Methods, equipment, and storage media for determining photovoltaic panel installation schemes

Abstract

This application provides a method, device, and storage medium for determining a photovoltaic (PV) panel installation scheme, including: acquiring the roof area of ​​the building where PV panels are to be installed and a pre-defined PV panel layout for the roof area; determining wind pressure distribution data for the roof area; dividing the roof area into multiple roof sub-areas based on the wind pressure distribution data and the PV panel layout; determining the installation reinforcement level for each roof sub-area; and generating a PV panel installation scheme for the roof area based on the installation reinforcement level. This application allows for different installation reinforcement schemes for different roof sub-areas, thereby adapting to the differences in wind loads borne by PV panels at different locations, improving the installation reliability of PV panels, and reducing the risk of PV panel damage.

Description

Technical Field

[0001] This application relates to the field of photovoltaic panel technology, specifically to a method, equipment, and storage medium for determining a photovoltaic panel installation scheme. Background Technology

[0002] With the development of solar energy technology, the application of photovoltaic panels is becoming increasingly widespread. For example, photovoltaic panels can be fixed to the roof of a building, which not only improves the efficiency of photovoltaic power generation, but also replaces the original building materials such as corrugated steel tiles, saving construction costs.

[0003] In related technologies, the same clamping block and the same fixing method are often used to fix photovoltaic panels in different positions. However, in severe weather such as strong winds, the wind load on photovoltaic panels in different positions varies greatly, and some photovoltaic panels may be blown away and damaged by strong winds. It can be seen that the installation reliability of photovoltaic panels is relatively low, and the risk of damage is relatively high. Summary of the Invention

[0004] The embodiments of this application provide a method, equipment, and storage medium for determining a photovoltaic panel installation scheme, which aims to improve the installation reliability of photovoltaic panels and reduce the risk of damage to photovoltaic panels.

[0005] Firstly, embodiments of this application provide a method for determining a photovoltaic panel installation scheme, the method comprising:

[0006] Obtain the roof area of ​​the building where photovoltaic panels are to be installed, and the preset photovoltaic panel layout for the roof area;

[0007] Determine the wind pressure distribution data for the roof area;

[0008] Based on the wind pressure distribution data and the photovoltaic panel layout, the roof area is divided into multiple roof sub-areas;

[0009] Determine the installation reinforcement level for each of the aforementioned roof sub-areas;

[0010] Based on the aforementioned installation reinforcement level, a photovoltaic panel installation plan for the roof area is generated.

[0011] In the above embodiments, based on refined wind pressure distribution data, the roof area is intelligently and dynamically divided into zones, and each divided roof sub-zone is matched with a better installation reinforcement level and a corresponding photovoltaic panel installation scheme. In this way, the installation strength of the roof sub-zone with high wind pressure can be strengthened in a targeted manner, improving the wind uplift resistance and long-term operational reliability of the entire photovoltaic system. It can also avoid unnecessary over-investment in the roof sub-zone with low wind pressure, saving the total installation cost of the photovoltaic panel installation project.

[0012] In one embodiment, dividing the roof area into multiple roof sub-areas based on the wind pressure distribution data and the photovoltaic panel layout includes:

[0013] Within the roof area, the installation location of each photovoltaic panel is determined according to the photovoltaic panel layout;

[0014] Based on the wind pressure distribution data, determine the wind pressure characteristic value for each of the installation locations;

[0015] Based on the different wind pressure characteristic values, the installation locations of multiple photovoltaic panels in the roof area are classified to obtain multiple categories of installation locations;

[0016] Based on multiple categories of installation locations, the roof area is divided into multiple roof sub-areas.

[0017] In the above embodiments, by determining the wind pressure characteristic value of each photovoltaic panel one by one and classifying them, and then transforming the discrete installation locations of the same category into regular and continuous roof sub-areas, the rationality of the roof area division results and the engineering practicality are ensured, providing the basic conditions for achieving the final differentiated and precise installation.

[0018] In one embodiment, dividing the roof area into multiple roof sub-areas based on multiple categories of installation locations includes:

[0019] Multiple installation locations for each category are mapped to pixels in a binary image to form an initial binary image region.

[0020] Morphological operations are performed on the initial binary image region to obtain the roof sub-region, wherein the morphological operations include at least one of opening and closing operations.

[0021] In the above embodiments, image morphology operations are used to optimize the division of roof sub-regions to avoid problems such as irregular boundaries and fragmented areas of the roof sub-regions. This makes the boundaries of the divided roof sub-regions clear and facilitates the implementation of photovoltaic panel installation projects, thereby further improving construction efficiency.

[0022] In one embodiment, determining the installation reinforcement level for each of the roof sub-regions includes:

[0023] For each of the aforementioned roof sub-regions, determine the maximum value among the wind pressure characteristic values ​​of multiple installation locations within the roof sub-region;

[0024] The installation reinforcement level of the roof sub-region is determined by comparing the maximum value of the wind pressure characteristic values ​​of multiple installation locations in the roof sub-region with a preset wind pressure characteristic threshold.

[0025] In the above embodiments, the wind pressure characteristic value of the most unfavorable point in each roof sub-area is used as the overall design benchmark for that roof sub-area. By comparing it with the wind pressure characteristic threshold, a unique and clear installation reinforcement level is matched for that roof sub-area to ensure the safety of the zonal installation strategy of photovoltaic panels and the installation reliability of photovoltaic panels.

[0026] In one embodiment, the photovoltaic panel installation scheme for the roof area includes multiple sub-installation schemes for the roof sub-areas. After generating the photovoltaic panel installation scheme for the roof area based on the installation reinforcement level, the scheme further includes:

[0027] Determine the total installation cost of the photovoltaic panel installation plan for the roof area;

[0028] If the total installation cost is greater than the preset installation cost threshold, then among the multiple sub-installation schemes for the roof sub-area, the target sub-installation scheme with the highest installation cost is determined, and in the photovoltaic panel installation scheme, the installation plan of at least one photovoltaic panel in the roof sub-area corresponding to the target sub-installation scheme is deleted.

[0029] Return to the step of determining the total installation cost of the photovoltaic panel installation plan for the roof area, until the total installation cost is less than or equal to a preset installation cost threshold.

[0030] In the above embodiments, an iterative cycle of "cost accounting - overspending judgment - high-efficiency cost reduction - recalculation" is established. By gradually reducing the photovoltaic panel installation plan in high-cost areas until the total installation cost meets the budget, the return on investment of the photovoltaic panel project is maximized.

[0031] In one embodiment, determining the wind pressure distribution data of the roof area includes:

[0032] Generate a fluid dynamics simulation model of the area where the building is located, wherein the fluid dynamics simulation model includes a first simulation module of the building and a second simulation module of wind field obstacles around the building;

[0033] Using the fluid dynamics simulation model, wind field simulation is performed on the area where the building is located to determine the wind pressure distribution data of the roof area.

[0034] In the above embodiments, by constructing a fluid dynamics simulation model that includes buildings and wind field obstacles around the buildings, and using the fluid dynamics simulation model to simulate the wind field in the area where the buildings are located, more accurate wind pressure distribution data for the roof area can be obtained quickly.

[0035] In one embodiment, the step of using the fluid dynamics simulation model to simulate the wind field in the area where the building is located to determine the wind pressure distribution data of the roof area includes:

[0036] Based on historical meteorological data of the area where the building is located, the boundary conditions of the fluid dynamics simulation model are determined;

[0037] Using the boundary conditions, the fluid dynamics simulation model is solved to obtain the simulated wind pressure data of multiple simulation grid nodes in the fluid dynamics simulation model;

[0038] Based on the simulated wind pressure data of multiple simulated grid nodes corresponding to the roof area, the wind pressure distribution data of the roof area is determined.

[0039] In the above embodiments, the boundary conditions of the fluid dynamics simulation model are determined based on real historical meteorological data to drive the numerical solution of the fluid dynamics simulation model, thereby realizing the accurate simulation of wind pressure distribution data in the roof area.

[0040] In one embodiment, determining the wind pressure distribution data of the roof area based on the simulated wind pressure data of multiple simulated grid nodes corresponding to the roof area includes:

[0041] For each simulation grid node corresponding to the roof area, the negative pressure data with the largest absolute value is determined from the simulation wind pressure data of the simulation grid node under multiple different wind direction angles;

[0042] Based on the negative pressure data with the largest absolute value, the wind pressure distribution data of the roof area is determined.

[0043] In the above embodiment, by calculating the simulated wind pressure data of each simulated grid node on the roof at multiple different wind angles and determining the extreme negative pressure value, it is ensured that all subsequent analyses and decisions are based on a full consideration of the worst-case scenario, thereby further improving the reliability and safety of the photovoltaic panel installation scheme.

[0044] Secondly, embodiments of this application provide a device for determining a photovoltaic panel installation scheme, the device comprising:

[0045] The acquisition module is used to acquire the roof area of ​​the building where photovoltaic panels are to be installed and the preset photovoltaic panel layout for the roof area;

[0046] A determination module is used to determine the wind pressure distribution data of the roof area; based on the wind pressure distribution data and the photovoltaic panel layout, the roof area is divided into multiple roof sub-areas; and the installation reinforcement level of each roof sub-area is determined.

[0047] The execution module is used to generate a photovoltaic panel installation plan for the roof area based on the installation reinforcement level.

[0048] Thirdly, embodiments of this application provide an electronic device including a processor and a memory, wherein the memory stores a computer program configured to be executed by the processor to implement the method for determining a photovoltaic panel installation scheme as described in any of the preceding claims.

[0049] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program configured to be executed by a processor to implement the method for determining a photovoltaic panel installation scheme as described in any of the preceding claims.

[0050] Fifthly, embodiments of this application provide a computer program product, including a computer program or instructions, which are executed by a processor to implement the method for determining a photovoltaic panel installation scheme as described in any of the preceding claims.

[0051] The beneficial effects of the embodiments of this application are as follows:

[0052] In the embodiments of this application, the roof area is divided into multiple roof sub-areas based on the wind pressure distribution data and photovoltaic panel layout of the roof area. Then, the installation reinforcement level of each roof sub-area is determined. Based on the installation reinforcement level, a photovoltaic panel installation scheme for the roof area is generated, so that the installation reinforcement schemes for different roof sub-areas can be different, thereby adapting to the differences in wind load borne by photovoltaic panels in different locations, improving the installation reliability of photovoltaic panels, and reducing the risk of photovoltaic panels being damaged. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic flowchart of an embodiment of the method for determining a photovoltaic panel installation scheme provided in this application;

[0055] Figure 2 This is a schematic flowchart of another embodiment of the method for determining a photovoltaic panel installation scheme provided in the embodiments of this application;

[0056] Figure 3 This is a schematic flowchart of another embodiment of the method for determining a photovoltaic panel installation scheme provided in the embodiments of this application;

[0057] Figure 4 This is a schematic diagram of another embodiment of the photovoltaic panel installation scheme determination device provided in the embodiments of this application;

[0058] Figure 5 This is a schematic diagram of an embodiment of the electronic device provided in this application. Detailed Implementation

[0059] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. In addition, in the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0060] Firstly, embodiments of this application provide a method for determining a photovoltaic panel installation scheme. Specifically, refer to... Figure 1 The methods for determining photovoltaic panel installation schemes may include:

[0061] 101. Obtain the roof area of ​​the building where photovoltaic panels are to be installed, and the preset photovoltaic panel layout for the roof area.

[0062] In the embodiments of this application, the building can be any building structure suitable for installing photovoltaic panels, including but not limited to industrial plants, commercial buildings, public facilities, residential buildings, etc. The roof area refers to the designated surface on the top of the building used for installing photovoltaic panels; this surface can be a flat roof, a pitched roof, or a roof with complex geometry. The preset photovoltaic panel layout is an initial design scheme, usually existing in digital or graphic format, containing preset installation position information for each photovoltaic panel within the roof area, such as the installation location (two-dimensional or three-dimensional coordinates), azimuth angle, tilt angle, etc. The photovoltaic panel layout can be automatically generated by professional photovoltaic design software or manually planned based on experience.

[0063] 102. Determine the wind pressure distribution data for the roof area.

[0064] In the embodiments of this application, wind pressure distribution data refers to a two-dimensional or three-dimensional dataset describing the magnitude and distribution of wind loads at different locations on the roof area. Wind pressure distribution data is typically represented as pressure values ​​associated with the roof area coordinates, particularly the negative pressure values ​​that exert an upward force on the photovoltaic panels. The wind pressure distribution data aims to accurately reflect the wind pressure differences at the roof edges, corners, ridges, and central areas caused by wind diversion, separation, and reattachment effects, and its spatial resolution is far higher than the coarse zoning of the roof in building load-related codes.

[0065] In some embodiments of this application, wind pressure distribution data can be obtained through on-site measurements by deploying multiple miniature wind pressure sensor arrays on a large scale on the roof of a building. Alternatively, fluid dynamics simulation technology can be used to generate high-precision wind pressure distribution data, thereby reducing the cost of acquiring wind pressure distribution data and increasing the speed of data acquisition.

[0066] 103. Based on wind pressure distribution data and photovoltaic panel layout, the roof area is divided into multiple roof sub-areas.

[0067] In the embodiments of this application, a roof sub-region refers to multiple independent, typically geometrically continuous regions formed by dividing the entire roof area. The core basis for this division can be to group photovoltaic panel installation locations with similar wind pressure characteristics together, thereby providing a foundation for subsequent differentiated installation.

[0068] 104. Determine the installation reinforcement level for each roof area.

[0069] In the embodiments of this application, the installation reinforcement level is a classification identifier or quantitative index used to characterize the required installation strength and safety standards for different roof sub-areas. This level is directly related to the hardware specifications, quantity, and construction techniques used in the subsequent photovoltaic panel installation. The installation reinforcement level may include, for example, three levels: "Standard Installation Reinforcement Level," "Medium Installation Reinforcement Level," and "Heavy Installation Reinforcement Level." Of course, the installation reinforcement level may also include values ​​ranging from [1, 10], so that different values ​​can characterize different installation reinforcement levels.

[0070] 105. Based on the installation reinforcement level, generate a photovoltaic panel installation plan for the roof area.

[0071] In the embodiments of this application, the photovoltaic panel installation scheme for the roof area includes multiple sub-installation schemes for different roof sub-areas, each sub-installation scheme corresponding to a specific reinforcement level for that roof sub-area. Each sub-installation scheme details the required component types and specifications, quantity and density of components, and specific construction methods for photovoltaic panel installation in the corresponding roof sub-area. Components include, for example, guide rails and clamping blocks for installing photovoltaic panels. Clamping blocks may include, for example, center clamping blocks and edge clamping blocks. For instance, the sub-installation scheme corresponding to the "heavy-duty installation reinforcement level" may include: requiring the use of reinforced aluminum alloy guide rails with a wall thickness 30% greater than standard components; using clamping blocks for fixing on both the long and short sides of the photovoltaic panels; increasing the weight of the foundation counterweight to 50 kg / block; or using higher-strength fixing methods such as drilled chemical anchors instead of expansion bolts. Following the photovoltaic panel installation scheme, the physical installation of photovoltaic panels can be completed within the corresponding roof sub-area.

[0072] As can be seen from the above embodiments of this application, based on refined wind pressure distribution data, the roof area is intelligently and dynamically divided into regions, and a better installation reinforcement level is matched for each divided roof sub-region. Based on the installation reinforcement level, a corresponding photovoltaic panel installation scheme is generated. In this way, the installation strength of the roof sub-region with high wind pressure can be strengthened in a targeted manner, improving the wind resistance and long-term operational reliability of the entire photovoltaic system. It can also avoid unnecessary over-investment in the roof sub-region with low wind pressure, saving the total installation cost of the photovoltaic panel installation project.

[0073] In some embodiments of this application, reference is made to Figure 2 ,exist Figure 1 Based on the illustrated embodiment, and using wind pressure distribution data and photovoltaic panel layout, the roof area is divided into multiple roof sub-areas, which may include:

[0074] 201. Within the roof area, determine the installation location of each photovoltaic panel based on the photovoltaic panel layout.

[0075] In the embodiments of this application, the installation location refers to the precise spatial location information of each individual photovoltaic panel in the roof area coordinate system, which is parsed from the photovoltaic panel layout file. This information is typically represented by the two-dimensional or three-dimensional coordinates (x, y, z) of the geometric center of the photovoltaic panel, thereby discretizing the continuous roof area into a set of spatial points equal to the number of photovoltaic panels.

[0076] 202. Based on the wind pressure distribution data, determine the wind pressure characteristic value of each installation location.

[0077] In the embodiments of this application, the wind pressure characteristic value is a numerical value used to quantify the wind load risk faced by a single installation location. Since wind pressure distribution data is a high-resolution continuous pressure field, and the photovoltaic panel occupies a certain area, it is necessary to use a specific algorithm to convert the pressure information of this area into a single characteristic value.

[0078] In some embodiments of this application, determining the wind pressure characteristic value of each installation location based on wind pressure distribution data may include: determining the projected coverage area of ​​the photovoltaic panel on the roof based on the preset standard size of the photovoltaic panel and the installation location; filtering out all pressure data points falling within the projected coverage area from the wind pressure distribution data; and finally, determining the negative pressure data with the largest absolute value (i.e., the extreme negative pressure value) among these data points as the wind pressure characteristic value of the installation location. This method ensures that the wind pressure characteristic value can reflect the local pressure peaks that may occur on the surface of the photovoltaic panel, thereby making the subsequent installation of the photovoltaic panel safer and more reliable.

[0079] 203. Based on the different wind pressure characteristic values, the installation locations of multiple photovoltaic panels in the roof area are classified to obtain multiple categories of installation locations.

[0080] In the embodiments of this application, the classification of multiple photovoltaic panel installation locations within the roof area refers to assigning all installation locations to different wind pressure categories based on one or more preset wind pressure thresholds. These wind pressure thresholds can be set according to relevant building load codes, the mechanical performance limits of photovoltaic panel components, or the project's own safety standards. For example, three threshold ranges can be set to define three categories: the first category (low wind pressure) corresponds to installation locations with a wind pressure characteristic value less than 800 Pascals (Pa); the second category (medium wind pressure) corresponds to installation locations with a wind pressure characteristic value between 800 and 1200 Pascals; and the third category (high wind pressure) corresponds to installation locations with a wind pressure characteristic value greater than 1200 Pascals. After this step, the originally indistinguishable set of installation locations within the roof area is transformed into a set of multiple categories with clear wind pressure labels, such as "low wind pressure set," "medium wind pressure set," and "high wind pressure set."

[0081] 204. Based on the multiple categories of installation locations, the roof area is divided into multiple roof sub-areas.

[0082] In the embodiments of this application, the discrete and classified set of installation locations obtained in the previous step is transformed into a geometrically continuous roof sub-region with clear boundaries.

[0083] In some embodiments of this application, a spatial clustering algorithm, such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise), can be used to determine the roof sub-regions. Specifically, the DBSCAN algorithm can be run independently for each category of installation locations. The DBSCAN algorithm treats each installation location as a point in a preset space, aggregates densely distributed points into clusters based on their spatial proximity, and identifies isolated noise points. Each generated cluster can be defined as a roof sub-region. This method can identify clusters of arbitrary shapes, making it ideal for handling irregularly shaped high-pressure areas caused by complex wind fields, and can automatically identify "enclave"-like installation locations that require special attention. Ultimately, the roof sub-regions generated for all categories together constitute a complete division of the entire roof area.

[0084] As can be seen from the above embodiments of this application, by determining the wind pressure characteristic value of each photovoltaic panel one by one and classifying them, and then transforming the discrete installation locations of the same category into regular and continuous roof sub-areas, the rationality of the roof area division results and the engineering practicality are guaranteed, providing the basic conditions for achieving the final differentiated and precise installation.

[0085] In some embodiments of this application, dividing the roof area into multiple roof sub-regions based on multiple categories of installation locations may include: mapping multiple installation locations of each category to pixels of a binary image to form an initial binary image region; performing morphological operations on the initial binary image region to obtain the roof sub-regions, wherein the morphological operations include at least one of opening and closing operations.

[0086] This step specifically transforms the spatial geometry problem into an image processing problem. A binary image is a digital image where pixel values ​​are only 0 or 1 (or black and white). The mapping process is as follows: First, the entire roof area is meshed on a two-dimensional plane, with each mesh cell corresponding to a pixel in the binary image. The size of the mesh cell can be adjusted according to the required precision, for example, set to 0.5 meters × 0.5 meters. Then, for a specific category (such as "high wind pressure") of installation locations, all mesh cells are traversed. If a mesh cell contains at least one installation location belonging to that category, the corresponding pixel is assigned a value of 1 (representing the foreground of the binary image, i.e., the high wind pressure area); conversely, if the mesh cell does not contain any installation locations belonging to that category, the corresponding pixel is assigned a value of 0 (representing the background of the binary image). After processing all mesh cells, all pixels assigned a value of 1 together constitute the "initial binary image region" representing that category. This initial binary image region may contain many isolated pixels, tiny holes inside, or jagged boundaries, making it difficult to use directly for engineering demarcation.

[0087] Therefore, morphological operations can be performed on the initial binary image region to at least partially address these defects. Morphological operations are a series of image processing techniques based on set theory, used to analyze and process the shape features of images. The basic operation of opening is to first perform erosion on the image, followed by dilation. Erosion "thins" the boundaries of the foreground region in the image, effectively eliminating tiny, isolated pixels (noise). The subsequent dilation "thickens" the boundaries of the foreground region, roughly restoring its size. Therefore, the overall effect of opening is to smooth the contours of objects, break narrow connections, and eliminate small protrusions, thereby effectively removing isolated installation locations that have been incorrectly classified as high-wind-pressure areas due to data mutations or calculation errors in individual wind pressure characteristic values. This avoids the formation of unnecessary, extremely small "enclave"-like roof sub-areas, thus simplifying the construction zoning of photovoltaic panels and reducing the installation difficulty.

[0088] The closing operation, in reverse order to the opening operation, performs dilation first, followed by erosion. Dilation fills small holes within the foreground region of the image and connects adjacent foreground areas. The subsequent erosion restores the outline of the foreground region to a near-original state. By performing the closing operation on the initial binary image region, "holes" within the same area that should be continuous due to sparse photovoltaic panel layout or localized depressions in wind pressure data can be filled, forming a complete and continuous geometric region. This makes the final roof sub-regions more regular, facilitating understanding and operation by photovoltaic panel installation personnel, and also facilitating the unified management and laying of photovoltaic panel-related materials.

[0089] In some embodiments of this application, opening and closing operations can be combined to achieve better roof sub-region regularization. For example, an opening operation is first performed on the initial binary image region to remove noise, and then a closing operation is performed to fill internal holes and connect neighboring regions. The boundary of the regularized foreground region obtained after morphological processing can be defined as the boundary of the final "roof sub-region".

[0090] As can be seen from the above embodiments of this application, image morphological operations are used to optimize the division of roof sub-regions to avoid problems such as irregular boundaries and fragmented areas of the roof sub-regions. This makes the boundaries of the divided roof sub-regions clear and facilitates the implementation of photovoltaic panel installation projects, thereby further improving construction efficiency.

[0091] In some embodiments of this application, determining the installation reinforcement level of each roof sub-region may include: for each roof sub-region, determining the maximum value among multiple wind pressure characteristic values ​​of the installation locations in the roof sub-region; and determining the installation reinforcement level of the roof sub-region based on a comparison between the maximum value among the multiple wind pressure characteristic values ​​of the installation locations in the roof sub-region and a preset wind pressure characteristic threshold.

[0092] This step specifically involves identifying the installation location with the highest wind pressure characteristic value from all installation locations within each roof sub-area; this is the "shortcoming" with the highest wind pressure. This "shortcoming" represents the extreme wind pressure that the roof area will withstand under the most unfavorable wind conditions. The preset wind pressure characteristic thresholds are one or more sets of critical pressure values ​​used to classify safety levels. These thresholds can be set based on relevant building structural load codes, product mechanical performance test reports provided by photovoltaic panel suppliers (e.g., mechanical load test data of photovoltaic panels under different pressures), and wind resistance performance parameters of the relevant installation and fixing structures for the photovoltaic panels. For example, the preset wind pressure characteristic thresholds may include two levels, T1 and T2, thereby defining three installation reinforcement levels.

[0093] As can be seen from the above embodiments of this application, the wind pressure characteristic value of the most unfavorable point in each roof sub-area is used as the overall design benchmark for that roof sub-area. By comparing it with the wind pressure characteristic threshold, a unique and clear installation reinforcement level is matched for that roof sub-area to ensure the safety of the zonal installation strategy of photovoltaic panels and the installation reliability of photovoltaic panels.

[0094] In some embodiments of this application, the photovoltaic panel installation scheme for the roof area includes multiple sub-installation schemes for the roof sub-areas. After generating the photovoltaic panel installation scheme for the roof area based on the installation reinforcement level, the process may further include: determining the total installation cost of the photovoltaic panel installation scheme for the roof area; if the total installation cost is greater than a preset installation cost threshold, then determining the target sub-installation scheme with the highest installation cost among the multiple sub-installation schemes for the roof sub-areas, and deleting the installation plan of at least one photovoltaic panel in the roof sub-area corresponding to the target sub-installation scheme; returning to the step of determining the total installation cost of the photovoltaic panel installation scheme for the roof area, until the total installation cost is less than or equal to the preset installation cost threshold.

[0095] The process of determining the total installation cost may include, for example, the following steps: First, iterate through all the divided sub-areas of the roof area. For each sub-area, obtain its determined installation reinforcement level and query the installation cost (e.g., yuan / panel) of each photovoltaic panel corresponding to that reinforcement level from a pre-set database. In the installation plans of all photovoltaic panels in each sub-area, count the number of photovoltaic panel installation locations in that sub-area, i.e., the number of photovoltaic panels. Multiply the installation cost of each photovoltaic panel by the number of photovoltaic panels to obtain the installation cost of that sub-area. Finally, sum up the installation costs of all sub-areas to obtain the total installation cost of the photovoltaic panel installation plan for the roof area.

[0096] The installation cost threshold refers to the maximum cost limit set in the budget for the photovoltaic (PV) panel installation project on the roof. When the calculated total installation cost exceeds this threshold, an optimization process is triggered. This involves identifying the target sub-installation plan with the highest installation cost among multiple sub-installation plans for the roof sub-area, and deleting at least one PV panel installation plan corresponding to the target sub-installation plan from the PV panel installation plans. This adjusts the PV panel installation plan for the roof area, and then returns to the step of determining the total installation cost of the PV panel installation plan for the roof area, until the total installation cost is less than or equal to the preset installation cost threshold. If the calculated total installation cost meets the budget requirement (i.e., less than or equal to the installation cost threshold), then no further adjustments are made to the PV panel installation plan for the roof area, resulting in a final, executable, and feasible PV panel installation plan.

[0097] As can be seen, in the above embodiments of this application, an iterative cycle of "cost accounting - overspending judgment - high-efficiency cost reduction - recalculation" is established. By gradually reducing the photovoltaic panel installation plan in high-cost areas until the total installation cost meets the budget, the return on investment of the photovoltaic panel project is improved.

[0098] In some embodiments of this application, reference is made to Figure 3 ,exist Figure 1 or Figure 2 Based on the illustrated embodiment, determining the wind pressure distribution data for the roof area may include:

[0099] 301. Generate a fluid dynamics simulation model of the area where the building is located. The fluid dynamics simulation model includes a first simulation module of the building and a second simulation module of the wind field obstacles around the building.

[0100] In the embodiments of this application, the fluid dynamics simulation model typically refers to a computational fluid dynamics (CFD) model. Fluid dynamics simulation specifically utilizes computer numerical calculations and image display to simulate systems involving physical phenomena such as fluid flow and heat conduction. Therefore, a digital "virtual wind tunnel" can be created using a fluid dynamics simulation model to reproduce the complex interaction between wind and buildings in the real world.

[0101] The first simulation module is the core of the fluid dynamics simulation model, representing the building itself where photovoltaic panels will be installed. This module was created using high-precision 3D modeling software, and its geometry perfectly matches the actual building. Furthermore, to achieve accurate prediction of local wind pressure, this module not only includes the main outline of the building but also precisely reproduces all the microstructures on the roof with a high level of detail, such as parapet walls, ventilation ducts, roof protrusions, and outdoor air conditioning units. These seemingly tiny structures are precisely the key factors causing drastic local wind speed changes, generating eddies, and creating areas of extreme high or negative pressure. Accurately modeling these microstructures greatly improves the accuracy and reliability of the simulation results.

[0102] The second simulation module is a digital representation of the environment surrounding the building. It includes all wind field obstacles that may affect the wind flow towards the building, such as nearby high-rise buildings, trees, hills, and other topographical features. Furthermore, to more realistically simulate the characteristics of incoming wind, the second simulation module employs multi-scale nested modeling technology. This technology simulates macroscopic airflow at the city or regional scale within a larger computational domain, and then uses the simulation results as boundary conditions input into the computational domain of wind field obstacles surrounding the building. This accurately captures the combined influence of upstream obstacles on wind speed, direction, and turbulence intensity, providing a more realistic incoming wind field environment for the first simulation module of the building.

[0103] In some embodiments of this application, in order to improve the realism of the incoming airflow field while controlling the computational cost of the simulation, the model accuracy (e.g., the density of the simulation mesh) at different locations in the second simulation module can be different. Specifically, in the second simulation module, the model accuracy is higher at locations closer to the center point of the roof area of ​​the first simulation module, thereby accurately capturing complex flow phenomena such as wakes and downwash near the roof area. Conversely, the model accuracy is lower at locations farther from the center point of the roof area of ​​the second simulation module, in order to reduce the computational cost of the simulation.

[0104] 302. Use fluid dynamics simulation models to simulate the wind field in the area where the building is located in order to determine the wind pressure distribution data of the roof area.

[0105] In the embodiments of this application, wind field simulation is the process of driving a fluid dynamics simulation model to perform calculations and solutions. This process first requires setting detailed simulation parameters, including the physical properties of the local air around the building (such as density and viscosity) and various settings of the solver. Through wind field simulation, the wind pressure distribution data of the roof area can be determined.

[0106] As can be seen from the above embodiments of this application, by constructing a fluid dynamics simulation model that includes buildings and wind field obstacles around the buildings, and by using the fluid dynamics simulation model to simulate the wind field in the area where the buildings are located, more accurate wind pressure distribution data for the roof area can be obtained quickly.

[0107] In some embodiments of this application, a fluid dynamics simulation model is used to simulate the wind field in the area where the building is located in order to determine the wind pressure distribution data of the roof area. This may include: determining the boundary conditions of the fluid dynamics simulation model based on historical meteorological data of the area where the building is located; solving the fluid dynamics simulation model using the boundary conditions to obtain the simulated wind pressure data of multiple simulation grid nodes in the fluid dynamics simulation model; and determining the wind pressure distribution data of the roof area based on the simulated wind pressure data of multiple simulation grid nodes corresponding to the roof area.

[0108] Historical meteorological data refers to statistical information such as wind speed, wind direction, and air density for historical periods in the area where the building is located, obtained from relevant meteorological information centers, meteorological stations, or certain commercial meteorological databases. Boundary conditions are the input parameters driving the entire wind field simulation calculation; they define the fluid physical state at the boundaries of the computational domain, such as the inlet, outlet, and walls. In determining the boundary conditions of the fluid dynamics simulation model based on historical meteorological data of the building's location, boundary conditions may include, for example:

[0109] Inlet boundary conditions: Based on the N-year return period (e.g., N=50 or 100) extreme wind speed values ​​from historical meteorological data, combined with the local topographic type (e.g., urban center, suburbs, open area), an exponential or logarithmic law wind profile formula is used to generate a velocity inlet profile that varies along the height direction and conforms to the characteristics of the Atmospheric Boundary Layer (ABL). Simultaneously, inlet conditions for turbulent kinetic energy and turbulent dissipation rate are set according to parameters such as the local turbulence integral scale.

[0110] Exit boundary conditions: Usually set to pressure outlet to ensure that the airflow in the computational domain can flow out smoothly without producing non-physical reflections.

[0111] Wall boundary conditions: All solid surfaces (including the ground, buildings, and wind field obstacles around buildings) are set to no-slip wall conditions, meaning the fluid velocity on the wall is zero.

[0112] The solution process for a fluid dynamics simulation model can be achieved by using the solver of appropriate fluid dynamics software to perform numerical iterative calculations on the Navier-Stokes equations describing fluid motion. Multiple simulation grids refer to discretizing a continuous computational domain into a collection of numerous small control volumes, with the simulation grid nodes being the vertices or center points of these control volumes. Specifically, under set boundary conditions, the solver calculates the flow field parameters at each simulation grid node, including velocity, pressure, and turbulent kinetic energy. After a sufficient number of iterative calculations until the solution converges (i.e., the residuals of each physical quantity reach a preset minimum value), the pressure value obtained at each simulation grid node is the simulated wind pressure data.

[0113] The multiple simulation mesh nodes corresponding to the roof area refer to the set of simulation mesh nodes in the entire three-dimensional simulation mesh of the fluid dynamics simulation model whose geometric positions exactly fall within the roof area of ​​the building. Since the simulated wind pressure data of the multiple simulation mesh nodes corresponding to the roof area may not cover all locations within the roof area, the simulated wind pressure data of the multiple simulation mesh nodes corresponding to the roof area can be used as simulated wind pressure data of multiple discrete nodes for interpolation processing, thereby generating continuous and high-resolution wind pressure distribution data of the roof area.

[0114] As can be seen, in the above embodiments of this application, the boundary conditions of the fluid dynamics simulation model are determined based on real historical meteorological data to drive the numerical solution of the fluid dynamics simulation model, thereby realizing the accurate simulation of wind pressure distribution data in the roof area.

[0115] In some embodiments of this application, determining the wind pressure distribution data of the roof area based on the simulated wind pressure data of multiple simulated grid nodes corresponding to the roof area may include: for each simulated grid node corresponding to the roof area, determining the negative pressure data with the largest absolute value among the simulated wind pressure data of the simulated grid node under multiple different wind direction angles; and determining the wind pressure distribution data of the roof area based on the negative pressure data with the largest absolute value.

[0116] The simulated wind pressure data under multiple wind direction angles refers to the fact that for any given location point (i.e., a simulated grid node) on the roof area, there are multiple simulated wind pressure data points. These different simulated wind pressure data points are obtained by simulating the wind field by setting different wind direction angles in the boundary conditions. It is understandable that the wind direction angles in historical meteorological data from different historical times may differ. Therefore, different boundary conditions can be generated based on historical meteorological data from different historical times, and wind field simulations can be performed sequentially for each boundary condition to obtain the simulated wind pressure data for the simulated grid node under multiple wind direction angles.

[0117] In fluid dynamics, negative pressure refers to pressure values ​​below the surrounding atmospheric pressure, generating a suction or uplift force on building surfaces. For rooftop photovoltaic panels, this uplift force is a major cause of them being overturned or damaged by strong winds, making it a crucial factor in structural safety design. Since the largest absolute value of negative pressure generates the greatest uplift force, it can be used as the wind pressure for the corresponding simulation grid node in the wind pressure distribution data. For example, a simulation grid node might have a wind pressure of -800 Pascals (Pa) at a wind angle of 0 degrees, +300 Pa at a wind angle of 45 degrees, and -1200 Pa at a wind angle of 90 degrees. Therefore, the largest absolute value of negative pressure can be determined as -1200 Pa, and the wind pressure for that simulation grid node in the wind pressure distribution data is -1200 Pa. It can be seen that the wind pressure at each point in the wind pressure distribution data represents the most extreme uplift force that point might encounter during the design period.

[0118] As can be seen, in the above embodiments of this application, by calculating the simulated wind pressure data of each simulated grid node on the roof at multiple different wind angles and determining the extreme negative pressure value, it is ensured that all subsequent analyses and decisions are based on a full consideration of the worst-case scenario, thereby further improving the reliability and safety of the photovoltaic panel installation scheme.

[0119] In some embodiments of this application, in order to further improve the reliability and safety of the photovoltaic panel installation scheme, at least one of the dominant pulsating frequency data and fatigue load spectrum data may be introduced in the step of determining the installation reinforcement level of each roof sub-area.

[0120] The dominant pulsation frequency data refers to one or more pressure pulsation frequencies with the most concentrated energy, obtained through wind pressure spectrum analysis of a fluid dynamics simulation model. This indicator reveals potential risk sources that may trigger resonance in the photovoltaic panel structure. The dominant pulsation frequency data can be determined through the following steps:

[0121] (1) Acquire long-term transient pressure data;

[0122] In the embodiments of this application, the aforementioned fluid dynamics simulation model can be used to perform transient wind field simulation, rather than steady-state wind field simulation, thereby determining the simulated wind pressure data (i.e., long-term transient pressure data) of multiple simulation grid nodes corresponding to the roof over a long time series. It is understood that steady-state wind field simulation only provides a time-averaged, convergent flow field solution, while transient wind field simulation calculates the complete process of flow field evolution over time, outputting a series of continuous flow field snapshots at small time steps (e.g., 0.01 seconds), thus obtaining long-term transient pressure data.

[0123] (2) Perform wind pressure spectrum analysis;

[0124] A Fast Fourier Transform (FFT) was performed on the long-term transient pressure data of multiple simulated grid nodes corresponding to the roof to obtain the power spectral density (PSD) function of wind pressure for each simulated grid node. The PSD function is the core output of the spectral analysis, and its physical meaning is the energy distribution density of the wind pressure signal at different frequencies. The horizontal axis of the PSD plot represents frequency (in Hertz, Hz), and the vertical axis represents power density (in Pa). 2 / Hz). The frequency corresponding to the peak value in the PSD plot is the "dominant pulsation frequency data". The dominant pulsation frequency data represents the frequency of vortex shedding or airflow pulsation where energy is most concentrated in the wind field.

[0125] Fatigue load spectrum is a mathematical representation of the variation law of load amplitude when a structure or material is subjected to alternating load. Therefore, the rain flow counting method can be used to process the long-term transient pressure data of multiple simulation grid nodes corresponding to the roof and count the number of pressure cycles with different amplitudes.

[0126] Specifically, long-term transient pressure data of the simulated mesh nodes corresponding to the roof can be used as input. The rainflow counting method simulates the process of rainwater flowing on the "roof" of the pressure-time curve to identify cycles. Each time "rainwater" falls from the peak point and encounters a specific termination condition, it constitutes a complete or half load cycle. The rainflow counting method accurately records two key parameters for each identified cycle: pressure amplitude (half the difference between the maximum and minimum values ​​of the cycle) and mean pressure (half the sum of the maximum and minimum values ​​of the cycle). After processing, the rainflow counting method can output a two-dimensional "pressure amplitude-cycle count" spectrum (i.e., fatigue load spectrum data). This spectrum clearly shows how many cycles of large stress amplitude, medium stress amplitude, and small stress amplitude the node experienced within the simulated time period.

[0127] Accordingly, determining the installation reinforcement level for each roof sub-area may include: for each roof sub-area, determining the wind pressure characteristic values ​​of multiple installation locations within the roof sub-area; based on at least one of the dominant pulsation frequency data and fatigue load spectrum data of each installation location within the roof sub-area, and the wind pressure characteristic value, determining the Comprehensive Wind-Induced Risk Index (CWRI) for the corresponding installation location; and based on a comparison between the maximum value of the CWRI among the multiple installation locations within the roof sub-area and a preset wind pressure characteristic threshold, determining the installation reinforcement level for the roof sub-area.

[0128] In the step of determining the comprehensive wind-induced risk index for each installation location based on at least one of the dominant pulsating frequency data, fatigue load spectrum data, and wind pressure characteristic values ​​for each installation location within the roof sub-region, the fatigue load spectrum data can be further processed. For example, based on the stress-life curve of the photovoltaic panel mounting material and the Palmgren-Miner linear damage rule, a single, standardized "cumulative fatigue damage index" can be calculated. This cumulative fatigue damage index intuitively quantifies the severity of the fatigue load borne by the photovoltaic panel mounting material at the corresponding simulation grid node within the wind field simulation cycle.

[0129] The formula for calculating the comprehensive wind risk index for the corresponding installation location may include, for example:

[0130] CWRI=w1×P_norm+w2×F_norm+w3×D_norm

[0131] Wherein, CWRI is the comprehensive wind-induced risk index, P_norm is the normalized wind pressure characteristic value, F_norm is the frequency resonance risk factor, and D_norm is the normalized cumulative fatigue damage index. w1, w2, and w3 are preset weighting coefficients, and their sum is 1.

[0132] The value of the frequency resonance risk factor F_norm can be determined based on the proximity between the dominant pulsating frequency data and the natural frequency of the photovoltaic panel's mounting materials. For example, the closer the ratio between the dominant pulsating frequency data and the natural frequency of the photovoltaic panel's mounting materials is to 1, the larger the value of the frequency resonance risk factor, indicating a greater risk of wind resonance in the photovoltaic panel's mounting materials. It is understandable that if the dominant pulsating frequency data happens to be close to or equal to the natural frequency of the photovoltaic panel's mounting materials, it may trigger structural resonance in the mounting materials. Resonance drastically amplifies the vibration amplitude of the structure, generating dynamic stress far exceeding the static wind pressure calculation value, which is the primary cause of structural fatigue failure and loosening of connections. Therefore, when determining the installation reinforcement level, the dominant pulsating frequency data can also be considered to further improve the reliability and safety of the photovoltaic panel installation scheme.

[0133] As can be seen from the above embodiments of this application, the corresponding comprehensive wind-induced risk index is determined based on the dominant pulsation frequency data, fatigue load spectrum data, and wind pressure characteristic value. It can comprehensively consider the resonant dynamic risk, long-term fatigue risk, and static wind pressure risk. In this way, the installation reinforcement level and photovoltaic panel installation scheme determined based on the comprehensive wind-induced risk index will be more accurate and reliable.

[0134] Secondly, referring to Figure 4 Based on the photovoltaic panel installation scheme determination method of the above embodiments, the embodiments of this application provide a photovoltaic panel installation scheme determination device 400, which is used to execute the steps of any embodiment of the photovoltaic panel installation scheme determination method described above. For example, the photovoltaic panel installation scheme determination device 400 may include:

[0135] The acquisition module 401 is used to acquire the roof area of ​​the building where photovoltaic panels are to be installed and the preset photovoltaic panel layout for the roof area;

[0136] Module 402 is used to determine the wind pressure distribution data of the roof area; based on the wind pressure distribution data and the layout of the photovoltaic panels, the roof area is divided into multiple roof sub-areas; and the installation reinforcement level of each roof sub-area is determined.

[0137] Execution module 403 is used to generate a photovoltaic panel installation plan for the roof area based on the installation reinforcement level.

[0138] Thirdly, embodiments of this application provide an electronic device that integrates the apparatus for determining any of the photovoltaic panel installation schemes provided in the embodiments of this application. The electronic device includes a processor and a memory, the memory storing a computer program configured to be executed by the processor to implement the photovoltaic panel installation scheme determination method as described in any of the above embodiments.

[0139] Fourthly, embodiments of this application provide an electronic device that integrates a device for determining any photovoltaic panel installation scheme provided in embodiments of this application. For example... Figure 5 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically:

[0140] The electronic device may include components such as a processor 501 with one or more processing cores, a memory 502 with one or more computer-readable storage media, a power supply 503, and an input unit 504. Those skilled in the art will understand that... Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0141] The processor 501 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 502, and by calling data stored in the memory 502, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 501.

[0142] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.

[0143] The electronic device also includes a power supply 503 that supplies power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 503 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0144] The electronic device may also include an input unit 504, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0145] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in the embodiments of this application, the processor 501 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 502 according to the following instructions, and the processor 501 runs the application programs stored in the memory 502 to realize various functions, such as the functions in the method for determining the photovoltaic panel installation scheme as described in any of the above claims.

[0146] Fifthly, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. The computer-readable storage medium stores a computer program configured to be executed by a processor to implement the method for determining a photovoltaic panel installation scheme as described in any of the preceding claims.

[0147] Sixthly, embodiments of this application provide a computer program product, including a computer program or instructions, which are executed by a processor to implement the method for determining a photovoltaic panel installation scheme as described in any of the preceding claims.

[0148] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for determining a photovoltaic panel installation scheme, characterized in that, The method for determining the photovoltaic panel installation scheme includes: Obtain the roof area of ​​the building where photovoltaic panels are to be installed, and the preset photovoltaic panel layout for the roof area; Determine the wind pressure distribution data for the roof area; Based on the wind pressure distribution data and the photovoltaic panel layout, the roof area is divided into multiple roof sub-areas; Determine the installation reinforcement level for each of the aforementioned roof sub-areas; Based on the aforementioned installation reinforcement level, a photovoltaic panel installation plan for the roof area is generated; The process of determining the wind pressure distribution data of the roof area includes: generating a fluid dynamics simulation model of the area where the building is located, wherein the fluid dynamics simulation model includes a first simulation module of the building and a second simulation module of wind field obstacles around the building, and in the second simulation module, the model accuracy is lower the further away from the center point of the roof area from the first simulation module; using the fluid dynamics simulation model, wind field simulation is performed on the area where the building is located to determine the wind pressure distribution data of the roof area; The step of dividing the roof area into multiple roof sub-areas based on the wind pressure distribution data and the photovoltaic panel layout includes: within the roof area, determining the installation location of each photovoltaic panel according to the photovoltaic panel layout; determining the wind pressure characteristic value of each installation location according to the wind pressure distribution data; classifying the installation locations of multiple photovoltaic panels within the roof area according to the different wind pressure characteristic values ​​to obtain multiple categories of installation locations; and dividing the roof area into multiple roof sub-areas based on the multiple categories of installation locations. Determining the installation reinforcement level for each of the aforementioned roof sub-regions includes: The fluid dynamics simulation model is used to simulate transient wind fields to determine the simulated wind pressure data of multiple simulation grid nodes corresponding to the roof over a long time series, which are then used as long-term transient pressure data. Based on the long-term transient pressure data, the wind pressure power spectral density function diagram of each simulation grid node is determined; the frequency corresponding to the peak value in the wind pressure power spectral density function diagram is taken as the dominant pulsating frequency data of the corresponding simulation grid node; based on the ratio between the dominant pulsating frequency data and the natural frequency of the photovoltaic panel mounting and fixing material, the frequency resonance risk factor of the corresponding installation location is determined. The long-term transient pressure data is processed using the rainflow counting method to obtain a pressure amplitude-cycle count spectrum, which is used as fatigue load spectrum data. Based on the stress-life curve of the photovoltaic panel mounting material and the Pamgren-Mainner linear cumulative damage criterion, the fatigue load spectrum data is processed to obtain the cumulative fatigue damage index of the corresponding installation location. The cumulative fatigue damage index is used to quantify the severity of the fatigue load borne by the photovoltaic panel mounting material at the corresponding simulation grid node during the wind field simulation cycle. For each of the aforementioned roof sub-regions, based on the frequency resonance risk factor, the cumulative fatigue damage index, and the wind pressure characteristic value of each installation location in the roof sub-region, a comprehensive wind-induced risk index for the corresponding installation location is determined. The installation reinforcement level of the roof sub-region is determined by comparing the maximum value of the comprehensive wind-induced risk index of multiple installation locations in the roof sub-region with a preset wind pressure characteristic threshold.

2. The method for determining the photovoltaic panel installation scheme as described in claim 1, characterized in that, The roof area is divided into multiple roof sub-areas based on multiple categories of installation locations, including: Multiple installation locations for each category are mapped to pixels in a binary image to form an initial binary image region. Morphological operations are performed on the initial binary image region to obtain the roof sub-region, wherein the morphological operations include at least one of opening and closing operations.

3. The method for determining the photovoltaic panel installation scheme as described in claim 1, characterized in that, The photovoltaic panel installation scheme for the roof area includes multiple sub-installation schemes for the roof sub-areas. After generating the photovoltaic panel installation scheme for the roof area based on the installation reinforcement level, it further includes: Determine the total installation cost of the photovoltaic panel installation plan for the roof area; If the total installation cost is greater than the preset installation cost threshold, then among the multiple sub-installation schemes for the roof sub-area, the target sub-installation scheme with the highest installation cost is determined, and in the photovoltaic panel installation scheme, the installation plan of at least one photovoltaic panel in the roof sub-area corresponding to the target sub-installation scheme is deleted. Return to the step of determining the total installation cost of the photovoltaic panel installation plan for the roof area, until the total installation cost is less than or equal to a preset installation cost threshold.

4. The method for determining the photovoltaic panel installation scheme as described in claim 1, characterized in that, The process of using the fluid dynamics simulation model to simulate the wind field in the area where the building is located to determine the wind pressure distribution data of the roof area includes: Based on historical meteorological data of the area where the building is located, the boundary conditions of the fluid dynamics simulation model are determined; Using the boundary conditions, the fluid dynamics simulation model is solved to obtain the simulated wind pressure data of multiple simulation grid nodes in the fluid dynamics simulation model; Based on the simulated wind pressure data of multiple simulated grid nodes corresponding to the roof area, the wind pressure distribution data of the roof area is determined.

5. The method for determining the photovoltaic panel installation scheme as described in claim 4, characterized in that, The step of determining the wind pressure distribution data of the roof area based on the simulated wind pressure data of multiple simulated grid nodes corresponding to the roof area includes: For each simulation grid node corresponding to the roof area, the negative pressure data with the largest absolute value is determined from the simulation wind pressure data of the simulation grid node under multiple different wind direction angles; Based on the negative pressure data with the largest absolute value, the wind pressure distribution data of the roof area is determined.

6. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing a computer program configured to be executed by the processor to implement the method for determining the photovoltaic panel installation scheme according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program configured to be executed by a processor to implement the method for determining a photovoltaic panel installation scheme as described in any one of claims 1 to 5.

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