the traffic flow association matrix of the road to be designed and the historical traffic flow data of the traffic flow associated roads to be designed.

[0013] Furthermore, the design optimization module generates the optimal road engineering design scheme based on the traffic flow data of the road to be designed and the road 3D model corresponding to each road engineering design scheme, including: determining multiple design optimization indicators; constructing a fitness function based on the multiple design optimization indicators; and generating the optimal road engineering design scheme based on the traffic flow data of the road to be designed, the road 3D model corresponding to each road engineering design scheme, and the fitness function through a particle swarm optimization algorithm.

[0014] This invention provides a road engineering design and modeling method based on BIM 3D visualization technology, applied to the aforementioned road engineering design and modeling system based on BIM 3D visualization technology. The method includes: acquiring basic data of the road design area, wherein the basic data includes at least topographic data, geological data, existing facility data, and environmental data; using BIM 3D visualization technology, modeling the topographic surface and geological layers of the road design area based on the basic data, generating a 3D topographic model of the road design area; acquiring multiple road engineering design schemes for the road design area, wherein the road engineering design schemes include at least horizontal alignment design, longitudinal profile design, and cross-sectional design; using BIM 3D visualization technology, generating a 3D road model corresponding to each road engineering design scheme based on the 3D topographic model of the road design area and the road engineering design schemes; and generating the optimal road engineering design scheme based on the 3D road model corresponding to each road engineering design scheme.

[0015] Compared to existing technologies, the road engineering design and modeling system and method based on BIM 3D visualization technology provided in this specification have at least the following advantages: 1. By collecting basic data on topography, geology, existing facilities, and meteorology, we can provide a rich basis for subsequent design and improve the rationality and comprehensiveness of the design. 2. By utilizing BIM 3D visualization technology, accurate 3D terrain models can be generated, providing an intuitive and precise basic model for road design; 3. Based on the 3D terrain model and various road engineering design schemes, generate corresponding 3D road models to visualize the abstract design schemes, making them easier to evaluate and compare intuitively.

[0016] 4. Generating optimal solutions based on 3D road models helps improve the quality of road engineering design and enable more scientific and rational decision-making. Attached Figure Description

[0017] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is a block diagram of a road engineering design and modeling system based on BIM 3D visualization technology shown in one embodiment of this application; Figure 2 This is a flowchart illustrating the acquisition of geological data for a road design area, as shown in one embodiment of this application; Figure 3 This is a flowchart illustrating the prediction of traffic flow data for a road to be designed, as shown in one embodiment of this application; Figure 4This is a flowchart illustrating a road engineering design and modeling method based on BIM three-dimensional visualization technology in one embodiment of this application. Detailed Implementation

[0018] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0019] Figure 1 This is a block diagram of a road engineering design and modeling system based on BIM 3D visualization technology, as shown in one embodiment of this application. Figure 1 As shown, a road engineering design and modeling system based on BIM 3D visualization technology can include a data acquisition module, a terrain modeling module, a design acquisition module, a design modeling module, and a design optimization module.

[0020] The data acquisition module is used to acquire basic data for the road design area.

[0021] The basic data includes at least topographic data, geological data, existing facility data, and meteorological data.

[0022] Specifically, topographic data is used to describe the geographical information of the landform in the road design area, including elevation, slope, aspect, and landform features.

[0023] Geological data reflects information about the underground soil and rock structure, physical and mechanical properties, and geological hazard risks in the road design area.

[0024] Existing facility data includes the distribution and attribute information of existing infrastructure within the road design area, such as transportation (e.g., the horizontal location and elevation of existing roads, bridges, tunnels, and railways), municipal pipelines (e.g., drainage pipes, power cables, communication lines, and gas pipes), and buildings.

[0025] Meteorological data reflects information on the long-term climate characteristics and short-term weather changes in the road design area, including temperature, precipitation, wind speed, etc.

[0026] In some embodiments, the data acquisition module acquires terrain data of the road design area, including: Acquire satellite imagery of the road design area. The satellite imagery provides a global macro view of the road design area, which is used to initially delineate the collection range and identify terrain features, providing a basis for subsequent UAV zoning. The satellite imagery can be medium resolution satellite (such as Sentinel-2, Landsat 8) imagery, which is used for large-area coverage and is less costly. Based on satellite imagery of the road design area, the road design area is divided into multiple first sub-regions; Based on multiple first sub-regions, determine the collaborative data collection route for the UAV cluster; Based on the collaborative collection of routes and multiple first sub-regions by a drone swarm, terrain data of the road design area is obtained.

[0027] Specifically, based on satellite imagery of the road design area, topographic features (e.g., elevation, slope, aspect, topographic relief, etc.) of multiple cells within the road design area are extracted, and the Euclidean distance between the topographic features of any two cells is calculated. Then, using clustering algorithms (e.g., K-Means clustering, hierarchical clustering, etc.), the road design area is divided into multiple first sub-regions based on the Euclidean distance between the topographic features of any two cells.

[0028] The following process can be used to determine the collaborative data collection route for the drone swarm based on multiple first sub-regions: S11. Construct a fitness function, where the fitness function is related to the standard deviation of the height of each drone's acquisition sub-route and the number of drones in the drone swarm collaborative acquisition route. The smaller the standard deviation of the height of each drone's acquisition sub-route and the fewer the number of drones, the larger the value of the fitness function. The standard deviation of the height of the drone's acquisition sub-route is the standard deviation of the height of multiple trajectory points in the drone's acquisition sub-route. S12. Construct a set of route generation constraints, which includes constraints on drone data collection altitude, maximum number of drones, and drone conflict. The drone data collection altitude constraint ensures that the drone's flight altitude is within a safe range, and the allowable altitude range can be dynamically adjusted through terrain data. The drone conflict constraint ensures that the horizontal distance between any two drones is greater than a safe threshold. S13. Based on the route generation constraint set and the terrain data of the road design area, generate multiple candidate drone cluster collaborative acquisition routes; S14. Using a genetic algorithm, determine the collaborative data collection route for the UAV swarm based on multiple candidate collaborative data collection routes and fitness functions.

[0029] Fitness function (as an example only) It can be:

[0030] in, The number of drones included in the drone swarm collaborative data collection route. Let be the total number of trajectory points along the data collection sub-route of the nth drone. Let m be the altitude of the m-th trajectory point on the data collection sub-route of the n-th drone. Let be the average altitude of the data collection sub-route of the nth drone.

[0031] In this fitness function, a smaller standard deviation indicates a more stable and less fluctuating drone flight altitude, encouraging routes with high altitude consistency and low resource consumption. By minimizing the altitude standard deviation, it ensures that the drone swarm maintains a similar altitude during collaborative data collection, avoiding collision risks and uneven energy consumption (altitude fluctuations increase climb / descent power consumption) caused by excessive altitude differences. It directly penalizes an increase in the number of drones, prompting the algorithm to minimize the number of drones used while meeting coverage requirements. Through the dual optimization of minimizing the altitude standard deviation and minimizing the number of drones, it achieves safety, efficiency, and resource conservation in collaborative drone swarm data collection, making it particularly suitable for low-cost, high-reliability mapping tasks in complex terrain.

[0032] This approach leverages satellite imagery to perform low-cost, large-scale preliminary terrain analysis, dividing the area into first sub-regions and guiding collaborative data collection by a drone swarm. Ultimately, it efficiently and accurately acquires terrain data for road design areas, balancing global coverage with local details. By constructing a fitness function that correlates altitude standard deviation with the number of drones, and combining dynamic altitude constraints and conflict avoidance mechanisms, a genetic algorithm is used to efficiently select highly consistent, resource-efficient, and safe collaborative data collection routes for the drone swarm.

[0033] Figure 2 This is a flowchart illustrating the acquisition of geological data for a road design area, as shown in one embodiment of this application. Figure 2 As shown, in some embodiments, the data acquisition module acquires geological data of the road design area, including: Based on meteorological data of the road design area, the road design area is divided into multiple second sub-areas; For any two second sub-regions, determine the correlation coefficient of the geological differences between the two second sub-regions; Obtain geological data from multiple initial sampling points in each second sub-region; For each second sub-region, the geological difference value of the second sub-region is calculated based on the geological data of multiple initial sampling points in the second sub-region; The density of geological sampling points in each second sub-region is determined based on the geological difference value of each second sub-region and the correlation coefficient of geological differences between any two second sub-regions; Geological data for the road design area are obtained based on the density of geological sampling points in each second sub-region.

[0034] Specifically, based on meteorological data of the road design area, meteorological characteristics of multiple cells are determined (e.g., mean temperature, standard deviation of temperature, mean humidity, standard deviation of humidity, etc.), and the Euclidean distance between the meteorological characteristics of any two cells is calculated. Using clustering algorithms (e.g., K-Means clustering, hierarchical clustering, etc.), the road design area is divided into multiple second sub-regions based on the meteorological characteristics of the terrain features of any two cells.

[0035] For each second sub-region, multiple initial sampling points can be evenly set up in the second sub-region. Based on the geological data of each initial sampling point, the geological characteristics of the initial sampling point are determined. The Euclidean distance between the geological characteristics of any two initial sampling points is calculated. The standard deviation of the Euclidean distance between the geological characteristics of any two initial sampling points in the second sub-region is calculated as the geological difference value of the second sub-region.

[0036] The density of geological sampling points in each second sub-region can be determined in the following way: S21. Obtain meteorological data and geological difference values ​​for multiple sample areas; S23. For each sample area, based on the meteorological data of the sample area, the sample area is divided into multiple sample second sub-regions, and the meteorological characteristics of each sample second sub-region are determined. S24. Based on the meteorological characteristics of the second sub-region of each sample in each sample area, determine multiple sub-region types, where the meteorological characteristics of the second sub-regions of different sample areas corresponding to the sub-region types are similar; S25. For any two sub-region types, take the geological difference values ​​of the two sub-region types in the sample second sub-region corresponding to the sample region containing the two sub-region types as two variables, substitute them into the calculation formula of the correlation coefficient (e.g., Pearson correlation coefficient, etc.), and calculate the geological difference correlation coefficient between the two sub-region types. S26. For any two second sub-regions, determine the sub-region type of each second sub-region, and use the geological difference correlation coefficient of the sub-region types of the two second sub-regions as the geological difference correlation coefficient of the two second sub-regions. S27. For each second sub-region, the geological difference correlation coefficient between the second sub-region and any other second sub-region can be used as a weight. The geological difference correlation coefficient of any other second sub-region can be weighted and summed to obtain the geological difference value correction amount. Then, the geological difference value and the geological difference value correction amount of the second sub-region can be summed to obtain the corrected geological difference value. Based on the corrected geological difference value, the density of geological sampling points in the second sub-region can be determined. The larger the corrected geological difference value, the higher the density of geological sampling points in the second sub-region.

[0037] Based on the density of geological sampling points in each second sub-region, multiple sampling points are evenly set up in the second sub-region, and geological data of each sampling point is collected. The geological data of the road design area may include the geological data of each sampling point.

[0038] By zoning meteorological data, quantifying geological differences, and performing correlation analysis, the sampling point density of each sub-region is dynamically adjusted to achieve high efficiency and specificity in geological data acquisition, balancing global coverage accuracy with local detail representation, while reducing the cost of redundant sampling.

[0039] The terrain modeling module is used to model the terrain surface and geological layers of the road design area based on the basic data of the road design area using BIM 3D visualization technology, and generate a 3D terrain model of the road design area.

[0040] Specifically, it includes: Based on the topographic and geological data of the road design area, identify similar historical road design areas; Retrieve 3D terrain models of areas with similar historical road designs; Based on the topographic and geological data of the road design area and similar historical road design areas, the three-dimensional topographic models of similar historical road design areas are adjusted to generate a three-dimensional topographic model of the road design area.

[0041] Specifically, the input topographic data (elevation point cloud, contour lines, DEM) and geological data (rock strata distribution, soil parameters) are standardized to unify the coordinate system and units. Slope, curvature, and surface roughness are calculated based on the topographic data, and rock strata thickness and soil type proportions are classified based on the geological data as key features of the road design area.

[0042] Calculate the cosine similarity between the key features of the road design area and the key features of the historical road design area, and regard the historical road design area with a cosine similarity greater than the cosine similarity threshold (e.g., 0.7) as a similar historical road design area.

[0043] Based on the candidate region ID, the corresponding 3D terrain model (including surface elevation grid and geological layer voxels) is extracted from the BIM database. The historical model coordinate system is aligned with the current design area, and key control points (such as ridge lines and valley points) are matched through a seven-parameter transformation (translation, rotation, scaling). The alignment accuracy is optimized using the ICP (Iterative Closest Point) algorithm, and a convergence threshold is set (such as root mean square error < 0.5 meters).

[0044] Adjusting the 3D terrain model of similar historical road design areas to generate a 3D terrain model of the road design area may include the following steps: S31, Terrain Surface Adaptation: Difference map generation: Calculates the difference map between the current area elevation and the historical model elevation, and identifies areas of abrupt elevation changes (such as differences > 5 meters).

[0045] Regional deformation: In smooth regions, bicubic interpolation is used to adjust the historical model mesh to gradually approximate the current elevation; in abrupt regions, local deformation is performed based on radial basis functions to preserve features such as geological fault zones.

[0046] Constraint handling: Ensure that the adjusted terrain meets engineering constraints (such as minimum cut and fill height, drainage slope). S32, Geological Layer Adaptation Stratigraphic mapping: Aligning the rock strata sequence in the current geological data with the stratigraphic levels in the historical model to establish a lithological correspondence table.

[0047] Thickness adjustment: For layers with large differences (such as thickness deviation >20%), conformal mapping is used to adjust the layer boundaries. For newly added or missing layers, a transition region is generated by extrapolation or interpolation of adjacent layers.

[0048] Attribute fusion: Weighted average of soil parameters (such as bearing capacity and permeability coefficient), with the weight determined by the similarity of individual soil parameters; S33, Multi-scale optimization: Global optimization: Finite element analysis is used to verify the stress distribution of the adjusted model to ensure geological stability.

[0049] Local optimization: The model within the road red line area is refined with high precision (e.g., the mesh resolution is increased by 5 times), while the area outside the red line is kept with low precision to save computing resources.

[0050] By matching 3D models of similar historical areas, the process avoids building terrain and geological layers from scratch, reducing the time spent on data acquisition, processing, and modeling. By combining current regional measured data with geological stratigraphic information from historical models, adjustments are made to improve the model's fit with actual conditions. Reusing historical data reduces the frequency of fieldwork.

[0051] The design acquisition module is used to acquire multiple road engineering design schemes for the road design area.

[0052] The road engineering design scheme shall include at least the horizontal alignment design, the longitudinal section design, and the cross section design.

[0053] Specifically, horizontal alignment design refers to determining the geometry and spatial position of the road centerline on the horizontal projection plane of the road design area, including combinations of elements such as straight sections, circular curves, and transition curves, to achieve a balance between driving safety, comfort, and engineering economy. Vertical profile design refers to the vertical section design along the road centerline, determining the combination of ground elevation changes and road design elevation, including elements such as longitudinal slopes and vertical curves, to ensure drainage, driving safety, and engineering economy. Cross-section design refers to the cross-section design perpendicular to the road centerline, determining the width, elevation, and spatial arrangement of various road components, including lanes, shoulders, median strips, and slopes, to ensure driving safety, drainage function, and landscape harmony.

[0054] The design modeling module is used to generate a 3D road model corresponding to each road engineering design scheme based on the 3D terrain model of the road design area and the road engineering design scheme using BIM 3D visualization technology.

[0055] Specifically, it includes: Based on the road engineering design schemes of the road design area and similar historical road design areas, identify similar historical road engineering design schemes. Obtain initial 3D road models of similar historical road engineering design schemes; The three-dimensional terrain model of the road design area and the initial three-dimensional road model of similar historical road engineering design schemes are fused to generate a three-dimensional road model corresponding to each road engineering design scheme.

[0056] Specifically, the similarity between the road engineering design scheme of the road design area and the historical road engineering design scheme of similar historical road design areas is calculated, and the historical road engineering design scheme of similar historical road design areas with a similarity greater than the similarity threshold (e.g., 0.7) is regarded as similar historical road engineering design scheme.

[0057] Retrieve initial 3D road models in IFC format from the historical project BIM library, containing the following hierarchical information: Geometric layers: road centerline, road surface edge, slope surface; Property layer: material parameters (asphalt thickness, concrete strength), construction information (number of fill layers, compaction degree); Relationship layer: The relationship between the model and sub-models such as terrain, drainage, and bridges.

[0058] A physics-based deformation algorithm is employed to dynamically adjust components such as road surfaces and slopes in the historical model according to the current terrain elevation. Boolean operations are performed on the deformed model and the current terrain to handle conflicts at the interface (such as road surface encroachment on the terrain or slope overhang). Attributes of the historical model (such as material parameters) are mapped to the fused model and validated against current standards. For example, if the initial 3D road model of a similar historical road engineering design scheme uses AC-13 asphalt, but current standards require the use of SMA-13, the attributes are automatically updated and the structural thickness is recalculated. The fused road engineering design scheme's corresponding 3D model is verified to have pavement smoothness (IRI < 2.5 m / km), slope stability (safety factor > 1.3), and that there are no missing or contradictory geometric, attribute, or relational layer data.

[0059] By reusing similar historical solutions, modeling from scratch is avoided, reducing the modeling time for a single project from 2-4 weeks using traditional methods to 3-5 days, improving efficiency by 70%-90%. Dynamic fusion technology enables parameter-driven model updates, reducing design change response time from several days to within 2 hours, and supporting rapid iterative optimization.

[0060] The design optimization module is used to generate the optimal road engineering design scheme based on the road 3D model corresponding to each road engineering design scheme.

[0061] Specifically, it includes: Obtain the road connectivity topology map corresponding to the road to be designed; Based on the road connectivity topology map corresponding to the road to be designed, predict the traffic flow data of the road to be designed; Based on the traffic flow data of the road to be designed and the 3D road model corresponding to each road engineering design scheme, the optimal road engineering design scheme is generated.

[0062] Specifically, regional road network vector data (such as Shapefile format) is obtained from the urban transportation planning department, including attributes such as road level (expressway / artery / secondary artery / local road), length, number of lanes, and intersection type (cross / T-shaped / roundabout). Roads are abstracted as "edges" and intersections as "nodes," and a directed graph is constructed as the road connectivity topology corresponding to the road to be designed. The edge weights can be set to traffic flow.

[0063] Figure 3 This is a flowchart illustrating the prediction of traffic flow data for a road to be designed, as shown in one embodiment of this application. Figure 3 As shown, in some embodiments, the design optimization module predicts traffic flow data for the road to be designed based on the road connectivity topology map corresponding to the road to be designed, including: Extract the graph features of the road connectivity topology corresponding to the road to be designed. The graph features may include node features (e.g., node type (e.g., highway exit, commercial area entrance, school entrance), node degree (number of connected roads)), edge features (e.g., road geometric attributes (length, number of lanes, design speed), topological attributes (whether it is a critical path, percentage of shortest paths), traffic flow attributes (average historical traffic flow of surrounding roads)), and global features (e.g., average clustering coefficient of the road network (reflecting road density), diameter (length of the longest path)). Based on the graph features of the road connectivity topology corresponding to the road to be designed, similar historical roads to be designed are identified. Obtain the traffic correlation matrix of similar historical roads to be designed; Based on the traffic flow correlation matrix of similar historical roads to be designed, the traffic flow correlation roads of the roads to be designed are determined; Based on the historical traffic flow data of the road to be designed, predict the traffic flow data of the road to be designed.

[0064] Specifically, the similarity between the graph features of the road connectivity topology graph corresponding to the road to be designed and the graph features of the road connectivity topology graph corresponding to the historical roads to be designed is calculated, and the historical roads to be designed with a similarity greater than the similarity threshold (e.g., 0.7) are considered as similar historical roads to be designed.

[0065] The traffic correlation matrix of similar historical roads to be designed can include the traffic correlation coefficient of any two roads in the road connectivity topology graph of similar historical roads to be designed. The historical traffic of the two roads can be substituted as two variables into the calculation formula of the correlation coefficient (e.g., Pearson correlation coefficient) to calculate the traffic correlation coefficient of the two roads.

[0066] The traffic association roads of the road to be designed can be determined in any way based on the traffic association matrix of similar historical roads to be designed. For example, the traffic association road can be determined by an association determination model based on the traffic association matrix of similar historical roads to be designed. The association determination model can be a convolutional neural network. The input of the association determination model can include the road connectivity topology graph corresponding to the road to be designed, the road connectivity topology graph of similar historical roads to be designed, and the traffic association matrix of similar historical roads to be designed. The output of the association determination model includes the traffic association roads of the road to be designed.

[0067] In some embodiments, the design optimization module predicts the traffic flow data of the road to be designed based on historical traffic flow data of the traffic flow associated roads, including: Based on the traffic flow correlation matrix of similar historical roads to be designed, the traffic flow correlation matrix of the roads to be designed is determined; Based on the traffic flow correlation matrix of the road to be designed and the historical traffic flow data of the roads associated with the traffic flow of the road to be designed, the traffic flow data of the road to be designed is predicted.

[0068] Specifically, the traffic flow correlation matrix of the road to be designed can be determined by using the aforementioned correlation model based on the traffic flow correlation matrix of similar historical roads to be designed.

[0069] Traffic flow prediction models can be used to predict the traffic flow data of the road to be designed based on the traffic flow correlation matrix of the road to be designed and the historical traffic flow data of the roads associated with the traffic flow of the road to be designed. The traffic flow prediction model can be a long short-term memory network model.

[0070] In some embodiments, the design optimization module generates an optimal road engineering design scheme based on the traffic flow data of the road to be designed and the road 3D model corresponding to each road engineering design scheme, including: Identify multiple design optimization metrics; Based on multiple design optimization indices, a fitness function is constructed. Specifically, each design optimization index is assigned a weight, and the fitness function is a weighted sum of the normalized values ​​of each function. Using the particle swarm optimization algorithm, the optimal road engineering design scheme is generated based on the traffic flow data of the road to be designed, the road 3D model corresponding to each road engineering design scheme, and the fitness function.

[0071] Specifically, multiple design optimization metrics can cover the following dimensions: 1. Traffic efficiency indicators: Traffic capacity: The maximum number of vehicles a road can carry per unit of time.

[0072] Average delay time: The average waiting time (in seconds) for vehicles to pass through the road.

[0073] Queue length: The length of vehicle queues during peak hours (in meters); 2. Safety indicators: Number of conflict points: The number of potential conflict points in the vehicle travel paths within the intersection (such as the intersection of left-turning and straight-going vehicles).

[0074] 3. Economic indicators: Construction cost: The cost of infrastructure such as roads, bridges, and tunnels (ten thousand yuan / km).

[0075] Maintenance cost: The average annual maintenance cost during the road's use period (ten thousand yuan / year).

[0076] Each road engineering design scheme is treated as a particle.

[0077] Export the BIM model to a common format (such as IFC or LandXML) and import it into traffic simulation software (such as VISSIM or SUMO). Define traffic flow parameters in the simulation software, simulate vehicle trajectories, and determine the score for traffic efficiency indicators. Mark the intersection points of vehicle paths in the BIM model (such as conflict points between left-turn lanes and oncoming straight lanes), count the number of conflict points per unit length of road, and obtain the score for safety indicators. The BIM model automatically calculates the volume of each part of the road (such as subgrade fill volume and pavement area), material usage (such as asphalt tons and concrete cubic meters), associates the material unit price (such as asphalt at 800 yuan / ton), generates a construction cost list, and estimates the average annual maintenance cost based on pavement material type (such as asphalt pavement life of 10 years and concrete pavement life of 20 years) and traffic load, obtaining the score for economic efficiency indicators.

[0078] Based on the scores of multiple design optimization indicators, the fitness function value of the road engineering design scheme is calculated.

[0079] The optimal road engineering design scheme is generated by iteratively searching for the fitness function value of the road engineering design scheme using the particle swarm optimization algorithm.

[0080] Figure 4 This is a flowchart illustrating a road engineering design and modeling method based on BIM 3D visualization technology in one embodiment of this application, as shown below. Figure 4 As shown, the road engineering design and modeling method based on BIM 3D visualization technology can include the following process.

[0081] Obtain basic data for the road design area, including at least topographic data, geological data, existing facility data, and environmental data; By using BIM-based 3D visualization technology, the topographic surface and geological layers of the road design area are modeled based on the basic data of the road design area, and a 3D topographic model of the road design area is generated. Obtain multiple road engineering design schemes for the road design area, including at least horizontal alignment design, longitudinal profile design, and cross-sectional design. Based on BIM 3D visualization technology, a 3D road model corresponding to each road engineering design scheme is generated according to the 3D terrain model of the road design area and the road engineering design scheme. The optimal road engineering design scheme is generated based on the three-dimensional road model corresponding to each road engineering design scheme.

[0082] The road engineering design and modeling method based on BIM 3D visualization technology can be applied to the road engineering design and modeling system based on BIM 3D visualization technology mentioned above, and will not be elaborated further here.

[0083] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and are considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A road engineering design and modeling system based on BIM 3D visualization technology, characterized in that, include: The data acquisition module is used to acquire basic data of the road design area, wherein the basic data includes at least topographic data, geological data, existing facility data and meteorological data; The terrain modeling module is used to model the terrain surface and geological layers of the road design area based on the basic data of the road design area using BIM 3D visualization technology, and generate a 3D terrain model of the road design area. The design acquisition module is used to acquire multiple road engineering design schemes for the road design area. The road engineering design schemes include at least the horizontal alignment design, the longitudinal profile design, and the cross-sectional design. The design modeling module is used to generate a road 3D model corresponding to each road engineering design scheme based on the 3D terrain model of the road design area and the road engineering design scheme using BIM 3D visualization technology. The design optimization module is used to generate the optimal road engineering design scheme based on the road 3D model corresponding to each road engineering design scheme.

2. The road engineering design and modeling system based on BIM three-dimensional visualization technology according to claim 1, characterized in that, The data acquisition module acquires terrain data of the road design area, including: Acquire satellite imagery of the road design area; Based on satellite imagery of the road design area, the road design area is divided into multiple first sub-regions; Based on multiple first sub-regions, determine the collaborative data collection route for the UAV cluster; Based on the collaborative collection of routes and multiple first sub-regions by a drone swarm, terrain data of the road design area is obtained.

3. The road engineering design and modeling system based on BIM three-dimensional visualization technology according to claim 2, characterized in that, The data acquisition module acquires geological data of the road design area, including: Based on meteorological data of the road design area, the road design area is divided into multiple second sub-areas; For any two second sub-regions, determine the correlation coefficient of the geological differences between the two second sub-regions; Obtain geological data from multiple initial sampling points in each second sub-region; For each second sub-region, the geological difference value of the second sub-region is calculated based on the geological data of multiple initial sampling points in the second sub-region; The density of geological sampling points in each second sub-region is determined based on the geological difference value of each second sub-region and the correlation coefficient of geological differences between any two second sub-regions; Geological data for the road design area are obtained based on the density of geological sampling points in each second sub-region.

4. The road engineering design and modeling system based on BIM three-dimensional visualization technology according to any one of claims 1-3, characterized in that, The terrain modeling module, based on BIM 3D visualization technology and using fundamental data of the road design area, models the terrain surface and geological layers of the road design area, generating a 3D terrain model of the road design area, including: Based on the topographic and geological data of the road design area, identify similar historical road design areas; Retrieve 3D terrain models of areas with similar historical road designs; Based on the topographic and geological data of the road design area and similar historical road design areas, the three-dimensional topographic models of similar historical road design areas are adjusted to generate a three-dimensional topographic model of the road design area.

5. The road engineering design and modeling system based on BIM three-dimensional visualization technology according to claim 4, characterized in that, The design modeling module, based on BIM 3D visualization technology, generates a 3D road model corresponding to each road engineering design scheme, based on the 3D terrain model of the road design area and the road engineering design scheme, including: Based on the road engineering design schemes of the road design area and similar historical road design areas, identify similar historical road engineering design schemes. Obtain initial 3D road models of similar historical road engineering design schemes; The three-dimensional terrain model of the road design area and the initial three-dimensional road model of similar historical road engineering design schemes are fused to generate a three-dimensional road model corresponding to each road engineering design scheme.

6. The road engineering design and modeling system based on BIM three-dimensional visualization technology according to any one of claims 1-3, characterized in that, The design optimization module generates the optimal road engineering design scheme based on the road 3D model corresponding to each road engineering design scheme, including: Obtain the road connectivity topology map corresponding to the road to be designed; Based on the road connectivity topology map corresponding to the road to be designed, predict the traffic flow data of the road to be designed; Based on the traffic flow data of the road to be designed and the 3D road model corresponding to each road engineering design scheme, the optimal road engineering design scheme is generated.

7. The road engineering design and modeling system based on BIM three-dimensional visualization technology according to claim 6, characterized in that, The design optimization module predicts traffic flow data for the road to be designed based on the road connectivity topology map corresponding to the road to be designed, including: Extract graph features from the road connectivity topology corresponding to the road to be designed; Based on the graph features of the road connectivity topology corresponding to the road to be designed, similar historical roads to be designed are identified. Obtain the traffic correlation matrix of similar historical roads to be designed; Based on the traffic flow correlation matrix of similar historical roads to be designed, the traffic flow correlation roads of the roads to be designed are determined; Based on the historical traffic flow data of the road to be designed, predict the traffic flow data of the road to be designed.

8. The road engineering design and modeling system based on BIM three-dimensional visualization technology according to claim 7, characterized in that, The design optimization module predicts the traffic flow data of the road to be designed based on the historical traffic flow data of the roads associated with the road to be designed, including: Based on the traffic flow correlation matrix of similar historical roads to be designed, the traffic flow correlation matrix of the roads to be designed is determined; Based on the traffic flow correlation matrix of the road to be designed and the historical traffic flow data of the roads associated with the traffic flow of the road to be designed, the traffic flow data of the road to be designed is predicted.

9. The road engineering design and modeling system based on BIM three-dimensional visualization technology according to claim 8, characterized in that, The design optimization module generates the optimal road engineering design scheme based on the traffic flow data of the road to be designed and the corresponding 3D road model for each road engineering design scheme, including: Identify multiple design optimization metrics; A fitness function is constructed based on multiple design optimization metrics. Using the particle swarm optimization algorithm, the optimal road engineering design scheme is generated based on the traffic flow data of the road to be designed, the road 3D model corresponding to each road engineering design scheme, and the fitness function.

10. A road engineering design and modeling method based on BIM 3D visualization technology, characterized in that, The road engineering design and modeling system based on BIM 3D visualization technology as described in claim 1 includes: Obtain basic data for the road design area, wherein the basic data includes at least topographic data, geological data, existing facility data, and environmental data; By using BIM-based 3D visualization technology, the topographic surface and geological layers of the road design area are modeled based on the basic data of the road design area, and a 3D topographic model of the road design area is generated. Obtain multiple road engineering design schemes for the road design area, wherein the road engineering design schemes include at least horizontal alignment design, longitudinal profile design and cross-sectional design; Based on BIM 3D visualization technology, a 3D road model corresponding to each road engineering design scheme is generated according to the 3D terrain model of the road design area and the road engineering design scheme. The optimal road engineering design scheme is generated based on the three-dimensional road model corresponding to each road engineering design scheme.

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