An intelligent line layout method, device and medium for high-drop mountainous railways

Abstract

The present application relates to the technical field of railway route design, and particularly relates to an intelligent route design method for high-fall mountainous railway, equipment and medium. The method comprises the following steps: obtaining DEM data of a steep section, dividing a grid to construct an elevation data set, and processing invalid data; determining core design parameters of route design of the steep section; calculating a target elevation of each step and generating local contour lines and guide nodes in a step-by-step cycle; iterating from a starting point to an ending point to form a node chain and a contour line set; generating a terminal node set; generating an initial route design path set, and obtaining a route plane scheme after plane fitting of the initial route design path set; and searching for an optimal route design path from the route plane scheme. The present application realizes parallel path search under precise terrain constraint through cooperation of local contour lines and multi-branch trees, can comprehensively consider the precision and efficiency of route design of high-fall mountainous railway, and provides reliable technical support for route design under complex terrain.

Description

Technical Field

[0001] This invention relates to the field of railway alignment design technology, specifically to an intelligent alignment method, equipment, and medium for railways in mountainous areas with high elevation differences. Background Technology

[0002] Mountainous areas with high elevation differences have dramatic terrain undulations and significant elevation differences. Contour lines are densely intersecting and terrain changes are frequent. Traditional line-planning methods rely on manual planning of routes based on terrain data. This is not only limited by the subjectivity and limitations of human experience, making it difficult to accurately adapt to complex and ever-changing terrain conditions, but also has prominent problems such as inconsistent elevation connections.

[0003] The core technological gap in intelligent route planning in high-altitude mountainous areas lies in the coordination between terrain adaptation and intelligent search: these areas experience extremely drastic local elevation changes with densely intersecting contour lines, lacking technical means to achieve smooth elevation transitions; at the same time, existing technologies do not construct targeted elevation benchmarks for local terrain, resulting in a lack of precise constraints on path search. This leads to either poor adaptation of the generated routes to the terrain and a large amount of excavation and filling work, or inefficiency due to blind searching, failing to achieve a balance between "precise terrain adaptation" and "efficient path generation".

[0004] To address the aforementioned technical challenges, relevant research has been conducted and a certain foundation has been accumulated: Some studies have integrated Digital Elevation Models (DEMs) and Geographic Information Systems (GIS) technologies into route selection, using terrain data visualization to assist manual judgment, playing a positive role in route selection in mountainous areas with medium to low elevation differences. However, they lack targeted solutions for intelligent path generation and dynamic terrain adaptation in high-elevation terrain. Other studies focus on the application of intelligent algorithms in path search, such as commonly used genetic algorithms and particle swarm optimization algorithms. These algorithms can optimize paths across an entire region, providing quantifiable analytical tools for route selection in mountainous areas. However, these algorithms generally have significant shortcomings: firstly, they are based on flatness... The original approach to route development focused excessively on avoiding planar obstacles, prioritizing short and straight routes while neglecting the steep terrain limitations of high-altitude mountainous areas. This resulted in issues such as excessive gradients and poor transitions between elevation changes, failing to meet the requirements for route development in high-altitude mountainous regions. Secondly, algorithms like genetic algorithms, when optimizing routes, may disrupt already found high-quality local route segments, while particle swarm optimization algorithms are easily "trapped" by steep terrain, struggling to find better directions and requiring repeated adjustments to break free from limitations. Both of these significantly slowed down path generation, failing to meet engineering construction needs. Furthermore, these algorithms did not fully consider the real-time situation of local terrain and the hierarchical search approach, making it difficult to provide effective technical support.

[0005] In summary, there is an urgent need to provide a method, equipment, and medium for intelligent track laying in mountainous areas with high elevation differences, in order to solve the technical problems existing in the current technology. Summary of the Invention

[0006] The purpose of this invention is to provide a method, equipment, and medium for intelligent track alignment of railways in mountainous areas with high elevation differences, in order to solve the technical problems existing in the prior art. The specific technical solution is as follows: A method for intelligent track alignment of railways in mountainous areas with high elevation differences includes the following steps: S1. Obtain DEM data for the steep slope section, divide the grid to construct the elevation dataset, and process invalid data; determine the core design parameters for the alignment of the steep slope section. S2. Calculate the target elevation for each step and generate local contour lines and guide nodes step by step in a loop; iterate from the starting point to the ending point to form a node chain and contour line set; S3. Initialize the multi-branch tree and associate it with the target elevation, expand the nodes at the same level in parallel, control the number of nodes bidirectionally, iterate and update the multi-branch tree to form a set of terminal child nodes; S4. Traverse the multi-branch tree to form an initial route layout path set. After plane fitting, the initial route layout path set is used to obtain the route plane scheme. Based on the ideal solution method, find the optimal route layout path from the route plane scheme.

[0007] Furthermore, S1 specifically refers to: Constructing an elevation dataset: Obtaining a digital elevation model of the steep slope area by dividing the study area into M×N regions with side lengths of... A square grid is constructed, and the elevation information of the grid is represented by the elevation value of the center point of each grid, thereby constructing the overall elevation dataset of the rectangular area. Processing invalid data: For elevation data that cannot be obtained due to the irregular shape of the boundary of the study area or the confidentiality of elevation data in some areas, the nearest neighbor assignment method is introduced to make it valid. Determine the core design parameters for alignment in steep slope sections, including: Maximum slope Determined by railway grade and terrain type; Maximum slope length With minimum slope length Determined by railway grade and design specifications; Search Step Size Preset based on terrain complexity and computational efficiency; Digital Elevation Model Grid Side Length : This is a built-in attribute of DEM data; Origin and Destination Information: Origin ,end ; Elevation forward direction coefficient If the overall trend of the line is upward, then If the overall trend of the line is downward, then... The expression is -1: ; in, The elevation value at the starting point of the route. This refers to the elevation value at the end of the route. Slope reduction factor The result is obtained by calculation using the following formula: ; In the formula, This represents the comprehensive reduction factor for railways. Indicates the gradient of a railway slope; Elevation difference per step The result is obtained by calculation using the following formula: .

[0008] Furthermore, S2 specifically refers to: S2-1. Obtain line nodes and calculate target elevation: Obtain node information And calculate the target elevation of the next node. That is, the elevation of the target contour lines has been determined. , Indicates the first The elevation of each node; if the starting point is used as the initial node for the first time, then the starting point information is obtained. Calculate the target elevation of the next node. That is, the elevation value of the first target contour line was determined. ; S2-2. Define the local effective area corresponding to the node: Centered on the DEM grid where the node is located, according to... Determine the number of grid cells to be expanded, and expand in four orthogonal directions: up, down, left, and right, to form a local effective area that only covers the key areas around the nodes; S2-3. Generate contour lines and guide nodes within the local effective area; S2-4, Repeat S2-1 to S2-3, and generate local contour lines and guide nodes synchronously and iteratively. S2-5, Iteration Termination Judgment: After generating a new guiding node in each iteration, calculate the elevation of the current node. Elevation of the endpoint The difference; if the difference is greater than or equal to the elevation difference at each step If the difference is less than the elevation difference at each step, then continue iterating; Then the end point of the route As the final guiding node, and also at the end of the route The surrounding area defines a local effective region, generates local contour lines close to the endpoint elevation, completes the iteration from the starting point to the endpoint, and forms a complete node chain and corresponding contour line set.

[0009] Furthermore, in S2-3, the specific steps for generating contour lines and guide nodes within the local effective region are as follows: ① Starting from the initial node, use the elevation matrix E The grid elevation information within the locally valid area defined by the record nodes is represented as follows: ; in: and These represent the number of rows and columns of the DEM elevation grid, respectively. Indicates the first Line 1 The center elevation of the grid, ; ; ② Construct state matrices, establishing the horizontal edge state matrices for contour lines and grid edge lengths within the local effective region of each node. and vertical edge state matrix The expressions are as follows: ; ; in: Indicates the first Line 1 Whether the horizontal edge of the grid after the elevation point intersects with the target contour line, 1 indicates intersection, 0 indicates non-intersection; Indicates the first Line 1 Whether a vertical edge of a grid below a grid elevation point intersects with the target contour line; 1 indicates intersection, 0 indicates non-intersection. ; ; The calculation formula is as follows: ; ; in: Indicates the first Line 1 Center elevation of the grid; Indicates the elevation value of the target contour line; ③ Based on the constructed state matrix, search for the starting point of the target contour line. Within the local effective region, if the target contour line is an open curve, then search at the edge of the effective region. , The first unit with a value of 1 is used as the starting point; if it is a closed curve, the search continues within the effective region. , The first cell with a value of 1 is taken as the starting point. Starting from the starting point, the coordinates of the starting point are calculated by linear interpolation. The departure edge is determined by searching clockwise and the coordinates of the departure point are calculated. The process is iterated until there are no intersecting edges. The coordinate points are connected to generate the target contour line with the corresponding elevation. If the state values ​​of three grid edges are all 1, the average elevation method is used to determine the unique departure edge to ensure the continuity of the contour line. ④ Generate guiding nodes: Using the node as the center, the search step size is... Draw a circle with radius and find its intersection with the target contour line. The intersection point is the feasible guide node with zero cut and fill volume. If there is no intersection, the distance between the node and the target contour line needs to be further determined: if the distance is greater than... Then, draw a perpendicular line from the current node to the target contour line, and take the first foot of the perpendicular as the guide node; if the distance is less than or equal to... Then adjust the search step size. SL Then, redraw the circle to find the intersection until the first guiding node is obtained.

[0010] Furthermore, in S2-4, the synchronous iterative generation of local contour lines and guide nodes includes: Elevation update: The guide node generated in the first round is used as the new current node, combined with the elevation forward direction coefficient. With each step of elevation difference The elevation values ​​of the next layer of target contour lines are calculated. ; Region re-frame: Using the new DEM grid where the current node is located as the center, continue using... The extended rules redefine the effective local area covering the key terrain around the current node; Local contour line regeneration: Within the newly defined local effective area, the elevation matrix and state matrix are reconstructed, and the grid edges are determined. The intersection of elevation contour lines is used to search for the starting point and iteratively generate a complete set of contour lines. Local contour lines are used to ensure that each guiding node has a corresponding local contour line as an elevation constraint. Node regeneration: with the new current node as the center, the search step size is... Draw a circle with radius and . Intersection of elevation contour lines generates a new set of guiding nodes.

[0011] Furthermore, S3 includes: S3-1. Initialize the multi-way tree and associate it with the target elevation: starting from the line origin. As the root node of the multi-way tree, construct the node set for the current level, which initially contains only... Simultaneously, correlate the elevation of the first target contour line obtained in S2-1. ; S3-2, Parallel Expansion of Nodes in the Same Layer: Using OpenMP parallel computing technology, each node in the current layer node set is encapsulated as an independent computing module and allocated to different threads of the CPU for synchronous processing; each node generates multiple feasible child nodes according to S2-3 to S2-4; the feasible child nodes generated by all threads are aggregated to form a temporary child node set; S3-3, Two-way control of the number of nodes: Node replenishment: If the number of nodes in a temporary child node set of a certain layer is less than a preset threshold, the number of nodes will be increased. Optimization for too many nodes: If the number of nodes in the temporary child node set exceeds the preset limit, the node set aggregation method is used for optimization to form an effective child node set; S3-4, Iterative Update of Multi-way Tree: Set the optimized set of valid child nodes as the new current layer node set, and update the target contour line elevation synchronously. Repeat the parallel expansion and node optimization process from S3-2 to S3-3 until the current target contour elevation is reached. Elevation of the endpoint The difference is less than Stop the hierarchical search and take the set of valid child nodes at this point as the terminal child node set of the multi-way tree.

[0012] Furthermore, S4 specifically refers to: S4-1, Initial layout path set formation: Connect each node in the terminal child node set to the endpoint. Establish parent node relationships; starting from each terminal child node, trace back level by level to its previous parent node in the multi-way tree, until the root node is reached. This forms multiple complete paths from the starting point to each layer of sub-nodes, then to the terminal sub-node, and finally to the end point. Paths without obvious engineering conflicts are selected to form an initial set of route development paths. S4-2: Path plane fitting: For each path in the initial alignment path set, the chord-tangent offset method based on the bisection approach is used to extract path feature points, and a preliminary alignment plan is fitted; the specific implementation steps are as follows: ① Generate initial baseline: Connect the start and end points of the original paths in the initial plotting path set to serve as the initial extraction path and the baseline for subsequent calculations; ② Find the farthest point: Traverse the points within the range of the start and end points, and find the node that is farthest from the baseline, i.e., the farthest node; ③ Constraint detection: Perform constraint detection on the farthest node. If the constraint conditions are met, the node is added to the path feature point set as a new path feature point; otherwise, it is removed from the set. ④ Binary extraction: The new path feature points divide the original set of path feature points into two segments: from the starting point to the node and from the node to the end point. For each segment, steps ①, ②, and ③ are repeated to find the path feature points that satisfy the constraints. ⑤ Preliminary route plan generation: For each segment in the newly generated path feature point set, perform a cyclic binary extraction operation until no path feature point that satisfies the constraints can be found in any segment of the path feature point set. Then, the path feature point extraction ends. Connect the new path feature point set as the route intersection point, and then fit it with the circular curve with the smallest radius and the shortest transition curve to obtain the preliminary route plan. S4-3: Optimize the generated route plan scheme based on the ideal solution method: ① Taking each path in the reconstructed route set as the evaluation scheme, and selecting engineering cost and cumulative turning angle value as evaluation attributes, a system is constructed. The decision matrix is ​​as follows: ; in, For the first The construction cost of the route, For the first The cumulative turning angle value of the path, ; ② Data standardization: Vector normalization is used to eliminate the influence of dimensions on the decision matrix. Each element in the dataset is standardized, and the calculation method is as follows: ; Obtain the standardized matrix: ; ③ Determine the positive and negative ideal solutions: Since both project cost and cumulative turning angles are negative indicators, the ideal solution is... Take the minimum value of each column of the standardized matrix, that is... Negative ideal solution Take the maximum value of each column of the standardized matrix, that is ; ④ Calculate the distance between each route's planar alignment and the ideal solution: Using the Euclidean distance method, calculate the first... Distance between the path and the ideal solution , No. Distance between the path and the negative ideal solution The calculation method is as follows: ; ; Calculate the first Relative fit of the path : ; according to Order from largest to smallest Sort the paths. The longest path is the optimal route.

[0013] Furthermore, in S4-2, the specific constraint conditions for constraint detection are: Planar alignment constraints: To determine whether the farthest node can be a route intersection, it is necessary to verify whether the length of the straight line between the node and the previous and subsequent intersections after inserting the minimum curve length at that intersection meets the constraint requirements; the length of the straight line must also meet the requirements of engineering specifications. Engineering structure constraints: Detect the elevation difference between the line connecting the farthest node and its preceding and following intersections and the ground elevation it traverses; Restricted Area Constraints: When performing constraint testing, the issue of restricted area crossings must be considered. Restricted area crossings are divided into two cases: the farthest node is inside the restricted area, and the line connecting the farthest point and the intersection points before and after it crosses the restricted area.

[0014] An electronic device includes a memory and a processor, wherein the memory stores a computer program; the processor executes the computer program to implement the intelligent track laying method for high-altitude mountain railways as described above.

[0015] A readable storage medium storing a computer program, wherein a processor executes the computer program to implement the intelligent track laying method for high-altitude mountain railways as described above.

[0016] The application of the technical solution of the present invention has the following beneficial effects: (1) This invention breaks through the redundancy limitations of traditional global contour line processing by using an innovative design of "generating local contour lines step by step". In each round, the local effective area is dynamically defined with the current guiding node as the center, according to the search step size and grid side length. Dedicated contour lines are generated only for the target elevation, which avoids interference from irrelevant terrain data and ensures accurate adaptation between contour lines and local terrain. Compared with the traditional manual global contour line generation method, it can significantly reduce the amount of terrain data processing, and avoid the problems of excessive cutting and filling of the line and broken elevation connection caused by terrain adaptation deviation. It provides real-time and accurate elevation constraint benchmarks for the extension of lines in high-altitude mountainous areas, and improves the adaptation accuracy between terrain and line from the source.

[0017] (2) This invention relies on the synchronous construction and bidirectional node management mechanism of multi-way trees to achieve a balanced innovation between "path diversity" and "computational efficiency": taking the starting point as the root node, multiple sets of child nodes are generated synchronously through OpenMP parallel computing to ensure the coverage of potential optimal paths; at the same time, through bidirectional control of "insufficient node supplementation" and "excessive node optimization", it avoids missing the optimal solution due to too few nodes and prevents the computation time from surging due to too many nodes. Compared with traditional single-direction search or unconstrained multi-node search, this invention can significantly improve the efficiency of path generation and greatly reduce the engineering conflict rate of the initial path, reduce the workload of subsequent manual adjustment, and make path generation both comprehensive and efficient.

[0018] (3) This invention forms a closed-loop innovation of "precise terrain constraints → intelligent node generation → efficient path management" through deep collaboration between "local contour lines and multi-branch trees": local contour lines provide exclusive elevation benchmarks for each layer of nodes in the multi-branch tree, ensuring that node generation always conforms to the terrain; the multi-branch tree, through layered iteration and parallel expansion, transforms local contour line constraints into multiple feasible paths, avoiding the problem of "disconnect between terrain constraints and path search" in traditional methods. In the scenario of railway line extension in high-altitude mountainous areas, the extension path generated by the scheme of this invention can fully take into account the accuracy and efficiency of railway line extension in high-altitude mountainous areas, providing reliable technical support for line design under complex terrain.

[0019] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of an intelligent railway alignment method for high-altitude mountainous areas; Figure 2 This is a diagram illustrating the grid numbering. Figure 3 This is a schematic diagram of local contour line generation and guide node generation; Figure 4 This is a schematic diagram of a multi-branch tree algorithm. Detailed Implementation

[0021] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] In the description of this invention, it should be noted that the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", "front", "back", "lateral", "longitudinal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0024] Example: See Figure 1 This invention provides an intelligent railway alignment method for mountainous areas with high elevation differences, comprising the following steps: S1. Obtain DEM (Digital Elevation Model) data for steep slope sections, divide the area into grids to construct an elevation dataset, and process invalid data; determine the core design parameters for the alignment of steep slope sections; specifically: ① Construct an elevation dataset: Obtain a digital elevation model of the steep slope area by dividing the study area into M×N quadrants with side lengths of... A square grid is constructed, and the elevation information of the grid is represented by the elevation value of the center point of each grid, thereby constructing the overall elevation dataset of the rectangular area. ② Handling invalid data: If the irregular shape of the study area boundary or the confidentiality of elevation data in some areas prevents the acquisition of actual valid elevation data, affecting subsequent calculations, then the nearest neighbor assignment method is introduced to validate this part of the elevation data. The specific processing method is as follows: First, locate the row and column numbers of the invalid elevation value grid, and then search along both the row and column directions: when searching along the row direction, record the first valid elevation value found and the search step number; when searching along the column direction, record the first valid elevation value found and the search step number. Compare the search step numbers along the row and column directions, and assign the valid elevation value with the smaller search step number to the grid of invalid elevation values. If the search step numbers are the same, the elevation of the invalid elevation value grid is taken as the arithmetic mean of the searched valid elevation values ​​in the row and column directions, thus resolving the problem of invalid elevation values ​​in the DEM dataset. ③ Determine the core design parameters for alignment in steep slope sections, including: Maximum slope Determined by railway grade and terrain type; Maximum slope length With minimum slope length Determined by railway grade and design specifications; Search Step Size : Preset by humans based on terrain complexity and computational efficiency; Digital Elevation Model (DEM) Grid Side Length : This is a built-in attribute of DEM data; Origin and Destination Information: Origin ,end ; Elevation forward direction coefficient This reflects the relative elevations of the endpoint and the starting point. If the overall trend of the route is upward, then... If the overall trend of the line is downward, then... The expression is -1: ; in, The elevation value at the starting point of the route. This refers to the elevation value at the end of the route. Slope reduction factor Based on railway design specifications and traction calculation logic, and taking into account the maximum limiting gradient, slope length constraints, gentle slope gradient, and comprehensive reduction factors, this coefficient is derived to balance terrain utilization and engineering constraints. It is used to reduce the maximum gradient to reserve space for gentle slope sections. It is calculated using the following formula: ; In the formula, This represents the comprehensive reduction factor for railways, typically taken as 0.8. Indicates the gradient (‰) of a gentle slope on a railway. Elevation difference per step It is the core elevation control parameter for multi-way tree hierarchical search, which is derived from known elevation control parameters. , , The result is obtained by calculation using the following formula: .

[0025] S2. Calculate the target elevation for each step and iteratively generate local contour lines and guide nodes step by step; iterate from the starting point to the ending point to form a node chain and contour line set; specifically: S2-1. Obtain line nodes and calculate target elevation: Obtain node information And calculate the target elevation of the next node. That is, the elevation of the target contour lines has been determined. , Indicates the first Elevation of each node; First, using the starting point as the initial node, obtain the starting point information. Combined with starting point information and the initial parameters 、 、 、 、 Calculate the slope reduction factor : ; Combine starting point information With destination information To clarify the overall elevation trend of the route, the elevation advance direction coefficient is obtained. : ; Combined with the obtained elevation forward direction coefficient Starting point elevation value The initial target elevation was calculated. This means that the elevation of the first target contour line has been determined. : .

[0026] S2-2. Define the local effective area corresponding to the node: Centered on the DEM grid where the node is located, according to... Determine the number of extended grids ( (Rounded up to the nearest integer, representing the side length of the DEM grid) and expands in four orthogonal directions (up, down, left, and right) to form a local effective area that only covers the key areas around the nodes; S2-3, Generate contour lines and guide nodes within the local effective area; specifically: ① Starting from the initial node, use the elevation matrix E The grid elevation information within the locally valid area defined by the record nodes is represented as follows: ; in: and These represent the number of rows and columns of the DEM elevation grid, respectively. Indicates the first Line 1 The center elevation of the grid, ; ; ② Construct the state matrix. Since the state matrix records the intersection relationship between contour lines and grid edge lengths, it is necessary to separately construct the horizontal edge state matrices of contour lines and grid edge lengths within the local effective region of a node. and vertical edge state matrix The expressions are as follows: ; ; in: Indicates the first Line 1 Whether the horizontal edge of the grid after the elevation point intersects with the target contour line, 1 indicates intersection, 0 indicates non-intersection; Indicates the first Line 1 Whether a vertical edge of a grid below a grid elevation point intersects with the target contour line; 1 indicates intersection, 0 indicates non-intersection. ; ; The calculation formula is as follows: ; ; in: Indicates the first Line 1 Center elevation of the grid; This represents the elevation value of the target contour line. Taking the starting point as an example, then... This represents the center elevation value of the grid within the effective range of the starting point. ; ③ Based on the constructed state matrix, search for the starting point of the target contour line. Within the local effective region, if the target contour line is an open curve (located at the boundary of the effective region), then search at the edge of the effective region. , The first unit with a value of 1 is used as the starting point; that is, using The matrix sequentially searches for the cell state values ​​in the first and last rows, and then uses... The matrix searches the cell state values ​​in the first and last columns in turn. The first value found, "1", is the interval where the starting point is located. At the same time, to avoid searching for duplicate positions when searching for the next contour line, the "1" found in the matrix is ​​changed to "0". If the curve is closed (located within the valid region), then search within the valid region. , The first cell with a value of 1 is taken as the starting point. Since the beginning and end points of the closed curve coincide, when the starting point is found, there is no need to change the "1" in the matrix to "0", otherwise it will not be able to close. When searching for the endpoints of the entire contour line, the value can be changed to "0" when the starting point is found. Starting from the origin, calculate the origin coordinates using linear interpolation. If the edge to be entered is the first... Line 1 The horizontal line after the grid elevation indicates the starting coordinates. The calculation formula is: ; in, Indicates the first Line 1 The x-coordinate of the elevation points in the grid. Indicates the first Line 1 The ordinate of the elevation points in the grid; If the edge is the first Line 1 The vertical edge of the grid's elevation point indicates the starting coordinates. The calculation formula is: ; See Figure 2 Starting from the incoming edge, the search proceeds clockwise. Edges with a state value of "1" are identified as the departing edges of the grid at that elevation point. The coordinates of the departing point are calculated using the same method. Simultaneously, the state value of this departing edge is changed from "1" to "0," and it becomes the incoming edge of the next elevation point grid. If three other edges have a state value of "1," the "average elevation method" can be used for judgment. This method involves determining the edge's elevation based on the relationship between the elevations of the two endpoints of the incoming edge, the average elevation of the four grid points, and the target elevation. For example, if the incoming edge is edge number 4, the judgment method is as follows: ; and These are the elevations at both ends of side 1. The average elevation of the grid shown is expressed by the following formula: ; Therefore, taking edge number 4 as an example, the way to determine the leaving edge is expressed as follows: ; After finding the departing edge, treat it as the entering edge of the next grid cell. Repeat the above operation, recording the coordinates of the departing point obtained each time, until the last entering grid cell has no state value of "1". At this point, the entire search of this contour line is completed. Connect the recorded point coordinates sequentially to form a contour line of the target elevation. Repeat this process until the state matrix is ​​obtained. and If all elements are 0, then all contour lines for the target elevation within the study area have been generated.

[0027] ④ Generate guide nodes: such as Figure 3 As shown, with the node as the center and the search step size... Draw a circle with radius and intersect it with the target contour line. The intersection point is the feasible guide node where the cut and fill volume is zero. Taking the starting point as an example, the starting point is... Center and search step size Draw a circle with radius and elevation . The intersection of contour lines is the feasible guiding node; If there is no intersection, the distance between the node and the target contour line needs to be further determined: if the distance is greater than... If the slope is gentle, draw a perpendicular line from the current node to the target contour line, and take the first foot of the perpendicular as the guide node; if the distance is less than or equal to... If the target elevation is characterized by a mountain peak or valley, delete the contour lines corresponding to that elevation and adjust the search step size. After reducing the search step size, re-draw the circle to find the intersection until the first round of guiding nodes is obtained.

[0028] S2-4, Repeat S2-1 to S2-3, generating local contour lines and guide nodes iteratively and synchronously; specifically: ① Elevation Update: The guide node generated in the first round is used as the new current node, combined with the elevation forward direction coefficient. With each step of elevation difference The elevation values ​​of the next layer of target contour lines are calculated. ; ②Regional re-frame: Using the new DEM grid where the current node is located as the center, continue using... The expansion rules redefine the effective local area covering the key terrain around the current node; this area focuses only on the terrain features around the current node, ensuring accurate adaptation to local terrain while avoiding redundant processing of irrelevant terrain data. ③ Local contour line regeneration: Within the newly defined local effective area, repeat the contour line generation logic in S2-3 to reconstruct the elevation matrix and state matrix, and determine the grid edges and... The intersection of elevation contour lines is used to search for the starting point and iteratively generate a complete set of contour lines. Local contour lines are used to ensure that each guiding node has a corresponding local contour line as an elevation constraint. ④ Node regeneration: Using the new current node as the center, the search step size is... Draw a circle with radius and . The elevation contour lines are intersected to generate a new round of guide nodes. When there are no intersections, intersections are generated according to the corresponding processing rules, so as to achieve the simultaneous progress of "local contour line generation - guide node generation".

[0029] S2-5, Iteration Termination Judgment: After generating a new guiding node in each iteration, calculate the elevation of the current node. Elevation of the endpoint The difference; if the difference is greater than or equal to the elevation difference at each step If the difference is less than the elevation difference at each step, then continue iterating; Then the end point of the route As the final guiding node, and also at the end of the route The surrounding area defines a local effective region, generates local contour lines close to the endpoint elevation, and completes the full-process cyclic generation of "local contour lines - guide nodes" from the starting point to the endpoint, forming a complete node chain and corresponding contour line set.

[0030] S3. Initialize the multi-branch tree and associate it with the target elevation, expand nodes at the same level in parallel, control the number of nodes bidirectionally, iteratively update the multi-branch tree, and form a set of terminal child nodes; for example... Figure 4 As shown, S3 includes: S3-1. Initialize the multi-way tree and associate it with the target elevation: starting from the line origin. As the root node of the multi-way tree, construct the node set for the current level, which initially contains only... Simultaneously, correlate the elevation of the first target contour line obtained in S2-1. This establishes the search elevation datum for the first-level child nodes of the multi-way tree, enabling synchronized startup of the multi-way tree and local contour line generation. During initialization, the root node... The coordinates and elevation information are linked with the terrain data processed in S2 to ensure that subsequent child node expansions can directly call the terrain parameters of the corresponding area, and to ensure data flow connection with the local contour line generation step in S2.

[0031] S3-2, Parallel Expansion of Nodes in the Same Layer: Using OpenMP parallel computing technology, each node in the current layer node set is encapsulated as an independent computing module and allocated to different threads of the CPU for synchronous processing. Each node follows the logic of "local contour line generation - node matching" in S2-3 to S2-4. That is, first, a corresponding local contour line is generated based on the current node position and the target elevation. Then, child nodes that meet the conditions are matched by intersection or adjustment to generate multiple feasible child nodes (i.e., multiple potential path directions corresponding to the same parent node). When summarizing the child nodes generated by all threads, the connection relationship between each child node and the parent node, the slope of the corresponding path, and the fit with the contour line are recorded to form a temporary child node set, ensuring the diversity and traceability of path schemes.

[0032] S3-3, Two-way control of the number of nodes: Node replenishment: If the number of nodes in a temporary child node set of a certain layer is less than the preset threshold (set according to the terrain complexity to ensure that enough potential paths are covered), the number of grid cells in the local effective area corresponding to the node of that layer is expanded (1-2 grid cells are added on the basis of the original expansion number). The child nodes are searched again according to the same range definition rules generated by the local contour lines in S2 and added to the temporary child node set. The added child nodes must meet the spatial discreteness requirements with the existing nodes to prevent insufficient path diversity due to too few nodes, which would prevent the optimal solution from being found. Optimization for too many nodes: If the number of nodes in the temporary child node set exceeds the preset limit, the point set aggregation method is used for optimization to form an effective child node set. Specifically, based on the DEM grid, the grid where each child node in the temporary child node set is located is determined, and multiple child nodes in the same grid are merged into one (the average coordinate of each node is taken as the coordinate of the merged node) to form an effective child node set.

[0033] S3-4, Iterative Update of Multi-way Tree: Set the optimized set of valid child nodes as the new current layer node set, and update the target contour line elevation synchronously. Repeat the parallel expansion and node optimization process from S3-2 to S3-3 until the current target contour elevation is reached. Elevation of the endpoint The difference is less than Stop the hierarchical search and take the set of valid child nodes at this point as the terminal child node set of the multi-way tree.

[0034] S4. Traverse the multi-branch tree to form an initial route layout path set. After plane fitting, the initial route layout path set yields a route plan scheme. Based on the ideal solution method, find the optimal route layout path from the route plan scheme. Specifically: S4-1, Initial layout path set formation: Connect each node in the terminal child node set to the endpoint. Establish parent node relationships (i.e., each terminal child node is a potential predecessor node of the endpoint); starting from each terminal child node, backtrack level by level to its previous parent node in the multi-way tree until the root node is reached. This process generates multiple complete paths from the starting point to each layer of sub-nodes, then to the terminal sub-node, and finally to the end point. Paths without significant engineering conflicts (such as those that do not cross known large obstacles or geological disaster areas) are selected to form an initial set of development paths.

[0035] S4-2: Path plane fitting: For each path in the initial alignment path set, the chord-tangent offset method based on the bisection approach is used to extract path feature points, and a preliminary alignment plan is fitted; the specific implementation steps are as follows: ① Generate initial baseline: Connect the start and end points of the original paths in the initial plotting path set to serve as the initial extraction path and the baseline for subsequent calculations; ② Find the farthest point: Traverse the points within the range of the start and end points, and find the node that is farthest from the baseline, i.e., the farthest node; ③ Constraint detection: Perform constraint detection on the farthest node. If the constraint conditions are met, the node is added to the path feature point set as a new path feature point; otherwise, it is removed from the set. The specific constraints for constraint testing are: Planar alignment constraints: To determine whether the farthest node can be a route intersection, it is necessary to verify whether the length of the straight line between the node and the previous and subsequent intersections after inserting the minimum curve length at that intersection meets the constraint requirements; the length of the straight line must also meet the requirements of engineering specifications. Engineering structure constraints: Since this method only focuses on the elevation of path points during the search process, it may cause the line connecting two path vertices to traverse areas with drastic changes in terrain elevation, resulting in high bridges. Therefore, it is also necessary to detect the elevation difference between the line connecting the farthest point and its preceding and following intersection points and the ground elevation it passes through. Restricted Area Constraints: In actual engineering route selection, it is necessary to avoid some restricted areas on the plane, such as nature reserves, military restricted areas, and cultural heritage sites. Therefore, the issue of restricted area crossings must be considered when conducting constraint testing. It should be divided into two cases: the farthest node is within the restricted area and the line connecting the farthest point and the intersection points before and after it crosses the restricted area.

[0036] ④ Binary extraction: The new path feature points divide the original set of path feature points into two segments: from the starting point to the node and from the node to the end point. For each segment, steps ①, ②, and ③ are repeated to find the path feature points that satisfy the constraints. ⑤ Preliminary route plan generation: For each segment in the newly generated path feature point set, perform a cyclic binary extraction operation until no path feature point that satisfies the constraints can be found in any segment of the path feature point set. Then, the current path feature point extraction ends. Connect the new path feature point set as the route intersection point, and then fit it with the circular curve with the smallest radius and the shortest transition curve to obtain the preliminary route plan.

[0037] S4-3: Optimize the generated route plan scheme based on the ideal solution method: ① Take each path in the reconstructed exhibition path set as the scheme to be evaluated (assuming a total of (path), select project cost ( C (lower is better) and cumulative turning angle value of the route ( (lower is better) is used as an evaluation attribute to construct... The decision matrix is ​​as follows: ; in, For the first The construction cost of the route, For the first The cumulative turning angle value of the path, ; ② Data standardization: Vector normalization is used to eliminate the influence of dimensions on the decision matrix. Each element in the dataset is standardized, and the calculation method is as follows: ; Obtain the standardized matrix: ; ③ Determine the positive and negative ideal solutions: Since both project cost and cumulative turning angles are negative indicators, the ideal solution is... Take the minimum value of each column of the standardized matrix, that is... Negative ideal solution Take the maximum value of each column of the standardized matrix, that is ; ④ Calculate the distance between each route's planar alignment and the ideal solution: Using the Euclidean distance method, calculate the first... Distance between the path and the ideal solution , No. Distance between the path and the negative ideal solution The calculation method is as follows: ; ; Calculate the first Relative fit of the path : ; according to Order from largest to smallest Sort the paths. The longest path is the optimal route.

[0038] The present invention also provides an electronic device corresponding to the above embodiments. The electronic device may be a processing device for a client, such as a mobile phone, a laptop, a tablet computer, a desktop computer, etc., to execute the methods of the above embodiments.

[0039] The electronic device of this embodiment includes a memory, a processor, and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method described in the above embodiment.

[0040] In some implementations, the memory may be high-speed random access memory (RAM), and may also include nonvolatile memory, such as at least one disk storage.

[0041] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0042] The present invention also provides a readable storage medium corresponding to the above embodiments, wherein a computer program / instructions are stored thereon. When the computer program / instructions are executed by a processor, they implement the steps of the methods described in the above embodiments.

[0043] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0044] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0045] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0046] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent track alignment in mountainous areas with high elevation differences, characterized in that: Includes the following steps: S1. Obtain DEM data for the steep slope section, divide the grid to construct the elevation dataset, and process invalid data; determine the core design parameters for the alignment of the steep slope section. Specifically: Constructing an elevation dataset: Obtaining a digital elevation model of the steep slope area by dividing the study area into M×N regions with side lengths of... A square grid is constructed, and the elevation information of the grid is represented by the elevation value of the center point of each grid, thereby constructing the overall elevation dataset of the rectangular area. Processing invalid data: For elevation data that cannot be obtained due to the irregular shape of the boundary of the study area or the confidentiality of elevation data in some areas, the nearest neighbor assignment method is introduced to make it valid. Determine the core design parameters for alignment in steep slope sections, including: Maximum slope Determined by railway grade and terrain type; Maximum slope length With minimum slope length Determined by railway grade and design specifications; Search Step Size Preset based on terrain complexity and computational efficiency; Digital Elevation Model Grid Side Length : This is a built-in attribute of DEM data; Origin and Destination Information: Origin ,end ; Elevation forward direction coefficient If the overall trend of the line is upward, then If the overall trend of the line is downward, then... The expression is -1: ； in, The elevation value at the starting point of the route. This refers to the elevation value at the end of the route. Slope reduction factor The result is obtained by calculation using the following formula: ； In the formula, This represents the comprehensive reduction factor for railways. Indicates the gradient of a railway slope; Elevation difference per step The result is obtained by calculation using the following formula: ； S2. Calculate the target elevation for each step and iteratively generate local contour lines and guide nodes step by step; iterate from the starting point to the ending point to form a node chain and contour line set; specifically: S2-1. Obtain line nodes and calculate target elevation: Obtain node information And calculate the target elevation of the next node. That is, the elevation of the target contour lines has been determined. , Indicates the first The elevation of each node; if the starting point is used as the initial node for the first time, then the starting point information is obtained. Calculate the target elevation of the next node. That is, the elevation value of the first target contour line was determined. ; S2-2. Define the local effective area corresponding to the node: Centered on the DEM grid where the node is located, according to... Determine the number of grid cells to expand, and expand in four orthogonal directions (up, down, left, and right) to form a local effective region that only covers the key areas surrounding the nodes. Indicates the search step size. Indicates the side length of the digital elevation model grid; S2-3, Generate contour lines and guide nodes within the local effective area; specifically: ① Starting from the initial node, use the elevation matrix E The grid elevation information within the locally valid area defined by the record nodes is represented as follows: ； in: and These represent the number of rows and columns in the DEM elevation grid, respectively. Indicates the first Line 1 The center elevation of the grid, ; ; ② Construct state matrices, establishing the horizontal edge state matrices for contour lines and grid edge lengths within the local effective region of each node. and vertical edge state matrix The expressions are as follows: ； ； in: Indicates the first Line 1 Whether the horizontal edge of the grid after the elevation point intersects with the target contour line, 1 indicates intersection, 0 indicates non-intersection; Indicates the first Line 1 Whether a vertical edge of a grid below a grid elevation point intersects with the target contour line; 1 indicates intersection, 0 indicates non-intersection. ; The calculation formula is as follows: ； ； in: Indicates the first Line 1 Center elevation of the grid; Indicates the elevation value of the target contour line; ③ Based on the constructed state matrix, search for the starting point of the target contour line. Within the local effective region, if the target contour line is an open curve, then search at the edge of the effective region. , The first unit with a value of 1 is used as the starting point; if it is a closed curve, the search continues within the effective region. , The first cell with a value of 1 is taken as the starting point. Starting from the starting point, the coordinates of the starting point are calculated by linear interpolation. The departure edge is determined by searching clockwise and the coordinates of the departure point are calculated. The process is iterated until there are no intersecting edges. The coordinate points are connected to generate the target contour line with the corresponding elevation. If the state values ​​of three grid edges are all 1, the average elevation method is used to determine the unique departure edge to ensure the continuity of the contour line. ④ Generate guiding nodes: Using the node as the center, the search step size is... Draw a circle with radius and find its intersection with the target contour line. The intersection point is the feasible guide node with zero cut and fill volume. If there is no intersection, the distance between the node and the target contour line needs to be further determined: if the distance is greater than... Then, draw a perpendicular line from the current node to the target contour line, and take the first foot of the perpendicular as the guide node; if the distance is less than or equal to... Then adjust the search step size. SL Then redraw the circle to find the intersection until the first guiding node is obtained; S2-4, Repeating S2-1 to S2-3, the local contour lines and guide nodes are generated synchronously and iteratively. Specifically: Elevation update: The guide node generated in the first round is used as the new current node, combined with the elevation forward direction coefficient. With each step of elevation difference The elevation values ​​of the next layer of target contour lines are calculated. ; Region re-frame: Using the new DEM grid where the current node is located as the center, continue using... The extended rules redefine the effective local area covering the key terrain around the current node; Local contour line regeneration: Within the newly defined local effective area, the elevation matrix and state matrix are reconstructed, and the grid edges are determined. The intersection of elevation contour lines is used to search for the starting point and iteratively generate a complete set of contour lines. Local contour lines are used to ensure that each guiding node has a corresponding local contour line as an elevation constraint. Node regeneration: with the new current node as the center, the search step size is... Draw a circle with radius and . Intersection of elevation contour lines generates a new set of guiding nodes; S2-5, Iteration Termination Judgment: After generating a new guiding node in each iteration, calculate the elevation of the current node. Elevation of the endpoint The difference; if the difference is greater than or equal to the elevation difference at each step If the difference is less than the elevation difference at each step, then continue iterating; Then the end point of the route will be... As the final guiding node, and also at the end of the route The surrounding area defines a local effective region, generates local contour lines close to the endpoint elevation, completes the iteration from the starting point to the endpoint, and forms a complete node chain and corresponding contour line set; S3. Initialize the multi-branch tree and associate it with the target elevation, expand nodes at the same level in parallel, control the number of nodes bidirectionally, iteratively update the multi-branch tree, and form a set of terminal child nodes; S3 includes: S3-1. Initialize the multi-way tree and associate it with the target elevation: starting from the route origin. As the root node of the multi-way tree, construct the node set for the current level, which initially contains only... Simultaneously, correlate the elevation of the first target contour line obtained in S2-1. ; S3-2, Parallel Expansion of Nodes in the Same Layer: Using OpenMP parallel computing technology, each node in the current layer node set is encapsulated as an independent computing module and allocated to different threads of the CPU for synchronous processing; each node generates multiple feasible child nodes according to S2-3 to S2-4; the feasible child nodes generated by all threads are aggregated to form a temporary child node set; S3-3, Two-way control of node quantity: Node replenishment: If the number of nodes in a temporary child node set of a certain layer is less than a preset threshold, the number of nodes will be increased. Optimization for too many nodes: If the number of nodes in the temporary child node set exceeds the preset limit, the node set aggregation method is used for optimization to form an effective child node set; S3-4, Iterative Update of Multi-way Tree: Set the optimized set of valid child nodes as the new current layer node set, and update the target contour line elevation synchronously. Repeat the parallel expansion and node optimization process from S3-2 to S3-3 until the current target contour elevation is reached. Elevation of the endpoint The difference is less than Stop the hierarchical search and take the set of valid child nodes at this point as the terminal child node set of the multi-way tree; S4. Traverse the multi-branch tree to form an initial route layout path set. After plane fitting, the initial route layout path set is used to obtain the route plane scheme. Based on the ideal solution method, find the optimal route layout path from the route plane scheme.

2. The intelligent track-laying method for railways in mountainous areas with high elevation differences according to claim 1, characterized in that, S4 specifically refers to: S4-1, Initial layout path set formation: Connect each node in the terminal child node set to the endpoint. Establish parent node association; Starting from each terminal child node, trace back level by level to its parent node in the multi-way tree, until the root node is reached. This forms multiple complete paths from the starting point to each layer of sub-nodes, then to the terminal sub-node, and finally to the end point. Paths without obvious engineering conflicts are selected to form an initial set of route development paths. S4-2: Path plane fitting: For each path in the initial alignment path set, the chord-tangent offset method based on the bisection approach is used to extract path feature points, and a preliminary alignment plan is fitted; the specific implementation steps are as follows: ① Generate initial baseline: Connect the start and end points of the original paths in the initial plotting path set to serve as the initial extraction path and the baseline for subsequent calculations; ② Find the farthest point: Traverse the points within the range of the start and end points, and find the node that is farthest from the baseline, i.e., the farthest node; ③ Constraint detection: Perform constraint detection on the farthest node. If the constraint conditions are met, the node is added to the path feature point set as a new path feature point; otherwise, it is removed from the set. ④ Binary extraction: The new path feature points divide the original set of path feature points into two segments: from the starting point to the node and from the node to the end point. For each segment, steps ①, ②, and ③ are repeated to find the path feature points that satisfy the constraints. ⑤ Preliminary route plan generation: For each segment in the newly generated path feature point set, perform a cyclic binary extraction operation until no path feature point that satisfies the constraints can be found in any segment of the path feature point set. Then, the path feature point extraction ends. Connect the new path feature point set as the route intersection point, and then fit it with the circular curve with the smallest radius and the shortest transition curve to obtain the preliminary route plan. S4-3: Optimize the generated route plan scheme based on the ideal solution method: ① Taking each path in the reconstructed route set as the evaluation scheme, and selecting engineering cost and cumulative turning angle value as evaluation attributes, a system is constructed. The decision matrix is ​​as follows: ； in, For the first The engineering cost of the route, For the first The cumulative turning angle value of the path, ; ② Data standardization: Vector normalization is used to eliminate the influence of dimensions on the decision matrix. Each element in the dataset is standardized, and the calculation method is as follows: ； The standardized matrix is ​​obtained as follows: ； ③ Determine the positive and negative ideal solutions: Since both project cost and cumulative turning angles are negative indicators, the positive ideal solution... Take the minimum value of each column of the standardized matrix, that is... Negative ideal solution Take the maximum value of each column of the standardized matrix, that is ; ④ Calculate the distance between each route's planar alignment and the ideal solution: Using the Euclidean distance method, calculate the first... Distance between the path and the ideal solution , No. Distance between the path and the negative ideal solution The calculation method is as follows: ； ； Calculate the first Relative fit of the path : ； according to Order from largest to smallest Sort the paths. The longest path is the optimal route.

3. The intelligent track-laying method for railways in mountainous areas with high elevation differences according to claim 2, characterized in that, In S4-2, the specific constraint conditions for constraint detection are: Planar alignment constraints: To determine whether the farthest node can be a route intersection, it is necessary to verify whether the length of the straight line between the node and the previous and subsequent intersections after inserting the minimum curve length at that intersection meets the constraint requirements; the length of the straight line must also meet the requirements of engineering specifications. Engineering structure constraints: Detect the elevation difference between the line connecting the farthest node and its preceding and following intersections and the ground elevation it traverses; Restricted Area Constraints: When performing constraint testing, the issue of restricted area crossings must be considered. Restricted area crossings are divided into two cases: the farthest node is within the restricted area, and the line connecting the farthest point and its preceding and following intersections crosses the restricted area.

4. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program; the processor executes the computer program to implement the intelligent track laying method for high-altitude mountain railways as described in any one of claims 1-3.

5. A readable storage medium, characterized in that, The readable storage medium stores a computer program, and the processor executes the computer program to implement the intelligent track layout method for high-altitude mountain railways as described in any one of claims 1-3.

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