Insulator zero value detection device and detection method

Abstract

The invention provides an insulator zero value detection device and method, and belongs to the technical field of power grid line insulation detection, and the insulator zero value detection device comprises a rack, a zero value detection assembly, a walking assembly and a control module. The unmanned aerial vehicle hoists or throws the detection robot through the rack. And the zero value detection assembly is arranged on the rack. The walking assembly is fixedly connected to the rack. The control module is in communication connection with the unmanned aerial vehicle; the control module comprises an image acquisition unit and a control unit, the control unit is in communication connection with the image acquisition unit and the unmanned aerial vehicle, and the image acquisition unit is used for acquiring image information of the insulator and sending the image information to the control unit, so that the control unit identifies the detection position and controls the unmanned aerial vehicle to put the detection robot. An automatic positioning and putting mechanism is constructed through cooperation of an image acquisition unit and a control unit in the control module. Manual operation errors are reduced, and the detection failure probability caused by putting deviation is reduced.

Description

Technical Field

[0001] This invention belongs to the technical field of power grid line insulation testing, and more specifically, relates to an insulator zero-value testing device and testing method. Background Technology

[0002] In the operation of high-voltage transmission lines, porcelain insulators are constantly affected by factors such as wind, rain, pollution, and electric fields, making them prone to zero-value defects. If these defects are not detected in time, they can lead to line faults. Currently, the mainstream detection solution in the industry is a collaborative model of "drone + detection robot": the drone is equipped with a hoisting mechanism to lift a robot with zero-value detection function. After flying to the vicinity of the target insulator string, the drone operator adjusts the drone's attitude and deploys the detection robot to the designated detection position on the insulator. The detection robot then starts the detection and transmits the data back.

[0003] The core of the existing technology relies on the human operation of the drone pilot to complete the robot deployment, which has significant drawbacks: First, the deployment accuracy depends entirely on the pilot's skill and visual judgment. When the insulator strings are dense, the tower structure is complex, or there are branches and leaves blocking the view, the pilot has difficulty in accurately judging the detection position, which can easily lead to robot deployment deviation and detection failure. Second, the detection efficiency is low. The pilot needs to repeatedly adjust the drone's attitude to calibrate the deployment position. The detection time for a single tower insulator usually exceeds 15 minutes, which is difficult to meet the efficiency requirements of large-scale transmission line inspection. Summary of the Invention

[0004] The purpose of this invention is to provide an insulator zero-value detection device and control method, which aims to solve the defect of the existing drone + detection robot collaborative mode that relies on manual operation by the drone operator.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, an insulator zero-value detection device is provided, comprising: Drones and inspection robots; the inspection robots include: The rack; the drone uses the rack to lift or deploy the inspection robot; The zero-value detection component is mounted on the rack; The walking assembly is fixedly connected to the frame; and A control module is communicatively connected to the UAV. The control module includes an image acquisition unit and a control unit. The control unit is communicatively connected to the image acquisition unit and the UAV. The image acquisition unit is used to acquire and send image information of the insulator to the control unit so that the control unit can identify the detection location and control the UAV to deploy the detection robot.

[0006] In one possible implementation, the rack includes: The lifting frame; the drone uses the lifting frame to lift or deploy the inspection robot; and An arc-shaped support, wherein the hanging frame is disposed on the top of the arc-shaped support; the walking assembly is disposed on the inner wall of the top of the arc-shaped support; and the zero-value detection assembly is disposed on the arc-shaped support.

[0007] In one possible implementation, two walking components are provided and are symmetrically arranged about the vertical central axis of the arc-shaped support.

[0008] In one possible implementation, the zero-value detection component includes: The zero-value detection mechanism is installed on the side wall of the arc-shaped support; A zero-value detection control mechanism is located at the bottom of the arc-shaped support; the height of the zero-value detection control mechanism is lower than that of the zero-value detection mechanism.

[0009] The beneficial effects of the insulator zero-value detection device provided by this invention are as follows: Compared with the prior art, the insulator zero-value detection device of this invention, through the cooperation of the image acquisition unit and the control unit in the control module, constructs an automated positioning and deployment mechanism. The image acquisition unit can acquire insulator image information in real time and transmit it to the control unit. The control unit can accurately identify the detection position by analyzing the image, and then directly control the drone to complete the deployment action. The entire process does not require manual visual judgment to adjust the drone's attitude, reducing human operation errors. Even in complex environments, it can ensure that the detection robot is accurately deployed to the target position, significantly improving deployment accuracy and reducing the probability of detection failure due to deployment deviation.

[0010] Furthermore, the automated operation mode of this device significantly improves detection efficiency. From image acquisition and location recognition to drone deployment, each step eliminates the need for repeated manual calibration and adjustment, shortening the detection time for single-tower insulators and enabling rapid identification of zero-value defects in insulators. This highly efficient detection capability can meet the needs of large-scale transmission line inspections, helping power grid maintenance personnel to more promptly identify and address insulator problems, ensuring the safe and stable operation of high-voltage transmission lines.

[0011] Secondly, a method for detecting zero value of an insulator is provided, applied to the insulator zero value detection device as described in the first aspect, comprising the following steps: Image information of insulators was obtained using drones; The detection location is identified based on the insulator image information, and the coordinates of the detection location are calculated so that the drone can deploy the detection robot to the detection location; Calculate the deployment position coordinates of the detection robot, and calculate the deviation between the deployment position coordinates and the detection position coordinates, so that the drone can adjust its own attitude according to the deviation; Deploy the detection robot and start it to perform zero-value detection.

[0012] In one possible implementation, insulator image information is acquired via a drone, including: Based on the GIS map of the power transmission line and the coordinates of the tower, the drone inspection route is planned so that the drone can fly to the vicinity of the target insulator in the shortest distance. Acquire visible light and infrared image information of the target insulator.

[0013] In one possible implementation, the detection location is identified based on the insulator image information, and the coordinates of the detection location are calculated so that the drone can deploy the detection robot to the detection location, including: Visible light image information and infrared image information are preprocessed, and the preprocessed images are fused to obtain fused image information; The detection location was identified based on the fused image information; Calculate the three-dimensional coordinates of the detection location in the UAV coordinate system.

[0014] In one possible implementation, the deployment position coordinates of the detection robot are calculated, and the deviation between the deployment position coordinates and the detection position coordinates is calculated, so that the UAV can adjust its own attitude according to the deviation, including: The insulator's local image information is obtained by capturing images of the detection location using a detection robot; The pixel coordinates of the detection position in the local insulator image information are converted into the three-dimensional coordinates of the detection position in the robot coordinate system; Convert the three-dimensional coordinates of the detection location in the UAV coordinate system to the theoretical coordinates in the robot coordinate system; Calculate the actual deviation between the theoretical coordinates in the robot coordinate system and the three-dimensional coordinates of the detection position in the robot coordinate system; If the deviation exceeds the deviation threshold, the drone's flight attitude is adjusted; if the deviation does not exceed the deviation threshold, the drone's flight attitude does not need to be adjusted.

[0015] In one possible implementation, the actual deviation includes both the combined deviation and the vertical deviation.

[0016] In one possible implementation, if the deviation exceeds a deviation threshold, the drone's flight attitude is adjusted, including: If the overall deviation exceeds the overall threshold, adjust the roll and pitch angles of the drone. If the vertical deviation exceeds the vertical threshold, adjust the drone's flight altitude.

[0017] The beneficial effects of the insulator zero-value detection device provided by this invention are as follows: Compared with the prior art, the insulator zero-value detection method of this invention utilizes drones to acquire insulator images, providing data support for accurate identification of the detection location. It eliminates the need for close-range manual observation, reducing safety risks and subjective judgment errors under high-voltage environments. By identifying the detection location and calculating coordinates through image information, ambiguous visual judgments can be transformed into precise numerical positioning, providing a clear target for the drone-deployed detection robot, avoiding repeated direction adjustments, shortening positioning time, and ensuring the accuracy of the deployment location.

[0018] Calculating the deviation between the deployment and detection positions and adjusting the drone's attitude accordingly, it can proactively correct deployment offsets without manual intervention or calibration, reducing the probability of detection failures due to inaccurate positioning and improving deployment tolerance. Zero-value detection is initiated immediately after deployment, achieving seamless "deployment-detection" integration. This eliminates manual commands or additional debugging steps, reduces the time interval between each step, significantly shortens the detection time for a single tower, improves overall efficiency, meets the needs of large-scale transmission line inspections, helps quickly identify zero-value defects in insulators, and ensures the safe and stable operation of the lines. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0020] Figure 1 This is a schematic diagram of the structure of the insulator zero-value detection device provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the detection robot provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the main steps of the insulator zero-value detection method provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the main steps of step S100 provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the main steps of step S200 provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the main steps of step S300 provided in an embodiment of the present invention.

[0021] Explanation of reference numerals in the attached figures: 1. Unmanned Aerial Vehicle (UAV); 2. Inspection Robot; 21. Frame; 211. Hanging Frame; 212. Arc-shaped Support; 22. Zero Value Detection Component; 221. Zero Value Detection Mechanism; 222. Zero Value Detection Control Mechanism; 23. Walking Component. Detailed Implementation

[0022] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0023] Reference Figures 1 to 6 The insulator zero-value detection device and detection method provided by the present invention will now be described.

[0024] Firstly, an insulator zero-value detection device is provided, comprising: a drone 1 and a detection robot 2. The detection robot 2 includes: a frame 21, a zero-value detection component 22, a walking component 23, and a control module. The drone 1 hoists or deploys the detection robot 2 via the frame 21. The zero-value detection component 22 is mounted on the frame 21. The walking component 23 is fixedly connected to the frame 21. The control module is communicatively connected to the drone 1; the control module includes an image acquisition unit and a control unit. The control unit is communicatively connected to the image acquisition unit and the drone 1. The image acquisition unit is used to acquire and send image information of the insulator to the control unit, so that the control unit can identify the detection location and control the drone 1 to deploy the detection robot 2.

[0025] The insulator zero-value detection device, through the coordinated operation of UAV 1 and detection robot 2, can effectively improve the automation level of the deployment and detection of detection robot 2. The control module of detection robot 2 includes an image acquisition unit and a control unit. The image acquisition unit can actively acquire insulator image information and transmit it to the control unit, eliminating the need for manual visual judgment to determine the detection position. The control unit can autonomously identify the detection position based on the image information, and then accurately control UAV 1 to complete the deployment of detection robot 2, greatly reducing the reliance on manual operation and avoiding deployment errors caused by human judgment bias.

[0026] The walking component 23 is fixed on the frame 21. After the inspection robot 2 is deployed to the inspection position, it can drive the robot to move stably on the insulator. In conjunction with the zero-value detection component 22, it can perform zero-value detection at different positions of the insulator, expanding the inspection coverage and improving the comprehensiveness of the inspection. At the same time, the control module maintains a communication connection with the drone 1, which can transmit control commands and image data in real time. This ensures more efficient cooperation between the drone 1 and the inspection robot 2, reduces the need for manual adjustment of the drone 1's attitude, shortens the inspection time of a single tower insulator, and meets the efficiency requirements of large-scale transmission line inspection.

[0027] In addition, the design of the drone 1 to hoist or deploy the inspection robot 2 via the frame 21 not only ensures the stability of the inspection robot 2 during transportation, but also allows for precise deployment at the appropriate location, avoiding detection failures caused by placement deviations. This further improves the reliability and accuracy of insulator zero-value detection, providing a stronger guarantee for the safe operation of high-voltage transmission lines.

[0028] In a preferred embodiment, the image acquisition unit includes a first image acquisition component mounted on the drone 1 and a second image acquisition component mounted on the inspection robot 2. The first image acquisition component includes a visible light camera and an infrared imaging camera. When the drone 1 carrying the inspection robot 2 flies to the vicinity of the insulator, the visible light camera and the infrared imaging camera simultaneously take pictures, obtaining visible light and infrared image information of the insulator, and then send this information to the control unit. The control unit identifies the detection location based on the visible light and infrared image information. After determining the detection location, the control unit controls the drone 1 to deploy the inspection robot 2 at a uniform and slow speed.

[0029] The second image acquisition component is a macro camera mounted on the detection robot 2. Before deploying the detection robot 2, the macro camera captures the actual location image information of the detection position and sends it to the control unit. The control unit compares the actual location image information with the identified detection position and dynamically adjusts the flight attitude and altitude of the drone 1.

[0030] In a preferred embodiment, the control unit uses an STM32H743VIT6 microcontroller as its core processing chip. This chip features a high-performance ARM Cortex-M7 core with a maximum clock frequency of 480MHz, enabling it to quickly process multi-path data and complex control algorithms, meeting the real-time and computational requirements of the inspection robot 2. The control unit integrates a power management module, a communication interface module, and a storage module, forming a complete control core hardware architecture.

[0031] The power management module uses the MP2359 DC-DC converter, which can stably convert the 12V voltage of the built-in lithium battery of the inspection robot 2 into 3.3V and 5V to power the microcontroller and peripheral circuits respectively. It is also equipped with a voltage monitoring chip to collect power supply voltage data in real time. When the voltage is lower than the preset threshold, it automatically sends a low power warning signal to the microcontroller to prevent the control unit from malfunctioning due to unstable power supply.

[0032] The communication interface module includes two communication channels. One is a UART serial port, which connects to the image acquisition unit via a MAX3232 level conversion chip to receive insulator image data transmitted by the image acquisition unit. The transmission baud rate is set to 115200bps to ensure the stability and integrity of image data transmission. The other is a 4G communication module, which uses an EC20 model 4G module to access the mobile network via a SIM card and establish bidirectional communication with the flight control system of UAV 1 to achieve real-time interaction of control commands and status information. The communication latency is controlled within 100ms to meet the real-time requirements of deployment control.

[0033] The storage module uses a 16GB SD card, connected to the microcontroller via an SPI interface. It stores insulator image information acquired by the image acquisition unit, detection position recognition results generated by the control unit, and communication logs with UAV 1, facilitating subsequent data traceability and fault diagnosis. Simultaneously, the control unit incorporates a deep learning-based insulator detection position recognition algorithm. This algorithm, trained and optimized with massive amounts of insulator image samples, can automatically identify key detection points on the insulator string from the image information transmitted by the image acquisition unit, achieving an accuracy rate of over 98% and a recognition time of no more than 500ms.

[0034] When the inspection robot 2 is in the deployment state, the image acquisition unit transmits one frame of insulator image to the control unit every 200ms. After receiving the image, the control unit first removes image noise and background interference through an image preprocessing algorithm, then calls the detection position recognition algorithm to determine the pixel coordinates of the detection position. Subsequently, combined with parameters such as the focal length and installation angle of the image acquisition unit, the pixel coordinates are converted into actual spatial coordinates, and a deployment control command for the drone 1 is generated. This command is sent to the drone 1 flight control system through the 4G communication module to control the drone 1 to adjust its attitude to the deployment position. After the drone 1 sends a signal that the attitude adjustment is complete, the control unit sends a further deployment command to complete the precise deployment of the inspection robot 2. The entire deployment control process does not require manual intervention, greatly improving the accuracy and efficiency of deployment.

[0035] In one possible implementation, the frame 21 includes a lifting frame 211 and an arc-shaped support 212. The lifting frame 211 is positioned on top of the arc-shaped support 212. The drone 1 uses the lifting frame 211 to lift or deploy the inspection robot 2. The arc-shaped support 212 is positioned on top of the lifting frame 211; the walking assembly 23 is positioned on the inner top wall of the arc-shaped support 212; and the zero-value detection assembly 22 is positioned on the arc-shaped support 212.

[0036] The connection between the drone 1 and the inspection robot 2 can be achieved by the robotic arm gripping the hanging frame 211. When carrying the inspection robot 2, the robotic arm grips the hanging frame 211. When deploying the inspection robot 2, after carrying the inspection robot 2 to the designated location, the robotic arm releases the hanging frame 211 to deploy the inspection robot 2.

[0037] The connection between the drone 1 and the inspection robot 2 can be achieved by connecting them via a rope to the hanging frame 211. The drone 1 carries or deploys the inspection robot 2 by retracting or unfolding the rope using a motor.

[0038] In one possible implementation, there are two walking components 23, which are symmetrically arranged about the vertical central axis of the arc-shaped support 212.

[0039] In a preferred embodiment, all walking components 23 employ tracked walking mechanisms. Each tracked walking mechanism includes a track body, a drive motor, a transmission gear set, and support wheel sets. The drive motor is connected to the transmission gear set, which meshes with the track body. The support wheel sets are evenly distributed inside the track body, supporting it and assisting its rotation. The two tracked walking mechanisms are symmetrical about the vertical central axis of the arc-shaped support 212. The drive motor can receive commands from the control module to drive the track body to rotate synchronously or differentially.

[0040] When the inspection robot 2 is placed on the insulator, the track body can fit against the surface of the insulator and move stably along the insulator string under the drive of the drive motor. This not only avoids the inspection robot 2 slipping on the surface of the insulator, but also adapts to the arc contour of the insulator, ensuring that the zero-value detection component 22 can perform comprehensive detection on different positions of the insulator, thereby improving the stability and reliability of the detection process.

[0041] In one possible implementation, the zero-value detection component 22 includes a zero-value detection mechanism 221 and a zero-value detection mechanism 222. The zero-value detection mechanism 221 is disposed on the side wall of the arc-shaped support 212. The zero-value detection control mechanism 222 is disposed at the bottom of the arc-shaped support 212; the height of the zero-value detection control mechanism 222 is lower than that of the zero-value detection mechanism 221.

[0042] The arc-shaped bracket 212 of the insulator zero-value detection device is made of high-strength aluminum alloy. Its curvature is adapted to the outer contour of common disc insulators, ensuring that the entire device can fit the surface of the insulator. The zero-value detection mechanism 221 and the zero-value detection control mechanism 222 of the zero-value detection component 22 are respectively assembled at different positions of the arc-shaped bracket 212 to achieve functional division and structural balance.

[0043] The zero-value detection mechanism 221 is specifically a high-voltage capacitive detection probe, of which two are provided. This detection probe is connected to the side wall of the arc-shaped bracket 212 through an elastic connector. It can adapt to the slight curvature changes of the insulator surface during the detection process, and always maintain stable contact with the insulator surface, thereby accurately collecting the capacitance value signal of the insulator and providing data support for the judgment of zero-value defects.

[0044] The zero-value detection control mechanism 222 includes a circuit box and a control box, both made of flame-retardant ABS plastic and fixed to the bottom of both ends of the arc-shaped bracket 212 by bolts. The circuit box integrates a signal amplification module, a filtering module, and a data transmission module, which can process the capacitance signal collected by the zero-value detection mechanism 221 and transmit the processed signal to the control box. The control box is equipped with a microprocessor and a wireless communication module, which can receive the processed signal transmitted from the circuit box, determine whether the insulator has a zero-value defect based on a preset threshold, and simultaneously interact with the control module of the detection robot 2.

[0045] The height of both the circuit box and the control box is lower than the height of the insulator, and their weights are similar. This ensures that the overall center of gravity of the inspection robot 2 falls below the bottom of the arc-shaped support 212 and is outside the height range of the insulator. This structural design effectively avoids the problem of tipping over due to the shift of the center of gravity when the inspection robot 2 moves on the insulator, ensuring that the device remains stable throughout the inspection process and improving the continuity and accuracy of zero-value detection.

[0046] The beneficial effects of the insulator zero-value detection device provided by this invention are as follows: Compared with the prior art, the insulator zero-value detection device of this invention, through the cooperation of the image acquisition unit and the control unit in the control module, constructs an automated positioning and deployment mechanism. The image acquisition unit can acquire insulator image information in real time and transmit it to the control unit. The control unit can accurately identify the detection position by analyzing the image, and then directly control the drone 1 to complete the deployment action. The entire process does not require manual visual judgment to adjust the attitude of the drone 1, reducing human operation errors. Even in complex environments, it can ensure that the detection robot 2 is accurately deployed to the target position, significantly improving deployment accuracy and reducing the probability of detection failure due to deployment deviation.

[0047] Furthermore, the automated operation mode of this device significantly improves detection efficiency. From image acquisition and location recognition to drone deployment, each step eliminates the need for repeated manual calibration and adjustment, shortening the detection time for single-tower insulators and enabling rapid identification of zero-value defects in insulators. This highly efficient detection capability can meet the needs of large-scale transmission line inspections, helping power grid maintenance personnel to more promptly identify and address insulator problems, ensuring the safe and stable operation of high-voltage transmission lines.

[0048] Secondly, a method for detecting zero value of an insulator is provided, applied to the insulator zero value detection device as described in the first aspect, comprising the following steps: S100. Obtain insulator image information via drone.

[0049] In one possible implementation, S100. Insulator image information is acquired via a drone, including: S110. Plan the drone inspection route based on the GIS map of the transmission line and the coordinates of the towers, so that the drone can fly to the vicinity of the target insulator in the shortest distance.

[0050] S120. Acquire visible light image information and infrared image information of the target insulator.

[0051] S200. Identify the detection location based on the insulator image information and calculate the coordinates of the detection location so that the drone can deploy the detection robot to the detection location.

[0052] In one possible implementation, S200. Identify the detection location based on the insulator image information and calculate the coordinates of the detection location so that the drone can deploy the detection robot to the detection location, including: S210. Preprocess the visible light image information and infrared image information, and fuse the preprocessed images to obtain fused image information.

[0053] The following formula is used to perform Gaussian filtering on visible light image information to remove high-frequency noise:

[0054] in, These are the pixel coordinates after denoising. These are the pixel coordinates before noise reduction. .

[0055] The following formula is used to perform non-uniform correction on the infrared image information to eliminate response differences in the infrared detector:

[0056] in, These are the corrected pixel coordinates. These are the pixel coordinates before correction. and This is the correction factor.

[0057] Based on SIFT feature point matching, the filtered and denoised visible light image information and the corrected infrared image information are aligned to obtain fused image information, ensuring that the registration error is controlled within 1 pixel, thereby improving image quality and ensuring the accuracy of subsequent feature extraction.

[0058] S220. Identify the detection location based on the fused image information.

[0059] The fused image information is input into a pre-trained insulator detection location recognition model. The model accurately identifies the detection location from a complex background and excludes non-target areas by recognizing geometric features such as the annular contour of the insulator cap and the spacing of the skirts, as well as textural features such as the smoothness of the cap surface and the gradient changes at the skirt edges. The insulator detection location recognition model is fine-tuned based on the YOLOv8 architecture, and its training samples include over 100,000 insulator images with different signals.

[0060] S230. Calculate the three-dimensional coordinates of the detection position in the UAV coordinate system.

[0061] Based on the principle of binocular vision, the three-dimensional coordinates of the detected position in the UAV coordinate system are calculated using the intrinsic and extrinsic parameters of the UAV camera and the following formula:

[0062]

[0063]

[0064] in, To detect the three-dimensional coordinates of the position in the UAV coordinate system, , To detect the pixel coordinates of the location, For the camera intrinsic parameter matrix, for Axial focal length, for Axial focal length, and Principal point coordinates For rotation matrix, It is a translation vector. The baseline distance between the two cameras. and For left and right cameras The coordinates are in pixel-axis format, with the origin of the UAV coordinate system being the UAV's center of gravity. The axis runs forward along the fuselage. The axis runs along the fuselage to the right. The axis runs downwards along the fuselage.

[0065] S300. Calculate the deployment position coordinates of the detection robot and the deviation between the deployment position coordinates and the detection position coordinates, so that the UAV can adjust its own attitude according to the deviation.

[0066] In one possible implementation, S300 calculates the deployment position coordinates of the detection robot and the deviation between the deployment position coordinates and the detection position coordinates, so that the UAV can adjust its own attitude according to the deviation, including: S310. Obtain local image information of the insulator by taking pictures of the detection location using the detection robot.

[0067] S320. Convert the pixel coordinates of the detection position in the local insulator image information into the three-dimensional coordinates of the detection position in the robot coordinate system.

[0068] The formula in step S230 is used to convert the pixel coordinates of the detection position in the local insulator image information into three-dimensional coordinates of the detection position in the robot coordinate system. .

[0069] S330. Convert the three-dimensional coordinates of the detected position in the UAV coordinate system to the theoretical coordinates in the robot coordinate system. .

[0070] S340. Calculate the actual deviation between the theoretical coordinates in the robot coordinate system and the three-dimensional coordinates of the detection position in the robot coordinate system.

[0071] In one possible implementation, the actual deviation includes both the combined deviation and the vertical deviation.

[0072] The overall deviation is calculated using the following formula:

[0073]

[0074]

[0075]

[0076] in, To account for the overall deviation, for Shaft deviation, for Shaft deviation, This represents the vertical deviation.

[0077] S350.

[0078] In one possible implementation, S350. "If the deviation exceeds the deviation threshold, adjust the UAV's flight attitude" includes: If the overall deviation exceeds the overall threshold, adjust the roll and pitch angles of the drone. If the vertical deviation exceeds the vertical threshold, adjust the drone's flight altitude.

[0079] If the overall deviation exceeds the overall threshold, the roll and pitch angles of the UAV are adjusted using the following formula:

[0080]

[0081] in, This is the roll angle. The pitch angle, , , For PID parameters, , , , , , , , .

[0082] S400. Deploy the detection robot and start the detection robot to begin zero-value detection.

[0083] The beneficial effects of the insulator zero-value detection device provided by this invention are as follows: Compared with the prior art, the insulator zero-value detection method of this invention utilizes drones to acquire insulator images, providing data support for accurate identification of the detection location. It eliminates the need for close-range manual observation, reducing safety risks and subjective judgment errors under high-voltage environments. By identifying the detection location and calculating coordinates through image information, ambiguous visual judgments can be transformed into precise numerical positioning, providing a clear target for the drone-deployed detection robot, avoiding repeated direction adjustments, shortening positioning time, and ensuring the accuracy of the deployment location.

[0084] Calculating the deviation between the deployment and detection positions and adjusting the drone's attitude accordingly, it can proactively correct deployment offsets without manual intervention or calibration, reducing the probability of detection failures due to inaccurate positioning and improving deployment tolerance. Zero-value detection is initiated immediately after deployment, achieving seamless "deployment-detection" integration. This eliminates manual commands or additional debugging steps, reduces the time interval between each step, significantly shortens the detection time for a single tower, improves overall efficiency, meets the needs of large-scale transmission line inspections, helps quickly identify zero-value defects in insulators, and ensures the safe and stable operation of the lines.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0086] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0087] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

Claims

1. An insulator zero-value detection device, characterized in that, include: Drones and inspection robots; The detection robot includes: The rack; the drone uses the rack to lift or deploy the inspection robot; The zero-value detection component is mounted on the rack; The walking assembly is fixedly connected to the frame; and A control module is communicatively connected to the UAV. The control module includes an image acquisition unit and a control unit. The control unit is communicatively connected to the image acquisition unit and the UAV. The image acquisition unit is used to acquire and send image information of the insulator to the control unit so that the control unit can identify the detection location and control the UAV to deploy the detection robot.

2. The insulator zero-value detection device as described in claim 1, characterized in that, The rack includes: The lifting frame; the drone uses the lifting frame to lift or deploy the inspection robot; and An arc-shaped support, wherein the hanging frame is disposed on the top of the arc-shaped support; the walking assembly is disposed on the inner wall of the top of the arc-shaped support; and the zero-value detection assembly is disposed on the arc-shaped support.

3. The insulator zero-value detection device as described in claim 2, characterized in that, Two walking components are provided and are symmetrically arranged about the vertical central axis of the arc-shaped support.

4. The insulator zero-value detection device as described in claim 2, characterized in that, The zero-value detection component includes: The zero-value detection mechanism is installed on the side wall of the arc-shaped support; A zero-value detection control mechanism is located at the bottom of the arc-shaped support; the height of the zero-value detection control mechanism is lower than that of the zero-value detection mechanism.

5. A method for detecting zero values ​​in insulators, applied to the insulator zero-value detection device as described in any one of claims 1 to 4, characterized in that, Includes the following steps: Image information of insulators was obtained using drones; The detection location is identified based on the insulator image information, and the coordinates of the detection location are calculated so that the drone can deploy the detection robot to the detection location; Calculate the deployment position coordinates of the detection robot, and calculate the deviation between the deployment position coordinates and the detection position coordinates, so that the drone can adjust its own attitude according to the deviation; Deploy the detection robot and start it to perform zero-value detection.

6. The insulator zero-value detection method as described in claim 5, characterized in that, Image information of insulators was obtained through drones, including: Based on the GIS map of the power transmission line and the coordinates of the tower, the drone inspection route is planned so that the drone can fly to the vicinity of the target insulator in the shortest distance. Acquire visible light and infrared image information of the target insulator.

7. The insulator zero-value detection method as described in claim 6, characterized in that, The detection location is identified based on the insulator image information, and the coordinates of the detection location are calculated to facilitate the deployment of the detection robot by the drone to the detection location, including: Visible light image information and infrared image information are preprocessed, and the preprocessed images are fused to obtain fused image information; The detection location was identified based on the fused image information; Calculate the three-dimensional coordinates of the detection location in the UAV coordinate system.

8. The insulator zero-value detection method as described in claim 5, characterized in that, Calculate the deployment position coordinates of the detection robot, and calculate the deviation between the deployment position coordinates and the detection position coordinates, so that the UAV can adjust its own attitude according to the deviation, including: The insulator's local image information is obtained by capturing images of the detection location using a detection robot; The pixel coordinates of the detection position in the local insulator image information are converted into the three-dimensional coordinates of the detection position in the robot coordinate system; Convert the three-dimensional coordinates of the detection location in the UAV coordinate system to the theoretical coordinates in the robot coordinate system; Calculate the actual deviation between the theoretical coordinates in the robot coordinate system and the three-dimensional coordinates of the detection position in the robot coordinate system; If the deviation exceeds the deviation threshold, the drone's flight attitude is adjusted; if the deviation does not exceed the deviation threshold, the drone's flight attitude does not need to be adjusted.

9. The insulator zero-value detection method as described in claim 8, characterized in that, Actual deviations include overall deviations and vertical deviations.

10. The insulator zero-value detection method as described in claim 9, characterized in that, If the deviation exceeds the deviation threshold, adjust the drone's flight attitude, including: If the overall deviation exceeds the overall threshold, adjust the roll and pitch angles of the drone. If the vertical deviation exceeds the vertical threshold, adjust the drone's flight altitude.

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