Automatic fault indicator hanging system based on unmanned aerial vehicle and hanging method thereof

Abstract

The invention relates to the technical field of power fault indicator automatic hanging, and provides an unmanned aerial vehicle-based fault indicator automatic hanging system and a hanging method thereof, which adopt a laser point cloud and image fusion method, effectively overcome the defect that single image recognition is influenced by a visual angle and noise, reduce the adjustment times and inertial accumulation, and improve the accuracy of image recognition. The real-time performance, the accuracy and the overall efficiency of the hanging operation are improved; based on an unmanned aerial vehicle platform, accurate identification and spatial positioning of a distribution line wire are realized, and high-precision installation of a fault indicator is completed by automatically controlling a hanging mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field related to automatic mounting of power fault indicators, and in particular to an automatic mounting system for a fault indicator based on a drone and a mounting method thereof. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As a vital component of the power system, the stable operation of distribution lines is directly related to the reliable power supply to countless households. When faults such as phase-to-phase short circuits and ground short circuits occur on distribution lines, quickly and accurately determining the fault location is crucial for timely troubleshooting and ensuring normal power supply to users. To achieve this goal, distribution line fault indicators are widely used as auxiliary devices. These devices display a striking color when a fault occurs, enabling operations and maintenance personnel to quickly identify and locate the fault point, significantly reducing troubleshooting time and improving the efficiency and reliability of the power supply system.

[0004] Currently, traditional methods for mounting fault indicators rely primarily on manual labor, often requiring the assistance of a boom truck, especially when working at height. This method is costly, inflexible, and unsafe. Alternatively, some technologies are exploring the use of image processing control. Using drones equipped with image acquisition devices, they use visual recognition to determine the location of distribution lines. This system then drives a mounting mechanism with bifurcated mounting legs, carrying a fixed fault indicator, toward the mounting line location. The fault indicator in the middle of the mounting legs touches the line, and the two spring-loaded legs of the fault indicator release and clamp the conductor, securing the indicator on the line. However, this method has significant limitations in complex field environments. Due to variations in image acquisition angles and background noise, the positioning accuracy of the mounting line obtained through single image recognition is insufficient. During the mounting process, the control system must continuously adjust the mounting mechanism's direction to compensate for recognition errors. During this adjustment process, due to the dynamic response characteristics and mechanical inertia of the drone or mounting mechanism, frequent directional corrections can cause control system oscillations, resulting in unstable mounting paths, increased energy consumption, and reduced overall system efficiency. In addition, too many adjustment actions prolong the installation time, and due to the cumulative inertia effect, the system is prone to control lag or overshoot when performing continuous direction changes, further reducing the installation accuracy and work efficiency. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes an automatic mounting system and mounting method for fault indicators based on drones. The system adopts the method of laser point cloud and image fusion to effectively overcome the defects of single image recognition being affected by viewing angle and noise, reduce the number of adjustments and inertia accumulation, and improve the real-time performance, accuracy and overall efficiency of the mounting operation. Based on the drone platform, the system realizes the accurate identification and spatial positioning of the distribution line conductors, and completes the high-precision installation of the fault indicator by automatically controlling the mounting mechanism.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] One or more embodiments provide a drone-based automatic mounting system for a fault indicator, comprising a mounting device and a drone, wherein the mounting device is mounted with the fault indicator, the mounting device being fixedly mounted on the drone, and the drone is used to transport the fault indicator and mount it at a target location.

[0008] It also includes a laser radar and an image acquisition device installed on the drone. The drone control system fuses the collected laser point cloud data and images, identifies the mounting target position and automatically generates a mounting strategy to realize the automatic mounting of the fault indicator.

[0009] One or more embodiments provide a method for automatically mounting a fault indicator on a drone, comprising the following steps:

[0010] Get the target section on the line;

[0011] Start the LiDAR to scan the target area and surrounding areas, obtain LiDAR point cloud data, acquire image data, and align the image data with the point cloud data along the time axis;

[0012] The acquired LiDAR data is integrated with the image data to obtain the target wire section to be installed;

[0013] The objective function is constructed with the goal of minimizing the path length, the energy consumption of the UAV, and the energy consumption of the UAV adjusting its attitude.

[0014] The UAV's position and flight data are acquired in real time, and the objective function is solved using a heuristic search algorithm to obtain the UAV's flight path and the UAV's flight control parameters on the path.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] In the present invention, by fusing lidar point cloud data with image acquisition data, the lidar realizes the collection of spatial data that is not restricted by viewing angle, ensures the positioning accuracy and realizes the three-dimensional precise positioning of the target hanging wire, effectively solves the recognition error problem caused by angle change and background interference of traditional image recognition methods, automatically generates flight trajectory and hanging strategy, keeps the hanging path stable, and reduces unnecessary posture adjustments.

[0017] Compared to traditional manual installation methods, this solution significantly reduces reliance on high-cost equipment such as boom trucks. It also improves the system's adaptability in confined or complex terrain, minimizing the risks associated with high-altitude operations. In terms of efficiency, the system quickly generates a mounting path and executes installation operations after identifying the target conductor, significantly reducing the number of posture adjustments and time delays, lowering flight energy consumption, and overall improving the intelligence, safety, and efficiency of fault indicator installation operations.

[0018] The advantages of the present invention and its additional aspects will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention but do not constitute a limitation of the present invention.

[0020] Figure 1 is a flow chart of an automatic mounting method according to embodiment 2 of the present invention;

[0021] Figure 2 1 is a schematic structural diagram of a mounting device according to embodiment 1 of the present invention;

[0022] Figure 3 1 is a schematic diagram of the three-dimensional structure of the fault indicator of Example 1 of the present invention;

[0023] Figure 4 1 is a side view of the structure of the indicator lamp body of the fault indicator of Example 1 of the present invention;

[0024] Figure 5 This is a bottom view of the indicator lamp body of the fault indicator of Example 1 of the present invention;

[0025] Figure 6 is a perspective view of an indicator lamp body of a fault indicator according to embodiment 1 of the present invention;

[0026] Figure 7 1 is a schematic structural diagram of a mounting base 504 of a fault indicator according to embodiment 1 of the present invention;

[0027] Among them: 1. Drive shaft; 2. Warning block; 3. Shielding plate; 4. Obstruction block; 5. Transparent protective cover; 6. Base; 7. External thread; 8. Internal thread; 10. Cavity; 100. First guide leg; 101. Trip slot; 102. Second guide leg; 103. Fixed slot; 104. Shock-absorbing slot; 105. Indicator placement slot; 200. Connector; 300. Insulating support rod; 400. Connecting base; 401. Fixing hole; 500. Limit block; 501. Protrusion; 502. Fixed shaft; 503. Spring leg; 504. Mounting seat; 505. Indicator lamp body. DETAILED DESCRIPTION

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0029] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0030] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, 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. It should be noted that, in the absence of conflict, the various embodiments of the present invention and the features in the embodiments can be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.

[0031] Example 1

[0032] In the technical solutions disclosed in one or more embodiments, Figures 1 to 7 As shown, the automatic mounting system for a fault indicator based on a UAV includes: a mounting device and a UAV, wherein the fault indicator is mounted on the mounting device, and the mounting device is fixedly arranged on the UAV, and the fault indicator is transported and mounted at a target location by the UAV;

[0033] It also includes a laser radar and an image acquisition device installed on the drone. The drone control system fuses the collected laser point cloud data and images, identifies the mounting target position and automatically generates a mounting strategy to realize the automatic mounting of the fault indicator.

[0034] In this embodiment, by fusing lidar point cloud data with image acquisition data, lidar is used to collect spatial data that is not restricted by viewing angle, ensuring positioning accuracy and achieving three-dimensional precise positioning of the target hanging wire. This effectively solves the recognition error problem caused by angle changes and background interference in traditional image recognition methods, automatically generates flight trajectories and hanging strategies, keeps the hanging path stable, and reduces unnecessary posture adjustments.

[0035] Compared to traditional manual installation methods, this solution significantly reduces reliance on high-cost equipment such as boom trucks. It also improves the system's adaptability in confined or complex terrain, avoiding the risks associated with high-altitude operations. In terms of efficiency, the system quickly generates a mounting path and executes installation operations after identifying the target conductor, significantly reducing the number of posture adjustments and time delays, lowering flight energy consumption, and overall improving the intelligence, safety, and efficiency of fault indicator installation operations.

[0036] In some embodiments, as Figure 2 As shown, the mounting device includes a Y-shaped bracket, which includes a first guide leg 100 and a second guide leg 102. The connecting ends of the first guide leg 100 and the second guide leg 102 form a U-shaped indicator placement slot 105. At the top of the indicator placement slot 105, a tripping slot 101 with an opening facing downward is provided on the first guide leg 100, and a fixing slot 103 with an opening facing upward is provided on the second guide leg 102. The tripping slot 101 and the fixing slot 103 are respectively adapted to the spring legs 503 on the fault indicator.

[0037] During installation, one spring leg 503 of the fault indicator is installed in the tripping slot 101, and the other spring leg 503 is stuck in the fixed slot 103. The tripping slot 101 opens downward, while the fixed slot 103 opens upward and is tilted inward. When tripping, the fault indicator set in the indicator placement slot 105 is touched by the distribution line guide, generating a downward force, causing the spring leg 503 to disengage from the tripping slot 101 and engage with the other spring leg 503. The drone moves downward, driving the hanging device downward, causing the spring leg 503 to disengage from the fixed slot 103.

[0038] A further technical solution is to provide a shock absorbing groove 104 on the second guide leg 102 near the fixed slot 103, and the shock absorbing groove 104 is configured as a triangular hollow area;

[0039] A further technical solution is to achieve the connection between the mounting device and the drone, wherein the mounting device further comprises a connector 200, an insulating support rod 300 and a connecting base 400 which are connected in sequence;

[0040] The connector 200 has a mounting hole at one end, which is threadedly connected to the Y-shaped bracket through the mounting hole, and a threaded hole or threaded column at the other end, which is detachably connected to the insulating support rod 300 through threads;

[0041] The insulating support rod 300 has a length set according to parameters such as the weight of the indicator and the resonance frequency of the drone;

[0042] The connecting base 400 is configured as a hollow cylinder, with a threaded hole provided on the top of the connecting base 400 for fixed connection with the insulating support rod 300; a fixing hole 401 is provided on the side of the cylinder;

[0043] During installation, after the connection base 400 is connected to the drone, a cable tie is passed through the fixing hole 401 and then passed around the drone. The cable tie is tightened to securely connect the device to the drone.

[0044] In some embodiments, in order to achieve the adaptive mounting with the mounting device, the structure of the fault indicator is improved, such as Figure 3 As shown, the fault indicator includes an indicator lamp body 505 and a mounting base 504. A V-shaped spring mounting bracket is provided on the mounting base 504. A through hole is provided on the spring mounting bracket. A fixed shaft 502 is installed in the through hole. A spring leg 503 is installed on the fixed shaft 502.

[0045] In this embodiment, a V-shaped spring mounting bracket is provided on the mounting base 504, which provides a deformation space for the spring, so that a large spring deformation amount can be provided before and after installation, thereby improving the mounting stability of the fault indicator on the wire.

[0046] A further technical solution is that a limit block 500 is provided on the mounting base 504 at the outer side of the position corresponding to the position of the spring leg 503 to form a space for the wall of the indicator placement slot 105 of the Y-shaped bracket on the mounting device;

[0047] Specifically, such as Figure 3 As shown in the overall structure diagram of the fault indicator, two limit blocks 500 form a group, with a total of two groups, distributed on both sides of the device, wherein there is a certain distance between each group of limit blocks. The function is that when the fault indicator is placed in the hanging device, it can be stuck on both sides of the "U"-shaped groove of the indicator placement groove 105, playing the role of limiting and fixing the indicator.

[0048] As a further technical solution, a protrusion 501 is provided on the mounting base 504 , corresponding to the inner side of the position where the spring leg 503 is provided; when the fault indicator is mounted on the wire, the contact with the wire increases the friction force.

[0049] In some embodiments, the indicator lamp body 505 includes a transparent protective cover 5 and a base 6, a shielding plate 3 and a warning block 2 that are sequentially attached to the transparent protective cover 5, and the shielding plate 3 is rotatably arranged outside the warning block 2; the shielding plate 3 is rotated to a first position to overlap the warning block 2, and the shielding plate 3 is rotated to a second position to not overlap the warning block 2; the warning light and control components are arranged on the base 6;

[0050] Optionally, the shielding plate 3 can be rotatably provided by providing a control assembly connected to the transmission shaft 1 on the base; the control assembly controls the transmission shaft 1 to rotate, thereby driving the shielding plate 3 to rotate;

[0051] Taking the shielding plate with three sectors of the present embodiment as an example, a blocking block 4 is extended inwardly from the shielding plate 3 so that the rotation range is limited to about 1 / 6 of the circumference angle.

[0052] Furthermore, the shielding plate 3 may also be provided with an obstruction block 4 for limiting the rotation angle of the shielding plate 3;

[0053] The warning block 2 is eye-catching in color and can be red. Under normal circumstances, it is hidden behind the shielding plate 3, which means that the line is operating normally. After being triggered to rotate by a fault signal, it is no longer blocked by the shielding plate 3, and can guide line patrol personnel to quickly determine the fault point; an obstruction block 4 is led inward on the shielding plate 3, so that the rotation range of the indicator is limited to about 1 / 6 of the circumference.

[0054] Specifically, a hole groove is provided on the base 6, which is connected to the transmission shaft 1 and serves to stabilize the transmission shaft; the transparent protective cover 5 is highly transparent and made of a hard material, protecting the internal structure from environmental erosion such as rain, and the high transparency can ensure the effectiveness of the color representation of the warning block; the transparent protective cover 5 is provided with an external thread 7, which is combined with the internal thread 8 on the mounting base 504, so that the mounting base 504 can be tightened and closed, protecting the control components in the cavity 10 of the mounting base 504;

[0055] Optionally, the control component includes a current sensor and a control module. The current sensor is used to detect current changes in the circuit, and the control module outputs a control signal after receiving the current changes. When a fault signal is detected, the control module triggers the electromagnetic drive module to drive the transmission shaft 1 to rotate and lights up the indicator light.

[0056] The electromagnetic drive module is used to generate electromagnetic force by controlling the current change to drive the transmission shaft 1 to rotate. It is an electromagnetic drive device;

[0057] Furthermore, it also includes a gimbal, on which an image acquisition device is provided, and the image acquisition device can be an image acquisition device; a laser radar is provided on the body of the UAV;

[0058] The structural feature of the mounting device of this embodiment is that it includes two legs, and the fault indicator is fixed between the two legs. When the designated position is reached, the fixing spring of the fault indicator is loosened from the legs by touching, so that the two fixing springs are engaged on the line, completing the mounting. Therefore, when mounting, the two legs need to be placed on both sides of the line at the mounting position, and the middle position of the legs needs to touch the line, so that the time difference between the fixing springs on both sides of the legs being loosened from the legs is small, and the fault indicator is mounted on the line. Therefore, manual control of the drone requires constant adjustment of the drone's attitude and flight status; automatic control of the mounting is performed through image recognition position; and since the positional relationship between the drone and the target line at the target position cannot be accurately identified through the collected images, the mounting failure rate is high and the efficiency is low.

[0059] To solve the above problems, this embodiment sets up a laser radar, which performs fusion processing based on the laser radar point cloud data and image data, and can accurately identify the target wire and its spatial position relationship with the target wire, thereby achieving accurate attachment;

[0060] In a further technical solution, the UAV control system fuses the collected laser point cloud data and images, identifies the mounting target location and automatically generates a mounting strategy to achieve automatic mounting of the fault indicator. The UAV control system is configured to perform the following process:

[0061] Step 1: Get the target section on the route;

[0062] Step 2: Start the LiDAR to scan the target area and surrounding areas, obtain LiDAR point cloud data, acquire image data, and align the image data with the point cloud data according to the time axis;

[0063] Step 3: Fusing the acquired LiDAR data with the image data to obtain the target wire section to be installed;

[0064] Step 4: Construct an objective function with the goal of minimizing the path length, the energy consumption of the UAV, and the energy consumption of the UAV adjusting its attitude;

[0065] Step 5: Acquire the UAV's position and flight data in real time, use a heuristic search algorithm to solve the objective function, and obtain the UAV's flight path and the UAV control strategy of the UAV's flight control parameters on the path;

[0066] Among them, the UAV flight control parameters include data such as speed, acceleration, flight direction, etc. at each path point or path segment.

[0067] In step 1, the method for obtaining the target section on the route includes the following steps:

[0068] Step 11: Control the drone to fly from the take-off point to the area where the wires are to be installed and hover;

[0069] Step 12: Turn on the image acquisition device and control the drone to move until the target section on the route is within the camera area of ​​the image acquisition device, and capture an image containing the target section;

[0070] Step 13: Identify the collected image to obtain the wire target, set the target frame and mark it in sections;

[0071] Step 131: Acquire the collected image and perform preprocessing;

[0072] Specifically, preprocessing includes:

[0073] 1) Denoising: using Gaussian filtering or bilateral filtering to reduce image noise while preserving edge information;

[0074] 2) Contrast enhancement: Adaptive histogram equalization is used to increase the contrast between the wire and the background in the image to improve the robustness of subsequent recognition.

[0075] Step 132: Using the Canny operator or the Sobel operator to extract image edges; based on the edge map, using the Hough transform to detect possible straight line objects and identify the geometric features of the wire, including the edge lines on both sides of the wire and the direction of the wire edge line;

[0076] Step 133: extracting two edge lines whose distance similarity rate is higher than a set value and within a set range to form a guide wire; among the obtained guide wires, identifying a guide wire extending along a straight line or an arc as a first target guide wire; the first target guide wire may be multiple guide wires;

[0077] The calculation formula of the distance sameness rate s is as follows:

[0078]

[0079] Among them, L d is the length of the edge line where the difference in distance between the two edge lines is less than d; L is the relative length of the two edge lines;

[0080] Specifically, the length of the edge line corresponding to the relative area of ​​two relative edge lines is identified as L, the relative sampling points on the two edge lines are extracted according to the set distance, and the distance di between the relative sampling points on the two edge lines is calculated. If the two sampling points are located on different edge lines, the distance between the two points is the distance of the edge lines at the sampling points. When di < d, the distance between the current sampling point and the previous sampling point on the same edge line is accumulated as the updated L d , continue the distance calculation of the next sampling point until the end of the edge line, and get the final L d, based on the obtained L d Calculate the distance similarity rate;

[0081] Step 134: Identify the intersection of the first target wire, use the intersection as a dividing point, segment the first target wire according to the set segmentation length, and mark the target frame;

[0082] Step 14: obtaining selection information of the target section through human-computer interaction, and determining the target section on the route according to the selection information;

[0083] Specifically, the pilot selects the target frame through the display end of the remote control, and obtains the selection information of the target section.

[0084] The method for selecting the target section in steps 11 to 13 is implemented manually. The pilot controls the drone from the takeoff point to hover below the conductor to be installed. The pilot uses the drone's remote control to activate the image acquisition device, which performs image recognition and automatically divides the conductor on the screen into segments and distinguishes them with different colors. The pilot can then click on the section of the conductor to be installed, and the drone activates the lidar to quickly scan the installed line.

[0085] Step 2 is the data synchronization step, in which the collected data is synchronized according to time. The laser radar is started to scan the target section and the surrounding area to obtain the laser radar point cloud data, obtain the image data, and align the image data with the point cloud data according to the time axis, that is, to establish an association relationship between the data at the same time point.

[0086] In step 3, the method of fusing the acquired lidar data with the image data to obtain the target wire segment to be installed includes the following steps:

[0087] Step 31: performing noise filtering on the original point cloud data acquired by the laser radar to extract the point cloud layer data corresponding to the wire body;

[0088] Step 32: performing image recognition on the image data to identify the wire and extracting the pixel coordinates and bounding box of the wire in the two-dimensional image;

[0089] Optionally, an image recognition algorithm such as YOLOv8 can be used to identify the wires and extract the pixel coordinates and bounding box of the wires in the two-dimensional image;

[0090] Step 33: Use the camera-lidar extrinsic calibration tool to calibrate the extrinsic parameters of the lidar and image acquisition device to obtain the rigid transformation matrix (R, T) between the two, that is, the rotation and translation relationship;

[0091] Step 34: Project the pixel coordinates of the wire obtained by image recognition into the three-dimensional coordinate system of the lidar point cloud according to the obtained transformation matrix to obtain the registered three-dimensional wire.

[0092] Step 35, matching the registered three-dimensional conductor with the target section determined in step 1, confirming the three-dimensional spatial position corresponding to the target section of the line, and obtaining the spatial coordinates and conductor direction information of the target conductor section to be installed;

[0093] In step 4, the objective function is constructed with the goal of minimizing the path length, the energy consumption of the UAV, and the energy consumption of the UAV adjusting its attitude as the minimum:

[0094] minJ=α·L1+β·E+γ·A

[0095] Among them, J: total objective function value, the smaller the better;

[0096] L1: total length of the path;

[0097] E: total energy consumption, the amount of electricity consumed during the flight;

[0098] A: Energy consumption of the UAV attitude adjustment, the additional energy consumption caused by the change of heading angle; α, β, γ: The weight of each item in the overall goal, which is the set value;

[0099] The path length term L uses the Euclidean distance and is calculated as follows:

[0100]

[0101] Among them, P i Represents the three-dimensional coordinates (x i ,y i ,z i );

[0102] The flight energy consumption term E is calculated as follows:

[0103]

[0104] Where C1 represents the energy consumption coefficient per meter of horizontal flight, and C2 represents the energy consumption gain factor of vertical flight. By equating the direct distance to the horizontal flight calculation and then calculating the energy consumption in the vertical direction separately, the flight energy consumption is simplified and the calculation efficiency is improved.

[0105] The energy consumption item A for attitude adjustment is calculated as follows:

[0106]

[0107] Among them, Δφ i , Δθ i , Δψi : are the attitude angle changes between step i and step i+1, namely roll angle, pitch angle and yaw angle;

[0108] W1, W2, W3: weighting factors for adjusting energy consumption in corresponding postures;

[0109] In step 5, a heuristic search algorithm is used to solve the objective function, and the cost function f(n) of the heuristic search algorithm is constructed based on the objective function J:

[0110] f(n)=g(n)+h(n)

[0111] g(n): The current path cost from the starting point to n, that is, the cumulative value of the objective function from the first point to the nth point;

[0112] h(n): estimated path cost from point n to the end point;

[0113] During the search process, the path length term, flight energy consumption term E, and attitude adjustment energy consumption term A are dynamically evaluated. The objective function is minimized to obtain all path points as the planned path, as well as the flight control parameters of the aircraft at each path point.

[0114] Example 2

[0115] Based on Example 1, this embodiment provides a method for automatically mounting a fault indicator on a drone, which is configured to be implemented in a drone control system and includes the following steps:

[0116] Step 1: Get the target section on the route;

[0117] Step 2: Start the LiDAR to scan the target area and surrounding areas, obtain LiDAR point cloud data, acquire image data, and align the image data with the point cloud data according to the time axis;

[0118] Step 3: Fusing the acquired LiDAR data with the image data to obtain the target wire section to be installed;

[0119] Step 4: Construct an objective function with the goal of minimizing the path length, the energy consumption of the UAV, and the energy consumption of the UAV adjusting its attitude;

[0120] Step 5: Acquire the UAV's position and flight data in real time, use a heuristic search algorithm to solve the objective function, and obtain the UAV's flight path and the UAV control strategy of the UAV's flight control parameters on the path;

[0121] Among them, the UAV flight control parameters include data such as speed, acceleration, flight direction, etc. at each path point or path segment.

[0122] In step 1, the method for obtaining the target section on the route includes the following steps:

[0123] Step 11: Control the drone to fly from the take-off point to the area where the wires are to be installed and hover;

[0124] Step 12: Turn on the image acquisition device and control the drone to move until the target section on the route is within the camera area of ​​the image acquisition device, and capture an image containing the target section;

[0125] Step 13: Identify the collected image to obtain the wire target, set the target frame and mark it in sections;

[0126] Step 131: Acquire the collected image and perform preprocessing;

[0127] Specifically, preprocessing includes:

[0128] 1) Denoising: using Gaussian filtering or bilateral filtering to reduce image noise while preserving edge information;

[0129] 2) Contrast enhancement: Adaptive histogram equalization is used to increase the contrast between the wire and the background in the image to improve the robustness of subsequent recognition.

[0130] Step 132: Using the Canny operator or the Sobel operator to extract image edges from the preprocessed image; based on the edge map, using the Hough transform to detect possible straight line objects and identify the geometric features of the wire, including the edge lines on both sides of the wire and the direction of the wire edge line;

[0131] Step 133: For edge lines extracted from the image, extract two edge lines whose distance similarity rate is greater than a set value and within a set range to form a guide wire; among the obtained guide wires, identify a guide wire extending along a straight line or an arc as a first target guide wire; the first target guide wire may be multiple guide wires;

[0132] The calculation formula of the distance sameness rate s is as follows:

[0133]

[0134] Among them, L d is the length of the edge line where the difference in distance between the two edge lines is less than d; L is the relative length of the two edge lines;

[0135] Specifically, the length of the edge line corresponding to the relative area of ​​two relative edge lines is identified as L, the relative sampling points on the two edge lines are extracted according to the set distance, and the distance di between the relative sampling points on the two edge lines is calculated. If the two sampling points are located on different edge lines, the distance between the two points is the distance of the edge lines at the sampling points. When di < d, the distance between the current sampling point and the previous sampling point on the same edge line is accumulated as the updated L d , continue the distance calculation of the next sampling point until the end of the edge line, and get the final L d , based on the obtained L d Calculate the distance similarity rate;

[0136] Step 134: Identify the intersection of the first target wire, use the intersection as a dividing point, segment the first target wire according to the set segmentation length, and mark the target frame;

[0137] Step 14: obtaining selection information of the target section through human-computer interaction, and determining the target section on the route according to the selection information;

[0138] Specifically, the pilot selects the target frame through the display end of the remote control, and obtains the selection information of the target section.

[0139] The method for selecting the target section in steps 11 to 13 is implemented manually. The pilot controls the drone from the takeoff point to hover below the conductor to be installed. The pilot uses the drone's remote control to activate the image acquisition device, which performs image recognition and automatically divides the conductor on the screen into segments and distinguishes them with different colors. The pilot can then click on the section of the conductor to be installed, and the drone activates the lidar to quickly scan the installed line.

[0140] Step 2 is the data synchronization step, in which the collected data is synchronized according to time. The laser radar is started to scan the target section and the surrounding area to obtain the laser radar point cloud data, obtain the image data, and align the image data with the point cloud data according to the time axis.

[0141] In step 3, the method of fusing the acquired lidar data with the image data to obtain the target wire segment to be installed includes the following steps:

[0142] Step 31: performing noise filtering on the original point cloud data acquired by the laser radar to extract the point cloud layer data corresponding to the wire body;

[0143] Step 32: performing image recognition on the image data to identify the wire and extracting the pixel coordinates and bounding box of the wire in the two-dimensional image;

[0144] Optionally, an image recognition algorithm such as YOLOv8 can be used to identify the wires and extract the pixel coordinates and bounding box of the wires in the two-dimensional image;

[0145] Step 33: Use the camera-lidar extrinsic calibration tool to calibrate the extrinsic parameters of the lidar and image acquisition device to obtain the rigid transformation matrix (R, T) between the two, that is, the rotation and translation relationship;

[0146] Step 34: Project the pixel coordinates of the wire obtained by image recognition into the three-dimensional coordinate system of the lidar point cloud according to the obtained transformation matrix to obtain the registered three-dimensional wire.

[0147] Step 35, matching the registered three-dimensional conductor with the target section determined in step 1, confirming the three-dimensional spatial position corresponding to the target section of the line, and obtaining the spatial coordinates and conductor direction information of the target conductor section to be installed;

[0148] In step 4, the objective function is constructed with the goal of minimizing the path length, the energy consumption of the UAV, and the energy consumption of the UAV adjusting its attitude as the minimum:

[0149] minJ=α·L1+β·E+γ·A

[0150] Among them, J: total objective function value, the smaller the better;

[0151] L1: total length of the path;

[0152] E: total energy consumption, the amount of electricity consumed during the flight;

[0153] A: Energy consumption of the UAV attitude adjustment, the additional energy consumption caused by the change of heading angle; α, β, γ: The weight of each item in the overall goal, which is the set value;

[0154] The path length term L uses the Euclidean distance and is calculated as follows:

[0155]

[0156] Among them, P i Represents the three-dimensional coordinates (x i ,y i ,z i );

[0157] The flight energy consumption term E is calculated as follows:

[0158]

[0159] Where C1 represents the energy consumption coefficient per meter of horizontal flight, and C2 represents the energy consumption gain factor of vertical flight. By equating the direct distance to the horizontal flight calculation and then calculating the energy consumption in the vertical direction separately, the flight energy consumption is simplified and the calculation efficiency is improved.

[0160] The energy consumption item A for attitude adjustment is calculated as follows:

[0161]

[0162] Among them, Δφ i , Δθ i , Δψ i : are the attitude angle changes between step i and step i+1, namely roll angle, pitch angle and yaw angle;

[0163] W1, W2, W3: weighting factors for adjusting energy consumption in corresponding postures;

[0164] In step 5, a heuristic search algorithm is used to solve the objective function, and the cost function f(n) of the heuristic search algorithm is constructed based on the objective function J:

[0165] f(n)=g(n)+h(n)

[0166] g(n): The current path cost from the starting point to n, that is, the cumulative value of the objective function from the first point to the nth point;

[0167] h(n): estimated path cost from point n to the end point;

[0168] During the search process, the path length term, flight energy consumption term E, and attitude adjustment energy consumption term A are dynamically evaluated. The objective function is minimized to obtain all path points as the planned path, as well as the flight control parameters of the aircraft at each path point.

[0169] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0170] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. The automatic mounting system for fault indicators based on drones is characterized by: The method comprises a mounting device and a drone, wherein the mounting device is mounted with a fault indicator, the mounting device is fixedly arranged on the drone, and the drone is used to carry the fault indicator and transport it to a target location; It also includes a laser radar and an image acquisition device installed on the drone. The drone control system fuses the collected laser point cloud data and images, identifies the mounting target position and automatically generates a mounting strategy to realize the automatic mounting of the fault indicator.

2. The automatic mounting system for a fault indicator based on a drone according to claim 1, characterized in that: The UAV control system fuses the collected laser point cloud data and images, identifies the mounting target location, and automatically generates a mounting strategy, including the following steps: Get the target section on the line; Start the LiDAR to scan the target area and surrounding areas, obtain LiDAR point cloud data, acquire image data, and align the image data with the point cloud data along the time axis; The acquired LiDAR data is integrated with the image data to obtain the target wire section to be installed; The objective function is constructed with the goal of minimizing the path length, the energy consumption of the UAV, and the energy consumption of the UAV adjusting its attitude. The UAV's position and flight data are acquired in real time, and the objective function is solved using a heuristic search algorithm to obtain the UAV's flight path and the UAV's flight control parameters on the path.

3. The automatic mounting system for a fault indicator based on a drone according to claim 1, characterized in that: The hanging device includes a Y-shaped bracket, which includes a first guide leg and a second guide leg. The connecting ends of the first guide leg and the second guide leg form a U-shaped indicator placement slot; at the top of the indicator placement slot, a tripping slot with an opening facing downward is provided on the first guide leg, and a fixed slot with an opening facing upward is provided on the second guide leg; the tripping slot and the fixed slot are respectively adapted to the spring legs on the fault indicator.

4. The automatic mounting system for a fault indicator based on a drone as claimed in claim 3, characterized in that: A shock absorber is provided on the second guide leg near the fixed slot, and the shock absorber slot is provided in a triangular hollow area.

5. The automatic mounting system for a UAV fault indicator according to claim 1, characterized in that: The fault indicator includes an indicator lamp body and a hanging seat body. A V-shaped spring mounting bracket is provided on the hanging seat body. A through hole is provided on the spring mounting bracket. A fixed shaft is installed in the through hole, and a spring leg is installed on the fixed bracket.

6. The automatic mounting system for a fault indicator based on a drone as claimed in claim 5, characterized in that: On the mounting seat, a limit block is arranged relatively outside the position where the spring leg is arranged, so as to form a setting space for the indicator placement groove wall of the Y-shaped bracket on the mounting device.

7. The automatic mounting system for a fault indicator based on a drone according to claim 5, characterized in that: A protrusion is provided on the mounting seat, corresponding to the inner side of the position where the spring leg is provided; Alternatively, the indicator lamp body includes a transparent protective cover and a base, with a shielding plate and a warning block set in sequence to fit the transparent protective cover, and the shielding plate can be rotatably set on the outside of the warning block; the shielding plate is rotated to a first position to overlap with the warning block, and the shielding plate is rotated to a second position to not overlap with the warning block; a warning light and a control component are set on the base.

8. The automatic mounting method of a fault indicator based on a drone is characterized by: The steps include: Get the target section on the line; Start the LiDAR to scan the target area and surrounding areas, obtain LiDAR point cloud data, acquire image data, and align the image data with the point cloud data along the time axis; The acquired LiDAR data is integrated with the image data to obtain the target wire section to be installed; The objective function is constructed with the goal of minimizing the path length, the energy consumption of the UAV, and the energy consumption of the UAV adjusting its attitude. The UAV's position and flight data are acquired in real time, and the objective function is solved using a heuristic search algorithm to obtain the UAV's flight path and the UAV's flight control parameters on the path.

9. The method for automatically mounting a fault indicator on a drone as claimed in claim 8, wherein: The method for obtaining a target section on a route includes the following steps: Control the drone from the take-off point to the area where the wires are to be installed and hover; Turn on the image acquisition device, control the drone to move until the target section on the route is within the camera area of ​​the image acquisition device, and capture an image containing the target section; Identify the collected image to obtain the wire target, set the target frame for segmentation marking; Obtain selection information of the target section through human-computer interaction, and determine the target section on the route based on the selection information; Alternatively, the collected image is identified to obtain the wire target, and a target frame is set for segmentation marking, including: Acquire the collected images and perform preprocessing; For the preprocessed image, the Canny operator or Sobel operator is used to extract the image edge; For edge lines extracted from the image, two edge lines with a distance consistency rate higher than a set value and within a set range are extracted to form a guide wire; among the obtained guide wires, a guide wire extending along a straight line or an arc is identified as a first target guide wire; Identify the intersection of the first target wire, use the intersection as the dividing point, split the first target wire according to the set split length, and mark the target box.

10. The method for automatically mounting a fault indicator on a drone as claimed in claim 8, wherein: The method of fusing the acquired laser radar data with the image data to obtain the target wire section to be installed includes the following steps: Perform noise filtering on the original point cloud data acquired by the lidar and extract the point cloud layer data corresponding to the wire body; Perform image recognition on the image data to identify the wire and extract the pixel coordinates and bounding box of the wire in the two-dimensional image; Use the camera-lidar extrinsic calibration tool to calibrate the extrinsic parameters of the lidar and image acquisition device to determine the rigid transformation matrix of the data collected by the lidar and image acquisition device; The pixel coordinates of the wire obtained by image recognition are projected into the three-dimensional coordinate system of the lidar point cloud according to the obtained transformation matrix to obtain the registered three-dimensional wire; The aligned three-dimensional conductor is matched with the target section to confirm the three-dimensional spatial position corresponding to the target section of the line, and the spatial coordinates and conductor direction information of the target conductor section to be installed are obtained.

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