A blade image acquisition method and device based on dynamic inspection of a fan

Abstract

The application discloses a kind of based on fan dynamic inspection blade image acquisition method and device, specifically related to unmanned aerial vehicle control field, including S1, obtain basic data, and construct three-dimensional coordinate;S2, based on basic data and position coordinates, plan unmanned aerial vehicle initial inspection route;S3, unmanned aerial vehicle flies in the process according to initial route, real-time execution data operation;S4, based on horizontal deviation degree, execute route horizontal adjustment and speed optimization;S5, based on image quality parameter, adjust the distance before and after unmanned aerial vehicle and blade;S6, based on unmanned aerial vehicle flight attitude parameter, adjust left and right offset;S7, based on blade dynamic waving change and unmanned aerial vehicle height deviation, adjust unmanned aerial vehicle attitude height;The application reaches the balance effect between deviation adjustment accuracy and wind resistance stability of unmanned aerial vehicle flight speed, both ensure that deviation is larger when reducing speed to improve adjustment accuracy, also ensure that speed is increased when wind speed increases to offset wind resistance.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and more specifically, to a method and apparatus for acquiring images of wind turbine blades based on dynamic inspection. Background Technology

[0002] Defect detection of wind turbine blades is a key link in ensuring the safe operation of wind turbines. Current wind turbine blade inspection technology mainly relies on drone inspection. The conventional method is to pre-set a fixed flight path, and the drone flies around the wind turbine blades at a fixed flight altitude and speed. It collects images of the blade surface through an onboard high-definition camera, and then imports the collected images into an image processing system to detect defects such as cracks, corrosion, and coating peeling on the blade surface.

[0003] Compared to traditional manual climbing inspection, existing drone inspection technology can significantly shorten inspection time and improve inspection efficiency, while avoiding the risk of falls from heights during manual climbing and reducing personnel safety hazards. However, this technology has obvious shortcomings: First, the flight path is a preset fixed pattern, which does not take into account the dynamic flapping and oscillating motion of the wind turbine blades under the action of wind, resulting in a continuous increase in the relative position deviation between the drone and the blades, and the acquired images may show motion blur or miss images of local areas of the blades. Second, the flight path adjustment is based solely on the straight-line distance between the drone and the blades, without taking into account dynamic parameters such as real-time wind speed and blade attitude change rate, resulting in low adjustment accuracy and difficulty in adapting to the dynamic position changes of the blades. Third, the drone's attitude and altitude adjustment rely solely on a preset fixed height value, without being linked to the real-time flapping position of the blades, which easily leads to the safety risk of collision between the drone and the blades, or incomplete image acquisition of the top and bottom areas of the blades due to excessive height deviation.

[0004] To address the shortcomings of existing technologies, a blade image acquisition method and device based on dynamic wind turbine inspection is proposed. By deploying parameter acquisition terminals on UAVs and wind turbines, the blade attitude parameters, environmental parameters, and UAV flight parameters are acquired in real time. A multi-dimensional dynamic flight path adjustment model is constructed, which sequentially realizes initial flight path planning, horizontal waypoint adjustment, flight speed optimization, forward and backward distance adaptation, left and right offset calibration, and attitude and elevation adjustment. This solves the problems of fixed flight paths being unsuitable for dynamic blades, low adjustment accuracy, and unreasonable attitude and elevation control, thereby improving the quality of blade image acquisition and the safety of inspection. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and device for acquiring blade images based on dynamic inspection of wind turbines, and solves the problems mentioned in the background art through the following scheme.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a blade image acquisition method based on dynamic inspection of wind turbines, comprising:

[0007] S1. Obtain basic data and construct three-dimensional coordinates;

[0008] S2. Based on basic data and location coordinates, plan the initial inspection route for the UAV;

[0009] S3. During the flight of the UAV along the initial route, data operations are performed in real time.

[0010] S4. Based on the horizontal deviation, perform horizontal adjustments to the flight path and speed optimization;

[0011] S5. Adjust the fore-and-aft distance between the drone and the blades based on image quality parameters;

[0012] S6. Adjust the left and right offset based on the UAV's flight attitude parameters;

[0013] S7. Adjust the drone's attitude and altitude based on the dynamic flapping changes of the blades and the drone's altitude deviation.

[0014] Preferably, the basic data includes basic geometric parameters of the wind turbine blades collected using a wind turbine condition monitoring terminal, initial static attitude parameters of the blades collected using a blade attitude acquisition terminal, and initial environmental parameters collected using an environmental sensing terminal. The basic geometric parameters of the wind turbine blades specifically include blade length and maximum chord length. The initial static attitude parameters of the blades specifically include initial flapping angle and initial oscillation angle. The initial environmental parameters specifically include initial wind speed and initial wind direction. The three-dimensional coordinate system is constructed with the wind turbine hub center as the origin O, the vertical upward direction of the wind turbine main shaft as the z-axis, the positive direction being upward, the windward direction perpendicular to the main shaft and parallel to the ground as the x-axis, the positive direction pointing towards the wind direction, and the horizontal direction perpendicular to the xz plane as the y-axis, where the positive direction conforms to the right-hand rule, thus constructing a three-dimensional rectangular coordinate system O-xyz.

[0015] Preferably, the initial inspection route of the UAV is as follows: the inspection section spacing is set, specifically the distance between two adjacent inspection sections along the blade length direction; the number of inspection sections is determined according to the blade length, each inspection section corresponds to a core waypoint, and the initial coordinates of each core waypoint are calculated; the initial flight speed of the UAV is calculated in combination with the initial wind speed, and the initial front-to-back distance and initial left-to-right offset between the UAV and the blade are set as auxiliary parameters of the initial route, wherein the initial left-to-right offset is specifically the horizontal offset of the UAV relative to the centerline of the blade.

[0016] Preferably, the data operation includes real-time parameter acquisition and flight path horizontal deviation calculation; the real-time parameter acquisition includes real-time acquisition of the current dynamic attitude parameters of the blades through the blade attitude acquisition terminal, real-time acquisition of the current environmental parameters through the environmental perception terminal, and real-time acquisition of the current position coordinates of the UAV through the UAV flight control terminal; the current dynamic attitude parameters of the blades specifically include the current flapping angle and the current oscillation angle; the current environmental parameters specifically include the current wind speed and the current wind direction; the flight path horizontal deviation calculation: calculate the current theoretical coordinates of each core waypoint, and then calculate the horizontal deviation between the current position of the UAV and the current theoretical waypoint.

[0017] Preferably, the specific method for adjusting the flight path horizontally is as follows: adjusting the current horizontal waypoint coordinates of the UAV according to a set horizontal deviation threshold; the specific method for speed optimization is as follows: simultaneously adjusting the current flight speed of the UAV in combination with the current wind speed and the horizontal deviation.

[0018] Preferably, the image quality parameters are obtained by the image processing terminal in real time by acquiring the quality parameters of the blade image, including image sharpness and image overlap. The image quality index is calculated based on the image quality parameters, and the current front-to-back distance between the UAV and the blade is adjusted according to the set image quality threshold.

[0019] Preferably, the flight attitude parameters are collected in real time by the UAV flight control terminal, including the current attitude parameters of the UAV, including roll angle and yaw angle. The attitude stability coefficient is calculated based on the set maximum allowable roll angle and maximum allowable yaw angle of the UAV, and the current left and right offset of the UAV is adjusted based on the set attitude stability threshold.

[0020] Preferably, the specific method for adjusting the drone's attitude and altitude is as follows: the blade attitude acquisition terminal collects the blade flapping angle change rate in real time; the drone flight control terminal collects the altitude deviation between the drone's current altitude and the current theoretical waypoint altitude in real time; the maximum blade flapping angle change rate is set to calculate the drone's attitude and altitude adjustment amount; if the drone's attitude and altitude adjustment amount is greater than 0, it indicates that the drone's current altitude is higher than the ideal altitude, and the drone is controlled to decrease the absolute value of the drone's attitude and altitude adjustment amount; if the drone's attitude and altitude adjustment amount is less than 0, it indicates that the drone's current altitude is lower than the ideal altitude, and the drone is controlled to increase the absolute value of the drone's attitude and altitude adjustment amount; after adjustment, the new blade flapping angle change rate and the altitude deviation between the drone's current altitude and the current theoretical waypoint altitude are collected in real time, and the calculation and adjustment are repeated until the absolute value of the blade flapping angle change rate and the altitude deviation between the drone's current altitude and the current theoretical waypoint altitude is less than the preset maximum allowable altitude deviation.

[0021] Preferably, a blade image acquisition device based on dynamic inspection of wind turbines includes:

[0022] Initialization module: Acquires basic data and constructs three-dimensional coordinates;

[0023] Initial inspection route planning module: Based on basic data and location coordinates, the module plans the initial inspection route for the UAV.

[0024] Flight path horizontal deviation calculation module: Performs data operations in real time during the UAV's flight along the initial flight path;

[0025] Drone adjustment module: Based on horizontal deviation, performs horizontal flight path adjustment and speed optimization;

[0026] Dynamic adjustment module: Adjusts the fore-and-aft distance between the drone and the blades based on image quality parameters;

[0027] UAV left and right offset calibration module: Adjusts the left and right offset based on the UAV's flight attitude parameters;

[0028] Drone attitude dynamic adaptation module: Adjusts the drone's attitude and altitude based on the dynamic changes in blade flapping and the drone's altitude deviation.

[0029] The technical effects and advantages of this invention are as follows:

[0030] 1. This invention collects the blade flapping angle, oscillation angle and ambient wind speed in real time, constructs the initial waypoint coordinate formula and the horizontal deviation calculation model, and obtains a horizontal waypoint adjustment scheme that dynamically adapts to changes in blade attitude. This solves the problem that fixed flight paths do not consider the dynamic flapping and oscillation motion of the blades, which leads to increased relative position deviation between the UAV and the blades, blurred images or missed shots. It achieves the effect of accurate matching between the UAV's horizontal flight path and the dynamic attitude of the blades, ensuring that there are no blind spots or blurs in the images of the entire blade length area, and providing clear basic image data for defect detection.

[0031] 2. This invention calculates the image quality index by collecting image clarity and overlap, and combines it with the front-to-back distance adjustment formula to obtain a dynamic front-to-back distance adaptation scheme based on image quality feedback. This solves the problem that the flight path adjustment is based solely on the straight-line distance between the UAV and the blade without considering image quality, which leads to uneven image clarity, missed shots, or excessive overlap. It achieves the dual goals of high-definition images of the entire blade surface and reasonable overlap, which avoids missing minor defects due to insufficient clarity, reduces invalid image redundancy, and improves the efficiency of inspection data processing.

[0032] 3. This invention calculates the attitude stability coefficient by collecting the roll angle and yaw angle of the UAV, and combines it with the left and right offset adjustment formula to obtain the left and right offset calibration scheme related to attitude stability. This solves the problem of missing the blade edge and image tilting caused by not considering the influence of UAV attitude deflection on the acquisition range. It achieves the effect of complete acquisition of the full width of the blade and no image distortion, avoids the misjudgment of defects caused by missing edge images, and improves the completeness of defect detection.

[0033] 4. This invention calculates the attitude height adjustment amount by collecting the change rate of the blade flapping angle and the height deviation of the UAV, and obtains a height dynamic adjustment scheme that integrates the blade flapping speed and wind speed. This solves the problem that the UAV attitude height relies only on the preset fixed height value, which is prone to collision risk with the blade or incomplete image acquisition of the top / bottom of the blade. It achieves the effect of precise matching between the height of the UAV and the dynamic flapping position of the blade, which not only eliminates the safety hazard of collision, but also ensures the integrity of the image of the entire height area from the bottom of the blade root to the top of the blade tip, covering the blind spots of traditional fixed height inspection.

[0034] 5. This invention obtains a flight speed optimization scheme that correlates deviation and wind speed by collecting current wind speed and horizontal deviation, combined with the flight speed adjustment formula. This solves the problem of low adjustment accuracy and UAV flight instability caused by not taking wind speed and deviation into account when adjusting flight routes. It achieves a balance between deviation adjustment accuracy and wind resistance stability of UAV flight speed. It ensures that the speed is reduced to improve adjustment accuracy when the deviation is large, and that the speed is appropriately increased to offset wind resistance when the wind speed increases, thus maintaining flight stability and improving overall inspection efficiency. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the method structure of the present invention;

[0036] Figure 2 This is a schematic diagram of the device structure of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] A blade image acquisition method based on dynamic inspection of wind turbines includes a wind turbine condition monitoring terminal, a UAV flight control terminal, a blade attitude acquisition terminal, an environmental perception terminal, and an image processing terminal. The wind turbine condition monitoring terminal is installed at the wind turbine hub to acquire basic geometric parameters of the wind turbine blades; the blade attitude acquisition terminal is installed at the blade root to acquire real-time dynamic attitude parameters of the blades; the environmental perception terminal is installed on the UAV fuselage to acquire real-time environmental parameters; the UAV flight control terminal is installed inside the UAV to plan flight paths and adjust flight parameters and attitude; and the image processing terminal is integrated into the UAV flight control terminal to analyze image quality parameters.

[0039] As attached Figure 1 The blade image acquisition method based on dynamic inspection of wind turbines, as shown, specifically includes the following steps:

[0040] S1. Obtain basic data and construct three-dimensional coordinates.

[0041] Specifically, the following should be noted: the basic data includes the basic geometric parameters of the wind turbine blades collected using a wind turbine condition monitoring terminal, the initial static attitude parameters of the blades collected using a blade attitude acquisition terminal, and the initial environmental parameters collected using an environmental sensing terminal; the basic geometric parameters of the wind turbine blades specifically include the blade length L (the straight-line distance from the blade root to the blade tip) and the maximum chord length C (the length at the widest point of the blade cross-section); the initial static attitude parameters of the blades specifically include the initial flapping angle. (Initial deflection angle of the blade around the flapping axis) and initial oscillation angle (Initial deflection angle of the blade around the oscillation axis); the initial environmental parameters specifically include the initial wind speed. (Ambient wind speed at drone takeoff) and initial wind direction (The angle between the initial wind direction and the turbine's main shaft); the three-dimensional coordinate system is constructed with the turbine hub center as the origin O, the vertical upward direction of the turbine's main shaft as the z-axis, the positive direction of which is upward, the windward direction perpendicular to the main shaft and parallel to the ground as the x-axis, the positive direction of which points towards the wind direction, and the horizontal direction perpendicular to the xz plane as the y-axis, where the positive direction conforms to the right-hand rule, constructing a three-dimensional rectangular coordinate system O-xyz, used to locate the real-time position of the UAV and the blades. Among them, the basic geometric parameters of the blades (length L, maximum chord length C) are the core basis for subsequent planning to cover the entire length of the blades. If L is missing, the number of inspection sections cannot be determined, which may lead to local missed inspections of the blades; the initial static attitude of the blades (initial flapping angle, initial oscillation angle) determines the reference attitude of the initial waypoint, because the initial deflection angle of the blades in the absence of wind will directly affect the initial relative position of the UAV and the blades; the initial environmental parameters (initial wind speed, initial wind direction) are related to the initial flight speed of the UAV, which conforms to the flight characteristics that the higher the wind speed of the UAV, the more appropriate the wind resistance speed needs to be to avoid instability. Coordinate system construction logic: Establishing a three-dimensional coordinate system with the center of the wind turbine hub as the origin, along the main axis, the windward direction, and the horizontal and vertical directions is the premise for realizing the unified quantification of the position of the UAV and the blade. Without a unified coordinate system, the flight coordinates of the UAV and the dynamic attitude coordinates of the blade will be at different references, and subsequent deviation calculation and waypoint adjustment will fail due to coordinate chaos. In actual wind turbine inspection scenarios, the blade sizes of different wind turbines vary (e.g., the blade length of a 1.5MW wind turbine is about 35m, and that of a 3MW wind turbine is about 50m). If geometric parameters such as L and C are not collected, using a flight path with a fixed cross-section spacing will lead to excessive overlap of small blades and missed inspection of large blades. Even when the turbine is stopped, the blades will still have initial flapping / wobbling due to their own weight. If this is ignored, the initial waypoint will deviate from the actual position of the blade. The wind speed when the drone takes off (e.g., the initial wind speed in coastal areas may reach 5-8m / s) directly affects takeoff stability. If the initial wind speed is not collected, a fixed initial speed (e.g., 5m / s) may cause the drone to be blown off course when the wind speed is high and become inefficient when the wind speed is low. Without a unified coordinate system, the drone can only locate its absolute position through GPS and cannot determine its relative position with the blades (e.g., the absolute coordinate change of the blade tip after flapping). The inspection will lose the core objective of flying around the blades.

[0042] S2. Based on basic data and location coordinates, plan the initial inspection route of the UAV.

[0043] Specifically, the initial inspection route of the UAV is as follows: The inspection section spacing d is set, which is the distance between two adjacent inspection sections along the blade length direction; the number of inspection sections is determined based on the blade length L. Each inspection section corresponds to a core waypoint, and the initial coordinates of each core waypoint are calculated. ,in ; ; ; ; (Origin point is the center of the wheel hub); combined with initial wind speed Calculate the initial flight speed of the drone. This formula dynamically adapts the initial flight speed to wind speed to prevent the drone from becoming unstable due to excessive wind speed; it also sets the initial fore-and-aft distance between the drone and the blades. (Vertical distance between the drone fuselage and the blade surface) and initial left and right offset As auxiliary parameters for the initial flight path, the initial left and right offsets specifically refer to the horizontal offset of the UAV relative to the blade centerline. The calculation of the number of inspection sections aims to uniformly cover the entire blade length. If the number of inspection sections is too large, blind spots will appear on the blade surface; if the number of inspection sections is too small, excessive image overlap will occur, wasting storage and computing power. This calculation balances coverage integrity and inspection efficiency. The core waypoint coordinate formula integrates the distance from the i-th blade section to the blade tip, the initial attitude, and the initial wind direction, ensuring that the initial waypoints are distributed parallel to the blade surface, rather than simply in a circle, conforming to the long and curved geometric characteristics of the blade. The initial flight speed formula uses an exponential function, meaning that the higher the wind speed, the more gradual the speed increase, avoiding UAV instability caused by abrupt speed adjustments when wind speed suddenly increases, conforming to the gradual speed adjustment principle of UAV flight control. Drone inspections must have an initial flight path; otherwise, the drone cannot determine the target direction after takeoff (e.g., whether to fly towards the blade tip or the blade root and maintain a certain distance). The initial flight path is the basis for the transition from takeoff to real-time adjustments. The drawback of existing fixed flight paths is that they do not combine blade parameters with environmental parameters (e.g., all wind turbines fly along a circular flight path with a radius of 10m), resulting in excessive photography of small blades and missed photography of large blades. The initial flight path in this step integrates multiple parameters and is the basic framework for dynamic flight paths. Without this framework, subsequent real-time adjustments will lose the reference target.

[0044] S3. The drone performs real-time operations while flying along the initial flight path.

[0045] Specifically, it should be noted that the data operations include real-time parameter acquisition and flight path horizontal deviation calculation; the real-time parameter acquisition includes real-time acquisition of the current dynamic attitude parameters of the blades through the blade attitude acquisition terminal, real-time acquisition of current environmental parameters through the environmental perception terminal, and real-time acquisition of the current position coordinates of the UAV through the UAV flight control terminal. The specific dynamic attitude parameters of the blade include the current flapping angle. and the current oscillation angle The current environmental parameters specifically include the current wind speed. and current wind direction The horizontal deviation calculation of the route involves calculating the current theoretical coordinates of each core waypoint. ,in , , Then calculate the horizontal deviation between the UAV's current position and the current theoretical waypoint. The real-time parameter acquisition dimensions include: the current dynamic attitude of the blades is invalidated because the blades continuously flap under wind force; the current wind speed and direction change the stress state of the UAV in real time (e.g., sudden changes in wind direction can cause the UAV to yaw); and the current position of the UAV is the actual value for calculating the deviation. These parameter acquisition dimensions perfectly match the needs of dynamic deviation analysis. The current theoretical waypoint is calculated by substituting the real-time blade attitude and wind direction into the initial waypoint logic to obtain the ideal position of the UAV. If not updated, the theoretical waypoint will remain in its initial state, deviating from the actual position of the blades. The horizontal deviation formula integrates positional deviation, wind speed influence, and blade attitude changes, avoiding the limitations of judging deviation solely by straight-line distance (e.g., at the same straight-line distance, higher wind speeds increase the likelihood of collisions), and comprehensively reflecting the actual risk deviation in the horizontal direction. In actual inspections, the blades cannot remain stationary (even if the drone is stopped, a slight breeze will cause them to flap), and the wind speed will not be constant. If parameters are not collected in real time, the drone will fly along the initial route, and the relative deviation from the blades will continue to increase (for example, if the blade tip deviates by 2m after flapping, the drone will still fly along the initial waypoint, resulting in the blade tip being missed in the image). The horizontal deviation is the core basis for judging whether adjustment is needed. Without the horizontal deviation, it is impossible to quantify the magnitude of the deviation (such as whether an adjustment is needed if the deviation is 1m). The adjustment operation will fall into the trap of judging based on experience, resulting in untimely or excessive adjustment.

[0046] S4. Based on the horizontal deviation, perform horizontal adjustments and speed optimizations for the flight path.

[0047] Specifically, it should be noted that the method for adjusting the horizontal flight path is as follows: based on a set horizontal deviation threshold... ,when > Adjust the current horizontal waypoint coordinates of the drone ,in , The specific method for speed optimization is as follows: The current flight speed of the drone is adjusted synchronously by combining the current wind speed and the horizontal deviation. The horizontal waypoint adjustment formula uses an exponential function logic: the greater the deviation, the larger the adjustment; the smaller the deviation, the smoother the adjustment, reaching an adjustment range of 86% to quickly reduce the deviation and avoid over-adjustment that could cause drone oscillation. The flight speed adjustment formula has two layers of logic: first, the greater the deviation, the lower the speed; second, the higher the wind speed, the more the speed is increased to offset the impact of wind resistance on flight stability, conforming to the dynamic characteristics of drones flying against wind. Meanwhile, if... > If the actual deviation is not adjusted to the horizontal waypoint, the drone will continue to deviate from the blade, resulting in the blade occupying only a small part (or even no blade) in the acquired image, which cannot meet the defect detection requirements. If the flight speed is not adjusted, when the deviation increases, the drone will still fly at high speed, which will lead to untimely adjustment (such as flying past the waypoint just after the deviation is detected). When the current wind speed increases, the drone will fly at low speed and be blown off course by the wind, further amplifying the deviation. Therefore, speed adjustment is a necessary supporting operation to ensure the effectiveness of horizontal adjustment.

[0048] S5. Adjust the front-to-back distance between the drone and the blades based on image quality parameters.

[0049] Specifically, it should be noted that the image quality parameters, including image sharpness, are quality parameters acquired in real time from leaf images via an image processing terminal. (Calculated using the edge gradient operator; a larger value indicates a sharper image) and image overlap. (The proportion of the overlapping area between the current image and the previous frame to the current image) is used to calculate the image quality index based on image quality parameters. Based on the set image quality threshold ,like > Adjust the current fore-and-aft distance between the drone and the blades. ,in To achieve the preset optimal image sharpness, when image sharpness is low, the fore-and-aft distance is reduced to improve sharpness; when overlap is low, the fore-and-aft distance is reduced to improve overlap, and vice versa. The image quality index formula integrates sharpness, overlap, and flight status. The ratio of wind speed to airspeed reflects flight stability; a smaller ratio indicates better stability and less image blurriness. This comprehensive evaluation of image quality is crucial. Focusing solely on image sharpness might overlook missed shots due to insufficient overlap; focusing solely on image overlap might overlook undetectable defects caused by low sharpness. This formula balances multi-dimensional quality requirements. The fore-and-aft distance adjustment formula follows the logic that the worse the image quality, the more the distance adjustment is tilted towards improving quality. Meanwhile, the distance between the drone and the blades directly affects image quality: if the distance is too large, the blades appear small in the image, and details (such as a 0.5mm crack) cannot be distinguished; if the distance is too small, the image overlap is too high, leading to a surge in storage (e.g., what could be covered by 100 images now requires 200), and post-processing stitching is time-consuming; existing technology does not consider image quality feedback, and a fixed distance will result in large differences in image quality at different locations of the same wind turbine (e.g., the blade tip is more prone to increased distance due to large waving amplitude, resulting in reduced clarity), so the distance must be adjusted according to real-time image quality.

[0050] S6. Adjust the left and right offset based on the UAV's flight attitude parameters.

[0051] Specifically, it should be noted that the flight attitude parameters, including roll angle, are collected in real time by the UAV flight control terminal. (The deflection angle of the UAV around the x-axis) and yaw angle (The deflection angle of the drone around the z-axis), based on the set maximum allowable roll angle of the drone. and maximum permissible yaw angle Calculate attitude stability coefficient The larger the value of this coefficient, the more stable the drone's attitude. The value range is [value range missing]. Based on the set attitude stability threshold ,like < Adjust the current left and right offset of the drone. When attitude stability is poor, the left and right offset is reduced to bring the UAV closer to the blade centerline, reducing the impact of attitude deflection on image acquisition. When the attitude is stable, the offset is maintained or moderately increased to ensure complete acquisition of the blade edge area, thereby ensuring UAV attitude stability and avoiding image tilt. The attitude stability coefficient formula quantifies the UAV's attitude stability. Excessive roll angle will cause the image to tilt left and right, making the blade appear tilted in the image; excessive yaw angle will cause the UAV to deviate from the blade centerline. A larger coefficient value indicates better stability, consistent with the quantitative logic of UAV flight attitude control. The left and right offset adjustment formula follows the logic that the more unstable the attitude, the smaller the offset. < To minimize the impact of attitude deflection on the acquisition of blade edge data, the drone is positioned closer to the blade centerline. This reduces the impact of attitude deflection on the acquisition of blade edge data (for example, in cases of attitude instability, a small offset prevents the drone from flying out of the acquisition range on either side of the blade), aligning with the control strategy of narrowing the flight range to ensure integrity when attitude is unstable. Simultaneously, the drone is affected by gusts during flight, inevitably resulting in roll and yaw. If the roll angle is too large, image tilt can lead to misjudgments by subsequent defect detection algorithms (e.g., misidentifying tilted blade edges as cracks). If the yaw angle is too large, the drone will shift to one side of the blade, missing the image of the other side (e.g., the right edge of the blade). Current technology does not adjust the left and right offsets, relying solely on the drone's own attitude stabilization system. When gusts are strong, the attitude stabilization system cannot completely counteract the deflection, leading to missed images of blade edges or tilted images. Therefore, adjusting the left and right offsets is a necessary operation to supplement the shortcomings of the attitude stabilization system.

[0052] S7. Adjust the drone's attitude and altitude based on the dynamic flapping changes of the blades and the drone's altitude deviation.

[0053] Specifically, it should be noted that the blade attitude acquisition terminal collects the rate of change of the blade flapping angle in real time. (The change in flapping angle per unit time reflects the blade flapping speed); the UAV flight control terminal collects the UAV's current altitude in real time. and current theoretical waypoint altitude Height deviation Set the rate of change of the maximum flapping angle of the blade. Calculate the altitude adjustment of the UAV The altitude deviation directly determines the adjustment direction. The greater the rate of change in the flapping angle (the more violent the blade flapping), the gentler the adjustment, avoiding excessive adjustment that could lead to a collision. The higher the wind speed, the smaller the adjustment, ensuring flight stability. If... >0 indicates that the drone's current altitude is higher than the ideal altitude; control the drone to descend. |, if <0 indicates the drone's current altitude is below the ideal altitude; control the drone to increase its altitude. |; After adjustment, the new blade flapping angle change rate and the altitude deviation between the UAV's current altitude and the current theoretical waypoint altitude are collected in real time, and the calculation and adjustment are repeated until... ,in This indicates the preset maximum allowable altitude deviation. The altitude adjustment formula integrates altitude deviation, blade flapping speed, and flight stability, with a rigorous adjustment logic: when the blade flapping angle change rate is large, the adjustment range is increased by 36.8%, but due to the attenuation of the exponential term, the actual adjustment is smoother, avoiding collisions caused by excessive adjustment when the blades flap rapidly; when the wind speed is high, the adjustment range is reduced by 80%, avoiding drone instability caused by high wind speeds, conforming to the safety-first inspection principle. The iterative adjustment logic ensures that the altitude deviation is controlled within a range that does not affect image acquisition, conforming to the closed-loop control principle in control theory until the error converges. Simultaneously, blade flapping directly changes the drone's altitude position. If the drone's altitude is not adjusted, it will lead to: drone too low altitude: collisions with the drone during blade flapping, damaging the equipment; drone too high altitude: missed images of the bottom area of ​​the blades (such as below the blade root); existing technology uses a fixed altitude, which cannot adapt to altitude changes caused by blade flapping, resulting in a high risk of collision and significant missed images of the bottom. Therefore, altitude adjustment is a core step in ensuring inspection safety and the integrity of the altitude direction.

[0054] As attached Figure 2 The blade image acquisition device shown includes:

[0055] Initialization module: Acquires basic data and constructs three-dimensional coordinates;

[0056] Initial inspection route planning module: Based on basic data and location coordinates, the module plans the initial inspection route for the UAV.

[0057] Flight path horizontal deviation calculation module: Performs data operations in real time during the UAV's flight along the initial flight path;

[0058] Drone adjustment module: Based on horizontal deviation, performs horizontal flight path adjustment and speed optimization;

[0059] Dynamic adjustment module: Adjusts the fore-and-aft distance between the drone and the blades based on image quality parameters;

[0060] UAV left and right offset calibration module: Adjusts the left and right offset based on the UAV's flight attitude parameters;

[0061] Drone attitude dynamic adaptation module: Adjusts the drone's attitude and altitude based on the dynamic changes in blade flapping and the drone's altitude deviation.

[0062] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0063] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for acquiring blade images based on dynamic inspection of wind turbines, characterized in that, include: S1. Obtain basic data and construct three-dimensional coordinates; S2. Based on basic data and location coordinates, plan the initial inspection route for the UAV; S3. During the flight of the UAV along the initial route, data operations are performed in real time. The data operations include real-time parameter acquisition and flight path horizontal deviation calculation. Real-time parameter acquisition includes acquiring the current dynamic attitude parameters of the blades in real-time via a blade attitude acquisition terminal, acquiring current environmental parameters in real-time via an environmental sensing terminal, and acquiring the current position coordinates of the UAV in real-time via a UAV flight control terminal. The current dynamic attitude parameters of the blades specifically include the current flapping angle and the current oscillation angle. The current environmental parameters specifically include the current wind speed and the current wind direction. The flight path horizontal deviation calculation involves calculating the current theoretical coordinates of each core waypoint, and then calculating the horizontal deviation between the UAV's current position and the current theoretical waypoint. ,in , These represent the current x and y coordinates of the drone, respectively. , These represent the current x and y coordinates of the i-th core waypoint, respectively. Indicates the current wind speed. This indicates the initial flight speed of the drone. Indicates the current waving angle. Indicates the initial swing angle; S4. Based on the horizontal deviation, perform horizontal adjustments to the flight path and speed optimization; S5. Adjust the fore-and-aft distance between the drone and the blades based on image quality parameters; S6. Adjust the left and right offset based on the UAV's flight attitude parameters; S7. Adjust the drone's attitude and altitude based on the dynamic flapping changes of the blades and the drone's altitude deviation.

2. The blade image acquisition method based on dynamic inspection of wind turbines according to claim 1, characterized in that: The basic data includes the collection of basic geometric parameters of the wind turbine blades using a wind turbine condition monitoring terminal, the collection of initial static attitude parameters of the blades using a blade attitude acquisition terminal, and the collection of initial environmental parameters using an environmental sensing terminal. The basic geometric parameters of the wind turbine blades specifically include the blade length and the maximum chord length of the blades. The initial static attitude parameters of the blades specifically include the initial flapping angle and the initial oscillation angle. The initial environmental parameters specifically include the initial wind speed and the initial wind direction. The three-dimensional coordinate system is constructed with the center of the wind turbine hub as the origin O, the vertical upward direction of the wind turbine main shaft as the z-axis, where the positive direction is upward, the windward direction perpendicular to the main shaft and parallel to the ground as the x-axis, where the positive direction points to the direction of the oncoming wind, and the horizontal direction perpendicular to the xz plane as the y-axis, where the positive direction conforms to the right-hand rule, thus constructing a three-dimensional rectangular coordinate system O-xyz.

3. The blade image acquisition method based on dynamic inspection of wind turbines according to claim 1, characterized in that: The initial inspection route of the UAV is as follows: the inspection section spacing is set, specifically the distance between two adjacent inspection sections along the blade length direction; the number of inspection sections is determined according to the blade length, each inspection section corresponds to a core waypoint, and the initial coordinates of each core waypoint are calculated; the initial flight speed of the UAV is calculated in combination with the initial wind speed, and the initial front-to-back distance and initial left-to-right offset between the UAV and the blade are set as auxiliary parameters of the initial route, wherein the initial left-to-right offset is specifically the horizontal offset of the UAV relative to the centerline of the blade.

4. The blade image acquisition method based on dynamic inspection of wind turbines according to claim 1, characterized in that: The specific method for adjusting the flight path horizontally is as follows: adjust the current horizontal waypoint coordinates of the UAV according to the set horizontal deviation threshold; the specific method for speed optimization is as follows: adjust the current flight speed of the UAV synchronously by combining the current wind speed and the horizontal deviation.

5. The blade image acquisition method based on dynamic inspection of wind turbines according to claim 1, characterized in that: The image quality parameters are acquired in real time by the image processing terminal. These parameters include image sharpness and image overlap. The image quality index is calculated based on the image quality parameters. The current front-to-back distance between the UAV and the blade is adjusted according to the set image quality threshold.

6. The blade image acquisition method based on dynamic inspection of wind turbines according to claim 1, characterized in that: The flight attitude parameters are collected in real time by the UAV flight control terminal. The current attitude parameters of the UAV include roll angle and yaw angle. The attitude stability coefficient is calculated based on the set maximum allowable roll angle and maximum allowable yaw angle of the UAV. The current left and right offset of the UAV is adjusted based on the set attitude stability threshold.

7. The blade image acquisition method based on dynamic inspection of wind turbines according to claim 1, characterized in that: The specific method for adjusting the drone's attitude and altitude is as follows: the blade attitude acquisition terminal collects the blade flapping angle change rate in real time; the drone flight control terminal collects the altitude deviation between the drone's current altitude and the current theoretical waypoint altitude in real time; the maximum blade flapping angle change rate is set to calculate the drone's attitude and altitude adjustment amount; if the drone's attitude and altitude adjustment amount is greater than 0, it means that the drone's current altitude is higher than the ideal altitude, and the drone is controlled to decrease the absolute value of the drone's attitude and altitude adjustment amount; if the drone's attitude and altitude adjustment amount is less than 0, it means that the drone's current altitude is lower than the ideal altitude, and the drone is controlled to increase the absolute value of the drone's attitude and altitude adjustment amount; after adjustment, the new blade flapping angle change rate and the altitude deviation between the drone's current altitude and the current theoretical waypoint altitude are collected in real time, and the calculation and adjustment are repeated until the absolute value of the blade flapping angle change rate and the altitude deviation between the drone's current altitude and the current theoretical waypoint altitude is less than the preset maximum allowable altitude deviation.

8. A blade image acquisition device based on dynamic inspection of wind turbines, characterized in that, The device includes: Initialization module: Acquires basic data and constructs three-dimensional coordinates; Initial inspection route planning module: Based on basic data and location coordinates, the module plans the initial inspection route for the UAV. Flight path horizontal deviation calculation module: Performs data operations in real time during the UAV's flight along the initial flight path; The data operations include real-time parameter acquisition and flight path horizontal deviation calculation. Real-time parameter acquisition includes acquiring the current dynamic attitude parameters of the blades in real-time via a blade attitude acquisition terminal, acquiring current environmental parameters in real-time via an environmental sensing terminal, and acquiring the current position coordinates of the UAV in real-time via a UAV flight control terminal. The current dynamic attitude parameters of the blades specifically include the current flapping angle and the current oscillation angle. The current environmental parameters specifically include the current wind speed and the current wind direction. The flight path horizontal deviation calculation involves calculating the current theoretical coordinates of each core waypoint, and then calculating the horizontal deviation between the UAV's current position and the current theoretical waypoint. ,in , These represent the current x and y coordinates of the drone, respectively. , These represent the current x and y coordinates of the i-th core waypoint, respectively. Indicates the current wind speed. This indicates the initial flight speed of the drone. Indicates the current waving angle. Indicates the initial swing angle; Drone adjustment module: Based on horizontal deviation, performs horizontal flight path adjustment and speed optimization; Dynamic adjustment module: Adjusts the fore-and-aft distance between the drone and the blades based on image quality parameters; UAV left and right offset calibration module: Adjusts the left and right offset based on the UAV's flight attitude parameters; Drone attitude dynamic adaptation module: Adjusts the drone's attitude and altitude based on the dynamic changes in blade flapping and the drone's altitude deviation.

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