Visual power transmission line multi-dimensional risk detection system and method

Abstract

The invention discloses a visual power transmission line multi-dimensional risk detection system and method, and relates to the technical field of power transmission line image data processing. The visual power transmission line multi-dimensional risk detection system comprises an inspection offset analysis module, an acquisition interference analysis module and a transmission interference analysis module. According to the method, the accuracy of the risk detection data collected in the inspection process is evaluated by combining the offset analysis result, whether inspection transmission interference analysis is executed or not is judged, and if inspection transmission interference analysis is executed, the transmission quality of the risk detection data is detected and analyzed to judge whether risk detection data visualization is executed or not. Therefore, the improvement of the data quality in the process of power transmission line risk detection visualization through unmanned aerial vehicle inspection is realized, and the problem of low data quality in the process of power transmission line risk detection visualization through unmanned aerial vehicle inspection in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line image data processing, and particularly to a visual multi-dimensional risk detection system and method for transmission lines. Background Art

[0002] Transmission lines are an important part of the power system, responsible for transmitting electrical energy from power plants to substations and end-users. Their safe and stable operation is crucial for ensuring power supply and maintaining the normal operation of the social economy. However, transmission lines are usually widely distributed, long in distance, and exposed to the natural environment, and are easily affected by various factors such as natural disasters, external damage, and equipment aging. These factors may lead to line failures and even large-scale power outages. Therefore, risk detection of transmission lines is a key link to ensure their safe operation. Through visual multi-dimensional risk detection of transmission lines, various potential threats can be effectively managed and predicted in a complex power system environment, ensuring the stable operation of the power grid, reducing accidents, and improving the overall safety of the power system.

[0003] Existing multi-dimensional risk detection of transmission lines collects various types of data of transmission lines through devices such as sensors, drones, and satellite remote sensing, including line temperature, vibration, meteorological data, image data, etc. Deep learning algorithms (such as convolutional neural networks) are used to analyze the image data of transmission lines to achieve rapid identification of line defects and faults. At the same time, real-time monitoring data and historical data are combined to dynamically evaluate the risk level of transmission lines and timely detect potential fault risks. In addition, a visualization platform can provide decision-makers with real-time reports, warning information, and maintenance suggestions to help formulate more scientific maintenance and inspection plans. Through visual multi-dimensional risk detection of transmission lines, not only the safety, reliability, and operation and maintenance efficiency of transmission lines are improved, but also the intelligent and sustainable development of the power industry is promoted, which has far-reaching significance in the process of promoting the digital and intelligent transformation of the power industry.

[0004] For example, a transmission line local heating monitoring method and system disclosed in the invention patent announcement with the publication number of CN118781095B includes: performing interpolation processing on the noise-reduced temperature data and enhanced thermal image data to obtain a target temperature matrix and a target thermal image matrix; fusing the temperature matrix and the thermal image matrix to obtain a fused temperature distribution matrix; performing threshold segmentation on the temperature distribution matrix to obtain local high-temperature regions, and performing positioning and quantification processing on the local high-temperature regions to obtain a set of heating position coordinates and a data set of heating degrees; calculating a comprehensive heating index, and determining a local heating warning level according to the comprehensive heating index; matching the local heating warning level with a pre-set BIM model of the transmission line to generate a visual warning model, and analyzing a differential inspection plan for the visual warning model according to the local heating warning level to generate a differential inspection and maintenance plan.

[0005] For example, a method and system for visual monitoring and early warning of transmission line status announced in the invention patent announcement with the announcement number CN116452594B includes: collecting infrared images of the transmission line; obtaining the optimal Gaussian kernel size according to the gray variance and local variance of each superpixel region; obtaining the reference range of each blank grid and several first weight parameters, and obtaining several second weight parameters according to the gray value and position distribution of each pixel point within the reference range; obtaining the guiding image of the infrared image, and obtaining the filling value of each blank grid according to the change of the gray variance between the guiding image and the infrared image, combined with the weight parameters, to obtain an upsampled image; obtaining the guiding filter intensity parameter according to the difference between the local variance statistical histograms of the upsampled image and the infrared image; guiding the filter to obtain a clear image, and completing the visual monitoring of the transmission line status.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems:

[0007] During the flight of the unmanned aerial vehicle (UAV), it may be affected by factors such as flight altitude and shooting angle, resulting in insufficient resolution or inappropriate viewing angle of the captured images, thereby affecting the clarity of the images and the capture of details, and further affecting the accuracy of the detection results.

[0008] It also needs to be considered that the UAV may encounter electromagnetic interference, radio frequency conflicts or other external signal interferences during flight, especially around power lines. The strong electromagnetic field may affect the data transmission quality, and further affect the final visual display. There is a problem of low data quality in the process of visualizing the risk detection of transmission lines through UAV inspections. Summary of the Invention

[0009] The embodiments of the present application provide a visual multi-dimensional risk detection system and method for transmission lines, which solve the problem of low data quality in the process of visualizing the risk detection of transmission lines through UAV inspections in the prior art, and realize the improvement of data quality in the process of visualizing the risk detection of transmission lines through UAV inspections.

[0010] An embodiment of the present application provides a visual multi-dimensional risk detection system for transmission lines, including: an inspection offset analysis module, a collection interference analysis module, and a transmission interference analysis module; the inspection offset analysis module is used to obtain the drone inspection status data of a preset transmission line inspection area, and analyze the offset degree between the drone inspection status data and the preset inspection status data to obtain an offset analysis result; the collection interference analysis module is used to evaluate the accuracy of the risk detection data collected during the inspection in combination with the offset analysis result, and judge whether to perform inspection transmission interference analysis; the transmission interference analysis module is used to, if the inspection transmission interference analysis is performed, detect and analyze the transmission quality of the risk detection data, and judge whether to perform visualization of the risk detection data.

[0011] Further, the specific process of analyzing the offset degree between the drone inspection status data and the preset inspection status data to obtain an offset analysis result is as follows: obtain the drone inspection offset amount within a preset time period; perform a weighted operation on the drone inspection offset amount and the corresponding offset analysis weight, and then combine it with the inspection environment influence factor for environmental influence correction to obtain an inspection offset correction factor, where the inspection offset correction factor is used to quantitatively evaluate the offset degree between the drone inspection status data and the preset inspection status data; if the inspection offset correction factor is within the preset offset degree threshold obtained from the preset database, record the offset analysis result of the corresponding preset time period as a normal offset to be corrected; if the inspection offset correction factor is not within the preset offset degree threshold obtained from the preset database, record the offset analysis result of the corresponding preset time period as an abnormal offset to be re-inspected.

[0012] Further, the drone inspection offset amount includes a position offset amount, a heading offset amount, a flight altitude offset amount, and a speed offset amount; the position offset amount represents the straight-line distance between the drone inspection coordinates and the preset inspection coordinates; the heading offset amount represents the result of the difference analysis between the drone inspection heading angle and the preset inspection heading angle; the flight altitude offset amount represents the result of the difference analysis between the relative altitude of the drone inspection and the preset relative altitude of the inspection; the speed offset amount represents the difference analysis result between the drone inspection speed and the preset inspection speed; the offset analysis weight includes a position offset weight, a heading offset weight, a flight altitude offset weight, and a speed offset weight; the inspection environment influence factor is used to correct the influence degree of the inspection environment wind speed on the offset analysis of the drone inspection status; the inspection offset correction factor represents the quantitative data of the combined influence of the position offset amount, the heading offset amount, the flight altitude offset amount, and the speed offset amount on the offset degree between the drone inspection status data and the preset inspection status data.

[0013] Further, the specific steps for evaluating the accuracy of the risk detection data collected during the inspection process by combining the offset analysis results are as follows: Obtain the quantization data for evaluating the accuracy of the risk detection data collected during the inspection process; Perform a weighted operation on the result of collecting and correcting the quantization data for accuracy evaluation with the corresponding collection correction factor and the collection evaluation weight, and then perform inspection deviation correction by combining the inspection offset correction factor to obtain the inspection collection evaluation value; The inspection collection evaluation value represents the quantization data of the combined influence degree of the inspection offset correction factor, the clarity of the collected image, the standard deviation of the thermal imaging, and the point cloud density of the lidar on the accuracy evaluation of the risk detection data collected during the inspection process.

[0014] Further, the risk detection data includes image detection data, thermal imaging detection data, and lidar data; The quantization data for accuracy evaluation includes the clarity of the collected image, the standard deviation of the thermal imaging, and the point cloud density of the lidar; The collection correction factor includes the collection illumination correction factor, the collection temperature correction factor, and the collection electromagnetic interference correction factor; The collection evaluation weight includes the image collection evaluation weight, the thermal imaging collection evaluation weight, and the lidar collection evaluation weight; The inspection collection evaluation value is used to quantitatively evaluate the accuracy of the risk detection data collected during the inspection process.

[0015] Further, the specific process for determining whether to perform inspection transmission interference analysis is as follows: According to the preset inspection collection threshold range obtained from the preset database, determine whether the obtained inspection collection evaluation value is within the preset inspection collection threshold range; If the inspection collection evaluation value is within the preset inspection collection threshold range obtained from the preset database, perform inspection transmission interference analysis and continuously monitor whether the inspection collection evaluation value is within the preset inspection collection threshold range; If the inspection collection evaluation value is not within the preset inspection collection threshold range obtained from the preset database, do not perform inspection transmission interference analysis, and at the same time remind the preset personnel to perform inspection collection data processing; The inspection collection data processing is used to perform image enhancement processing, thermal imaging data smoothing processing, and point cloud registration processing on the risk detection data.

[0016] Further, the specific process of inspection and collection data processing is as follows: A1. Perform image enhancement processing on the image detection data, and determine whether the inspection and collection evaluation value after the image enhancement processing is within the preset inspection and collection threshold range. If so, stop the inspection and collection data processing and perform marker transmission; otherwise, execute A2; A2. Perform thermal imaging data smoothing processing on the thermal imaging detection data, and determine whether the inspection and collection evaluation value after the thermal imaging data smoothing processing is within the preset inspection and collection threshold range. If so, stop the inspection and collection data processing and perform marker transmission; otherwise, execute A3; A3. Perform point cloud registration processing on the lidar data, and perform marker transmission on the risk detection data after the inspection and collection data processing; the marker transmission is used to perform marking on the risk detection data after the inspection and collection data processing and then perform transmission.

[0017] Further, the specific steps for detecting and analyzing the transmission quality of the risk detection data are as follows: Obtain the transmission quality quantization data during the transmission process of the risk detection data, where the transmission quality quantization data includes the average inspection transmission rate, transmission error bit rate, and signal strength at the transmission receiving end; Analyze the result of the proportion of the average inspection transmission rate, the transmission error bit rate, the signal strength at the transmission receiving end, and the result of environmental electromagnetic interference correction of the transmission electromagnetic interference correction factor, and perform weighted operation in combination with the corresponding transmission evaluation weights and perform inspection deviation correction with the inspection offset correction factor to obtain the inspection transmission evaluation value; The transmission evaluation weights include the transmission rate evaluation weight, the transmission accuracy evaluation weight, and the transmission signal strength evaluation weight; The inspection transmission evaluation value represents the quantization data of the combined influence degree of the inspection offset correction factor, the average inspection transmission rate, the transmission error bit rate, and the signal strength at the transmission receiving end on the detection and analysis of the transmission quality of the risk detection data; The inspection transmission evaluation value is used to perform quantitative analysis on the transmission quality of the risk detection data.

[0018] Further, the specific process for determining whether to perform risk detection data visualization is as follows: According to the preset inspection transmission threshold range obtained from the preset database, determine whether the obtained inspection transmission evaluation value is within the preset inspection transmission threshold range; If the inspection transmission evaluation value is within the preset inspection transmission threshold range, perform risk detection data visualization; If the inspection transmission evaluation value is not within the preset inspection transmission threshold range, do not perform risk detection data visualization, and at the same time remind the preset personnel to perform inspection transmission optimization; The inspection transmission optimization includes inspection transmission data optimization and inspection transmission protocol optimization; The inspection transmission data optimization is used to reduce the amount of transmission data during the inspection transmission process; The inspection transmission protocol optimization is used to perform transmission congestion control on the inspection transmission process.

[0019] An embodiment of the present application provides a visual multi-dimensional risk detection method for transmission lines, including the following steps: S1, obtaining the drone inspection status data of a preset transmission line inspection area, and analyzing the deviation degree between the drone inspection status data and the preset inspection status data to obtain a deviation analysis result; S2, evaluating the accuracy of the risk detection data collected during the inspection in combination with the deviation analysis result, and determining whether to perform inspection transmission interference analysis; S3, if performing inspection transmission interference analysis, detecting and analyzing the transmission quality of the risk detection data, and determining whether to perform visualization of the risk detection data.

[0020] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0021] 1. By evaluating the accuracy of the risk detection data collected during the inspection in combination with the deviation analysis result, and determining whether to perform inspection transmission interference analysis, and then if performing inspection transmission interference analysis, detecting and analyzing the transmission quality of the risk detection data to determine whether to perform visualization of the risk detection data, it realizes the analysis and judgment of the accuracy of the risk detection data collection and the transmission quality during the inspection process by the deviation analysis result, and further realizes the improvement of the data quality in the process of visualizing the risk detection of transmission lines through drone inspection, effectively solving the problem of low data quality in the process of visualizing the risk detection of transmission lines through drone inspection in the prior art.

[0022] 2. By obtaining the quantization data of the accuracy evaluation of the risk detection data collected during the inspection process, then performing a collection correction operation on the result of the collection correction of the quantization data of the accuracy evaluation and the corresponding collection correction factor with the collection evaluation weight, and then performing a patrol deviation correction in combination with the patrol offset correction factor to obtain a patrol collection evaluation value, it realizes the interference correction of the accuracy of the risk detection data collected during the inspection process due to the drone inspection deviation degree, and further realizes a more accurate evaluation of the accuracy of the risk detection data collected during the inspection process.

[0023] 3. By obtaining the quantization data of the transmission quality during the transmission process of the risk detection data, then performing an environmental electromagnetic influence correction on the result of the analysis of the ratio of the average patrol transmission rate, the transmission bit error rate, the signal strength at the transmission receiving end, and the result of the correction of the transmission electromagnetic interference correction factor in combination with the weighted operation of the corresponding transmission evaluation weight and performing a patrol deviation correction with the patrol offset correction factor to obtain a patrol transmission evaluation value, it realizes the interference correction of the transmission quality of the risk detection data due to the drone inspection deviation degree, and further realizes a more accurate evaluation of the transmission quality of the risk detection data. Description of the Drawings

[0024] Figure 1 It is a schematic structural diagram of a visual multi-dimensional risk detection system for transmission lines provided by an embodiment of the present application;

[0025] Figure 2 This is a flowchart of a visual multi-dimensional risk detection method for transmission lines provided by an embodiment of the present application. Detailed implementation manners

[0026] By providing a visual multi-dimensional risk detection system and method for transmission lines, the embodiment of the present application solves the problem of low data quality in the visualization process of risk detection of transmission lines through drone patrol inspection. By obtaining the drone patrol inspection status data of a preset transmission line patrol inspection area, and analyzing the deviation degree between the drone patrol inspection status data and the preset patrol inspection status data to obtain a deviation analysis result, then combining the deviation analysis result to evaluate the accuracy of the risk detection data collected during the patrol inspection process to determine whether to perform patrol inspection transmission interference analysis. If the patrol inspection transmission interference analysis is performed, the transmission quality of the risk detection data is detected and analyzed. Finally, it is determined whether to perform visualization of the risk detection data, realizing the improvement of data quality in the visualization process of risk detection of transmission lines through drone patrol inspection.

[0027] The technical solution in the embodiment of the present application for solving the problem of low data quality in the visualization process of risk detection of transmission lines through drone patrol inspection is generally as follows:

[0028] By combining the deviation analysis result to evaluate the accuracy of the risk detection data collected during the patrol inspection process, and determining whether to perform patrol inspection transmission interference analysis, then detecting and analyzing the transmission quality of the risk detection data to determine whether to perform visualization of the risk detection data, the effect of improving the data quality in the visualization process of risk detection of transmission lines through drone patrol inspection is achieved.

[0029] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0030] As Figure 1As shown in the figure, it is a schematic structural diagram of a visual multi-dimensional risk detection system for transmission lines provided by an embodiment of the present application. A visual multi-dimensional risk detection system for transmission lines provided by an embodiment of the present application includes: an inspection deviation analysis module, a collection interference analysis module, and a transmission interference analysis module; the inspection deviation analysis module is used to obtain the drone inspection status data of a preset transmission line inspection area, and perform deviation degree analysis on the drone inspection status data and the preset inspection status data to obtain a deviation analysis result; the collection interference analysis module is used to evaluate the accuracy of the risk detection data collected during the inspection in combination with the deviation analysis result, and judge whether to perform inspection transmission interference analysis; the transmission interference analysis module is used to, if inspection transmission interference analysis is performed, detect and analyze the transmission quality of the risk detection data, and judge whether to perform visualization of the risk detection data.

[0031] In this embodiment, traditional risk detection of transmission lines usually requires personnel to work at high altitudes or near power facilities, often facing problems such as high costs, low efficiency, and high risks. The introduction of drone inspection technology has greatly changed this situation.

[0032] The drone is equipped with a high-definition camera and other sensors (such as thermal imagers, lidar, etc.), which can obtain data in real time and provide a basis for subsequent analysis and decision-making. In addition, the drone can quickly complete the inspection task in bad weather and complex terrain. At the same time, the drone can cross long distances and monitor the transmission lines in multiple areas according to the set flight path, providing real-time remote monitoring and data transmission, reducing the delay and loopholes of manual inspections.

[0033] During the drone inspection process, the data quality directly affects the reliability of the transmission line risk detection and the accuracy of decision-making. In the actual application process of the algorithm of the present application, the interference impact evaluation of the analysis of the deviation degree during the drone inspection process on the accuracy and transmission quality of the risk detection data collected during the inspection is considered (for example, deviation may lead to insufficient area monitoring during the drone inspection process, especially in high-risk areas (such as wire intersections), and deviation may cause these areas to not be effectively scanned or monitored), thereby effectively improving the inspection efficiency, reducing the labor cost, and enhancing the inspection coverage.

[0034] Further, the specific process of analyzing the offset degree between the UAV inspection status data and the preset inspection status data to obtain the offset analysis result is as follows: Obtain the UAV inspection offset amount within a preset time period. The UAV inspection offset amount includes position offset amount, heading offset amount, flight altitude offset amount, and speed offset amount; After performing a weighted operation on the UAV inspection offset amount with the corresponding offset analysis weight and combining it with the inspection environment impact factor for environmental impact correction, obtain the inspection offset correction factor; Judge the inspection offset correction factor according to the preset offset degree threshold range obtained from the preset database; If the inspection offset correction factor is within the preset offset degree threshold range obtained from the preset database (including the boundary values of the preset offset degree threshold range), record the offset analysis result of the corresponding preset time period as a normal offset to be corrected; If the inspection offset correction factor is not within the preset offset degree threshold range obtained from the preset database, record the offset analysis result of the corresponding preset time period as an abnormal offset to be re-inspected.

[0035] It should be understood that the preset inspection status data refers to the relevant parameters of UAV inspection preset in advance to ensure the smooth and effective inspection before UAV inspection, including preset inspection coordinates (latitude and longitude coordinates), preset inspection heading angle, preset inspection relative altitude, and preset inspection speed.

[0036] In addition, the preset offset degree threshold range is set by professionals according to the standards in the field. For example, the preset offset degree threshold range is set to 1.0 to 2.0; The position offset amount, heading offset amount, flight altitude offset amount, and speed offset amount are all processed without units.

[0037] Among them, the position offset amount represents the straight-line distance between the UAV inspection coordinates and the preset inspection coordinates; Obtain the UAV inspection coordinates through the geographic information system, and use GIS tools (such as QGIS, Quantum Geographic Information System) to obtain the position offset amount.

[0038] The heading offset amount represents the result of the difference analysis between the UAV inspection heading angle and the preset inspection heading angle; Obtain the UAV inspection heading angle through the heading sensor. The units of the UAV inspection heading angle and the preset inspection heading angle are the same, both in degrees.

[0039] The method to obtain the heading offset amount is as follows:

[0040] XH = |θ W - θ Y |;

[0041] In the formula, XH represents the heading offset amount, θ W represents the UAV inspection heading angle, and θ Y represents the preset inspection heading angle.

[0042] The flight altitude offset represents the analysis result of the difference between the relative altitude of the UAV inspection and the preset relative altitude of the inspection. The relative altitude of the UAV inspection represents the vertical distance between the UAV and the transmission line; the relative altitude of the UAV inspection is obtained by lidar scanning, and the unit of the relative altitude of the UAV inspection and the preset relative altitude of the inspection is the same, both in meters.

[0043] The method for obtaining the flight altitude offset is as follows:

[0044] XG = |G W - G Y |;

[0045] In the formula, XG represents the flight altitude offset, G W represents the relative altitude of the UAV inspection, and G Y represents the preset relative altitude of the inspection.

[0046] The speed offset represents the analysis result of the difference between the UAV inspection speed and the preset inspection speed; the UAV inspection speed is obtained through the UAV flight control system, and the unit of the UAV inspection speed and the preset inspection speed is the same, both in kilometers per hour.

[0047] The method for obtaining the speed offset is as follows:

[0048] XV = |V W - V Y |;

[0049] In the formula, XV represents the speed offset, V W represents the UAV inspection speed, and V Y represents the preset inspection speed.

[0050] The offset analysis weights include the position offset weight, the heading offset weight, the flight altitude offset weight, and the speed offset weight; the position offset weight, the heading offset weight, the flight altitude offset weight, and the speed offset weight respectively describe the influence degree of the position offset, the heading offset, the flight altitude offset, and the speed offset on the inspection offset correction factor. For example, the position offset, the heading offset, the flight altitude offset, and the speed offset are input into the mapping set of the preset position offset, heading offset, flight altitude offset, and speed offset and their respective weight factors in the database to obtain the corresponding weights.

[0051] The inspection environment influence factor is used to correct the influence degree of the inspection environment wind speed on the UAV inspection state offset analysis, and its value range is [0, 1]. For example, the real-time inspection environment wind speed (obtained through an anemometer) is input into the mapping set of the preset inspection environment wind speed and the inspection environment influence factor in the database to obtain the inspection environment influence factor.

[0052] When the offset analysis result is a normal offset to be corrected, the accuracy of the risk detection data collected during the inspection process is evaluated; when the offset analysis result is an abnormal offset to be re-inspected, a re-inspection instruction is sent to the drone. If the number of times the re-inspection instruction is sent reaches the preset number of times, multi-drone inspection is adopted, where the preset number of times is set by professionals and is generally five times.

[0053] The method for obtaining the inspection offset correction factor is as follows:

[0054]

[0055] In the formula, ξ XJP represents the inspection offset correction factor within a preset time period, ξ JF represents the inspection environment impact factor, n represents the inspection time within a preset time period, n = 1, 2,..., N, and N represents the total number of inspection times within a preset time period. ξ a represents the position offset weight, ξ b represents the heading offset weight, ξ c represents the flight altitude offset weight, ξ d represents the speed offset weight, XW n represents the position offset amount at the nth inspection time within a preset time period, XH n represents the heading offset amount at the nth inspection time within a preset time period, XG n represents the flight altitude offset amount at the nth inspection time within a preset time period, XV n represents the speed offset amount at the nth inspection time within a preset time period.

[0056] It should be added that the inspection offset correction factor represents the quantitative data of the degree of offset between the drone inspection status data and the preset inspection status data caused by the position offset amount, heading offset amount, flight altitude offset amount, and speed offset amount. The inspection offset correction factor includes parameters in multiple aspects, and there are connections between various parameters and they do not exist independently. For example, the position offset is usually correlated with factors such as heading, flight altitude, and speed. If the heading of the drone has an offset, the change in position often follows. That is, as the heading offset amount increases, the position offset amount also increases; at the same time, the flight altitude offset and speed offset are also correlated. Excessive speed may cause the position to exceed the predetermined orbit, thereby affecting the heading and altitude of the drone.

[0057] The inspection deviation correction factor is used to quantitatively evaluate the deviation degree between the UAV inspection status data and the preset inspection status data. As the position deviation, heading deviation, flight altitude deviation, and speed deviation increase, the inspection deviation correction factor increases accordingly. Furthermore, the correction and adjustment of the inspection deviation correction factor for the inspection acquisition and transmission processes become more obvious. Through quantification, a numerical evaluation of the deviation degree between the UAV inspection status data and the preset inspection status data is achieved, and further correction of the process of detecting risk detection data for UAV inspection acquisition and transmission is realized.

[0058] Furthermore, the specific steps for evaluating the accuracy of risk detection data collected during the inspection process in combination with the deviation analysis results are as follows: Obtain the quantification data for evaluating the accuracy of risk detection data collected during the inspection process; perform a weighted operation on the result of collecting and correcting the accuracy evaluation quantification data with the corresponding collection correction factor and the collection evaluation weight, and then perform inspection deviation correction in combination with the inspection deviation correction factor to obtain the inspection acquisition evaluation value.

[0059] The method for obtaining the inspection acquisition evaluation value is as follows:

[0060]

[0061] In the formula, XJC represents the inspection acquisition evaluation value within the preset time period, ξ XJP represents the inspection deviation correction factor within the preset time period, δ1 represents the image acquisition evaluation weight, δ2 represents the thermal imaging acquisition evaluation weight, δ3 represents the lidar acquisition evaluation weight, ξ JG represents the acquisition illumination correction factor, ξ JW represents the acquisition temperature correction factor, ξ JD represents the acquisition electromagnetic interference correction factor, m represents the number of times of collecting risk detection data during the inspection process, m = 1, 2,..., M, and M represents the total number of times of collecting risk detection data during the inspection process. TJQ m represents the clarity of the acquisition image of the m-th collection of risk detection data during the inspection process, RJP represents the thermal imaging standard deviation, and JJD represents the lidar point cloud density.

[0062] It should be added that the risk detection data includes image detection data, thermal imaging detection data, and lidar data; the image detection data is obtained through a high-resolution RGB camera on the UAV, and the thermal imaging detection data and lidar data are obtained through an infrared thermal imager and a lidar sensor loaded on the UAV.

[0063] The precision evaluation quantization data includes the clarity of the acquired image, the standard deviation of the thermal imaging, and the point cloud density of the lidar. At the same time, the clarity of the acquired image, the standard deviation of the thermal imaging, and the point cloud density of the lidar have all been de-unified. The clarity of the acquired image is obtained by using gradient operators (such as Sobel and Canny edge detection). The standard deviation of the thermal imaging is obtained by statistically analyzing the thermal imaging detection data using libraries such as NumPy and OpenCV in Python. The point cloud density of the lidar is obtained by using the tools of the PCL (Point Cloud Library) open-source library.

[0064] The acquisition correction factors include the acquisition light correction factor, the acquisition temperature correction factor, and the acquisition electromagnetic interference correction factor. Among them, the value ranges of the acquisition light correction factor, the acquisition temperature correction factor, and the acquisition electromagnetic interference correction factor are all [0, 1]. For example, the real-time inspection acquisition light intensity (obtained through a light sensor), the inspection acquisition temperature (obtained through a temperature sensor), and the inspection acquisition electromagnetic interference intensity (obtained through an electromagnetic field detector) are respectively input into the mapping sets of the preset real-time inspection acquisition light intensity, inspection acquisition temperature, inspection acquisition electromagnetic interference intensity and their corresponding acquisition light correction factors, acquisition temperature correction factors, and acquisition electromagnetic interference correction factors in the database to obtain the corresponding acquisition light correction factors, acquisition temperature correction factors, and acquisition electromagnetic interference correction factors.

[0065] The acquisition evaluation weights include the image acquisition evaluation weight, the thermal imaging acquisition evaluation weight, and the lidar acquisition evaluation weight. Among them, the value ranges of the image acquisition evaluation weight, the thermal imaging acquisition evaluation weight, and the lidar acquisition evaluation weight are all [0, 1] and their sum is 1. For example, the clarity of the real-time acquired image, the standard deviation of the thermal imaging, and the point cloud density of the lidar are input into the mapping sets of the preset clarity of the acquired image, the standard deviation of the thermal imaging, the point cloud density of the lidar and their respective weight factors in the database to obtain the corresponding weights.

[0066] It should be understood that the inspection acquisition evaluation value represents the quantization data of the combined influence degree of the inspection offset correction factor, the clarity of the acquired image, the standard deviation of the thermal imaging, and the point cloud density of the lidar on the precision evaluation of the risk detection data acquired during the inspection process. The inspection acquisition evaluation value includes parameters in multiple aspects and is used to quantitatively evaluate the precision of the risk detection data acquired during the inspection process. Among them, the inspection acquisition evaluation value includes parameters in multiple aspects. As the clarity of the acquired image and the point cloud density of the lidar increase, the inspection acquisition evaluation value increases accordingly. As the standard deviation of the thermal imaging decreases, the inspection acquisition evaluation value also increases accordingly.

[0067] In addition, there are relationships among the various parameters in the inspection and collection evaluation values, and they do not exist independently. For example, with the increase in the lidar point cloud density, more spatial information can usually be provided, which helps to improve the image clarity and the quality of thermal imaging data. For example, high-density point cloud data can assist in improving the object recognition accuracy in the image, thereby improving the image clarity. At the same time, the data density of lidar can help identify abnormal temperature distributions in thermal imaging and reduce the standard deviation of thermal imaging data. In addition, the inspection offset correction factor corrects the inspection offset degree corresponding to the image clarity of the collected image, the standard deviation of thermal imaging, and the lidar point cloud density. Therefore, this algorithm realizes a more accurate evaluation of the accuracy of the risk detection data collected during the inspection process through a quantitative method.

[0068] Further, the specific process for determining whether to perform inspection transmission interference analysis is as follows: According to the preset inspection collection threshold range obtained from the preset database, determine whether the obtained inspection collection evaluation value is within the preset inspection collection threshold range; if the inspection collection evaluation value is within the preset inspection collection threshold range obtained from the preset database, perform inspection transmission interference analysis and continuously monitor whether the inspection collection evaluation value is within the preset inspection collection threshold range (excluding the boundary values of the preset inspection collection threshold range); if the inspection collection evaluation value is not within the preset inspection collection threshold range obtained from the preset database, do not perform inspection transmission interference analysis, and at the same time remind the preset personnel to perform inspection collection data processing; the inspection collection data processing is used to perform image enhancement processing, thermal imaging data smoothing processing, and point cloud registration processing on the risk detection data.

[0069] In this embodiment, the preset inspection collection threshold range is set by professionals according to the standards in the field. For example, the preset inspection collection threshold range is set to be from 2.0 to 3.0; among them, the inspection transmission interference analysis is to transmit the risk detection data through satellite communication and detect and analyze the transmission quality of the risk detection data.

[0070] It should be added that the specific process of the inspection collection data processing is as follows: A1, perform image enhancement processing on the image detection data, and determine whether the inspection collection evaluation value after the image enhancement processing is within the preset inspection collection threshold range. If so, stop the inspection collection data processing and perform marked transmission; otherwise, execute A2; A2, perform thermal imaging data smoothing processing on the thermal imaging detection data, and determine whether the inspection collection evaluation value after the thermal imaging data smoothing processing is within the preset inspection collection threshold range. If so, stop the inspection collection data processing and perform marked transmission; otherwise, execute A3; A3, perform point cloud registration processing on the lidar data, and perform marked transmission on the risk detection data after the inspection collection data processing; the marked transmission is used to mark the risk detection data after the inspection collection data processing and then perform transmission.

[0071] Among them, image enhancement processing is achieved through histogram equalization. Specifically, OpenCV is used for equalization processing; thermal imaging data smoothing processing is achieved through mean filtering. Specifically, the blur() function in OpenCV is used to perform mean filtering on thermal imaging data; point cloud registration processing is achieved through the ICP (Iterative Closest Point) algorithm. Specifically, the Open3D library is used to align lidar data to a common coordinate system; tag transmission is performed by using the scapy library in Python.

[0072] By combining the preset inspection acquisition threshold range to judge whether to perform inspection acquisition data processing, a more accurate judgment of the accuracy of risk detection data acquisition during the inspection process is achieved. Furthermore, the reliability of the acquired data during the transmission line detection by the UAV inspection is improved.

[0073] Furthermore, the specific steps for detecting and analyzing the transmission quality of risk detection data are as follows: Obtain the transmission quality quantization data during the transmission process of risk detection data. The transmission quality quantization data includes the average inspection transmission rate, transmission bit error rate, and signal strength at the transmission receiving end; Analyze the results of the proportion of the average inspection transmission rate, the transmission bit error rate, the signal strength at the transmission receiving end, and the results of environmental electromagnetic influence correction of the transmission electromagnetic interference correction factor, and perform weighted operations in combination with the corresponding transmission evaluation weights and perform inspection deviation correction with the inspection offset correction factor to obtain the inspection transmission evaluation value; The transmission evaluation weights include the transmission rate evaluation weight, the transmission accuracy evaluation weight, and the transmission signal strength evaluation weight.

[0074] The method for obtaining the inspection transmission evaluation value is as follows:

[0075]

[0076] In the formula, XJS represents the inspection transmission evaluation value within the preset time period, ξ XJP represents the inspection offset correction factor within the preset time period, ξ1 represents the transmission rate evaluation weight, ξ2 represents the transmission accuracy evaluation weight, ξ3 represents the transmission signal strength evaluation weight, represents the average inspection transmission rate, represents the reference minimum transmission rate, BER represents the transmission bit error rate, ξ CD represents the transmission electromagnetic interference correction factor, represents the signal strength at the transmission receiving end.

[0077] In this embodiment, the average inspection transmission rate is obtained by using network performance monitoring tools (such as Wireshark, NetFlow, iPerf), the transmission bit error rate is obtained by a signal quality test instrument (such as BERT, Bit Error Rate Tester), and the signal strength at the transmission receiving end is obtained by a wireless signal analyzer (such as a network spectrum analyzer). The reference minimum transmission rate is the minimum value obtained by summing and averaging the historical inspection transmission average rates collected. Here, the unit of the inspection transmission average rate is the same as that of the reference minimum transmission rate, both being bits per second; the unit of the signal strength at the transmission receiving end is dBm, and the unit of the signal strength at the transmission receiving end is de-unified before calculation.

[0078] The transmission electromagnetic interference correction factor is used to correct the influence degree of the electromagnetic interference intensity during the inspection transmission process on the quality detection and analysis of risk detection data transmission. For example, the electromagnetic interference intensity during the real-time inspection transmission process (obtained by an electromagnetic field detector) is input into the mapping set of the electromagnetic interference intensity and the transmission electromagnetic interference correction factor preset in the database to obtain the transmission electromagnetic interference correction factor.

[0079] The transmission rate evaluation weight, the transmission accuracy evaluation weight, and the transmission signal strength evaluation weight respectively describe the influence degree of the inspection transmission average rate, the transmission bit error rate, and the signal strength at the transmission receiving end on the quality detection and analysis of risk detection data transmission. For example, the real-time inspection transmission average rate, the transmission bit error rate, and the signal strength at the transmission receiving end are input into the mapping set of the inspection transmission average rate, the transmission bit error rate, the signal strength at the transmission receiving end, and their respective weight factors preset in the database to obtain the corresponding weights.

[0080] It should be understood that the inspection transmission evaluation value represents the quantitative data of the combined influence degree of the inspection offset correction factor, the inspection transmission average rate, the transmission bit error rate, and the signal strength at the transmission receiving end on the quality detection and analysis of risk detection data transmission; this algorithm comprehensively considers multiple parameters for comprehensive analysis to obtain the inspection transmission evaluation value. As the inspection transmission average rate and the signal strength at the transmission receiving end increase, the inspection transmission evaluation value increases accordingly. As the transmission bit error rate decreases, the inspection transmission evaluation value also increases accordingly.

[0081] It should also be considered that the inspection transmission evaluation value is used for quantitative analysis of the transmission quality of risk detection data. The various parameters included are related and do not exist independently. For example, signal strength is usually one of the key factors affecting the transmission rate. A strong signal helps to increase the transmission rate, while a weak signal may lead to a rate decrease. At the same time, good signal transmission reduces the occurrence of error codes, that is, as the signal strength at the transmission receiving end increases, the transmission bit error rate decreases. In addition, the inspection offset correction factor corrects the average inspection transmission rate, the signal strength at the transmission receiving end, and the transmission bit error rate according to the corresponding inspection offset degree. In summary, through the quantitative analysis of the transmission quality of risk detection data, a more accurate evaluation of the transmission quality of risk detection data is achieved.

[0082] Furthermore, the specific process for determining whether to perform risk detection data visualization is as follows: According to the preset inspection transmission threshold range obtained from the preset database, it is determined whether the obtained inspection transmission evaluation value is within the preset inspection transmission threshold range (excluding the boundary values of the preset inspection transmission threshold range); if the inspection transmission evaluation value is within the preset inspection transmission threshold range, risk detection data visualization is performed; if the inspection transmission evaluation value is not within the preset inspection transmission threshold range, risk detection data visualization is not performed, and at the same time, the preset personnel are reminded to optimize the inspection transmission; the inspection transmission optimization includes inspection transmission data optimization and inspection transmission protocol optimization; the inspection transmission data optimization is used to reduce the amount of transmission data during the inspection transmission process; the inspection transmission protocol optimization is used to perform transmission congestion control on the inspection transmission process.

[0083] In this embodiment, the preset inspection transmission threshold range is set by professionals according to the standards in the field. For example, the preset inspection transmission threshold range is set to be from 1.0 to 3.0; among them, risk detection data visualization refers to the visual processing of risk detection data through visualization tools. Specifically, the visual display of image detection data is performed through an image processing library (such as matplotlib and OpenCV in Python), the visual display of thermal imaging detection data is performed through a visualization tool (such as a heat map), and the visual display of lidar data is performed through a visualization tool (such as PCL, Point Cloud Library).

[0084] The optimization of inspection and transmission data reduces the volume of risk detection data during inspection and the amount of transmitted data during the inspection transmission process by compressing the risk detection data transmitted during the inspection using a data compression algorithm (such as ZIP); the optimization of the inspection transmission protocol is achieved by using an adaptive congestion avoidance algorithm to continuously evaluate the congestion status of the network and thereby dynamically adjust the data sending rate; by combining a preset inspection transmission threshold range to judge the visualization of risk detection data, a more accurate judgment of the transmission quality of risk detection data is realized, and further, the improvement of the reliability of the transmitted data during the power transmission line inspection by the UAV inspection is achieved.

[0085] As Figure 2 shown in the figure, it is a flowchart of a visual multi-dimensional risk detection method for a power transmission line provided by an embodiment of the present application. The method includes the following steps: S1, obtaining the UAV inspection status data of a preset power transmission line inspection area, and performing an offset degree analysis on the UAV inspection status data and the preset inspection status data to obtain an offset analysis result; S2, evaluating the accuracy of the risk detection data collected during the inspection in combination with the offset analysis result, and judging whether to perform an inspection transmission interference analysis; S3, if the inspection transmission interference analysis is performed, detecting and analyzing the transmission quality of the risk detection data, and judging whether to perform the visualization of the risk detection data.

[0086] In this embodiment, by analyzing the UAV inspection offset degree and evaluating and judging the double interference during the inspection collection and transmission of risk detection data based on the offset analysis result, the improvement of the data reliability during the power transmission line risk detection by the UAV inspection is realized, and further, the improvement of the data quality during the visualization of the power transmission line risk detection by the UAV inspection is realized.

[0087] In summary, in the embodiment of the present application, the accuracy of the risk detection data collected during the inspection is evaluated in combination with the offset analysis result, and it is judged whether to perform an inspection transmission interference analysis. Then, if the inspection transmission interference analysis is performed, the transmission quality of the risk detection data is detected and analyzed to judge whether to perform the visualization of the risk detection data, so as to realize the analysis and judgment of the offset analysis result on the collection accuracy and transmission quality of the risk detection data during the inspection, and further realize the improvement of the data quality during the visualization of the power transmission line risk detection by the UAV inspection, effectively solving the problem of low data quality during the visualization of the power transmission line risk detection by the UAV inspection in the prior art.

[0088] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0089] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0090] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0092] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0093] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A visual multi-dimensional risk detection system for transmission lines, characterized in that Including: An inspection deviation analysis module, a collection interference analysis module, and a transmission interference analysis module; The inspection deviation analysis module is used to obtain the drone inspection status data of a preset power transmission line inspection area, and perform deviation degree analysis on the drone inspection status data and the preset inspection status data to obtain a deviation analysis result; The collection interference analysis module is used to evaluate the accuracy of the risk detection data collected during the inspection in combination with the deviation analysis result, and judge whether to perform inspection transmission interference analysis; The transmission interference analysis module is used to detect and analyze the transmission quality of the risk detection data if the inspection transmission interference analysis is performed, and judge whether to perform visualization of the risk detection data.

2. The visual multi-dimensional risk detection system for transmission lines according to claim 1, wherein The specific process of performing deviation degree analysis on the drone inspection status data and the preset inspection status data to obtain a deviation analysis result is as follows: Obtain the drone inspection deviation amount within a preset time period; Perform weighted operation on the drone inspection deviation amount and the corresponding deviation analysis weight, and then combine it with the inspection environment impact factor for environmental impact correction to obtain an inspection deviation correction factor, which is used to quantitatively evaluate the deviation degree between the drone inspection status data and the preset inspection status data; If the inspection deviation correction factor is within the preset deviation degree threshold obtained from the preset database, record the deviation analysis result of the corresponding preset time period as a normal deviation to be corrected; If the inspection deviation correction factor is not within the preset deviation degree threshold obtained from the preset database, record the deviation analysis result of the corresponding preset time period as an abnormal deviation to be re-inspected.

3. The visual multi-dimensional risk detection system for transmission lines according to claim 2, characterized in that, The drone inspection deviation amount includes a position deviation amount, a heading deviation amount, a flight altitude deviation amount, and a speed deviation amount; The position deviation amount represents the straight-line distance between the drone inspection coordinates and the preset inspection coordinates; The heading deviation amount represents the result of the difference analysis between the drone inspection heading angle and the preset inspection heading angle; The flight altitude deviation amount represents the result of the difference analysis between the relative altitude of the drone inspection and the preset relative altitude of the inspection; The speed deviation amount represents the result of the difference analysis between the drone inspection speed and the preset inspection speed; The deviation analysis weight includes a position deviation weight, a heading deviation weight, a flight altitude deviation weight, and a speed deviation weight; The inspection environment impact factor is used to correct the influence degree of the inspection environment wind speed on the deviation analysis of the drone inspection status; The inspection deviation correction factor represents the quantitative data of the combined influence of the position deviation amount, the heading deviation amount, the flight altitude deviation amount, and the speed deviation amount on the deviation degree between the drone inspection status data and the preset inspection status data.

4. The visual multi-dimensional risk detection system for transmission lines according to claim 1, wherein The specific steps for evaluating the accuracy of the risk detection data collected during the inspection in combination with the deviation analysis result are as follows: Obtain the accuracy evaluation quantitative data of the risk detection data collected during the inspection; Perform weighted operation on the result of the acquisition correction of the accuracy evaluation quantitative data and the corresponding acquisition correction factor and the acquisition evaluation weight, and then combine it with the inspection deviation correction factor for inspection deviation correction to obtain an inspection acquisition evaluation value; The inspection and collection evaluation value represents the quantitative data of the combined influence degree of the inspection offset correction factor, the clarity of the collected image, the standard deviation of the thermal imaging, and the lidar point cloud density on the accuracy evaluation of the risk detection data collected during the inspection process.

5. The visual multi-dimensional risk detection system for transmission lines according to claim 4, characterized in that The risk detection data includes image detection data, thermal imaging detection data, and lidar data; The accuracy evaluation quantitative data includes the clarity of the collected image, the standard deviation of the thermal imaging, and the lidar point cloud density; The collection correction factor includes the collection light correction factor, the collection temperature correction factor, and the collection electromagnetic interference correction factor; The collection evaluation weight includes the image collection evaluation weight, the thermal imaging collection evaluation weight, and the lidar collection evaluation weight; The inspection and collection evaluation value is used to quantitatively evaluate the accuracy of the risk detection data collected during the inspection process.

6. The visual multi-dimensional risk detection system for transmission lines according to claim 4, wherein The specific process for determining whether to perform inspection transmission interference analysis is as follows: According to the preset inspection and collection threshold range obtained from the preset database, determine whether the obtained inspection and collection evaluation value is within the preset inspection and collection threshold range; If the inspection and collection evaluation value is within the preset inspection and collection threshold range obtained from the preset database, perform inspection transmission interference analysis and continuously monitor whether the inspection and collection evaluation value is within the preset inspection and collection threshold range; If the inspection and collection evaluation value is not within the preset inspection and collection threshold range obtained from the preset database, do not perform inspection transmission interference analysis, and at the same time remind the preset personnel to perform inspection and collection data processing; The inspection and collection data processing is used to perform image enhancement processing, thermal imaging data smoothing processing, and point cloud registration processing on the risk detection data.

7. The visual multi-dimensional risk detection system for transmission lines according to claim 6, characterized in that The specific process of the inspection and collection data processing is as follows: A1. Perform image enhancement processing on the image detection data, and determine whether the inspection and collection evaluation value after the image enhancement processing is within the preset inspection and collection threshold range. If so, stop the inspection and collection data processing and perform marked transmission. Otherwise, execute A2; A2. Perform thermal imaging data smoothing processing on the thermal imaging detection data, and determine whether the inspection and collection evaluation value after the thermal imaging data smoothing processing is within the preset inspection and collection threshold range. If so, stop the inspection and collection data processing and perform marked transmission. Otherwise, execute A3; A3. Perform point cloud registration processing on the lidar data, and perform marked transmission on the risk detection data after the inspection and collection data processing; The marked transmission is used to perform marking on the risk detection data after the inspection and collection data processing and then perform transmission.

8. The visual multi-dimensional risk detection system for transmission lines according to claim 1, wherein The specific steps for detecting and analyzing the transmission quality of the risk detection data are as follows: Obtain the transmission quality quantitative data during the transmission process of the risk detection data. The transmission quality quantitative data includes the average inspection transmission rate, the transmission bit error rate, and the signal strength at the transmission receiving end; Perform weighted operation on the result of the ratio analysis of the average inspection transmission rate, the transmission bit error rate, the signal strength at the transmission receiving end, the result of environmental electromagnetic influence correction of the transmission electromagnetic interference correction factor, and combine the corresponding transmission evaluation weights, and perform inspection deviation correction with the inspection offset correction factor to obtain the inspection transmission evaluation value; The transmission evaluation weight includes a transmission rate evaluation weight, a transmission accuracy evaluation weight, and a transmission signal strength evaluation weight; The inspection transmission evaluation value represents the quantified data of the combined influence degree of the inspection offset correction factor, the average inspection transmission rate, the transmission bit error rate, and the transmission receiving end signal strength on the detection and analysis of the transmission quality of risk detection data; The inspection transmission evaluation value is used for quantitative analysis of the transmission quality of risk detection data.

9. The visual multi-dimensional risk detection system for transmission lines according to claim 8, wherein The specific process of determining whether to perform risk detection data visualization is as follows: According to the preset inspection transmission threshold range obtained from the preset database, determine whether the obtained inspection transmission evaluation value is within the preset inspection transmission threshold range; If the inspection transmission evaluation value is within the preset inspection transmission threshold range, perform risk detection data visualization; If the inspection transmission evaluation value is not within the preset inspection transmission threshold range, do not perform risk detection data visualization, and at the same time remind the preset personnel to optimize the inspection transmission; The inspection transmission optimization includes inspection transmission data optimization and inspection transmission protocol optimization; The inspection transmission data optimization is used to reduce the amount of transmission data during the inspection transmission process; The inspection transmission protocol optimization is used to perform transmission congestion control on the inspection transmission process.

10. A visual multi-dimensional risk detection method for transmission lines, characterized in that, It includes the following steps: S1. Obtain the drone inspection status data of the preset power transmission line inspection area, and perform offset degree analysis on the drone inspection status data and the preset inspection status data to obtain an offset analysis result; S2. Evaluate the accuracy of the risk detection data collected during the inspection process in combination with the offset analysis result, and determine whether to perform inspection transmission interference analysis; S3. If inspection transmission interference analysis is performed, detect and analyze the transmission quality of the risk detection data, and determine whether to perform risk detection data visualization.

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