Power transmission line insulator hydrophobicity detection method and system based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle detection, and discloses a power transmission line insulator hydrophobicity detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the relative position information of the unmanned aerial vehicle and a target insulator; according to the relative position information, identifying whether the unmanned aerial vehicle is in a key stage of insulator hydrophobicity detection; when it is recognized that the unmanned aerial vehicle is in the key stage, the flight control system is switched to a fine hovering mode, the unmanned aerial vehicle is controlled to execute insulator hydrophobicity detection tasks in the mode, and the tasks include spraying water flow to the surface of the insulator and collecting water drop images; and after the task is detected to be completed, the flight control system is switched to a default flight mode. Flight control of the unmanned aerial vehicle is deeply coupled with a specific detection task, so that an intelligent detection system integrating sensing, decision making and execution is realized, and hovering stability and efficiency and objectivity of data analysis are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) inspection technology, and more specifically, to a method and system for detecting the hydrophobicity of power transmission line insulators based on UAVs. Background Technology

[0002] The hydrophobicity of transmission line insulators is crucial to the safe operation of power systems. Traditional inspection methods are inefficient and pose safety risks; therefore, automated inspection using drones has become an industry trend. Drones are typically equipped with high-pressure water spray devices and high-definition camera systems to assess the hydrophobicity of insulators by spraying water onto their surfaces and capturing images of the water droplets.

[0003] However, in practical applications, when drones perform hydrophobicity testing of power transmission line insulators, especially in complex local airflow environments (such as valleys, hills, or river confluences), their flight control systems may raise the response threshold to subtle fuselage sway signals due to software updates. This optimization aims to reduce unnecessary motor corrections to extend flight time, but under the influence of persistent and directional eddies or gusts, the drone's response to small and continuous fuselage sway is sluggish, resulting in slight and irregular wobbling at critical moments such as water spraying and photography.

[0004] This slight, irregular swaying of the drone exceeded the compensation capabilities of the onboard high-definition camera's stabilization system, directly affecting image quality. This resulted in blurred, discontinuous, or ghosting edges on the water droplets, leading to inaccuracies in subsequent basic data such as water droplet diameter measurements and contact angle calculations. Simultaneously, the water jets ejected by the drone during swaying could not maintain an ideal trajectory, causing uneven distribution of the water film on the insulator surface, with differences in water droplet size, quantity, and adhesion morphology compared to the ideal state.

[0005] Furthermore, to conserve battery power, the water spraying and photography actions were set to be closely linked. However, with uneven water film distribution, some water droplets require a longer stabilization time to fully form and present their true shape. This tight timing sequence may cause the camera to capture the instantaneous state of the water droplet formation process, rather than its final stable state, further exacerbating the difficulty and inaccuracy of image analysis. Ultimately, these problems lead to a significant increase in the misjudgment rate of hydrophobicity levels, greatly reducing the reliability of the detection results. This prevents power companies from accurately assessing the actual operating status of insulators, increasing the potential risk of discharge flashover accidents on transmission lines. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this application discloses a method and system for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs). The aim is to solve the problem that when UAVs perform hydrophobicity detection of power transmission line insulators in complex airflow environments, the sluggish response of the flight control system causes fuselage swaying, which in turn affects the water jet trajectory and the quality of water droplet image acquisition, ultimately leading to misjudgment of the hydrophobicity level.

[0007] The technical solution of this application is as follows: In a first aspect, this application discloses a method for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs), comprising the following steps: Obtain the relative position information between the drone and the target insulator; Based on relative position information, identify whether the drone is in the critical stage of insulator hydrophobicity testing; When the drone is identified as being in a critical stage of insulator hydrophobicity testing, the flight control system is switched to fine hovering mode, and the drone is controlled to perform the insulator hydrophobicity testing task in fine hovering mode, which includes spraying water onto the surface of the insulator and acquiring water droplet images. After the insulator hydrophobicity detection task is completed, the flight control system will switch to the default flight mode.

[0008] Through this technical solution, this application can intelligently switch flight modes according to the relative position of the UAV and the target insulator, and activate the fine hovering mode in the critical detection stage. This effectively suppresses the irregular swing of the UAV in complex airflow environments, ensures the accuracy of water jetting and the clarity of water droplet image acquisition, thereby improving the accuracy and reliability of hydrophobicity detection.

[0009] Furthermore, based on the above methods, the task of testing the hydrophobicity of insulators also includes: Before spraying water onto the surface of the target insulator, activate the air curtain generating device; The air curtain generating device controls the formation of an annular airflow area around the nozzles used for spraying water to isolate external airflow from interfering with the water flow trajectory. The steps of spraying water onto the surface of the target insulator and acquiring images of water droplets are performed after the annular airflow region has been stabilized. After the water droplet image is acquired, turn off the air curtain generator.

[0010] Furthermore, the air curtain generating device includes a miniature high-speed fan or air pump integrated around the spray nozzle; The steps of controlling the air curtain generating device to form an annular airflow region around the nozzle used for spraying water to isolate external airflow from interference with the water flow trajectory include: Start a miniature high-speed fan or air pump to generate a ring-shaped airflow that blows toward the target insulator; The system obtains ambient wind speed information from an ambient wind speed sensor, and adjusts the speed of a miniature high-speed fan or air pump based on the relative position information and ambient wind speed information to maintain the stability of the annular airflow.

[0011] Based on the above, this application further proposes that the steps of spraying water onto the surface of the target insulator and acquiring images of water droplets include: After the water jet is completed, the real-time environmental parameters of the detection area are acquired, including wind speed, temperature and humidity. The water droplet stabilization waiting time is dynamically adjusted based on real-time environmental parameters. After the dynamically adjusted water droplet stabilization waiting time has ended, the control acquisition device will acquire images.

[0012] Specifically, the acquisition device includes a low-resolution acquisition device and a high-resolution acquisition device; After the dynamically adjusted water droplet stabilization waiting time has ended, the steps for controlling the acquisition device to acquire images include: After the dynamically adjusted water droplet stabilization waiting time has ended, the low-resolution acquisition device is controlled to continuously capture a sequence of water droplet images on the surface of the target insulator. Real-time analysis of each frame in the water droplet image sequence is performed to extract the morphological features of the water droplets; When the rate of change of morphological characteristics is lower than the preset stability threshold within the preset monitoring time, it is determined that the water droplet has reached a stable state. After determining that the water droplets have reached a stable state, the high-resolution acquisition device is controlled to acquire high-resolution images.

[0013] Furthermore, the steps for obtaining the relative position information between the UAV and the target insulator include: Preload the precise three-dimensional coordinates of all insulators to be tested; It receives the latitude, longitude, and altitude information of the drone in real time, as well as the relative position correction data from the airborne visual positioning system; During the flight of the drone, the relative position information between the drone and the next target insulator is calculated based on the precise three-dimensional coordinates, the drone's latitude, longitude, altitude information, and relative position correction data.

[0014] Furthermore, based on relative position information, the steps to identify whether a drone is in a critical stage of insulator hydrophobicity testing include: The straight-line distance between the current position of the UAV and the position of the target insulator is determined based on relative position information; When the straight-line distance is less than the preset threshold and the detection task sequence is started, it is identified that the UAV is in the critical stage of insulator hydrophobicity detection.

[0015] To enhance functionality, the steps to switch the flight control system to fine hover mode include: In fine hovering mode, the flight control system loads preset aggressive PID parameters to increase the proportional, integral, and derivative gains of the attitude error and reduce the attitude deviation response threshold, updating motor commands at a preset frequency.

[0016] To improve the solution, the steps to switch the flight control system to the default flight mode include: In the default flight mode, the attitude deviation response threshold is increased to reduce motor correction actions.

[0017] Secondly, this application also discloses a drone-based system for detecting the hydrophobicity of power transmission line insulators, the system comprising: The location information acquisition module is used to acquire the relative position information between the UAV and the target insulator; The critical stage identification module is used to identify whether the drone is in the critical stage of insulator hydrophobicity detection based on relative position information; The mode switching module is used to switch the flight control system to fine hovering mode when the UAV is identified as being in a critical stage of insulator hydrophobicity detection; it is also used to switch the flight control system to the default flight mode after the insulator hydrophobicity detection task is completed. The task execution module is used to control the UAV to perform the hydrophobicity detection task of insulators in fine hovering mode. The hydrophobicity detection task of insulators includes spraying water onto the surface of the insulator and acquiring images of water droplets.

[0018] This application provides a system that integrates location information acquisition, key stage identification, mode switching and task execution functions, enabling intelligent and high-precision control of UAVs in the process of detecting the hydrophobicity of power transmission line insulators. This effectively solves the problem of low detection accuracy of UAVs in complex airflow environments in the prior art.

[0019] In summary, this application provides a method and system for detecting the hydrophobicity of transmission line insulators based on unmanned aerial vehicles (UAVs). The method acquires the relative position information between the UAV and the target insulator and identifies whether the UAV is in a critical stage of the hydrophobicity detection process based on this information. When the UAV is in the critical detection stage, the flight control system automatically switches to a fine hovering mode. In this mode, the UAV can perform the hydrophobicity detection task with higher accuracy and stability, including spraying water onto the insulator surface and acquiring images of water droplets. After the detection task is completed, the flight control system switches back to the default flight mode. This method effectively solves the problem of irregular fuselage swaying caused by the sluggish response of the flight control system in complex airflow environments, a problem found in existing technologies. By enabling a fine hovering mode at critical stages, this application can significantly improve the stability of the UAV during water spraying and photography, ensure the accuracy of the water jet trajectory, avoid uneven water film distribution, and guarantee the clarity of water droplet images and the integrity of edge information. This greatly improves the accuracy of basic data such as water droplet diameter measurement and contact angle calculation, ultimately reducing the misjudgment rate of hydrophobicity level and improving the reliability of detection results. This provides a strong guarantee for power companies to accurately assess the operating status of insulators and reduce the risk of transmission line discharge flashover accidents. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a method for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs) according to an embodiment of this application.

[0021] Figure 2 This is a schematic diagram of a hydrophobicity detection system for power transmission line insulators based on an embodiment of this application.

[0022] Labeling Explanation: 210, Location Information Acquisition Module; 220, Key Stage Identification Module; 230, Mode Switching Module; 240, Task Execution Module. Detailed Implementation

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] When performing hydrophobicity testing of power transmission line insulators, especially in complex local airflow environments, existing drones' flight control systems may experience increased response thresholds to subtle fuselage swaying signals due to software updates. This optimization aims to reduce unnecessary motor corrections to extend flight time; however, under the influence of persistent and directional eddies or gusts, the drone's response to minute and continuous fuselage swaying becomes sluggish, resulting in slight and irregular wobbling at critical moments of water spraying and photography. This wobbling exceeds the compensation capabilities of the onboard high-definition camera stabilization system, directly affecting image quality, blurring water droplet edges, leading to inaccurate subsequent data, and ultimately significantly increasing the misjudgment rate of hydrophobicity levels.

[0026] Firstly, please see Figure 1 This application proposes a method for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs), comprising: S1. Obtain the relative position information between the UAV and the target insulator; S2. Based on the relative position information, identify whether the drone is in the critical stage of insulator hydrophobicity detection; S3. When it is identified that the UAV is in the critical stage of insulator hydrophobicity detection, switch the flight control system to fine hover mode, and control the UAV to perform insulator hydrophobicity detection task in fine hover mode. The insulator hydrophobicity detection task includes spraying water onto the surface of the insulator and collecting water droplet images. S4. After the insulator hydrophobicity detection task is completed, switch the flight control system to the default flight mode.

[0027] In practice, the relative position information between the drone and the target insulator can be obtained in several ways. For example, precise three-dimensional coordinate data of all insulators to be detected can be pre-loaded into the drone system. During the drone's flight, its own latitude, longitude, and altitude information, as well as relative position correction data from the onboard visual positioning system, are received in real time. Subsequently, based on these precise three-dimensional coordinates, the drone's latitude, longitude, altitude information, and relative position correction data, the relative position information between the drone and the next target insulator is calculated. Another approach is that the drone can be equipped with sensors such as lidar or millimeter-wave radar to actively detect the target insulator and obtain its relative distance and angle information in real time. Furthermore, image recognition technology can be used, utilizing the drone's onboard camera to identify the target insulator and estimating its relative position using visual algorithms.

[0028] After obtaining the relative position information, it is necessary to identify whether the drone is in the critical stage of insulator hydrophobicity detection. One approach is to determine the straight-line distance between the drone's current position and the target insulator position based on the relative position information. When this straight-line distance is less than a preset threshold (e.g., 5 meters) and the detection task sequence has been initiated, the drone is identified as being in the critical stage of insulator hydrophobicity detection. For example, a safe distance can be set; when the drone enters this distance range and prepares to perform water spraying and image acquisition tasks, it is considered to have entered the critical stage. Another approach is to use the drone's internal task management system to determine whether it has entered the critical stage based on preset detection procedures and task nodes. For example, when the task management system issues a "prepare to spray water" or "prepare to take a picture" command, it is considered to have entered the critical stage.

[0029] When the drone is identified as being in a critical phase of insulator hydrophobicity detection, the flight control system switches to a fine hovering mode. In fine hovering mode, the flight control system can load preset aggressive PID (proportional-integral-derivative) parameters to increase the proportional, integral, and derivative gains of the attitude error and reduce the attitude deviation response threshold, while updating motor commands at a preset higher frequency. For example, the proportional gain (Kp) and integral gain (Ki) in the PID parameters can be appropriately increased, making the drone's response to attitude deviations faster and more accurate. Simultaneously, the attitude deviation response threshold is reduced from the default 0.5 degrees to 0.1 degrees, meaning that even minute attitude changes will immediately trigger motor correction actions. The motor command update frequency can be increased from the default 100Hz to 200Hz for finer control. Alternatively, fine hovering mode can enable additional sensor fusion algorithms, such as combining data from the inertial measurement unit (IMU) and visual odometry (VO), to provide more accurate attitude estimation, thereby achieving more stable hovering.

[0030] In fine-hover mode, the drone will perform a hydrophobicity testing task for insulators, which involves spraying water onto the insulator surface and capturing images of the water droplets. For example, the drone can be equipped with a high-pressure water jet device, which sprays a measured amount of water onto the target insulator surface by controlling the opening and closing of the nozzles. Simultaneously, the drone's onboard high-definition camera system will activate immediately after water spraying to capture images of the water droplets on the insulator surface. The water jet can be applied using a single pulse or a continuous short-duration spray to ensure uniform water film coverage. Image acquisition can be set to continuously capture multiple images or to capture a single high-resolution image after the water droplets have stabilized.

[0031] After completing the insulator hydrophobicity detection task, the flight control system switches back to the default flight mode. In the default flight mode, the attitude deviation response threshold can be increased to reduce unnecessary motor corrections. For example, restoring the attitude deviation response threshold to the default 0.5 degrees makes the drone less sensitive to minor disturbances during normal flight, thus reducing energy consumption from frequent motor adjustments. Alternatively, the default flight mode can reload a more conservative set of PID parameters to optimize flight efficiency and endurance, rather than pursuing extreme hovering accuracy.

[0032] The proposed method for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs) effectively solves the problem of inaccurate detection results caused by insufficient flight stability of UAVs in complex airflow environments in existing technologies by dynamically switching flight control modes during critical detection stages. Specifically, after acquiring the relative position information between the UAV and the target insulator, the system can intelligently identify whether the UAV has entered the critical stage of insulator hydrophobicity detection. Once the critical stage is entered, the flight control system immediately switches to a fine hovering mode. In this mode, by loading aggressive PID parameters, reducing the attitude deviation response threshold, and increasing the motor command update frequency, the UAV can achieve high-precision stable hovering, effectively resisting external airflow disturbances. This high stability ensures that the water jet sprayed onto the insulator surface maintains an ideal trajectory, forming a uniform water film, while ensuring that the high-definition camera system can capture clear and accurate water droplet images without shaking. After the detection task is completed, the system will promptly switch back to the default flight mode to balance flight efficiency and endurance. The entire process forms a closed-loop control system, enabling the UAV to ensure detection accuracy and optimize overall flight performance when performing hydrophobicity detection tasks.

[0033] Compared to existing technologies, the core innovation of this application lies in the introduction of an intelligent switching mechanism between a "fine hovering mode" and a "default flight mode." In traditional methods, the UAV flight control system may raise the response threshold to subtle fuselage sway signals to extend endurance, resulting in a sluggish response to persistent eddies or gusts during critical detection moments, causing slight irregular oscillations. This oscillation directly affects the accuracy of water jetting and the clarity of image acquisition, blurring water droplet edges and ultimately increasing the misjudgment rate of hydrophobicity levels. This application significantly improves the flight stability of the UAV in complex airflow environments by switching the flight control system to fine hovering mode when the UAV is identified as being in a critical stage of insulator hydrophobicity detection, and performing the detection task in this mode. In fine hovering mode, by loading aggressive PID parameters, reducing the attitude deviation response threshold, and increasing the motor command update frequency, the UAV can respond quickly and accurately to minor disturbances, thereby ensuring the stability of water jetting and the clarity of image acquisition. This dynamic mode switching mechanism enables the drone to maintain efficient flight to save power during non-critical phases, while providing extreme stability during critical detection phases. This effectively solves the contradiction between detection accuracy and flight efficiency in existing technologies, and greatly improves the reliability and accuracy of hydrophobicity detection results.

[0034] In some embodiments described above, this application proposes a method for spraying water onto the surface of an insulator and acquiring images of water droplets for hydrophobicity testing. However, in actual operation, when a drone performs water spraying in an outdoor environment, external airflow (such as ambient wind or downwash generated by the drone's own rotor) may interfere with the water flow trajectory, causing the water to fail to accurately spray onto the target insulator surface. This affects the quality of water droplet formation and the accuracy of image acquisition, reducing the reliability of hydrophobicity testing. If these problems are not addressed, it may lead to deviations in the test results, or even require repeated testing, thereby increasing testing costs and time. Therefore, this application further proposes an optimized method for insulator hydrophobicity testing to improve the accuracy of water spraying and the reliability of the test results.

[0035] Specifically, the aforementioned insulator hydrophobicity testing tasks also include: Before spraying water onto the surface of the target insulator, activate the air curtain generating device; The air curtain generating device controls the formation of an annular airflow area around the nozzles used for spraying water to isolate external airflow from interfering with the water flow trajectory. The steps of spraying water onto the surface of the target insulator and acquiring images of water droplets are performed after the annular airflow region has been stabilized. After the water droplet image is acquired, turn off the air curtain generator.

[0036] The air curtain generating device can be understood as a device that generates directional airflow to form a physical barrier, aiming to create a relatively stable microenvironment around the water nozzle. This device is typically integrated into the water spray mechanism of a drone, positioned close to the nozzle. The nozzle is the outlet for spraying water onto the surface of the target insulator, and its design should ensure that the water flow is ejected at a predetermined speed and direction. The annular airflow region refers to the airflow barrier with a specific shape and velocity, generated by the air curtain generating device and surrounding the nozzle. This annular airflow region acts as an "air wall," effectively blocking or weakening the influence of external environmental winds and the downwash airflow from the drone's own rotor on the trajectory of the sprayed water. Isolating the water flow trajectory from external airflow specifically means that by forming an annular airflow region, the flight path of the sprayed water remains stable before reaching the insulator surface, unaffected by uncertain external airflow, thus ensuring that the water accurately hits the target area and forms water droplets on the insulator surface in the desired shape.

[0037] The solution proposed in this application establishes a locally stable airflow environment along the water jet path by activating an air curtain generation device before the water jet is sprayed, thus forming an annular airflow region around the nozzle. This annular airflow region effectively isolates external airflow interference with the water jet trajectory, ensuring that the water jet is accurately sprayed onto the target insulator surface according to a preset path and speed. The steps of spraying the water jet and acquiring water droplet images are performed only after the annular airflow region is stably established, guaranteeing the accuracy of the water jet spray and the stability of water droplet formation. Turning off the air curtain generation device after the water droplet images are acquired saves energy and reduces additional impact on the drone's flight.

[0038] In some embodiments described above, an air curtain generating device is proposed to form an annular airflow region around the water nozzle to isolate external airflow interference with the water flow trajectory. However, in practical applications, changes in external airflow, especially wind speed, can cause instability in the formed annular airflow region, affecting the accuracy of water jetting and water droplet image acquisition. If the stability issue of the annular airflow region is not resolved, the reliability of the insulator hydrophobicity detection task will be difficult to ensure. Therefore, this application further proposes a specific implementation of the air curtain generating device and its control method to effectively maintain the stability of the annular airflow.

[0039] Air curtain generating devices include miniature high-speed fans or air pumps integrated around the spray nozzles; The steps of controlling the air curtain generating device to form an annular airflow region around the nozzle used for spraying water to isolate external airflow from interference with the water flow trajectory include: Start a miniature high-speed fan or air pump to generate a ring-shaped airflow that blows toward the target insulator; The system obtains ambient wind speed information from an ambient wind speed sensor, and adjusts the speed of a miniature high-speed fan or air pump based on the relative position information and ambient wind speed information to maintain the stability of the annular airflow.

[0040] Specifically, the aforementioned air curtain generating device can be understood as a device capable of generating directional airflow, designed to be integrated around a water nozzle used for spraying water. For example, the device can consist of multiple miniature high-speed fans or miniature air pumps arranged in a ring array outside the water nozzle, with the aim of forming a stable annular airflow area around the nozzle. The activation of the miniature high-speed fans or air pumps involves supplying power to them via a control system, causing them to start operating and generate airflow. The generated airflow is directed towards the target insulator, thereby forming an air barrier between the water nozzle and the target insulator. To ensure the stability of the annular airflow, the solution further includes acquiring ambient wind speed information from an environmental wind speed sensor. The environmental wind speed sensor can be installed on a drone to monitor the airflow around the drone in real time. Based on the acquired ambient wind speed information and the relative position information of the drone and the target insulator, the control system can dynamically adjust the rotation speed of the miniature high-speed fans or air pumps.

[0041] The proposed solution concretizes the air curtain generating device as a miniature high-speed fan or air pump integrated around the water nozzle, and incorporates an environmental wind speed sensor to acquire real-time environmental wind speed information. This allows for dynamic adjustment of the fan or air pump speed based on this real-time data. It is precisely this dynamic adjustment mechanism that enables the generated annular airflow to adapt to constantly changing environmental wind speeds, effectively counteracting the interference of external airflow on the water flow trajectory. By precisely controlling the intensity and stability of the annular airflow, the accuracy of the water jet is ensured, preventing the water flow from deviating from the target insulator surface due to airflow instability, thereby guaranteeing the reliability of subsequent water droplet image acquisition.

[0042] In some embodiments described above, an air curtain generating device is used to create an annular airflow region around the nozzle before spraying water onto the target insulator surface. This isolates the water flow trajectory from external airflow interference, ensuring accurate water spraying onto the insulator surface. However, even with effective water flow trajectory control, the shape and position of water droplets after contact with the insulator surface and formation can still be affected by real-time environmental parameters (e.g., wind speed, temperature, and humidity) in the detection area. These environmental factors may cause the water droplets to become unstable for a short period, such as swaying, deforming, or moving, thus affecting the accuracy of image acquisition. If image acquisition is performed before the water droplets have fully stabilized, the acquired image may not accurately reflect the hydrophobicity of the insulator, thereby affecting the reliability of the detection results.

[0043] To address this, this application further proposes an optimization of the steps described above for spraying water onto the surface of the target insulator and acquiring images of the water droplets, ensuring that image acquisition is performed only after the water droplets have reached a stable state. This optimization includes: After the water jet is completed, the real-time environmental parameters of the detection area are acquired, including wind speed, temperature and humidity. The water droplet stabilization waiting time is dynamically adjusted based on real-time environmental parameters. After the dynamically adjusted water droplet stabilization waiting time has ended, the control acquisition device will acquire images.

[0044] Specifically, real-time environmental parameters refer to a quantitative description of the current environmental state of the detection area after the water jet has been sprayed. These parameters mainly include wind speed, temperature, and humidity. These parameters can be acquired in real time using environmental sensors integrated on the drone or ground sensors deployed near the detection area. The purpose of acquiring these parameters is to comprehensively understand the external environmental conditions after water droplets form on the insulator surface, as these conditions directly affect the stabilization process of the water droplets.

[0045] The dynamic adjustment of water droplet stabilization waiting time refers to intelligently calculating and setting an appropriate time interval based on real-time acquired environmental parameters to ensure that water droplets have reached a relatively stable form before image acquisition. For example, in environments with high wind speeds, low temperatures, or high humidity, water droplets may require a longer time to stabilize, and the waiting time will be extended accordingly; conversely, in more stable environmental conditions, the waiting time can be appropriately shortened. This dynamic adjustment mechanism can be implemented based on a pre-established physical model, empirical formula, or machine learning algorithm, which takes environmental parameters as input and outputs the optimal water droplet stabilization waiting time.

[0046] This application's solution effectively solves the problem of inaccurate water droplet image acquisition caused by environmental changes in traditional methods by acquiring real-time environmental parameters of the detection area after water jetting and dynamically adjusting the water droplet stabilization waiting time based on these parameters. Specifically, after water jetting onto the insulator surface forms water droplets, the shape and position of the droplets are affected by ambient wind speed, temperature, and humidity. By acquiring real-time environmental parameters, the system can accurately assess the degree of influence of the current environment on water droplet stability. Furthermore, by dynamically adjusting the water droplet stabilization waiting time based on these real-time parameters, the system ensures that image acquisition occurs after the water droplet shape and position have reached a relatively stable state. This avoids the impact of water droplet instability caused by environmental disturbances on image acquisition quality, thereby ensuring the accuracy and reliability of subsequent hydrophobicity assessment.

[0047] In some embodiments described above, a scheme is proposed to acquire real-time environmental parameters of the detection area after water jetting, and to dynamically adjust the water droplet stabilization waiting time based on these parameters, aiming to acquire images after the water droplets have stabilized. However, the actual stabilization process of water droplets may be affected by various complex factors. Relying solely on a preset or dynamically adjusted waiting time may not accurately determine whether the water droplets have reached their optimal stabilization state, potentially leading to image acquisition too early or too late, thus affecting the quality of the acquired images and the accuracy of subsequent hydrophobicity assessments. Therefore, this application further proposes an optimized acquisition device and its image acquisition control method to ensure high-resolution image acquisition when the water droplets have reached a stable state.

[0048] In this regard, this application further proposes that the steps for the above-mentioned control and acquisition device to perform image acquisition include: After the dynamically adjusted water droplet stabilization waiting time has ended, the low-resolution acquisition device is controlled to continuously capture a sequence of water droplet images on the surface of the target insulator. Real-time analysis of each frame in the water droplet image sequence is performed to extract the morphological features of the water droplets; When the rate of change of morphological characteristics is lower than the preset stability threshold within the preset monitoring time, it is determined that the water droplet has reached a stable state. After determining that the water droplets have reached a stable state, the high-resolution acquisition device is controlled to acquire high-resolution images.

[0049] The acquisition devices include low-resolution and high-resolution acquisition devices. Specifically, a low-resolution acquisition device can be understood as an image sensor with a high frame rate but relatively low resolution. Its purpose is to quickly and continuously capture the dynamic changes of water droplets during their formation and stabilization on the insulator surface; for example, a CMOS sensor or a CCD sensor can be used. A high-resolution acquisition device, on the other hand, can be understood as an image sensor with higher resolution but less stringent frame rate requirements. Its purpose is to acquire clear, detailed images of the water droplets after they have stabilized for accurate morphological analysis; for example, a high-pixel industrial camera can be used.

[0050] Furthermore, controlling the low-resolution acquisition device to continuously capture the image sequence of water droplets on the surface of the target insulator means that after the water droplets have stabilized and waited for a certain period of time, the low-resolution acquisition device is activated and continuously captures images of the water droplets on the surface of the insulator at a certain frame rate, forming a time-series image data stream.

[0051] Based on this, each frame of the water droplet image sequence is analyzed in real time to extract the morphological features of the water droplets. These morphological features may include, but are not limited to, geometric parameters such as the diameter, height, contact angle, spreading area, and perimeter of the water droplets, as well as topological features such as the smoothness and symmetry of the water droplet's outline. The extraction of these features can be achieved through image processing algorithms (such as edge detection, region segmentation, and shape fitting).

[0052] When the rate of change of morphological features falls below a preset stability threshold within a preset monitoring time, the water droplet is considered to have reached a stable state. Specifically, the rate of change refers to the ratio of the numerical change in extracted morphological features (such as droplet diameter or contact angle) to the time interval between consecutive image frames. The preset monitoring time is the time window used to evaluate the rate of change, such as the time period covered by several consecutive image frames. The preset stability threshold is an empirical value or a value determined experimentally; when the rate of change of morphological features falls below this threshold, it indicates that the water droplet's morphology has stabilized and no longer undergoes significant changes.

[0053] Therefore, after determining that the water droplets have reached a stable state, the high-resolution acquisition device is controlled to acquire high-resolution images. This means that the high-resolution acquisition device is only triggered to take pictures after the water droplet morphology is confirmed to be stable, thereby ensuring that the acquired images can accurately reflect the hydrophobic state of the insulator surface.

[0054] This application's solution addresses the problem that relying solely on time-based waiting may not accurately determine the stability of water droplets by introducing a collaborative mechanism between low-resolution and high-resolution acquisition devices, combined with real-time monitoring and analysis of water droplet morphology characteristics. Specifically, after the dynamically adjusted water droplet stabilization waiting time, the low-resolution acquisition device continuously captures dynamic image sequences of the water droplets. Its high frame rate ensures that even minute changes in water droplet morphology are recorded promptly. Subsequently, by analyzing these image sequences in real-time, the morphological features and rate of change of the water droplets are extracted, allowing for an objective and accurate determination of whether the water droplets have truly reached a stable state. Once the water droplet morphology is confirmed to be stable—that is, its rate of change is below a preset stabilization threshold—the high-resolution acquisition device is triggered to acquire images. This real-time feedback-based control strategy avoids the problem of acquiring images too early or too late, which can occur in traditional methods, ensuring the acquisition of high-quality images when the water droplets are in their optimal stable state.

[0055] Specifically, the steps for obtaining the relative position information between the UAV and the target insulator include: Preload the precise three-dimensional coordinates of all insulators to be tested; It receives the latitude, longitude, and altitude information of the drone in real time, as well as the relative position correction data from the airborne visual positioning system; During the flight of the drone, the relative position information between the drone and the next target insulator is calculated based on the precise three-dimensional coordinates, the drone's latitude, longitude, altitude information, and relative position correction data.

[0056] The pre-loading of precise three-dimensional coordinates for all insulators to be tested refers to storing the geographical location information, such as longitude, latitude, and altitude, of all insulators in the transmission line requiring hydrophobicity testing in a high-precision manner in the UAV's mission planning system or ground control system before the UAV performs its testing mission. These coordinates can originate from preliminary surveys, Geographic Information System (GIS) data, or high-precision mapping. The purpose is to provide a basic reference for the UAV's autonomous navigation and target recognition.

[0057] Furthermore, real-time reception of the UAV's latitude, longitude, and altitude information, as well as relative position correction data from the onboard visual positioning system, refers to the UAV acquiring its real-time latitude and longitude information via its onboard Global Positioning System (GPS) module and real-time altitude information via sensors such as barometers or lidar during flight. Simultaneously, the onboard visual positioning system, such as systems based on visual odometry or Simultaneous Localization and Mapping (SLAM) algorithms, continuously analyzes the image data captured by the UAV to provide accurate relative position correction data relative to the local environment. This correction data can compensate for potential drift or errors in the GPS signal, improving the UAV's positioning accuracy within a local space.

[0058] Specifically, during UAV flight, the calculation of the relative position between the UAV and the next target insulator is based on precise three-dimensional coordinates, the UAV's latitude, longitude, altitude information, and relative position correction data. This involves the flight control system comparing the pre-loaded precise three-dimensional coordinates of the target insulator with the UAV's real-time latitude, longitude, and altitude information. Based on this, and combined with relative position correction data provided by the onboard visual positioning system, parameters such as the straight-line distance, azimuth angle, and relative altitude between the UAV and the target insulator are precisely calculated. This allows for the acquisition of the UAV's precise three-dimensional relative position with respect to the next insulator to be inspected, providing accurate positioning data for subsequent flight path planning and inspection task execution.

[0059] This application's solution achieves precise acquisition of the relative position information between the UAV and the target insulator by combining multiple positioning data sources. Pre-loaded precise three-dimensional coordinates of the insulator provide a global, high-precision target position reference for the mission. Real-time latitude, longitude, and altitude information received by the UAV provides its own global position awareness. However, GPS positioning alone may suffer from insufficient accuracy or signal obstruction, especially in complex environments such as power transmission lines. Therefore, by introducing relative position correction data from an airborne visual positioning system, the shortcomings of global positioning can be effectively compensated for, providing high-precision relative positioning information in local environments. This correction data can correct positioning deviations caused by GPS errors or environmental interference, ensuring that the UAV achieves centimeter-level positioning accuracy when approaching the target insulator. Finally, by comprehensively utilizing these multi-source data for fusion calculations, highly accurate relative position information between the UAV and the target insulator can be generated, providing reliable positioning assurance for subsequent precise hovering and inspection tasks.

[0060] In some embodiments of this application, the step of identifying whether a drone is in a critical stage of insulator hydrophobicity testing based on relative position information can be further refined. Specifically, the step of identifying whether a drone is in a critical stage of insulator hydrophobicity testing based on relative position information includes: The straight-line distance between the current position of the UAV and the position of the target insulator is determined based on relative position information; When the straight-line distance is less than the preset threshold and the detection task sequence is started, it is identified that the UAV is in the critical stage of insulator hydrophobicity detection.

[0061] The relative position information refers to the position data of the UAV relative to the target insulator in three-dimensional space, which can be provided by the UAV's navigation system, visual positioning system, or a combination of both. The target insulator position refers to the precise spatial coordinates of the insulator to be inspected, either pre-set or acquired in real time. The straight-line distance can be understood as the Euclidean distance between the UAV's current position and the target insulator's position. The preset threshold is a distance value set according to actual inspection needs and the UAV's flight accuracy; for example, it can be set to several meters to tens of meters. Its purpose is to determine whether the UAV has entered the close-range inspection range requiring precise operation. The start of the inspection task sequence refers to the start signal of the entire insulator hydrophobicity inspection process, its purpose being to ensure that the identification of key stages only occurs when the inspection task is officially activated.

[0062] This application's solution uses the straight-line distance between the UAV's current position and the target insulator's position as the criterion, combined with the start status of the detection task sequence, to accurately determine when the UAV enters the critical stage of insulator hydrophobicity detection. Specifically, when the UAV is close enough to the target insulator and the detection task is ready, the system considers the UAV to be in a critical state where it needs to switch to fine hovering mode and execute the detection task. This distance- and task-state-based judgment mechanism provides clear and reliable triggering conditions for subsequent flight mode switching and task execution.

[0063] In some of the embodiments described above in this application, it is proposed to switch the flight control system to a fine hovering mode to perform the hydrophobicity detection task of insulators. However, in actual operation, if only the mode is switched without optimizing the flight control parameters, the attitude stability, anti-interference ability and response speed of the UAV in fine hovering mode may not fully meet the requirements of high-precision water jet and image acquisition, especially in scenarios with light wind disturbance or requiring rapid attitude adjustment, which may lead to a decrease in detection accuracy or low task execution efficiency.

[0064] In response, this application further proposes a step for switching the flight control system to a fine hovering mode, which includes: in the fine hovering mode, controlling the flight control system to load preset aggressive PID parameters, increasing the proportional, integral and derivative gains of the attitude error, and reducing the attitude deviation response threshold, and updating motor commands at a preset frequency.

[0065] Specifically, flight control systems typically employ PID (Proportional-Integral-Derivative) controllers to maintain the attitude stability of the UAV. The proportional term (P) reflects the current error, the integral term (I) eliminates steady-state error, and the derivative term (D) suppresses the trend of error change. Loading preset aggressive PID parameters means adjusting these parameters to relatively high values ​​to enhance the controller's response speed and correction strength to attitude errors. Increasing the proportional, integral, and derivative gains of the attitude error means that when the UAV's attitude deviates, the controller will output correction commands faster and more forcefully, enabling the UAV to quickly return to the target attitude. Lowering the attitude deviation response threshold means that even small attitude deviations will trigger corrective actions from the controller, further improving the precision of control. Updating motor commands at a preset frequency ensures that the control system can adjust the motor output in real time and at high frequency, thereby achieving precise and rapid control of the UAV's attitude.

[0066] This application's solution, by loading aggressive PID parameters in a refined hovering mode and lowering the attitude deviation response threshold, enables the flight control system to have higher sensitivity and faster response speed to minute changes in UAV attitude. Specifically, increasing the proportional gain allows the system to react quickly to current attitude errors, reducing instantaneous deviations; increasing the integral gain helps eliminate small attitude errors that persist for extended periods, ensuring the UAV can hover stably at the target position; increasing the derivative gain allows for prediction of attitude change trends and early correction, effectively suppressing the impact of external disturbances (such as light winds) on the UAV's attitude. Simultaneously, lowering the attitude deviation response threshold ensures that even extremely small attitude deviations can be detected and corrected promptly, avoiding cumulative errors. Updating motor commands at a preset frequency ensures that these highly sensitive correction commands are transmitted to the UAV motors in a timely and accurate manner, thereby achieving high-precision attitude maintenance and position stability, providing a stable platform for water jetting and image acquisition in insulator hydrophobicity testing tasks.

[0067] In some embodiments described above, the flight control system switches to the default flight mode after the insulator hydrophobicity testing task is completed. However, if the control parameters of the default flight mode are set too sensitively, the UAV may perform unnecessary attitude correction maneuvers after the task switch, thereby affecting flight stability or increasing energy consumption. Therefore, this application further proposes a scheme to optimize the switching method of the flight control system in the default flight mode, in order to improve the flight efficiency and stability of the UAV after the task is completed.

[0068] The steps to switch the flight control system to the default flight mode include: increasing the attitude deviation response threshold in the default flight mode to reduce motor correction actions.

[0069] Specifically, the default flight mode refers to the conventional flight control mode used by the drone during non-precise hovering or specific mission execution, with the primary goal of ensuring flight stability and efficiency. The attitude deviation response threshold can be understood as the minimum deviation by which the flight control system triggers motor correction actions when it detects a deviation in the drone's attitude from a preset value. When the attitude deviation is less than this threshold, the system will not immediately correct it. Motor correction actions refer to the flight control system's actions of adjusting the speed of each motor in the drone to correct its attitude based on the attitude deviation. Increasing the attitude deviation response threshold means that the flight control system will no longer immediately respond to and correct small fluctuations in the drone's attitude; motor correction will only be triggered when the attitude deviation reaches a greater level. The purpose is to reduce unnecessary and frequent motor corrections, thereby reducing energy consumption and extending motor life.

[0070] The proposed solution increases the attitude deviation response threshold in the default flight mode, thereby enhancing the flight control system's tolerance for minute fluctuations in the UAV's attitude. After completing the insulator hydrophobicity testing task, when the UAV switches back to the default flight mode from the fine hovering mode, extremely high attitude accuracy is typically no longer required. At this point, if the lower attitude deviation response threshold of the fine hovering mode is used, even slight airflow disturbances or sensor noise could cause the flight control system to frequently issue motor correction commands, resulting in unnecessary frequent motor starts and stops or speed adjustments. This not only increases motor wear but also consumes additional energy. By increasing the attitude deviation response threshold, these minute attitude deviations that do not affect overall flight stability can be effectively filtered out. Motor correction actions are only triggered when larger attitude deviations requiring actual correction occur, thus avoiding over-correction, ensuring flight stability, and reducing energy consumption.

[0071] When performing hydrophobicity testing of power transmission line insulators, especially in complex local airflow environments, existing drones' flight control systems may experience increased response thresholds to subtle fuselage swaying signals due to software updates. This optimization aims to reduce unnecessary motor corrections to extend flight time; however, under the influence of persistent and directional eddies or gusts, the drone's response to minute and continuous fuselage swaying becomes sluggish, resulting in slight and irregular wobbling at critical moments of water spraying and photography. This wobbling exceeds the compensation capabilities of the onboard high-definition camera stabilization system, directly affecting image quality, blurring water droplet edges, leading to inaccurate subsequent data, and ultimately significantly increasing the misjudgment rate of hydrophobicity levels.

[0072] Secondly, see Figure 2 This application proposes a UAV-based system for detecting the hydrophobicity of power transmission line insulators. The system includes: The location information acquisition module 210 is used to acquire the relative position information between the UAV and the target insulator; The critical stage identification module 220 is used to identify whether the UAV is in the critical stage of insulator hydrophobicity detection based on relative position information; The mode switching module 230 is used to switch the flight control system to fine hovering mode when the UAV is identified as being in a critical stage of insulator hydrophobicity detection; it is also used to switch the flight control system to the default flight mode after the insulator hydrophobicity detection task is completed. The task execution module 240 is used to control the UAV to perform the hydrophobicity detection task of insulators in fine hovering mode. The hydrophobicity detection task of insulators includes spraying water onto the surface of the insulator and acquiring images of water droplets.

[0073] Compared with existing technologies, the core innovation of the UAV-based hydrophobicity testing system for transmission line insulators proposed in this application lies in the introduction of an intelligent modular collaborative working mechanism to dynamically switch flight control modes. Traditional UAV systems, in complex airflow environments, may experience irregular swaying at critical detection moments due to the flight control system's sluggish response to subtle fuselage movement signals. This affects the accuracy of water jetting and the clarity of image acquisition, ultimately leading to inaccurate detection results. The system in this application, through the close cooperation of the position information acquisition module 210, the critical stage identification module 220, the mode switching module 230, and the task execution module 240, automatically switches the flight control system to a fine hovering mode when the UAV enters the critical stage of insulator hydrophobicity testing. In this mode, the UAV can respond quickly and accurately to minor disturbances, thus ensuring the stability of water jetting and the clarity of image acquisition. This system-level dynamic mode switching mechanism enables the drone to maintain efficient flight to save power during non-critical phases, while providing extreme stability during critical detection phases. It effectively solves the contradiction between detection accuracy and flight efficiency in existing technologies, and greatly improves the reliability and accuracy of hydrophobicity detection results.

[0074] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs), characterized in that, include: Obtain the relative position information between the drone and the target insulator; Based on the relative position information, identify whether the drone is in the critical stage of insulator hydrophobicity detection; When the drone is identified as being in a critical stage of the insulator hydrophobicity detection, the flight control system is switched to fine hovering mode, and the drone is controlled to perform the insulator hydrophobicity detection task in fine hovering mode. The insulator hydrophobicity detection task includes spraying water onto the surface of the insulator and acquiring water droplet images. After the insulator hydrophobicity detection task is completed, the flight control system will switch to the default flight mode.

2. The method for detecting the hydrophobicity of transmission line insulators based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The insulator hydrophobicity testing task also includes: Before spraying water onto the surface of the target insulator, the air curtain generating device is activated; The air curtain generating device is controlled to form an annular airflow area around the nozzle used for spraying water flow, so as to isolate the interference of external airflow on the water flow trajectory. The step of spraying water onto the surface of the target insulator and acquiring images of water droplets is performed after the annular airflow region has been stabilized. After the water droplet image is acquired, the air curtain generating device is turned off.

3. The method for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, The air curtain generating device includes a miniature high-speed fan or air pump integrated around the water nozzle; The step of controlling the air curtain generating device to form an annular airflow region around the nozzle used for spraying water to isolate external airflow from interference with the water flow trajectory includes: The miniature high-speed fan or air pump is activated to generate an annular airflow that blows toward the target insulator; The ambient wind speed information is obtained from the ambient wind speed sensor. Based on the relative position information and the ambient wind speed information, the rotation speed of the miniature high-speed fan or air pump is adjusted to maintain the stability of the annular airflow.

4. The method for detecting the hydrophobicity of transmission line insulators based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, The step of spraying water onto the surface of the target insulator and acquiring images of water droplets includes: After the water jet is completed, the real-time environmental parameters of the detection area are acquired, including wind speed, temperature and humidity. The water droplet stabilization waiting time is dynamically adjusted based on the real-time environmental parameters. After the dynamically adjusted water droplet stabilization waiting time has ended, the control acquisition device performs image acquisition.

5. The method for detecting the hydrophobicity of transmission line insulators based on unmanned aerial vehicles (UAVs) according to claim 4, characterized in that, The acquisition device includes a low-resolution acquisition device and a high-resolution acquisition device; The step of controlling the acquisition device to acquire images after the dynamically adjusted water droplet stabilization waiting time has ended includes: After the dynamically adjusted water droplet stabilization waiting time has ended, the low-resolution acquisition device is controlled to continuously capture a sequence of water droplet images on the surface of the target insulator. The morphological features of the water droplets are extracted by real-time analysis of each frame in the water droplet image sequence. When the rate of change of the morphological features is lower than the preset stability threshold within a preset monitoring time, it is determined that the water droplet has reached a stable state. After determining that the water droplets have reached a stable state, the high-resolution acquisition device is controlled to acquire high-resolution images.

6. The method for detecting the hydrophobicity of transmission line insulators based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The step of obtaining the relative position information between the UAV and the target insulator includes: Preload the precise three-dimensional coordinates of all insulators to be tested; It receives the latitude, longitude, and altitude information of the drone in real time, as well as the relative position correction data from the airborne visual positioning system; During the flight of the UAV, the relative position information between the UAV and the next target insulator is calculated based on the precise three-dimensional coordinates, the latitude and longitude of the UAV, altitude information, and the relative position correction data.

7. The method for detecting the hydrophobicity of transmission line insulators based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The step of identifying whether the drone is in a critical stage of insulator hydrophobicity detection based on the relative position information includes: Based on the relative position information, determine the straight-line distance between the current position of the UAV and the position of the target insulator; When the straight-line distance is less than a preset threshold and the detection task sequence is started, it is identified that the UAV is in the critical stage of the insulator hydrophobicity detection.

8. The method for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The step of switching the flight control system to the fine hovering mode includes: In the fine hovering mode, the flight control system loads preset aggressive PID parameters to increase the proportional, integral, and derivative gains of the attitude error and reduce the attitude deviation response threshold, and updates motor commands at a preset frequency.

9. A method for detecting the hydrophobicity of transmission line insulators based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The step of switching the flight control system to the default flight mode includes: In the default flight mode, the attitude deviation response threshold is increased to reduce motor correction actions.

10. A system for detecting the hydrophobicity of power transmission line insulators based on unmanned aerial vehicles (UAVs), characterized in that, The system includes: The location information acquisition module is used to acquire the relative position information between the UAV and the target insulator; The critical stage identification module is used to identify whether the UAV is in a critical stage of insulator hydrophobicity detection based on the relative position information. The mode switching module is used to switch the flight control system to fine hovering mode when the UAV is identified as being in a critical stage of the insulator hydrophobicity detection; it is also used to switch the flight control system to the default flight mode after the insulator hydrophobicity detection task is completed. The task execution module is used to control the UAV to perform the hydrophobicity detection task of the insulator in the fine hovering mode. The hydrophobicity detection task of the insulator includes spraying water onto the surface of the insulator and acquiring images of water droplets.

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