A tethered unmanned aerial vehicle operation method based on visual positioning

Abstract

The application discloses a kind of tethered unmanned aerial vehicle operation methods based on visual positioning, it is related to unmanned aerial vehicle positioning adjustment technical field.The kind of tethered unmanned aerial vehicle operation methods based on visual positioning, comprising the following steps: tethered unmanned aerial vehicle positioning pre-monitoring, tethered unmanned aerial vehicle visual image analysis and tethered unmanned aerial vehicle operation positioning monitoring.The application obtains tethered unmanned aerial vehicle positioning pre-monitoring result by executing tethered unmanned aerial vehicle positioning pre-monitoring, obtains tethered unmanned aerial vehicle visual image analysis result by executing tethered unmanned aerial vehicle visual image analysis, obtains tethered unmanned aerial vehicle operation positioning monitoring result by executing tethered unmanned aerial vehicle operation positioning monitoring, reaches the effect of improving the accuracy of tethered unmanned aerial vehicle operation, solves the problem of low accuracy of tethered unmanned aerial vehicle operation based on visual positioning in the prior art.

Description

Technical Field

[0001] This invention relates to the field of drone positioning and adjustment technology, and in particular to a method for operating tethered drones based on visual positioning. Background Technology

[0002] During tethered drone operations, visual sensors such as visible light cameras and thermal imaging cameras onboard the tethered drone acquire environmental visual images at a frequency of tens to hundreds of frames per second. These images include reference environmental visual images of the target object, such as power transmission lines, insulators, and the surrounding scene—i.e., the initially acquired reference frames and real-time environmental visual images. These images are transmitted back to the ground processing system in real time via the tethered cable to construct a positioning and comparison dataset. Edge detection algorithms are used to extract the drone's own contour or environmental feature points identified by the visual sensors. Combined with preset reference coordinates, the current position is calculated. Furthermore, optical flow algorithms are used to analyze the motion trajectory of feature points in consecutive frames of environmental visual images, accurately calculating the tethered drone's position in three-dimensional space. The displacement changes (including lateral and longitudinal displacement, elevation and angular deviations of rotation) are used to form real-time positioning data. Then, the real-time position collected by the vision sensor is compared with a preset benchmark point, such as an inspection position 0.5 meters away from the power transmission line. The deviation value of the tethered drone's position is calculated and repeatedly corrected by a PID (Proportional-Integral-Derivative) control system. After the tethered drone is positioned stably, the angle and focal length of the vision sensor are adjusted to ensure that the lens is accurately aligned with the target and captures high-definition images. The images are transmitted back to the ground via the tether cable for defect detection, ultimately improving the accuracy of tethered drone operations based on vision positioning.

[0003] For example, Chinese invention patent application CN114740876B discloses a vehicle-mounted tethered unmanned aerial vehicle (UAV) guidance and control system and method, including: a satellite-guided positioning differential subsystem, a visual positioning subsystem, and a control and tracking subsystem. When the vehicle can be located, the satellite-guided positioning differential subsystem acquires the vehicle's position and speed information; when the vehicle cannot be located, the vehicle-mounted positioning and orientation subsystem and the airborne visual positioning and navigation subsystem within the visual positioning subsystem combine to acquire the vehicle's position and speed information, and the control and tracking subsystem receives relevant vehicle information to perform PID control, thereby controlling the flight of the tethered UAV.

[0004] For example, the Chinese invention patent application CN119690119A discloses a method and system for visual positioning and obstacle avoidance of unmanned aerial vehicles (UAVs), which includes the following steps: a binocular camera acquires images of the environment around the UAV in real time; an onboard processor performs three-dimensional modeling of the UAV body and surrounding obstacles based on the images, generating relative relationship data between the UAV body and surrounding obstacles; the relative relationship data is transmitted to the UAV remote control terminal via a data link; the UAV remote control terminal parses the relative relationship data, guides the operator to view the relative relationship data, and guides the operator to choose whether to intervene in the relative relationship data.

[0005] The above-mentioned technology has at least the following technical problems: During tethered drone operations, the tension in the tether cable dynamically fluctuates with changes in the drone's attitude, altitude, and wind speed (e.g., when the tether cable generates oblique tension, it exacerbates the roll-pitch axis coupling). If the coupling compensation coefficient, response speed, and other parameters of the decoupling algorithm in the PID control system are only set based on the static tethered drone, they cannot adapt to the dynamic coupling changes brought about by the tether cable in real time. This leads to improper parameter settings when designing the decoupling algorithm for the coupling characteristics of the tethered drone in the PID control system. There may be lag or over-compensation of torque in the control dimension of the PID control system, and the attitude deviation of the coupled axis cannot be suppressed in time and will continue to accumulate. When compensation is excessive, it will exacerbate the attitude overshoot of the main control dimension and the coupled axis, leading to... When PID corrects attitude deviations, coupling axis deviations repeatedly occur, eventually causing attitude oscillations in the tethered drone. This leads to jitter in the tethered drone's visual sensor, resulting in blurred environmental visual images captured by the sensor. Consequently, the visual target feature points on the environmental visual images are unclear, causing the extraction and matching of visual target feature points to fail. Since existing positioning technologies (such as PnP algorithm and optical flow method) are based on visual target feature points for positioning, when the extraction and matching of visual target feature points fails, the positioning of the tethered drone based on the failed visual target feature points may become blurred, resulting in low accuracy of visual positioning-based tethered drone operations. Summary of the Invention

[0006] This invention provides a visual positioning-based tethered drone operation method, which solves the problem of low accuracy in existing visual positioning-based tethered drone operations and improves the accuracy of tethered drone operations.

[0007] To achieve the aforementioned objectives, the present invention provides the following technical solution: A method for operating a tethered drone based on visual positioning is provided. This method includes: during the operation of the tethered drone, performing tethered drone positioning pre-monitoring, first acquiring the tethered drone positioning pre-monitoring results, and then determining whether to perform tethered drone positioning pre-optimization to improve the accuracy of the initial positioning of the tethered drone's shooting angle; after the tethered drone positioning pre-monitoring is qualified, performing tethered drone visual image analysis, first acquiring the tethered drone visual image analysis results, and then determining whether to perform tethered drone visual image optimization to improve the completeness of visual image feature extraction; after the tethered drone visual image analysis is qualified, performing tethered drone operation positioning monitoring, first acquiring the tethered drone operation positioning monitoring results, and then determining whether to perform drone operation positioning optimization to improve the stability of visual positioning based on tethered drone operation.

[0008] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. By performing pre-monitoring of tethered drone positioning to obtain the results, and then determining whether to perform pre-optimization based on these results, we can accurately assess the qualification level of the pre-monitoring, proactively identify potential visual positioning deviations in complex environments, ensure that the tethered drone accurately maintains its preset position during subsequent operations, and reduce problems such as operational range deviations and data acquisition errors caused by inaccurate initial positioning, thereby improving the accuracy of initial positioning. 2. By performing visual image analysis of the tethered drone to obtain the results, and then determining whether to perform visual image optimization based on these results, we can accurately assess the qualification level of visual image feature extraction and matching, and reduce the risk of visual image optimization errors. This study aims to address the interference of noise, distortion, color imbalance, or blurred details in the visual images of tethered drones with their visual positioning. Improving the clarity and feature integrity of these images ensures the accuracy of feature extraction for tethered drone operations based on visual images, thereby enhancing the reliability of operational decisions. By performing tethered drone operation positioning monitoring to obtain monitoring results, and using these results to determine whether to optimize drone operation positioning, the study helps to accurately assess the matching degree between the current positioning status of the tethered drone and the visual positioning of the tethered drone operation. This reduces visual positioning drift caused by changes in environmental factors, ensures the accuracy of tethered drone visual positioning, and reduces situations such as repeated operations and missed detection areas caused by positioning deviations, thus improving the efficiency of tethered drone operations.

[0009] 2. Pre-optimization of tethered UAV positioning includes: determining the attitude coupling of the tethered UAV, adjusting the attitude of the tethered UAV, and determining the PID response of the tethered UAV. Compared with existing technologies, since the tension of the tether cable fluctuates dynamically with changes in UAV attitude, altitude, and wind speed (e.g., when the tether cable generates oblique tension, it exacerbates the roll-pitch axis coupling), if the coupling compensation coefficient, response speed, and other parameters of the decoupling algorithm in the PID control system are only set based on the static tethered UAV, they cannot adapt to the dynamic coupling changes brought about by the tether cable in real time. This leads to improper parameter settings when the PID control system designs the decoupling algorithm for the coupling characteristics of the tethered UAV. This solution helps to reduce the interference of tether cable tension on the attitude stability of the tethered UAV in advance through pre-optimization of tethered UAV positioning, and reduces problems such as attitude control lag, excessive overshoot, or positioning deviation caused by improper decoupling algorithm parameter settings. It improves the attitude control accuracy and positioning stability of the tethered UAV in dynamic coupling scenarios, and ensures that the tethered UAV provides accurate visual positioning for subsequent tethered UAV operations even when changes in wind speed and altitude adjustment cause fluctuations in tether cable tension.

[0010] 3. Optimization of tethered drone visual images includes: filtering and optimizing the resolution of tethered drone visual images. Compared to existing technologies, the vibration of the tethered drone's visual sensor causes blurring of the environmental visual images acquired by the sensor, resulting in unclear visual target feature points on the environmental visual images. This leads to failure in the extraction and matching of visual target feature points. This solution, through optimization of tethered drone visual images, helps improve the completeness of feature extraction and matching, reduces the loss of key feature information due to low resolution, improves the accuracy and reliability of tethered drone visual positioning, reduces the visual image processing load, and thus ensures the real-time performance of tethered drone visual positioning. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a visual positioning-based tethered drone operation method provided in an embodiment of the present invention; Figure 2 This is a general overview diagram of a visual positioning-based tethered drone operation method provided in an embodiment of the present invention; Figure 3This is a framework diagram of visual image optimization for a tethered drone operation method based on visual positioning provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or “connected,” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0015] It should be noted that the terms "up", "down", "left", "right", "front", and "back" used in this invention are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0016] like Figure 1 The flowchart shown is a visual positioning-based tethered drone operation method provided in this application embodiment. The visual positioning-based tethered drone operation method includes: Tethered UAV Positioning Pre-monitoring: Performing tethered UAV positioning pre-monitoring first obtains the tethered UAV positioning pre-monitoring results, and then determines whether to perform tethered UAV positioning pre-optimization. Performing tethered UAV positioning pre-monitoring helps reduce the potential visual positioning deviation risks that may exist in the visual positioning of tethered UAVs in complex environments, ensures the accuracy of visual positioning during subsequent tethered UAV operations, reduces problems such as tethered UAV operation range deviation and data acquisition deviation caused by inaccurate initial positioning of tethered UAVs, and improves the accuracy of initial positioning of tethered UAVs.

[0017] Tethered UAV Visual Image Analysis: Performing tethered UAV visual image analysis first obtains the analysis results, then determines whether to optimize the tethered UAV visual image. Performing tethered UAV visual image analysis helps reduce the interference of noise, distortion, color imbalance, or blurred details in the tethered UAV visual image on the visual positioning of the tethered UAV, improves the clarity and feature integrity of the tethered UAV visual image, ensures the accuracy of feature extraction for tethered UAV operations based on visual images, and thus improves the reliability of operation judgment.

[0018] Tethered Drone Operation Positioning Monitoring: Tethered drone operation positioning monitoring is performed by first obtaining the monitoring results, and then determining whether to optimize the drone's positioning. This monitoring helps reduce visual positioning drift caused by changes in environmental factors, ensuring the accuracy of visual positioning and reducing duplicate operations and missed detection areas due to positioning deviations, thereby improving the accuracy of tethered drone operations.

[0019] like Figure 2As shown in the diagram, this invention provides an overall overview of a visual positioning-based tethered drone operation method. First, tethered drone positioning pre-monitoring is performed to determine if the acquired tethered drone positioning pre-monitoring results meet the qualification conditions. If they do, tethered drone image quality analysis is performed; otherwise, tethered drone positioning pre-optimization is performed. Then, the tethered drone positioning pre-monitoring results, after pre-optimization, are re-acquired to determine if they meet the qualification conditions. If they do, tethered drone image quality analysis is performed; otherwise, a tethered drone positioning pre-optimization anomaly alert is sent to a pre-defined personnel. Next, tethered drone image quality analysis is performed to determine if the acquired tethered drone visual image analysis results meet the qualification conditions. If they do, tethered drone operation positioning monitoring is performed; otherwise, tethered drone visual image optimization is performed. Finally, the tethered drone visual image analysis results, after optimization, are re-acquired to determine if they meet the qualification conditions. If the results meet the qualification conditions for tethered drone visual image analysis, and if the resolution of the tethered drone visual image in the low-resolution visual image area reaches the tethered drone visual image resolution, then tethered drone operation positioning monitoring is performed. Otherwise, a tethered drone visual image optimization anomaly prompt is sent to the preset personnel. Finally, tethered drone operation positioning monitoring is performed again, and it is determined whether the obtained tethered drone operation positioning monitoring results meet the qualification conditions. If they do, a visual positioning qualification prompt is sent to the preset personnel, and the tethered drone operation continues. Otherwise, drone operation positioning optimization is performed, and it is determined whether the tethered drone operation positioning monitoring results obtained after drone operation positioning optimization meet the qualification conditions. If they do, a visual positioning qualification prompt is sent to the preset personnel, and the tethered drone operation continues. Otherwise, a visual positioning anomaly prompt is sent to the preset personnel.

[0020] It should be added that, prior to the design of the visual positioning-based tethered drone operation method in this application, a database storing various preset data is established. The database includes, but is not limited to, preset tethered drone positioning pre-monitoring results, preset tethered drone attitude coupling judgment results, and preset tethered drone PID response judgment results. The various values ​​are directly set by technical personnel. Specifically, the database is a set of preset values ​​set by relevant technical personnel based on the technical parameters of the tethered drone, the characteristics of the operation scenario, and the control algorithm objectives, through theoretical calculations, simulation tests, and engineering experience. Core data tables are divided around positioning pre-monitoring, attitude coupling judgment, and PID response judgment. Each table has corresponding fields set according to data attributes and is linked through key fields such as the tethered drone operation scenario number to ensure that the tethered drone can be quickly called according to the operation status. A hybrid architecture of "local and cloud" is used for storage. The core preset data is stored locally using high-speed media to ensure the real-time performance of the operation, while the complete data is backed up in the cloud to achieve secure storage and unified management. All data is stored in a standardized format to ensure compatibility.

[0021] In this embodiment, firstly, pre-monitoring of tethered drone positioning is performed to obtain the pre-monitoring results and determine whether they meet the qualification conditions. If they do, image quality analysis of the tethered drone is performed; otherwise, pre-optimization of tethered drone positioning is performed. This helps to monitor in advance the impact of the dynamic fluctuations in the tension of the tethering cable with changes in drone attitude, altitude, and wind speed on the initial visual positioning of the tethered drone, thereby improving the reliability of the tethering drone operation. Next, visual image analysis of the tethered drone is performed to obtain the visual image analysis results and determine whether they meet the qualification conditions. If they do, positioning monitoring of the tethered drone operation is performed; otherwise, the results are analyzed within a preset visual image analysis period. Optimizing the visual images of tethered drones based on the analysis results of abnormal visual images helps reduce the blurring of environmental visual images acquired by the visual sensors of tethered drones, and improves the clarity of visual target feature points on the environmental visual images. Finally, tethered drone operation positioning monitoring is performed to obtain the monitoring results and determine whether they meet the qualified conditions for tethered drone operation positioning monitoring. If they do, tethered drone operation positioning monitoring is performed; otherwise, the visual images of tethered drones based on the analysis results of abnormal visual images acquired within a preset visual image analysis period are optimized to help reduce the blurring of visual target feature points on the tethered drones, thereby increasing the accuracy of visual positioning-based tethered drone operations.

[0022] In the first embodiment, the specific process of first obtaining the tethered drone positioning pre-monitoring results and then determining whether to perform tethered drone positioning pre-optimization is as follows: The ratio of the change in tether cable tension during the preset positioning pre-monitoring time period to the preset tether cable tension change is quantified, i.e., a ratio calculation is performed to obtain the tethered drone positioning pre-monitoring results reflecting the initial positioning fluctuations of the tethered drone; it is then determined whether the obtained tethered drone positioning pre-monitoring results meet the qualified conditions for tethered drone positioning pre-monitoring; the preset positioning pre-monitoring time period refers to the preset time period corresponding to the execution of tethered drone positioning pre-monitoring set in advance by the preset personnel; the change in tether cable tension is represented by the absolute value corresponding to the difference between the tether cable tension at the beginning and end of the preset positioning pre-monitoring time period monitored by a tension sensor; the qualified conditions for tethered drone positioning pre-monitoring indicate that the tethered drone... If the pre-monitoring result of the tethered drone's positioning is less than the preset pre-monitoring result of the tethered drone's positioning, which is represented by the average value of the pre-monitoring results of the tethered drone's positioning over a historical period, then if the pre-monitoring result of the tethered drone's positioning meets the qualification criteria for pre-monitoring the tethered drone's positioning, then the tethered drone image quality analysis, which reflects the qualification level of the tethered drone's visual image feature extraction and matching, is performed; otherwise, the tethered drone positioning pre-optimization is performed. The tethered drone positioning pre-optimization includes: tethered drone attitude coupling determination, which assesses the degree of interference of the tethered drone's attitude coupling on the tethered drone's visual positioning; tethered drone attitude adjustment, which reduces the visual positioning position deviation caused by the inherent distortion of the visual sensor; and tethered drone PID response determination, which assesses the degree of interference of the tethered drone's attitude coupling on the tethered drone's positioning adjustment.

[0023] In this embodiment, by performing pre-monitoring of tethered drone positioning, the initial fluctuation state of tethered drone positioning can be accurately assessed to form a pre-monitoring result, thereby enabling the judgment of the accuracy of the early positioning of the tethered drone, ensuring the basic quality of the core link of subsequent visual positioning of tethered drone, visual image feature extraction and matching of tethered drone, reducing the hidden danger of positioning deviation caused by cable tension fluctuation, avoiding the interference of initial positioning fluctuation on the subsequent visual positioning process, and improving the stability of visual positioning of tethered drone.

[0024] Preferably, the specific process for determining the attitude coupling of a tethered drone is as follows: The coupling torque of the tethered drone during a preset positioning and pre-monitoring time period is monitored using a torque sensor. This torque is then quantified by comparing it with the maximum controllable torque of the tethered drone to obtain a tethered drone attitude coupling determination result reflecting the degree of attitude coupling interference. It is then determined whether the obtained tethered drone attitude coupling determination result meets the qualified conditions for tethered drone attitude coupling determination. The qualified conditions for tethered drone attitude coupling determination indicate that the tethered drone attitude coupling determination result is less than a preset tethered drone attitude coupling determination result, which is represented by the average value of tethered drone attitude coupling determination results over historical time periods. If the tethered drone attitude coupling determination result meets the qualified conditions, then tethered drone image quality analysis is performed; otherwise, tethered drone attitude adjustment is performed.

[0025] In this embodiment, the attitude coupling determination of the tethered drone can accurately assess the degree of interference of the attitude coupling of the tethered drone on the precise positioning of the tethered drone in visual positioning-based tethered drone operations. This reduces the redundancy of torque control caused by the attitude coupling interference of the tethered drone, prevents the coupling torque from causing the drone's attitude deviation, and thus avoids the visual sensor's shooting angle deviation and image feature position distortion caused by the attitude deviation of the tethered drone. This ensures that the visual sensor can acquire visual images of the tethered drone based on a stable attitude reference, and guarantees the accuracy of subsequent visual positioning.

[0026] Preferably, the specific process for adjusting the attitude of a tethered drone is as follows: After calibrating the initial coordinate reference of the tethered drone image acquired by the visual sensor of the tethered drone based on a preset calibration method, the compensation angle of the tethered drone is determined; the initial coordinate reference calibration means establishing the initial coordinate axis; the specific process for determining the compensation angle of the tethered drone is as follows: it is determined whether the compensation angle of the tethered drone is greater than the preset compensation angle of the tethered drone, which is represented by the average value of the compensation angle of the tethered drone over a historical time period, wherein the compensation angle of the tethered drone is monitored by the attitude sensor; if the compensation angle of the tethered drone is greater than or equal to the preset compensation angle of the tethered drone, the visual sensor angle is acquired to reflect the completeness of the field of view of the tethered drone visual image; otherwise, an abnormal compensation angle prompt is sent to the preset personnel; the visual sensor angle acquisition means that the number of shooting angles of the tethered drone visual image corresponding to the acquired combination of tethered drone shooting angles is... The quantity serves as the final adjustment value, increasing the number of shooting angles for the current environmental visual images to the final adjustment value. The combination of shooting angles of the tethered drone is represented by a three-dimensional cross combination of the radial layer distance, the circumferential layer angle, and the pitch layer angle of the tethered drone. For example, to inspect the potholes and guardrails of a section of highway, the combination of shooting angles of the tethered drone is determined by "distance from the road surface (radial layer, viewing the overall road section from a distance, viewing pothole details from a close distance)," "different positions along the extension direction of the highway (circumferential layer, such as shooting from the beginning, middle, and end of the road section)," and "the angle of looking up or down (pitch layer, shooting the road surface from a low angle, shooting the top of the guardrail from a high angle)." For example, "close distance, middle of the road section, and looking down" and "far distance, starting point of the road section, and eye level" are all different combinations. The visual sensor first calculates the number of combinations required to cover the road surface and guardrails as the final adjustment value, and then allows the tethered drone with insufficient shooting angles to take supplementary shots until the number of angles meets the standard, ensuring that no road surface and guardrail are missed during inspection.

[0027] In this embodiment, adjusting the attitude of the tethered drone helps establish a precise coordinate reference for subsequent attitude adjustment and visual positioning of the tethered drone. This ensures that the visual sensor can capture more comprehensive visual image feature information of the tethered drone that meets the positioning requirements, reducing the impact of insufficient compensation on the accuracy of visual positioning of the tethered drone. By adjusting the reference coordinates, the positioning deviation caused by the inherent distortion of the visual sensor can be effectively reduced, further improving the accuracy of visual positioning pre-monitoring. This provides accurate and stable data support for the visual positioning of the tethered drone, ensuring the accuracy of subsequent visual image feature extraction and matching of the tethered drone, and improving the positioning accuracy of tethered drone operations.

[0028] Preferably, the specific process for determining the PID response of a tethered drone is as follows: The time taken by the PID control system from receiving the coupling torque signal of the tethered drone to outputting the PID compensation command is expressed as the tethered drone PID response determination result, which reflects the degree of positioning adjustment delay in the tethered drone operation; it is determined whether the obtained tethered drone PID response determination result is less than the preset tethered drone PID response determination result; if the tethered drone PID response determination result is less than or equal to the preset tethered drone PID response determination result, then the tethered drone image quality analysis is performed; otherwise, a positioning pre-monitoring anomaly prompt is sent to the preset personnel.

[0029] In this embodiment, the PID response determination of the tethered UAV can effectively assess the degree of delay in the positioning adjustment of the tethered UAV, reduce the cumulative impact of attitude deviation caused by the attitude adjustment delay on the accuracy of visual image feature extraction and matching of the tethered UAV, reduce the loss of attitude control of the tethered UAV under the interference of coupling torque due to excessive positioning adjustment delay, thereby causing the visual sensor shooting angle to shift and the environmental feature capture to be distorted, affecting the accuracy and stability of visual positioning, and ensuring the real-time response capability of the tethered UAV's visual positioning under dynamic interference.

[0030] Preferably, the specific process of performing visual image analysis on a tethered drone is as follows: The visual image of the tethered drone is converted into an image signal by a visual inspection camera and transmitted to an image processing system. Then, based on pixel distribution and information such as brightness and color, the image signal is processed to extract target features. The number of visual feature points extracted within a preset visual image analysis time period is statistically obtained, and this number is quantified by comparing it with a preset number of visual feature points extracted to obtain a result reflecting the richness of the extracted visual feature points. The preset number of visual feature points extracted is represented by the average number of visual feature points extracted over historical time periods. The result of quantifying the ratio between the number of matched visual feature points and the number of extracted visual feature points yields a result reflecting the qualification of the visual feature point matching. It is then determined whether the obtained visual image analysis results of the tethered drone meet the qualification conditions for visual image analysis of the tethered drone. The visual image analysis results of the tethered drone include: the number of visual feature points extracted and the number of successful visual feature point matching. The number of visual feature points extracted represents the number of visual operation target feature points that can be detected in the environmental visual image. The number of matched visual feature points represents the number of visual operation target feature points in the environmental visual image that match the preset number of visual feature points. The number of successfully matched visual feature points is determined by the number of visual feature points extracted. Visual operation target feature points are monitored by an infrared camera. The qualified condition for tethered drone visual image analysis indicates that the tethered drone visual image analysis result is greater than the preset tethered drone visual image analysis result. The preset tethered drone visual image analysis result includes: the preset number of extracted visual feature points and the preset number of successfully matched visual feature points. The preset number of extracted visual feature points is represented by the average of the number of extracted visual feature points over a historical time period, and the preset number of successfully matched visual feature points is represented by the average of the number of successfully matched visual feature points over a historical time period. If the tethered drone visual image analysis result meets the qualified condition, then tethered drone operation positioning monitoring, reflecting the stability of visual positioning during tethered drone operations, is performed. Conversely, for abnormal tethered drone visual image analysis results obtained within the preset visual image analysis time period, tethered drone visual image optimization is performed. The preset visual image analysis time period refers to the preset time period for performing tethered drone visual image analysis, pre-set by personnel. Abnormal tethered drone visual image analysis results refer to tethered drone visual image analysis results that do not meet the qualified condition.

[0031] In this embodiment, by performing visual image analysis of tethered drones, the richness and matching effectiveness of extracted feature points in visual images of tethered drones in visual positioning operations can be effectively evaluated. This ensures that subsequent visual positioning is based on high-quality feature data, thereby accurately reflecting the stability of tethered drone positioning. This provides a reliable basis for the continuous guarantee of positioning accuracy during operations and avoids positioning deviations or failures caused by low-quality visual images of tethered drones, which would affect the accuracy of tethered drone positioning operations.

[0032] Preferably, the specific process for optimizing the visual images of tethered drones is as follows: First, the resolution of the tethered drone visual images is selected. Based on a pyramid layering strategy, the scaling ratio of adjacent layers in the pyramid structure is used as the layer interval. The visual images of the tethered drone are acquired in layers. Images with a resolution greater than or equal to the tethered drone's visual image resolution are marked as high-resolution visual images; conversely, images with a resolution lower than the pyramid structure are marked as low-resolution visual images. The scaling ratio of adjacent layers in the pyramid structure is pre-set by a designated person. The pyramid layering strategy means first performing multi-scale downsampling on the original tethered drone visual image using Gaussian blur. A Laplacian pyramid is generated by acquiring visual images of a Gaussian tethered drone and then performing differential operations on the resolutions of adjacent Gaussian tethered drone visual images. Laplacian differential encoding is used to quantify the resolution differences between different levels of Gaussian tethered drone visual images. This pyramid layering strategy helps the tethered drone flexibly access visual images of corresponding resolutions when the flight environment changes, such as changes in distance from the target or fluctuations in lighting conditions. This reduces redundant computation in long-distance scenarios and detail loss in close-range scenarios, achieving greater flexibility and reliability for the tethered drone. Furthermore, industrial cameras monitor the tethered drones within a preset visual image analysis time period. The resolution of the human-machine visual image is averaged and expressed as the tethered drone visual image resolution. Tethered drone visual image resolution filtering is performed to reduce the visual image processing load and improve the tethered drone's response speed. The second step is tethered drone visual image resolution optimization. Low-resolution visual images are subjected to directional magnification optimization based on a preset local super-resolution reconstruction algorithm, such as an improved bilinear interpolation algorithm. This directional magnification optimization operation means magnifying the low-resolution visual image region by a magnification factor based on the visual image deviation value, where the visual image deviation value is calculated by comparing the tethered drone visual image resolution with the low-resolution... The resolution of the visual image is interpolated, i.e., the result of the subtraction operation is represented. If the analysis result of the tethered drone visual image re-acquired after optimization meets the qualified conditions for tethered drone visual image analysis, and the resolution of the tethered drone visual image in the low-resolution visual image area reaches the tethered drone visual image resolution, then tethered drone operation positioning monitoring is performed; otherwise, an abnormal prompt for tethered drone visual image optimization is sent to the preset personnel. Optimizing the resolution of tethered drone visual images is used to improve the integrity of the tethered drone visual image features. Low-resolution visual images represent images with a resolution lower than that corresponding to the tethered drone visual image resolution.

[0033] It should be added that, such as Figure 3As shown in the figure, this invention provides a framework diagram for tethered drone visual image optimization in a visual positioning-based tethered drone operation method. First, tethered drone image quality analysis is performed to determine whether the acquired tethered drone visual image analysis results meet the qualification conditions for tethered drone visual image analysis. If they do, tethered drone operation positioning monitoring is conducted; otherwise, tethered drone visual image optimization is performed. Tethered drone visual image optimization includes: tethered drone visual image resolution acquisition and filtering, and tethered drone visual image resolution optimization. It is determined whether the tethered drone visual image analysis results re-acquired after optimization meet the qualification conditions for tethered drone visual image analysis, and whether the tethered drone visual image resolution in low-resolution visual image areas reaches the required tethered drone visual image resolution. If they do, tethered drone operation positioning monitoring is conducted; otherwise, a visual positioning anomaly alert for tethered drone operation is sent to preset personnel.

[0034] In this embodiment, optimizing the visual images of the tethered drone helps improve the response speed of the tethered drone, reduces the delay in updating the tethered drone's positioning data due to excessive processing pressure, ensures that the visual positioning of the tethered drone can adapt to changes in the tethered drone's operating environment in a timely manner, effectively improves the integrity of the tethered drone's visual image features, reduces the loss and blurring of visual image feature points due to insufficient image resolution, and thus enhances the recognizability and integrity of the tethered drone's visual image features.

[0035] Preferably, the specific process for performing tethered drone operation positioning monitoring is as follows: The straight-line distance between the tethered drone operation positioning monitoring location point and a preset tethered drone operation positioning monitoring location point is represented as the tethered drone operation positioning monitoring result. The tethered drone operation positioning monitoring location point represents the location point pre-set by the tethered drone. The tethered drone operation positioning monitoring location point within a preset operation positioning monitoring time period is monitored using satellite positioning equipment. The preset operation positioning monitoring time period represents the preset time period corresponding to the tethered drone operation positioning monitoring set in advance by preset personnel. It is determined whether the obtained tethered drone operation positioning monitoring result meets the qualified conditions for tethered drone operation positioning monitoring. The tethered drone operation positioning monitoring result is used to reflect the positioning accuracy of the tethered drone operation; tethered... The qualified condition for tethered drone operation positioning monitoring indicates that the positioning monitoring results of the tethered drone operation are within the preset range of tethered drone operation positioning monitoring results. The preset range of tethered drone operation positioning monitoring results is represented by the range between the maximum and minimum values ​​over a historical time period, including both the maximum and minimum endpoints. If the tethered drone operation positioning monitoring results meet the qualified condition, a visual positioning qualified prompt for the tethered drone operation is sent to the preset personnel, and the tethered drone operation continues. Otherwise, the drone operation positioning is optimized based on multiple acquisitions of abnormal tethered drone operation positioning monitoring results. Abnormal tethered drone operation positioning monitoring results indicate tethered drone operation positioning monitoring results that do not meet the qualified condition.

[0036] In this embodiment, by performing tethered drone operation positioning monitoring, the accuracy of tethered drone operation positioning can be effectively evaluated, reducing the failure of tethered drone operation data and task execution deviations caused by substandard visual positioning accuracy of tethered drones, ensuring the stability of visual positioning accuracy of tethered drones, ensuring that tethered drone operation tasks can be carried out efficiently based on accurate positioning, and improving the reliability and stability of tethered drone operations.

[0037] Preferably, the specific process for optimizing the positioning of unmanned aerial vehicles (UAVs) is as follows: Based on the PnP algorithm, the visual image features of the tethered UAV are nonlinearly optimized and solved with a reference 3D map, outputting the tethered UAV pose estimation value in real time; based on the tethered UAV pose estimation value, IMU inertial measurement data is fused through an EKF filter to obtain the UAV's positioning for tethered UAV operations. For example, firstly, based on the PnP algorithm, real-time visual images of the tethered UAV containing identifiable feature points are captured by a visual sensor, combined with a pre-constructed reference 3D map with precisely calibrated 3D coordinates of the feature points, to match the tethered UAV visual image feature points from "2D image coordinates to 3D world coordinates," and then through nonlinear optimization... The algorithm solves for the tethered drone pose estimate without cumulative bias. Then, based on the EKF filter, the tethered drone pose estimate and the real-time inertial measurement data output by the IMU are input. First, the instantaneous pose change of the drone is quickly predicted by integrating the IMU data to solve the short-term positioning error caused by the lag in visual pose update. Then, when a new tethered drone pose estimate is obtained, the difference between it and the tethered drone pose estimate predicted by the IMU is calculated. The tethered drone pose is corrected by dynamically allocating weights through Kalman gain, while correcting for long-term IMU drift and short-term visual lag bias. The fused tethered drone pose estimate is output, providing support for tethered drones to hover in fixed areas for extended periods (such as power line inspection and agricultural spraying). To ensure the drone stays on the planned operational path at all times, avoiding risks caused by positioning deviations from the outset, the entire process forms a continuously optimized closed loop by providing visual references in advance and neutralizing two types of deviations in real time, achieving operational positioning stability. IMU inertial measurement data represents physical quantities that are collected and output in real time by the IMU's built-in accelerometers and gyroscopes, reflecting the tethered drone's own motion state and attitude changes. These include: linear acceleration along the X, Y, and Z axes of the drone's body coordinate system, and angular velocity around the X, Y, and Z axes of the tethered drone's body coordinate system. IMU inertial measurement data is used to assist the tethered drone's visual positioning and correct attitude deviations, thereby ensuring the tethered drone's operational stability. Operational stability; the tethered drone pose estimation value is represented by the tethered drone position point corresponding to the qualified tethered drone attitude angle, which is monitored by satellite positioning equipment; the qualified tethered drone attitude angle indicates that the current attitude angle of the tethered drone is within the preset tethered drone attitude angle range, which is monitored by gyroscope; if the tethered drone operation positioning monitoring result re-acquired after drone operation positioning optimization meets the qualified tethered drone operation positioning monitoring conditions, a visual positioning qualified prompt for the tethered drone operation is sent to the preset personnel, and the tethered drone operation continues; otherwise, a visual positioning abnormality prompt for the tethered drone operation is sent to the preset personnel. The preset tethered drone attitude angle range is set in advance by the preset personnel.

[0038] In this embodiment, optimizing the positioning of tethered drone operations helps to improve and correct the visual positioning of tethered drone operations, enhancing positioning accuracy from the perspective of visual and inertial data collaboration. This provides accurate and reliable visual positioning support for the tethered drone operation process, reduces errors in tethered drone operations caused by continuous positioning deviations, thereby enhancing the accuracy of tethered drone positioning data and providing key assurance for the accuracy and stability of tethered drone visual positioning, ensuring the efficiency of tethered drone operations.

[0039] Based on the first embodiment, in the second embodiment, during the hovering phase of the tethered drone, since the IMU (Inertial Measurement Unit) has not yet completed zero-bias calibration, there are noise signals in the sensor output. If the PID control system is directly activated at this time, the PID control system algorithm may misjudge the non-converged drift as a valid displacement command, thereby outputting excessive torque compensation, causing attitude overshoot in the main control channel (such as altitude or heading), and also affecting the stability of other axes through dynamic coupling effect. This control oscillation caused by the initial calibration delay will be superimposed on the measurement process of the vision positioning system, resulting in low positioning accuracy of vision-based tethered drone operations.

[0040] Specifically, the process for obtaining the tethered drone positioning pre-monitoring results is as follows: The tethered drone positioning pre-monitoring results are obtained by quantifying the ratio of the IMU drift amount to a preset IMU drift amount, which reflects the degree of interference of the IMU drift amount on the tethered drone positioning pre-monitoring. The preset IMU drift amount is represented by the average value of the IMU drift amount over a historical time period. Based on the tethered drone positioning pre-monitoring results, it is determined whether the IMU drift qualification condition is met. The IMU drift qualification condition indicates that the tethered drone positioning pre-monitoring results are less than the preset tethered drone positioning pre-monitoring results. If the tethered drone positioning pre-monitoring results meet the IMU drift qualification condition, visual image analysis of the tethered drone is performed; otherwise, a visual sensor noise anomaly alert is sent to the preset personnel.

[0041] Among them, the results of pre-monitoring of tethered drone positioning The specific expression is as follows:

[0042] It should be added that, , and , where k is the weighting factor and k is the proportional coefficient, are both set in advance by designated personnel.

[0043] in addition, The actual angular drift of the IMU represents the average drift of the IMU on the roll, pitch, or yaw axis during the start-up hovering phase, measured in degrees per hour, and monitored by a high-precision IMU. The standard angle drift of the IMU represents the manufacturer's specified angle drift threshold of the IMU under normal temperature and pressure, in degrees per hour, and is set at the factory for tethered drones. The actual velocity drift of the IMU is measured in meters per second squared, monitored by a high-precision IMU and calibrated with GNSS assistance. The standard velocity drift of the IMU represents the maximum allowable velocity drift of the IMU as specified by the manufacturer, in meters per second squared. This value is set at the factory for tethered drones. The actual position drift of the IMU represents the cumulative relative position drift calculated by the IMU during the start-up and hovering phase, in meters. It is monitored by a high-precision IMU and calibrated by a laser rangefinder. This is the standard IMU position drift, representing the maximum position drift threshold required for tethered drone operations. It is measured in meters and is set at the factory when the tethered drone leaves the factory. The actual ambient wind speed represents the real-time wind speed around the drone during the start-up and hovering phase, measured in meters per second, and is monitored by a wind speed sensor. Maximum permissible wind speed indicates the upper limit of safe wind speed for visual operations of tethered drones, in meters per second, and is set by the tethered drone at the factory.

[0044] It's important to note that the actual IMU angle drift, actual IMU velocity drift, actual IMU position drift, and actual ambient wind speed are not independent but rather interconnected. Since ambient wind speed directly exerts an external force on the tethered drone, increased wind speed intensifies wind disturbances, potentially causing rapid changes in roll, pitch, or yaw angles. This leads to an increase in the actual IMU angle drift. Because the IMU needs to continuously sense and reflect changes in the drone's angle, unstable angle variations caused by wind will cause the angle measured by the IMU to deviate more significantly from its initial state. Furthermore, since velocity drift is calculated from IMU data... When calculating the IMU's position, angle information is one of the key parameters. Therefore, an increase in the actual angle drift of the IMU will affect the velocity calculation, leading to deviations in the velocity obtained from integration and other calculations. This, in turn, increases the actual velocity drift of the IMU. The velocity deviation will further affect the position calculation, which is obtained by integrating the actual velocity of the tethered UAV's IMU. If the actual velocity of the tethered UAV's IMU is inaccurate, it will accumulate over time, resulting in an increase in the actual position drift of the IMU. Therefore, there is a positive correlation between the parameters of actual angle drift, actual velocity drift, actual position drift, and actual ambient wind speed.

[0045] In this embodiment, acquiring the positioning pre-monitoring results of the tethered drone helps to accurately reflect the degree of interference of IMU drift on positioning pre-monitoring in visual positioning-based tethered drone operations, ensuring the reliability of subsequent visual positioning of the tethered drone, reducing the impact of inertial data deviation caused by IMU drift on the fusion accuracy of visual positioning, reducing the superposition interference formed by IMU drift and visual sensor data, which could lead to deviations or even failures in visual positioning pre-monitoring of the tethered drone, improving the reliability of visual positioning inertial data of the tethered drone, and ensuring that visual positioning can be combined with stable IMU auxiliary information during tethered drone operations to maintain high accuracy and high stability.

[0046] In summary, this embodiment obtains tethered drone positioning pre-monitoring results by performing tethered drone positioning pre-monitoring, and determines whether to perform tethered drone positioning pre-optimization based on these results. This helps to accurately assess the pass rate of tethered drone positioning pre-monitoring, proactively identify potential visual positioning deviations in complex environments, ensure that the tethered drone accurately maintains its preset position during subsequent operations, and reduce problems such as tethered drone operation range deviation and data acquisition deviation caused by inaccurate initial positioning, thereby improving the accuracy of initial positioning. Furthermore, by performing tethered drone visual image analysis to obtain the results, and determining whether to perform tethered drone visual image optimization based on these results, this helps to accurately assess the pass rate of tethered drone visual image feature extraction and matching. This method reduces interference from noise, distortion, color imbalance, or blurred details in the visual images of tethered drones, thereby improving the clarity and feature integrity of the visual images and ensuring the accuracy of feature extraction for tethered drone operations based on visual images. This enhances the reliability of operation judgment. By performing tethered drone operation positioning monitoring to obtain monitoring results, and using these results to determine whether to optimize the drone operation positioning, this method helps to accurately assess the matching degree between the current positioning status of the tethered drone and the visual positioning of the tethered drone operation. It reduces visual positioning drift caused by changes in environmental factors, ensures the accuracy of visual positioning of the tethered drone, and reduces situations such as repeated operations and missed detection areas caused by positioning deviations, thereby improving the efficiency of tethered drone operations.

[0047] The following points need to be explained: (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0048] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0049] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0050] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for operating a tethered unmanned aerial vehicle (UAV) based on visual positioning, characterized in that, Includes the following steps: During tethered drone operations, tethered drone positioning pre-monitoring is performed. The tethered drone positioning pre-monitoring results are obtained first, and then it is determined whether to perform tethered drone positioning pre-optimization to improve the accuracy of the initial positioning of the tethered drone's shooting angle. After the tethered drone's positioning and pre-monitoring are qualified, the visual image analysis of the tethered drone is performed. First, the visual image analysis results of the tethered drone are obtained, and then it is determined whether to optimize the visual image of the tethered drone to improve the completeness of the visual image feature extraction. After the visual image analysis of the tethered drone is qualified, the tethered drone operation positioning monitoring is performed. First, the tethered drone operation positioning monitoring results are obtained, and then it is determined whether to optimize the drone operation positioning to improve the stability of the tethered drone operation visual positioning.

2. The method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 1, characterized in that, The specific process of first obtaining the positioning pre-monitoring results of the tethered drone and then determining whether to perform tethered drone positioning pre-optimization is as follows: The tethered drone positioning pre-monitoring results are obtained by quantifying the ratio of the change in tether cable tension during the preset positioning pre-monitoring period to the preset change in tether cable tension, which reflects the initial positioning fluctuation of the tethered drone. Determine whether the obtained pre-monitoring results of tethered drone positioning meet the qualification conditions for pre-monitoring of tethered drone positioning; The preset positioning and pre-monitoring time period refers to the preset time period corresponding to the pre-set positioning and pre-monitoring of the tethered drone by the preset personnel. The change in the tension of the mooring cable is represented by the absolute value of the difference between the tension of the mooring cable at the beginning and the end of the preset positioning and pre-monitoring time period; The qualified condition for tethered UAV positioning pre-monitoring indicates that the tethered UAV positioning pre-monitoring result is less than the preset tethered UAV positioning pre-monitoring result; If the tethered drone positioning pre-monitoring results meet the tethered drone positioning pre-monitoring qualification conditions, then perform tethered drone image quality analysis to reflect the qualification level of tethered drone visual image feature extraction and matching; otherwise, perform tethered drone positioning pre-optimization. The tethered UAV positioning pre-optimization includes: tethered UAV attitude coupling determination for assessing the degree of interference of tethered UAV attitude coupling on tethered UAV visual positioning; tethered UAV attitude adjustment for reducing visual positioning position deviation caused by inherent distortion of visual sensors; and tethered UAV PID response determination for assessing the degree of interference of tethered UAV attitude coupling on tethered UAV positioning adjustment.

3. The method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 2, characterized in that, The specific process for determining the attitude coupling of the tethered UAV is as follows: The attitude coupling determination result of the tethered UAV is obtained by quantifying the ratio of the coupling torque of the tethered UAV to the maximum controllable torque of the tethered UAV. Determine whether the obtained attitude coupling determination results of the tethered UAV meet the qualified conditions for attitude coupling determination of the tethered UAV; The qualified condition for attitude coupling determination of tethered UAVs indicates that the attitude coupling determination result of tethered UAVs is less than the preset attitude coupling determination result of tethered UAVs; If the attitude coupling determination result of the tethered UAV meets the qualified conditions for attitude coupling determination of the tethered UAV, then the image quality analysis of the tethered UAV is performed; otherwise, the attitude adjustment of the tethered UAV is performed.

4. The method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 3, characterized in that, The specific process for adjusting the attitude of the tethered drone is as follows: After calibrating the initial coordinate reference of the tethered drone based on the visual sensor of the tethered drone using a preset calibration method, the compensation angle of the tethered drone is determined. The initial coordinate reference calibration represents the establishment of initial coordinate axes; The specific process for determining the compensation angle of the tethered drone is as follows: Determine whether the compensation angle of the tethered drone is greater than the preset compensation angle for the tethered drone; If the compensation angle of the tethered drone is greater than or equal to the preset compensation angle of the tethered drone, the angle of the visual sensor used to reflect the completeness of the visual field of the tethered drone will be collected; otherwise, an abnormal compensation angle prompt of the tethered drone will be sent to the preset personnel. The visual sensor angle acquisition means that the number of shooting angles of the tethered drone visual image corresponding to the combination of shooting angles of the tethered drone is used as the final adjustment value, and the number of shooting angles of the current environment visual image is increased to the final adjustment value. The combination of shooting angles of the tethered drone is represented by a three-dimensional cross combination of the radial layer distance of the tethered drone, the circumferential layer angle of the tethered drone, and the pitch layer angle of the tethered drone.

5. A method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 2, characterized in that, The specific process for determining the PID response of the tethered UAV is as follows: The time taken by the PID control system from receiving the coupling torque signal of the tethered UAV to outputting the PID compensation command is expressed as the tethered UAV PID response judgment result, which reflects the degree of positioning adjustment delay in the operation of the tethered UAV. Determine whether the obtained tethered drone PID response result is less than the preset tethered drone PID response result; If the PID response judgment result of the tethered drone is less than or equal to the preset PID response judgment result of the tethered drone, then the image quality analysis of the tethered drone is performed; otherwise, an abnormality prompt for the tethered drone positioning pre-monitoring is sent to the preset personnel.

6. The method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 1, characterized in that, The specific process for obtaining the positioning and pre-monitoring results of the tethered UAV is as follows: The tethered UAV positioning pre-monitoring results are obtained by quantifying the ratio of IMU drift to preset IMU drift, which reflects the degree of interference of IMU drift on tethered UAV positioning pre-monitoring. Determine whether the IMU drift qualification conditions are met based on the pre-monitoring results of the tethered UAV positioning; The IMU drift qualification condition indicates that the tethered UAV positioning pre-monitoring result is less than the preset tethered UAV positioning pre-monitoring result; If the positioning and pre-monitoring results of the tethered drone meet the IMU drift qualification conditions, then the visual image analysis of the tethered drone will be performed; otherwise, a visual sensor noise anomaly alert will be sent to the designated personnel.

7. A method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 6, characterized in that, The specific process of performing visual image analysis on the tethered drone is as follows: The result of the number of visual feature points extracted is obtained by quantifying the ratio of the number of visual feature points extracted to the preset number of visual feature points extracted. The successful visual feature point matching result is obtained by quantifying the ratio of the number of visual feature points matched to the number of visual feature points extracted, which reflects the qualification of the visual feature point matching. Determine whether the obtained visual image analysis results of the tethered drone meet the qualification conditions for visual image analysis of tethered drones; The visual image analysis results of the tethered UAV include: the number of visual feature points extracted and the results of successful visual feature point matching; The number of visual feature points extracted represents the number of visual task target feature points that can be detected in the environmental visual image; The qualified condition for visual image analysis of tethered drones indicates that the visual image analysis result of the tethered drone is greater than the preset visual image analysis result of the tethered drone; If the visual image analysis results of the tethered drone meet the qualification conditions for visual image analysis of the tethered drone, then the tethered drone operation positioning monitoring, which reflects the stability of visual positioning during tethered drone operation, will be performed; otherwise, the abnormal visual image analysis results of the tethered drone obtained within the preset visual image analysis time period will be optimized. The preset visual image analysis time period refers to the preset time period corresponding to the visual image analysis of the tethered drone, which is set in advance by the preset personnel. The abnormal visual image analysis results of the tethered drones indicate visual image analysis results of tethered drones that do not meet the qualified conditions for visual image analysis of tethered drones.

8. A method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 7, characterized in that, The specific process for optimizing the visual images of tethered drones is as follows: The first step is to filter the resolution of tethered drone visual images. Based on the pyramid layering strategy, the scaling ratio of adjacent layers in the pyramid structure is used as the layer interval. The tethered drone visual images are collected in layers. The tethered drone visual images with a resolution greater than or equal to the resolution of the tethered drone visual images are marked as high-resolution visual images, and the corresponding tethered drone visual images with a resolution lower than the resolution of the tethered drone visual images are marked as low-resolution visual images. The pyramid layering strategy first involves multi-scale downsampling of the original tethered drone visual image through Gaussian blur to obtain a Gaussian tethered drone visual image, then generating a Laplacian pyramid by performing differential operations on the resolutions of adjacent Gaussian tethered drone visual images, and finally quantifying the differences in resolution between different levels of Gaussian tethered drone visual images using Laplacian differential coding. The resolution of the tethered drone visual images is represented by the average resolution of the tethered drone visual images obtained over a preset visual image analysis period. The aforementioned visual image resolution filtering for tethered drones is used to reduce the visual image processing load of tethered drones and improve their response speed. The second step is to optimize the visual image resolution of the tethered drone. Low-resolution visual images are magnified and optimized in a targeted manner based on a preset local super-resolution reconstruction algorithm. The directional magnification optimization operation means magnifying the visual image resolution of the tethered UAV in the low-resolution visual image region by using the visual image deviation value as the magnification factor. If the visual image analysis results of the tethered drone re-acquired after visual image optimization meet the qualified conditions for visual image analysis of tethered drones, and the visual image resolution of the tethered drone in the low-resolution visual image area reaches the visual image resolution of the tethered drone, then the tethered drone operation positioning monitoring will be performed; otherwise, a visual image optimization anomaly prompt will be sent to the preset personnel. The aforementioned optimization of the visual image resolution of the tethered drone is used to improve the integrity of the visual image features of the tethered drone. The low-resolution visual image refers to an image with a resolution lower than that of the tethered drone's visual image.

9. A method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 8, characterized in that, The specific process for performing tethered drone operation positioning and monitoring is as follows: The straight-line distance between the tethered drone operation positioning and monitoring location point and the preset tethered drone operation positioning and monitoring location point is expressed as the tethered drone operation positioning and monitoring result; Determine whether the obtained tethered drone operation positioning monitoring results meet the qualification conditions for tethered drone operation positioning monitoring; The tethered UAV operation positioning monitoring results are used to reflect the positioning accuracy of the tethered UAV operation. The qualified condition for tethered drone operation positioning and monitoring indicates that the tethered drone operation positioning and monitoring results are within the preset range of tethered drone operation positioning and monitoring results; If the positioning monitoring results of the tethered drone operation meet the qualified conditions for tethered drone operation positioning monitoring, a visual positioning qualified prompt for the tethered drone operation will be sent to the preset personnel, and the tethered drone operation will continue. Otherwise, the drone operation positioning will be optimized based on multiple abnormal tethered drone operation positioning monitoring results. The abnormal tethered drone operation positioning monitoring results indicate tethered drone operation positioning monitoring results that do not meet the qualified conditions for tethered drone operation positioning monitoring.

10. A method for operating a tethered unmanned aerial vehicle based on visual positioning according to claim 9, characterized in that, The specific process for optimizing the positioning of unmanned aerial vehicles (UAVs) is as follows: Based on the PnP algorithm, the visual image features of the tethered UAV and the benchmark 3D map are nonlinearly optimized to solve the problem and output the pose estimation value of the tethered UAV in real time. The drone's operational positioning is obtained by fusing IMU inertial measurement data with the pose estimation value of the tethered drone through an EKF filter; The IMU inertial measurement data refers to the physical quantity data that is collected and output in real time by the two core sensors built into the IMU, namely the accelerometer and the gyroscope, to reflect the motion state and attitude changes of the tethered UAV itself. The IMU inertial measurement data is used to assist the visual positioning of the tethered UAV and correct attitude deviations, thereby ensuring the operational stability of the tethered UAV; The estimated pose of the tethered drone is represented by the tethered drone position point corresponding to the attitude angle of the qualified tethered drone; The qualified tethered drone attitude angle indicates that the current attitude angle of the tethered drone is within the preset tethered drone attitude angle range; If the re-acquired tethered drone operation positioning monitoring results after drone operation positioning optimization meet the qualified conditions for tethered drone operation positioning monitoring, a visual positioning qualified prompt for the tethered drone operation will be sent to the preset personnel, and the tethered drone operation will continue. Otherwise, a visual positioning abnormality prompt for the tethered drone operation will be sent to the preset personnel.

Images