Unmanned aerial vehicle inspection method and device for wind power blades

Abstract

The invention discloses a wind power blade unmanned aerial vehicle inspection method and device, and belongs to the technical field of wind driven generators, and the wind power blade unmanned aerial vehicle inspection method comprises the steps: carrying out the unmanned aerial vehicle inspection of the front surface of a wind power blade to form first inspection information, and carrying out the unmanned aerial vehicle inspection of the back surface of the wind power blade to form second inspection information, the first inspection information comprises a first track point data set and a blade front image set, and the second inspection information comprises a second track point data set and a blade back image set; according to the first inspection information and the second inspection information, in combination with a wind power blade defect judgment condition, obtaining a wind power blade unmanned aerial vehicle inspection result; according to the wind power blade unmanned aerial vehicle inspection result, whether an unmanned aerial vehicle re-inspection mode is started is judged, if yes, the unmanned aerial vehicle re-inspection mode is started, and the wind power blade unmanned aerial vehicle inspection result is updated; and if not, the re-inspection mode is not started. The method can reduce the influence on the wind power plant operation and power generation efficiency, and improves the accuracy of the wind power blade unmanned aerial vehicle inspection result.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbines, and more specifically, to a method and device for unmanned aerial vehicle inspection of wind turbine blades. Background Art

[0002] During wind power generation, the wind turbine blades of wind turbines play a key role. Defective or damaged blades will directly affect the components of the entire wind turbine, making the wind turbine unable to operate normally, which puts higher requirements on the blade inspection of wind turbines.

[0003] Drone inspection technology has begun to be used in the field of wind turbine blade inspection. Drone autonomous inspection technology usually uses a rotorcraft with stable hovering capabilities. The wind turbine blade drone inspection process usually includes: when the wind turbine unit is shut down, the drone flies to the vicinity of the wind turbine blade according to the planned track line. The drone obtains a complete surface image of the wind turbine blade at a specified location along a track line including multiple track points based on its own positioning and the position of the wind turbine blade. Compared with traditional manual inspection technology for wind turbine blades, wind turbine blade drone inspection technology has greatly improved the inspection efficiency of wind turbine blades in wind turbine units.

[0004] However, due to the high wind speed in the wind farm, the drones have to inspect the wind turbine blades at close range, which puts high demands on the flight stability, control reliability and safety of the drones. It will increase the hardware conditions and software algorithm costs of the drones, resulting in a decrease in the endurance performance of the drones. Insufficient endurance performance often causes the wind turbine to be forced to stop the inspection in the middle of the inspection, and the drone returns to the take-off position, which greatly affects the efficiency of the drone inspection of the wind turbine blades. In addition, the existing drone inspection technology for wind turbine blades must be carried out when the wind turbine is shut down. The shutdown inspection of the wind turbine will affect the power generation efficiency of the wind farm. Therefore, it is of great significance to study and develop drone inspection technology for wind turbine blades of wind turbines, monitor the operating status of wind turbine blades of wind turbines, collect defect data of wind turbine blades of wind turbines, determine the defect type of wind turbine blades of wind turbines, improve the efficiency and accuracy of drone inspections, reduce the impact on the operation and power generation efficiency of wind farms, and realize the automatic inspection of wind turbine blades of wind farms by drones. Summary of the invention

[0005] Based on this, the present invention provides a wind turbine blade drone inspection method and device, aiming to partially or completely solve the above technical problems. The present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a method for inspecting wind turbine blades using a drone, comprising the following steps: Step S100, obtaining structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm, wherein the structural parameters at least include the center height of the impeller, the blade profile curve, the blade length, the maximum chord length, the impeller diameter and the number of blades; the motion parameters at least include the speed of the wind turbine; and the environmental parameters at least include the altitude and the wind speed; Step S200, based on the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm, a drone inspection is performed on the front of the wind turbine blade to form first inspection information, and a drone inspection is performed on the back of the wind turbine blade to form second inspection information, wherein the first inspection information includes a first track point data set and a blade front image set, and the second inspection information includes a second track point data set and a blade back image set; the blade front image set includes a plurality of wind turbine blade overall front images; and the blade back image set includes a plurality of wind turbine blade overall back images; Step S300, based on the first inspection information and the second inspection information, combined with the wind turbine blade defect judgment conditions, obtain the wind turbine blade drone inspection results; based on the wind turbine blade drone inspection results, determine whether to start the drone re-inspection mode, if yes, start the drone re-inspection mode and update the wind turbine blade drone inspection results; if not, do not start the re-inspection mode.

[0006] Optionally, starting the drone re-inspection mode includes the following steps: Step S401: acquiring a defect track point image sequence corresponding to the defect type wind turbine blade according to the wind turbine blade drone inspection result; Step S402, acquiring defect track points corresponding to the defect type wind turbine blade according to the defect track point image sequence, the defect track points forming a first re-inspection track point set and / or a second re-inspection track point set; Step S403: planning a drone re-inspection route according to the first re-inspection track point set and / or the second re-inspection track point set, and starting the drone re-inspection of the wind turbine blades.

[0007] Optionally, updating the wind turbine blade drone inspection result includes the following steps: Step S501: Performing a UAV re-inspection on a wind turbine blade according to a UAV re-inspection route to obtain re-inspection information of the wind turbine blade, wherein the re-inspection information includes a blade front re-inspection image set and a blade back re-inspection image set; the blade front re-inspection image set includes a plurality of wind turbine blade front re-inspection images; and the blade front re-inspection image set includes a plurality of wind turbine blade back re-inspection images; Step S502: Obtain the wind turbine blade drone re-inspection result according to the re-inspection information, and update the wind turbine blade drone inspection result according to the wind turbine blade drone re-inspection result and the wind turbine blade drone inspection result.

[0008] Optionally, step S502 includes: Step S5021: determine whether the wind turbine blade drone re-inspection result is consistent with the wind turbine blade drone inspection result. If yes, update the wind turbine blade drone re-inspection result as the wind turbine blade drone inspection result. If not, proceed to step S5022. Step S5022: at least according to the overall front images of the multiple wind turbine blades corresponding to the defect types and / or the overall back images of the multiple wind turbine blades corresponding to the defect types, and the corresponding multiple front re-inspection images of the wind turbine blades and / or the corresponding back re-inspection images of the wind turbine blades, use the first classifier, the second classifier, and the third classifier to classify the overall front images of the multiple wind turbine blades corresponding to the defect types and / or the overall back images of the multiple wind turbine blades corresponding to the defect types, and the corresponding multiple front re-inspection images of the wind turbine blades and / or the multiple back re-inspection images of the wind turbine blades, respectively, to obtain the results of the first defect type set C1 and the first probability set P1 of the first classifier, the second defect type set C2 and the second probability set P2 of the second classifier, and the third defect type set C3 and the third probability set P3 of the third classifier; Step S5023: According to the first defect type set C1, the second defect type set C2 and the third defect type set C3, the first probability set P1, the second probability set P2 and the third probability set P3, calculate the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types, evaluate the defect types of the wind turbine blades according to the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types, and update the results of evaluating the defect types of the wind turbine blades as the wind turbine blade drone inspection results.

[0009] Optionally, the defect track point includes a first track point and a second track point, the UAV re-inspection route includes a first re-inspection route, a re-inspection switching route and a second re-inspection route, the re-inspection switching route connects the first re-inspection route and the second re-inspection route, the first re-inspection track point set is used to form a first re-inspection route, the first re-inspection route is used to perform a front UAV inspection of the wind turbine blade at the defect first track point to form a first re-inspection information, the second re-inspection track point set is used to form a second re-inspection route, the second re-inspection route is used to perform a back UAV inspection of the wind turbine blade at the defect second track point to form a second re-inspection information; obtain the defect first track point 1 at the beginning of the first re-inspection route and the defect first track point P at the end, The defective second track point 1 at the beginning of the second re-inspection route and the defective second track point Q at the end are used to calculate the first distance D1 between the defective first track point 1 and the defective second track point 1, the second distance D2 between the defective first track point 1 and the defective second track point Q, the third distance D3 between the defective first track point P and the defective second track point 1, and the fourth distance D4 between the defective first track point P and the defective second track point Q; according to the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4, the minimum distance Dmin=min[D1, D2, D3, D4] among the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4 is obtained, and according to the minimum distance Dmin, the UAV re-inspection route is planned.

[0010] In a second aspect, the present invention provides a wind turbine blade drone inspection device, which implements any wind turbine blade drone inspection method described in the first aspect, including a parameter acquisition unit, an inspection unit, a display unit, a re-inspection unit and a server: A parameter acquisition unit, which acquires structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm and sends them to the inspection unit, wherein the structural parameters at least include the center height of the impeller, the blade profile curve, the blade length, the maximum chord length, the impeller diameter and the number of blades; the motion parameters at least include the speed of the wind turbine; and the environmental parameters at least include the altitude and the wind speed; The inspection unit performs drone inspection on the front of the wind turbine blade according to the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm to form first inspection information and sends it to the server, and performs drone inspection on the back of the wind turbine blade to form second inspection information and sends it to the server, wherein the first inspection information includes a first track point data set and a blade front image set, and the second inspection information includes a second track point data set and a blade back image set; the blade front image set includes a plurality of wind turbine blade overall front images; and the blade back image set includes a plurality of wind turbine blade overall back images; The server obtains the wind turbine blade UAV inspection result according to the first inspection information and the second inspection information and the wind turbine blade defect judgment condition, and sends the result to the re-inspection unit and the display unit, and the display unit displays the wind turbine blade UAV inspection result; The re-inspection unit, based on the wind turbine blade drone inspection results, provides an interactive interface through the display unit to indicate whether to start the drone re-inspection mode; if yes, start the drone re-inspection mode and update the wind turbine blade drone inspection results; if not, do not start the re-inspection mode.

[0011] Optionally, the re-inspection unit includes a defect image search unit, a defect track point forming unit, a re-inspection route planning unit and a starting unit. The defect image search unit obtains a defect track point image sequence corresponding to the defect type of the wind turbine blade based on the UAV inspection result of the wind turbine blade; the defect track point forming unit obtains the defect track points corresponding to the defect type of the wind turbine blade based on the defect track point image sequence, and forms the defect track points into a first re-inspection track point set and / or a second re-inspection track point set; the re-inspection route planning unit plans the UAV re-inspection route based on the first re-inspection track point set and / or the second re-inspection track point set; the starting unit starts the UAV re-inspection of the wind turbine blade.

[0012] Optionally, the UAV re-inspection route includes a first re-inspection route, a re-inspection switching route T and a second re-inspection route, the re-inspection switching route connects the first re-inspection route and the second re-inspection route, the first re-inspection track point set is used to form the first re-inspection route, the first re-inspection route is used to perform a frontal UAV inspection of the defect type of the wind turbine blade to form first re-inspection information, the second re-inspection track point set is used to form a second re-inspection route, the second re-inspection route is used to perform a back UAV inspection of the defect type of the wind turbine blade to form second re-inspection information.

[0013] Optionally, the re-inspection route planning unit includes a distance calculation sub-unit, which obtains the defective first track point 1 at the start and the defective first track point P at the end of the first re-inspection route, the defective second track point 1 at the start and the defective second track point Q at the end of the second re-inspection route, calculates the first distance D1 between the defective first track point 1 and the defective second track point 1, the second distance D2 between the defective first track point 1 and the defective second track point Q, the third distance D3 between the defective first track point P and the defective second track point 1, and the fourth distance D4 between the defective first track point P and the defective second track point Q, obtains the minimum distance Dmin=min[D1, D2, D3, D4] among the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4, and sends the minimum distance Dmin to the re-inspection route planning unit.

[0014] Optionally, the re-inspection unit also includes a re-inspection information acquisition unit, a first evaluation unit and a second evaluation unit. The re-inspection information acquisition unit performs a UAV re-inspection of the wind turbine blades according to the UAV re-inspection route to obtain re-inspection information of the wind turbine blades; the first evaluation unit obtains the UAV re-inspection results of the wind turbine blades according to the re-inspection information; and the second evaluation unit updates the UAV inspection results of the wind turbine blades according to the UAV re-inspection results of the wind turbine blades and the UAV inspection results of the wind turbine blades.

[0015] In a third aspect, the present invention application provides a wind turbine blade drone inspection device, which includes a processor, a memory, and a computing program stored in the memory and executable by the processor, wherein when the computing program is executed by the processor, the steps of the wind turbine blade drone inspection method as described in any one of the first aspects are implemented.

[0016] In a fourth aspect, the present invention application provides a computer-readable storage medium having a computing program stored thereon, wherein when the computing program is executed by a processor, the steps of the wind turbine blade drone inspection method as described in any one of the first aspects are implemented.

[0017] In summary, the beneficial technical effects of the present invention are: (1) In the present invention, firstly, the wind turbine blade drone inspection technology does not require the wind turbine to be shut down. When the wind turbine is running, the wind turbine blade drone inspection work can be carried out synchronously, the working conditions of multiple wind turbine blades of at least one wind turbine in the wind farm can be photographed, and it can be determined whether multiple wind turbine blades have defects, which will not affect the operation and power generation efficiency of the wind farm; in addition, the first track point 1, the first track point 2, ..., the first track point M, the second track point 1, the second track point 2, ..., the second track point M can be planned and set in advance, as long as there is a drone that can take pictures of the front or back of the blade, even if the endurance performance of the drone is reduced or insufficient, the standby drone can take off in advance or immediately take over the existing drone with poor endurance performance, and continue to take pictures of the front and back of the wind turbine blades at the first track point 1, the first track point 2, ..., the first track point M, the second track point 1, the second track point 2, ..., the second track point M, thereby improving the efficiency of the wind turbine blade drone inspection.

[0018] (2) In the present invention, the re-inspection route of the UAV is planned according to the minimum distance Dmin. With the help of the advanced navigation and positioning technology of the UAV, the UAV can accurately fly according to the planned re-inspection route to obtain high-resolution images and data, and by setting appropriate shooting angles and segmented shooting images, the integrity and consistency of the data are ensured, which is convenient for comparative analysis and timely detection of subtle changes. The planned re-inspection route enables the UAV to cover the target area in the shortest time, greatly improving the inspection efficiency. There is no need for a large number of manpower to conduct long-term inspections on site, which reduces manpower costs and labor intensity, and reduces equipment loss. Reasonable route planning can reduce unnecessary flights and operations of the UAV, extend its service life, and reduce equipment maintenance and replacement costs. According to the planned re-inspection route, the consistency and comparability of the data are guaranteed, which helps to obtain more accurate and comprehensive data, improve the accuracy and reliability of data analysis, and provide strong support for the decision-making of subsequent wind turbine blade defect detection results.

[0019] (3) In the present invention, the conflict coefficient CT, the first classifier, the second classifier, the third classifier, the confidence expectation and the true voting expectation are set. When CT ≥ 0.8, the confidence expectation and the voting expectation can be comprehensively considered to determine the defect type. By introducing a comparison mechanism of the product and the average expectation, when the product of the confidence expectation and the true voting expectation of different defect types is still less than the average expectation, the defect types corresponding to these expectations are used as the results of evaluating the defect types of wind turbine blades. In this way, reliable defect types can be screened out more rigorously, and classification errors caused by classifier conflicts can be reduced, making the evaluation of wind turbine blade defect types more accurate and stable. It is also helpful to improve the accuracy of wind turbine blade defect identification in complex classification situations, reduce the misleading of subsequent maintenance decisions by misjudgment, and reduce unnecessary maintenance costs and resource waste caused by incorrect judgment of defect types by classifiers, thereby ensuring the safe operation of wind turbine blades, allowing wind farms to continue to operate, and thus improving the operating efficiency of the entire wind farm; when CT<0.8, focus on the true voting expectation. When the true voting expectations of different defect types are still greater than the average true voting expectation, the defect types corresponding to these true voting expectations are used as the results of evaluating the defect types of wind turbine blades. In this way, the defect type of the wind turbine blade can be quickly identified, the complex multi-factor calculation process can be avoided, and the efficiency and timeliness of the wind turbine blade defect assessment can be improved. This is particularly important for the inspection of large-scale wind turbine blades, and a large amount of data can be processed in a short time to obtain reliable results. At the same time, when the classifier consistency is high, the advantages of multiple classifiers can be fully utilized to reduce the misclassification caused by individual factors or accidental errors, avoid unnecessary repairs or miss the best repair time due to misjudgment, reduce maintenance costs and wind turbine blade downtime, and improve the availability and power generation efficiency of wind power equipment. Moreover, with the accumulation of data and the improvement of the deep learning performance of the classifier, the accuracy and reliability are expected to be further improved, which can promote the intelligent drone inspection in the wind power industry to develop in a more refined and precise direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the wind turbine blade drone inspection method applied by the present invention; Figure 2 This is a schematic diagram of the principle of the wind turbine blade drone inspection method applied by the present invention. Figure 1 ; Figure 3 is a flow chart of step S400 of the present invention; Figure 4 This is a schematic diagram of the principle of the wind turbine blade drone inspection method applied by the present invention. Figure 2 ; Figure 5 This is a schematic diagram of the principle of the wind turbine blade drone inspection method applied by the present invention. Figure 3 ; Figure 6 This is a schematic diagram of a wind turbine blade drone inspection device applied for by the present invention; Figure 7 It is a schematic diagram of the composition of the multiple inspection unit of the present invention; Description of the drawings: first blade - 101, second blade - 102, third blade - 103, first unmanned unit - 201, second drone unit one - 2021, second drone unit two - 2022, third flight unit - 203, parameter acquisition unit - 100, inspection unit - 200, display unit - 300, re-inspection unit - 400, server - 500, defect image search unit - 401, defect track point formation unit - 402, route planning unit - 403, start-up unit - 404, re-inspection information acquisition unit - 405, first evaluation unit - 406, second evaluation unit - 407, judgment sub-unit - 4071, processing sub-unit - 4072, evaluation update sub-unit - 4073. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure more clear, the embodiments of the present disclosure are further described in detail below in conjunction with the accompanying drawings and embodiments; it should be understood that the specific embodiments described herein are only used to explain the embodiments of the present disclosure and are not used to limit the embodiments of the present disclosure; The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no substantial technical significance. Any structural modification, change in proportion relationship or adjustment in size, without affecting the effects and purposes that can be produced by the present invention, should still fall within the scope of the technical contents disclosed by the present invention; at the same time, the terms such as "and" and "or" quoted in this specification are only for the convenience of description, and are not used to limit the scope of implementation. The change or adjustment of their relative relationship should also be regarded as the scope of implementation of the present invention without substantially changing the technical contents; in addition, the various embodiments of the present invention are not independent of each other, but can be combined; The terms "first", "second" and "third" are used for descriptive purposes only and should not be understood as indicating or suggesting relative importance or implicitly indicating the number of the indicated technical features; thus, the features defined as "first", "second" and "third" may explicitly or implicitly include one or more of the features, and in the description of the present application, unless otherwise specified, "multiple" means two or more.

[0022] In the existing field of wind power generation technology, a horizontal-axis wind turbine refers to a wind turbine whose blades' rotation axis is parallel to the ground and perpendicular to the wind turbine blades; a horizontal-axis wind turbine usually includes an impeller, a nacelle and a tower body. The impeller part is mainly composed of three blades, the nacelle is usually a horizontal nacelle containing key operating components of the horizontal-axis wind turbine (such as the impeller), and the tower body usually includes a tower column vertical to the ground. Due to the simple structure of the horizontal-axis wind turbine, the large rotation range of the blades, the convenient track planning during the inspection in the drone inspection technology when the horizontal-axis wind turbine is in the shutdown state, and the high power generation efficiency, the horizontal-axis wind turbine has been widely used in most wind farms for power generation. The working state of the horizontal-axis wind turbine also greatly affects the operation and maintenance efficiency and power generation income of most wind farms. These are the technical knowledge of ordinary technicians in the existing field of wind power generation technology.

[0023] First, as Figure 1-Figure 5 As shown, the present invention provides a wind turbine blade drone inspection method, comprising the following steps: Step S100, obtaining structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm, wherein the structural parameters at least include the center height of the impeller, the blade profile curve, the blade length, the maximum chord length, the impeller diameter and the number of blades; the motion parameters at least include the speed of the wind turbine; and the environmental parameters at least include the altitude and the wind speed; In some embodiments, the structural parameters, motion parameters and environmental parameters of the wind turbine can be obtained in advance or measured, and the structural parameters include at least the center height of the impeller, the blade profile curve, the blade length, the maximum chord length, the impeller diameter, and the number of blades; the motion parameters include at least the speed of the wind turbine; and the environmental parameters include at least the terrain and the wind speed. Exemplarily, at least one wind turbine can be photographed multiple times by a drone to obtain multiple images of at least one wind turbine, and the multiple images can be used to obtain the structural parameters and altitude of the wind turbine. At the same time, a wind speed sensor can be used to measure the wind speed around the wind turbine, and then the structural parameters, motion parameters, and environmental parameters of the wind turbine can be measured.

[0024] Exemplarily, in the present application, the first drone unit 201 can be used to measure and obtain some or all of the structural parameters and some environmental parameters (altitude) of the wind turbine. The wind turbine includes three wind turbine blades, namely a first wind turbine blade 101, a second wind turbine blade 102 and a third wind turbine blade 103, an impeller center height of 75m, a blade length of 44m, a maximum chord length of 3.7m, an impeller diameter of about 90m, a wind turbine generator speed of about 25r / min, an altitude of about 1500m, a wind speed of about 4m / s, etc.

[0025] Step S200, based on the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm, a drone inspection is performed on the front of the wind turbine blade to form first inspection information, and a drone inspection is performed on the back of the wind turbine blade to form second inspection information, wherein the first inspection information includes a first track point data set and a blade front image set, and the second inspection information includes a second track point data set and a blade back image set; the blade front image set includes a complete plurality of wind turbine blade overall front images; and the blade back image set includes a complete plurality of wind turbine blade overall back images; In some embodiments, the plurality of wind turbine blades are the three wind turbine blades mentioned above, and the wind turbine blades of the wind turbine generator may include: the front side, the back side, the blade root, and the blade tip. The front side may be understood as at least: the windward side or the leeward side, and correspondingly, the back side may be understood as at least: the leeward side or the windward side, and those skilled in the art may reasonably understand it according to the actual situation. When the horizontal axis wind turbine generator is running clockwise, the front side and the back side of the blades may generally be considered as important areas of the wind turbine blades affected by environmental factors such as wind and sand, lightning, and rain, and accordingly, it may be considered that images of the above areas of the wind turbine blades need to be acquired.

[0026] In some embodiments, a first inspection information is formed by performing a drone inspection on the front of a wind turbine blade. The first inspection information includes a first track point data set and a first blade front image set. The drone inspection usually includes a drone and a camera. The camera is installed on the drone. The drone can take off according to the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm. The camera is located above the blade. The first track point data set includes a first track point 1, a first track point 2, ..., a first track point M, where M is a positive integer, M is greater than or equal to 3. The drone hovers at the first track point 1, the first track point 2, ..., the first track point M, and the camera takes pictures in a downward shooting mode facing the blade, thereby obtaining a blade front image 1 of the first track point 1, a blade front image 2 of the first track point 2, ..., and a blade front image M of the first track point M. It should be noted that, since the wind turbine blades are relatively long and the camera shooting range is limited, the blade front image 1 of the first track point 1, the blade front image 2 of the first track point 2, ..., the blade front image M of the first track point M are segmented images obtained by photographing the front of the wind turbine blade at each first track point, and the number of segments of the segmented images can correspond to the total number of the first track points, that is, the blade front image 1, the blade front image 2, ..., the blade front image M can be combined to form an overall front image of the wind turbine blade. Accordingly, the blade front image 1 of the first track point 1, the blade front image 2 of the first track point 2, ..., the blade front image M of the first track point M can be processed, and then a blade front image set of the wind turbine blade can be obtained, and the blade front image set includes a complete plurality of overall front images of the wind turbine blades.

[0027] For example, Figure 2 As shown, in the application of the present invention, the first track point 1, the first track point 2, ..., the first track point M can be planned and set in advance, as long as a drone can appear at the first track point 1, the first track point 2, ..., the first track point M to take pictures of the front of the wind turbine blade; at the same time, a second drone unit 2021 can be used to perform drone inspections on the front of the first wind turbine blade 101, the front of the second wind turbine blade 102, and the front of the third wind turbine blade 103 to form first inspection information, and the second drone unit 2021 hovers at the first track point 1P11, the first track point 2P12, ..., the first track point iP1i, ..., the first track point MP1M, i=1, 2, 3, ..., M.

[0028] Obtain the blade front image 1 of the first track point 1P11, the blade front image 2 of the first track point 2P12, ..., the blade front image M of the first track point MP1M, the blade front image 1 includes the first wind turbine blade 101 front image 1, the second wind turbine blade 102 front image 1, and the third wind turbine blade 103 front image 1, the blade front image 2 includes the first wind turbine blade 101 front image 2, the second wind turbine blade 102 front image 2, and the third wind turbine blade 103 front image 2, ..., the blade front image M includes the first wind turbine blade 101 front image M, the second wind turbine blade 102 front image M, and the third wind turbine blade 103 front image M. On this basis, blade front image 1, blade front image 2, ..., blade front image M can be combined to form an overall front image of the wind turbine blade. The blade front image set will include a complete overall front image of the first wind turbine blade 101, a complete overall front image of the second wind turbine blade 102, and a complete overall front image of the third wind turbine blade 103, that is, the blade front image set can include multiple complete overall front images of wind turbine blades.

[0029] In some embodiments, a second inspection information is formed by performing a drone inspection on the back of a wind turbine blade. The second inspection information includes a second track point data set and a second blade back image set. Similarly, the drone inspection usually includes a drone and a camera. The camera is installed on the drone. The drone can take off according to the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm. The camera is located above the blade. The second track point data set includes a second track point 1, a second track point 2, ..., a second track point N, where N is a positive integer, N is greater than or equal to 3. The drone hovers at the second track point 1, the second track point 2, ..., the second track point N, and the camera takes pictures in a downward shooting mode facing the blade, thereby obtaining a blade back image 1 of the second track point 1, a blade back image 2 of the second track point 2, ..., and a blade back image N of the second track point N. It should also be noted that, since the wind turbine blades are relatively long and the camera's shooting range is limited, blade back image 1 of the second track point 1, blade back image 2 of the second track point 2, ..., blade back image N of the second track point N are segmented images obtained by photographing the back of the wind turbine blade at each second track point, and the number of segments of the segmented images may correspond to the total number of second track points, that is, blade back image 1, blade back image 2, ..., blade back image N may be combined to form an overall back image of the wind turbine blade, and accordingly, blade back image 1 of the second track point 1, blade back image 2 of the second track point 2, ..., blade back image N of the second track point M may be processed, and then may be combined to form a blade back image set of the wind turbine blade, and the blade back image set includes a complete plurality of overall back images of wind turbine blades.

[0030] For example, Figure 2 As shown, in the application of the present invention, the second track point 1, the second track point 2, ..., the second track point N can be planned and set in advance, as long as the second track point 1, the second track point 2, ..., the second track point N can appear when the drone takes pictures of the back side of the wind turbine blade; at the same time, the second drone unit 2022 can be used to perform drone inspections on the back side of the first wind turbine blade 101, the back side of the second wind turbine blade 102, and the back side of the third wind turbine blade 103 to form second inspection information, and the second drone 2022 hovers at the second track point 1P21, the second track point 2P22, ..., the second track point jP2j, ..., the second track point NP2N, j=1, 2, 3, ..., N.

[0031] Obtain blade back image 1 of the second track point 1P21, blade back image 2P22 of the second track point 2, ..., second track point jP2j, ..., blade back image N of the second track point NP2N, blade back image 1 includes first wind turbine blade 101 back image 1, second wind turbine blade 102 back image 1, third wind turbine blade 103 back image 1, blade back image 2 includes second wind turbine blade 101 back image 2, second wind turbine blade 102 back image 2, third wind turbine blade 103 back image 2, ..., blade back image N includes second wind turbine blade 101 back image N, second wind turbine blade 102 back image N, third wind turbine blade 103 back image N. On this basis, blade back image 1, blade back image 2, ..., blade back image N can be combined to form an overall back image of the wind turbine blade. The blade back image set will include a complete overall back image of the first wind turbine blade 101, a complete overall back image of the second wind turbine blade 102, and a complete overall back image of the third wind turbine blade 103, that is, the blade back image set includes multiple complete overall back images of wind turbine blades.

[0032] In the present application, the camera takes pictures by pointing downward toward the blade, and the foreground in the front image or the back image is usually the ground or the sea surface, so the front image or the back image of the blade can be easily extracted. At the same time, generally speaking, the camera starts taking pictures only when the first wind turbine blade 101, the second wind turbine blade 102, and the third wind turbine blade 103 are basically level with the ground and enter the shooting range of the camera. The camera can rotate or not rotate to ensure a sufficient shooting range, so that the front image or the back image of the first wind turbine blade 101, the second wind turbine blade 102, and the third wind turbine blade 103 can have more pixels occupied by the spine of the wind turbine blade, and the front image or the back image of the first wind turbine blade 101, the second wind turbine blade 102, and the third wind turbine blade 103 has a high degree of similarity and a low difficulty in image stitching, which is convenient for subsequent combination to form a blade front image set or a blade back image set, and then the complete overall front image of the first wind turbine blade 101, the second wind turbine blade 102, and the third wind turbine blade 103 and the overall back image of the first wind turbine blade 101, the second wind turbine blade 102, and the third wind turbine blade 103 can be obtained.

[0033] Step S300, based on the first inspection information and the second inspection information, combined with the wind turbine blade defect judgment conditions, obtain the wind turbine blade drone inspection results; based on the wind turbine blade drone inspection results, determine whether to start the drone re-inspection mode, if yes, start the drone re-inspection mode and update the wind turbine blade drone inspection results; if not, do not start the re-inspection mode.

[0034] In some embodiments, the defect types of wind turbine blades generally include: (1) fatigue defects (which can be named C-1). Due to fatigue, wind turbine blades have tiny cracks, elongated cracks, etc. The width of the cracks or cracks is narrow and the length is short, and they can be directly observed on the surface of the wind turbine blades; 2) impact defects (which can be named C-2). When the wind turbine blades are subjected to external impact or collision, such as lightning strikes, bird collisions, etc., the surface of the wind turbine blades may be burned or have open cracks, and may be broken, cracked, and fractured; 3) erosion defects (which can be named C-3). When the surface of the wind turbine blades is eroded by factors such as chemicals, wind and sand, seawater, or air pollution, erosion damage may occur, such as sand holes (i.e., a continuous area of ​​the blade has relatively dense and discrete small holes); 4) shedding defects (which can be named C-4). Due to unreliable bonding of the wind turbine blades, the internal fiber cloth of the wind turbine blades is exposed, and surface shedding occurs.

[0035] Based on the above four common types of defects of wind turbine blades, after learning and training, multiple samples of wind turbine blade defects can usually form a classifier for detecting the types of defects of wind turbine blades. Usually, the image of the wind turbine blade taken can be input into the classifier to detect and classify the defects of the wind turbine blade taken (for example, neural network technology, deep learning technology, etc. can be used), that is, combined with the wind turbine blade defect judgment conditions, the classifier is used to obtain the wind turbine blade drone inspection results, which are all common technical knowledge of existing wind turbine blades. Therefore, the wind turbine blade defect judgment conditions applied in the present invention include: judging whether the wind turbine blade has fatigue defects, impact defects, erosion defects and shedding defects, that is, judging whether the wind turbine blade has one or more of fatigue defects, impact defects, erosion defects and shedding defects according to the front image set of the blade and the back image set of the blade, that is, judging whether the wind turbine blade has defects and the defect types of the wind turbine blade. More specifically, based on the overall front image or the overall back image of the first wind turbine blade, the overall front image or the overall back image of the second wind turbine blade, and the overall front image or the overall back image of the third wind turbine blade, it can be judged whether the first wind turbine blade, the second wind turbine blade, and the third wind turbine blade have defects and the corresponding defect types, and the defect types are one or more of fatigue defects, impact defects, erosion defects, and shedding defects.

[0036] In some embodiments, obtaining the wind turbine blade drone inspection result includes: the wind turbine blade has defects and the wind turbine blade defect type result, or the wind turbine blade has no defects and the wind turbine blade defect type is none. Obviously, after detecting the wind turbine blade has defects and the wind turbine blade defect type result, it is determined that the drone re-inspection mode can be started, and the wind turbine blade is inspected by the drone again, which can improve the accuracy and reliability of the wind turbine blade defect detection result; after detecting the wind turbine blade has no defects and the wind turbine blade defect type is none, it is determined that the drone re-inspection mode can be not started.

[0037] Therefore, in the application of the present invention, firstly, the wind turbine blade drone inspection technology does not require the wind turbine to be shut down. When the wind turbine is running, the wind turbine blade drone inspection work can be carried out synchronously to photograph the working conditions of the wind turbine blades of at least one wind turbine in the wind farm, and determine whether there are defects in the wind turbine blades, which will not affect the power generation efficiency of the wind farm; in addition, the first track point 1, the first track point 2, ..., the first track point M, the second track point 1, the second track point 2, ..., the second track point M can be planned and set in advance, as long as the drone can inspect the blades It only needs to take pictures of the front or back of the wind turbine blade. Even if the endurance performance of the drone decreases or is insufficient, the backup drone can take off in advance or quickly take over the existing drone with poor endurance performance, and continue to take pictures of the front and back of the wind turbine blade at the first track point 1, the first track point 2, ..., the first track point M, the second track point 1, the second track point 2, ..., the second track point M, without affecting the drone inspection efficiency of the wind turbine blade. Performing drone inspection on the wind turbine blade again can further improve the accuracy and reliability of the wind turbine blade defect detection results.

[0038] Optionally, the first inspection information includes a first track point image sequence one, a first track point image sequence two, and a first track point image sequence three; The first track point image sequence 1 at least includes: a first track point front image sequence SNPF11, a first track point front image sequence SNPF12; ...; a first track point front image sequence SNPF1i; ...; a first track point front image sequence SNPF1M; the first track point front image sequence SNPF1i includes: [first track point iP1i, first wind turbine blade 101 front image i]; The first track point image sequence 2 at least includes: a first track point front image sequence SNPF21, a first track point front image sequence SNPF22; ...; a first track point front image sequence SNPF2i; ...; a first track point front image sequence SNPF2M; the first track point front image sequence SNPF2i includes: [first track point iP1i, second wind turbine blade 102 front image i]; The first track point image sequence three includes at least: the first track point front image sequence SNPF31, the first track point front image sequence SNPF32; ...; the first track point front image sequence SNPF3i; ...; the first track point front image sequence SNPF3M; the first track point front image sequence SNPF3i includes: [first track point iP1i, third wind turbine blade 103 front image i], i=1, 2, 3, ..., M.

[0039] In the present invention, the first inspection information includes a first track point image sequence 1, a first track point image sequence 2 and a first track point image sequence 3. The first track point image sequence 1 may include: [first track point 1P11, front image 1 of the first wind turbine blade 101; first track point 1P12, front image 2 of the first wind turbine blade 101; ...; first track point 1P1M, front image M of the first wind turbine blade 101; overall front image of the first wind turbine blade 101]; The first track point image sequence 2 may include: [first track point 1P11, front image 1 of the second wind turbine blade 102; first track point 1P12, front image 2 of the second wind turbine blade 101; ...; first track point MP1M, front image M of the second wind turbine blade 102; overall front image of the second wind turbine blade 102]; The first track point image sequence three may include: [first track point 1P11, front image 1 of the third wind turbine blade 103; first track point 1P12, front image 2 of the third wind turbine blade 101; ...; first track point 1P1M, front image M of the third wind turbine blade 103; overall front image of the third wind turbine blade 103].

[0040] On this basis, it can at least be understood that: the first track point image sequence one is used to represent the front image (segmented local) of the first wind turbine blade and the overall front image of the first wind turbine blade at different first track points after the drone inspection of the front of the first wind turbine blade, and the front images of the first wind turbine blade at different first track points can be spliced ​​and combined to form the overall front image of the first wind turbine blade; the first track point image sequence two is used to represent the front image (segmented local) of the second wind turbine blade and the overall front image of the second wind turbine blade at different first track points after the drone inspection of the front of the second wind turbine blade, and the front images of the second wind turbine blade at different first track points can be spliced ​​and combined to form the overall front image of the second wind turbine blade; the first track point image sequence three is used to represent the front image (segmented local) of the third wind turbine blade and the overall front image of the third wind turbine blade at different first track points after the drone inspection of the front of the third wind turbine blade, and the front image (segmented local) of the third wind turbine blade at different first track points can be spliced ​​and combined to form the overall front image of the third wind turbine blade.

[0041] Optionally, the second inspection information includes the second track point image sequence one, the second track point image sequence two, and the second track point image sequence three; The second track point image sequence 1 at least includes: a second track point back image sequence SNPB11, a second track point back image sequence SNPB12; ...; a second track point front image sequence SNPB1j; ...; a second track point back image sequence SNPB1N; the second track point back image sequence SNPB1j includes: [second track point jP2j, first wind turbine blade 101 back image j]; The second track point image sequence 2 at least includes: a second track point back image sequence SNPB21, a second track point back image sequence SNPB22; ...; a second track point front image sequence SNPB2j; ...; a second track point back image sequence SNPB2N; the second track point back image sequence SNPB2j includes: [second track point jP2j, second wind turbine blade 102 back image j]; The second track point image sequence three includes at least: the second track point back side image sequence SNPB31, the second track point back side image sequence SNPB32; ...; the second track point front side image sequence SNPB3j; ...; the second track point back side image sequence SNPB3N; the second track point back side image sequence SNPB3j includes: [second track point jP2j, third wind turbine blade 103 back side image j], j=1, 2, 3, ..., N.

[0042] In the present invention application, the second inspection information includes a second track point image sequence one, a second track point image sequence two, and a second track point image sequence three; The second track point image sequence 1 may include: [second track point 1P21, image 1 of the back side of the first wind turbine blade 101; second track point 2P22, image 2 of the back side of the first wind turbine blade 101; ...; second track point NP2N, image N of the back side of the first wind turbine blade 101; overall back side image of the first wind turbine blade 101]; The second track point image sequence 2 may include: [second track point 1P21, image 1 of the back side of the second wind turbine blade 102; second track point 2P22, image 2 of the back side of the second wind turbine blade 102; ...; second track point NP2N, image N of the back side of the second wind turbine blade 102; overall back side image of the second wind turbine blade 102]; The second track point image sequence three may include: [second track point 1P21, third wind turbine blade 103 back side image 1; second track point 2P22, third wind turbine blade 103 back side image 2; ...; second track point NP2N, third wind turbine blade 103 back side image N; third wind turbine blade 103 overall back side image].

[0043] On this basis, it can at least be understood that: the second track point image sequence one is used to represent the back image (segmented local) of the first wind turbine blade and the overall back image of the first wind turbine blade at different second track points after the drone inspection of the back of the first wind turbine blade, and the back images of the first wind turbine blade at different first track points can be spliced ​​and combined to form the overall back image of the first wind turbine blade; the second track point image sequence two is used to represent the back image (segmented local) of the second wind turbine blade and the overall back image of the second wind turbine blade at different second track points after the drone inspection of the back of the second wind turbine blade, and the back images of the second wind turbine blade at different second track points can be spliced ​​and combined to form the overall back image of the second wind turbine blade; the second track point image sequence three is used to represent the back image (segmented local) of the third wind turbine blade and the overall back image of the third wind turbine blade at different second track points after the drone inspection of the back of the third wind turbine blade, and the back images of the third wind turbine blade at different second track points can be spliced ​​and combined to form the overall back image of the third wind turbine blade.

[0044] Alternatively, if Figure 3 As shown, starting the drone re-inspection mode includes the following steps: Step S401: acquiring a defect track point image sequence corresponding to the defect type wind turbine blade according to the wind turbine blade drone inspection result; In the present application, the defect track point image sequence includes one or more of the first track point front image sequence of the wind turbine blade corresponding to the defect type, and / or one or more of the second track point back image sequence; the result of the wind turbine blade having defects and the defect type of the wind turbine blade, or the result of the wind turbine blade having no defects and the wind turbine blade having no defect type. Accordingly, according to the wind turbine blade drone inspection result, that is, according to the result of the wind turbine blade having defects and the defect type of the wind turbine blade, it can be reversely determined which one or several of the front images and / or back images of the wind turbine blade with the defect type appear from the multiple wind turbine blades, and according to which one or several of the multiple front images and / or back images of the wind turbine blades, It can be determined which one or several front images and / or back images of the multiple wind turbine blades come from one or more of the first track point image sequence 1, the first track point image sequence 2 and the first track point image sequence 3, and / or one or more of the second track point image sequence 1, the second track point image sequence 2 and the second track point image sequence 3, that is, more specifically, the defective track point image sequence comes from one or more of the first track point front image sequence SNPF1i, SNPF2i, SNPF3i of the first inspection information, and / or one or more of the second track point back image sequence SNPB1j, SNPB2j, SNPB3j in the second inspection information. In this way, it is possible to determine one or more first track point front image sequences and / or one or more second track point back image sequences corresponding to the defective type wind turbine blade, and complete the acquisition of the defective track point image sequence corresponding to the defective type wind turbine blade, that is, the defective track point image sequence includes one or more of the first track point front image sequence and / or one or more of the second track point back image sequence of the wind turbine blade corresponding to the defective type.

[0045] Step S402, acquiring defect track points corresponding to the defect type wind turbine blade according to the defect track point image sequence, the defect track points forming a first re-inspection track point set and / or a second re-inspection track point set; In the present application, the defect track point image sequence comes from one or more of the first track point front image sequence SNPF1i, SNPF2i, SNPF3i corresponding to the defect type wind turbine blade, and / or comes from one or more of the second track point back image sequence SNPB1i, SNPB2i, SNPB3i. According to the defect track point image sequence, the track point corresponding to the defect type wind turbine blade (also called defect track point) and the corresponding front and / or back of the wind turbine blade can be determined.

[0046] In the present invention application, the defective track point comes from one or more of the first track point iP1i, ..., the first track point MP1M, and / or, from one or more of the second track point, the second track point 1P21, the second track point 2P22, ..., the second track point jP2j, ..., the second track point NP2N. Accordingly, the defective track point includes defective first track point 1, defective first track point 2, ..., defective first track point r, ..., defective first track point P, and / or, defective second track point 1, defective second track point 2, ..., defective second track point s, ..., defective second track point Q, r = 1, 2, 3, ..., P; s = 1, 2, 3, ..., Q. On this basis, the first re-inspection track point set includes defective first track point 1, defective first track point 2,..., defective first track point r,..., defective first track point P, the second re-inspection track point set includes defective second track point 1, defective second track point 2,..., defective second track point s,..., defective second track point Q, the corresponding defective track points include the first track point and / or the second track point, the first track point can be one or more, and / or, the second track point can be one or more, the defective track points can be used to form the first re-inspection track point set and / or the second re-inspection track point set.

[0047] Step S403: planning a drone re-inspection route according to the first re-inspection track point set and / or the second re-inspection track point set, and starting the drone re-inspection of the wind turbine blades.

[0048] In some embodiments, the first re-inspection track point set and / or the second re-inspection track point set, that is, based on defective first track point 1, defective first track point 2,..., defective first track point r,..., defective first track point P, and / or defective second track point 1, defective second track point 2,..., defective second track point s,..., defective second track point Q, can plan a UAV re-inspection route to start the UAV re-inspection of wind turbine blades.

[0049] In the present application, according to the defect first track point 1, the defect first track point 2, ..., the defect first track point r, ..., the defect first track point P, and / or the defect second track point 1, the defect second track point 2, ..., the defect second track point s, ..., the defect second track point Q, the re-inspection route of the third UAV unit 203 can be planned, and the third UAV unit 203 starts the UAV re-inspection of the wind turbine blade, returns to one or more first track point positions and / or second track point positions corresponding to the defect type wind turbine blade given by the defect track point image sequence, and performs UAV inspection on the wind turbine blade again.

[0050] like Figure 4 , Figure 5As shown, optionally, the UAV re-inspection route includes a first re-inspection route, a re-inspection switching route T and a second re-inspection route, the re-inspection switching route T connects the first re-inspection route and the second re-inspection route, the first re-inspection track point set is used to form the first re-inspection route, the first re-inspection route is used to perform a front UAV inspection of the wind turbine blade at the defective first track point to form first re-inspection information, the second re-inspection track point set is used to form a second re-inspection route, the second re-inspection route is used to perform a back UAV inspection of the wind turbine blade at the defective second track point to form second re-inspection information.

[0051] It should be noted that the drone inspection of the front side of the wind turbine blade and the drone inspection of the back side of the wind turbine blade at the defect track point can preferably only be carried out on the front side of the wind turbine blade and / or the back side of the wind turbine blade corresponding to the defect type, so as to improve the efficiency of the re-inspection; for the defect track point, the front side of the wind turbine blade and / or the back side of the wind turbine blade corresponding to the non-defect type can also be inspected (the camera takes pictures of the front side of the wind turbine blade and / or the back side of the wind turbine blade corresponding to the non-defect type) or not inspected (that is, the camera does not take pictures of the front side of the wind turbine blade and / or the back side of the wind turbine blade corresponding to the non-defect type). Technical personnel in this field can make a reasonable choice based on the image processing needs and the actual situation.

[0052] Obviously, in the present application, the first re-inspection route is used to perform UAV inspection on the front side of the wind turbine blade at the defective track point to form the first re-inspection information, and the second re-inspection route is used to perform UAV inspection on the back side of the wind turbine blade at the defective track point to form the second re-inspection information. Correspondingly, there will be a technical problem of how to switch the UAV to the second re-inspection route for flight inspection after the UAV completes the flight inspection on the first re-inspection route, or how to switch the UAV to the first re-inspection route for flight inspection after the UAV completes the flight inspection on the second re-inspection route.

[0053] Therefore, the applicant conducted technical research on this and proposed a technical solution for the re-inspection switching route T, wherein the UAV can switch to the re-inspection switching route after the flight inspection of the first re-inspection route is completed, and after the flight of the re-inspection switching route is completed, it reaches the starting position of the second re-inspection route, and the UAV performs flight inspection on the second re-inspection route. Alternatively, the UAV can switch to the re-inspection switching route after the flight inspection of the second re-inspection route is completed, and after the flight of the re-inspection switching route is completed, it reaches the starting position of the first re-inspection route, and the UAV performs flight inspection on the first re-inspection route.

[0054] like Figure 4 , Figure 5As shown, optionally, obtain the defective first track point 1CF1 at the start and the defective first track point PCFP at the end of the first re-inspection route, the defective second track point 1CB1 at the start and the defective second track point QCBQ at the end of the second re-inspection route, calculate the first distance D1 between the defective first track point 1 and the defective second track point 1, the second distance D2 between the defective first track point 1 and the defective second track point Q, the third distance D3 between the defective first track point P and the defective second track point 1, and the fourth distance D4 between the defective first track point P and the defective second track point Q; according to the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4, obtain the minimum distance Dmin=min[D1, D2, D3, D4] among the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4, and plan the UAV re-inspection route according to the minimum distance Dmin.

[0055] Therefore, in the application of the present invention, the drone re-inspection route is planned according to the minimum distance Dmin. With the help of the drone's advanced navigation and positioning technology, the drone can accurately fly according to the planned re-inspection route to obtain high-resolution images and data, and by setting appropriate shooting angles and segmented shooting images, the integrity and consistency of the data are ensured, which is convenient for comparative analysis and timely discovery of subtle changes. The planned re-inspection route enables the drone to cover the target area in the shortest time, greatly improving the inspection efficiency, eliminating the need for a large amount of manpower to conduct long-term inspections on site, reducing labor costs and labor intensity, and reducing equipment wear. Reasonable route planning can reduce unnecessary flights and operations of drones, extend their service life, and reduce equipment maintenance and replacement costs. According to the planned re-inspection route, the consistency and comparability of the data are guaranteed, which helps to obtain more accurate and comprehensive data, improve the accuracy and reliability of data analysis, and provide strong support for subsequent decision-making on wind turbine blade defect detection results.

[0056] In some embodiments, the spatial coordinates of the first defect track point 1CF1 are (x1, y1, z1), the spatial coordinates of the first defect track point PCFP are (xp, yp, zp), the spatial coordinates of the second defect track point 1CB1 are (x2, y2, z2), and the spatial coordinates of the second defect track point QCBQ are (xq, yq, zq). Accordingly, the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4 are respectively: D1=((x1-x2)2+(y1-y2)2+(z1-z2)2)1 / 2; D2=((x1-xq)2+(y1-yq)2+(z1-zq)2)1 / 2; D3=((xp-x2)2+(yp-y2)2+(zp-z2)2)1 / 2; D4=((xp-xq)2+(yp-yq)2+(zp-zq)2)1 / 2; Dmin=min[D1, D2, D3, D4].

[0057] Based on the minimum distance Dmin, the planned drone inspection route includes: (1) When the minimum distance Dmin=D1, the planned UAV re-inspection route includes: the UAV reverse inspection of the first re-inspection route, switching to the re-inspection switching route T, and the UAV forward inspection of the second re-inspection route; When the minimum distance Dmin=D2, the planned UAV re-inspection route includes: the reverse UAV inspection of the first re-inspection route, switching to the re-inspection switching route T, and the reverse UAV inspection of the second re-inspection route; (2) When the minimum distance Dmin=D3, the planned UAV re-inspection route includes: the UAV forward inspects the first re-inspection route, switches to the re-inspection switching route T, and the UAV forward inspects the second re-inspection route; when the minimum distance Dmin=D4, the planned UAV re-inspection route includes: the UAV forward inspects the first re-inspection route, switches to the re-inspection switching route T, and the UAV reverse inspects the second re-inspection route.

[0058] It should be noted that the above-mentioned UAV can use the third UAV unit 203. For the first re-inspection route, the forward inspection means: the UAV hovers at the first defect track point 1CF1 from takeoff, and hovers at the first defect track point PCFP at the end after the flight inspection is completed; the reverse inspection means: the UAV hovers at the first defect track point PCFP from takeoff, and hovers at the first defect track point 1CF1 at the end after the flight inspection is completed; for the second re-inspection route, the forward inspection means: the UAV hovers at the second defect track point 1CB1 from takeoff, and hovers at the second defect track point QCBQ at the end after the flight inspection is completed; the reverse inspection means: the UAV hovers at the second defect track point QCBQ from takeoff, and hovers at the second defect track point 1CB1 at the end after the flight inspection is completed.

[0059] Optionally, updating the wind turbine blade drone inspection result includes the following steps: Step S501: Performing a UAV re-inspection on a wind turbine blade according to a UAV re-inspection route to obtain re-inspection information of the wind turbine blade, wherein the re-inspection information includes a blade front re-inspection image set and a blade back re-inspection image set; the blade front re-inspection image set includes a plurality of wind turbine blade front re-inspection images; and the blade front re-inspection image set includes a plurality of wind turbine blade back re-inspection images; In the present application, at least one of the first wind turbine blade front re-inspection image CF11, the second wind turbine blade front re-inspection image CF21 and the third wind turbine blade front re-inspection image CF31 of the defective first track point 1CF1, at least one of the first wind turbine blade front re-inspection image CF12, the second wind turbine blade front re-inspection image CF22 and the third wind turbine blade front re-inspection image CF32 of the defective first track point 2CF2, ..., at least one of the first wind turbine blade front re-inspection image CF1r, the second wind turbine blade front re-inspection image CF2r and the third wind turbine blade front re-inspection image CF3r of the defective first track point r, ..., at least one of the first wind turbine blade front re-inspection image CF1P, the second wind turbine blade front re-inspection image CF2P and the third wind turbine blade front re-inspection image CF3P of the defective first track point PCFP is obtained. Correspondingly, one or more of the first wind turbine blade front side re-inspection image CF11, the first wind turbine blade front side re-inspection image CF12, ..., the first wind turbine blade front side re-inspection image CF1r, ..., and the first wind turbine blade front side re-inspection image CF1P are combined to form the first wind turbine blade front side re-inspection image; one or more of the second wind turbine blade front side re-inspection image CF21, the second wind turbine blade front side re-inspection image CF22, ..., the second wind turbine blade front side re-inspection image CF2r, ..., and the second wind turbine blade front side re-inspection image CF2P are combined to form the second wind turbine blade front side re-inspection image; one or more of the third wind turbine blade front side re-inspection image CF31, the third wind turbine blade front side re-inspection image CF32, ..., the third wind turbine blade front side re-inspection image CF3r, ..., and the third wind turbine blade front side re-inspection image CF3P form the second wind turbine blade front side re-inspection image. On this basis, the blade front side re-inspection image set includes: one or more of: a first wind turbine blade front side re-inspection image, a second wind turbine blade front side re-inspection image, and a third front side re-inspection image, that is, the blade front side re-inspection image set includes: multiple wind turbine blade front side re-inspection images.

[0060] In the present application, at least one of the first wind turbine blade back side re-inspection image CB11, the second wind turbine blade back side re-inspection image CB21 and the third wind turbine blade back side re-inspection image CB31 of the defective second track point 1CB1, at least one of the first wind turbine blade back side re-inspection image CB12, the second wind turbine blade back side re-inspection image CB22 and the third wind turbine blade back side re-inspection image CB32 of the defective second track point 2CB2, ..., at least one of the first wind turbine blade back side re-inspection image CB1s, the second wind turbine blade back side re-inspection image CB2s and the third wind turbine blade back side re-inspection image CB3s of the defective second track point s, ..., at least one of the first wind turbine blade back side re-inspection image CB1Q, the second wind turbine blade back side re-inspection image CB2Q and the third wind turbine blade back side re-inspection image C3Q of the defective second track point QCBQ is obtained. Correspondingly, one or more of the first wind turbine blade back side re-inspection image CB11, the first wind turbine blade back side re-inspection image CB12, ..., the first wind turbine blade back side re-inspection image CB1s, ..., and the first wind turbine blade back side re-inspection image CB1Q are combined to form the first wind turbine blade back side re-inspection image; one or more of the second wind turbine blade back side re-inspection image CB21, the second wind turbine blade back side re-inspection image CB22, ..., the second wind turbine blade back side re-inspection image CB2s, ..., and the second wind turbine blade back side re-inspection image CB2Q are combined to form the second wind turbine blade back side re-inspection image; one or more of the third wind turbine blade back side re-inspection image CB31, the third wind turbine blade back side re-inspection image CB32, ..., the third wind turbine blade back side re-inspection image CB3s, ..., and the third wind turbine blade back side re-inspection image CB3Q form the second wind turbine blade back side re-inspection image. On this basis, the blade back side re-inspection image set includes: one or more of: a first wind turbine blade back side re-inspection image, a second wind turbine blade back side re-inspection image, and a third back side re-inspection image, and the blade back side re-inspection image set includes: multiple wind turbine blade back side re-inspection images.

[0061] In some embodiments, the third drone unit 203 may also take off according to the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm, and the camera installed on the third drone unit 203 may only take pictures of the wind turbine blades with defects detected in the drone inspection results of the wind turbine blades at the defect track point (i.e., only the front and / or back of the wind turbine blades corresponding to the defect type), and take pictures to obtain the front and / or back images of the wind turbine blades with defects detected in the drone inspection results of the wind turbine blades at the defect track point, so as to improve the efficiency of the third drone unit 203 in re-inspecting the wind turbine blades.

[0062] Step S502: Obtain the wind turbine blade drone re-inspection result according to the re-inspection information, and update the wind turbine blade drone inspection result according to the wind turbine blade drone re-inspection result and the wind turbine blade drone inspection result.

[0063] In some embodiments, the wind turbine blade drone re-inspection result is obtained according to the re-inspection information, and the wind turbine blade defect judgment condition also includes: judging whether the wind turbine blade has fatigue defects, impact defects, erosion defects and shedding defects, that is, judging whether the wind turbine blade has one or more of fatigue defects, impact defects, erosion defects and shedding defects according to the blade front re-inspection image set (i.e., multiple wind turbine blade front re-inspection images) and the blade back re-inspection image set (i.e., multiple wind turbine blade back re-inspection images), that is, judging whether the wind turbine blade has defects and the defect type of the wind turbine blade. More specifically, according to the front image of the wind turbine blade with defects detected in the drone inspection result of the wind turbine blade at one or more defect first track points, and / or the back image of the wind turbine blade with defects detected in the drone inspection result of the wind turbine blade at one or more defect second track points, it is judged whether at least one of the first wind turbine blade, the second wind turbine blade and the third wind turbine blade still has one or more of fatigue defects, impact defects, erosion defects and shedding defects.

[0064] In some embodiments, the wind turbine blade drone re-inspection result includes: the wind turbine blade has defects and the wind turbine blade re-inspection defect type result, or the wind turbine blade has no defects and the wind turbine blade defect type is none. On this basis, the wind turbine blade drone inspection result can be updated according to the wind turbine blade drone re-inspection result and the wind turbine blade drone inspection result. Specifically, step S502 includes: Step S5021: Determine whether the wind turbine blade drone re-inspection result is consistent with the wind turbine blade drone inspection result. If so, update the wind turbine blade drone re-inspection result as the wind turbine blade drone inspection result. If not, proceed to step S5022.

[0065] Step S5022: at least according to the overall front images of the multiple wind turbine blades corresponding to the defect types and / or the overall back images of the multiple wind turbine blades corresponding to the defect types, and the corresponding multiple front re-inspection images of the wind turbine blades and / or the corresponding back re-inspection images of the wind turbine blades, use the first classifier, the second classifier, and the third classifier to classify the overall front images of the multiple wind turbine blades corresponding to the defect types and / or the overall back images of the multiple wind turbine blades corresponding to the defect types, and the corresponding multiple front re-inspection images of the wind turbine blades and / or the multiple back re-inspection images of the wind turbine blades, respectively, to obtain the results of the first defect type set C1 and the first probability set P1 of the first classifier, the second defect type set C2 and the second probability set P2 of the second classifier, and the third defect type set C3 and the third probability set P3 of the third classifier; Step S5023: According to the first defect type set C1, the second defect type set C2 and the third defect type set C3, the first probability set P1, the second probability set P2 and the third probability set P3, calculate the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types, evaluate the defect types of the wind turbine blades according to the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types, and update the results of evaluating the defect types of the wind turbine blades as the wind turbine blade drone inspection results.

[0066] Specifically, step S5023 includes: Step S50231: calculating the conflict coefficient CT according to the first defect type set C1, the second defect type set C2 and the third defect type set C3 includes; CT=m / (3*k) Among them, the total number of different defect types is m, and the average number of defect types is k.

[0067] In the present application, the first defect type set C1 is at least one of [C-1, C-2, C-3, C-4], and the total number of defect types of the first classifier is k1; the second defect type set C2 is at least one of [C-1, C-2, C-3, C-4], and the total number of defect types of the second classifier is k2; the third defect type set C3 is at least one of [C-1, C-2, C-3, C-4], and the total number of defect types of the third classifier is k3; the number of different types between the first defect type set C1 and the second defect type set C2 is m1, the number of different types between the second defect type set C2 and the third defect type set C3 is m2, and the number of different types between the third defect type set C3 and the third defect type set C1 is m3. Accordingly, the total number m of different defect types is: m=m1+m2+m3, and the average number of defect types is k=(k1+k2+k3) / 3.

[0068] In the present invention, the first probability set P1 corresponding to the defect types in the first defect type set C1 is: P1=[P11, P12, ..., P1k1], the second probability set P2 corresponding to the defect types in the second defect type set C2 is: P2=[P21, P22, ..., P2k2], and the third probability set P3 corresponding to the defect types in the third defect type set C3 is: P3=[P31, P32, ..., P3k3]. On this basis, the confidence expectations E(C-1), E(C-2), E(C-3), and E(C-4) of the defect types C-1, C-2, C-3, and C-4 can be calculated respectively, and the general calculation formula is: E(CT)=w1P1T+w2P2T+w3P3T Among them, T=1, 2, 3, 4, w1 is the confidence of the first classifier, w2 is the confidence of the second classifier, w3 is the confidence of the third classifier, w1+w2+w3=1, P1T is the corresponding probability of the corresponding defect type in the first probability set P1, P2T is the corresponding probability of the corresponding defect type in the second probability set P2, and P3T is the corresponding probability of the corresponding defect type in the third probability set P3.

[0069] The average confidence expectation of different defect types C-1, C-2, C-3, and C-4 is: EC=(E(C-1)+E(C-2)+E(C-3)+E(C-4)) / 4.

[0070] In the present invention, based on the first defect type set C1, the second defect type set C2 and the third defect type set, the number of votes R1 of defect type C-1, the number of votes R2 of defect type C-2, the number of votes R3 of defect type C-3 and the number of votes R4 of defect type C-4 can be calculated, and the number of votes of different types can be normalized. Accordingly, the real voting expectation V1 of defect type C-1, the real voting expectation V2 of defect type C-2, the real voting expectation V3 of defect type C-3, the real voting expectation V4 of defect type C-4 and the average real voting expectation V are respectively: V1=G1*R1 / (R1+R2+R3+R4), V2=G2*R2 / (R1+R2+R3+R4); V3=G3*R3 / (R1+R2+R3+R4), V4=G4*R4 / (R1+R2+R3+R4); V = (V1 + V2 + V3 + V4) / 4; Among them, G1 is the first voting adjustment coefficient, G2 is the second voting adjustment coefficient, G3 is the third voting adjustment coefficient, G4 is the fourth voting adjustment coefficient, G1, G2, G3, G4 are constants, G1, G2, G3, G4 are constants and can take values ​​between 0.1-20 (including values ​​0.1, 1.0, 1.5, 2.0, 20, etc.), which can be reasonably set according to actual conditions.

[0071] Step S50232: evaluating the defect type of the wind turbine blade according to the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types.

[0072] Specifically, step S50232 includes: Step S502321 includes: calculating the expectations of different defect types according to the conflict coefficient CT, the confidence expectations of different defect types and the voting expectations of different defect types. Accordingly, the expectations and average expectations of different defect types C-1, C-2, C-3 and C-4 are E1, E2, E3, E4 and E4 respectively: E1=E(C-1)*V1, E2=E(C-2)*V2; E3=E(C-3)*V3, E4=E(C-4)*V4; E=(E1+E2+E3+E4) / 4; Step S502322 includes: When CT≥0.8, the defect type that satisfies the expectation [E1, E2, E3, E4]≤E is taken as the result of evaluating the defect type of the wind turbine blade; when CT<0.8, the defect type that satisfies the true voting expectation [V1, V2, V3, V4]≥V is taken as the result of evaluating the defect type of the wind turbine blade.

[0073] In the present application, when CT≥0.8, it indicates that the differences between the first classifier, the second classifier, and the third classifier are relatively large. In this case, it is necessary to comprehensively consider the confidence expectation and the voting expectation to determine the defect type. By introducing a comparison mechanism of the product and the average expectation, when the product of the confidence expectation and the true voting expectation of different defect types is still less than the average expectation, the defect type corresponding to these expectations is used as the result of evaluating the defect type of the wind turbine blade. In this way, reliable defect types can be screened out more rigorously, and classification errors caused by conflicts between classifiers can be reduced, making the evaluation of wind turbine blade defect types more accurate and stable. It also helps to improve the accuracy of wind turbine blade defect identification in complex classification situations, reduce the misleading of subsequent maintenance decisions by misjudgment, and reduce unnecessary maintenance costs and resource waste caused by incorrect judgment of defect types by classifiers, thereby ensuring the safe operation of wind turbine blades, allowing wind farms to continue to operate, and thus improving the operating efficiency of the entire wind farm.

[0074] In the present application, when CT<0.8, it means that the differences among the first classifier, the second classifier, and the third classifier are not large, and the confidence of the first classifier, the second classifier, and the third classifier is high. In this way, the relatively consistent judgments between the classifiers can be efficiently utilized, focusing on the true voting expectations. Only the true voting expectations need to be comprehensively considered to determine the defect type. When the true voting expectations of different defect types are still greater than the average true voting expectations, the defect types corresponding to these true voting expectations are used as the results of evaluating the defect types of wind turbine blades. In this way, the defect type of the wind turbine blade can be quickly identified, the complex multi-factor calculation process can be avoided, and the efficiency and timeliness of the wind turbine blade defect assessment can be improved. This is particularly important for the inspection of large-scale wind turbine blades, and a large amount of data can be processed in a short time to obtain reliable results. At the same time, when the classifier consistency is high, the advantages of multiple classifiers can be fully utilized to reduce the misclassification caused by individual factors or accidental errors, avoid unnecessary repairs or miss the best repair time due to misjudgment, reduce maintenance costs and wind turbine blade downtime, and improve the availability and power generation efficiency of wind power equipment. Moreover, with the accumulation of data and the improvement of the deep learning performance of the classifier, the accuracy and reliability are expected to be further improved, which can promote the intelligent drone inspection in the wind power industry to develop in a more refined and precise direction.

[0075] like Figure 6 As shown, the present invention provides a wind turbine blade drone inspection device, which implements any wind turbine blade drone inspection method described in the first aspect, including a parameter acquisition unit 100, an inspection unit 200, a display unit 300, a re-inspection unit 400 and a server 500: The parameter acquisition unit 100 acquires the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm and sends them to the inspection unit, wherein the structural parameters at least include the center height of the impeller, the blade profile curve, the blade length, the maximum chord length, the impeller diameter and the number of blades; the motion parameters at least include the speed of the wind turbine; and the environmental parameters at least include the altitude and the wind speed; The inspection unit 200 performs drone inspection on the front of the wind turbine blade according to the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm to form first inspection information and sends it to the server, and performs drone inspection on the back of the wind turbine blade to form second inspection information and sends it to the server, wherein the first inspection information includes a first track point data set and a blade front image set, and the second inspection information includes a second track point data set and a blade back image set; the blade front image set includes a complete plurality of wind turbine blade overall front images; and the blade back image set includes a complete plurality of wind turbine blade overall back images; The server 500 obtains the wind turbine blade UAV inspection result according to the first inspection information and the second inspection information and combines the wind turbine blade defect judgment condition and sends it to the re-inspection unit 400 and the display unit 300, and the display unit displays the wind turbine blade UAV inspection result; The re-inspection unit 400, based on the wind turbine blade drone inspection results, provides an interactive interface for displaying whether to start the drone re-inspection mode through the display unit 300; if yes, the drone re-inspection mode is started and the wind turbine blade drone inspection results are updated; if not, the re-inspection mode is not started.

[0076] It should be noted that a wind turbine blade drone inspection device applied for in the present invention is used for any of the wind turbine blade drone inspection methods described in the first aspect above, and accordingly includes: all the technical problems, technical solutions and technical effects recorded in any of the wind turbine blade drone inspection methods in the first aspect, which will not be repeated in the present invention application.

[0077] like Figure 7 As shown, optionally, the re-inspection unit includes a defect image search unit 401, a defect track point forming unit 402, a re-inspection route planning unit 403 and a start unit 404. The defect image search unit 401 obtains a defect track point image sequence corresponding to the defect type of the wind turbine blade according to the UAV inspection result of the wind turbine blade; the defect track point forming unit 402 obtains the defect track points corresponding to the defect type of the wind turbine blade according to the defect track point image sequence, and forms the defect track points into a first re-inspection track point set and / or a second re-inspection track point set; the re-inspection route planning unit 403 plans the UAV re-inspection route according to the first re-inspection track point set and / or the second re-inspection track point set; the start unit 404 starts the UAV re-inspection of the wind turbine blade.

[0078] In the present invention application, the defect track point image sequence includes one or more of the first track point front image sequence of the wind turbine blade corresponding to the defect type, and / or one or more of the second track point back image sequence. All technical contents such as the first track point front image sequence and the second track point back image sequence can be found in the first aspect of the present invention application and will not be repeated.

[0079] In the present application, the defect image search unit can obtain a defect track point image sequence corresponding to the defect type of the wind turbine blade according to the inspection result of the unmanned aerial vehicle of the wind turbine blade; the defect track point formation unit can obtain the defect track points corresponding to the defect type of the wind turbine blade according to the defect track point image sequence, and form the defect track points into a first re-inspection track point set and / or a second re-inspection track point set; the re-inspection route planning unit can plan the unmanned aerial vehicle re-inspection route according to the first re-inspection track point set and / or the second re-inspection track point set; the start unit can start the unmanned aerial vehicle re-inspection of the wind turbine blade. Exemplarily, the unmanned aerial vehicle re-inspection route and the start unit can also be displayed on the interactive interface of the display unit 300 or another human-computer interactive interface.

[0080] Optionally, the drone re-inspection route includes a first re-inspection route, a re-inspection switching route T and a second re-inspection route, the re-inspection switching route connects the first re-inspection route and the second re-inspection route, the first re-inspection track point set is used to form the first re-inspection route, the first re-inspection route is used to perform a front drone inspection on the defect type of the wind turbine blade to form the first re-inspection information, the second re-inspection track point set is used to form the second re-inspection route, the second re-inspection route is used to perform a back drone inspection on the defect type of the wind turbine blade to form the second re-inspection information; the re-inspection route planning unit 403 includes a distance calculation subunit, the distance calculation subunit obtains the defect first track point 1 starting from the first re-inspection route and the defective first track point P at the end, the defective second track point 1 at the starting point of the second re-inspection route and the defective second track point Q at the end, calculate the first distance D1 between the defective first track point 1 and the defective second track point 1, the second distance D2 between the defective first track point 1 and the defective second track point Q, the third distance D3 between the defective first track point P and the defective second track point 1, the fourth distance D4 between the defective first track point P and the defective second track point Q, obtain the minimum distance Dmin=min[D1, D2, D3, D4] among the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4, and send the minimum distance Dmin to the re-inspection route planning unit 403.

[0081] In some embodiments, the spatial coordinates of the first defect track point 1CF1 are (x1, y1, z1), the spatial coordinates of the first defect track point PCFP are (xp, yp, zp), the spatial coordinates of the second defect track point 1CB1 are (x2, y2, z2), and the spatial coordinates of the second defect track point QCBQ are (xq, yq, zq). Accordingly, the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4 are respectively: D1=((x1-x2)2+(y1-y2)2+(z1-z2)2)1 / 2; D2=((x1-xq)2+(y1-yq)2+(z1-zq)2)1 / 2; D3=((xp-x2)2+(yp-y2)2+(zp-z2)2)1 / 2; D4=((xp-xq)2+(yp-yq)2+(zp-zq)2)1 / 2; Dmin=min[D1, D2, D3, D4].

[0082] Based on the minimum distance Dmin, the planned drone inspection route includes: (1) When the minimum distance Dmin=D1, the planned UAV re-inspection route includes: the UAV reverse inspection of the first re-inspection route, switching to the re-inspection switching route T, and the UAV forward inspection of the second re-inspection route; When the minimum distance Dmin=D2, the planned UAV re-inspection route includes: the reverse UAV inspection of the first re-inspection route, switching to the re-inspection switching route T, and the reverse UAV inspection of the second re-inspection route; (2) When the minimum distance Dmin=D3, the planned UAV re-inspection route includes: the UAV forward inspects the first re-inspection route, switches to the re-inspection switching route T, and the UAV forward inspects the second re-inspection route; when the minimum distance Dmin=D4, the planned UAV re-inspection route includes: the UAV forward inspects the first re-inspection route, switches to the re-inspection switching route T, and the UAV reverse inspects the second re-inspection route.

[0083] Optionally, the re-inspection unit 400 also includes a re-inspection information acquisition unit 405, a first evaluation unit 406 and a second evaluation unit 407. The re-inspection information acquisition unit 405 performs a UAV re-inspection on the wind turbine blades according to the UAV re-inspection route to obtain re-inspection information of the wind turbine blades; the first evaluation unit 406 obtains the UAV re-inspection results of the wind turbine blades according to the re-inspection information; the second evaluation unit 407 updates the UAV inspection results of the wind turbine blades according to the UAV re-inspection results of the wind turbine blades and the UAV inspection results of the wind turbine blades.

[0084] In the present application, the re-inspection information acquisition unit 405 performs UAV re-inspection of the wind turbine blades according to the UAV re-inspection route to obtain the re-inspection information of the wind turbine blades; the first evaluation unit 406 obtains the UAV re-inspection results of the wind turbine blades according to the re-inspection information; the second evaluation unit 407 updates the UAV inspection results of the wind turbine blades according to the UAV re-inspection results and the UAV inspection results of the wind turbine blades.

[0085] The second evaluation unit 407 includes a judgment subunit 4071, a processing subunit 4072 and an evaluation update subunit 4073; the judgment subunit 4071 judges whether the wind turbine blade UAV re-inspection result and the wind turbine blade UAV inspection result are consistent. If so, the evaluation update subunit 4073 updates the wind turbine blade UAV re-inspection result as the wind turbine blade UAV inspection result; if not, the processing subunit 4072 uses the first classifier, the second classifier, and the third classifier to respectively classify the overall front images of the multiple wind turbine blades corresponding to the defect type and / or the overall back images of the multiple wind turbine blades corresponding to the defect type, and the corresponding multiple wind turbine blade front re-inspection images and / or the multiple wind turbine blade back re-inspection images according to at least the overall front images of the multiple wind turbine blades corresponding to the defect type and / or the overall back images of the multiple wind turbine blades corresponding to the defect type, and the corresponding multiple wind turbine blade front re-inspection images and / or the multiple wind turbine blade back re-inspection images. Classify the re-inspection images of the front side of the wind turbine blades and / or the re-inspection images of the back side of the wind turbine blades to obtain the results of the first defect type set C1 and the first probability set P1 of the first classifier, the second defect type set C2 and the second probability set P2 of the second classifier, and the third defect type set C3 and the third probability set P3 of the third classifier; calculate the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types according to the first defect type set C1, the second defect type set C2 and the third defect type set C3, the first probability set P1, the second probability set P2 and the third probability set P3, and evaluate the results of the defect types of the wind turbine blades according to the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types. The evaluation update sub-unit 4073 updates the results of evaluating the defect types of the wind turbine blades as the wind turbine blade drone inspection results.

[0086] In some embodiments, according to the first defect type set C1, the second defect type set C2, and the third defect type set C3, calculating the conflict coefficient CT includes: CT=m / (3*k) Among them, the total number of different defect types is m, and the average number of defect types is k.

[0087] In some embodiments, the first defect type set C1 is at least one of [C-1, C-2, C-3, C-4], and the total number of defect types of the first classifier is k1; the second defect type set C2 is at least one of [C-1, C-2, C-3, C-4], and the total number of defect types of the second classifier is k2; the third defect type set C3 is at least one of [C-1, C-2, C-3, C-4], and the total number of defect types of the third classifier is k3; the number of different types between the first defect type set C1 and the second defect type set C2 is m1, the number of different types between the second defect type set C2 and the third defect type set C3 is m2, and the number of different types between the third defect type set C3 and the third defect type set C1 is m3. Accordingly, the total number m of different defect types is: m=m1+m2+m3, and the average number of defect types is k=(k1+k2+k3) / 3.

[0088] In some embodiments, the first probability set P1 corresponding to the defect types in the first defect type set C1 is: P1=[P11, P12, ..., P1k1], the second probability set P2 corresponding to the defect types in the second defect type set C2 is: P2=[P21, P22, ..., P2k2], and the third probability set P3 corresponding to the defect types in the third defect type set C3 is: P3=[P31, P32, ..., P3k3]. On this basis, the confidence expectations E(C-1), E(C-2), E(C-3), and E(C-4) of the defect types C-1, C-2, C-3, and C-4 can be calculated respectively, and the general calculation formula is: E(CT)=w1P1T+w2P2T+w3P3T Among them, T=1, 2, 3, 4, w1 is the confidence of the first classifier, w2 is the confidence of the second classifier, w3 is the confidence of the third classifier, w1+w2+w3=1, P1T is the corresponding probability of the corresponding defect type in the first probability set P1, P2T is the corresponding probability of the corresponding defect type in the second probability set P2, and P3T is the corresponding probability of the corresponding defect type in the third probability set P3.

[0089] The average confidence expectation of different defect types C-1, C-2, C-3, and C-4 is: EC=(E(C-1)+E(C-2)+E(C-3)+E(C-4)) / 4.

[0090] In some embodiments, based on the first defect type set C1, the second defect type set C2, and the third defect type set, the number of votes R1 for defect type C-1, the number of votes R2 for defect type C-2, the number of votes R3 for defect type C-3, and the number of votes R4 for defect type C-4 can be calculated, and the number of votes of different types can be normalized. Accordingly, the real voting expectation V1 for defect type C-1, the real voting expectation V2 for defect type C-2, the real voting expectation V3 for defect type C-3, the real voting expectation V4 for defect type C-4, and the average real voting expectation V are respectively: V1=G1*R1 / (R1+R2+R3+R4), V2=G2*R2 / (R1+R2+R3+R4); V3=G3*R3 / (R1+R2+R3+R4), V4=G4*R4 / (R1+R2+R3+R4); V = (V1 + V2 + V3 + V4) / 4; Among them, G1 is the first voting adjustment coefficient, G2 is the second voting adjustment coefficient, G3 is the third voting adjustment coefficient, G4 is the fourth voting adjustment coefficient, G1, G2, G3, G4 are constants, G1, G2, G3, G4 are constants and can take values ​​between 0.1-20 (including values ​​0.1, 1.0, 1.5, 2.0, 20, etc.), which can be reasonably set according to actual conditions.

[0091] In some embodiments, the result of evaluating the defect type of the wind turbine blade according to the conflict coefficient CT, the confidence expectations of different defect types, and the true voting expectations of different defect types includes: According to the conflict coefficient CT, the confidence expectation of different defect types and the voting expectation of different defect types, the expectations of different defect types are calculated. Correspondingly, the expectations and average expectations of different defect types C-1, C-2, C-3, and C-4 are E1, E2, E3, E4, and E are: E1=E(C-1)*V1, E2=E(C-2)*V2; E3=E(C-3)*V3, E4=E(C-4)*V4; E=(E1+E2+E3+E4) / 4; When CT≥0.8, the defect type that satisfies the expectation [E1, E2, E3, E4]≤E is taken as the result of evaluating the defect type of the wind turbine blade; when CT<0.8, the defect type that satisfies the true voting expectation [V1, V2, V3, V4]≥V is taken as the result of evaluating the defect type of the wind turbine blade.

[0092] In the present application, when CT≥0.8, it indicates that the differences between the first classifier, the second classifier, and the third classifier are relatively large. In this case, it is necessary to comprehensively consider the confidence expectation and the voting expectation to determine the defect type. By introducing a comparison mechanism of the product and the average expectation, when the product of the confidence expectation and the true voting expectation of different defect types is still less than the average expectation, the defect type corresponding to these expectations is used as the result of evaluating the defect type of the wind turbine blade. In this way, reliable defect types can be screened out more rigorously, and classification errors caused by conflicts between classifiers can be reduced, making the evaluation of wind turbine blade defect types more accurate and stable. It also helps to improve the accuracy of wind turbine blade defect identification in complex classification situations, reduce the misleading of subsequent maintenance decisions by misjudgment, and reduce unnecessary maintenance costs and resource waste caused by incorrect judgment of defect types by classifiers, thereby ensuring the safe operation of wind turbine blades, allowing wind farms to continue to operate, and thus improving the operating efficiency of the entire wind farm.

[0093] In the present application, when CT<0.8, it means that the differences among the first classifier, the second classifier, and the third classifier are not large, and the confidence of the first classifier, the second classifier, and the third classifier is high. In this way, the relatively consistent judgments between the classifiers can be efficiently utilized, focusing on the true voting expectations. Only the true voting expectations need to be comprehensively considered to determine the defect type. When the true voting expectations of different defect types are still greater than the average true voting expectations, the defect types corresponding to these true voting expectations are used as the results of evaluating the defect types of wind turbine blades. In this way, the defect type of the wind turbine blade can be quickly identified, the complex multi-factor calculation process can be avoided, and the efficiency and timeliness of the wind turbine blade defect assessment can be improved. This is particularly important for the inspection of large-scale wind turbine blades, and a large amount of data can be processed in a short time to obtain reliable results. At the same time, when the classifier consistency is high, the advantages of multiple classifiers can be fully utilized to reduce the misclassification caused by individual factors or accidental errors, avoid unnecessary repairs or miss the best repair time due to misjudgment, reduce maintenance costs and wind turbine blade downtime, and improve the availability and power generation efficiency of wind power equipment. Moreover, with the accumulation of data and the improvement of the deep learning performance of the classifier, the accuracy and reliability are expected to be further improved, which can promote the intelligent drone inspection in the wind power industry to develop in a more refined and precise direction.

[0094] In a third aspect, the present invention application provides a wind turbine blade drone inspection device, which includes a processor, a memory, and a computing program stored in the memory and executable by the processor, wherein when the computing program is executed by the processor, the steps of the wind turbine blade drone inspection method as described in any one of the first aspects are implemented.

[0095] In a fourth aspect, the present invention application provides a computer-readable storage medium having a computing program stored thereon, wherein when the computing program is executed by a processor, the steps of the wind turbine blade drone inspection method as described in any one of the first aspects are implemented.

[0096] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. It can be understood by those skilled in the art that the various operations, methods, steps, measures, and schemes in the process discussed in the present invention application can be alternated, changed, combined or deleted; further, other steps, measures, and schemes in the various operations, methods, and processes discussed in the present invention application can also be alternated, changed, rearranged, decomposed, combined or deleted; further, the various operations, methods, and steps, measures, and schemes in the prior art disclosed in the present invention application can also be alternated, changed, rearranged, decomposed, combined or deleted.

[0097] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, 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 present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device, and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0098] In several embodiments provided in the present invention application, any function, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention application, 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 several instructions for 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 method described in each embodiment of the present invention application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-OMly Memory; hereinafter referred to as: ROM), random access memory (RaMdom Access Memory; hereinafter referred to as: RAM), disk or optical disk and other media that can store program codes.

[0099] The above-described embodiments only express several implementation methods of the embodiments of the present disclosure, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the patent scope of the embodiments of the present disclosure. It should be pointed out that, for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the embodiments of the present disclosure, which all belong to the protection scope of the embodiments of the present disclosure. Therefore, the protection scope of the embodiments of the present disclosure shall be based on the attached claims.

Claims

1. A wind turbine blade drone inspection method, characterized in that: The following steps are involved: Step S100, obtaining structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm, wherein the structural parameters at least include the center height of the impeller, the blade profile curve, the blade length, the maximum chord length, the impeller diameter and the number of blades; the motion parameters at least include the speed of the wind turbine; and the environmental parameters at least include the altitude and the wind speed; Step S200, based on the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm, a drone inspection is performed on the front side of the wind turbine blade to form first inspection information, and a drone inspection is performed on the back side of the wind turbine blade to form second inspection information, wherein the first inspection information includes a first track point data set and a blade front side image set, and the second inspection information includes a second track point data set and a blade back side image set; The blade front image set includes a plurality of wind turbine blade overall front images; The blade back image set includes a plurality of wind turbine blade overall back images; Step S300, obtaining the wind turbine blade UAV inspection result according to the first inspection information and the second inspection information in combination with the wind turbine blade defect judgment condition; According to the inspection results of the wind turbine blades by the UAV, it is determined whether to start the UAV re-inspection mode. If so, the UAV re-inspection mode is started and the inspection results of the wind turbine blades by the UAV are updated; If not, the re-inspection mode is not started.

2. A wind turbine blade drone inspection method according to claim 1, characterized in that: Starting the drone re-inspection mode includes the following steps: Step S401: acquiring a defect track point image sequence corresponding to the defect type wind turbine blade according to the wind turbine blade drone inspection result; Step S402, acquiring defect track points corresponding to the defect type wind turbine blade according to the defect track point image sequence, the defect track points forming a first re-inspection track point set and / or a second re-inspection track point set; Step S403: planning a drone re-inspection route according to the first re-inspection track point set and / or the second re-inspection track point set, and starting the drone re-inspection of the wind turbine blades.

3. A wind turbine blade inspection method using a drone according to claim 2, characterized in that: Updating wind turbine blade drone inspection results includes the following steps: Step S501: Performing a UAV re-inspection on a wind turbine blade according to a UAV re-inspection route to obtain re-inspection information of the wind turbine blade, wherein the re-inspection information includes a blade front re-inspection image set and a blade back re-inspection image set; the blade front re-inspection image set includes a plurality of wind turbine blade front re-inspection images; and the blade front re-inspection image set includes a plurality of wind turbine blade back re-inspection images; Step S502: Obtain the wind turbine blade drone re-inspection result according to the re-inspection information, and update the wind turbine blade drone inspection result according to the wind turbine blade drone re-inspection result and the wind turbine blade drone inspection result.

4. A wind turbine blade inspection method using a drone according to claim 3, characterized in that: Step S502 includes: Step S5021: determine whether the wind turbine blade drone re-inspection result is consistent with the wind turbine blade drone inspection result. If yes, update the wind turbine blade drone re-inspection result as the wind turbine blade drone inspection result. If not, proceed to step S5022. Step S5022: at least according to the overall front images of the multiple wind turbine blades corresponding to the defect types and / or the overall back images of the multiple wind turbine blades corresponding to the defect types, and the corresponding multiple front re-inspection images of the wind turbine blades and / or the corresponding back re-inspection images of the wind turbine blades, use the first classifier, the second classifier, and the third classifier to classify the overall front images of the multiple wind turbine blades corresponding to the defect types and / or the overall back images of the multiple wind turbine blades corresponding to the defect types, and the corresponding multiple front re-inspection images of the wind turbine blades and / or the multiple back re-inspection images of the wind turbine blades, respectively, to obtain the results of the first defect type set C1 and the first probability set P1 of the first classifier, the second defect type set C2 and the second probability set P2 of the second classifier, and the third defect type set C3 and the third probability set P3 of the third classifier; Step S5023: According to the first defect type set C1, the second defect type set C2 and the third defect type set C3, the first probability set P1, the second probability set P2 and the third probability set P3, calculate the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types, evaluate the defect types of the wind turbine blades according to the conflict coefficient CT, the confidence expectations of different defect types and the true voting expectations of different defect types, and update the results of evaluating the defect types of the wind turbine blades as the wind turbine blade drone inspection results.

5. A wind turbine blade inspection method using a drone according to claim 4, characterized in that: The defect track point includes a first track point and a second track point, the UAV re-inspection route includes a first re-inspection route, a re-inspection switching route and a second re-inspection route, the re-inspection switching route connects the first re-inspection route and the second re-inspection route, the first re-inspection track point set is used to form a first re-inspection route, the first re-inspection route is used to perform a front UAV inspection on the wind turbine blade at the defect first track point to form first re-inspection information, the second re-inspection track point set is used to form a second re-inspection route, the second re-inspection route is used to perform a back UAV inspection on the wind turbine blade at the defect second track point to form second re-inspection information; Obtain the defective first track point 1 at the start and the defective first track point P at the end of the first re-inspection route, the defective second track point 1 at the start and the defective second track point Q at the end of the second re-inspection route, calculate the first distance D1 between the defective first track point 1 and the defective second track point 1, the second distance D2 between the defective first track point 1 and the defective second track point Q, the third distance D3 between the defective first track point P and the defective second track point 1, and the fourth distance D4 between the defective first track point P and the defective second track point Q; obtain the minimum distance Dmin=min[D1, D2, D3, D4] among the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4 according to the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4, and plan the UAV re-inspection route according to the minimum distance Dmin.

6. A wind turbine blade drone inspection device, which implements the wind turbine blade drone inspection method according to any one of claims 1 to 5, comprising a parameter acquisition unit, an inspection unit, a display unit, a re-inspection unit and a server: A parameter acquisition unit, which acquires structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm and sends them to the inspection unit, wherein the structural parameters at least include the center height of the impeller, the blade profile curve, the blade length, the maximum chord length, the impeller diameter and the number of blades; the motion parameters at least include the speed of the wind turbine; and the environmental parameters at least include the altitude and the wind speed; The inspection unit performs drone inspection on the front of the wind turbine blade according to the structural parameters, motion parameters and environmental parameters of at least one wind turbine in the wind farm to form first inspection information and sends it to the server, and performs drone inspection on the back of the wind turbine blade to form second inspection information and sends it to the server, wherein the first inspection information includes a first track point data set and a blade front image set, and the second inspection information includes a second track point data set and a blade back image set; the blade front image set includes a plurality of wind turbine blade overall front images; The blade back image set includes a plurality of wind turbine blade overall back images; The server obtains the wind turbine blade UAV inspection result according to the first inspection information and the second inspection information and the wind turbine blade defect judgment condition, and sends the result to the re-inspection unit and the display unit, and the display unit displays the wind turbine blade UAV inspection result; The re-inspection unit displays an interactive interface for starting the re-inspection mode of the drone through the display unit according to the inspection result of the drone of the wind turbine blade; If yes, start the drone re-inspection mode and update the wind turbine blade drone inspection results; If not, the re-inspection mode is not started.

7. The wind turbine blade inspection device using an unmanned aerial vehicle according to claim 6, characterized in that: The re-inspection unit includes a defect image search unit, a defect track point formation unit, a re-inspection route planning unit and a start-up unit. The defect image search unit obtains a defect track point image sequence corresponding to the defect type of the wind turbine blade according to the inspection result of the wind turbine blade UAV; the defect track point formation unit obtains the defect track points corresponding to the defect type of the wind turbine blade according to the defect track point image sequence, and forms the defect track points into a first re-inspection track point set and / or a second re-inspection track point set; The re-inspection route planning department plans the UAV re-inspection route according to the first re-inspection track point set and / or the second re-inspection track point set; The startup department initiated the drone re-inspection of wind turbine blades.

8. The wind turbine blade inspection device using an unmanned aerial vehicle according to claim 7, characterized in that: The UAV re-inspection route includes a first re-inspection route, a re-inspection switching route T and a second re-inspection route. The re-inspection switching route connects the first re-inspection route and the second re-inspection route. The first re-inspection track point set is used to form the first re-inspection route. The first re-inspection route is used to perform a frontal UAV inspection of the defect type of the wind turbine blade to form the first re-inspection information. The second re-inspection track point set is used to form a second re-inspection route. The second re-inspection route is used to perform a back UAV inspection of the defect type of the wind turbine blade to form the second re-inspection information.

9. The wind turbine blade inspection device using an unmanned aerial vehicle according to claim 8, characterized in that: The re-inspection route planning unit includes a distance calculation sub-unit, which obtains the defective first track point 1 at the start and the defective first track point P at the end of the first re-inspection route, the defective second track point 1 at the start and the defective second track point Q at the end of the second re-inspection route, calculates the first distance D1 between the defective first track point 1 and the defective second track point 1, the second distance D2 between the defective first track point 1 and the defective second track point Q, the third distance D3 between the defective first track point P and the defective second track point 1, and the fourth distance D4 between the defective first track point P and the defective second track point Q, obtains the minimum distance Dmin=min[D1, D2, D3, D4] among the first distance D1, the second distance D2, the third distance D3 and the fourth distance D4, and sends the minimum distance Dmin to the re-inspection route planning unit.

10. The wind turbine blade drone inspection device according to claim 9, characterized in that: The re-inspection unit also includes a re-inspection information acquisition unit, a first evaluation unit and a second evaluation unit. The re-inspection information acquisition unit re-inspects the wind turbine blades by the drone according to the re-inspection route of the drone to obtain the re-inspection information of the wind turbine blades; the first evaluation unit obtains the re-inspection result of the wind turbine blades by the drone according to the re-inspection information; The second assessment department updates the wind turbine blade drone inspection results based on the wind turbine blade drone re-inspection results and the wind turbine blade drone inspection results.

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