An intelligent search method for railway alignment channels and an intelligent recommendation method for station positions based on a triangular mesh cost map

Abstract

The present invention discloses an intelligent search method for railway alignment channels and an intelligent station position recommendation method based on a triangular mesh cost map. The method includes the following steps: setting project parameters (S1), initializing basic data (S2), creating an artificial guiding line (S3), recommending a cost parameter set based on a differential evolution algorithm (S4), searching for multiple alignment channels based on the triangular mesh cost map (S5), performing horizontal and vertical alignment design of the alignment channels (S6), constructing an economic stronghold data set based on the alignment channels (S7), performing intelligent station position recommendation for the alignment channels based on the economic stronghold data set (S8), calculating and outputting the engineering cost of the alignment channels (S9). This method has the characteristics of high automation, fast calculation speed, and strong practicability. It can be applied to intelligent railway route selection, significantly improving the efficiency of railway alignment channel search and station position recommendation, and has high popularization and application value.

Description

Technical Field

[0001] The present invention relates to the field of railway route selection design, and particularly relates to an intelligent search method for railway alignment channels and an intelligent station position recommendation method based on a triangular mesh cost map. Background Art

[0002] During the intelligent railway route selection process, the search for alignment channels is a crucial step, and the generated alignment channels are the basis for subsequent line scheme design. When searching for alignment channels, it is necessary to not only consider the diversity of alignment channel schemes and quickly generate multiple alignment channels, but also consider the factor of station position selection to avoid adjusting the alignment scheme due to unreasonable station positions.

[0003] The traditional alignment channel search generally first constructs a regular grid, discretely stores the route selection-related information in the grid, then takes the cells as units, conducts path search on the grid at a fixed step length, and then performs constraint processing and fitting of the paths to finally generate a channel scheme.

[0004] The above method has a large amount of data redundancy and a large amount of calculation. In the case of route selection in a large scene, the channel search generally takes a long time. For example, when searching within a range of 40km * 20km, it takes about 2 hours or even longer to generate multiple alignment channels. Summary of the Invention

[0005] Aiming at the problems existing in the current intelligent railway route selection, the present invention provides a method for quickly searching for railway alignment channels and intelligent station position recommendation based on a triangular mesh cost map, which can quickly generate multiple railway alignment channels considering the station position factor and lay a foundation for subsequent line scheme design based on the alignment channels.

[0006] Therefore, the present invention adopts the following technical solutions:

[0007] An intelligent search method for railway alignment channels and an intelligent station position recommendation method based on a triangular mesh cost map, comprising the following steps:

[0008] S1, setting project parameters: According to the project design principles, set the longitudinal section design parameters, cross-section design parameters, station position design parameters, and cost parameters; the station position design parameters include the minimum station spacing, the minimum station platform length, and the maximum station platform slope; the cost parameters include the unit price per meter of subgrade, bridge, and tunnel;

[0009] S2, initializing basic data: Construct a digital elevation model based on the ground point cloud data, read the cost map information in the cost map file, construct a triangular mesh cost map model in memory, and construct a cost parameter set according to the information in the triangular mesh cost map model, and load the economic node data;

[0010] S3. Create an artificial guiding line: Set the starting point of channel search, the ending point of channel search, an array of mandatory points, and connect these points to generate a polyline to obtain the artificial guiding line, and use the artificial guiding line as the constraint line in the path search process; when no mandatory points are set, the artificial guiding line is a line segment connecting the starting point and the ending point of channel search.

[0011] S4. Recommend a set of cost parameters based on the differential evolution algorithm: Use the cost map information to initialize the set of cost parameters, set the evolution parameters, and use the differential evolution algorithm to optimize the initialized set of cost parameters to generate an optimal set of cost parameters, and modify the triangular mesh cost map according to the optimal set of cost parameters to obtain a modified triangular mesh cost map; where:

[0012] When initializing the set of cost parameters, the cost type cost group is based on the subgrade cost; the bridge cost and the tunnel cost are determined respectively according to the ratio of the unit price per meter of the bridge, the unit price per meter of the tunnel to the unit price per meter of the subgrade; the penalty type cost group is set according to the cost type; the reward type cost group is set according to the cost type.

[0013] When optimizing the initialized set of cost parameters, first generate an array of cost parameter sets according to the population size and the value range of cost parameters, and extract the starting and ending points of the artificial guiding line as the calculation starting and ending points; use the initialized set of cost parameters for optimization calculation, and use the optimization result of the current generation to generate an array of cost parameter sets for the next generation through the crossover factor and the scaling factor, and perform the optimization calculation for the next generation until the optimal set of cost parameters is found.

[0014] S5. Search for multiple alignment channels based on the modified triangular mesh cost map: Select the artificial guiding line created in S3 and set the number of alignment channels, and use the improved Dijkstra algorithm to perform multi-channel search on the modified triangular mesh cost map to form multiple alignment channels.

[0015] S6. Horizontal and vertical alignment design of the alignment channels: Smooth the polyline of each alignment channel, and use the polyline to construct a line plane model with a radius and easement length of 0; perform automatic fitting and constraint processing on the vertical section according to the line plane model, and automatically generate bridge and tunnel gaps on the vertical section, and generate subgrade, bridge, and tunnel work point paragraphs according to the bridge and tunnel gaps.

[0016] S7. Construct an economic stronghold data set based on the alignment channels: Construct a filtering area according to the line plane model of each alignment channel, extract economic stronghold data and sort it.

[0017] S8. Perform intelligent recommendation of the alignment positions of the alignment channels based on the economic stronghold data set:

[0018] First, use the economic stronghold dataset obtained by S7. Calculate each economic stronghold data one by one in descending order of the economic stronghold level. If the distance between the center mileage of the stations on both sides of the center mileage of the economic stronghold is greater than or equal to twice the minimum station spacing, add a new station position, initialize the center mileage of the station position, and perform station position adjustment; otherwise, do nothing.

[0019] After completing the station position recommendation based on the economic stronghold dataset, recheck the distance between the center mileages of all adjacent station positions. If this distance is greater than or equal to twice the minimum station spacing, add a new station position according to the station spacing parameter, initialize the center mileage of the station position, and perform station position adjustment; otherwise, do nothing.

[0020] S9. Calculation and output of the cost of the alignment channel project:

[0021] Perform subgrade cross-section design on the plane model and longitudinal section model of each alignment channel, and calculate the project cost using the cost parameters; finally, output the information of the alignment channel in ascending order of cost, including the plane model, longitudinal section model, total cost, total expense, line length, total bridge length, total tunnel length, bridge-tunnel ratio, and total number of station positions.

[0022] In the above method, the cost map information in S2 includes vertex coordinates, a set of cost types, side lengths, and edge costs; the cost types are divided into three groups according to their functions: cost-type costs, penalty-type costs, and reward-type costs, where:

[0023] The cost-type cost group includes cost types such as subgrade, bridge, and tunnel.

[0024] The penalty-type cost group includes cost types such as rivers, lakes, existing railways, existing highways, poor geological areas, restricted areas, economic strongholds, planned areas, environmental protection core areas, environmental protection buffer areas, and environmental protection experimental areas; among them, the economic stronghold data includes the name, level, boundary coordinates, center coordinates, population, GDP, center projection mileage, and projection distance of the economic stronghold.

[0025] The reward-type cost group includes cost types such as railway transportation corridor belts, highway transportation corridor belts, and economic radiation areas.

[0026] In step S4, the evolutionary parameters include the objective function, the value range of the cost parameters in the cost types, the calculation start and end points, the evolutionary mode, the population size, the total number of evolutionary generations, the scaling factor, and the crossover factor; the objective function is the project cost. Preferably, in the cost-type cost group, the value range of the cost parameters is [1.0, 10.0]; in the penalty-type cost group, the value range of the cost parameters is [1.0, 100.0]; in the reward-type cost group, the value range of the cost parameters is [0.0, 1.0].

[0027] In the above-mentioned step S4, the calculation of each cost parameter set in each generation includes:

[0028] Updating the triangular mesh cost map according to the cost parameter set, searching for the shortest path using Dijkstra's algorithm in the triangular mesh cost map, constructing a line plane model using the shortest path, performing automatic vertical profile design, generating roadbed, bridge, tunnel and gap, performing automatic roadbed cross-section design and engineering cost calculation;

[0029] When updating the triangular mesh cost map according to the cost parameter set, for each edge in the triangular mesh cost map, first accumulate all cost-type and penalty-type cost parameters, then multiply the accumulated result by the reward-type cost parameter, and finally multiply the multiplied result by the edge length to generate the edge cost.

[0030] In the above-mentioned step S5:

[0031] The improved Dijkstra's algorithm is as follows: Search for a shortest path according to Dijkstra's algorithm, and generate a guiding channel based on the coordinate information of the shortest path; For each edge on the shortest path, increase the edge cost by a set multiple, update the copy of the cost map, and re-use Dijkstra's algorithm to search for the shortest path; Repeat the above steps until the specified number of guiding channels are generated;

[0032] When performing multi-channel search, first segment the artificial guiding line, with each segment taking two adjacent points as the starting and ending points, and perform multi-channel search independently; Then combine the multiple channels obtained from the segmented search to form a complete guiding channel; Finally, calculate the total cost of each guiding channel, sort the guiding channels from low to high according to the total cost, and retain the specified number of guiding channels with the lowest total cost; For each segment, first create a copy of the triangular mesh cost map, and then generate a guiding channel using the improved Dijkstra's algorithm based on the copy of the triangular mesh cost map.

[0033] In the above-mentioned step S7, first set the offset, offset the line plane model to both sides, construct a filtering area, obtain the economic strongholds that intersect with the filtering area, calculate the projected mileage and projected distance from the center coordinates of each economic stronghold to the line plane model, and construct an economic stronghold data set; Finally, sort the economic stronghold data set, first sort from high to low according to the economic stronghold level, and when the economic stronghold levels are the same, then sort from high to low according to the economic stronghold population.

[0034] In step S8, the specific method for the station position adjustment is as follows: Calculate the starting and ending mileage of the station position according to the initial value of the station position center mileage and the minimum station apron length, set the adjustment range of the starting and ending mileage of the station position, construct a stake number array at a mileage step within the starting and ending mileage range of the station position, calculate the recommended station position parameters for each stake number one by one, recalculate the station position center mileage and the starting and ending mileage of the station position using the recommended station position parameters, and adjust the longitudinal section model according to the starting and ending mileage of the station position.

[0035] In step S8, the recommended station position parameters include the economic stronghold distance parameter, the work point type parameter, and the longitudinal section slope parameter, where:

[0036] The economic stronghold distance parameter is the ratio of the distance between the stake number coordinate and the economic center to the projected distance of the economic stronghold center, indicating the influence of the distance between the stake number coordinate and the economic stronghold on the station position setting;

[0037] The work point type parameter indicates the influence of the work point type at the stake number on the station position setting, and the priority of the work point type for setting the station position is subgrade > bridge > tunnel;

[0038] The longitudinal section slope parameter is the ratio of the algebraic difference between the absolute value of the longitudinal section slope at the stake number and the maximum station apron slope to the maximum limit slope, indicating the influence of the longitudinal section slope value at the stake number on the station position setting;

[0039] The recommended station position parameter is the weighted sum of the economic stronghold distance parameter, the work point type parameter, and the longitudinal section slope parameter; recalculating the station position center mileage and the starting and ending mileage of the station position using the recommended station position parameter is specifically:

[0040] Within the adjustment range of the starting and ending mileage of the station position, with the minimum sum of the recommended station position parameters within the starting and ending mileage range of the station position as the adjustment target, recalculate the starting and ending mileage of the station position and the station position center mileage;

[0041] Adjusting the longitudinal section model according to the starting and ending mileage of the station position means setting the starting and ending mileage range of the station position as the elevation control section, setting the slope of this control section as the maximum station apron slope, and adjusting the longitudinal section model of the line.

[0042] Preferably, in step S8, the work point type parameters of the subgrade, bridge, and tunnel are respectively set to 0.0, 0.7, and 1.0.

[0043] The intelligent search method for the railway alignment channel and the intelligent recommendation method for the station position based on the triangular mesh cost map of the present invention include setting project parameters, initializing basic data, creating an artificial guiding line, recommending a cost parameter set based on the differential evolution algorithm, searching for multiple alignment channels based on the triangular mesh cost map, automatically designing the horizontal and longitudinal sections of the alignment channels, initializing the recommended station position parameters, making intelligent recommendations for the station position based on the alignment channels, calculating the project cost of the alignment channels, and outputting.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The triangular mesh cost map in the present invention is constructed by using route selection boundary control information through Delaunay triangulation. Compared with the regular grid, it can not only better express data features, but also greatly reduce data redundancy. In the case of the same amount of data, it can represent a larger range of route selection scenarios;

[0046] 2. The present invention uses the differential evolution algorithm to optimize the cost parameter set of the triangular mesh cost map through multiple iterative calculations; when searching for the alignment channel based on the triangular mesh cost map, an improved Dijkstra algorithm is used, which can generate multiple alignment channels in a very short time. Compared with the grid-based channel search method, the search efficiency of the present invention is greatly improved;

[0047] 3. The present invention realizes the diversity of the line alignment channel through the segmented combination and sorting of multiple alignment channels; when searching for the alignment channel, the search process can be intervened by the artificial guiding line, realizing the coverage of the alignment channel to the necessary points, and can better realize the design intention of the designer;

[0048] 4. When making intelligent station position recommendations, the present invention filters economic strongholds along the line, sets station positions in order from high to low according to the economic stronghold level, and interpolates station positions according to the minimum station spacing, which not only ensures that high-level economic strongholds are preferentially provided with station positions, but also ensures the requirements of the station spacing.

[0049] The present invention generates high-quality line alignment channels and station position schemes by applying the above technical means, providing a solid foundation for the subsequent optimization of the line scheme. Brief Description of the Drawings

[0050] Figure 1 is a flow chart of the intelligent search method for railway alignment channels and intelligent station position recommendation method of the present invention.

[0051] Figure 2 is an example of the cost map obtained in S2 of the present invention. Detailed Embodiment

[0052] The method of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] See Figure 1 , the intelligent search method for railway alignment channels and intelligent station position recommendation method based on the triangular mesh cost map of the present invention includes the following steps:

[0054] S1, set project parameters:

[0055] According to the project design principles, set the vertical section design parameters, cross-section design parameters, station location design parameters, and cost parameters. Among them, the station location design parameters include station spacing, minimum platform length, and maximum platform slope; the cost parameters include the unit price per linear meter of subgrade, bridge, and tunnel;

[0056] S2. Initialize the basic data: Construct a digital elevation model based on the ground point cloud data, read the cost map information in the cost map file, construct a triangular mesh cost map model in memory, and construct a cost parameter set according to the information in the triangular mesh cost map model, and load the economic stronghold data. Among them:

[0057] The cost map information includes vertex coordinates, cost type set, side length, and edge cost. The cost types are divided into three groups according to their functions: cost-type cost, penalty-type cost, and reward-type cost. Among them:

[0058] The cost types included in the cost-type cost group are subgrade, bridge, and tunnel.

[0059] The cost types included in the penalty-type cost group are river, lake, existing railway, existing highway, bad geological area, restricted area, economic stronghold, planning area, environmental protection core area, environmental protection buffer area, and environmental protection experimental area. The economic stronghold data includes economic stronghold name, level, boundary coordinates, center coordinates, population, GDP, central projection mileage, and projection distance.

[0060] The cost types included in the reward-type cost group are railway traffic corridor belt, highway traffic corridor belt, and economic radiation area.

[0061] Among them, the method for generating the cost map file is as follows:

[0062] 1) Construct a route selection boundary control region:

[0063] Obtain the route selection boundary control data layer information from the original data file of the project design, obtain the region data according to the route selection boundary control data layer information, generate an auxiliary boundary through the region data, and construct a route selection boundary control region;

[0064] The region data includes river, lake, existing railway, existing highway, bad geological area, restricted area, economic stronghold, planning area, environmental protection core area, environmental protection buffer area, and environmental protection experimental area; if the existing highway or existing railway is a multi-segment line, convert it into a closed region by setting the road width;

[0065] The auxiliary boundaries include railway traffic corridor belt, highway traffic corridor belt, and economic radiation area, which are respectively formed by offsetting the boundaries of the existing railway, existing highway, and economic stronghold outward by a set offset amount.

[0066] 2) Construct a set of terrain exploration line plane models:

[0067] Set the exploration mode and exploration parameters, and construct an exploration path array. In the exploration path array, each exploration path is a set of point coordinates. Generate an intersection point array according to the exploration path. Each intersection point in the intersection point array includes coordinates, radius, and easement length attributes. Set the radius and easement length to 0, and use the intersection point method to construct a line plane model. Each exploration path constructs a line plane model, thereby forming a set of terrain exploration line plane models.

[0068] The exploration parameters include an exploration starting point, an exploration ending point, an exploration passing point array, a maximum exploration width, and an exploration spacing.

[0069] The exploration mode is rectangular parallel line exploration. Specifically: First, construct a basic exploration path according to the exploration starting point, the exploration passing point array, and the exploration ending point, and then offset the basic exploration path multiple times to both sides at integer multiples of the exploration spacing to generate multiple new exploration paths, obtaining an exploration path array. The maximum amount of offset on each side is limited to half of the maximum exploration width.

[0070] 3) Construct a set of terrain exploration line vertical section models:

[0071] Perform vertical section design on each of the line plane models in the set of terrain exploration line plane models obtained in step 2) to obtain a corresponding line vertical section model. Multiple line vertical section models constitute a set of terrain exploration line vertical section models; the vertical section models and the line plane models are in one-to-one correspondence. The vertical section design includes generating a ground line according to the plane model and the digital elevation model, and performing vertical section slope fitting and constraint processing.

[0072] 4) Construct a set of terrain exploration bridge and tunnel high-cost regions:

[0073] First, use the line vertical section model and the ground line in step 3) to automatically generate a bridge and tunnel gap array, and then use the line plane model corresponding to the line vertical section model to calculate the offset amounts on the left and right sides of each bridge and tunnel gap at a mileage step of 1m, and calculate the offset coordinates on the left and right sides. In this way, the offset coordinates on the left side of the bridge and tunnel gap form a point array, and the points in the point array are connected in sequence; the offset coordinates on the right side of the bridge and tunnel gap form a point array, and the points in the point array are connected in sequence; then connect the starting points of the two connecting lines and connect the ending points to form a closed polygon, obtaining a bridge and tunnel gap region; finally, perform a union Boolean operation on all bridge and tunnel gap regions to merge similar regions, forming a set of terrain exploration bridge and tunnel high-cost regions.

[0074] 5) Construct a triangular network based on the region boundary:

[0075] First, extract the boundary polygons of the line selection boundary control region in step 1) and the terrain exploration bridge and tunnel high-cost region in step 4) as feature lines, perform Delaunay triangulation, and construct a triangular network. Then, set the maximum side length parameter of the triangle (for example, set it to 100 meters), perform vertex interpolation in the triangular network, and construct an encrypted vertex array. Finally, use the boundary polygon feature line and the encrypted vertex array to perform Delaunay triangulation again to generate the final triangular network;

[0076] The method of performing vertex interpolation in the triangular network is as follows: Check each edge of each triangle. If the length of a certain edge is greater than the set maximum side length, then perform interpolation on this edge with the maximum side length as the step size and add interpolation vertices. Connect lines from the interpolation vertices to the corner points of the triangle to obtain line segments, and perform interpolation again on these line segments (the connections between the interpolation vertices and the corner points) according to the minimum side length to obtain new vertices. Add all the generated interpolation vertices to the encrypted vertex array.

[0077] 6) Construct an undirected graph of the triangular network based on the triangular network:

[0078] First, extract the vertex array and edge array of the final triangular network obtained in step 5), and process the edge array to delete the edges that overlap but have opposite directions. Then, create an empty undirected graph, add nodes to the undirected graph according to the vertex array, and use the vertex coordinates as node attributes. Then, add edges to the undirected graph according to the edge array, and use the side lengths as edge attributes, thereby obtaining the undirected graph of the triangular network.

[0079] 7) Set the region cost parameters:

[0080] Group the line selection boundary control region constructed in step 2) and the terrain exploration bridge and tunnel high-cost region obtained in step 4), use the region type as the cost type, and set the cost per meter for each cost type.

[0081] The cost types are divided into three groups according to their functions: cost-type costs, penalty-type costs, and reward-type costs. The cost-type cost group includes subgrade, bridge, and tunnel, representing the structural types of railway projects during intelligent railway line selection. The penalty-type cost group includes rivers, lakes, existing railways, existing highways, poor geological areas, restricted areas, economic strongholds, planning areas, environmental protection core areas, environmental protection buffer areas, and environmental protection experimental areas, representing the areas that should be avoided as much as possible during intelligent railway line selection. The reward-type cost group includes railway traffic corridor belts, highway traffic corridor belts, and economic radiation areas, representing the areas that should be approached as much as possible to reduce land occupation and station setting during intelligent railway line selection.

[0082] 8) Construct a cost graph of the triangular network based on the undirected graph of the triangular network:

[0083] First, calculate the midpoint coordinates of each edge in the triangulation undirected graph in step 6), then use the midpoint coordinates of the edges to query the corresponding regions, obtain the set of region types (equivalent to cost types), and save them to the edge attributes; finally, for each type in the set of region types, obtain the cost per meter, calculate the cost of each edge, and form a triangulation cost graph; the edge attributes include edge cost, edge length, and the set of region types.

[0084] When calculating the edge cost, first accumulate the cost per meter of the cost-based region group and the penalty-based region group, then multiply the accumulated result by the cost per meter of the reward-based region group to generate the total cost per meter; finally, multiply the total cost per meter by the edge length to generate the edge cost.

[0085] 9) Draw and save the triangulation cost graph:

[0086] For each edge in the triangulation cost graph, set different colors according to the size of the edge cost and draw it to visually reflect the high and low characteristics of the costs in different regions in the route selection scenario, and save the triangulation cost graph as a cost graph file.

[0087] Figure 2 An example of the cost graph obtained in this step is shown, where different colors represent different costs.

[0088] S3. Create an artificial guiding line:

[0089] Set the starting point of channel search, the ending point of channel search, and the array of mandatory points, and connect these points to generate a polyline to obtain the artificial guiding line, using the artificial guiding line as the constraint line in the path search process; when there are no mandatory points set, the artificial guiding line is a line segment connecting the starting point and the ending point of channel search.

[0090] S4. Recommend a set of cost parameters based on the differential evolution algorithm: Use the cost graph information to initialize the set of cost parameters, set the evolution parameters, and use the differential evolution algorithm to optimize the initialized set of cost parameters to generate the best set of cost parameters, and modify the triangulation cost graph according to the best set of cost parameters to obtain the modified triangulation cost graph.

[0091] When initializing the set of cost parameters, the cost-based cost group uses the subgrade cost as the benchmark; the bridge cost and the tunnel cost are determined respectively according to the ratios of the unit price per meter of the bridge and the unit price per meter of the tunnel to the unit price per meter of the subgrade; the penalty-based cost group is set according to the cost type; the reward-based cost group is set according to the cost type.

[0092] The evolution parameters include the objective function, the value range of the cost parameters in the cost type, the calculation start and end points, the evolution mode, the population size, the total number of evolution generations, the scaling factor, and the crossover factor. The objective function is the project cost.

[0093] In the cost-based cost group, the value range of the cost parameter is [1.0, 10.0]. In the penalty-based cost group, the value range of the cost parameter is [1.0, 100.0]. In the reward-based cost group, the value range of the cost parameter is [0.0, 1.0].

[0094] When optimizing the initialized cost parameter set, first generate an array of cost parameter sets according to the population size and the value range of the cost parameter, and extract the starting and ending points of the artificial guiding line as the calculation starting and ending points; use the initialized cost parameter set for optimization calculation, and generate an array of cost parameter sets for the next generation through the crossover factor and the scaling factor based on the optimization result of the current generation, and perform the optimization calculation for the next generation until the optimal cost parameter set is found.

[0095] The calculation of each cost parameter set in each generation includes:

[0096] Update the triangular mesh cost map according to the cost parameter set, search for the shortest path in the triangular mesh cost map using Dijkstra's algorithm, construct a line plane model using the shortest path, perform automatic longitudinal section design, generate roadbed, bridge, tunnel and gap, and perform automatic roadbed cross-section design and engineering cost calculation;

[0097] When updating the triangular mesh cost map according to the cost parameter set, for each edge in the triangular mesh cost map, first accumulate all the cost-based and penalty-based cost parameters, then multiply the accumulated result by the reward-based cost parameter, and finally multiply the multiplied result by the side length to generate the edge cost.

[0098] S5. Search for multiple alignment channels based on the modified triangular mesh cost map:

[0099] Select the artificial guiding line created in S3 and set the number of alignment channels (which can be 10 - 100, preferably 10 - 20), and use the improved Dijkstra's algorithm to perform multi-channel search on the modified triangular mesh cost map to form multiple alignment channels; the improved Dijkstra's algorithm means first searching for a shortest path according to Dijkstra's algorithm, generating an alignment channel based on the coordinate information of the shortest path; for each edge on the shortest path, increase the edge cost by a set multiple (instead of setting the edge cost to a maximum value or deleting the edge), and update the cost map copy, and then use Dijkstra's algorithm again to search for the shortest path; repeat the above steps until the specified number of alignment channels is generated.

[0100] When performing multi-channel search, first segment the artificial guiding line. Each segment takes adjacent two points as the starting and ending points, and performs multi-path search independently. Then combine the multiple channels obtained from the segmented search to form a complete alignment channel. Finally, calculate the total cost of each alignment channel (using the original triangular mesh cost map), and sort the alignment channels from low to high according to the total cost, and retain the specified number of alignment channels with the lowest total cost. For each segment, first create a copy of the triangular mesh cost map (ensuring that the original triangular mesh cost map is not damaged), and then generate alignment channels based on the copy of the triangular mesh cost map using the improved Dijkstra algorithm.

[0101] S6, Horizontal and vertical alignment design of the alignment channel:

[0102] Smooth the polyline of each alignment channel, and use the polyline to construct a line plane model with a radius and easement length of 0. Automatically fit and constrain the vertical alignment according to the line plane model, and automatically generate bridge and tunnel gaps on the vertical alignment. Generate subgrade, bridge, and tunnel work point paragraphs according to the bridge and tunnel gaps.

[0103] S7, Construct an economic stronghold dataset based on the alignment channel:

[0104] Construct a filtering area according to the line plane model of each alignment channel, extract economic stronghold data and sort it.

[0105] First set the offset, offset the line plane model to both sides, construct a filtering area, obtain economic strongholds that intersect with the filtering area, calculate the projection mileage and projection distance from the center coordinates of each economic stronghold to the line plane model, and construct an economic stronghold dataset. Finally, sort the economic stronghold dataset, first sort from high to low according to the economic stronghold level, and when the economic stronghold levels are the same, then sort from high to low according to the economic stronghold population.

[0106] S8, Intelligent recommendation of alignment channel stations based on the economic stronghold dataset:

[0107] First use the economic stronghold dataset obtained in S7, and calculate each economic stronghold data one by one in the order from high to low according to the economic stronghold level. If the distance between the center mileage of the stations on both sides of the center mileage of the economic stronghold is greater than or equal to twice the minimum station spacing, then add a new station, initialize the center mileage of the station and perform station adjustment, otherwise do nothing.

[0108] After completing the station position recommendation according to the economic stronghold dataset, re-check the distance between the center mileage of all adjacent stations. If the distance is greater than or equal to twice the minimum station spacing, then add a new station according to the station spacing parameter, initialize the center mileage of the station, and perform station adjustment, otherwise do nothing.

[0109] The specific method for the above-mentioned station position adjustment is as follows: Calculate the starting and ending mileage of the station position according to the initial value of the center mileage of the station position and the minimum platform length, and set the adjustment range of the starting and ending mileage of the station position. Construct a pile number array within the starting and ending mileage of the station position according to the mileage step length, and calculate the recommended station position parameters for each pile number. Use the recommended station position parameters to recalculate the center mileage of the station position and the starting and ending mileage of the station position, and adjust the vertical section model according to the starting and ending mileage of the station position. The recommended station position parameters include the economic stronghold distance parameter, the work point type parameter, and the vertical section slope parameter. Among them: The economic stronghold distance parameter is the ratio of the distance between the pile number coordinate and the economic center to the projected distance of the economic stronghold center, indicating the influence of the distance between the pile number coordinate and the economic stronghold on the station position setting; The work point type parameter indicates the influence of the work point type at the pile number on the station position setting. The priority of the work point type for setting the station position is subgrade > bridge > tunnel. Therefore, the work point type parameters for subgrade, bridge, and tunnel are set to 0.0, 0.7, and 1.0 respectively; The vertical section slope parameter is the ratio of the algebraic difference between the absolute value of the vertical section slope at the pile number and the maximum platform slope to the maximum limit slope, indicating the influence of the vertical section slope value at the pile number on the station position setting. The recommended station position parameter is the weighted sum of the economic stronghold distance parameter, the work point type parameter, and the vertical section slope parameter.

[0110] Using the recommended station position parameters to recalculate the center mileage of the station position and the starting and ending mileage of the station position means that within the adjustment range of the starting and ending mileage of the station position, with the goal of minimizing the sum of the recommended station position parameters within the starting and ending mileage of the station position, recalculate the starting and ending mileage of the station position and the center mileage of the station position.

[0111] Adjusting the vertical section model according to the starting and ending mileage of the station position means setting the starting and ending mileage range of the station position as the elevation control section, setting the slope of this control section as the maximum platform slope, and adjusting the line vertical section model.

[0112] S9. Calculation and output of the engineering cost of the access channel:

[0113] Perform subgrade cross-section design on the plane model and vertical section model of each access channel, and calculate the engineering cost using the cost parameters; Finally, output the information of the access channel in ascending order of cost, including the plane model, vertical section model, total cost, total expense, line length, total bridge length, total tunnel length, bridge-tunnel ratio, and total number of station positions.

[0114] In an embodiment of the present invention, the route selection scenario range is 300 km * 200 km, the number of read route selection boundary surface regions is 583, the number of bridge-tunnel high-cost surface regions generated by terrain exploration is 2196, and the time taken is 3 s. For the cost map constructed after triangulation, the number of vertices in the cost map is 145,000, the number of edges is 436,000, and the time taken is 10 s; when using the differential evolution algorithm to optimize the cost parameter set, the population size is 100, the total number of generations is 30, and the time taken is 210 s; when constructing the artificial guiding line, the aerial distance between the starting and ending points is 290 km, and 2 economic strongholds are set as passing points; when using the triangular mesh cost map to search for the alignment channel, 20 alignment channels are generated, and the time taken is 6 s; when making intelligent station position recommendations based on the alignment channels, 9 - 12 station positions are set for each alignment channel, and the total time taken is 50 s.

Claims

1. A method for intelligent search of railway direction channels and intelligent recommendation of railway stations based on triangulated network cost graph, characterized in that: The following steps are involved: S1, setting project parameters: according to the project design principles, setting longitudinal section design parameters, cross section design parameters, station location design parameters and cost parameters; the station location design parameters include minimum station spacing, minimum station apron length, and maximum station apron slope; The cost parameters include the unit price per meter of roadbed, bridges and tunnels; S2, initializing basic data: building a digital elevation model based on ground point cloud data, reading the cost map information in the cost map file, building a triangulated network cost map model in memory, building a cost parameter set based on the information in the triangulated network cost map model, and loading economic base data; S3, creating an artificial guide line: setting a channel search starting point, a channel search end point, and an array of necessary points, and connecting these points to generate a multi-segment line to obtain an artificial guide line, and using the artificial guide line as a constraint line in the path search process; When no must-pass point is set, the artificial guide line is a line segment connecting the channel search start point and the channel search end point; S4, recommending a cost parameter set based on a differential evolution algorithm: using the cost map information, initializing the cost parameter set, setting evolution parameters, optimizing the initialized cost parameter set using a differential evolution algorithm, generating an optimal cost parameter set, modifying the triangulated network cost map according to the optimal cost parameter set, and obtaining a modified triangulated network cost map; wherein: When initializing the cost parameter set, the cost type cost group uses the roadbed cost as a benchmark; the bridge cost and the tunnel cost are determined according to the ratio of the bridge per meter unit price, the tunnel per meter unit price and the roadbed per meter unit price respectively; the penalty type cost group is set according to the cost type; and the reward type cost group is set according to the cost type; When optimizing the initialized cost parameter set, first generate a cost parameter set array according to the population size and the cost parameter value range, and extract the start and end points of the artificial guide line as the calculation start and end points; use the initialized cost parameter set for optimization calculation, and use the optimization results of the current generation to generate the next generation of cost parameter set arrays through the cross factor and scaling factor, and perform the next generation of optimization calculations until the best cost parameter set is found; S5, searching for multiple strike channels based on the modified triangulated network cost map: selecting the artificial guide line created in S3 and setting the number of strike channels, using the improved Dijkstra algorithm to perform a multi-channel search on the modified triangulated network cost map to form multiple strike channels; The improved Dijkstra algorithm is as follows: searching for a shortest path according to the Dijkstra algorithm, generating a direction channel according to the coordinate information of the shortest path; increasing the edge cost of each edge on the shortest path by a set multiple, updating the cost graph copy, and reusing the Dijkstra algorithm to search for the shortest path; repeating the above steps until a specified number of direction channels are generated; S6, longitudinal section design of the strike channel: smooth the polyline of each strike channel, and use the polyline to construct a line plane model with a radius and a slow length of 0; automatically fit and constrain the longitudinal section according to the line plane model, and automatically generate bridge and tunnel gaps on the longitudinal section, and generate roadbed, bridge, and tunnel work point sections according to the bridge and tunnel gaps; S7, constructing an economic stronghold data set based on the trend channel: constructing a filtering area based on the line plane model of each trend channel, extracting the economic stronghold data and sorting them; S8, intelligent recommendation of station locations along the corridor based on the economic stronghold dataset: First, use the economic base data set obtained in S7 to calculate the economic base data one by one in the order of economic base levels from high to low. If the distance between the station center mileage on both sides of the economic base center mileage is greater than or equal to twice the minimum station spacing, then add a new station, initialize the station center mileage and adjust the station, otherwise no processing is done; After completing the station recommendation based on the economic base data set, recheck the distance between the center mileages of all adjacent stations. If the distance is greater than or equal to twice the minimum station spacing, add a new station according to the station spacing parameter, initialize the station center mileage, and adjust the station. Otherwise, no processing is performed. S9. Calculation and output of the project cost of the direction channel: The roadbed cross-section design is carried out for the plane model and longitudinal section model of each direction channel, and the cost parameters are used to calculate the engineering cost; finally, the information of the direction channel is output in the order of cost from low to high, including the plane model, longitudinal section model, total cost, total cost, line length, total length of bridges, total length of tunnels, bridge-tunnel ratio and total number of stations.

2. The method for intelligently searching for railway direction channels and intelligently recommending railway stations based on triangulated network cost graph according to claim 1 is characterized in that: The cost graph information in S2 includes vertex coordinates, cost type set, edge length, and edge cost; the cost types are divided into three groups according to their functions: cost type, penalty type, and reward type, among which: The cost type group includes the cost types of roadbed, bridge and tunnel; The penalty cost group includes cost types such as rivers, lakes, existing railways, existing roads, bad geological areas, restricted areas, economic strongholds, planning areas, environmental protection core areas, environmental protection buffer areas, and environmental protection experimental areas; wherein the economic stronghold data includes the name of the economic stronghold, level, boundary coordinates, center coordinates, population, GDP, center projection mileage, and projection distance; The reward-type cost group includes cost types such as railway transportation corridors, highway transportation corridors, and economic radiation zones.

3. The method for intelligently searching for railway direction channels and intelligently recommending railway stations based on triangulated network cost graph according to claim 2 is characterized in that: In step S4: The evolution parameters include the objective function, the value range of the cost parameter in the cost type, the calculation start and end points, the evolution mode, the population size, the total number of evolution generations, the scaling factor, and the crossover factor; the objective function is the engineering cost; In the cost type cost group, the cost parameter value range is [1.0, 10.0]; In the penalty cost group, the cost parameter value range is [1.0,100.0]: In the reward-type cost group, the value range of the cost parameter is [0.0, 1.0].

4. The method for intelligently searching for railway direction channels and intelligently recommending railway stations based on triangulated network cost graph according to claim 1, characterized in that: In step S4, the calculation of each cost parameter set in each generation includes: Update the triangulated network cost map according to the cost parameter set, use the Dijkstra algorithm to search for the shortest path in the triangulated network cost map, use the shortest path to build the line plane model, perform automatic longitudinal section design, generate roadbed bridge and tunnel gaps, perform automatic roadbed cross-section design and project cost calculation; When updating the triangulated network cost graph according to the cost parameter set, for each edge in the triangulated network cost graph, first accumulate all the fee-type and penalty-type cost parameters, then multiply the accumulated result by the reward-type cost parameter, and finally multiply the accumulated result by the edge length to generate the edge cost.

5. The method for intelligent search of railway direction channels and intelligent recommendation of railway stations based on triangulated network cost graph according to claim 1, characterized in that: In step S5: When performing multi-channel search, first divide the artificial guide line into segments, and each segment uses two adjacent points as the starting and ending points, and independently performs multi-channel search; then combine the multiple channels obtained by the segmented search to form a complete direction channel; finally calculate the total cost of each direction channel, and sort the direction channels from low to high according to the total cost, and retain the specified number of direction channels with the lowest total cost; For each segment, a copy of the triangulated network cost map is first created, and then the improved Dijkstra algorithm is used to generate the strike channel based on the copy of the triangulated network cost map.

6. The method for intelligently searching for railway direction channels and intelligently recommending railway stations based on triangulated network cost graph according to claim 1, characterized in that: In step S7, first, an offset is set to offset the route plane model to both sides, a filtering area is constructed, economic strongholds that intersect with the filtering area are obtained, the projected mileage and projection distance from the center coordinates of each economic stronghold to the route plane model are calculated, and an economic stronghold data set is constructed; finally, the economic stronghold data set is sorted, first from high to low according to the economic stronghold level, and if the economic stronghold levels are the same, then sorted from high to low according to the economic stronghold population.

7. The method for intelligently searching for railway direction channels and intelligently recommending railway stations based on triangulated network cost graph according to claim 1, characterized in that: The specific method of the station adjustment in step S8 is: calculate the station start and end mileages according to the initial value of the station center mileage and the minimum station apron length, and set the station start and end mileage adjustment range, construct a pile number array according to the mileage step within the station start and end mileage range, and calculate the station recommended parameters for each pile number, use the station recommended parameters to recalculate the station center mileage and the station start and end mileages, and adjust the longitudinal section model according to the station start and end mileages.

8. The method for intelligently searching for railway direction channels and intelligently recommending railway stations based on triangulated network cost graph according to claim 1, characterized in that: The site recommendation parameters in step S8 include economic base distance parameters, work site type parameters, and longitudinal section slope parameters, where: The economic stronghold distance parameter is the ratio of the distance between the pile coordinate and the economic center to the projection distance of the economic stronghold center, indicating the influence of the distance between the pile coordinate and the economic stronghold on the station setting; The work point type parameter indicates the influence of the work point type at the pile number on the station setting. The work point type priority of the station setting is roadbed>bridge>tunnel; The longitudinal section slope parameter is the ratio of the slope algebraic difference between the absolute value of the longitudinal section slope at the pile number and the maximum station slope to the maximum limit slope, which indicates the influence of the longitudinal section slope value at the pile number on the station setting; The station recommendation parameters are the weighted sum of the economic base distance parameter, the work point type parameter, and the longitudinal slope parameter. The station center mileage and the station start and end mileage are recalculated using the station recommendation parameters, specifically: Within the adjustment range of the station start and end mileages, the minimum sum of the station recommended parameters within the station start and end mileages is taken as the adjustment target, and the station start and end mileages and station center mileage are recalculated; The longitudinal section model is adjusted according to the starting and ending mileages of the station, which means setting the starting and ending mileages of the station as the elevation control section, setting the slope of the control section as the maximum slope of the station apron, and adjusting the longitudinal section model of the line.

9. The method for intelligent search of railway direction channels and intelligent recommendation of railway stations based on triangulated network cost graph according to claim 1, characterized in that: In step S8, the work point type parameters of the roadbed, bridge, and tunnel are set to 0.0, 0.7, and 1.0, respectively.

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