Mountain photovoltaic layout optimization method and system based on multi-point illumination dynamic data

Abstract

The invention discloses a mountain photovoltaic layout optimization method and system based on multi-point illumination dynamic data, and relates to the technical field of photovoltaic construction, and the method comprises the steps: constructing a virtual mountain structure and configuring a three-dimensional landform stereo structure through mountain geographical and landform data; based on the historical illumination data, establishing an illumination simulation model, and quantifying the illumination performance of the virtual mountain structure; randomly arranging virtual photovoltaic modules in the photovoltaic layout area, determining position nodes and angles, and forming a virtual photovoltaic arrangement strategy; and calculating an illumination intensity accumulation parameter and a total parameter of each virtual photovoltaic module, evaluating layout optimization in combination with layout cost, and screening an optimization strategy. According to the method, through refined terrain modeling, dynamic illumination simulation and multi-target optimization, the energy efficiency and economical efficiency of mountain photovoltaic layout are remarkably improved, and scientific support is provided for planning of a complex terrain photovoltaic power station.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic construction, and in particular to a mountain photovoltaic layout optimization method and system based on multi-point illumination dynamic data. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, photovoltaic power generation, as an important form of clean energy, has been widely used. However, the construction of photovoltaic power stations in mountainous areas faces many challenges, mainly including complex terrain conditions, dynamically changing lighting environments, and the multi-objective requirements of layout optimization. The high undulations, slope changes, and surface irregularities of mountainous terrain make traditional layout methods based on planes or simple geometry difficult to apply, resulting in inefficient photovoltaic module layout and difficulty in fully utilizing the potential of light resources. In addition, lighting conditions in mountainous environments are highly dynamic, affected by terrain obstruction, seasonal changes, and time series. Traditional static optimization methods cannot accurately capture the changing trends of light intensity and angle, thus limiting the improvement of power generation efficiency. At the same time, photovoltaic layout needs to strike a balance between light utilization, construction costs, and terrain constraints, involving a multi-objective optimization problem. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system capable of optimizing mountain photovoltaic layout.

[0004] The present invention discloses a mountain photovoltaic layout optimization method based on multi-point illumination dynamic data, comprising:

[0005] Step S100: acquiring mountain geographic data, and constructing a virtual mountain structure based on the mountain geographic data; acquiring mountain landform data, and configuring a three-dimensional landform structure for the virtual mountain structure based on the mountain landform data;

[0006] Step S200, acquiring historical lighting data, and determining a lighting representation of a lighting simulation model of a virtual mountain structure based on the historical lighting data;

[0007] Step S300: Delineating a photovoltaic layout area for the virtual mountain structure and randomly arranging virtual photovoltaic modules within the photovoltaic layout area based on a preset photovoltaic layout volume. The arrangement method includes determining the location node of each virtual photovoltaic module and the arrangement angle of the virtual photovoltaic module to form a virtual photovoltaic layout strategy.

[0008] In step S400, the accumulated light intensity parameters of each virtual photovoltaic component in each virtual photovoltaic layout strategy are calculated, and the accumulated light intensity parameters of all virtual photovoltaic components are calculated. Combined with the photovoltaic layout cost, the layout optimization evaluation of this virtual photovoltaic layout strategy is determined, and based on the level of the layout optimization evaluation, several virtual photovoltaic layout strategies are determined.

[0009] In some embodiments disclosed herein, a method for constructing a mountain structure based on mountain geographic data includes:

[0010] Step S101: establishing a virtual three-dimensional coordinate system, determining a height parameter axis of the virtual three-dimensional coordinate system, and uniformly demarcating the height parameter axis to obtain a number of height reference points;

[0011] In step S102, the shape of the mountain structure transverse slice corresponding to each height reference point is determined, and the mountain structure edge in the mountain structure transverse slice is configured in a virtual three-dimensional system with reference to the height reference point, and the mountain structure edge is connected with a structural surface to form a mountain structure.

[0012] In some embodiments disclosed herein, a method for connecting the edges of a mountain structure with a structural surface includes:

[0013] Step S1021: Obtain historical mountain structure edges, and obtain historically recorded actual structural surfaces between the mountain structure edges, and associate the actual structural surfaces with the historical mountain structure edges to obtain a corresponding group of historical structural edges and structural surfaces;

[0014] Step S1022: determining the structural center point of each historical structural edge in the corresponding group of historical structural edges and structural faces, constructing a structural center line based on the structural center point, and associating the structural center lines parallel to the historical structural edges to obtain a structural center line group;

[0015] Step S1023: Determine the same end of the structure center lines in the structure center line group, determine the actual structure surface lines that pass through the ends of the structure center lines on the actual structure surface, segment the historical structure edge, and obtain a plurality of historical structure edge segments;

[0016] Step S1024: constructing historical structural edge segments and their corresponding actual structural curves-on-surface into structural edge segment-structural curve-on-surface correspondence groups, and constructing a structural edge segment-structural curve-on-surface correspondence library from a plurality of structural edge segment-structural curve-on-surface correspondence groups;

[0017] Step S1025: When connecting the mountain structure edge to the structural surface, the mountain structure edge is cut off to obtain the mountain structure edge segment, and the mountain structure edge segment is used as the search condition. The adapted actual structural surface line is determined in the structural edge segment ~ structural surface line correspondence library, and the actual structural surface line is adapted to the mountain structure edge segment to form a structural surface.

[0018] In some embodiments disclosed herein, a method for determining an adapted actual structural curve-on-surface from a structure edge segment to structural curve-on-surface correspondence library includes:

[0019] Step S10251: Overlap and compare the upper and lower opposite structural edge segments with the corresponding historical edge structural segments to determine the structural adaptation parameters between the two, and determine the structural correlation parameters between the historical structural segments.

[0020] Step S10252: Determine the comprehensive adaptation parameters of the structure edge segment and the historical structure edge segment based on the structure adaptation parameters and the structure association parameters, and determine the actual structure surface curve selected from the structure edge segment to structure surface curve correspondence library based on the comprehensive adaptation parameters.

[0021] The method for determining the structural adaptation parameters between the structural edge segment and the historical edge structural segment includes:

[0022] Align the structural edge segment with the historical edge structural segment and gradually advance them from one end to the other. During the advancement process, calculate the change in the intersection area between the two and determine the structural adaptation parameters based on the change in the intersection area.

[0023] The method for determining the structural correlation parameters between historical structural segments includes:

[0024] When segmenting the historical structure edge, several segmented historical structure segments are associated with each other to form a historical structure segment group;

[0025] Based on the historical structure segment groups, the determined historical structure segments are classified to determine the largest structure segment set with the largest number of classifications. Based on the ratio of the number of historical structure segments in the largest structure segment set to the number of all historical structure segments, the structural correlation parameters between the historical structure segments are determined.

[0026] In some embodiments disclosed in the present invention, the expression for calculating the comprehensive adaptation parameter is:

[0027] P2=P1×Q×exp(L×G+b);

[0028] Among them, P2 is the comprehensive adaptation parameter, P1 is the structural adaptation parameter, G is the structural correlation parameter, L is the structural correlation parameter influence adjustment coefficient, b is the structural correlation parameter influence adjustment constant, and Q is the preset inter-parameter influence adjustment coefficient.

[0029] Among them, the expression of the structural adaptation parameter is:

[0030] ;

[0031] Among them, P1 is the structural adaptation parameter, K1 is the area change rate performance weight adjustment coefficient, α(i) is the area change rate judgment function corresponding to the i-th propulsion node, and the change influencing parameter of α(i) output is determined based on the preset change rate interval to which the area change rate belongs. n is the number of propulsion nodes, K2 is the cross-area weight adjustment coefficient, and S is the accumulated amount of cross-area during the propulsion process.

[0032] In some embodiments disclosed herein, a method for determining a lighting representation of a lighting simulation model of a virtual mountain structure based on historical lighting data includes:

[0033] Step S201, determining changes in light intensity and light angle within a preset time period based on historical light data;

[0034] Step S202 : constructing a virtual illumination line according to illumination intensity, and determining a line density change of the virtual illumination line over time based on illumination intensity change, and determining a line angle change of the virtual illumination line over time based on illumination angle change.

[0035] In some embodiments disclosed herein, a method for calculating a cumulative parameter of light intensity of a photovoltaic module includes:

[0036] In step S401, the virtual illumination line is advanced along the line direction to determine the illumination line component perpendicular to the virtual photovoltaic assembly, and the accumulated illumination line component of the virtual illumination line passing through the virtual photovoltaic assembly is counted and recorded as the illumination intensity accumulation parameter.

[0037] In some embodiments disclosed herein, a method for determining a layout optimization evaluation of a virtual photovoltaic deployment strategy includes:

[0038] Step S402, determining the photovoltaic deployment cost of each virtual photovoltaic assembly based on the difficulty of photovoltaic deployment and the cost of consumables;

[0039] Step S403, determine the cost preset interval to which the photovoltaic layout cost of each virtual photovoltaic component belongs, determine the first layout optimization sub-evaluation value, determine the illumination intensity accumulation parameter interval to which the illumination intensity accumulation parameter belongs, determine the second layout optimization sub-evaluation value, calculate the sum of the first layout optimization sub-evaluation value and the second layout optimization sub-evaluation value, and obtain the layout optimization evaluation.

[0040] In some embodiments disclosed in the present invention, a mountain photovoltaic layout optimization system based on multi-point illumination dynamic data is also disclosed, including:

[0041] The first module is used to obtain mountain geographic data, and based on the mountain geographic data, construct a virtual mountain structure, obtain mountain landform data, and configure a three-dimensional landform structure for the virtual mountain structure based on the mountain landform data;

[0042] The second module is used to obtain historical lighting data and determine the lighting performance of the lighting simulation model of the virtual mountain structure based on the historical lighting data;

[0043] The third module is used to delineate a photovoltaic layout area for the virtual mountain structure and randomly arrange virtual photovoltaic modules in the photovoltaic layout area based on the preset photovoltaic layout volume. The arrangement method includes determining the location node of each virtual photovoltaic module and the arrangement angle of the virtual photovoltaic module to form a virtual photovoltaic layout strategy;

[0044] The fourth module is used to calculate the accumulated light intensity parameters of each virtual photovoltaic module in each virtual photovoltaic layout strategy, and calculate the total accumulated light intensity parameters of all virtual photovoltaic modules. Combined with the photovoltaic layout cost, it determines the layout optimization evaluation of this virtual photovoltaic layout strategy, and based on the level of the layout optimization evaluation, determines several virtual photovoltaic layout strategies.

[0045] The present invention discloses a method and system for optimizing mountain photovoltaic layout based on dynamic multi-point illumination data, relating to the field of photovoltaic construction technology. Using mountain geographic and landform data, a virtual mountain structure is constructed and configured with a three-dimensional landform structure. Based on historical illumination data, an illumination simulation model is established to quantify the illumination performance of the virtual mountain structure. Virtual photovoltaic components are randomly arranged within the photovoltaic layout area, and their location nodes and angles are determined to form a virtual photovoltaic layout strategy. The accumulated and total illumination intensity parameters of each virtual photovoltaic component are calculated, and the layout optimization is evaluated in combination with the layout cost to select the optimal strategy. Through refined terrain modeling, dynamic illumination simulation, and multi-objective optimization, the present invention significantly improves the energy efficiency and economic efficiency of mountain photovoltaic layout, providing scientific support for the planning of photovoltaic power stations in complex terrain.

[0046] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a diagram of the steps of a method for optimizing mountain photovoltaic layout based on multi-point illumination dynamic data disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0049] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solutions of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should have the common meanings understood by those skilled in the art of the present invention.

[0050] Example:

[0051] The purpose of the present invention is to provide a method and system capable of optimizing mountain photovoltaic layout.

[0052] The present invention discloses a mountain photovoltaic layout optimization method based on multi-point illumination dynamic data, comprising:

[0053] Step S100: acquiring mountain geographic data, and constructing a virtual mountain structure based on the mountain geographic data; acquiring mountain landform data, and configuring a three-dimensional landform structure for the virtual mountain structure based on the mountain landform data.

[0054] Step S100 aims to construct a virtual mountain structure and configure a three-dimensional topographic structure by acquiring mountain geographic and geomorphological data, providing a precise geographic foundation for subsequent photovoltaic layout optimization. Mountainous terrain is complex, characterized by high elevation fluctuations, varying slopes, and surface irregularities, making it difficult to accurately describe its characteristics using traditional two-dimensional or simple geometric modeling. This step first acquires mountain geographic data (such as latitude and longitude, elevation, and slope) through remote sensing, geographic information systems (GIS), or on-site surveys. Based on this data, a virtual mountain structure is constructed in a virtual three-dimensional coordinate system, forming a preliminary digital representation of the terrain. Next, more detailed mountain topographic data (vegetation cover) is acquired, and the virtual mountain structure is refined using algorithms (such as triangulation interpolation or surface fitting) to configure a three-dimensional structure that reflects the actual topographic features. This structure not only incorporates terrain elevation and slope information, but also takes into account the constraints imposed by topographic details on photovoltaic module layout. This provides a highly accurate virtual environment for subsequent lighting simulation and layout optimization, ensuring the model's consistency with the actual mountain terrain.

[0055] Step S200 : acquiring historical lighting data, and determining lighting representation of a lighting simulation model of a virtual mountain structure based on the historical lighting data.

[0056] Step S200 establishes a lighting simulation model of a virtual mountain structure by analyzing historical lighting data to quantify lighting performance and provide a dynamic lighting basis for photovoltaic layout optimization. The lighting conditions in the mountainous environment are affected by terrain shading, seasonal changes and time series, and present complex dynamic characteristics. Traditional static lighting models are difficult to accurately predict the light receiving efficiency of photovoltaic modules. This step first collects historical lighting data of the target area, including light intensity, angle, time series and meteorological conditions (such as cloud cover, haze). Based on these data, combined with the three-dimensional landform characteristics of the virtual mountain structure, a lighting simulation model is constructed. The model simulates the propagation path of sunlight in mountainous terrain through ray tracing or radiation transfer algorithm, calculates the light intensity and angle of each location at different time points, and generates lighting performance parameters (such as sunshine duration, cumulative radiation). This process takes into account the dynamic effects of terrain shading and slope on lighting, ensuring that the simulation results can truly reflect the light receiving potential of mountain photovoltaic modules and provide a scientific basis for subsequent layout optimization.

[0057] In step S300, a photovoltaic layout area is defined for the virtual mountain structure, and based on a preset photovoltaic layout volume, virtual photovoltaic components are randomly arranged in the photovoltaic layout area. The arrangement method includes determining the location node of each virtual photovoltaic component and the arrangement angle of the virtual photovoltaic component to form a virtual photovoltaic layout strategy.

[0058] Step S300 defines a suitable photovoltaic layout area and randomly arranges virtual photovoltaic modules to form a virtual photovoltaic layout strategy, providing a variety of candidate solutions for layout optimization. The complexity of mountainous terrain means that not all areas are suitable for photovoltaic module installation. Factors such as terrain stability, slope constraints, and construction feasibility must be considered. This step first uses the three-dimensional topographic data of the virtual mountain structure to comprehensively analyze conditions such as slope, load-bearing capacity, and vegetation cover to delineate areas suitable for photovoltaic layout. Subsequently, a randomized algorithm is used to generate a layout plan for virtual photovoltaic modules within the layout area, subject to preset photovoltaic layout volume constraints (such as the number of modules and total area). The location node (3D coordinates) and layout angle (inclination and azimuth) of each virtual photovoltaic module are determined through random sampling, forming a complete virtual photovoltaic layout strategy. This randomized layout method can cover a variety of possible layout combinations, fully exploring the optimization space. At the same time, through diverse angle and position configurations, it adapts to the dynamic changes in mountain light, laying the foundation for subsequent light intensity assessment and optimization screening.

[0059] In step S400, the accumulated light intensity parameters of each virtual photovoltaic component in each virtual photovoltaic layout strategy are calculated, and the accumulated light intensity parameters of all virtual photovoltaic components are calculated. Combined with the photovoltaic layout cost, the layout optimization evaluation of this virtual photovoltaic layout strategy is determined, and based on the level of the layout optimization evaluation, several virtual photovoltaic layout strategies are determined.

[0060] Step S400 determines the merits of virtual photovoltaic layout strategies by calculating the accumulated light intensity parameters of virtual photovoltaic modules and combining them with cost assessment, thereby selecting several preferred layout options. The power generation efficiency and economic efficiency of a photovoltaic power station depend on a balance between light utilization and construction costs. The complexity of mountainous environments further increases the difficulty of optimization. This step first calculates the accumulated light intensity parameters (i.e., the cumulative radiation over a period of time) for each virtual photovoltaic layout strategy at a specific location and angle based on the illumination simulation model of step S200. Subsequently, the parameters of all virtual photovoltaic modules are aggregated to obtain the total accumulated light intensity parameter for the strategy, reflecting the overall light utilization efficiency. Simultaneously, the layout optimization evaluation value of each strategy is calculated using a weighted evaluation or multi-objective optimization algorithm, taking into account the photovoltaic layout costs (including module installation, infrastructure, and maintenance costs). The evaluation value comprehensively considers light efficiency and economic efficiency, and strategies with higher evaluation values ​​are retained. By comparing the evaluation results of multiple random layout strategies, several optimized virtual photovoltaic layout strategies are selected, providing a scientific layout solution for actual projects and maximizing the overall benefits of mountain photovoltaic power stations.

[0061] In some embodiments disclosed herein, a method for constructing a mountain structure based on mountain geographic data includes:

[0062] Step S101 : establishing a virtual three-dimensional coordinate system, determining a height parameter axis of the virtual three-dimensional coordinate system, and uniformly demarcating the height parameter axis to obtain a number of height reference points.

[0063] In step S102, the shape of the mountain structure transverse slice corresponding to each height reference point is determined, and the mountain structure edge in the mountain structure transverse slice is configured in a virtual three-dimensional system with reference to the height reference point, and the mountain structure edge is connected with a structural surface to form a mountain structure.

[0064] In some embodiments disclosed herein, a method for connecting the edges of a mountain structure with a structural surface includes:

[0065] Step S1021: Obtain historical mountain structure edges and actual structural surfaces recorded in historical records between the mountain structure edges, associate the actual structural surfaces with the historical mountain structure edges, and obtain a corresponding group of historical structural edges and structural surfaces.

[0066] Step S1022 , determining the structural center point of each historical structural edge in the corresponding group of historical structural edge to structural face, constructing a structural center line for the structural center point, and associating the structural center lines parallel to the historical structural edges to obtain a structural center line group.

[0067] In step S1023, the same end of the structure centerline in the structure centerline group is determined, and the actual structure surface line that passes through the end of the structure centerline at the same time is determined on the actual structure surface, and the historical structure edge is segmented to obtain a plurality of historical structure edge segments.

[0068] Step S1024 , constructing historical structural edge segments and their corresponding actual structural curves-on-surface into structural edge segment-structural curve-on-surface correspondence groups, and constructing a structural edge segment-structural curve-on-surface correspondence library from several structural edge segment-structural curve-on-surface correspondence groups.

[0069] Step S1025: When connecting the mountain structure edge to the structural surface, the mountain structure edge is cut off to obtain the mountain structure edge segment, and the mountain structure edge segment is used as the search condition. The adapted actual structural surface line is determined in the structural edge segment ~ structural surface line correspondence library, and the actual structural surface line is adapted to the mountain structure edge segment to form a structural surface.

[0070] In some embodiments disclosed herein, a method for determining an adapted actual structural curve-on-surface from a structure edge segment to structural curve-on-surface correspondence library includes:

[0071] Step S10251: Overlap and compare the upper and lower opposite structural edge segments with the corresponding historical edge structural segments to determine the structural adaptation parameters between the two, and determine the structural correlation parameters between the historical structural segments.

[0072] Step S10252, based on the structural adaptation parameters and the structural association parameters, determine the comprehensive adaptation parameters of the structural edge segment and the historical structural edge segment, and based on the comprehensive adaptation parameters, determine the actual structural surface curve selected in the structural edge segment ~ structural surface curve correspondence library.

[0073] The method for determining the structural adaptation parameters between the structural edge segment and the historical edge structural segment includes:

[0074] The structural edge segment and the historical edge structure segment are aligned and gradually advanced from one end to the other. During the advancement process, the change in the intersection area between the two is calculated, and the structural adaptation parameters are determined based on the change in the intersection area.

[0075] The method for determining the structural correlation parameters between historical structural segments includes:

[0076] When segmenting the edge of the historical structure, several segmented historical structure segments are associated with each other to form a historical structure segment group.

[0077] Based on the historical structure segment groups, the determined historical structure segments are classified to determine the largest structure segment set with the largest number of classifications. Based on the ratio of the number of historical structure segments in the largest structure segment set to the number of all historical structure segments, the structural correlation parameters between the historical structure segments are determined.

[0078] In some embodiments disclosed in the present invention, the expression for calculating the comprehensive adaptation parameter is:

[0079] P2=P1×Q×exp(L×G+b);

[0080] Among them, P2 is the comprehensive adaptation parameter, P1 is the structural adaptation parameter, G is the structural correlation parameter, L is the structural correlation parameter influence adjustment coefficient, b is the structural correlation parameter influence adjustment constant, and Q is the preset inter-parameter influence adjustment coefficient.

[0081] Among them, the expression of the structural adaptation parameter is:

[0082] .

[0083] Among them, P1 is the structural adaptation parameter, K1 is the area change rate performance weight adjustment coefficient, α(i) is the area change rate judgment function corresponding to the i-th propulsion node, and the change influencing parameter of α(i) output is determined based on the preset change rate interval to which the area change rate belongs. n is the number of propulsion nodes, K2 is the cross-area weight adjustment coefficient, and S is the accumulated amount of cross-area during the propulsion process.

[0084] In some embodiments disclosed herein, a method for determining a lighting representation of a lighting simulation model of a virtual mountain structure based on historical lighting data includes:

[0085] Step S201 : determining changes in light intensity and light angle within a preset time period based on historical light data.

[0086] Step S201 aims to analyze historical illumination data and extract the dynamic change patterns of illumination intensity and illumination angle within a preset time period, providing basic data for the subsequent construction of illumination simulation models. The illumination in mountainous environments is affected by time, season, weather, and terrain, and exhibits significant dynamic characteristics. This step first collects historical illumination data for the target area, including illumination intensity (radiation per unit area, usually expressed in W / m2). 2The data is then cleaned and analyzed over time to determine the changing trends in light intensity (e.g., the intensity curve from sunrise to sunset) and the changing patterns in light angles (e.g., the angular displacement of the sun's trajectory) over a preset time period (e.g., one day, one month, or one year). For example, hourly or daily intensity and angle data sets can be generated using statistical averages or interpolation methods. This process provides quantitative input parameters for lighting simulation, ensuring that the model reflects the real-world dynamics of lighting and laying the foundation for evaluating the lighting performance of virtual mountain structures.

[0087] Step S202 : constructing a virtual illumination line according to illumination intensity, and determining a line density change of the virtual illumination line over time based on illumination intensity change, and determining a line angle change of the virtual illumination line over time based on illumination angle change.

[0088] Step S202 simulates the dynamic performance of light on the virtual mountain structure by constructing virtual light lines and analyzing their changes over time, providing a detailed description of the spatiotemporal characteristics of the lighting simulation model. Virtual light lines are abstract representations of light propagation paths. Combined with the dynamic changes in light intensity and angle, they can intuitively reflect the distribution characteristics of light in the three-dimensional terrain. This step first constructs virtual light lines based on the light intensity data from step S201, representing the projection trajectory of light on the virtual mountain structure. Changes in light intensity are converted into changes in the line density of virtual light lines. That is, higher light intensity results in greater line density (indicating a greater concentration of light per unit area). The dynamic changes in line density over time are quantified using mathematical mapping (such as linear or nonlinear functions). Simultaneously, based on changes in light angle, the line angle changes of virtual light lines are determined. That is, the incident angle of light is adjusted over time to reflect the movement trajectory of the sun's position (such as changes in altitude and azimuth). Through ray tracing or geometric algorithms, combined with the three-dimensional topographic characteristics of the virtual mountain structure, the projection and occlusion effects of light lines on the terrain are calculated to generate the spatiotemporal distribution of the lighting performance. This process provides an accurate simulation basis for the calculation of the light intensity accumulation of photovoltaic modules and enhances the model's adaptability to complex mountain lighting conditions.

[0089] In some embodiments disclosed herein, a method for calculating a cumulative parameter of light intensity of a photovoltaic module includes:

[0090] In step S401, the virtual illumination line is advanced along the line direction to determine the illumination line component perpendicular to the virtual photovoltaic assembly, and the accumulated illumination line component of the virtual illumination line passing through the virtual photovoltaic assembly is counted and recorded as the illumination intensity accumulation parameter.

[0091] In some embodiments disclosed herein, a method for determining a layout optimization evaluation of a virtual photovoltaic deployment strategy includes:

[0092] Step S402 : determining the photovoltaic deployment cost of each virtual photovoltaic assembly based on the difficulty of photovoltaic deployment and the cost of consumables.

[0093] Step S403, determine the cost preset interval to which the photovoltaic layout cost of each virtual photovoltaic component belongs, determine the first layout optimization sub-evaluation value, determine the illumination intensity accumulation parameter interval to which the illumination intensity accumulation parameter belongs, determine the second layout optimization sub-evaluation value, calculate the sum of the first layout optimization sub-evaluation value and the second layout optimization sub-evaluation value, and obtain the layout optimization evaluation.

[0094] In some embodiments disclosed in the present invention, a mountain photovoltaic layout optimization system based on multi-point illumination dynamic data is also disclosed, including:

[0095] The first module is used to obtain mountain geographic data, and based on the mountain geographic data, construct a virtual mountain structure, obtain mountain landform data, and configure a three-dimensional landform structure for the virtual mountain structure based on the mountain landform data;

[0096] The second module is used to obtain historical lighting data and determine the lighting performance of the lighting simulation model of the virtual mountain structure based on the historical lighting data;

[0097] The third module is used to delineate a photovoltaic layout area for the virtual mountain structure and randomly arrange virtual photovoltaic modules in the photovoltaic layout area based on the preset photovoltaic layout volume. The arrangement method includes determining the location node of each virtual photovoltaic module and the arrangement angle of the virtual photovoltaic module to form a virtual photovoltaic layout strategy;

[0098] The fourth module is used to calculate the accumulated light intensity parameters of each virtual photovoltaic module in each virtual photovoltaic layout strategy, and calculate the total accumulated light intensity parameters of all virtual photovoltaic modules. Combined with the photovoltaic layout cost, it determines the layout optimization evaluation of this virtual photovoltaic layout strategy, and based on the level of the layout optimization evaluation, determines several virtual photovoltaic layout strategies.

[0099] The present invention discloses a method and system for optimizing mountain photovoltaic layout based on dynamic multi-point illumination data, relating to the field of photovoltaic construction technology. Using mountain geographic and landform data, a virtual mountain structure is constructed and configured with a three-dimensional landform structure. Based on historical illumination data, an illumination simulation model is established to quantify the illumination performance of the virtual mountain structure. Virtual photovoltaic components are randomly arranged within the photovoltaic layout area, and their location nodes and angles are determined to form a virtual photovoltaic layout strategy. The accumulated and total illumination intensity parameters of each virtual photovoltaic component are calculated, and the layout optimization is evaluated in combination with the layout cost to select the optimal strategy. Through refined terrain modeling, dynamic illumination simulation, and multi-objective optimization, the present invention significantly improves the energy efficiency and economic efficiency of mountain photovoltaic layout, providing scientific support for the planning of photovoltaic power stations in complex terrain.

[0100] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented through hardware or by using software plus the necessary general hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) and includes a number of instructions for enabling a computer device (such as a personal computer, a server, or a network device) to execute the methods described in various implementation scenarios of the present invention.

[0101] 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 the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A mountain photovoltaic layout optimization method based on multi-point illumination dynamic data, characterized by: include: Step S100: acquiring mountain geographic data, and constructing a virtual mountain structure based on the mountain geographic data; acquiring mountain landform data, and configuring a three-dimensional landform structure for the virtual mountain structure based on the mountain landform data; Step S200, acquiring historical lighting data, and determining a lighting representation of a lighting simulation model of a virtual mountain structure based on the historical lighting data; Step S300: Delineating a photovoltaic layout area for the virtual mountain structure and randomly arranging virtual photovoltaic modules within the photovoltaic layout area based on a preset photovoltaic layout volume. The arrangement method includes determining the location node of each virtual photovoltaic module and the arrangement angle of the virtual photovoltaic module to form a virtual photovoltaic layout strategy. In step S400, the accumulated light intensity parameters of each virtual photovoltaic component in each virtual photovoltaic layout strategy are calculated, and the accumulated light intensity parameters of all virtual photovoltaic components are calculated. Combined with the photovoltaic layout cost, the layout optimization evaluation of this virtual photovoltaic layout strategy is determined, and based on the level of the layout optimization evaluation, several virtual photovoltaic layout strategies are determined.

2. The mountain photovoltaic layout optimization method based on multi-point illumination dynamic data according to claim 1 is characterized in that: Based on mountain geographic data, the methods for constructing mountain structures include: Step S101: establishing a virtual three-dimensional coordinate system, determining a height parameter axis of the virtual three-dimensional coordinate system, and uniformly demarcating the height parameter axis to obtain a number of height reference points; In step S102, the shape of the mountain structure transverse slice corresponding to each height reference point is determined, and the mountain structure edge in the mountain structure transverse slice is configured in a virtual three-dimensional system with reference to the height reference point, and the mountain structure edge is connected with a structural surface to form a mountain structure.

3. The mountain photovoltaic layout optimization method based on multi-point illumination dynamic data according to claim 2 is characterized in that: Methods for connecting the edges of mountain structures with structural surfaces include: Step S1021: Obtain historical mountain structure edges, and obtain historically recorded actual structural surfaces between the mountain structure edges, and associate the actual structural surfaces with the historical mountain structure edges to obtain a corresponding group of historical structural edges and structural surfaces; Step S1022: determining the structural center point of each historical structural edge in the corresponding group of historical structural edges and structural faces, constructing a structural center line based on the structural center point, and associating the structural center lines parallel to the historical structural edges to obtain a structural center line group; Step S1023: Determine the same end of the structure center lines in the structure center line group, determine the actual structure surface lines that pass through the ends of the structure center lines on the actual structure surface, segment the historical structure edge, and obtain a plurality of historical structure edge segments; Step S1024: constructing historical structural edge segments and their corresponding actual structural curves-on-surface into structural edge segment-structural curve-on-surface correspondence groups, and constructing a structural edge segment-structural curve-on-surface correspondence library from a plurality of structural edge segment-structural curve-on-surface correspondence groups; Step S1025: When connecting the mountain structure edge to the structural surface, the mountain structure edge is cut off to obtain the mountain structure edge segment, and the mountain structure edge segment is used as the search condition. The adapted actual structural surface line is determined in the structural edge segment ~ structural surface line correspondence library, and the actual structural surface line is adapted to the mountain structure edge segment to form a structural surface.

4. The mountain photovoltaic layout optimization method based on multi-point illumination dynamic data according to claim 3 is characterized in that: The method for determining the adapted actual structural curve-on-surface from the structural edge segment to structural curve-on-surface correspondence library includes: Step S10251: Overlap and compare the upper and lower opposite structural edge segments with the corresponding historical edge structural segments to determine the structural adaptation parameters between the two, and determine the structural correlation parameters between the historical structural segments. Step S10252: Determine the comprehensive adaptation parameters of the structure edge segment and the historical structure edge segment based on the structure adaptation parameters and the structure association parameters, and determine the actual structure surface curve selected from the structure edge segment to structure surface curve correspondence library based on the comprehensive adaptation parameters. The method for determining the structural adaptation parameters between the structural edge segment and the historical edge structural segment includes: Align the structural edge segment with the historical edge structural segment and gradually advance them from one end to the other. During the advancement process, calculate the change in the intersection area between the two and determine the structural adaptation parameters based on the change in the intersection area. The method for determining the structural correlation parameters between historical structural segments includes: When segmenting the historical structure edge, several segmented historical structure segments are associated with each other to form a historical structure segment group; Based on the historical structure segment groups, the determined historical structure segments are classified to determine the largest structure segment set with the largest number of classifications. Based on the ratio of the number of historical structure segments in the largest structure segment set to the number of all historical structure segments, the structural correlation parameters between the historical structure segments are determined.

5. The mountain photovoltaic layout optimization method based on multi-point illumination dynamic data according to claim 4 is characterized in that: The expression for calculating the comprehensive adaptation parameter is: P2=P1×Q×exp(L×G+b); Among them, P2 is the comprehensive adaptation parameter, P1 is the structural adaptation parameter, G is the structural correlation parameter, L is the structural correlation parameter influence adjustment coefficient, b is the structural correlation parameter influence adjustment constant, and Q is the preset inter-parameter influence adjustment coefficient. Among them, the expression of the structural adaptation parameter is: Among them, P1 is the structural adaptation parameter, K1 is the area change rate performance weight adjustment coefficient, α(i) is the area change rate judgment function corresponding to the i-th propulsion node, and the change influencing parameter of α(i) output is determined based on the preset change rate interval to which the area change rate belongs. n is the number of propulsion nodes, K2 is the cross-area weight adjustment coefficient, and S is the accumulated amount of cross-area during the propulsion process.

6. The mountain photovoltaic layout optimization method based on multi-point illumination dynamic data according to claim 1, characterized in that: Methods for determining the lighting performance of a lighting simulation model for a virtual mountain structure based on historical lighting data include: Step S201, determining changes in light intensity and light angle within a preset time period based on historical light data; Step S202 : constructing a virtual illumination line according to illumination intensity, and determining a line density change of the virtual illumination line over time based on illumination intensity change, and determining a line angle change of the virtual illumination line over time based on illumination angle change.

7. The mountain photovoltaic layout optimization method based on multi-point illumination dynamic data according to claim 6 is characterized in that: Methods for calculating the accumulated parameters of light intensity of photovoltaic modules include: In step S401, the virtual illumination line is advanced along the line direction to determine the illumination line component perpendicular to the virtual photovoltaic assembly, and the accumulated illumination line component of the virtual illumination line passing through the virtual photovoltaic assembly is counted and recorded as the illumination intensity accumulation parameter.

8. The mountain photovoltaic layout optimization method based on multi-point illumination dynamic data according to claim 1, characterized in that: The layout optimization evaluation method for determining the virtual photovoltaic layout strategy includes: Step S402, determining the photovoltaic deployment cost of each virtual photovoltaic assembly based on the difficulty of photovoltaic deployment and the cost of consumables; Step S403, determine the cost preset interval to which the photovoltaic layout cost of each virtual photovoltaic component belongs, determine the first layout optimization sub-evaluation value, determine the illumination intensity accumulation parameter interval to which the illumination intensity accumulation parameter belongs, determine the second layout optimization sub-evaluation value, calculate the sum of the first layout optimization sub-evaluation value and the second layout optimization sub-evaluation value, and obtain the layout optimization evaluation.

9. Mountain photovoltaic layout optimization system based on multi-point illumination dynamic data, characterized by: A method for optimizing mountain photovoltaic layout based on multi-point illumination dynamic data for executing any one of claims 1 to 8, comprising: The first module is used to obtain mountain geographic data, and based on the mountain geographic data, construct a virtual mountain structure, obtain mountain landform data, and configure a three-dimensional landform structure for the virtual mountain structure based on the mountain landform data; The second module is used to obtain historical lighting data and determine the lighting performance of the lighting simulation model of the virtual mountain structure based on the historical lighting data; The third module is used to delineate a photovoltaic layout area for the virtual mountain structure and randomly arrange virtual photovoltaic modules in the photovoltaic layout area based on the preset photovoltaic layout volume. The arrangement method includes determining the location node of each virtual photovoltaic module and the arrangement angle of the virtual photovoltaic module to form a virtual photovoltaic layout strategy; The fourth module is used to calculate the accumulated light intensity parameters of each virtual photovoltaic module in each virtual photovoltaic layout strategy, and calculate the total accumulated light intensity parameters of all virtual photovoltaic modules. Combined with the photovoltaic layout cost, it determines the layout optimization evaluation of this virtual photovoltaic layout strategy, and based on the level of the layout optimization evaluation, determines several virtual photovoltaic layout strategies.

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