Unmanned aerial vehicle control method and system based on wind driven generator blade damage detection

Abstract

The invention relates to an unmanned aerial vehicle control method and system based on wind driven generator blade damage detection, and belongs to the technical field of unmanned aerial vehicles. The method comprises the steps that the real-time distance between the current position of an unmanned aerial vehicle and the center of a wind driven generator is obtained through an RGB-D camera, and the position of the unmanned aerial vehicle is corrected; a wind driven generator image is obtained through an RGB-D camera, a space coordinate system is established, and a wind driven generator blade recognition result is obtained through calculation of an image detection and recognition model; presetting a detection point sequence, obtaining related parameters, and calculating to obtain change coordinates of the detection points through a coordinate calculation model according to the related parameters; inputting the related parameters into an unmanned aerial vehicle control network model to obtain a motor change control quantity of the unmanned aerial vehicle; the intelligent terminal generates a self-adaptive signal according to the motor change control quantity to control the unmanned aerial vehicle to move to a detection point and perform defect detection; and the execution is repeated until all the blades are detected. High-precision unmanned aerial vehicle control is achieved for wind driven generator blade detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a control method and system of an unmanned aerial vehicle based on wind turbine blade damage detection. Background Art

[0002] With the rapid development of renewable energy technology, wind power generation has become an important part of the global energy structure. In the wind power industry, the reliability and performance of wind turbine blades are crucial. However, due to the long-term influence of the natural environment and operating conditions, defects on the blades often occur, which may lead to performance degradation or even failure. Current detection methods usually require downtime for maintenance or the use of expensive equipment, relying on manual climbing or rope suspension detection. These methods have problems such as high labor intensity, low efficiency, high risk, and cannot achieve real-time monitoring and tracking of blade defects.

[0003] Current problems:

[0004] (1) Damage detection of wind turbine blades mainly relies on manual labor, which has problems such as high labor intensity, low efficiency and poor safety.

[0005] (2) The existing UAV control system cannot meet the requirements of accurate inspection of wind turbine blades and lacks professional control strategies and algorithms. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the present invention provides a UAV control method and system based on wind turbine blade damage detection.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] S1: Control the drone to fly relative to the wind turbine, obtain the real-time distance between the current position of the drone and the center of the wind turbine through the RGB-D camera carried by the drone and transmit it to the smart terminal, the smart terminal compares the real-time distance with the preset optimal detection distance to obtain a comparison result, and corrects the drone position according to the comparison result;

[0009] S2: The wind turbine image is acquired in real time by the RGB-D camera and transmitted to the smart terminal. The smart terminal establishes a spatial coordinate system according to the wind turbine image, and calculates blade No. 1, blade No. 2, and blade No. 3 by using an image detection and recognition model, and uses blade No. 1 as the detection blade.

[0010] S3: Preset a detection point sequence for the detection blade, and obtain the initial coordinates of each detection point in the detection point sequence in the spatial coordinate system, obtain the rotation angular velocity of the test blade, the distance data between the detection point and the center point, the instantaneous speed of the drone, the detection running speed of the drone and the current coordinates of the drone, calculate the transformed coordinates of the detection point within a preset time length according to the rotation angular velocity and the initial coordinates of the detection point through a coordinate calculation model, and calculate the coordinate difference between the current coordinates of the drone and the transformed coordinates;

[0011] S4: Input the rotation angular velocity, the UAV detection running speed, the UAV instantaneous speed, the distance data between the detection point and the center point, the motor control amount of the UAV, and the coordinate difference as state quantities into the UAV control network model to obtain the motor change control amount of the UAV through calculation;

[0012] S5: the intelligent terminal generates an adaptive signal according to the motor change control amount to control the propeller operation state of the drone, so that the drone runs to the transformed coordinates, and uses the ultrasonic machine carried by the drone to perform defect detection on the area corresponding to the transformed coordinates;

[0013] S6. Repeat steps S1-S5 until the second blade and the third blade are all detected.

[0014] As a preferred technical solution of the present invention, the specific steps of the UAV control network model training are as follows:

[0015] S201: Setting a virtual environment for detecting wind turbine blades;

[0016] S202: obtaining the current state quantity of all detection points in the detection virtual environment, and setting a reward function according to the current state quantity, inputting the current state quantity into a preset UAV control network model to obtain the motor change control quantity of the UAV by calculation, obtaining the state quantity at the next moment, establishing an experience quadruple with the current state quantity, the motor change control quantity, the reward function, and the next moment state quantity, and storing all the experience quadruple in a special experience playback pool for reinforcement learning. When all blades are detected, it indicates that the interaction of this game is over;

[0017] S203: taking n groups of experience quadruple groups with poor correlation from the reinforcement learning dedicated experience replay pool as test groups through a priority experience replay algorithm, and continuously updating the preset drone control network model parameters according to the test groups;

[0018] S204: Obtain a reward function curve graph, and determine whether the reward function tends to be stable based on the reward function curve graph. If the reward function tends to be stable, end the loop and obtain the UAV control network model; if the reward function does not tend to be stable, continue to repeat steps S202-S204 for training until the reward function tends to be stable.

[0019] Specifically, the reward function r t (s t ) is expressed as:

[0020]

[0021] Among them, ω1, ω2, ω3 represent hyperparameters, ω v is the rotation angular velocity, l r is the distance data between the detection point and the center point, v0 is the detection running speed of the drone, and v t is the instantaneous speed of the drone, Δx, Δy, Δz are the coordinate differences, are the motor control quantities of the first wing, the second wing, the third wing, and the fourth wing of the UAV, respectively. It is the motor change control amount of wing No. 1, wing No. 2, wing No. 3 and wing No. 4 of the UAV.

[0022] Specifically, the UAV control network model consists of an Actor network, a Target Actor network, a Critic1 network, a Target Critic1 network, a Critic2 network, and a Target Critic2 network. The input of the Actor network is the state quantity s perceived by the UAV at the current moment. t The output of the Actor network is the control amount a sent to the drone motor at the current moment. t , the input of the Target Actor network is the state quantity s perceived by the drone at the next moment t ', the output of the TargetActor network is the control amount a sent to the drone motor at the next moment t '; The input of the Critic1 network and the Critic2 network is the state-action pair at the current moment (s t ,a t ), the output is the action pair according to the current state (s t ,a t ) The state value evaluation value given by the application neural network; the input of the Target Critic1 network and the TargetCritic2 network is the state action pair (s t ',a t'), the output is the state action pair (s t ',a t ') Apply the state value target value given by the neural network.

[0023] Specifically, the calculation formula of the motor control amount is as follows:

[0024] a t =μ(s t |θ μ )+∈

[0025] ∈=clip(N(0,σ),-b,b)

[0026] where s t Represents the current state, θ μ represents the Actor network parameters, μ(·) represents the Actor network, ∈ represents the random noise parameters, and a t is the motor control variable. The clip function indicates that when N(0,σ)<-b, ∈=-b; when N(0,σ)>b, ∈=b. ∈=N(0,σ) means that ∈ satisfies the normal distribution. -b and b represent fixed parameters.

[0027] Specifically, the priority experience playback algorithm selects the experience data from the experience buffer pool and the calculation formula is:

[0028]

[0029] Wherein, P(c) represents a set of experience quadruplets (s t ,a t ,r t ,s t '); c represents the sequence number of the extracted empirical data; p k represents the priority of the extracted empirical data; α represents the preset parameter used to adjust the priority sampling degree of data samples.

[0030] Specifically, the coordinate calculation model regards the wind turbine as rotating about the Z axis of the spatial coordinate system, calculates the rotation angle of the detection blade around the Z axis according to the rotation angular velocity, and establishes a rotation matrix according to the rotation angle, and then calculates the transformed coordinates according to the rotation matrix and the initial coordinates through a coordinate transformation algorithm. The coordinate transformation algorithm calculation formula is:

[0031]

[0032] Among them, (x1, y1, z1) is the transformed coordinate, a is the scale parameter, b is the rotation angle, and (x, y, z) is the initial coordinate.

[0033] A UAV control system based on wind turbine blade damage detection includes: a position correction module, an identification module, a data acquisition module, a control calculation module, a detection module and a circulation module;

[0034] The position correction module is used to control the UAV to fly relative to the wind turbine. The real-time distance between the current position of the UAV and the center of the wind turbine is obtained through the RGB-D camera carried by the UAV and transmitted to the smart terminal. The smart terminal compares the real-time distance with the preset optimal detection distance to obtain a comparison result, and corrects the UAV position according to the comparison result.

[0035] The recognition module is used to obtain the wind turbine image in real time through the RGB-D camera and transmit it to the smart terminal. The smart terminal establishes a spatial coordinate system according to the wind turbine image, and calculates blade No. 1, blade No. 2, and blade No. 3 through the image detection and recognition model, and uses blade No. 1 as the detection blade;

[0036] The data acquisition module is used to preset the detection point sequence of the detection blade, and obtain the initial coordinates of each detection point in the detection point sequence in the spatial coordinate system, obtain the rotation angular velocity of the test blade, the distance data between the detection point and the center point, the instantaneous speed of the drone, the detection running speed of the drone and the current coordinates of the drone, calculate the transformed coordinates of the detection point within a preset time length according to the rotation angular velocity and the initial coordinates of the detection point through a coordinate calculation model, and calculate the coordinate difference between the current coordinates of the drone and the transformed coordinates;

[0037] The control calculation module is used to input the rotation angular velocity, the UAV detection running speed, the UAV instantaneous speed, the distance data between the detection point and the center point, the motor control amount of the UAV, and the coordinate difference as state quantities into the UAV control network model to obtain the motor change control amount of the UAV through calculation;

[0038] The detection module is used for the intelligent terminal to generate an adaptive signal according to the motor change control amount to control the propeller operation state of the drone, so that the drone runs to the transformation coordinates and uses the ultrasonic machine carried by the drone to perform defect detection on the detection blade corresponding to the transformation coordinates;

[0039] The loop module is used to repeat steps S1-S5 until the second blade and the third blade are all detected.

[0040] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned UAV control method based on wind turbine blade damage detection is implemented.

[0041] A storage medium containing computer executable instructions, characterized in that the computer executable instructions are used to execute the drone control method based on wind turbine blade damage detection as described above when executed by a computer processor.

[0042] The beneficial effects of the present invention are:

[0043] (1) The wind turbine images are collected by a drone equipped with an RGB-D camera, and a spatial coordinate system is established based on the wind turbine images. The recognition results of the wind turbine blades are obtained through the image detection and recognition model, and the detection point sequence is marked on the spatial coordinate system. The drone tracks the detection point coordinates in real time and transmits ultrasonic waves to the detection point area through an ultrasonic machine to perform detection, which solves the problem of the wind turbine needing to be shut down for detection and avoids the need for manual detection.

[0044] (2) The rotation angular velocity, the UAV detection running speed, the UAV instantaneous speed, the distance data between the detection point and the center point, the UAV motor control quantity, and the coordinate difference are input as state quantities into the UAV control network model to obtain the motor change control quantity of the UAV through calculation, and the UAV heading and flight speed are changed according to the motor change control quantity, thereby improving the UAV's tracking and positioning of the detection point.

[0045] (3) The priority experience replay algorithm is introduced into the UAV control network model, which improves the learning rate of the UAV control network model, reduces sample bias, and improves stability, robustness, and generalization ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0047] Figure 1 The present invention is a flowchart of a method for controlling a UAV based on wind turbine blade damage detection.

[0048] Figure 2 This is a flow chart of the training process of the drone control network model of the present invention.

[0049] Figure 3 This is a diagram of the network architecture of the Actor class network.

[0050] Figure 4 Schematic diagram of the network architecture of the Critic network. DETAILED DESCRIPTION

[0051] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0052] See also Figure 1 , A UAV control method based on wind turbine blade damage detection,

[0053] S1: Control the drone to fly relative to the wind turbine, obtain the real-time distance between the current position of the drone and the center of the wind turbine through the RGB-D camera carried by the drone and transmit it to the smart terminal, the smart terminal compares the real-time distance with the preset optimal detection distance to obtain a comparison result, and corrects the drone position according to the comparison result;

[0054] S2: The wind turbine image is acquired in real time by the RGB-D camera and transmitted to the smart terminal. The smart terminal establishes a spatial coordinate system according to the wind turbine image, and calculates blade No. 1, blade No. 2, and blade No. 3 by using an image detection and recognition model, and uses blade No. 1 as the detection blade.

[0055] S3: Preset a detection point sequence for the detection blade, and obtain the initial coordinates of each detection point in the detection point sequence in the spatial coordinate system, obtain the rotation angular velocity of the test blade, the distance data between the detection point and the center point, the instantaneous speed of the drone, the detection running speed of the drone and the current coordinates of the drone, calculate the transformed coordinates of the detection point within a preset time length according to the rotation angular velocity and the initial coordinates of the detection point through a coordinate calculation model, and calculate the coordinate difference between the current coordinates of the drone and the transformed coordinates;

[0056] S4: Input the rotation angular velocity, the UAV detection running speed, the UAV instantaneous speed, the distance data between the detection point and the center point, the motor control amount of the UAV, and the coordinate difference as state quantities into the UAV control network model to obtain the motor change control amount of the UAV through calculation;

[0057] S5: the intelligent terminal generates an adaptive signal according to the motor change control amount to control the propeller operation state of the drone, so that the drone runs to the transformed coordinates and uses the ultrasonic machine carried by the drone to perform defect detection on the inspection blade in the area corresponding to the transformed coordinates;

[0058] S6. Repeat steps S1-S5 until the second blade and the third blade are all detected.

[0059] In this embodiment, the image detection and recognition model is an image recognition model based on deep learning, which effectively learns the features of the image through multiple convolutional layers and pooling layers, and can recognize the wind turbine blades from the wind turbine image.

[0060] As a preferred technical solution of the present invention, the specific steps of the UAV control network model training are as follows:

[0061] S201: Setting a virtual environment for detecting wind turbine blades;

[0062] S202: obtaining the current state quantity of all detection points in the detection virtual environment, and setting a reward function according to the current state quantity, inputting the current state quantity into a preset UAV control network model to obtain the motor change control quantity of the UAV by calculation, obtaining the state quantity at the next moment, establishing an experience quadruple with the current state quantity, the motor change control quantity, the reward function, and the next moment state quantity, and storing all the experience quadruple in a special experience playback pool for reinforcement learning. When all blades are detected, it indicates that the interaction of this game is over;

[0063] S203: taking n groups of experience quadruple groups with poor correlation from the reinforcement learning dedicated experience replay pool as test groups through a priority experience replay algorithm, and continuously updating the preset drone control network model parameters according to the test groups;

[0064] S204: Obtain a reward function curve graph, and determine whether the reward function tends to be stable based on the reward function curve graph. If the reward function tends to be stable, end the loop and obtain the UAV control network model; if the reward function does not tend to be stable, continue to repeat steps S202-S204 for training until the reward function tends to be stable.

[0065] In this embodiment, the drone control network model adopts an improved double-delay deep deterministic policy gradient algorithm.

[0066] Specifically, the reward function r t (s t ) is expressed as:

[0067]

[0068] Among them, ω1, ω2, ω3 represent hyperparameters, ω v is the rotation angular velocity, l r is the distance data between the detection point and the center point, v0 is the detection running speed of the drone, and v t is the instantaneous speed of the drone, Δx, Δy, Δz are the coordinate differences, are the motor control quantities of the first wing, the second wing, the third wing, and the fourth wing of the UAV, respectively. It is the motor change control amount of wing No. 1, wing No. 2, wing No. 3 and wing No. 4 of the UAV.

[0069] Specifically, the UAV control network model consists of an Actor network, a Target Actor network, a Critic1 network, a Target Critic1 network, a Critic2 network, and a Target Critic2 network. The input of the Actor network is the state quantity s perceived by the UAV at the current moment. t The output of the Actor network is the control amount a sent to the drone motor at the current moment. t , the input of the Target Actor network is the state quantity s perceived by the drone at the next moment t ', the output of the TargetActor network is the control amount a sent to the drone motor at the next moment t '; The input of the Critic1 network and the Critic2 network is the state-action pair at the current moment (s t ,a t ), the output is the action pair according to the current state (s t ,a t ) The state value evaluation value given by the application neural network; the input of the Target Critic1 network and the TargetCritic2 network is the state action pair (s t ',a t '), the output is the state action pair (s t ',a t ') Apply the state value target value given by the neural network.

[0070] In this embodiment, the network parameter update process of Critic1 and Critic2 is as follows:

[0071] Use the Target Actor network to calculate the next state s t 'The next action a t ', action a t The calculation formula of ' is as follows:

[0072] a t ′=μ′(s t ′|θ μ′ ),

[0073] Among them, θ μ ' is the network parameter of the Target Actor network, μ'(·) represents the Target Actor network;

[0074] Then based on the target strategy smoothing regularization, in the target action a t 'Add noise to it:

[0075] a t =μ(s t |θ μ )+∈

[0076] ∈=clip(N(0,σ),-b,b)

[0077] Then, based on the idea of ​​dual networks, the Target Critic network is used to calculate the state-action pair (s t ,a t )’s state value target value y t , state value target value y t The calculation formula is as follows:

[0078]

[0079] Among them, r t is the reward function, γ is the discounted return rate, and θ i Q' are the network parameters of Target Critic1 and TargetCritic2 networks, Q i '(·) represents the Target Critic1 and Target Critic2 networks;

[0080] Finally, the gradient descent algorithm is used to minimize the error L between the evaluation value and the target value. ci , thereby updating the parameters in the Critic1 and Critic2 networks:

[0081]

[0082] Where Q i (·) represents the Target Critic1 and Target Critic2 networks, s t and a t Represents the current state and action quantity, θ i Q are the network parameters of Critic1 and Critic2 networks.

[0083] Actor network parameter update process:

[0084] After the Ctitic1 and Critic2 networks are updated for d steps, the Actor network update is started and the state s is calculated using the Actor network. t The next action a t :

[0085] a t =μ(s t |θ μ ),

[0086] Among them, θ μ is the network parameter of the Actor network, μ(·) represents the Actor network. It should be noted here that there is no need to add noise after calculating the action, because here we hope that the Actor network can be updated towards the maximum value, and adding noise does not make any sense;

[0087] Use Critic1 or Critic2 network to calculate the state action pair (s t ,a t )’s state value evaluation value q t , state value evaluation value q t The calculation formula is as follows, assuming that the Critic1 network is used:

[0088] q t =Q1(s t ,a t |θ Q1 ),

[0089] Among them, θ Q1 are the network parameters of Critic1 network, Q1(·) represents the Critic1 network;

[0090] Finally, the gradient ascent algorithm is used to maximize the state value evaluation value q t , thereby updating the parameters θ in the Actor network μ The reason why either Critic1 or Critic2 can be used to calculate the Q value is mainly because the purpose of the Actor network is to maximize the cumulative expected return, and there is no need to use the minimum value.

[0091] The TD3 algorithm uses a soft update method, in which the parameter update process of the Target Actor network is as follows:

[0092] θ μ′ =τθ μ +(1-τ)θ μ′ ,

[0093] The Target Critic1 and Target Critic2 network update process is as follows:

[0094]

[0095] Where τ is the learning rate (momentum), τ∈(0,1), and is usually set to 0.005.

[0096] Specifically, noise needs to be added when calculating the motor control amount, and the calculation formula of the motor control amount is as follows:

[0097] a t =μ(s t |θ μ )+∈

[0098] ∈=clip(N(0,σ),-b,b)

[0099] where s t Represents the current state, θ μ represents the Actor network parameters, μ(·) represents the Actor network, a t is the motor control variable, ∈ represents the random noise parameter; the clip function means that when N(0,σ)<-b, ∈=-b, when N(0,σ)>b, ∈=b, ∈=N(0,σ) means that ∈ satisfies the normal distribution, -b and b represent fixed parameters.

[0100] In this embodiment, the Actor network mainly includes 1 input layer, 4 hidden layers and 1 output layer. The input layer contains 11 nodes, corresponding to the state quantity s t The hidden layer is composed of linear layers. The four hidden layers contain 512, 512, 256, and 128 nodes respectively. The output layer contains 4 nodes, corresponding to the control amount a t The four parameters of the Critic network architecture include two branches. The first branch is similar to the Actor network, including 1 input layer and 4 hidden layers. The input layer contains 11 nodes, corresponding to the state quantity s t The hidden layer is composed of linear layers. The four hidden layers contain 512, 512, 256, and 128 nodes respectively. The first branch consists of 1 input layer and 2 hidden layers. The input layer is the output layer of the Actor network, that is, the action amount a t , contains 4 nodes, the two hidden layers contain 256 and 128 nodes respectively, and the two branches have the same size of 128-node hidden layer. Therefore, the outputs of the two 128-node hidden layers are added and normalized and then output to the output layer. The output layer contains 1 node, which is the evaluation value or target value of the state-action pair.

[0101] Specifically, the priority experience playback algorithm selects the experience data from the experience buffer pool and the calculation formula is:

[0102]

[0103] Wherein, P(c) represents a set of experience quadruplets (s t ,a t ,rt ,s t '); c represents the sequence number of the extracted empirical data; p k represents the priority of the extracted empirical data; α represents the preset parameter used to adjust the priority sampling degree of data samples.

[0104] Specifically, the coordinate calculation model regards the wind turbine as rotating about the Z axis of the spatial coordinate system, calculates the rotation angle of the detection blade around the Z axis according to the rotation angular velocity, and establishes a rotation matrix according to the rotation angle, and then calculates the transformed coordinates according to the rotation matrix and the initial coordinates through a coordinate transformation algorithm. The coordinate transformation algorithm calculation formula is:

[0105]

[0106] Among them, (x1, y1, z1) is the transformed coordinate, a is the scale parameter, b is the rotation angle, and (x, y, z) is the initial coordinate.

[0107] A UAV control system based on wind turbine blade damage detection includes: a position correction module, an identification module, a data acquisition module, a control calculation module, a detection module and a circulation module;

[0108] The position correction module is used to control the UAV to fly relative to the wind turbine. The real-time distance between the current position of the UAV and the center of the wind turbine is obtained through the RGB-D camera carried by the UAV and transmitted to the smart terminal. The smart terminal compares the real-time distance with the preset optimal detection distance to obtain a comparison result, and corrects the UAV position according to the comparison result.

[0109] The recognition module is used to obtain the wind turbine image in real time through the RGB-D camera and transmit it to the smart terminal. The smart terminal establishes a spatial coordinate system according to the wind turbine image, and calculates blade No. 1, blade No. 2, and blade No. 3 through the image detection and recognition model, and uses blade No. 1 as the detection blade;

[0110] The data acquisition module is used to preset the detection point sequence of the detection blade, and obtain the initial coordinates of each detection point in the detection point sequence in the spatial coordinate system, obtain the rotation angular velocity of the test blade, the distance data between the detection point and the center point, the instantaneous speed of the drone, the detection running speed of the drone and the current coordinates of the drone, calculate the transformed coordinates of the detection point within a preset time length according to the rotation angular velocity and the initial coordinates of the detection point through a coordinate calculation model, and calculate the coordinate difference between the current coordinates of the drone and the transformed coordinates;

[0111] The control calculation module is used to input the rotation angular velocity, the UAV detection running speed, the UAV instantaneous speed, the distance data between the detection point and the center point, the motor control amount of the UAV, and the coordinate difference as state quantities into the UAV control network model to obtain the motor change control amount of the UAV through calculation;

[0112] The detection module is used for the intelligent terminal to generate an adaptive signal according to the motor change control amount to control the propeller operation state of the drone, so that the drone runs to the transformation coordinates and uses the ultrasonic machine carried by the drone to perform defect detection on the detection blade corresponding to the transformation coordinates;

[0113] The loop module is used to repeat steps S1-S5 until the second blade and the third blade are all detected.

[0114] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned UAV control method based on wind turbine blade damage detection is implemented.

[0115] A storage medium containing computer executable instructions, characterized in that the computer executable instructions are used to execute the drone control method based on wind turbine blade damage detection as described above when executed by a computer processor.

[0116] The program code included in the method in the embodiment of the present invention can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, etc., or any suitable combination mentioned above. The computer program code for performing the operation of the present invention can be written with one or more programming languages ​​or their combination, and the programming language includes object-oriented programming languages--such as Java, Smalltalk, C++, and also includes conventional procedural programming languages--such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on the remote computer, or executed completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0117] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A UAV control method based on wind turbine blade damage detection, characterized in that: The following steps are involved: S1: Control the drone to fly relative to the wind turbine, obtain the real-time distance between the current position of the drone and the center of the wind turbine through the RGB-D camera carried by the drone and transmit it to the smart terminal, the smart terminal compares the real-time distance with the preset optimal detection distance to obtain a comparison result, and corrects the drone position according to the comparison result; S2: The wind turbine image is acquired in real time by the RGB-D camera and transmitted to the smart terminal. The smart terminal establishes a spatial coordinate system according to the wind turbine image, and calculates blade No. 1, blade No. 2, and blade No. 3 by using an image detection and recognition model, and uses blade No. 1 as the detection blade. S3: Preset a detection point sequence for the detection blade, and obtain the initial coordinates of each detection point in the detection point sequence in the spatial coordinate system, obtain the rotation angular velocity of the test blade, the distance data between the detection point and the center point, the instantaneous speed of the drone, the detection running speed of the drone and the current coordinates of the drone, calculate the transformed coordinates of the detection point within a preset time length according to the rotation angular velocity and the initial coordinates of the detection point through a coordinate calculation model, and calculate the coordinate difference between the current coordinates of the drone and the transformed coordinates; S4: Input the rotation angular velocity, the UAV detection running speed, the UAV instantaneous speed, the distance data between the detection point and the center point, the motor control amount of the UAV, and the coordinate difference as state quantities into the UAV control network model to obtain the motor change control amount of the UAV through calculation; S5: the intelligent terminal generates an adaptive signal according to the motor change control amount to control the propeller operation state of the drone, so that the drone runs to the transformed coordinates, and uses the ultrasonic machine carried by the drone to perform defect detection on the area corresponding to the transformed coordinates; S6. Repeat steps S1-S5 until the second blade and the third blade are all detected.

2. The UAV control method based on wind turbine blade damage detection according to claim 1 is characterized in that: The specific steps of training the UAV control network model are as follows: S201: Setting a virtual environment for detecting wind turbine blades; S202: obtaining the current state quantity of all detection points in the detection virtual environment, and setting a reward function according to the current state quantity, inputting the current state quantity into a preset UAV control network model to obtain the motor change control quantity of the UAV by calculation, obtaining the state quantity at the next moment, establishing an experience quadruple with the current state quantity, the motor change control quantity, the reward function, and the next moment state quantity, and storing all the experience quadruple in a special experience playback pool for reinforcement learning. When all blades are detected, it indicates that the interaction of this game is over; S203: taking n groups of experience quadruple groups with poor correlation from the reinforcement learning dedicated experience replay pool as test groups through a priority experience replay algorithm, and continuously updating the preset drone control network model parameters according to the test groups; S204: Obtain a reward function curve graph, and determine whether the reward function tends to be stable based on the reward function curve graph. If the reward function tends to be stable, end the loop and obtain the UAV control network model; if the reward function does not tend to be stable, continue to repeat steps S202-S204 for training until the reward function tends to be stable.

3. The UAV control method based on wind turbine blade damage detection according to claim 2 is characterized in that: The reward function r t (s t ) is expressed as: Among them, ω1, ω2, ω3 represent hyperparameters, ω v is the rotation angular velocity, l r is the distance data between the detection point and the center point, v0 is the detection running speed of the drone, and v t is the instantaneous speed of the drone, Δx, Δy, Δz are the coordinate differences, are the motor control quantities of the first wing, the second wing, the third wing, and the fourth wing of the UAV, respectively. It is the motor change control amount of wing No. 1, wing No. 2, wing No. 3 and wing No. 4 of the UAV.

4. The UAV control method based on wind turbine blade damage detection according to claim 1 is characterized in that: The UAV control network model consists of Actor network, Target Actor network, Critic1 network, Target Critic1 network, Critic2 network, and Target Critic2 network. The input of the Actor network is the state quantity s perceived by the UAV at the current moment. t The output of the Actor network is the control amount a sent to the drone motor at the current moment. t The input of the TargetActor network is the state s perceived by the drone at the next moment t ', the output of the Target Actor network is the control amount a sent to the drone motor at the next moment t '; The input of the Critic1 network and the Critic2 network is the state-action pair at the current moment (s t ,a t ), the output is the action pair according to the current state (s t ,a t ) The state value evaluation value given by the application neural network; the input of the Target Critic1 network and the Target Critic2 network is the state action pair (s t ',a t '), the output is the state action pair (s t ',a t ') Apply the state value target value given by the neural network.

5. The UAV control method based on wind turbine blade damage detection according to claim 2 is characterized in that: The calculation formula of the motor control quantity is as follows: a t =μ(s t ||θ μ )+∈ ∈=clip(N(0,σ),-b,b) where s t Indicates the current state, a t is the motor control quantity, θ μ represents the Actor network parameters, μ(·) represents the Actor network, ∈ represents the random noise parameters, the clip function represents that ∈=-b when N(0,σ)<-b, and ∈=b when N(0,σ)>b, ∈=N(0,σ) represents that ∈ satisfies the normal distribution, -b and b represent fixed parameters.

6. The UAV control method based on wind turbine blade damage detection according to claim 2 is characterized in that: The priority experience replay algorithm selects the experience data from the experience buffer pool and the calculation formula is: Wherein, P(c) represents a set of experience quadruplets (s t ,a t ,r t ,s t '); c represents the sequence number of the extracted empirical data; p k represents the priority of the extracted empirical data; α represents the preset parameter used to adjust the priority sampling degree of data samples.

7. The UAV control method based on wind turbine blade damage detection according to claim 1 is characterized in that: The coordinate calculation model regards the wind turbine as rotating about the Z axis of the spatial coordinate system, calculates the rotation angle of the detection blade around the Z axis according to the rotation angular velocity, establishes a rotation matrix according to the rotation angle, and then calculates the transformed coordinates according to the rotation matrix and the initial coordinates through a coordinate conversion algorithm. The coordinate conversion algorithm calculation formula is: Among them, (x1, y1, z1) is the transformed coordinate, a is the scale parameter, b is the rotation angle, and (x, y, z) is the initial coordinate.

8. A UAV control system based on wind turbine blade damage detection, characterized in that: include: Position correction module, identification module, data acquisition module, control calculation module, detection module and circulation module; The position correction module is used to control the UAV to fly relative to the wind turbine. The real-time distance between the current position of the UAV and the center of the wind turbine is obtained through the RGB-D camera carried by the UAV and transmitted to the smart terminal. The smart terminal compares the real-time distance with the preset optimal detection distance to obtain a comparison result, and corrects the UAV position according to the comparison result. The recognition module is used to obtain the wind turbine image in real time through the RGB-D camera and transmit it to the smart terminal. The smart terminal establishes a spatial coordinate system according to the wind turbine image, and calculates blade No. 1, blade No. 2, and blade No. 3 through the image detection and recognition model, and uses blade No. 1 as the detection blade; The data acquisition module is used to preset the detection point sequence of the detection blade, and obtain the initial coordinates of each detection point in the detection point sequence in the spatial coordinate system, obtain the rotation angular velocity of the test blade, the distance data between the detection point and the center point, the instantaneous speed of the drone, the detection running speed of the drone and the current coordinates of the drone, calculate the transformed coordinates of the detection point within a preset time length according to the rotation angular velocity and the initial coordinates of the detection point through a coordinate calculation model, and calculate the coordinate difference between the current coordinates of the drone and the transformed coordinates; The control calculation module is used to input the rotation angular velocity, the UAV detection running speed, the UAV instantaneous speed, the distance data between the detection point and the center point, the motor control amount of the UAV, and the coordinate difference as state quantities into the UAV control network model to obtain the motor change control amount of the UAV through calculation; The detection module is used for the intelligent terminal to generate an adaptive signal according to the motor change control amount to control the propeller operation state of the drone, so that the drone runs to the transformed coordinates, and uses the ultrasonic machine carried by the drone to perform defect detection on the area corresponding to the transformed coordinates; The loop module is used to repeat steps S1-S5 until the second blade and the third blade are all detected.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the drone control method based on wind turbine blade damage detection as described in any one of claims 1-7 is implemented.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to execute the drone control method based on wind turbine blade damage detection as described in any one of claims 1 to 7.

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