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

Abstract

The application discloses a mountainous photovoltaic layout optimization method and system based on multi-point light illumination dynamic data, relates to the photovoltaic construction technical field, and constructs a virtual mountain structure and configures a three-dimensional landform stereo structure through mountainous geographical and landform data; establishes a light illumination simulation model based on historical light illumination data, and quantifies the light illumination performance of the virtual mountain structure; randomly arranges virtual photovoltaic components in a photovoltaic layout area, determines position nodes and angles, and forms a virtual photovoltaic arrangement strategy; calculates the light illumination intensity accumulation parameters and total parameters of each virtual photovoltaic component, evaluates layout optimization in combination with arrangement cost, and selects an optimal strategy. Through fine terrain modeling, dynamic light illumination simulation and multi-target optimization, the application significantly improves the energy efficiency and economy of mountainous photovoltaic layout, and provides scientific support for complex terrain photovoltaic power station planning.

Description

TECHNICAL FIELD

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

[0002] With the transformation of 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 station in mountainous areas faces many challenges, mainly including complex terrain conditions, dynamic changes of light environment and multi-objective requirements of layout optimization. The height fluctuation, slope change and surface irregularity of mountainous terrain make it difficult to apply traditional layout methods based on plane or simple geometry, resulting in low efficiency of photovoltaic module arrangement and difficulty in fully utilizing the potential of light resources. In addition, the light conditions in mountainous environment are affected by terrain obstruction, seasonal changes and time series, showing high dynamic characteristics. Traditional static optimization methods cannot accurately capture the trend of light intensity and angle changes, thus limiting the improvement of power generation efficiency. At the same time, photovoltaic layout needs to balance between light utilization rate, construction cost and terrain constraints, involving multi-objective optimization problem. SUMMARY

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

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

[0005] Step S100, obtaining mountain geographical data, and based on the mountain geographical data, constructing a virtual mountain structure, obtaining mountain landform data, and configuring a three-dimensional landform structure to the virtual mountain structure based on the mountain landform data;

[0006] Step S200, obtaining historical light data, and based on the historical light data, determining the light performance of the light simulation model of the virtual mountain structure;

[0007] Step S300, defining a photovoltaic layout area for the virtual mountain structure, and based on a preset photovoltaic arrangement volume, arranging a random virtual photovoltaic module in the photovoltaic layout area, the arrangement mode including determining the position node of each virtual photovoltaic module and the arrangement angle of the virtual photovoltaic module, forming a virtual photovoltaic arrangement strategy;

[0008] Step S400, calculating the light intensity accumulation parameter of each virtual photovoltaic module in each virtual photovoltaic arrangement strategy, and calculating the total light intensity accumulation parameter of all virtual photovoltaic modules, combining the photovoltaic arrangement cost to determine the layout optimization evaluation of this virtual photovoltaic arrangement strategy, and based on the high and low of the layout optimization evaluation, determining a plurality of virtual photovoltaic arrangement strategies.

[0009] In some embodiments of the present disclosure, based on mountainous geographical data, the method for constructing a mountainous structure comprises:

[0010] Step S101, a virtual three-dimensional coordinate system is established, a height parameter axis of the virtual three-dimensional coordinate system is determined, the height parameter axis is uniformly divided, and a plurality of height reference points are obtained;

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

[0012] In some embodiments of the present disclosure, the method for connecting the mountainous structure edge with the structure surface comprises:

[0013] Step S1021, historical mountainous structure edges are obtained, and historical records of actual structure surfaces between the mountainous structure edges are obtained, the actual structure surfaces are associated with the historical mountainous structure edges to obtain a historical structure edge-structure surface corresponding group;

[0014] Step S1022, the structure center point of each historical structure edge in the historical structure edge-structure surface corresponding group is determined, and a structure center line is constructed with reference to the structure center point, the structure center lines parallel to each other between the historical structure edges are associated to obtain a structure center line group;

[0015] Step S1023, the same end of the structure center line in the structure center line group is determined, the actual structure surface line passing through the end of the structure center line is determined on the actual structure surface, and the historical structure edge is segmented to obtain a plurality of historical structure edge sections;

[0016] Step S1024, the historical structure edge section and the corresponding actual structure surface line are constructed as a structure edge section-structure surface line corresponding group, and a plurality of structure edge section-structure surface line corresponding groups are constructed as a structure edge section-structure surface line corresponding library;

[0017] Step S1025, when connecting the mountainous structure edge with the structure surface, the mountainous structure edge is cut to obtain a mountainous structure edge section, the mountainous structure edge section is taken as a search condition, the adaptive actual structure surface line is determined in the structure edge section-structure surface line corresponding library, and the actual structure surface line is adapted between the mountainous structure edge sections to form a structure surface.

[0018] In some embodiments of the present disclosure, the method for determining the adaptive actual structure surface line in the structure edge section-structure surface line corresponding library comprises:

[0019] In step S10251, the structure fitting parameter between the upper and lower opposite structure edge sections and the corresponding historical edge structure sections is determined by aligning the structure edge sections and the historical edge structure sections, and the structure correlation parameter between the historical structure sections is determined.

[0020] In step S10252, the comprehensive fitting parameter of the structure edge sections and the historical edge structure sections is determined based on the structure fitting parameter and the structure correlation parameter, and the actual structure surface line on the selected structure surface line in the corresponding library is determined based on the comprehensive fitting parameter.

[0021] The method for determining the structure fitting parameter between the structure edge sections and the historical edge structure sections comprises the following steps:

[0022] The structure edge sections and the historical edge structure sections are aligned and gradually pushed from one end to the other end, and the cross-sectional area change of the two is calculated during the pushing process, and the structure fitting parameter is determined based on the cross-sectional area change.

[0023] The method for determining the structure correlation parameter between the historical structure sections comprises the following steps:

[0024] When the historical structure edge is segmented, the segmented historical structure sections are correlated with each other to form a historical structure section group.

[0025] Based on the historical structure section group, the determined historical structure sections are classified, the maximum structure section set with the largest number of classified historical structure sections is determined, and the structure correlation parameter between the historical structure sections is determined based on the section number ratio of the number of historical structure sections in the maximum structure section set to the total number of historical structure sections.

[0026] In some embodiments of the present application, the expression for calculating the comprehensive fitting parameter is:

[0027] P2=P1*Q*exp(L*G+b);

[0028] Wherein, P2 is the comprehensive fitting parameter, P1 is the structure fitting parameter, G is the structure correlation parameter, L is the structure correlation parameter influence adjustment coefficient, b is the structure correlation parameter influence adjustment constant, and Q is a preset parameter influence adjustment coefficient.

[0029] The expression of the structure fitting parameter is:

[0030] ;

[0031] Wherein, P1 is a structure adaptation parameter, K1 is an area change rate performance weight adjustment coefficient, a(i) is an area change rate judgment function corresponding to the i-th advancing node, based on the preset change rate interval to which the area change rate belongs, the change influence parameter output by a(i) is determined, n is the number of advancing nodes, K2 is a cross-sectional area weight adjustment coefficient, and S is the accumulation of the cross-sectional area in the advancing process.

[0032] In some embodiments disclosed in the present application, based on historical lighting data, a method for determining the lighting performance of a lighting simulation model of a virtual mountain structure includes:

[0033] Step S201, based on historical lighting data, determine the lighting intensity change and the lighting angle change in a preset time period;

[0034] Step S202, construct a virtual lighting line for the lighting intensity, and based on the lighting intensity change, determine the line density change of the virtual lighting line over time, and based on the lighting angle change, determine the line angle change of the virtual lighting line over time.

[0035] In some embodiments disclosed in the present application, a method for calculating the lighting intensity accumulation parameter of a photovoltaic module includes:

[0036] Step S401, advance the virtual lighting line according to the line direction, determine the virtual lighting line component perpendicular to the virtual photovoltaic module, and count the accumulation of the virtual lighting line component passing through the virtual lighting module, denoted as the lighting intensity accumulation parameter.

[0037] In some embodiments disclosed in the present application, a method for determining the layout optimization evaluation of a virtual photovoltaic arrangement strategy includes:

[0038] Step S402, based on the difficulty of photovoltaic arrangement and the cost of materials, determine the photovoltaic arrangement cost of each virtual photovoltaic module;

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

[0040] In some embodiments disclosed in the present application, a multi-point lighting dynamic data mountain photovoltaic layout optimization system is also disclosed, which includes:

[0041] The first module is used for obtaining mountain geographical data, and based on the mountain geographical data, constructing a virtual mountain structure, obtaining mountain landform data, and configuring a three-dimensional landform structure to the virtual mountain structure based on the mountain landform data;

[0042] The second module is configured to acquire historical light data and determine light performance of a light simulation model of the virtual mountain structure based on the historical light data.

[0043] The third module is configured to demarcate a photovoltaic layout area for the virtual mountain structure and perform random arrangement of virtual photovoltaic components in the photovoltaic layout area based on a preset photovoltaic arrangement volume, the arrangement mode including determination of a position node of each virtual photovoltaic component and an arrangement angle of the virtual photovoltaic component to form a virtual photovoltaic arrangement strategy.

[0044] The fourth module is configured to calculate light intensity accumulation parameters of each virtual photovoltaic component in each virtual photovoltaic arrangement strategy and calculate total light intensity accumulation parameters of all virtual photovoltaic components, determine layout optimization evaluation of the virtual photovoltaic arrangement strategy in combination with a photovoltaic arrangement cost, and determine a plurality of virtual photovoltaic arrangement strategies based on the layout optimization evaluation.

[0045] The application discloses a mountain photovoltaic layout optimization method and system based on multi-point light dynamic data, relates to the technical field of photovoltaic construction, and constructs a virtual mountain structure and configures a three-dimensional landform structure through mountain geographical and topographical data; establishes a light simulation model based on historical light data, quantifies light performance of the virtual mountain structure; randomly arranges virtual photovoltaic components in a photovoltaic layout area, determines position nodes and angles, and forms a virtual photovoltaic arrangement strategy; calculates light intensity accumulation parameters and total parameters of the virtual photovoltaic components, evaluates layout optimization in combination with arrangement cost, and selects an optimal strategy. The application significantly improves energy efficiency and economy of mountain photovoltaic layout through fine topographic modeling, dynamic light simulation and multi-target optimization, and provides scientific support for complex topographic photovoltaic power station planning.

[0046] The technical solutions of the application will be further described in detail below with reference to the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The application discloses a mountain photovoltaic layout optimization method and system based on multi-point light dynamic data, relates to the technical field of photovoltaic construction, and constructs a three-dimensional landform structure through mountain geographical and topographical data; establishes a light simulation model based on historical light data, quantifies light performance of the virtual mountain structure; randomly arranges virtual photovoltaic components in a photovoltaic layout area, determines position nodes and angles, and forms a virtual photovoltaic arrangement strategy; calculates light intensity accumulation parameters and total parameters of the virtual photovoltaic components, evaluates layout optimization in combination with arrangement cost, and selects an optimal strategy. The application significantly improves energy efficiency and economy of mountain photovoltaic layout through fine topographic modeling, dynamic light simulation and multi-target optimization, and provides scientific support for complex topographic photovoltaic power station planning. DETAILED DESCRIPTION

[0048] The technical solutions of the application will be further described in detail below with reference to the drawings and examples.

[0049] The technical solutions of the present application will be described clearly and completely in combination with the drawings and specific embodiments. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and cannot be understood as a limitation on the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments according to the content of the present application. In the present application, unless otherwise explicitly specified and limited, the technical terms used in the present application should be understood as the general meaning understood by the skilled person in the art.

[0050] Embodiments:

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

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

[0053] Step S100, obtain mountain geographic data, and based on the mountain geographic data, construct a virtual mountain structure, obtain mountain topographic data, and based on the mountain topographic data, configure a three-dimensional topographic solid structure to the virtual mountain structure.

[0054] Step S100 aims to provide accurate geographic basis for subsequent photovoltaic layout optimization by obtaining mountain geographic and topographic data, constructing a virtual mountain structure and configuring a three-dimensional topographic solid structure. Mountain terrain is complex, with high relief, slope change and surface irregularity, and traditional two-dimensional or simple geometric modeling is difficult to accurately describe its characteristics. This step first obtains mountain geographic data (such as latitude, longitude, elevation, slope, etc.) through remote sensing, geographic information system (GIS) or field survey, constructs a virtual mountain structure in a virtual three-dimensional coordinate system based on these data, and preliminarily forms a digital expression of the terrain. Then, obtain more detailed mountain topographic data (vegetation coverage), and configure a three-dimensional solid structure reflecting the real topographic features by fine processing the virtual mountain structure through algorithms (such as triangular mesh interpolation or surface fitting). This structure not only contains the elevation and slope information of the terrain, but also considers the constraints of topographic details on photovoltaic module arrangement, providing a high-precision virtual environment for subsequent lighting simulation and layout optimization, ensuring the consistency of the model with the actual mountain terrain.

[0055] Step S200, obtain historical lighting data, and based on the historical lighting data, determine the lighting performance of the lighting simulation model of the virtual mountain structure.

[0056] Step S200 establishes a light simulation model of the virtual mountain structure by analyzing historical light data to quantify light performance and provide dynamic light basis for photovoltaic layout optimization. The light conditions in mountainous environments are influenced by terrain obstruction, seasonal changes, and time series, presenting complex dynamic characteristics. Traditional static light models cannot accurately predict the light efficiency of photovoltaic modules. This step first collects historical light data for the target area, including light intensity, angle, time series, and weather conditions (such as cloud cover, haze). Based on these data, combined with the three-dimensional topographic features of the virtual mountain structure, a light simulation model is constructed. This model simulates the propagation path of sunlight in the mountainous terrain through ray tracing or radiative transfer algorithms, calculates the light intensity and angle at different time points for each location, and generates light performance parameters (such as sunshine duration, cumulative radiation). This process takes into account the dynamic effects of terrain obstruction and slope on light, ensuring that the simulation results accurately reflect the light potential of photovoltaic modules in the mountains, providing a scientific basis for subsequent layout optimization.

[0057] Step S300 defines the photovoltaic layout area for the virtual mountain structure and randomly arranges virtual photovoltaic modules in the photovoltaic layout area based on the preset photovoltaic arrangement volume. The arrangement method includes determining the position node of each virtual photovoltaic module and the arrangement angle of the virtual photovoltaic module to form a virtual photovoltaic arrangement strategy.

[0058] Step S300 defines the photovoltaic layout area and randomly arranges virtual photovoltaic modules to form a virtual photovoltaic arrangement strategy, providing a variety of candidate solutions for layout optimization. The complexity of mountainous terrain makes it not suitable for photovoltaic module installation in all areas, considering factors such as terrain stability, slope constraints, and construction feasibility. This step first analyzes the slope, load-bearing capacity, and vegetation coverage based on the three-dimensional topographic data of the virtual mountain structure to define the area suitable for photovoltaic arrangement. Then, under the constraint of the preset photovoltaic arrangement volume (such as the number of modules and total area), a randomization algorithm is used to generate a virtual photovoltaic module arrangement scheme in the layout area. The position node (three-dimensional coordinates) and arrangement angle (inclination, azimuth) of each virtual photovoltaic module are determined by random sampling to form a complete virtual photovoltaic arrangement strategy. This random arrangement method can cover a variety of possible layout combinations, fully exploring the optimization space, and through the diversified angle and position configuration, it can adapt to the dynamic changes of mountain light, laying a foundation for subsequent light intensity evaluation and optimization selection.

[0059] Step S400 calculates the light intensity accumulation parameter of each virtual photovoltaic module in each virtual photovoltaic arrangement strategy and calculates the total light intensity accumulation parameter of all virtual photovoltaic modules. Combined with the photovoltaic arrangement cost, the layout optimization evaluation of this virtual photovoltaic arrangement strategy is determined, and based on the high and low of the layout optimization evaluation, a number of virtual photovoltaic arrangement strategies are determined.

[0060] Step S400 determines the pros and cons of the virtual photovoltaic arrangement strategy by calculating the light intensity accumulation parameter of the virtual photovoltaic component and combining the cost evaluation, and screens out several preferred layout schemes. The power generation efficiency and economy of the photovoltaic power station depend on the balance of light utilization rate and construction cost, and the complexity of the mountainous environment further increases the difficulty of optimization. This step first calculates the light intensity accumulation parameter (i.e. the cumulative radiation amount in a period of time) of each virtual photovoltaic component at a specific position and angle based on the light simulation model of step S200 for each virtual photovoltaic arrangement strategy. Then, the parameters of all virtual photovoltaic components are summarized to obtain the total light intensity accumulation parameter of the strategy, reflecting the overall light utilization efficiency. At the same time, combined with the photovoltaic arrangement cost (including component installation, infrastructure construction, maintenance, etc.), the layout optimization evaluation value of the strategy is calculated through weighted evaluation or multi-objective optimization algorithm. The evaluation value considers the light efficiency and economy, and the strategy with higher evaluation value is retained. By comparing the evaluation results of multiple random arrangement strategies, several optimized virtual photovoltaic arrangement strategies are screened out to provide scientific layout schemes for actual engineering and maximize the comprehensive benefits of the mountain photovoltaic power station.

[0061] In some embodiments disclosed in the present application, the method for constructing a mountain structure based on mountain geographical data comprises:

[0062] Step S101 establishes a virtual three-dimensional coordinate system, determines a height parameter axis of the virtual three-dimensional coordinate system, uniformly divides the height parameter axis, and obtains a plurality of height reference points.

[0063] Step S102 determines the shape of the mountain structure transverse slice corresponding to each height reference point, configures the mountain structure edge in the mountain structure transverse slice with reference to the height reference point in the virtual three-dimensional system, connects the mountain structure edges, and forms the mountain structure.

[0064] In some embodiments disclosed in the present application, the method for connecting the mountain structure edges comprises:

[0065] Step S1021 obtains historical mountain structure edges and historical actual structure surfaces between the mountain structure edges, associates the actual structure surfaces with the historical mountain structure edges, and obtains a historical structure edge-structure surface corresponding group.

[0066] Step S1022 determines the structure center point of each historical structure edge in the historical structure edge-structure surface corresponding group, constructs a structure center line with reference to the structure center point, associates the structure center lines parallel between the historical structure edges, and obtains a structure center line group.

[0067] Step S1023, the same end of the structural center line in the structural center line group is determined, the actual structural surface passing through the end of the structural center line is determined as the actual structural surface line, the historical structural edge is segmented to obtain a plurality of historical structural edge sections.

[0068] Step S1024, the historical structural edge section and the corresponding actual structural surface line are constructed as a structural edge section-structural surface line corresponding group, and a plurality of structural edge section-structural surface line corresponding groups are constructed as a structural edge section-structural surface line corresponding library.

[0069] Step S1025, when connecting the mountain structural edge with the structural surface, the mountain structural edge is cut to obtain a mountain structural edge section, the mountain structural edge section is taken as a search condition, the adaptive actual structural surface line is determined in the structural edge section-structural surface line corresponding library, and the actual structural surface line is adapted between the mountain structural edge sections to form a structural surface.

[0070] In some embodiments of the present disclosure, the method for determining the adaptive actual structural surface line in the structural edge section-structural surface line corresponding library comprises:

[0071] Step S10251, the upper and lower opposite structural edge sections and the corresponding historical edge structural sections are superimposed and compared to determine the structural adaptation parameters therebetween and the structural correlation parameters between the historical structural sections.

[0072] Step S10252, based on the structural adaptation parameters and the structural correlation parameters, the comprehensive adaptation parameters of the structural edge sections and the historical structural edge sections are determined, and based on the comprehensive adaptation parameters, the selected actual structural surface line in the structural edge section-structural surface line corresponding library is determined.

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

[0074] The structural edge sections and the historical edge structural sections are aligned and gradually advanced from one end to the other end, in the advancing process, the cross-sectional area change is calculated, and based on the cross-sectional area change, the structural adaptation parameters are determined.

[0075] The method for determining the structural correlation parameters between the historical structural sections comprises:

[0076] When the historical structural edge is segmented, the segmented historical structural sections are correlated with each other to form a historical structural section group.

[0077] Based on the historical structure section group, the determined historical structure sections are classified, the maximum structure section set with the largest number of classified historical structure sections is determined, and based on the proportion of the number of historical structure sections in the maximum structure section set to the number of all historical structure sections, a structure correlation parameter between the historical structure sections is determined.

[0078] In some embodiments disclosed in the present application, the expression of the comprehensive adaptation parameter is calculated as:

[0079] P2=P1×Q×exp(L×G+b);

[0080] Wherein, P2 is the comprehensive adaptation parameter, P1 is the structure adaptation parameter, G is the structure correlation parameter, L is the structure correlation parameter influence adjustment coefficient, b is the structure correlation parameter influence adjustment constant, and Q is a preset parameter influence adjustment coefficient.

[0081] Wherein, the expression of the structure adaptation parameter is:

[0082] .

[0083] Wherein, P1 is the structure adaptation parameter, K1 is the area change rate performance weight adjustment coefficient, a(i) is the area change rate judgment function corresponding to the i-th advancing node, the change influence parameter output by a(i) is determined based on the preset change rate interval to which the area change rate belongs, n is the number of advancing nodes, K2 is the cross-sectional area weight adjustment coefficient, and S is the accumulation of the cross-sectional area in the advancing process.

[0084] In some embodiments disclosed in the present application, based on historical light data, a method for determining the light performance of a light simulation model of a virtual mountain structure includes:

[0085] Step S201, based on historical light data, determine the light intensity change and light angle change in a preset time period.

[0086] Step S201 aims to analyze historical light data and extract the dynamic change rule of light intensity and light angle in a preset time period, providing basic data for subsequent construction of a light simulation model. The light of a mountainous environment is affected by time, season, weather and terrain, and presents significant dynamic characteristics. This step first collects historical light data of the target region, including light intensity (radiation per unit area, usually in W / m 2representations), illumination angles (solar elevation and azimuth angles), and corresponding timestamps and weather conditions (e.g., sunny, cloudy). Through data cleaning and time series analysis, the illumination intensity variation trend (e.g., intensity curve from sunrise to sunset) and illumination angle variation law (e.g., angular displacement of the sun's trajectory) within a preset time period (e.g., a day, a month, or a year) are determined. For example, the hourly or daily intensity and angle data sets can be generated by statistical averaging or interpolation methods. This process provides quantitative input parameters for illumination simulation, ensuring that the model can reflect the real illumination dynamic characteristics and laying the foundation for the evaluation of the virtual mountain structure's illumination performance.

[0087] In step S202, a virtual illumination line is constructed for the illumination intensity, and the line density variation of the virtual illumination line over time is determined based on the illumination intensity variation, and the line angle variation of the virtual illumination line over time is determined based on the illumination angle variation.

[0088] Step S202 simulates the dynamic performance of illumination on the virtual mountain structure by constructing a virtual illumination line and analyzing its variation over time, providing a refined spatiotemporal feature description for the illumination simulation model. The virtual illumination line is an abstract representation of the illumination propagation path, combining the dynamic variations of illumination intensity and angle, and can intuitively reflect the distribution characteristics of illumination in three-dimensional terrain. This step first constructs a virtual illumination line based on the illumination intensity data of step S201, representing the projection trajectory of the light ray on the virtual mountain structure. The variation of illumination intensity is converted into the line density variation of the virtual illumination line, i.e., the higher the illumination intensity, the greater the line density (indicating that the light rays are more concentrated per unit area), and the dynamic variation of the line density over time is quantified through mathematical mapping (e.g., linear or nonlinear functions). At the same time, based on the variation of the illumination angle, the line angle variation of the virtual illumination line is determined, i.e., the incident angle of the light ray is adjusted over time, reflecting the moving trajectory of the sun's position (e.g., the variation of the elevation angle and the azimuth angle). Through ray tracing or geometric algorithms, combined with the three-dimensional topographic features of the virtual mountain structure, the projection and shading effects of the illumination line on the terrain are calculated, generating the spatiotemporal distribution of illumination performance. This process provides an accurate simulation basis for the calculation of the illumination intensity accumulation of photovoltaic modules, enhancing the adaptability of the model to complex illumination conditions in mountainous areas.

[0089] In some embodiments disclosed in the present application, the method for calculating the illumination intensity accumulation parameter of the photovoltaic module includes:

[0090] In step S401, the virtual illumination line is advanced according to the line direction, the vertical illumination line component of the virtual illumination line relative to the virtual photovoltaic module is determined, and the accumulation amount of the illumination line component of the virtual illumination line passing through the virtual photovoltaic module is counted, denoted as the illumination intensity accumulation parameter.

[0091] In some embodiments disclosed in the present application, the method for determining the layout optimization evaluation of the virtual photovoltaic arrangement strategy includes:

[0092] In step S402, the photovoltaic arrangement cost of each virtual photovoltaic component is determined based on the difficulty of the photovoltaic arrangement and the cost of the consumables.

[0093] In step S403, the cost preset interval to which the photovoltaic arrangement cost of each virtual photovoltaic component belongs is determined, the first layout optimization sub-evaluation value is determined, the illumination intensity accumulation parameter interval to which the illumination intensity accumulation parameter belongs is determined, the second layout optimization sub-evaluation value is determined, the sum of the first layout optimization sub-evaluation value and the second layout optimization sub-evaluation value is calculated, and the layout optimization evaluation is obtained.

[0094] In some embodiments of the present disclosure, a mountain photovoltaic layout optimization system based on multi-point light illumination dynamic data is also disclosed, comprising:

[0095] The first module is configured to obtain mountain geographical data, construct a virtual mountain structure based on the mountain geographical data, obtain mountain landform data, and configure a three-dimensional landform structure to the virtual mountain structure based on the mountain landform data.

[0096] The second module is configured to obtain historical light data, and determine the light performance of a light simulation model of the virtual mountain structure based on the historical light data.

[0097] The third module is configured to demarcate a photovoltaic layout area for the virtual mountain structure, and perform random arrangement of virtual photovoltaic components in the photovoltaic layout area based on a preset photovoltaic arrangement volume, the arrangement mode comprising determining the position node of each virtual photovoltaic component and the arrangement angle of the virtual photovoltaic component, thereby forming a virtual photovoltaic arrangement strategy.

[0098] The fourth module is configured to calculate the illumination intensity accumulation parameter of each virtual photovoltaic component in each virtual photovoltaic arrangement strategy, calculate the total illumination intensity accumulation parameter of all virtual photovoltaic components, determine the layout optimization evaluation of the virtual photovoltaic arrangement strategy based on the photovoltaic arrangement cost, and determine a plurality of virtual photovoltaic arrangement strategies based on the high and low of the layout optimization evaluation.

[0099] The application discloses a mountainous photovoltaic layout optimization method and system based on multi-point light illumination dynamic data, relates to the photovoltaic construction technical field, and constructs a virtual mountain structure and configures a three-dimensional landform structure through mountainous geographical and landform data; establishes a light simulation model based on historical light data, and quantifies light performance of the virtual mountain structure; randomly arranges virtual photovoltaic components in a photovoltaic layout area, determines position nodes and angles, and forms a virtual photovoltaic layout strategy; calculates light intensity accumulation parameters and total parameters of each virtual photovoltaic component, evaluates layout optimization in combination with layout cost, and selects an optimal strategy. The application significantly improves energy efficiency and economy of mountainous photovoltaic layout through fine topographic modeling, dynamic light simulation and multi-target optimization, and provides scientific support for complex topography photovoltaic power station planning.

[0100] Those skilled in the art can clearly understand the present application through the above description of the embodiments that the present application can be implemented by hardware or by means of software and necessary general hardware platform. Based on such understanding, the technical scheme of the present application can be embodied in the form of a software product, which can be stored in a nonvolatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.), and includes a plurality of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method described in each embodiment scenario of the present application.

[0101] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application rather than limit them, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can still be modified or replaced by equivalents, and these modifications or replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for optimizing the layout of photovoltaic power generation in mountainous areas based on dynamic multi-point illumination data, characterized in that: include: Step S100: Obtain mountain geographic data, and construct a virtual mountain structure based on the mountain geographic data; obtain mountain landform data, and configure a three-dimensional landform structure for the virtual mountain structure based on the mountain landform data. Step S200: Obtain historical lighting data and, based on the historical lighting data, determine the lighting performance of the lighting simulation model of the virtual mountain structure. Step S300: Define the photovoltaic deployment area for the virtual mountain structure, and based on the preset photovoltaic deployment volume, randomly deploy virtual photovoltaic modules in the photovoltaic deployment area. The deployment method includes determining the location node of each virtual photovoltaic module and the deployment angle of the virtual photovoltaic module to form a virtual photovoltaic deployment strategy. Step S400: Calculate the light intensity accumulation parameter of each virtual photovoltaic module in each virtual photovoltaic deployment strategy, and calculate the total light intensity accumulation parameter of all virtual photovoltaic modules. Combined with the photovoltaic deployment cost, determine the layout optimization evaluation of this virtual photovoltaic deployment strategy, and determine several virtual photovoltaic deployment strategies based on the level of the layout optimization evaluation. Methods for constructing mountain structures based on mountain geographic data include: Step S101: Establish a virtual three-dimensional coordinate system, determine the height parameter axis of the virtual three-dimensional coordinate system, and uniformly delineate the height parameter axis to obtain several height reference points. Step S102: Determine the shape of the horizontal slice of the mountain structure corresponding to each height reference point, configure the mountain structure edge in the horizontal slice of the mountain structure with reference height reference points in the virtual three-dimensional system, and connect the mountain structure edge with structural surfaces to form a mountain structure. Methods for connecting the edges of mountain structures using structural surfaces include: Step S1021: Obtain the historical mountain structure edge and the actual structural surface of the historical records between the mountain structure edges. Associate the actual structural surface with the historical mountain structure edge to obtain the historical structure edge ~ structural surface corresponding group. Step S1022: Determine the structural center point of each historical structural edge in the corresponding group of historical structural edges and structural surfaces, construct structural center lines for the structural center points, and associate the parallel structural center lines between historical structural edges to obtain a group of structural center lines. Step S1023: Determine the same end of the structural centerline in the structural centerline group, determine the actual structural surface line that passes through the end of the structural centerline on the actual structural surface, cut the edge of the historical structure to obtain several historical structural edge segments. Step S1024: Construct historical structural edge segments and their corresponding actual structural surface online as structural edge segment ~ structural surface online corresponding groups, and construct a structural edge segment ~ structural surface online corresponding library from several structural edge segment ~ structural surface online corresponding groups; Step S1025: When connecting the mountain structure edges with structural surfaces, the mountain structure edges are cut off to obtain mountain structure edge segments. The mountain structure edge segments are used as search conditions to determine the corresponding actual structural surface online in the structural edge segment ~ structural surface online correspondence library. The actual structural surface online is then adapted to the mountain structure edge segments to form structural surfaces.

2. The method for optimizing the layout of photovoltaic power generation in mountainous areas based on multi-point illumination dynamic data according to claim 1, characterized in that, Methods for determining the appropriate actual structural surface top line in the structural edge segment ~ structural surface top line correspondence library include: Step S10251: Overlap and compare the upper and lower relative structural edge segments with the corresponding historical edge structural segments to determine the structural adaptation parameters between them, and determine the structural correlation parameters between the historical structural segments. Step S10252: Based on the structural adaptation parameters and 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 selected in the corresponding library of the structural edge segment to the structural surface online. The methods for determining the structural adaptation parameters between structural edge segments and historical edge structural segments include: Align the structural edge segment with the historical edge structural segment, and gradually advance from one end to the other. During the advancement, 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 methods for determining the structural correlation parameters between historical structural segments include: When segmenting the edges of historical structures, the segments are linked together to form groups of historical structure segments. Based on the historical structural segment groups, the identified historical structural segments are classified, and the largest set of structural segments with the most classifications is determined. Based on the proportion of the number of historical structural segments in the largest set to the total number of historical structural segments, the structural correlation parameters between historical structural segments are determined.

3. The method for optimizing the layout of photovoltaic power generation in mountainous areas based on multi-point illumination dynamic data according to claim 2, characterized in that, The expression for calculating the integrated adaptation parameters is: ； in, To comprehensively adapt the parameters, To adapt parameters to the structure, For structural correlation parameters, This is an adjustment coefficient for the influence of structural correlation parameters. Changshu was adjusted to account for the influence of structural correlation parameters. This is a preset adjustment coefficient for the influence between parameters; The expression for the structural adaptation parameter is as follows: ； in, To adapt parameters to the structure, The area change rate is represented by a weighting adjustment factor. For the first The area change rate judgment function corresponding to each advancement node determines the area change rate based on the preset change rate interval to which the area change rate belongs. Changes in output affect parameters. To increase the number of nodes, This is the cross-area weighting adjustment factor. The amount of accumulated cross-area during the process of advancement.

4. The method for optimizing the layout of photovoltaic power generation in mountainous areas based on multi-point dynamic illumination data according to claim 1, characterized in that, Methods for determining the lighting representation of a virtual mountain structure lighting simulation model based on historical lighting data include: Step S201: Based on historical illumination data, determine the changes in illumination intensity and illumination angle under a preset time period; Step S202: Construct virtual illumination lines for illumination intensity, and determine the linear density change of the virtual illumination lines over time based on the change in illumination intensity, and determine the linear angle change of the virtual illumination lines over time based on the change in illumination angle.

5. The method for optimizing the layout of photovoltaic power generation in mountainous areas based on multi-point illumination dynamic data according to claim 4, characterized in that, Methods for calculating the accumulated irradiance parameters of photovoltaic modules include: Step S401: Advance the virtual illumination line along the line direction, determine the illumination line component perpendicular to the virtual photovoltaic module, and calculate the accumulated amount of the illumination line component passing through the virtual illumination module, which is recorded as the light intensity accumulation parameter.

6. The method for optimizing the layout of photovoltaic power generation in mountainous areas based on multi-point dynamic illumination data according to claim 1, characterized in that, Methods for determining the layout optimization assessment of virtual photovoltaic deployment strategies include: Step S402: Based on the difficulty of photovoltaic deployment and the cost of consumables, determine the photovoltaic deployment cost of each virtual photovoltaic module; Step S403: Determine the preset cost range to which the photovoltaic arrangement cost of each virtual photovoltaic module belongs, determine the first layout optimization sub-evaluation value, determine the light intensity accumulation parameter range to which the light 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.

7. A mountain photovoltaic layout optimization system based on multi-point illumination dynamic data, characterized in that, The method for optimizing the layout of mountain photovoltaic systems based on multi-point dynamic illumination data, used to execute any one of claims 1 to 6, includes: The first module is used to acquire mountain geographic data, and based on the mountain geographic data, construct a virtual mountain structure, acquire 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 acquire historical lighting data and, based on the historical lighting data, determine the lighting performance of the lighting simulation model of the virtual mountain structure. The third module is used to delineate the photovoltaic deployment area of ​​the virtual mountain structure, and based on the preset photovoltaic deployment volume, to randomly deploy virtual photovoltaic modules in the photovoltaic deployment area. The deployment method includes determining the location node of each virtual photovoltaic module and the deployment angle of the virtual photovoltaic module to form a virtual photovoltaic deployment strategy. The fourth module is used to calculate the accumulated irradiance parameter of each virtual photovoltaic module in each virtual photovoltaic deployment strategy, and to calculate the total accumulated irradiance parameter of all virtual photovoltaic modules. Combined with the photovoltaic deployment cost, the module determines the layout optimization evaluation of this virtual photovoltaic deployment strategy, and determines several virtual photovoltaic deployment strategies based on the level of the layout optimization evaluation.

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