Fan blade defect identification system and method

Abstract

The invention relates to the technical field of wind power generation, and discloses a fan blade defect identification system and method.The system comprises an unmanned aerial vehicle platform end and a remote management terminal, and the unmanned aerial vehicle platform end is wirelessly connected with the remote management terminal; the remote management terminal is used for acquiring unmanned aerial vehicle trajectory planning data and transmitting the unmanned aerial vehicle trajectory planning data to the unmanned aerial vehicle platform end; and the unmanned aerial vehicle platform end is used for controlling the unmanned aerial vehicle to fly to the target scanning area based on the unmanned aerial vehicle trajectory planning data, scanning the fan blade in the target scanning area to obtain an infrared spliced image, and performing defect identification on the infrared spliced image to obtain a fan blade defect identification result. According to the invention, intelligent inspection of the fan blade is realized, the inspection operation efficiency of the fan blade is effectively improved, and the operation and maintenance cost is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a system and method for identifying defects in wind turbine blades. Background Art

[0002] Blades are key components of wind turbines. They face harsh environments such as heavy loads, high torques, and salt spray erosion, which can easily lead to defects such as cracks, corrosion, and cracking in the blades. If they are not discovered and repaired in time, the expansion of cracks and corrosion holes will cause the blades to break, causing huge economic losses and safety issues to the wind farm.

[0003] Usually, inspection personnel perform high-altitude operations to complete the inspection of wind turbine blades, which poses certain personal safety risks. At the same time, this inspection method has low efficiency. Summary of the invention

[0004] In view of this, the present invention provides a fan blade defect identification system and method to solve the problems of certain personal safety hazards and low inspection efficiency in fan blade inspection.

[0005] In a first aspect, the present invention provides a wind turbine blade defect identification system, the system comprising: a drone platform end and a remote management terminal, the drone platform end and the remote management terminal being wirelessly connected;

[0006] Remote management terminal, used to obtain the UAV trajectory planning data and transmit the UAV trajectory planning data to the UAV platform;

[0007] The UAV platform is used to control the UAV to fly to the target scanning area based on the UAV trajectory planning data, scan the wind blades in the target scanning area to obtain infrared stitching images, and perform defect identification on the infrared stitching images to obtain the fan blade defect identification results.

[0008] The present embodiment provides a fan blade defect recognition system, which obtains an infrared stitching image by scanning the fan blades in the target scanning area, and performs defect recognition on the infrared stitching image to obtain a fan blade defect recognition result. Through information interaction between the UAV platform and the remote management terminal, the personal safety hazards caused by high-altitude operations of inspection personnel during the inspection process and the problem of low inspection efficiency are avoided, and intelligent inspection of fan blades is realized, which effectively improves the inspection operation efficiency of fan blades and saves operation and maintenance costs.

[0009] In an optional implementation, the UAV platform side includes: a first wireless data transmission module, a control module and an optoelectronic device, the first wireless data transmission module being wirelessly connected to a remote management terminal;

[0010] The first wireless data transmission module is used to receive the UAV trajectory planning data sent by the remote management terminal and transmit the UAV trajectory planning data to the control module;

[0011] A control module, used to control the UAV to fly to a target scanning area based on the UAV trajectory planning data; wherein the target scanning area is an area including at least two wind turbine blades;

[0012] The optoelectronic device is used to scan the fan blades in the target scanning area to obtain an infrared stitching image, and perform defect recognition on the infrared stitching image to obtain a fan blade defect recognition result.

[0013] A wind blade defect identification system provided in this embodiment controls a drone to fly to a target scanning area through a control module, wherein the target scanning area is set to an area including at least two wind blades, so that the inspection area is maximized and the inspection efficiency is greatly improved; and the wind blades are inspected by rapid scanning with a photoelectric device, thereby avoiding the personal safety risks in the wind blade inspection method. At the same time, compared with the mode in which the drone hovers to take pictures, the photoelectric device is used to scan the wind blades in the target scanning area, thereby further improving the inspection efficiency and greatly reducing the operation and maintenance costs of the wind blades.

[0014] In an optional embodiment, the optoelectronic device comprises:

[0015] A stitching unit is used to scan the fan blades in the target scanning area to obtain a plurality of blade scanning images, and stitch the plurality of blade scanning images to obtain an infrared stitching image;

[0016] The recognition unit is used to recognize and classify the blade defects in the infrared stitching image to obtain the fan blade defects and defect pixel locations;

[0017] a determination unit, used to obtain the infrared field of view angle and the azimuth position of the fan blade, and determine the position of the fan blade based on the azimuth position of the fan blade and the infrared field of view angle;

[0018] The calculation unit is used to calculate the defect position of the fan blade based on the position of the fan blade and the defect pixel position, and determine the fan blade defect recognition result based on the fan blade defect and the defect position of the fan blade.

[0019] The present embodiment provides a fan blade defect recognition system, which obtains an infrared stitched image by stitching multiple blade scanning images, and then recognizes and classifies the blade defects in the infrared stitched image, thereby realizing rapid extraction and identification of blade defects in the infrared stitched image, and calculating the fan blade defect position by the position of the fan blade and the defect pixel position, thereby realizing rapid positioning of the fan blade defects and improving the accuracy of defect recognition.

[0020] In an optional implementation, the stitching unit is specifically configured to arrange the plurality of blade scanning images based on the azimuth position of the wind turbine blades to obtain an infrared stitching image.

[0021] The present embodiment provides a fan blade defect recognition system, which arranges multiple blade scanning images according to the azimuth position of the fan blades to obtain an infrared stitching image. The fan blades in the target scanning area can be stitched in a short time to form an infrared stitching image, thereby improving the image stitching efficiency and accuracy.

[0022] In an optional implementation, the recognition unit is specifically used to use an image detection model to identify and classify blade defects in the infrared stitching image to obtain wind turbine blade defects and defect pixel locations.

[0023] The present embodiment provides a fan blade defect recognition system, which extracts and identifies fan blade defects from infrared stitching images through an image detection model, thereby improving the recognition accuracy of fan blade defects and improving inspection efficiency.

[0024] In an optional embodiment, the optoelectronic device further includes:

[0025] The superposition unit is used to superimpose the fan blade defect and the defect position of the fan blade into the infrared stitching image, and transmit the infrared stitching image after superimposing the fan blade defect and the defect position of the fan blade to the remote management terminal through the first wireless data transmission module.

[0026] A fan blade defect recognition system provided in this embodiment superimposes the fan blade defects and the defect locations of the fan blades onto an infrared stitching image, thereby allowing a user to quickly and accurately grasp the fan blade defects and the defect locations of the fan blades, and then maintain the defect locations of the fan blades to ensure the normal operation of the fan.

[0027] In an optional implementation, the remote management terminal includes: a data management terminal and a second wireless data transmission module, the data management terminal is connected to the second wireless data transmission module, and the second wireless data transmission module is connected to the first wireless data transmission module;

[0028] A data management terminal, used to obtain the UAV trajectory planning data and transmit the UAV trajectory planning data to the first wireless data transmission module;

[0029] The second wireless data transmission module is used to transmit the UAV trajectory planning data to the UAV platform end, and receive the infrared stitching image after superimposing the fan blade defects and the defect positions of the fan blades, and send the infrared stitching image after superimposing the fan blade defects and the defect positions of the fan blades to the data management terminal for display.

[0030] A fan blade defect recognition system provided in this embodiment realizes the sending of UAV trajectory planning data and the display of fan blade defects and defect locations of fan blades in infrared stitching images through information interaction between a data management terminal and a second wireless data transmission module, thereby laying a foundation for the subsequent rapid maintenance of fan blades.

[0031] In an optional implementation, the UAV platform further includes:

[0032] The inertial navigation module is used to collect the flight attitude information of the UAV; the flight attitude information of the UAV is used to control the flight of the UAV.

[0033] A wind turbine blade defect recognition system provided in this embodiment realizes precise control of the flight attitude of the UAV through the flight attitude information of the UAV, laying a foundation for subsequent accurate scanning of wind turbine blades in the target scanning area by the UAV.

[0034] In an optional implementation, the UAV platform further includes:

[0035] Beidou module is used to collect the real-time location of the drone and transmit it to the remote management terminal.

[0036] A wind turbine blade defect identification system provided in this embodiment realizes accurate positioning of the real-time position of the UAV through the Beidou module. The remote management terminal can control the inspection direction and hovering position according to the real-time position of the UAV, thereby improving the inspection efficiency.

[0037] In a second aspect, the present invention provides a method for identifying defects in a wind turbine blade, which is applied to a wind turbine blade defect identification system according to the first aspect or any corresponding embodiment thereof, and the method comprises:

[0038] The remote management terminal obtains the UAV trajectory planning data and transmits the UAV trajectory planning data to the UAV platform;

[0039] The UAV platform controls the UAV to fly to the target scanning area based on the UAV trajectory planning data, scans the wind turbine blades in the target scanning area, obtains an infrared stitching image, and performs defect identification on the infrared stitching image to obtain the fan blade defect identification result. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 is a structural block diagram of a fan blade defect recognition system according to an embodiment of the present invention;

[0042] Figure 2 is a structural block diagram of a drone platform according to an embodiment of the present invention;

[0043] Figure 3 is a structural block diagram of an optoelectronic device according to an embodiment of the present invention;

[0044] Figure 4 is a schematic diagram of an infrared stitching image after superimposing a fan blade defect and a defect position of the fan blade according to an embodiment of the present invention;

[0045] Figure 5 is a structural block diagram of a remote management terminal according to an embodiment of the present invention;

[0046] Figure 6 is a schematic diagram of an enlarged infrared stitching image according to an embodiment of the present invention;

[0047] Figure 7 It is a flow chart of a method for identifying defects in wind turbine blades according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0049] An embodiment of the present invention provides a fan blade defect recognition system, which quickly scans fan blades in a certain area through the infrared channel of a photoelectric device to form a clear spliced ​​strip image, and uses AI (Artificial Intelligence) technology to identify and extract defects such as cracks and seams in the spliced ​​strip image, thereby effectively reducing the hidden dangers of fan safety accidents caused by the expansion of fan blade defects, avoiding the occurrence of personal safety accidents during high-altitude operations, and at the same time saving the cost of fan blade inspection work, thereby improving the economic benefits of the enterprise.

[0050] In this embodiment, a wind turbine blade defect recognition system is provided. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0051] This embodiment provides a fan blade defect recognition system. Figure 1 As shown, it includes: a drone platform end 101 and a remote management terminal 102, and the drone platform end 101 and the remote management terminal 102 are wirelessly connected;

[0052] The remote management terminal 102 is used to obtain the UAV trajectory planning data and transmit the UAV trajectory planning data to the UAV platform terminal 101.

[0053] Specifically, the remote management terminal 102 sends the UAV trajectory planning data to the UAV platform end 101 according to the wind turbine blade inspection task; after completing the wind turbine blade inspection task, the remote management terminal 102 sends a recovery command to the UAV platform end 101, and the UAV platform end 101 controls the UAV to return and complete the inspection.

[0054] The drone platform end 101 is used to control the drone to fly to the target scanning area based on the drone trajectory planning data, scan the wind blades in the target scanning area to obtain an infrared stitching image, and perform defect identification on the infrared stitching image to obtain a fan blade defect identification result.

[0055] The present embodiment provides a fan blade defect recognition system, which obtains an infrared stitching image by scanning the fan blades in the target scanning area, and performs defect recognition on the infrared stitching image to obtain a fan blade defect recognition result. Through information interaction between the UAV platform and the remote management terminal, the personal safety hazards caused by high-altitude operations of inspection personnel during the inspection process and the problem of low inspection efficiency are avoided, and intelligent inspection of fan blades is realized, which effectively improves the inspection operation efficiency of fan blades and saves operation and maintenance costs.

[0056] In some optional embodiments, such as Figure 2 As shown, the drone platform end 101 includes: a first wireless data transmission module 1011, a control module 1012 and an optoelectronic device 1013, and the first wireless data transmission module 1011 is wirelessly connected to the remote management terminal 102;

[0057] The first wireless data transmission module 1011 is used to receive the UAV trajectory planning data sent by the remote management terminal 102 and transmit the UAV trajectory planning data to the control module 1012 .

[0058] Specifically, the first wireless data transmission module 1011 of the drone platform 101 performs wireless information exchange with the remote management terminal 102 , mainly transmitting the real-time positioning information of the drone and the infrared stitching image of the optoelectronic device 1013 .

[0059] The control module 1012 is used to control the UAV to fly to a target scanning area based on the UAV trajectory planning data; wherein the target scanning area is an area including at least two wind turbine blades.

[0060] Specifically, the control module 1012 mainly receives the UAV trajectory planning data transmitted by the remote management terminal 102, and then controls the flight trajectory of the UAV.

[0061] Further, the control module 1012 controls the UAV to fly to the first planned blade area (generally, there are no less than 2 wind turbine blades around the target scanning area) and hover there. At this time, the remote management terminal 102 sends a start scanning instruction to the optoelectronic device 1013.

[0062] The optoelectronic device 1013 is used to scan the fan blades in the target scanning area to obtain an infrared stitched image, and perform defect recognition on the infrared stitched image to obtain a fan blade defect recognition result.

[0063] Specifically, the infrared channel of the optoelectronic device 1013 is responsible for quickly scanning the wind turbine blades in the target scanning area to form a clear infrared stitching image, and identifying and marking the defects of the wind turbine blades in the infrared stitching image through an AI algorithm.

[0064] Furthermore, after the optoelectronic device 1013 receives the start scanning instruction sent by the remote management terminal 102, the infrared channel of the optoelectronic device 1013 starts to drive the azimuth motor to perform a rapid scan of 0.5 seconds per circle. The infrared channel of the optoelectronic device 1013 can trigger high-resolution infrared 4K imaging of multiple blades (such as 4) in the target scanning area within 0.5 seconds, and thus can complete 360° panoramic stitching images of multiple blades at the same time.

[0065] A wind blade defect identification system provided in this embodiment controls a drone to fly to a target scanning area through a control module, wherein the target scanning area is set to an area including at least two wind blades, so that the inspection area is maximized and the inspection efficiency is greatly improved; and the wind blades are inspected by rapid scanning with a photoelectric device, thereby avoiding the personal safety risks in the wind blade inspection method. At the same time, compared with the mode in which the drone hovers to take pictures, the photoelectric device is used to scan the wind blades in the target scanning area, thereby further improving the inspection efficiency and greatly reducing the operation and maintenance costs of the wind blades.

[0066] In some optional embodiments, such as Figure 3 As shown, the optoelectronic device 1013 includes:

[0067] The stitching unit 10131 is used to scan the fan blades in the target scanning area to obtain a plurality of blade scanning images, and stitch the plurality of blade scanning images to obtain an infrared stitching image.

[0068] The identification unit 10132 is used to identify and classify the blade defects in the infrared stitching image to obtain the fan blade defects and defect pixel locations.

[0069] Specifically, while performing image stitching, AI is used to identify, extract and mark typical blade defects in the blade scanning image to obtain the wind turbine blade defects and defect pixel locations.

[0070] Furthermore, AI recognition, extraction and identification of typical blade defects in the infrared stitching image output by the stitching unit 10131 can also be performed to obtain the fan blade defects and defect pixel locations.

[0071] The determination unit 10133 is used to obtain the infrared field of view angle and the azimuth position of the fan blade, and determine the position of the fan blade based on the azimuth position of the fan blade and the infrared field of view angle.

[0072] Specifically, according to the azimuth position of the fan blade, the specific position of the fan blade in the infrared stitching image (i.e., the position of the fan blade) n is calculated by the following formula:

[0073] n = azimuth position% (infrared field of view angle ÷ 1.2) + 1

[0074] The calculation unit 10134 is used to calculate the defect position of the fan blade based on the position of the fan blade and the defect pixel point position, and determine the fan blade defect recognition result based on the fan blade defect and the defect position of the fan blade.

[0075] Specifically, the defective pixel points include azimuth pixels and pitch pixels, and the defective position of the wind turbine blade is composed of the azimuth angle A and the pitch angle E, wherein the calculation formula of the azimuth angle A is as follows:

[0076] A = azimuth pixel / (1280*4)*15°+15°*(n-1)

[0077] Furthermore, the calculation formula of the pitch angle E is as follows:

[0078] E = pitch pixels / (1024*4)*12°

[0079] The present embodiment provides a fan blade defect recognition system, which obtains an infrared stitched image by stitching multiple blade scanning images, and then recognizes and classifies the blade defects in the infrared stitched image, thereby realizing rapid extraction and identification of blade defects in the infrared stitched image, and calculating the fan blade defect position by the position of the fan blade and the defect pixel position, thereby realizing rapid positioning of the fan blade defects and improving the accuracy of defect recognition.

[0080] In some optional implementations, the stitching unit 10131 is specifically used to arrange multiple blade scanning images based on the azimuth position of the wind turbine blades to obtain an infrared stitching image.

[0081] Specifically, the number of infrared stitching images is calculated according to the field of view of the infrared channel (i.e., the infrared field of view angle), and the calculation formula for the number of imaging images N is as follows:

[0082] N = (360 / infrared field of view × 1.2)

[0083] The multiple of 1.2 is used to set a 20% redundancy to prevent errors when imaging the image.

[0084] Furthermore, assuming that the field of view is 18°, the number of images N = (360 / 18×1.2) = 24, and the panoramic 360° stitching strips can be set to 4 rows with every 90° as a row. Each row is made up of 6 images that are scaled and stitched in equal proportions, and then arranged in order from left to back according to the azimuth position of the wind turbine blades to obtain an infrared stitching image.

[0085] The present embodiment provides a fan blade defect recognition system, which arranges multiple blade scanning images according to the azimuth position of the fan blades to obtain an infrared stitching image. The fan blades in the target scanning area can be stitched in a short time to form an infrared stitching image, thereby improving the image stitching efficiency and accuracy.

[0086] In some optional implementations, the identification unit 10132 is specifically used to identify and classify blade defects in the infrared stitched image using an image detection model to obtain wind turbine blade defects and defect pixel locations.

[0087] Specifically, since upgrading and improving the neural network framework can realize target classification, target frame coordinate determination and target object determination, the neural network framework is used to identify and classify blade defects. The specific steps include: when identifying wind turbine blade defects, first use the blade defect data set for training to enhance the generalization ability, and then input the training samples in the blade defect data set to iteratively train the pre-trained model to obtain the final trained model, namely the image detection model.

[0088] Furthermore, when implementing AI defect detection, the scan-triggered image (multiple blade scanning images, or infrared stitching images) is input into the image detection model through the optoelectronic device 1013, and the image detection model outputs the fan blade defects and the defect pixel positions.

[0089] The present embodiment provides a fan blade defect recognition system, which extracts and identifies fan blade defects from infrared stitching images through an image detection model, thereby improving the recognition accuracy of fan blade defects and improving inspection efficiency.

[0090] In some optional implementations, the optoelectronic device 1013 further includes:

[0091] The superimposition unit 10135 is used to superimpose the fan blade defects and the defect positions of the fan blades onto the infrared stitched image, and transmit the infrared stitched image after superimposing the fan blade defects and the defect positions of the fan blades to the remote management terminal 102 through the first wireless data transmission module 1011.

[0092] Specifically, the defect position of the fan blade (such as Figure 4 The infrared stitching image is transmitted to the remote management terminal 102 through the first wireless data transmission module 1011. After scanning the target scanning area once, the optoelectronic device 1013 stops scanning.

[0093] A fan blade defect recognition system provided in this embodiment superimposes the fan blade defects and the defect locations of the fan blades onto an infrared stitching image, thereby allowing a user to quickly and accurately grasp the fan blade defects and the defect locations of the fan blades, and then maintain the defect locations of the fan blades to ensure the normal operation of the fan.

[0094] In some optional embodiments, such as Figure 5 As shown, the remote management terminal 102 includes: a data management terminal 1021 and a second wireless data transmission module 1022, the data management terminal 1021 is connected to the second wireless data transmission module 1022, and the second wireless data transmission module 1022 is connected to the first wireless data transmission module 1011;

[0095] The data management terminal 1021 is used to obtain the UAV trajectory planning data and transmit the UAV trajectory planning data to the first wireless data transmission module 1011.

[0096] Specifically, when the wind turbine blade inspection task begins, the data management terminal 1021 sends the UAV trajectory planning data to the UAV platform 101, and the infrared channel of the optoelectronic device 1013 is powered on and cooled in advance. After the infrared channel is imaged, the wind turbine blades in the target scanning area are scanned.

[0097] Furthermore, the data management terminal 1021 acts as a general server, runs the management software, and is responsible for issuing the drone trajectory planning plan and receiving the stitched images and status information sent by the drone platform 101.

[0098] The second wireless data transmission module 1022 is used to transmit the UAV trajectory planning data to the UAV platform end 101, and receive the infrared stitching image after superimposing the fan blade defects and the defect positions of the fan blades, and send the infrared stitching image after superimposing the fan blade defects and the defect positions of the fan blades to the data management terminal 1021 for display.

[0099] Specifically, Figure 6 As shown, after the data management terminal 1021 receives the infrared stitching image, the data management terminal 1021 can display the infrared stitching image, and click on the stitching image to enlarge it as needed to view the defect details of the wind turbine blades in the infrared stitching image, and at the same time, the infrared stitching image can be saved.

[0100] Furthermore, after the data management terminal 1021 completes the saving, it sends a command to the drone platform end 101 to fly to the next blade area and hover and scan, and uses the same method to detect subsequent blades in sequence until all are completed.

[0101] A fan blade defect recognition system provided in this embodiment realizes the sending of UAV trajectory planning data and the display of fan blade defects and defect locations of fan blades in infrared stitching images through information interaction between a data management terminal and a second wireless data transmission module, thereby laying a foundation for the subsequent rapid maintenance of fan blades.

[0102] In some optional implementations, the drone platform 101 further includes:

[0103] The inertial navigation module 1014 is used to collect the flight attitude information of the UAV; the flight attitude information of the UAV is used to control the flight of the UAV.

[0104] Specifically, the inertial navigation module 1014 is responsible for collecting the attitude information of the roll, pitch and heading of the drone, and then calculating the attitude information of the roll, pitch and heading of the drone, and using the calculation results to control the flight of the drone.

[0105] A wind turbine blade defect recognition system provided in this embodiment realizes precise control of the flight attitude of the UAV through the flight attitude information of the UAV, laying a foundation for subsequent accurate scanning of wind turbine blades in the target scanning area by the UAV.

[0106] In some optional implementations, the drone platform 101 further includes:

[0107] The Beidou module 1015 is used to collect the real-time position of the drone and transmit the real-time position of the drone to the remote management terminal 102.

[0108] Specifically, the Beidou module 1015 is responsible for providing high-precision real-time latitude, longitude and altitude information of the UAV, so as to facilitate the remote management terminal 102 to adjust the position of the UAV.

[0109] A wind turbine blade defect identification system provided in this embodiment realizes accurate positioning of the real-time position of the UAV through the Beidou module. The remote management terminal can control the inspection direction and hovering position according to the real-time position of the UAV, thereby improving the inspection efficiency.

[0110] The following is a specific example to illustrate the working process of a fan blade defect recognition system.

[0111] Embodiment 1:

[0112] A workflow of a fan blade defect recognition system includes:

[0113] Step 1: When the wind turbine blade inspection task begins, the data management terminal sends the drone trajectory planning technology to the drone platform. The infrared channel of the optoelectronic equipment is powered on and cooled in advance. After the infrared channel is imaged, the drone platform controls the drone to fly to the first planned blade area (generally, there are no less than 2 wind turbine blades around the area) and hover.

[0114] Step 2: The remote management terminal sends a start scanning command to the optoelectronic device, and the infrared channel of the optoelectronic device starts to drive the azimuth motor to perform a rapid scan of 0.5 seconds per circle. This method can trigger high-resolution infrared 4K imaging of multiple blades (such as 4) in the area within 0.5 seconds. At this time, 360° panoramic stitching images of multiple blades can be completed simultaneously; and according to the azimuth position of the fan blade, the specific position of the blade in the panoramic stitching image is calculated.

[0115] Step 3: Since the upgrade and improvement of the neural network framework can realize target classification, target frame coordinate determination and target object determination, the neural network framework is used to identify and classify blade defects. The specific steps include: when identifying fan blade defects, first use the blade defect data set for training to enhance the generalization ability, and then input the training samples in the blade defect data set to iteratively train the pre-trained model to obtain the final trained model, that is, the image detection model. When implementing AI defect detection, the scan-triggered image (multiple blade scan images, or infrared stitched images) is input into the image detection model through the optoelectronic device, and the image detection model outputs the fan blade defects and the defect pixel locations.

[0116] Step 4: The optoelectronic device combines the high-precision positioning information of the Beidou module and the location of the image pixel points where the wind turbine blade defects are located output by the model, and superimposes the specific location information on the infrared stitching image. The infrared stitching image and the original image (i.e., the blade scanning image) are transmitted to the remote management terminal through the first wireless data transmission module. The optoelectronic device stops scanning after scanning one circle.

[0117] Step 5: After receiving the stitched image, the remote management terminal displays the infrared stitched image, and clicks on the infrared stitched image to enlarge it as required to view the defect details of the wind turbine blade; at the same time, the infrared stitched image can be saved.

[0118] Step 6: After the remote management terminal completes the save, it sends a command to the drone platform, which controls the drone to fly to the next blade area and hover; the same method is used to detect subsequent blades in sequence until all are completed.

[0119] Step 7: After the inspection is completed, the remote management terminal sends a recovery command to the drone platform, and the drone platform controls the drone to return, and the entire inspection process is completed.

[0120] According to an embodiment of the present invention, an embodiment of a method for identifying defects in wind blades is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0121] In this embodiment, a fan blade defect recognition method is provided, which can be used in the above-mentioned fan blade defect recognition system. Figure 7 is a flow chart of a method for identifying defects in fan blades according to an embodiment of the present invention. Figure 7 As shown, the process includes the following steps:

[0122] Step S701: The remote management terminal obtains the UAV trajectory planning data and transmits the UAV trajectory planning data to the UAV platform.

[0123] In step S702, the UAV platform controls the UAV to fly to the target scanning area based on the UAV trajectory planning data, scans the wind blades in the target scanning area to obtain an infrared stitching image, and performs defect identification on the infrared stitching image to obtain a wind blade defect identification result.

[0124] A fan blade defect recognition method of this embodiment is applied to Figure 1 The fan blade defect recognition system in the embodiment shown in the figure, so the specific implementation of step S701 and step S702 can refer to the above Figure 1 The corresponding description of the illustrated embodiment will not be repeated here.

[0125] It is understandable that the effects and beneficial effects of the method of this embodiment are similar to those of Figure 1 The functions and beneficial effects of a fan blade defect identification system in the illustrated embodiment correspond to each other and will not be described in detail here.

[0126] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0127] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0129] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0131] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0132] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A fan blade defect recognition system, characterized in that: The system comprises: a drone platform end and a remote management terminal, wherein the drone platform end and the remote management terminal are wirelessly connected; The remote management terminal is used to obtain the UAV trajectory planning data and transmit the UAV trajectory planning data to the UAV platform end; The drone platform is used to control the drone to fly to a target scanning area based on the drone trajectory planning data, scan the wind blades in the target scanning area to obtain an infrared stitching image, and perform defect identification on the infrared stitching image to obtain a fan blade defect identification result.

2. The system according to claim 1, characterized in that The UAV platform includes: a first wireless data transmission module, a control module and an optoelectronic device, wherein the first wireless data transmission module is wirelessly connected to the remote management terminal; The first wireless data transmission module is used to receive the UAV trajectory planning data sent by the remote management terminal, and transmit the UAV trajectory planning data to the control module; The control module is used to control the UAV to fly to the target scanning area based on the UAV trajectory planning data; wherein the target scanning area is an area including at least two wind turbine blades; The optoelectronic device is used to scan the fan blades in the target scanning area to obtain an infrared stitching image, and perform defect recognition on the infrared stitching image to obtain a fan blade defect recognition result.

3. The system according to claim 2, characterized in that The optoelectronic device comprises: A splicing unit, configured to scan the fan blades in the target scanning area to obtain a plurality of blade scanning images, and splice the plurality of blade scanning images to obtain the infrared splicing image; An identification unit, used to identify and classify blade defects in the infrared stitched image to obtain fan blade defects and defect pixel locations; a determination unit, configured to obtain an infrared field of view angle and an azimuth position of a fan blade, and determine a position of the fan blade based on the azimuth position of the fan blade and the infrared field of view angle; A calculation unit is used to calculate the defect position of the fan blade based on the position of the fan blade and the defect pixel point position, and determine the fan blade defect recognition result based on the fan blade defect and the defect position of the fan blade.

4. The system according to claim 3, characterized in that The stitching unit is specifically used to arrange the multiple blade scanning images based on the azimuth position of the wind turbine blade to obtain the infrared stitching image.

5. The system according to claim 3, characterized in that The recognition unit is specifically used to use an image detection model to identify and classify blade defects in the infrared stitching image to obtain the fan blade defects and the defect pixel locations.

6. The system according to claim 3, characterized in that The optoelectronic device further comprises: A superimposition unit is used to superimpose the fan blade defect and the defect position of the fan blade into the infrared stitching image, and transmit the infrared stitching image after superimposing the fan blade defect and the defect position of the fan blade to the remote management terminal through the first wireless data transmission module.

7. The system according to claim 6, characterized in that The remote management terminal comprises: a data management terminal and a second wireless data transmission module, the data management terminal is connected to the second wireless data transmission module, and the second wireless data transmission module is connected to the first wireless data transmission module; The data management terminal is used to obtain the UAV trajectory planning data and transmit the UAV trajectory planning data to the first wireless data transmission module; The second wireless data transmission module is used to transmit the UAV trajectory planning data to the UAV platform end, and receive the infrared stitching image after superimposing the fan blade defect and the defect position of the fan blade, and send the infrared stitching image after superimposing the fan blade defect and the defect position of the fan blade to the data management terminal for display.

8. The system according to claim 2, characterized in that The UAV platform also includes: The inertial navigation module is used to collect the flight attitude information of the UAV; the flight attitude information of the UAV is used to control the flight of the UAV.

9. The system according to claim 2, characterized in that The UAV platform also includes: The Beidou module is used to collect the real-time position of the drone and transmit the real-time position of the drone to the remote management terminal.

10. A method for identifying defects in fan blades, characterized in that: Applied to the wind turbine blade defect recognition system according to any one of claims 1 to 9, the method comprising: The remote management terminal obtains the UAV trajectory planning data and transmits the UAV trajectory planning data to the UAV platform end; The UAV platform controls the UAV to fly to a target scanning area based on the UAV trajectory planning data, scans the wind blades in the target scanning area to obtain an infrared stitching image, and performs defect identification on the infrared stitching image to obtain a fan blade defect identification result.

Images