A transmission line UAV micro-machine nest intelligent inspection system and method

Abstract

The present invention discloses a transmission line UAV micro-nest intelligent inspection system and method, which relates to the field of power inspection, solves the problems of great difficulty and unsafe inspection of transmission lines in mountainous areas, and comprises a level determination module, a route formulation module, a route deviation module, an interference confirmation module and an image analysis module; the level determination module is used to determine the fault level and importance level of the transmission line section, the route formulation module is used to formulate a customized inspection route when the UAV inspects the transmission line section according to the importance level and the historical fault level, the interference confirmation module is used to confirm whether the UAV enters an interference area, the route deviation module is used to determine whether the UAV deviates during flight in the interference area according to the real-time position coordinates, and the image analysis module is used to analyze the real-time image of the transmission line section in real time. The present invention realizes intelligent inspection and accurate inspection of the transmission line based on the UAV technology.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power inspection, and specifically is a transmission line UAV micro-nest intelligent inspection system and method. Background Art

[0002] The transmission line is realized by using a transformer to step up the voltage of the electric energy generated by the generator, and then connecting it to the transmission line through control equipment such as circuit breakers. In terms of structural form, the transmission line is divided into overhead transmission lines and cable lines. Overhead transmission lines are composed of line towers, conductors, insulators, line hardware, guy wires, tower foundations, grounding devices, etc., and are erected above the ground. According to the nature of the transmitted current, power transmission is divided into AC transmission and DC transmission. With the continuous development of power technology, transmission lines have been widely used, and the inspection task of transmission lines has become increasingly important.

[0003] However, in the prior art, transmission lines are erected in various mountainous areas. Due to the large ups and downs in the terrain of the mountainous areas, the high-voltage line inspection distance is long and the workload is large. At the same time, it is high-risk. Once encountering severe natural disasters such as ice and snow, heavy rain, and landslides, the line inspection work cannot be carried out effectively;

[0004] To this end, the present invention proposes a transmission line UAV micro-nest intelligent inspection system and method. Summary of the invention

[0005] In view of the deficiencies in the prior art, the object of the present invention is to provide a transmission line UAV micro-nest intelligent inspection system and method.

[0006] The technical problems to be solved by the present invention are:

[0007] How to achieve intelligent and accurate inspection of transmission lines based on drone technology.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A transmission line UAV micro-machine nest intelligent inspection system, comprising an intelligent segmentation module, a user terminal, a task processing module, a level determination module, a database, a route formulation module, a data acquisition module, a route deviation module, an interference confirmation module and an image analysis module; the intelligent segmentation module is used to segment the transmission line into transmission line segments; the user terminal is used to issue an initial inspection task instruction and send it to the task processing module, and the task processing module is used to control the UAV to start the inspection after receiving the initial inspection task instruction; when executing the initial inspection task, the data acquisition module is used to collect the initial inspection data of the transmission line segment and send it to the level determination module; the database is used to store the historical fault data of the transmission line segment and send it to the level determination module; the level determination module is used to determine the fault level and importance level of the transmission line segment, obtain the historical fault level and importance level and send them to the route formulation module; the route formulation module is used to formulate a customized inspection route when the UAV inspects the transmission line segment according to the importance level and the historical fault level, and send the customized inspection route to the task processing module;

[0010] When the UAV performs the inspection task according to the customized inspection route, the data acquisition module is used to collect the real-time position coordinates of the UAV and send them to the interference confirmation module; the database is also used to store the interference sources in the transmission line section and the interference data corresponding to the interference sources and send them to the interference confirmation module; the interference confirmation module is used to confirm whether the UAV enters the interference area, and if an offset detection signal is generated, the real-time position coordinates of the UAV in the interference area are sent to the route offset module; the route offset module is used to determine whether the UAV deviates during the flight in the interference area based on the real-time position coordinates, and if it deviates, the flight path adjustment instruction is sent to the task processing module;

[0011] The data acquisition module is used to collect real-time images of the transmission line section and send them to the image analysis module. The image analysis module is used to analyze the real-time images of the transmission line section in real time. If the analysis generates a damage signal or an abnormal signal, the location coordinates and the corresponding real-time image are sent to the user terminal. The user terminal is used to view the real-time image of the transmission line section.

[0012] Furthermore, the initial inspection data includes the number of line branches of the transmission line section and the number of households supplied by the line;

[0013] The historical fault data includes the number of historical faults of the transmission line section and the maintenance duration at each fault.

[0014] Furthermore, the determination process of the level determination module is as follows:

[0015] Obtain the number of historical faults of each transmission line section, and then obtain the maintenance duration of each transmission line section at each fault, and add and average the maintenance duration at each fault to obtain the average maintenance duration of each transmission line section;

[0016] Calculate the failure coefficient of each transmission line section based on the number of historical failures and the average maintenance duration;

[0017] The fault coefficient is compared with the preset fault coefficient to determine whether the historical fault level of the transmission line section is the first fault level, the second fault level or the third fault level;

[0018] At the same time, the number of line branches and the number of households powered by each transmission line section are obtained, and the important coefficient of each transmission line section is calculated;

[0019] The importance coefficient is compared with the preset importance coefficient to determine whether the importance level of the transmission line section is the first importance level, the second importance level or the third importance level.

[0020] Further, the fault degree of the third fault level is greater than that of the second fault level, and the fault degree of the second fault level is greater than that of the first fault level;

[0021] The third importance level is more important than the second importance level, and the second importance level is more important than the first importance level.

[0022] Furthermore, the working process of the route planning module is as follows:

[0023] The drone uses the importance level of the transmission line section as the first selection factor, the historical fault level of the transmission line section as the second selection factor, and the distance between the drone's corresponding nest and the transmission line section as the third selection factor;

[0024] The drone takes off from the current location of the nest. If there are multiple transmission line sections with the same importance level around the nest, the drone will first select the transmission line section with a higher historical fault level to start inspection;

[0025] If the importance level and historical fault level of the transmission line section are equal, the UAV will select the transmission line section closest to the machine nest to start inspection;

[0026] When the drone reaches the node;

[0027] If the corresponding node is only connected to one transmission line segment, the drone directly inspects the transmission line segment; where the node is the tower of the transmission line segment;

[0028] If the node is connected to two or more transmission line segments, the drone will prioritize the transmission line segment with a higher importance level; when the importance levels of multiple transmission line segments connected to the node are equal, the transmission line segment with a higher historical fault level will be prioritized; when the importance levels and historical fault levels of multiple transmission line segments connected to the node are equal, the drone will randomly select a transmission line segment for inspection;

[0029] If the node is not connected to other transmission line segments, and there are transmission line segments that have not been inspected, the drone will prioritize inspecting the nearest uninspected transmission line segment. When the drone reaches any node, if all other transmission line segments have been inspected, the drone will directly return to the starting point and land.

[0030] According to the above rules, a customized inspection route is obtained when the UAV inspects the transmission line section;

[0031] When flying along the customized inspection route, the UAV flies at different speeds in the transmission line sections corresponding to different importance levels or third fault levels.

[0032] Furthermore, the interference data includes the center position coordinates, center interference intensity and diffusion attenuation rate of the interference source.

[0033] Furthermore, the confirmation process of the interference confirmation module is specifically as follows:

[0034] Calculate the real-time distance between the real-time position coordinates of the drone and the corresponding center position coordinates of the interference source;

[0035] Obtain interference data corresponding to the interference source in the transmission line section, obtain the center position coordinates, center interference intensity and diffusion attenuation rate of the interference source, and obtain the interference radius of the interference source by dividing the center interference intensity by the diffusion attenuation rate;

[0036] The interference area of ​​the interference source is constructed with the center position coordinates as the origin and the interference radius as the radius;

[0037] When the real-time distance is greater than the interference radius, the drone performs inspection tasks outside the interference area and does not perform any operations;

[0038] When the real-time distance is less than or equal to the interference radius, the UAV performs inspection tasks in the interference area of ​​the interference source and generates an offset detection signal.

[0039] Furthermore, the working process of the route deviation module is as follows:

[0040] Construct inspection height ranges and allowable deviation ranges for UAV inspection of transmission line sections;

[0041] If the laser pulse reflection duration is within the allowable deviation range of the drone and the vertical distance between the drone and the transmission line section is within the inspection height range, no operation will be performed;

[0042] If the laser pulse reflection duration is not within the allowable deviation range of the UAV or the vertical distance between the UAV and the power transmission line section is not within the inspection height range, a flight path adjustment instruction is generated.

[0043] Furthermore, the analysis process of the image analysis module is specifically as follows:

[0044] capturing visible light images corresponding to transmission line segments;

[0045] Convert the color image input by the visible light camera into a grayscale image; use Gaussian filtering to smooth the image, remove the non-transmission line insulation layer or background noise in the image, set the threshold range to extract the color of the transmission line insulation layer according to the color difference between the transmission line insulation layer and the background; convert the image to HSV color space, use the threshold segmentation method to extract the pixel area that meets the color range of the transmission line insulation layer, and retain the transmission line insulation layer area; use the Canny edge detection algorithm to extract the edge, match the detected edge shape with the straight line characteristics of the transmission line insulation layer, and filter out the part that is not the transmission line insulation layer; apply the connectivity algorithm to extract the connected area formed by adjacent pixels, and filter out the area that meets the characteristics of the transmission line insulation layer; analyze the area that meets the characteristics of the transmission line insulation layer to determine the situation of the area with the characteristics of the transmission line insulation layer;

[0046] Acquire the thermal image of the insulation layer of the transmission line section in real time, set the temperature range according to the temperature difference between the insulation layer and the background, and extract the area in the thermal image of the insulation layer that meets the set temperature range; use pseudo-color enhancement technology to map the temperature range to color, combine the pseudo-color contrast effect, set a threshold to filter out the background low-temperature area, apply the edge detection algorithm to extract the edge in the thermal image of the insulation layer, combine with shape analysis, retain the slender line features of the insulation layer, and eliminate non-related areas;

[0047] Capture the temperature distribution information of the thermal image of the insulation layer, and mark the areas with increased temperature in the thermal image of the insulation layer and the abnormalities;

[0048] If both the thermal image and the visible light image of the insulation layer show abnormalities, a damage signal is generated, and the position coordinates and the corresponding real-time image are obtained;

[0049] If there is no abnormality in both the thermal image and the visible light image of the insulation layer, no operation is performed;

[0050] If there is an abnormality in either the thermal image or the visible light image of the insulation layer, an abnormality signal is generated, and the position coordinates and the corresponding real-time image are obtained.

[0051] In the second aspect, a transmission line UAV micro-machine nest intelligent inspection method is provided, and the method is as follows:

[0052] Step S101, segmenting the transmission line into transmission line segments, and issuing an initial inspection task instruction at the same time, when the drone performs the initial inspection task;

[0053] Step S102, collecting initial inspection data of the transmission line section and obtaining historical fault data of the transmission line section, and determining the fault level and importance level of the transmission line section according to the initial inspection data and the historical fault data;

[0054] Step S103, formulating a customized inspection route for the drone when inspecting the transmission line section according to the importance level and the historical fault level;

[0055] Step S104, when performing the inspection task according to the customized inspection route, obtain the interference source in the transmission line section and the interference data corresponding to the interference source, and collect the real-time position coordinates of the drone to confirm whether the drone enters the interference area;

[0056] Step S105, if the UAV enters the interference area, the real-time position coordinates of the UAV in the interference area are obtained, and whether the UAV deviates during the flight in the interference area is determined according to the real-time position coordinates;

[0057] Step S106: collecting a real-time image of the power transmission line section, and analyzing the real-time image of the power transmission line section in real time.

[0058] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention first divides the transmission line into transmission line segments, and then when the drone performs the initial inspection task, the initial inspection data and historical fault data are combined to determine the fault level and importance level of the transmission line segment, and then a customized inspection route is formulated when the drone inspects the transmission line segment according to the importance level and historical fault level, so as to realize intelligent inspection of the transmission line;

[0060] 2. The UAV in the present invention performs inspection tasks according to customized inspection routes, and obtains interference sources in the transmission line section and interference data corresponding to the interference sources during the inspection process, and determines whether the UAV enters the interference area in combination with the real-time position coordinates of the UAV. If the UAV enters the interference area, it determines whether the UAV deviates during the flight in the interference area. After the deviance processing is completed, the real-time image of the transmission line section is collected, and the real-time image of the transmission line section is analyzed in real time, thereby realizing accurate inspection of the transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0062] Figure 1 is the overall system block diagram of the present invention;

[0063] Figure 2 A schematic diagram of a customized inspection route in the present invention;

[0064] Figure 3 It is a schematic diagram of the inspection height interval in the present invention;

[0065] Figure 4 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0066] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Example 1: Please refer to Figure 1-Figure 3 As shown, the technical solution provided by the present invention is: a transmission line UAV micro-machine nest intelligent inspection system, including an intelligent segmentation module, a user terminal, a task processing module, a level determination module, a database, a route formulation module, a data acquisition module, a route deviation module, an interference confirmation module and an image analysis module;

[0068] The intelligent segmentation module is used to segment the transmission line. Specifically, the transmission line can be divided into a plurality of transmission line segments e based on the iron towers of the transmission line, where e=1, 2, 3, ..., n, where n is a positive integer;

[0069] In specific implementation, the user terminal is used for the user to issue an initial inspection task instruction in the cloud, and send the initial inspection task instruction to the task processing module, and the task processing module is used to receive the initial inspection task instruction. The UAV starts inspection after the task processing module receives the initial inspection task instruction;

[0070] When the UAV is performing the initial inspection task, the data acquisition module is used to collect the initial inspection data of the transmission line section and send the initial inspection data to the level determination module; wherein the initial inspection data includes the number of line branches of the transmission line section and the number of households powered by the line. Specifically, the data acquisition module can actually be a counter;

[0071] In this embodiment, the database is used to store historical fault data of the transmission line segment, and send the historical fault data of the transmission line segment to the level determination module; wherein the historical fault data is the number of historical faults of the transmission line segment and the maintenance duration of each fault;

[0072] The level determination module is used to determine the fault level and importance level of the transmission line section. The determination process is as follows:

[0073] Step S1: Obtain the number of historical faults of each transmission line section, and mark the number of historical faults of the transmission line section as Ge; then obtain the maintenance duration of each transmission line section at each fault, add and average the maintenance duration of each fault to obtain the average maintenance duration of each transmission line section, and mark the average maintenance duration of the transmission line section as We;

[0074] Step S2: The fault coefficient GZe of each transmission line section is calculated by the formula GZe=Ge×a1+We×a2, wherein a1 and a2 are weight coefficients, and the values ​​of a1 and a2 are both greater than zero. The fault coefficient is used to indicate the fault degree of the transmission line section. The larger the fault coefficient, the higher the fault degree of the transmission line section. Similarly, the smaller the fault coefficient, the lower the fault degree of the transmission line section. The fault coefficient is directly proportional to the number of historical faults and the maintenance time of the transmission line section.

[0075] Step S3: if GZe<X1, the historical fault level of the transmission line section is the first fault level; if X1≤GZe<X2, the historical fault level of the transmission line section is the second fault level; if X2≤GZe, the historical fault level of the transmission line section is the third fault level; wherein the fault degree of the third fault level is greater than the second fault level, the fault degree of the second fault level is greater than the first fault level, X1 and X2 are both preset fault coefficients with fixed values, and X1<X2;

[0076] Step S4: Obtain the number of line branches Se and the number of households supplied by the line Ye of each transmission line segment;

[0077] Step S5: The importance coefficient ZYe of each transmission line section is calculated by the formula ZYe=Se×a3+Ye×a4, wherein a3 and a4 are weight coefficients, and the values ​​of a3 and a4 are both greater than zero; the importance coefficient indicates the importance of the transmission line section, and the larger the importance coefficient, the higher the importance of the transmission line section, and similarly, the smaller the importance coefficient, the lower the importance of the transmission line section, and the importance coefficient is directly proportional to the number of line branches of each transmission line section, and is directly proportional to the number of households supplied by each transmission line section;

[0078] Step S6: if ZYe<Y1, the importance level of the transmission line section is the first importance level; if Y1≤ZYe<Y2, the importance level of the transmission line section is the second importance level; if Y2≤ZYe, the importance level of the transmission line section is the third importance level; wherein the importance of the third importance level is greater than the second importance level, the importance of the second importance level is greater than the first importance level, Y1 and Y2 are both preset importance coefficients of fixed values, and Y1<Y2;

[0079] The level determination module sends the historical fault level and importance level of the transmission line segment to the route formulation module; the route formulation module is used to formulate a customized inspection route when the drone inspects the transmission line segment according to the importance level and historical fault level of each transmission line segment. The specific formulation rules are as follows:

[0080] like Figure 2 As shown, Figure 2 The numerical sequence in is the inspection order of the drone. First, the drone uses the importance level of the transmission line section as the first selection factor, the historical fault level of the transmission line section as the second selection factor, and the distance between the drone's corresponding nest and the transmission line section as the third selection factor;

[0081] Step P1: The drone takes off from the current location of the nest. If there are multiple transmission line sections with the same importance level around the nest, the drone will first select the transmission line section with a higher historical fault level to start inspection;

[0082] If the importance level and historical fault level of the transmission line section are equal, the UAV will select the transmission line section closest to the machine nest to start inspection;

[0083] Step P2: When the drone arrives at a node, if the node is only connected to one transmission line segment, the drone directly inspects the transmission line segment without considering its importance level and historical fault level; the node is a tower of the transmission line segment;

[0084] Step P3: If the node is connected to two or more transmission line segments, the drone will prioritize the transmission line segment with a higher importance level;

[0085] When the importance levels of multiple transmission line sections connected to a node are equal, the transmission line section with a higher historical fault level will be inspected first;

[0086] When the importance levels and historical fault levels of multiple transmission line segments connected to the node are equal, the drone can randomly select a transmission line segment for inspection;

[0087] Step P4: When the drone arrives at a node, if the node is not connected to other transmission line segments and there are transmission line segments that have not been inspected, the drone will prioritize inspecting the nearest uninspected transmission line segment;

[0088] When the drone reaches any node, if all other transmission line sections have been inspected, the drone will directly return to the starting point and land;

[0089] Step P5: According to the above rules, a customized inspection route is obtained when the UAV inspects the transmission line section;

[0090] Step P6: When the UAV flies along the customized inspection route, it will fly at a speed b in the transmission line section of the third importance level or the third fault level, fly at a speed c in the transmission line section of the second importance level or the second fault level, and fly at a speed d in the transmission line section of the first importance level or the first fault level, wherein b, c, and d are all natural numbers, and b<c<d, that is, the UAV flies slower in the transmission line section with a higher importance level or historical fault level, so as to collect information of the transmission line section more accurately;

[0091] The route making module sends the customized inspection route of the transmission line section to the task processing module after the route making module is completed.

[0092] Furthermore, when the drone is conducting inspections according to a customized inspection route, interference may occur in the customized inspection route, thereby interfering with the positioning information of the drone;

[0093] The database is used to store interference sources in the transmission line section and interference data corresponding to the interference sources, and send the interference data to the interference confirmation module, the interference data is the center position coordinates, center interference intensity and diffusion attenuation rate of the interference source, the coordinates of the interference source are its GPS coordinate points, and the center interference intensity is the signal interference intensity of the interference source at the center position coordinates;

[0094] The data acquisition module is used to collect the real-time position coordinates of the drone and send the real-time position coordinates to the interference confirmation module. The interference confirmation module is used to confirm whether the drone has entered the interference area. The confirmation process is as follows:

[0095] The real-time distance D between the real-time position coordinates of the drone and the corresponding center position coordinates of the interference source is calculated by the formula. When the interference radius of the interference source is small, the influence of the earth's curvature can be ignored. The plane distance is directly used to approximate the spherical distance. The three-dimensional coordinate system is constructed with the position of the drone as the coordinate origin, the earth's longitude line as the x-axis, the latitude line as the y-axis, and the height direction as the z-axis. The specific formula is as follows:

[0096] ;

[0097] Among them, lat1, lon1 and h1 are the latitude, longitude and altitude of the current position of the drone, lat2, lon2 and h2 are the latitude, longitude and altitude of the interference source, △y represents the latitude difference, △x represents the longitude difference, and △z represents the altitude difference;

[0098] Obtain interference data corresponding to the interference source in the transmission line section, obtain the center position coordinates, center interference intensity and diffusion attenuation rate of the interference source, and obtain the interference radius of the interference source by dividing the center interference intensity by the diffusion attenuation rate;

[0099] The interference area of ​​the interference source is constructed with the center position coordinates as the origin and the interference radius as the radius;

[0100] When the real-time distance is greater than the interference radius, the drone performs inspection tasks outside the interference area. At this time, the interference confirmation module does not work and no operation is performed;

[0101] When the real-time distance is less than or equal to the interference radius, the UAV performs the inspection task in the interference area of ​​the interference source, and the interference confirmation module generates an offset detection signal;

[0102] If the interference confirmation module generates an offset detection signal, the real-time position coordinates of the UAV in the interference area are sent to the route offset module; the route offset module is used to determine whether the UAV deviates during flight in the interference area based on the real-time position coordinates. The specific determination process is as follows:

[0103] First, when the UAV performs the inspection task of the transmission line section, the UAV should maintain the optimal acquisition altitude directly above the transmission line section. Flying below the minimum acquisition altitude or above the maximum acquisition altitude is not conducive to the UAV accurately collecting image information of the transmission line section. The minimum acquisition altitude is taken as the left endpoint and the maximum acquisition altitude is taken as the right endpoint to construct the inspection height range of the UAV. Among them, the optimal acquisition altitude refers to the UAV being directly above the transmission line section. At this time, the vertical distance between the UAV and the upper section of the transmission line section is calculated;

[0104] The UAV conducts inspections directly above the transmission line section according to the customized inspection route. The data acquisition module emits laser pulses to illuminate the transmission line section and records the emission time of the laser pulse and the reception time of the laser pulse reflection. The reception time minus the emission time is the reflection time of the laser pulse. The reflection time of the laser pulse emitted by the UAV at the lowest collection height directly above the transmission line section is called the shortest reflection time. At the same time, the UAV emits laser pulses to illuminate the transmission line section at the highest collection height and at the maximum allowable deviation angle. The longest reflection time of the laser pulse is calculated, and the shortest reflection time is used as the left endpoint and the longest reflection time is used as the right endpoint, thereby constructing the allowable deviation range of the UAV.

[0105] If the laser pulse reflection duration is within the allowable deviation range of the drone and the vertical distance between the drone and the transmission line section is within the inspection height range, it means that the drone is currently navigating normally;

[0106] If the laser pulse reflection time is not within the allowable deviation range of the UAV or the vertical distance between the UAV and the transmission line section is not within the inspection height range, such as Figure 3 As shown in the figure, although the yaw position of the UAV is within the maximum offset angle, it is lower than the lowest altitude collection point, indicating that the current position of the UAV has deviated. At this time, a flight path adjustment instruction for adjusting the flight trajectory of the UAV is generated and sent to the task processing module to ensure that the UAV returns to the customized inspection route.

[0107] When the drone is performing the inspection task, no deviation occurs or the deviation has been corrected. At this time, the data acquisition module is used to collect real-time images of the transmission line section and send the real-time images to the image analysis module. Specifically, the data acquisition module can use the infrared camera and the visible light camera set on the drone to monitor the status of the transmission line section in real time. The real-time image can be a thermal image of the insulation layer or a visible light image.

[0108] In this embodiment, only the damage of the insulation layer of the transmission line section is analyzed. The image analysis module is used to analyze the real-time image of the transmission line section in real time through the data acquisition module. The specific analysis process is as follows:

[0109] Visible light analysis can clearly capture the morphology and surface details of transmission line sections. At the same time, visible light cameras are relatively inexpensive and do not require complex post-processing equipment, making them suitable for large-scale deployment and daily inspections. By combining computer vision technologies (such as edge detection, texture analysis, and deep learning), visible light images can identify rich information such as defect shape and size. However, at night or in low-light environments, the quality of visible light imaging is significantly reduced, requiring additional light source support. At the same time, visible light cannot penetrate smoke, haze, and dust, which may cause blurred images and unreliable detection results. Visible light mainly reflects the appearance information of transmission line sections, and cannot accurately detect internal defects of the insulation layer (such as thermal damage and aging).

[0110] Infrared thermal imaging detection can detect thermal damage, aging or overheated areas of the insulation layer, which are usually difficult to observe directly through visible light. At the same time, infrared imaging relies on the thermal radiation of the object itself and does not require external light support. It can work normally at night or in other low-light environments. The infrared band has a certain penetrating ability and can work in environments such as haze and smoke, improving the detection effect in complex scenes. Furthermore, early hidden faults (such as overheating points) can be identified through temperature differences, which is convenient for preventing further damage in advance. However, the resolution of infrared imaging is lower than that of visible light imaging, and it cannot accurately display the surface details of the insulation layer, and is not suitable for identifying small cracks or minor surface damage. At the same time, infrared detection may be affected by ambient temperature fluctuations. For example, solar radiation can cause misjudgment and infrared thermal imaging equipment is expensive and has high maintenance costs. Infrared imaging relies on the temperature difference between the insulation layer and the environment or other components of the transmission line section. When the temperature difference is small, the detection effect may not be obvious.

[0111] Infrared thermal imaging and visible light analysis are complementary solutions for detecting insulation damage in transmission line sections. Infrared thermal imaging can be used to identify abnormal temperature areas, and then combined with visible light images to confirm the specific damage characteristics of the insulation layer. This method can effectively improve the accuracy and reliability of detection.

[0112] In this solution, the image analysis module uses infrared thermal imaging detection method and visible light analysis to analyze the damage of the insulation layer of the transmission line section. When the drone inspects the transmission line section, the infrared camera captures the thermal image of the insulation layer of the transmission line section and the insulation layer in real time, and the visible light camera collects the surface image of the insulation layer in real time, that is, the visible light image;

[0113] The visible light image input by the visible light camera is converted into a grayscale image to reduce the computational complexity. Specifically, the RGB values ​​in the color image are converted into grayscale values ​​by weighted averaging;

[0114] Use Gaussian filtering to smooth the image and reduce the interference of noise on edge detection; at the same time, remove possible non-transmission line insulation layer targets or background noise in the image, set a threshold range to extract the color of the transmission line insulation layer according to the color difference between the transmission line insulation layer and the background; for example, the transmission line insulation layer is usually dark, and the color threshold can be set according to the HSV (hue, saturation, brightness) space; convert the image to the HSV color space, use the threshold segmentation method to extract the pixel area that meets the color range of the transmission line insulation layer, and retain the transmission line insulation layer area; use the Canny edge detection algorithm to extract the edge, match the detected edge shape with the straight line characteristics of the transmission line insulation layer, filter out the parts that are obviously not the transmission line insulation layer, such as large areas or isolated points, apply the connectivity algorithm to extract the connected areas formed by adjacent pixels, and filter out the areas that meet the characteristics of the transmission line insulation layer according to parameters such as area, shape, aspect ratio, etc.;

[0115] Analyze whether the detected edges form a closed area, mark possible damaged contours, use contour tracking algorithm to extract the contour of each closed area, record the geometric features of the contour, such as shape, area and side length, to distinguish the damage type. If the edge shape is irregular, jagged or curved lines appear, or the pixel value inside the closed area is significantly different from the surrounding area, the insulation layer may be damaged; highlight the contour that meets the damage condition in red, and mark the coordinates of the damaged contour;

[0116] Analyze the detected edge lengths and calculate the total length of each edge. If the edge is smaller than a certain threshold, it may be noise or a non-target feature; a long edge may represent a crack.

[0117] Analyze the detected edge shape to check whether the edge is smooth. Use curvature analysis to calculate the smoothness of the edge. If the edge is too jagged, it may be an irregular shape caused by burn damage.

[0118] Analysis of the closure of the detected edges: Use the closed area detection algorithm to determine whether the edge forms a closed area; if the grayscale inside the closed area is uniform, it may be dirt or pollution; if the grayscale distribution is uneven, it may be material peeling;

[0119] Combine geometric features (such as edge length, shape, and closure) to determine the damage of the insulation layer: long cracks may indicate early cracks in the insulation layer, irregular edge areas may indicate peeling or burning, and closed edge areas may indicate dirt or pollution, and mark the insulation layer abnormalities of the relevant transmission line section;

[0120] Use an infrared thermal imager to obtain the thermal image of the insulation layer of the transmission line section in real time. Set the temperature range according to the temperature difference between the insulation layer and the background. The insulation layer is usually slightly higher in temperature than the background. Extract the area in the thermal image of the insulation layer that meets the set temperature range. Use pseudo-color enhancement technology to map the temperature range to color. Combined with the pseudo-color contrast effect, set a threshold to filter out the background low-temperature area. Apply an edge detection algorithm to extract the edges in the thermal image of the insulation layer. Combined with shape analysis, retain the slender line features of the insulation layer and eliminate non-related areas.

[0121] Capture the temperature distribution information of the thermal image of the insulation layer, analyze the areas with obvious temperature rise in the thermal image of the insulation layer and mark the anomalies; in fact, the temperature rise area in the thermal image of the insulation layer can be compared with the ambient temperature to determine whether it exceeds the normal operating threshold. At the same time, the visible light camera shoots the temperature rise area from multiple angles to ensure that the complete surface details are captured;

[0122] If both the thermal image and the visible light image of the insulation layer show abnormalities, it is usually a serious damage situation, such as the insulation layer is burned or peeled off in a large area, which needs to be repaired in time. At this time, the image analysis module generates a damage signal and sends the relevant position coordinates and the corresponding real-time image to the user terminal;

[0123] If both images show no abnormalities, the insulation layer is in normal condition and no further processing is required;

[0124] If there is any image anomaly in the thermal image or visible light image of the insulation layer, it means that the transmission line section needs further inspection. The image analysis module generates an abnormal signal and sends the relevant location coordinates and the corresponding real-time image to the user terminal;

[0125] Specifically, when the thermal image of the insulation layer shows abnormality, but the visible light image shows no surface damage, it may be due to internal aging or local heating of the insulation layer;

[0126] When only visible light shows surface defects and the thermal image of the insulation layer shows no obvious abnormalities, it may be an early crack or surface contamination that has not yet caused thermal damage;

[0127] The user terminal is used to view the real-time image of the transmission line section.

[0128] Embodiment 2: This embodiment also provides a transmission line UAV micro-machine nest intelligent inspection system, the system also includes a temperature monitoring module, which is applied to the UAV to monitor the battery temperature in real time during the inspection of the transmission line section. The specific monitoring process is as follows:

[0129] The data acquisition module is used to collect temperature information and power information of the drone performing the inspection task, and send the temperature information to the temperature monitoring module. The temperature information includes the ambient temperature of the drone's location and the battery temperature of the drone. The collection process is as follows:

[0130] First, the battery temperature of the drone before takeoff is 20 degrees Celsius, and then the inspection task begins. The data acquisition module collects the current temperature information at every 1 kilometer of the drone's voyage and sends it to the temperature monitoring module. There are a total of m=1, 2, ..., m is a positive integer collection point, and the temperature information collection of all collection points on the inspection path is completed in sequence;

[0131] The temperature monitoring module is used to receive temperature information and analyze the real-time changes in battery temperature during the drone inspection process. The specific analysis process is as follows:

[0132] Obtain the temperature information of the drone at each collection point, and obtain the ambient temperature value of the drone's location and the drone's battery temperature value;

[0133] Analyze the temperature information of adjacent collection points;

[0134] First, the battery temperature difference threshold and the ambient temperature difference threshold of the drone are set, and the battery temperature difference and the ambient temperature difference corresponding to the drone between each adjacent collection point are calculated;

[0135] If the battery temperature difference between any two adjacent collection points is less than the battery temperature difference threshold, it means that the battery temperature does not change sharply when the drone performs the transmission line inspection task, and the drone continues to perform the inspection task;

[0136] If the battery temperature difference between any two adjacent collection points is greater than or equal to the battery temperature difference threshold, it means that the battery temperature of the drone changes sharply during the inspection of the transmission line section. At this time, the ambient temperature difference between the adjacent collection points is analyzed. If the ambient temperature difference is greater than or equal to the ambient temperature difference threshold, it means that the battery temperature of the drone changes sharply and it is related to the ambient temperature change. The drone continues to maintain the inspection state. If the ambient temperature difference is less than the ambient temperature difference threshold, it means that the battery temperature of the drone changes sharply and it is not related to the ambient temperature change. At this time, the drone makes corresponding speed adjustments.

[0137] At the same time, during the next inspection process, the battery temperature of the drone may continue to change sharply, causing the battery temperature to overheat or overcool; at this time, the temperature monitoring module will monitor the battery temperature of the drone at a time interval of 30S as the collection point. If the battery temperature difference between any two adjacent time collection points is still greater than or equal to the battery temperature difference threshold, the battery management module will send the temperature information to the task processing module. After the task processing module receives the temperature information, the drone will make corresponding adjustments; if the battery temperature difference between any two adjacent time collection points is less than the battery temperature difference threshold, the drone will continue to maintain the inspection state;

[0138] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0139] Example 3: Please refer to Figure 4 As shown, based on another concept of the same invention, a method for intelligent inspection of a transmission line section by using a drone micro-nest is proposed, and the method comprises the following steps:

[0140] Step S101, segmenting the transmission line into transmission line segments, and issuing an initial inspection task instruction at the same time, when the drone performs the initial inspection task;

[0141] Step S102, collecting initial inspection data of the transmission line section and obtaining historical fault data of the transmission line section, and determining the fault level and importance level of the transmission line section according to the initial inspection data and the historical fault data;

[0142] Step S103, formulating a customized inspection route for the drone when inspecting the transmission line section according to the importance level and the historical fault level;

[0143] Step S104, when performing the inspection task according to the customized inspection route, obtain the interference source in the transmission line section and the interference data corresponding to the interference source, and collect the real-time position coordinates of the drone to confirm whether the drone enters the interference area;

[0144] Step S105, if the UAV enters the interference area, the real-time position coordinates of the UAV in the interference area are obtained, and whether the UAV deviates during the flight in the interference area is determined according to the real-time position coordinates;

[0145] Step S106, collecting a real-time image of the power transmission line section, and analyzing the real-time image of the power transmission line section in real time;

[0146] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A transmission line drone micro-machine nest intelligent inspection system, characterized in that: It includes an intelligent segmentation module, a user terminal, a task processing module, a level determination module, a database, a route formulation module, a data acquisition module, a route deviation module, an interference confirmation module and an image analysis module; the intelligent segmentation module is used to segment the transmission line into transmission line segments; the user terminal is used to issue an initial inspection task instruction and send it to the task processing module, and the task processing module is used to control the UAV to start the inspection after receiving the initial inspection task instruction; when executing the initial inspection task, the data acquisition module is used to collect the initial inspection data of the transmission line segment and send it to the level determination module; the database is used to store the historical fault data of the transmission line segment and send it to the level determination module; the level determination module is used to determine the fault level and importance level of the transmission line segment, obtain the historical fault level and importance level and send them to the route formulation module; the route formulation module is used to formulate a customized inspection route when the UAV inspects the transmission line segment according to the importance level and the historical fault level, and send the customized inspection route to the task processing module; The working process of the route planning module is as follows: The drone uses the importance level of the transmission line section as the first selection factor, the historical fault level of the transmission line section as the second selection factor, and the distance between the drone's corresponding nest and the transmission line section as the third selection factor; The drone takes off from the current location of the nest. If there are multiple transmission line sections with the same importance level around the nest, the drone will first select the transmission line section with a higher historical fault level to start inspection; If the importance level and historical fault level of the transmission line section are equal, the UAV will select the transmission line section closest to the machine nest to start inspection; When the drone reaches the node; If the corresponding node is only connected to one transmission line segment, the drone directly inspects the transmission line segment; where the node is the tower of the transmission line segment; If the node is connected to two or more transmission line segments, the drone will prioritize the transmission line segment with a higher importance level; when the importance levels of multiple transmission line segments connected to the node are equal, the transmission line segment with a higher historical fault level will be prioritized; when the importance levels and historical fault levels of multiple transmission line segments connected to the node are equal, the drone will randomly select a transmission line segment for inspection; If the node is not connected to other transmission line segments, and there are transmission line segments that have not been inspected, the drone will prioritize inspecting the nearest uninspected transmission line segment. When the drone reaches any node, if all other transmission line segments have been inspected, the drone will directly return to the starting point and land. According to the above rules, a customized inspection route is obtained when the UAV inspects the transmission line section; When the drone flies along the customized inspection route, it flies at different speeds in the transmission line sections corresponding to different importance levels or third fault levels; When the UAV performs the inspection task according to the customized inspection route, the data acquisition module is used to collect the real-time position coordinates of the UAV and send them to the interference confirmation module; the database is also used to store the interference sources in the transmission line section and the interference data corresponding to the interference sources and send them to the interference confirmation module; the interference confirmation module is used to confirm whether the UAV enters the interference area, and if an offset detection signal is generated, the real-time position coordinates of the UAV in the interference area are sent to the route offset module; the route offset module is used to determine whether the UAV deviates during the flight in the interference area based on the real-time position coordinates, and if it deviates, the flight path adjustment instruction is sent to the task processing module; The data acquisition module is used to collect real-time images of the transmission line section and send them to the image analysis module. The image analysis module is used to analyze the real-time images of the transmission line section in real time. If the analysis generates a damage signal or an abnormal signal, the location coordinates and the corresponding real-time image are sent to the user terminal. The user terminal is used to view the real-time image of the transmission line section.

2. According to claim 1, a transmission line UAV micro-machine nest intelligent inspection system is characterized in that: The initial inspection data includes the number of line branches in the transmission line section and the number of households supplied by the line; The historical fault data includes the number of historical faults of the transmission line section and the maintenance duration at each fault.

3. According to claim 2, a transmission line UAV micro-machine nest intelligent inspection system is characterized in that: The determination process of the level determination module is as follows: Obtain the number of historical faults of each transmission line section, and then obtain the maintenance duration of each transmission line section at each fault, and add and average the maintenance duration at each fault to obtain the average maintenance duration of each transmission line section; Calculate the failure coefficient of each transmission line section based on the number of historical failures and the average maintenance duration; The fault coefficient is compared with the preset fault coefficient to determine whether the historical fault level of the transmission line section is the first fault level, the second fault level or the third fault level; At the same time, the number of line branches and the number of households powered by each transmission line section are obtained, and the important coefficient of each transmission line section is calculated; The importance coefficient is compared with the preset importance coefficient to determine whether the importance level of the transmission line section is the first importance level, the second importance level or the third importance level.

4. The power transmission line UAV micro-nest intelligent inspection system according to claim 3 is characterized in that: The fault degree of the third fault level is greater than that of the second fault level, and the fault degree of the second fault level is greater than that of the first fault level; The third importance level is more important than the second importance level, and the second importance level is more important than the first importance level.

5. The power transmission line UAV micro-machine nest intelligent inspection system according to claim 1 is characterized in that: The interference data are the center position coordinates of the interference source, the center interference intensity and the diffusion attenuation rate.

6. The power transmission line UAV micro-nest intelligent inspection system according to claim 5 is characterized in that: The confirmation process of the interference confirmation module is as follows: Calculate the real-time distance between the real-time position coordinates of the drone and the corresponding center position coordinates of the interference source; Obtain interference data corresponding to the interference source in the transmission line section, obtain the center position coordinates, center interference intensity and diffusion attenuation rate of the interference source, and obtain the interference radius of the interference source by dividing the center interference intensity by the diffusion attenuation rate; The interference area of ​​the interference source is constructed with the center position coordinates as the origin and the interference radius as the radius; When the real-time distance is greater than the interference radius, the drone performs inspection tasks outside the interference area and does not perform any operations; When the real-time distance is less than or equal to the interference radius, the UAV performs inspection tasks in the interference area of ​​the interference source and generates an offset detection signal.

7. The power transmission line UAV micro-machine nest intelligent inspection system according to claim 6 is characterized in that: The working process of the route deviation module is as follows: Construct inspection height ranges and allowable deviation ranges for UAV inspection of transmission line sections; If the laser pulse reflection duration is within the allowable deviation range of the drone and the vertical distance between the drone and the transmission line section is within the inspection height range, no operation will be performed; If the laser pulse reflection duration is not within the allowable deviation range of the UAV or the vertical distance between the UAV and the power transmission line section is not within the inspection height range, a flight path adjustment instruction is generated.

8. The power transmission line UAV micro-nest intelligent inspection system according to claim 1 is characterized in that: The analysis process of the image analysis module is as follows: capturing visible light images corresponding to transmission line segments; Convert the color image input by the visible light camera into a grayscale image; use Gaussian filtering to smooth the image, remove the non-transmission line insulation layer or background noise in the image, set the threshold range to extract the color of the transmission line insulation layer according to the color difference between the transmission line insulation layer and the background; convert the image to HSV color space, use the threshold segmentation method to extract the pixel area that meets the color range of the transmission line insulation layer, and retain the transmission line insulation layer area; use the Canny edge detection algorithm to extract the edge, match the detected edge shape with the straight line characteristics of the transmission line insulation layer, and filter out the part that is not the transmission line insulation layer; apply the connectivity algorithm to extract the connected area formed by adjacent pixels, and filter out the area that meets the characteristics of the transmission line insulation layer; analyze the area that meets the characteristics of the transmission line insulation layer to determine the situation of the area with the characteristics of the transmission line insulation layer; Acquire the thermal image of the insulation layer of the transmission line section in real time, set the temperature range according to the temperature difference between the insulation layer and the background, and extract the area in the thermal image of the insulation layer that meets the set temperature range; use pseudo-color enhancement technology to map the temperature range to color, combine the pseudo-color contrast effect, set a threshold to filter out the background low-temperature area, apply the edge detection algorithm to extract the edge in the thermal image of the insulation layer, combine with shape analysis, retain the slender line features of the insulation layer, and eliminate non-related areas; Capture the temperature distribution information of the thermal image of the insulation layer, and mark the areas with increased temperature in the thermal image of the insulation layer and the abnormalities; If both the thermal image and the visible light image of the insulation layer show abnormalities, a damage signal is generated, and the position coordinates and the corresponding real-time image are obtained; If there is no abnormality in both the thermal image and the visible light image of the insulation layer, no operation is performed; If there is an abnormality in either the thermal image or the visible light image of the insulation layer, an abnormality signal is generated, and the position coordinates and the corresponding real-time image are obtained.

9. A method for intelligent inspection of power transmission lines by using drone micro-nests, characterized in that: Based on the transmission line drone micro-machine nest intelligent inspection system according to any one of claims 1 to 8, the method is as follows: Step S101, segmenting the transmission line into transmission line segments, and issuing an initial inspection task instruction at the same time, when the drone performs the initial inspection task; Step S102, collecting initial inspection data of the transmission line section and obtaining historical fault data of the transmission line section, and determining the fault level and importance level of the transmission line section according to the initial inspection data and the historical fault data; Step S103, formulating a customized inspection route for the drone when inspecting the transmission line section according to the importance level and the historical fault level; Step S104, when performing the inspection task according to the customized inspection route, obtain the interference source in the transmission line section and the interference data corresponding to the interference source, and collect the real-time position coordinates of the drone to confirm whether the drone enters the interference area; Step S105, if the UAV enters the interference area, the real-time position coordinates of the UAV in the interference area are obtained, and whether the UAV deviates during the flight in the interference area is determined according to the real-time position coordinates; Step S106: collecting a real-time image of the power transmission line section, and analyzing the real-time image of the power transmission line section in real time.

Images