Intelligent optimization system and method for design scheme of photovoltaic power station

Abstract

The embodiment of the invention provides an intelligent optimization system and method for a photovoltaic power station design scheme, and the system comprises a data input module which receives and analyzes input data which specifically comprises topographic data, meteorological data and equipment parameters, and transmits the input data to a data processing module; the data processing module is used for carrying out standardization processing and cleaning processing on the input data, complementing missing values and obtaining a constructed high-precision terrain and meteorological model; and the optimization algorithm module adopts a multi-objective optimization algorithm, takes the power generation efficiency, the economic cost and the land utilization rate as optimization objectives, dynamically adjusts the layout, the inclination angle and the line spacing of the photovoltaic modules based on the input data, and obtains an intelligent optimization result and a related report of the design scheme of the photovoltaic power station. The generating capacity and the cost of the intelligent optimization result of the photovoltaic power station design scheme are predicted through a high-precision terrain and meteorological model; and the result output module is used for outputting an intelligent optimization result and a related report of the photovoltaic power station design scheme.

Description

Technical Field

[0001] This document relates to the technical field of photovoltaic power station design, and in particular to a system and method for intelligent optimization of photovoltaic power station design solutions. Background Art

[0002] The design of photovoltaic power stations requires comprehensive consideration of a variety of complex factors, such as terrain conditions, meteorological data (such as sunshine, temperature, wind speed, etc.), equipment parameters (such as component power, inverter efficiency, etc.), as well as project budget and construction restrictions. Traditional design methods usually rely on manual experience or simple single-objective optimization algorithms, which have the following shortcomings:

[0003] 1. Limitations: It is difficult to achieve global optimization under multi-objective conditions, and often only suboptimal solutions can be achieved.

[0004] 2. Inefficiency: Faced with complex terrain and diverse meteorological conditions, the design process is time-consuming and has low accuracy.

[0005] 3. Lack of flexibility: The design is difficult to adjust dynamically and adapt to rapid changes in actual needs.

[0006] Although there are some studies based on intelligent optimization, most of them focus on a single optimization goal (such as maximizing power generation efficiency) and lack the optimization of comprehensive goals such as economy and land utilization. Summary of the invention

[0007] The purpose of the present invention is to provide a photovoltaic power station design intelligent optimization system and method, aiming to solve the above-mentioned problems in the prior art.

[0008] The present invention provides a photovoltaic power station design scheme intelligent optimization system, comprising:

[0009] A data input module is used to receive and analyze input data, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, and send the input data to a data processing module;

[0010] A data processing module is used to standardize and clean the input data, fill in missing values, and obtain a constructed high-precision terrain and meteorological model;

[0011] An optimization algorithm module is used to adopt a multi-objective optimization algorithm, take power generation efficiency, economic cost and land utilization rate as optimization targets, dynamically adjust the layout, inclination angle and row spacing of photovoltaic modules based on the input data, obtain the intelligent optimization results and related reports of the photovoltaic power station design scheme, and predict the power generation and cost of the intelligent optimization results of the photovoltaic power station design scheme through the high-precision terrain and meteorological model;

[0012] The result output module is used to output the intelligent optimization results and related reports of the photovoltaic power station design scheme.

[0013] The present invention provides a method for intelligent optimization of photovoltaic power station design scheme, which is used in the above system, and the method specifically includes:

[0014] Receive and parse input data through a data input module, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, and send the input data to a data processing module;

[0015] The input data is standardized and cleaned by a data processing module, missing values ​​are filled, and a high-precision terrain and meteorological model is constructed;

[0016] The optimization algorithm module adopts a multi-objective optimization algorithm, takes power generation efficiency, economic cost and land utilization rate as optimization targets, dynamically adjusts the layout, inclination angle and row spacing of photovoltaic modules based on the input data, obtains the intelligent optimization results and related reports of the photovoltaic power station design scheme, and predicts the power generation and cost of the intelligent optimization results of the photovoltaic power station design scheme through the high-precision terrain and meteorological model;

[0017] The intelligent optimization results and related reports of the photovoltaic power station design scheme are output through the result output module.

[0018] An embodiment of the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned method for intelligent optimization of photovoltaic power station design schemes when executed by the processor.

[0019] An embodiment of the present invention further provides a computer-readable storage medium, on which a program for implementing information transmission is stored, and when the program is executed by a processor, the steps of the above-mentioned method for intelligent optimization of photovoltaic power station design schemes are implemented.

[0020] By adopting the embodiment of the present invention, through multi-objective optimization algorithms (such as genetic algorithms, particle swarm algorithms) and machine learning models, by integrating terrain, meteorology, equipment parameters and economic indicators, and using intelligent algorithms, global optimization of the design scheme is achieved, thereby improving the economic benefits and operating efficiency of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0022] Figure 1 is a schematic diagram of an intelligent optimization system for photovoltaic power station design solutions according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a method for intelligent optimization of a photovoltaic power station design scheme according to an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0026] System Example

[0027] According to an embodiment of the present invention, a photovoltaic power station design scheme intelligent optimization system is provided. Figure 1 Schematic diagram of an intelligent optimization system for designing a photovoltaic power station according to an embodiment of the present invention. Figure 1 As shown, the photovoltaic power station design scheme intelligent optimization system according to the embodiment of the present invention specifically includes:

[0028] The data input module 10 is used to receive and analyze input data, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, and send the input data to the data processing module; the data input module is specifically used to:

[0029] Receive and parse input data, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, wherein the terrain data is a three-dimensional terrain model generated based on DEM, and is obtained by using GIS tools to analyze terrain slope and orientation and extract effective areas, the meteorological data is an hourly data sequence generated by predicting historical meteorological data using an LSTM deep learning model, and the equipment parameters are dynamically selected from different models of components and inverters in a pre-built database.

[0030] The data processing module 12 is used to perform standardization and cleaning on the input data, fill in missing values, and obtain a constructed high-precision terrain and meteorological model;

[0031] The optimization algorithm module 14 is used to adopt a multi-objective optimization algorithm, with power generation efficiency, economic cost and land utilization rate as optimization targets, dynamically adjust the layout, inclination and row spacing of photovoltaic modules based on the input data, obtain the intelligent optimization results and related reports of the photovoltaic power station design scheme, and predict the power generation and cost of the intelligent optimization results of the photovoltaic power station design scheme through the high-precision terrain and meteorological model; the optimization algorithm module is specifically used to:

[0032] Based on the constraints of terrain slope limitation, maximum load-bearing capacity of components, and inter-row shading, a multi-objective genetic algorithm MOGA is used to generate an initial layout plan based on terrain characteristics, simulate biological genetic mechanisms, explore the solution space, and determine the convergence conditions according to the changing trend of the fitness value. The intelligent optimization results and related reports of the photovoltaic power station design scheme are obtained. The fitness function of the multi-objective genetic algorithm is shown in Formula 1:

[0033] F(x)=α·Efficiency(x)-β·Cost(x)+γ·LandUsage(x) Formula 1;

[0034] Among them, α, β, and γ are adjustable weights, representing the weights of power generation efficiency, economic cost, and land utilization rate, respectively. Efficiency(x) represents power generation efficiency, Cost(x) represents economic cost, and LandUsage(x) represents land utilization rate.

[0035] The result output module 16 is used to output the intelligent optimization results of the photovoltaic power station design scheme and related reports.

[0036] The system further comprises:

[0037] A simulation verification module is used to use PVsyst to simulate and verify the credibility and accuracy of the intelligent optimization results of the photovoltaic power station design scheme, evaluate the shading effect of the component arrangement and the actual power generation performance, and perform Monte Carlo simulation to evaluate the robustness of the scheme under different meteorological conditions;

[0038] The visualization module is used to display the intelligent optimization results and related reports of photovoltaic power station design schemes through three-dimensional terrain maps and interactive interfaces.

[0039] By adopting the embodiment of the present invention, comprehensive multi-objective optimization is carried out, taking into account power generation efficiency, economic cost and land utilization rate, and improving the overall design quality. Dynamic terrain modeling and machine learning prediction are introduced to improve the adaptability under complex terrain and changeable meteorological conditions. The whole process is automated, from data input to solution output, significantly reducing design time and labor costs.

[0040] The technical solution of the embodiment of the present invention is described in detail below.

[0041] The embodiment of the present invention proposes an intelligent optimization system for photovoltaic power station design based on artificial intelligence, which generates a globally optimal design solution by combining actual terrain data, meteorological conditions and equipment parameters through multi-objective optimization algorithms (such as genetic algorithms, particle swarm algorithms) and machine learning models. The system covers the following functional modules:

[0042] 1. Data input module

[0043] Receive and analyze terrain data (such as digital elevation model DEM), meteorological data (such as annual sunshine, temperature, wind speed) and equipment parameters (such as component power, inverter efficiency). Specifically:

[0044] The system receives and parses terrain data (such as digital elevation model DEM), meteorological data (such as annual sunshine, temperature, wind speed) and equipment parameters (such as component power, inverter efficiency), then standardizes and cleans the input data, uses interpolation algorithms to fill in missing values, and builds high-precision terrain and meteorological models.

[0045] Terrain data: Terrain data is obtained through digital elevation models (DEM), and GIS tools are used to analyze the slope and orientation of the area. The system customizes and optimizes the design plan based on the complexity of the terrain (such as flat, hilly and mountainous).

[0046] Meteorological data: The system uses the LSTM deep learning model to train the region's past historical meteorological data and predict future hourly meteorological data. These meteorological data contain information such as sunshine, temperature, wind speed, etc., which can provide support for dynamic optimization algorithms to ensure that the design plan is adjusted according to seasonal and weather changes.

[0047] Equipment parameters: The system builds a database to support dynamic selection of different types of modules and inverters. It has built-in parameters of common photovoltaic modules (such as monocrystalline silicon, polycrystalline silicon, and double-glass photovoltaic modules) and inverters, including key data such as power, efficiency, and cost, to ensure the accuracy and operability of the design solution.

[0048] 2. Data processing module

[0049] The input data is standardized and cleaned, missing values ​​are filled using interpolation algorithms, and high-precision terrain and meteorological models are constructed.

[0050] 3. Optimization algorithm module

[0051] Using multi-objective optimization algorithms (such as MOGA), with power generation efficiency, economic cost and land utilization as optimization targets, dynamically adjust the component layout, inclination angle and row spacing. Specifically:

[0052] Using multi-objective optimization algorithms (such as MOGA), with power generation efficiency, economic cost and land utilization as optimization targets, the component layout, inclination angle and row spacing are dynamically adjusted.

[0053] Adopting Multi-Objective Genetic Algorithm (MOGA): The system uses Multi-Objective Genetic Algorithm (MOGA) to optimize the design scheme. The optimization objectives include: Power generation efficiency: Optimize the layout of components to reduce the shading effect and improve the power generation efficiency. Economic cost: Minimize the initial investment and long-term operation and maintenance costs, and reduce the overall cost by optimizing the layout of components and selecting the appropriate equipment combination. Land utilization rate: Improve land utilization efficiency, especially in areas with large slopes, to ensure the maximum use of land resources.

[0054] Fitness function: F(x) = α·Efficiency(x)-β·Cost(x)+γ·LandUsage(x).

[0055] Among them, α, β, and γ are adjustable weights, representing the weights of power generation efficiency, economic cost, and land utilization rate, respectively.

[0056] Constraints include: terrain slope restrictions, maximum component load capacity, inter-row shading effects, etc.

[0057] Population initialization: Generate an initial layout plan based on terrain features.

[0058] Crossover and mutation: simulate biological genetic mechanisms and explore solution space.

[0059] Convergence judgment: Determine the convergence condition based on the changing trend of the fitness value.

[0060] The initial design is generated by terrain features, and the algorithm simulates biological genetic mechanisms through the crossover and mutation process to explore different design solutions. Through the fitness function and weighting coefficients (α, β, γ), the system can balance multiple optimization objectives and ultimately find the global optimal solution.

[0061] Machine learning prediction: The system predicts the power generation and cost of different design solutions by training machine learning models (such as regression models), thereby reducing the number of simulation calculations and improving optimization speed and accuracy. The model can quickly screen out the most promising design solutions for further verification.

[0062] 4.Simulation verification module

[0063] The optimization scheme was verified using simulation tools, with a focus on evaluating the shading effect of the module arrangement and the actual power generation performance. Monte Carlo simulation was introduced to evaluate the robustness of the scheme under different meteorological conditions.

[0064] PVsyst simulation tool: simulates the optimized design scheme, focusing on evaluating the shading effect and power generation performance of the component arrangement. By comparing with the traditional design scheme, the optimized scheme has improved the power generation efficiency by 8% and reduced the land use area by about 12%.

[0065] Monte Carlo simulation: Monte Carlo simulation is used to evaluate the robustness of design solutions under different meteorological conditions. The optimized design can maintain relatively stable power generation performance under extreme meteorological conditions (such as high temperature and strong winds), showing strong adaptability.

[0066] 5. Result output module

[0067] Provide optimized design solutions, including component layout, inclination and spacing recommendations, as well as power generation estimates and cost analysis reports. This not only improves power generation efficiency, but also reduces construction costs and land use costs.

[0068] 6. Visualization Module

[0069] The optimization results are displayed using 3D terrain maps and interactive interfaces to enhance the intuitiveness of the design and user experience. The visual interface displays key indicators such as the specific layout of components, terrain slope, and sunshine data to enhance the intuitiveness of the design and user experience.

[0070] The following are three typical examples that demonstrate the application of the intelligent optimization system for photovoltaic power station design based on artificial intelligence technology under different terrain conditions. These examples illustrate how the system can be effectively applied in actual projects and are implemented through existing technical means, with high feasibility.

[0071] Example 1: Design of photovoltaic power station on flat terrain

[0072] Input conditions:

[0073] Terrain: Flat terrain is assumed.

[0074] Meteorology: Assuming the average annual sunshine is 1800kWh / m 2 , the annual average temperature is 25℃.

[0075] Equipment: Assume the use of monocrystalline silicon photovoltaic modules (power 450W) equipped with a centralized inverter (efficiency 98%).

[0076] Optimization results:

[0077] Module layout: Through the system's multi-objective optimization algorithm (such as MOGA), it is predicted that under flat terrain conditions, the modules should be arranged in a north-south direction, with an inclination angle of 25° and a row spacing of 3.5 meters. This layout can make full use of sunlight resources and meet the needs of economic cost optimization.

[0078] Power generation efficiency: According to theoretical model calculations, the optimized solution may achieve a power generation efficiency of about 85.2%.

[0079] Cost Savings: Based on the optimization results, it is assumed that this design can save about 8% in construction and material costs.

[0080] Analysis and evaluation: In this embodiment, based on flat terrain, the system comprehensively considers terrain, weather and equipment parameters through simulation algorithms. The optimized design scheme can improve power generation efficiency and reduce costs, which meets the actual needs of photovoltaic power station design.

[0081] Example 2: Design of photovoltaic power station in hilly terrain

[0082] Input conditions:

[0083] Terrain: The terrain is assumed to be hilly with a slope between 10° and 20°.

[0084] Meteorology: Assuming the average annual sunshine is 1600kWh / m 2 , the annual average temperature is 22℃.

[0085] Equipment: Assume the use of multicrystalline silicon photovoltaic modules (power 420W) equipped with string inverters (efficiency 97%).

[0086] Optimization results:

[0087] Module layout: According to the prediction of the optimization algorithm, the system recommends that the modules be installed along the slope, with an inclination of 15° and a row spacing of 3.2 meters. This layout can adapt to hilly terrain and improve land use efficiency.

[0088] Power Generation Efficiency: The predicted power generation efficiency is likely to be 82.7%.

[0089] Land utilization rate: Based on the optimization algorithm, it is assumed that the design can effectively improve the land utilization rate by about 10%.

[0090] Analysis and evaluation: In hilly terrain, the slope-down installation method is used to optimize the layout of components. Although the power generation efficiency is reduced, the overall benefits are improved by optimizing land use, which can provide strong design support.

[0091] Example 3: Mountain photovoltaic power station design

[0092] Input conditions:

[0093] Terrain: Complex mountainous terrain with a slope greater than 25° is assumed.

[0094] Meteorology: Assume that the annual average sunshine is 2000kWh / m 2 , and the area has high wind speed.

[0095] Equipment: Assume the use of double-glass photovoltaic panels (power 500W) equipped with distributed inverters (efficiency 96%).

[0096] Optimization results:

[0097] Component layout: Based on the complexity of the mountainous terrain, the system recommends a zoning design and adjusting the inclination and spacing of the components according to the slope differences in each area to adapt to the characteristics of the terrain.

[0098] Power generation efficiency: The predicted power generation efficiency is 79.5%.

[0099] Construction cost savings: Theoretical deduction results show that optimized design can reduce construction costs by about 12%.

[0100] Analysis and evaluation: In mountainous terrain, by studying the terrain characteristics and adjusting the component layout and construction design according to the complexity of the terrain, although the power generation efficiency has decreased, the construction cost has been effectively controlled through reasonable optimization, thus enhancing the economy of the design.

[0101] The above embodiment uses a multi-objective optimization algorithm based on artificial intelligence to comprehensively consider various complex factors in the design of photovoltaic power stations, effectively overcoming the limitations of traditional design methods. The system can combine deep learning and simulation tools to provide efficient and accurate optimization solutions under complex terrain and changeable weather conditions. At the same time, the automated data processing and optimization process greatly shortens the design cycle, reduces labor costs, and improves the flexibility and adaptability of the system in practical applications.

[0102] By adopting the technical solution of the embodiment of the present invention, the system is applied to the design of a 1GW photovoltaic power station, which can reduce the overall cost by 5%-8%. The average annual increase in power generation revenue is about RMB 1 million. According to the global annual increase of 300GW installed capacity, the potential market value is about RMB 3 billion. The embodiment of the present invention solves the multi-objective optimization problem of the traditional photovoltaic power station design method through artificial intelligence technology, realizes design automation and global optimization in complex environments, and provides strong technical support for the development of the photovoltaic industry.

[0103] It should be noted that:

[0104] The system can obtain terrain and meteorological data through existing remote sensing technology and meteorological prediction models. Data processing and standardization technologies currently have mature solutions in the industry. Multi-objective genetic algorithm (MOGA) has been widely used in the optimization of various complex systems. The algorithm framework and implementation method adopted in this project have strong feasibility. Machine learning models (such as LSTM) can effectively predict meteorological data and have achieved application results in many fields. PVsyst is a photovoltaic simulation tool widely used in the industry with high accuracy and reliability, which is suitable for solution verification in this project.

[0105] The present invention solves the multi-objective optimization problem of traditional photovoltaic power station design methods through artificial intelligence technology. The system can not only optimize the design of photovoltaic power stations, improve power generation efficiency and reduce costs, but also adapt to changes in different geographical and climatic conditions, providing strong technical support for the development of the photovoltaic industry.

[0106] Method Embodiment

[0107] According to an embodiment of the present invention, a method for intelligent optimization of photovoltaic power station design scheme is provided, which is used in the above system. Figure 2 is a flow chart of a method for intelligent optimization of a photovoltaic power station design scheme according to an embodiment of the present invention. Figure 2 As shown, the intelligent optimization method for photovoltaic power station design scheme according to an embodiment of the present invention specifically includes:

[0108] Step S201, receiving and parsing input data through a data input module, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, and sending the input data to a data processing module; specifically including:

[0109] Receive and parse input data, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, wherein the terrain data is a three-dimensional terrain model generated based on DEM, and is obtained by using GIS tools to analyze terrain slope and orientation and extract effective areas, the meteorological data is an hourly data sequence generated by predicting historical meteorological data using an LSTM deep learning model, and the equipment parameters are dynamically selected from different models of components and inverters in a pre-built database.

[0110] Step S202, standardizing and cleaning the input data through a data processing module, filling in missing values, and obtaining a constructed high-precision terrain and meteorological model;

[0111] Step S203, using a multi-objective optimization algorithm through an optimization algorithm module, taking power generation efficiency, economic cost and land utilization rate as optimization targets, dynamically adjusting the layout, inclination angle and row spacing of photovoltaic modules based on the input data, obtaining the intelligent optimization results and related reports of the photovoltaic power station design scheme, and predicting the power generation and cost of the intelligent optimization results of the photovoltaic power station design scheme through the high-precision terrain and meteorological model; specifically including:

[0112] Based on the constraints of terrain slope limitation, maximum load-bearing capacity of components, and inter-row shading, a multi-objective genetic algorithm MOGA is used to generate an initial layout plan based on terrain characteristics, simulate biological genetic mechanisms, explore the solution space, and determine the convergence conditions according to the changing trend of the fitness value. The intelligent optimization results and related reports of the photovoltaic power station design scheme are obtained. The fitness function of the multi-objective genetic algorithm is shown in Formula 1:

[0113] F(x)=α·Efficiency(x)-β·Cost(x)+γ·LandUsage(x) Formula 1;

[0114] Among them, α, β, and γ are adjustable weights, representing the weights of power generation efficiency, economic cost, and land utilization rate, respectively. Efficiency(x) represents power generation efficiency, Cost(x) represents economic cost, and LandUsage(x) represents land utilization rate.

[0115] Step S204: outputting the intelligent optimization results of the photovoltaic power station design scheme and related reports through the result output module.

[0116] The method further comprises:

[0117] The simulation verification module uses PVsyst to simulate and verify the credibility and accuracy of the intelligent optimization results of the photovoltaic power station design scheme, evaluate the shading effect of the component arrangement and the actual power generation performance, and conduct Monte Carlo simulation to evaluate the robustness of the scheme under different meteorological conditions;

[0118] The visualization module displays the intelligent optimization results and related reports of photovoltaic power station design schemes based on three-dimensional terrain maps and interactive interfaces.

[0119] The embodiment of the present invention is a method embodiment corresponding to the above-mentioned device embodiment. The specific operation of each step can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0120] Device Example 1

[0121] An embodiment of the present invention provides an electronic device, such as Figure 3 As shown, it includes: a memory 30, a processor 32, and a computer program stored in the memory 30 and executable on the processor 32. When the computer program is executed by the processor 32, the steps described in the method embodiment are implemented.

[0122] Device Example 2

[0123] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by the processor 32, the steps described in the method embodiment are implemented.

[0124] The computer-readable storage medium in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk or optical disk, etc.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic power station design intelligent optimization system, characterized in that: include: A data input module is used to receive and analyze input data, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, and send the input data to a data processing module; A data processing module is used to standardize and clean the input data, fill in missing values, and obtain a constructed high-precision terrain and meteorological model; An optimization algorithm module is used to adopt a multi-objective optimization algorithm, take power generation efficiency, economic cost and land utilization rate as optimization targets, dynamically adjust the layout, inclination angle and row spacing of photovoltaic modules based on the input data, obtain the intelligent optimization results and related reports of the photovoltaic power station design scheme, and predict the power generation and cost of the intelligent optimization results of the photovoltaic power station design scheme through the high-precision terrain and meteorological model; The result output module is used to output the intelligent optimization results and related reports of the photovoltaic power station design scheme.

2. The system according to claim 1, characterized in that The system further comprises: A simulation verification module is used to use PVsyst to simulate and verify the credibility and accuracy of the intelligent optimization results of the photovoltaic power station design scheme, evaluate the shading effect of the component arrangement and the actual power generation performance, and perform Monte Carlo simulation to evaluate the robustness of the scheme under different meteorological conditions; The visualization module is used to display the intelligent optimization results and related reports of photovoltaic power station design schemes through three-dimensional terrain maps and interactive interfaces.

3. The system according to claim 1, characterized in that The data input module is specifically used for: Receive and parse input data, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, wherein the terrain data is a three-dimensional terrain model generated based on DEM, and is obtained by using GIS tools to analyze terrain slope and orientation and extract effective areas, the meteorological data is an hourly data sequence generated by predicting historical meteorological data using an LSTM deep learning model, and the equipment parameters are dynamically selected from different models of components and inverters in a pre-built database.

4. The system according to claim 1, characterized in that The optimization algorithm module is specifically used for: Based on the constraints of terrain slope limitation, maximum load capacity of components, and inter-row shading, a multi-objective genetic algorithm MOGA is used to generate an initial layout plan based on terrain characteristics, simulate biological genetic mechanisms, explore the solution space, and determine the convergence conditions according to the changing trend of the fitness value. The intelligent optimization results and related reports of the photovoltaic power station design scheme are obtained. The fitness function of the multi-objective genetic algorithm is shown in Formula 1: F(x)=α·Efficiency(x)-β·Cost(x)+γ·LandUsage(x) Formula 1; Among them, α, β, and γ are adjustable weights, representing the weights of power generation efficiency, economic cost, and land utilization rate, respectively. Efficiency(x) represents power generation efficiency, Cost(x) represents economic cost, and LandUsage(x) represents land utilization rate.

5. A method for intelligent optimization of photovoltaic power station design scheme, characterized in that: For use in a system according to any one of claims 1 to 4, the method specifically comprises: Receiving and parsing input data through a data input module, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, and sending the input data to a data processing module; The input data is standardized and cleaned by a data processing module, missing values ​​are filled, and a high-precision terrain and meteorological model is constructed; The optimization algorithm module adopts a multi-objective optimization algorithm, takes power generation efficiency, economic cost and land utilization rate as optimization targets, dynamically adjusts the layout, inclination angle and row spacing of photovoltaic modules based on the input data, obtains the intelligent optimization results and related reports of the photovoltaic power station design scheme, and predicts the power generation and cost of the intelligent optimization results of the photovoltaic power station design scheme through the high-precision terrain and meteorological model; The intelligent optimization results and related reports of the photovoltaic power station design scheme are output through the result output module.

6. The method according to claim 5, characterized in that The method further comprises: The simulation verification module uses PVsyst to simulate and verify the credibility and accuracy of the intelligent optimization results of the photovoltaic power station design scheme, evaluate the shading effect of the component arrangement and the actual power generation performance, and conduct Monte Carlo simulation to evaluate the robustness of the scheme under different meteorological conditions; The visualization module displays the intelligent optimization results and related reports of photovoltaic power station design schemes based on three-dimensional terrain maps and interactive interfaces.

7. The method according to claim 5, characterized in that The receiving and parsing of input data by the data input module specifically includes: Receive and parse input data, wherein the input data specifically includes: terrain data, meteorological data and equipment parameters, wherein the terrain data is a three-dimensional terrain model generated based on DEM, and is obtained by using GIS tools to analyze terrain slope and orientation and extract effective areas, the meteorological data is an hourly data sequence generated by predicting historical meteorological data using an LSTM deep learning model, and the equipment parameters are dynamically selected from different models of components and inverters in a pre-built database.

8. The method according to claim 5, characterized in that The optimization algorithm module adopts a multi-objective optimization algorithm, takes power generation efficiency, economic cost and land utilization rate as optimization targets, and dynamically adjusts the layout, inclination angle and row spacing of photovoltaic modules based on the input data to obtain the intelligent optimization results of the photovoltaic power station design scheme and related reports, which specifically include: Based on the constraints of terrain slope limitation, maximum load capacity of components, and inter-row shading, a multi-objective genetic algorithm MOGA is used to generate an initial layout plan based on terrain characteristics, simulate biological genetic mechanisms, explore the solution space, and determine the convergence conditions according to the changing trend of the fitness value. The intelligent optimization results and related reports of the photovoltaic power station design scheme are obtained. The fitness function of the multi-objective genetic algorithm is shown in Formula 1: F(x)=α·Efficiency(x)-β·Cost(x)+γ·LandUsage(x) Formula 1; Among them, α, β, and γ are adjustable weights, representing the weights of power generation efficiency, economic cost, and land utilization rate, respectively. Efficiency(x) represents power generation efficiency, Cost(x) represents economic cost, and LandUsage(x) represents land utilization rate.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for intelligent optimization of a photovoltaic power station design scheme as described in any one of claims 5 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by the processor, the steps of the intelligent optimization method for photovoltaic power station design scheme according to any one of claims 5 to 8 are implemented.

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