Blade attitude detection and air route correction method in fan unmanned aerial vehicle inspection

Abstract

The invention provides a blade attitude detection and route correction method in fan unmanned aerial vehicle inspection, and relates to the technical field of unmanned aerial vehicle application. The method comprises the following steps: configuring an unmanned aerial vehicle, carrying a distance measuring sensor and a YOLO identification module, and obtaining the length of a fan blade; the unmanned aerial vehicle is controlled to fly to the front of a fan hub and vertically rise to the blade midpoint and the blade tip position, and related data are recorded; calculating an attitude angle of the blade according to the measured deviation angle and the actual shape of the blade, and correcting an ideal route of the unmanned aerial vehicle; and generating an inspection route adaptive to the actual position of the blade. The method can improve the inspection precision and safety, is suitable for the complex shape and position deviation of the fan blade, and improves the detection efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle applications, and in particular to a method for blade attitude detection and route correction in wind turbine unmanned aerial vehicle inspection. Background Art

[0002] As one of the core technologies in the field of renewable energy, wind power generation has become an important part of the global energy transformation. In recent years, with the continuous growth of energy demand and the improvement of environmental protection awareness, my country's wind power installed capacity has also increased rapidly. In order to meet market demand and improve energy efficiency, major wind power equipment manufacturers at home and abroad have stepped up their technological innovation efforts to develop and produce larger and more efficient wind turbines. These innovations have not only significantly improved the power generation capacity of a single wind turbine, but also promoted the improvement of the technical level of the entire industry. However, with the expansion of the scale of wind power equipment and the advancement of technology, the complexity and cost of its operation and maintenance are also gradually increasing.

[0003] Among the various components of a wind turbine, the blades are undoubtedly the most critical and complex part. As the core component of the wind turbine, the blades operate for a long time in extreme environments and are subjected to erosion by natural factors such as strong wind, rain, ice and snow, making them a weak link with frequent failures. Common forms of blade failure include surface peeling, crack formation, sand hole appearance, structural fracture, surface icing, and material aging. These problems not only affect the power generation efficiency of the wind turbine, but also pose a serious threat to the overall structural safety of the wind turbine. Especially during the high-speed rotation of the wind turbine, if the blade fails and is not detected and repaired in time, it may lead to catastrophic consequences and even cause the entire wind turbine to be scrapped. Therefore, efficient and accurate inspection and maintenance of blades has become a key measure to ensure the safe operation of wind turbines and extend the service life of equipment.

[0004] Traditional wind turbine blade inspection methods mainly rely on manual operation, including long-distance observation through telescopes, visual inspection by circling and descending at high altitude, and close-up inspection using blade inspection platforms. However, although these methods can detect obvious defects on the blade surface to a certain extent, there are great safety hazards in their operation process and the inspection efficiency is also low. In particular, the dangers of high-altitude operations and complex environmental conditions have greatly limited the application effect of these methods, which in turn affects the utilization rate and economic benefits of wind turbines.

[0005] In view of the limitations of traditional methods, in recent years, drone-based wind turbine blade inspection technology has gradually become a hot topic in industry research. Drone inspection technology uses advanced navigation and image recognition technology to accurately obtain the position and orientation information of the wind turbine hub, plan the flight route of the drone, and realize program-controlled automatic inspection. This method not only greatly improves the inspection efficiency, but also effectively ensures the safety of operators and reduces the risk of high-altitude operations. In addition, drones can flexibly approach different positions of the blades and conduct comprehensive inspections from multiple angles, which makes up for the shortcomings of traditional inspection methods and can detect potential faults of blades earlier.

[0006] However, although drone inspection technology has significant advantages in detection efficiency and safety, it still faces some challenges in practical applications. First, when the wind turbine stops running, the actual position of its blades often deviates from the ideal state, which makes the pre-planned flight route inconsistent with the actual inspection needs. In addition, the wind turbine blades are not ideal straight lines, their shapes are complex and varied, and the planes where the three blades are located are usually not ideally perpendicular to the ground. Therefore, simple route planning cannot meet the needs of precise inspections, and a certain measurement method must be combined to correct the generated ideal route.

[0007] The present invention proposes an innovative measurement method to address the difficulties in the above-mentioned UAV wind turbine blade inspection technology. By acquiring the position information and orientation data of the wind turbine hub in real time and combining it with the actual shape of the blade, the ideal flight route of the UAV is dynamically adjusted. This method can accurately correct the route to ensure that the UAV can accurately cover all key parts of the blade during the inspection process, thereby achieving efficient and safe inspection. This innovative technology not only improves the accuracy and reliability of UAV inspections, but also provides a more scientific and efficient solution for the maintenance of wind power generation equipment. Summary of the invention

[0008] In order to solve the technical problems in the prior art of backward detection means, potential safety hazards, low detection efficiency, poor adaptability and high maintenance cost, the present invention provides a blade attitude detection and route correction method in wind turbine UAV inspection.

[0009] The technical solution provided by the present invention is as follows:

[0010] First aspect:

[0011] The present invention provides a method for blade attitude detection and route correction in wind turbine drone inspection, comprising:

[0012] S1. Configure the drone, carry a range sensor, and integrate an onboard YOLO recognition module, so that the drone can automatically adjust the gimbal angle to ensure that the camera is aimed at the wind turbine blades, obtain the length L of the wind turbine blades, and control the drone to fly to the initial position p0(0,-d,0) directly in front of the wind turbine hub 0(0,0,0), where d is the horizontal distance in front of the hub;

[0013] S2. Control the drone to rise vertically and fly to the forward waypoint position pb1 = (0, -d, L / 2) at the midpoint of wind blade 1 hb1 = B*C*A*(0, 0, L / 2). Adjust the camera to aim at the blade and record the gimbal heading angle and the drone heading angle deviation ab1, and record the distance db1 between the drone and the blade. Control the drone to continue to rise and fly to the tip of wind blade 1 pb2 (0, -d, L). During the process from the middle of the blade to the tip of the blade, record the aircraft coordinates pb at equal intervals. List , gimbal heading angle and drone heading angle deviation ab List and the distance between the drone and the blade db List ;

[0014] S3, control the drone to fly to the midpoint of wind turbine blade 2 ha1 = B·C·A fan ·A·Waypoint position pa1 at hb1 = B fan pb1, records the distance da1 between the drone and the blade;

[0015] S4, according to the data ab1, db1 and da1 obtained in step S2 and step S3, establish a set of equations to solve the left deflection angle β, the backward tilt angle θ and the forward tilt angle α of the fan blade, and according to the data pb obtained in step S2 List , ab List and db List , by looping and calculating the blade lateral offset x at a specific position diff , determine the actual coordinates of the rear half of the fan blade;

[0016] S5. According to the blade attitude angles β, θ and α, the ideal waypoint is rotated and transformed to correct the waypoint of the fan blade 1. The actual waypoint bpoint Real Calculated by the following formula:

[0017]

[0018] For the waypoints of fan blade 2 and fan blade 3, the actual waypoints are calculated according to the following formulas:

[0019] Waypoint apoint of wind turbine blade 2 Real :

[0020]

[0021] Waypoint cpoint of wind turbine blade 3 Real :

[0022]

[0023] Finally, all actual flight routes are generated.

[0024] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0025] (1) In the present invention, the inspection route of the drone is dynamically corrected by measuring the left deflection angle, backward pitch angle and forward pitch angle of the wind turbine blades in real time. This method can effectively avoid the detection blind spots caused by the shape and position deviation of the wind turbine blades, significantly improve the inspection accuracy, reduce the potential collision risk, and improve the safety of the inspection process;

[0026] (2) In the present invention, the integrated YOLO recognition module and ranging sensor are used to enable the drone to automatically adjust the gimbal angle and accurately identify the position of the wind turbine blades, realizing fully automated inspection route planning and correction, greatly improving the efficiency of wind turbine blade inspection and reducing the need for manual intervention;

[0027] (3) In the present invention, according to the actual complex posture and shape of the wind turbine blades, a more practical inspection route can be generated through multiple rotation transformations and posture angle adjustments to adapt to various complex wind field environments. In particular, when the wind turbine shutdown position deviation is large or the blade posture is complex, the inspection task can still be completed efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0029] Figure 1 A schematic flow chart of a method for blade attitude detection and route correction in wind turbine drone inspection provided by an embodiment of the present invention;

[0030] Figure 2 A bird's-eye view of an ideal route trajectory of a method for blade attitude detection and route correction in a wind turbine drone inspection provided by an embodiment of the present invention;

[0031] Figure 3 A three-view image of a wind turbine blade in a method for blade attitude detection and route correction in a wind turbine drone inspection provided by an embodiment of the present invention;

[0032] Figure 4 A schematic diagram of attitude angles of a method for blade attitude detection and route correction in wind turbine drone inspection provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0034] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0035] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0036] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0037] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0038] Reference Manual Attached Figure 1 , shows a flow chart of a method for blade attitude detection and route correction in wind turbine drone inspection provided by an embodiment of the present invention.

[0039] The embodiment of the present invention provides a method for blade attitude detection and route correction in wind turbine drone inspection. The processing flow may include the following steps:

[0040] S1. Configure the drone, carry a range sensor, and integrate an onboard YOLO recognition module, so that the drone can automatically adjust the gimbal angle to ensure that the camera is aimed at the wind turbine blades, obtain the length L of the wind turbine blades, and control the drone to fly to the initial position p0(0,-d,0) directly in front of the wind turbine hub 0(0,0,0), where d is the horizontal distance in front of the hub;

[0041] S2. Control the drone to rise vertically and fly to the forward waypoint position pb1 = (0, -d, L / 2) at the midpoint of wind blade 1 hb1 = B*C*A*(0, 0, L / 2). Adjust the camera to aim at the blade and record the gimbal heading angle and the drone heading angle deviation ab1, and record the distance db1 between the drone and the blade. Control the drone to continue to rise and fly to the tip of wind blade 1 pb2 (0, -d, L). During the process from the middle of the blade to the tip of the blade, record the aircraft coordinates pb at equal intervals. List , gimbal heading angle and drone heading angle deviation ab List and the distance between the drone and the blade db List ;

[0042] S3, control the drone to fly to the midpoint of wind turbine blade 2 ha1 = B·C·A fan ·A·Waypoint position pa1 at hb1 = B fan pb1, records the distance da1 between the drone and the blade;

[0043] S4, according to the data ab1, db1 and da1 obtained in step S2 and step S3, establish a set of equations to solve the left deflection angle β, the backward tilt angle θ and the forward tilt angle α of the fan blade, and according to the data pb obtained in step S2 List , ab List and db List , by looping and calculating the blade lateral offset x at a specific position diff , determine the actual coordinates of the rear half of the fan blade;

[0044] S5. According to the blade attitude angles β, θ and α, the ideal waypoint is rotated and transformed to correct the waypoint of the fan blade 1. The actual waypoint bpoint Real Calculated by the following formula:

[0045]

[0046] For the waypoints of fan blade 2 and fan blade 3, the actual waypoints are calculated according to the following formulas:

[0047] Waypoint apoint of wind turbine blade 2 Real :

[0048]

[0049] Waypoint cpoint of wind turbine blade 3 Real :

[0050]

[0051] Finally, all actual flight routes are generated.

[0052] In a possible implementation, the fan blade 1 is a blade installed vertically upward, and the fan blade 2 and the fan blade 3 are obtained by rotating 120 degrees clockwise or counterclockwise around the hub, respectively.

[0053] In a possible implementation, the rotation matrix corresponding to the forward tilt angle α is:

[0054]

[0055] The rotation matrix corresponding to the left deflection angle β is:

[0056]

[0057] The rotation matrix corresponding to the back tilt angle θ is:

[0058]

[0059] In a possible implementation, the waypoint of wind blade 1 is rotated to the rotation matrix A of wind blade 2. fan as follows:

[0060]

[0061] The rotation matrix C of the waypoint of wind blade 1 to wind blade 3 fan as follows:

[0062]

[0063] In a possible implementation, the equation group for the left deflection angle β, the rearward tilt angle θ and the forward tilt angle α of the wind blade is solved as follows:

[0064] ‖hb1-pb1‖=db1

[0065]

[0066] ‖ha1-pa1‖=da1

[0067] Solving the above equations can obtain the left deflection angle β, the backward tilt angle θ and the forward tilt angle α of the wind blade.

[0068] In a possible implementation manner, the S4 specifically includes:

[0069] The blade lateral offset x diff By pb List , ab List and db List It is calculated that for a specific value (0, -d, h), a in the three list loops l and d l Calculate the blade lateral offset x diff :

[0070] x diff =d l ×sin a l -h×tanβ.

[0071] In a possible implementation manner, the S5 specifically includes:

[0072] According to the blade lateral offset x diff The actual coordinates of each point on the rear half of the wind turbine blade are obtained. According to the number of waypoints required, some key points are selected as actual waypoints to obtain the actual offset distances in the x direction of the four routes of wind turbine blade 1 at multiple heights, thereby obtaining the ideal route waypoints. Add the x-direction deviation to get the corrected waypoint

[0073] The basic logic of the basic route generation method is:

[0074] The shape and position of the fan blades are in an ideal state, which means:

[0075] 1. All the fan blades are straight and in the same plane vertical to the ground;

[0076] 2. The fan blade 1 is perpendicular to the ground and upward from the hub;

[0077] 3. Fan blades 2 and 3 can be obtained by rotating fan blade 1 120 degrees around the hub.

[0078] The inspection route of wind turbine blade 1 is a vertical straight line, flying in front of, behind, left and right of the wind turbine blade. The front and back routes face the wind turbine blade directly. To avoid the risk of collision caused by accidental rotation of the wind turbine, the left and right routes of the drone face the wind turbine blade diagonally. Figure 2 As shown, the four magenta routes are photo routes, named L1, L2, L3 and L4, and the rest are transition routes. The routes are connected head to tail. During the transition process from L1 to L2 and from L3 to L4, the drone will cross the vertical plane formed by the three wind blades from the blade tip. To avoid collision with the wind turbine blades, the heights at both ends of the L1 to L4 routes correspond to the hub height and the blade tip height + 5 meters respectively; the transition route uses a discounted detour to increase the distance between the drone and the wind blade. After the L4 flight is completed, the drone returns to the starting position of L1, which is directly in front of the hub.

[0079] Due to the symmetry of the three blades, the flight path of the fan blade 1 can be rotated 120 degrees clockwise and counterclockwise respectively with the hub as the center to obtain the flight paths of the fan blade 2 and the fan blade 3.

[0080] Depend on Figure 3 It can be seen that:

[0081] 1. The shape of the fan blade is not an ideal straight line, and the rear half has a significant curvature;

[0082] 2. The plane where the three wind turbine blades are located is not ideally perpendicular to the ground, but slightly tilted backward. According to the wind turbine operation logic, the tilt angle will change according to the real-time wind force;

[0083] 3. When installing the fan blades, they should be slightly extended from the root to the tip;

[0084] 4. The fan parking position cannot ensure that the fan blade 1 is perpendicular to the ground from the hub and may be slightly deflected along the Y axis.

[0085] The above characteristics of wind turbine deployment result in poor results of ideal route inspection aerial photography and collision risks. Therefore, it is necessary to calculate the corresponding attitude angle through flight survey and correct the ideal route through rotation operation. The attitude angle to be calculated is as follows: Figure 4 As shown, it includes the left deviation angle β, the rearward tilt angle θ and the forward tilt angle α.

[0086] The rotation matrices corresponding to the forward tilt angle α, backward tilt angle θ and left tilt angle β are:

[0087]

[0088] The rotation matrix A of the waypoint of wind blade 1 to wind blade 2 fan and the rotation matrix C of the fan blade 3 fan as follows:

[0089]

[0090] The front half of the wind turbine can be approximately regarded as a straight line. Under this assumption, the specific method of flight survey is as follows:

[0091] 1. Configure the drone, carry a range-finding sensor, and integrate an onboard YOLO recognition module, so that the drone can automatically adjust the gimbal angle to ensure that the camera is aimed at the wind turbine blades;

[0092] 2. Get the length L of the wind turbine blades and control the drone to fly to the initial position p0(0,-d,0) directly in front of the wind turbine hub 0(0,0,0), where d is the horizontal distance in front of the hub;

[0093] 3. Control the drone to rise vertically and fly to the forward waypoint position pb1 = (0, -d, L / 2) at the midpoint of wind turbine blade 1 hb1 = B*C*A*(0, 0, L / 2). Adjust the camera to aim at the blade and record the gimbal heading angle and the drone heading angle deviation ab1, and record the distance db1 between the drone and the blade.

[0094] 4. Control the drone to continue to rise and fly to the tip of wind turbine blade 1, pb2 (0, -d, L). During the process from the middle of the blade to the tip of the blade, record the aircraft coordinates pb at equal intervals. List , gimbal heading angle and drone heading angle deviation ab List and the distance between the drone and the blade db List ;

[0095] 5. Control the drone to fly to the midpoint of wind turbine blade 2, ha1 = B·C·A fan ·A·Waypoint position pa1 at hb1 = B fan pb1, records the distance da1 between the drone and the blade.

[0096] The following equations are established based on the data ab1, db1 and da1 obtained from steps 3 and 5 above:

[0097] ‖hb1-pb1‖=db1

[0098]

[0099] ‖ha1-pa1‖=da1

[0100] Solving the above equations can obtain the left deflection angle β, rearward tilt angle θ and forward tilt angle α of the wind blade.

[0101] The data pb obtained from step 4 above List , ab List and db List , for a specific value (0, -d, h), a in the three list loops l and d l Calculate the blade lateral offset x diff :

[0102] x diff =d l ×sin a l -h×tanβ

[0103] Then, the actual coordinates of each point on the rear half of the wind turbine blade can be obtained. According to the number of waypoints required, some key points can be selected as actual waypoints to obtain the actual offset distances in the x direction of the four routes of blade 1 at multiple heights, thereby obtaining the ideal route waypoints. Add the x-direction deviation to get the corrected waypoint

[0104] According to the obtained blade attitude angles β, θ and α, all the ideal path waypoints after the offset are rotated and any waypoint of the four ideal paths after the offset of wind turbine blade 1 is rotated. The actual waypoint bpoint Real Calculated by the following formula:

[0105]

[0106] For the waypoints of fan blade 2 and fan blade 3, the actual waypoints are calculated according to the following formulas:

[0107] Waypoint apoint of wind turbine blade 2 Real :

[0108]

[0109] Waypoint cpoint of wind turbine blade 3 Real :

[0110]

[0111] Finally, all actual flight routes are generated

[0112] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0113] (1) In the present invention, the inspection route of the drone is dynamically corrected by measuring the left deflection angle, backward pitch angle and forward pitch angle of the wind turbine blades in real time. This method can effectively avoid the detection blind spots caused by the shape and position deviation of the wind turbine blades, significantly improve the inspection accuracy, reduce the potential collision risk, and improve the safety of the inspection process;

[0114] (2) In the present invention, the integrated YOLO recognition module and ranging sensor are used to enable the drone to automatically adjust the gimbal angle and accurately identify the position of the wind turbine blades, realizing fully automated inspection route planning and correction, greatly improving the efficiency of wind turbine blade inspection and reducing the need for manual intervention;

[0115] (3) In the present invention, according to the actual complex posture and shape of the wind turbine blades, a more practical inspection route can be generated through multiple rotation transformations and posture angle adjustments to adapt to various complex wind field environments. In particular, when the wind turbine shutdown position deviation is large or the blade posture is complex, the inspection task can still be completed efficiently.

[0116] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0117] There are a few points to note:

[0118] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0119] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0120] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0121] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for blade attitude detection and route correction in wind turbine drone inspection, characterized in that: include: S1. Configure the drone, carry a range sensor, and integrate an onboard YOLO recognition module, so that the drone can automatically adjust the gimbal angle to ensure that the camera is aimed at the wind turbine blades, obtain the length L of the wind turbine blades, and control the drone to fly to the initial position p0(0,-d,0) directly in front of the wind turbine hub 0(0,0,0), where d is the horizontal distance in front of the hub; S2. Control the drone to rise vertically and fly to the forward waypoint position pb1 = (0, -d, L / 2) at the midpoint of wind blade 1 hb1 = B*C*A*(0, 0, L / 2). Adjust the camera to aim at the blade and record the gimbal heading angle and the drone heading angle deviation ab1, and record the distance db1 between the drone and the blade. Control the drone to continue to rise and fly to the tip of wind blade 1 pb2 (0, -d, L). During the process from the middle of the blade to the tip of the blade, record the aircraft coordinates pb at equal intervals. List , gimbal heading angle and drone heading angle deviation ab List and the distance between the drone and the blade db List ; S3, control the drone to fly to the midpoint of wind turbine blade 2 ha1 = B·C·A fan· A·Waypoint position pa1 at hb1 = B fan pb1, records the distance da1 between the drone and the blade; S4, according to the data ab1, db1 and da1 obtained in step S2 and step S3, establish a set of equations to solve the left deflection angle β, the backward tilt angle θ and the forward tilt angle α of the fan blade, and according to the data pb obtained in step S2 List , ab List and db List , by looping and calculating the blade lateral offset x at a specific position diff , determine the actual coordinates of the rear half of the fan blade; S5. According to the blade attitude angles β, θ and α, the ideal waypoint is rotated and transformed to correct the waypoint of the fan blade 1. The actual waypoint bpoint Real Calculated by the following formula: For the waypoints of fan blade 2 and fan blade 3, the actual waypoints are calculated according to the following formulas: Waypoint apoint of wind turbine blade 2 Real : Waypoint cpoint of wind turbine blade 3 Real : Finally, all actual flight routes are generated.

2. The method for blade attitude detection and route correction in wind turbine UAV inspection according to claim 1 is characterized in that: include: The fan blade 1 is a blade installed vertically upward, and the fan blade 2 and the fan blade 3 are obtained by rotating 120 degrees clockwise or counterclockwise around the hub respectively.

3. The method for blade attitude detection and route correction in wind turbine drone inspection according to claim 1 is characterized in that: include: The rotation matrix corresponding to the forward tilt angle α is: The rotation matrix corresponding to the left deflection angle β is: The rotation matrix corresponding to the back tilt angle θ is:

4. The method for blade attitude detection and route correction in wind turbine drone inspection according to claim 1 is characterized in that: include: The rotation matrix A of the waypoint of wind blade 1 to wind blade 2 fan as follows: The rotation matrix C of the waypoint of wind blade 1 to wind blade 3 fan as follows:

5. The method for blade attitude detection and route correction in wind turbine drone inspection according to claim 1 is characterized in that: include: The equations for the left deflection angle β, the backward tilt angle θ and the forward tilt angle α of the fan blade are as follows: ||hb1-pb1||=db1 ||ha1-pa1||=da1 Solving the above equations can obtain the left deflection angle β, the backward tilt angle θ and the forward tilt angle α of the wind blade.

6. The method for blade attitude detection and route correction in wind turbine drone inspection according to claim 1 is characterized in that: The S4 specifically includes: The blade lateral offset x diff By pb List , ab List and db List It is calculated that for a specific value (0, -d, h), a in the three list loops l and d l Calculate the blade lateral offset x diff : x diff =d l ×sin a l -h×tanβ。 7. The method for blade attitude detection and route correction in wind turbine drone inspection according to claim 6 is characterized in that: The S5 specifically includes: According to the blade lateral offset x diff The actual coordinates of each point on the rear half of the wind turbine blade are obtained. According to the number of waypoints required, some key points are selected as actual waypoints to obtain the actual offset distances in the x direction of the four routes of wind turbine blade 1 at multiple heights, thereby obtaining the ideal route waypoints. Add the x-direction deviation to get the corrected waypoint

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