Wind and light power station area road planning method and system based on terrain analysis

Abstract

The invention provides a wind and light power station area road planning method and system based on terrain analysis. According to the method, digital elevation model data and vehicle turning radius parameters of a wind and light power station area are processed, layered reconstruction and optimization are carried out, optimized sampling data are generated, and a road network is constructed by using the optimized sampling data; path optimization is further carried out based on the road network data to obtain a path candidate set, and final road path data suitable for the wind and light power station area is generated through an electric field layout and equipment adaptation algorithm, construction adaptability processing and a safe and smooth correction means, so that the road planning precision is improved, the construction adaptability is enhanced, and the construction efficiency is improved. The actual requirements under different topographic conditions are met, the problem that local environment characteristics and operation safety requirements cannot be reasonably integrated in traditional path planning is avoided, and the feasibility of wind and light power station road planning is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of energy electric field road design and planning, and particularly relates to a method and system for planning the roads in a wind-solar power station area based on terrain analysis. Background Art

[0002] As an important form of renewable energy power generation, the scale of wind-solar power stations is expanding day by day, and the road planning in the power station area has become a key link in project implementation. Roads not only need to provide equipment transportation channels, but also are involved in subsequent construction, equipment maintenance, and daily safety operations. The terrain conditions in the wind-solar power station area are usually complex, such as hills, mountains, undulations, etc. These factors pose higher requirements for road design. A highly adaptable and precise road planning is crucial for ensuring the efficient operation of the power station and reducing construction and maintenance costs. Therefore, a reasonable road planning in the wind-solar power station area can not only effectively support equipment transportation and construction, but also improve the convenience of equipment installation and maintenance, and ensure the safety and smoothness of traffic during the operation of the power station.

[0003] Most of the existing road planning methods for wind-solar power station areas are based on traditional terrain data analysis and path optimization algorithms, and usually adopt path planning techniques based on digital elevation models (DEMs). These methods can usually achieve certain effects under relatively simple terrain conditions, but under complex terrain and changing environmental conditions, the existing methods fail to fully consider the special requirements of wind-solar power station areas. For example, the existing technologies often ignore the actual requirements during construction and maintenance, and fail to consider the restricted conditions for equipment passage such as turning radius, slope, and road surface width during the planning stage, nor can they effectively integrate local environmental features with the safety requirements of electric field operations. Traditional path optimization methods are often limited to single terrain features or simple constraint conditions, and fail to comprehensively consider the actual construction environment and equipment passage requirements.

[0004] Therefore, the path planning schemes of the existing technologies fail to meet the complex terrain features, construction requirements, and equipment passage and safety requirements of wind-solar power station areas. The accuracy of path planning and construction adaptability are poor, and it is difficult to effectively meet the actual requirements under different terrain conditions. At the same time, during the path optimization process of the existing technologies, the local environmental features and the safety requirements of electric field operations are not reasonably integrated, so that the planned paths not only have adaptability problems during construction, but also face great challenges in later equipment installation and daily maintenance. Summary of the Invention

[0005] In view of the above actual situation, the present application proposes a method and system for planning roads in a wind-solar power station area based on terrain analysis, so as to solve the problems that the existing road planning methods fail to fully consider the complex terrain characteristics, construction and maintenance requirements, and equipment passage and safety requirements of the wind-solar power station area, resulting in insufficient accuracy of path planning, poor construction adaptability, difficulty in effectively adapting to the actual requirements under different terrain conditions, and failure to reasonably integrate local environmental characteristics and operation safety requirements in the process of path optimization.

[0006] A method for planning roads in a wind-solar power station area based on terrain analysis, the method comprising the following steps: S1, obtaining data to be processed, the data to be processed including digital elevation model data of the wind-solar power station area, vehicle turning radius parameters, and facility distribution data, the digital elevation model data including elevation data and DEM pixel size, and the facility distribution data being the location and distribution information of all relevant equipment and infrastructure within the power station; S2, performing hierarchical reconstruction and optimization processing on the data to be processed to generate optimized sampling data; S3, constructing a road network based on the optimized sampling data to obtain road network data; S4, performing path screening and constraint adjustment based on the road network data to obtain a path candidate set; S5, generating power station road path data applicable to the wind-solar power station area based on the path candidate set in combination with the facility distribution data.

[0007] Further, the S2 step includes the following sub-steps: S201, performing multi-scale decomposition on the elevation data based on gradient decomposition, extracting terrain features at different scales, setting an adaptive sampling step according to each terrain feature, comparing the sampling step with the DEM pixel size, and when the sampling step is greater than the pixel, performing a sampling operation to generate terrain scale data; S202, defining a local path sensitive area based on the terrain scale data, vehicle turning radius, and slope information, and optimizing the sampling density of the sensitive area through weighted convolution to generate optimized sampling data.

[0008] Further, the S3 step includes the following sub-steps: S301, based on the optimized sampling data, using a weighted Voronoi diagram partitioning algorithm to perform spatial segmentation on the terrain of the power station area to obtain each terrain Voronoi unit; S302, combining the gradient information and terrain resistance coefficient of each Voronoi unit, and using a multi-level weighted connection method to connect paths between adjacent units to generate a preliminary road network candidate map; In S303, based on the topological structure of the preliminary road network candidate graph and combined with the minimum spanning tree algorithm in graph theory, the road network is optimized and generated to obtain the final road network data.

[0009] Furthermore, the S4 step includes the following sub-steps: In S401, based on the road network data, the shortest path from the starting point to the ending point is calculated through heuristic search to generate a preliminary path candidate set; In S402, the edge weights are adjusted based on the local environmental characteristics, and the preliminary path candidate set is optimized by using the edge weight adjustment model to generate an adjusted path weight matrix; In S403, based on the path weight matrix and combined with the Lagrange multiplier method, the paths are constrained and optimized to eliminate the paths that do not meet the constraint conditions and generate a path candidate set.

[0010] Furthermore, the S5 step includes the following sub-steps: In S501, based on the path candidate set, the electric field layout and equipment adaptation algorithm is used to correct the path structure to ensure that the path avoids key equipment and infrastructure and optimize the path layout to generate a corrected path structure; In S502, combined with the construction and maintenance requirements in the electric field, the corrected path is processed for construction adaptability to generate construction adaptability path data; In S503, based on the construction adaptability path data and combined with the electric field operation and safety requirements, path smoothing and correction processing are carried out to generate the final electric field road path data.

[0011] Furthermore, the S303 step includes the following sub-steps: The minimum spanning tree algorithm is carried out through the following steps: In S3031, all the edges in the preliminary road network candidate graph are sorted to generate edge weight sequence data; In S3032, based on the edge weight sequence data, the edges are selected one by one by using the Kruskal algorithm or the Prim algorithm, the edge with the smallest weight is selected, and the Voronoi cells are gradually connected until the spanning tree contains all the cells to generate the minimum spanning tree data; In S3033, based on the minimum spanning tree data, loop generation is avoided to ensure that the selected path can connect all the cells and form a tree structure to generate the road network data, and the road network data contains a connected path set of all Voronoi cells and with the smallest weight.

[0012] Furthermore, the heuristic search calculation in S401 includes the shortest path search of the Dijkstra algorithm and the heuristic estimation of the A algorithm, and the heuristic search calculation is carried out according to the following steps: S4011, Initialize the path cost of the nodes. Set the path cost of the starting point to 0, and the path costs of other nodes to infinity; S4012, Calculate the total cost of each node to be processed, including the actual cost and the heuristic estimate, and select the node with the minimum cost for expansion; S4013, Update the path costs of adjacent nodes according to the edge weights and the heuristic estimate, continuously expand the nodes until the end node is expanded or all nodes are processed; S4014, Generate the shortest path from the starting point to the end point, and add the path to the preliminary path candidate set.

[0013] Furthermore, the constraint conditions in S403 are the minimum road surface width and the maximum slope limit.

[0014] Furthermore, the electric field layout and device adaptation algorithm in S501 includes path obstacle avoidance processing and path optimization processing; the construction adaptability processing in S502 includes path width adjustment, slope adjustment, turning radius optimization, and path flatness improvement processing; the path smoothing and correction processing in S503 includes path curve smoothing, slope flattening, turning radius correction, and safety area avoidance processing.

[0015] In addition, the present application also discloses a scenic power station site road planning system based on terrain analysis, characterized in that the system includes: An acquisition unit for acquiring data to be processed, the data to be processed including digital elevation model data of the scenic power station site, vehicle turning radius parameters, and facility distribution data, the digital elevation model data including elevation data and DEM pixel size, and the facility distribution data being the position and distribution information of all relevant devices and infrastructure in the electric field; An optimization sampling unit for performing hierarchical reconstruction and optimization processing on the data to be processed to generate optimized sampling data; A road network construction unit for constructing a road network based on the optimized sampling data to obtain road network data; A path screening unit for performing path screening and constraint adjustment based on the road network data to obtain a path candidate set; A power station path generation unit for generating power station road path data applicable to the scenic power station site based on the path candidate set in combination with the facility distribution data.

[0016] A method and system for planning roads in a wind-solar power station area based on terrain analysis proposed in this application achieve path optimization for the complex terrain and actual construction requirements of the wind-solar power station area by means of multi-scale terrain analysis and optimization processing, combined with local path-sensitive areas and environmental characteristics, ensuring that the generated road paths can adapt to changes in different terrains, equipment passage requirements, and actual conditions of construction and maintenance, and ultimately providing a road planning scheme with high precision, strong feasibility, and meeting safety requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of a method for planning roads in a wind-solar power station area based on terrain analysis proposed in this application; Figure 2 It is a schematic flow chart of optimizing sampled data in a method for planning roads in a wind-solar power station area based on terrain analysis proposed in this application; Figure 3 It is a schematic flow chart of obtaining road network data in a method for planning roads in a wind-solar power station area based on terrain analysis proposed in this application Figure 4 It is a schematic flow chart of generating a candidate path set in a method for planning roads in a wind-solar power station area based on terrain analysis proposed in this application; Figure 5 It is a schematic flow chart of generating final electric field road path data in a method for planning roads in a wind-solar power station area based on terrain analysis proposed in this application; Figure 6 It is a schematic structural diagram of a system for planning roads in a wind-solar power station area based on terrain analysis provided in an embodiment of this application; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the simulation technical route in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0019] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0020] The features and performance of the present invention will be further described in detail below in conjunction with embodiments. Please refer to the attached Figure 1 As shown, a method for planning roads in a wind-solar power station area based on terrain analysis, the method includes the following steps: S1. Obtain the data to be processed, where the data to be processed includes digital elevation model data of the wind-solar power station site area, vehicle turning radius parameters, and facility distribution data. The digital elevation model data includes elevation data and DEM pixel size, and the facility distribution data is the location and distribution information of all relevant equipment and infrastructure within the power station. In some embodiments, obtaining the data to be processed is the primary step in road planning, involving the collection and integration of multiple data sources. Digital Elevation Model (DEM) data is the basis for obtaining the terrain information of the site area, and can provide necessary terrain data support for subsequent path planning. DEM data includes ground elevation information and pixel size. The former reflects the ground elevation values of different points within the site area, and the latter defines the spatial resolution of the data, that is, the actual area size of each pixel in reality.

[0021] In this embodiment, the obtained digital elevation model data is obtained through remote sensing technology or laser scanning technology (such as LiDAR). These technologies can accurately record the terrain undulations of the site area and generate elevation data with high precision. Specifically, the elevation data of the digital elevation model represents the height information of different positions on the ground. It not only helps to analyze the slope and aspect of the terrain, but also can provide detailed terrain features for path selection in subsequent steps. These elevation data are represented in the form of a grid, where each grid point corresponds to a certain spatial coordinate and the elevation of that point, forming an elevation map covering the entire site area.

[0022] In addition, the pixel size in the DEM data defines the actual geographical range represented by each elevation point within the site area, in meters. For example, a pixel size of 1 meter means that each elevation point represents an area of 1 square meter. The selection of the pixel size directly affects the spatial resolution of the data and the accuracy of subsequent calculations. In some embodiments, a smaller pixel size means that higher-precision terrain information can be obtained, but it may also lead to an increase in the data volume. Therefore, it is necessary to make a trade-off according to specific requirements when obtaining the data.

[0023] The vehicle turning radius parameter, as another input data in road planning, is mainly used to describe the turning radius limit of the roads within the wind-solar power station site area. The turning radius refers to the radius of the trajectory along which the wheels rotate when the vehicle turns. In some embodiments, considering the dimensions and driving requirements of different types of vehicles within the site area, the determination of the turning radius needs to be set according to the specific vehicle type. A smaller turning radius requires the road to have a larger curve radius to avoid derailment or inability to pass smoothly during the turning process of the vehicle. The turning radius data will affect the construction of the road network in the subsequent path planning process, especially when considering curve design and slope limitations.

[0024] By integrating digital elevation model data and vehicle turning radius parameters, this step provides accurate input information for subsequent data processing and path optimization. The acquisition and processing methods of these data provide a solid foundation for the implementation of the road planning in the wind-solar power plant area, ensuring the scientificity and feasibility of road planning under complex terrains and various driving conditions. In this embodiment, relying on high-precision remote sensing technology and reasonable vehicle parameter settings, it is possible to provide necessary terrain support and driving constraints for path planning, providing guarantee for achieving a reasonable road layout.

[0025] S2. Perform hierarchical reconstruction and optimization processing on the data to be processed to generate optimized sampling data; Specifically, please refer to the appendix Figure 2 As shown, this step includes the following sub-steps: S201. Based on gradient decomposition, perform multi-scale decomposition on the elevation data, extract terrain features at different scales, set adaptive sampling steps according to each terrain feature, compare the sampling step with the DEM pixel size, and when the sampling step is greater than the pixel, perform the sampling operation to generate terrain scale data; In some embodiments, gradient decomposition is used to extract terrain features at different scales from digital elevation model (DEM) data. During the gradient decomposition process, first, the elevation data is processed. By decoupling information at different scales, local and global features of the terrain can be identified and extracted. Gradient decomposition is based on the terrain surface change rate, that is, the gradient, which reflects the amplitude and direction of elevation change in space and is a core index in terrain analysis.

[0026] The gradient describes the rate of change of the surface relief. In this embodiment, given elevation data , where x and y respectively represent the horizontal and vertical coordinates in the geographic coordinate system, the gradient is expressed as: , where and are the partial derivatives of the elevation data in the x and y directions respectively, reflecting the slope and aspect of the terrain.

[0027] When performing multi-scale decomposition, wavelet transform or scale space method is used to decompose the elevation data layer by layer. In some embodiments, through multi-level processing of the scale space of the gradient data, terrain features at different scales can be extracted respectively. By processing the terrain gradient at each scale, different levels of terrain information can be obtained, including local terrain undulations and global terrain morphology.

[0028] In this embodiment, different selections of the scale factor are used to extract topographic features suitable for different analysis requirements through gradient decomposition. For example, a smaller scale factor can be used to carefully analyze local changes in a small area, such as the detection of overly steep slopes or obstacles; a larger scale factor focuses on the overall topographic trend over a large area to assist in macroscopic topographic recognition during path planning. Through this multi-scale decomposition, the appropriate scale can be adaptively selected according to different scenario requirements to accurately describe and model topographic data. Let be the topographic data at scale s, where s represents the scale factor. By performing scale decomposition on the gradient data, the gradient values at different scales obtained can be used for subsequent path planning and topographic analysis. The decomposition formula of the gradient data is expressed as: , where is the wavelet function corresponding to the scale factor s, represents the offset expression of the elevation data after the abscissa is offset by t positions. By performing convolution processing on the topographic data with this wavelet function, multi-scale topographic data is obtained.

[0029] Next, according to the topographic features extracted at different scales, an adaptive sampling step size is set. The selection of the sampling step size directly affects the accuracy of the data and the computational efficiency. In some embodiments, the sampling step size is adjusted according to the degree of change of the topographic features. In areas with large topographic changes, the sampling step size is relatively small to ensure that fine topographic changes can be captured; in areas with relatively gentle topographic changes, the sampling step size is large to reduce data redundancy and computational burden.

[0030] Let be the sampling step size at scale s, and the selection of is determined according to the change of the topographic gradient. Specifically, when the topographic change is relatively drastic, the gradient value is large, and a smaller sampling step size should be selected at this time; while in areas with relatively slow topographic changes, the gradient is small and the sampling step size is increased accordingly. The comparison between the sampling step size and the DEM pixel size determines the execution condition of the sampling operation. Let be the size of the DEM pixel. If the sampling step size is greater than the pixel size , then the sampling operation is performed. Specifically, when: , the sampling operation is executed, and the sampling points are added to the generated topographic scale data. This operation ensures sufficient sampling in areas with large topographic features and reduces unnecessary calculations in flat areas, thereby optimizing the distribution of the data.

[0031] The generated terrain scale data contains information about terrain features at different scales. This data not only reflects detailed changes in the terrain but also facilitates path planning and road network construction in subsequent steps. In this embodiment, this multi-scale decomposition and adaptive sampling process effectively improves the utilization efficiency of terrain data while ensuring the accuracy and rationality of sampling for different terrain features.

[0032] S202, defining a local path sensitive area based on terrain scale data, vehicle turning radius, and slope information, and optimizing the sampling density of the sensitive area through weighted convolution to generate optimized sampling data; In some embodiments, this step integrates terrain scale data, vehicle turning radius, and slope information to identify local areas that have a significant impact on path planning. These areas are referred to as path-sensitive areas. Path-sensitive areas primarily refer to areas with a significant impact on road planning in complex terrain or where vehicle movement is restricted, such as steep slopes and sharp turns. The sampling density of these areas needs to be appropriately adjusted based on their impact on path planning to ensure the rationality and feasibility of the path.

[0033] The definition of the local path sensitive area is based on the comprehensive consideration of terrain information, vehicle turning radius, and slope factors. is the gradient data at scale s, which represents the rate of change of terrain; let Slope information, indicating the magnitude of the ground slope. Calculated by the following formula: ,in and The x and y gradients of the terrain, respectively. A larger slope indicates a more dramatic change in the terrain, and thus a higher requirement for the path the vehicle must follow.

[0034] In addition, the vehicle turning radius r plays a decisive role in path planning. In this embodiment, the impact of the turning radius is reflected in the turning restrictions of the terrain area. The smaller the turning radius, the smaller the space the vehicle can turn in, requiring a more compact road curve design. In the site, it is necessary to combine the turning radius information with the slope and terrain characteristics to determine which areas are path-sensitive areas. The demarcation of path-sensitive areas is achieved through the following model: ,in Indicates at point The path sensitivity at are weight coefficients, which control the gradient data , slope information and turning radius Impact on path sensitivity. In some embodiments, the weight coefficient is dynamically adjusted according to specific requirements to ensure a higher sampling density in areas with drastic terrain changes and a lower sampling density in relatively flat areas. The higher the path sensitivity of an area, the greater its impact on road planning. Therefore, it is necessary to optimize the sampling density for processing.

[0035] Once the path-sensitive area is defined, it enters the stage of optimizing the sampling density through weighted convolution. Weighted convolution is a technique for weighted averaging in space, and its goal is to adjust the density of sampling points according to the path sensitivity. Let be the sampling density at the position. When using the weighted convolution operation, the sampling density is determined by the following formula: , where is the convolution kernel function, which controls the influence of the surrounding area on the current sampling point, is the path sensitivity value, which reflects the sensitivity of this position. Weighted convolution adjusts the sampling density of each position by weighting the sensitivity values of the neighborhood in the spatial domain. Through this operation, the sampling density of sensitive areas is optimized to ensure that there are more sampling points in high-sensitivity areas (such as steep slopes and sharp turns), while reducing sampling points in relatively gentle areas to reduce the computational load. In this embodiment, the weighted convolution kernel function uses a Gaussian function or other functions suitable for spatial weighting, and the specific form is as follows: , where is a parameter that controls the width of the convolution kernel. A smaller value indicates a smaller weighted area, while a larger value indicates a larger weighted area. In some embodiments, the shape and size of the convolution kernel can be adjusted according to terrain features and path planning requirements to achieve the best sampling effect.

[0036] After being processed by weighted convolution, the obtained sampling density will reflect the terrain changes and the actual needs of vehicle driving, and the generated optimized sampling data is the final set of sampling points. These sampling points will be used as basic data in the subsequent road network construction and path planning processes to ensure the accuracy and feasibility of path planning.

[0037] S3. Construct a road network based on the optimized sampling data to obtain road network data; Specifically, as shown in the attached Figure 3 , this step includes the following sub-steps: S301. Based on the optimized sampling data, use the weighted Voronoi diagram partitioning algorithm to spatially partition the field terrain to obtain each terrain Voronoi unit; In some embodiments, in this step, the optimized sampling data is used to perform spatial segmentation of the field terrain through the weighted Voronoi diagram partitioning algorithm. The weighted Voronoi diagram partitioning algorithm is a mathematical method for partitioning space based on a set of points. It generates polygonal regions around a given set of points (sampling data points) such that all points within each polygon are closer to the generating point of that polygon than to other generating points. In this embodiment, the optimized sampling data provides a series of sampling points related to the terrain features, serving as the seed points in the weighted Voronoi diagram for spatial partitioning to obtain different terrain Voronoi cells.

[0038] The construction of the weighted Voronoi diagram not only considers the distance relationship between points but also introduces additional weight information. In this embodiment, the complexity of the terrain is represented by the sensitivity of the sampling points, and the sensitivity value is the optimized sampling density in the previous step. Specifically, for a given sampling point and its corresponding weight , the spatial segmentation of the weighted Voronoi diagram is calculated according to the following formula: , where represents any spatial point, is the sampling point, represents the spatial distance, represents the weight of the sampling point . This formula indicates that the distance from each spatial point to the sampling point is determined not only by the Euclidean distance but also affected by the weight of the sampling point, so that regions with larger weights (such as places with greater terrain changes or path-sensitive areas) have stronger attraction.

[0039] The partitioning of the weighted Voronoi diagram is achieved through the following steps. First, a set of sampling point sets is defined, where each sampling point has a weight . Then, according to the principle of weighted distance, the space is segmented into several Voronoi cells. For any spatial point , to determine the Voronoi cell it belongs to, the following condition is used for determination: , where the Voronoi cell corresponding to the sampling point that minimizes the weighted distance is the cell to which the spatial point P belongs.

[0040] In this embodiment, the site's terrain is partitioned using the weighted Voronoi diagram algorithm described above, generating multiple Voronoi cells based on the distribution and weights of the sampling points. Each Voronoi cell corresponds to a topographic region, where every point within that region has the shortest weighted distance to a sampling point within that cell. This information, based on the distribution and weights of the sampling points, allows the site's terrain to be divided into multiple regions with specific inherent characteristics, providing foundational data for subsequent path connection and road network construction.

[0041] In some embodiments, the distribution and weight information of the sampling points are adjusted according to different needs. The size of the weight value directly affects the boundary position of the Voronoi unit, thereby having a significant impact on the accuracy of terrain segmentation. When optimizing the sampling data, the sampling points in sensitive areas have higher weights, so that the weighted Voronoi diagram can generate smaller Voronoi units in these areas, refining the terrain division. By constructing the weighted Voronoi diagram, the terrain of the site is divided into multiple Voronoi units. These units have good spatial segmentation, which is conducive to the subsequent path connection and optimization based on terrain characteristics.

[0042] S302, combining the gradient information of each Voronoi cell with the terrain resistance coefficient, using a multi-level weighted connection method to connect adjacent cells by paths, and generating a preliminary road network candidate map; In some embodiments, this step combines the gradient information of each Voronoi cell with the terrain resistance coefficient and uses a multi-level weighted connection method to connect adjacent cells to form a preliminary road network candidate map. This step considers terrain variations and resistance characteristics to establish path connections between adjacent Voronoi cells, thereby preliminarily constructing the site's road network structure.

[0043] In this embodiment, path connectivity is primarily determined by two factors: the gradient information of each Voronoi cell and the terrain resistance coefficient. Gradient information indicates the rate of terrain change; areas with larger gradients typically indicate more undulating terrain and greater driving difficulty. The terrain resistance coefficient indicates the driving resistance in a specific area, which is related to factors such as slope, soil type, and vegetation cover. Combining this information helps optimize path selection in the road network, ensuring that path connections avoid areas with high gradients and high resistance, thereby reducing construction and travel costs.

[0044] Specifically, the gradient information is calculated from the elevation data of the terrain. Given any Voronoi cell Elevation data, gradient It is expressed as the rate of change of elevation of any point within the unit. The gradient is calculated as: ,in Denote the elevation, where x and y are the spatial coordinates within the unit. The slope information of each point within the unit is obtained by calculating the gradient, which is used for subsequent path selection.

[0045] Terrain resistance coefficient Then it is set based on specific terrain attributes. In this embodiment, the terrain resistance coefficient is calculated according to the slope and other influencing factors (such as surface type, vegetation coverage). The specific resistance coefficient is represented by the following model: , where f is a function related to terrain attributes, and its specific form depends on the terrain data and environmental model used. In some embodiments, the terrain resistance coefficient is obtained through polynomial fitting, interpolation method, or preset empirical values.

[0046] The process of path connection uses a multi-level weighting method. Specifically, for each pair of adjacent Voronoi units and , the connection cost between them is calculated through a weighting method. The connection cost is jointly determined by the gradient information and the terrain resistance coefficient. Its calculation formula is: , where and are the weights of the gradient information and the terrain resistance coefficient respectively, represents the difference in gradients of adjacent units, and are the terrain resistance coefficients of the two Voronoi units respectively. Through this weighting method, areas with smaller gradient changes and lower resistance are preferentially selected during path connection to ensure the passability and economy of the road network.

[0047] In this embodiment, the result of path connection is a graph structure containing multiple Voronoi units and their connection paths, and the weight of each edge is obtained through weighted calculation. This graph structure serves as a preliminary road network candidate graph, providing basic data for subsequent road network optimization and path selection.

[0048] S303. Based on the topological structure of the preliminary road network candidate graph, combined with the minimum spanning tree algorithm in graph theory, the road network is optimized and generated to obtain the final road network data.

[0049] In some embodiments, this step utilizes the topological structure of the preliminary road network candidate graph and combines it with the minimum spanning tree (MST) algorithm in graph theory to optimize and generate the road network in order to obtain the final road network data. By optimizing the path connections in the preliminary road network candidate graph through the minimum spanning tree algorithm, redundant path connections are reduced, thereby generating a road network structure with the minimum cost and connectivity.

[0050] In this embodiment, the topological structure of the preliminary road network candidate graph is composed of multiple Voronoi cells and the connection paths between them. This graph structure represents all potential path connection methods in the field area. The weight of each edge is usually obtained by a weighted connection method, representing the connection cost between two cells. On this basis, by applying the minimum spanning tree algorithm, the path connections are optimized to ensure that the generated road network has the lowest overall construction cost while meeting the connectivity requirements of the road network.

[0051] The minimum spanning tree algorithm is to select a certain number of edges in a connected graph to generate a subgraph that contains all vertices and no loops, and the total weight of the edges is the smallest. In this step, each node of the graph corresponds to a Voronoi cell, each edge corresponds to the connection path between two adjacent cells, and the weight of the edge is calculated by weighting the gradient difference and the terrain resistance coefficient. According to the definition in graph theory, the goal of the minimum spanning tree is to solve a tree that contains all nodes in the graph and the sum of the path weights is the smallest. Specifically, given a graph G=(V,E), where V is the set of vertices, representing Voronoi cells, and E is the set of edges, representing the connection paths between cells. Each edge weight is calculated by the aforementioned weighted connection formula. The minimum spanning tree problem is described by the following mathematical model: , where T represents the set of edges in the minimum spanning tree, is the weight of edge . The goal is to select several edges from the candidate graph G so that these edges form a tree, and all nodes in the tree are connected, and the total weight of the edges is the smallest.

[0052] In this embodiment, the minimum spanning tree algorithm can be implemented by the Kruskal algorithm or the Prim algorithm. In some embodiments, the Kruskal algorithm sorts all edges by weight, and then sequentially selects the edge with the smallest weight and adds it to the spanning tree until the spanning tree contains all nodes. When selecting an edge each time, if the two nodes connected by the edge are already in the same spanning tree, the edge is skipped, otherwise it is added to the spanning tree. This algorithm can efficiently generate a minimum spanning tree.

[0053] Another common implementation is to use the Prim algorithm. This algorithm starts from an initial node and gradually expands the spanning tree. Each time, it selects an edge with the smallest weight and connects it to the tree until the spanning tree contains all nodes. The advantage of the Prim algorithm is that only the edges of the currently generated part need to be considered each time the spanning tree is expanded, and the computational complexity is relatively small.

[0054] During the implementation process, the minimum spanning tree algorithm is carried out through the following steps: S3031. Sort all the edges in the preliminary road network candidate graph to generate edge weight sequence data; S3032. Based on the edge weight sequence data, use the Kruskal algorithm or the Prim algorithm to select edges one by one, select the edge with the smallest weight, and gradually connect the Voronoi cells until the spanning tree contains all the cells to generate the minimum spanning tree data; S3033. Based on the minimum spanning tree data, avoid generating loops, ensure that the selected path can connect all the cells and form a tree structure to generate road network data, and the road network data contains a set of connected paths with the smallest weight for all Voronoi cells; By applying the minimum spanning tree algorithm, this step can effectively optimize the topological structure of the road network, reduce unnecessary path connections, so as to ensure that while maintaining connectivity, the road network reaches the minimum construction cost and the highest efficiency. This process is a key step in road network generation and can provide a reasonable preliminary framework for subsequent path optimization and construction.

[0055] S4. Based on the road network data, perform path screening and constraint adjustment to obtain a path candidate set; Specifically, please refer to the appendix Figure 4 As shown, this step includes the following sub-steps: S401. Based on the road network data, calculate the shortest path from the starting point to the ending point through heuristic search to generate a preliminary path candidate set; In some embodiments, this step combines the road network data and the heuristic search algorithm. In this embodiment, the heuristic search calculation includes the shortest path search of the Dijkstra algorithm and the heuristic estimation of the A algorithm.

[0056] In this embodiment, the road network data consists of the aforementioned minimum spanning tree data and the connection paths between Voronoi cells. The nodes represent the key points of the road network, and the edges represent the connection paths between the nodes. The heuristic search algorithm is applied to calculate the shortest path from the starting node to the ending node and optimize the path based on the cumulative cost of the current path and the estimated cost of the target node.

[0057] The Dijkstra algorithm selects paths by calculating the shortest distances of all possible paths during this process and is applicable to graphs without negative weight edges. Specifically, the mathematical formula of the Dijkstra algorithm is: , where represents the shortest path cost from the starting point to node , represents the edge weight from node to node , It is the set of nodes in the graph. The Dijkstra algorithm continuously updates the shortest path values of each node until the shortest path from the starting point to the ending point is calculated.

[0058] In the A algorithm, heuristic estimation is introduced into the path search, making the search process depend not only on the actual cost from the current node to the starting point but also on the estimated cost from the current node to the ending point. The cost function of the A algorithm is defined as: , where g(n) represents the actual cost from the starting point to the current node n, h(n) is the heuristic estimated cost from node n to the ending point, and f(n) is the total cost of node n. During the heuristic search process, the A* algorithm expands the node with the minimum f(n) value, thus being able to find the shortest path more effectively.

[0059] The Dijkstra-A hybrid algorithm combines the shortest path search of the Dijkstra algorithm with the heuristic estimation of the A algorithm, maintaining both the global optimization of the path by the Dijkstra algorithm and adding the guidance of the A algorithm for the target direction, improving the efficiency of the path search. During the implementation process, the Dijkstra-A hybrid algorithm proceeds according to the following steps: S4011, Initialize the path cost of the nodes. Set the path cost of the starting point to 0, and the path costs of other nodes to infinity; S4012, Calculate the total cost of each node to be processed, including the actual cost and the heuristic estimation, and select the node with the minimum cost for expansion; S4013, Update the path costs of adjacent nodes according to the edge weights and the heuristic estimation, continuously expand the nodes until the ending point node is expanded or all nodes are processed; S4014, Generate the shortest path from the starting point to the ending point and add the path to the preliminary path candidate set.

[0060] In some implementation manners, through the combination of heuristic estimation and shortest path search, the Dijkstra-A* hybrid algorithm can find the optimal path in a relatively short time while avoiding blind search. This algorithm provides an efficient and accurate path calculation method for the generation of the path candidate set, providing preliminary candidate path data for subsequent path optimization.

[0061] S402, Adjust the edge weights based on the local environment characteristics, optimize the preliminary path candidate set using the edge weight adjustment model, and generate the adjusted path weight matrix; In some embodiments, this step combines local environmental features including slope and turning radius to further adjust the edge weights of the path, and optimizes the preliminary path candidate set according to the adjusted weights to generate an adjusted path weight matrix. By considering the local terrain and vehicle driving characteristics, the superiority and inferiority of the path are refined, providing more reasonable edge weights for subsequent path selection and optimization.

[0062] In this embodiment, the adjustment of the edge weights is to dynamically adjust the edges in the preliminary path candidate set by introducing a series of environmental factors. Specifically, the adjustment formula for the edge weights is: , where represents the adjusted edge weight, is the original edge weight in the preliminary path candidate set, is the adjustment factor related to the slope, is the adjustment factor related to the turning radius, and are the coefficients that affect the weights of the slope and turning radius, controlling the influence degree of each environmental factor in the path weight adjustment. The slope adjustment factor represents the influence of the slope of each segment of the edge on driving. A path with a larger slope will increase the driving difficulty of the vehicle. Therefore, its adjustment factor is calculated by the following formula: , where is the coefficient that controls the influence of the slope on the path weight, is the slope angle of the edge e.

[0063] The turning radius adjustment factor reflects the influence of the path curve on the driving difficulty. A path with a smaller turning radius has a sharper curve, and the resistance when the vehicle passes is greater. Therefore, its adjustment factor is calculated by the following formula: , where is the coefficient that controls the influence of the turning radius on the path weight, is the turning radius of the edge e.

[0064] By introducing the adjustment factors of the slope and turning radius, the weights of each edge on the path can be dynamically adjusted, ensuring that the path selection not only considers the influence of the terrain but also the adaptability of vehicle driving. The adjusted edge weights reflect the comprehensive passing ability of the path and can more accurately express the relative superiority and inferiority of each path.

[0065] In some embodiments, the adjusted edge weight matrix is used to optimize the preliminary path candidate set. By applying the edge weight adjustment model, relatively difficult paths in the candidate paths (such as paths with steep slopes or sharp turns) are assigned larger weights, thereby reducing the probability of these paths being selected. Conversely, for paths that are relatively flat and have a larger turning radius, their weight values are lower, increasing the likelihood of their being selected. The generated adjusted path weight matrix contains the edge weight information of all paths and provides a refined path selection basis based on terrain features for subsequent path optimization. Through this step, path optimization is not limited to the shortest path calculation but also fully considers the road traffic performance and the actual driving characteristics of the vehicle.

[0066] S403. Based on the path weight matrix, the Lagrange multiplier method is used to perform constraint optimization on the path, eliminating paths that do not meet the constraint conditions (minimum road surface width and maximum slope limit), and generating a path candidate set.

[0067] In some embodiments, the constraint conditions are the minimum road surface width and the maximum slope limit; In this embodiment, the Lagrange multiplier method is applied to the path optimization process, with the goal of minimizing the weight of the path while satisfying a series of constraint conditions. Assume that the path weight matrix is represented as , where is the weight of the i-th edge. The constraint conditions include: Minimum road surface width constraint: The road surface width of each path should be greater than or equal to a certain threshold , that is: , , where represents the road surface width of path e, and P is the set of paths.

[0068] Maximum slope limit: The slope of each path should be less than or equal to the maximum slope threshold , that is: , , where is the slope of path e.

[0069] In some embodiments, the optimization goal of the path is to minimize the sum of the path weights while satisfying the above constraint conditions. To achieve this goal, a Lagrangian function can be constructed to incorporate the constraint conditions into the objective function. The form of the Lagrangian function is: , where is the Lagrangian function, is the adjusted edge weight obtained above, and are the Lagrange multipliers, which are related to the minimum road surface width constraint and the maximum slope limit respectively. By solving the extreme value problem of the Lagrangian function, the optimal path set that meets the constraint conditions can be obtained.

[0070] In some embodiments, the balance between path weights and constraint conditions is obtained by solving the partial derivatives of the Lagrangian equation. In this embodiment, by taking the partial derivatives of the Lagrangian function and setting them to zero, the following equations are obtained: , , The solutions of these equations are the set of optimal paths , that is, the paths that satisfy the minimum road surface width and maximum slope limit. The solved optimal paths will be the path candidate set with the minimum path weight and meeting the constraint conditions.

[0071] In this embodiment, after the path candidate set generated based on this optimization process undergoes constraint optimization, all ineligible paths are eliminated, ensuring that the finally selected path has the lowest driving difficulty and optimal driving efficiency on the premise of meeting the actual constraints.

[0072] S5. Based on the path candidate set and in combination with the facility distribution data, generate a road planning scheme applicable to the wind and solar power plant site, and output the final power station road path data; Specifically, please refer to the attached Figure 5 As shown, this step includes the following sub-steps: S501. Based on the path candidate set, use the electric field layout and equipment adaptation algorithm to correct the path structure, ensure that the path avoids key equipment and infrastructure, optimize the path layout, and generate a corrected path structure; In some embodiments, the electric field layout and equipment adaptation algorithm includes path obstacle avoidance processing and path optimization processing, aiming to correct the path candidate set, ensure that the path avoids key equipment and infrastructure in the electric field, and at the same time optimize the path layout. This algorithm includes two main parts: path obstacle avoidance processing and path optimization processing. Path obstacle avoidance processing mainly analyzes the paths in the path candidate set, eliminates the paths that conflict with equipment or infrastructure, and re-plans the path according to the terrain and equipment distribution; path optimization processing is based on the requirements of the electric field and the distribution of equipment, optimizes the selection of paths, makes the final path layout more reasonable, avoids obstacles that affect path safety or operation efficiency, and ensures the coherence and optimality of the path.

[0073] In this embodiment, the core of path obstacle avoidance processing is to ensure that the path does not pass through these equipment or facilities by modifying the shape or direction of the path according to the position constraint conditions of the equipment and infrastructure. Suppose there are several key equipment or infrastructure in the electric field , each piece of equipment has an occupied area , and the paths in the path candidate set are represented as . The goal of path obstacle avoidance processing is to adjust the path , so that the path does not overlap with the occupied area of any device occupied area overlap.

[0074] In some embodiments, the correction of the path can be achieved by adding constraint conditions. For example, the turning points of the path can be adjusted so that the path avoids approaching the device area. This can be handled by introducing an "obstacle avoidance function". Let be the shortest distance between the path p and the device , and the goal is to maximize the shortest distance between the path and the device , that is: , in this embodiment, the path optimization process uses an adaptation algorithm based on the requirements of the electric field. The goal of this algorithm is to optimize each path in the path candidate set, reduce the path length or minimize the energy loss on the path. The goal of path optimization is represented by the following optimization function: , where represents the path weight on (which is the weighted sum of the slope and turning degree factors), represents the objective function of path optimization.

[0075] In order to consider the actual requirements of the electric field layout, the path optimization process also needs to combine the adaptation requirements of the devices to ensure that the path can reasonably connect each device and serve the functional requirements of the electric field. For example, if some paths need to pass through the device area to connect different devices, then this part of the path should be carefully designed to ensure mutual adaptation with the devices. For example, in this embodiment, the adaptation between the device and the path is optimized through the following constraints: , where is the adaptation degree function between the path p and the device , and α is the threshold of device adaptation. The optimization goal is to minimize the total weight of the path under the premise of meeting device adaptation. This optimization problem obtains the optimal solution by solving the combination of the minimization function f(P) and the constraint condition .

[0076] In summary, through the above steps, the path candidate set generates a corrected path structure after being structurally corrected by the electric field layout and device adaptation algorithm. This path structure avoids key devices and infrastructure, optimizes the path layout, meets the actual requirements of the electric field, and ensures the safety, feasibility, and efficiency of the path.

[0077] S502, in combination with the construction and maintenance requirements within the electric field, perform construction adaptability processing on the corrected path, thereby generating construction adaptability path data; In some embodiments, the construction adaptability treatment includes path width adjustment, slope adjustment, turning radius optimization, and path flatness improvement treatment. The main purpose of these treatments is to ensure that the path can meet the space requirements for equipment installation and maintenance operations during construction and to ensure good construction accessibility and convenience for later maintenance. Specifically, path width adjustment takes into account the passage of heavy machinery, equipment transportation, and construction tools. Slope adjustment is to ensure that construction machinery and maintenance equipment can pass safely and smoothly through different terrain changes. Turning radius optimization is to improve the passing efficiency of equipment transportation and construction vehicles, while path flatness improvement helps with the precise installation of equipment during construction and the smooth progress of later maintenance operations.

[0078] In this embodiment, the core of the construction adaptability treatment is to adjust the width, slope, turning radius, and ground flatness of the path based on the corrected path structure to meet the requirements of construction and maintenance. The goal of path width adjustment is to ensure that the path can accommodate necessary construction equipment, materials, and transportation tools. Let the width of path p be w(p), and it is required that the path width at least meets the width required for equipment passage , that is: . If the original path width does not meet this condition, the path will be expanded, and the adjusted path width is expressed as: . In addition, slope adjustment is to apply slope limits to the paths in the path candidate set to ensure that the slope change of the path does not affect the passage of construction machinery. In some embodiments, the slope of the path is calculated from the height difference and horizontal distance of each point on the path. If the slope of the path exceeds the preset maximum slope limit , the path needs to be adjusted. Assume that z(p) is the height of a certain point on path p, and the slope between two adjacent points on the path is expressed as: , where is the horizontal distance between two adjacent points. When the calculated slope is greater than the maximum slope limit , the slope of the path will be adjusted by flattening the slope or selecting a more suitable path to meet the requirements. The adjusted slope is expressed as: . Turning radius optimization is mainly to ensure that the turning angle of the path is not too large to ensure that large construction equipment can turn smoothly. Let the turning radius between two consecutive points on path p be , and it is required that this turning radius is greater than or equal to the preset minimum turning radius.

[0079] Path flatness improvement is achieved by leveling the uneven parts of the path to ensure that construction equipment can drive smoothly and avoid steep slopes or sharp turns on the path from affecting construction operations. The flatness improvement is achieved in the following ways: For each segment and on path p, calculate the path curvature , and control the curvature within an appropriate range to avoid overly steep changes in the path. The calculation formula for path curvature is: . When the curvature exceeds the predetermined range, the path will be smoothed to reduce the curvature, and the adjusted curvature will be smoothed by methods such as path interpolation or curve fitting.

[0080] In some embodiments, the result of the construction adaptability processing is to generate construction adaptability path data that meets the requirements of equipment installation and maintenance. This path data meets the requirements of construction machinery, equipment transportation and installation, and post-maintenance, and can effectively avoid obstacles on the path during actual construction to ensure the safety and efficiency of construction. The width, slope, turning radius, and flatness of the path all meet the requirements of internal construction operations in the electric field, and the finally generated construction adaptability path data provides effective support for subsequent construction stages.

[0081] S503, based on the construction adaptability path data combined with the electric field operation and safety requirements, perform path smoothing and correction processing to generate the final electric field road path data.

[0082] In some embodiments, the path smoothing and correction processing includes path curve smoothing, slope flattening, turning radius correction, and safety area avoidance processing. This further improves the traffic capacity of the path and ensures the smoothness of the path to meet the safety requirements of equipment passage and personnel operation within the electric field. In this embodiment, the final smoothing and correction processing of the path involves the following aspects: path curve optimization, slope correction and flattening, turning radius optimization, and avoidance of environmental features that meet the safety requirements within the electric field.

[0083] Path curve optimization is to improve the smoothness of the path and avoid traffic obstacles caused by sharp turns or discontinuous curves. In some embodiments, each segment of the path curve on the path will be smoothed. Let the curvature of the path curve be , then the optimization of the curve can be achieved by reducing the curvature change rate. The curvature change amount between each segment of the path on the path is expressed as: . To make the path curve smooth, the adjustment goal of the path is to correct the path through curve fitting or spline interpolation techniques (such as B-spline or cubic spline interpolation) to make the curvature change as gentle as possible. After curve optimization, the new curvature It will meet certain smoothness requirements and avoid drastic changes in curvature. The optimized path of the curve can be controlled by the following constraints: , where is the maximum allowable curvature of the path curve.

[0084] The slope correction and smoothing are mainly to ensure that the slope of the path meets the requirements of the electric field operation. The slope requirements within the electric field should meet the needs of equipment passage and avoid overly steep slopes that cause difficulties in construction and maintenance. In some embodiments, the slope of the path will be corrected according to the set maximum slope limit . When the slope of the path exceeds the limit, the path will be smoothed through interpolation or reconstruction to ensure that the slope of each section of the path does not exceed the maximum slope limit. The corrected slope is expressed as: .

[0085] The turning radius correction is to ensure that the turning angle of the path adapts to the passage requirements of mechanical equipment within the electric field. In some embodiments, the turning radius of the path is corrected to the minimum turning radius that meets the safety requirements of the electric field . The corrected turning radius is ensured to meet this requirement through local adjustment of the path: , this formula means that the larger radius between the turning radius of the path and the minimum turning radius is taken as the corrected turning radius. The correction of the path also needs to avoid conflicts with key areas, equipment and safety areas within the electric field. Therefore, during the path optimization process, the path will be avoided from the safety area according to the functional area division and environmental characteristics within the electric field. Let the safety area within the electric field be , the path p should avoid overlapping with . The correction process of the path will adjust the position of the path according to the avoidance requirements of the area, so that the corrected path does not cross the safety area. The safety of the corrected path can be controlled by the following constraints: the path p does not overlap with the safety area , that is , indicating that the path P does not overlap with the safety area , otherwise it is 0, that is, there is an overlap and intersection.

[0086] In this embodiment, the final smoothing and correction processing generates path data that meets the safety requirements of electric field operations. This path data not only ensures the smooth passage of equipment during construction and transportation but also takes into account the internal environment and safety requirements of the electric field, avoiding interference with the electric field functional areas, equipment, and safety areas. Through the above series of smoothing and correction processes, the final path not only has construction adaptability but also meets the actual operation and safety requirements of the electric field, generating the final electric field road path data, which provides effective support for subsequent construction, operation, and maintenance.

[0087] Based on the description of the above embodiments of the method for planning roads in a wind-solar power station area based on terrain analysis, an embodiment of the present application also discloses a system for planning roads in a wind-solar power station area based on terrain analysis. The system for planning roads in a wind-solar power station area based on terrain analysis can be a computer program (including program code) that runs the above-mentioned method for planning roads in a wind-solar power station area based on terrain analysis. Please refer to the attached Figure 6 As shown, the system for planning roads in a wind-solar power station area based on terrain analysis can run the following units: An acquisition unit 110, configured to acquire data to be processed, where the data to be processed includes digital elevation model data of a wind-solar power station area, vehicle turning radius parameters, and facility distribution data. The digital elevation model data includes elevation data and DEM pixel size, and the facility distribution data is the location and distribution information of all relevant equipment and infrastructure within the electric field; An optimization sampling unit 120, configured to perform hierarchical reconstruction and optimization processing on the data to be processed to generate optimized sampling data; A road network construction unit 130, configured to construct a road network based on the optimized sampling data to obtain road network data; A path screening unit 140, configured to perform path screening and constraint adjustment based on the road network data to obtain a path candidate set; A power station path generation unit 150, configured to generate power station road path data applicable to the wind-solar power station area based on the path candidate set in combination with the facility distribution data.

[0088] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications, and environments, and can be changed within the scope of the concept described herein through the above teachings or the technology or knowledge in related fields. Any changes and variations made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A method for planning roads in a wind and solar power station area based on terrain analysis, characterized in that, The method includes the following steps: S1. Obtain the data to be processed, where the data to be processed includes digital elevation model data of a wind-solar power station site, vehicle turning radius parameters, and facility distribution data. The digital elevation model data includes elevation data and DEM pixel size, and the facility distribution data is the location and distribution information of all relevant equipment and infrastructure within the power station; S2. Perform hierarchical reconstruction and optimization processing on the data to be processed to generate optimized sampling data; S3. Construct a road network based on the optimized sampling data to obtain road network data; S4. Perform path screening and constraint adjustment based on the road network data to obtain a path candidate set; S5. Generate power station road path data applicable to the wind-solar power station site based on the path candidate set in combination with the facility distribution data.

2. The method for planning the roads in the scenic power station area based on terrain analysis according to claim 1, wherein, The S2 step includes the following sub-steps: S201. Perform multi-scale decomposition on the elevation data based on gradient decomposition, extract terrain features at different scales, set adaptive sampling steps according to each terrain feature, compare the sampling step with the DEM pixel size, and when the sampling step is greater than the pixel, perform a sampling operation to generate terrain scale data; S202. Define local path sensitive areas based on the terrain scale data, vehicle turning radius, and slope information, and optimize the sampling density of the sensitive areas through weighted convolution to generate optimized sampling data.

3. A method for planning a road in a wind-solar power station area based on terrain analysis according to claim 1, characterized in that The S3 step includes the following sub-steps: S301. Based on the optimized sampling data, use the weighted Voronoi diagram partitioning algorithm to perform spatial segmentation on the site terrain to obtain each terrain Voronoi unit; S302. Combine the gradient information and terrain resistance coefficient of each Voronoi unit, and use a multi-level weighted connection method to connect adjacent units to generate a preliminary road network candidate map; S303. Based on the topological structure of the preliminary road network candidate map, combine the minimum spanning tree algorithm in graph theory to optimize the generation of the road network to obtain road network data.

4. A method for planning a road in a wind-solar power station area based on terrain analysis according to claim 1, characterized in that, The S4 step includes the following sub-steps: S401. Calculate the shortest path from the starting point to the ending point through heuristic search based on the road network data to generate a preliminary path candidate set; S402. Adjust the edge weights based on the local environmental characteristics, and use the edge weight adjustment model to optimize the preliminary path candidate set to generate an adjusted path weight matrix; S403. Based on the path weight matrix, combine the Lagrange multiplier method to perform constraint optimization on the path, eliminate paths that do not meet the constraint conditions, and generate a path candidate set.

5. A method for planning a road in a wind-solar power station area based on terrain analysis according to any one of claims 1-4, characterized in that, The S5 step includes the following sub-steps: S501. Based on the path candidate set, use the electric field layout and equipment adaptation algorithm to correct the path structure, ensure that the path avoids key equipment and infrastructure, and optimize the path layout to generate a corrected path structure; S502. Combine the construction and maintenance requirements within the electric field to perform construction adaptability processing on the corrected path structure to generate construction adaptability path data; S503. Based on the construction adaptability path data, combine the electric field operation and safety requirements to perform path smoothing and correction processing to generate the final electric field road path data.

6. The method for planning the roads in a wind-solar power station area based on terrain analysis according to claim 3, wherein, The S303 step includes the following sub-steps: The minimum spanning tree algorithm proceeds through the following steps: S3031, Sort all the edges in the preliminary road network candidate graph to generate edge weight sequence data; S3032, Based on the edge weight sequence data, use the Kruskal algorithm or the Prim algorithm to select edges one by one, select the edge with the smallest weight, and gradually connect Voronoi cells until the spanning tree contains all cells, generating minimum spanning tree data; S3033, Based on the minimum spanning tree data, avoid generating loops, ensure that the selected path can connect all cells and form a tree structure to generate road network data, where the road network data contains all Voronoi cells and the set of connected paths with the smallest weight.

7. A method for planning a road in a wind-solar power station area based on terrain analysis according to claim 4, characterized in that, The heuristic search calculation in S401 includes the shortest path search of the Dijkstra algorithm and the heuristic estimation of the A algorithm. The heuristic search calculation proceeds according to the following steps: S4011, Initialize the path cost of the nodes, set the path cost of the starting point to 0, and the path cost of other nodes to infinity; S4012, Calculate the total cost of each node to be processed, including the actual cost and the heuristic estimation, and select the node with the smallest cost for expansion; S4013, Update the path cost of adjacent nodes according to the edge weight and the heuristic estimation, continuously expand the nodes until the end node is expanded or all nodes are processed; S4014, Generate the shortest path from the starting point to the end point and add the path to the preliminary path candidate set.

8. A method for planning roads in a wind-solar power station area based on terrain analysis according to claim 4, wherein, The constraint conditions in S403 are the minimum road surface width and the maximum slope limit.

9. A method for planning roads in a wind-solar power station area based on terrain analysis according to claim 5, characterized in that, The electric field layout and equipment adaptation algorithm in S501 includes path obstacle avoidance processing and path optimization processing; the construction adaptability processing in S502 includes path width adjustment, slope adjustment, turning radius optimization, and path flatness improvement processing; the path smoothing and correction processing in S503 includes path curve smoothing, slope flattening, turning radius correction, and safety area avoidance processing.

10. A road planning system for a wind-solar power station area based on terrain analysis, characterized in that, The system includes: An acquisition unit for acquiring data to be processed, where the data to be processed includes digital elevation model data of a wind-solar power station site area, vehicle turning radius parameters, and facility distribution data. The digital elevation model data includes elevation data and DEM pixel size, and the facility distribution data is the location and distribution information of all relevant equipment and infrastructure within the electric field; An optimized sampling unit for performing hierarchical reconstruction and optimization processing on the data to be processed to generate optimized sampling data; A road network construction unit for constructing a road network based on the optimized sampling data to obtain road network data; A path screening unit for performing path screening and constraint adjustment based on the road network data to obtain a path candidate set; A power station path generation unit for generating power station road path data applicable to a wind-solar power station site based on the path candidate set in combination with the facility distribution data.

Images