Global constraint heuristic method and system for optimization of complex mountainous power transmission line route

Abstract

This invention proposes a globally constrained heuristic method and system for optimizing the routing of transmission lines in complex mountainous areas, belonging to the field of power engineering planning and design and disaster prevention and mitigation technology. The method includes: acquiring and processing geospatial elevation data of the target area to output wind speed cloud maps influenced by terrain; calculating terrain flow field data based on the wind speed cloud maps influenced by terrain, and constructing a discretized terrain model containing three-dimensional coordinates and environmental cost values; setting transmission line selection constraints, including target straight-line distance, distance tolerance, target direction angle, angle tolerance, and minimum distance between path points; defining the path cost function as the integral of the path segment length and environmental feature values; using a minimum heap structure for iterative search based on the discretized terrain model to calculate multiple discrete path point sequences with the minimum cumulative cost while avoiding constraints on already selected path nodes; and evaluating the selected optimal paths to obtain the transmission line selection results.

Description

Technical Field

[0001] This invention belongs to the field of power engineering planning and design and disaster prevention and mitigation technology, and in particular relates to a global constraint heuristic method and system for optimizing the routing of transmission lines in complex mountainous areas. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Mountainous areas with micro-topography are characterized by dramatic undulations, steep slopes, and diverse landforms, resulting in significant differences in micro-topography and imposing strict constraints on the selection of transmission line routes. High-altitude mountainous areas generally suffer from complex geological structures and frequent adverse geological phenomena, which are core constraints on transmission line route selection. Furthermore, the variable meteorological conditions and pronounced micro-meteorological effects in high-altitude mountainous areas have a significant impact on route selection. In addition, the fragile ecosystems, high vegetation coverage, and predominantly ecologically sensitive areas contain habitats for rare plants and wildlife, requiring route selection to strictly comply with ecological environmental protection requirements, making the constraints extremely stringent.

[0004] With the construction of the global energy internet and the increasing scale of ultra-high voltage power grid construction, transmission lines inevitably extend into mountainous areas with high altitudes and complex micro-topography. The terrain in southwestern and northwestern my country is extremely complex, with micro-topography features such as canyons, saddles, and ridges exhibiting significant funneling and acceleration effects on near-surface wind fields. Statistics show that local wind field distortion induced by micro-topography is a major cause of disasters such as wind-induced flashover, tower buckling, and even tower collapse and line breakage in transmission lines.

[0005] The inventors discovered in their research that existing transmission line route selection technologies primarily rely on Geographic Information Systems (GIS) for path planning, focusing on optimizing static indicators such as path length, terrain elevation differences, and construction costs, often neglecting the dynamic influence of external environmental loads (especially wind loads). In wind field analysis, traditional engineering design often uses simple wind pressure height variation coefficients to estimate wind loads, which fails to reflect the three-dimensional flow field characteristics under complex micro-topography. Although some studies have begun to introduce CFD technology for wind field simulation, it often faces problems such as excessively large computational grids and convergence difficulties when dealing with large-scale complex mountainous terrain. Furthermore, conventional CFD simulations are prone to boundary truncation effects and outlet backflow interference due to terrain truncation when handling boundary conditions, severely impacting simulation accuracy.

[0006] At the level of route selection algorithms, traditional Dijkstra's algorithm or A* algorithm often struggles to find the optimal solution that balances aerodynamic safety and engineering feasibility on a global scale when dealing with engineering constraints unique to transmission lines, such as strict span limits, line corridor skew angle limits, and multi-circuit safety distances. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a global constraint heuristic method and system for optimizing the routing of transmission lines in complex mountainous areas. It can refine the construction of a micro-topography wind field model in mountainous areas and, based on this model, achieve global multi-constraint optimization for transmission line routing.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: Firstly, a globally constrained heuristic method for optimizing the routing of transmission lines in complex mountainous areas is disclosed, including: Acquire and process geospatial elevation data of the target area, and output wind speed cloud map affected by terrain. Based on wind speed cloud maps influenced by terrain, terrain flow field data are calculated, and a discretized terrain model containing three-dimensional coordinates and environmental cost values ​​is constructed. Set the route selection constraints for transmission lines, including target straight distance, distance tolerance, target direction angle, angle tolerance, and minimum distance between path points; The path cost function is defined as the integral of the path segment length and the environmental feature value. Based on the discretized terrain model, the min-heap structure is used for iterative search to calculate multiple discrete path point sequences with the minimum cumulative cost while avoiding the constraints of the selected path nodes. The calculated optimal paths are evaluated for transmission line selection to obtain the selected transmission line results.

[0009] As a further technical solution, the geospatial elevation data of the target area is processed to construct a detailed grid model of the target area that reflects the real landform features; The detailed mesh model of the target area is materialized, and a long-distance flatland fitting transition layer is constructed around the mountainous terrain to establish a three-dimensional wind field model. Define the computational domain and initial boundary conditions for the three-dimensional wind field model, and complete the mesh generation; A turbulence model was selected as the solver. The ground was set as a no-slip wall and the other walls were set as symmetry planes. Fluid dynamics numerical simulation was performed to capture the airflow separation and eddy response process in mountainous terrain and output wind speed cloud map under the influence of terrain.

[0010] As a further technical solution, the discrete space heuristic search algorithm based on priority queues introduces strict geometric topological constraints as heuristic pruning rules during the search process when setting transmission line selection constraints, so as to ensure that the generated transmission line path conforms to engineering specifications.

[0011] As a further technical solution, when calculating the sequence of discrete path points with the minimum cumulative cost, the following is included: After successfully finding the first optimal path, the spatial avoidance mechanism is automatically activated, marking all nodes on the path and their surrounding neighborhoods as the avoidance set. In subsequent iterations to find the second and more alternative paths, the search process is forced to avoid these high-risk overlapping areas, thereby automatically generating multiple independent path sequences in three-dimensional space that do not overlap and have the lowest cumulative wind disaster risk.

[0012] As a further technical solution, the calculated optimal paths are evaluated for transmission line selection, including: The rendering engine is used to draw a 3D terrain scatter cloud map containing environmental feature color mapping, and the planned path is overlaid as a highlighted line to intuitively present the spatial relationship between the route corridor and high wind speed areas.

[0013] As a further technical solution, it also includes: constructing a mathematical representation of the transmission line, assuming that the transmission line path consists of a series of discrete tower location sequences; Define a global path selection objective function that aims to find a path that minimizes the line integral of the wind speed and topographic coefficients of the regions it traverses.

[0014] Secondly, a globally constrained heuristic system for optimizing the routing of transmission lines in complex mountainous areas is disclosed, including: The wind speed cloud map acquisition module is configured to: acquire and process the geospatial elevation data of the target area, and output the wind speed cloud map affected by the terrain. The discretized terrain model construction module is configured to: calculate terrain flow field data based on wind speed cloud maps affected by terrain, and construct a discretized terrain model containing three-dimensional coordinates and environmental cost values; The discrete path point sequence calculation module is configured to: set transmission line selection constraints, including target straight-line distance, distance tolerance, target direction angle, angle tolerance, and minimum distance between path points; The path cost function is defined as the integral of the path segment length and the environmental feature value. Based on the discretized terrain model, the min-heap structure is used for iterative search to calculate multiple discrete path point sequences with the minimum cumulative cost while avoiding the constraints of the selected path nodes. The route selection module is configured to: perform 3D visualization rendering of multiple calculated optimal paths, overlay terrain cloud map display, conduct route selection evaluation of transmission lines, and obtain the route selection results of the evaluated transmission lines.

[0015] The above one or more technical solutions have the following beneficial effects: This invention's technical solution is based on a discretized terrain model, setting constraints for transmission line selection, including target straight-line distance, distance tolerance, target direction angle, angle tolerance, and minimum distance between path points. The objective function for the path is defined as the integral of the path segment length and environmental characteristic values. Using a min-heap structure for iterative search, multiple discrete path point sequences with minimum cumulative cost are calculated while avoiding the constraints of already selected path nodes. After calculating the first optimal path, multiple non-overlapping alternative paths that satisfy engineering constraints can be continuously searched through state memory and spatial avoidance strategies. This design fully considers potential land acquisition difficulties or geological hazards in actual engineering, providing designers with flexible and diverse route planning schemes and avoiding the dilemma that a single mathematically optimal solution cannot be implemented in practical engineering.

[0016] Furthermore, the global multi-constraint heuristic route selection strategy adopted in this invention innovatively integrates path length, turning angle constraints, and wind field environmental costs (wind speed / turbulence intensity integral) into a unified evaluation system. Unlike traditional geometric shortest path planning, this method can proactively identify and avoid high-velocity, strong turbulence "wind gap" areas during the search process, reducing the risk of future transmission line galloping, wind-induced flashover, and other disasters from the planning source, thereby improving the inherent safety level of power grid operation.

[0017] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] 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 improper limitation of the invention.

[0019] Figure 1 This is an overall flowchart of Embodiment 1 of the present invention; Figure 2 This is an overall flowchart of Embodiment 3 of the present invention; Figure 3 This is an ASTER GDEM 30M resolution digital elevation data map extracted from the case study project; Figure 4 The mountainous terrain of the case was segmented and preprocessed in a refined manner; Figure 5 The following is a probability distribution chart of the average monthly wind direction over the past 20 years for this case study. Figure 6 The case study wind field CFD wind speed topographic coefficient distribution cloud map; Figure 7 A 3D visualization cloud map for global multi-constraint heuristic line selection in case studies; Figure 8This is another case study of a global multi-constraint heuristic route selection 3D visualization cloud map. Detailed Implementation

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0022] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] Example 1 This embodiment discloses a globally constrained heuristic method for optimizing the routing of transmission lines in complex mountainous areas, including: Step S1: Obtain high-resolution digital elevation data of the target area, construct a three-dimensional terrain entity model including a flat-land fitting transition layer, define the computational domain and boundary conditions, mesh the three-dimensional terrain entity model, and drive the CFD solver through an automated control module to perform fluid dynamics numerical simulation to obtain high-precision three-dimensional flow field data reflecting micro-topographic features. This step obtains high-precision three-dimensional wind field simulation results through block simulation and transition layer settings, and outputs wind speed cloud maps affected by terrain. Step S2: Based on the wind speed cloud map affected by the terrain, perform data cleaning and discretization mapping to construct a discretized terrain model containing three-dimensional coordinates and environmental cost values; A global heuristic search algorithm based on priority queues is established, and multiple engineering constraints are set, including target distance, direction angle and multi-path avoidance strategy. The route selection rules include geometric topological constraints and environmental risk costs. Step S3: Based on the discretized terrain model, the algorithm selects the transmission line route in mountainous areas based on CFD simulation and heuristic search. The algorithm uses the integral of the path segment length and the wind field environmental characteristic value as the cost function. Under the premise of satisfying the above geometric topological constraints, iteratively searches in the three-dimensional discrete space for one or more transmission line routes with the minimum cumulative environmental risk cost.

[0024] Step S4: Use 3D interactive visualization technology to intuitively display the terrain cloud map and the planned route.

[0025] This embodiment uses the above-mentioned method to construct a high-precision wind field through block simulation and transition layer setting technology. By using a global heuristic search strategy based on priority queues, it can effectively identify and avoid risk areas such as high wind speed and strong turbulence, and provide a quantitative route selection scheme for transmission lines in complex mountainous areas that takes into account both aerodynamic safety and engineering feasibility. Under the premise of meeting multiple engineering constraints, it actively searches for the transmission line path with the lowest wind disaster risk.

[0026] In one implementation example, see Appendix Figure 1 As shown, in step S1, high-resolution digital elevation data (DEM) of the target area can be obtained through remote sensing, UAV laser scanning, or geospatial data download, etc. See Appendix. Figure 3 As shown, geographic data processing software is used to denoise, filter, and finely resample the high-resolution digital elevation data of the target area to construct a detailed grid model of the target area that reflects the real landform features.

[0027] Specifically, Global Mapper is used to process digital elevation data to extract topographic contour lines at precise regional locations. These contour lines are then imported into Rhino software for further fitting into a high-precision rough mountain surface. Next, 3D modeling software is used to convert the detailed mesh model of the target area into a 3D solid. Based on this, Rhino is used to smooth the terrain boundaries and wind field plane, constructing a long-distance flatland fitting transition layer around the mountainous terrain to establish a 3D wind field model. By establishing this extended computational domain model, the blocking effect of the flow field boundaries is eliminated, ensuring that the inlet airflow reaches a fully developed and stable state before entering complex terrain.

[0028] The wind field walls and top surface were constructed, and a flat ground buffer transition layer was set at the outer edge of the mountain surface. This ensured that the airflow process through the mountain had better stability during the wind field simulation, and the simulation results were more in line with the actual working conditions.

[0029] In one implementation example, the annual wind direction probability distribution of the analysis area is also obtained, which is then used to set the wind direction angle of the wind field. Based on the wind direction probability distribution, it is determined from which direction the wind blows across the terrain. The constructed three-dimensional wind field model is imported into the wind field preprocessing software to accurately define the fluid computation domain and the initial boundary condition surface. Subsequently, the model is imported into the fluid dynamics calculation software for mesh discretization to generate a high-quality computational mesh adapted to the complex micro-topographic surface.

[0030] Specifically, the model is imported into SpaceClaim software to define boundary surface properties, and it is checked whether the wind field model is a solid element and the quality of its boundary connections is checked. The three-dimensional micro-topography wind field is meshed, and the mesh in the area that fits the mountain needs to be further refined to ensure the accuracy of the output; The meshed wind field is imported into Fluent software for preprocessing and calculation. The wind turbulence model, inlet wind speed, outlet wind pressure, and wall properties are set. Once the calculation is complete, a wind speed contour map influenced by the terrain is output.

[0031] An accurate turbulence model is selected as the core solver, and the ground boundary condition is set as a no-slip wall, while the boundaries of the remaining computational domain are set as symmetry planes. Fluid dynamics numerical simulation calculations are performed, specifically using fluid dynamics simulation software such as Fluen in ANSYS software, to accurately capture the airflow separation, recirculation, and eddy response processes under the micro-topography of mountainous areas, and output high-precision three-dimensional wind field simulation results.

[0032] In one implementation example, regarding step S2, a graphical interactive interface is provided to output high-precision three-dimensional wind field simulation results. The interface receives the CAD geometry file path, automatically generates a Fluent batch processing command stream based on the user-input mesh parameters, boundary conditions, and solution parameters, and drives the solver to calculate the terrain flow field data.

[0033] Specifically, during the solution process, the system receives user-input parameters, such as mesh size and wind speed, through an automated control script, and automatically generates a batch command stream. An accurate turbulence model is selected as the solver, the ground boundary condition is set to a no-slip wall, and the boundaries of the remaining computational domain are set to symmetry planes. The CFD solver is driven to calculate the separation and eddy response process of airflow under micro-topography, and outputs an unstructured flow field result file as a three-dimensional wind field simulation result, including a wind speed contour map.

[0034] Data cleaning and mapping: Parse the unstructured result file output by the solver, perform format conversion and spatial point downsampling, and construct a discretized terrain model containing three-dimensional coordinates and environmental cost values. This model is a three-dimensional point cloud map, with each point having a wind speed data.

[0035] In one implementation example, see Appendix Figure 2 As shown, step S3 includes the following steps: Heuristic route selection calculation: Based on the set start and end distances, azimuth angles and avoidance strategies, a heuristic algorithm with state memory is used to search for multiple optimal paths in three-dimensional space that satisfy geometric constraints and minimize the integral of environmental impact.

[0036] Regarding step S4, the 3D interactive visualization uses Matplotlib to draw a 3D terrain scatter plot and path connection, dynamically marks the starting point (S) and the ending point (E), and provides functions such as click to zoom in, relative coordinate transformation, and text output of detailed path parameters (coordinates, feature values).

[0037] In one implementation example, a discrete space heuristic search algorithm based on a priority queue is established, and line selection constraints are set, including the target straight-line distance, distance tolerance, target direction angle, angle tolerance, and minimum distance between path points.

[0038] The algorithm utilizes a min-heap data structure to manage the state of the nodes in the 3D mesh to be expanded. During the search process, the system introduces strict geometric topological constraints as heuristic pruning rules to ensure that the generated transmission line paths conform to engineering specifications: on the one hand, distance tolerance constraints are set, allowing the algorithm to search only adjacent nodes whose Euclidean distance is within a preset range, thereby effectively controlling the span between adjacent towers; on the other hand, direction angle constraints are applied, by calculating the extension azimuth angle of candidate nodes relative to the current path segment, only nodes located within the fan-shaped area of ​​the target direction are retained, and invalid paths with backtracking or large-angle turns are excluded. At the same time, by setting a minimum safe distance parameter between points, redundant spatial points that are too close to existing path nodes are filtered out in real time during the search.

[0039] The path cost function is defined as a linear integral of the physical length of the path segment and environmental characteristic values ​​(such as wind speed and turbulence intensity), and a multi-path iterative search strategy with state memory is adopted. Using a min-heap structure for iterative search, multiple discrete path point sequences with minimum cumulative cost are calculated while avoiding constraints on already selected path nodes. Specifically, the system uses numerical integration to calculate the cumulative environmental risk cost of each path segment, and leverages the min-heap property to prioritize expanding the path branch with the lowest current total cost until a complete path sequence is generated.

[0040] After successfully finding the first optimal path, the algorithm automatically activates the spatial avoidance mechanism, marking all nodes on the path and their surrounding neighborhoods as an avoidance set. In subsequent iterations to find the second and more alternative paths, the search process is forced to avoid these high-risk overlapping areas, thereby automatically generating multiple independent path sequences in three-dimensional space that do not overlap and have the lowest cumulative wind disaster risk, providing designers with diversified route selection schemes that take into account both safety and economy.

[0041] A route selection evaluation module based on 3D interactive technology is constructed to visualize and analyze multiple optimal paths calculated. The paths are ranked based on the integral of wind speed characteristic values ​​(i.e., the cost function) of the three paths. A 3D terrain scatter plot cloud map with environmental feature color mapping is drawn using a rendering engine, and the planned paths are displayed as highlighted lines. The module supports interactive viewing of the relative coordinate details of specific paths and the cumulative cost value obtained through the objective function, intuitively presenting the spatial relationship between the route corridor and high-wind-speed areas.

[0042] Specifically, a 3D graphics engine is used to draw a scatter plot of terrain, and the magnitude of the wind speed terrain coefficient is represented by color mapping; the calculated optimal path is overlaid on the terrain plot as a line, and the start and end points are dynamically marked; in response to user interaction, the relative coordinate details of the specific path and the text output of the accumulated environmental cost value are provided.

[0043] Furthermore, such as Figure 2 As shown, the technical solution of this embodiment is implemented with reference to the following content: Step 1: Defining the objective function and discretizing the model for line selection.

[0044] (1) Construct a mathematical representation of the transmission line and assume the transmission line path L Consists of a series of discrete tower site sequences P= {p 1 ,p 2 ,…,p n } Composition, in which p i =(x i ,y i ,z i ) For the first i Spatial coordinates of each tower location; For all data points, first, the data is filtered by constraints, and then the objective function is used to select the point with the minimum cost among the remaining points.

[0045] (2) Define the objective function for global line selection J , K(r) For spatial points r The wind speed topography coefficient is used to find a path that passes through an area with a wind speed topography coefficient. K(x,y,z) The line integral is minimized, thereby reducing the overall wind drift risk of the line. The calculation formula is as follows:

[0046] (3) To adapt to computer numerical solutions, the above continuous integral formula is discretized into a path segment accumulation form. In the code implementation, the integral value is approximated by interpolating and sampling the path segments between adjacent towers:

[0047] in: N The number of path points. p i ，p i+1 The coordinates of two adjacent tower locations. K(q i,k） For the first i Within each gear range k Wind speed topography coefficient at each interpolated sampling point q i,k For the first i Within the first gear range k One interpolation sampling point, Δl i,k Let be the infinitesimal length between sampling points. The algorithm uses a min-heap structure to manage nodes to be expanded, prioritizing the expansion of the current accumulated cost. J The smallest path branch.

[0048] Step 2: Heuristic Search Strategy Based on Geometric Constraints (4) A global heuristic search strategy based on a priority queue is adopted. The algorithm first uses Euclidean formulas and the square root of the sum of squares of the differences in three-dimensional point coordinates to calculate the global distance matrix of the wind field point cloud to construct the topological foundation, and adds the starting nodes that meet the conditions to the initialization sequence. During the iteration process, based on Dijkstra's idea, the current cumulative wind field effect integral is expanded first. J The smallest path branch; (5) In expanding the next tower position p i+1 At the same time, strict spacing constraints are imposed. The selected tower sites must meet the tower site quantity limit. N Furthermore, the span between adjacent transmission towers must be controlled within the design optimal span. L opt Tolerance δ L Within the range:

[0049] in,‖ ‖ represents the three-dimensional Euclidean distance. L opt To design the optimal gear ratio, δ L This refers to the allowable error in the gear spacing.

[0050] (6) Introduce route corridor direction angle constraints to prevent path looping or significant deviation. By calculating the angle between the candidate line segment and the coordinate axis, ensure that the route always follows the predetermined corridor direction. θ target extend:

[0051] in, δ θ This is the azimuth tolerance value. θ target The azimuth angle of the target route corridor.

[0052] Step 3: Multipath Mutual Exclusion Mechanism and Security Assessment (7) To avoid selecting a single route and provide multiple options for comparison, the algorithm designs a multi-path mutual exclusion generation mechanism: after finding the first optimal path, a spatial avoidance mechanism is activated, marking all nodes on that path and their surrounding neighborhoods as an avoidance set. Continuous output within the same region... M Find the optimal path and force subsequent paths to avoid the already generated path area; (8) For the first m Apply safe distance constraints to the newly generated path nodes to ensure safety. p (m） Compared to the past m-1 All nodes in the selected path p (k) Maintain minimum safe distance D min This generates spatially non-overlapping alternative paths, preventing overlapping of paths.

[0053] In the formula, p j (m) The current search term is m The first path j 1 node p l (k) This indicates that a path already exists. k The l One node; (9) Through simultaneous iterative stacking of multiple lines and constrained priority search, the algorithm finally outputs an optimal list containing multiple discrete paths. Each path has been verified by multiple geometric filters with specific span ranges, line turning directions, and safe avoidance distances to ensure convergence to the transmission line scheme that meets engineering requirements and has the lowest global wind disaster risk.

[0054] This invention constructs an automated workflow integrating CFD simulation-driven and intelligent route selection. It achieves headless invocation and batch processing control of ANSYS Fluent through Python scripts, encapsulating the cumbersome processes of geometry import, mesh generation, boundary setting, and solution into a standardized workflow. This not only significantly lowers the technical barrier for power transmission designers using fluid dynamics software but also effectively avoids parameter setting errors caused by manual operation, significantly improving the efficiency and consistency of wind field data acquisition in complex mountainous areas.

[0055] This invention employs a global multi-constraint heuristic route selection strategy, innovatively integrating path length, turning angle constraints, and wind field environmental costs (wind speed / turbulence intensity integral) into a unified evaluation system. Unlike traditional geometric shortest path planning, this method can proactively identify and avoid high-velocity, highly turbulent "wind gap" areas during the search process, reducing the risk of future transmission line galloping, wind-induced flashover, and other disasters from the planning stage, thereby improving the inherent safety level of power grid operation.

[0056] The path search algorithm proposed in this invention features a "avoidance mechanism" that generates multiple alternative paths. After calculating the first optimal path, it continuously searches for multiple non-overlapping alternative paths that meet engineering constraints through state memory and spatial avoidance strategies. This design fully considers the potential difficulties in land acquisition or geological hazards in actual engineering projects, providing designers with flexible and diverse route planning schemes and avoiding the dilemma that a single mathematically optimal solution cannot be implemented in practical engineering.

[0057] This invention designs a dedicated data cleaning and downsampling module that can efficiently process massive amounts of unstructured node data from CFD calculations. While preserving key terrain features and flow field gradients, it significantly compresses the data size, solving the problems of excessive computation and slow convergence speed in alignment algorithms under large-scale complex terrain, and achieving the best balance between engineering accuracy and computational efficiency.

[0058] This invention develops a route selection post-processing analysis platform based on 3D interactive technology, overcoming the limitations of traditional 2D contour maps in representing complex spatial flow fields. The system supports direct overlay and display of planned paths on 3D terrain cloud maps and provides relative coordinate transformation and local detail magnification functions, enabling designers to intuitively view the wind speed distribution at each tower location, achieving a leap from "data calculation" to "visualized decision-making".

[0059] A 765kV transmission line in a high-altitude mountainous area serves as the basis for a refined construction method for simulating wind fields in mountainous micro-topography.

[0060] Step 1: Acquisition of high-resolution geographic digital elevation data and establishment of 3D wind field First, the ASTER GDEM 30M resolution digital elevation data for the case area was obtained, such as... Figure 3 As shown. To ensure the accuracy of the results and to improve the computational speed and reduce costs, the region is divided into nine terrain subdomains, as follows. Figure 4 As shown.

[0061] Import the data into Global Mapper to extract terrain elevation data and contour lines, and then use Rhino to further process it into terrain surfaces. Set a smooth transition surface around the terrain to create a three-dimensional closed wind field model.

[0062] Step 2: Setting wind field boundary conditions and mesh generation Obtain the annual average wind direction probability distribution for the case area from 2004 to 2024, such as... Figure 5 As shown in the figure, the wind direction in the case area is predominantly east-west, so the east-west facing wall of the terrain wind field is set as the airflow inlet, and the southwest facing wall is set as the airflow outlet.

[0063] The three-dimensional micro-topographic wind field is meshed, the unit length of the regional wind field mesh is set to 50m, the surface of the mountain mesh is densified, and the mesh density varies linearly with the wind field height, thereby reducing unnecessary calculations. The meshed wind field was imported into Fluent software for preprocessing and calculation. The turbulence model was selected as SSTk-omega, the airflow inlet was set to Velocity-inlet, and the airflow outlet was set to Pressure-outlet. The ground boundary condition was set to a no-slip wall, and the remaining computational domain boundaries were set to symmetry planes. After calculation, the wind speed contour map influenced by terrain was obtained, as shown below. Figure 6 As shown.

[0064] Furthermore, based on the aforementioned 765kV transmission line in the plateau and mountainous areas as a supporting project, the objects used for the mountain transmission line selection method based on CFD simulation and heuristic search specifically include: Step (1): Defining the objective function for line selection and discretization modeling.

[0065] This embodiment uses a 765kV transmission line project in a high-altitude mountainous area as an example. Employing the refined construction method for mountain micro-topography wind field simulation described in Embodiment 1, high-resolution digital elevation data acquisition, 3D solid modeling, and CFD fluid dynamics numerical calculations were completed for the target area. This directly constructed a discretized wind field model containing accurate 3D coordinates and wind speed-topographic coefficients, transforming the continuous physical field into computer-processable discrete data nodes, providing a standardized data foundation for subsequent heuristic route selection.

[0066] Step (2): Heuristic search strategy based on geometric constraints Based on the discretized wind field model established above, route selection analysis was conducted for nine typical terrain areas. The set engineering parameters were: 10 tower sites, 500m standard span, and 45° target route. The wind field boundary conditions were set as follows: airflow from infinity at the X and Y coordinates, perpendicular to the preset route direction, flows towards the origin of the coordinates.

[0067] Line selection results are as follows Figure 7 , 8As shown, the optimized transmission routes exhibit significant risk avoidance characteristics. The lines are mainly distributed on the leeward slopes of mountains, tending to bypass peaks, saddles, or gently sloping areas, thus effectively avoiding high-wind-speed risk zones such as sharp peaks. Overall, all lines are located in areas with low wind speed topographic coefficients, and no lines cross high-value areas, further demonstrating the effectiveness of the heuristic route selection algorithm incorporating geometric constraints.

[0068] Step (3): Multipath Mutual Exclusion Mechanism and Security Assessment To provide diverse alternatives, the algorithm employs a spatial avoidance mechanism, ensuring that the three generated paths maintain a clear spatial interval and guaranteeing the differentiation of the options. The path shape is represented as a broken line formed by connecting tower locations, constrained by a preset directional angle. This shape conforms to the physical characteristics of the "tower-line" transmission line system while avoiding the line looping problem caused by excessive pursuit of mathematical optimality.

[0069] The algorithm strictly follows terrain elevation constraints and safety assessment standards, ensuring that all tower locations are effectively aligned with the ground surface and that the span can adapt to engineering requirements such as crossing gullies and varying slopes, thus verifying the applicability of the mechanism in complex terrain.

[0070] In one implementation example, let's look at the appendix. Figure 2 As shown, the specific steps include: Step (1) Input Data and Preprocessing: The input data includes 3D point cloud data, wind field feature data, and route selection constraints. The 3D point cloud data includes the spatial coordinates (x, y, z) of each discrete node. The wind field feature data includes the environmental feature values ​​corresponding to each node, which can be one or more of wind speed-terrain coefficient, wind speed, and turbulence intensity. The route selection constraints include at least the target span value, span tolerance, target direction angle, angle tolerance, number of path nodes, and minimum safe distance between paths. When preprocessing the above input data, the wind field solution results are first cleaned, format converted, downsampled, and discretized to construct a discretized terrain model containing "node coordinates + environmental cost value," which serves as the basic dataset for subsequent path search.

[0071] Step (2) Calculate the global used point set and the full distance matrix. Based on the discretized terrain model obtained in step (1), extract all candidate discrete nodes to form the complete set of search nodes. At the same time, initialize the global used point set, which is initially empty, to record nodes that have been occupied or need to be avoided in the previously generated paths. Then, calculate the three-dimensional Euclidean distance between any two points in the complete set of nodes to construct the full distance matrix N×N. This distance matrix is ​​used to quickly determine whether the candidate nodes meet the gap constraint and serves as the basis for path topology search. The global used point set comes from the multi-path iterative search strategy. The set is empty when the first path is searched. After each optimal path is obtained, the nodes of that path and their neighborhoods are added to the set to support spatial avoidance of subsequent paths.

[0072] Step (3) For each path, initialize the search, set the outer loop, and perform the search sequentially for each path m=1 to M; for the current m-th path, initialize the priority queue PQ, and push the starting node or the state that meets the starting condition into the priority queue; at the same time, initialize the cumulative cost, visited state, and current path sequence of the path. The priority queue adopts a min-heap structure, according to the cumulative cost of the path. J Sort the paths from smallest to largest to ensure that the path with the lowest total cost is expanded first each time.

[0073] Step (4) Pop the current optimal state and determine the target. Pop the current path state with the minimum cumulative cost from the priority queue as the current optimal expansion branch. Determine whether the state meets the target condition, which is that the number of path nodes reaches the preset value N. If the target is met, store the current path as a valid optimal path and mark the nodes on the path as global "used" nodes. If the target is not met, continue to perform candidate node search and filtering.

[0074] Step (5) involves finding candidate points by traversing all unused nodes in the current state and combining the full distance matrix established in step (2) to find a set of candidate nodes connected to the current end node. This candidate point search does not directly expand all nodes, but instead uses the distance matrix to quickly filter out points that obviously do not meet the connection conditions, thereby improving search efficiency.

[0075] Step (6) Filter 1: Determine if a node is not used globally. First, determine if the candidate node belongs to the set of globally used points or if it falls within the neighborhood avoidance range of the preceding path node. If the candidate node has been used or belongs to the avoidance area, discard the candidate point directly. Only globally unused candidate nodes are retained for the next step of filtering. This step corresponds to the multi-path mutual exclusion mechanism and is the basis for generating multiple non-overlapping candidate paths.

[0076] Step (7) Filter 2: Span length constraint determination. For candidate nodes that have passed through filter 1, further determine whether the three-dimensional distance between them and the end node of the current path meets the span requirement, that is, whether it falls within the design optimal span. L opt and its tolerance δL Within the defined range, if the span constraint is not met, the candidate point is discarded; if it is met, it is retained and proceeds to the next step. This step is used to ensure that the distance between adjacent tower sites complies with the transmission line engineering specifications.

[0077] Step (8) Filter 3: Corridor direction angle constraint determination. For candidate nodes that meet the distance requirements, it is further determined whether the angle between them and the current path direction or the target corridor direction meets the preset angle constraint, that is, whether the extension direction of the candidate line segment is within the fan-shaped range allowed by the target direction angle θtarget and its tolerance δθ. If the direction angle requirement is not met, the candidate point is discarded; if it is met, the candidate point is regarded as a valid extension node. This step is used to prevent path reversal, looping, or large-angle deflection.

[0078] Step (9) Calculate the step cost and update the total cost. For candidate nodes that pass all filters, calculate the step cost Δ from the current node to that candidate node. J The step cost is calculated using the line integral discrete approximation of the path segment length and environmental feature values. It can be obtained by interpolating and sampling the path segment between two nodes and accumulating the environmental costs of each micro-segment. Then, this step cost is added to the cumulative cost of the current path to obtain the total cumulative cost of the new path state.

[0079] Step (10) Enqueue the new path state and push the updated new path state into the priority queue PQ, waiting for further expansion; repeat the loop process of "pop the optimal state - find candidate points - multiple filtering - calculate cost - push into the queue" until the current m-th path search is completed.

[0080] Step (11) Path storage and global avoidance update: When a path meets the target conditions, it is stored as the current optimal path, and all nodes on the path and their surrounding neighborhoods are marked as globally "used" or avoided nodes and written into the global used point set; then the outer loop search of the next path begins. Through this state memory and spatial avoidance mechanism, multiple non-overlapping candidate paths can be continuously generated.

[0081] Step (12) Output the final optimal path list. After searching for M paths, output the final optimal path list. The result is a sequence of multiple discrete path points that satisfy the constraints of the number of nodes, the distance between the paths, the direction angle, and the minimum safe distance between paths. It can be further used for three-dimensional visualization and route selection evaluation.

[0082] The core technologies of this embodiment include heuristic route selection calculation: based on the set start and end distances, azimuth angles, and avoidance strategies, a heuristic algorithm with state memory is used to search for multiple optimal paths in three-dimensional space that satisfy geometric constraints and minimize the integral of environmental influence. The three-dimensional interactive visualization uses Matplotlib to draw a three-dimensional terrain scatter plot and path connections, dynamically marking the start point (S) and end point (E), and providing functions such as click-to-zoom, relative coordinate transformation, and text output of detailed path parameters (coordinates, feature values).

[0083] Example 2 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0084] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0085] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0086] Example 4 The purpose of this embodiment is to provide a globally constrained heuristic system for optimizing the routing of transmission lines in complex mountainous areas, including: The wind speed cloud map acquisition module is configured to: acquire and process the geospatial elevation data of the target area, and output the wind speed cloud map affected by the terrain. The discretized terrain model construction module is configured to: calculate terrain flow field data based on wind speed cloud maps affected by terrain, and construct a discretized terrain model containing three-dimensional coordinates and environmental cost values; The discrete path point sequence calculation module is configured to: set transmission line selection constraints, including target straight-line distance, distance tolerance, target direction angle, angle tolerance, and minimum distance between path points; The path cost function is defined as the integral of the path segment length and the environmental feature value. Based on the discretized terrain model, the min-heap structure is used for iterative search to calculate multiple discrete path point sequences with the minimum cumulative cost while avoiding the constraints of the selected path nodes. The route selection module is configured to: perform 3D visualization rendering of multiple calculated optimal paths, overlay terrain cloud map display, conduct route selection evaluation of transmission lines, and obtain the route selection results of the evaluated transmission lines.

[0087] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions involved in any of the above embodiments.

[0088] The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0089] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0090] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A global constraint heuristic method for optimization of the route of a complex mountainous power transmission line, characterized in that, include: Acquire and process geospatial elevation data of the target area, and output wind speed cloud map affected by terrain. Based on wind speed cloud maps influenced by terrain, terrain flow field data are calculated, and a discretized terrain model containing three-dimensional coordinates and environmental cost values ​​is constructed. Set the route selection constraints for transmission lines, including target straight distance, distance tolerance, target direction angle, angle tolerance, and minimum distance between path points; The path cost function is defined as the integral of the path segment length and the environmental feature value. Based on the discretized terrain model, the min-heap structure is used for iterative search to calculate multiple discrete path point sequences with the minimum cumulative cost while avoiding the constraints of the selected path nodes. The calculated optimal paths are evaluated for transmission line selection to obtain the selected transmission line results.

2. The globally constrained heuristic method for optimizing the routing of transmission lines in complex mountainous areas as described in claim 1, characterized in that, The geospatial elevation data of the target area is processed to construct a detailed grid model of the target area that reflects the real landform features; The detailed mesh model of the target area is materialized, and a long-distance flatland fitting transition layer is constructed around the mountainous terrain to establish a three-dimensional wind field model. Define the computational domain and initial boundary conditions for the three-dimensional wind field model, and complete the mesh generation; A turbulence model was selected as the solver. The ground was set as a no-slip wall and the other walls were set as symmetry planes. Fluid dynamics numerical simulation was performed to capture the airflow separation and eddy response process in mountainous terrain and output wind speed cloud map under the influence of terrain.

3. The globally constrained heuristic method for optimizing the routing of transmission lines in complex mountainous areas as described in claim 1, characterized in that, The discrete space heuristic search algorithm based on priority queues introduces strict geometric topological constraints as heuristic pruning rules during the search process when setting transmission line route selection constraints, so as to ensure that the generated transmission line paths conform to engineering specifications.

4. The globally constrained heuristic method for optimizing the routing of transmission lines in complex mountainous areas as described in claim 1, characterized in that, When calculating the sequence of discrete path points with the minimum cumulative cost, the following steps are taken: After successfully finding the first optimal path, the spatial avoidance mechanism is automatically activated, marking all nodes on the path and their surrounding neighborhoods as the avoidance set. In subsequent iterations to find the second and more alternative paths, the search process is forced to avoid these high-risk overlapping areas, thereby automatically generating multiple independent path sequences in three-dimensional space that do not overlap and have the lowest cumulative wind disaster risk.

5. The globally constrained heuristic method for optimizing the routing of transmission lines in complex mountainous areas as described in claim 1, characterized in that, The transmission line selection evaluation is performed on the calculated multiple optimal paths, including: The rendering engine is used to draw a 3D terrain scatter cloud map containing environmental feature color mapping, and the planned path is overlaid as a highlighted line to intuitively present the spatial relationship between the route corridor and high wind speed areas.

6. The globally constrained heuristic method for optimizing the routing of transmission lines in complex mountainous areas as described in claim 1, characterized in that it also... include: A mathematical representation of the transmission line is constructed, assuming that the transmission line path consists of a series of discrete tower location sequences; Define a global path selection objective function that aims to find a path that minimizes the line integral of the wind speed and topographic coefficients of the regions it traverses.

7. A globally constrained heuristic system for optimizing the routing of transmission lines in complex mountainous areas, characterized by: include: The wind speed cloud map acquisition module is configured to: acquire and process the geospatial elevation data of the target area, and output the wind speed cloud map affected by the terrain. The discretized terrain model construction module is configured to: calculate terrain flow field data based on wind speed cloud maps affected by terrain, and construct a discretized terrain model containing three-dimensional coordinates and environmental cost values; The discrete path point sequence calculation module is configured to: set transmission line selection constraints, including target straight-line distance, distance tolerance, target direction angle, angle tolerance, and minimum distance between path points; The path cost function is defined as the integral of the path segment length and the environmental feature value. Based on the discretized terrain model, the min-heap structure is used for iterative search to calculate multiple discrete path point sequences with the minimum cumulative cost while avoiding the constraints of the selected path nodes. The route selection module is configured to: perform 3D visualization rendering of multiple calculated optimal paths, overlay terrain cloud map display, conduct route selection evaluation of transmission lines, and obtain the route selection results of the evaluated transmission lines.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the steps of the method described in any one of claims 1-6 above.

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