Photovoltaic panel positioning modeling method and system based on unmanned aerial vehicle image measurement

Abstract

The invention relates to the technical field of photovoltaic panel positioning modeling, and discloses a photovoltaic panel positioning modeling method and system based on unmanned aerial vehicle image determination, and the method comprises the steps: recognizing the region characteristics of a photovoltaic panel determination region, obtaining the adjustable parameters of an unmanned aerial vehicle, fitting a positioning path, driving the unmanned aerial vehicle to fly, and shooting an image in real time in the flight process, performing image preprocessing operation on the detection image set to obtain a clear detection image set; performing feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features; obtaining a target monitoring course shooting angle and a target monitoring lateral shooting angle; obtaining a target recognition image set; and obtaining a photovoltaic panel model, storing the photovoltaic panel model to obtain a photovoltaic panel reference model, and completing photovoltaic panel positioning modeling based on unmanned aerial vehicle image measurement based on the photovoltaic panel reference model. According to the invention, the efficiency and accuracy of photovoltaic panel positioning measurement can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic panel positioning modeling, and in particular to a photovoltaic panel positioning modeling method and system based on unmanned aerial vehicle image measurement. Background Art

[0002] Drone image measurement is the process of using drones equipped with image acquisition equipment to obtain relevant image data during flight in a specific area and analyze these image data. Photovoltaic panel positioning modeling is the process of determining the exact position of photovoltaic panels in three-dimensional space based on the data obtained from drone image measurement through some algorithms and technologies, and constructing a three-dimensional model of the photovoltaic panels and the scene in which they are located.

[0003] Traditional photovoltaic panel positioning and detection mainly rely on workers to carry measurement tools to measure the position and related parameters of photovoltaic panels one by one in the photovoltaic power station. This method is not only inefficient, but also consumes a lot of manpower and time. In addition, some photovoltaic power stations use fixed monitoring equipment with limited monitoring range, which cannot fully cover the entire photovoltaic power station. It is difficult to effectively monitor photovoltaic panels in some remote or obstructed areas. Therefore, how to improve the efficiency and accuracy of photovoltaic panel positioning measurement is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a photovoltaic panel positioning modeling method based on unmanned aerial vehicle image measurement and a computer-readable storage medium, the main purpose of which is to improve the efficiency and accuracy of photovoltaic panel positioning measurement.

[0005] To achieve the above purpose, the present invention provides a photovoltaic panel positioning modeling method based on drone image measurement, comprising:

[0006] Confirming a photovoltaic panel measurement area, identifying regional features of the photovoltaic panel measurement area, wherein the regional features include: height and slope, and the photovoltaic panel measurement area includes a plurality of photovoltaic panels;

[0007] Obtaining adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle, and lateral shooting angle;

[0008] Fitting a positioning path according to the photovoltaic panel measurement area, regional features and adjustable parameters of the drone, using the positioning path to drive the pre-built drone to fly, and during the flight, using preset shooting parameters to take images in real time to obtain a detection image set;

[0009] Performing image preprocessing operations on each detection image in the detection image set to obtain a clear detection image set, and performing feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features;

[0010] According to the characteristics of the photovoltaic panel, a target monitoring heading shooting angle and a target monitoring side shooting angle are obtained, and a target recognition image set is obtained based on the target monitoring heading shooting angle and the target monitoring side shooting angle;

[0011] Obtaining a photovoltaic panel model based on a target recognition image set;

[0012] The photovoltaic panel model is stored to obtain a photovoltaic panel benchmark model, and the photovoltaic panel positioning modeling based on drone image measurement is completed based on the photovoltaic panel benchmark model.

[0013] Optionally, fitting the positioning path according to the photovoltaic panel measurement area, regional characteristics and adjustable parameters of the drone includes:

[0014] Gridding the photovoltaic panel measurement area to obtain a grid measurement area set, wherein the grid measurement area set includes a plurality of grid measurement areas, and each grid measurement area has the same area;

[0015] Obtain the flight start and end points of the drone based on the grid measurement area set, and generate multiple initial positioning paths based on the pre-built route planning algorithm, the flight start and end points;

[0016] Extract initial positioning paths from multiple initial positioning paths in sequence, and perform the following operations on the extracted initial positioning paths:

[0017] Calculate the actual energy consumption of the drone based on the initial positioning path and regional characteristics, and compare the actual energy consumption with the preset energy consumption threshold;

[0018] If it is confirmed that the actual energy consumption is greater than the preset energy consumption threshold, the initial positioning path is eliminated, and the retained initial positioning paths are summarized to obtain multiple retained positioning paths, and the multiple retained positioning paths are used as multiple initial positioning paths, and the step of extracting the initial positioning paths from the multiple initial positioning paths is returned to the step of extracting the initial positioning paths from the multiple initial positioning paths in sequence until the actual energy consumption is less than or equal to the preset energy consumption threshold, and the optimized positioning path is obtained;

[0019] The optimized positioning paths are summarized to obtain an optimized positioning path set, and the optimized positioning path corresponding to the minimum actual energy consumption is extracted from the optimized positioning path set to obtain the positioning path.

[0020] Optionally, the actual energy consumption of the UAV is calculated according to the initial positioning path and regional characteristics, including:

[0021] Acquire the air density according to the flight altitude and regional characteristics, acquire the regional wind direction and regional wind speed of the photovoltaic panel measurement area, and acquire the flight direction angle of the drone according to the regional wind direction and regional wind speed;

[0022] Obtain the flight speed of the drone based on the flight direction angle, regional wind direction and regional wind speed, and calculate the air resistance based on the flight speed;

[0023] The energy consumption of overcoming resistance is calculated according to air resistance and air density, the energy consumption of overcoming gravity of the UAV is obtained, the path length of the initial positioning path is obtained, and the heading shooting angle change and the side shooting angle change are obtained according to the heading shooting angle and the side shooting angle;

[0024] The energy consumption of adjusting the shooting angle is calculated according to the path length, the change amount of the heading shooting angle and the change amount of the lateral shooting angle, wherein the energy consumption calculation formula of adjusting the shooting angle is as follows:

[0025] W z =k(|Δα|+|Δβ|)×s

[0026] Among them, W z represents the energy consumption of adjusting the shooting angle, k represents the preset attitude adjustment force coefficient, Δα represents the change of the heading shooting angle, Δβ represents the change of the side shooting angle, and s represents the path length;

[0027] The actual energy consumption of the drone is calculated based on the energy consumption for overcoming resistance, the energy consumption for overcoming gravity, the energy consumption for adjusting the shooting angle, and the preset power efficiency of the drone.

[0028] Optionally, the calculating the actual energy consumption of the drone according to the energy consumption for overcoming resistance, the energy consumption for overcoming gravity, the energy consumption for adjusting the shooting angle and a preset power efficiency of the drone includes:

[0029] The actual energy consumption of the drone is calculated based on the energy consumption of overcoming resistance, the energy consumption of overcoming gravity, the energy consumption of adjusting the shooting angle, the preset drone power efficiency and the pre-built total energy consumption formula, wherein the total energy consumption formula is as follows:

[0030]

[0031] Where E represents the actual energy consumption, W d Represents the energy consumption to overcome resistance, W g represents the energy consumption to overcome gravity, η represents the preset UAV power efficiency, ρ0 represents the air density, e represents the natural constant, H h Indicates the flight altitude, H0 indicates the preset reference altitude, H indicates the preset atmospheric altitude, v indicates the regional wind direction, and v w represents the regional wind speed, cosθ represents the flight direction angle, C d represents the preset air resistance coefficient, and A represents the preset projection area.

[0032] Optionally, performing an image preprocessing operation on each detection image in the detection image set to obtain a clear detection image set includes:

[0033] Extract detection images from the detection image set in sequence, and perform the following operations on the extracted detection images:

[0034] Cropping the detected image to obtain a cropped image, and acquiring flight attitude data according to the shooting parameters;

[0035] Obtain a distortion model and a projection model, and use the distortion model and camera parameters to perform distortion correction on the cropped image to obtain an initial corrected image;

[0036] Performing geometric correction on the initial correction image using the projection model and the flight attitude data to obtain a correction image;

[0037] A histogram equalization operation is performed on the corrected image to obtain a clear detection image, and the clear detection images are summarized to obtain a clear detection image set corresponding to the detection image set.

[0038] Optionally, acquiring a photovoltaic panel model based on a target recognition image set includes:

[0039] Generate a three-dimensional point cloud image using the target recognition image set, fit the photovoltaic panel geometric representation plane using the three-dimensional point cloud image, identify the photovoltaic panel geometric representation plane, and obtain the photovoltaic panel recognition plane;

[0040] Determine whether the photovoltaic panel identification plane meets the photovoltaic panel characteristics;

[0041] If the photovoltaic panel identification plane meets the photovoltaic panel characteristics, the photovoltaic panel identification plane is analyzed to obtain a photovoltaic panel model;

[0042] If the photovoltaic panel recognition plane does not meet the photovoltaic panel characteristics, return to the step of using the positioning path to drive the pre-built drone to fly until the photovoltaic panel recognition plane meets the photovoltaic panel characteristics, and obtain the photovoltaic panel model.

[0043] Optionally, fitting a photovoltaic panel geometric representation plane using a three-dimensional point cloud image includes:

[0044] Acquire three-dimensional point cloud data according to the three-dimensional point cloud image, filter the three-dimensional point cloud data to obtain filtered point cloud data, and acquire a filtered point cloud coordinate set according to the filtered point cloud data, wherein the three-dimensional point cloud data includes a plurality of three-dimensional point cloud points;

[0045] Determine a plane fitting algorithm, and obtain an initial plane equation according to the plane fitting algorithm, where the initial plane equation is expressed as:

[0046] z=ax+by+c

[0047] Among them, a, b, c are all initial parameters in the initial plane equation, x represents the x-axis filtered point cloud coordinates, y represents the y-axis filtered point cloud coordinates, and z represents the z-axis filtered point cloud coordinates;

[0048] The matrix equation is constructed according to the filtered point cloud coordinate set and the initial plane equation, where the matrix equation is expressed as:

[0049]

[0050] Among them, z1 represents the first z-axis filtered point cloud coordinates, z2 represents the second z-axis filtered point cloud coordinates, and z n represents the nth z-axis filtered point cloud coordinates, x1 represents the first x-axis filtered point cloud coordinates, x2 represents the second x-axis filtered point cloud coordinates, and x n represents the nth x-axis filtered point cloud coordinate, y1 represents the first y-axis filtered point cloud coordinate, y2 represents the second y-axis filtered point cloud coordinate, y n Represents the nth y-axis filtered point cloud coordinates;

[0051] Solve the matrix equation to obtain the plane parameters, determine the fitting plane according to the plane parameters, calculate the distance between each filtered point cloud coordinate in the filtered point cloud coordinate set and the fitting plane, and obtain the point cloud distance set;

[0052] Calculate the average error of the point cloud distance set and determine whether the average error is within the preset error range;

[0053] If it is confirmed that the average error is within the preset error range, the fitting plane is confirmed as the photovoltaic panel geometric characterization plane.

[0054] Optionally, filtering the three-dimensional point cloud data to obtain filtered point cloud data includes:

[0055] Extracting a plurality of sample point cloud data from the three-dimensional point cloud data, wherein the sample point cloud data includes a plurality of sample point cloud points;

[0056] A sample point cloud data is extracted from the multiple sample point cloud data, and the multiple retained sample point cloud data are aggregated to obtain multiple remaining point cloud data, and the following operations are performed on the extracted sample point cloud data:

[0057] Extract sample point cloud points from the sample point cloud data in sequence, and perform the following operations on the extracted sample point cloud points:

[0058] Obtain a neighborhood point set of the sample point cloud point, calculate the distance between the sample point cloud point and each neighborhood point in the neighborhood point set, and obtain a neighborhood distance set;

[0059] Determine whether there is a neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval;

[0060] If there is a neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval, the sample point cloud point corresponding to the neighborhood distance is confirmed as an abnormal point cloud point;

[0061] If there is no neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval, returning to the step of sequentially extracting sample point cloud points from the sample point cloud data until the sample point cloud data is an empty set;

[0062] Summarize the abnormal point cloud points to obtain the abnormal point cloud point set, and build the abnormal point cloud model according to the abnormal point cloud point set;

[0063] The abnormal point cloud model is used to filter multiple remaining point cloud data to obtain filtered point cloud data.

[0064] Optionally, calculating the average error of the point cloud distance set includes:

[0065] Get the median, maximum point cloud distance and minimum point cloud distance in the point cloud distance set;

[0066] The average error is calculated using the median, the maximum point cloud distance, the minimum point cloud distance, and the point cloud distance set. The average error calculation formula is as follows:

[0067]

[0068] in, represents the average error, n represents the number of point cloud distance sets, d i represents the i-th point cloud distance, i represents the index of the point cloud distance set, m represents the median, d max Indicates the maximum point cloud distance, d min represents the minimum point cloud distance, γ1 represents the weight coefficient of the arithmetic mean of the point cloud distance, γ2 represents the weight coefficient of the median, and γ3 represents the weight coefficient of the mean of the maximum point cloud distance and the minimum point cloud distance.

[0069] To achieve the above purpose, the present invention also provides a photovoltaic panel positioning modeling system based on drone image measurement, comprising:

[0070] The path fitting module is used to confirm the photovoltaic panel measurement area, identify the regional characteristics of the photovoltaic panel measurement area, wherein the regional characteristics include: height and slope, the photovoltaic panel measurement area includes multiple photovoltaic panels, and obtain the adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle and sideways shooting angle;

[0071] An image acquisition module is used to drive the pre-built UAV to fly using the positioning path, and during the flight, take images in real time using preset shooting parameters to obtain a detection image set;

[0072] An image analysis module is used to perform image preprocessing operations on each detection image in the detection image set to obtain a clear detection image set, perform feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features, obtain a target monitoring heading shooting angle and a target monitoring lateral shooting angle according to the photovoltaic panel features, and obtain a target recognition image set based on the target monitoring heading shooting angle and the target monitoring lateral shooting angle;

[0073] The modeling completion module is used to obtain a photovoltaic panel model based on a target recognition image set, store the photovoltaic panel model, obtain a photovoltaic panel benchmark model, and complete photovoltaic panel positioning modeling based on drone image measurement based on the photovoltaic panel benchmark model.

[0074] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0075] A memory storing at least one instruction;

[0076] The processor executes the instructions stored in the memory to implement the photovoltaic panel positioning modeling method based on drone image measurement as described above.

[0077] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned photovoltaic panel positioning modeling method based on drone image measurement.

[0078] The present invention is to solve the problem described in the background technology. The present invention confirms the photovoltaic panel measurement area and identifies the regional characteristics of the photovoltaic panel measurement area, wherein the regional characteristics include: height and slope. The photovoltaic panel measurement area includes multiple photovoltaic panels. The present invention clarifies the photovoltaic panel measurement area, which helps to limit the operation range of the drone within the target area, avoid unnecessary flight and data collection, improve work efficiency, reduce resource waste, and obtain adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle and lateral shooting angle. Different photovoltaic panel measurement areas and regional characteristics of the present invention may require different shooting angles and altitudes. By obtaining adjustable parameters such as flight altitude, heading shooting angle and lateral shooting angle, the drone can be adjusted according to actual conditions. The flight and shooting modes of the drone can be flexibly adjusted according to the situation to obtain the best image data, so as to meet the needs of photovoltaic panel information collection in different scenarios. The positioning path is fitted according to the photovoltaic panel measurement area, regional characteristics and adjustable parameters of the drone, and the pre-built drone is driven to fly using the positioning path. During the flight, images are taken in real time using preset shooting parameters to obtain a detection image set. The present invention combines the measurement area, regional characteristics and the positioning path fitted by the adjustable parameters of the drone to plan the optimal flight route, ensure that the drone covers the entire photovoltaic panel measurement area, and reduce the energy consumption required for the drone to locate the photovoltaic panel. The image preprocessing operation is performed on each detection image in the detection image set to obtain a clear detection image set. Each clear detection image in the clear detection image set is subjected to feature recognition to obtain photovoltaic panel features. The image preprocessing operation of the present invention can remove interference factors such as noise and distortion in the image, improve the clarity and quality of the image, make the features of the photovoltaic panel more obvious, and facilitate subsequent feature recognition work. The target monitoring heading shooting angle and the target monitoring lateral shooting angle are obtained according to the characteristics of the photovoltaic panel, and the target recognition image set is obtained based on the target monitoring heading shooting angle and the target monitoring lateral shooting angle. The present invention determines the target monitoring heading shooting angle and the target monitoring lateral shooting angle according to the characteristics of the photovoltaic panel, and can specifically obtain clearer and more accurate photovoltaic panel image data, highlight the key features of the photovoltaic panel, reduce the interference of irrelevant information, and improve subsequent The accuracy of model construction is achieved by obtaining a photovoltaic panel model based on a target recognition image set. The present invention utilizes the rich information in the target recognition image set to construct an intuitive and accurate three-dimensional model of a photovoltaic panel. The model can truly reflect the actual shape, position and layout of the photovoltaic panel, and provide a visual decision-making basis for the planning, design, operation and maintenance of photovoltaic power stations. The photovoltaic panel model is stored to obtain a photovoltaic panel benchmark model. Based on the photovoltaic panel benchmark model, photovoltaic panel positioning modeling based on drone image measurement is completed. The present invention stores the photovoltaic panel model as a photovoltaic panel benchmark model to facilitate subsequent query, comparison and analysis. At the same time, the benchmark model can be used as a reference standard and reused in the subsequent construction, transformation and operation and maintenance of photovoltaic power stations to improve work efficiency and accuracy.Therefore, the present invention can improve the efficiency and accuracy of photovoltaic panel positioning measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic diagram of a process flow of a photovoltaic panel positioning modeling method based on drone image measurement provided by an embodiment of the present invention;

[0080] Figure 2 A functional module diagram of a photovoltaic panel positioning modeling system based on drone image measurement provided by an embodiment of the present invention;

[0081] Figure 3 A schematic diagram of the structure of an electronic device for implementing the photovoltaic panel positioning modeling method based on drone image measurement provided by one embodiment of the present invention.

[0082] Description of reference numerals:

[0083] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0084] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0085] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0086] The embodiment of the present application provides a photovoltaic panel positioning modeling method based on drone image measurement. The execution subject of the photovoltaic panel positioning modeling method based on drone image measurement includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the photovoltaic panel positioning modeling method based on drone image measurement can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0087] Reference Figure 1 FIG. 1 is a flow chart of a photovoltaic panel positioning modeling method based on drone image measurement provided by an embodiment of the present invention. In this embodiment, the photovoltaic panel positioning modeling method based on drone image measurement includes:

[0088] S1. Confirm the photovoltaic panel measurement area and identify the regional features of the photovoltaic panel measurement area.

[0089] Specifically, the regional characteristics include: height and slope, and the photovoltaic panel measurement area includes multiple photovoltaic panels.

[0090] It should be explained that the PV panel measurement area refers to the geographic spatial area containing the PV panels to be detected, analyzed and modeled. For example, a large centralized PV power plant covers the area occupied by many PV panel arrays on a large piece of land or a small distributed PV power plant, such as the roof area where PV panels are installed on the roof of a residential building.

[0091] It should be explained that the identification of the regional characteristics of the photovoltaic panel measurement area refers to the use of a geographic information system to identify the photovoltaic panel measurement area to obtain regional characteristics. The use of a geographic information system to identify the photovoltaic panel measurement area in the embodiment of the present invention is a prior art and will not be repeated here.

[0092] S2. Obtaining adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle, and lateral shooting angle.

[0093] It should be explained that the acquisition of the adjustable parameters of the drone refers to the acquisition of the adjustable parameters of the drone from the technical manual of the drone or related equipment documents. The adjustable parameters of the drone are parameters composed of the flight altitude, the heading shooting angle and the sideways shooting angle. The flight altitude refers to the vertical distance of the drone relative to the ground. The heading shooting angle refers to the angle between the optical axis of the drone's camera in the flight direction and the vertical direction. The sideways shooting angle refers to the angle between the optical axis of the drone's camera in the direction perpendicular to the flight direction and the vertical direction.

[0094] S3. Fitting a positioning path according to the photovoltaic panel measurement area, regional characteristics and adjustable parameters of the drone.

[0095] In detail, the method of fitting the positioning path according to the photovoltaic panel measurement area, regional characteristics and adjustable parameters of the drone includes:

[0096] Gridding the photovoltaic panel measurement area to obtain a grid measurement area set, wherein the grid measurement area set includes a plurality of grid measurement areas, and each grid measurement area has the same area;

[0097] Obtain the flight start and end points of the drone based on the grid measurement area set, and generate multiple initial positioning paths based on the pre-built route planning algorithm, the flight start and end points;

[0098] Extract initial positioning paths from multiple initial positioning paths in sequence, and perform the following operations on the extracted initial positioning paths:

[0099] Calculate the actual energy consumption of the drone based on the initial positioning path and regional characteristics, and compare the actual energy consumption with the preset energy consumption threshold;

[0100] If it is confirmed that the actual energy consumption is greater than the preset energy consumption threshold, the initial positioning path is eliminated, and the retained initial positioning paths are summarized to obtain multiple retained positioning paths, and the multiple retained positioning paths are used as multiple initial positioning paths, and the step of extracting the initial positioning paths from the multiple initial positioning paths is returned to the step of extracting the initial positioning paths from the multiple initial positioning paths in sequence until the actual energy consumption is less than or equal to the preset energy consumption threshold, and the optimized positioning path is obtained;

[0101] The optimized positioning paths are summarized to obtain an optimized positioning path set, and the optimized positioning path corresponding to the minimum actual energy consumption is extracted from the optimized positioning path set to obtain the positioning path.

[0102] It should be explained that the gridded photovoltaic panel measurement area refers to the operation of gridding the photovoltaic panel measurement area using a division tool and a preset grid size. For example, the division tool can be ArcGIS, QGIS, GiobalMapper, etc. The grid size is a pre-set size. The grid measurement area set refers to the set of all grid measurement areas obtained after gridding the photovoltaic panel measurement area. The acquisition of the flight starting point and flight end point of the drone based on the grid measurement area set refers to selecting a vertex on the boundary of the photovoltaic panel measurement area close to the drone take-off point and entering the grid measurement area as the flight starting point, and selecting a vertex in the grid measurement area on the boundary of the photovoltaic panel measurement area that is relative to the flight starting point as the flight end point.

[0103] For example, the grid measurement area set is on a plane rectangular coordinate system. When the drone takes off from the south side of the grid measurement area, the southwest corner vertex of the grid located at the south boundary of the area with the coordinates (50,10) is selected as the flight starting point, and the northeast corner vertex of the grid located at the north boundary of the area with the coordinates (50,200) is selected as the flight end point.

[0104] Importantly, the route planning algorithm refers to an algorithm used to generate the flight path of the UAV. For example, genetic algorithm, ant colony algorithm, Dijkstra algorithm, etc. The initial positioning path is a plurality of feasible UAV flight paths generated according to the route planning algorithm, the flight starting point and the flight end point. The energy consumption threshold is a pre-set upper limit value to ensure that the UAV will not be unable to complete the task or return safely due to excessive energy consumption during the flight. The retained positioning path refers to the path retained after the energy consumption screening of the initial positioning path. The optimized positioning path refers to the retained positioning path whose actual energy consumption is greater than the preset energy consumption threshold after continuously comparing the actual energy consumption of the retained positioning path with the preset energy consumption threshold, until the actual energy consumption of all retained positioning paths is less than or equal to the energy consumption threshold. The optimized positioning path set refers to a set composed of optimized positioning paths. The positioning path refers to the path with the minimum actual energy consumption extracted from the optimized positioning path set. The purpose of the positioning path is to enable the UAV to capture all images used to characterize the photovoltaic panel measurement area during the flight, while reducing the energy consumption of the UAV during the photovoltaic panel positioning process.

[0105] In detail, the actual energy consumption of the drone is calculated based on the initial positioning path and regional characteristics, including:

[0106] Acquire the air density according to the flight altitude and regional characteristics, acquire the regional wind direction and regional wind speed of the photovoltaic panel measurement area, and acquire the flight direction angle of the drone according to the regional wind direction and regional wind speed;

[0107] Obtain the flight speed of the drone based on the flight direction angle, regional wind direction and regional wind speed, and calculate the air resistance based on the flight speed;

[0108] The energy consumption of overcoming resistance is calculated according to air resistance and air density, the energy consumption of overcoming gravity of the UAV is obtained, the path length of the initial positioning path is obtained, and the heading shooting angle change and the side shooting angle change are obtained according to the heading shooting angle and the side shooting angle;

[0109] The energy consumption of adjusting the shooting angle is calculated according to the path length, the change amount of the heading shooting angle and the change amount of the lateral shooting angle, wherein the energy consumption calculation formula of adjusting the shooting angle is as follows:

[0110] W z =k(|Δα|+|Δβ|)×s

[0111] Among them, W z represents the energy consumption of adjusting the shooting angle, k represents the preset attitude adjustment force coefficient, Δα represents the change of the heading shooting angle, Δβ represents the change of the side shooting angle, and s represents the path length;

[0112] The actual energy consumption of the drone is calculated based on the energy consumption for overcoming resistance, the energy consumption for overcoming gravity, the energy consumption for adjusting the shooting angle, and the preset power efficiency of the drone.

[0113] It should be explained that the air density obtained according to the flight altitude and regional characteristics means that the air density will decrease as the flight altitude and regional characteristics increase. The regional wind direction and regional wind speed obtained in the photovoltaic panel measurement area refers to the regional wind direction and regional wind speed obtained by equipment such as meteorological stations, meteorological satellites, and ground wind speed and direction meters in the photovoltaic panel measurement area. The flight direction angle refers to the angle between the flight direction of the drone and the regional wind direction. The flight speed refers to the speed at which the drone flies. When the drone flies against the wind, the flight speed will decrease, and when the drone flies with the wind, the flight speed will increase. The formula for calculating the air resistance in the step of calculating the air resistance according to the flight speed is as follows:

[0114]

[0115] Among them, F d represents air resistance, v f Indicates the flight speed.

[0116] It is understandable that the energy consumption for overcoming resistance refers to the energy consumption calculated by air resistance and path length. The energy consumption for overcoming gravity refers to the energy consumption obtained by multiplying the mass of the drone, the flight altitude and the acceleration of gravity. The path length is the length of the initial positioning path. The change in heading shooting angle refers to the change in the heading shooting angle of the drone on the initial positioning path. The change in lateral shooting angle refers to the change in the lateral shooting angle of the drone on the initial positioning path. The energy consumption for adjusting the shooting angle is the energy consumption calculated by the attitude adjustment force coefficient, the heading shooting angle change, the lateral shooting angle change and the path length. The attitude adjustment force coefficient refers to a preset coefficient that reflects the proportional relationship between the energy required by the drone to adjust the heading shooting angle and the lateral shooting angle and the angle change and the path length.

[0117] In detail, the actual energy consumption of the drone is calculated based on the energy consumption of overcoming resistance, the energy consumption of overcoming gravity, the energy consumption of adjusting the shooting angle, and the preset power efficiency of the drone, including:

[0118] The actual energy consumption of the drone is calculated based on the energy consumption of overcoming resistance, the energy consumption of overcoming gravity, the energy consumption of adjusting the shooting angle, the preset drone power efficiency and the pre-built total energy consumption formula, wherein the total energy consumption formula is as follows:

[0119]

[0120] Where E represents the actual energy consumption, W d Represents the energy consumption to overcome resistance, W grepresents the energy consumption to overcome gravity, η represents the preset UAV power efficiency, ρ0 represents the air density, e represents the natural constant, H h Indicates the flight altitude, H0 indicates the preset reference altitude, H indicates the preset atmospheric altitude, v indicates the regional wind direction, and v w represents the regional wind speed, cosθ represents the flight direction angle, C d represents the preset air resistance coefficient, and A represents the preset projection area.

[0121] It should be explained that the actual energy consumption refers to the energy actually consumed by the drone to overcome air resistance, gravity and camera angle adjustment during flight. The reference altitude refers to a preset reference altitude value used to describe the relationship between air density and flight altitude. The air resistance coefficient refers to the coefficient that reflects the relationship between the size of the air resistance encountered by the drone when it moves in the air and factors such as the shape and surface smoothness of the drone. For example, the air resistance coefficient of a streamlined drone is small, which can reduce energy consumption and improve flight efficiency during flight. The projected area refers to the projected area of ​​the drone in the flight direction. The projected area described in the embodiment of the present invention is obtained from the design specifications of the drone. Atmospheric scale altitude is a physical quantity related to the change of air density with altitude. The characteristic length of air density exponentially decays with altitude. The power efficiency of a drone refers to a preset measure of the efficiency of the drone power system in converting input energy into energy required for effective flight.

[0122] S4. Use the positioning path to drive the pre-built UAV to fly, and during the flight, use the preset shooting parameters to shoot images in real time to obtain a detection image set.

[0123] It should be explained that a drone is an unmanned aircraft controlled by a self-contained program control device. Shooting parameters are pre-set parameters for obtaining detection images during the flight of the drone. For example, aperture, shutter speed and focal length. The detection image set refers to the set of images obtained by shooting using preset shooting parameters while the drone is flying along the positioning path.

[0124] S5. Perform image preprocessing operations on each detection image in the detection image set to obtain a clear detection image set.

[0125] In detail, performing an image preprocessing operation on each detection image in the detection image set to obtain a clear detection image set includes:

[0126] Extract detection images from the detection image set in sequence, and perform the following operations on the extracted detection images:

[0127] Cropping the detected image to obtain a cropped image, and acquiring flight attitude data according to the shooting parameters;

[0128] Obtain a distortion model and a projection model, and use the distortion model and camera parameters to perform distortion correction on the cropped image to obtain an initial corrected image;

[0129] Performing geometric correction on the initial correction image using the projection model and the flight attitude data to obtain a correction image;

[0130] A histogram equalization operation is performed on the corrected image to obtain a clear detection image, and the clear detection images are summarized to obtain a clear detection image set corresponding to the detection image set.

[0131] It should be explained that the cropping of the detection image refers to the operation of cropping the detection image according to a preset image size. Flight attitude data refers to the spatial attitude data of the drone during flight, including: the heading and roll of the drone. Heading refers to the flight direction of the drone. Roll refers to the rotational movement of the drone around the longitudinal axis of the fuselage. The longitudinal axis of the fuselage is a straight line running from the nose of the drone to the tail. The distortion model is a mathematical model used to describe the image distortion law produced by the camera lens of the drone. The distortion model described in the embodiment of the present invention can perform distortion correction on the cropped image and restore the distorted image to an image closer to the real scene. The projection model is a mathematical model that describes the projection relationship from the photovoltaic panel in three-dimensional space to the two-dimensional image plane. The projection model described in the embodiment of the present invention can be combined with the flight attitude data to perform geometric correction on the initial correction image after distortion correction, eliminate the image deformation caused by the shooting attitude and projection relationship, and restore the photovoltaic panel in the image to the correct geometric shape and position relationship.

[0132] Importantly, geometric correction refers to the operation of processing the initial corrected image using a projection model and flight attitude data to eliminate the geometric deformation of the initial corrected image caused by factors such as the flight attitude of the drone and the shooting angle. The corrected image refers to the image obtained after geometric correction of the initial corrected image. Histogram equalization is an image processing technology used to enhance image contrast. The histogram equalization operation performed on the corrected image in the embodiment of the present invention refers to the operation of adjusting the grayscale histogram of the corrected image, remapping the grayscale value distribution of the corrected image, so that the grayscale value of the corrected image is more evenly distributed in the entire grayscale range, enhancing the overall contrast of the corrected image, and making the details in the corrected image more clearly visible. Histogram equalization is a prior art and will not be described in detail here.

[0133] S6. Perform feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features.

[0134] It should be explained that the clear detection image set is a set consisting of all clear detection images. The photovoltaic panel features are obtained by extracting features from the clear detection image set using a CNN model to obtain photovoltaic panel features. The CNN model refers to a model obtained by historical training and is used to identify photovoltaic panel features. Photovoltaic panel features include: photovoltaic panel shape and photovoltaic panel texture, etc.

[0135] S7. Obtain a target monitoring heading shooting angle and a target monitoring lateral shooting angle according to the characteristics of the photovoltaic panel, and obtain a target recognition image set based on the target monitoring heading shooting angle and the target monitoring lateral shooting angle.

[0136] It should be explained that the target monitoring heading shooting angle refers to the shooting angle of the drone along the flight direction relative to a specific reference direction (such as the front, geographic north, etc.) when monitoring photovoltaic panels. The target monitoring lateral shooting angle refers to the lateral shooting angle of the drone relative to the flight direction during the flight when monitoring photovoltaic panels, that is, the shooting angle in the direction perpendicular to the target monitoring heading shooting angle. The target recognition image set refers to the set of images taken by the drone based on the target monitoring heading shooting angle and the target monitoring lateral shooting angle.

[0137] S8. Obtain a photovoltaic panel model based on the target recognition image set.

[0138] In detail, the method of acquiring a photovoltaic panel model based on a target recognition image set includes:

[0139] Generate a three-dimensional point cloud image using the target recognition image set, fit the photovoltaic panel geometric representation plane using the three-dimensional point cloud image, identify the photovoltaic panel geometric representation plane, and obtain the photovoltaic panel recognition plane;

[0140] Determine whether the photovoltaic panel identification plane meets the photovoltaic panel characteristics;

[0141] If the photovoltaic panel identification plane meets the photovoltaic panel characteristics, the photovoltaic panel identification plane is analyzed to obtain a photovoltaic panel model;

[0142] If the photovoltaic panel recognition plane does not meet the photovoltaic panel characteristics, return to the step of using the positioning path to drive the pre-built drone to fly until the photovoltaic panel recognition plane meets the photovoltaic panel characteristics, and obtain the photovoltaic panel model.

[0143] It should be explained that the step of generating a three-dimensional point cloud map using the target recognition image set is a prior art and will not be repeated here. The photovoltaic panel recognition plane refers to a plane obtained after further judgment and confirmation based on the identified geometric characterization plane of the photovoltaic panel. The step of parsing the photovoltaic panel recognition plane to obtain the photovoltaic panel model is as follows: extracting the geometric parameters of the photovoltaic panel recognition plane, including the position of the plane (coordinates in three-dimensional space), size (length, width), tilt angle, etc., combining the image information in the target recognition image set, extracting the texture and material information of the photovoltaic panel recognition plane, integrating the extracted geometric parameters, texture and material information, and constructing a photovoltaic panel model. The photovoltaic panel model refers to a three-dimensional virtual model that integrates multiple feature information such as the geometric shape, size, position, texture, material, etc. of the photovoltaic panel.

[0144] In detail, the method of fitting the photovoltaic panel geometric representation plane using the three-dimensional point cloud image includes:

[0145] Acquire three-dimensional point cloud data according to the three-dimensional point cloud image, filter the three-dimensional point cloud data to obtain filtered point cloud data, and acquire a filtered point cloud coordinate set according to the filtered point cloud data, wherein the three-dimensional point cloud data includes a plurality of three-dimensional point cloud points;

[0146] Determine a plane fitting algorithm, and obtain an initial plane equation according to the plane fitting algorithm, where the initial plane equation is expressed as:

[0147] z=ax+by+c

[0148] Among them, a, b, c are all initial parameters in the initial plane equation, x represents the x-axis filtered point cloud coordinates, y represents the y-axis filtered point cloud coordinates, and z represents the z-axis filtered point cloud coordinates;

[0149] The matrix equation is constructed according to the filtered point cloud coordinate set and the initial plane equation, where the matrix equation is expressed as:

[0150]

[0151] Among them, z1 represents the first z-axis filtered point cloud coordinates, z2 represents the second z-axis filtered point cloud coordinates, and z n represents the nth z-axis filtered point cloud coordinates, x1 represents the first x-axis filtered point cloud coordinates, x2 represents the second x-axis filtered point cloud coordinates, and x n represents the nth x-axis filtered point cloud coordinate, y1 represents the first y-axis filtered point cloud coordinate, y2 represents the second y-axis filtered point cloud coordinate, y n Represents the nth y-axis filtered point cloud coordinates;

[0152] Solve the matrix equation to obtain the plane parameters, determine the fitting plane according to the plane parameters, calculate the distance between each filtered point cloud coordinate in the filtered point cloud coordinate set and the fitting plane, and obtain the point cloud distance set;

[0153] Calculate the average error of the point cloud distance set and determine whether the average error is within the preset error range;

[0154] If it is confirmed that the average error is within the preset error range, the fitting plane is confirmed as the photovoltaic panel geometric characterization plane.

[0155] It should be explained that the three-dimensional point cloud image refers to a graphic representation of the digital representation of the photovoltaic panel in three-dimensional space. Three-dimensional point cloud data refers to data containing multiple three-dimensional point cloud points. Filtered point cloud data refers to data obtained after filtering the three-dimensional point cloud data. The purpose of filtering the three-dimensional point cloud data is to remove abnormal data such as noise points and outliers in the three-dimensional point cloud data, so as to make the data smoother and more accurate, thereby improving the accuracy and reliability of subsequent plane fitting. The acquisition of the filtered point cloud coordinate set based on the filtered point cloud data means that each point in the filtered point cloud data has a corresponding three-dimensional coordinate (x, y, z), and the x-coordinate, y-coordinate and z-coordinate of all points in the filtered point cloud data are extracted respectively, and arranged in sequence according to the order of the points to obtain the filtered point cloud coordinate set. The initial parameter refers to an unknown parameter. The filtered point cloud coordinate set refers to a set consisting of all filtered point cloud coordinates.

[0156] Importantly, the plane fitting algorithm is an algorithm for finding a plane in three-dimensional point cloud data so that the plane can best approximate the distribution of these three-dimensional point cloud data. The plane fitting algorithm described in the embodiment of the present invention refers to the least squares method. The initial plane equation is a mathematical equation used to represent the fitting plane. The solution of the matrix equation is a prior art and will not be repeated here. The plane parameters refer to the parameters obtained by solving the matrix equation. The fitting plane refers to the plane determined according to the plane parameters obtained by the solution. The point cloud distance set refers to a set consisting of the distance between each filtered point cloud coordinate in the filtered point cloud coordinate set and the fitting plane. The calculation formula in the step of calculating the distance between each filtered point cloud coordinate in the filtered point cloud coordinate set and the fitting plane is the point-to-plane distance formula. The error interval is a pre-set interval used to determine whether the fitting plane meets the requirements.

[0157] In detail, filtering the three-dimensional point cloud data to obtain filtered point cloud data includes:

[0158] Extracting a plurality of sample point cloud data from the three-dimensional point cloud data, wherein the sample point cloud data includes a plurality of sample point cloud points;

[0159] A sample point cloud data is extracted from the multiple sample point cloud data, and the multiple retained sample point cloud data are aggregated to obtain multiple remaining point cloud data, and the following operations are performed on the extracted sample point cloud data:

[0160] Extract sample point cloud points from the sample point cloud data in sequence, and perform the following operations on the extracted sample point cloud points:

[0161] Obtain a neighborhood point set of the sample point cloud point, calculate the distance between the sample point cloud point and each neighborhood point in the neighborhood point set, and obtain a neighborhood distance set;

[0162] Determine whether there is a neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval;

[0163] If there is a neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval, the sample point cloud point corresponding to the neighborhood distance is confirmed as an abnormal point cloud point;

[0164] If there is no neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval, returning to the step of sequentially extracting sample point cloud points from the sample point cloud data until the sample point cloud data is an empty set;

[0165] Summarize the abnormal point cloud points to obtain the abnormal point cloud point set, and build the abnormal point cloud model according to the abnormal point cloud point set;

[0166] The abnormal point cloud model is used to filter multiple remaining point cloud data to obtain filtered point cloud data.

[0167] It should be explained that the sample point cloud data refers to a part of the point cloud data extracted from the three-dimensional point cloud data. The remaining point cloud data refers to the remaining sample point cloud data after extracting a sample point cloud data from multiple sample point cloud data. The step of obtaining the neighborhood point set of the sample point cloud point is: setting a distance threshold, selecting a sample point cloud point from the sample point cloud data, calculating the distance from the sample point cloud point to all other sample point cloud points, and collecting the sample point cloud points whose distance is less than or equal to the distance threshold to obtain a neighborhood point set. The calculation formula in the step of calculating the distance between the sample point cloud point and each neighborhood point in the neighborhood point set to obtain the neighborhood distance set is as follows:

[0168]

[0169] Among them, o represents the neighborhood distance, (t0, p0, q0) represents the sample point cloud point, and (t1, p1, q1) represents the neighborhood point.

[0170] Importantly, the neighborhood distance set refers to a set consisting of the distance between a sample point cloud point and each of its neighborhood points in the neighborhood point set. An abnormal point cloud point refers to a sample point cloud point in the sample point cloud data, for which there is a neighborhood distance in the neighborhood distance set that is not within a preset standard distance interval. An abnormal point cloud point set refers to a set consisting of all abnormal point cloud points. Constructing an abnormal point cloud model based on an abnormal point cloud point set refers to using the abnormal point cloud point set as training data, and using a machine learning algorithm (such as a clustering algorithm, a classification algorithm, etc.) for training to obtain a model that can identify abnormal points. Using the abnormal point cloud model to filter multiple remaining point cloud data to obtain filtered point cloud data refers to identifying and removing abnormal points that match the characteristics of the abnormal point cloud model from the remaining point cloud data, thereby obtaining filtered point cloud data. The standard distance interval refers to a pre-set distance range.

[0171] In detail, the calculation of the average error of the point cloud distance set includes:

[0172] Get the median, maximum point cloud distance and minimum point cloud distance in the point cloud distance set;

[0173] The average error is calculated using the median, the maximum point cloud distance, the minimum point cloud distance, and the point cloud distance set. The average error calculation formula is as follows:

[0174]

[0175] in, represents the average error, n represents the number of point cloud distance sets, d i represents the i-th point cloud distance, i represents the index of the point cloud distance set, m represents the median, d max Indicates the maximum point cloud distance, d min represents the minimum point cloud distance, γ1 represents the weight coefficient of the arithmetic mean of the point cloud distance, γ2 represents the weight coefficient of the median, and γ3 represents the weight coefficient of the mean of the maximum point cloud distance and the minimum point cloud distance.

[0176] It should be explained that the average error refers to the average value of the point cloud distance set. The weight coefficient of the arithmetic mean of the point cloud distance is the coefficient that determines the weight of the arithmetic mean of the point cloud distance in the final average error calculation. When the weight coefficient of the arithmetic mean of the point cloud distance is large, it means that the arithmetic mean of the point cloud distance has a greater impact on the average error. The weight coefficient of the median determines the coefficient of the weight of the median of the point cloud distance in the final average error calculation. When the weight coefficient of the median is large, it means that the weight coefficient of the median has a greater impact on the average error. The weight coefficient of the maximum point cloud distance and the minimum point cloud distance mean determines the coefficient of the weight of the maximum point cloud distance and the minimum point cloud distance mean in the average error calculation. When the weight coefficient of the median is large, it means that the maximum point cloud distance and the minimum point cloud distance mean have a greater impact on the average error. In an embodiment of the present invention, γ1+γ2+γ3=1.

[0177] S9. Store the photovoltaic panel model, obtain the photovoltaic panel benchmark model, and complete the photovoltaic panel positioning modeling based on the drone image measurement based on the photovoltaic panel benchmark model.

[0178] It should be explained that the storage of the photovoltaic panel model refers to the operation of storing the photovoltaic panel model file in the hard disk of the local computer or in an external storage device. For example, the external storage device is a USB flash drive, a mobile hard disk, etc. The photovoltaic panel benchmark model refers to the model obtained after storage. The photovoltaic panel benchmark model can be used as a reference standard and reused in the subsequent construction, transformation and operation and maintenance of the photovoltaic power station to improve work efficiency and accuracy.

[0179] The present invention is to solve the problem described in the background technology. The present invention confirms the photovoltaic panel measurement area and identifies the regional characteristics of the photovoltaic panel measurement area, wherein the regional characteristics include: height and slope. The photovoltaic panel measurement area includes multiple photovoltaic panels. The present invention clarifies the photovoltaic panel measurement area, which helps to limit the operation range of the drone within the target area, avoid unnecessary flight and data collection, improve work efficiency, reduce resource waste, and obtain adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle and lateral shooting angle. Different photovoltaic panel measurement areas and regional characteristics of the present invention may require different shooting angles and altitudes. By obtaining adjustable parameters such as flight altitude, heading shooting angle and lateral shooting angle, the drone can be adjusted according to actual conditions. The flight and shooting modes of the drone can be flexibly adjusted according to the situation to obtain the best image data, so as to meet the needs of photovoltaic panel information collection in different scenarios. The positioning path is fitted according to the photovoltaic panel measurement area, regional characteristics and adjustable parameters of the drone, and the pre-built drone is driven to fly using the positioning path. During the flight, images are taken in real time using preset shooting parameters to obtain a detection image set. The present invention combines the measurement area, regional characteristics and the positioning path fitted by the adjustable parameters of the drone to plan the optimal flight route, ensure that the drone covers the entire photovoltaic panel measurement area, and reduce the energy consumption required for the drone to locate the photovoltaic panel. The image preprocessing operation is performed on each detection image in the detection image set to obtain a clear detection image set. Each clear detection image in the clear detection image set is subjected to feature recognition to obtain photovoltaic panel features. The image preprocessing operation of the present invention can remove interference factors such as noise and distortion in the image, improve the clarity and quality of the image, make the features of the photovoltaic panel more obvious, and facilitate subsequent feature recognition work. The target monitoring heading shooting angle and the target monitoring lateral shooting angle are obtained according to the characteristics of the photovoltaic panel, and the target recognition image set is obtained based on the target monitoring heading shooting angle and the target monitoring lateral shooting angle. The present invention determines the target monitoring heading shooting angle and the target monitoring lateral shooting angle according to the characteristics of the photovoltaic panel, and can specifically obtain clearer and more accurate photovoltaic panel image data, highlight the key features of the photovoltaic panel, reduce the interference of irrelevant information, and improve subsequent The accuracy of model construction is achieved by obtaining a photovoltaic panel model based on a target recognition image set. The present invention utilizes the rich information in the target recognition image set to construct an intuitive and accurate three-dimensional model of a photovoltaic panel. The model can truly reflect the actual shape, position and layout of the photovoltaic panel, and provide a visual decision-making basis for the planning, design, operation and maintenance of photovoltaic power stations. The photovoltaic panel model is stored to obtain a photovoltaic panel benchmark model. Based on the photovoltaic panel benchmark model, photovoltaic panel positioning modeling based on drone image measurement is completed. The present invention stores the photovoltaic panel model as a photovoltaic panel benchmark model to facilitate subsequent query, comparison and analysis. At the same time, the benchmark model can be used as a reference standard and reused in the subsequent construction, transformation and operation and maintenance of photovoltaic power stations to improve work efficiency and accuracy.Therefore, the present invention can improve the efficiency and accuracy of photovoltaic panel positioning measurement.

[0180] like Figure 2 , is a functional module diagram of a photovoltaic panel positioning modeling system based on drone image measurement provided by an embodiment of the present invention.

[0181] The photovoltaic panel positioning modeling system 100 based on drone image measurement of the present invention can be installed in an electronic device. According to the functions to be implemented, the photovoltaic panel positioning modeling system 100 based on drone image measurement can include a path fitting module 101, an image acquisition module 102, an image analysis module 103 and a modeling completion module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device;

[0182] The path fitting module 101 is used to confirm the photovoltaic panel measurement area, identify the regional characteristics of the photovoltaic panel measurement area, wherein the regional characteristics include: height and slope, the photovoltaic panel measurement area includes a plurality of photovoltaic panels, and obtain the adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle and lateral shooting angle;

[0183] The image acquisition module 102 is used to drive the pre-built UAV to fly using the positioning path, and during the flight, take images in real time using preset shooting parameters to obtain a detection image set;

[0184] The image analysis module 103 is used to perform image preprocessing operations on each detection image in the detection image set to obtain a clear detection image set, perform feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features, obtain a target monitoring heading shooting angle and a target monitoring lateral shooting angle according to the photovoltaic panel features, and obtain a target recognition image set based on the target monitoring heading shooting angle and the target monitoring lateral shooting angle;

[0185] The modeling completion module 104 is used to obtain a photovoltaic panel model based on a target recognition image set, store the photovoltaic panel model, obtain a photovoltaic panel benchmark model, and complete photovoltaic panel positioning modeling based on drone image measurement based on the photovoltaic panel benchmark model.

[0186] In detail, each module in the photovoltaic panel positioning modeling system 100 based on drone image measurement in the embodiment of the present invention is used in the same manner as described above. Figure 1 The photovoltaic panel positioning modeling method based on drone image measurement described in the present invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0187] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a photovoltaic panel positioning modeling method based on drone image measurement provided by an embodiment of the present invention.

[0188] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a photovoltaic panel positioning modeling method program based on drone image measurement.

[0189] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the photovoltaic panel positioning modeling method program based on drone image measurement, but also can be used to temporarily store data that has been output or is to be output.

[0190] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a photovoltaic panel positioning modeling method program based on drone image measurement, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0191] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0192] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0193] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0194] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0195] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0196] The photovoltaic panel positioning modeling method program based on drone image measurement stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0197] Confirming a photovoltaic panel measurement area, identifying regional features of the photovoltaic panel measurement area, wherein the regional features include: height and slope, and the photovoltaic panel measurement area includes a plurality of photovoltaic panels;

[0198] Obtaining adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle, and lateral shooting angle;

[0199] Fitting a positioning path according to the photovoltaic panel measurement area, regional features and adjustable parameters of the drone, using the positioning path to drive the pre-built drone to fly, and during the flight, using preset shooting parameters to take images in real time to obtain a detection image set;

[0200] Performing image preprocessing operations on each detection image in the detection image set to obtain a clear detection image set, and performing feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features;

[0201] According to the characteristics of the photovoltaic panel, a target monitoring heading shooting angle and a target monitoring side shooting angle are obtained, and a target recognition image set is obtained based on the target monitoring heading shooting angle and the target monitoring side shooting angle;

[0202] Obtaining a photovoltaic panel model based on a target recognition image set;

[0203] The photovoltaic panel model is stored to obtain a photovoltaic panel benchmark model, and the photovoltaic panel positioning modeling based on drone image measurement is completed based on the photovoltaic panel benchmark model.

[0204] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0205] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0206] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0207] Confirming a photovoltaic panel measurement area, identifying regional features of the photovoltaic panel measurement area, wherein the regional features include: height and slope, and the photovoltaic panel measurement area includes a plurality of photovoltaic panels;

[0208] Obtaining adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle, and lateral shooting angle;

[0209] Fitting a positioning path according to the photovoltaic panel measurement area, regional features and adjustable parameters of the drone, using the positioning path to drive the pre-built drone to fly, and during the flight, using preset shooting parameters to take images in real time to obtain a detection image set;

[0210] Performing image preprocessing operations on each detection image in the detection image set to obtain a clear detection image set, and performing feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features;

[0211] According to the characteristics of the photovoltaic panel, a target monitoring heading shooting angle and a target monitoring side shooting angle are obtained, and a target recognition image set is obtained based on the target monitoring heading shooting angle and the target monitoring side shooting angle;

[0212] Obtaining a photovoltaic panel model based on a target recognition image set;

[0213] The photovoltaic panel model is stored to obtain a photovoltaic panel benchmark model, and the photovoltaic panel positioning modeling based on drone image measurement is completed based on the photovoltaic panel benchmark model.

[0214] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0215] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0216] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0217] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A photovoltaic panel positioning modeling method based on drone image measurement, characterized in that: The method comprises: Confirming a photovoltaic panel measurement area, identifying regional features of the photovoltaic panel measurement area, wherein the regional features include: height and slope, and the photovoltaic panel measurement area includes a plurality of photovoltaic panels; Obtaining adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle, and lateral shooting angle; Fitting a positioning path according to the photovoltaic panel measurement area, regional features and adjustable parameters of the drone, using the positioning path to drive the pre-built drone to fly, and during the flight, using preset shooting parameters to take images in real time to obtain a detection image set; Performing image preprocessing operations on each detection image in the detection image set to obtain a clear detection image set, and performing feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features; According to the characteristics of the photovoltaic panel, a target monitoring heading shooting angle and a target monitoring side shooting angle are obtained, and a target recognition image set is obtained based on the target monitoring heading shooting angle and the target monitoring side shooting angle; Obtaining a photovoltaic panel model based on a target recognition image set; The photovoltaic panel model is stored to obtain a photovoltaic panel benchmark model, and the photovoltaic panel positioning modeling based on drone image measurement is completed based on the photovoltaic panel benchmark model.

2. The photovoltaic panel positioning modeling method based on drone image measurement as claimed in claim 1, characterized in that: The method of fitting a positioning path according to the photovoltaic panel measurement area, regional characteristics and adjustable parameters of the drone includes: Gridding the photovoltaic panel measurement area to obtain a grid measurement area set, wherein the grid measurement area set includes a plurality of grid measurement areas, and each grid measurement area has the same area; Obtain the flight start and end points of the drone based on the grid measurement area set, and generate multiple initial positioning paths based on the pre-built route planning algorithm, the flight start and end points; Extract initial positioning paths from multiple initial positioning paths in sequence, and perform the following operations on the extracted initial positioning paths: Calculate the actual energy consumption of the drone based on the initial positioning path and regional characteristics, and compare the actual energy consumption with the preset energy consumption threshold; If it is confirmed that the actual energy consumption is greater than the preset energy consumption threshold, the initial positioning path is eliminated, and the retained initial positioning paths are summarized to obtain multiple retained positioning paths, and the multiple retained positioning paths are used as multiple initial positioning paths, and the step of extracting the initial positioning paths from the multiple initial positioning paths is returned to the step of extracting the initial positioning paths from the multiple initial positioning paths in sequence until the actual energy consumption is less than or equal to the preset energy consumption threshold, and the optimized positioning path is obtained; The optimized positioning paths are summarized to obtain an optimized positioning path set, and the optimized positioning path corresponding to the minimum actual energy consumption is extracted from the optimized positioning path set to obtain the positioning path.

3. The photovoltaic panel positioning modeling method based on drone image measurement as claimed in claim 2, characterized in that: The actual energy consumption of the drone is calculated based on the initial positioning path and regional characteristics, including: Acquire the air density according to the flight altitude and regional characteristics, acquire the regional wind direction and regional wind speed of the photovoltaic panel measurement area, and acquire the flight direction angle of the drone according to the regional wind direction and regional wind speed; Obtain the flight speed of the drone based on the flight direction angle, regional wind direction and regional wind speed, and calculate the air resistance based on the flight speed; The energy consumption of overcoming resistance is calculated according to air resistance and air density, the energy consumption of overcoming gravity of the UAV is obtained, the path length of the initial positioning path is obtained, and the heading shooting angle change and the side shooting angle change are obtained according to the heading shooting angle and the side shooting angle; The energy consumption of adjusting the shooting angle is calculated according to the path length, the change amount of the heading shooting angle and the change amount of the lateral shooting angle, wherein the energy consumption calculation formula of adjusting the shooting angle is as follows: W z =k(|Δα|+|Δβ|)×s Among them, W z represents the energy consumption of adjusting the shooting angle, k represents the preset attitude adjustment force coefficient, Δα represents the change of the heading shooting angle, Δβ represents the change of the side shooting angle, and s represents the path length; The actual energy consumption of the drone is calculated based on the energy consumption for overcoming resistance, the energy consumption for overcoming gravity, the energy consumption for adjusting the shooting angle, and the preset power efficiency of the drone.

4. The photovoltaic panel positioning modeling method based on drone image measurement as claimed in claim 3 is characterized in that: The actual energy consumption of the drone is calculated according to the energy consumption of overcoming resistance, the energy consumption of overcoming gravity, the energy consumption of adjusting the shooting angle and the preset power efficiency of the drone, including: The actual energy consumption of the drone is calculated based on the energy consumption of overcoming resistance, the energy consumption of overcoming gravity, the energy consumption of adjusting the shooting angle, the preset drone power efficiency and the pre-built total energy consumption formula, wherein the total energy consumption formula is as follows: Where E represents the actual energy consumption, W d Represents the energy consumption to overcome resistance, W g represents the energy consumption to overcome gravity, η represents the preset UAV power efficiency, ρ0 represents the air density, e represents the natural constant, H h Indicates the flight altitude, H0 indicates the preset reference altitude, H indicates the preset atmospheric altitude, v indicates the regional wind direction, and v w represents the regional wind speed, cosθ represents the flight direction angle, C d represents the preset air resistance coefficient, and A represents the preset projection area.

5. The photovoltaic panel positioning modeling method based on drone image measurement as claimed in claim 4, characterized in that: The image preprocessing operation is performed on each detection image in the detection image set to obtain a clear detection image set, including: Extract detection images from the detection image set in sequence, and perform the following operations on the extracted detection images: Cropping the detected image to obtain a cropped image, and acquiring flight attitude data according to the shooting parameters; Obtain a distortion model and a projection model, and use the distortion model and camera parameters to perform distortion correction on the cropped image to obtain an initial corrected image; Performing geometric correction on the initial correction image using the projection model and the flight attitude data to obtain a correction image; A histogram equalization operation is performed on the corrected image to obtain a clear detection image, and the clear detection images are summarized to obtain a clear detection image set corresponding to the detection image set.

6. The photovoltaic panel positioning modeling method based on drone image measurement as claimed in claim 5, characterized in that: The method of acquiring a photovoltaic panel model based on a target recognition image set includes: Generate a three-dimensional point cloud image using the target recognition image set, fit the photovoltaic panel geometric representation plane using the three-dimensional point cloud image, identify the photovoltaic panel geometric representation plane, and obtain the photovoltaic panel recognition plane; Determine whether the photovoltaic panel identification plane meets the photovoltaic panel characteristics; If the photovoltaic panel identification plane meets the photovoltaic panel characteristics, the photovoltaic panel identification plane is analyzed to obtain a photovoltaic panel model; If the photovoltaic panel recognition plane does not meet the photovoltaic panel characteristics, return to the step of using the positioning path to drive the pre-built drone to fly until the photovoltaic panel recognition plane meets the photovoltaic panel characteristics, and obtain the photovoltaic panel model.

7. The photovoltaic panel positioning modeling method based on drone image measurement according to claim 6, characterized in that: The method of fitting the photovoltaic panel geometric representation plane using the three-dimensional point cloud image includes: Acquire three-dimensional point cloud data according to the three-dimensional point cloud image, filter the three-dimensional point cloud data to obtain filtered point cloud data, and acquire a filtered point cloud coordinate set according to the filtered point cloud data, wherein the three-dimensional point cloud data includes a plurality of three-dimensional point cloud points; Determine a plane fitting algorithm, and obtain an initial plane equation according to the plane fitting algorithm, where the initial plane equation is expressed as: z=ax+by+c Among them, a, b, c are all initial parameters in the initial plane equation, x represents the x-axis filtered point cloud coordinates, y represents the y-axis filtered point cloud coordinates, and z represents the z-axis filtered point cloud coordinates; The matrix equation is constructed according to the filtered point cloud coordinate set and the initial plane equation, where the matrix equation is expressed as: Among them, z1 represents the first z-axis filtered point cloud coordinates, z2 represents the second z-axis filtered point cloud coordinates, and z n represents the nth z-axis filtered point cloud coordinates, x1 represents the first x-axis filtered point cloud coordinates, x2 represents the second x-axis filtered point cloud coordinates, and x n represents the nth x-axis filtered point cloud coordinate, y1 represents the first y-axis filtered point cloud coordinate, y2 represents the second y-axis filtered point cloud coordinate, y n Represents the nth y-axis filtered point cloud coordinates; Solve the matrix equation to obtain the plane parameters, determine the fitting plane according to the plane parameters, calculate the distance between each filtered point cloud coordinate in the filtered point cloud coordinate set and the fitting plane, and obtain the point cloud distance set; Calculate the average error of the point cloud distance set and determine whether the average error is within the preset error range; If it is confirmed that the average error is within the preset error range, the fitting plane is confirmed as the photovoltaic panel geometric characterization plane.

8. The photovoltaic panel positioning modeling method based on drone image measurement as claimed in claim 7, characterized in that: The filtering of the three-dimensional point cloud data to obtain filtered point cloud data includes: Extracting a plurality of sample point cloud data from the three-dimensional point cloud data, wherein the sample point cloud data includes a plurality of sample point cloud points; A sample point cloud data is extracted from the multiple sample point cloud data, and the multiple retained sample point cloud data are aggregated to obtain multiple remaining point cloud data, and the following operations are performed on the extracted sample point cloud data: Extract sample point cloud points from the sample point cloud data in sequence, and perform the following operations on the extracted sample point cloud points: Obtain a neighborhood point set of the sample point cloud point, calculate the distance between the sample point cloud point and each neighborhood point in the neighborhood point set, and obtain a neighborhood distance set; Determine whether there is a neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval; If there is a neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval, the sample point cloud point corresponding to the neighborhood distance is confirmed as an abnormal point cloud point; If there is no neighborhood distance in the neighborhood distance set that is not within the preset standard distance interval, returning to the step of sequentially extracting sample point cloud points from the sample point cloud data until the sample point cloud data is an empty set; Summarize the abnormal point cloud points to obtain the abnormal point cloud point set, and build the abnormal point cloud model according to the abnormal point cloud point set; The abnormal point cloud model is used to filter multiple remaining point cloud data to obtain filtered point cloud data.

9. The photovoltaic panel positioning modeling method based on drone image measurement as claimed in claim 8, characterized in that: The calculating the average error of the point cloud distance set includes: Get the median, maximum point cloud distance and minimum point cloud distance in the point cloud distance set; The average error is calculated using the median, the maximum point cloud distance, the minimum point cloud distance, and the point cloud distance set. The average error calculation formula is as follows: in, represents the average error, n represents the number of point cloud distance sets, d i represents the i-th point cloud distance, i represents the index of the point cloud distance set, m represents the median, d max Indicates the maximum point cloud distance, d min represents the minimum point cloud distance, γ1 represents the weight coefficient of the arithmetic mean of the point cloud distance, γ2 represents the weight coefficient of the median, and γ3 represents the weight coefficient of the mean of the maximum point cloud distance and the minimum point cloud distance.

10. A photovoltaic panel positioning modeling system based on drone image measurement, characterized in that: The system comprises: The path fitting module is used to confirm the photovoltaic panel measurement area, identify the regional characteristics of the photovoltaic panel measurement area, wherein the regional characteristics include: height and slope, the photovoltaic panel measurement area includes multiple photovoltaic panels, and obtain the adjustable parameters of the drone, wherein the adjustable parameters of the drone include: flight altitude, heading shooting angle and sideways shooting angle; An image acquisition module is used to drive the pre-built UAV to fly using the positioning path, and during the flight, take images in real time using preset shooting parameters to obtain a detection image set; An image analysis module is used to perform image preprocessing operations on each detection image in the detection image set to obtain a clear detection image set, perform feature recognition on each clear detection image in the clear detection image set to obtain photovoltaic panel features, obtain a target monitoring heading shooting angle and a target monitoring lateral shooting angle according to the photovoltaic panel features, and obtain a target recognition image set based on the target monitoring heading shooting angle and the target monitoring lateral shooting angle; The modeling completion module is used to obtain a photovoltaic panel model based on a target recognition image set, store the photovoltaic panel model, obtain a photovoltaic panel benchmark model, and complete photovoltaic panel positioning modeling based on drone image measurement based on the photovoltaic panel benchmark model.

Images