Obstacle modeling method and computer program product for photovoltaic power generation system

Abstract

The present invention provides an obstacle modeling method and computer program product for a photovoltaic power generation system. The above method includes: determining the photovoltaic installation area where the photovoltaic power generation system is located; collecting real-time obstacle information of the photovoltaic installation area; searching for historical obstacle information of the photovoltaic installation area based on the real-time obstacle information; analyzing the changing trend of obstacles through the real-time obstacle information and the historical obstacle information; predicting obstacles and modeling them to obtain obstacle models at different time points based on the changing trend. Through this method, obstacle models at different time points can be predicted and modeled by analyzing the changing trend of obstacles, so as to adjust the planning of the photovoltaic power station according to the obstacle model in the future, and avoid the obstacles changing over time and thus having an adverse effect on the photovoltaic power station.

Description

Technical Field

[0001] The present invention relates to the field of power station design, and in particular to an obstacle modeling method and a computer program product of a photovoltaic power generation system. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy technology, photovoltaic power generation systems are becoming more and more widely used as a clean and renewable energy utilization method. However, in the actual design and operation of photovoltaic power stations, the impact of obstacles on photovoltaic modules cannot be ignored. Obstacles such as trees, buildings, and utility poles will block sunlight and reduce the amount of solar radiation received by photovoltaic modules, thereby affecting the power generation efficiency and overall economic benefits of the photovoltaic system. Therefore, accurate and efficient modeling of obstacles in photovoltaic systems is of great significance for optimizing photovoltaic layout and improving power generation efficiency. Summary of the invention

[0003] In view of the above problems, the present invention proposes an obstacle modeling method and a computer program product for a photovoltaic power generation system that overcome the above problems or at least partially solve the above problems.

[0004] One object of the present invention is to build obstacle models at different time points.

[0005] A further object of the present invention is to provide data support for photovoltaic planning.

[0006] In particular, the present invention provides an obstacle modeling method for a photovoltaic power generation system, comprising:

[0007] Determine the photovoltaic installation area where the photovoltaic power generation system is located;

[0008] Collect real-time obstacle information in the PV installation area;

[0009] Obtain historical obstacle information of the photovoltaic installation area based on real-time obstacle information search;

[0010] Analyze obstacle change trends through real-time obstacle information and historical obstacle information;

[0011] According to the changing trend, obstacles are predicted and modeled to obtain obstacle models at different time points.

[0012] Optionally, the step of searching for historical obstacle information of the photovoltaic installation area according to the real-time obstacle information includes:

[0013] Determine the location and type of obstacles in the photovoltaic installation area based on real-time obstacle information;

[0014] Determine the existing obstacle set based on obstacle locations and obstacle types;

[0015] Obtain historical image data of photovoltaic installation areas;

[0016] According to the existing obstacle set, historical obstacle information corresponding to the existing obstacle set is obtained by searching in the historical image data.

[0017] Optionally, the real-time obstacles include natural obstacles; and

[0018] The steps of analyzing obstacle change trends through real-time obstacle information and historical obstacle information include:

[0019] Identify the natural state information of natural obstacles at different times from the real-time obstacle information and historical obstacle information;

[0020] Establish plant growth model based on natural state information;

[0021] Generate trends of natural obstacles based on plant growth models.

[0022] Optionally, after the step of generating the change trend of the natural obstacles according to the plant growth model, the step further includes:

[0023] Real-time monitoring of the status changes of natural obstacles in the photovoltaic installation area;

[0024] Verify and correct the changing trends of natural obstacles based on the status change information.

[0025] Optionally, the real-time obstacles include architectural obstacles; and

[0026] The steps of analyzing obstacle change trends through real-time obstacle information and historical obstacle information include:

[0027] Identify and obtain building status information corresponding to building obstacles at different times from real-time obstacle information and historical obstacle information;

[0028] Model building changes based on building status information;

[0029] Generate the change trend of building obstacles based on the building change model.

[0030] Optionally, after the step of generating the change trend of the building obstacle according to the building change model, the step further includes:

[0031] Obtain the urban planning plan and project construction plan of the city where the photovoltaic installation area is located;

[0032] Verify and correct the changing trends of building obstacles according to urban planning plans and project construction plans.

[0033] Optionally, the step of collecting real-time obstacle information of the photovoltaic installation area includes:

[0034] Plan the obstacle shooting plan of the drone according to the photovoltaic installation area;

[0035] Control the drone to shoot according to the obstacle shooting plan to obtain real-time obstacle information.

[0036] Optionally, the obstacle shooting scheme includes a shooting path, a flight altitude, and a camera setting of the drone;

[0037] The steps for planning the obstacle shooting scheme of the drone according to the photovoltaic installation area include:

[0038] Parameter requirements for obtaining real-time obstacle information, including image overlap and image clarity;

[0039] Determine the shooting path of the UAV according to the image overlap so that the image captured by the UAV meets the image overlap requirement;

[0040] Determine the flight altitude of the drone and the camera settings based on the image clarity so that the images taken by the drone meet the image clarity requirements.

[0041] Optionally, after the step of collecting real-time obstacle information of the photovoltaic installation area, the step further includes:

[0042] Perform consistency checks on images taken by drones to avoid images with large lighting changes;

[0043] The image is corrected for errors, including lens distortion correction and color correction.

[0044] According to another aspect of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above-mentioned methods for obstacle modeling of a photovoltaic power generation system when the computer program is executed by a processor.

[0045] According to yet another aspect of the present invention, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for obstacle modeling of a photovoltaic power generation system are implemented.

[0046] According to another aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a machine executable program stored in the memory and running on the processor, and the processor implements the steps of any of the above-mentioned methods for obstacle modeling of a photovoltaic power generation system when executing the machine executable program.

[0047] The obstacle modeling method of the photovoltaic power generation system of the present invention first determines the photovoltaic installation area where the photovoltaic power generation system is located; collects real-time obstacle information of the photovoltaic installation area; searches for historical obstacle information of the photovoltaic installation area based on the real-time obstacle information; analyzes the change trend of obstacles through the real-time obstacle information and the historical obstacle information; predicts and models obstacles at different time points based on the change trend. Through this method, obstacle models at different time points can be predicted and modeled by analyzing the change trend of obstacles, so as to adjust the planning of the photovoltaic power station according to the obstacle model in the future, and avoid the obstacles changing over time and thus having an adverse effect on the photovoltaic power station.

[0048] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0050] Figure 1 is a schematic diagram of a design system architecture of an obstacle modeling method for a photovoltaic power generation system using an embodiment of the present invention;

[0051] Figure 2 is a flow chart of an obstacle modeling method for a photovoltaic power generation system according to an embodiment of the present invention;

[0052] Figure 3 It is a schematic diagram of a process of obtaining real-time obstacle information and historical obstacle information in an obstacle modeling method of a photovoltaic power generation system according to an embodiment of the present invention;

[0053] Figure 4 is a schematic diagram of a flow chart of obtaining a change trend of natural obstacles in an obstacle modeling method for a photovoltaic power generation system according to an embodiment of the present invention;

[0054] Figure 5 is a schematic diagram of a process for obtaining a change trend of a building obstacle in an obstacle modeling method for a photovoltaic power generation system according to an embodiment of the present invention;

[0055] Figure 6 is a schematic diagram of a computer program product according to an embodiment of the present invention;

[0056] Figure 7 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; and

[0057] Figure 8 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] It should be understood by those skilled in the art that the embodiments described below are only some embodiments of the present invention, rather than all embodiments of the present invention, and these embodiments are intended to explain the technical principles of the present invention, rather than to limit the protection scope of the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present invention.

[0059] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or equipment (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or equipment and execute instructions), or used in combination with these instruction execution systems, devices or equipment.

[0060] Figure 1 The schematic diagram of the design system architecture of the obstacle modeling method for photovoltaic power generation system according to one embodiment of the present invention is shown in FIG. The design system may include: a drone 10 , a wireless communication device 20 , a data processing device 30 , and an interactive terminal 40 .

[0061] The drone 10 may include: a flight control system, a positioning system, an aerial photography system, and a measurement system. The flight control system is used to adjust the flight attitude (including altitude, speed, and direction) of the drone, and the positioning system is used to obtain the location information of the drone 10, which may use positioning devices such as GPS and Beidou. The aerial photography system is used to photograph the target during flight. In order to realize the functions of the method of this embodiment, the aerial photography system uses a camera with adjustable focal length and shooting parameters that meet the design requirements. The measurement system is used to record the flight process of the drone 10 and collect status data such as flight altitude, flight attitude, and shooting parameters.

[0062] The drone 10 exchanges data with the data processing device 30 through the wireless communication device 20, and transmits its own flight data and the data of the airborne equipment (positioning system, aerial photography system, and measurement system) to the data processing device 30. The data processing device 30 collects and processes the data provided by the drone 10 to complete the flight control of the drone 10. With the characteristics of the drone's flexibility and low cost, it completes the functions that cannot be achieved by traditional manual surveys, greatly improving the processing efficiency.

[0063] The wireless communication device 20 can select a communication method according to actual communication conditions, such as 5G, Wi-Fi, Bluetooth, etc. The interactive terminal 40 is data-connected to the drone 10 and the data processing device 30, respectively, and can display the interface generated by the drone 10 and the data processing device 30, and serve as an interactive device with survey and design personnel to provide manual instructions to the drone 10 and the data processing device 30, and adjust the operating status and processing results of the drone 10 and the data processing device 30. The interactive terminal 40 may include a personal computer, a smart phone, a tablet computer, or other dedicated electronic client devices with a survey application installed.

[0064] This embodiment first provides a method for obstacle modeling of a photovoltaic power generation system, which can be executed by the above-mentioned data processing device 30 to complete information search, prediction and modeling functions. Figure 2 1 is a flow chart of an obstacle modeling method for a photovoltaic power generation system according to an embodiment of the present invention. The obstacle modeling method for a photovoltaic power generation system may generally include:

[0065] Step S201, determining a photovoltaic installation area where a photovoltaic power generation system is located.

[0066] Step S202, collecting real-time obstacle information of the photovoltaic installation area. In some optional embodiments, the step of collecting real-time obstacle information of the photovoltaic installation area may generally include: planning an obstacle shooting plan for the drone according to the photovoltaic installation area, the obstacle shooting plan includes the shooting path, flight altitude and camera settings of the drone; controlling the drone to shoot according to the obstacle shooting plan to obtain real-time obstacle information. When planning a drone obstacle shooting plan for a photovoltaic installation area, it is usually necessary to conduct a comprehensive analysis of the area, including but not limited to topography, photovoltaic facility layout, surrounding environmental conditions and potential air restrictions. This step involves the use of a geographic information system (GIS) and professional software to divide the shooting blocks to ensure that the drone can cover all key areas. Specifically, a detailed flight trajectory will be set. This trajectory must not only ensure effective shooting of all photovoltaic components and other important infrastructure, but also take into account flight efficiency and battery life to plan the most optimized path.

[0067] The drone can be controlled to execute the shooting plan by the operator or through pre-programmed instructions, so that the drone can fly autonomously according to the predetermined flight plan. During the flight, the high-resolution camera on the drone will continuously take photos of the ground according to the established parameter settings. These photos will be transmitted to the ground station or cloud server in real time, and the image processing algorithm will be used for instant analysis to identify and mark obstacles that may hinder the installation or maintenance of photovoltaic panels, such as rubble piles, uncleaned vegetation, ground subsidence, buildings or other structural abnormalities.

[0068] Optionally, the obstacle shooting plan includes the shooting path, flight altitude and camera settings of the drone; the steps of planning the obstacle shooting plan of the drone according to the photovoltaic installation area include: obtaining parameter requirements for real-time obstacle information, the parameter requirements including image overlap and image clarity; determining the shooting path of the drone according to the image overlap, so that the image captured by the drone meets the image overlap requirements; determining the flight altitude and camera settings of the drone according to the image clarity, so that the image captured by the drone meets the image clarity requirements.

[0069] Among them, image overlap refers to the ratio of the common coverage area between two consecutive images, which is very critical for later construction of three-dimensional maps or environmental analysis. By setting appropriate image overlap parameters, the flight path of the drone can be planned to ensure that the same area is fully photographed and effectively stitched from different perspectives, reduce omissions or blind spots, and improve the accuracy and completeness of map reconstruction. Image clarity is related to the accuracy of obstacle recognition. It depends on the flight altitude of the drone and the specific configuration of the camera, such as aperture size, shutter speed and ISO value. A lower flight altitude usually obtains clearer ground images, but at the same time, it is also necessary to avoid blur or safety risks caused by being too close. Therefore, according to the required image clarity standard, it is necessary to optimize the flight altitude of the drone and fine-tune the camera settings to capture images that are both clear and can identify the characteristics of obstacles in detail. Therefore, in order to efficiently and accurately obtain real-time obstacle information, the flight strategy of the drone needs to comprehensively consider the requirements of image overlap and clarity. Through carefully planned flight paths and appropriate shooting parameters, it is ensured that the collected data not only meets the requirements of reconstructing the environmental model, but also accurately identifies and locates potential obstacles.

[0070] Optionally, the step of collecting real-time obstacle information in the photovoltaic installation area also includes: performing consistency check on the images taken by the drone to avoid excessive changes in illumination in the images; and performing error correction on the images, which includes lens distortion correction and color correction. When processing image data taken by the drone, consistency check is a key step to ensure that all images remain relatively uniform in terms of illumination conditions and avoid drastic differences in image brightness and color due to changes in time, angle or weather. This process usually automatically compares statistical characteristics such as brightness and color histograms of adjacent images through algorithms, and performs brightness balance and hue adjustment on some images when necessary, so that the entire image sequence looks harmonious and consistent, which is convenient for subsequent splicing and analysis. For example, images taken at different times of the day can be adjusted to eliminate the inconsistency caused by the intensity of light in the morning and evening. The following error correction is equally important, and it includes two main aspects: 1. Lens distortion correction: The drone camera lens may produce barrel or pincushion distortion when shooting at a wide angle, affecting the true geometric shape of the image. Distortion correction is to restore the original shape of the object through mathematical model calculation to ensure the accuracy of measurement and analysis. This step is usually performed based on the calibration parameters of the lens, which are either preset at the factory or obtained by calibrating by shooting a specific pattern. 2. Color correction: Since different camera sensors may have different perceptions of color, especially between images taken under different light sources, the color may shift. Color correction is to adjust the color space of the image so that all images in the same scene have consistent color performance, thereby improving the naturalness and realism of the image after stitching. After these rigorous post-processing, the image data captured by the drone will be more accurate and reliable, providing solid data support for obstacle identification in the photovoltaic installation area.

[0071] Step S203, searching for historical obstacle information of the photovoltaic installation area based on the real-time obstacle information. In some optional embodiments, step S203 may generally include: determining the obstacle position and obstacle type in the photovoltaic installation area based on the real-time obstacle information; determining the existing obstacle set based on the obstacle position and obstacle type; obtaining historical image data of the photovoltaic installation area; searching the historical image data based on the existing obstacle set to obtain the historical obstacle information corresponding to the existing obstacle set. Among them, the existing obstacle set generally lists in detail the location coordinates and types of all identified obstacles, and the historical obstacle information can generally come from satellite images, previous drone reconnaissance or ground photography records. Those skilled in the art can determine the data source according to actual conditions.

[0072] Step S204: analyzing the obstacle change trend through the real-time obstacle information and the historical obstacle information.

[0073] Step S205 , predicting obstacles according to the change trend and modeling them to obtain obstacle models at different time points.

[0074] Through this method, the obstacle change trend can be analyzed to predict and model the obstacle model at different time points, so as to adjust the planning of the photovoltaic power station according to the obstacle model in the future and avoid the adverse effects of obstacles changing over time on the photovoltaic power station.

[0075] Optionally, the real-time obstacles generally include natural obstacles. And the step of analyzing the change trend of obstacles through the real-time obstacle information and the historical obstacle information includes: identifying the natural state information corresponding to the natural obstacles at different times in the real-time obstacle information and the historical obstacle information; establishing a plant growth model according to the natural state information; and generating the change trend of the natural obstacles according to the plant growth model.

[0076] Optionally, the step of generating the change trend of natural obstacles according to the plant growth model can generally include: real-time monitoring of the state change information of natural obstacles in the photovoltaic installation area; verifying and correcting the change trend of natural obstacles according to the state change information. Among them, natural obstacles are generally related to vegetation, such as trees, bushes, etc. By recording their appearance changes over time, such as seasonal growth, withering, or morphological changes caused by climate. This process can use image recognition algorithms and machine learning techniques to distinguish and track the growth trajectory of natural obstacles by comparing features such as color, texture, and shape. Based on the collected natural state information, a plant growth model is further established. This type of model attempts to simulate the biophysical process of plant growth, considering the effects of environmental factors such as temperature, humidity, soil type, and sunshine duration on plant growth rate. By fitting historical data into the model, the model parameters can be calibrated so that it can more accurately predict the future growth trend of plants. Finally, using the established and calibrated plant growth model, we can generate the change trend of natural obstacles. This means that the model will output a series of predictions showing the expected growth of each natural obstacle, such as the expected height, expansion of coverage area or changes in shape caused by seasonal leaf fall. These predictions are particularly important for planning long-term maintenance and expansion of photovoltaic facilities, and can help decision makers take measures in advance, such as arranging vegetation pruning time reasonably, or adjusting the layout of photovoltaic panels if necessary to adapt to possible natural changes in the future.

[0077] In other optional embodiments, real-time obstacles may generally also include building obstacles; and the step of analyzing the changing trend of obstacles through real-time obstacle information and historical obstacle information may generally include: identifying the building status information corresponding to the building obstacles at different times in the real-time obstacle information and historical obstacle information; establishing a building change model based on the building status information; and generating the changing trend of the building obstacles based on the building change model.

[0078] Optionally, the step of generating the change trend of building obstacles according to the building change model can generally include: obtaining the urban planning plan and project construction plan of the city where the photovoltaic installation area is located; verifying and correcting the change trend of building obstacles according to the urban planning plan and project construction plan. In the preparation work of the photovoltaic installation area, it is an important link to identify the state information of building obstacles at different times. This refers to identifying all building-related obstacles, such as abandoned houses, warehouses or structures under construction, from real-time drone images and historical image records, and recording their conditions at different time points in detail, including the increase, decrease, expansion, demolition or damage repair of buildings. This step is achieved through advanced image analysis technology, which detects pixel-level changes to ensure that the state changes of each building are accurately captured. Based on these detailed building state information, a building change model can be further established. The model comprehensively considers the impact of multiple factors on the change of building state, such as external driving factors such as urban planning changes, land development and utilization policies, and natural disasters, as well as internal factors such as building aging, repair and maintenance. Through statistical analysis or machine learning algorithms, the model attempts to capture the regularity and trend of building changes to provide a basis for prediction.

[0079] Then, using the constructed building change model, we can generate the change trend of building obstacles. This process will output a series of possible development trends of each building obstacle in the future, such as the expected demolition time of an old building, the completion date of a new construction project, or any planning changes that may cause layout changes. This is crucial for the long-term planning of photovoltaic installation projects, helping to avoid potential construction conflicts and optimize the design layout to adapt to future environmental changes.

[0080] Finally, incorporating urban planning and specific project construction plans into the model for verification and correction is a key step in ensuring that the prediction is consistent with actual development. Through comparative analysis, we can adjust model parameters, eliminate unrealistic predictions, and absorb newly released planning information, so that the prediction of the trend of building obstacles is closer to reality, laying a solid foundation for the land selection, risk assessment and long-term development strategy of photovoltaic projects.

[0081] Figure 3FIG. 1 is a flow chart of obtaining real-time obstacle information and historical obstacle information in an obstacle modeling method for a photovoltaic power generation system according to an embodiment of the present invention. Figure 3 As shown, the process of obtaining real-time obstacle information and historical obstacle information includes at least the following steps S301 to S306.

[0082] Step S301, determining a photovoltaic installation area where a photovoltaic power generation system is located.

[0083] Step S302, collecting real-time obstacle information in the photovoltaic installation area.

[0084] Step S303: determining the location and type of obstacles in the photovoltaic installation area according to the real-time obstacle information.

[0085] Step S304, determine the existing obstacle set according to the obstacle location and obstacle type. In the preliminary preparation work for handling the photovoltaic installation area, determining real-time obstacle information is a basic step. This includes using drone photography and data analysis technology to identify various obstacles in the area, such as rocks, abandoned buildings, vegetation overgrowth points, etc., and record their exact locations. Based on this data, an existing obstacle set can be established, which lists in detail the location coordinates and types of all identified obstacles. This set is the core basis for planning photovoltaic panel layout, path optimization and construction strategy.

[0086] Step S305, obtaining historical image data of the photovoltaic installation area. In order to better understand the changing trend of obstacles, historical image data of the photovoltaic installation area is selected. These historical data may come from satellite images, previous drone reconnaissance or ground photography records, so as to trace the environmental changes in the area.

[0087] Step S306: searching the historical image data according to the existing obstacle set to obtain historical obstacle information corresponding to the existing obstacle set.

[0088] Through this method, the existing obstacle set can be compared and analyzed with historical image data. Through image processing technology and machine learning algorithms, the corresponding obstacle information can be searched in the historical data to find the dynamic changes of obstacles, such as whether some obstacles are newly added or the migration of some natural obstacles such as sand dunes. Such analysis is particularly critical for predicting similar problems that may be encountered in the future and evaluating the feasibility of long-term projects.

[0089] Figure 4 FIG. 1 is a flow chart of obtaining the changing trend of natural obstacles in the obstacle modeling method of a photovoltaic power generation system according to an embodiment of the present invention. Figure 4As shown, the process of obtaining the change trend of natural obstacles at least includes the following steps S401 to S405.

[0090] Step S401, the natural state information corresponding to the natural obstacles at different times is identified in the real-time obstacle information and the historical obstacle information. The natural state information of the natural obstacles at different times can generally be identified through modern remote sensing technology and data analysis, and the growth, withering, distribution changes and other states of natural obstacles such as trees and bushes can be tracked in detail from real-time monitoring data and past historical records to provide accurate baseline data for subsequent analysis.

[0091] Step S402, establishing a plant growth model based on the natural state information. On the basis of step S401, a plant growth model is established. In this embodiment, the model integrates the principles of ecology, meteorology and specific regional conditions, and simulates key growth parameters in the plant life cycle, such as growth rate, seasonal variation, etc., through mathematical formulas or algorithms, so that the plant growth model can estimate its future development trend based on the current state.

[0092] Step S403, generating a change trend of natural obstacles according to the plant growth model. The change trend of natural obstacles generally indicates possible changes of natural obstacles over time, such as expected vegetation height growth, coverage expansion or density changes due to seasonal changes, which have a direct impact on the light reception of photovoltaic panels.

[0093] Step S404, real-time monitoring of the state change information of natural obstacles in the photovoltaic installation area. In order to further ensure the accuracy of the change trend, the present invention chooses to monitor the state change information of natural obstacles in the photovoltaic installation area in real time. This step ensures that sudden or abnormal changes that may not be considered by the model can be captured, such as rapid growth or damage caused by extreme weather events.

[0094] Step S405: verify and correct the change trend of the natural obstacles according to the state change information.

[0095] To clearly illustrate the above steps, we take willow as an example. First, we collect the growth data of willow, including the average height, diameter at breast height, crown diameter and other information of willows of different ages. These data can be obtained from forestry research institutions, past literature or field measurements. Then, we record the environmental factors that affect the growth of willow, such as the average annual temperature, precipitation, soil type, etc. These can be obtained from meteorological stations and soil databases. Based on the collected data, we select a suitable growth model, such as the Logistic model or the Chapman-Richards growth model, which can better describe the changes in the growth of organisms over time. Then, we use statistical software to estimate the model parameters, and fit the model by the least squares method or other optimization methods to ensure that the model can accurately reflect the growth trend of willow. Then, we use the selected and parameterized willow plant growth model, input the virtual age or growth cycle stage at the target time point, and predict the expected height, crown width and other growth status of the willow at that time point. Then, we monitor the state change information of the willow in the photovoltaic installation area in real time, and verify and correct the change trend of the willow predicted by the plant growth model.

[0096] Through this method, the real-time monitored state change information can be fed back to the plant growth model to verify and correct the changing trend of natural obstacles. This improves the accuracy of the prediction, ensures that the model prediction is synchronized with the actual situation, and provides more reliable data support for the layout planning and maintenance strategy of photovoltaic projects.

[0097] Figure 5 FIG. 1 is a flow chart of obtaining the changing trend of building obstacles in the obstacle modeling method of a photovoltaic power generation system according to an embodiment of the present invention. Figure 5 As shown, the process of obtaining the change trend of the building obstacle includes at least the following steps S501 to S505.

[0098] Step S501, identify and obtain the building status information corresponding to the building obstacles at different times from the real-time obstacle information and the historical obstacle information. Specifically, the identification process generally involves carefully identifying and recording the detailed status of the building obstacles from the real-time and historical obstacle information. This covers the location, height, structure type of the building and any changes over time, such as new construction, demolition or renovation, laying a data foundation for subsequent analysis.

[0099] Step S502: establishing a building change model according to the building status information.

[0100] Step S503: Generate a change trend of building obstacles according to the building change model, wherein the change trend of building obstacles can generally describe the possible existence status, growth mode or reduction trend of each building in the future time period.

[0101] Step S504, obtaining the urban planning plan and project construction plan of the city where the photovoltaic installation area is located.

[0102] Step S505: verify and correct the change trend of building obstacles according to the urban planning plan and the project construction plan.

[0103] The official urban planning plans and project construction plans of the cities where the photovoltaic installation areas are located usually include the legal framework and future vision of urban construction, such as the demarcation of new development zones and old city renovation projects, which are directly related to the future pattern of the built environment. Integrating these authoritative planning information into the building change model for verification and correction is a key step to ensure that the prediction is close to actual development. By comparing and adjusting the consistency of the model prediction with the official plan, unreasonable assumptions can be eliminated, and the realistic adaptability and reliability of the prediction model can be enhanced, thereby providing more scientific and rigorous decision-making support for the layout and long-term planning of photovoltaic projects.

[0104] The flow chart provided by the present embodiment is not intended to indicate that the operation of the method will be performed in any particular order, or that all operations of the method are included in all every case. In addition, the method may include additional operations. Within the scope of the technical thinking provided by the present embodiment method, additional changes may be made to the above method.

[0105] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0106] This embodiment also provides a computer program product 112 , a computer readable storage medium 113 , and a computer device 114 . Figure 6 is a schematic diagram of a computer program product 112 according to one embodiment of the present invention, Figure 7 is a schematic diagram of a computer-readable storage medium 113 according to an embodiment of the present invention, Figure 8 is a schematic block diagram of a computer device 114 according to one embodiment of the present invention.

[0107] The computer program product 112 includes a computer program 111, which implements the steps of any of the above-mentioned methods for modeling obstacles for photovoltaic power generation systems when executed by the processor 310. The computer-readable storage medium 113 stores the above-mentioned computer program 111, which implements the steps of any of the above-mentioned methods for modeling obstacles for photovoltaic power generation systems when executed by the processor 310. The computer device 114 may include a memory 320, a processor 310, and a computer program 111 stored in the memory 320 and running on the processor 310.

[0108] The computer program 111 used to perform the operations of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data of an integrated circuit, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages.

[0109] The computer program 111 may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, in order to perform various aspects of the present invention, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuits.

[0110] In the description of this embodiment, the computer program product 112 is a related product including the computer program 111 .

[0111] For the purposes of the description of this embodiment, the computer-readable storage medium 113 is a tangible device capable of retaining and storing the computer program 111, which may be any device that can contain, store, communicate, propagate or transmit the program 111 for use with or in conjunction with an instruction execution system, device or apparatus. More specific examples (a non-exhaustive list) of the computer-readable storage medium 113 include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the foregoing.

[0112] At this point, those skilled in the art should recognize that, although multiple exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications that conform to the principles of the present invention can still be directly determined or derived based on the content disclosed in the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and recognized as covering all these other variations or modifications.

Claims

1. A method for modeling obstacles in a photovoltaic power generation system, comprising: Determine a photovoltaic installation area where the photovoltaic power generation system is located; Collecting real-time obstacle information in the photovoltaic installation area; Searching and obtaining historical obstacle information of the photovoltaic installation area according to the real-time obstacle information; Analyzing the change trend of the obstacle through the real-time obstacle information and the historical obstacle information; The obstacle is predicted and modeled according to the change trend to obtain the obstacle model at different time points.

2. The obstacle modeling method for a photovoltaic power generation system according to claim 1, wherein: The step of searching for historical obstacle information of the photovoltaic installation area according to the real-time obstacle information comprises: Determining the obstacle location and obstacle type in the photovoltaic installation area according to the real-time obstacle information; Determining an existing obstacle set according to the obstacle position and the obstacle type; Acquiring historical image data of the photovoltaic installation area; The historical obstacle information corresponding to the existing obstacle set is obtained by searching the historical image data according to the existing obstacle set.

3. The obstacle modeling method for a photovoltaic power generation system according to claim 1, wherein: The real-time obstacles include natural obstacles; and The step of analyzing the change trend of the obstacle through the real-time obstacle information and the historical obstacle information includes: Identify and obtain the natural state information corresponding to the natural obstacle at different times from the real-time obstacle information and the historical obstacle information; Establishing a plant growth model according to the natural state information; The change trend of the natural obstacle is generated according to the plant growth model.

4. The obstacle modeling method for a photovoltaic power generation system according to claim 3, wherein: After the step of generating the change trend of the natural obstacle according to the plant growth model, the step further includes: Real-time monitoring of status change information of the natural obstacles in the photovoltaic installation area; The change trend of the natural obstacle is verified and corrected according to the state change information.

5. The obstacle modeling method for a photovoltaic power generation system according to claim 1, wherein: The real-time obstacles include building obstacles; and The step of analyzing the change trend of the obstacle through the real-time obstacle information and the historical obstacle information includes: Identify and obtain building status information corresponding to the building obstacle at different times from the real-time obstacle information and the historical obstacle information; Establishing a building change model according to the building status information; The change trend of the building obstacle is generated according to the building change model.

6. The obstacle modeling method for a photovoltaic power generation system according to claim 5, wherein: After the step of generating the change trend of the building obstacle according to the building change model, the step further includes: Obtain the urban planning plan and project construction plan of the city where the photovoltaic installation area is located; The changing trend of the building obstacles is verified and corrected according to the urban planning plan and the project construction plan.

7. The obstacle modeling method for a photovoltaic power generation system according to claim 1, wherein: The step of collecting real-time obstacle information of the photovoltaic installation area comprises: Planning the obstacle shooting plan of the drone according to the photovoltaic installation area; The drone is controlled to shoot according to the obstacle shooting scheme to obtain the real-time obstacle information.

8. The obstacle modeling method for a photovoltaic power generation system according to claim 7, wherein: The obstacle shooting scheme includes the shooting path, flight altitude and camera settings of the drone; The step of planning an obstacle shooting scheme of a drone according to the photovoltaic installation area comprises: Obtaining parameter requirements for the real-time obstacle information, the parameter requirements including image overlap and image clarity; Determining the shooting path of the drone according to the image overlap, so that the image shot by the drone meets the image overlap requirement; The flight altitude of the UAV and the camera settings are determined according to the image clarity, so that the image captured by the UAV meets the image clarity requirement.

9. The obstacle modeling method for a photovoltaic power generation system according to claim 8, wherein: After the step of collecting real-time obstacle information of the photovoltaic installation area, the following step is further included: Performing a consistency check on the image captured by the drone to avoid excessive changes in illumination in the image; Error correction is performed on the image, and the error correction includes lens distortion correction and color correction.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the obstacle modeling method for a photovoltaic power generation system according to any one of claims 1 to 9 are implemented.

11. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the obstacle modeling method for a photovoltaic power generation system according to any one of claims 1 to 9 are implemented.

12. A computer device comprising a memory, a processor, and a machine executable program stored in the memory and running on the processor, wherein the processor implements the steps of the obstacle modeling method for a photovoltaic power generation system according to any one of claims 1 to 9 when executing the machine executable program.

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