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

Abstract

The invention discloses a road structure optimization design method and system based on multi-source data analysis, and belongs to the technical field of multi-source data analysis. The method comprises the following steps: generating a first connected graph by using a minimum spanning tree, and optimizing the first connected graph by using a Dijkstra algorithm with a weight to obtain a second connected graph; performing traffic flow simulation on the second connected graph, inserting a middle transition node into an edge with sudden gradient change between adjacent nodes, and refining a terrain grid to obtain a road structure connected graph; extracting all curve segments from the road structure connected graph, converting the curve segments into a geometric model, and determining the vehicle speed constraint of each curve segment; determining a minimum radius constraint according to the landform data; constructing a curve radius optimization model to obtain a first optimized road structure; an initial longitudinal slope is calculated, a longitudinal slope optimization model is constructed, an optimal safe longitudinal slope is solved, and a second optimized road structure is obtained; dividing road section features, and matching a material data set to obtain a road structure optimization design scheme.

Description

Technical Field

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

[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, and has limited quantitative analysis capabilities for multi-source data such as traffic flow, topography, and geological conditions, making it difficult to achieve accurate 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 single indicators and lacks 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, and no linkage optimization mechanism is formed. 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 risks of side slip and braking failure. Existing material selection is mostly based on engineering experience and specification lookup tables, and no quantitative matching model for section characteristics, material properties, and environmental constraints is established. 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: In the first aspect, the present application provides a highway structure optimization design method based on multi-source data analysis, including the following steps: 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; 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; Extract all curve sections from the highway structure connected graph, convert them into geometric models, calculate the initial curve radius, and determine the speed constraint of each curve section in combination with traffic flow data; analyze the terrain complexity of the area where the curve section is located based on 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 speed constraint and the minimum radius constraint to obtain the first optimized highway structure; Extract elevation data from the highway structure connectivity graph, obtain constraint conditions, segment the longitudinal slope, and calculate the initial longitudinal slope; construct a longitudinal slope optimization model, use the dynamic programming algorithm to solve the optimal safe longitudinal slope, adjust the slope segments, and 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 with the second optimized highway structure to obtain the optimized design scheme of the highway structure.

[0006] Combined with the first aspect, in the first implementation manner of the first aspect of this application, the obtaining of the original highway route, defining core nodes, generating candidate edges between adjacent nodes, and using the minimum spanning tree to generate the first connectivity graph includes: Import the CAD design drawing, extract the coordinates of the original highway route, generate a 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 ArcGIS buffer analysis to generate a buffer zone for the prohibited construction areas; 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 perform edge attribute assignment; 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 areas and their buffer zones; 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.

[0007] Combined with the first aspect, in the second implementation manner of the first aspect of this application, the defining of the constraint against high filling and deep excavation to avoid geological risks and using the weighted Dijkstra algorithm to optimize the first connectivity graph to obtain the second connectivity graph includes: Set the cut and fill volume limit according to the highway grade and terrain conditions; define deep cut risk sections and high fill risk sections in combination with engineering experience; calculate the cut and fill volume and average slope corresponding to each edge based on the nodes and edges of the first connected graph and in combination with terrain data, and label whether each edge belongs to high fill and deep cut sections in the first connected graph; set the weight T of high fill and deep cut sections, which increases proportionally according to the degree of the cut and fill 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. 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 cut factor, is the geological risk factor, which is determined by the analytic hierarchy process 、 and ; Arbitrarily 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, and 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, and obtain the shortest paths from the starting node to all nodes, forming the second connected graph.

[0008] Combined with the first aspect, in the third implementation manner of the first aspect of the present application, for the traffic flow simulation of 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: Import the second connected 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 passenger cars, 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 sudden 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 perform engineering feasibility verification. Generate a Delaunay triangulation based on the nodes of the second connected graph, 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 points of the new edges being the original nodes and the ending points being the inserted transition nodes; 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. Determine the multi-objective function and use the algorithm to preferentially search for gentle slope 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 includes the refined gentle slope paths to construct a highway structure connectivity graph.

[0009] 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 a geometric model, calculating the initial curve radius, and combining traffic flow data to determine the vehicle speed constraints for each curve segment, including: Traverse all the edges in the highway structure connectivity graph, identify the sequence of continuously turning edges 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 data points by linear interpolation for non-node positions; for the curve segments of circular arcs, use circular arc fitting to construct a circular arc geometric model by calculating the center coordinates and radius of the fitted circular arc; for the curve segments containing multiple shapes, use cubic spline curve fitting; calculate the geometric parameters of the curve segment to construct a geometric model. For the curve segments fitted with circular arcs, directly obtain the radius of the fitted circular arc as the initial curve radius; for the curve segments fitted with 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 of the vehicle 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 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.

[0010] Combined with the first aspect, in the fifth implementation manner of the first aspect of the present application, 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, including: Collect topographic and geomorphic data, conduct curve segment area division and topographic feature extraction, 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 highway design specifications.

[0011] Combined with the first aspect, in the sixth implementation manner of the first aspect of this application, when constructing 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 vehicle 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. The safety objective and the comfort objective are weighted and combined 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.

[0012] Combined with the first aspect, in the seventh implementation manner of the first aspect of this application, when constructing 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, transfer equation, and boundary conditions to 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.

[0013] Combined with the first aspect, in the eighth implementation manner of the first aspect of this application, when matching the material data set from the pavement material library according to different section characteristics and combining it with the second optimized highway structure to obtain the highway structure optimized design scheme, including: Build a pavement material library, formulate pavement material matching rules according to different road section characteristics; screen out 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 rank 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-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 road structure to obtain an optimized road structure design scheme.

[0014] In a second aspect, the present application provides a highway structure optimization design system based on multi-source data analysis, 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 edges with sudden slope changes between adjacent nodes, refines the terrain grid, and recalculates gentle paths 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 geomorphic 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: including: 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 highway structure; the optimized design solution 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 design solution for the highway structure.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. Through the Dijkstra algorithm with weights and the dynamic programming algorithm, the present 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 at the road sections with sudden slope changes can reduce the emergency braking frequency of vehicles and reduce the engineering quantity of high fills and deep cuts.

[0016] 2. The present application constructs a collaborative optimization model of curve radius and longitudinal slope, and conducts mechanical coupling analysis on vehicle speed constraints, terrain complexity, longitudinal slope gradient, and slope length; at sharp curve 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.

[0017] 3. The present application establishes a matching model of road section features and material properties based on the analytic hierarchy process, and dynamically screens the optimal materials in combination with requirements such as climate, traffic load, and ecological protection, which can extend the rutting resistance life of the road surface, improve the rainwater penetration rate while reducing construction pollution emissions. Description of the drawings

[0018] 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; 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 implementation manners

[0019] 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 of 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.

[0020] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution, As Figure 1 shown in a 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: Step S100: Obtain the original highway route, define core nodes, generate candidate edges between adjacent nodes, and use the minimum spanning tree to generate the first connected graph; define the constraint of prohibiting high fills and deep cuts to avoid geological risks, and use the weighted Dijkstra algorithm to optimize the first connected graph to obtain the second connected graph; perform traffic flow simulation on the second connected graph, insert intermediate transition nodes for the edges with abrupt slope changes between adjacent nodes, refine the terrain grid, and recalculate the gentle paths to obtain the highway structure connected graph. Specifically, import the CAD design drawings, extract the coordinates of the original highway route, generate a 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 ArcGIS buffer analysis to generate buffer zones for the prohibited construction areas. 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 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. 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, exclude the prohibited edges located in the prohibited construction areas and their buffer zones; 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 nodes 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.

[0021] Furthermore, set the fill and cut volume limits according to the highway grade and terrain conditions; combine engineering experience to define deep cut risk sections and high fill risk sections; based on the nodes and edges of the first connected graph, combine with the terrain data, calculate the fill and cut volume and average slope corresponding to each edge, and label the attribute of whether each edge belongs to a high fill or deep cut section in the first connected graph; set the weight T of the high fill and deep cut sections, which increases proportionally according to the degree of the fill and cut 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. 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 cut factor, is the geological risk factor, which is determined by the analytic hierarchy process , and ; Select a core node in the first connected 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 smallest 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 visiting and neighbor node updating until the set Q is empty, obtaining the shortest paths from the starting node to all nodes, which form the second connected graph.

[0022] Furthermore, import the second connected graph into the traffic simulation software Vissim, generate a road network according to the node coordinates, and mark the designed vehicle speeds, number of lanes, and subgrade widths 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. Obtain the terrain grid, determine the insertion density of the edges with slope mutations between adjacent nodes according to the slope difference, use linear interpolation to determine the elevations of the intermediate transition nodes, and conduct engineering feasibility verification. Based on the nodes of the second connected graph, generate a Delaunay triangulation, and identify the triangular elements corresponding to the edges with slope mutations. Adopt the Loop subdivision algorithm to perform several subdivisions on the triangles containing the mutated edges to generate a high-density sub-grid. Divide the edges with slope mutations 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 adopt algorithm to preferentially search for paths with gentle slopes; use the path of the second connected graph as the initial solution to find the sub-path corresponding to the mutated edge in the refined grid. For each mutated edge, search for an alternative path in its neighborhood grid. Use the union-find algorithm to check the connectivity of the new graph. 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 connected graph.

[0023] In a specific embodiment, a CAD drawing with a scale of 1:2000 is imported, 127 coordinate points are extracted, a GIS line layer is generated, and 10m-resolution DEM and no-construction area data are overlaid. The mandatory connection 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 optimization nodes include: terrain mutation points G, H, and I. Node importance ranking is performed to obtain the core node set, including: Town A, Town B, Industrial Zone C, and Scenic Area D.

[0024] The initial candidate edges are 56, 12 edges in the no-construction area are excluded, 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 connected graph: total length 5200m, avoiding all no-construction areas, shortening by 18% compared to the original route.

[0025] Constraint conditions are set. The high-fill and deep-excavation constraints include: fill threshold > 8m (weight T = 1.5), excavation threshold > 10m (weight T = 2.0); the geological risk weights include: soft soil area (grade 4) G = 2.0, karst area (grade 5) G = 3.0 (edge H-D crosses the karst edge, G = 2.5).

[0026] Therefore, the comprehensive weight formula is W = 0.4D + 0.3T + 0.3G.

[0027] The optimization results of the Dijkstra algorithm are 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: 2 high-fill and deep-excavation edges (C-G, F-H) are avoided, and the proportion of geological risk edges is reduced from 15% to 5%.

[0028] The Vissim simulation parameters are as follows: Peak flow: 5000 pcu / d, heavy-duty vehicles account for 20%, designed vehicle speed 60 km / h; Monitoring indicators: For edge F-B (original slope difference 7%), the emergency braking frequency is 8 times / km (exceeding the standard), and the average vehicle speed is 45 km / h (25% lower than the designed value).

[0029] An intermediate node is inserted. Node J is inserted at the midpoint of edge F-B (station number K4+500), and it is divided into F-J (slope 4.5%) and J-B (slope 3.2%), and the slope difference is reduced to 1.3%.

[0030] Grid refinement is carried out. The grid in the mountainous section is encrypted from 500m to 200m, generating 18 new candidate edges, and 3 geological risk edges are excluded.

[0031] Perform Algorithm optimization, new path F→J→B, slope difference ≤ 3%, emergency braking frequency reduced to 2 times / km, average vehicle speed increased to 58 km / h.

[0032] Step S200: Extract all curve segments from the highway structure connectivity graph, convert them into geometric models, calculate the initial curve radius, combine traffic flow data to determine the vehicle speed constraints for each curve segment; based on topographic 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 vehicle speed constraints and minimum radius constraints to obtain the first optimized highway structure; Specifically, traverse all edges in the highway structure connectivity graph, identify the edge sequences with continuous turns 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, and construct an arc geometric model by calculating the center coordinates and radius of the fitted arc; for the curve segments containing multiple shapes, use cubic spline curve fitting; calculate the geometric parameters of the curve segment and construct a geometric model; 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 of the vehicle 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 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.

[0033] Furthermore, collect topographic data, conduct curve segment area division 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 highway design specifications.

[0034] 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. The safety objective and the comfort objective are weighted and combined to obtain the objective function. The sequential quadratic programming algorithm is used to solve the objective function based on the vehicle speed constraint and the minimum radius constraint. The optimal curve radius obtained by the solution is applied to the corresponding curve segments in the highway structure connection diagram to update the geometric model of the curve segments. According to the updated geometric model, the highway structure connection diagram is reconstructed to obtain the first optimized highway structure.

[0035] In a specific embodiment, curve segment identification and marking are carried out. Curve segment 1 (C1): The reverse curve between Town A and Industrial Zone C, including 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, including 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 turning edge (node I→D), is marked as "low-traffic curve segment".

[0036] 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).

[0037] C2 (cubic spline fitting): Node coordinates: F(2800,2200), J(3100,2150), B(3500,1800); Equivalent curvature radius calculation: The curvature k at the midpoint J is 0.002, R = 1 / k = 500m (weighted average of 3 sampling points).

[0038] Safety vehicle speed calculation is carried out. The parameters include: g = 9.8m / s 2 , superelevation cross slope α = 6%, and 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%).

[0039] Flow-adapted vehicle speed is calculated. For C1 (peak flow k = 150 vehicles / km), , taking the smaller value of 20km / h, and actually corrected to 30km / h according to the heavy vehicle ratio. For C2 (medium flow k = 100 vehicles / km), .

[0040] Extract topographic features. Taking C1 as an example, slope: 15° (calculated from DEM), terrain undulation: 200 m, surface roughness: 0.8 (on a scale of 1 - 5, the larger the value, the more complex); The index weights include: slope 0.4, undulation 0.3, roughness 0.3; 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 complexity" level).

[0041] Secondary highway in medium - complex hilly area: minimum radius 300 m (design speed 60 km / h); The initial radius of C1 is 500 m ≥ 300 m, which meets the requirement; for C2, it is necessary to check whether the minimum radius needs to be increased due to complex terrain.

[0042] For the safety objective, , ; For the comfort objective, for C1, .

[0043] Then the comprehensive objective is: .

[0044] The vehicle speed constraint is: , the minimum radius is: For C2, due to its proximity to the bridge, the minimum radius is increased to 400 m.

[0045] Perform SQP algorithm to solve.

[0046] After optimization, the center coordinates of C1 are adjusted to (1350, 1600), the radius is 600 m, and the horizontal curve elements are marked as follows: R = 600 m, L = 350 m, E = 15 m. For C2, an intermediate point K is inserted to refine the curve segment into 2 segments, and the curvature change rate is reduced from 0.0008 to 0.00058.

[0047] Perform CarSim simulation. When a heavy - duty truck passes through C1, the standard deviation of 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 optimization, the accident rate of this section is reduced by 40% compared with the average value of the previous three years.

[0048] Step S300: Extract elevation data from the highway structure connectivity map, obtain 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 for the optimal safety longitudinal slope, adjust the slope segments, and obtain the second - optimized highway structure; Specifically, the safety objective of the longitudinal slope optimization model is set to approach the safe longitudinal slope, the comfort objective is to equalize the slope length, and geological constraints, slope length constraints, and the slope difference constraint between 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.

[0049] In a specific embodiment, the longitudinal slope of a secondary highway in a mountainous area is optimized. The elevation data is extracted, the longitudinal slope is segmented, and the initial longitudinal slope calculation and problem identification are carried out. It is found 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%.

[0050] A longitudinal slope optimization model is constructed, and the results are solved by the dynamic programming algorithm. The optimized longitudinal slope plan is as follows: Section P1: Divided into 2 sections (+6% / 300m, +3% / 500m), the slope length is ≤ 500m, and the longitudinal slope is ≤ 6%; Section P2: Insert a break point C, divided into +2% / 500m (soft soil section), +1.5% / 900m (exceeding the limit, further divided into +1.5% / 500m, +1.5% / 400m); Section P3: Divided into -6% / 500m, -2% / 500m, the longitudinal slope is ≤ 6%, and the slope length is ≤ 500m.

[0051] The number of over-limit longitudinal slope sections is reduced from 3 to 0, and the longitudinal slope of the soft soil section is optimized from 1.43% to 2% (≤ 3%), meeting the geological constraints; In the CarSim simulation data, the climbing speed of the truck is increased from 25 km / h to 35 km / h.

[0052] The average slope length is reduced from 1100m to 467m, the slope length uniformity is increased by 67%, and the difference between adjacent slopes is ≤ 3%.

[0053] 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.

[0054] 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 highway structure optimization design plan.

[0055] Specifically, construct a pavement material library, formulate pavement material matching rules according to different road section characteristics; screen out material data sets suitable for 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 highway structure to obtain an optimized highway structure design scheme.

[0056] In a specific embodiment, select a section of a main east-west arterial highway from K10+000 to K15+000 (with a total length of 5 km), covering three typical road sections: Heavy traffic section (K10+000~K12+000): Passing through an industrial park, with 2,000 trucks with a load of 30 t passing through daily, 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).

[0057] Ecologically sensitive section (K12+000~K13+500): Passing through a provincial water source protection area, with an annual rainfall of 800 mm, the pavement needs to meet a water permeability rate of ≥2 mm / s, the design speed is 60 km / h, the horizontal curve radius is 500 m, and the subgrade is slightly weathered rock.

[0058] General mixed traffic section (K13+500~K15+000): Connecting towns, with a daily traffic flow of 3,000 pcu, small cars accounting for 60%, the highest summer temperature is 35°C, frequent heavy rains, a longitudinal slope of 4%, and the subgrade is silty clay.

[0059] Formulate matching rules, including: Heavy load section: Prioritize meeting the compressive strength (≥15 MPa) and freeze-thaw resistance (≥150 times), take into account the subgrade coordination (soft soil requires flexible materials), and exclude cement concrete (rigid structure is prone to cracking).

[0060] 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).

[0061] General section: Balance strength, cost, and construction convenience, consider the heavy rain drainage demand (water permeability rate ≥0.5 mm / s), and allow medium-strength materials.

[0062] In the hierarchical model: The target layer (O) specifically is: 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, skid resistance performance (friction coefficient ≥ 0.55); C2 Cost economy: material unit price, construction cost (including mechanical loss), full 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 emission (kg / CO2 / ㎡), ecological friendliness (whether it contains heavy metals).

[0063] Construct the criterion layer judgment matrix and score it by experts. Conduct single ranking for the scheme layer and calculate the total ranking weight. Determine the optimal materials for each road section and integrate the schemes.

[0064] 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: Highway structure connectivity graph generation module: including: 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 the core nodes, generates candidate edges between adjacent nodes, and generates the first connectivity graph using the minimum spanning tree; The second connectivity graph generation unit defines the constraint of prohibiting 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 sudden slope changes between adjacent nodes, refines the terrain grid, and recalculates the gentle path to obtain the highway structure connectivity graph; The first optimized highway structure generation module: including: 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 the 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 determines the vehicle speed constraints for each curve segment in combination with the traffic flow data; The minimum radius constraint determination unit analyzes the terrain complexity of the area where the curve segment is located based on the terrain and landform 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 the safety objective and the comfort objective, and solves the objective function based on the vehicle speed constraint and the minimum radius constraint to obtain the 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; 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 safe longitudinal slope, adjusts the slope section, and obtains the second optimized highway structure; Optimized design scheme generation module: including: 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 road surface material library according to different road section features, and combines with the second optimized highway structure to obtain an optimized design scheme for the highway structure.

[0065] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. 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 included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A highway structure optimization design method based on multi-source data analysis, characterized in that: The following steps are involved: The original highway route is obtained, core nodes are defined, candidate edges are generated between adjacent nodes, and the first connected graph is generated using the minimum spanning tree; constraints prohibiting high filling and deep excavation are defined to avoid geological risks, and the first connected graph is optimized using the weighted Dijkstra algorithm to obtain the second connected graph; traffic flow simulation is performed on the second connected graph, and for edges with sudden slope changes between adjacent nodes, intermediate transition nodes are inserted, the terrain grid is refined, and the gentle path is recalculated to obtain the highway structure connected graph; Extract all curve segments from the highway structure connectivity diagram, convert them into a geometric model, calculate the initial curve radius, and determine the speed constraints of each curve segment in combination with traffic flow data; analyze the terrain complexity of the area where the curve segment is located based on topographic data to determine the minimum radius constraint; construct a curve radius optimization model, set the objective function, including safety objectives and comfort objectives, solve the objective function based on the speed constraint and the minimum radius constraint, and obtain the first optimized highway structure; Extract elevation data from the highway structure connectivity diagram, obtain constraint conditions, divide the longitudinal slope into sections, and calculate the initial longitudinal slope; build a longitudinal slope optimization model, use a dynamic programming algorithm to solve the optimal safe longitudinal slope, adjust the slope section, and obtain the second optimized highway structure; Acquire climate data and divide road section characteristics based on the second optimized highway structure; According to different road section characteristics, the material data set is matched from the pavement material library, and combined with the second optimized highway structure, an optimized design scheme for the highway structure is obtained.

2. A highway structure optimization design method based on multi-source data analysis according to claim 1, characterized in that: The method of obtaining the original highway route, defining core nodes, generating candidate edges between adjacent nodes, and using a minimum spanning tree to generate a first connected graph includes: Import CAD design drawings, extract the coordinates of the original highway route, generate GIS linear feature layers, overlay terrain data, supplement the geographic elements around the route, and perform data cleaning and standardization; determine the type of prohibited construction area, use ArcGIS buffer analysis, and generate buffer zones for prohibited construction areas; Nodes are divided into must-connect nodes, optional nodes, and optimized nodes according to their priorities from high to low. The PageRank algorithm is used to sort the importance of nodes and generate a core node set. Based on the core node set, candidate edges are generated between adjacent nodes, candidate edges without engineering feasibility are filtered out, and edge attributes are assigned. The minimum spanning tree selects the Prim algorithm, randomly selects a node in the core node set as the root node, initializes the minimum spanning tree node set and the edge set; for all nodes in the minimum spanning tree node set, traverses their candidate edges, and excludes the prohibited construction edges located in the prohibited construction area and its buffer zone; calculates the comprehensive weights of the candidate edges, selects the edge with the smallest weight to add to the edge set; adds the unvisited nodes connected by the candidate edge to the minimum spanning tree node set, and repeats until all core nodes are connected; performs connectivity verification and redundant edge pruning, and outputs a first connected graph.

3. The highway structure optimization design method based on multi-source data analysis according to claim 1 is characterized in that: 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: According to the highway grade and terrain conditions, the filling and excavation volume limit is set; based on engineering experience, deep excavation risk sections and high filling and risk sections are defined; based on the nodes and edges of the first connected graph, combined with terrain data, the filling and excavation volume and average slope corresponding to each edge are calculated, and the attributes of whether each edge belongs to the high filling and deep excavation section are marked in the first connected graph; the weight T of the high filling and deep excavation section is set, and it is increased proportionally according to the degree to which the filling and excavation volume exceeds the threshold, so as to limit the Dijkstra algorithm from selecting such sections; the geological risk level is divided and the corresponding geological risk weight G is assigned to each level, and the geological risk weight is associated with the edge of the first connected graph; Calculate the comprehensive weight W of the edge, the formula is: , where D is the distance weight, For the distance factor, For high filling and deep excavation factors, As geological risk factors, the , and ; Select any 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 of nodes to be visited Q; 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 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 of v and the predecessor node to u; repeat the node visit and neighbor node update until the set Q is empty, and obtain the shortest path from the starting node to all nodes to form the second connected graph.

4. The highway structure optimization design method based on multi-source data analysis according to claim 1 is characterized in that: The method of performing traffic flow simulation on the second connected graph, inserting intermediate transition nodes for edges with sudden slope changes between adjacent nodes, refining the terrain grid, and recalculating the gentle path to obtain a highway structure connected graph includes: Import the second connected graph into the traffic simulation software Vissim, generate a road network according to the node coordinates, and mark the design speed, number of lanes and roadbed width of each edge; input traffic flow data, distribute traffic according to peak and off-peak periods, distinguish between cars, heavy trucks and buses, set vehicle acceleration and deceleration characteristics, and conduct simulation index monitoring; extract the elevation data of adjacent nodes and calculate the original slope; when the slope difference of adjacent edges or the slope of a single point exceeds the limit, judge the edge between adjacent nodes as a slope mutation; Obtain the terrain grid, determine the insertion density of the edges with sudden 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 perform engineering feasibility verification; Based on the nodes of the second connected graph, a Delaunay triangulation is generated to identify the triangular units corresponding to the slope mutation edges; the Loop subdivision algorithm is used to subdivide the triangles containing the slope mutation edges several times to generate high-density subgrids; the slope mutation edges are divided 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, new candidate edges are generated according to the adjacency relationship of the Delaunay triangulation, the edges in the prohibited construction area are excluded, and the edge weights are updated; Determine the multi-objective function, using The algorithm first searches for paths with gentle slopes; takes the second connected graph path as the initial solution, and searches for subpaths corresponding to the mutation edges in the refined grid; for each mutation edge, searches for alternative paths in its neighborhood grid; uses the union-find algorithm to check the connectivity of the new graph, and enables the bridge edge mechanism when isolated nodes appear; merges the original core nodes and transition nodes into a node set, and the edge set contains the refined gentle paths, and constructs a highway structure connectivity graph.

5. The highway structure optimization design method based on multi-source data analysis according to claim 1 is characterized in that: The method of extracting all curve segments from the highway structure connectivity diagram, converting them into a geometric model, calculating the initial curve radius, and combining the traffic flow data to determine the speed constraints of each curve segment includes: Traverse all the edges in the highway structure connectivity graph, identify the edge sequence with continuous turning 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 the data points for non-node positions through linear interpolation; for arc curve segments, use arc fitting, and construct the arc geometric model by calculating the coordinates of the center of the fitted arc and the radius; for curve segments containing multiple shapes, use cubic spline curve fitting; calculate the geometric parameters of the curve segment and construct the geometric model; For curve segments that adopt arc fitting, the radius of the fitted arc is directly obtained as the initial curve radius; for curve segments that adopt spline curve fitting, the equivalent curve radius is determined by calculating the curvature of the curve; the first reasonable speed for the vehicle to drive safely in the curve segment is calculated by the mechanical formula in combination with the curve radius, superelevation setting and road friction coefficient; the Greenshields model is used to establish a relationship model between traffic flow and speed, and the second reasonable speed under the traffic flow conditions is calculated according to the traffic flow density and saturation of the curve segment; the smaller value between the first reasonable speed and the second reasonable speed is taken 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 is characterized in that: The method of analyzing the terrain complexity of the area where the curve segment is located based on the terrain data and determining the minimum radius constraint includes: Collect topographic data, divide the curve segment area and extract terrain features, determine the indicator weights of different terrain features, use weighted summation to calculate the terrain complexity index of the area where each curve segment is located, divide the terrain complexity into different levels, and determine the minimum radius constraint according to the highway design specifications.

7. The highway structure optimization design method based on multi-source data analysis according to claim 1 is characterized in that: The curve radius optimization model is constructed, an objective function is set, including a safety objective and a comfort objective, and the objective function is solved based on a vehicle speed constraint and a minimum radius constraint to obtain a first optimized highway structure, including: In the curve radius optimization model, the safety target is expressed as minimizing the centrifugal force coefficient of each curve segment, and the comfort target is expressed as minimizing the curvature change rate of each curve segment. The safety target and the comfort target are weightedly combined to obtain the objective function; the sequential quadratic programming algorithm is used to solve the objective function based on the vehicle speed constraint and the minimum radius constraint; the optimal curve radius obtained by the solution is applied to the corresponding curve segment in the highway structure connectivity graph, and the geometric model of the curve segment is updated; according to the updated geometric model, the highway structure connectivity graph is reconstructed to obtain the first optimized highway structure.

8. The highway structure optimization design method based on multi-source data analysis according to claim 1 is characterized in that: The method of constructing a longitudinal slope optimization model, solving the optimal safe longitudinal slope using a dynamic programming algorithm, adjusting the slope section, and obtaining a second optimized highway structure includes: The safety target of the longitudinal slope optimization model is determined to be approaching the safe longitudinal slope, and the comfort target is to make the slope length uniform. Geological constraints, slope length constraints and constraints on the slope difference between adjacent slope sections are set. The dynamic programming algorithm is used to define the state space, transfer equations and boundary conditions to solve 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.

9. The highway structure optimization design method based on multi-source data analysis according to claim 1 is characterized in that: The method of matching material data sets from a pavement material library according to different road section characteristics and combining the second optimized highway structure to obtain a highway structure optimization design scheme includes: Construct a pavement material library and formulate pavement material matching rules according to different road section characteristics; select material data sets suitable for each road section from the pavement material library according to the road section classification and matching rules; establish a hierarchical model, including the target layer, criterion layer and scheme layer; construct a judgment matrix, and rank 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 corresponding eigenvector of the judgment matrix; perform a consistency check on the judgment matrix; calculate the total ranking weight of each material in the scheme layer relative to the target layer by combining the single ranking results of each material in the scheme layer relative to the criterion layer with the weight of the criterion layer relative to the target layer, and determine the optimal pavement material for each road section; combine with the second optimized highway structure to obtain the highway structure optimization design scheme.

10. A highway structure optimization design system based on multi-source data analysis, using a highway structure optimization design method based on multi-source data analysis according to any one of claims 1 to 9, characterized in that: include: 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 a minimum spanning tree to generate a first connectivity graph; the second connectivity graph generation unit defines a constraint prohibiting high filling and deep excavation, avoids geological risks, and uses a weighted Dijkstra algorithm to optimize the first connectivity graph to obtain a second connectivity graph; the highway structure connectivity graph generation unit performs traffic flow simulation on the second connectivity graph, inserts intermediate transition nodes for edges with sudden slope changes between adjacent nodes, refines the terrain grid, and recalculates the gentle path to obtain a highway structure connectivity graph; The first optimized highway structure generation module includes: a geometric model conversion unit, a 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 diagram and converts them into a geometric model; the speed constraint determination unit calculates the initial curve radius and determines the speed constraint of each curve segment in combination with the traffic flow data; the minimum radius constraint determination unit analyzes the terrain complexity of the area where the curve segment is located based on the terrain data and determines 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, solves the objective function based on the speed constraint and the minimum radius constraint, and obtains the first optimized highway structure; The second optimized highway structure generation module includes: 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 diagram, 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 a dynamic programming algorithm to solve the optimal safe longitudinal slope, adjusts the slope section, and obtains the second optimized highway structure; The optimization design scheme generation module includes: a road section feature division unit and an optimization design scheme generation unit; wherein the road section feature division unit obtains climate data and divides the road section features based on the second optimized highway structure; the optimization design scheme generation unit matches the material data set from the pavement material library according to different road section features, and obtains the highway structure optimization design scheme in combination with the second optimized highway structure.

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