Component backboard fault detection method and device, equipment and storage medium

Abstract

The invention belongs to the technical field of unmanned aerial vehicle inspection, and discloses an assembly backboard fault detection method and device, equipment and a storage medium. Target detection is performed on the current image transmission interface generated by the unmanned aerial vehicle at the current waypoint to obtain the to-be-shot target area, and when the definition value corresponding to the to-be-shot target area does not meet the preset definition requirement, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position. And obtaining a target image transmission interface collected by the unmanned aerial vehicle at the target waypoint position, and carrying out component backboard fault detection according to the target image transmission interface. When the definition value corresponding to the to-be-shot target area does not meet the preset definition requirement, the current waypoint position is adjusted, then the clear target image transmission interface collected by the unmanned aerial vehicle at the target waypoint position is obtained, and the component backboard fault is detected according to the clear target image transmission interface, so that the inspection data quality can be improved, and the inspection efficiency is improved. Therefore, the fault detection accuracy of the assembly backboard is improved.

Description

Technical Field

[0001] The present application relates to the field of drone inspection technology, and in particular to a component backplane fault detection method, device, equipment and storage medium. Background Art

[0002] With the increasing global demand for renewable energy, photovoltaic power generation has developed rapidly as a clean energy solution, and photovoltaic installed capacity has continued to grow, which provides a broad market space for drone inspections.

[0003] While drones have shown great potential for PV power station inspections, module backsheet inspections still rely on manual climbing ladders or using lifting equipment for close-up observation, a time-consuming and inefficient approach. This is primarily because the environment in which module backsheet inspections are typically performed is complex. The routes planned through point cloud reconstruction can become disrupted after a certain period of time due to changes in the underlying environment, such as sudden vegetation growth or the presence of workers. Furthermore, because PV panels are typically installed facing the sun, backsheet inspections are subject to backlighting, often resulting in blurry drone images and poorly-defined inspection data. While existing drone inspection technology can increase inspection speed, the quality of the inspection data cannot be guaranteed. Therefore, improving the quality of inspection data, and thereby the accuracy of module backsheet fault detection, has become a pressing issue. Summary of the Invention

[0004] The main purpose of this application is to provide a component backplane fault detection method, device, equipment and storage medium, aiming to solve the technical problem of how to improve the quality of inspection data and thus improve the accuracy of component backplane fault detection.

[0005] To achieve the above objectives, the present application provides a component backplane fault detection method, which includes the following steps:

[0006] Perform target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed;

[0007] When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, adjusting the current waypoint position corresponding to the current waypoint to obtain the target waypoint position;

[0008] A target image transmission interface collected by the drone at the target waypoint position is obtained, and component backplane fault detection is performed based on the target image transmission interface.

[0009] Optionally, the step of performing target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed specifically includes:

[0010] Get the current image transmission interface collected by the gimbal camera on the drone at the current waypoint;

[0011] Performing target detection on the current image transmission interface using a preset target detection model to obtain a target detection result;

[0012] When the target detection result indicates that there is a target to be photographed in the current image transmission interface, a target area to be photographed corresponding to the target to be photographed is selected from the current image transmission interface.

[0013] Optionally, after the step of performing target detection on the current image transmission interface using a preset target detection model to obtain a target detection result, the method further includes:

[0014] When the target detection result indicates that the target to be photographed does not exist in the current image transmission interface, adjusting parameters corresponding to the pan-tilt camera to determine a first adjusted pan-tilt camera;

[0015] Return to the step of obtaining the current image transmission interface captured by the gimbal camera on the drone at the current waypoint, until a new target detection result indicates that there is a target to be photographed in the new image transmission interface.

[0016] Optionally, when the clarity value corresponding to the target area to be photographed does not meet a preset clarity requirement, the step of adjusting the current waypoint position corresponding to the current waypoint to obtain the target waypoint position specifically includes:

[0017] When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, calculating the maximum adjustment distance corresponding to the drone;

[0018] Determine the route execution direction of the current waypoint according to the preset inspection route;

[0019] Based on the maximum adjustment distance, the current waypoint position corresponding to the current waypoint is adjusted along the route execution direction to obtain the target waypoint position.

[0020] Optionally, the step of calculating the maximum adjustment distance corresponding to the drone specifically includes:

[0021] Calculate the zoom field of view corresponding to the gimbal camera on the drone;

[0022] Determine the width of a single photovoltaic module photographed by the drone at the current waypoint;

[0023] The maximum adjustment distance corresponding to the drone is calculated according to the zoom field of view and the component width.

[0024] Optionally, the step of calculating the zoom field of view corresponding to the gimbal camera on the drone specifically includes:

[0025] Determining the focal length of a lens of a pan-tilt camera on the drone, and determining the sensor width of an image sensor on the pan-tilt camera;

[0026] Calculating the horizontal viewing angle of the pan / tilt camera according to the lens focal length and the sensor width;

[0027] Calculating the shooting distance between the drone and the shooting point;

[0028] The zoom field of view corresponding to the pan / tilt camera is calculated according to the shooting distance and the horizontal viewing angle.

[0029] Optionally, the step of calculating the shooting distance between the drone and the shooting point specifically includes:

[0030] The shooting distance between the drone and the shooting point is calculated based on the photovoltaic panel spacing, the drone safety distance and the drone size.

[0031] Optionally, when the clarity value corresponding to the target area to be photographed does not meet a preset clarity requirement, the step of adjusting the current waypoint position corresponding to the current waypoint to obtain the target waypoint position specifically includes:

[0032] When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, adjusting the camera parameters corresponding to the pan-tilt camera on the drone, and obtaining the adjusted target area to be photographed captured by the second adjusted pan-tilt camera;

[0033] When the clarity value corresponding to the adjusted target area to be photographed does not meet the preset clarity requirement, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position.

[0034] Optionally, before the step of performing target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed, the step further includes:

[0035] When the UAV flies along a preset inspection route, obtaining the obstacle distance between the UAV and the obstacle at the current waypoint;

[0036] Determining whether to adjust the current waypoint position corresponding to the current waypoint according to the obstacle distance;

[0037] If not, target detection is performed on the current image transmission interface generated by the UAV at the current waypoint to obtain the target area to be photographed.

[0038] Optionally, after the step of determining whether to adjust the current waypoint position corresponding to the current waypoint according to the obstacle distance, the method further includes:

[0039] If yes, collect real-time point cloud data of the surrounding environment of the UAV and construct a real-time point cloud model based on the real-time point cloud data;

[0040] Determine an adjusted waypoint position corresponding to the current waypoint based on the real-time point cloud model and the route planning algorithm;

[0041] The adjusted waypoint position is used as a new waypoint, and the process returns to the step of obtaining the obstacle distance between the drone and the obstacle at the current waypoint until the new waypoint position corresponding to the new waypoint is not adjusted.

[0042] In addition, to achieve the above-mentioned purpose, the present application also provides a component backplane fault detection device, the component backplane fault detection device comprising:

[0043] The target detection module is used to detect the target on the current image transmission interface generated by the drone at the current waypoint and obtain the target area to be photographed;

[0044] a position adjustment module, configured to adjust the current waypoint position corresponding to the current waypoint to obtain the target waypoint position when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement;

[0045] The fault detection module is used to obtain a target image transmission interface collected by the drone at the target waypoint position, and perform component backplane fault detection based on the target image transmission interface.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a component backplane fault detection device, which includes: a memory, a processor, and a computer program stored on the memory and runnable on the processor, and the computer program is configured to implement the steps of the component backplane fault detection method as described above.

[0047] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the component backplane fault detection method as described above are implemented.

[0048] This application performs target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed. When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position, and then the target image transmission interface collected by the drone at the target waypoint position is obtained, and component backplane fault detection is performed based on the target image transmission interface. This application performs target detection on the current waypoint position to obtain the target area to be photographed when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, and then the clear target image transmission interface collected by the drone at the target waypoint position is obtained, and component backplane fault detection is performed based on the clear target image transmission interface, which can improve the quality of inspection data and thus improve the accuracy of component backplane fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 This is a flow chart of the first embodiment of the component backplane fault detection method of the present application;

[0052] Figure 2 A schematic diagram of an obstacle in accordance with an embodiment of the component backplane fault detection method of the present application;

[0053] Figure 3 This is a flow chart of a second embodiment of the component backplane fault detection method of the present application;

[0054] Figure 4 This is a flow chart of a third embodiment of the component backplane fault detection method of the present application;

[0055] Figure 5 A schematic diagram of adjusting the current waypoint position of an embodiment of the component backplane fault detection method of the present application;

[0056] Figure 6 This is a schematic diagram of the overall flow of an embodiment of a component backplane fault detection method of the present application;

[0057] Figure 7 This is a structural block diagram of the first embodiment of the component backplane fault detection device of the present application;

[0058] Figure 8It is a structural diagram of a component backplane fault detection device in a hardware operating environment involved in an embodiment of the present application.

[0059] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0060] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0061] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0062] It should be noted that the execution entity of this application can be a built-in chip of a drone, specifically a main control chip, such as a microcontroller unit (MCU) or a system on chip (SoC).

[0063] Based on this, the embodiment of the present application provides a component backplane fault detection method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the component backplane fault detection method of the present application.

[0064] In this embodiment, the component backplane fault detection method includes the following steps:

[0065] Step S10: Target detection is performed on the current image transmission interface generated by the UAV at the current waypoint to obtain the target area to be photographed.

[0066] Furthermore, in order to improve the safety of drone inspections, in this embodiment, before step S10, it also includes: when the drone flies according to the preset inspection route, obtaining the obstacle distance between the drone and the obstacle at the current waypoint; judging whether to adjust the current waypoint position corresponding to the current waypoint based on the obstacle distance; if not, performing target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed.

[0067] It is understood that in this embodiment, a preset inspection route can be pre-set for the drone. The preset inspection route refers to the inspection route of the drone in the photovoltaic power station, and the module backplane can be inspected based on this inspection route. Specifically, a historical 3D point cloud model of the photovoltaic power station can be constructed based on the point cloud data collected in the early stage. This can be point cloud data from the previous day or the previous week. Based on this historical 3D point cloud model, a route planning algorithm embedded in the drone's built-in chip is used to obtain a preset inspection route. The preset inspection route is then imported into the drone. This preset inspection route can be the route between every two rows of module channels. The drone flies within the photovoltaic power station according to the preset inspection route.

[0068] It should be understood that the drone in this embodiment can be equipped with an obstacle avoidance laser radar device, which maintains real-time communication with the drone through the PSDK interface. When the drone flies according to the preset inspection route, the obstacle avoidance laser radar scans the drone's surrounding conditions in real time and obtains the obstacle distance between the drone and the obstacle when the drone is shooting at the current waypoint. The obstacle can be vegetation, equipment, etc. Figure 2 As shown, Figure 2 This is a schematic diagram of an obstacle in an embodiment of the component backplane fault detection method of the present application. Figure 2 The red box in the figure shows vegetation growing in the channel, which is identified as an obstacle in the photovoltaic power station.

[0069] It can be understood that whether there are obstacles around the drone that affect safe flight can be detected based on the obstacle distance, and the obstacle distance can be transmitted to the drone's built-in chip in real time through the PSDK interface. Based on the obstacle distance, it can be determined whether a route detour is required, that is, whether the current waypoint position corresponding to the current waypoint needs to be adjusted.

[0070] It should be understood that the obstacle distance can be compared with the safety distance, and the safety distance can be adjusted accordingly according to the wheelbase of different drones. Considering the RTK (Real-Time Kinematic, real-time dynamic positioning technology) fluctuation factor, the safety distance is generally required to be greater than 0.5 meters of the aircraft wheelbase. In this embodiment, the safety distance can be set to 1 meter.

[0071] In specific implementations, if the obstacle distance is greater than or equal to the safety distance, it means there are no obstacles around the drone that would affect safe flight, and no obstacle avoidance instructions will be sent to the drone's built-in chip. That is, there is no need to adjust the current waypoint position corresponding to the current waypoint, and the drone will continue to inspect according to the preset inspection route. If the obstacle distance is less than the safety distance, an obstacle avoidance instruction will be sent to the drone's built-in chip, the route will be interrupted, and the current waypoint position corresponding to the current waypoint will need to be adjusted.

[0072] It should be understood that if the current waypoint position corresponding to the current waypoint does not need to be adjusted, it means that the drone is far away from surrounding obstacles at the current waypoint and the drone's flight environment is relatively safe. In this case, the current image transmission interface can be captured by the drone's gimbal camera at the current waypoint and sent to the drone's built-in chip for target detection. The target area to be captured in the current image transmission interface can be the area containing the component backplane target, the target can be a junction box, MC4 connector, bracket bolts, etc.

[0073] Furthermore, in order to adjust the current waypoint position in real time, in this embodiment, after the step of determining whether to adjust the current waypoint position corresponding to the current waypoint according to the obstacle distance, it also includes: if so, collecting real-time point cloud data of the surrounding environment of the UAV, and constructing a real-time point cloud model based on the real-time point cloud data; determining the adjusted waypoint position corresponding to the current waypoint according to the real-time point cloud model and the route planning algorithm; using the adjusted waypoint position as the new waypoint, and returning to the step of obtaining the obstacle distance between the UAV and the obstacle at the current waypoint, until the new waypoint position corresponding to the new waypoint is not adjusted.

[0074] It is understandable that when the current waypoint position corresponding to the current waypoint needs to be adjusted, the real-time point cloud data of the drone's surrounding environment can be collected by the obstacle avoidance lidar, and a real-time point cloud model can be constructed based on the real-time point cloud data. Based on the real-time point cloud model, the adjusted waypoint position corresponding to the current waypoint can be determined by the route planning algorithm.

[0075] In specific implementations, after adjusting the current waypoint position, if the adjusted waypoint position still does not meet the safety distance requirement, that is, the obstacle distance of the drone at the adjusted waypoint position is less than the safety distance, the waypoint position will need to be readjusted until the waypoint position no longer needs to be adjusted. If the waypoint position still does not meet the safety distance requirement after multiple adjustments, the breakpoint position information at this time will be saved and a detour command will be sent to the drone's built-in chip to make the aircraft fly to the next waypoint to continue the mission. The breakpoint position can then be adjusted by staff.

[0076] Step S20: When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position.

[0077] It is understandable that the clarity value corresponding to the target area to be photographed can be calculated by an image quality assessment algorithm. The image quality assessment algorithm may include a PSNR (peak signal-to-noise ratio) algorithm and an SSIM (structural similarity index). Different preset clarity requirements can be set for different image quality assessment algorithms. For example, the higher the clarity value obtained by the PSNR algorithm, the better the image quality. When the clarity value is higher than the threshold, it is determined that the preset clarity requirement is met; when the clarity value is lower than the threshold, it is determined that the preset clarity requirement is not met; the clarity value obtained by the SSIM algorithm ranges from 0 to 1. The closer the clarity value is to 1, the more it is determined that the preset clarity requirement is met, indicating that the image quality is better.

[0078] It should be understood that when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, it means that the image transmission interface photographed by the drone at this waypoint is not clear enough. At this time, the current waypoint position corresponding to the current waypoint where the drone is located can be adjusted to obtain the target waypoint position.

[0079] Step S30: obtaining a target image transmission interface collected by the drone at the target waypoint location, and performing component backplane fault detection based on the target image transmission interface.

[0080] It is understandable that after the current waypoint position of the drone is adjusted to the target waypoint position, the target image transmission interface collected by the drone at the target waypoint position can be obtained, and component backplane fault detection can be performed based on the target image transmission interface.

[0081] In the specific implementation, when the drone flies according to the preset inspection route, if the obstacle distance corresponding to the waypoint is greater than or equal to the safety distance, it means that there are no obstacles around the drone that affect safe flight. If the clarity value corresponding to the target area to be photographed in the drone image transmission interface captured by the drone at this waypoint meets the preset clarity requirement, the image transmission interface will be uploaded to the cloud, and the cloud will then call the component backplane fault detection algorithm to analyze and determine whether there is a fault in the captured image transmission interface, and further issue an inspection analysis report.

[0082] This embodiment performs target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed. When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position, and then the target image transmission interface collected by the drone at the target waypoint position is obtained, and component backplane fault detection is performed based on the target image transmission interface. This embodiment performs target detection on the current waypoint position to obtain the target area to be photographed when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, and then the clear target image transmission interface collected by the drone at the target waypoint position is obtained, and component backplane fault detection is performed based on the clear target image transmission interface, which can improve the quality of inspection data and thus improve the accuracy of component backplane fault detection.

[0083] refer to Figure 3 , Figure 3 This is a flow chart of the second embodiment of the component backplane fault detection method of the present application.

[0084] Based on the above first embodiment, in this embodiment, step S10 includes:

[0085] Step S101: Obtain the current image transmission interface collected by the gimbal camera on the drone at the current waypoint.

[0086] It is understandable that if there is no need to adjust the current waypoint position corresponding to the current waypoint, the gimbal camera carried by the drone can be used to collect the picture of the shooting point as the current image transmission interface. The shooting point refers to the point that the drone needs to shoot. Every two photovoltaic modules can be used as shooting points, or every three photovoltaic modules can be used as shooting points.

[0087] Step S102: performing target detection on the current image transmission interface using a preset target detection model to obtain a target detection result.

[0088] It should be understood that this embodiment can train the initial target detection model to obtain a preset target detection model, which can be a YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector) model. Specifically, component backplane images within a historical period can be collected, which can be the previous day or the previous two days, and then target annotations are performed on the component backplane images. The targets may include junction boxes, MC4 connectors, bracket bolts, etc. The initial target detection model is trained using the target-annotated images, and the trained model is compressed and accelerated. Unimportant neural network connections are removed to reduce model complexity to obtain a preset target detection model, and the preset target detection model is deployed in the drone's built-in chip.

[0089] In specific implementation, due to the influence of uncertain factors such as gimbal angle, backlight, shooting distance, etc. during the inspection of the drone board, problems such as blurred focus, target offset, dim picture, etc. may occur during the inspection, affecting the subsequent backplane fault detection effect. Therefore, the preset target detection model in the built-in chip of the drone can be used to perform target detection on the current image transmission interface to obtain the target detection result. The target detection result can be whether there is a target in the image transmission interface. If so, the location of the target can also be determined.

[0090] Furthermore, in this embodiment, after step S302, it also includes: when the target detection result is that the target to be photographed does not exist in the current image transmission interface, adjusting the parameters corresponding to the gimbal camera to determine the first adjusted gimbal camera; returning to the step of obtaining the current image transmission interface collected by the gimbal camera on the drone at the current waypoint, until the new target detection result is that the target to be photographed exists in the new image transmission interface.

[0091] It is understandable that when there is no target to be photographed in the current image transmission interface, it is necessary to adjust the corresponding parameters of the gimbal camera, such as the gimbal camera angle and zoom ratio, and re-capture the new image transmission interface through the first adjusted gimbal camera, and then perform target detection on the new image transmission interface until the new target detection result shows that there is a target to be photographed in the new image transmission interface.

[0092] Step S103: When the target detection result indicates that there is a target to be photographed in the current image transmission interface, a target area to be photographed corresponding to the target to be photographed is selected from the current image transmission interface.

[0093] It should be understood that when there is a target to be photographed in the current image transmission interface, a target area to be photographed corresponding to the target to be photographed can be captured from the current image transmission interface.

[0094] This embodiment obtains the image transmission interface captured by the gimbal camera on the drone at the current waypoint, then performs target detection on the current image transmission interface using a preset target detection model to obtain a target detection result. If the target detection result indicates that a target to be photographed exists in the current image transmission interface, the target area to be photographed corresponding to the target to be photographed is selected from the current image transmission interface. This embodiment performs target detection on the current image transmission interface using a preset target detection model. Only when a target to be photographed exists in the current image transmission interface is the target area to be photographed corresponding to the target to be photographed selected from the current image transmission interface. This allows analysis of the component backplane target during subsequent component backplane fault detection to obtain accurate fault detection results.

[0095] refer to Figure 4 , Figure 4 This is a flow chart of the third embodiment of the component backplane fault detection method of the present application.

[0096] Based on the above embodiments, in this embodiment, step S20 includes:

[0097] Step S201: When the clarity value corresponding to the target area to be photographed does not meet a preset clarity requirement, a maximum adjustment distance corresponding to the drone is calculated.

[0098] It is understandable that when the clarity value corresponding to the adjusted target area to be photographed does not meet the preset clarity requirement, the current waypoint position needs to be adjusted. Specifically, the maximum adjustment distance corresponding to the drone can be calculated first, that is, the maximum distance the drone can move relative to the current waypoint position.

[0099] Furthermore, in order to accurately calculate the maximum adjustment distance corresponding to the drone, in this embodiment, step S201 includes: calculating the zoom field of view corresponding to the gimbal camera on the drone; determining the component width of a single photovoltaic component photographed by the drone at the current waypoint; and calculating the maximum adjustment distance corresponding to the drone based on the zoom field of view and the component width.

[0100] It should be understood that when a drone is inspecting the component backplane, in order to ensure that the component backplane inspection target can be clearly photographed, the drone needs to operate between two rows of components. To ensure the safety of the inspection, the corresponding maximum adjustment distance of the drone needs to be set, that is, the maximum distance the drone can move.

[0101] In the specific implementation, the zoom field of view corresponding to the gimbal camera on the drone can be calculated first, that is, the range of the scene that the gimbal camera can capture, and the horizontal width of the single photovoltaic module photographed by the drone at the current waypoint can be determined. Finally, the maximum adjustment distance corresponding to the drone is calculated based on the zoom field of view and the module width. The calculation formula is L = dn × W, where L represents the maximum adjustment distance, d represents the zoom field of view, n represents the number of complete modules that the gimbal camera can capture, and W represents the module width.

[0102] Furthermore, in order to accurately calculate the zoom field of view corresponding to the gimbal camera, in this embodiment, the step of calculating the zoom field of view corresponding to the gimbal camera on the drone specifically includes: determining the focal length of the lens of the gimbal camera on the drone, and determining the sensor width of the image sensor on the gimbal camera; calculating the horizontal viewing angle of the gimbal camera based on the lens focal length and the sensor width; calculating the shooting distance between the drone and the shooting point; and calculating the zoom field of view corresponding to the gimbal camera based on the shooting distance and the horizontal viewing angle.

[0103] It's understandable that to calculate the zoom field of view corresponding to a gimbal camera, one must first determine the camera's lens focal length and the image sensor's sensor width. The horizontal viewing angle of the gimbal camera is then calculated using the formula FOV = 2 × arctan(w / 2 × f), where FOV represents the camera's horizontal viewing angle, w represents the sensor width, and f represents the lens focal length. For a gimbal camera, a longer focal length results in a smaller field of view and a larger zoom factor. However, when conducting under-the-board inspections, to improve inspection efficiency and capture quality, the minimum zoom factor is generally selected to ensure image quality and coverage. The shooting distance between the drone and the capture point is then calculated. Finally, the zoom field of view corresponding to the gimbal camera is calculated based on the shooting distance and horizontal viewing angle. The formula is d = 2 × D × tan(FOV / 2), where d represents the zoom field of view corresponding to the gimbal camera, D represents the shooting distance, and FOV represents the camera's horizontal viewing angle.

[0104] Furthermore, in order to accurately calculate the shooting distance between the drone and the shooting point and improve the safety of drone inspections, in this embodiment, the step of calculating the shooting distance between the drone and the shooting point specifically includes: calculating the shooting distance between the drone and the shooting point based on the photovoltaic panel spacing, the drone safety distance and the drone size.

[0105] In specific implementation, the shooting distance between the drone and the shooting point can be calculated based on the photovoltaic panel spacing, the drone safety distance and the drone size, that is, the shooting distance D = photovoltaic panel spacing - (drone safety distance + drone size). The drone safety distance refers to the safe distance between the drone and the obstacle.

[0106] Step S202: determining the route execution direction of the current waypoint according to the preset inspection route.

[0107] It is understandable that the route execution direction may be the direction from the current waypoint to the next waypoint in the preset inspection route.

[0108] Step S203: adjusting the current waypoint position corresponding to the current waypoint along the route execution direction based on the maximum adjustment distance to obtain the target waypoint position.

[0109] It should be understood that the current waypoint position can be moved in the direction of route execution, and the moving distance must be less than or equal to the maximum adjustment distance.

[0110] It is understandable that a preset distance can be set first, that is, the distance moved in the direction of route execution. The preset distance is less than the maximum adjustment distance. For example, the maximum adjustment distance is 0.66 meters, and the preset distance is 0.3 meters. The current waypoint position can be moved 0.3 meters along the direction of route execution first, and it is determined whether the clarity value of the target area to be photographed by the drone at this position meets the preset clarity requirement. If not, the position will continue to be moved 0.3 meters along the direction of route execution. If the clarity value moved to the maximum adjustment distance still does not meet the preset clarity requirement, the background will automatically record this waypoint position and the aircraft will automatically jump to the next waypoint to continue executing the route. The waypoint position needs to be skipped for subsequent component back fault detection.

[0111] In the specific implementation, refer to Figure 5 , Figure 5 This is a schematic diagram of adjusting the current waypoint position of an embodiment of the component backplane fault detection method of this application, as shown in FIG. Figure 5 As shown, the distance between photovoltaic panels is 3 meters. The safe hovering point that meets the inspection requirements is 3 meters away from the component, that is, the distance between the drone and the shooting point is 3 meters. Taking the gimbal camera as the DJI M30T lens as an example, the zoom lens focal length is: 21-75mm (equivalent focal length: 113-405mm), DFOV (Diagonal Field of View): 22°-6° corresponding relationship: 22° corresponds to a focal length of 21mm (equivalent focal length 113mm), 6° corresponds to a focal length of 75mm (equivalent focal length: 405mm). When using 2x zoom, assuming its focal length f is 4.5mm and the sensor width w is 4.8mm, the horizontal FOV angle of view is 70.75°, and the zoom field of view horizontal field of view range d is 4.26m. Figure 5 The horizontal width W of a single component in the image is about 1.8m, so only two complete components can be photographed at 2x camera zoom. To ensure that the target search is not missed after the drone adjusts its position, the maximum adjustment distance allowed is L = d - 2 × W = 0.66m along the route execution direction.

[0112] Furthermore, in this embodiment, the step S20 also includes: when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, adjusting the camera parameters corresponding to the gimbal camera on the drone, and obtaining the adjusted target area to be photographed captured by the second adjusted gimbal camera; when the clarity value corresponding to the adjusted target area to be photographed does not meet the preset clarity requirement, adjusting the current waypoint position corresponding to the current waypoint to obtain the target waypoint position.

[0113] It is understandable that when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirements, it means that the target photographed by the gimbal camera on the drone is offset or blurred. At this time, the camera parameters corresponding to the gimbal camera on the drone need to be adjusted, which may include parameters such as the gimbal camera angle, zoom factor, and exposure value, to obtain a second adjusted gimbal camera, that is, a gimbal camera after parameter adjustment, and collect the adjusted image transmission interface through the second adjusted gimbal camera, and then select the adjusted target area to be photographed from the adjusted image transmission interface. The number of parameter adjustments may be 4 times, 5 times, etc.

[0114] It should be understood that if, after multiple adjustments to the camera parameters, the clarity of the target area to be photographed still does not meet the preset clarity requirements, the current waypoint position corresponding to the drone's current waypoint can be adjusted to obtain the target waypoint position. If the clarity of the target area to be photographed after the adjustments meet the preset clarity requirements, there is no need to adjust the current waypoint position, and component backplane fault detection can be performed using the image transmission interface captured by the gimbal camera after the parameter adjustments.

[0115] In the specific implementation, refer to Figure 6 , Figure 6This is a schematic diagram of the overall process of an embodiment of the component backplane fault detection method of the present application. First, a component backplane target detection model is obtained through component backplane detection algorithm training, the model is compressed and accelerated, and deployed to the built-in chip of the drone, that is, the initial target detection model is trained, and the trained model is compressed and accelerated, and the obtained preset target detection model is deployed to the built-in chip of the drone; then the drone is equipped with a light-proof laser radar device to inspect the component backplane according to the autonomously planned route, that is, the drone flies according to the preset inspection route; then it is detected whether there are obstacles around the aircraft that affect safe flight, that is, according to the obstacle distance between the drone and the obstacle, it is determined whether the current waypoint position corresponding to the current waypoint is adjusted. If so, the hovering position of the aircraft is adjusted, that is, the current waypoint position is adjusted; if not, the drone map obtained in real time by the drone is The transmission interface is input into the fuselage AI target detection algorithm to detect whether the shooting target is clear and accurate, that is, target detection is performed on the image transmission interface corresponding to the drone, and it is determined whether the clarity value corresponding to the target area to be photographed meets the preset clarity requirement. If so, the inspection picture is taken, uploaded to the cloud for image analysis and an inspection report is issued; if not, the gimbal camera angle is automatically adjusted to find the shooting target focus, that is, the camera parameters corresponding to the gimbal camera on the drone are adjusted, and the adjusted target area to be photographed collected by the second adjusted gimbal camera is obtained. If the number of adjustments is greater than 5 times, the clarity value of the adjusted target area to be photographed still does not meet the preset clarity requirement, and the hovering position of the aircraft needs to be adjusted, that is, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position, and there are no obstacles that affect safe flight around the target waypoint position.

[0116] In this embodiment, when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirements, the maximum adjustment distance corresponding to the drone is calculated, and then the route execution direction of the current waypoint is determined based on the preset inspection route. The current waypoint position corresponding to the current waypoint is then adjusted along the route execution direction based on the maximum adjustment distance to obtain the target waypoint position. In this embodiment, when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirements, the maximum adjustment distance that the drone can move is first calculated, and then the current waypoint position corresponding to the current waypoint is adjusted along the route execution direction based on the maximum adjustment distance. This allows the current waypoint position corresponding to the current waypoint to be safely adjusted to obtain the target waypoint position.

[0117] Reference Figure 7 , Figure 7 This is a structural block diagram of the first embodiment of the component backplane fault detection device of the present application.

[0118] like Figure 7 As shown, the component backplane fault detection device proposed in the embodiment of the present application includes:

[0119] The target detection module 10 is used to perform target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed;

[0120] A position adjustment module 20 is configured to adjust the current waypoint position corresponding to the current waypoint to obtain the target waypoint position when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement;

[0121] The fault detection module 30 is used to obtain the target image transmission interface collected by the drone at the target waypoint position, and perform component backplane fault detection based on the target image transmission interface.

[0122] This embodiment performs target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed. When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position, and then the target image transmission interface collected by the drone at the target waypoint position is obtained, and component backplane fault detection is performed based on the target image transmission interface. This embodiment performs target detection on the current waypoint position to obtain the target area to be photographed when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, and then the clear target image transmission interface collected by the drone at the target waypoint position is obtained, and component backplane fault detection is performed based on the clear target image transmission interface, which can improve the quality of inspection data and thus improve the accuracy of component backplane fault detection.

[0123] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In actual applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of this embodiment scheme, and no restrictions are imposed here.

[0124] In addition, for technical details not fully described in this embodiment, please refer to the component backplane fault detection method provided in any embodiment of the present application, and will not be repeated here.

[0125] Based on the first embodiment of the component backplane fault detection device of the present application, a second embodiment of the component backplane fault detection device of the present application is proposed.

[0126] In this embodiment, the target detection module 10 is used to obtain the current image transmission interface collected by the gimbal camera on the drone at the current waypoint; perform target detection on the current image transmission interface through a preset target detection model to obtain a target detection result; when the target detection result is that there is a target to be photographed in the current image transmission interface, select the target area to be photographed corresponding to the target to be photographed from the current image transmission interface.

[0127] Furthermore, the target detection module 10 is also used to adjust the parameters corresponding to the gimbal camera when the target detection result is that the target to be photographed does not exist in the current image transmission interface, and determine the first adjusted gimbal camera; return to the step of obtaining the current image transmission interface collected by the gimbal camera on the drone at the current waypoint until the new target detection result is that the target to be photographed exists in the new image transmission interface.

[0128] Furthermore, the position adjustment module 20 is also used to calculate the maximum adjustment distance corresponding to the UAV when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement; determine the route execution direction of the current waypoint according to the preset inspection route; and adjust the current waypoint position corresponding to the current waypoint along the route execution direction based on the maximum adjustment distance to obtain the target waypoint position.

[0129] Furthermore, the position adjustment module 20 is also used to calculate the zoom field of view corresponding to the gimbal camera on the drone; determine the component width of a single photovoltaic component photographed by the drone at the current waypoint; and calculate the maximum adjustment distance corresponding to the drone based on the zoom field of view and the component width.

[0130] Furthermore, the position adjustment module 20 is also used to determine the lens focal length of the gimbal camera on the drone and the sensor width of the image sensor on the gimbal camera; calculate the horizontal viewing angle of the gimbal camera based on the lens focal length and the sensor width; calculate the shooting distance between the drone and the shooting point; and calculate the zoom field of view corresponding to the gimbal camera based on the shooting distance and the horizontal viewing angle.

[0131] Furthermore, the position adjustment module 20 is further configured to calculate the shooting distance between the drone and the shooting point according to the photovoltaic panel spacing, the drone safety distance, and the size of the drone.

[0132] Furthermore, the position adjustment module 20 is further configured to adjust the camera parameters corresponding to the pan-tilt camera on the drone when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, and obtain the adjusted target area to be photographed captured by the second adjusted pan-tilt camera;

[0133] When the clarity value corresponding to the adjusted target area to be photographed does not meet the preset clarity requirement, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position.

[0134] Furthermore, the target detection module 10 is also used to obtain the obstacle distance between the drone and the obstacle at the current waypoint when the drone flies according to the preset inspection route; determine whether to adjust the current waypoint position corresponding to the current waypoint based on the obstacle distance; if not, perform target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed.

[0135] Furthermore, the target detection module 10 is also used to collect real-time point cloud data of the surrounding environment of the UAV when adjusting the current waypoint position corresponding to the current waypoint, and construct a real-time point cloud model based on the real-time point cloud data; determine the adjusted waypoint position corresponding to the current waypoint based on the real-time point cloud model and the route planning algorithm; use the adjusted waypoint position as the new waypoint, and return to the step of obtaining the obstacle distance between the UAV and the obstacle at the current waypoint until the new waypoint position corresponding to the new waypoint is not adjusted.

[0136] Other embodiments or specific implementations of the component backplane fault detection device of the present application can refer to the above-mentioned method embodiments and will not be repeated here.

[0137] The present application provides a component backplane fault detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the component backplane fault detection method in the above-mentioned embodiment one.

[0138] Reference below Figure 8 , which shows a schematic structural diagram of a module backplane fault detection device suitable for implementing an embodiment of the present application. The module backplane fault detection device in the embodiment of the present application can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 8 The component backplane fault detection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0139] like Figure 8As shown, the component backplane fault detection device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the component backplane fault detection device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. Communication device 1009 can allow the component backplane fault detection device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a component backplane fault detection device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have instead.

[0140] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0141] The module backplane fault detection device provided in this application, employing the module backplane fault detection method of the aforementioned embodiment, can address the technical problem of improving the quality of inspection data and thereby increasing the accuracy of module backplane fault detection. Compared to the prior art, the module backplane fault detection device provided in this application has the same beneficial effects as the module backplane fault detection method provided in the aforementioned embodiment. Other technical features of the module backplane fault detection device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0142] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0143] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0144] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the component backplane fault detection method in the above embodiment.

[0145] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0146] The computer-readable storage medium may be included in the component backplane fault detection device; or may exist independently without being assembled into the component backplane fault detection device.

[0147] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the component backplane fault detection device, the component backplane fault detection device: performs target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed; when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, adjusts the current waypoint position corresponding to the current waypoint to obtain the target waypoint position; obtains the target image transmission interface collected by the drone at the target waypoint position, and performs component backplane fault detection according to the target image transmission interface.

[0148] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0149] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0150] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0151] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned component backplane fault detection method. This computer-readable storage medium can address the technical problem of improving the quality of inspection data and, therefore, the accuracy of component backplane fault detection. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the component backplane fault detection method provided in the aforementioned embodiment, and are not further elaborated here.

[0152] The above description is only part of the embodiments of the present application and does not limit the scope of protection of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the scope of protection of the present application.

Claims

1. A component backplane fault detection method, characterized in that: The method comprises the following steps: Perform target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed; When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, adjusting the current waypoint position corresponding to the current waypoint to obtain the target waypoint position; A target image transmission interface collected by the drone at the target waypoint position is obtained, and component backplane fault detection is performed based on the target image transmission interface.

2. The component backplane fault detection method according to claim 1, wherein: The step of performing target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed specifically includes: Get the current image transmission interface collected by the gimbal camera on the drone at the current waypoint; Performing target detection on the current image transmission interface using a preset target detection model to obtain a target detection result; When the target detection result indicates that there is a target to be photographed in the current image transmission interface, a target area to be photographed corresponding to the target to be photographed is selected from the current image transmission interface.

3. The component backplane fault detection method according to claim 2, wherein: After the step of performing target detection on the current image transmission interface using a preset target detection model to obtain a target detection result, the method further includes: When the target detection result indicates that the target to be photographed does not exist in the current image transmission interface, adjusting parameters corresponding to the pan-tilt camera to determine a first adjusted pan-tilt camera; Return to the step of obtaining the current image transmission interface captured by the gimbal camera on the drone at the current waypoint, until a new target detection result indicates that there is a target to be photographed in the new image transmission interface.

4. The component backplane fault detection method according to claim 1, wherein: When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, the step of adjusting the current waypoint position corresponding to the current waypoint to obtain the target waypoint position specifically includes: When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, calculating the maximum adjustment distance corresponding to the drone; Determine the route execution direction of the current waypoint according to the preset inspection route; Based on the maximum adjustment distance, the current waypoint position corresponding to the current waypoint is adjusted along the route execution direction to obtain the target waypoint position.

5. The component backplane fault detection method according to claim 4, wherein: The step of calculating the maximum adjustment distance corresponding to the drone specifically includes: Calculate the zoom field of view corresponding to the gimbal camera on the drone; Determine the width of a single photovoltaic module photographed by the drone at the current waypoint; The maximum adjustment distance corresponding to the drone is calculated according to the zoom field of view and the component width.

6. The component backplane fault detection method according to claim 5, wherein: The step of calculating the zoom field of view corresponding to the pan-tilt camera on the drone specifically includes: Determining the focal length of a lens of a pan-tilt camera on the drone, and determining the sensor width of an image sensor on the pan-tilt camera; Calculating the horizontal viewing angle of the pan / tilt camera according to the lens focal length and the sensor width; Calculating the shooting distance between the drone and the shooting point; The zoom field of view corresponding to the pan / tilt camera is calculated according to the shooting distance and the horizontal viewing angle.

7. The component backplane fault detection method according to claim 6, wherein: The step of calculating the shooting distance between the drone and the shooting point specifically includes: The shooting distance between the drone and the shooting point is calculated based on the photovoltaic panel spacing, the drone safety distance and the drone size.

8. The component backplane fault detection method according to claim 1, wherein: When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, the step of adjusting the current waypoint position corresponding to the current waypoint to obtain the target waypoint position specifically includes: When the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement, adjusting the camera parameters corresponding to the pan-tilt camera on the drone, and obtaining the adjusted target area to be photographed captured by the second adjusted pan-tilt camera; When the clarity value corresponding to the adjusted target area to be photographed does not meet the preset clarity requirement, the current waypoint position corresponding to the current waypoint is adjusted to obtain the target waypoint position.

9. The component backplane fault detection method according to any one of claims 1 to 8, characterized in that: Before the step of performing target detection on the current image transmission interface generated by the drone at the current waypoint to obtain the target area to be photographed, the method further includes: When the UAV flies along a preset inspection route, obtaining the obstacle distance between the UAV and the obstacle at the current waypoint; Determining whether to adjust the current waypoint position corresponding to the current waypoint according to the obstacle distance; If not, target detection is performed on the current image transmission interface generated by the UAV at the current waypoint to obtain the target area to be photographed.

10. The component backplane fault detection method according to claim 9, wherein: After the step of determining whether to adjust the current waypoint position corresponding to the current waypoint according to the obstacle distance, the method further includes: If yes, collect real-time point cloud data of the surrounding environment of the UAV and construct a real-time point cloud model based on the real-time point cloud data; Determine an adjusted waypoint position corresponding to the current waypoint based on the real-time point cloud model and the route planning algorithm; The adjusted waypoint position is used as a new waypoint, and the process returns to the step of obtaining the obstacle distance between the drone and the obstacle at the current waypoint until the new waypoint position corresponding to the new waypoint is not adjusted.

11. A component backplane fault detection device, characterized in that: The component backplane fault detection device comprises: The target detection module is used to detect the target on the current image transmission interface generated by the drone at the current waypoint and obtain the target area to be photographed; a position adjustment module, configured to adjust the current waypoint position corresponding to the current waypoint to obtain the target waypoint position when the clarity value corresponding to the target area to be photographed does not meet the preset clarity requirement; The fault detection module is used to obtain a target image transmission interface collected by the drone at the target waypoint position, and perform component backplane fault detection based on the target image transmission interface.

12. A component backplane fault detection device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the component backplane failure detection method according to any one of claims 1 to 10.

13. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the component backplane fault detection method according to any one of claims 1 to 10 are implemented.

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