A highway structure optimization design method and system based on multi-source data analysis

Abstract

The present invention discloses a highway structure optimization design method and system based on multi-source data analysis, belonging to the technical field of multi-source data analysis. The present invention uses a minimum spanning tree to generate a first connected graph, and optimizes the first connected graph by using a weighted Dijkstra algorithm to obtain a second connected graph; conducts traffic flow simulation on the second connected graph, inserts intermediate transition nodes for the edges with sudden slope changes between adjacent nodes, and refines the terrain grid to obtain a highway structure connected graph; extracts all curve segments from the highway structure connected graph, converts them into geometric models, and determines the vehicle speed constraints for each curve segment; determines the minimum radius constraint according to the terrain and landform data; constructs a curve radius optimization model to obtain a first optimized highway structure; calculates the initial longitudinal slope, constructs a longitudinal slope optimization model, and solves for the optimal safe longitudinal slope to obtain a second optimized highway structure; divides the section characteristics and matches the material data set to obtain a highway structure optimization design scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source data analysis, and specifically to a highway structure optimization design method and system based on multi-source data analysis. Background Technique

[0002] With the continuous expansion of the scale of China's highway network and the complexity of traffic demands, highway structure design faces challenges such as great difficulty in integrating multi-source data and insufficient adaptability to terrain and geology. Traditional highway design relies on empirical formulas and manual trial calculations, with limited quantitative analysis capabilities for multi-source data such as traffic flow, topography, and geological conditions, making it difficult to achieve precise decision-making in aspects such as route planning, horizontal and vertical alignment optimization, and material selection.

[0003] Traditional route optimization mostly uses heuristic algorithms with a single index, lacking comprehensive processing of multiple constraints such as prohibiting high fills and deep cuts, geological risk avoidance, and traffic flow balance. The optimization of the plane curve radius and the longitudinal slope design are often carried out independently without forming a linkage optimization mechanism. For example, the speed constraint in the curve section is not mechanically coupled with the longitudinal slope gradient and slope length, which may lead to the superposition of centrifugal force and gravity component exceeding the limit in the horizontal and vertical combined section, increasing the risk of side slip and braking failure. Existing material selection is mostly based on engineering experience and specification lookup tables, without establishing a quantitative matching model for section characteristics, material properties, and environmental constraints. Summary of the Invention

[0004] The purpose of the present invention is to provide a highway structure optimization design method and system based on multi-source data analysis to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] In the first aspect, the present application provides a highway structure optimization design method based on multi-source data analysis, including the following steps:

[0007] Obtain the original highway route, define core nodes, generate candidate edges between adjacent nodes, and generate a first connected graph using the minimum spanning tree; define the constraint of prohibiting high fills and deep cuts, avoid geological risks, and optimize the first connected graph using the weighted Dijkstra algorithm to obtain a second connected graph; conduct traffic flow simulation on the second connected graph, insert intermediate transition nodes for the edges with sudden slope changes between adjacent nodes, refine the terrain grid, and recalculate the gentle path to obtain the highway structure connected graph;

[0008] Extract all curve segments from the highway structure connectivity graph, convert them into geometric models, calculate the initial curve radius, and determine the vehicle speed constraints for each curve segment in combination with traffic flow data; based on the topographic and geomorphic data, analyze the terrain complexity of the area where the curve segment is located and determine the minimum radius constraint; construct an optimization model for the curve radius, set the objective function, including safety objectives and comfort objectives, and solve the objective function based on the vehicle speed constraint and the minimum radius constraint to obtain the first optimized highway structure;

[0009] Extract elevation data from the highway structure connectivity graph, obtain the constraint conditions, perform longitudinal slope segmentation, and calculate the initial longitudinal slope; construct an optimization model for the longitudinal slope, use the dynamic programming algorithm to solve the optimal safe longitudinal slope, and adjust the slope section to obtain the second optimized highway structure;

[0010] Obtain climate data and divide the road section characteristics based on the second optimized highway structure; match the material data set from the pavement material library according to different road section characteristics, and combine it with the second optimized highway structure to obtain the optimized design scheme of the highway structure.

[0011] Combined with the first aspect, in the first implementation manner of the first aspect of the present application, the obtaining of the original highway route, defining the core nodes, generating candidate edges between adjacent nodes, and generating the first connectivity graph using the minimum spanning tree includes:

[0012] Import the CAD design drawings, extract the coordinates of the original highway route, generate a GIS line feature layer, overlay the topographic data, supplement the geographical elements around the route, and perform data cleaning and standardization; determine the types of prohibited construction areas, and use ArcGIS buffer analysis to generate buffer zones for the prohibited construction areas;

[0013] According to the priority of the nodes from high to low, they are divided into mandatory connection nodes, optional connection nodes, and optimization nodes. Use the PageRank algorithm to sort the node importance and generate a core node set; based on the core node set, generate candidate edges between adjacent nodes, filter out the candidate edges without engineering feasibility, and assign edge attributes;

[0014] The minimum spanning tree selects the Prim algorithm, arbitrarily selects a node in the core node set as the root node, and initializes the minimum spanning tree node set and edge set; for all nodes in the minimum spanning tree node set, traverse their candidate edges, and exclude the prohibited edges located in the prohibited construction area and its buffer zone; calculate the comprehensive weight of the candidate edges, select the edge with the smallest weight and add it to the edge set; add the unvisited node connected by the candidate edge to the minimum spanning tree node set, and repeat until all core nodes are connected; perform connectivity verification and redundant edge pruning, and output the first connectivity graph.

[0015] Combined with the first aspect, in the second implementation manner of the first aspect of the present application, the definition prohibits high filling and deep excavation constraints, avoids geological risks, and uses the weighted Dijkstra algorithm to optimize the first connected graph to obtain the second connected graph, including:

[0016] Set the filling and excavation volume limit according to the highway grade and terrain conditions; combine engineering experience to define deep excavation risk sections and high filling risk sections; based on the nodes and edges of the first connected graph, combine with terrain data, calculate the filling and excavation volume and average slope corresponding to each edge, and label each edge in the first connected graph with the attribute of whether it belongs to a high filling and deep excavation section; set the weight T of the high filling and deep excavation section, which increases proportionally according to the degree of the filling and excavation volume exceeding the threshold to limit the Dijkstra algorithm from selecting such sections; divide the levels of geological risks and assign corresponding geological risk weights G to each level, and associate the geological risk weights with the edges of the first connected graph;

[0017] Calculate the comprehensive weight W of the edge, and the formula is: , where D is the distance weight, is the distance factor, is the high filling and deep excavation factor, is the geological risk factor, which is determined by the analytic hierarchy process 、 and ;

[0018] Optionally select a core node in the first connected graph as the starting node, set its distance to 0, and the distances of the remaining nodes to infinity, and establish a set Q of nodes to be visited; select the node u with the smallest distance from the set Q, mark it as visited, and remove it from Q; traverse all neighbor nodes v of the node u, calculate the new distance d from the starting node to v, specifically the sum of the actual geometric distance from the node u to v and the comprehensive weight of the edge from the node u to v; when d is less than the currently recorded distance of v, update the distance and predecessor node of v to u; repeat node access and neighbor node update until the set Q is empty, obtain the shortest paths from the starting node to all nodes, and form the second connected graph.

[0019] Combined with the first aspect, in the third implementation manner of the first aspect of the present application, traffic flow simulation is performed on the second connected graph, for the edges with sudden slope changes between adjacent nodes, insert intermediate transition nodes, refine the terrain grid, and recalculate the gentle path to obtain the highway structure connected graph, including:

[0020] Import the second connected graph into the traffic simulation software Vissim, generate a road network according to the node coordinates, mark the designed vehicle speed, number of lanes and roadbed width of each edge; input traffic flow data, allocate the flow according to peak and off-peak periods, distinguish cars, heavy trucks and buses, set the vehicle acceleration and deceleration characteristics, and monitor the simulation indicators; extract the elevation data of adjacent nodes and calculate the original slope; when the slope difference between adjacent edges or the single-point slope exceeds the limit, determine that the edge between adjacent nodes is a slope mutation;

[0021] Obtain the terrain grid, determine the insertion density of the edges with slope mutations between adjacent nodes according to the slope difference, use the linear interpolation method to determine the elevation of the intermediate transition nodes, and conduct engineering feasibility verification;

[0022] Based on the nodes of the second connected graph, generate a Delaunay triangulation, and identify the triangular elements corresponding to the slope mutation edges; adopt the Loop subdivision algorithm to perform several subdivisions on the triangles containing the mutation edges to generate a high-density sub-grid; divide the slope mutation edges into several new edges, with the starting point of the new edge being the original node and the ending point being the inserted transition node; within the refined grid, generate new candidate edges according to the adjacency relationship of the Delaunay triangulation, exclude the edges in the prohibited construction area, and update the edge weights;

[0023] Determine the multi-objective function and adopt the algorithm to preferentially search for gently sloping paths; use the path of the second connected graph as the initial solution to find the sub-path corresponding to the mutation edge in the refined grid; for each mutation edge, search for alternative paths within its neighborhood grid; use the union-find algorithm to check the connectivity of the new graph, and when isolated nodes appear, enable the bridge edge mechanism; combine the original core nodes and transition nodes into a node set, and the edge set contains the refined gentle paths to construct a highway structure connectivity graph.

[0024] Combined with the first aspect, in the fourth implementation manner of the first aspect of the present application, extracting all curve segments from the highway structure connectivity graph, converting them into geometric models, calculating the initial curve radius, and combining traffic flow data to determine the vehicle speed constraints for each curve segment, including:

[0025] Traverse all edges in the highway structure connectivity graph, identify the sequence of continuously turning edges as potential curve segments according to the node connection relationship, and mark the curve segments; extract the plane coordinates of each node of the curve segment, and supplement data points by linear interpolation for non-node positions; for the curve segments of arcs, use arc fitting to construct an arc geometric model by calculating the center coordinates and radius of the fitted arc; for the curve segments containing various shapes, use cubic spline curve fitting; calculate the geometric parameters of the curve segments and construct geometric models;

[0026] For the curve segment fitted by an arc, directly obtain the radius of the fitted arc as the initial curve radius; for the curve segment fitted by a spline curve, determine the equivalent curve radius by calculating the curvature of the curve; combine the curve radius, superelevation setting, and road surface friction coefficient, and calculate the first reasonable vehicle speed for safe driving on the curve segment through mechanical formulas; use the Greenshields model to establish a relationship model between traffic flow and vehicle speed, and calculate the second reasonable vehicle speed under this traffic flow condition according to the traffic flow density and saturation degree of the curve segment; take the smaller value of the first reasonable vehicle speed and the second reasonable vehicle speed as the vehicle speed constraint for the curve segment.

[0027] Combined with the first aspect, in the fifth implementation manner of the first aspect of the present application, the analyzing the terrain complexity of the area where the curve segment is located based on the terrain and landform data to determine the minimum radius constraint includes:

[0028] Collect terrain and landform data, conduct regional division of the curve segment and terrain feature extraction, determine the index weights of different terrain features, calculate the terrain complexity index of the area where each curve segment is located by weighted summation, divide the terrain complexity into different levels, and determine the minimum radius constraint according to the highway design specifications.

[0029] Combined with the first aspect, in the sixth implementation manner of the first aspect of the present application, the constructing a curve radius optimization model and setting the objective function, including a safety objective and a comfort objective, and solving the objective function based on the vehicle speed constraint and the minimum radius constraint to obtain the first optimized highway structure includes:

[0030] In the curve radius optimization model, the safety objective is expressed as minimizing the centrifugal force coefficient of each curve segment, and the comfort objective is expressed as minimizing the curvature change rate of each curve segment. Combine the safety objective and the comfort objective by weighting to obtain the objective function; use the sequential quadratic programming algorithm to solve the objective function based on the vehicle speed constraint and the minimum radius constraint; apply the obtained optimal curve radius to the corresponding curve segment in the highway structure connection diagram to update the geometric model of the curve segment; according to the updated geometric model, reconstruct the highway structure connection diagram to obtain the first optimized highway structure.

[0031] Combined with the first aspect, in the seventh implementation manner of the first aspect of the present application, the constructing a longitudinal slope optimization model, using the dynamic programming algorithm to solve the optimal safe longitudinal slope, and adjusting the slope section to obtain the second optimized highway structure includes:

[0032] The safety objective of the longitudinal slope optimization model is set to approach the safe longitudinal slope, the comfort objective is set to uniformize the slope length, and geological constraints, slope length constraints, and the slope difference constraint of adjacent slope sections are set. The dynamic programming algorithm is used to define the state space, transition equation, and boundary conditions to solve for the optimal safe longitudinal slope. Based on the optimal safe longitudinal slope, the slope sections in the first optimized highway structure are adjusted to obtain the second optimized highway structure.

[0033] Combined with the first aspect, in the eighth implementation manner of the first aspect of the present application, the matching of the material data set from the pavement material library according to different road section characteristics, combined with the second optimized highway structure, to obtain the highway structure optimization design scheme includes:

[0034] Construct a pavement material library, formulate pavement material matching rules according to different road section characteristics; screen out the material data sets applicable to each road section from the pavement material library according to road section classification and matching rules; establish a hierarchical structure model, including an objective layer, a criterion layer, and a scheme layer; construct a judgment matrix, and by calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix, rank the importance of each criterion in the criterion layer and each material in the scheme layer relative to the criterion layer; conduct a consistency test on the judgment matrix; combine the single-rank results of each material in the scheme layer relative to the criterion layer with the weights of the criterion layer relative to the objective layer, calculate the total-rank weights of each material in the scheme layer relative to the objective layer, determine the optimal pavement materials for each road section; combine with the second optimized highway structure to obtain the highway structure optimization design scheme.

[0035] In a second aspect, the present application provides a highway structure optimization design system based on multi-source data analysis, including:

[0036] Highway structure connectivity graph generation module: including: a first connectivity graph generation unit, a second connectivity graph generation unit, and a highway structure connectivity graph generation unit; wherein, the first connectivity graph generation unit obtains the original highway route, defines core nodes, generates candidate edges between adjacent nodes, and uses the minimum spanning tree to generate the first connectivity graph; the second connectivity graph generation unit defines the constraint of prohibiting high fills and deep cuts to avoid geological risks, and uses the weighted Dijkstra algorithm to optimize the first connectivity graph to obtain the second connectivity graph; the highway structure connectivity graph generation unit conducts traffic flow simulation on the second connectivity graph, inserts intermediate transition nodes for the edges with sudden slope changes between adjacent nodes, refines the terrain grid, and recalculates the gentle path to obtain the highway structure connectivity graph;

[0037] The first optimized highway structure generation module: includes: a geometric model conversion unit, a vehicle speed constraint determination unit, a minimum radius constraint determination unit, and a first optimized highway structure generation unit; among them, the geometric model conversion unit extracts all curve segments from the highway structure connectivity graph and converts them into a geometric model. The vehicle speed constraint determination unit calculates the initial curve radius, combines traffic flow data, and determines the vehicle speed constraints for each curve segment; the minimum radius constraint determination unit analyzes the terrain complexity of the area where the curve segment is located based on terrain and geomorphic data, and determines the minimum radius constraint; the first optimized highway structure generation unit constructs a curve radius optimization model, sets the objective function, including safety objectives and comfort objectives, and solves the objective function based on vehicle speed constraints and minimum radius constraints to obtain the first optimized highway structure;

[0038] The second optimized highway structure generation module: includes: an initial longitudinal slope calculation unit and a second optimized highway structure generation unit; among them, the initial longitudinal slope calculation unit extracts elevation data from the highway structure connectivity graph, obtains constraint conditions, performs longitudinal slope segmentation, and calculates the initial longitudinal slope; the second optimized highway structure generation unit constructs a longitudinal slope optimization model, uses the dynamic programming algorithm to solve the optimal safety longitudinal slope, and adjusts the slope section to obtain the second optimized highway structure;

[0039] The optimized design scheme generation module: includes: a road section feature division unit and an optimized design scheme generation unit; among them, the road section feature division unit obtains climate data and divides road section features based on the second optimized highway structure; the optimized design scheme generation unit matches material data sets from the pavement material library according to different road section features, and combines with the second optimized highway structure to obtain an optimized highway structure design scheme.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. Through the weighted Dijkstra algorithm and the dynamic programming algorithm, this application realizes the quantitative processing of multiple constraint conditions such as prohibiting high fills and deep cuts, geological risks, and traffic flow balance. Inserting transition nodes and refining the terrain grid in sections with sudden slope changes can reduce the emergency braking frequency of vehicles and reduce the amount of high fill and deep cut engineering.

[0042] 2. This application constructs a collaborative optimization model for curve radius and longitudinal slope, and conducts mechanical coupling analysis on vehicle speed constraints, terrain complexity, longitudinal slope gradient, and slope length; in sharp turn sections, through the algorithm searches for gentle paths, which can improve the climbing speed and reduce the slope difference between adjacent slope sections at the same time.

[0043] 3. Based on the analytic hierarchy process, this application establishes a matching model for road section features and material properties, dynamically screens the optimal materials in combination with requirements such as climate, traffic load, and ecological protection, prolongs the rutting resistance life of the road surface, and reduces construction pollution emissions while improving the rainwater penetration rate. Brief Description of the Drawings

[0044] Figure 1 is a schematic diagram of the steps of a method for optimizing the design of a highway structure based on multi-source data analysis according to the present invention;

[0045] Figure 2 is a system structure diagram of a system for optimizing the design of a highway structure based on multi-source data analysis according to the present invention. Detailed Embodiment

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution,

[0048] As Figure 1 shown in the schematic diagram of the steps of a method for optimizing the design of a highway structure based on multi-source data analysis, the present application provides a method for optimizing the design of a highway structure based on multi-source data analysis, including the following steps:

[0049] Step S100: Obtain the original highway route, define the core nodes, generate candidate edges between adjacent nodes, and generate the first connected graph using the minimum spanning tree; define the constraint of prohibiting high fills and deep cuts to avoid geological risks, and optimize the first connected graph using the weighted Dijkstra algorithm to obtain the second connected graph; perform traffic flow simulation on the second connected graph, insert intermediate transition nodes for the edges with sudden slope changes between adjacent nodes, refine the terrain grid, and recalculate the gentle path to obtain the highway structure connected graph;

[0050] Specifically, import the CAD design drawings, extract the coordinates of the original highway route, generate the GIS line feature layer, overlay the terrain data, supplement the geographical features around the route, and perform data cleaning and standardization; determine the types of prohibited construction areas, and use the ArcGIS buffer analysis to generate buffer zones for the prohibited construction areas;

[0051] According to the priority of the nodes from high to low, they are divided into mandatory connection nodes, optional connection nodes, and optimization nodes. Use the PageRank algorithm to sort the importance of the nodes to generate a set of core nodes; based on the set of core nodes, generate candidate edges between adjacent nodes, filter out the candidate edges without engineering feasibility, and assign edge attributes;

[0052] The Prim algorithm for minimum spanning tree selection selects a node from the core node set as the root node, initializes the minimum spanning tree node set and edge set; for all nodes in the minimum spanning tree node set, traverse their candidate edges, and exclude the prohibited edges located in the prohibited construction area and its buffer zone; calculate the comprehensive weight of the candidate edges, and select the edge with the minimum weight to add to the edge set; add the unvisited node connected by the candidate edge to the minimum spanning tree node set, and repeat until all core nodes are connected; perform connectivity verification and redundant edge pruning, and output the first connected graph.

[0053] Furthermore, according to the highway grade and terrain conditions, set the limit of earthwork volume; based on engineering experience, define the deep excavation risk sections and high fill risk sections; based on the nodes and edges of the first connected graph, combined with terrain data, calculate the earthwork volume and average slope corresponding to each edge, and label each edge in the first connected graph with the attribute of whether it belongs to the high fill and deep excavation sections; set the weight T of the high fill and deep excavation sections, which increases proportionally according to the degree of the earthwork volume exceeding the threshold to limit the Dijkstra algorithm to select such sections; divide the levels of geological risks and assign corresponding geological risk weights G to each level, and associate the geological risk weights with the edges of the first connected graph;

[0054] Calculate the comprehensive weight W of the edge, and the formula is: , where D is the distance weight, is the distance factor, is the high fill and deep excavation factor, is the geological risk factor, which is determined by the analytic hierarchy process , and ;

[0055] Select a core node in the first connected graph as the starting node, set its distance to 0, and the distances of the other nodes to infinity, and establish a set Q of nodes to be visited; select the node u with the minimum distance from the set Q, mark it as visited, and remove it from Q; traverse all neighbor nodes v of node u, and calculate the new distance d from the starting node to v, specifically the sum of the actual geometric distance from node u to v and the comprehensive weight of the edge from node u to v; when d is less than the currently recorded distance of v, update the distance and predecessor node of v to u; repeat node access and neighbor node update until the set Q is empty, and obtain the shortest path from the starting node to all nodes, forming the second connected graph.

[0056] Further, import the second connectivity graph into the traffic simulation software Vissim, generate a road network according to the node coordinates, mark the designed vehicle speed, number of lanes, and roadbed width of each edge; input traffic flow data, allocate the flow according to peak and off-peak periods, distinguish cars, heavy trucks, and passenger cars, set the vehicle acceleration and deceleration characteristics, and monitor the simulation indicators; extract the elevation data of adjacent nodes and calculate the original slope; when the slope difference between adjacent edges or the single-point slope exceeds the limit, determine that the edge between adjacent nodes is a slope mutation;

[0057] Obtain the terrain grid, determine the insertion density of the edges with slope mutations between adjacent nodes according to the slope difference, use the linear interpolation method to determine the elevation of the intermediate transition nodes, and conduct engineering feasibility verification;

[0058] Based on the nodes of the second connectivity graph, generate a Delaunay triangulation, and identify the triangular elements corresponding to the slope mutation edges; adopt the Loop subdivision algorithm to perform several subdivisions on the triangles containing the mutation edges to generate a high-density sub-grid; divide the slope mutation edges into several new edges, with the starting point of the new edge being the original node and the end point being the inserted transition node; within the refined grid, generate new candidate edges according to the adjacency relationship of the Delaunay triangulation, exclude the edges in the prohibited construction area, and update the edge weights;

[0059] Determine the multi-objective function and adopt the algorithm to preferentially search for gentle-slope paths; use the path of the second connectivity graph as the initial solution to find the sub-path corresponding to the mutation edge in the refined grid; for each mutation edge, search for alternative paths within its neighborhood grid; use the union-find algorithm to check the connectivity of the new graph, and when isolated nodes appear, enable the bridge edge mechanism; combine the original core nodes and transition nodes into a node set, and the edge set contains the refined gentle-slope paths to construct a highway structure connectivity graph.

[0060] In a specific embodiment, import the CAD drawing of 1:2000, extract 127 coordinate points, generate a GIS line layer, and overlay the DEM with a resolution of 10m and the data of the prohibited construction area. The must-be-connected nodes include: Town A, Town B, Industrial Zone C, and Scenic Area D. The optional connection nodes include: Township E and Bridge Endpoint F. The optimized nodes include: Terrain Mutation Points G, H, and I. Conduct a ranking of the node importance to obtain the core node set, including: Town A, Town B, Industrial Zone C, and Scenic Area D.

[0061] The initial candidate edges are 56, and the edges excluded from the prohibited construction area are 12, and edge attribute assignment is performed. The iterative results of the Prim algorithm are as follows: Root node: Town A, generated edges: A-C (1200m), C-E (800m), C-F (900m), F-B (1100m), B-I (700m), I-D (500m), First connectivity graph: Total length 5200m, avoiding all prohibited construction areas, and shortening by 18% compared to the original route.

[0062] Set constraint conditions. The high-fill and deep-excavation constraints include: fill threshold > 8m (weight T = 1.5), cut threshold > 10m (weight T = 2.0); geological risk weights include: soft soil area (level 4) G = 2.0, karst area (level 5) G = 3.0 (when passing through the edge of the karst area with side H-D, G = 2.5).

[0063] Therefore, the comprehensive weight formula is W = 0.4D + 0.3T + 0.3G.

[0064] The optimization result of the Dijkstra algorithm is as follows: starting node: Town A; the total weight of the optimal path A→C→F→B→I→D is 481.2 + 350.5 + 420.8 + 310.3 + 280.6 = 1843.4. Compared with the original first-connected graph: avoid 2 high-fill and deep-excavation edges (C-G, F-H), and the proportion of geological risk edges drops from 15% to 5%.

[0065] The Vissim simulation parameters are as follows:

[0066] Peak flow: 5000 pcu / d, heavy-duty vehicles account for 20%, designed speed 60 km / h;

[0067] Monitoring indicators: for edge F-B (original slope difference 7%), the emergency braking frequency is 8 times / km (exceeding the standard), and the average speed is 45 km / h (25% lower than the designed value).

[0068] Insert a transition node. Insert node J at the midpoint of edge F-B (milepost K4+500), dividing it into F-J (slope 4.5%) and J-B (slope 3.2%), and the slope difference drops to 1.3%.

[0069] Perform grid refinement. Refine the grid in the mountainous section from 500m to 200m, generating 18 new candidate edges and excluding 3 geological risk edges.

[0070] Perform Algorithm optimization. The new path F→J→B, with a slope difference ≤ 3%, the emergency braking frequency drops to 2 times / km, and the average speed increases to 58 km / h.

[0071] Step S200: Extract all curve segments from the highway structure connectivity graph, convert them into geometric models, calculate the initial curve radius, combine with traffic flow data to determine the speed constraints for each curve segment; based on the topographic and geomorphic data, analyze the terrain complexity of the area where the curve segment is located to determine the minimum radius constraint; construct a curve radius optimization model, set the objective function, including safety objectives and comfort objectives, and solve the objective function based on the speed constraint and the minimum radius constraint to obtain the first optimized highway structure;

[0072] Specifically, traverse all the edges in the road structure connectivity graph, identify the edge sequence with continuous turns as potential curve segments based on the node connection relationship, and mark the curve segments; extract the plane coordinates of each node of the curve segment, and supplement the data points by linear interpolation for non-node positions; for the curve segments of arcs, use arc fitting, calculate the center coordinates and radius of the fitted arc, and construct the geometric model of the arc; for the curve segments containing multiple shapes, use cubic spline curve fitting; calculate the geometric parameters of the curve segments and construct the geometric model.

[0073] For the curve segments fitted by arcs, directly obtain the radius of the fitted arc as the initial curve radius; for the curve segments fitted by spline curves, determine the equivalent curve radius by calculating the curvature of the curve; combine the curve radius, superelevation setting, and road surface friction coefficient, and calculate the first reasonable vehicle speed for safe driving on the curve segment through mechanical formulas; use the Greenshields model to establish the relationship model between traffic flow and vehicle speed, and calculate the second reasonable vehicle speed under this traffic flow condition according to the traffic flow density and saturation of the curve segment; take the smaller value of the first reasonable vehicle speed and the second reasonable vehicle speed as the speed constraint for the curve segment.

[0074] Furthermore, collect topographic and geomorphic data, conduct regional division of the curve segment and extraction of topographic features, determine the index weights of different topographic features, calculate the topographic complexity index of the area where each curve segment is located by weighted summation, divide the topographic complexity into different levels, and determine the minimum radius constraint according to the highway design specifications.

[0075] Furthermore, in the curve radius optimization model, the safety objective is expressed as minimizing the centrifugal force coefficient of each curve segment, and the comfort objective is expressed as minimizing the curvature change rate of each curve segment. Combine the safety objective and the comfort objective by weighting to obtain the objective function; use the sequential quadratic programming algorithm to solve the objective function based on the speed constraint and the minimum radius constraint; apply the obtained optimal curve radius to the corresponding curve segments in the road structure connectivity graph, and update the geometric model of the curve segments; according to the updated geometric model, reconstruct the road structure connectivity graph to obtain the first optimized road structure.

[0076] In a specific embodiment, conduct curve segment identification and marking. Curve segment 1 (C1): The reverse curve between Town A and Industrial Zone C, containing 3 turning edges (Node A→G→C), is marked as "sharp curve segment"; Curve segment 2 (C2): The transition curve from the bridge end point F to Town B, containing 2 turning edges (Node F→J→B), is marked as "general curve segment"; Curve segment 3 (C3): The circular curve at the entrance of Scenic Area D, with a single-edge turn (Node I→D), is marked as "low-traffic curve segment".

[0077] Coordinate extraction and fitting are carried out. C1 (circular arc fitting): Node coordinates: A(1000, 2000), G(1200, 1800), C(1500, 1700); Fitting center (1300, 1650), radius R = 500m (calculated by the least squares method).

[0078] C2 (cubic spline fitting): Node coordinates: F(2800, 2200), J(3100, 2150), B(3500, 1800); Equivalent curvature radius calculation: Curvature k = 0.002 at the midpoint J, R = 1 / k = 500m (weighted average of 3 sampling points).

[0079] Safety vehicle speed calculation is carried out. The parameters include: g = 9.8m / s 2 , superelevation cross slope α = 6%, pavement friction coefficient μ = 0.5 (wet condition). Then the safety vehicle speed of C1 is: ; The safety vehicle speed of C2 is: (superelevation cross slope 4%).

[0080] Calculate the flow - adapted vehicle speed. For C1 (peak flow k = 150 vehicles / km), , take the smaller value of 20km / h and actually correct it to 30km / h according to the proportion of heavy - duty vehicles. For C2 (medium flow k = 100 vehicles / km), .

[0081] Extract the terrain features. Taking C1 as an example, slope: 15° (calculated by DEM), terrain undulation: 200m, surface roughness: 0.8 (1 - 5 levels, the larger the value, the more complex);

[0082] Index weights include: slope 0.4, undulation 0.3, roughness 0.3;

[0083] Terrain complexity index (TCI): 0.4×15 + 0.3×200 / 100 + 0.3×0.8×5 = 6 + 6 + 1.2 = 13.2 (belongs to the "medium - complex" level).

[0084] Secondary highway in medium - complex hilly area: minimum radius 300m (design vehicle speed 60km / h);

[0085] The initial radius of C1 is 500m ≥ 300m, which meets the requirement; It is necessary to check whether the minimum radius of C2 needs to be increased due to complex terrain.

[0086] For the safety objective, , ;

[0087] For the comfort objective, for C1, .

[0088] The comprehensive objective is: .

[0089] The vehicle speed constraint is: , and the minimum radius is: , and due to the proximity of C2 to the bridge, the minimum radius is increased to 400 m.

[0090] Perform the SQP algorithm to solve.

[0091] After the optimization of C1, the center coordinates of the circle are adjusted to (1350, 1600), the radius is 600 m, and the flat curve elements are marked as follows: R = 600 m, L = 350 m, E = 15 m. A transition point K is inserted into C2, and the curve segment is refined into 2 segments, and the curvature change rate is reduced from 0.0008 to 0.00058.

[0092] Perform CarSim simulation. When the heavy truck passes through C1, the standard deviation of the lateral displacement is reduced from 0.35 m to 0.28 m, and the number of emergency steering operations is reduced from 5 times / km to 2 times / km. After the optimization, the accident rate of this section of the road is reduced by 40% compared with the average value of the previous three years.

[0093] Step S300: Extract the elevation data from the road structure connectivity graph, obtain the constraint conditions, perform longitudinal slope segmentation, and calculate the initial longitudinal slope; construct a longitudinal slope optimization model, use the dynamic programming algorithm to solve the optimal safe longitudinal slope, and adjust the slope section to obtain the second optimized road structure;

[0094] Specifically, determine that the safety objective of the longitudinal slope optimization model is to approach the safe longitudinal slope, the comfort objective is to equalize the slope length, and set the geological constraint, slope length constraint, and adjacent slope section slope difference constraint; use the dynamic programming algorithm to define the state space, transition equation, and boundary conditions, and solve the optimal safe longitudinal slope; based on the optimal safe longitudinal slope, adjust the slope section in the first optimized road structure to obtain the second optimized road structure.

[0095] In a specific embodiment, optimize the longitudinal slope design of a secondary highway in a mountainous area, extract the elevation data, segment the longitudinal slope, perform the initial longitudinal slope calculation and problem identification, and obtain that the longitudinal slopes of sections P1 and P3 exceed the limit, the slope length of section P2 exceeds the limit, and the longitudinal slope of section P2 in the soft soil area needs to be ≤ 3%.

[0096] Construct a longitudinal slope optimization model, solve the result through the dynamic programming algorithm, and the optimized longitudinal slope plan is as follows:

[0097] Section P1: Divided into 2 segments (+6% / 300 m, +3% / 500 m), the slope length is ≤ 500 m, and the longitudinal slope is ≤ 6%;

[0098] Section P2: Insert a break point C, divided into +2% / 500 m (soft soil section), +1.5% / 900 m (exceeding the limit, further divided into +1.5% / 500 m, +1.5% / 400 m);

[0099] Section P3: Divided into -6% / 500m and -2% / 500m, longitudinal slope ≤ 6%, and slope length ≤ 500m for both.

[0100] The over-limit longitudinal slope section is reduced from 3 to 0, and the longitudinal slope of the soft soil section is optimized from 1.43% to 2% (≤ 3%) to meet the geological constraints.

[0101] In the CarSim simulation data, the climbing speed of the truck is increased from 25 km / h to 35 km / h.

[0102] The average slope length is reduced from 1100m to 467m, the slope length uniformity is increased by 67%, and the adjacent slope difference ≤ 3% for all.

[0103] The total volume of filling and excavation is increased from 8900 m³ to 9200 m³ (+3.4%), but the risk of uneven settlement in the soft soil section is avoided.

[0104] Step S400: Obtain climate data and divide the section characteristics based on the second optimized highway structure; match the material data set from the pavement material library according to different section characteristics, and combine with the second optimized highway structure to obtain the optimized design scheme of the highway structure.

[0105] Specifically, construct a pavement material library, formulate pavement material matching rules according to different section characteristics; screen out the material data sets applicable to each section from the pavement material library according to section classification and matching rules; establish a hierarchical structure model, including the target layer, criterion layer, and scheme layer; construct a judgment matrix, and through calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix, rank the importance of each criterion in the criterion layer and each material in the scheme layer relative to the criterion layer; conduct a consistency test on the judgment matrix; combine the single ranking results of each material in the scheme layer relative to the criterion layer with the weights of the criterion layer relative to the target layer, calculate the total ranking weights of each material in the scheme layer relative to the target layer, determine the optimal pavement materials for each section; combine with the second optimized highway structure to obtain the optimized design scheme of the highway structure.

[0106] In a specific embodiment, select a section of a certain east-west arterial highway from K10+000 to K15+000 (with a total length of 5 km), covering three typical sections:

[0107] Heavy traffic section (K10+000~K12+000): Passing through an industrial park, with 2000 trucks with a daily average load of 30t passing through, accounting for 30%, the lowest winter temperature is -15°C, the freeze-thaw cycle is 40 times / year, and the subgrade is soft soil foundation (bearing capacity 120 kPa).

[0108] Ecologically Sensitive Section (K12+000~K13+500): Passing through the provincial water source protection area, with an annual rainfall of 800 mm, the road surface needs to meet a water permeability rate of ≥2 mm / s, a design speed of 60 km / h, a horizontal curve radius of 500 m, and the roadbed is moderately weathered rock stratum.

[0109] Ordinary Mixed Traffic Section (K13+500~K15+000): Connecting towns and townships, with a daily average traffic flow of 3000 pcu, a small passenger car proportion of 60%, the highest summer temperature of 35℃, frequent heavy rains, a longitudinal slope of 4%, and the roadbed is silty clay.

[0110] Formulate matching rules, including:

[0111] Heavy Load Section: Prioritize meeting the compressive strength (≥15 MPa) and freeze-thaw resistance (≥150 times), take into account the roadbed coordination (flexible materials are needed for soft soil), and exclude cement concrete (rigid structures are prone to cracking).

[0112] Ecological Section: Mandatory water permeability rate ≥2 mm / s, prioritize environmental protection level (reuse of waste materials or low emissions), and exclude impermeable materials (such as cement concrete).

[0113] Ordinary Section: Balance strength, cost, and construction convenience, consider the heavy rain drainage requirements (water permeability rate ≥0.5 mm / s), and allow medium-strength materials.

[0114] In the hierarchical structure model: The target layer (O) is specifically: the selection of the optimal pavement material for each road section; the criterion layer (C) includes: C1 Performance Adaptability: including compressive strength, freeze-thaw resistance performance, anti-skid performance (friction coefficient ≥0.55); C2 Cost Economy: material unit price, construction cost (including mechanical loss), and life-cycle maintenance cost; C3 Construction Convenience: construction temperature range (whether it adapts to the local climate), process complexity (such as whether special equipment is required); C4 Environmental Protection Feasibility: production energy consumption (kWh / ㎡), carbon emissions (kg / CO2 / ㎡), and ecological friendliness (whether it contains heavy metals).

[0115] Construct the criterion layer judgment matrix and score by experts. Conduct single ranking of the scheme layer and calculate the total ranking weight. Determine the optimal materials for each road section and integrate the schemes.

[0116] Such as Figure 2 As shown in the system structure diagram of a highway structure optimization design system based on multi-source data analysis, the present application provides a highway structure optimization design system based on multi-source data analysis, including:

[0117] Highway Structure Connectivity Graph Generation Module: It includes: the First Connectivity Graph Generation Unit, the Second Connectivity Graph Generation Unit, and the Highway Structure Connectivity Graph Generation Unit; among them, the First Connectivity Graph Generation Unit obtains the original highway route, defines core nodes, generates candidate edges between adjacent nodes, and uses the minimum spanning tree to generate the First Connectivity Graph; the Second Connectivity Graph Generation Unit defines the constraint of avoiding high fills and deep cuts, avoids geological risks, and optimizes the First Connectivity Graph using the weighted Dijkstra algorithm to obtain the Second Connectivity Graph; the Highway Structure Connectivity Graph Generation Unit conducts traffic flow simulation on the Second Connectivity Graph, inserts intermediate transition nodes for the edges with abrupt slope changes between adjacent nodes, refines the terrain grid, and recalculates the gentle path to obtain the Highway Structure Connectivity Graph;

[0118] First Optimized Highway Structure Generation Module: It includes: the Geometric Model Conversion Unit, the Vehicle Speed Constraint Determination Unit, the Minimum Radius Constraint Determination Unit, and the First Optimized Highway Structure Generation Unit; among them, the Geometric Model Conversion Unit extracts all curve segments from the highway structure connectivity graph and converts them into geometric models, the Vehicle Speed Constraint Determination Unit calculates the initial curve radius, and combines with traffic flow data to determine the vehicle speed constraints for each curve segment; the Minimum Radius Constraint Determination Unit analyzes the terrain complexity of the area where the curve segment is located based on terrain and geomorphic data to determine the minimum radius constraint; the First Optimized Highway Structure Generation Unit constructs a curve radius optimization model, sets the objective function, including safety objectives and comfort objectives, and solves the objective function based on vehicle speed constraints and minimum radius constraints to obtain the First Optimized Highway Structure;

[0119] Second Optimized Highway Structure Generation Module: It includes: the Initial Longitudinal Slope Calculation Unit and the Second Optimized Highway Structure Generation Unit; among them, the Initial Longitudinal Slope Calculation Unit extracts elevation data from the highway structure connectivity graph, obtains constraint conditions, conducts longitudinal slope segmentation, and calculates the initial longitudinal slope; the Second Optimized Highway Structure Generation Unit constructs a longitudinal slope optimization model, uses the dynamic programming algorithm to solve the optimal safe longitudinal slope, and adjusts the slope segments to obtain the Second Optimized Highway Structure;

[0120] Optimized Design Scheme Generation Module: It includes: the Road Section Feature Division Unit and the Optimized Design Scheme Generation Unit; among them, the Road Section Feature Division Unit obtains climate data and divides road section features based on the Second Optimized Highway Structure; the Optimized Design Scheme Generation Unit matches the material data set from the pavement material library according to different road section features and combines with the Second Optimized Highway Structure to obtain the optimized design scheme for the highway structure.

[0121] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A highway structure optimization design method based on multi-source data analysis, characterized in that The steps include: Obtain the original highway route, define core nodes, generate candidate edges between adjacent nodes, and generate the first connected graph using the minimum spanning tree; define the constraint of prohibiting high filling and deep excavation to avoid geological risks, and optimize the first connected graph using the weighted Dijkstra algorithm to obtain the second connected graph; conduct traffic flow simulation on the second connected graph, insert intermediate transition nodes for the edges with sudden slope changes between adjacent nodes, refine the terrain grid, and recalculate the gentle paths to obtain the highway structure connected graph; Extract all curve segments from the highway structure connected graph, convert them into geometric models, calculate the initial curve radius, and determine the vehicle speed constraints for each curve segment in combination with traffic flow data; analyze the terrain complexity of the area where the curve segments are located based on the terrain and geomorphology data to determine the minimum radius constraint; construct a curve radius optimization model, set the objective function, including safety objectives and comfort objectives, and solve the objective function based on the vehicle speed constraint and the minimum radius constraint to obtain the first optimized highway structure; Extract elevation data from the highway structure connected graph, obtain the constraint conditions, conduct longitudinal slope segmentation, and calculate the initial longitudinal slope; construct a longitudinal slope optimization model, use the dynamic programming algorithm to solve the optimal safe longitudinal slope, and adjust the slope segments to obtain the second optimized highway structure; Obtain climate data and divide the road section characteristics based on the second optimized highway structure; Match the material data set from the pavement material library according to different road section characteristics, and combine it with the second optimized highway structure to obtain the optimized design scheme of the highway structure.

2. The highway structure optimization design method based on multi-source data analysis according to claim 1, characterized in that The steps of obtaining the original highway route, defining core nodes, generating candidate edges between adjacent nodes, and generating the first connected graph using the minimum spanning tree include: Import CAD design drawings, extract the coordinates of the original highway route, generate a GIS line feature layer, overlay terrain data, supplement the geographical elements around the route, and conduct data cleaning and standardization; determine the types of prohibited construction areas, and use ArcGIS buffer analysis to generate buffer zones for the prohibited construction areas; Classify the nodes into mandatory connection nodes, optional connection nodes, and optimization nodes according to the priority from high to low, use the PageRank algorithm to sort the node importance, and generate a core node set; based on the core node set, generate candidate edges between adjacent nodes, filter the candidate edges without engineering feasibility, and assign edge attributes; The minimum spanning tree selects the Prim algorithm, arbitrarily selects a node from the core node set as the root node, and initializes the minimum spanning tree node set and edge set; for all nodes in the minimum spanning tree node set, traverse their candidate edges, and exclude the prohibited edges located in the prohibited construction areas and their buffer zones; calculate the comprehensive weight of the candidate edges, select the edge with the minimum weight and add it to the edge set; add the unvisited node connected by the candidate edge to the minimum spanning tree node set, and repeat until all core nodes are connected; conduct connectivity verification and redundant edge pruning, and output the first connected graph.

3. A method for optimizing the design of highway structures based on multi-source data analysis according to claim 1, characterized in that The steps of defining the constraint of prohibiting high filling and deep excavation to avoid geological risks, and optimizing the first connected graph using the weighted Dijkstra algorithm to obtain the second connected graph include: Set the filling and excavation volume limits according to the highway grade and terrain conditions; define deep excavation risk sections and high fill risk sections in combination with engineering experience; based on the nodes and edges of the first connectivity graph, calculate the filling and excavation volume and average slope corresponding to each edge in combination with terrain data, and label each edge in the first connectivity graph with the attribute of whether it belongs to a high fill or deep excavation section; set the weight T of the high fill and deep excavation sections, which increases proportionally according to the degree of the filling and excavation volume exceeding the threshold to limit the Dijkstra algorithm from selecting such sections; divide the geological risk levels and assign corresponding geological risk weights G to each level, and associate the geological risk weights with the edges of the first connectivity graph. Calculate the comprehensive weight W of the edge, and the formula is: , where D is the distance weight, is the distance factor, is the high fill and deep excavation factor, is the geological risk factor, which is determined by the analytic hierarchy process , and ; Arbitrarily select a core node in the first connectivity graph as the starting node, set its distance to 0, and set the distances of the remaining nodes to infinity. Establish a set Q of nodes to be visited; select the node u with the minimum distance from the set Q, mark it as visited, and remove it from Q; traverse all neighbor nodes v of node u, and calculate the new distance d from the starting node to v, which is specifically the sum of the actual geometric distance from node u to v and the comprehensive weight of the edge from node u to v; when d is less than the currently recorded distance of v, update the distance and predecessor node of v to u; repeat node visiting and neighbor node updating until the set Q is empty to obtain the shortest paths from the starting node to all nodes, forming the second connectivity graph.

4. A highway structure optimization design method based on multi-source data analysis according to claim 1, characterized in that For the traffic flow simulation of the second connectivity graph, for the edges with abrupt slope changes between adjacent nodes, insert intermediate transition nodes, refine the terrain grid, and recalculate the gentle paths to obtain the highway structure connectivity graph, including: Import the second connectivity graph into the traffic simulation software Vissim, generate a road network according to the node coordinates, and label the design vehicle speed, number of lanes, and subgrade width of each edge; input traffic flow data, allocate the flow according to peak and off-peak periods, distinguish cars, heavy trucks, and buses, set the acceleration and deceleration characteristics of the vehicles, and monitor the simulation indicators; extract the elevation data of adjacent nodes and calculate the original slope; when the slope difference between adjacent edges or the single-point slope exceeds the limit, determine that the edge between adjacent nodes is a slope mutation. Obtain the terrain grid, determine the insertion density of the edges with abrupt slope changes between adjacent nodes according to the slope difference, use the linear interpolation method to determine the elevation of the intermediate transition nodes, and conduct engineering feasibility verification. Based on the nodes of the second connectivity graph, generate a Delaunay triangulation, and identify the triangular elements corresponding to the slope mutation edges; use the Loop subdivision algorithm to perform several subdivisions on the triangles containing the mutation edges to generate a high-density sub-grid; divide the slope mutation edges into several new edges, with the starting point of the new edge being the original node and the end point being the inserted transition node; in the refined grid, generate new candidate edges according to the adjacency relationship of the Delaunay triangulation, exclude the edges in the no-construction area, and update the edge weights. Determine the multi-objective function and use the algorithm to preferentially search for paths with gentle slopes; use the path of the second connected graph as the initial solution, and find the sub-path corresponding to the mutated edge in the refined grid; for each mutated edge, search for alternative paths in its neighborhood grid; use the union-find algorithm to check the connectivity of the new graph, and when isolated nodes appear, enable the bridge edge mechanism; combine the original core nodes and transition nodes into a node set, the edge set contains the refined gentle paths, and construct a connected graph of the highway structure.

5. The highway structure optimization design method based on multi-source data analysis according to claim 1, characterized in that Extract all the curve segments from the highway structure connectivity graph, convert them into geometric models, calculate the initial curve radius, and determine the vehicle speed constraints for each curve segment in combination with the traffic flow data, including: Traverse all the edges in the connected graph of the highway structure. According to the node connection relationship, identify the edge sequence with continuous turning as the potential curve segment and mark the curve segment; extract the plane coordinates of each node of the curve segment. For non-node positions, supplement data points through linear interpolation; for the curve segment of an arc, use arc fitting, calculate the center coordinates and radius of the fitted arc, and construct the geometric model of the arc; for the curve segment containing multiple shapes, use cubic spline curve fitting; calculate the geometric parameters of the curve segment and construct the geometric model. For the curve segment fitted by an arc, directly obtain the radius of the fitted arc as the initial curve radius; for the curve segment fitted by a spline curve, determine the equivalent curve radius by calculating the curvature of the curve; combine the curve radius, superelevation setting and pavement friction coefficient, and calculate the first reasonable vehicle speed for safe driving on the curve segment through mechanical formulas; use the Greenshields model to establish the relationship model between traffic flow and vehicle speed, and calculate the second reasonable vehicle speed under the traffic flow density and saturation of the curve segment; take the smaller value of the first reasonable vehicle speed and the second reasonable vehicle speed as the speed constraint of the curve segment.

6. The highway structure optimization design method based on multi-source data analysis according to claim 1, characterized in that Based on the topographic and geomorphic data, analyze the topographic complexity of the area where the curve segment is located and determine the minimum radius constraint, including: Collect topographic and geomorphic data, conduct regional division of the curve segment and extraction of topographic features, determine the index weights of different topographic features, calculate the topographic complexity index of the area where each curve segment is located by weighted summation, divide the topographic complexity into different levels, and determine the minimum radius constraint according to the highway design specifications.

7. A highway structure optimization design method based on multi-source data analysis according to claim 1, characterized in that Construct the curve radius optimization model, set the objective function, including the safety objective and the comfort objective, and solve the objective function based on the speed constraint and the minimum radius constraint to obtain the first optimized highway structure, including: In the curve radius optimization model, the safety objective is expressed as minimizing the centrifugal force coefficient of each curve segment, and the comfort objective is expressed as minimizing the curvature change rate of each curve segment. Combine the safety objective and the comfort objective through weighting to obtain the objective function; use the sequential quadratic programming algorithm to solve the objective function based on the speed constraint and the minimum radius constraint; apply the obtained optimal curve radius to the corresponding curve segment in the connected graph of the highway structure, and update the geometric model of the curve segment; according to the updated geometric model, reconstruct the connected graph of the highway structure to obtain the first optimized highway structure.

8. A highway structure optimization design method based on multi-source data analysis according to claim 1, characterized in that Construct the longitudinal slope optimization model, use the dynamic programming algorithm to solve the optimal safe longitudinal slope, and adjust the slope section to obtain the second optimized highway structure, including: Determine that the safety objective of the longitudinal slope optimization model is to approach the safe longitudinal slope, and the comfort objective is to equalize the slope length. Set the geological constraint, slope length constraint and adjacent slope section slope difference constraint; use the dynamic programming algorithm to define the state space, transition equation and boundary conditions, and solve the optimal safe longitudinal slope; adjust the slope section in the first optimized highway structure based on the optimal safe longitudinal slope to obtain the second optimized highway structure.

9. A highway structure optimization design method based on multi-source data analysis according to claim 1, characterized in that Matching a material dataset from a pavement material library according to different road section characteristics, and combining with the second optimized road structure to obtain an optimized road structure design scheme, including: Construct a pavement material library, and formulate pavement material matching rules according to different road section characteristics; screen out the material datasets applicable to each road section from the pavement material library according to road section classification and matching rules; establish a hierarchical structure model, including an objective layer, a criterion layer, and a scheme layer; construct a judgment matrix, and sort the importance of each criterion in the criterion layer and each material in the scheme layer relative to the criterion layer by calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix; conduct a consistency test on the judgment matrix; combine the single sorting results of each material in the scheme layer relative to the criterion layer with the weights of the criterion layer relative to the objective layer, calculate the total sorting weights of each material in the scheme layer relative to the objective layer, determine the optimal pavement materials for each road section; and combine with the second optimized road structure to obtain an optimized road structure design scheme.

10. A highway structure optimization design system based on multi-source data analysis, using a highway structure optimization design method according to any one of claims 1-9, characterized in that, Including: Highway structure connectivity graph generation module: including: a first connectivity graph generation unit, a second connectivity graph generation unit, and a highway structure connectivity graph generation unit; wherein, the first connectivity graph generation unit obtains the original highway route, defines core nodes, generates candidate edges between adjacent nodes, and generates a first connectivity graph using a minimum spanning tree; the second connectivity graph generation unit defines a constraint against high fills and deep cuts, avoids geological risks, and optimizes the first connectivity graph using a weighted Dijkstra algorithm to obtain a second connectivity graph; the highway structure connectivity graph generation unit conducts traffic flow simulation on the second connectivity graph, inserts intermediate transition nodes for the edges with sudden slope changes between adjacent nodes, refines the terrain grid, and recalculates the gentle path to obtain a highway structure connectivity graph; First optimized highway structure generation module: including: a geometric model conversion unit, a vehicle speed constraint determination unit, a minimum radius constraint determination unit, and a first optimized highway structure generation unit; wherein, the geometric model conversion unit extracts all curve segments from the highway structure connectivity graph and converts them into a geometric model, the vehicle speed constraint determination unit calculates the initial curve radius, and combines with traffic flow data to determine the vehicle speed constraints for each curve segment; the minimum radius constraint determination unit analyzes the terrain complexity of the area where the curve segment is located based on terrain and landform data to determine the minimum radius constraint; the first optimized highway structure generation unit constructs a curve radius optimization model, sets an objective function, including a safety objective and a comfort objective, and solves the objective function based on the vehicle speed constraint and the minimum radius constraint to obtain a first optimized highway structure; Second optimized highway structure generation module: including: an initial longitudinal slope calculation unit and a second optimized highway structure generation unit; wherein, the initial longitudinal slope calculation unit extracts elevation data from the highway structure connectivity graph, obtains constraint conditions, conducts longitudinal slope segmentation, and calculates the initial longitudinal slope; the second optimized highway structure generation unit constructs a longitudinal slope optimization model, uses a dynamic programming algorithm to solve for the optimal safe longitudinal slope, and adjusts the slope segments to obtain a second optimized highway structure; Optimized design solution generation module: includes: road section feature division unit and optimized design solution generation unit; among them, the road section feature division unit obtains climate data and divides road section features based on the second optimized road structure; the optimized design solution generation unit matches material data sets from the road surface material library according to different road section features and combines them with the second optimized road structure to obtain an optimized design solution for the road structure.

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