A complex mountain photovoltaic grounding system design method and system based on adaptive optimization clustering

Abstract

The present application relates to photovoltaic engineering design technical field, especially to a kind of complex mountain photovoltaic grounding system design method and system based on adaptive optimization clustering.The method obtains photovoltaic field vector drawing data, extracts group string spatial feature to construct high-dimensional space point set distribution model;Adaptive density clustering is carried out to the model to divide multiple electrical clusters, and the closed grounding grid contour of each cluster is generated by constructing entity geometry and morphological operation;With electrical cluster as node, cluster space distance as edge weight, construct weighted complete graph, and generate the shortest main trunk grounding path connecting each cluster by minimum spanning tree algorithm;Through loop search algorithm, add redundant edges to main trunk path to construct multi-link closed loop network, generate vertical grounding pole coordinates along the path by combining spatial interpolation, and output design drawing.The present application can automatically adapt to complex mountain arrangement scene without manual intervention, can greatly reduce grounding network material cost, and significantly improve system electrical safety.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic engineering design technology, and in particular to a design method and system for complex mountain photovoltaic grounding systems based on adaptive optimization clustering. Background Technology

[0002] As photovoltaic (PV) power generation technology expands into complex terrains such as mountains and hills, the arrangement of PV arrays exhibits highly discrete and unstructured characteristics. A corresponding grounding system is often designed and implemented within the PV field, and its design is a core element in ensuring the personal safety of power plant maintenance personnel and the electrical safety of PV equipment. Current PV field grounding system design methods often rely on engineers manually wiring using general CAD software. However, existing technologies still face the following technical challenges when dealing with complex mountainous environments: First, the sheer volume of discrete photovoltaic string data leads to low processing efficiency for engineers and a lack of envelope adaptive capabilities. In practical applications, complex mountain photovoltaic projects often involve tens of thousands of photovoltaic strings arranged at varying angles, including those arranged due south along the coordinate direction and those arranged along the slope of the mountain terrain. This results in a huge workload for manually drawing grounding grids, and it is difficult to quickly generate envelope grids that fit the edges when dealing with the slight misalignment between strings caused by terrain undulations.

[0003] Secondly, existing technologies lack global optimization capabilities, resulting in high redundancy in grounding materials. Manual wiring is often based on local experience and visual intuition, making it impossible to achieve the mathematically optimal solution for the connection path between electrical nodes across the entire field. This leads to a huge amount of material used for grounding flat steel and cables, significantly increasing project costs.

[0004] On the other hand, existing technologies suffer from poor topological reliability and non-compliant grounding electrode placement. Grounding networks require dual-link or multi-link redundancy to ensure electrical reliability. Manually drawn tree-like or simple mesh paths are prone to islanding effects, and manually placing vertical grounding electrodes makes it difficult to strictly balance spacing compliance and terrain avoidance requirements. Summary of the Invention

[0005] To address the technical problems of existing grounding system design methods, such as low efficiency in processing massive amounts of data, lack of effective identification of string misalignment caused by minor terrain changes, poor reliability of generated schemes, lack of global optimization capabilities, high redundancy of grounding materials, and difficulty in meeting the needs of practical engineering applications, this invention provides a design method and system for complex mountain photovoltaic grounding systems based on adaptive optimization clustering.

[0006] One method for designing complex mountain photovoltaic grounding systems based on adaptive optimization clustering includes the following steps: S1. Obtain vector map data of the target mountain photovoltaic field, extract spatial feature information of each photovoltaic string, and construct a high-dimensional spatial point set distribution model; S2. Based on the high-dimensional spatial point set distribution model, adaptive density clustering is performed on each photovoltaic string to divide it into multiple topologically independent electrical clusters, and a closed grounding grid outline enveloping each electrical cluster is generated by constructing a solid geometry algorithm. S3. Based on the closed grounding grid outline, construct a weighted complete graph with each electrical cluster as a node and the spatial distance between electrical clusters as the edge weight, and generate the shortest main grounding path connecting each electrical cluster in the weighted complete graph using the minimum spanning tree algorithm; S4. Based on the shortest main grounding path, and combined with the weighted complete graph, a multi-link closed loop network is constructed using a loop search algorithm. Along the contour of the multi-link closed loop network and the closed grounding grid, the coordinates of each vertical grounding pole are generated using a spatial interpolation algorithm to obtain the photovoltaic grounding system design drawing.

[0007] Furthermore, step S1 includes the following sub-steps: S101. Obtain vector map data of the target mountain photovoltaic field area, and extract the geometric centroid coordinates, rotation vectors, and boundary dimensions of each photovoltaic string as spatial feature information; S102. Based on the spatial feature information, construct a geometric mapping model to map each photovoltaic string with different orientations to a unified global coordinate system of the target mountain photovoltaic field area, thereby obtaining a high-dimensional spatial point set distribution model.

[0008] Furthermore, step S2 includes the following sub-steps: S201. Based on the high-dimensional spatial point set distribution model, construct the R-Tree spatial index structure; S202. Based on the R-Tree spatial index structure, a neighborhood search is performed on the high-dimensional spatial point set distribution model to obtain adjacent photovoltaic strings within a preset distance threshold. Combined with the spatial density characteristics of the photovoltaic string arrangement, adaptive density clustering is performed on the adjacent photovoltaic strings to obtain multiple topologically independent electrical clusters. S203. For each electrical cluster, a preliminary geometric figure is obtained by performing a union operation on all photovoltaic strings within the cluster using a solid geometry algorithm. S204. Based on the preliminary geometry, and using morphological expansion and erosion calculations, generate a closed grounding grid outline that fits the edge of the photovoltaic string.

[0009] Furthermore, step S204 also includes the following sub-steps: S2041. Within the target mountain photovoltaic field coordinate system, when the initial geometric shape is a scene arranged due south, perform morphological expansion and erosion operations based on the north-south axis direction of the target mountain photovoltaic field coordinate system to generate a rectangular grounding grid outline that is parallel or perpendicular to the direction of the photovoltaic strings. S2042. Within the coordinate system of the target mountain photovoltaic field area, when the initial geometry is a scene arranged along the slope following the terrain undulations, a polygonal grounding grid outline is generated through morphological dilation and erosion operations.

[0010] Furthermore, step S3 includes the following sub-steps: S301. Extract the centroid coordinates of each electrical cluster based on the closed grounding grid outline, calculate the Euclidean distance between each pair of centroids of each electrical cluster, and generate the spatial distance matrix between electrical clusters; S302. Construct a weighted complete graph using the spatial distance matrix between electrical clusters as edge weights, and extract the acyclic connected subgraph with the minimum sum of edge weights that connects all electrical clusters using the minimum spanning tree algorithm, as the shortest main grounding path.

[0011] Furthermore, step S4 includes the following sub-steps: S401. Based on the shortest main grounding path, a loop search algorithm is used to search for the second shortest main grounding path that avoids the shortest main grounding path in the weighted complete graph, which is then used as a redundant grounding path. S402. The redundant grounding path is superimposed with the shortest main grounding path to obtain a multi-link closed loop network to meet the N-1 reliability criterion of the power system. S403. Extract the total path of the horizontal grounding body based on the outline of the multi-link closed loop network and the closed grounding grid; S404. Based on the total path of the horizontal grounding electrode, and using preset distance constraints and terrain avoidance conditions, perform equidistant calculations along the total path of the horizontal grounding electrode using a spatial interpolation algorithm to generate the coordinates of each vertical grounding electrode point and construct a photovoltaic grounding system design drawing.

[0012] This invention also provides a design system for complex mountain photovoltaic grounding systems based on adaptive optimization clustering. This system is implemented based on any of the aforementioned design methods for complex mountain photovoltaic grounding systems based on adaptive optimization clustering, and includes: The data parsing and mapping module is used to extract the spatial feature information of each photovoltaic string based on the vector drawing data of the target mountain photovoltaic field and to construct a high-dimensional spatial point set distribution model. The adaptive clustering and envelope generation module is used to perform adaptive density clustering on each photovoltaic string according to the high-dimensional spatial point set distribution model, divide it into multiple topologically independent electrical clusters, and generate the closed grounding grid outline that encloses each electrical cluster through a solid geometry construction algorithm. The backbone topology optimization module is used to construct a weighted complete graph based on the closed grounding grid outline, with each electrical cluster as a node and the spatial distance between electrical clusters as the edge weight, and to generate the shortest backbone grounding path connecting each electrical cluster in the weighted complete graph through the minimum spanning tree algorithm. The grounding electrode layout module is used to construct a multi-link closed loop network based on the shortest main grounding path using a loop search algorithm. It then generates the coordinates of each vertical grounding electrode point along the contour of the multi-link closed loop network and the closed grounding grid using a spatial interpolation algorithm, and outputs the photovoltaic grounding system design drawing.

[0013] A computer-readable storage medium for storing a computer program that, when run on a computer, causes the computer to perform any of the above-mentioned design methods for complex mountain photovoltaic grounding systems based on adaptive optimization clustering.

[0014] An electronic device includes: a memory for storing a computer program; and a processor for executing the computer program to implement a design method for a complex mountain photovoltaic grounding system based on adaptive optimization clustering, as described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention utilizes spatial point set modeling and adaptive density clustering, based on a combination of R-Tree and DBSCAN, to reduce unstructured components into topologically independent electrical clusters. Furthermore, by employing morphological dilation and erosion operations, it automatically absorbs terrain misalignment errors without complex coordinate conversions, generating a closed grounding grid perfectly aligned with slopes or due south, significantly reducing traditional manual design time and greatly improving design efficiency. Secondly, this invention abstracts electrical clusters as graph nodes, rigorously using the Minimum Spanning Tree (MST) algorithm in graph theory to find the shortest main grounding path connecting the entire field. This ensures global optimization of material usage based on data, effectively reducing the cost of grounding flat steel and cables, and significantly lowering project costs. Finally, based on the shortest trunk line, this invention further introduces a loop search algorithm to supplement suboptimal redundant edges, upgrading the fragile tree structure into a multi-link closed loop network satisfying the N-1 criterion. By combining spatial interpolation and terrain avoidance, it automatically completes the equidistant and compliant deployment of vertical grounding electrodes, eliminating safety risks caused by manual omissions or errors in connection. This constructs a highly reliable and practical photovoltaic field grounding system design network, effectively eliminating electrical hazards. Attached Figure Description

[0016] Figure 1 This is a flowchart of a design method for a complex mountain photovoltaic grounding system based on adaptive optimization clustering, as proposed in an embodiment of the present invention.

[0017] Figure 2This is a schematic diagram of the structure of a complex mountain photovoltaic grounding system design system based on adaptive optimization clustering, as proposed in an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of the closed grounding grid outline proposed in an embodiment of the present invention.

[0019] Figure 4 This is a schematic diagram of the grounding grid arranged along the slope proposed in an embodiment of the present invention.

[0020] Figure 5 This is a schematic diagram of the shortest main grounding path proposed in an embodiment of the present invention.

[0021] Figure 6 This is a schematic diagram of the overall layout of the grounding system proposed in an embodiment of the present invention.

[0022] Figure 7 This is a magnified schematic diagram of a local detail proposed in an embodiment of the present invention.

[0023] Figure 8 This is a schematic diagram of the terminal equipment structure of a complex mountain photovoltaic grounding system design method based on adaptive optimization clustering proposed in an embodiment of the present invention.

[0024] Figure 9 This is a schematic diagram of a computer-readable storage medium structure for a design method of a complex mountain photovoltaic grounding system based on adaptive optimization clustering, as proposed in an embodiment of the present invention.

[0025] In the diagram, 200 is the terminal device, 210 is the memory, 211 is the RAM, 212 is the cache memory, 213 is the ROM, 214 is the program / utility, 215 is the program module, 220 is the processor, 230 is the bus, 240 is the external device, 250 is the I / O interface, 260 is the network adapter, and 300 is the program product. Detailed Implementation

[0026] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, and not all of them. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0028] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0029] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or machine that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or machine. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or machine that includes said element.

[0030] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0031] Example 1: like Figure 1 As shown, this embodiment provides a design method for complex mountain photovoltaic grounding systems based on adaptive optimization clustering, including the following steps: S1. Obtain vector map data of the target mountain photovoltaic field, extract spatial feature information of each photovoltaic string, and construct a high-dimensional spatial point set distribution model; Furthermore, step S1 includes the following sub-steps: S101. Obtain vector map data of the target mountain photovoltaic field area, and extract the geometric centroid coordinates, rotation vectors, and boundary dimensions of each photovoltaic string as spatial feature information; S102. Based on the spatial feature information, construct a geometric mapping model to map each photovoltaic string with different orientations to a unified global coordinate system of the target mountain photovoltaic field area, thereby obtaining a high-dimensional spatial point set distribution model.

[0032] Specifically, this step analyzes the vector drawing data of the target mountain photovoltaic field, extracts the spatial feature information of the photovoltaic strings, and constructs a geometric mapping model. This solves the technical problem of automating the processing of photovoltaic strings with different orientations under complex terrain in the same coordinate system. Existing technologies typically rely on designers manually selecting elements or manually setting local coordinate systems for different orientations to perform data transformation when solving this problem. The technical solution of this method unifies the extracted local geometric coordinates into the global coordinate system through the geometric mapping model, avoiding manual coordinate conversion operations, improving the efficiency of the initial data analysis, and providing a standardized data model for subsequent automated clustering and envelope calculation.

[0033] Specifically, the above implementation principle is as follows: First, the block reference data of each photovoltaic string is identified from the input target mountain photovoltaic field vector map. The centroid coordinates, rotation angles and boundary dimensions are extracted as spatial feature information. Then, a geometric mapping model is established based on the extracted spatial feature information. Through this model, the spatial parameters of each photovoltaic string with different orientations are uniformly transformed into the global coordinate system of the target mountain photovoltaic field. Finally, a high-dimensional spatial point set distribution model composed of the global coordinate parameters of all strings is generated.

[0034] S2. Based on the high-dimensional spatial point set distribution model, adaptive density clustering is performed on each photovoltaic string to divide it into multiple topologically independent electrical clusters, and a closed grounding grid outline enveloping each electrical cluster is generated by constructing a solid geometry algorithm. Furthermore, step S2 includes the following sub-steps: S201. Based on the high-dimensional spatial point set distribution model, construct the R-Tree spatial index structure; S202. Based on the R-Tree spatial index structure, a neighborhood search is performed on the high-dimensional spatial point set distribution model to obtain adjacent photovoltaic strings within a preset distance threshold. Combined with the spatial density characteristics of the photovoltaic string arrangement, adaptive density clustering is performed on the adjacent photovoltaic strings to obtain multiple topologically independent electrical clusters. S203. For each electrical cluster, a preliminary geometric figure is obtained by performing a union operation on all photovoltaic strings within the cluster using a solid geometry algorithm. S204. Based on the preliminary geometry, and using morphological expansion and erosion calculations, generate a closed grounding grid outline that fits the edge of the photovoltaic string.

[0035] Furthermore, step S204 also includes the following sub-steps: S2041. Within the target mountain photovoltaic field coordinate system, when the initial geometric shape is a scene arranged due south, perform morphological expansion and erosion operations based on the north-south axis direction of the target mountain photovoltaic field coordinate system to generate a rectangular grounding grid outline that is parallel or perpendicular to the direction of the photovoltaic strings. S2042. Within the coordinate system of the target mountain photovoltaic field area, when the initial geometry is a scene arranged along the slope following the terrain undulations, a polygonal grounding grid outline is generated through morphological dilation and erosion operations.

[0036] Specifically, this step utilizes R-Tree spatial indexing and density clustering algorithms to divide photovoltaic strings into electrical clusters, and generates closed grounding grid outlines based on solid geometry construction algorithms. This solves the technical problem that it is difficult to automatically divide grounding grids for massive discrete photovoltaic strings and that the grid envelope cannot adapt to complex terrain. Existing technologies typically solve this problem by manually selecting local components and manually drawing rectangular or polygonal boundaries to determine the grounding grid, which cannot automatically adapt to the misaligned arrangement of components. The technical solution of this method first uses density clustering to identify sets of strings with close physical distances, and then performs union, morphological dilation, and erosion operations on the strings within the set, so that the generated grounding envelope boundary can automatically fit the edge of the string. At the same time, rectangular or polyline outlines are generated for due south or downhill scenes, respectively, improving the fit between the grid outline and the actual engineering terrain.

[0037] Specifically, the above implementation principle is as follows: an R-Tree spatial index is established based on a high-dimensional spatial point set distribution model. This index is used to retrieve adjacent photovoltaic strings within a preset distance threshold. Combined with density features, clustering is performed to generate multiple independent electrical clusters. For each electrical cluster, a geometric union operation is performed on all photovoltaic strings within it to form a preliminary geometric shape. Expansion and erosion operations are performed on the preliminary geometric shape. When it is determined to be arranged due south, a rectangular grid outline is generated along the global north-south axis. When it is determined to be arranged along a slope, a polygonal grid outline is directly generated based on the operation results.

[0038] S3. Based on the closed grounding grid outline, construct a weighted complete graph with each electrical cluster as a node and the spatial distance between electrical clusters as the edge weight, and generate the shortest main grounding path connecting each electrical cluster in the weighted complete graph using the minimum spanning tree algorithm; Furthermore, step S3 includes the following sub-steps: S301. Extract the centroid coordinates of each electrical cluster based on the closed grounding grid outline, calculate the Euclidean distance between each pair of centroids of each electrical cluster, and generate the spatial distance matrix between electrical clusters; S302. Construct a weighted complete graph using the spatial distance matrix between electrical clusters as edge weights, and extract the acyclic connected subgraph with the minimum sum of edge weights that connects all electrical clusters using the minimum spanning tree algorithm, as the shortest main grounding path.

[0039] Specifically, this step constructs a weighted complete graph by calculating the Euclidean distance between electrical clusters and applies the minimum spanning tree algorithm to generate the main grounding path. This solves the technical problem of redundant grounding trunk materials caused by the lack of a global perspective in traditional manual wiring. Existing technologies mainly rely on designers to connect adjacent photovoltaic arrays one by one based on visual experience from two-dimensional drawings, making it difficult to achieve the optimal solution for the total length of the connection between all nodes across the entire field. The technical solution of this method abstracts each independent electrical cluster into a graph node and inputs the spatial distance as the edge weight into the minimum spanning tree algorithm to obtain the minimum connected subgraph that connects all nodes, thereby reducing the total length of the grounding trunk and the material cost of the system.

[0040] Specifically, the above implementation principle is as follows: Based on the generated closed grounding grid outline, the centroid coordinates of each electrical cluster are extracted, the Euclidean distance between each pair of centroids of all electrical clusters is calculated, and the Euclidean distances are summarized to generate a spatial distance matrix between electrical clusters; each electrical cluster is used as a node of the graph, and the spatial distance matrix is ​​used as the edge weight connecting the nodes to establish a weighted complete graph; the weighted complete graph is input into the minimum spanning tree algorithm to calculate and extract the acyclic subgraph that connects all electrical clusters and has the smallest sum of edge weights, which is used as the shortest main grounding path connecting the entire field.

[0041] S4. Based on the shortest main grounding path, and combined with the weighted complete graph, a multi-link closed loop network is constructed using a loop search algorithm. Along the contour of the multi-link closed loop network and the closed grounding grid, the coordinates of each vertical grounding pole are generated using a spatial interpolation algorithm to obtain the photovoltaic grounding system design drawing. Furthermore, step S4 includes the following sub-steps: S401. Based on the shortest main grounding path, a loop search algorithm is used to search for the second shortest main grounding path that avoids the shortest main grounding path in the weighted complete graph, which is then used as a redundant grounding path. S402. Superimpose the redundant grounding path with the shortest main grounding path to obtain a multi-link closed loop network; S403. Extract the total path of the horizontal grounding body based on the outline of the multi-link closed loop network and the closed grounding grid; S404. Based on the total path of the horizontal grounding electrode, and using preset distance constraints and terrain avoidance conditions, perform equidistant calculations along the total path of the horizontal grounding electrode using a spatial interpolation algorithm to generate the coordinates of each vertical grounding electrode point and construct a photovoltaic grounding system design drawing.

[0042] Specifically, this step utilizes a loop search algorithm to add redundant edges to the main path to construct a closed loop network, and combines it with a spatial interpolation algorithm to automatically generate vertical grounding electrodes. This solves the technical problems of single-point circuit breakage hazards in a single tree-like main trunk and the low efficiency of manual grounding electrode deployment. Existing technologies typically address this problem by manually adding lines between arrays based on experience to form a grid, and manually placing grounding electrode elements at equal intervals along the wiring path. This process is time-consuming and prone to missing isolated nodes or ignoring terrain obstacles. The technical solution of this approach automatically searches for suboptimal edges to form a closed loop based on the shortest main trunk, ensuring that the topological connectivity of the grounding network satisfies multi-link redundancy. At the same time, based on the total path and distance constraints of the horizontal grounding bodies, the coordinates of the grounding electrodes are directly calculated through spatial interpolation, and invalid points that trigger avoidance conditions are eliminated, improving the electrical safety and computational efficiency of the system design.

[0043] Specifically, the implementation principle is as follows: Based on the shortest main grounding path, a loop search algorithm is applied to find other suboptimal connection edges that avoid the current main path in the weighted complete graph, which serve as redundant grounding paths; these redundant grounding paths are superimposed on the shortest main grounding path to form a multi-link closed loop network with multiple closed loops; the multi-link closed loop network is merged with the closed grounding grid outline to extract the total path of the horizontal grounding body; according to the preset distance constraints and terrain avoidance conditions, a spatial interpolation algorithm is used to calculate equidistant coordinates along the total path, and after filtering out the points in the terrain avoidance area, the coordinates of the vertical grounding electrode are output to generate the final photovoltaic grounding system design drawing.

[0044] Example 2

[0045] like Figure 2 As shown, as a preferred embodiment of the above embodiments, a design system for complex mountain photovoltaic grounding systems based on adaptive optimization clustering is provided. This system is implemented based on any of the above-described design methods for complex mountain photovoltaic grounding systems based on adaptive optimization clustering, and includes: The data parsing and mapping module is used to extract the spatial feature information of each photovoltaic string based on the vector drawing data of the target mountain photovoltaic field and to construct a high-dimensional spatial point set distribution model. The adaptive clustering and envelope generation module is used to perform adaptive density clustering on each photovoltaic string according to the high-dimensional spatial point set distribution model, divide it into multiple topologically independent electrical clusters, and generate the closed grounding grid outline that encloses each electrical cluster through a solid geometry construction algorithm. The backbone topology optimization module is used to construct a weighted complete graph based on the closed grounding grid outline, with each electrical cluster as a node and the spatial distance between electrical clusters as the edge weight, and to generate the shortest backbone grounding path connecting each electrical cluster in the weighted complete graph through the minimum spanning tree algorithm. The grounding electrode layout module is used to construct a multi-link closed loop network based on the shortest main grounding path using a loop search algorithm. It then generates the coordinates of each vertical grounding electrode point along the contour of the multi-link closed loop network and the closed grounding grid using a spatial interpolation algorithm, and outputs the photovoltaic grounding system design drawing.

[0046] Specifically, the implementation principle of the above embodiments is as follows: First, the data parsing and mapping module obtains vector map data of the target mountain photovoltaic field area, extracts the geometric centroid coordinates, rotation vectors and boundary dimensions of each photovoltaic string as spatial feature information, and constructs a geometric mapping model based on the spatial feature information to uniformly map photovoltaic strings with different orientations to the global coordinate system, generating a high-dimensional spatial point set distribution model. Then, the adaptive clustering and envelope generation module receives the high-dimensional spatial point set distribution model, performs neighborhood retrieval on the model by constructing an R-Tree spatial index structure, obtains adjacent photovoltaic strings within a preset distance threshold, performs adaptive density clustering based on the spatial density characteristics of the string arrangement, divides multiple topologically independent electrical clusters, and for each electrical cluster, performs a union operation on the strings within the cluster by constructing a solid geometry algorithm to obtain a preliminary geometric shape, and generates a closed grounding grid outline that fits the edge of the string based on morphological dilation and erosion operations. Next, the backbone topology optimization module extracts the centroid coordinates of each electrical cluster based on the generated closed grounding grid outline, calculates the Euclidean distance between each pair of centroids to generate a distance matrix, constructs a weighted complete graph with electrical clusters as nodes and the distance matrix as edge weights, and extracts the acyclic connected subgraph with the minimum total edge weights from the weighted complete graph using the minimum spanning tree algorithm, generating the shortest backbone grounding path connecting each electrical cluster. Finally, the grounding electrode deployment module, based on the shortest main grounding path, uses a loop search algorithm to search for the second shortest main grounding path that avoids the main path in the weighted complete map as a redundant grounding path. The redundant grounding path is superimposed with the main path to form a multi-link closed loop network. Then, the total path of the horizontal grounding body formed by the multi-link closed loop network and the closed grounding grid outline is extracted. Based on the preset distance constraints and terrain avoidance conditions, the spatial interpolation algorithm is used to perform equidistant calculations along the total path of the horizontal grounding body to generate the coordinates of each vertical grounding electrode point and output the final photovoltaic grounding system design drawing. Furthermore, in the entire system processing flow, the adaptive clustering and envelope generation module performs classification judgment based on the initial geometric features of the input when generating the mesh outline. When the drawing is determined to be a south-facing scene, the algorithm forces morphological operations based on the north-south axis of the coordinate system to generate a rectangular grounding mesh outline. When it is determined to be a slope-facing scene, the algorithm directly generates a polygonal grounding mesh outline based on the envelope boundary of morphological dilation and erosion. Thus, it achieves automated matching and processing of different mountain terrain layout features without human intervention.

[0047] Example 3

[0048] Based on Examples 1 and 2, there is a practical application scenario for the design of a grounding system for a mountain photovoltaic system. This scenario adopts a complex mountain photovoltaic grounding system design method and system based on adaptive optimization clustering as described in the above examples. The project is located in a typical complex mountainous terrain, and the component arrangement exhibits highly discrete characteristics due to the terrain undulations. Specifically, the processing flow is as follows: Step 1: Using the data parsing and mapping module, obtain vector map data of the target mountain photovoltaic field, extract the spatial feature information of each photovoltaic string, and construct a high-dimensional spatial point set distribution model; Specifically, the system automatically identifies and reads information such as photovoltaic strings, terrain, and roads in the CAD vector file, and identifies the spatial location and orientation of all 10,284 photovoltaic modules by parsing the Block Reference primitive information. Specifically, since the original CAD drawings contain a large number of disordered lines, this embodiment achieves data structuring through the following sub-steps: (1) Obtain vector drawing data of the target mountain photovoltaic field, analyze the BlockReference attribute in the drawing, and extract the geometric centroid coordinates (X, Y), rotation vector (angle) and boundary dimensions (length, width) of each photovoltaic string as spatial feature information.

[0049] (2) Due to the undulating terrain, the normal directions of each string are different. Based on the spatial feature information, a geometric mapping model is constructed to map each photovoltaic string with different arrangement orientations to the global coordinate system of the target mountain photovoltaic field area, thereby transforming the discrete physical primitives into a high-dimensional spatial point set distribution model that is easy for the algorithm to process.

[0050] Specifically, in this step, by parsing the vector map data of the photovoltaic field, the geometric centroid coordinates, rotation vectors, and boundary dimensions of the photovoltaic module primitives are extracted to construct a high-dimensional spatial point set distribution model. This invention constructs an adaptive multi-directional geometric mapping model, which can handle both south-facing and slope-following arrangements through a unified geometric topological logic. For non-orthogonal scenarios such as slope-following arrangements, the algorithm adaptively generates a grounding grid that closely fits the terrain and module edges through high-precision geometric envelope calculations, without the need for pre-setting complex coordinate transformation rules, thus achieving universal support for different layout forms in complex mountainous photovoltaic field areas.

[0051] Step 2: Using the adaptive clustering and envelope generation module, adaptive density clustering is performed on each photovoltaic string according to the high-dimensional spatial point set distribution model to divide it into multiple topologically independent electrical clusters. Then, a closed grounding grid outline that encloses each electrical cluster is generated by constructing a solid geometry algorithm.

[0052] Specifically, the system constructs an R-Tree spatial index structure to accelerate neighborhood retrieval, applies a density-based clustering algorithm to automatically divide all field components into 27 topologically independent electrical clusters, performs a geometric union operation on the components within each cluster for each cluster, and executes a 6-meter outward buffer operation to generate a continuous and closed grounding grid profile, such as... Figure 3 As shown.

[0053] Specifically, traditional manual zoning suffers from blurry boundaries. This embodiment addresses this issue through the following rigorous mathematical algorithm: (1) Based on the high-dimensional spatial point set distribution model, construct the R-Tree spatial index structure to greatly improve the efficiency of spatial query.

[0054] (2) Based on the R-Tree spatial index structure, a neighborhood search is performed on the high-dimensional spatial point set distribution model to quickly obtain adjacent photovoltaic strings within a preset distance threshold (such as the tolerance of the spacing between the front and rear rows). Then, combined with the spatial density characteristics of the photovoltaic string arrangement (i.e. the density of the strings in the region), adaptive density clustering (such as the improved DBSCAN algorithm) is performed on the adjacent photovoltaic strings to automatically cluster the strings that are close in distance and densely arranged to obtain multiple topologically independent "electrical clusters".

[0055] (3) For each electrical cluster, Boolean union operation is performed on all photovoltaic strings in the cluster by constructing solid geometry (CSG) algorithm to eliminate the overlapping space inside the strings and obtain the overall preliminary geometry.

[0056] (4) Based on the preliminary geometry, a closed grounding grid outline that fits the edge of the photovoltaic string is generated based on morphological dilation and erosion operations.

[0057] Specifically, this embodiment optimizes the algorithm for two typical scenarios commonly seen in complex mountain photovoltaic projects: "south-facing arrangement" and "slope-facing arrangement." This ensures that the generated grounding system complies with electrical specifications and is easy to construct. First, within the coordinate system of the target mountain photovoltaic field, for south-facing module arrays that aim for the best illumination angle, the algorithm locks the north-south axis of the global coordinate system. Based on the north-south axis of the global coordinate system, morphological expansion and erosion are performed to forcibly straighten the edges and generate a regular "rectangular grounding grid outline" that is parallel or perpendicular to the direction of the photovoltaic strings. This ensures that the direction of the grounding body is strictly parallel or perpendicular to the module array, facilitating construction layout.

[0058] Secondly, within the coordinate system of the target mountain photovoltaic field, for areas with significant terrain undulations, the modules are often arranged along the slope. When the initial geometry is a scenario of arrangement along the slope following the terrain undulations (with staggered string orientations), this invention abandons the complex coordinate system rotation operations and forced rectangularization of traditional manual design, and innovatively introduces a global morphological buffer algorithm based on Constructed Solid Geometry (CSG). By performing unified geometric dilation and erosion operations on the module cluster, the algorithm can automatically absorb small angular deviations between modules, generating a highly robust stepped or polygonal grounding envelope, resulting in... Figure 4 The grounding grid shown is arranged along the slope. This design not only avoids connection failures caused by terrain errors, but also the generated zigzag path naturally has a certain thermal expansion and contraction compensation capability, preventing the grounding flat steel from breaking. It is particularly suitable for mountainous environments with large temperature differences.

[0059] Specifically, in this step, spatial indexing algorithms (such as R-Tree) are used to accelerate neighborhood search, and an improved density-based clustering algorithm (DBSCAN) is employed to automatically divide discrete photovoltaic modules into several topologically compact electrical clusters based on the inherent arrangement patterns of the photovoltaic strings. For each electrical cluster, morphological operations (dilation and erosion) in Constructed Solid Geometry (CSG) are applied to generate a closed grounding mesh profile that tightly encloses all modules in the cluster.

[0060] Step 3: Using the backbone topology optimization module, a weighted complete graph is constructed based on the closed grounding grid outline, with each electrical cluster as a node and the spatial distance between electrical clusters as the edge weight. The shortest backbone grounding path connecting each electrical cluster is then generated using the minimum spanning tree algorithm.

[0061] Specifically, the Euclidean distances between the centroids of the 27 electrical clusters are calculated to construct a fully connected graph. The Minimum Spanning Tree (MST) algorithm is used to generate 26 main connecting lines to ensure electrical connectivity throughout the field. Furthermore, a graph theory loop search algorithm is applied to automatically plan 88 redundant connecting paths, forming the shortest main grounding path that satisfies N-1 reliability, such as... Figure 5 As shown.

[0062] Specifically, in order to achieve the absolute minimization of material costs, the following implementation measures will be taken: (1) Extract the geometric centroid coordinates of each electrical cluster based on the closed grounding grid outline generated in step S2. Calculate the Euclidean distance between each pair of centroids of each electrical cluster using the distance formula to generate the spatial distance matrix between electrical clusters.

[0063] (2) In the graph theory model, each electrical cluster is regarded as a "node", and a weighted complete graph is constructed using the spatial distance matrix between electrical clusters as "edge weights". The minimum spanning tree (MST) algorithm (such as Prim's algorithm or Kruskal's algorithm) is applied to extract an acyclic connected subgraph from the weighted complete graph that can connect all electrical clusters and has the minimum total length of all connections (edge ​​weights). This subgraph serves as the shortest main grounding path in the entire field, theoretically eliminating the waste of materials from manual wiring.

[0064] Specifically, in this step, a weighted complete graph is constructed with each electrical cluster as a node, and the Euclidean distance matrix between clusters is calculated as the edge weights. The Prim algorithm or Kruskal algorithm is applied to generate the minimum spanning tree (MST) connecting all electrical clusters, establishing the globally shortest trunk grounding path and achieving the theoretical optimization of material usage.

[0065] Step 4: Using the grounding electrode layout module, based on the shortest main grounding path and combined with the weighted complete graph, construct a multi-link closed loop network using a loop search algorithm, and generate the coordinates of the vertical grounding electrode points to obtain the photovoltaic grounding system design drawing; Specifically, the algorithm automatically plans the vertical grounding electrode locations along the generated horizontal grounding electrode path (envelope + connecting line). The algorithm incorporates an avoidance mechanism and distance verification logic, ultimately generating 341 vertical grounding electrode coordinates, as shown below. Figure 6 The overall layout of the grounding system is shown in the enlarged schematic diagram below. Figure 7 As shown.

[0066] Specifically, since there may be a single point of failure risk when there is only a tree-like trunk, it is necessary to construct a redundant ring network to improve the reliability of the design scheme, as detailed below: (1) Based on the shortest main grounding path, the loop search algorithm is used to search for the "second shortest edge" (i.e. the second best path) that avoids the selected main path in the weighted complete graph, and then use it as a redundant grounding path.

[0067] (2) The redundant grounding path is superimposed with the shortest main grounding path. At this time, a closed loop is generated in the original loopless tree structure, resulting in a multi-link closed loop network, which ensures that if any part of the grounding wire is disconnected, the equipment can still be grounded through another link, thus satisfying the N-1 electrical reliability criterion.

[0068] (3) Based on the multi-link closed ring network and the closed grounding grid outline of each cluster, the path is merged and the total path of the horizontal grounding body of the whole field is extracted.

[0069] (4) Based on the total path of the horizontal grounding electrode, and based on the preset distance constraints (such as the specification requiring a grounding electrode to be driven every 10 meters) and terrain avoidance conditions (such as avoiding exposed rock areas or drainage ditches), the algorithm performs equidistant calculations along the total path of the horizontal grounding electrode using a spatial interpolation algorithm. After automatically filtering out invalid points that trigger terrain avoidance conditions, the algorithm generates the precise coordinates of each vertical grounding electrode (pile), and finally renders and outputs the photovoltaic grounding system design drawing.

[0070] Specifically, in this step, based on the MST backbone path, a graph-based loop search strategy is used to add suboptimal connection paths, constructing a dual-link or multi-link closed loop network to meet the N-1 reliability criterion. Simultaneously, along the generated horizontal grounding electrode path, combined with distance constraints, a spatial interpolation algorithm is used to automatically plan the placement points of the vertical grounding electrodes.

[0071] Example 4

[0072] like Figure 8 As shown, this embodiment proposes a terminal device for a design method of complex mountain photovoltaic grounding system based on adaptive optimization clustering. The terminal device includes at least one memory, at least one processor, and a bus connecting different platform systems.

[0073] The memory may include readable media in the form of volatile memory, such as RAM 211 and / or cache memory, and may further include ROM 213.

[0074] The memory also stores a computer program that can be executed by a processor, causing the processor to perform any of the above-described methods for designing complex mountain photovoltaic grounding systems based on adaptive optimization clustering in this application. The specific implementation and technical effects are consistent with those described in the embodiments of the above methods, and some details will not be repeated here. The memory may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0075] Accordingly, the processor can execute the aforementioned computer programs, as well as executable programs / utilities.

[0076] A bus can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus structures.

[0077] The terminal device can also communicate with one or more external devices, such as keyboards, pointing devices, Bluetooth devices, etc., and with one or more devices capable of interacting with the terminal device, and / or with any device that enables the terminal device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed through I / O interfaces. Furthermore, the terminal device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter can communicate with other modules of the terminal device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the terminal device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0078] Example 5

[0079] like Figure 9 As shown in Example 1, this example proposes a computer-readable storage medium for a design method of a complex mountain photovoltaic grounding system based on adaptive optimization clustering. The computer-readable storage medium stores instructions that, when executed by a processor, implement any of the aforementioned design methods for a complex mountain photovoltaic grounding system based on adaptive optimization clustering. The specific implementation method and the achieved technical effects are consistent with those described in the examples above, and some details will not be repeated.

[0080] This embodiment provides a program product for implementing the above-described method, which may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this embodiment, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0081] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet through an Internet service provider).

[0082] This invention is described from the perspectives of its intended use, effectiveness, progress, and novelty. Its practical and progressive features meet the functional enhancement and use requirements emphasized by the Patent Law. The above description and drawings are merely preferred embodiments of this application and are not intended to limit this application. Therefore, all structures, devices, features, etc., that are similar to or identical to those of this application, i.e., all equivalent substitutions or modifications made in accordance with the scope of this patent application, shall fall within the scope of protection of this patent application.

[0083] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A design method for complex mountain photovoltaic grounding systems based on adaptive optimization clustering, characterized in that, Includes the following steps: S1. Obtain vector map data of the target mountain photovoltaic field, extract spatial feature information of each photovoltaic string, and construct a high-dimensional spatial point set distribution model; S2. Based on the high-dimensional spatial point set distribution model, adaptive density clustering is performed on each photovoltaic string to divide it into multiple topologically independent electrical clusters, and a closed grounding grid outline enveloping each electrical cluster is generated by constructing a solid geometry algorithm. S3. Based on the closed grounding grid outline, construct a weighted complete graph with each electrical cluster as a node and the spatial distance between electrical clusters as the edge weight, and generate the shortest main grounding path connecting each electrical cluster in the weighted complete graph using the minimum spanning tree algorithm; S4. Based on the shortest main grounding path, and combined with the weighted complete graph, a multi-link closed loop network is constructed using a loop search algorithm. Along the contour of the multi-link closed loop network and the closed grounding grid, the coordinates of each vertical grounding pole are generated using a spatial interpolation algorithm to obtain the photovoltaic grounding system design drawing. Step S4 includes the following sub-steps: S401. Based on the shortest main grounding path, a loop search algorithm is used to search for the second shortest main grounding path that avoids the shortest main grounding path in the weighted complete graph, which is then used as a redundant grounding path. S402. Superimpose the redundant grounding path with the shortest main grounding path to obtain a multi-link closed loop network; S403. Extract the total path of the horizontal grounding body based on the outline of the multi-link closed loop network and the closed grounding grid; S404. Based on the total path of the horizontal grounding electrode, and using preset distance constraints and terrain avoidance conditions, perform equidistant calculations along the total path of the horizontal grounding electrode using a spatial interpolation algorithm to generate the coordinates of each vertical grounding electrode point and construct a photovoltaic grounding system design drawing.

2. The design method for complex mountain photovoltaic grounding systems based on adaptive optimization clustering according to claim 1, characterized in that, Step S1 includes the following sub-steps: S101. Obtain vector map data of the target mountain photovoltaic field area, and extract the geometric centroid coordinates, rotation vectors, and boundary dimensions of each photovoltaic string as spatial feature information; S102. Based on the spatial feature information, construct a geometric mapping model to map each photovoltaic string with different orientations to a unified global coordinate system of the target mountain photovoltaic field area, thereby obtaining a high-dimensional spatial point set distribution model.

3. The design method for complex mountain photovoltaic grounding systems based on adaptive optimization clustering according to claim 1, characterized in that, Step S2 includes the following sub-steps: S201. Based on the high-dimensional spatial point set distribution model, construct the R-Tree spatial index structure; S202. Based on the R-Tree spatial index structure, a neighborhood search is performed on the high-dimensional spatial point set distribution model to obtain adjacent photovoltaic strings within a preset distance threshold. Combined with the spatial density characteristics of the photovoltaic string arrangement, adaptive density clustering is performed on the adjacent photovoltaic strings to obtain multiple topologically independent electrical clusters. S203. For each electrical cluster, a preliminary geometric figure is obtained by performing a union operation on all photovoltaic strings within the cluster using a solid geometry algorithm. S204. Based on the preliminary geometry, and using morphological expansion and erosion calculations, generate a closed grounding grid outline that fits the edge of the photovoltaic string.

4. The design method for complex mountain photovoltaic grounding systems based on adaptive optimization clustering according to claim 3, characterized in that, Step S204 also includes the following sub-steps: S2041. Within the target mountain photovoltaic field coordinate system, when the initial geometric shape is a scene arranged due south, perform morphological expansion and erosion operations based on the north-south axis direction of the target mountain photovoltaic field coordinate system to generate a rectangular grounding grid outline that is parallel or perpendicular to the direction of the photovoltaic strings. S2042. Within the coordinate system of the target mountain photovoltaic field area, when the initial geometry is a scene arranged along the slope following the terrain undulations, a polygonal grounding grid outline is generated through morphological dilation and erosion operations.

5. The design method for complex mountain photovoltaic grounding systems based on adaptive optimization clustering according to claim 1, characterized in that, Step S3 includes the following sub-steps: S301. Extract the centroid coordinates of each electrical cluster based on the closed grounding grid outline, calculate the Euclidean distance between each pair of centroids of each electrical cluster, and generate the spatial distance matrix between electrical clusters; S302. Construct a weighted complete graph using the spatial distance matrix between electrical clusters as edge weights, and extract the acyclic connected subgraph with the minimum sum of edge weights that connects all electrical clusters using the minimum spanning tree algorithm, as the shortest main grounding path.

6. A design system for complex mountain photovoltaic grounding systems based on adaptive optimization clustering, the system being implemented based on the design method for complex mountain photovoltaic grounding systems based on adaptive optimization clustering as described in any one of claims 1-5, characterized in that, include: The data parsing and mapping module is used to extract the spatial feature information of each photovoltaic string based on the vector drawing data of the target mountain photovoltaic field and to construct a high-dimensional spatial point set distribution model. The adaptive clustering and envelope generation module is used to perform adaptive density clustering on each photovoltaic string according to the high-dimensional spatial point set distribution model, divide it into multiple topologically independent electrical clusters, and generate the closed grounding grid outline that encloses each electrical cluster through a solid geometry construction algorithm. The backbone topology optimization module is used to construct a weighted complete graph based on the closed grounding grid outline, with each electrical cluster as a node and the spatial distance between electrical clusters as the edge weight, and to generate the shortest backbone grounding path connecting each electrical cluster in the weighted complete graph through the minimum spanning tree algorithm. The grounding electrode layout module is used to construct a multi-link closed loop network based on the shortest main grounding path using a loop search algorithm. It then generates the coordinates of each vertical grounding electrode point along the contour of the multi-link closed loop network and the closed grounding grid using a spatial interpolation algorithm, and outputs the photovoltaic grounding system design drawing.

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