A method for generating an intelligent three-dimensional road network for a complex mountainous terrain

Abstract

The application discloses a kind of complex mountain terrain-oriented city three-dimensional road network intelligent generation method, contain design site data acquisition and processing, trunk road network node identification, trunk road network generation, secondary trunk road network generation, road network smoothing and angle optimization, road network output and interactive optimization;The present application is unified to the present situation topography and environmental data of site processing, combined with improved A* algorithm carries out path search and optimization, and intelligently generates primary and secondary road network and carries out adaptive smoothing processing;The three-dimensional road network finally output has higher adaptability and rationality, interactive optimization display is more intuitive and three-dimensional, and effective support is provided to complex terrain city road planning.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban and rural intelligent planning, and particularly relates to a three-dimensional urban road network intelligent generation method for complex mountain terrain. BACKGROUND

[0002] The urban road network layout is an important part of urban planning work and an important means of controlling urban spatial structure, and deeply affects the operation efficiency and future development of the city. The road network generation technology mainly exists in two application scenarios of new city construction road planning and old city expansion road planning. In actual application scenarios, there are often problems of complex terrain, dense present situation reservation, high construction cost and the like. The traditional road network generation method is difficult to balance the relationship between complex present situation conditions and planning intention, is difficult to adapt to the design requirements of road network in different scenarios, and cannot dynamically and intuitively interactively adjust and improve the generation scheme.

[0003] Therefore, it is urgent to solve the above problems. SUMMARY

[0004] The purpose of the application is to provide a three-dimensional urban road network intelligent generation method for complex mountain terrain, which solves the problems that the existing urban road network generation method is difficult to adapt to complex scenarios, difficult to balance the relationship between complex present situation conditions and planning intention, and cannot dynamically and interactively adjust and improve the generation scheme.

[0005] Technical scheme: In order to achieve the above purpose, the application discloses a three-dimensional urban road network intelligent generation method for complex mountain terrain, which comprises the following steps:

[0006] (1) Collecting design site present situation terrain data and environment data, collecting design site related upper planning data, importing into a geographic information platform, uniformly processing into grid data or vector data in a format, and generating a design site database;

[0007] (2) Calculating the development suitability of each district of the design site based on the design site database, identifying the center point in each region in the high suitability area as a trunk road node after threshold screening;

[0008] (3) Generating a three-dimensional grid of the design site, using an improved A* algorithm for path search, connecting the trunk road nodes and extending to the edge of the design site, forming several trunk roads, deleting unreasonable paths, and calculating the distance between the trunk roads for road network supplement and adjustment;

[0009] (4) Based on the generated trunk road network, selecting secondary trunk road nodes on the trunk roads according to the road distance specification, using the improved A* algorithm for path search, and generating a secondary trunk road network;

[0010] (5) Using adaptive smoothing algorithm to process the generated primary and secondary road network, optimizing the road angle;

[0011] (6) Output the generated road network, and perform three-dimensional visualization interaction optimization.

[0012] Optionally, step (1) specifically comprises the following steps:

[0013] (1.1) Collecting the design site current terrain data, obtaining the DEM raster data through the geographic spatial data cloud or the geological exploration bureau of the city where the design site is located;

[0014] (1.2) Collecting the design site current environmental data, which includes the vector environmental data of rivers and current buildings, the atmospheric environmental data, and the raster data of land cover types;

[0015] (1.3) Using a drone equipped with a high-resolution camera and LiDAR to take high-altitude images of the design site, and identifying the design site current terrain data and the design site current environmental data except the atmospheric environmental data from the high-altitude images, which are used to assist in perfecting the design site current terrain data and environmental data;

[0016] (1.4) Obtaining the design site upper planning data from the local urban planning related department, which includes the city overall planning map, regional development planning map, traffic planning map, and upper planning farmland index;

[0017] (1.5) Importing the collected terrain data, environmental data, and upper planning data into a high-performance workstation, using a geographic information platform, selecting a coordinate system based on the site coordinates, adding geographic registration control points, and spatially positioning the upper planning data; converting the planning map of the upper planning data from raster data to vector elements in the geographic information platform, correcting errors in the conversion process, identifying the range of each functional area in the planning map, assigning values to different functional areas, and storing as raster data;

[0018] (1.6) Integrating the data to generate a design site database, which is stored in a RAID 10 storage array and managed using a database manager.

[0019] Optionally, in step (1.2), the vector environmental data of rivers and current buildings are obtained through an open source data platform; the atmospheric environmental data are obtained through the China Meteorological Data Network; and the raster data of land cover types are obtained through the National Geographic Information Resource Directory Service System.

[0020] Optionally, step (2) specifically comprises the following steps:

[0021] (2.1) Based on the design site database, the design site is analyzed using a geographic information platform to obtain the evaluation results of the normalized slope index, regional climate comfort degree index, land redevelopability index, and functional area redeveloping timing index;

[0022] (2.2) According to the analytic hierarchy process (AHP), a judgment matrix is constructed to obtain the weight of each factor; the slope index, regional climate comfort degree index, land redevelopability index, and functional area redeveloping timing index are superimposed according to the weight calculated by the judgment matrix to obtain a development suitability base map of the design site and store it in a raster data format;

[0023] (2.3) A grid is established, the development suitability base map is converted into point data, the value of the point data is extracted into the corresponding grid using a spatial join tool, the extracted data is classified into 5 levels using a natural segmentation method, and the development suitability is from low to high corresponding to 1 to 5, and the classification results are stored in a new field of "development suitability"; in the attribute table, the cell with a value of 5 in the development suitability field is selected using the attribute selection tool, and the geometric center of each high suitability area is taken as the main road node;

[0024] (2.4) Check whether the location of each node has a steep slope or unsuitable terrain for development, and if so, move the node to the nearest flat area.

[0025] Optionally, step (2.1) specifically includes the following steps:

[0026] The slope is calculated using DEM raster data, the slope is evaluated, and is divided into 5 levels of high, higher, medium, lower, and low according to ≤2°, 2-6°, 6-15°, 15-25°, and >25°, and is assigned a score of 1, 2, 3, 4, and 5, wherein the high level corresponds to flat land, the higher level corresponds to flat slope land, the medium level corresponds to gentle slope land, the lower level corresponds to gentle steep slope land, and the low level corresponds to steep slope land, and a slope classification map is generated;

[0027] The monthly average temperature and the monthly average air relative humidity are calculated using the atmospheric environment data, the thermal comfort index is calculated, the regional climate comfort degree is evaluated, the regional climate comfort degree is divided into 7 levels and is assigned according to the level, the thermal comfort index 60-65 corresponds to the comfort degree level 7, and is assigned 7 points; the thermal comfort index 56-60 or 65-70 corresponds to the comfort degree level 6, and is assigned 6 points; the thermal comfort index 50-56 or 70-75 corresponds to the comfort degree level 5, and is assigned 5 points; the thermal comfort index 45-50 or 75-80 corresponds to the comfort degree level 4, and is assigned 4 points; the thermal comfort index 40-45 or 80-85 corresponds to the comfort degree level 3, and is assigned 3 points; the thermal comfort index 32-40 or 85-90 corresponds to the comfort degree level 2, and is assigned 2 points; the thermal comfort index < 32 or > 90 corresponds to the comfort degree level 1, and is assigned 1 point; the higher the comfort degree level is, the higher the comfort degree is;

[0028] The land reformation degree is evaluated according to the upper planning arable land index using the land surface coverage type data, the land of various types is divided into 3 levels of reformation, moderate reformation and non-reformation, and is assigned 5 points, 3 points and 1 point;

[0029] The functional areas are divided into 3 levels of to be developed, short-term development and key development using the urban overall planning map, and are assigned 1 point, 2 points and 3 points;

[0030] The four evaluation scores are normalized to obtain the slope index, the regional climate comfort degree index, the land reformation degree index and the functional area reformation time sequence index.

[0031] Optionally, the importance ranking of the judgment matrix in step (2.2) is: the slope index > the land reformation degree index > the functional area reformation time sequence index > the regional climate comfort degree index; the sum of each column of the judgment matrix is calculated; each element is divided by the column sum to obtain the normalized judgment matrix; and the average value of each row is calculated to obtain the weight of each factor;

[0032] The development suitability superposition calculation formula is: k1xw1+k2xw2+k3xw3+k4xw4, wherein k1 is the slope index, w1 is the weight of the slope index, k2 is the regional climate comfort degree index, w2 is the weight of the regional climate comfort degree index; k3 is the land reformation degree index, w3 is the weight of the land reformation degree index; k4 is the functional area reformation time sequence index, and w4 is the weight of the functional area reformation time sequence index.

[0033] Optionally, step (3) specifically includes the following steps:

[0034] (3.1) Identify the center point of each grid of the design site DEM grid data containing height coordinates, connect all grid center points to generate a three-dimensional grid, write the three-dimensional grid using high-speed storage device NVMe SSD, and use it to generate road network storage;

[0035] (3.2) Use improved A* algorithm for path search, define the main road node as the starting point and the end point of path search, and the formula of the function f(n) of the improved A* algorithm is:

[0036] f(n)=g(n)+w·h(n)+α·S(n)+β·C(n)

[0037] g(n) is the actual cost from the starting point to the current node n, that is, the path length;

[0038] h(n) is the Euclidean distance heuristic function, which estimates the cost from node n to the target node;

[0039] w is a dynamic weight used to adjust the influence of h(n), which changes dynamically according to the search progress;

[0040] S(n) is the terrain cost function, which reflects the slope or terrain relief degree of the area where node n is located;

[0041] α is the weight of terrain cost, which determines the influence of terrain on path selection;

[0042] C(n) is the road design rule cost function, which considers the road spacing and road curvature factors;

[0043] β is the weight of road design rule, which determines the influence of road design rule on path selection;

[0044] wherein,

[0045] g(n)=g(p)+d(p,n)

[0046] g(p) is the cost of the previous node p;

[0047] d(p,n) is the path length from node p to node n;

[0048]

[0049] Δh(n) is the height difference from the previous node p to the current node n, that is, the slope;

[0050] d(p,n) is the horizontal distance from the previous node p to the current node n;

[0051]

[0052] (x n ,yn ,z n ) is the three-dimensional coordinate of the current node n;

[0053] (x goal ,y goal ,z goal ) is the three-dimensional coordinate of the target node;

[0054]

[0055] d road (n) is the actual distance between the current node n and the nearest adjacent road;

[0056] d target is the predefined ideal road distance, determined as 800-1000 meters according to urban design standards;

[0057] θ(n) is the turning angle of the current path at node n, expressed in degrees;

[0058] θmax is the maximum allowed road turning angle, determined as 60 degrees according to design standards;

[0059] (3.3) Use the improved A* algorithm in step (3.2) for path search, define the main road nodes as the starting point of path search, and the three-dimensional grid boundary points of the design site as the end point of path search, generate a path with the smallest f(n) from each main road node n to the grid boundary, and preliminarily form the main road network;

[0060] (3.4) After generating the road network, check the node degree of the main road nodes, identify all paths connected to the nodes whose degree exceeds the expected value of 4, prioritize the paths, and delete the path with the lowest priority until the degree of the node is less than or equal to 4;

[0061] The cost function formula is:

[0062]

[0063] w1 and w2 are weight coefficients, respectively controlling the influence degree of slope cost and distance cost;

[0064] Δh r is the height difference between the starting point and the end point of the path r;

[0065] d r is the length of the path r;

[0066] (3.5) Calculate the distance between the generated trunk road network, according to the trunk road distance standard, mark all the areas with a distance greater than 1200 meters; in each marked area, calculate its geometric center point as a new node, and from the node to the existing trunk road node with the longest distance from the edge of the area, use the improved A* algorithm in step (3.2) to search the path to obtain a connecting path; extend the trunk road from both ends of the connecting path to the design site three-dimensional grid boundary to form several trunk roads.

[0067] Optionally, step (4) specifically comprises the following steps:

[0068] (4.1) Mark the secondary trunk road nodes on the generated trunk road network according to the distance rule, which is greater than 350 meters and less than 500 meters, and the distance is greater as it gets closer to the three-dimensional grid boundary;

[0069] (4.2) For each region divided by the trunk road network, use the improved A* algorithm in step (3.2) to search the path, adjust the function formula of the improved A* algorithm, increase the weight of the road design rule cost, that is, increase the value of β in the formula, define the secondary trunk road nodes on the boundary of the region as the starting point and the ending point, and the nodes on the same trunk road path cannot be used as a pair of starting point and ending point;

[0070] (4.3) Check the node degree of the secondary trunk road nodes after generating the path, identify the nodes whose degree exceeds the expected value 1, do not calculate the trunk road network path and the path in the non-region, delete the redundant path so that the degree of all nodes is less than or equal to 1, and obtain the secondary trunk road network.

[0071] Optionally, step (5) specifically comprises the following steps:

[0072] (5.1) Divide the road network into different regions, and divide the road network into flat regions with a slope of 0°-5°, hilly regions with a slope of 5°-15°, and mountainous regions with a slope of more than 15° according to the terrain characteristics;

[0073] (5.2) Dynamically select and adjust the smoothing algorithm and window according to different regions, and the window represents the number of adjacent data points selected within a given range. For flat areas, select the linear interpolation method, and use a larger smoothing window for parameters; for hilly areas, select the Bezier curve, and use a medium-sized smoothing window for parameters; for mountainous areas, use the Gaussian filter, and use a smaller smoothing window for parameters;

[0074] (5.3) Convert the smoothed road network into a multi-dimensional vector dataset, calculate the direction vector using Python, and calculate the included angle based on the direction vector;

[0075] (5.4) Identify the intersection node A with the included angle less than 70 degrees, and divide the two direction vectors involved in the included angle into the main road section and the secondary road section; take the node as the starting point, generate an alternative node B on the main road section with cot(θ)*10m as a step length; generate a new road section perpendicular to the main road section from the alternative node B, calculate the intersection point C of the new road section and the secondary road section; recalculate the local direction vector and calculate the included angle, and loop until the road included angle is greater than 70 degrees;

[0076] (5.5) Write the generated road network into a high-speed storage device.

[0077] Optionally, step (6) specifically comprises the following steps:

[0078] (6.1) Input the generated road network data into the virtual reality application software on the development workstation equipped with high-performance CPU and GPU for processing and rendering;

[0079] (6.2) Connect the VR head-mounted display and the matching controller, integrate and test different VR devices, create a VR scene; connect the AR device, and place and track virtual objects through a mobile device or AR glasses; perform three-dimensional visual interactive optimization of the generated road network on a 4K or above high-resolution display, and adjust and optimize the scene using VR and AR tracking devices.

[0080] Advantages: Compared with the prior art, the present application has the following advantages:

[0081] (1) The present application collects relevant design site vector data and inputs it into a geographic information platform for format unification processing, constructs a design site digital sand table, forms a design site condition data set, ensures the standardization and accuracy of the data, and improves the accuracy and efficiency of the generated city road network;

[0082] (2) The present application analyzes the terrain slope, climate comfort, ground cover type and other factors, comprehensively calculates the "development suitability" of the region and standardizes it. Effectively filter out high suitability areas as the main road node, so that the subsequent main road and secondary road generation is based on more scientific and reasonable selection, thereby improving the planning quality of the road network. This technology ensures that the generated road network not only conforms to the regional natural environment characteristics, but also meets the actual needs of regional development, providing a solid data foundation and spatial basis for subsequent road network planning, improving the overall planning efficiency and accuracy;

[0083] (3) The improved A* algorithm considers path length, terrain complexity, road design rules and other factors, so that the planning of the main road is more accurate; the weight parameter introduced can be dynamically adjusted according to actual needs, so that the generated main road is more in line with the terrain characteristics, avoiding the problem of insufficient processing of complex terrain in traditional path algorithms; in addition, the application of node priority sorting and degree value detection reduces the calculation of redundant paths, improves the efficiency of the main road generation process, and makes the algorithm maintain high computational efficiency and reasonable path when running in large-scale three-dimensional grid data;

[0084] (4) The application adopts a partition optimization search strategy based on the main road, ensures that the planning of the secondary road follows the overall structure of the main road, and through reasonable weight parameter setting, the secondary road can adapt to terrain changes and specific road requirements in different areas; through degree value detection and priority sorting of the secondary road nodes, redundant paths are effectively removed, the structure of the secondary road network is simplified, and the generated secondary road can reasonably connect the main road network while maintaining high planning efficiency;

[0085] (5) The application optimizes the main and secondary road network through an adaptive smoothing algorithm, significantly improves the practicability and safety of the road network, and makes the generated road network naturally fit the terrain changes in complex terrain. Through the intersection optimization based on the direction vector, the unreasonable design such as sharp turns is reduced, the smoothness of the connection between the secondary road and the main road is improved, and the rationality and fluency of the generated road network are greatly improved;

[0086] (6) The application inputs the generated road network into an intelligent city design platform to generate a visual three-dimensional model, and uses VR\AR technology to assist in realizing dynamic interactive adjustment and improvement of the generated scheme. BRIEF DESCRIPTION OF DRAWINGS

[0087] Figure 1 is the flowchart of the application;

[0088] Figure 2 is the principle diagram of optimizing the road angle in the application. DETAILED DESCRIPTION

[0089] The technical solutions of the application will be further described below with reference to the drawings.

[0090] The technical solutions of the application will be further described below with reference to the drawings.

[0091] As Figure 1 shown, a city three-dimensional road network intelligent generation method for complex mountainous terrain includes the following steps:

[0092] S1. Design site database construction: collect design site current topographic data and environmental data, collect design site related upper planning data, import into geographic information platform, process into raster data or vector data in unified format, generate design site database;

[0093] S1 specifically includes the following steps:

[0094] S1.1 Collect design site current topographic data, obtain DEM (Digital Elevation Model) raster data through geographic spatial data cloud or city geological exploration bureau;

[0095] S1.2 Collect design site current environmental data, which includes river and current building vector environmental data, atmospheric environmental data and land cover type raster data;

[0096] Obtain river and current building vector environmental data through an open source data platform; obtain atmospheric environmental data through China Meteorological Data Network; obtain land cover type raster data through the National Geographic Information Resource Directory Service System;

[0097] S1.3 Use a drone equipped with a high-resolution camera and LiDAR to take aerial images of the design site, and identify design site current topographic data and design site current environmental data except atmospheric environmental data from the aerial images, which are used to assist in perfecting design site current topographic data and environmental data;

[0098] S1.4 Obtain design site upper planning data from local city planning related departments, which includes city overall planning map, regional development planning map, traffic planning map and upper planning farmland index;

[0099] S1.5 Import the collected topographic data, environmental data and upper planning data into a high-performance workstation equipped with a multi-core CPU and a large-capacity memory, with at least 64GB RAM, use a geographic information platform, select a coordinate system based on site coordinates, add geographic registration control points, and perform spatial positioning on upper planning data; convert the planning map of upper planning data from raster data to vector elements in the geographic information platform, correct errors in the conversion process, identify the range of various functional areas in the planning map, assign values to different functional areas, and store as raster data;

[0100] S1.6 Integrate data to generate design site database, store in RAID 10 storage array, use database manager for data management;

[0101] S2. Identification of main road nodes based on development suitability of the plot: Based on the design site database, the development suitability of each plot of the design site is calculated, and after threshold screening, the central point in the high suitability area is identified as the main road node;

[0102] S2 specifically includes the following steps:

[0103] S2.1 Based on the design site database, use the geographic information platform to analyze the design site, and obtain the evaluation results of the normalized slope index, regional climate comfort index, land redevelopability index, and functional area reconstruction timing index;

[0104] The slope is calculated using DEM grid data, the slope is evaluated, and it is divided into high, higher, medium, lower, and low levels according to ≤2°, 2-6°, 6-15°, 15-25°, and >25°, and assigned 1 point, 2 points, 3 points, 4 points, and 5 points, respectively. The high level corresponds to flat land, the higher level corresponds to flat slope land, the medium level corresponds to gentle slope land, the lower level corresponds to gentle steep slope land, and the low level corresponds to steep slope land, and a slope classification map is generated.

[0105] The monthly average temperature and monthly average air relative humidity are calculated using atmospheric environment data, the temperature and humidity index is calculated, and the regional climate comfort degree is evaluated. The regional climate comfort degree is divided into 7 levels and assigned according to the level. The temperature and humidity index 60-65 corresponds to a comfort level of 7, assigned 7 points; the temperature and humidity index 56-60 or 65-70 corresponds to a comfort level of 6, assigned 6 points; the temperature and humidity index 50-56 or 70-75 corresponds to a comfort level of 5, assigned 5 points; the temperature and humidity index 45-50 or 75-80 corresponds to a comfort level of 4, assigned 4 points; the temperature and humidity index 40-45 or 80-85 corresponds to a comfort level of 3, assigned 3 points; the temperature and humidity index 32-40 or 85-90 corresponds to a comfort level of 2, assigned 2 points; the temperature and humidity index <32 or >90 corresponds to a comfort level of 1, assigned 1 point; the higher the comfort level, the higher the comfort.

[0106] Using land cover type data, according to the upper planning farmland index, the land redevelopability is evaluated, and each type of land is divided into 3 levels: redevelopable, moderately redevelopable, and non-redevelopable, and assigned 5 points, 3 points, and 1 point, respectively.

[0107] Using the overall urban planning map, each functional area is divided into 3 levels: to be developed, short-term development, and key development, and assigned 1 point, 2 points, and 3 points, respectively.

[0108] The scores of the four evaluations are normalized to obtain the slope index, regional climate comfort index, land redevelopability index, and functional area reconstruction timing index.

[0109] S2.2 According to the analytic hierarchy process (AHP), a judgment matrix is constructed, and the importance of the matrix is ranked as follows: slope index > land redevelopability index > functional area redeveloping time sequence index > regional climate comfort index; the sum of each column of the judgment matrix is calculated; each element is divided by the sum of its column to obtain a normalized judgment matrix; the average value of each row is calculated to obtain the weight of each factor;

[0110] The slope index, regional climate comfort index, land redevelopability index, and functional area redeveloping time sequence index are superimposed according to the weight calculated by the judgment matrix to obtain a development suitability basic map of the design site and store it in a raster data format;

[0111] The superimposition calculation formula of the development suitability is: k1xw1+k2xw2+k3xw3+k4xw4, wherein k1 is the slope index, w1 is the weight of the slope index, k2 is the regional climate comfort index, w2 is the weight of the regional climate comfort index; k3 is the land redevelopability index, w3 is the weight of the land redevelopability index; k4 is the functional area redeveloping time sequence index, and w4 is the weight of the functional area redeveloping time sequence index;

[0112] S2.3 A 1000m grid is established, and a grid-to-point tool is used to convert the development suitability basic map into point data; a spatial join tool is used to extract the values of the point data into the corresponding grid, and a natural segmentation method is used to classify the extracted data into 5 levels, from 1 to 5 corresponding to low to high development suitability, and the classification results are stored in a new field of “development suitability”; in the attribute table, the cells with a value of 5 in the development suitability field, i.e., high development suitability, are selected using the attribute selection tool, and the geometric center of each high suitability area is taken as a trunk road node;

[0113] S2.4 Check whether the location of each node has a steep slope or unsuitable terrain for development, and if so, move the node to the nearest flat area;

[0114] S3. Generation of the trunk road network based on the improved A* algorithm: generate a three-dimensional grid of the design site, perform path search using the improved A* algorithm, connect the trunk road nodes and extend to the edge of the design site to form several trunk roads, delete unreasonable paths, and calculate the distance between the trunk roads for road network supplement and adjustment;

[0115] S3 specifically includes the following steps:

[0116] S3.1 Identify the center point of each grid of the design site DEM grid data containing height coordinates, connect all the grid center points to generate a three-dimensional grid, write the three-dimensional grid to a high-speed storage device NVMe SSD, and use it to generate a road network storage;

[0117] S3.2 Path search is performed using the improved A* algorithm, the main road nodes are defined as the start and end points of the path search, and the formula of the function f(n) of the improved A* algorithm is:

[0118] f(n) = g(n) + w h(n) + a S(n) + b C(n)

[0119] g(n) is the actual cost from the start point to the current node n, i.e. the path length;

[0120] h(n) is the Euclidean distance heuristic function, which estimates the cost from node n to the target node;

[0121] w is a dynamic weight used to adjust the influence of h(n), which changes dynamically according to the progress of the search;

[0122] S(n) is the terrain cost function, which reflects the slope or terrain undulation of the area where node n is located;

[0123] a is the weight of the terrain cost, which determines the influence of the terrain on path selection;

[0124] C(n) is the road design rule cost function, which considers the road spacing and road curvature factors;

[0125] b is the weight of the road design rule, which determines the influence of the road design rule on path selection;

[0126] wherein,

[0127] g(n) = g(p) + d(p, n)

[0128] g(p) is the cost of the previous node p;

[0129] d(p, n) is the path length from node p to node n;

[0130]

[0131] Ah(n) is the height difference from the previous node p to the current node n, i.e. the slope;

[0132] d(p, n) is the horizontal distance from the previous node p to the current node n;

[0133]

[0134] (x n , y n , z n ) is the three-dimensional coordinates of the current node n;

[0135] (x goal , y goal , z goal) is the three-dimensional coordinate of the target node;

[0136]

[0137] d road (n) is the actual distance between the road at the current node n and the nearest adjacent road;

[0138] d target is the predefined ideal road distance, which is determined as 800-1000 meters according to urban design standards;

[0139] θ(n) is the turning angle of the current path at node n, expressed in degrees;

[0140] θmax is the maximum allowed road turning angle, which is determined as 60 degrees according to design standards;

[0141] S3.3 Utilize the improved A* algorithm in step S3.2 to perform path search, define the main road nodes as the starting points of path search, and design the three-dimensional grid boundary points as the endpoints of path search, generate a path with the smallest f(n) from each main road node n to the grid boundary, and preliminarily form the main road network;

[0142] S3.4 Check the node degree of the generated main road nodes, identify all paths connected to the nodes whose degree exceeds the expected value of 4, prioritize the paths, and the path connected to the grid boundary has the highest priority, the remaining paths are sorted based on the cost function, and the cost function considers slope and distance, delete the path with the lowest priority until the degree of the node is less than or equal to 4;

[0143] The cost function formula is:

[0144]

[0145] w1 and w2 are weight coefficients, which control the influence degree of slope cost and distance cost respectively;

[0146] Δh r is the height difference between the starting point and the endpoint of the path r;

[0147] d r is the length of the path r;

[0148] S3.5 Calculate the distance between the generated main road networks, according to the distance standard of the main road, mark all areas with a distance greater than 1200 meters; in each marked area, calculate its geometric center point as a new node, and from this node to the existing main road node with the longest distance in the area edge, utilize the improved A* algorithm in step S3.2 to perform path search, obtain a connecting path; extend the main road from both ends of the connecting path to the design site three-dimensional grid boundary, form several main roads;

[0149] S4. Sub-trunk road network generation: based on the generated trunk road network, select sub-trunk road nodes on the trunk road according to the road spacing specification, and use the improved A* algorithm to search the path to generate the sub-trunk road network;

[0150] S4 specifically includes the following steps:

[0151] S4.1 Label the selected sub-trunk road nodes on the generated trunk road network according to the spacing rule, which is greater than 350 meters and less than 500 meters, and the spacing is larger as it is closer to the three-dimensional grid boundary;

[0152] S4.2 For each region divided by the trunk road network, use the improved A* algorithm in step S3.2 to search the path, adjust the function formula of the improved A* algorithm, increase the weight of the road design rule cost, that is, increase the value of β in the formula, define the sub-trunk road nodes on the boundary of the region as the starting point and the end point, and the nodes on the same trunk road path cannot be used as a pair of starting point and end point;

[0153] S4.3 Check the node degree of the generated sub-trunk road nodes, identify the nodes whose degree exceeds the expected value 1, do not calculate the trunk road network path and the path in the non-regional area, delete the redundant path so that the degree of all nodes is less than or equal to 1, and obtain the sub-trunk road network;

[0154] S5. Smooth processing of main and sub-trunk road networks and optimization of road angles: using an adaptive smoothing algorithm to process the generated main and sub-trunk road networks and optimize the road angles;

[0155] S5 specifically includes the following steps:

[0156] S5.1 Divide the road network into different regions, and divide the road network into flat regions with a slope of 0°-5°, hilly regions with a slope of 5°-15°, and mountainous regions with a slope of 15° or more according to the terrain characteristics;

[0157] S5.2 Dynamically select and adjust the smoothing algorithm and window according to different regions, and the window represents the number of adjacent data points selected within a given range. For flat regions, select the linear interpolation method, and use a larger smoothing window for parameters; for hilly regions, select Bezier curve, and use a medium-sized smoothing window for parameters; for mountainous regions, use a Gaussian filter, and use a smaller smoothing window for parameters;

[0158] S5.3 Convert the smoothed road network into a multi-dimensional vector dataset, calculate the direction vector using Python, and calculate the angle based on the direction vector;

[0159] S5.4 Identify the intersection node A with the included angle less than 70 degrees, and divide the two direction vectors involved in the included angle into the main road segment and the secondary road segment; take the node as the starting point, generate an alternative node B on the main road segment with cot(θ)*10m as a step length; generate a new road segment perpendicular to the main road segment from the alternative node B, calculate the intersection C of the new road segment and the secondary road segment; recalculate the local direction vector and calculate the included angle, and loop until the road included angle is greater than 70 degrees, as shown in Figure 2

[0160] S5.5 Write the generated road network into a high-speed storage device;

[0161] S6. Visualize the output and interactively optimize: output the generated road network and perform three-dimensional visual interactive optimization;

[0162] S6 specifically includes the following steps:

[0163] S6.1 Input the generated road network data into a virtual reality application software on a development workstation equipped with a high-performance CPU and GPU for processing and rendering;

[0164] S6.2 Connect the VR headset and the matching controller, integrate and test different VR devices, create a VR scene; connect the AR device, create an AR scene, and place and track virtual objects through a mobile device or AR glasses; perform three-dimensional visual interactive optimization of the generated road network on a 4K or higher high-resolution display, and accurately adjust and optimize the scene using VR and AR tracking devices.​

Claims

1. A method for generating an intelligent three-dimensional road network for a complex mountainous terrain, characterized in that, Comprise the following steps: (1) Collecting design site topographic data and environmental data, collecting design site planning data, importing geographic information platform, processing into raster data or vector data in unified format, generating design site database; (2) Based on the design site database, the development suitability of each area of the design site is calculated, and after threshold screening, the center point in each region in the high suitability area is identified as the trunk road node; Said step (2) specifically comprises the following steps: (2.1) Based on the design site database, the design site is analyzed using the geographic information platform to obtain the evaluation results of the normalized slope index, regional climate comfort index, land redevelopability index and functional area reconstruction timing index; (2.2) According to the analytic hierarchy process AHP, a judgment matrix is constructed to obtain the weight of each factor; The slope index, regional climate comfort index, land redevelopability index and functional area reconstruction timing index are superimposed and calculated according to the weight calculated by the judgment matrix to obtain the development suitability base map of the design site and stored in raster data format; (2.3) Establish a grid, convert the development suitability base map to point data, use the spatial connection tool to extract the value of the point data to the corresponding grid, use the natural segmentation method to classify the extracted data into 5 levels, from 1 to 5 corresponding to low to high development suitability, and store the classification results in a new field of "development suitability"; In the attribute table, use the attribute selection tool to filter out the cells with a value of 5 in the development suitability field, that is, the high development suitability, and take the geometric center of each high development suitability area as the trunk road node; (2.4) Check whether the location of each node has steep slope or unsuitable development terrain, if so, move the node to the nearest flat area; (3) Generate a three-dimensional grid of the design site, use the improved A* algorithm for path search, connect the trunk road nodes and extend to the edge of the design site to form several trunk roads, delete unreasonable paths, calculate the distance between the trunk roads and supplement and adjust the road network to generate the trunk road network; (4) Based on the generated trunk road network, select the secondary trunk road nodes on the trunk road according to the road distance specification, use the improved A* algorithm for path search to generate the secondary trunk road network; (5) Use the adaptive smoothing algorithm to process the generated main and secondary trunk road networks to optimize the road angle; (6) Output the generated road network for three-dimensional visualization and interactive optimization. 2.The method of claim 1, wherein the method comprises: Said step (1) specifically comprises the following steps: (1.1) Collecting design site topographic data to obtain DEM raster data; (1.2) Collecting design site current environmental data, including river and current building vector environmental data, atmospheric environmental data and land cover type raster data; (1.3) Taking aerial images of the design site, identifying the design site topographic data and the design site current environmental data except the atmospheric environmental data from the aerial images to assist in improving the design site topographic data and environmental data; (1.4) Obtain the design site master planning data, including the city overall planning map, regional development planning map, traffic planning map and master planning farmland index; (1.5) Import the collected terrain data, environmental data and master planning data into a high-performance workstation, use a geographic information platform, select a coordinate system based on the site coordinates, add geographic registration control points, and spatially locate the master planning data; Convert the planning map in the master planning data from raster data to vector elements in the geographic information platform, correct errors in the conversion process, identify the range of each functional area in the planning map, assign values to different functional areas, and store them as raster data; (1.6) Integrate the data to generate a design site database, store and manage the data using a database manager.

3. The method of claim 2, wherein the method is a method for intelligent generation of a three-dimensional road network for a city facing complex mountainous terrain. In step (1.2), the vector environmental data of rivers and existing buildings is obtained through an open source data platform, atmospheric environmental data is obtained through the China Meteorological Data Network, and raster data of land cover types is obtained through the National Geographic Information Resource Directory Service System.

4. The method of claim 1, wherein the method is a method for generating an intelligent urban three-dimensional road network for complex mountainous terrain. The step (2.1) specifically comprises the following steps: Using DEM raster data to calculate slope and perform slope evaluation, dividing into high, higher, medium, lower and low levels according to ≤2°, 2~6°, 6~15°, 15~25° and >25° and assigning scores of 1, 2, 3, 4 and 5, wherein the high level corresponds to flat land, the higher level corresponds to flat slope land, the medium level corresponds to gentle slope land, the lower level corresponds to gentle steep slope land, and the low level corresponds to steep slope land, to generate a slope classification map; Using atmospheric environmental data to calculate monthly average temperature and monthly average air relative humidity, calculating the temperature and humidity index, evaluating the regional climate comfort level, dividing the regional climate comfort level into 7 levels and assigning scores according to the level, and the temperature and humidity index 60~65 corresponds to the comfort level of 7 levels, and the score is 7 points; the temperature and humidity index 56~60 or 65~70 corresponds to the comfort level of 6 levels, and the score is 6 points; the temperature and humidity index 50~56 or 70~75 corresponds to the comfort level of 5 levels, and the score is 5 points; the temperature and humidity index 45~50 or 75~80 corresponds to the comfort level of 4 levels, and the score is 4 points; the temperature and humidity index 40~45 or 80~85 corresponds to the comfort level of 3 levels, and the score is 3 points; the temperature and humidity index 32~40 or 85~90 corresponds to the comfort level of 2 levels, and the score is 2 points; the temperature and humidity index <32 or >90 corresponds to the comfort level of 1 level, and the score is 1 point; the higher the comfort level, the higher the comfort level; Using land cover type data, according to the master planning farmland index, evaluating the land reclamation degree, dividing each type of land into reclamation, moderate reclamation and non-reclamation, and assigning scores of 5, 3 and 1; Using the city overall planning map, dividing each functional area into development, near-term development and key development, and assigning scores of 1, 2 and 3; Normalizing the scores of the four evaluations respectively to obtain the slope index, regional climate comfort level index, land reclamation degree index and functional area reclamation timing index.

5. The method of claim 1, wherein the method is a method for intelligent generation of a three-dimensional road network for a city facing complex mountainous terrain. The step (2.2) judges the matrix importance ranking as: slope index > land reclamation degree index > functional area reclamation time sequence index > regional climate comfort degree index; Calculate the sum of each column of the judgment matrix; Divide each element by its column sum to obtain the normalized judgment matrix; then calculate the average of each row to obtain the weight of each factor; Develop the suitability superposition calculation formula as: k1×w1+k2×w2+k3×w3+k4×w4, wherein k1 is the slope index, w1 is the weight of the slope index, k2 is the regional climate comfort degree index, w2 is the weight of the regional climate comfort degree index; k3 is the land reclamation degree index, w3 is the weight of the land reclamation degree index; k4 is the functional area reclamation time sequence index, and w4 is the weight of the functional area reclamation time sequence index.

6. The method of claim 1, wherein the method is a method for intelligent generation of a three-dimensional road network for a city facing complex mountainous terrain. The step (3) specifically comprises the following steps: (3.1) identifying the center point of each grid of the design site DEM grid data containing height coordinates, connecting all grid center points to generate a three-dimensional grid, using a high-speed storage device to write the three-dimensional grid, and using the three-dimensional grid to generate a road network storage; (3.2) using the improved A* algorithm for path search, defining the backbone road node as the starting point and the end point of the path search, the function of the improved A* algorithm The formula is: ， is the actual cost from the start to the current node n, i.e. the path length; A Euclidean distance heuristic function estimates the cost from node n to the goal node. Dynamic weights are used to adjust the influence of h(n) and vary dynamically according to the search progress. is a terrain cost function, reflecting the slope or degree of terrain undulation of the region where the node n is located; is a weight of the terrain cost, which determines the influence of the terrain on path selection; A road design rule cost function is designed, considering road spacing and road curvature factors; weights for the road design rules, which determine the influence of the road design rules on the path selection; Wherein, ， cost of the previous node p; is the path length from node p to node n; ， height difference from the previous node p to the current node n, i.e. the slope; is the horizontal distance from the previous node p to the current node n; ， is the three-dimensional coordinate of the current node n;​ three-dimensional coordinates of the target node; ， is the actual distance of the road at the current node n from the nearest adjacent road; a predefined ideal road distance; is the turn angle of the current path at node n in degrees; maximal road corner allowed; (3.3) Path search is performed by using the improved A* algorithm in step (3.2), the trunk road node is defined as the starting point of path search, and the three-dimensional grid boundary point of the site is designed as the end point of path search, and a minimum path connecting to the grid boundary is generated from each trunk road node n to preliminarily form a trunk road network; the minimum path, and a trunk road network is preliminarily formed; (3.4) checking the node degree of the main road nodes after generating the road network, identifying all paths connected to the nodes whose degree exceeds the expected value 4, and performing priority sorting on the paths, the paths connecting the grid boundary have the highest priority, the remaining paths are sorted based on the cost function, the cost function considers slope and distance, and the path with the lowest priority is deleted until the degree of the node is less than or equal to 4; The cost function formula is: ， w1 and w2 are weight coefficients, which control the influence degree of slope cost and distance cost respectively; is the height difference between the start and end points of the path r; d 𝑟 is the length of the path r; (3.5) calculating the distance between the generated main road networks, marking all regions with a distance greater than 1200 meters according to the main road distance standard; in each marked region, calculate its geometric center point as a new node, and use the improved A* algorithm in step (3.2) to search for a connection path from the node to the existing main road node with the longest distance to the edge of the region; extend the main road from the connection path to the boundary of the design site three-dimensional grid to form several main roads.

7. The method of claim 6, wherein the method is a method for intelligent generation of a three-dimensional road network for a city facing complex mountainous terrain. The step (4) specifically comprises the following steps: (4.1) marking the secondary road nodes on the generated main road network according to the distance rule, the distance rule is greater than 350 meters and less than 500 meters, and the distance is greater as it is closer to the three-dimensional grid boundary; (4.2) for each region divided by the main road network, use the improved A* algorithm in step (3.2) to search for a path, adjust the function formula of the improved A* algorithm, increase the weight of the road design rule cost, that is, increase the value of β in the formula, define the secondary road node on the boundary of the region as the starting point and the ending point, and the nodes on the same main road path cannot be used as a pair of starting point and ending point; (4.3) checking the node degree of the secondary road nodes after generating the path, identifying the nodes whose degree exceeds the expected value 1, not calculating the path of the main road network and the path in the region, deleting the redundant path to make the node degree of all nodes less than or equal to 1, and obtaining the secondary road network.

8. The method of claim 7, wherein the method is a method for intelligent generation of a three-dimensional road network for a city facing complex mountainous terrain. The step (5) specifically comprises the following steps: (5.1) divide the road network into different regions, divide the road network into flat regions with a slope of 0°-5°, hilly regions with a slope of 5°-15°, and mountainous regions with a slope of 15° or more according to the terrain characteristics; (5.2) dynamically select and adjust the smoothing algorithm and window according to different regions, the window represents the number of adjacent data points selected in a given range, for flat regions, select linear interpolation method, use larger smoothing window for parameters; for hilly regions, select Bezier curve, use medium-sized smoothing window for parameters; for mountainous regions, use Gaussian filter, use smaller smoothing window for parameters; (5.3) convert the smoothed road network into a multi-dimensional vector dataset, calculate the direction vector using Python, and calculate the included angle based on the direction vector; (5.4) identify the intersection node A with an included angle less than 70 degrees, divide the two direction vectors involved in the included angle into the main road segment and the secondary road segment; Take the node as the starting point, generate a candidate node B on the main road segment with cot(θ)*10m as a step; generate a new road segment perpendicular to the main road segment from the candidate node B, calculate the intersection point C of the new road segment and the secondary road segment; Recalculate the local direction vector and calculate the included angle, and loop until the road included angle is greater than 70 degrees; (5.5) write the generated road network into a high-speed storage device.

9. The method of claim 8, wherein the method is a method for intelligent generation of a three-dimensional road network for a city facing complex mountainous terrain. The step (6) specifically comprises the following steps: (6.1) input the generated road network data into the virtual reality application software on the development workstation for processing and rendering; (6.2) connect the VR headset and the matching controller, integrate and test different VR devices, and create a VR scene; Connect the AR device, and place and track virtual objects through the mobile device or AR glasses; perform three-dimensional visualization interaction optimization of the generated road network on the display, and adjust and optimize the scene using VR and AR tracking devices.

Images