Digital elevation-based automatic route selection method for wind farm transportation road

Abstract

The application discloses a kind of wind farm transport road automatic line selection method based on digital elevation.The steps include: S1, obtaining the vector data of wind field range and ground object distribution, as design limit area and obstacle respectively;S2, obtain DEM data and carry out slope analysis, and determine the slope limit area as the slope limit area;S3, determine the avoidance condition by comprehensively considering the above conditions, discretize the wind field range into grid units and calculate the passing cost of each unit, and use A* algorithm to search the optimal path from the starting point to the end point with the minimum total road construction cost as the target;S4, take the wind turbine point as the starting point, connect the existing road network as the target, and generate a new road line selection scheme based on the optimal path.The application realizes automatic line selection of wind farm road by integrating digital elevation model and intelligent algorithm, can accurately avoid unfavorable terrain and optimize construction cost, thereby improving design efficiency, engineering safety and economy.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and more specifically, to an automatic route selection method for wind farm transportation routes based on digital elevation. Background Technology

[0002] In the construction of wind farms, the selection of on-site transportation routes is a critical factor affecting project costs, schedule, and safety. Traditional route selection methods mainly rely on the site survey experience of designers and two-dimensional drawings for qualitative planning. This method is inefficient and struggles to systematically quantify and analyze multiple engineering constraints such as complex terrain, slope limitations, turning radii, and earthwork volumes. This results in highly subjective and inaccurate planning schemes, easily leading to problems such as detours, excessive workloads, or frequent design changes later on, which constrain the project's economic viability and construction progress.

[0003] Especially in mountainous and hilly areas with significant undulations, traditional manual alignment methods struggle to quickly and accurately assess the global impact of continuous terrain changes on road longitudinal slope and cut-fill balance. The design process can easily overlook unfavorable geological conditions such as steep slopes and catchment areas, potentially leading to a disconnect between the road alignment and the terrain. This not only significantly increases construction costs and difficulty but also introduces safety hazards such as slope instability during the operational phase. Therefore, current technologies lack an intelligent alignment method that can automatically and accurately handle multi-dimensional constraints and optimize road construction costs. Summary of the Invention

[0004] In view of the above-mentioned technical problems in related technologies, the present invention proposes an automatic route selection method for wind farm transportation routes based on digital elevation, which can overcome the above-mentioned shortcomings of the prior art.

[0005] To achieve the above-mentioned technical objectives, the technical solution of the present invention is implemented as follows: An automatic route selection method for wind farm transportation routes based on digital elevation; The automatic route selection method for wind farm transportation routes based on digital elevation includes the following steps: S1. Obtain vector data of wind field range and ground feature distribution; wherein, the wind field range serves as the limiting area for automatic road design, and the ground feature distribution serves as the obstacles for automatic road design; S2. Obtain the digital elevation model (DEM) data within the wind field area, and perform slope analysis on the DEM data to determine the area where the slope exceeds a preset threshold as the slope restriction area. S3. Based on the wind field range, ground feature distribution, and slope restriction area, determine the avoidance conditions, and discretize the wind field range into multiple grid units; Calculate the passage cost corresponding to each of the grid cells, wherein the passage cost is positively correlated with the terrain complexity of the corresponding grid cell; Under the premise of satisfying the avoidance conditions, the A* path planning algorithm is used in the grid cell to search for the optimal path from the starting point to the ending point with the goal of minimizing the total road construction cost. S4. Taking the wind turbine locations within the wind farm as the starting point of the road and the existing road network connecting to or around the wind farm as the target, generate a route selection scheme for the newly built transportation road of the wind farm based on the optimal path.

[0006] Furthermore, in step S2, the preset threshold is greater than or equal to 50 degrees.

[0007] Furthermore, the terrain complexity is measured by the elevation standard deviation within the corresponding grid cell.

[0008] Furthermore, the path planning algorithm is an improved A algorithm, and the cost in the evaluation function of the improved A algorithm is represented by the road construction cost, which includes at least the earthwork volume.

[0009] Furthermore, the earthwork volume is calculated as follows: The optimal path is divided into multiple road segments. For each road segment, the areas A1 and A2 of its two adjacent cross-sections are obtained based on the DEM data, and the distance L between the two cross-sections is determined. Then, the earthwork volume V of this road segment is: ; The total earthwork volume V of the optimal path is the sum of the earthwork volumes of each road segment: ; Where n is the total number of cross sections.

[0010] Furthermore, step S4 specifically includes: S41. Determine the wind turbine locations within the wind farm as the starting points of the roads, and sample the existing roads within the wind farm to obtain multiple pre-selected endpoints; S42. Select the wind turbine location closest to the existing road as the calculation starting point, calculate the path cost from the calculation starting point to each of the pre-selected endpoints, and select the path with the lowest path cost as the new road corresponding to the current wind turbine location. S43. Incorporate the newly built road into the existing road, and repeat step S42 until all wind turbine locations are connected to the existing road through the newly built road.

[0011] Furthermore, if the existing road does not exist, an access road will be manually planned as the initial existing road.

[0012] Furthermore, the resolution of the DEM data is 30 meters, and the vertical accuracy is better than 5 meters.

[0013] Furthermore, the vector data type of the wind field range is Polygon or MultiPolygon.

[0014] Furthermore, in step S3, the total cost of road construction is the objective function, and the path planning algorithm searches for the optimal path by minimizing this objective function.

[0015] The beneficial effects of this invention are as follows: By integrating digital elevation model analysis, multi-constraint fusion, and intelligent path search algorithms, automated and quantitative route selection for wind farm transportation roads in complex terrain environments is achieved. This enables the planning process to accurately avoid unfavorable terrain, significantly improve the matching degree between the route and the terrain, and enhance engineering safety. Simultaneously, global optimization is performed with the goal of minimizing the total road construction cost. Ultimately, this results in a comprehensive improvement in design efficiency and accuracy, effective control of engineering investment and risks, and a shortened project lead time. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an automatic route selection method for wind farm transportation routes based on digital elevation, as described in an embodiment of the present invention; Figure 2 This is an automatic route selection method for wind farm transportation routes based on digital elevation, as described in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1-2 As shown in the figure, an automatic route selection method for wind farm transportation roads based on digital elevation according to an embodiment of the present invention includes the following steps: S1. Obtain vector data of wind field range and ground feature distribution; wherein, the wind field range serves as the limiting area for automatic road design, and the ground feature distribution serves as the obstacles for automatic road design; S2. Obtain the digital elevation model (DEM) data within the wind field area, and perform slope analysis on the DEM data to determine the area where the slope exceeds a preset threshold as the slope restriction area. S3. Based on the wind field range, ground feature distribution, and slope restriction area, determine the avoidance conditions, and discretize the wind field range into multiple grid units; Calculate the passage cost corresponding to each of the grid cells, wherein the passage cost is positively correlated with the terrain complexity of the corresponding grid cell; Under the premise of satisfying the avoidance conditions, the A* path planning algorithm is used in the grid cell to search for the optimal path from the starting point to the ending point with the goal of minimizing the total road construction cost. S4. Taking the wind turbine locations within the wind farm as the starting point of the road and the existing road network connecting to or around the wind farm as the target, generate a route selection scheme for the newly built transportation road of the wind farm based on the optimal path.

[0020] According to an embodiment of the present invention, in a specific embodiment of the automatic route selection method for wind farm transportation roads based on digital elevation, the preset threshold in step S2 is greater than or equal to 50 degrees.

[0021] According to an embodiment of the present invention, an automatic route selection method for wind farm transportation routes based on digital elevation is provided. In a specific embodiment, the terrain complexity is measured by the elevation standard deviation within the corresponding grid cell.

[0022] According to an embodiment of the present invention, an automatic route selection method for wind farm transportation roads based on digital elevation is provided. In a specific embodiment, the route planning algorithm is an improved A algorithm, and the cost in the evaluation function of the improved A algorithm is represented by the road construction cost, which includes at least the earthwork volume.

[0023] According to an embodiment of the present invention, an automatic route selection method for wind farm transportation roads based on digital elevation is provided. In a specific embodiment, the earthwork volume is calculated as follows: The optimal path is divided into multiple road segments. For each road segment, the areas A1 and A2 of its two adjacent cross-sections are obtained based on the DEM data, and the distance L between the two cross-sections is determined. Then, the earthwork volume V of this road segment is: ; The total earthwork volume V of the optimal path is the sum of the earthwork volumes of each road segment: ; Where n is the total number of cross sections.

[0024] According to an embodiment of the present invention, an automatic route selection method for wind farm transportation routes based on digital elevation is provided. In a specific embodiment, step S4 specifically includes: S41. Determine the wind turbine locations within the wind farm as the starting points of the roads, and sample the existing roads within the wind farm to obtain multiple pre-selected endpoints; S42. Select the wind turbine location closest to the existing road as the calculation starting point, calculate the path cost from the calculation starting point to each of the pre-selected endpoints, and select the path with the lowest path cost as the new road corresponding to the current wind turbine location. S43. Incorporate the newly built road into the existing road, and repeat step S42 until all wind turbine locations are connected to the existing road through the newly built road.

[0025] According to an embodiment of the present invention, an automatic route selection method for wind farm transportation roads based on digital elevation is provided. In a specific embodiment, if the existing road does not exist, an access road is manually planned as the initial existing road.

[0026] According to an embodiment of the present invention, an automatic route selection method for wind farm transportation routes based on digital elevation modeling (DEM) is provided. In a specific embodiment, the resolution of the DEM data is 30 meters, and the vertical accuracy is better than 5 meters.

[0027] According to an embodiment of the present invention, an automatic route selection method for wind farm transportation routes based on digital elevation is provided. In a specific embodiment, the vector data type of the wind farm range is Polygon or MultiPolygon.

[0028] According to an embodiment of the present invention, an automatic route selection method for wind farm transportation roads based on digital elevation is provided. In a specific embodiment, in step S3, the total road construction cost is the objective function, and the path planning algorithm searches for the optimal path by minimizing the objective function.

[0029] To facilitate understanding of the above technical solutions of the present invention, the following detailed description of the above technical solutions of the present invention will be provided through specific usage methods.

[0030] In practical application, the automatic route selection method for wind farm transportation routes based on digital elevation as described in this invention can be executed through a computer system or professional software platform equipped with the corresponding algorithm, and specifically includes the following steps: First, data preparation and preprocessing are performed. Vector boundary data of the wind farm area is imported through a Geographic Information System (GIS) platform; this data is typically in the form of polygon or multi-polygon isometric features. Simultaneously, point, line, and polygon vector data of existing features within the area that need to be avoided, such as buildings, water bodies, and woodlands, are imported. Digital elevation model (DEM) data for the wind farm area is then obtained. Slope calculations are performed on the DEM using GIS spatial analysis tools, generating a slope raster map. Slope thresholds are set based on engineering experience and safety regulations; for example, areas with slopes greater than or equal to 50 degrees are classified as infeasible areas and converted into isometric vector data as new avoidance conditions. This data is then merged with the aforementioned feature distribution data to form the avoidance constraint set for path search.

[0031] Secondly, spatial discretization and cost modeling are performed. The outer rectangular region of the wind field is regularly discretized into square grid cells, with the grid size set according to the DEM resolution and accuracy requirements. For each grid cell, the standard deviation of elevation of all DEM pixels within it is calculated as an indicator of the terrain complexity of that cell. This indicator is then linearly or non-linearly mapped to the "passage cost" of that grid cell; the more fragmented and undulating the terrain, the higher the cost value. This step constructs a discretized raster network with cost values.

[0032] Next, the core automatic path optimization is performed. The starting and ending points of the road are clearly defined. An improved A* algorithm is used to search within the discretized grid network. Both the heuristic function and the actual cost function of the algorithm use "road construction cost" as a metric. The G value accumulates during path expansion, and its calculation not only considers the "passage cost" of the grids traversed, but more importantly, it uses DEM data to estimate the earthwork volume generated by road construction in real time. The earthwork volume is estimated using the cross-sectional method: assuming the road path passes through the center points of two adjacent grids, these are considered as a road segment. Based on DEM data interpolation, the cross-sectional terrain perpendicular to the path direction at the starting and ending points of this road segment is obtained. The cut and fill areas A1 and A2 between the design elevation and the original terrain elevation are calculated. Combined with the road segment length L, the earthwork volume of this road segment is calculated using the formula V=L / 2×(A1+A2), and this is converted into economic cost and included in the G value. During runtime, the algorithm automatically avoids all grids marked as obstacle avoidance conditions, and finally searches for a path from the starting point to the ending point that minimizes the estimated total construction cost while satisfying all obstacle avoidance conditions. This path is the recommended optimal path.

[0033] Finally, the entire site's road network is iteratively generated. For a wind farm with multiple turbine locations, the turbine closest to the existing access road is selected as the first starting point. Existing roads are discretized at certain intervals to obtain a series of pre-selected endpoints. The aforementioned path optimization algorithm is called to calculate the optimal path and cost from the turbine starting point to each pre-selected endpoint, and the path with the lowest cost is selected as the connecting road for that turbine. Subsequently, this newly generated road is incorporated into the "existing road network" data. Next, from the unconnected turbines, the turbine closest to the currently formed road network is selected as the next starting point, and the above optimization and connection process is repeated. This iterative process continues until all turbine locations are connected to the road network through newly built roads, thereby automatically generating the route selection scheme for the entire site's transportation roads. The intermediate and final results of the entire process can be visualized, output, and adjusted through a GIS platform.

[0034] In summary, by utilizing the technical solution described above in this invention, and through the integration of digital elevation model analysis, multi-constraint fusion, and intelligent path search algorithms, automated and quantitative route selection for wind farm transportation roads in complex terrain environments is achieved. This enables the planning process to accurately avoid unfavorable terrain, significantly improve the matching degree between the route and the terrain, and enhance engineering safety, while simultaneously optimizing the overall road construction cost. Ultimately, this results in a comprehensive improvement in design efficiency and accuracy, effective control of engineering investment and risks, and a shortened project lead time.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automatic route selection method for wind farm transportation routes based on digital elevation, characterized in that, Includes the following steps: S1. Obtain vector data of wind field range and ground feature distribution; wherein, the wind field range serves as the limiting area for automatic road design, and the ground feature distribution serves as the obstacles for automatic road design; S2. Obtain the digital elevation model (DEM) data within the wind field area, and perform slope analysis on the DEM data to determine the area where the slope exceeds a preset threshold as the slope restriction area. S3. Based on the wind field range, ground feature distribution, and slope restriction area, determine the avoidance conditions, and discretize the wind field range into multiple grid units; Calculate the passage cost corresponding to each of the grid cells, wherein the passage cost is positively correlated with the terrain complexity of the corresponding grid cell; Under the premise of satisfying the avoidance conditions, the A* path planning algorithm is used in the grid cell to search for the optimal path from the starting point to the ending point with the goal of minimizing the total road construction cost. S4. Taking the wind turbine locations within the wind farm as the starting point of the road and the existing road network connecting to or around the wind farm as the target, generate a route selection scheme for the newly built transportation road of the wind farm based on the optimal path.

2. The automatic route selection method for wind farm transportation routes based on digital elevation as described in claim 1, characterized in that, In step S2, the preset threshold is greater than or equal to 50 degrees.

3. The automatic route selection method for wind farm transportation routes based on digital elevation as described in claim 1, characterized in that, The terrain complexity is measured by the elevation standard deviation within the corresponding grid cell.

4. The automatic route selection method for wind farm transportation routes based on digital elevation as described in claim 1, characterized in that, The path planning algorithm is an improved A algorithm, and the cost in the evaluation function of the improved A algorithm is represented by the road construction cost, which includes at least the earthwork volume.

5. The automatic route selection method for wind farm transportation routes based on digital elevation as described in claim 4, characterized in that, The earthwork volume is calculated as follows: The optimal path is divided into multiple road segments. For each road segment, the areas A1 and A2 of its two adjacent cross-sections are obtained based on the DEM data, and the distance L between the two cross-sections is determined. Then, the earthwork volume V of this road segment is: ； The total earthwork volume V of the optimal path is the sum of the earthwork volumes of each road segment: ； Where n is the total number of cross sections.

6. The automatic route selection method for wind farm transportation routes based on digital elevation as described in claim 1, characterized in that, Step S4 specifically includes: S41. Determine the wind turbine locations within the wind farm as the starting points of the roads, and sample the existing roads within the wind farm to obtain multiple pre-selected endpoints; S42. Select the wind turbine location closest to the existing road as the calculation starting point, calculate the path cost from the calculation starting point to each of the pre-selected endpoints, and select the path with the lowest path cost as the new road corresponding to the current wind turbine location. S43. Incorporate the newly built road into the existing road, and repeat step S42 until all wind turbine locations are connected to the existing road through the newly built road.

7. The automatic route selection method for wind farm transportation routes based on digital elevation as described in claim 6, characterized in that, If no existing road exists, an access road will be manually planned as the initial existing road.

8. The automatic route selection method for wind farm transportation routes based on digital elevation as described in claim 1, characterized in that, The resolution of the DEM data is 30 meters, and the vertical accuracy is better than 5 meters.

9. The automatic route selection method for wind farm transportation roads based on digital elevation as described in claim 1, characterized in that, The vector data type for the wind field range is Polygon or MultiPolygon.

10. The automatic route selection method for wind farm transportation roads based on digital elevation as described in claim 1, characterized in that, In step S3, the total cost of road construction is the objective function, and the path planning algorithm searches for the optimal path by minimizing this objective function.

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