Offshore wind turbine generator data acquisition method and system

Abstract

The invention relates to an offshore wind turbine generator data acquisition method and system, and the method comprises the steps: dividing a target offshore wind power plant into a plurality of inspection regions, and obtaining the structure parameters and topographic data of a wind turbine generator of each inspection region; according to the wind turbine generator structure parameters and topographic data, respectively constructing an unmanned aerial vehicle flight path of each inspection area, and presetting a surrounding radius and a vehicle position angle; based on the flight path, the surrounding radius and the camera position angle, controlling the unmanned aerial vehicle to carry out surrounding flight on the wind turbine generator in each inspection area, and collecting a wind turbine generator image and point cloud data in the surrounding flight process; and constructing a wind turbine generator three-dimensional model based on the wind turbine generator image and the point cloud data, constructing a three-dimensional map in combination with the topographic data, and fusing a flight path into the three-dimensional map to complete data acquisition of the target offshore wind plant. According to the invention, autonomous intelligent inspection of the offshore wind turbine generator can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power, and in particular to a method and system for collecting data of an offshore wind turbine generator set. Background Art

[0002] The wide distribution of offshore wind turbines and the large number of units bring many challenges to drone inspection operations. On the one hand, the endurance and range of a single drone are limited, making it difficult to complete large-scale inspection tasks in a short period of time; on the other hand, harsh offshore environmental conditions such as strong winds and typhoons will directly affect the flight posture and safety of drones. In addition, offshore wind turbines are different from land units. Their unique layout and environment require drones to have more intelligent obstacle avoidance and route planning capabilities.

[0003] When multiple drones are working together for inspection, how to achieve efficient task allocation and dynamic scheduling is also a major technical challenge. Wind turbine failures are somewhat random and sudden, and drones need to dynamically adjust inspection tasks based on real-time unit status, respective locations and other factors. However, during the scheduling process, problems such as repeated inspections and missed units by drones may occur, affecting overall operational efficiency. At the same time, the availability of drones is reduced in severe weather. How to maximize the use of limited drone resources and reasonably allocate inspection tasks is also an optimization problem that needs to be solved urgently. Therefore, the present invention proposes a method and system for collecting data from offshore wind turbines. Summary of the invention

[0004] The purpose of the present invention is to solve the above technical problems and provide a method and system for collecting data of offshore wind turbines, so as to realize autonomous intelligent inspection of wind turbines, greatly improve inspection efficiency and accuracy, and reduce the safety risks and costs of manual inspection.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for collecting data of an offshore wind turbine generator system, comprising:

[0007] Divide the target offshore wind farm into several inspection areas, and obtain the structural parameters and terrain data of the wind turbines in each inspection area;

[0008] Construct the UAV flight trajectory of each inspection area according to the structural parameters of the wind turbine and the terrain data, and preset the circling radius and the camera angle;

[0009] Based on the flight trajectory, circling radius and aircraft position angle, the drone is controlled to circumvent the wind turbines in each inspection area, and images and point cloud data of the wind turbines are collected during the circling flight;

[0010] A three-dimensional model of the wind turbine is constructed based on the wind turbine image and point cloud data, and a three-dimensional map is constructed in combination with the terrain data. The flight trajectory is integrated into the three-dimensional map to complete data collection of the target offshore wind farm.

[0011] Optionally, dividing the target offshore wind farm into a number of inspection areas includes:

[0012] Acquire geographic information data and a wind turbine distribution map of the target offshore wind farm, and construct a topological structure map, wherein the topological structure map uses wind turbines as nodes and distances between wind turbines as edges;

[0013] Obtain the maximum range and load capacity of the drone, and calculate the maximum single inspection range and the maximum number of wind turbines in a single inspection mission;

[0014] A graph theory algorithm is used, and the maximum single inspection range of the UAV and the maximum number of wind turbines at a single time are taken as constraints. The topological structure diagram is divided to obtain a number of connected subgraphs, that is, a number of inspection areas.

[0015] Optionally, constructing the UAV flight trajectory of each inspection area according to the wind turbine structure parameters and terrain data includes:

[0016] Constructing a three-dimensional scene model according to the structural parameters of the wind turbine and the terrain data;

[0017] Using an artificial potential field algorithm to perform path planning in the three-dimensional scene model to obtain a flight trajectory;

[0018] The flight trajectory is locally optimized based on collision risk detection, and the flight trajectory is globally optimized based on a particle swarm optimization algorithm to obtain a final flight trajectory.

[0019] Optionally, using an artificial potential field algorithm to perform path planning in the three-dimensional scene model to obtain a flight trajectory includes:

[0020] Acquire the positions of wind turbines and terrain obstacles based on the three-dimensional scene model;

[0021] Using the position of the wind turbine as the target point in the artificial potential field algorithm, and using the position of the terrain obstacle as the obstacle point in the artificial potential field algorithm;

[0022] The flight trajectory of the UAV is planned by dynamically adjusting the distance between the UAV and the target point and the obstacle point.

[0023] Optionally, the method for presetting the surround radius and the camera angle is:

[0024] Get the camera parameters and lidar parameters carried by the drone;

[0025] Simulating flight in the three-dimensional scene model based on the final flight trajectory, and calculating data collection efficiency and quality under different circling radii and camera angles according to the camera parameters and the laser radar parameters;

[0026] Based on the data collection efficiency and quality, combined with flight safety, the optimal circling radius and the optimal aircraft position angle are determined.

[0027] Optionally, controlling the drone to fly around the wind turbines in each inspection area based on the flight trajectory, the circling radius and the aircraft position angle includes:

[0028] Discretize the flight trajectory into a number of waypoints, each of which contains position, attitude, and speed information, wherein the position and attitude information are calculated by the orbiting radius and the aircraft position angle, and the speed information is a preset speed;

[0029] After controlling the drone to take off, the real-time position, real-time attitude, and real-time speed of the drone are obtained, and the real-time position, real-time attitude, and real-time speed are compared with the position, attitude, and speed information of the next waypoint to obtain the real-time heading adjustment amount and the real-time speed adjustment amount;

[0030] The heading and speed of the UAV are continuously corrected by the real-time heading adjustment amount and the real-time speed adjustment amount until the flight trajectory ends.

[0031] Optionally, constructing a three-dimensional model of a wind turbine generator set based on the wind turbine generator set image and point cloud data includes:

[0032] Preprocessing the wind turbine image and point cloud data respectively and then fusing them;

[0033] Based on the fused wind turbine image and point cloud data, the component features of the wind turbine and the geometric outline and position information of each component are extracted to construct a three-dimensional model of the wind turbine.

[0034] Optionally, preprocessing the wind turbine image and point cloud data respectively and then fusing them comprises:

[0035] splicing a plurality of wind turbine group images into a panoramic image of the wind turbine group, and then performing distortion correction processing on the panoramic image of the wind turbine group to obtain a processed panoramic image;

[0036] The point cloud data is processed to remove noise and smooth the point cloud to obtain the processed point cloud data;

[0037] The processed panoramic image is registered and mapped with the processed point cloud data to obtain a panoramic image containing the point cloud data.

[0038] To further achieve the above object, the present invention also provides an offshore wind turbine data acquisition system, comprising:

[0039] The inspection area division module is used to divide the target offshore wind farm into several inspection areas and obtain the structural parameters and terrain data of the wind turbines in each inspection area;

[0040] A flight information construction module is used to construct the UAV flight trajectory of each inspection area according to the structural parameters of the wind turbine set and the terrain data, and to preset the circling radius and the aircraft position angle;

[0041] The inspection data acquisition module is used to control the UAV to fly around the wind turbines in each inspection area based on the flight trajectory, the circling radius and the aircraft position angle, and to collect images and point cloud data of the wind turbines during the circling flight;

[0042] The inspection data processing module is used to construct a three-dimensional model of the wind turbine based on the wind turbine image and point cloud data, and to construct a three-dimensional map in combination with the terrain data, and to integrate the flight trajectory into the three-dimensional map to complete the data collection of the target offshore wind farm.

[0043] The beneficial effects of the present invention are:

[0044] The present invention first obtains the geographic information and wind turbine distribution map of the target offshore wind farm, and divides the wind farm into several inspection areas according to the maximum range and load capacity of the drone, using a graph theory algorithm. The number and spacing of the units in each area meet the operating capacity of a single drone. For each inspection area, the present invention obtains the structural parameters and surrounding terrain data of the wind turbine, plans the inspection path through the improved artificial potential field method, and generates an optimal flight trajectory that covers all wind turbines and avoids terrain obstacles. During the circling flight, the drone continuously shoots multi-angle, high-resolution unit images through the onboard camera, and the laser radar scans the wind turbine in real time to obtain dense and uniform point cloud data, transmits the acquired unit images and point cloud data to the ground station, builds a realistic three-dimensional map in the virtual scene, and integrates the drone flight trajectory, obstacle avoidance strategy and other information into the map. The present invention can realize autonomous intelligent inspection of wind turbines, greatly improve inspection efficiency and accuracy, and reduce the safety risks and costs of manual inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 The present invention is a flow chart of a method for collecting data of an offshore wind turbine generator system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Although the application of drone inspection in offshore wind turbines has achieved remarkable results, it still faces some technical and environmental challenges: (1) Battery life: Offshore wind turbines are usually located in remote sea areas, and the endurance of drones is an important limiting factor. Developing drones with long flight time and improving battery technology is a future direction. (2) Climate impact: Offshore wind turbines are usually located in extreme climate conditions. How to ensure the stability of drones under weather conditions such as strong winds and heavy rains is a technical challenge. (3) Data storage and processing: The storage and real-time processing of massive data requires drones to have powerful computing and transmission capabilities. The use of drone inspections to collect data from offshore wind turbines is gradually replacing traditional manual inspection methods. Through advanced technologies such as high-definition visual imaging, thermal imaging, and lidar, drones can efficiently and accurately collect various types of data from wind turbines, help detect the health status of wind turbines, reduce failures and downtime, and improve the operational efficiency and safety of wind farms. With the continuous advancement of technology, the application prospects of drone inspections will be broader.

[0050] Therefore, this embodiment provides a method for collecting data of an offshore wind turbine generator set, comprising:

[0051] Divide the target offshore wind farm into several inspection areas, and obtain the structural parameters and terrain data of the wind turbines in each inspection area;

[0052] The flight trajectory of the drone in each inspection area is constructed according to the structural parameters of the wind turbine and the terrain data, and the circling radius and the camera angle are preset;

[0053] Based on the flight trajectory, circling radius and camera angle, the drone is controlled to circle the wind turbines in each inspection area, and images and point cloud data of the wind turbines are collected during the circling process.

[0054] A three-dimensional model of the wind turbine is constructed based on the wind turbine image and point cloud data, and a three-dimensional map is constructed in combination with the terrain data. The flight trajectory is integrated into the three-dimensional map to complete the data collection of the target offshore wind farm.

[0055] Specifically, this embodiment first obtains the geographic information and wind turbine distribution map of the target offshore wind farm, and divides the wind farm into several inspection areas according to the maximum range and load capacity of the drone, using a graph theory algorithm. The number and spacing of the units in each area meet the operating capacity of a single drone. For each inspection area, this embodiment obtains the structural parameters and surrounding terrain data of the wind turbine, plans the inspection path through the improved artificial potential field method, and generates an optimal flight trajectory that covers all wind turbines and avoids terrain obstacles. During the circling flight, the drone continuously captures multi-angle, high-resolution unit images through the onboard camera, and the laser radar scans the wind turbine in real time to obtain dense and uniform point cloud data, and transmits the acquired unit images and point cloud data to the ground station, constructs a realistic three-dimensional map in the virtual scene, and integrates the drone flight trajectory, obstacle avoidance strategy and other information into the map. This embodiment can realize autonomous intelligent inspection of wind turbines, greatly improve inspection efficiency and accuracy, and reduce the safety risks and costs of manual inspections.

[0056] Combine the following Figure 1 A method for collecting data of an offshore wind turbine set proposed in this embodiment is described in detail, and specifically includes the following steps:

[0057] Step 1: Divide the target offshore wind farm into several inspection areas, and obtain the structural parameters and terrain data of the wind turbines in each inspection area.

[0058] Furthermore, the target offshore wind farm is divided into several inspection areas including:

[0059] Obtain geographic information data and a wind turbine distribution map of the target offshore wind farm, and construct a topological structure map, wherein the topological structure map uses wind turbines as nodes and the distances between wind turbines as edges;

[0060] Obtain the maximum range and load capacity of the drone, and calculate the maximum single inspection range and the maximum number of wind turbines in a single inspection mission;

[0061] Using graph theory algorithm, the maximum single inspection range of the UAV and the maximum number of wind turbines at a time are taken as constraints, the topological structure graph is divided, and several connected subgraphs, i.e., several inspection areas, are obtained.

[0062] At the same time, if the number of inspection areas divided exceeds the total number of drones, the division results need to be adjusted through the optimization algorithm to balance the inspection task volume of each area and the workload of the drones.

[0063] Specifically, in this embodiment, the geographic information data of the wind farm is obtained, including the latitude and longitude coordinates, altitude, topography, etc. of each wind turbine, as well as the distribution map data of the wind turbine. These data can be obtained through a wind farm management system, a GIS platform or a field survey. For example, a wind farm has 100 wind turbines, and the precise location information and surrounding terrain data of each unit can be obtained. These data are converted into a data format suitable for graph theory algorithm processing, including an adjacency matrix or an adjacency list. The adjacency matrix is ​​a two-dimensional array used to represent the connection relationship between nodes in the graph. For example, if there is a path connection between unit 1 and unit 2, the corresponding position element value in the adjacency matrix is ​​1, otherwise it is 0. Construct a topological structure diagram of the wind farm, that is, the wind turbines are regarded as nodes in the graph, and the distances between the units are regarded as edges in the graph, thereby abstracting the wind farm into a graph theory model. According to the maximum range and load capacity parameters of the drone, the maximum inspection range and the maximum number of wind turbines that can be inspected by a single drone in a flight mission are calculated. For example, the maximum flight distance is 200 kilometers, the maximum load is 10 kilograms, and the UAV is flying at a cruising speed, and the inspection time of each unit is 30 minutes, then the maximum inspection time of a single flight mission is about 5 hours. Considering the round-trip flight time and standby time, a single UAV can inspect up to 8 wind turbines at a time. Using graph theory algorithms, including depth-first search or breadth-first search, the topological structure graph of the wind farm is divided into several connected subgraphs, each of which represents an inspection area. By setting appropriate threshold conditions, the scale and density of each inspection area are controlled to ensure that it meets the operational capacity limit of a single UAV. For example, the wind farm is divided into 5 inspection areas, the number of units in each inspection area does not exceed 20, and the distance between units does not exceed 10 kilometers. This can avoid a single mission exceeding the maximum range and inspection time limit of the UAV. If the number of inspection areas obtained by division exceeds the total number of UAVs, the division results need to be adjusted through the optimization algorithm to balance the inspection task volume of each area and the workload of the UAV. Two smaller areas can be merged into one, or a larger area can be split into two smaller areas to ensure that each drone is responsible for a roughly equal amount of inspection tasks.

[0064] Step 2: Construct the UAV flight trajectory for each inspection area based on the wind turbine structural parameters and terrain data, and preset the circling radius and camera angle.

[0065] Furthermore, the UAV flight trajectory of each inspection area is constructed according to the structural parameters of the wind turbine and the terrain data, including:

[0066] Construct a three-dimensional scene model based on wind turbine structural parameters and terrain data;

[0067] The artificial potential field algorithm is used to plan the path in the three-dimensional scene model and obtain the flight trajectory;

[0068] The flight trajectory is locally optimized based on collision risk detection, and globally optimized based on the particle swarm optimization algorithm to obtain the final flight trajectory.

[0069] Among them, the artificial potential field algorithm is used to perform path planning in the three-dimensional scene model, and the flight trajectory is obtained including:

[0070] Obtain the locations of wind turbines and terrain obstacles based on the three-dimensional scene model;

[0071] The location of the wind turbine is used as the target point in the artificial potential field algorithm, and the location of the terrain obstacle is used as the obstacle point in the artificial potential field algorithm;

[0072] Plan the flight trajectory of the drone by dynamically adjusting the distance between the drone and the target point and obstacle point.

[0073] Local optimization of flight trajectory based on collision risk detection: Collision detection and safety distance judgment are performed on the flight trajectory in the three-dimensional scene model according to the structural parameters of the wind turbine set, and local optimization adjustments are made to the paths with collision risks or too close distances.

[0074] Global optimization of flight trajectory based on particle swarm optimization algorithm: The particle swarm optimization algorithm is used to globally optimize the flight trajectory, with the shortest flight distance as the optimization goal, to obtain an optimal flight trajectory that takes into account coverage, safety and efficiency.

[0075] Specifically, in this embodiment, the precise position coordinates, height, blade length and other structural parameters of the wind turbine in each inspection area, as well as the terrain elevation data around the wind turbine, are obtained to construct a three-dimensional scene model. For example, the position coordinates (latitude and longitude, altitude), tower height, blade length and elevation data (DEM data) of the 5 wind turbines in area A of a certain wind farm are obtained, and a three-dimensional scene model is constructed in the software using these data, which includes the three-dimensional model of the wind turbine and the terrain surface. This can intuitively show the actual situation of the wind farm and provide a basis for subsequent path planning. The position of the wind turbine is used as the target point in the artificial potential field algorithm, and the position of the terrain obstacle is used as the obstacle point in the artificial potential field algorithm. The artificial potential field function is improved so that the drone is subject to the gravitational force of the target point and the repulsive force of the obstacle during flight. For example, the position of the 5 wind turbines in area A is set as the target point to generate gravitational force on the drone; the position of obstacles such as mountains and buildings in the terrain that are higher than the set threshold is set as an obstacle point to generate repulsive force on the drone. The improved artificial potential field function can dynamically adjust the magnitude of gravity and repulsion according to the distance between the UAV and the target point and obstacle point, guide the UAV to fly to the target point and avoid obstacles. This method can effectively deal with path planning problems in complex environments. Through the improved artificial potential field algorithm for path planning, a flight path that covers all wind turbines and avoids terrain obstacles is obtained. This path can be used as the basis for subsequent optimization. According to the structural parameters such as the height and blade length of the wind turbine, the flight path is collided and the safety distance is judged in the three-dimensional scene, and the path with collision risk or too close distance is locally optimized. For example, in the scene of area A, check whether the initial path collides with the blades or tower of the wind turbine, or whether it meets the set safety distance requirements. If there is a collision risk or the distance is too close, the path is locally adjusted, such as changing the waypoint position or flight altitude, to ensure the flight safety of the UAV. The particle swarm optimization algorithm is used to globally optimize the flight path, with the shortest flight distance as the optimization goal, to obtain an optimal flight trajectory that takes into account coverage, safety and efficiency. For example, the initial path of area A is taken as input, and the particle swarm optimization algorithm is used to globally optimize the path, with the shortest total flight distance as the objective function. At the same time, the safety distance and obstacle avoidance constraints are considered, and finally an optimal flight trajectory is obtained that covers all wind turbines, avoids obstacles and has the shortest flight distance.

[0076] Furthermore, the method of presetting the surround radius and camera angle is:

[0077] Get the camera parameters and lidar parameters carried by the drone;

[0078] Based on the final flight trajectory, the flight is simulated in the 3D scene model, and the data collection efficiency and quality under different orbital radii and camera angles are calculated according to the camera parameters and lidar parameters;

[0079] Based on data collection efficiency and quality, combined with flight safety, the optimal circling radius and optimal aircraft position angle are determined.

[0080] Specifically, in this embodiment, the simulated flight of the drone is performed according to the three-dimensional scene model and the final flight trajectory, and the data acquisition efficiency and quality at different circling radii and camera angles in the simulated flight are calculated according to the camera parameters (such as resolution, focal length, pixel size) and laser radar parameters (such as scanning frequency, scanning angle, point cloud density) carried by the drone. For example, assuming that the tower height of a wind turbine is 100 meters and the blade length is 80 meters, the drone can be set to fly around at a distance of 150 meters from the center of the tower and a height of 120 meters, and the camera angle can be adjusted to simulate the data acquisition effect at different viewing angles. The camera parameters and laser radar parameters carried by the drone will directly affect the resolution and accuracy of data acquisition. Assuming that the camera resolution of a drone is 4K and the point cloud density of the laser radar is 1000 points per square meter, the image resolution and point cloud density of key parts such as blades and towers can be calculated at different circling radii and camera angles. Considering flight safety, data acquisition efficiency and data quality, it is necessary to determine the optimal circling radius and the optimal camera angle. A smaller circling radius can improve the data acquisition resolution, but may increase flight risks. A larger orbit radius can improve flight safety, but it will reduce data collection efficiency. A suitable camera angle can ensure complete data collection of key parts and avoid occlusion and viewing angle deviation.

[0081] Step 3: Based on the flight trajectory, circling radius and camera angle, the drone is controlled to circle the wind turbines in each inspection area, and images and point cloud data of the wind turbines are collected during the circling flight.

[0082] Further, controlling the UAV to fly around the wind turbines in each inspection area based on the flight trajectory, the circling radius and the aircraft position angle includes:

[0083] The flight trajectory is discretized into several waypoints, which contain position, attitude, and speed information. The position and attitude information are calculated by the orbit radius and the aircraft position angle, and the speed information is the preset speed.

[0084] After controlling the drone to take off, obtain the real-time position, real-time attitude, and real-time speed of the drone, and compare the real-time position, real-time attitude, and real-time speed with the position, attitude, and speed information of the next waypoint to obtain the real-time heading adjustment amount and real-time speed adjustment amount;

[0085] The heading and speed of the drone are continuously corrected through the real-time heading adjustment amount and the real-time speed adjustment amount until the flight trajectory ends.

[0086] Specifically, in this embodiment, the trajectory is discretized into a series of waypoints, each of which contains information such as position, attitude, and speed. For example, the orbital trajectory can be divided into 100 waypoints, each of which is 10 meters apart. The first waypoint may be located 10 meters in front of the wind turbine, at a height of 50 meters, with a horizontal attitude toward the turbine and a speed of 5 meters per second. The setting of each waypoint requires precise calculation to ensure that the drone can collect data stably and efficiently during flight. After the drone takes off, the control system obtains the real-time position and attitude information of the drone through GPS and inertial measurement unit (IMU), compares this information, and calculates the position deviation and attitude deviation. If the deviation value exceeds the preset threshold, the adjustment amount of the drone's heading and speed is calculated according to the direction and size of the deviation, and the drone corrects the heading and speed according to the adjustment amount so that the actual flight trajectory coincides with the planned trajectory.

[0087] In this embodiment, the position and attitude information of the next waypoint is dynamically calculated according to the preset circling radius R and the aircraft position angle θ, and the information of the next waypoint is sent to the drone. The drone adjusts the heading and speed according to the waypoint information and continues to perform the circling flight mission to ensure that it reaches the predetermined position accurately until the drone completes the all-round circling flight of the wind turbine.

[0088] In this embodiment, the wind turbine images are collected by an onboard camera during the circling flight, which are multi-angle high-resolution images; the point cloud data are scanned by a laser radar, which are the three-dimensional spatial coordinates of the wind turbine.

[0089] Step 4: Build a 3D model of the wind turbine based on the wind turbine image and point cloud data, build a 3D map in combination with the terrain data, integrate the flight trajectory into the 3D map, and complete the data collection of the target offshore wind farm.

[0090] Furthermore, constructing a three-dimensional model of a wind turbine based on the wind turbine image and point cloud data includes:

[0091] After pre-processing the wind turbine image and point cloud data respectively, they are fused;

[0092] Based on the fused wind turbine image and point cloud data, the component features of the wind turbine, as well as the geometric outline and position information of each component are extracted to construct a three-dimensional model of the wind turbine.

[0093] The preprocessing of the wind turbine image includes: stitching a plurality of wind turbine images into a panoramic image of the wind turbine, and then performing distortion correction processing on the panoramic image of the wind turbine to obtain a processed panoramic image.

[0094] The pre-processing of the point cloud data includes: removing noise from the point cloud data, smoothing the point cloud data, and obtaining the processed point cloud data.

[0095] The fusion includes: registering and mapping the processed panoramic image with the processed point cloud data to obtain the panoramic image containing the point cloud data.

[0096] Specifically, in this embodiment, a feature matching algorithm, such as the scale-invariant feature transform (SIFT), is used to identify the same feature points between different images, so as to accurately align and stitch the images; a distortion correction algorithm, such as strip distortion correction, is used to eliminate the distortion caused by the lens curvature and stitching technology by adjusting the pixel position in the image.

[0097] In this embodiment, the point cloud data is subjected to noise removal processing through statistical analysis methods, such as calculating the local density of each point and removing isolated points with a density lower than a threshold; the point cloud is smoothed using the moving least squares method (MLS) to smooth the point cloud to improve the quality of the point cloud data.

[0098] In this embodiment, the fusion processing is performed by matching feature points to correspond specific points in the image with corresponding points in the point cloud, thereby achieving the fusion of the image and the point cloud. During the fusion processing, the iterative closest point (ICP) algorithm is used to optimize the registration accuracy to ensure the optimal alignment between the image and the point cloud.

[0099] In this embodiment, based on the fused wind turbine image and point cloud data, the component features of the wind turbine, as well as the geometric outline and position information of each component, are extracted to construct a three-dimensional model of the wind turbine, specifically including:

[0100] First, the fused data is used to extract the key component features of the wind turbine, such as the damage or wear of the blades. The edges of the blades are identified through edge detection algorithms, such as the Canny algorithm, and then their shape and integrity are analyzed. Then, the region segmentation algorithm is used to identify and isolate different components in the image, such as the nacelle and tower, for further analysis and evaluation. Then, based on the extracted features and point cloud data, a surface reconstruction algorithm is used to construct a 3D model of the wind turbine. Finally, the 3D model is post-processed, including model simplification and texture mapping, to obtain a lightweight 3D model suitable for visualization and analysis. The 3D model of the wind turbine is then imported into the virtual map, and the flight trajectory and environmental parameters are also integrated into the virtual map. The visual effect of the model is enhanced through advanced graphics rendering technology to improve the overall realism of the scene.

[0101] At the same time, the geometric parameters and semantic attributes of the key components of the wind turbine are extracted, the 3D models and attribute information of the key components are encoded and compressed, and the data is sent to the ground station through wireless or wired transmission to achieve remote monitoring and analysis. After the ground station receives the data, it decodes and decompresses the data, stores the 3D model and attribute information in the database, and provides visualization and analysis tools to support the status monitoring, fault diagnosis and maintenance decision-making of the key components of the wind turbine.

[0102] To further optimize the above technical solution, this embodiment also provides an offshore wind turbine data acquisition system, including:

[0103] The inspection area division module is used to divide the target offshore wind farm into several inspection areas and obtain the structural parameters and terrain data of the wind turbines in each inspection area;

[0104] The flight information construction module is used to construct the UAV flight trajectory of each inspection area according to the structural parameters of the wind turbine and the terrain data, and preset the circling radius and the aircraft position angle;

[0105] The inspection data acquisition module is used to control the UAV to fly around the wind turbines in each inspection area based on the flight trajectory, circling radius and aircraft position angle, and collect wind turbine images and point cloud data during the circling flight;

[0106] The inspection data processing module is used to build a three-dimensional model of the wind turbine based on the wind turbine image and point cloud data, and to build a three-dimensional map in combination with terrain data, and to integrate the flight trajectory into the three-dimensional map to complete the data collection of the target offshore wind farm.

[0107] Furthermore, the inspection area division module divides the target offshore wind farm into several inspection areas including:

[0108] Obtain geographic information data and a wind turbine distribution map of the target offshore wind farm, and construct a topological structure map, wherein the topological structure map uses wind turbines as nodes and the distances between wind turbines as edges;

[0109] Obtain the maximum range and load capacity of the drone, and calculate the maximum single inspection range and the maximum number of wind turbines in a single inspection mission;

[0110] Using graph theory algorithm, the maximum single inspection range of the UAV and the maximum number of wind turbines at a time are taken as constraints, the topological structure graph is divided, and several connected subgraphs, i.e., several inspection areas, are obtained.

[0111] Furthermore, the flight information construction module constructs the UAV flight trajectory of each inspection area according to the structural parameters of the wind turbine and the terrain data, including:

[0112] Construct a three-dimensional scene model based on wind turbine structural parameters and terrain data;

[0113] The artificial potential field algorithm is used to plan the path in the three-dimensional scene model and obtain the flight trajectory;

[0114] The flight trajectory is locally optimized based on collision risk detection, and globally optimized based on the particle swarm optimization algorithm to obtain the final flight trajectory.

[0115] Among them, the artificial potential field algorithm is used to perform path planning in the three-dimensional scene model, and the flight trajectory is obtained including:

[0116] Obtain the locations of wind turbines and terrain obstacles based on the three-dimensional scene model;

[0117] The location of the wind turbine is used as the target point in the artificial potential field algorithm, and the location of the terrain obstacle is used as the obstacle point in the artificial potential field algorithm;

[0118] Plan the flight trajectory of the drone by dynamically adjusting the distance between the drone and the target point and obstacle point.

[0119] Furthermore, the method for presetting the orbiting radius and the aircraft position angle in the flight information construction module is:

[0120] Get the camera parameters and lidar parameters carried by the drone;

[0121] Based on the final flight trajectory, the flight is simulated in the 3D scene model, and the data collection efficiency and quality under different orbital radii and camera angles are calculated according to the camera parameters and lidar parameters;

[0122] Based on data collection efficiency and quality, combined with flight safety, the optimal circling radius and optimal aircraft position angle are determined.

[0123] Furthermore, the inspection data acquisition module controls the UAV to fly around the wind turbines in each inspection area based on the flight trajectory, the circling radius and the aircraft position angle, including:

[0124] The flight trajectory is discretized into several waypoints, which contain position, attitude, and speed information. The position and attitude information are calculated by the orbit radius and the aircraft position angle, and the speed information is the preset speed.

[0125] After controlling the drone to take off, obtain the real-time position, real-time attitude, and real-time speed of the drone, and compare the real-time position, real-time attitude, and real-time speed with the position, attitude, and speed information of the next waypoint to obtain the real-time heading adjustment amount and real-time speed adjustment amount;

[0126] The heading and speed of the drone are continuously corrected through the real-time heading adjustment amount and the real-time speed adjustment amount until the flight trajectory ends.

[0127] Furthermore, the inspection data processing module constructs a three-dimensional model of the wind turbine based on the wind turbine image and point cloud data, including:

[0128] After pre-processing the wind turbine image and point cloud data respectively, they are fused;

[0129] Based on the fused wind turbine image and point cloud data, the component features of the wind turbine, as well as the geometric outline and position information of each component are extracted to construct a three-dimensional model of the wind turbine.

[0130] Among them, the wind turbine image and point cloud data are pre-processed and then fused, including:

[0131] splicing a plurality of wind turbine images into a panoramic image of the wind turbine, and then performing distortion correction processing on the panoramic image of the wind turbine to obtain a processed panoramic image;

[0132] The point cloud data is processed to remove noise and smooth the point cloud to obtain the processed point cloud data;

[0133] The processed panoramic image is registered and mapped with the processed point cloud data to obtain a panoramic image containing the point cloud data.

[0134] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for collecting data of an offshore wind turbine, characterized in that: include: Divide the target offshore wind farm into several inspection areas, and obtain the structural parameters and terrain data of the wind turbines in each inspection area; Construct the UAV flight trajectory of each inspection area according to the structural parameters of the wind turbine and the terrain data, and preset the circling radius and the camera angle; Based on the flight trajectory, circling radius and aircraft position angle, the drone is controlled to circumvent the wind turbines in each inspection area, and images and point cloud data of the wind turbines are collected during the circling flight; A three-dimensional model of the wind turbine is constructed based on the wind turbine image and point cloud data, and a three-dimensional map is constructed in combination with the terrain data. The flight trajectory is integrated into the three-dimensional map to complete data collection of the target offshore wind farm.

2. The offshore wind turbine data collection method according to claim 1, characterized in that: The target offshore wind farm is divided into several inspection areas including: Acquire geographic information data and a wind turbine distribution map of the target offshore wind farm, and construct a topological structure map, wherein the topological structure map uses wind turbines as nodes and distances between wind turbines as edges; Obtain the maximum range and load capacity of the drone, and calculate the maximum single inspection range and the maximum number of wind turbines in a single inspection mission; A graph theory algorithm is used, and the maximum single inspection range of the UAV and the maximum number of wind turbines at a single time are taken as constraints. The topological structure diagram is divided to obtain a number of connected subgraphs, that is, a number of inspection areas.

3. The offshore wind turbine data collection method according to claim 1, characterized in that: Constructing the UAV flight trajectory of each inspection area according to the wind turbine structure parameters and terrain data includes: Constructing a three-dimensional scene model according to the structural parameters of the wind turbine and the terrain data; Using an artificial potential field algorithm to perform path planning in the three-dimensional scene model to obtain a flight trajectory; The flight trajectory is locally optimized based on collision risk detection, and the flight trajectory is globally optimized based on a particle swarm optimization algorithm to obtain a final flight trajectory.

4. The offshore wind turbine data collection method according to claim 3, characterized in that: Using an artificial potential field algorithm to perform path planning in the three-dimensional scene model, obtaining a flight trajectory includes: Acquire the positions of wind turbines and terrain obstacles based on the three-dimensional scene model; Using the position of the wind turbine as the target point in the artificial potential field algorithm, and using the position of the terrain obstacle as the obstacle point in the artificial potential field algorithm; The flight trajectory of the UAV is planned by dynamically adjusting the distance between the UAV and the target point and the obstacle point.

5. The offshore wind turbine data collection method according to claim 3, characterized in that: The method for presetting the surround radius and camera angle is: Get the camera parameters and lidar parameters carried by the drone; Simulating flight in the three-dimensional scene model based on the final flight trajectory, and calculating data collection efficiency and quality under different circling radii and camera angles according to the camera parameters and the laser radar parameters; Based on the data collection efficiency and quality, combined with flight safety, the optimal circling radius and the optimal aircraft position angle are determined.

6. The offshore wind turbine data collection method according to claim 1, characterized in that: Controlling the UAV to fly around the wind turbines in each inspection area based on the flight trajectory, the circling radius and the aircraft position angle includes: Discretize the flight trajectory into a number of waypoints, each of which contains position, attitude, and speed information, wherein the position and attitude information are calculated by the orbiting radius and the aircraft position angle, and the speed information is a preset speed; After controlling the drone to take off, the real-time position, real-time attitude, and real-time speed of the drone are obtained, and the real-time position, real-time attitude, and real-time speed are compared with the position, attitude, and speed information of the next waypoint to obtain the real-time heading adjustment amount and the real-time speed adjustment amount; The heading and speed of the UAV are continuously corrected by the real-time heading adjustment amount and the real-time speed adjustment amount until the flight trajectory ends.

7. The offshore wind turbine data collection method according to claim 1, characterized in that: Constructing a three-dimensional model of a wind turbine generator system based on the wind turbine generator system image and point cloud data includes: Preprocessing the wind turbine image and point cloud data respectively and then fusing them; Based on the fused wind turbine image and point cloud data, the component features of the wind turbine and the geometric outline and position information of each component are extracted to construct a three-dimensional model of the wind turbine.

8. The offshore wind turbine data collection method according to claim 7, characterized in that: After preprocessing the wind turbine image and point cloud data respectively, the fusion includes: splicing a plurality of wind turbine group images into a panoramic image of the wind turbine group, and then performing distortion correction processing on the panoramic image of the wind turbine group to obtain a processed panoramic image; The point cloud data is processed to remove noise and smooth the point cloud to obtain the processed point cloud data; The processed panoramic image is registered and mapped with the processed point cloud data to obtain a panoramic image containing the point cloud data.

9. An offshore wind turbine data acquisition system, applied to the offshore wind turbine data acquisition method according to any one of claims 1 to 8, characterized in that: include: The inspection area division module is used to divide the target offshore wind farm into several inspection areas and obtain the structural parameters and terrain data of the wind turbines in each inspection area; A flight information construction module is used to construct the UAV flight trajectory of each inspection area according to the structural parameters of the wind turbine set and the terrain data, and to preset the circling radius and the aircraft position angle; The inspection data acquisition module is used to control the UAV to fly around the wind turbines in each inspection area based on the flight trajectory, the circling radius and the aircraft position angle, and to collect images and point cloud data of the wind turbines during the circling flight; The inspection data processing module is used to construct a three-dimensional model of the wind turbine based on the wind turbine image and point cloud data, and to construct a three-dimensional map in combination with the terrain data, and to integrate the flight trajectory into the three-dimensional map to complete the data collection of the target offshore wind farm.

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