Country roof photovoltaic array automatic arrangement design method based on point cloud data

Abstract

The invention belongs to the technical field of three-dimensional modeling, and discloses a rural roof photovoltaic array automatic arrangement design method based on point cloud data. Comprising the steps of obtaining point cloud data of a target rural roof, and performing denoising processing on the point cloud data to obtain denoised point cloud data; extracting roof plane points in the de-noised point cloud data, obtaining a roof plane point set, further obtaining a roof plane, and establishing a roof plane model according to the roof plane; obtaining inclination angles and areas of the roof plane models, and screening the roof plane models to obtain candidate roof plane models; obtaining illumination time, illumination intensity and temperature of a roof plane corresponding to the candidate roof plane model, and further obtaining a to-be-installed roof plane model; constructing a photovoltaic array-roof plane model, and installing a corresponding photovoltaic array on a corresponding roof plane according to the photovoltaic array-roof plane model; and the rationality of photovoltaic array installation and the power generation efficiency of the photovoltaic array are further improved.

Description

Technical Field

[0001] This invention relates to the field of 3D modeling technology, and more specifically, to a method for automatic layout design of rural rooftop photovoltaic arrays based on point cloud data. Background Technology

[0002] Patent application CN110135049A discloses an automatic layout design method for distributed photovoltaic power stations based on three-dimensional point clouds, including the following steps: Step (1): Point cloud model processing; Step (2): Roof search; Step (3): Roof ridge line search; Step (4): North and south roof segmentation; Step (5): Photovoltaic layout on the north and south roofs; Step (6): Rendering and displaying the photovoltaic layout. Using UAV technology, multiple perspective motion images of buildings can be easily obtained. Three-dimensional modeling technology is used to model the image data, resulting in three-dimensional point cloud data. This solves the following problems: Automated search and measurement of point clouds for roofs, industrial plants, etc.; Accurate calculation of roof size and area, avoiding errors caused by manual work; Parametric layout method, flexibly responding to various layout needs; Low product cost, easy commercialization and market promotion.

[0003] However, in the design process of photovoltaic (PV) arrays, traditional methods often rely on manual labor or simplified two-dimensional images to complete the layout design. This results in a lack of geometric information, an inaccurate reflection of the actual roof structure, and a rough or misjudged assessment of the installation area. Consequently, the PV array cannot be installed according to the designed installation method, leading to a waste of PV array resources and increased installation costs. Furthermore, failing to select a suitable roof plane for the PV array during the layout design process can cause unstable PV array output power due to frequent fluctuations in sunlight intensity on the roof plane. This can also lead to PV array fatigue and aging, reducing the PV array's lifespan. Frequent temperature fluctuations on the roof plane can also cause unstable PV array power generation efficiency, resulting in poor PV array power generation. Installing PV arrays on roof planes with large fluctuations in sunlight intensity or temperature will cause unstable PV array output power, reducing the PV array's lifespan and power generation efficiency.

[0004] In view of this, the present invention proposes an automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data, comprising: Step S1: Obtain point cloud data of the target rural rooftops and perform denoising processing on the point cloud data of the target rural rooftops to obtain denoised point cloud data; Step S2: Extract roof plan points from the denoised point cloud data to obtain a roof plan point set, obtain the roof plan based on the roof plan point set, and build a roof plan model based on the roof plan. Step S3: Obtain the tilt angle and area of ​​the roof plan model, and filter the roof plan models based on the tilt angle and area to obtain candidate roof plan models; Step S4: During the monitoring period, obtain the illumination time, illumination intensity and temperature of the roof plane corresponding to the candidate roof plane model, and obtain the roof plane model to be installed based on the illumination time, illumination intensity and temperature of the roof plane corresponding to the candidate roof plane model. Step S5: Construct a photovoltaic array model, arrange the photovoltaic array model on the roof plane model to be installed, obtain the photovoltaic array-roof plane model, and install the corresponding photovoltaic array on the corresponding roof plane according to the photovoltaic array-roof plane model.

[0006] Furthermore, the method for denoising the point cloud data of the target rural rooftops includes: To obtain the Euclidean distance between each point and all other points in the point cloud data of the target rural rooftops, a neighbor number threshold k is set. The Euclidean distances between each point and all other points are sorted in ascending order. The average of the first k Euclidean distances for each point is obtained, and this average is used as the noise coefficient for that point. The average noise coefficient of all points is then calculated. and standard deviation ; in, ; ; in, The index of points in the point cloud data of the target rural rooftops. The number of points in the point cloud data of the target rural rooftops. The first point cloud data of the target rural rooftops Noise figure at each point; Based on the average noise figure of all points and standard deviation Set noise figure threshold ; in, ; It is a multiple of the standard deviation, and ; When the noise coefficient of a point in the point cloud data of the target rural roof is greater than the noise coefficient threshold, the point is removed; when the noise coefficient of a point in the point cloud data of the target rural roof is less than or equal to the noise coefficient threshold, the point is not processed, thus obtaining denoised point cloud data.

[0007] Furthermore, the method for extracting roof plan points from the denoised point cloud data includes: Step A1: Randomly select three non-collinear points from the denoised point cloud data. The three non-collinear points form a plane and are denoted as the first plane. Obtain the distance from the remaining points in the denoised point cloud data to the first plane and set a distance threshold. The distance threshold can be set through experimental data analysis or experience. When the distance from the remaining points in the denoised point cloud data to the first plane is less than or equal to the distance threshold, the point is recorded as an interior point. Collect all interior points to form a candidate roof plane point set. Step A2: Repeat step A1 N times. The value of N can be set by experimental data analysis or experience to obtain N candidate roof plane point sets. Compare the number of interior points in all candidate roof plane point sets, select the candidate roof plane point set with the most interior points as the roof plane point set, and remove the interior points in the roof plane point set from the denoised point cloud data. Step A3: Set the remaining point threshold. The remaining point threshold can be set through experimental data analysis or experience. Repeat steps A1 and A2 until the remaining points in the denoised point cloud data are less than or equal to the remaining point threshold, and obtain the set of all roof plan points.

[0008] Furthermore, the method for obtaining the roof plane based on the set of roof plane points and establishing a roof plane model based on the roof plane includes: All interior points in the roof plane point set constitute the roof plane, thus obtaining all roof planes. A roof plane model is then created based on the roof planes using 3D modeling software.

[0009] Furthermore, the method for filtering roof plan models based on their tilt angle and area includes: Set a tilt angle threshold and an area threshold. When the tilt angle of the roof plan model is less than or equal to the tilt angle threshold and the area of ​​the roof plan model is greater than or equal to the area threshold, the roof plan model is selected as a candidate roof plan model. When the tilt angle of the roof plan model is greater than the tilt angle threshold or the area of ​​the roof plan model is less than the area threshold, the roof plan model is not selected as a candidate roof plan model.

[0010] Furthermore, the method for obtaining the illumination time, illumination intensity, and temperature of the roof plane corresponding to the candidate roof plane model includes: The light intensity and temperature of the roof plane corresponding to the candidate roof plane model are obtained by using a light intensity measuring device and a temperature measuring device during the monitoring period. A light intensity threshold is set, and the time when the light intensity of the roof plane corresponding to the candidate roof plane model is greater than or equal to the light intensity threshold is obtained during the monitoring period. The time when the light intensity of the roof plane corresponding to the candidate roof plane model is greater than or equal to the light intensity threshold is taken as the illumination time of the roof plane corresponding to the candidate roof plane model.

[0011] Furthermore, the method for obtaining the roof plan model to be installed based on the illumination time, illumination intensity, and temperature of the roof plan corresponding to the candidate roof plan model includes: The monitoring period is evenly divided into Q monitoring time points, and the light intensity and temperature of the roof plane corresponding to the candidate roof plane model at each monitoring time point are obtained. Construct a light intensity-temperature dual Y-axis line graph based on the light intensity and temperature of the roof plane corresponding to the candidate roof plane model at each monitoring time point; The light intensity fluctuation index and temperature fluctuation index at the monitoring time point were obtained from the light intensity-temperature dual Y-axis broken line graph; Set thresholds for light intensity fluctuation index and temperature fluctuation index. When the light intensity fluctuation index at a monitoring time point is greater than or equal to the light intensity fluctuation index threshold or the temperature fluctuation index at a monitoring time point is greater than or equal to the temperature fluctuation index threshold, the monitoring time point is recorded as an abnormal fluctuation time point. Obtain the number of abnormal fluctuation time points, and set a number threshold and a light exposure time threshold. Obtain the candidate roof plane models corresponding to roof planes with light exposure time greater than or equal to the light exposure time threshold and the number of abnormal fluctuation time points less than or equal to the number threshold, and record them as the roof plane models to be installed.

[0012] Furthermore, the method for constructing a light intensity-temperature dual Y-axis line graph includes: Establish a blank dual Y-axis coordinate system. Set the x-axis of the blank dual Y-axis coordinate system to time, the first Y-axis to light intensity, and the second Y-axis to temperature. Fill the blank dual Y-axis coordinate system with the light intensity and temperature of the roof plane corresponding to the candidate roof plane model at each monitoring time point. Obtain the light intensity mapping point and temperature mapping point at each monitoring time point. Draw straight lines in chronological order to connect the light intensity mapping points and the temperature mapping points in chronological order to obtain a light intensity-temperature dual Y-axis line graph.

[0013] Furthermore, the method for obtaining the light intensity fluctuation index and temperature fluctuation index at the monitoring time point based on the light intensity-temperature dual Y-axis line graph includes: Starting from the second monitoring time point, draw a line parallel to the vertical axis through the light intensity mapping point at the monitoring time point, and record it as the first light intensity parallel line. Draw a line parallel to the horizontal axis through the light intensity mapping point at the monitoring time point, and record it as the second light intensity parallel line. Draw a line parallel to the horizontal axis through the light intensity mapping point before the light intensity mapping point at the monitoring time point, and record it as the third light intensity parallel line. Draw a line parallel to the vertical axis through the light intensity mapping point before the light intensity mapping point at the monitoring time point, and record it as the fourth light intensity parallel line. Obtain the area of ​​the closed figure enclosed by the first, second, third, and fourth light intensity parallel lines. Use the area of ​​the closed figure enclosed by the first, second, third, and fourth light intensity parallel lines as the light intensity fluctuation index at the monitoring time point, until the last monitoring time point ends. Starting from the second monitoring time point, draw a line parallel to the vertical axis through the temperature mapping point at the monitoring time point, and record it as the first temperature parallel line. Draw a line parallel to the horizontal axis through the temperature mapping point at the monitoring time point, and record it as the second temperature parallel line. Draw a line parallel to the horizontal axis through the temperature mapping point preceding the temperature mapping point at the monitoring time point, and record it as the third temperature parallel line. Draw a line parallel to the vertical axis through the temperature mapping point preceding the temperature mapping point at the monitoring time point, and record it as the fourth temperature parallel line. Obtain the area of ​​the closed figure enclosed by the first, second, third, and fourth temperature parallel lines. Use the area of ​​the closed figure enclosed by the first, second, third, and fourth temperature parallel lines as the temperature fluctuation index at the monitoring time point, until the last monitoring time point ends.

[0014] Furthermore, the method for constructing the photovoltaic array model and arranging the photovoltaic array model on the roof plan model to be installed includes: A photovoltaic array model is constructed using 3D modeling software, and then the photovoltaic array model is evenly arranged on the roof plane model to be installed to obtain the photovoltaic array-roof plane model. Based on the photovoltaic array-roof plane model, the corresponding photovoltaic array is installed on the corresponding roof plane.

[0015] The technical effects and advantages of the automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data in this invention are as follows: 1. By using 3D modeling to automatically design the layout of photovoltaic arrays on rural rooftops, the geometric information is complete, accurately reflecting the real roof structure, and the installation area is accurately assessed. Suitable roof planes are selected for photovoltaic array installation, and the installation of photovoltaic arrays can be completed according to the designed installation plan, thereby saving photovoltaic array resources and reducing photovoltaic array installation costs. 2. Obtain the tilt angle and area of ​​the roof plan model. Based on the tilt angle and area of ​​the roof plan model, filter the roof plan models to obtain candidate roof plan models. This ensures that the area of ​​the selected roof plan is reasonable, that there is enough space to install the photovoltaic array, and that a suitable tilt angle is selected to ensure that there will be no problems such as difficult installation, unstable support, or inability to install the photovoltaic array during the installation process. In addition, the direction of gravity of the installed photovoltaic array is parallel to the roof, reducing the risk of the photovoltaic array slipping. 3. During the monitoring period, the illumination time, illumination intensity, and temperature of the roof plane corresponding to the candidate roof plane model are obtained. Based on the illumination time, illumination intensity, and temperature of the roof plane corresponding to the candidate roof plane model, the roof plane model to be installed is obtained. Roof planes with infrequent fluctuations in illumination intensity and temperature are selected. Installing photovoltaic arrays on roof planes with infrequent fluctuations in illumination intensity and temperature improves the stability of the output power of the photovoltaic array, increases the service life of the photovoltaic array, improves the stability of the power generation efficiency of the photovoltaic array, and also improves the power generation efficiency of the photovoltaic array. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to the present invention; Figure 2 This is a schematic diagram of the automatic layout design system for rural rooftop photovoltaic arrays based on point cloud data according to the present invention; Figure 3 This is a flowchart illustrating the noise reduction process for point cloud data of target rural rooftops according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 Please see Figure 1 and Figure 3As shown in this embodiment, the automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data includes: Step S1: Obtain point cloud data of the target rural rooftops and perform denoising processing on the point cloud data of the target rural rooftops to obtain denoised point cloud data; Step S2: Extract roof plan points from the denoised point cloud data to obtain a roof plan point set, obtain the roof plan based on the roof plan point set, and build a roof plan model based on the roof plan. Step S3: Obtain the tilt angle and area of ​​the roof plan model, and filter the roof plan models based on the tilt angle and area to obtain candidate roof plan models; Step S4: During the monitoring period, obtain the illumination time, illumination intensity and temperature of the roof plane corresponding to the candidate roof plane model, and obtain the roof plane model to be installed based on the illumination time, illumination intensity and temperature of the roof plane corresponding to the candidate roof plane model. Step S5: Construct a photovoltaic array model, arrange the photovoltaic array model on the roof plane model to be installed, obtain the photovoltaic array-roof plane model, and install the corresponding photovoltaic array on the corresponding roof plane according to the photovoltaic array-roof plane model.

[0019] The process of acquiring point cloud data of the rooftops of the target rural area includes: Point cloud data of the rooftops of the target rural area is acquired using point cloud data acquisition devices (such as LiDAR, UAV aerial surveying, etc.). When using LiDAR to acquire point cloud data of the rooftops of a target village, the LiDAR scans the rooftops of the target village to obtain the point cloud data. When using a drone to acquire point cloud data of the rooftops of a target village, the drone scans the rooftops of the target village from high altitude to obtain the point cloud data. The process of denoising the point cloud data of the target rural rooftops to obtain denoised point cloud data includes: To obtain the Euclidean distance between each point and all other points in the point cloud data of the target rural rooftops, a neighbor number threshold k is set. This threshold k can be determined through experimental data analysis or experience. The Euclidean distances between each point and all other points are sorted in ascending order, and the average of the first k Euclidean distances for each point is obtained. This average of the first k Euclidean distances is used as the noise coefficient for that point. The average noise coefficient of all points is then obtained. and standard deviation ; in, ; ; in, The index of points in the point cloud data of the target rural rooftops. The number of points in the point cloud data of the target rural rooftops. The first point cloud data of the target rural rooftops Noise figure at each point; Based on the average noise figure of all points and standard deviation Set noise figure threshold ; in, ; It is a multiple of the standard deviation, and The multiple of the standard deviation can be set through experimental data analysis or experience; When the noise coefficient of a point in the point cloud data of the target rural roof is greater than the noise coefficient threshold, the point is removed; when the noise coefficient of a point in the point cloud data of the target rural roof is less than or equal to the noise coefficient threshold, the point is not processed, thus obtaining denoised point cloud data. It should be explained that in the process of photovoltaic array layout design, traditional methods usually rely on manual labor or simplified two-dimensional images to complete the photovoltaic array layout design, resulting in missing geometric information, inaccurate reflection of the actual roof structure, rough or misjudged installation area assessment, and consequently, failure to complete the installation of the photovoltaic array according to the designed installation method, thus wasting photovoltaic array resources and increasing photovoltaic array installation costs. Therefore, this invention uses three-dimensional modeling to automatically design the layout of photovoltaic arrays on rural roofs, thereby ensuring complete geometric information, accurately reflecting the actual roof structure, accurately assessing the installation area, and selecting suitable roof planes for photovoltaic array installation. This allows the photovoltaic array to be installed according to the designed installation plan, thereby saving photovoltaic array resources and reducing photovoltaic array installation costs.

[0020] Extracting roof plan points from denoised point cloud data to obtain a roof plan point set, and then obtaining the roof plan from the roof plan point set, includes the following steps: Step A1: Randomly select three non-collinear points from the denoised point cloud data. The three non-collinear points form a plane and are denoted as the first plane. Obtain the distance from the remaining points in the denoised point cloud data to the first plane and set a distance threshold. The distance threshold can be set through experimental data analysis or experience. When the distance from the remaining points in the denoised point cloud data to the first plane is less than or equal to the distance threshold, the point is recorded as an interior point. Collect all interior points to form a candidate roof plane point set. Step A2: Repeat step A1 N times. The value of N can be set by experimental data analysis or experience to obtain N candidate roof plane point sets. Compare the number of interior points in all candidate roof plane point sets, select the candidate roof plane point set with the most interior points as the roof plane point set, and remove the interior points in the roof plane point set from the denoised point cloud data. Step A3: Set the remaining point threshold. The remaining point threshold can be set through experimental data analysis or experience. Repeat steps A1 and A2 until the remaining points in the denoised point cloud data are less than or equal to the remaining point threshold, and obtain the set of all roof plan points. Step A4: All interior points in the roof plane point set constitute the roof plane, thus obtaining all roof planes; The process of creating a roof plan model based on the roof plan includes: A roof plan model is created based on the roof plan using 3D modeling software.

[0021] When choosing a rooftop for installing a photovoltaic array, if the rooftop area is too small, there will not be enough space to install the array, making the rooftop unsuitable. If the rooftop angle is too large, problems such as difficulty in installing the array, unstable brackets, or inability to install it will occur. Furthermore, the direction of gravity of the installed array will not be parallel to the roof, posing a risk of slippage. Therefore, it is necessary to choose a suitable rooftop for installing the photovoltaic array. The process of obtaining the slope angle and area of ​​the roof plan model, and then filtering the roof plan models based on their slope angle and area to obtain candidate roof plan models includes: Obtain the normal vector of the roof plane and the ground normal vector corresponding to the roof plane model. Based on the normal vector of the roof plane and the ground normal vector, obtain the tilt angle of the roof plane model. Set the tilt angle threshold and the area threshold. The tilt angle threshold and the area threshold can be set through experimental data analysis or experience. When the tilt angle of the roof plan model is less than or equal to the tilt angle threshold and the area of ​​the roof plan model is greater than or equal to the area threshold, the roof plan model is selected as a candidate roof plan model. If the tilt angle of the roof plan model is greater than the tilt angle threshold or the area of ​​the roof plan model is less than the area threshold, the roof plan model is not a candidate roof plan model.

[0022] During the monitoring period, the process of obtaining the illumination time, illumination intensity, and temperature of the roof plane corresponding to the candidate roof plane model, and then obtaining the roof plane model to be installed based on the illumination time, illumination intensity, and temperature of the roof plane corresponding to the candidate roof plane model, includes: Install a light intensity measuring device (such as an illuminance recorder) and a temperature measuring device (such as a temperature sensor) on the roof plane corresponding to the candidate roof plane model. Use the light intensity measuring device to obtain the light intensity of the roof plane corresponding to the candidate roof plane model during the monitoring period, and use the temperature measuring device to obtain the temperature of the roof plane corresponding to the candidate roof plane model during the monitoring period. The illumination time of the roof plane corresponding to the candidate roof plane model is obtained based on the illumination intensity of the roof plane corresponding to the candidate roof plane model within the monitoring period, specifically including: Set a light intensity threshold. The light intensity threshold can be set through experimental data analysis or experience. During the monitoring period, obtain the time when the light intensity of the roof plane corresponding to the candidate roof plane model is greater than or equal to the light intensity threshold. The time when the light intensity of the roof plane corresponding to the candidate roof plane model is greater than or equal to the light intensity threshold is taken as the light time of the roof plane corresponding to the candidate roof plane model. Based on the light intensity and temperature of the roof plane corresponding to the candidate roof plane model, a dual Y-axis line graph of light intensity and temperature is constructed, specifically including: The monitoring period is evenly divided into Q monitoring time points. The value of Q can be set by experimental data analysis or experience. The light intensity and temperature of the roof plane corresponding to the candidate roof plane model at each monitoring time point are obtained. Establish a blank dual Y-axis coordinate system. Set the x-axis of the blank dual Y-axis coordinate system to time, set the first Y-axis of the blank dual Y-axis coordinate system to light intensity, and set the second Y-axis of the blank dual Y-axis coordinate system to temperature. Fill the light intensity and temperature of the roof plane corresponding to the candidate roof plane model at each monitoring time point into the blank dual Y-axis coordinate system. Obtain the light intensity mapping point and temperature mapping point at each monitoring time point. Draw straight lines in chronological order to connect the light intensity mapping points and the temperature mapping points in chronological order to obtain a light intensity-temperature dual Y-axis line graph. The light intensity fluctuation index and temperature fluctuation index at the monitoring time point were obtained from the light intensity-temperature dual Y-axis line graph, specifically including: Starting from the second monitoring time point, draw a line parallel to the vertical axis through the light intensity mapping point at the monitoring time point, and record it as the first light intensity parallel line. Draw a line parallel to the horizontal axis through the light intensity mapping point at the monitoring time point, and record it as the second light intensity parallel line. Draw a line parallel to the horizontal axis through the light intensity mapping point before the light intensity mapping point at the monitoring time point, and record it as the third light intensity parallel line. Draw a line parallel to the vertical axis through the light intensity mapping point before the light intensity mapping point at the monitoring time point, and record it as the fourth light intensity parallel line. Obtain the area of ​​the closed figure enclosed by the first, second, third, and fourth light intensity parallel lines. Use the area of ​​the closed figure enclosed by the first, second, third, and fourth light intensity parallel lines as the light intensity fluctuation index at the monitoring time point, until the last monitoring time point ends. Starting from the second monitoring time point, draw a line parallel to the vertical axis through the temperature mapping point at the monitoring time point, and record it as the first temperature parallel line. Draw a line parallel to the horizontal axis through the temperature mapping point at the monitoring time point, and record it as the second temperature parallel line. Draw a line parallel to the horizontal axis through the temperature mapping point before the temperature mapping point at the monitoring time point, and record it as the third temperature parallel line. Draw a line parallel to the vertical axis through the temperature mapping point before the temperature mapping point at the monitoring time point, and record it as the fourth temperature parallel line. Obtain the area of ​​the closed figure enclosed by the first, second, third, and fourth temperature parallel lines. Use the area of ​​the closed figure enclosed by the first, second, third, and fourth temperature parallel lines as the temperature fluctuation index at the monitoring time point, until the last monitoring time point ends. Set thresholds for light intensity fluctuation index and temperature fluctuation index. These thresholds can be set through experimental data analysis or experience. When the light intensity fluctuation index at a monitoring time point is greater than or equal to the light intensity fluctuation index threshold or the temperature fluctuation index at a monitoring time point is greater than or equal to the temperature fluctuation index threshold, that monitoring time point is recorded as an abnormal fluctuation time point. Obtain the number of abnormal fluctuation time points and set the number threshold and the illumination time threshold. The number threshold and the illumination time threshold can be set through experimental data analysis or experience. Obtain the candidate roof plane model corresponding to the roof plane with illumination time greater than or equal to the illumination time threshold and the number of abnormal fluctuation time points less than or equal to the number threshold. Record the candidate roof plane model corresponding to the roof plane with illumination time greater than or equal to the illumination time threshold and the number of abnormal fluctuation time points less than or equal to the number threshold as the roof plane model to be installed. It should be explained that traditional methods often fail to select a suitable rooftop plane for photovoltaic (PV) array installation during the layout design process. Frequent fluctuations in sunlight intensity on the rooftop plane can cause unstable PV array output power, leading to fatigue and aging, and reducing the PV array's lifespan. Frequent temperature fluctuations on the rooftop plane can also cause unstable PV array power generation efficiency, resulting in poor PV array power generation. Installing PV arrays on rooftop planes with large fluctuations in sunlight intensity or temperature will further degrade PV array output power, lifespan, and power generation efficiency. Therefore, this invention acquires the sunlight duration, sunlight intensity, and temperature of the rooftop plane corresponding to the candidate rooftop plane model during a monitoring period. Based on these parameters, a rooftop plane model for installation is obtained, selecting rooftop planes with infrequent fluctuations in sunlight intensity and temperature. Installing PV arrays on these rooftop planes improves the stability of PV array output power, extends PV array lifespan, and enhances both the stability and efficiency of PV array power generation.

[0023] The process of constructing a photovoltaic array model, arranging the photovoltaic array model on the roof plane model to be installed, and obtaining the photovoltaic array-roof plane model, and then installing the corresponding photovoltaic array on the corresponding roof plane based on the photovoltaic array-roof plane model includes: A photovoltaic array model is constructed using 3D modeling software, and then the photovoltaic array model is evenly arranged on the roof plane model to be installed to obtain the photovoltaic array-roof plane model. Based on the photovoltaic array-roof plane model, the corresponding photovoltaic array is installed on the corresponding roof plane.

[0024] In this embodiment, 3D modeling is used to automatically design the layout of photovoltaic arrays on rural rooftops. This ensures complete geometric information, accurately reflects the actual roof structure, accurately assesses the installation area, and selects suitable roof planes for photovoltaic array installation. The installation of the photovoltaic arrays can be completed according to the designed installation plan, thus saving photovoltaic array resources and reducing installation costs. The tilt angle and area of ​​the roof plane model are obtained, and the roof plane models are filtered based on these parameters to obtain candidate roof plane models. This ensures that the selected roof planes have reasonable areas, sufficient space for photovoltaic array installation, and suitable tilt angles, ensuring that there are no installation difficulties during the photovoltaic array installation process. The system addresses issues such as large size, unstable support, or inability to install the photovoltaic array. Furthermore, the installed photovoltaic array's gravity direction is parallel to the roof, reducing the risk of it slipping. During the monitoring period, the system obtains the illumination time, intensity, and temperature of the roof plane corresponding to the candidate roof plane model. Based on these parameters, a roof plane model for installation is obtained, selecting roof planes with infrequent fluctuations in illumination intensity and temperature. Installing the photovoltaic array on these roof planes improves the stability of the array's output power, extends its lifespan, and enhances both the stability and efficiency of its power generation.

[0025] Example 2 Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. An automatic layout design system for rural rooftop photovoltaic arrays based on point cloud data is provided, including: The data acquisition module is used to acquire point cloud data of the rooftops of the target rural area and to perform noise reduction processing on the point cloud data of the rooftops of the target rural area to obtain noise-reduced point cloud data. The model building module is used to extract roof plane points from the denoised point cloud data, obtain a roof plane point set, obtain the roof plane based on the roof plane point set, and build a roof plane model based on the roof plane. The model screening module is used to obtain the tilt angle and area of ​​the roof plan model, and to screen the roof plan model based on the tilt angle and area to obtain candidate roof plan models. During the monitoring period, the module obtains the illumination time, illumination intensity and temperature of the roof plan corresponding to the candidate roof plan model, and obtains the roof plan model to be installed based on the illumination time, illumination intensity and temperature of the roof plan corresponding to the candidate roof plan model. The photovoltaic array layout module is used to construct a photovoltaic array model, arrange the photovoltaic array model on the roof plane model to be installed, obtain the photovoltaic array-roof plane model, and install the corresponding photovoltaic array on the corresponding roof plane according to the photovoltaic array-roof plane model.

[0026] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0027] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0028] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

[0029] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data, characterized in that, The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data includes: Step S1: Obtain point cloud data of the target rural rooftops and perform denoising processing on the point cloud data of the target rural rooftops to obtain denoised point cloud data; Step S2: Extract roof plan points from the denoised point cloud data to obtain a roof plan point set, obtain the roof plan based on the roof plan point set, and build a roof plan model based on the roof plan. Step S3: Obtain the tilt angle and area of ​​the roof plan model, and filter the roof plan models based on the tilt angle and area to obtain candidate roof plan models; Step S4: During the monitoring period, obtain the illumination time, illumination intensity and temperature of the roof plane corresponding to the candidate roof plane model, and obtain the roof plane model to be installed based on the illumination time, illumination intensity and temperature of the roof plane corresponding to the candidate roof plane model. Step S5: Construct a photovoltaic array model, arrange the photovoltaic array model on the roof plane model to be installed, obtain the photovoltaic array-roof plane model, and install the corresponding photovoltaic array on the corresponding roof plane according to the photovoltaic array-roof plane model.

2. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 1, characterized in that, The method for denoising the point cloud data of the target rural rooftops includes: Obtain the Euclidean distance between each point and the other points in the point cloud data of the target rural roof. Set the neighbor number threshold k, sort the Euclidean distances between each point and the other points in ascending order, obtain the average of the first k Euclidean distances for each point, use the average of the first k Euclidean distances as the noise coefficient of the corresponding point, and obtain the average and standard deviation of the noise coefficients of all points. A noise coefficient threshold is set based on the average and standard deviation of the noise coefficients of all points. When the noise coefficient of a point in the point cloud data of the target rural roof is greater than the noise coefficient threshold, the point is removed to obtain denoised point cloud data.

3. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 2, characterized in that, The method for extracting roof plan points from denoised point cloud data includes: Step A1: Randomly select three non-collinear points from the denoised point cloud data to form a plane, and denote it as the first plane. Obtain the distance from the remaining points in the denoised point cloud data to the first plane, and set a distance threshold. When the distance from the remaining points in the denoised point cloud data to the first plane is less than or equal to the distance threshold, the point is recorded as an interior point. Collect all interior points to form a candidate roof plane point set. Step A2: Repeat step A1 N times to obtain N candidate roof plan point sets. Select the candidate roof plan point set with the most interior points as the roof plan point set, and remove the interior points in the roof plan point set from the denoised point cloud data. Step A3: Set the remaining point threshold, and repeat steps A1 and A2 until the remaining points in the denoised point cloud data are less than or equal to the remaining point threshold, thus obtaining the set of all roof plan points.

4. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 3, characterized in that, The method for obtaining the roof plane based on the set of roof plane points and establishing a roof plane model based on the roof plane includes: All interior points in the roof plane point set constitute the roof plane, thus obtaining all roof planes. A roof plane model is then created based on the roof planes using 3D modeling software.

5. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 4, characterized in that, The method for selecting roof plan models based on their tilt angle and area includes: Set a tilt angle threshold and an area threshold. When the tilt angle of the roof plan model is less than or equal to the tilt angle threshold and the area of ​​the roof plan model is greater than or equal to the area threshold, the roof plan model is selected as a candidate roof plan model.

6. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 5, characterized in that, The method for obtaining the illumination time, illumination intensity, and temperature of the roof plane corresponding to the candidate roof plane model includes: The light intensity and temperature of the roof plane corresponding to the candidate roof plane model are obtained by using a light intensity measuring device and a temperature measuring device, respectively, during the monitoring period. The illumination time of the roof plane corresponding to the candidate roof plane model is obtained based on the light intensity of the roof plane corresponding to the candidate roof plane model.

7. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 6, characterized in that, The method for obtaining the roof plan model to be installed based on the illumination time, illumination intensity, and temperature of the roof plan corresponding to the candidate roof plan model includes: The monitoring period is evenly divided into Q monitoring time points, and the light intensity and temperature of the roof plane corresponding to the candidate roof plane model at each monitoring time point are obtained. Construct a light intensity-temperature dual Y-axis line graph based on the light intensity and temperature of the roof plane corresponding to the candidate roof plane model at each monitoring time point; The light intensity fluctuation index and temperature fluctuation index at the monitoring time point were obtained from the light intensity-temperature dual Y-axis broken line graph; Set thresholds for light intensity fluctuation index and temperature fluctuation index. When the light intensity fluctuation index at a monitoring time point is greater than or equal to the light intensity fluctuation index threshold or the temperature fluctuation index at a monitoring time point is greater than or equal to the temperature fluctuation index threshold, the monitoring time point is recorded as an abnormal fluctuation time point. Obtain the number of abnormal fluctuation time points, and set a number threshold and a light exposure time threshold. Obtain the candidate roof plane models corresponding to roof planes with light exposure time greater than or equal to the light exposure time threshold and the number of abnormal fluctuation time points less than or equal to the number threshold, and record them as the roof plane models to be installed.

8. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 7, characterized in that, The method for constructing a light intensity-temperature dual Y-axis line graph includes: Establish a blank dual Y-axis coordinate system. Set the x-axis of the blank dual Y-axis coordinate system to time, the first Y-axis to light intensity, and the second Y-axis to temperature. Fill the blank dual Y-axis coordinate system with the light intensity and temperature of the roof plane corresponding to the candidate roof plane model at each monitoring time point. Obtain the light intensity mapping point and temperature mapping point at each monitoring time point. Draw straight lines in chronological order to connect the light intensity mapping points and the temperature mapping points in chronological order to obtain a light intensity-temperature dual Y-axis line graph.

9. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 8, characterized in that, The method for obtaining the light intensity fluctuation index and temperature fluctuation index at the monitoring time point based on the light intensity-temperature dual Y-axis broken line graph includes: Starting from the second monitoring time point, draw parallel lines to the vertical and horizontal axes respectively through the light intensity mapping point at the monitoring time point, and record them as the first light intensity parallel line and the second light intensity parallel line. Draw parallel lines to the horizontal and vertical axes respectively through the previous light intensity mapping point at the monitoring time point, and record them as the third light intensity parallel line and the fourth light intensity parallel line. Obtain the area of ​​the closed figure enclosed by the first light intensity parallel line, the second light intensity parallel line, the third light intensity parallel line, and the fourth light intensity parallel line, and record it as the light intensity fluctuation index at the monitoring time point, until the last monitoring time point ends. The temperature fluctuation index at the monitoring time point was obtained using the same method as that used to obtain the light intensity fluctuation index.

10. The automatic layout design method for rural rooftop photovoltaic arrays based on point cloud data according to claim 9, characterized in that, The method for constructing a photovoltaic array model and arranging the photovoltaic array model on the roof plan model to be installed includes: A photovoltaic array model is constructed using 3D modeling software, and then the photovoltaic array model is evenly arranged on the roof plan model to be installed to obtain the photovoltaic array-roof plan model.

Images