Photovoltaic array layout method and system based on mountain terrain

Abstract

The invention provides a photovoltaic array layout method and system based on mountain topography, and relates to the field of photovoltaic array layout, and the method comprises the steps: obtaining the gradient, the slope direction, the vegetation coverage rate and the current sunshine duration corresponding to each sub-grid region; inputting the gradient, the slope direction, the vegetation coverage rate and the current sunshine duration corresponding to the sub-grid region to a preset photovoltaic installation preset model to obtain a layout expected value; determining the layout areas as expected layout areas, splicing all the expected layout areas with the adjacency relation to obtain all to-be-laid areas, and removing the to-be-laid areas with the area smaller than a preset area to obtain all removed areas; and by taking generation power maximization, shadow shielding loss minimization and photovoltaic module installation total cost minimization as objective functions, outputting a layout optimal solution by using a genetic algorithm, and performing photovoltaic array layout on the region after elimination by using the layout optimal solution. According to the method, the suitability of each sub-grid region is accurately evaluated, so that the accuracy of the photovoltaic array layout is improved.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic array layout, and particularly to a photovoltaic array layout method and system based on mountain terrain. Background Art

[0002] A Chinese invention patent with the application publication number CN 118898105 A discloses a method, device, equipment and medium for optimizing the installation layout of roof photovoltaic panels. The method includes: inputting an image of a building roof into a photovoltaic installable area recognition model that has been trained to a convergent state to obtain a mask image of the photovoltaic installable area, so as to determine the contour information corresponding to the photovoltaic installable area; using the greedy algorithm in the heuristic polygon packing algorithm model to determine the initial photovoltaic panel installation layout according to the constraint conditions, the installation priority of the photovoltaic panels, the photovoltaic panel numbers, the installation postures of the photovoltaic panels and the storage point numbers of the photovoltaic panels; using a transformation operator to generate a new photovoltaic panel installation layout according to the initial photovoltaic panel installation layout, and using the simulated annealing algorithm in the heuristic polygon packing algorithm model to iterate the new photovoltaic panel installation layout to determine multiple iteration results, and selecting the layout with the largest photovoltaic panel area as the final photovoltaic panel installation layout.

[0003] However, it is mainly used for the installation of roof photovoltaic panels and is not applied to mountain terrain. Due to factors such as slope changes, terrain undulations, and potential installation risks in mountain terrain, it has become a technical problem to select a suitable area for installing photovoltaic panels. If the selected area is inappropriate, it may lead to low power generation efficiency, potential hazards in installation and use, and due to the complexity of mountain terrain, after selecting an area for installation, it may also lead to low power generation efficiency, high installation costs or excessive waste of material use due to inappropriate installation layout.

[0004] However, there is no technical solution in the prior art that can solve the above technical problems, and there is no photovoltaic array layout method and system based on mountain terrain. Summary of the Invention

[0005] The present invention provides a photovoltaic array layout method and system based on mountain terrain, which can not only select the most suitable area for installing photovoltaic arrays in complex mountain terrain, but also specifically maximize the layout advantages in different installation areas, aiming to improve the layout efficiency and power generation benefits of photovoltaic arrays in complex mountain environments.

[0006] In the first aspect, the present invention provides a photovoltaic array layout method based on mountain terrain, including:

[0007] Dividing the current mountain terrain area into grids to obtain multiple sub-grid areas;

[0008] Obtain the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area. For each sub-grid area, input the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into a preset photovoltaic installation preset model to obtain the layout expected value output by the preset photovoltaic installation preset model;

[0009] Determine all sub-grid areas with a layout expected value greater than the preset expected value as the expected layout areas, splice all adjacent expected layout areas to obtain all areas to be laid out, and exclude the areas to be laid out with an area smaller than the preset area from all areas to be laid out to obtain all areas after exclusion;

[0010] For any area after exclusion, use multiple photovoltaic arrays with different sizes and different power generation powers to perform multiple initial layouts on the area after exclusion at different installation positions with different installation tilts to obtain an initial population. Take the maximization of power generation power, the minimization of shadow occlusion loss, and the minimization of the total cost of photovoltaic module installation as the objective function, use the genetic algorithm to output the optimal layout solution, and use the optimal layout solution to perform the layout of the photovoltaic array on the area after exclusion. Traverse all areas after exclusion until the layout of the photovoltaic array for the current mountain terrain area is completed.

[0011] According to the photovoltaic array layout method based on mountain terrain provided by the present invention, the obtaining of the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area includes:

[0012] Use an airborne lidar measurement system of a drone to conduct an aerial survey of the current mountain terrain, obtain a remote sensing image corresponding to the current mountain terrain, process the remote sensing image to obtain a digital elevation model and a digital surface model, and analyze the digital elevation model and the digital surface model in a preset map software to obtain the slope and aspect corresponding to each sub-grid area;

[0013] According to the remote sensing image, obtain the vegetation index corresponding to each sub-grid area, and determine the vegetation coverage rate according to the vegetation index;

[0014] Input the current geographical location information and the current date corresponding to each sub-grid area into a preset meteorological data platform to obtain the current sunshine duration output by the preset meteorological data platform.

[0015] According to the photovoltaic array layout method based on mountain terrain provided by the present invention, the grid division of the current mountain terrain area to obtain multiple sub-grid areas includes:

[0016] Perform grid division on the current mountain terrain area to obtain all grid division areas;

[0017] Eliminate non-layout areas from all grid division areas to obtain multiple sub-grid areas;

[0018] The non-layout area at least includes an area with a ravine, an area with protected plants, an area with a mountain water flow path, and an area with a building that cannot be removed.

[0019] According to the photovoltaic array layout method based on mountain terrain provided by the present invention, before inputting the slope, slope direction, vegetation coverage and current sunshine duration corresponding to the sub-grid area into the preset photovoltaic installation preset model, the method further includes:

[0020] Obtain each slope, each slope aspect, each vegetation coverage rate and each current sunshine duration corresponding to different sample areas;

[0021] The initial model is trained according to each slope, each slope direction, each vegetation coverage rate and each current sunshine duration corresponding to different sample areas, as well as the expected value of the sample layout corresponding to each sample area, to obtain the preset photovoltaic installation preset model.

[0022] According to the photovoltaic array layout method based on mountainous terrain provided by the present invention, determining all sub-grid areas whose layout expected values ​​are greater than a preset expected value as expected layout areas includes:

[0023] For each sub-grid area, the average wind force level of the sub-grid area within a preset time period is obtained using a meteorological observation station or an anemometer, and the soil compaction degree of the sub-grid area is obtained using a soil compaction degree meter;

[0024] An actual expected value is determined according to the layout expected value, the average wind force level, and the soil compactness, and all sub-grid areas whose actual expected value is greater than a preset expected value are determined as expected layout areas.

[0025] According to the photovoltaic array layout method based on mountainous terrain provided by the present invention, the actual expected value is determined according to the layout expected value, the average wind force level and the soil compactness, including:

[0026]

[0027] Among them, E is the layout expectation value, W is the average wind force level, and W max is the maximum wind force level, S is the soil tightness, S max is the maximum value of soil looseness and A is the actual expected value.

[0028] According to the photovoltaic array layout method based on mountainous terrain provided by the present invention, the step of splicing all desired layout areas having an adjacent relationship to obtain all areas to be laid out includes:

[0029] For any desired layout area, determine the areas that are also desired layout areas among the upper area, lower area, left area, and right area of the desired layout area as the areas to be spliced;

[0030] Splice the desired layout area and the areas to be spliced until all the areas to be laid out are obtained.

[0031] According to the method for arranging a photovoltaic array based on mountain terrain provided by the present invention, with the maximization of power generation, the minimization of shadow occlusion loss, and the minimization of the total installation cost of photovoltaic modules as the objective functions, using a genetic algorithm to output the optimal layout solution, including:

[0032] With the maximization of power generation, the minimization of shadow occlusion loss, and the minimization of the total installation cost of photovoltaic modules as the objective functions, calculate the fitness value of each initial layout in the initial population, apply selection, crossover, and mutation to generate new layout solutions, form a new population, and calculate the fitness value of each new layout in the new population again until a preset number of iterations is reached, and determine the layout solution with the highest fitness value as the optimal layout solution.

[0033] According to the method for arranging a photovoltaic array based on mountain terrain provided by the present invention, calculating the fitness value of each initial layout in the initial population includes:

[0034]

[0035] Wherein, F is the fitness value, w1, w2, and w3 are weight coefficients, P gen is the power generation of the initial layout, is the maximum and minimum values of the power generation, L shade is the shadow occlusion loss of the initial layout, is the maximum and minimum values of the shadow occlusion loss, C total is the total installation cost of the photovoltaic modules of the initial layout, is the minimum and maximum values of the total installation cost of the photovoltaic modules.

[0036] In a second aspect, a system for arranging a photovoltaic array based on mountain terrain is provided, including:

[0037] An acquisition unit, the acquisition unit is used to divide the current mountain terrain area into grids and acquire a plurality of sub-grid areas;

[0038] An input unit, which is used to obtain the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area. For each sub-grid area, input the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into a preset photovoltaic installation preset model to obtain the layout expectation value output by the preset photovoltaic installation preset model;

[0039] A splicing unit, which is used to determine all sub-grid areas with layout expectation values greater than the preset expectation value as the expected layout areas, splice all adjacent expected layout areas to obtain all areas to be laid out, and exclude the areas to be laid out with an area smaller than the preset area from all areas to be laid out to obtain all areas after exclusion;

[0040] A layout unit, which is used for any area after exclusion, use multiple photovoltaic arrays with different sizes and different power generation powers to perform multiple initial layouts on the area after exclusion at different installation positions with different installation tilts to obtain an initial population, use the maximization of power generation power, the minimization of shadow occlusion loss, and the minimization of the total cost of photovoltaic module installation as the objective function, use the genetic algorithm to output the optimal layout solution, and use the optimal layout solution to perform the photovoltaic array layout on the area after exclusion, and traverse all areas after exclusion until the photovoltaic array layout of the current mountain terrain area is completed.

[0041] By dividing the mountain terrain area into multiple sub-grids, comprehensively considering multiple factors such as slope, aspect, vegetation coverage rate, and sunshine duration, and using the preset photovoltaic installation model to calculate the layout expectation value, the present invention can more accurately evaluate the suitability of each sub-grid area, thereby improving the accuracy of the photovoltaic array layout; using the genetic algorithm to optimize the initial layout, through multiple iterative calculations, can quickly find the optimal layout plan and improve the layout efficiency; by excluding the areas to be laid out with smaller areas, it avoids inefficient installation in these areas and further saves costs; by considering environmental factors such as wind force level and soil looseness, it can screen out areas more suitable for installing photovoltaic arrays and enhance the adaptability to complex mountain terrains;

[0042] Taking the maximization of power generation power as one of the objective functions, by optimizing the layout of the photovoltaic array, the present invention ensures the maximum power generation benefit within the limited installation area; by reducing the shadow occlusion loss, it improves the light-receiving area and light intensity of the photovoltaic array, further enhancing the power generation efficiency; optimizing the total cost of photovoltaic module installation as one of the objective functions helps to reduce the installation cost while ensuring the power generation benefit. Description of the Drawings

[0043] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 is a schematic flowchart of a photovoltaic array layout method based on mountain terrain provided by the present invention;

[0045] Figure 2 is a schematic diagram of regional distribution based on mountain terrain provided by the present invention;

[0046] Figure 3 is a schematic structural diagram of a photovoltaic array layout system based on mountain terrain provided by the present invention;

[0047] Figure 4 is a schematic structural diagram of an electronic device provided by the present invention. Specific Embodiments

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0049] Figure 1 is a schematic flowchart of a photovoltaic array layout method based on mountain terrain provided by the present invention. The photovoltaic array layout method based on mountain terrain includes:

[0050] Step 101: Divide the current mountain terrain area into grids to obtain multiple sub-grid areas;

[0051] Step 102: Obtain the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area. For each sub-grid area, input the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into a preset photovoltaic installation preset model to obtain the layout expectation value output by the preset photovoltaic installation preset model;

[0052] Step 103: Determine all sub-grid areas with layout expectation values greater than the preset expectation value as expected layout areas, splice all expected layout areas with adjacency relationships to obtain all areas to be laid out, and exclude from all areas to be laid out those with an area smaller than the preset area to obtain all areas after exclusion;

[0053] Step 104: For any of the excluded areas, use multiple photovoltaic arrays with different sizes and different power generation capacities to perform multiple initial layouts on the excluded area at different installation positions and different installation inclinations to obtain an initial population. Taking the maximization of power generation capacity, the minimization of shadow occlusion loss, and the minimization of the total installation cost of photovoltaic modules as the objective functions, use the genetic algorithm to output the optimal layout solution, and use the optimal layout solution to perform the layout of photovoltaic arrays on the excluded area. Traverse all the excluded areas until the layout of photovoltaic arrays for the current mountainous terrain area is completed.

[0054] In step 101, the present invention can use Geographic Information System (GIS) software to divide the current mountainous terrain area into grids. According to the terrain complexity and the accuracy requirements for the installation of photovoltaic arrays, determine the size of the grids. The grid division can adopt regular grids or irregular grids. Regular grids are easy to calculate and process, but are not suitable for complex terrains; irregular grids can better adapt to complex terrains, but the calculation and processing are relatively complex. The present invention preferably adopts the form of regular grids. The grid division divides the terrain area into multiple small areas that are easy to manage and process, providing convenience for subsequent steps. The size of the grids can be adjusted according to the actual situation to adapt to the complexity of different terrains.

[0055] In step 102, the obtaining of the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area includes:

[0056] Use an airborne lidar measurement system of an unmanned aerial vehicle to conduct an aerial survey of the current mountainous terrain, obtain a remote sensing image corresponding to the current mountainous terrain, process the remote sensing image to obtain a digital surface model, and analyze the digital surface model in a preset map software to obtain the slope and aspect corresponding to each sub-grid area;

[0057] According to the remote sensing image, obtain the vegetation index corresponding to each sub-grid area, and determine the vegetation coverage rate according to the vegetation index;

[0058] Input the current geographical location information and the current date corresponding to each sub-grid area into a preset meteorological data platform to obtain the current sunshine duration output by the preset meteorological data platform.

[0059] Optionally, select an unmanned aerial vehicle (UAV)-borne lidar measurement system to carry a high-resolution camera for aerial photography of the current mountain terrain. When conducting aerial photography, factors such as light conditions, flight altitude, and overlap rate should be considered to ensure the quality of the acquired remote sensing images. Import the acquired remote sensing images into professional image processing software, and use the digital surface model (DSM) generation technology of stereo image pairs or single images, combined with ground control points, to generate a digital elevation model (DEM). The DEM is a digital representation of the surface elevation and can reflect the undulation changes of the terrain. Import the DEM data into a preset mapping software (such as ArcGIS, QGIS, etc.), and use the terrain analysis function of the software to calculate the slope (i.e., the degree of surface inclination) and aspect (i.e., the direction of surface inclination) of each sub-grid area. In addition to UAV aerial photography, multi-source data such as satellite remote sensing data and lidar (LiDAR) data can also be combined to improve the accuracy and reliability of the DEM. Based on the slope and aspect analysis, further evaluate the complexity of the terrain, such as terrain undulation and valley density, to provide more detailed terrain information for the photovoltaic array layout.

[0060] Optionally, calculate the normalized difference vegetation index (NDVI) or other vegetation indices based on the reflectance of the red band and near-infrared band in the remote sensing images. The NDVI is an important indicator for measuring vegetation coverage and green biomass, and its value ranges from -1 to 1. The larger the value, the better the vegetation coverage. Based on the calculated vegetation index, combined with on-site surveys or empirical formulas, determine the vegetation coverage rate of each sub-grid area. Usually, the vegetation coverage rate can be estimated through the linear or non-linear relationship between the NDVI value and vegetation coverage. The present invention can also use remote sensing images of different seasons or different years to calculate multi-temporal vegetation indices and analyze the spatio-temporal changes of vegetation coverage to provide a dynamic basis for the photovoltaic array layout. Or, conduct on-site measurements in key sub-grid areas to verify the accuracy of the vegetation coverage rate estimated from remote sensing images and improve the reliability of the layout scheme.

[0061] Optionally, collect the current geographical location information (including longitude, latitude, altitude, etc.) and the current date of each sub-grid area. These information are the basis for obtaining accurate sunshine duration. Input the geographical location information and the current date into a preset meteorological data platform (such as the meteorological information center, ECMWF, etc.). These platforms provide meteorological data services globally. Query and obtain the sunshine duration data corresponding to the geographical location and date in the platform. In other embodiments, when obtaining the sunshine duration, considering the influence of cloud cover on sunshine, data such as satellite cloud images and radar echo images can be used, combined with meteorological models to predict cloud cover conditions and correct the sunshine duration. In addition to obtaining the sunshine duration of the current date, long-term (such as the past few years) sunshine data can also be analyzed to understand the change trend and seasonal differences of sunshine duration and provide more comprehensive sunshine information for the photovoltaic array layout.

[0062] Optionally, the impact of slope on the installation of a photovoltaic array is mainly reflected in two aspects: power generation efficiency and safety. A reasonable installation slope can enable the photovoltaic array to better receive sunlight, thereby increasing power generation. Due to different latitudes and longitudes in different regions, the irradiation angle and intensity of sunlight will also vary. For mountainous terrains with a large slope, special installation brackets and fixing methods are required to ensure the stability and safety of the photovoltaic modules; the impact of slope aspect on the power generation of the photovoltaic array is also significant. Taking the Northern Hemisphere as an example, the photovoltaic modules should be installed facing due south as much as possible, so as to receive solar energy to the greatest extent. However, in actual applications, due to layout conditions and the limited area of the scene, it is often impossible for all photovoltaic modules to be installed in the optimal orientation. There are differences in the power generation efficiency of photovoltaic modules facing east-west slopes. Generally, the power generation efficiency of the east slope is higher in the morning, while that of the west slope is higher in the afternoon; sunshine duration is an important factor determining the power generation of the photovoltaic array. At the same time, under sunny weather, solar radiant energy shines on the photovoltaic array without obstruction, and the power generation is higher; while in bad weather such as rain, fog, and haze, solar radiant energy is weakened or blocked, resulting in a reduction in power generation. Therefore, slope, slope aspect, and sunshine duration are all important factors affecting the installation of the photovoltaic array, and they jointly determine the power generation efficiency and safety of the photovoltaic array. The present invention inputs the slope, slope aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into a preset photovoltaic installation preset model to obtain the layout expectation value output by the preset photovoltaic installation preset model. By comprehensively considering various factors, the model can output a more accurate layout expectation value, providing a reliable basis for subsequent steps.

[0063] Furthermore, the present invention collects data on the slope, slope aspect, vegetation coverage rate, and sunshine duration of each sub-grid area to ensure the accuracy and integrity of the data. Generally, slope data is continuous and can be directly used. If the model requires discretization, it can be divided into several intervals, such as 0 - 5°, 5 - 15°, 15 - 30°, greater than 30°, and a value or label is assigned to each interval; slope aspect is usually represented by azimuth angle. For example, north is 0°, east is 90°, south is 180°, and west is 270°. The azimuth angle can be converted into a numerical feature, or it can be converted into multiple binary features using one-hot encoding; vegetation coverage rate is usually continuous data in the form of a percentage and can be directly used. If the model requires discretization, it can be divided into several intervals, such as 0 - 10%, 10 - 30%, 30 - 50%, 50 - 70%, 70 - 100%, and a value or label is assigned to each interval; sunshine duration is usually continuous data and can be directly used. If the model requires discretization, it can be divided into several intervals, such as 0 - 2 hours, 2 - 4 hours, 4 - 6 hours, 6 - 8 hours, greater than 8 hours, and a value or label is assigned to each interval.

[0064] Optionally, the grid division of the current mountain terrain area is performed to obtain multiple sub-grid areas, which includes:

[0065] Perform grid division on the current mountain terrain area to obtain all grid division areas;

[0066] Remove non-layout areas from all grid division areas to obtain multiple sub-grid areas;

[0067] The non-layout areas at least include areas with mountain valleys, areas with protected plants, areas with mountain water flow paths, and areas with immovable buildings.

[0068] Figure 2 It is a schematic diagram of the regional distribution based on the mountain terrain provided by the present invention. First, detailed geographical information of the current mountain terrain area is collected, including topographic maps, satellite remote sensing images, digital elevation models (DEM), etc. These information are the basis for grid division. According to the requirements of the photovoltaic array layout and terrain features, parameters for grid division are set, such as grid size, shape, direction, etc. According to the collected terrain data and field surveys, areas with mountain valleys, areas with protected plants, areas with mountain water flow paths, and areas with immovable buildings are identified. These areas are not suitable for photovoltaic array layout due to complex terrain, ecological sensitivity, or the presence of obstacles. The present invention can use techniques such as manual annotation and neural network model recognition to label the identified non-layout areas. According to the annotation information, non-layout areas are removed from the generated grids.

[0069] Optionally, the present invention can utilize high-resolution satellite remote sensing images and aerial survey data of an airborne lidar measurement system, combined with machine learning or deep learning algorithms, to automatically identify and remove non-layout areas. This method can improve the recognition efficiency and accuracy, reduce manual intervention, and fully consider ecological impacts when identifying non-layout areas, such as the distribution of protected plants and the scope of ecologically sensitive areas. For example, input the remote sensing image corresponding to the current mountain terrain into a preset recognition model to obtain the non-layout areas output by the preset recognition model. The preset recognition model is determined after being trained based on sample remote sensing images and sample marked areas. The sample marked areas include areas with mountain valleys, areas with protected plants, areas with mountain water flow paths, and areas with immovable buildings. The present invention improves the layout efficiency through automated and intelligent grid division and non-layout area identification methods, reduces manual intervention and errors, and provides strong support for subsequent determination of a more optimized layout plan by combining terrain features and the requirements of photovoltaic array layout.

[0070] Optionally, before inputting the slope, slope aspect, vegetation coverage and current sunshine duration corresponding to the sub-grid area into the preset photovoltaic installation preset model, the method further includes:

[0071] Obtain each slope, each slope aspect, each vegetation coverage rate and each current sunshine duration corresponding to different sample areas;

[0072] The initial model is trained according to each slope, each slope direction, each vegetation coverage rate and each current sunshine duration corresponding to different sample areas, as well as the expected value of the sample layout corresponding to each sample area, to obtain the preset photovoltaic installation preset model.

[0073] Optionally, the present invention selects multiple sample areas under representative different terrain and climatic conditions. These sample areas should cover various combinations of slopes, slope aspects, vegetation coverage and sunshine duration to ensure the generalization ability of the model. For each sample area, the corresponding slope, slope aspect, vegetation coverage and current sunshine duration data are collected. At the same time, a sample layout expectation value is set for each sample area. The sample layout expectation value is used to characterize the willingness to install a photovoltaic array in the sample area. It can be determined based on multiple factors such as the difficulty of installation, post-installation maintenance costs, post-installation power generation efficiency requirements, and cost budget. A suitable machine learning or deep learning model is selected as the initial model, which can be one of the models such as decision tree, random forest, support vector machine, neural network, etc. The preprocessed data is input into the initial model, and the model is trained according to the sample layout expectation value. During the training process, the parameters and structure of the model are adjusted. After training and evaluation, a preset photovoltaic installation preset model is obtained. Furthermore, in actual application, the present invention can also continuously collect new data and input it into the model for online learning to update the model parameters and structure so that it can adapt to changing environmental conditions. By training and optimizing the preset photovoltaic installation preset model, the prediction accuracy of the layout expectation value can be significantly improved, providing data support for the subsequent selection of areas suitable for photovoltaic array installation.

[0074] In step 103, the present invention pre-sets a preset expected value as a screening criterion, such as Figure 2 As shown, the sub-grid area with a layout expectation value greater than a preset expected value is determined as the expected layout area. The spatial analysis function of the GIS software is used to splice all the expected layout areas with an adjacent relationship to obtain the area to be laid out. At the same time, a preset area value is set to eliminate the area to be laid out with an area smaller than the preset area to obtain all the eliminated areas. The present invention takes into account the impact of terrain continuity on the layout, and further optimizes the spliced ​​area to be laid out. At the same time, in order to reduce the layout cost and maintenance difficulty, the area with a smaller area is eliminated.

[0075] Optionally, determining all sub-grid regions with layout expected values greater than a preset expected value includes:

[0076] For each sub-grid region, use a meteorological observation station or an anemometer to obtain the average wind force level of the sub-grid region within a preset time period, and use a soil compactness meter to obtain the soil looseness of the sub-grid region;

[0077] According to the layout expected value, the average wind force level, and the soil looseness, determine the actual expected value, and determine all sub-grid regions with the actual expected value greater than the preset expected value as the expected layout region.

[0078] Optionally, for each sub-grid region, first use a meteorological observation station or an anemometer to obtain the average wind force level of the region within a preset time period (such as the past month or quarter). This can be achieved by continuously measuring the wind speed and wind volume through a wind speed sensor to ensure the accuracy and continuity of the data. At the same time, use a soil compactness meter (such as a digital soil hardness meter) to obtain the soil looseness of the sub-grid region. When measuring, ensure that the soil surface is leveled, and insert the tip of the soil hardness meter completely into the soil. After pulling it out vertically and smoothly, read the hardness indication value. According to the layout expected value (i.e., the expected value for this region in the original plan or design), the average wind force level, and the soil looseness, calculate the actual expected value through a certain algorithm or model. The determining the actual expected value according to the layout expected value, the average wind force level, and the soil looseness includes:

[0079]

[0080] where E is the layout expected value, W is the average wind force level, W max is the maximum wind force level, S is the soil looseness, S max is the maximum soil looseness, A is the actual expected value, and compare the calculated actual expected value with the preset expected value. If the actual expected value is greater than the preset expected value, then determine this sub-grid region as the expected layout region.

[0081] The magnitude of the wind force level directly affects the stability of the photovoltaic array. In areas with strong winds, the photovoltaic array requires a stronger support structure to resist wind pressure. Otherwise, it may cause deformation of the photovoltaic array, loosening or even damage of the brackets. Correspondingly, the soil compactness directly affects the stability of the basic structure of the photovoltaic array. If the soil is too soft, the basic structure of the photovoltaic array may not be firmly fixed on the ground, resulting in the photovoltaic array being prone to loosening or tilting after installation. In mountainous terrains, due to the complex terrain and variable geological conditions, the unevenness of soil compactness may lead to an increase in the instability of the basic structure of the photovoltaic array. By comprehensively considering the wind force level and soil compactness, the present invention can more accurately determine the desired layout area, improving the accuracy and rationality of the layout.

[0082] Optionally, splicing all the desired layout areas with adjacency relationships to obtain all the areas to be laid out, including:

[0083] For any desired layout area, determine the areas that are also desired layout areas among the upper area, lower area, left area, and right area of the desired layout area as the areas to be spliced;

[0084] Splice the desired layout area and the areas to be spliced until all the areas to be laid out are obtained.

[0085] Optionally, as Figure 2 shown, for each desired layout area, check the adjacent areas in the up, down, left, and right directions. If the adjacent areas are also desired layout areas, mark them as areas to be spliced, and splice the desired layout area with its areas to be spliced to form a larger layout area. Repeat the above steps until all the desired layout areas are spliced to complete the formation of all the areas to be laid out.

[0086] In step 104, the present invention randomly generates initial layout schemes of photovoltaic arrays with different sizes and different power generation capacities at different installation positions and different installation inclinations to form an initial population, sets objective functions including maximizing power generation, minimizing shadow occlusion loss, and minimizing the total installation cost of photovoltaic modules, uses a genetic algorithm to optimize the initial population, iteratively searches for the optimal solution through operations such as selection, crossover, and mutation, applies the obtained optimal solution to the photovoltaic array layout in the removed areas, and traverses all the removed areas until the photovoltaic array layout of the entire mountainous terrain area is completed. The photovoltaic array layout method based on mountainous terrain of the present invention realizes the effective layout of the mountainous terrain area through steps such as grid division, information acquisition and model prediction, area screening and splicing, and genetic algorithm optimization.

[0087] Optionally, taking the maximization of power generation, the minimization of shadow occlusion loss, and the minimization of the total installation cost of photovoltaic modules as the objective function, using a genetic algorithm to output the optimal layout solution, including:

[0088] Taking the maximization of power generation, the minimization of shadow occlusion loss, and the minimization of the total installation cost of photovoltaic modules as the objective function, calculating the fitness value of each initial layout in the initial population, applying selection, crossover, and mutation to generate new layout solutions, forming a new population, calculating the fitness value of each new layout in the new population again until a preset number of iterations is reached, and determining the layout solution with the highest fitness value as the optimal layout solution.

[0089] Optionally, calculating the fitness value of each initial layout in the initial population includes:

[0090]

[0091] where F is the fitness value, w1, w2, and w3 are weight coefficients, P gen is the power generation of the initial layout, is the maximum and minimum values of power generation, L shade is the shadow occlusion loss of the initial layout, is the maximum and minimum values of shadow occlusion loss, C total is the total installation cost of photovoltaic modules of the initial layout, is the minimum and maximum values of the total installation cost of photovoltaic modules.

[0092] Optionally, a series of photovoltaic array layout schemes are randomly generated as the initial population. Each layout scheme represents an individual, and its gene encoding can represent parameters such as the position and angle of the photovoltaic array. The fitness value of each initial layout is calculated using the given fitness function formula. This function comprehensively considers three objectives: power generation, shadow occlusion loss, and the total installation cost of photovoltaic modules, and is balanced through weight coefficients. The relatively better individuals are selected according to the fitness values to enter the next generation. Commonly used selection strategies include roulette wheel selection, tournament selection, etc. In this embodiment, the roulette wheel selection strategy can be adopted, that is, the probability of an individual being selected is determined according to the proportion of its fitness value. The selected individuals are subjected to crossover operations to generate new offspring individuals. The crossover operation can simulate the gene recombination phenomenon in the biological evolution process to increase the diversity of the population. In this embodiment, single-point crossover or multi-point crossover can be used for the crossover operation; with a certain probability, the offspring individuals are subjected to mutation operations to change some of their gene encodings. The mutation operation can introduce new genetic information to prevent the algorithm from falling into a local optimal solution. In this embodiment, the mutation points can be randomly selected, and operations such as flipping or replacing the mutation points can be performed; the newly generated individuals are used to replace some of the original individuals to form a new generation of population. Then, the fitness function calculation, selection, crossover, and mutation operations are repeated until the preset number of iterations is reached. After the iteration ends, the layout scheme with the highest fitness value is selected as the optimal solution.

[0093] Optionally, in different application scenarios, the importance of power generation, shadow occlusion loss, and the total installation cost of photovoltaic modules may vary. Therefore, the weight coefficients can be adjusted to meet different optimization requirements. The performance of the genetic algorithm is affected by parameters such as population size, number of iterations, crossover probability, and mutation probability. These parameters can be optimized through experiments or simulations to improve the operation efficiency and convergence speed of the algorithm. For example, methods such as grid search, random search, or Bayesian optimization can be used to find the best parameter combination; in practical applications, the photovoltaic array layout scheme may be restricted by factors such as terrain, buildings, and safety. Therefore, constraint conditions can be added to the fitness function to ensure that the generated layout scheme meets the actual requirements.

[0094] By optimizing the photovoltaic array layout scheme, the present invention can maximize the power generation and improve the utilization efficiency of solar energy, which helps to reduce the dependence on traditional energy sources and promote sustainable development; by reducing the shadow occlusion loss, the utilization rate of the photovoltaic array can be improved, and by optimizing the layout scheme, the installation position and quantity of the photovoltaic array can be reasonably planned to reduce the total installation cost.

[0095] Those skilled in the art understand that in addition to using genetic algorithms, the present invention can also use other algorithms to implement the layout of photovoltaic arrays. For example, the particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence. It simulates the foraging behavior of bird flocks. In the problem of photovoltaic array layout, each particle can represent a layout scheme, and its position vector represents parameters such as the position and angle of the photovoltaic array. By continuously updating the speed and position of the particles, the algorithm can gradually converge to the optimal solution. Another example is the simulated annealing algorithm, which is an optimization algorithm based on the physical annealing process. It simulates the process of metal heating and then slowly cooling to find the lowest energy state. In the problem of photovoltaic array layout, the fitness value of the layout scheme can be regarded as an energy function, and by continuously trying new layout schemes and accepting solutions better than the current scheme, it gradually approaches the optimal solution. There is also the ant colony optimization algorithm, which is an optimization algorithm based on the foraging behavior of ants. It simulates the process of ants transmitting information through pheromones during the process of finding food. In the problem of photovoltaic array layout, each layout scheme can be regarded as a path, and ants choose the next direction based on the pheromone concentration on the path during the search process. By continuously updating the pheromone concentration, the algorithm can gradually converge to the optimal path, that is, the optimal layout scheme.

[0096] By dividing the mountain terrain area into multiple sub-grids and comprehensively considering multiple factors such as slope, aspect, vegetation coverage rate, and sunshine duration, and using a preset photovoltaic installation model to calculate the layout expectation value, the present invention can more accurately evaluate the suitability of each sub-grid area, thereby improving the accuracy of photovoltaic array layout; by using a genetic algorithm to optimize the initial layout and through multiple iterative calculations, it can quickly find the optimal layout scheme and improve the layout efficiency; by eliminating small-sized areas to be laid out, it avoids inefficient installation in these areas and further saves costs; by considering environmental factors such as wind force level and soil looseness, it can screen out areas more suitable for installing photovoltaic arrays and enhance the adaptability to complex mountain terrains;

[0097] Taking the maximization of power generation as one of the objective functions, by optimizing the layout of the photovoltaic array, the present invention ensures the realization of the maximum power generation benefit within a limited installation area; by reducing the loss of shadow occlusion, increasing the light-receiving area and light intensity of the photovoltaic array, it further improves the power generation efficiency; optimizing the total installation cost of photovoltaic modules as one of the objective functions helps to reduce the installation cost while ensuring the power generation benefit.

[0098] Figure 3 FIG. is a schematic structural diagram of a photovoltaic array layout system based on mountain terrain provided by the present invention. The photovoltaic array layout system based on mountain terrain includes an acquisition unit 1. The acquisition unit is used to divide the current mountain terrain area into grids and obtain multiple sub-grid areas. The working principle of the acquisition unit 1 can refer to the foregoing step 101 and will not be elaborated here.

[0099] The photovoltaic array layout system based on mountain terrain further includes an input unit 2. The input unit is used to obtain the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area. For each sub-grid area, the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area are input into a preset photovoltaic installation preset model to obtain the layout expectation value output by the preset photovoltaic installation preset model. The working principle of the input unit 2 can refer to the foregoing step 102 and will not be elaborated here.

[0100] The photovoltaic array layout system based on mountain terrain further includes a splicing unit 3. The splicing unit is used to determine all sub-grid areas with layout expectation values greater than the preset expectation value as the expected layout areas, splice all expected layout areas with adjacency relationships to obtain all areas to be laid out, and exclude the areas to be laid out with an area smaller than the preset area from all areas to be laid out to obtain all areas after exclusion. The working principle of the splicing unit 3 can refer to the foregoing step 103 and will not be elaborated here.

[0101] The photovoltaic array layout system based on mountain terrain further includes a layout unit 4. The layout unit is used to perform multiple initial layouts on any area after exclusion with multiple photovoltaic arrays of different sizes and different power generation powers at different installation positions and different installation inclinations to obtain an initial population. Taking the maximization of power generation power, the minimization of shadow occlusion loss, and the minimization of the total cost of photovoltaic module installation as the objective functions, use the genetic algorithm to output the optimal layout solution, and use the optimal layout solution to perform the photovoltaic array layout on the area after exclusion. Traverse all areas after exclusion until the photovoltaic array layout of the current mountain terrain area is completed. The working principle of the layout unit 4 can refer to the foregoing step 104 and will not be elaborated here.

[0102] By dividing the mountain terrain area into multiple sub-grids, comprehensively considering multiple factors such as slope, aspect, vegetation coverage rate, and sunshine duration, and using a preset photovoltaic installation model to calculate the layout expectation value, the present invention can more accurately evaluate the suitability of each sub-grid area, thereby improving the accuracy of the photovoltaic array layout; using the genetic algorithm to optimize the initial layout, through multiple iterative calculations, can quickly find the optimal layout plan and improve the layout efficiency; by excluding the areas to be laid out with smaller areas, it avoids inefficient installation in these areas and further saves costs; by considering environmental factors such as wind force level and soil looseness, it can screen out areas more suitable for installing photovoltaic arrays and enhance the adaptability to complex mountain terrains;

[0103] One of the objective functions of the present invention is to maximize the power generation. By optimizing the layout of the photovoltaic array, it ensures the maximum power generation benefit within a limited installation area; by reducing the shadow occlusion loss, it increases the light-receiving area and light intensity of the photovoltaic array, further improving the power generation efficiency; optimizing the total installation cost of the photovoltaic modules as one of the objective functions helps to reduce the installation cost while ensuring the power generation benefit.

[0104] Figure 4 It is a schematic structural diagram of the electronic device provided by the present invention. As Figure 3 shown, the electronic device may include: a processor 110, a communications interface 120, a memory 130, and a communication bus 140. Among them, the processor 110, the communications interface 120, and the memory 130 complete mutual communication through the communication bus 140. The processor 110 can call the logical instructions in the memory 130 to execute a method for the layout of a photovoltaic array based on mountain terrain. The method includes: dividing a current mountain terrain area into grids to obtain a plurality of sub-grid areas; obtaining the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area. For each sub-grid area, input the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into a preset photovoltaic installation preset model to obtain the layout expected value output by the preset photovoltaic installation preset model; determine all sub-grid areas with a layout expected value greater than the preset expected value as expected layout areas, splice all expected layout areas with an adjacency relationship to obtain all areas to be laid out, exclude the areas to be laid out with an area smaller than the preset area from all areas to be laid out to obtain all areas after exclusion; for any area after exclusion, perform multiple initial layouts on the area after exclusion at different installation positions with different installation angles using a plurality of photovoltaic arrays with different sizes and different power generations to obtain an initial population. Taking the maximization of power generation, the minimization of shadow occlusion loss, and the minimization of the total installation cost of photovoltaic modules as objective functions, use a genetic algorithm to output the optimal layout solution, and use the optimal layout solution to perform the layout of the photovoltaic array on the area after exclusion. Traverse all areas after exclusion until the layout of the photovoltaic array for the current mountain terrain area is completed.

[0105] In addition, the logical instructions in the above-mentioned memory 130 can be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used independently as a product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0106] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a photovoltaic array layout method based on mountain terrain provided by the above-mentioned various methods. The method includes: dividing the current mountain terrain area into grids to obtain multiple sub-grid areas; obtaining the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area. For each sub-grid area, input the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into a preset photovoltaic installation preset model to obtain the layout expected value output by the preset photovoltaic installation preset model; determining all sub-grid areas with a layout expected value greater than the preset expected value as expected layout areas, splicing all expected layout areas with an adjacency relationship to obtain all areas to be laid out, removing the areas to be laid out with an area smaller than the preset area from all areas to be laid out to obtain all areas after removal; for any area after removal, performing multiple initial layouts on the area after removal at different installation positions with different installation inclinations using multiple photovoltaic arrays with different sizes and different power generation powers to obtain an initial population, using the maximization of power generation power, the minimization of shadow occlusion loss, and the minimization of the total cost of photovoltaic module installation as the objective function, outputting the optimal layout solution using a genetic algorithm, and using the optimal layout solution to perform photovoltaic array layout on the area after removal, and traversing all areas after removal until the photovoltaic array layout of the current mountain terrain area is completed.

[0107] In another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a photovoltaic array layout method based on mountain terrain provided by the above-mentioned various methods. The method includes: dividing a current mountain terrain area into grids to obtain a plurality of sub-grid areas; obtaining the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area. For each sub-grid area, input the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into a preset photovoltaic installation preset model to obtain the layout expected value output by the preset photovoltaic installation preset model; determine all sub-grid areas with a layout expected value greater than the preset expected value as expected layout areas, splice all expected layout areas with an adjacency relationship to obtain all areas to be laid out, remove the areas to be laid out with an area smaller than the preset area from all areas to be laid out to obtain all areas after removal; for any area after removal, perform multiple initial layouts on the area after removal at different installation positions with different installation inclinations using multiple photovoltaic arrays with different sizes and different power generation powers to obtain an initial population. Taking the maximization of power generation power, the minimization of shadow occlusion loss, and the minimization of the total cost of photovoltaic module installation as objective functions, use a genetic algorithm to output the optimal layout solution, and use the optimal layout solution to perform photovoltaic array layout on the area after removal. Traverse all areas after removal until the photovoltaic array layout of the current mountain terrain area is completed.

[0108] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0109] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, also by hardware. Based on such an understanding, the above technical solutions, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic array layout method based on mountain terrain, characterized in that Including: Performing grid division on the current mountain terrain area to obtain multiple sub-grid areas; Obtaining the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area. For each sub-grid area, inputting the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into a preset photovoltaic installation preset model to obtain the layout expected value output by the preset photovoltaic installation preset model; Determining all sub-grid areas with layout expected values greater than the preset expected value, splicing all adjacent expected layout areas to obtain all areas to be laid out, and excluding from all areas to be laid out those with an area smaller than the preset area to obtain all areas after exclusion; For any area after exclusion, using multiple photovoltaic arrays with different sizes and different power generation powers to perform multiple initial layouts on the area after exclusion at different installation positions with different installation tilts to obtain an initial population. Using the maximization of power generation power, the minimization of shadow occlusion loss, and the minimization of the total cost of photovoltaic module installation as the objective function, using a genetic algorithm to output the optimal layout solution, and using the optimal layout solution to perform photovoltaic array layout on the area after exclusion. Traversing all areas after exclusion until the photovoltaic array layout of the current mountain terrain area is completed.

2. The photovoltaic array layout method based on mountain terrain according to claim 1, characterized in that, The obtaining of the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid area includes: Using an airborne lidar measurement system of a drone to perform aerial photography on the current mountain terrain to obtain a remote sensing image corresponding to the current mountain terrain, processing the remote sensing image to obtain a digital elevation model and a digital surface model, and analyzing the digital elevation model and the digital surface model in a preset map software to obtain the slope and aspect corresponding to each sub-grid area; According to the remote sensing image, obtaining the vegetation index corresponding to each sub-grid area, and determining the vegetation coverage rate according to the vegetation index; Inputting the current geographical location information and the current date corresponding to each sub-grid area into a preset meteorological data platform to obtain the current sunshine duration output by the preset meteorological data platform.

3. The photovoltaic array layout method based on mountain terrain according to claim 1, wherein The performing of grid division on the current mountain terrain area to obtain multiple sub-grid areas includes: Performing grid division on the current mountain terrain area to obtain all grid division areas; Excluding non-layout areas from all grid division areas to obtain multiple sub-grid areas; The non-layout areas at least include areas with mountain valleys, areas with protected plants, areas with mountain water flow paths, and areas with immovable buildings.

4. The photovoltaic array layout method based on mountain terrain according to claim 1, characterized in that Before inputting the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to the sub-grid area into the preset photovoltaic installation preset model, the method further includes: Obtaining each slope, each aspect, each vegetation coverage rate, and each current sunshine duration corresponding to different sample areas; Training an initial model according to each slope, each aspect, each vegetation coverage rate, and each current sunshine duration corresponding to different sample areas, and the sample layout expected value corresponding to each sample area to obtain the preset photovoltaic installation preset model.

5. The photovoltaic array layout method based on mountain terrain according to claim 1, wherein Determining all sub-grid regions with layout expected values greater than the preset expected value includes: For each sub-grid region, obtaining the average wind force level of the sub-grid region within a preset time period by using a meteorological observation station or an anemometer, and obtaining the soil looseness of the sub-grid region by using a soil compactness meter; According to the layout expected value, the average wind force level, and the soil looseness, determining the actual expected value, and determining all sub-grid regions with the actual expected value greater than the preset expected value as the expected layout regions.

6. The photovoltaic array layout method based on mountain terrain according to claim 5, wherein, The determining the actual expected value according to the layout expected value, the average wind force level, and the soil looseness includes: Among them, E is the layout expected value, W is the average wind force level, W max is the maximum value of the wind force level, S is the soil looseness, S max is the maximum value of the soil looseness, and A is the actual expected value.

7. The method for photovoltaic array layout based on mountain terrain according to claim 1, wherein Splicing all expected layout regions with adjacency relationships to obtain all regions to be laid out, including: For any expected layout region, determining the regions that are also expected layout regions among the upper region, the lower region, the left region, and the right region of the expected layout region as the regions to be spliced; Splicing the expected layout region and the regions to be spliced until all regions to be laid out are obtained.

8. The method for arranging a photovoltaic array based on mountain terrain according to claim 1, wherein Taking the maximization of power generation, the minimization of shadow occlusion loss, and the minimization of the total cost of photovoltaic module installation as the objective function, and using a genetic algorithm to output the optimal layout solution, including: Taking the maximization of power generation, the minimization of shadow occlusion loss, and the minimization of the total cost of photovoltaic module installation as the objective function, calculating the fitness value of each initial layout in the initial population, applying selection, crossover, and mutation to generate new layout solutions to form a new population, and calculating the fitness value of each new layout in the new population again until the preset number of iterations is reached, and determining the layout solution with the highest fitness value as the optimal layout solution.

9. The method for arranging a photovoltaic array based on mountain terrain according to claim 8, characterized in that The calculating the fitness value of each initial layout in the initial population includes: Among them, F is the fitness value, w1, w2, and w3 are weight coefficients, and P gen is the power generation of the initial layout, is the maximum and minimum values of the power generation, and L shade is the shadow occlusion loss of the initial layout, is the maximum and minimum values of the shadow occlusion loss, and C total is the total installation cost of the photovoltaic modules in the initial layout, is the minimum and maximum values of the total installation cost of the photovoltaic modules.

10. A photovoltaic array layout system based on mountain terrain, characterized in that, Including: An acquisition unit for dividing the current mountain terrain area into grids to obtain a plurality of sub-grid regions; An input unit for obtaining the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid region, and inputting the slope, aspect, vegetation coverage rate, and current sunshine duration corresponding to each sub-grid region into a preset photovoltaic installation preset model to obtain the layout expected value output by the preset photovoltaic installation preset model; A splicing unit for determining all sub-grid regions with layout expected values greater than the preset expected value as the expected layout regions, splicing all expected layout regions with adjacency relationships to obtain all regions to be laid out, and removing the regions to be laid out with an area smaller than the preset area from all regions to be laid out to obtain all regions after removal; Layout unit, which is used for any remaining area. Multiple photovoltaic arrays with different sizes and different power generation capacities are used to perform multiple initial layouts on the remaining area at different installation positions with different installation inclinations to obtain an initial population. Taking the maximization of power generation capacity, the minimization of shadow occlusion loss, and the minimization of the total installation cost of photovoltaic modules as the objective function, the genetic algorithm is used to output the optimal layout solution. The remaining area is laid out with photovoltaic arrays using the optimal layout solution, and all remaining areas are traversed until the photovoltaic array layout of the current mountainous terrain area is completed.

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