Path planning method for UAV power grid inspection operation based on Beidou navigation

Abstract

The present invention relates to the technical field of unmanned aerial vehicle path control, and specifically discloses a path planning method for unmanned aerial vehicle power grid inspection operations based on Beidou navigation. The method comprises: obtaining historical inspection contribution values ​​of various power grid areas, combining actual operation status contribution values ​​of various power grid areas, determining priority values ​​of unmanned aerial vehicle power grid inspection operations of various power grid areas, determining initial paths of unmanned aerial vehicle power grid inspection operations, determining heights of unmanned aerial vehicle power grid inspection operations of various power grid areas, obtaining obstacle position coordinates of connection paths of various power grid areas based on Beidou navigation, determining detour plans for unmanned aerial vehicle power grid inspection operations of connection paths of various power grid areas, and correcting the initial paths of unmanned aerial vehicle power grid inspection operations. The present invention solves the problem that traditional path planning methods can no longer meet the needs of efficient and intelligent inspections, combines the problem of insufficient dynamic optimization capabilities of real-time data, improves the effectiveness of inspections, reduces the flight risks of unmanned aerial vehicles, and ensures the smooth progress of inspection tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) path control, and in particular to a path planning method for unmanned aerial vehicle (UAV) power grid inspection operations based on Beidou navigation. Background Art

[0002] With the expansion of the scale and increase of complexity of the power grid, the inspection tasks of key facilities such as power grid equipment and transmission lines are increasing year by year. Traditional manual inspection methods are difficult to cover a wide range of power grid areas. Manual inspection is even more difficult in remote areas, mountainous areas and severe weather conditions. The guarantee of stable operation of the power grid requires inspection work to become more frequent and accurate. UAV inspection has gradually become an ideal way to replace manual inspection. UAVs can quickly cover a large range of power grid areas while reducing the safety risks of inspection operations. UAVs can carry high-precision cameras, infrared thermal imaging and other sensors, making transmission line monitoring, equipment fault detection and environmental risk identification more efficient. The Beidou satellite navigation system has become one of the global satellite navigation systems, which can provide high-precision positioning services worldwide and is suitable for UAVs to inspect power grid areas with complex terrain and many obstacles.

[0003] At present, there are still some shortcomings in the research on path planning of Beidou navigation-based drone power grid inspection operations. Specifically, traditional path planning methods (such as manually set paths or single optimization algorithms) can no longer meet the needs of efficient and intelligent inspections. The dynamic optimization capabilities combined with real-time data are insufficient. Traditional manual inspection methods are difficult to cover a wide range of power grid areas, and are inefficient, costly, and unsafe. During the inspection process, the power grid status, meteorological environment, etc. may change. Traditional path planning lacks integration with real-time data, and it is difficult to adjust the path in time to respond to emergencies, resulting in reduced inspection efficiency and increased potential safety risks. In complex environments, traditional methods cannot optimize the path in time in combination with meteorological data, resulting in reduced inspection safety and imaging quality, which may affect the detection accuracy of equipment. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a path planning method for unmanned aerial vehicle power grid inspection operations based on Beidou navigation, which can effectively solve the problems involved in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a path planning method for unmanned aerial vehicle power grid inspection operations based on Beidou navigation, comprising the following steps: analyzing the actual operating status of each area of ​​the power grid to obtain the actual operating status contribution value of each area of ​​the power grid; analyzing the historical inspection characteristics of each area of ​​the power grid to obtain the historical inspection contribution value of each area of ​​the power grid, and combining the actual operating status contribution value of each area of ​​the power grid to determine the priority value of the unmanned aerial vehicle power grid inspection operation in each area of ​​the power grid; determining the initial path of the unmanned aerial vehicle power grid inspection operation based on the priority value of the unmanned aerial vehicle power grid inspection operation in each area of ​​the power grid, and the initial path of the unmanned aerial vehicle power grid inspection operation includes the inspection path in each area of ​​the power grid and the connection path of each area; analyzing the unmanned aerial vehicle power grid inspection operation environment in each area of ​​the power grid to determine the height of the unmanned aerial vehicle power grid inspection operation in each area of ​​the power grid; based on Beidou navigation, obtaining the obstacle position coordinates of the connection path of each area of ​​the power grid, and determining the unmanned aerial vehicle power grid inspection operation detour plan of the connection path of each area of ​​the power grid; based on the unmanned aerial vehicle power grid inspection operation height of each area of ​​the power grid and the unmanned aerial vehicle power grid inspection operation detour plan of the connection path of each area of ​​the power grid, the initial path of the unmanned aerial vehicle power grid inspection operation is corrected.

[0006] As a further method, the actual operating status of each area of ​​the power grid is analyzed. The specific analysis process is: obtaining the actual operating status data set of each area of ​​the power grid, the actual operating status data set of each area of ​​the power grid specifically includes the regional voltage deviation rate, regional power factor, and regional total harmonic distortion rate; based on the actual operating status data set of each area of ​​the power grid obtained, a comprehensive analysis is performed to obtain the actual operating status contribution value, and the actual operating status contribution value is used as the analysis basis for determining the priority value of drone power grid inspection operations in each area of ​​the power grid.

[0007] As a further method, the historical inspection characteristics of each area of ​​the power grid are analyzed. The specific analysis process is: obtaining the historical inspection feature data set of each area of ​​the power grid, the historical inspection feature data set of each area of ​​the power grid specifically includes the inspection frequency of key nodes of regional historical inspections, the number of regional historical inspection failures, and the number of regional historical inspection current overloads; based on the obtained historical inspection feature data set of each area of ​​the power grid, a comprehensive analysis is performed to obtain the historical inspection contribution value, and the historical inspection contribution value is used as the analysis basis for determining the priority value of drone power grid inspection operations in each area of ​​the power grid.

[0008] As a further method, the historical inspection contribution value, the specific analysis process is as follows:

[0009] ;

[0010] In the formula, Contribute to historical inspections. The inspection frequency of key nodes in the regional historical inspection. is the number of historical inspection failures in the region, The number of current overloads in the regional historical inspections. For setting The compensation factor, For setting The compensation factor, For setting The compensation factor of , e is a natural constant.

[0011] As a further method, the priority value of the drone power grid inspection operation in each area of ​​the power grid is determined. The specific analysis process is: the actual operation status contribution value of each area of ​​the power grid and the corresponding historical inspection contribution value of each area of ​​the power grid are stored as a designated label, and the designated label is compared with the drone power grid inspection operation priority value of the power grid area corresponding to each designated label stored in the database to obtain the drone power grid inspection operation priority value of the power grid area corresponding to the designated label.

[0012] As a further method, the initial path of the UAV power grid inspection operation is determined based on the priority value of the UAV power grid inspection operation in each power grid area. The specific analysis process is: sort the priority values ​​of the UAV power grid inspection operation in each power grid area from large to small; mark the power grid area corresponding to the UAV power grid inspection operation priority value ranked as the Nth starting area, where N is the power grid area number, , J is the total number of power grid areas; the inspection starting point of the UAV power grid inspection operation corresponding to the first starting area is marked as the starting point of the initial path of the UAV power grid inspection operation; the connection line between the inspection end point of the UAV power grid inspection operation of the Nth starting area and the inspection starting point of the UAV power grid inspection operation corresponding to the N+1th starting area is used as the regional connection path between the Nth starting area and the N+1th starting area, marked as the regional connection path P, P is the regional connection path number, ; Mark the inspection endpoint of the UAV power grid inspection operation corresponding to the Jth starting area as the initial path endpoint of the UAV power grid inspection operation.

[0013] As a further method, the operating height of the drone power grid inspection in each area of ​​the power grid is determined. The specific analysis process is: obtaining the drone power grid inspection operating environment data set of each area of ​​the power grid, based on the obtained drone power grid inspection operating environment data set of each area of ​​the power grid, comprehensive analysis to obtain the drone power grid inspection operating environment characteristic values, the drone power grid inspection operating environment characteristic values ​​are used as the analysis basis for determining the drone power grid inspection operating height of each area of ​​the power grid; the drone power grid inspection operating environment characteristic values ​​are stored as designated tags, the designated tags are compared with the drone power grid inspection operating heights of the power grid areas corresponding to the designated tags stored in the database, and the drone power grid inspection operating heights of the power grid areas corresponding to the designated tags are obtained.

[0014] As a further approach, the UAV power grid inspection operation environment dataset specifically includes the maximum height of trees in the regional environment, the average height of trees in the regional environment, and the concentration of particulate matter in the regional environment.

[0015] As a further method, a bypass plan for UAV power grid inspection operations on the connection paths of each area of ​​the power grid is determined. The specific analysis process is: based on Beidou navigation, the obstacle position coordinates of the connection paths of each area of ​​the power grid are obtained; based on the obstacle position coordinates of the connection paths of each area of ​​the power grid, the obstacle characteristics of the connection paths of each area of ​​the power grid are collected from the geological database; based on the obstacle characteristics of the connection paths of each area of ​​the power grid, the shortest distance for the UAV to bypass the top of the obstacle and the shortest distance for the UAV to bypass the side of the obstacle are obtained; the energy required for the UAV to rise per unit height and the energy required for the UAV to deviate per unit distance are obtained; based on the shortest distance for the UAV to bypass the top of the obstacle and the shortest distance for the UAV to bypass the obstacle from the side The shortest distance to bypass the side of the object, the energy required for the drone to rise per unit height, and the energy required for the drone to deviate per unit distance are comprehensively analyzed to obtain the obstacle characteristic value, which is used as the analysis basis for determining the bypass plan for the drone power grid inspection operation of the connection paths of various power grid areas; if the obstacle characteristic value is not greater than the bypass threshold stored in the database, the path with the shortest distance that the drone bypasses from above the obstacle is used as the bypass plan for the drone power grid inspection operation of the power grid area connection path; if the obstacle characteristic value is greater than the bypass threshold stored in the database, the path with the shortest distance that the drone bypasses from the side of the obstacle is used as the bypass plan for the drone power grid inspection operation of the power grid area connection path.

[0016] As a further method, obstacle characteristic value, the specific analysis process is:

[0017] ;

[0018] In the formula, is the obstacle characteristic value, is the shortest distance that the drone can bypass an obstacle. The shortest distance for the drone to bypass an obstacle from the side. The energy required for the drone to rise to a certain height. The energy required for the drone to deviate a unit distance. is the compensation factor for the drone's ascent. The compensation factor for the set drone offset.

[0019] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0020] (1) The present invention provides a path planning method for unmanned aerial vehicle (UAV) power grid inspection operations based on Beidou navigation. Areas with high priority values ​​usually contain characteristics such as high fault frequency and equipment aging. This ensures that the UAV first inspects key and problem-prone areas, thereby improving the effectiveness of the inspection. The initial path of the UAV inspection operation not only covers the inspection points in the area, but also includes the connection paths between the areas, ensuring the continuity and coverage of the path. After analyzing the UAV inspection operation environment in the power grid area, the optimal operating height can be determined. The obstacle coordinates are obtained through Beidou navigation and the detour path is determined, thereby reducing the path deviation caused by obstacles, reducing the flight risk of the UAV, and ensuring the smooth progress of the inspection task.

[0021] (2) The present invention determines the priority value of drone power grid inspection operations in each area of ​​the power grid through the historical inspection contribution value of each area of ​​the power grid, combined with the actual operating status contribution value of each area of ​​the power grid. Combining the historical inspection contribution value with the actual operating status contribution value can more comprehensively and accurately reflect the actual risks and health conditions of each area, avoid the deviation caused by single data, and make inspection decisions more scientific. The inspection path based on the priority value ensures priority inspection of high-risk areas, so that drone resources are mainly invested in areas with high probability of failure and unstable operating status, thereby reducing resource waste and improving inspection efficiency. Inspection decisions are based on data analysis rather than subjective judgment, which is in line with the direction of intelligent and data-based management of power grids and helps to build a scientific power grid operation and maintenance management system.

[0022] (3) The present invention corrects the initial path of the drone power grid inspection operation by means of a detour plan for the drone power grid inspection operation based on the operation height of the drone power grid inspection operation in each area of ​​the power grid and the connection paths of each area of ​​the power grid. This can effectively avoid obstacles (such as buildings, trees, etc.) near the transmission lines and reduce the risk of drone collision. By adjusting the path through a dynamic detour plan, the drone can complete the inspection along the optimal path, avoid time waste caused by redundant paths, and ensure that the inspection operation is completed in the shortest time. It can reduce the energy consumption of the drone during flight due to obstacles and path adjustments, thereby extending the drone's flight time, reducing the impact of external interference and sudden factors, ensuring the stability of the inspection data, and making the inspection results more reliable, which is conducive to subsequent equipment status evaluation and fault analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0024] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0025] Figure 2 Flowchart of the steps for a detour plan for drone grid inspection operations to determine the connection paths for each area of ​​the grid. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] Reference Figure 1 As shown, the present invention provides a path planning method for UAV power grid inspection operations based on Beidou navigation, including: analyzing the actual operating status of each area of ​​the power grid to obtain the actual operating status contribution value of each area of ​​the power grid.

[0028] The specific analysis process is as follows: obtain the actual operating status data set of each area of ​​the power grid, which specifically includes the regional voltage deviation rate, regional power factor, and regional total harmonic distortion rate; based on the actual operating status data set of each area of ​​the power grid, a comprehensive analysis is performed to obtain the actual operating status contribution value, which is used as the analysis basis for determining the priority value of drone power grid inspection operations in each area of ​​the power grid.

[0029] The voltage deviation rate indicates the difference between the actual voltage and the rated voltage, usually expressed as a percentage, reflecting the voltage stability during grid operation. The voltage deviation rate is generally obtained through a voltage sensor or a voltage transmitter. The power factor is the ratio of active power to apparent power in the grid, and is used to measure the efficiency of electric energy use in the grid. The higher the power factor, the higher the electric energy conversion efficiency and the smaller the loss. The power factor is generally measured by power meters, power factor meters or smart energy meters. The total harmonic distortion rate is used to quantify the level of harmonic content in the grid. It is the ratio of the sum of the harmonic voltages to the fundamental wave. The total harmonic distortion rate is obtained through a harmonic analyzer or a power quality monitoring device.

[0030] When the power factor is low, the reactive power demand in the power grid increases. Reactive power will cause voltage loss during transmission, which will increase the voltage deviation rate and cause the actual voltage to deviate from the rated voltage. This is particularly evident during long-distance power transmission and when high-power equipment is started. Harmonic distortion will deteriorate the power factor of the power grid because harmonic current increases the reactive power component and reduces the actual power factor in the power grid, resulting in reduced power grid energy utilization efficiency. High total harmonic distortion will cause voltage waveform distortion, thereby increasing the voltage deviation rate and causing the voltage to deviate from the rated value. Voltage fluctuations caused by harmonics will also affect the normal operation of power grid equipment, especially precision electronic equipment.

[0031] Through the real-time data of key indicators such as voltage deviation rate, power factor and harmonic distortion rate, the health of power grid operation in each area can be accurately identified, and priority inspections can be given to those areas with higher actual operation status contribution values ​​(areas with a high possibility of abnormal status), ensuring the targeted inspection. Areas with high voltage deviation rate, low power factor or high total harmonic distortion rate usually face higher failure risks. Setting priority values ​​based on these data can help drones focus their inspections on areas with more potential problems and increase the effectiveness of inspection results. The analysis of the actual operation status contribution value enables priority setting based on real-time status rather than fixed static area division, ensuring that drone inspection tasks at different time points have higher flexibility and adaptability, can quickly adjust tasks as the power grid status changes, and can identify possible unstable factors in the power grid in advance. Focusing on these high contribution value areas helps to deal with potential faults in a timely manner and improve the safety of power grid operation. After analyzing the actual operation status contribution value, the inspection path can be optimized in real time as the power grid operation status changes, forming an adaptive inspection path, making the drone inspection system more flexible and practical.

[0032] The actual operating status contribution value, the specific analysis process is as follows:

[0033] ;

[0034] In the formula, Contribute value to the actual running status, is the regional voltage deviation rate, is the regional power factor, is the regional harmonic total distortion rate, For setting The compensation factor, For setting The compensation factor, For setting The three parameters with yellow background do not match the formula.

[0035] It should be explained that the above actual operating status contribution value is calculated by regional voltage deviation rate, regional power factor, and regional total harmonic distortion rate. , , After normalization, the voltage deviation rate, power factor and total harmonic distortion rate reflect the operation of the power grid from the aspects of voltage stability, power utilization efficiency and power grid harmonic pollution respectively. The contribution value obtained through comprehensive calculation can more comprehensively represent the actual state of the power grid and avoid the limitation of a single indicator. The contribution value calculated by comprehensive indicators can sensitively reflect the weak links of the power grid, help identify areas where power quality problems may occur, and improve the accuracy of power grid status assessment. Areas with high contribution values ​​indicate that their operating status fluctuates greatly or has high potential risks. UAV inspections can be arranged as a priority to ensure effective monitoring of key areas. The actual operating status contribution value can help quickly locate areas with poor power quality in the power grid, provide support for fault warning, and avoid sudden failures caused by large state fluctuations.

[0036] Set , , The compensation factor is obtained from the database, and the regional voltage deviation rate, regional power factor, regional total harmonic distortion rate and , , The mapping set of compensation factors is obtained , , Corresponding , , compensation factor.

[0037] It should be noted that the following , , , , , , , They are also obtained through a mapping set of historical data and compensation factors established in a database, that is, the corresponding compensation factors are obtained according to the current data.

[0038] The historical inspection characteristics of each area of ​​the power grid are analyzed to obtain the historical inspection contribution value of each area of ​​the power grid. Combined with the actual operation status contribution value of each area of ​​the power grid, the priority value of drone power grid inspection operations in each area of ​​the power grid is determined.

[0039] The specific analysis process is as follows: obtaining the historical inspection feature data set of each area of ​​the power grid, which specifically includes the inspection frequency of key nodes in the regional historical inspection, the number of regional historical inspection failures, and the number of regional historical inspection current overloads; based on the obtained historical inspection feature data set of each area of ​​the power grid, a comprehensive analysis is performed to obtain the historical inspection contribution value, which is used as the analysis basis for determining the priority value of drone power grid inspection operations in each area of ​​the power grid.

[0040] The inspection frequency of key nodes in regional historical inspections refers to the number of times drones inspect important equipment or nodes (such as substations, important transmission towers, cable joints, etc.) in a specific power grid area. The higher the inspection frequency, the more likely the key node is to be a high-risk device that requires more frequent monitoring. The inspection frequency is usually obtained through the power grid inspection management system. The number of historical inspection failures refers to the cumulative number of equipment failures in the area in the past inspection records. The number of historical failures is mainly obtained through the fault recording system or the power grid equipment management system. The number of regional historical inspection current overloads refers to the number of times power grid equipment (such as transformers, lines, etc.) exceeds the rated current during operation. The number of current overloads is usually obtained through current sensors or smart ammeters.

[0041] If the number of failures in a certain area is high, it means that the equipment in this area is unstable and prone to problems. The inspection frequency needs to be increased to promptly detect and handle new failures. Current overload means that the equipment is overloaded for a long time, which will accelerate equipment aging and loss, thereby increasing the probability of failure. Areas with a high number of overloads are usually also high-incidence areas of failures. Areas with a high number of current overloads are usually heavily loaded and have a higher potential risk of failure. Increasing the inspection frequency of this area can detect equipment abnormalities caused by overload as early as possible and take timely measures.

[0042] The inspection frequency, number of faults and current overloads of key nodes in historical inspections are important data for judging regional reliability and safety. They can accurately identify areas with high fault incidence and heavy loads, thereby scientifically setting inspection priorities and ensuring the accuracy of decision-making. Traditional inspection priority setting relies more on manual experience, and generating contribution values ​​through comprehensive analysis of historical data can reduce human bias and make priority setting more objective and reasonable. For areas with low historical inspection contribution values, the inspection frequency can be appropriately reduced to avoid unnecessary inspections, and inspection resources can be concentrated in areas with high contribution values ​​to improve the operating efficiency and resource utilization of drones. Generating priority values ​​based on historical inspection data will help establish a data-driven inspection system, improve the level of intelligent power grid management, and adapt to the development trend of intelligent and digital power grids.

[0043] Furthermore, the specific analysis process of historical inspection contribution value is as follows:

[0044] ;

[0045] In the formula, Contribute to historical inspections. The inspection frequency of key nodes in the regional historical inspection. is the number of historical inspection failures in the region, The number of current overloads in the regional historical inspections. For setting The compensation factor, For setting The compensation factor, For setting The compensation factor of , e is a natural constant.

[0046] It should be explained that the above historical inspection contribution value is calculated by the inspection frequency of key nodes in the regional historical inspection, the number of regional historical inspection failures, and the number of regional historical inspection current overloads. , , After normalization, the inspection frequency, number of faults and number of overloads reflect the health status and operational stability of power grid equipment. The comprehensive contribution value provides a more scientific basis for inspection priority, which helps to more accurately identify high-risk areas that need priority inspection. High-frequency fault areas and areas with a large number of overloads are usually potential high-incidence points of faults. Historical contribution values ​​can serve as early warnings, helping power grid management departments to give priority to these areas, thereby reducing sudden failures. The conversion of historical inspection data into contribution values ​​helps the intelligent system to automatically set inspection priorities based on data, reduce manual intervention, and improve the automation of inspection tasks.

[0047] The specific analysis process is as follows: the actual operating status contribution value of each power grid area and the corresponding historical inspection contribution value of each power grid area are stored as designated labels, and the designated labels are compared with the UAV power grid inspection operation priority values ​​of the power grid areas corresponding to each designated label stored in the database to obtain the UAV power grid inspection operation priority values ​​of the power grid areas corresponding to the designated labels.

[0048] After the operating status and historical inspection contribution values ​​of each area are managed by labeling, they can be directly compared with the standard priority values ​​in the database to ensure that the priority setting is based on systematic analysis and data support, which improves the scientific nature of inspection decisions. The actual operating status and historical data of each area are stored in a labeled manner, which can be easily compared with other tags in the database, simplifying the priority management process and speeding up the inspection priority determination process. The system can automatically compare the priority value tags of each area in the database based on the latest status contribution value and historical inspection contribution value, and dynamically adjust the inspection priority, ensuring that the inspection plan can respond to changes in the power grid status in real time and realize the reasonable allocation of inspection resources. Through the analysis and comparison of historical and real-time data, areas that have been at a high priority for a long time can be identified, providing a basis for predictive maintenance plans, arranging equipment maintenance and optimization in advance, and improving the foresight and initiative of power grid operation and maintenance.

[0049] The initial path of the drone power grid inspection operation is determined based on the priority value of the drone power grid inspection operation in each area of ​​the power grid. The initial path of the drone power grid inspection operation includes the inspection paths in each area of ​​the power grid and the connection paths of each area.

[0050] The specific analysis process is as follows: sort the priority values ​​of the UAV power grid inspection operations in each power grid area from large to small; mark the power grid area corresponding to the UAV power grid inspection operation priority value ranked as the Nth starting area, where N is the power grid area number. , J is the total number of power grid areas; the inspection starting point of the UAV power grid inspection operation corresponding to the first starting area is marked as the starting point of the initial path of the UAV power grid inspection operation; the connection line between the inspection end point of the UAV power grid inspection operation of the Nth starting area and the inspection starting point of the UAV power grid inspection operation corresponding to the N+1th starting area is used as the regional connection path between the Nth starting area and the N+1th starting area, marked as the regional connection path P, P is the regional connection path number, ; Mark the inspection endpoint of the UAV power grid inspection operation corresponding to the Jth starting area as the initial path endpoint of the UAV power grid inspection operation.

[0051] After sorting the inspection priority values ​​from large to small, the drone can give priority to inspecting areas with higher risks, ensuring that important areas and equipment with potential hidden dangers are detected in a timely manner. The areas are connected in order of priority, and the end point of the Nth area is directly connected to the starting point of the N+1th area to form the shortest path, reducing unnecessary flight distance and energy consumption, and improving inspection efficiency. The drone does not need to fly back and forth between different areas, reducing battery consumption and flight time, thereby extending the drone's battery life and improving the completion rate of the operation. The inspection end point of each area is connected to the starting point of the next area, avoiding breakpoints between inspection areas, making the inspection task have good continuity and integrity, and ensuring that each high-priority area is effectively covered. If the inspection area needs to be dynamically adjusted, the system can easily reorder or change the connection path, quickly generate a new optimal path, and improve the intelligence level of the inspection system. The inspection paths are connected one by one and the high-priority areas are inspected step by step, which helps to ensure the normal operation and safety of power grid equipment and reduce the probability of sudden failures in power grid operation.

[0052] Analyze the operating environment of UAV power grid inspection in various areas of the power grid, and determine the operating height of UAV power grid inspection in various areas of the power grid.

[0053] The specific analysis process is as follows: obtaining the drone power grid inspection operating environment data set of each power grid area, and based on the obtained drone power grid inspection operating environment data set of each power grid area, comprehensively analyzing to obtain the drone power grid inspection operating environment characteristic values, and using the drone power grid inspection operating environment characteristic values ​​as the analysis basis for determining the drone power grid inspection operating heights of each power grid area; storing the drone power grid inspection operating environment characteristic values ​​as designated tags, and comparing the designated tags with the drone power grid inspection operating heights of the power grid areas corresponding to each designated tag stored in the database to obtain the drone power grid inspection operating heights of the power grid areas corresponding to the designated tags.

[0054] By judging the height through the characteristic values ​​of the operating environment, the UAV can better adapt to the environmental conditions of different areas and ensure the accurate setting of the inspection height. The characteristic value analysis can help identify obstacles and vegetation in the area, so as to set a reasonable flight height, avoid collision between the UAV and obstacles, and improve the safety of the inspection. After comparing the database labels, the UAV can quickly obtain the appropriate flight height, reduce the time wasted by frequently adjusting the height during the operation, and improve the inspection efficiency. Reasonable height setting reduces unnecessary flight height changes and reduces the energy consumption of the UAV, thereby extending the flight time and improving the economy of the inspection operation. High-risk or complex environmental areas can be inspected more safely by adapting the height, and accurate status information can be continuously obtained to ensure the safety of key areas of the power grid.

[0055] Furthermore, the drone power grid inspection operation environment dataset specifically includes the maximum height of trees in the regional environment, the average height of trees in the regional environment, and the concentration of particulate matter in the regional environment.

[0056] The maximum tree height refers to the height of the highest tree measured among the trees in a certain power grid area. It is used to evaluate the height of obstacles that drones need to avoid when inspecting the area. The maximum tree height is usually obtained through LiDAR or high-precision drone mapping equipment. The average tree height refers to the average height of all trees in a certain power grid area. The average tree height can also be obtained through LiDAR technology. The particle concentration indicates the concentration level of suspended particulate matter (PM2.5) in the air, usually in micrograms per cubic meter (µg / m³). The higher the particle concentration, the worse the air quality, which may affect the image quality of drone sensors and cameras. The particle concentration can be obtained through air quality monitors (such as PM2.5 monitors).

[0057] The maximum height of trees and the average height of trees together reflect the overall height of trees in the area. The maximum height of trees is usually higher than the average height by a certain range. The maximum height will be used as the upper limit reference value for setting the flight altitude of drones, while the average height will be used as a reference for the overall flight altitude of the area. The greater the tree density, the more compact the trees in the area are. There may be situations where trees of different heights are closely adjacent to each other. In densely populated areas, drone inspections may need to set a higher flight altitude based on the average and maximum heights of trees to avoid safety risks caused by traveling through dense trees. In areas with high particulate matter concentrations, especially dense vegetation areas with high tree heights, lower flight altitudes may result in image blur or sensor errors.

[0058] The specific analysis process of the characteristic values ​​of the UAV power grid inspection operation environment is as follows:

[0059] ;

[0060] In the formula, is the characteristic value of the UAV power grid inspection operation environment, is the maximum height of trees in the regional environment, is the average height of trees in the regional environment, is the regional ambient particulate matter concentration, For setting The compensation factor, For setting The compensation factor, For setting The compensation factor of , e is a natural constant.

[0061] It should be explained that the above-mentioned UAV power grid inspection operation environment characteristic values ​​are calculated through the maximum height of trees in the regional environment, the average height of trees in the regional environment, and the concentration of particulate matter in the regional environment. , , After normalization and combining the maximum and average heights of trees, environmental characteristic values ​​can help determine a safe flight altitude, ensuring that the UAV can effectively avoid obstacles, especially interference from tall trees, and reduce flight risks. By measuring particulate matter concentration, environmental characteristic values ​​can help set the flight altitude or path, allowing the UAV to avoid areas with high concentrations of particulate matter, thereby improving the clarity of the camera and the accuracy of the sensor and ensuring the quality of data collection. By pre-evaluating environmental characteristic values, the UAV can maintain an appropriate flight altitude during the inspection process, reducing the energy consumption of repeated altitude adjustments, thereby extending the flight time. Environmental characteristic values ​​can help UAVs inspect on smoother paths, reduce detour distances and unnecessary altitude increases, reduce energy consumption, and extend battery life. Environmental characteristic values ​​can help the system autonomously decide on inspection altitudes and paths without the need for frequent manual adjustments, thereby improving the intelligence level and automated management capabilities of the inspection system.

[0062] like Figure 2 As shown, based on Beidou navigation, the obstacle position coordinates of the connection paths of each area of ​​the power grid are obtained, and the detour plan for the UAV power grid inspection operation on the connection paths of each area of ​​the power grid is determined.

[0063] The specific analysis process is as follows: based on Beidou navigation, obtain the obstacle position coordinates of the connection paths of each area of ​​the power grid; based on the obstacle position coordinates of the connection paths of each area of ​​the power grid, collect the obstacle characteristics of the connection paths of each area of ​​the power grid from the geological database; based on the obstacle characteristics of the connection paths of each area of ​​the power grid, obtain the shortest distance for the drone to bypass the top of the obstacle and the shortest distance for the drone to bypass the side of the obstacle; obtain the energy required for the drone to rise per unit height and the energy required for the drone to deviate per unit distance; based on the shortest distance for the drone to bypass the top of the obstacle, the shortest distance for the drone to bypass the side of the obstacle, and the shortest distance for the drone to rise per unit height, the energy required for the drone to deviate per unit distance The energy required for the height of the obstacle and the energy required for the unit distance of the UAV offset are comprehensively analyzed to obtain the obstacle characteristic value, which is used as the analysis basis for determining the bypass plan of the UAV power grid inspection operation for the connection paths of each area of ​​the power grid; if the obstacle characteristic value is not greater than the bypass threshold stored in the database, the path with the shortest distance that the UAV bypasses from the top of the obstacle is used as the bypass plan for the UAV power grid inspection operation for the power grid area connection path; if the obstacle characteristic value is greater than the bypass threshold stored in the database, the path with the shortest distance that the UAV bypasses from the side of the obstacle is used as the bypass plan for the UAV power grid inspection operation for the power grid area connection path.

[0064] It needs to be explained that the Beidou navigation system is used to provide accurate geographic location coordinates (latitude, longitude and altitude) for each obstacle on the power grid connection path. These coordinates are combined with the power grid connection path to generate a list of obstacle locations in each area. The collected obstacle location coordinates are input into the geographic information system (GIS), and the specific locations of the obstacles are marked on the power grid connection path to form a map view, which is convenient for the next step of extracting obstacle features. According to the location coordinates of the obstacle, the geological database is accessed to query the corresponding obstacle features. The geological database usually contains attributes such as the type of obstacle (such as trees, buildings, mountains, etc.), height, width, material density, and surrounding environment. The obstacle height, width, volume and other features are extracted from the database. According to the obstacle height characteristics and the safe flight height margin of the drone, the minimum height for the drone to bypass is obtained. For example, if the obstacle height is 30 meters, considering the safety margin of 5 meters, the minimum height for the drone to bypass is 35 meters. If the current flight altitude of the drone is lower than the bypass altitude, the shortest vertical distance is the difference between the required bypass altitude and the current altitude. Assuming that the current altitude of the drone is 20 meters and the minimum bypass altitude is 35 meters, the shortest distance for the drone to bypass the obstacle is 15 meters. According to the obstacle width characteristics and the safe avoidance distance of the drone, the shortest horizontal distance required for side bypass is obtained. For example, if the obstacle width is 15 meters and a safety distance of 5 meters is added, the shortest distance for the drone to bypass the obstacle from the side is 20 meters.

[0065] The intelligent battery management system based on the drone battery can record real-time energy consumption data and display the power consumption during vertical and horizontal flight. By reading the data in the BMS system, the unit energy consumption during ascent and deviation can be directly obtained.

[0066] By determining the location of obstacles through Beidou navigation and combining the obstacle characteristics of the geological database, the UAV can accurately select the upper or side bypass path to effectively avoid obstacles, reduce the risk of collision, and ensure inspection safety. By calculating the shortest distances for upper and side bypasses and combining energy consumption data, the bypass scheme with less energy consumption is given priority (a shorter path is selected according to the characteristic value of the obstacle), reducing battery consumption and thus extending the flight time of the UAV. By selecting the shortest bypass path, the UAV can bypass obstacles faster and reduce the time spent on bypassing, making inspections more efficient, especially in long-distance inspection tasks, where the efficiency advantage is more obvious. The bypass scheme based on the characteristic value takes into account the size, location, shape and height of the obstacle. The UAV can adapt to the requirements of different terrains and obstacles, and is particularly effective in complex and changing geographical environments, which helps to smoothly carry out inspection operations.

[0067] Furthermore, the obstacle characteristic value, the specific analysis process is:

[0068] ;

[0069] In the formula, is the obstacle characteristic value, is the shortest distance the drone can bypass an obstacle. The shortest distance for the drone to bypass an obstacle from the side. The energy required for the drone to rise to a certain height. The energy required for the drone to deviate a unit distance. is the compensation factor for the drone's ascent. The compensation factor for the set drone offset.

[0070] It should be explained that the above obstacle characteristic values ​​are calculated by the shortest distance that the drone bypasses from above the obstacle, the shortest distance that the drone bypasses from the side of the obstacle, the energy required for the drone to rise per unit height, and the energy required for the drone to deviate per unit distance. , , , After normalization, the obstacle eigenvalue combines the detour distance and energy consumption factors, which can help the drone choose the path with the lowest energy consumption in the overhead or lateral detour, ensuring the efficient use of energy. The eigenvalue helps the drone choose a safer detour strategy in areas with complex obstacles, avoid invalid height adjustments or offsets, and reduce the risk of collision. The optimal energy consumption path selection reduces frequent height adjustments and lateral offsets, reduces the loss of motors and batteries, and extends the service life of drone equipment. In high-rise buildings or areas with dense trees, the eigenvalue can help select a suitable detour path, avoid signal interference sources, and improve the stability of data transmission.

[0071] The initial path of the UAV power grid inspection operation is corrected based on the UAV power grid inspection operation height of each area of ​​the power grid and the UAV power grid inspection operation detour plan of each area of ​​the power grid.

[0072] In a specific embodiment, the initial path correction based on the inspection height and detour plan realizes the intelligence and dynamic nature of path planning, which makes it convenient for the UAV system to adjust the path according to real-time data at any time, thereby improving the autonomy of the inspection. The corrected path allows the UAV to more flexibly respond to environmental changes that may occur during the inspection (such as temporarily added obstacles or changes in environmental conditions), enabling it to more intelligently adjust the flight altitude and detour strategy. For the regional connection paths that need to be detoured, the original initial path is replaced with the detoured path, and the initial UAV power grid inspection operation height is replaced with the latest UAV power grid inspection operation height.

[0073] In a specific embodiment, the actual operating status of five areas of the power grid is analyzed. Table 1 is the analysis results of the actual operating status contribution values ​​of the five areas of the power grid, Table 2 is the analysis results of the historical inspection contribution values ​​of the five areas of the power grid, and Table 3 is the result of determining the priority values ​​of drone power grid inspection operations in each area of ​​the power grid.

[0074] Table 1: Analysis results of actual operation status contribution values ​​of five areas of the power grid

[0075] ;

[0076] Table 2: Analysis results of historical inspection contribution values ​​of five power grid areas

[0077] ;

[0078] Table 3: Results of determining the priority values ​​of UAV power grid inspection operations in various power grid areas

[0079] ;

[0080] The inspection order of the drone is area 4, area 2, area 5, area 1, and area 3.

[0081] Determine the initial path of the UAV power grid inspection operation, and mark the inspection starting point of the UAV power grid inspection operation corresponding to the first starting area as the starting point of the initial path of the UAV power grid inspection operation; the line between the inspection end point of the UAV power grid inspection operation in the first starting area and the inspection starting point of the UAV power grid inspection operation corresponding to the second starting area is used as the regional connection path between the first starting area and the second starting area, marked as regional connection path 1, and the line between the inspection end point of the UAV power grid inspection operation in the second starting area and the inspection starting point of the UAV power grid inspection operation corresponding to the third starting area is used as the regional connection path between the second starting area and the third starting area, marked. The regional connection path 2 is used as the regional connection path 3. The line between the inspection end point of the drone power grid inspection operation in the third starting area and the inspection starting point of the drone power grid inspection operation corresponding to the fourth starting area is used as the regional connection path between the third starting area and the fourth starting area, marked as regional connection path 3. The line between the inspection end point of the drone power grid inspection operation in the fourth starting area and the inspection starting point of the drone power grid inspection operation corresponding to the fifth starting area is used as the regional connection path between the fourth starting area and the fifth starting area, marked as regional connection path 4; the inspection end point of the drone power grid inspection operation corresponding to the fifth starting area is calibrated as the end point of the initial path of the drone power grid inspection operation.

[0082] The detour threshold stored in the database is 3.5. Table 4 is the analysis results of the characteristic values ​​of the UAV power grid inspection operation environment. Table 5 is the analysis results of the height of the UAV power grid inspection operation in the power grid area. Table 6 is the analysis results of the obstacle characteristic values. Table 7 is the analysis results of the detour plan for the UAV power grid inspection operation to determine the connection path in the power grid area.

[0083] Table 4: Analysis results of the characteristic values ​​of the UAV power grid inspection operation environment

[0084] ;

[0085] Table 5: Analysis results of the UAV power grid inspection operation height in the power grid area

[0086] ;

[0087] Table 6: Analysis results of obstacle characteristic values

[0088] ;

[0089] Table 7: Analysis results of the detour scheme for UAV power grid inspection operations to determine the connection path of the power grid area

[0090] ;

[0091] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A path planning method for unmanned aerial vehicle power grid inspection based on Beidou navigation, characterized in that: The following steps are involved: Analyze the actual operation status of each area of ​​the power grid and obtain the actual operation status contribution value of each area of ​​the power grid; Analyze the historical inspection characteristics of each area of ​​the power grid to obtain the historical inspection contribution value of each area of ​​the power grid. Combined with the actual operation status contribution value of each area of ​​the power grid, determine the priority value of drone power grid inspection operations in each area of ​​the power grid; The above analysis of the historical inspection characteristics of each area of ​​the power grid is as follows: Obtain a historical inspection feature data set for each area of ​​the power grid, which specifically includes the inspection frequency of key nodes in the regional historical inspection, the number of regional historical inspection faults, and the number of regional historical inspection current overloads; Based on the historical inspection feature data set obtained for each area of ​​the power grid, a comprehensive analysis is performed to obtain the historical inspection contribution value, which is used as the analysis basis for determining the priority value of drone power grid inspection operations in each area of ​​the power grid; The specific analysis process of the historical inspection contribution value is as follows: Where β is the historical inspection contribution value, jp is the inspection frequency of key nodes in the regional historical inspection, gz is the number of regional historical inspection faults, gn is the number of regional historical inspection current overloads, τ1 is the compensation factor of the set jp, τ2 is the compensation factor of the set gz, τ3 is the compensation factor of the set gn, and e is a natural constant; Determine the initial path of the UAV power grid inspection operation based on the priority value of the UAV power grid inspection operation in each area of ​​the power grid, wherein the initial path of the UAV power grid inspection operation includes the inspection path in each area of ​​the power grid and the connection path of each area; Analyze the operating environment of UAV power grid inspection in various areas of the power grid and determine the operating height of UAV power grid inspection in various areas of the power grid; Based on Beidou navigation, the coordinates of obstacles in the connection paths of each area of ​​the power grid are obtained, and the detour plan for UAV power grid inspection operations in the connection paths of each area of ​​the power grid is determined; The initial path of the UAV power grid inspection operation is corrected based on the UAV power grid inspection operation height of each area of ​​the power grid and the UAV power grid inspection operation detour plan of each area of ​​the power grid.

2. The path planning method for Beidou navigation-based UAV power grid inspection operation according to claim 1 is characterized in that: The actual operation status of each area of ​​the power grid is analyzed, and the specific analysis process is as follows: Obtaining the actual operation status data set of each area of ​​the power grid, the actual operation status data set of each area of ​​the power grid specifically includes the regional voltage deviation rate, regional power factor, and regional total harmonic distortion rate; Based on the actual operation status data set of each area of ​​the power grid, a comprehensive analysis is performed to obtain the actual operation status contribution value, which is used as the analysis basis for determining the priority value of drone power grid inspection operations in each area of ​​the power grid.

3. The path planning method for Beidou navigation-based UAV power grid inspection operation according to claim 1 is characterized in that: The specific analysis process of determining the priority value of the UAV power grid inspection operation in each area of ​​the power grid is as follows: The actual operating status contribution value of each power grid area and the corresponding historical inspection contribution value of each power grid area are stored as designated tags, and the designated tags are compared with the UAV power grid inspection operation priority values ​​of the power grid areas corresponding to each designated tag stored in the database to obtain the UAV power grid inspection operation priority values ​​of the power grid areas corresponding to the designated tags.

4. The path planning method for Beidou navigation-based UAV power grid inspection operation according to claim 1 is characterized in that: The initial path of the UAV power grid inspection operation is determined based on the priority value of the UAV power grid inspection operation in each area of ​​the power grid. The specific analysis process is as follows: Sort the priority values ​​of drone power grid inspection operations in each area of ​​the power grid from large to small; The power grid area corresponding to the priority value of the UAV power grid inspection operation ranked as the Nth is marked as the Nth starting area, where N is the power grid area number, N = 1, 2,,, J, and J is the total number of power grid areas; The inspection starting point of the UAV power grid inspection operation corresponding to the first starting area is marked as the starting point of the initial path of the UAV power grid inspection operation; The line between the inspection end point of the UAV power grid inspection operation of the Nth starting area and the inspection start point of the UAV power grid inspection operation corresponding to the N+1th starting area is used as the regional connection path between the Nth starting area and the N+1th starting area, marked as the regional connection path P, where P is the regional connection path number, P=1, 2,,, J-1; The inspection endpoint of the UAV power grid inspection operation corresponding to the Jth starting area is marked as the initial path endpoint of the UAV power grid inspection operation.

5. The path planning method for Beidou navigation-based UAV power grid inspection operation according to claim 1 is characterized in that: The specific analysis process for determining the UAV power grid inspection operation height in each area of ​​the power grid is as follows: Obtain the drone power grid inspection operation environment data set of each power grid area, and based on the obtained drone power grid inspection operation environment data set of each power grid area, obtain the drone power grid inspection operation environment characteristic value through comprehensive analysis. The drone power grid inspection operation environment characteristic value is used as the analysis basis for determining the drone power grid inspection operation height of each power grid area; The characteristic value of the UAV power grid inspection operation environment is stored as a designated label, and the designated label is compared with the UAV power grid inspection operation height of the power grid area corresponding to each designated label stored in the database to obtain the UAV power grid inspection operation height of the power grid area corresponding to the designated label.

6. The path planning method for Beidou navigation-based UAV power grid inspection operation according to claim 5 is characterized in that: The UAV power grid inspection operation environment dataset specifically includes the maximum height of trees in the regional environment, the average height of trees in the regional environment, and the concentration of particulate matter in the regional environment.

7. The path planning method for Beidou navigation-based UAV power grid inspection operation according to claim 1 is characterized in that: The specific analysis process of the detour plan for the UAV power grid inspection operation to determine the connection paths of each area of ​​the power grid is as follows: Based on Beidou navigation, obtain the coordinates of obstacles in the connection paths of each area of ​​the power grid; Based on the coordinates of the obstacles in the connection paths of each area of ​​the power grid, the obstacle characteristics of the connection paths of each area of ​​the power grid are collected from the geological database; Based on the obstacle characteristics of the connection paths in each area of ​​the power grid, the shortest distance for the drone to bypass the obstacle from above and the shortest distance for the drone to bypass the obstacle from the side are obtained; Get the energy required for the drone to rise to a certain height and the energy required for the drone to deviate a certain distance; Based on the shortest distance that a drone bypasses from above an obstacle, the shortest distance that a drone bypasses from the side of an obstacle, the energy required for a drone to rise per unit height, and the energy required for a drone to deviate per unit distance, a comprehensive analysis is performed to obtain the obstacle characteristic value. The obstacle characteristic value is used as the analysis basis for determining the bypass plan for drone power grid inspection operations to determine the connection paths of various power grid areas. If the obstacle characteristic value is not greater than the detour threshold stored in the database, the shortest path for the drone to bypass the obstacle is used as the detour plan for the drone power grid inspection operation, which connects the power grid area. If the obstacle characteristic value is greater than the detour threshold stored in the database, the shortest path for the drone to bypass the obstacle from the side is used as the drone power grid inspection operation detour plan for the power grid area connection path.

8. The path planning method for Beidou navigation-based UAV power grid inspection operation according to claim 7 is characterized in that: The obstacle characteristic value, the specific analysis process is: Wherein, δ is the characteristic value of the obstacle, hd is the shortest distance that the UAV bypasses from the top of the obstacle, cd is the shortest distance that the UAV bypasses from the side of the obstacle, nh is the energy required for the UAV to rise per unit height, nc is the energy required for the UAV to deviate per unit distance, θ1 is the compensation factor for the set UAV rise, and θ2 is the compensation factor for the set UAV deviation.

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