Unmanned aerial vehicle fan blade inspection path adaptive planning method and control system

Abstract

The invention discloses an unmanned aerial vehicle fan blade inspection path self-adaptive planning method and a control system, relates to the technical field of electric power inspection, and aims to solve the technical problems that a high-altitude wind power plant is complex in environment, fan blades are different in shape, and traditional inspection path planning is low in efficiency, prone to omission defects and difficult to adapt to sudden meteorological changes. Comprising the following steps: S1, acquiring three-dimensional position coordinates of all wind turbine generators in a region where a wind turbine generator needing to be inspected is located, judging feasibility of air routes among the wind turbine generators, and screening an optimal flight route; and S2, defining the orientations of any two wind turbine generators by using the optimal flight route obtained in the step S1. According to the method, deep coupling of high-altitude multi-environmental factors, blade static characteristics and paths is realized, the core problem of insufficient path adaptability of the existing method can be directly solved, the risks of power compensation misalignment and blade-path space mismatching are avoided, and basic feasibility guarantee is provided for routing inspection of the paths.

Description

Technical Field

[0001] This invention relates to the field of power line inspection technology, and more specifically, to an adaptive planning method and control system for unmanned aerial vehicle (UAV) wind turbine blade inspection paths. Background Technology

[0002] In the field of high-altitude wind farm operation and maintenance, wind turbine blade inspection is a crucial step in preventing the expansion of defects such as blade cracks and corrosion, and ensuring the power generation efficiency of the unit. Due to their high altitude (usually above 3000m) and complex environment, these wind farms generally exhibit characteristics such as the coupling of multiple factors including low temperature, thin air, and fluctuating wind loads; dynamic displacement of blades during operation, such as flapping / swaying; and significant chain changes in the status of drone equipment (battery capacity, motor temperature) due to environmental influences. These factors place stringent requirements on the "environmental adaptability, dynamic matching, and equipment coordination" of the inspection path.

[0003] Existing drone-based wind turbine blade inspection path planning methods have core technical limitations: they only use the straight-line distance between units and static blade parameters as planning basis, without coupling the combined effects of multiple environmental factors at high altitudes, ignoring dynamic attitude changes of the blades, and failing to correlate with the real-time operating status of the drone equipment, resulting in path planning remaining at the theoretical level.

[0004] In practical applications, three major problems often arise:

[0005] First, the power compensation is inaccurate. The power output calculated based on a single altitude parameter cannot meet the combined demands of "low temperature power consumption + thin air and insufficient lift + strong wind load and energy consumption". The drone is prone to a sudden drop in lift or premature exhaustion of its range.

[0006] Second, there is a mismatch between the blade and the path space. The path planned according to the static blade position is either too far apart from the dynamically waving blade (leading to missed detection in critical areas) or too close together (leading to collision risk).

[0007] Third, the real-time response is delayed. When environmental disturbances such as sudden vertical wind shear and short-term gusts occur, the pre-planned path cannot be dynamically adjusted, resulting in blurred sampled images or subsequent inspection timeouts. Ultimately, the existing methods are insufficient to meet the actual needs of blade inspection in complex wind fields at high altitudes, where they require "stability, feasibility, high efficiency, continuity, and comprehensive coverage."

[0008] In view of this, we propose an adaptive planning method and control system for the inspection path of wind turbine blades by unmanned aerial vehicles (UAVs). Summary of the Invention

[0009] The purpose of this invention is to provide an adaptive planning method and control system for unmanned aerial vehicle (UAV) wind turbine blade inspection paths, in order to solve the technical problems of complex environments and varied blade shapes in high-altitude wind farms, where traditional inspection path planning is inefficient, prone to missing defects, and difficult to adapt to sudden weather changes.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution, including the following steps:

[0011] S1: Obtain the three-dimensional position coordinates of all wind turbines in the area where the wind turbines to be inspected are located; construct triple constraints of flight time, wind load, and air thinning compensation parameters; combine the minimum flight time, minimum wind load, and minimum power compensation coefficient dynamic weight objective function to determine the feasibility of the route between turbines and select the optimal flight route.

[0012] S2: Using the optimal flight route obtained in step S1, define the orientation of any two wind turbines and define the orientation information between the wind turbines; based on the obtained orientation information and the structural characteristics of the wind turbines, pre-plan the blade inspection path on the optimal flight route between the turbines.

[0013] S3: Obtain the planned time for the inspection path of each unit, integrate the planned time for the inspection path of each unit to obtain the path duration of the inspection path between all units; then optimize and adjust the inspection time of the units in the predicted path based on the path duration.

[0014] S4: Utilize the optimized and adjusted inspection path to plan the inspection path: determine whether the path meets the unit's inspection conditions; if it does not meet the inspection conditions, perform adaptive adjustments; if it meets the inspection conditions, output the path as the final inspection path.

[0015] This invention constructs a triple constraint condition of flight path time, wind load, and air rarefaction compensation parameters. It combines this with a dynamic weighted objective function of minimum flight path time, minimum wind load, and minimum dynamic compensation coefficient, and incorporates structural features such as blade length and installation angle, along with high-altitude-specific inertial navigation attitude correction technology. This achieves deep coupling between multiple environmental factors at high altitudes, blade static characteristics, and the path. It directly solves the core problem of insufficient path adaptability in existing methods, avoids the risks of dynamic compensation inaccuracies and blade-path spatial mismatch, and provides a fundamental feasibility guarantee for inspection paths.

[0016] Preferably, step S1 includes the following steps:

[0017] S101: Obtain the three-dimensional position coordinates (X) of all wind turbines in the wind farm to be inspected. n ,Y n Z n This method combines barometric altimeter and lidar ranging data for multi-source fusion positioning, correcting positioning errors caused by ionospheric interference and air pressure fluctuations.

[0018] S102: Using flight time, wind load, and air thinning compensation parameters as triple constraints and objective functions, determine the feasibility of inter-unit routes where the air density decreases in high-altitude environments, leading to changes in the lift coefficient of UAVs, and select the optimal route.

[0019] Preferably, the triple constraint conditions include: flight time ≤ preset time threshold T0, wind load ≤ preset load threshold F0, and air thinning compensation parameters;

[0020] The objective function is to determine the optimal flight routes between each pair of aircraft for routes that meet the constraints, using a weighted combination of minimum flight time, minimum wind load, and minimum power compensation coefficient as the optimization objective.

[0021] Preferably, the flight time of the route is determined by... The calculation is as follows: L1 is the actual flight distance between the two wind turbines, V1 is the flight speed of the UAV on the flight path, and α is the altitude correction factor.

[0022] Preferably, step S2 includes the following steps:

[0023] S201: Based on the unit position coordinates obtained from S1, establish a geodetic coordinate system and introduce a high-altitude dedicated inertial navigation system for real-time attitude correction. Define the orientation information between any two units in this coordinate system, and combine terrain data to plan obstacle avoidance routes to avoid collision risks caused by terrain undulations.

[0024] S202: Combining the structural characteristics of wind turbine units, including blade length L blade Based on the optimal flight path between units and combined with the vertical wind shear of complex wind fields, the blade installation angle θ and nacelle height H are used to optimize the dynamic adjustment strategy of path height and pre-plan the blade inspection path.

[0025] Preferably, step S3 includes the following steps:

[0026] S301: Inter-group cruise time: The time it takes for a drone to travel from its current group to an adjacent group during the cruise phase (excluding the inspection and sampling phase), via... Calculate, where L is the straight-line distance between the two units, and V c1 β represents the drone's cruising speed, and β is the power loss coefficient in low-temperature environments.

[0027] S302: Total path duration, total path duration T total cruise time T cruise Flight time T of the route flight and inspection sampling time T sample The sum;

[0028] S303: If T total If the value is >T0, then the patrol time is optimized and adjusted to obtain the optimized patrol path.

[0029] Preferably, optimizing and adjusting the patrol time to obtain an optimized patrol path includes the following methods:

[0030] Fine-tuning cruise speed V c1 The adjusted cruise speed shall not exceed 80% of the drone's maximum cruise speed, and the speed limit shall be limited in combination with the air thinness compensation parameters;

[0031] Reduce the number of sampling points in non-critical areas: In the process of reducing the number of sampling points, it is necessary to ensure that the sampling rate of critical areas of the blade is ≥98%;

[0032] Reselect flight routes with lower wind loads: Reduced wind loads decrease flight drag, thereby shortening the T-shortage time. flight This reduces the total path duration. When re-selecting routes, the constraints of flight time and wind load must be met simultaneously.

[0033] Preferably, to determine whether the path meets the unit's inspection conditions, it is necessary to determine whether the optimized path meets the following conditions: total path duration T total ≤T0, wind load ≤F0, blade inspection coverage ≥95%, equipment temperature within safe range, and air thinning compensation parameters within allowable range.

[0034] Preferably, if the inspection conditions are not met, an adaptive adjustment is made, specifically in the following manner:

[0035] If the wind load exceeds the limit, return to step S1 to recalculate the optimal flight route between computer groups, reduce the flight speed, and increase the motor output power;

[0036] If the equipment temperature is abnormal, the temperature control system will be triggered, and the path will be adjusted to avoid the heat source or cold source area.

[0037] If the air thinness compensation is insufficient, recalculate the dynamic compensation coefficient;

[0038] Repeat steps S2-S4 after adjustment until the path meets all inspection conditions.

[0039] An adaptive planning and control system for unmanned aerial vehicle (UAV) wind turbine blade inspection paths includes:

[0040] The detection module is used to acquire real-time three-dimensional coordinates of wind turbines and drones, detect environmental meteorological data, and collect images of blade surfaces;

[0041] The inspection path acquisition module is used to calculate and select the optimal flight route between wind turbines based on the wind turbine coordinates and environmental data, with flight time, wind load and air thinning compensation parameters as constraints.

[0042] The path planning module is used to plan the inspection paths between crews and optimize the path duration based on the optimal flight route, combined with the crew structure characteristics and time parameters, to ensure path feasibility.

[0043] The control module is used to generate drone motion control commands based on the planned path and dynamically adjust them according to real-time environmental data to ensure that the drone completes the blade inspection task according to the plan.

[0044] The storage module is used to store raw data, historical planning and execution results related to inspections, and supports data backtracking and statistical analysis.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. This invention constructs a triple constraint condition of flight path time, wind load, and air rarefaction compensation parameters, combined with a dynamic weighted objective function of minimum flight path time - minimum wind load - minimum dynamic compensation coefficient, and incorporates structural features such as blade length and installation angle, along with high-altitude-specific inertial navigation attitude correction technology, to achieve deep coupling between multiple environmental factors at high altitudes, blade static characteristics, and the path. This directly solves the core problem of insufficient path adaptability in existing methods, avoids the risks of dynamic compensation inaccuracy and blade-path spatial mismatch, and provides a fundamental feasibility guarantee for inspection paths.

[0047] 2. This invention also integrates the total path duration model based on cruise time, flight route time, and inspection sampling time. Through a triple optimization strategy—fine-tuning cruise speed, reducing sampling points in non-critical areas, and re-selecting routes with lower wind loads—the total path duration is precisely controlled. This further addresses the issue of total path duration exceeding the UAV's endurance threshold caused by environmental adaptation and blade coverage requirements, ensuring that multi-unit continuous inspection tasks are completed efficiently within the UAV's endurance range.

[0048] 3. This invention also establishes a path satisfaction judgment mechanism that includes total path duration ≤ preset threshold T0, wind load ≤ preset threshold F0, blade inspection coverage ≥ 95%, equipment temperature within a safe range, and air thinning compensation parameters compliance. For scenarios with excessive wind load, abnormal equipment temperature, and insufficient air thinning compensation, an adaptive adjustment closed loop is designed. This can further solve the response lag problem caused by real-time environmental changes and equipment status chain effects in the main beneficial effects, ensure the continuous and stable inspection process, and avoid defect omissions or task interruptions caused by sudden situations. Attached Figure Description

[0049] Figure 1 This is a flowchart of the method in this invention. Detailed Implementation

[0050] Example 1: As Figure 1 As shown, the present invention relates to an adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection paths, comprising the following steps:

[0051] S1: Obtain the three-dimensional position coordinates of all wind turbines in the area where the wind turbines to be inspected are located; based on the obtained three-dimensional position coordinates of the wind turbines, obtain the optimal flight route for the pairwise interconnection of all wind turbines;

[0052] S101: Obtain the three-dimensional position coordinates (X) of all wind turbines in the wind farm to be inspected. n ,Y n Z n ), where n is the number of wind turbine units (n≥2), and multi-source fusion positioning is performed by combining barometric altimeter and lidar ranging data to correct positioning errors caused by ionospheric interference and air pressure fluctuations, ensuring the accuracy of three-dimensional coordinates;

[0053] S102: Using flight time, wind load, and air thinning compensation parameters as triple constraints and objective functions, determine the feasibility of inter-team routes where the air density decreases at high altitudes, causing changes in the lift coefficient of UAVs, and select the optimal route; where the air density decreases at high altitudes, causing changes in the lift coefficient of UAVs, flight parameters need to be dynamically corrected using air thinning compensation parameters (based on pre-stored data of altitude-air density curves).

[0054] In embodiments of the present invention, the triple constraint conditions include:

[0055] Flight time of the flight route ≤ preset time threshold T0 (set according to the drone's endurance, such as T0 = 300s);

[0056] Wind load ≤ preset load threshold F0 (set according to the drone's load-bearing capacity, such as F0 = 15N);

[0057] Air thinness compensation parameters: Establish an altitude-power compensation coefficient table (for every 1000 meters increase in altitude, the motor output power needs to be increased by 8%-12%) to ensure that the UAV maintains stable lift in low-pressure environments.

[0058] In an embodiment of the present invention, the objective function is to determine the optimal flight route between each pair of aircraft for routes that meet the constraints, using a weighted combination of the minimum flight time, minimum wind load, and minimum power compensation coefficient as the optimization objective (the weights can be dynamically adjusted according to the operating conditions, such as emphasizing the weight of wind load in a strong wind environment).

[0059] In practical applications, the corresponding optimization target can be selected according to the priority of the inspection task. If the inspection efficiency is pursued, the minimum flight time of the route can be selected as the optimization target. If the flight safety of the UAV is more important, the minimum wind load can be selected as the optimization target.

[0060] In an embodiment of the present invention, the flight time of the route is calculated using the following formula:

[0061]

[0062] In the formula, L1 is the actual flight distance between the two wind turbines, in meters (m); V1 is the flight speed of the UAV along the flight path, in meters per second (m / s), and V1 is dynamically adjusted according to the wind load. When the wind load is large, the flight speed is appropriately reduced to ensure flight stability; when the wind load is small, the flight speed can be appropriately increased to improve inspection efficiency. α is the altitude correction coefficient (calculated from the air thinness compensation parameter).

[0063] S2: Using the optimal flight route obtained in step S1, define the orientation of any two wind turbines and define the orientation information between the wind turbines; based on the obtained orientation information and the structural characteristics of the wind turbines, pre-plan the blade inspection path on the optimal flight route between the turbines.

[0064] S201: Based on the unit position coordinates obtained from S1, a geodetic coordinate system is established, and a high-altitude dedicated inertial navigation system (INS) is introduced for real-time attitude correction. Within this coordinate system, the orientation information between any two units is defined, such as relative angles and distance ranges. Simultaneously, terrain data (using a high-precision digital elevation model, DEM) is combined to plan obstacle avoidance routes, avoiding collision risks caused by terrain undulations. Clear orientation information provides directional guidance for the subsequent pre-planning of blade inspection paths, ensuring the accuracy of path planning.

[0065] S202: Combining the structural characteristics of wind turbine units (such as blade length L) blade Based on the optimal flight path between units and combined with the vertical wind shear of complex wind fields, the dynamic adjustment strategy of path height is optimized and the blade inspection path is pre-planned, taking into account the blade installation angle θ and nacelle height H.

[0066] In embodiments of the present invention, the blade inspection path parameters include:

[0067] Path coverage: ±D on both sides of the unit connection line (D = blade radius + 2m, to ensure full coverage of the blade surface). This setting ensures that the UAV flight path covers the full surface of the blade, avoiding missing blade defects due to incomplete coverage.

[0068] Path height range: [H-0.5L] blade H+0.5L blade A vertical wind shear early warning model based on real-time data from an ultrasonic anemometer is introduced. When the detected vertical wind shear intensity exceeds 3 m / s², an early warning model is established. 2 At that time, the path height is automatically adjusted to a stable air layer. This height range is adapted to the vertical distribution range of the blades, which can ensure that the UAV can conduct comprehensive inspection of different height positions of the blades during the inspection process.

[0069] Path inspection density: An inspection sampling point is set at every 5m interval. In complex wind field areas such as valleys and wind gaps, an adaptive sampling strategy is adopted to dynamically increase the number of sampling points according to the turbulence intensity (up to one sampling point every 2m). A reasonable inspection density can ensure the accuracy of defect detection while avoiding unnecessary inspection time due to too many sampling points.

[0070] S3: Obtain the planned time for the inspection path of each unit, integrate the planned time for the inspection path of each unit to obtain the path duration of the inspection path between all units; then optimize and adjust the inspection time of the units in the predicted path based on the path duration.

[0071] S301: Cruise time between computer groups: The time it takes for a drone to fly from its current group to an adjacent group during the cruise phase (excluding the inspection and sampling phase), calculated using the following formula:

[0072]

[0073] In the formula: L is the straight-line distance between the two units, in meters; V c1 The drone's cruising speed, measured in m / s, is typically V. c1 >V1, as V c1 =15m / s, β is the power loss coefficient under low temperature conditions (dynamically calculated based on the battery temperature and discharge efficiency curve). No inspection sampling is required during the cruise phase; appropriately increasing the cruise speed can shorten the overall inspection time and improve inspection efficiency.

[0074] S302: Total path duration, total path duration T total cruise time T cruise Flight time T of the route flight and inspection sampling time T sample (The sum of the numbers set according to the number of sampling points, such as 2 seconds for each sampling point);

[0075] S303: If T total If the value is >T0, then the patrol time is optimized and adjusted to obtain the optimized patrol path;

[0076] In embodiments of the present invention, optimizing and adjusting the inspection time to obtain an optimized inspection path includes the following methods:

[0077] Fine-tuning cruise speed V c1 The adjusted cruise speed should not exceed 80% of the drone's maximum cruise speed. Combined with the air thinness compensation parameter to limit the upper limit of speed (for example, at an altitude of over 5000 meters, the maximum cruise speed is reduced to 12 m / s), under the premise of ensuring the drone's flight safety, appropriately increasing the cruise speed can effectively shorten the cruise time, thereby reducing the total path duration.

[0078] Reduce the number of sampling points in non-critical areas: In the process of reducing the number of sampling points, it is necessary to ensure that the sampling rate of critical areas of the blade is ≥98%. Critical areas of the blade are high-incidence areas of defects, and ensuring their sampling rate can ensure the quality of inspection. At the same time, reducing the number of sampling points in non-critical areas can reduce the inspection sampling time.

[0079] Reselect flight routes with lower wind loads: Reduced wind loads decrease flight drag, thereby shortening the T-shortage time. flight This reduces the total path duration. When re-selecting routes, the constraints of flight time and wind load must be met simultaneously.

[0080] S4: Calculate the optimized inspection path from the predicted path obtained in step S3, and plan the inspection path according to the optimized inspection path: determine whether the path meets the inspection conditions of the unit; if it does not meet the inspection conditions, make adaptive adjustments; if it meets the inspection conditions, output the path as the final inspection path.

[0081] In an embodiment of the present invention, determining whether the path meets the unit inspection conditions requires determining whether the optimized path meets the following condition: total path duration T. total The path is evaluated from five dimensions: inspection efficiency, flight safety, inspection comprehensiveness, equipment reliability, and environmental adaptability. The conditions are: ≤T0, wind load ≤F0, blade inspection coverage ≥95%, equipment temperature within the safe range (-20℃~60℃), and air rarefaction compensation parameters within the allowable range. Only when all five conditions are met is the path feasible.

[0082] In an embodiment of the present invention, if the inspection conditions are not met, adaptive adjustment is performed, specifically as follows:

[0083] If the wind load exceeds the limit, return to step S1 to recalculate the optimal flight route between computer groups and activate the wind-resistant mode (reduce flight speed and increase motor output power).

[0084] If the equipment temperature is abnormal, the temperature control system will be triggered (such as starting the heating / heat dissipation device), and the path will be adjusted to avoid the heat source or cold source area.

[0085] If the compensation for air thinness is insufficient, recalculate the power compensation coefficient and replace the propeller with one specifically designed for high altitudes if necessary.

[0086] Repeat steps S2-S4 after adjustment until the path meets all inspection conditions. This cyclical adjustment mechanism ensures that the final planned path meets the actual inspection needs, guaranteeing the smooth execution of the inspection task.

[0087] Example 2: The present invention relates to an adaptive planning and control system for unmanned aerial vehicle (UAV) wind turbine blade inspection paths, comprising:

[0088] The detection module is used to acquire real-time three-dimensional coordinates of wind turbines and drones, detect environmental meteorological data, and collect images of blade surfaces to provide basic data for subsequent path planning and control.

[0089] The inspection path acquisition module is used to calculate and select the optimal flight route between wind turbines based on the wind turbine coordinates and environmental data, with flight time, wind load and air thinning compensation parameters as constraints.

[0090] The path planning module is used to plan the inspection paths between crews and optimize the path duration based on the optimal flight route, combined with the crew structure characteristics and time parameters, to ensure path feasibility.

[0091] The control module is used to generate drone motion control commands based on the planned path and dynamically adjust them according to real-time environmental data to ensure that the drone completes the blade inspection task according to the plan.

[0092] The storage module is used to store raw data, historical planning and execution results related to inspections, and supports data backtracking and statistical analysis.

[0093] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. An adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection paths, characterized in that, Includes the following steps: S1: Obtain the three-dimensional position coordinates of all wind turbines in the area where the wind turbines to be inspected are located; construct triple constraints of flight time, wind load, and air thinning compensation parameters; combine the minimum flight time, minimum wind load, and minimum power compensation coefficient dynamic weight objective function to determine the feasibility of the route between turbines and select the optimal flight route. S2: Using the optimal flight route obtained in step S1, define the orientation of any two wind turbines and define the orientation information between the wind turbines; based on the obtained orientation information and the structural characteristics of the wind turbines, pre-plan the blade inspection path on the optimal flight route between the turbines. S3: Obtain the planning time of the inspection path for each unit, integrate the planning time of the inspection path for each unit, and obtain the path duration of the inspection path between all units; Then, based on the path duration, the inspection time of the units in the predicted path is optimized and adjusted; S4: Using the optimized and adjusted inspection path, plan the inspection path: determine whether the path meets the inspection conditions of the unit; If the inspection conditions are not met, adaptive adjustments will be made; if the inspection conditions are met, the path will be output as the final inspection path.

2. The adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection path according to claim 1, characterized in that, Step S1 includes the following steps: S101: Obtain the three-dimensional position coordinates (X) of all wind turbines in the wind farm to be inspected. n ,Y n Z n This method combines barometric altimeter and lidar ranging data for multi-source fusion positioning, correcting positioning errors caused by ionospheric interference and air pressure fluctuations. S102: Using flight time, wind load, and air thinning compensation parameters as triple constraints and objective functions, determine the feasibility of inter-unit routes where the air density decreases in high-altitude environments, leading to changes in the lift coefficient of UAVs, and select the optimal route.

3. The adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection path according to claim 2, characterized in that, The triple constraint conditions include: flight time ≤ preset time threshold T0, wind load ≤ preset load threshold F0, and air thinning compensation parameters; The objective function is to determine the optimal flight routes between each pair of aircraft for routes that meet the constraints, using a weighted combination of minimum flight time, minimum wind load, and minimum power compensation coefficient as the optimization objective.

4. The adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection path according to claim 3, characterized in that, The flight time of the route is determined by... The calculation is as follows: L1 is the actual flight distance between the two wind turbines, V1 is the flight speed of the UAV on the flight path, and α is the altitude correction factor.

5. The adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection path according to claim 4, characterized in that, Step S2 includes the following steps: S201: Based on the unit position coordinates obtained from S1, establish a geodetic coordinate system and introduce a high-altitude dedicated inertial navigation system for real-time attitude correction. Define the orientation information between any two units in this coordinate system, and combine terrain data to plan obstacle avoidance routes to avoid collision risks caused by terrain undulations. S202: Combining the structural characteristics of wind turbine units, including blade length L blade Based on the optimal flight path between units and combined with the vertical wind shear of complex wind fields, the blade installation angle θ and nacelle height H are used to optimize the dynamic adjustment strategy of path height and pre-plan the blade inspection path.

6. The adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection path according to claim 5, characterized in that, Step S3 includes the following steps: S301: Inter-group cruise time: The time it takes for a drone to travel from its current group to an adjacent group during the cruise phase (excluding the inspection and sampling phase), via... Calculate, where L is the straight-line distance between the two units, and V c1 β represents the drone's cruising speed, and β is the power loss coefficient in low-temperature environments. S302: Total path duration, total path duration T total cruise time T cruise Flight time T of the route flight and inspection sampling time T sample The sum; S303: If T total If the value is >T0, then the patrol time is optimized and adjusted to obtain the optimized patrol path.

7. The adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection path according to claim 6, characterized in that, Optimize and adjust patrol times to obtain optimized patrol routes, including the following methods: Fine-tuning cruise speed V c1 The adjusted cruise speed shall not exceed 80% of the drone's maximum cruise speed, and the speed limit shall be limited in combination with the air thinness compensation parameters; Reduce the number of sampling points in non-critical areas: In the process of reducing the number of sampling points, it is necessary to ensure that the sampling rate of critical areas of the blade is ≥98%; Reselect flight routes with lower wind loads: Reduced wind loads decrease flight drag, thereby shortening the T-shortage time. flight This reduces the total path duration. When re-selecting routes, the constraints of flight time and wind load must be met simultaneously.

8. The adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection path according to claim 7, characterized in that, To determine whether a route meets the unit's inspection conditions, it is necessary to check whether the optimized route meets the following condition: total route duration T. total ≤T0, wind load ≤F0, blade inspection coverage ≥95%, equipment temperature within safe range, and air thinning compensation parameters within allowable range.

9. The adaptive planning method for unmanned aerial vehicle (UAV) wind turbine blade inspection path according to claim 8, characterized in that, If the inspection conditions are not met, an adaptive adjustment will be made, specifically as follows: If the wind load exceeds the limit, return to step S1 to recalculate the optimal flight route between computer groups, reduce the flight speed, and increase the motor output power; If the equipment temperature is abnormal, the temperature control system will be triggered, and the path will be adjusted to avoid the heat source or cold source area. If the air thinness compensation is insufficient, recalculate the dynamic compensation coefficient; Repeat steps S2-S4 after adjustment until the path meets all inspection conditions.

10. An adaptive planning and control system for unmanned aerial vehicle (UAV) wind turbine blade inspection paths, which uses the adaptive planning method for UAV wind turbine blade inspection paths as described in claim 9, characterized in that... include: The detection module is used to acquire real-time three-dimensional coordinates of wind turbines and drones, detect environmental meteorological data, and collect images of blade surfaces; The inspection path acquisition module is used to calculate and select the optimal flight route between wind turbines based on the wind turbine coordinates and environmental data, with flight time, wind load and air thinning compensation parameters as constraints. The path planning module is used to plan the inspection paths between crews and optimize the path duration based on the optimal flight route, combined with the crew structure characteristics and time parameters, to ensure path feasibility. The control module is used to generate drone motion control commands based on the planned path and dynamically adjust them according to real-time environmental data to ensure that the drone completes the blade inspection task as planned. The storage module is used to store raw data, historical planning and execution results related to inspections, and supports data backtracking and statistical analysis.

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