Measurement method and device based on unmanned aerial vehicle and edge computing equipment

Abstract

The invention provides a measurement method and device based on an unmanned aerial vehicle and edge computing equipment. The measurement method comprises the steps of controlling the unmanned aerial vehicle to cruise a distribution wire according to a preset route; the unmanned aerial vehicle is controlled to carry out image acquisition on the power distribution wire through a camera in the cruising process to obtain a line image; the unmanned aerial vehicle is controlled to obtain spatial position coordinates of at least three key points on the power distribution wire and spatial position coordinates of base poles located at the two ends of the at least three key points in the cruising process; and preprocessing the line image to obtain a processed image. The technical problem solved by the invention is that sag measurement of a power distribution wire generally depends on a traditional manual measurement method in the power industry. Although the method meets the basic measurement requirement to a certain extent, the inherent limitation of the method is increasingly prominent, and the method becomes an important bottleneck restricting the improvement of the power operation and maintenance efficiency and precision.

Description

Technical Field

[0001] The present application relates to the field of electric power inspection technology, and more specifically, to a drone-based measurement method, device, and edge computing device. Background Art

[0002] In the power transmission system, the sag of the distribution conductor is a key indicator to measure the degree of natural droop of the conductor under the action of gravity. Its accuracy is directly related to the efficiency, stability and even the safety of the entire power grid of power transmission. Reasonable control of sag is not only the basis for ensuring that power transmission is not disturbed by external environmental factors (such as wind and temperature changes), but also a necessary measure to prevent potential safety risks such as conductor breakage and short circuit.

[0003] For a long time, the power industry has generally relied on traditional manual measurement methods for sag measurement of distribution conductors. Although this method has met basic measurement needs to a certain extent, its inherent limitations have become increasingly prominent, becoming an important bottleneck restricting the improvement of power operation and maintenance efficiency and accuracy. Summary of the invention

[0004] The technical problem solved by this application is the sag measurement of distribution conductors. The power industry generally relies on traditional manual measurement methods. Although this method meets the basic measurement needs to a certain extent, its inherent limitations are becoming increasingly prominent, becoming an important bottleneck restricting the improvement of power operation and maintenance efficiency and accuracy.

[0005] To solve the above problems, the present application provides a measurement method based on a drone, which is used to measure the sag data of a distribution conductor; the measurement method comprises: controlling the drone to cruise the distribution conductor according to a preset route; controlling the drone to collect images of the distribution conductor through a camera during the cruising process to obtain a line image; controlling the drone to obtain the spatial position coordinates of at least three key points on the distribution conductor and the spatial position coordinates of base poles at both ends of the at least three key points during the cruising process; preprocessing the line image to obtain a processed image; using an edge detection algorithm to extract multiple edge points from the processed image to obtain an edge point set composed of multiple edge points; fitting each edge point in the edge point set to obtain a curve model; calibrating the curve model through the spatial position coordinates of at least three key points and the spatial position coordinates of the base poles at both ends of the at least three key points; calculating the sag value of the calibrated curve model to obtain a calculation result; comparing the calculation result with a preset safety standard value to generate a sag measurement report.

[0006] Compared with the existing technology, the technical effect achieved by adopting this technical solution is: the autonomous cruising of the drone on the preset route replaces the manual sag detection method, thereby improving the efficiency of data collection. Combined with the high-resolution camera carried by the drone, the clarity and accuracy of image acquisition are ensured, providing a high-quality data source for subsequent processing. In the image processing link, the advanced image recognition algorithm is used to automatically and accurately identify the contour and position of the distribution wire, and effectively extract the key feature of sag. This process not only reduces manual intervention and human errors, but also greatly improves the processing speed and efficiency. The data analysis module relies on powerful computing power and uses an accurate mathematical model to calculate the sag value based on the extracted sag characteristics. Through intelligent comparison with the preset standard, it can quickly identify whether the line status is abnormal, providing a timely and accurate reference for the maintenance and maintenance of power lines. This process not only reflects the high precision and reliability of the technology, but also demonstrates its strong adaptability and stability in complex environments.

[0007] In an example of the present application, the preprocessing includes: image denoising, grayscale adjustment, and edge enhancement.

[0008] In one example of the present application, a drone is controlled to capture images of distribution wires through a camera during a cruise to obtain a line image, including: formulating a cruise route for the drone according to the geographical location of the distribution wires and a preset safety distance; wherein the preset safety distance is a distance set between the drone and the distribution wires; while the drone is moving along the cruise route, the camera is controlled to capture images of the distribution wires according to a shooting angle associated with the cruise route to obtain a line image.

[0009] In one example of the present application, the calculation result is compared with a preset range threshold to generate a sag measurement report, including: if the actual sag value in the calculation result does not fall within the preset range threshold, it is determined that the distribution conductor is in a potential risk state; the actual sag value is matched with multiple preset sag values ​​stored in a database to obtain a matching result; the risk level associated with the actual sag value is determined based on the matching result; risk avoidance measures are formulated for the distribution conductor based on the risk level; the risk avoidance measures are input into the sag measurement report; if the actual sag value in the calculation result falls within the preset range threshold, it is determined that the distribution conductor is in a safe state.

[0010] In an example of the present application, three base poles arranged adjacent to each other in sequence are defined as a first base pole, a second base pole and a third base pole, the distribution conductor formed between the first base pole and the second base pole is a first distribution conductor, and the distribution conductor formed between the second base pole and the third base pole is a second distribution conductor; any base pole is provided with a pulling structure for pulling the distribution conductor; the risk avoidance measures include: when controlling the drone to cruise the first distribution conductor, if the first distribution conductor is in a potential risk state and the second distribution conductor is in a safe state, then controlling the pulling structure to transport the first distribution conductor in the direction of the second distribution conductor to reduce the actual sag value of the first distribution conductor.

[0011] In one example of the present application, the parabola approximation method or the catenary formula is used to calculate the sag value of the calibrated curve model.

[0012] In one example of the present application, a polynomial curve is used to represent the curve model, and the least square method is used to calculate each polynomial coefficient of the polynomial.

[0013] In one example of the present application, the Canny edge detection algorithm is used to extract edge points in the line graph.

[0014] On the other hand, the present application example also provides a drone-based measuring device, the measuring method is used to measure the sag data of the distribution conductor; the measuring device adopts the measuring method in any of the above examples; the measuring device includes: A control module, which is used to control the UAV to cruise the power distribution wires according to a preset route; Control the UAV to collect images of the distribution wires through the camera during the cruise process to obtain line images; Control the UAV to obtain the spatial position coordinates of at least three key points on the distribution wire and the spatial position coordinates of the base poles located at both ends of the at least three key points during the cruise process; A processing module, the processing module is used to pre-process the line image to obtain a processed image; The edge computing module is used to extract multiple edge points in the processed image using an edge detection algorithm to obtain an edge point set consisting of the multiple edge points; A fitting module, which is used to fit each edge point in the edge point set to obtain a curve model; A calibration module, the calibration module is used to calibrate the curve model through the spatial position coordinates of at least three key points and the spatial position coordinates of the base poles located at both ends of the at least three key points; A calculation module, the calculation module is used to calculate the sag value of the calibrated curve model to obtain a calculation result; The generation module is used to compare the calculation result with the preset range threshold and generate a sag measurement report.

[0015] Compared with the prior art, the technical effect achieved by adopting this technical solution is: it can achieve the technical effect corresponding to the technical solution in any of the above examples, which will not be repeated here.

[0016] On the other hand, the example of the present application also provides an edge computing device, including: a memory and a processor; the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the various steps of the measurement method in any of the above examples.

[0017] Compared with the prior art, the technical effect achieved by adopting this technical solution is: it can achieve the technical effect corresponding to any of the above examples, which will not be repeated here.

[0018] After adopting the technical solution of this application, the following technical effects can be achieved: The autonomous cruising of the drone on the preset route replaces the manual sag detection method, which improves the efficiency of data collection. Combined with the high-resolution camera carried by the drone, the clarity and accuracy of image acquisition are ensured, providing a high-quality data source for subsequent processing. In the image processing link, the advanced image recognition algorithm is used to automatically and accurately identify the outline and position of the distribution wire, and effectively extract the key feature of sag. This process not only reduces manual intervention and human errors, but also greatly improves the processing speed and efficiency. The data analysis module relies on powerful computing power and uses an accurate mathematical model to calculate the sag value based on the extracted sag characteristics. Through intelligent comparison with the preset standard, it can quickly identify whether the line status is abnormal, providing a timely and accurate reference for the maintenance and maintenance of power lines. This process not only reflects the high precision and reliability of the technology, but also demonstrates its strong adaptability and stability in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. Figure 1 A schematic diagram of the system architecture provided for an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of a measurement method based on a drone 102 provided in an embodiment of the present application; Figure 3 A schematic diagram of module connections of a drone-based measurement device is also provided for the embodiment of the present application; Figure 4 A structural diagram of an edge computing device 101 is also provided for an embodiment of the present application.

[0020] Description of reference numerals: 101, edge computing device; 102, drone; 103, power facility; 401, processor; 402, communication bus; 403, network interface; 404, memory; 10. Measuring device; 11. Control module; 12. Processing module; 13. Edge computing module; 14. Fitting module; 15. Calibration module; 16. Calculation module; 17. Generation module. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] Specifically, manual sag measurement often faces the following challenges: Inefficiency: Manual measurement requires sending professionals with professional equipment to the site to collect and calculate data point by point. The process is cumbersome and time-consuming, especially when facing large-scale and long-distance distribution networks. Its low efficiency is particularly significant, and it is difficult to meet the modern power management requirements of rapid response and efficient operation and maintenance.

[0023] Insufficient accuracy: Manual measurement is susceptible to human errors, environmental factors (such as line of sight interference, weather changes) and equipment accuracy limitations, resulting in deviations in measurement results. It is difficult to accurately reflect the actual sag state of the distribution conductor, which may affect subsequent analysis decisions and operation and maintenance strategy formulation.

[0024] Complex operation: Traditional measurement methods often require measurement personnel to have high professional skills and rich practical experience to accurately perform measurement tasks. At the same time, a variety of correction factors need to be considered during the measurement process, such as wire material, temperature coefficient, etc., making the entire measurement process complex and changeable, increasing the difficulty and cost of operation.

[0025] In view of the above problems, see Figure 2 , which is a flow chart of a measurement method based on drone 102 provided in an embodiment of the present application. Specifically, the measurement method is used to measure the sag data of the distribution conductor; the measurement method specifically includes: S1, controlling the drone 102 to cruise the distribution wires according to a preset route; S2, controlling the drone 102 to collect images of the distribution wires through a camera during cruising to obtain line images; S3, controlling the drone 102 to obtain the spatial position coordinates of at least three key points on the distribution wire and the spatial position coordinates of the base poles located at both ends of the at least three key points during the cruising process; Among them, the edge detection algorithm is executed by the edge computing device 101. First, according to the geographical location of the distribution wire and the preset safety distance, the cruising route of the drone 102 is planned. The cruising route must ensure that the drone 102 can fully cover the distribution wire area while avoiding obstacles. The edge computing device 101 sends a cruising instruction to the drone 102 through a wireless communication module (such as 4G / 5G, Wi-Fi or a dedicated communication frequency band), including parameters such as route coordinates, flight altitude, and speed. During the cruising process, the drone 102 uses the high-definition camera on board to capture images of the distribution wire in real time, and transmits the image data back to the edge computing device 101 in real time. At the same time, the drone 102 obtains the spatial position coordinates of at least three key points on the distribution wire (such as the midpoint, turning point, etc.) and the spatial position coordinates of the two end base poles through the built-in GPS module or visual positioning algorithm, and transmits this information together.

[0026] S4, preprocessing the line image to obtain a processed image; After receiving the line image data sent back by the drone 102, the edge computing device 101 uses algorithms such as median filtering and Gaussian filtering to remove noise in the image and improve the image quality. The color image is converted into a grayscale image to reduce the amount of calculation for subsequent processing. The image contrast is enhanced using methods such as histogram equalization and contrast stretching to make the edges of the distribution wires clearer.

[0027] S5, extracting a plurality of edge points from the processed image using an edge detection algorithm to obtain an edge point set consisting of the plurality of edge points; Among them, edge detection algorithms specifically include Canny, Sobel, Prewitt and other edge detection algorithms. The most appropriate algorithm is selected according to the characteristics of the preprocessed image. The selected edge detection algorithm is applied to the preprocessed image to extract the edge points of the distribution wires and form an edge point set.

[0028] Furthermore, in some embodiments of the present application, edge points are extracted using the Canny edge detection algorithm, which specifically includes the following steps: Vertical gradient calculation: Calculate the horizontal and vertical gradients of the processed image. .

[0029] in, and The images are and Directional gradient.

[0030] Gradient magnitude and direction: .

[0031] in, is the gradient magnitude, is the gradient direction.

[0032] Non-maxima suppression: Refine edges by suppressing non-maxima.

[0033] Double threshold processing: set high and low thresholds, mark points with gradient amplitude greater than the high threshold as strong edges, points with gradient amplitude less than the low threshold as non-edges, and points between the two as weak edges according to their connectivity.

[0034] Edge connection: Connect strong edges and weak edges to get the final edge graph.

[0035] S6, fitting each edge point in the edge point set to obtain a curve model; Among them, according to the characteristics of the edge point set, one or more methods such as least squares method, Hough transform, random sample consensus algorithm (RANSAC) are selected for curve fitting. Using the selected fitting method, the points in the edge point set are fitted into a smooth curve model, which can better reflect the actual shape of the distribution conductor.

[0036] In some embodiments of the present application, the curve model to be fitted can be represented by an n-order polynomial, and the edge point set is fitted by the least squares method to obtain the left and right polynomial coefficients of the n-order polynomial.

[0037] Among them, let the n-order polynomial be: in, , ,…, are the coefficients of the polynomial, and x is the independent variable (in the case of distribution conductors, it may be some measure along the conductor direction, such as distance, angle, etc.). For each point (xi,yi) in the set of edge points (where yi is the vertical position of the distribution conductor at that point obtained by edge detection), the square of the vertical distance from the point to the polynomial curve can be calculated and summed as the error function E. Apply the least squares method: In order to find the polynomial coefficients that minimize the error function E, it is necessary to take the partial derivative of the error function and set it to 0. This usually leads to a system of linear equations (normal equations), and solving this system of equations can obtain the coefficients of the polynomial. Solve the linear equation system: Use Gaussian elimination, LU decomposition, QR decomposition or other numerical methods to solve the linear equation system Ax=b and obtain the coefficients a0, a1,…, an of the polynomial. Verify and adjust: Use the obtained coefficients to build a polynomial model and optionally verify how well the model fits the set of edge points.

[0038] S7, calibrating the curve model through the spatial position coordinates of at least three key points and the spatial position coordinates of base poles located at both ends of the at least three key points; The spatial position coordinates of at least three key points and the spatial position coordinates of the base poles at both ends acquired by the drone 102 are integrated with the curve model obtained by fitting. The parameters of the curve model are adjusted by mathematical methods (such as least squares optimization) so that it passes through these key points more accurately and is consistent with the position of the base poles.

[0039] In some embodiments of the present application, the edge computing device 101 uses the GPS module on the drone 102 to obtain the spatial position coordinates of three key points on the distribution line (x1, y1, z1), (x2, y2, z2), (x3, y3, z3). The pixel coordinates of the fitted curve model are converted into spatial position coordinates, and the spatial position coordinates of the fitted curve model are calibrated by the spatial position coordinates of the three key points to improve the accuracy.

[0040] During the cruise, the drone 102 can obtain the precise spatial position coordinates (x1, y1, z1), (x2, y2, z2), (x3, y3, z3) of three key points (such as midpoints, turning points, etc.) on the distribution wire through its onboard GPS module and possible visual positioning algorithms (such as SLAM). These coordinates are relative to a global coordinate system (such as WGS-84 or a local coordinate system).

[0041] Conversion of the curve model from pixel coordinates to spatial coordinates: The fitted curve model is initially based on the image pixel coordinate system. In order to convert it to the actual spatial coordinate system, the following steps are required: Camera calibration: First, the camera on the drone 102 needs to be calibrated to obtain the camera's intrinsic parameters (focal length, optical center, etc.) and extrinsic parameters (the camera's position and attitude in the world coordinate system).

[0042] Coordinate transformation: Using the parameters obtained from camera calibration, the pixel coordinates in the image are transformed into three-dimensional coordinates in the camera coordinate system. Then, these coordinates are further transformed into spatial position coordinates in the global coordinate system through the camera's external parameters.

[0043] Calibration of the curve model: Once the curve model is converted to a spatial coordinate system, the model can be calibrated using the spatial position coordinates of three key points. The purpose of calibration is to ensure that the model is as consistent as possible with the actual distribution conductor position at the key points.

[0044] Optimizing model parameters: If the curve model is represented by a polynomial or other parameterized form, these parameters can be optimized to minimize the model's error at key points. This usually involves solving an optimization problem where the objective function is some distance measure (such as Euclidean distance) between the model's predicted position at the key points and the actual position.

[0045] Reshape the model: If the curve model is not parametric, or optimizing the parameters is not sufficient to achieve the desired accuracy, more complex reshape methods may be required, such as spline-based curve editing or machine learning-based shape regression.

[0046] S8, calculating the sag value of the calibrated curve model to obtain a calculation result; Among them, sag refers to the maximum vertical distance that the distribution conductor naturally sags between two points (such as base poles) under the action of its own weight and wind force. The sag value is obtained through mathematical calculation (such as calculating the vertical distance from the lowest point on the curve to the base pole connection) based on the calibrated curve model.

[0047] In some embodiments of the present application, the sag value of the distribution conductor is calculated using a parabola approximation method or a catenary formula.

[0048] The parabola approximation method is a simplified calculation method, which assumes that the curve formed by the distribution wire under the action of its own weight is close to a parabola. This method is simple to calculate and is suitable for occasions where the wire span is not large, the sag is small, and the accuracy requirement is not particularly high.

[0049] Calculation steps: Determine the parameters: First, you need to know the span L of the distribution conductor (that is, the distance between the two end base poles), the weight w of the conductor per unit length (including the weight of the conductor itself and the weight of ice, snow, etc. that may be attached), and the horizontal tension T of the conductor.

[0050] Calculate sag: For the parabola approximation, the sag f (i.e. the vertical distance from the lowest point of the conductor to the connection line between the two end poles) can be calculated using the following formula: f=wL2 / 8T. Note: This formula is based on some simplified assumptions and may need to be adjusted according to specific circumstances in actual applications.

[0051] In the above embodiment, if the parameters such as the span, weight and tension of the conductor have been obtained through the drone 102 and the edge computing device 101, and it is decided to use the parabolic approximation method, then these parameters can be directly substituted into the above formula to calculate the sag value.

[0052] Among them, the catenary formula is a more accurate calculation method, which is based on the actual physical form of the wire under its own weight - the catenary. The catenary formula can more accurately describe the sag of the wire under different spans and tensions.

[0053] Calculation steps: Determine the parameters: It is also necessary to know the span L of the distribution conductor, the weight w per unit length of the conductor, and the horizontal tension T of the conductor.

[0054] Using the catenary formula: The shape of the catenary can be described by a complex mathematical formula, but usually we use approximate numerical methods or lookup tables to solve it. The general form of the catenary formula is relatively complicated and is omitted here, which is not convenient for writing a simple analytical solution directly. In practical applications, you can use ready-made software tools or library functions to calculate the sag value under given parameters.

[0055] Application: If the embodiment requires a high accuracy of the sag value, or the conductor span is large and the sag is obvious, the catenary formula can be used for calculation. On the edge computing device 101, the calculation of the catenary formula can be implemented by integrating a corresponding algorithm library or calling a cloud service.

[0056] It should be noted that when choosing to use the parabola approximation method or the catenary formula, it is necessary to weigh the actual situation and accuracy requirements. No matter which method is used, it is necessary to ensure that the input parameters (such as span, weight, tension, etc.) are accurate and reliable. In practical applications, it may be necessary to consider the influence of other factors (such as wind direction, wind speed, temperature, etc.) on the sag value, but these factors usually need to be analyzed and corrected through more complex models or experimental data.

[0057] S9, comparing the calculated result with a preset range threshold, and generating a sag measurement report.

[0058] The calculated sag value is recorded and related parameters (such as cruising time, weather conditions, drone 102 status, etc.) are attached. The calculated sag value is compared with the preset range threshold to determine whether there are safety hazards in the distribution wire. Based on the comparison results, a sag measurement report is generated. The sag measurement report includes, for example, measurement data, comparison results, safety assessment, and possible recommended measures. The generated sag measurement report is saved in the form of an electronic document and sent to relevant personnel (such as operation and maintenance personnel, management personnel, etc.) through the network or other means for viewing and processing.

[0059] In an embodiment of the present application, the drone 102 can set the flight route, flight altitude and other parameters through a remote control or a ground station, and supports the automatic flight function of a preset route. The drone 102 is equipped with a high-resolution camera and a GPS positioning system. During the cruise flight, the distribution guide is collected in real time with real-time images and GPS spatial coordinates, and the flight status is uploaded in real time. The image data and GPS spatial coordinates are processed and modeled by dedicated analysis software. The edge computing device 101 can be connected to the drone 102 by wire or wireless means to receive and process the collected data. After the processing is completed, the edge computing device 101 generates a sag measurement report and archives or outputs the results for further analysis. The remote control is equipped with an application background that can display the sag analysis results. The results generated by the edge computing device 101 are transmitted to the application background of the remote control, and the analysis results are displayed on the remote control screen.

[0060] Among them, through the remote control or ground station, the operator can pre-set the flight route, flight altitude, speed and other necessary flight parameters of the drone 102. The setting of these parameters should take into account the layout of the distribution wires, terrain conditions and safe flight requirements. The drone 102 supports the automatic flight function of the preset route. Once the parameters are set and the automatic flight mode is started, the drone 102 will cruise along the preset route without manual real-time control. During the cruise flight, the high-resolution camera equipped with the drone 102 is responsible for real-time capture of the image of the distribution wires, and the GPS positioning system records the spatial position coordinates of the drone 102 in real time. These data are uploaded to the edge computing device 101 in real time through the built-in communication module of the drone 102.

[0061] The edge computing device 101 establishes a connection with the drone 102 via a wired or wireless method, and receives image data and GPS spatial coordinates from the drone 102 in real time. This real-time data transmission ensures the timeliness and accuracy of the data. The received image data is first preprocessed, including steps such as denoising and contrast enhancement, to improve the accuracy of subsequent analysis. Then, the image is edge detected using dedicated analysis software to extract the edge contour of the distribution conductor. At the same time, the GPS spatial coordinates are matched with the pixel coordinates in the image data to achieve the conversion of image coordinates to actual spatial coordinates. Based on the extracted edge contour of the distribution conductor and the converted spatial coordinates, the sag value is calculated using mathematical models such as the parabola approximation method or the catenary formula. Multiple iterations and optimizations may be required during the calculation process to ensure the accuracy of the results.

[0062] After completing data processing and model calculation, the edge computing device 101 automatically generates a sag measurement report. The report content includes but is not limited to measurement data, calculation results, safety assessment, and possible recommended measures. The generated sag measurement report is archived in the storage medium of the edge computing device 101 for subsequent review and audit. At the same time, the report can also be output to other devices or systems in the form of an electronic document for further analysis and utilization. The application background equipped with the remote control can receive and display the sag analysis results generated by the edge computing device 101. The operator can intuitively view the measurement results and analysis reports through the remote control screen, so as to quickly understand the sag of the distribution conductor and make corresponding decisions.

[0063] Therefore, in combination with the technical solution of the present application, the autonomous cruising of the drone 102 on the preset route replaces the manual sag detection method, thereby improving the efficiency of data collection. Combined with the high-resolution camera carried by the drone 102, the clarity and accuracy of image acquisition are ensured, providing a high-quality data source for subsequent processing. In the image processing link, advanced image recognition algorithms are used to automatically and accurately identify the contour and position of the distribution wire, and effectively extract the key feature of sag. This process not only reduces manual intervention and human errors, but also greatly improves the processing speed and efficiency. The data analysis module relies on powerful computing power and uses an accurate mathematical model to calculate the sag value based on the extracted sag features. Through intelligent comparison with the preset standard, it is possible to quickly identify whether the line status is abnormal, providing a timely and accurate reference basis for the maintenance and maintenance of power lines. This process not only reflects the high precision and reliability of the technology, but also demonstrates its strong adaptability and stability in complex environments.

[0064] See also Figure 2 , which is an exemplary system architecture of the measurement method based on drone 102 or the measurement device based on drone 102 applied to the present application. Specifically, the system architecture may include: edge computing device 101, drone 102, power facility 103 and remote control device ( Figure 2 The edge computing device 101 and the drone 102 can communicate with each other through a network, and the network is used as a medium for providing communication links between the above-mentioned units. The network may include various types of wired communication links or wireless communication links, for example: the wired communication link includes an optical fiber, a twisted pair or a coaxial cable, etc., and the wireless communication link includes a Bluetooth communication link, a wireless fidelity (WIreless-FIdelity, Wi-Fi) communication link or a microwave communication link, etc.

[0065] Among them, the remote control device is used to control the flight of the drone 102. The drone 102 also supports automatic cruise mode. The drone 102 cruises the distribution wires in the power facilities 103, collects images of the distribution wires in real time through the camera, and sends the collected images to the edge computing device 101 for processing. The edge computing device 101 calculates the sag value of the distribution wire according to the graphics, and then pushes the calculation results to the remote control device.

[0066] It should be understood that Figure 1 The number of edge computing devices 101, remote control devices, power facilities 103, and drones 102 in the figure is only illustrative. According to the implementation requirements, there can be any number of edge computing devices 101, remote control devices, power facilities 103, networks, and drones 102.

[0067] Preferably, the preprocessing includes: image denoising, grayscale adjustment, and edge enhancement.

[0068] Preferably, controlling the drone 102 to collect images of the distribution wires through a camera during cruising to obtain line images includes: The cruise route of the UAV 102 is formulated according to the geographical location of the power distribution wire and the preset safety distance; wherein the preset safety distance is the distance between the UAV 102 and the power distribution wire; When the UAV 102 moves along the cruise route, the camera is controlled to capture images of the distribution wires according to a shooting angle associated with the cruise route to obtain a line image.

[0069] Preferably, the calculation result is compared with a preset range threshold to generate a sag measurement report, including: If the actual sag value in the calculation result does not fall within the preset range threshold, the distribution conductor is determined to be in a potential risk state; Matching the actual sag value with a plurality of preset sag values ​​stored in a database to obtain a matching result; Determine the risk level associated with the actual sag value based on the matching result; Develop risk avoidance measures for distribution conductors according to risk levels; Input risk avoidance measures into sag measurement reports; If the actual sag value in the calculation result falls within the preset range threshold, it is determined that the distribution conductor is in a safe state.

[0070] Preferably, three base poles arranged adjacent to each other are defined as a first base pole, a second base pole and a third base pole, a distribution conductor formed between the first base pole and the second base pole is a first distribution conductor, and a distribution conductor formed between the second base pole and the third base pole is a second distribution conductor; Any electric pole is provided with a pulling structure for pulling the distribution wire; Risk mitigation measures include: When the drone 102 is controlled to cruise the first distribution wire, if the first distribution wire is in a potential risk state and the second distribution wire is in a safe state, the pulling structure is controlled to transport the first distribution wire in the direction of the second distribution wire to reduce the actual sag value of the first distribution wire.

[0071] Preferably, the sag value of the calibrated curve model is calculated using a parabola approximation method or a catenary formula.

[0072] Preferably, a polynomial curve is used to represent the curve model, and the least square method is used to calculate each polynomial coefficient of the polynomial.

[0073] Preferably, the Canny edge detection algorithm is used to extract edge points in the line graph.

[0074] On the other hand, see Figure 3 , which is a module connection diagram of a measuring device 10 based on a drone 102, which is an embodiment of the present application. Specifically, the measuring method is used to measure the sag data of the distribution line, and the measuring device 10 adopts any measuring method in the above embodiments; the measuring device 10 specifically includes a control module 11, a processing module 12, an edge computing module 13, a fitting module 14, a calibration module 15, a computing module 16 and a generating module 17.

[0075] Furthermore, the control module 11 is used to control the UAV 102 to cruise the distribution wires according to a preset route; the control module 11 is used to control the UAV 102 to capture images of the distribution wires through a camera during the cruise to obtain a line image; the control module 11 is used to control the UAV 102 to obtain the spatial position coordinates of at least three key points on the distribution wires and the spatial position coordinates of the base poles at both ends of at least three key points during the cruise.

[0076] Furthermore, the processing module 12 is used to preprocess the line image to obtain a processed image; the edge calculation module 13 is used to extract multiple edge points in the processed image using an edge detection algorithm to obtain an edge point set composed of multiple edge points; the fitting module 14 is used to fit each edge point in the edge point set to obtain a curve model; the calibration module 15 is used to calibrate the curve model through the spatial position coordinates of at least three key points and the spatial position coordinates of the base poles located at both ends of at least three key points; the calculation module 16 is used to calculate the sag value of the calibrated curve model to obtain a calculation result; the generation module 17 is used to compare the calculation result with a preset range threshold to generate a sag measurement report.

[0077] Correspondingly, in this embodiment, the technical effect corresponding to any technical solution in the above embodiments can be achieved, which will not be repeated here.

[0078] On the other hand, see Figure 4 , which is a structural diagram of an edge computing device 101 provided in an embodiment of the present application. The edge computing device 101, for example, includes: a memory and a processor; the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the various steps of the measurement method in the above embodiment.

[0079] Specifically, the edge computing device 101 also includes, for example, at least one network interface 403 and at least one communication bus 402. The communication bus 402 is used to realize the connection and communication between these components. The network interface 403 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface) for communicating with a remote control device.

[0080] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect the various parts of the entire edge computing device 101, and executes various functions and processes data of the edge computing device 101 by running or executing instructions, programs, code sets or instruction sets stored in the memory 404, and calling data stored in the memory 404. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented separately through a chip.

[0081] Among them, the memory 404 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 404 includes a non-transitory computer-readable storage medium. The memory 404 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 404 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 404 may also be optionally at least one storage device located away from the aforementioned processor 401. As Figure 4 As shown, the memory 404 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program.

[0082] exist Figure 4 In the edge computing device 101 shown, the processor 401 can be used to call the application stored in the memory 404 and specifically execute the following Figure 2 The specific process can be referred to Figure 2 As shown, no further description is given here.

[0083] Correspondingly, in this embodiment, the technical effect corresponding to any technical solution in the above embodiments can be achieved, which will not be repeated here.

[0084] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A measurement method based on drone, characterized in that: The measuring method is used to measure the sag data of the distribution conductor; the measuring method comprises: Controlling the UAV to cruise the power distribution wire according to a preset route; Controlling the UAV to collect images of the power distribution wires through a camera during cruising to obtain line images; Controlling the UAV to obtain the spatial position coordinates of at least three key points on the distribution wire and the spatial position coordinates of the base poles located at both ends of the at least three key points during cruising; Preprocessing the line image to obtain a processed image; Extracting a plurality of edge points from the processed image using an edge detection algorithm to obtain an edge point set consisting of the plurality of edge points; Fitting each edge point in the edge point set to obtain a curve model; The curve model is calibrated by using the spatial position coordinates of the at least three key points and the spatial position coordinates of the base poles located at both ends of the at least three key points; Calculating the sag value of the calibrated curve model to obtain a calculation result; The calculation result is compared with a preset range threshold to generate a sag measurement report.

2. The measuring method according to claim 1, characterized in that: The preprocessing includes: image denoising, grayscale adjustment, and edge enhancement.

3. The measuring method according to claim 1, characterized in that: The controlling the UAV to collect images of the distribution wires through a camera during cruising to obtain line images includes: Formulate a cruising route for the UAV according to the geographical location of the power distribution wire and a preset safety distance; wherein the preset safety distance is a distance between the UAV and the power distribution wire; When the UAV moves along the cruise route, the camera is controlled to capture images of the power distribution wires according to a shooting angle associated with the cruise route to obtain the line image.

4. The measuring method according to claim 1, characterized in that: The step of comparing the calculation result with a preset range threshold and generating a sag measurement report includes: If the actual sag value in the calculation result does not fall within the preset range threshold, it is determined that the distribution conductor is in a potential risk state; Matching the actual sag value with a plurality of preset sag values ​​stored in a database to obtain a matching result; determining a risk level associated with the actual sag value according to the matching result; Formulate risk avoidance measures for the distribution conductors according to the risk level; inputting the risk avoidance measures into the sag measurement report; If the actual sag value in the calculation result falls within the preset range threshold, it is determined that the distribution conductor is in a safe state.

5. The measuring method according to claim 4, characterized in that: Define three base poles arranged adjacent to each other in sequence as a first base pole, a second base pole and a third base pole, a distribution conductor formed between the first base pole and the second base pole is a first distribution conductor, and a distribution conductor formed between the second base pole and the third base pole is a second distribution conductor; Any of the base poles is provided with a pulling structure for pulling the distribution wire; The risk mitigation measures include: When the drone is controlled to cruise the first distribution wire, if the first distribution wire is in the potential risk state and the second distribution wire is in a safe state, the pulling structure is controlled to transport the first distribution wire in the direction of the second distribution wire to reduce the actual sag value of the first distribution wire.

6. The measuring method according to claim 1, characterized in that: The sag value of the calibrated curve model is calculated using a parabola approximation method or a catenary formula.

7. The measuring method according to claim 1, characterized in that: The curve model is represented by a polynomial curve, and each polynomial coefficient of the polynomial is calculated using a least square method.

8. The measuring method according to claim 1, characterized in that: The edge points are extracted from the line graph using a Canny edge detection algorithm.

9. A drone-based measurement device, characterized in that: The measuring method is used to measure the sag data of the distribution conductor; The measuring device adopts the measuring method according to any one of claims 1 to 8; the measuring device comprises: A control module, the control module is used to control the UAV to cruise the power distribution wire according to a preset route; Controlling the UAV to collect images of the power distribution wires through a camera during cruising to obtain line images; Controlling the UAV to obtain the spatial position coordinates of at least three key points on the distribution wire and the spatial position coordinates of the base poles located at both ends of the at least three key points during cruising; A processing module, the processing module is used to pre-process the line image to obtain a processed image; An edge computing module, the edge computing module is used to extract a plurality of edge points from the processed image using an edge detection algorithm to obtain an edge point set consisting of the plurality of edge points; A fitting module, the fitting module is used to fit each edge point in the edge point set to obtain a curve model; A calibration module, the calibration module is used to calibrate the curve model through the spatial position coordinates of the at least three key points and the spatial position coordinates of the base poles located at both ends of the at least three key points; A calculation module, wherein the calculation module is used to calculate the sag value of the calibrated curve model to obtain a calculation result; A generation module is used to compare the calculation result with a preset range threshold and generate a sag measurement report.

10. An edge computing device, characterized in that: include: A memory and a processor; the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing each step in the measurement method as claimed in any one of claims 1 to 8.

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