A method and system for automatically generating take-off and landing points for electric power inspection drones

Abstract

The present invention discloses a method and system for automatically generating take-off and landing points for electric power inspection drones. The method includes: S1, importing tower location data, basic terrain and geographic data, airspace traffic data, and RTK signal coverage area data; S2, judging the terrain type of the inspection area based on the DEM elevation model, if the terrain type is a plain area, obtaining the layer of the area and using it as a surface analysis base map; if the terrain type is a mountainous area, performing regional high point analysis to obtain a high point set layer and using it as a point analysis base map; S3, extracting geological areas that meet the take-off and landing conditions in the area accessible to personnel at the take-off and landing point based on the remote sensing image feature analysis and recognition method, and obtaining a passable geological area layer; S4, performing spatial overlay analysis on the analysis base map, the passable geological area layer, the RTK strong network signal layer, and the airspace non-restricted area layer to obtain alternative take-off and landing points / areas. The present invention solves the problem that inspection personnel need to undergo long-term training and practice and long-term evaluation to find the take-off point.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle inspection, and more specifically, to a method and system for automatically generating take-off and landing points of an electric power inspection unmanned aerial vehicle. Background Art

[0002] In recent years, with the rapid development of my country's economy and the overall improvement of science and technology, especially electronic information technology, the drone industry has made substantial progress. At present, drone products are complete in variety and diverse in functions. We have the ability to independently develop and design low-, medium- and high-end drones, and have basically formed a complete R&D, manufacturing, sales and service system. Some technologies have reached the international advanced level. The drone industry has embarked on a path of comprehensive development. Domestic small drone companies represented by DJI Innovations, Zero Degree Intelligent Control, Ehang Technology, and Zhendi Intelligent are developing rapidly, and their scale far exceeds that of foreign companies. Breakthroughs in this field mainly rely on my country's cost advantage and technological first-mover advantage in civilian small drone hardware. With the help of China's perfect electronic component supply chain, domestic drone companies can produce and sell products at a lower cost. Domestic companies such as DJI and Zero Degree Intelligent Control mostly originated from universities and military institutions, and have more technical reserves. Coupled with the reserves of relevant software and algorithm technologies of domestic companies, companies have gained first-mover advantages and are ahead of foreign companies in technology.

[0003] As a high-tech inspection tool, drones have been rapidly promoted and applied in the power grid industry. With the continuous increase in the number of drones, the number of pilots and the skill level in the national power grid industry, the era of drone inspection for power grid inspection has arrived. Since 2016, drone companies and software companies have continuously invested in the research and development of drone + AI intelligent systems. In 2016, Tovos Technology Co., Ltd. developed the Tovos Powerline Analyzer module, a professional application system and solution for power transmission line safety detection and intelligent identification and reconstruction based on lidar. This module is conducive to the integrated geospatial solution of point cloud data management, display, analysis, expression and processing obtained by the lidar carried by drones. In 2018, Fuya Intelligent officially launched the MindSky "Ruile" second-generation fully automatic flight system. The system allows drones to take off and land automatically and change batteries through automatic airports. The onboard AI algorithm realizes fully automatic flight of drones, and image data is transmitted to the cloud in real time for recognition; China Electric Haohai proposed a drone automatic cruise system based on Beidou high-precision location services. In 2019, Zhongke Yuntu launched an unmanned aerial automatic inspection system to realize functions such as drone automatic cruise and real-time data return. In 2020, DJI launched "DJI Zhitu", which uses drone laser point cloud for real-time 3D modeling to realize automatic drone inspection operations. Not only have various companies made major breakthroughs in drone automatic cruising, but they have also worked with universities and research institutes to achieve key results in intelligent defect identification. For example, Chongqing University has developed a new type of intelligent detection technology for power transmission lines; State Grid has worked with Shandong Luneng Intelligent Technology Co., Ltd. to propose a method for intelligent automatic identification of power transmission line components; the Institute of Intelligent Visual Computing of North China Electric Power University has proposed a comprehensive solution for intelligent detection of power transmission line components, etc.

[0004] At present, more and more domestic companies are focusing on how to use artificial intelligence algorithms such as image recognition to solve practical production problems, such as face recognition, security, finance, transportation, and medical care. Well-known domestic image recognition companies include SenseTime, Megvii, and CloudWalk. At present, drone intelligent autonomous inspections are developing rapidly, but most autonomous inspections are based on existing manually planned paths to complete the automatic inspection and real-time data transmission functions of drones. There is no research on automatic selection of drone take-off points based on basic geographic information.

[0005] my country's power grid has become the largest in the world, with wide range, high density, and complex network. Existing UAV power inspection technology requires high manpower and material costs, and cannot achieve real-time online inspection and processing. The automation of UAV inspection and real-time intelligent defect identification have become the core issues that urgently need to be solved in power grid maintenance. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for automatically generating take-off and landing points of a power inspection UAV, so as to overcome the defects existing in the prior art.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A method for automatically generating take-off and landing points of a power inspection UAV comprises the following steps:

[0009] S1. Import the location data of the tower to be inspected, basic terrain and geographic data, airspace traffic data, and RTK signal coverage area data, and unify them into a standard spatial coordinate system database;

[0010] S2. Determine the terrain type of the inspection area based on the DEM elevation model. The terrain types include plains and mountains. If the terrain type is a plain area, obtain the layer of the area and use it as the surface analysis base map; if the terrain type is a mountainous area, perform regional high point analysis to obtain a high point set layer and use it as the point analysis base map;

[0011] S3. Obtain the passable area for personnel at the take-off and landing point based on the basic topographic data in the standard spatial coordinate system database, and extract the geological area that meets the take-off and landing conditions in the passable area for personnel at the take-off and landing point based on the remote sensing image ground feature analysis and recognition method to obtain a passable geological area layer;

[0012] S4. Perform spatial overlay analysis on the analysis base map, accessible geological area layer, RTK strong network signal layer, and airspace non-restricted area layer to obtain alternative take-off and landing points / areas for power inspection drones.

[0013] Furthermore, it is characterized in that the location data of the electric tower to be inspected in step S1 includes the spatial data of the electric tower and the electric wire, and the basic topographical data includes DEM and road traffic vector data.

[0014] Furthermore, the specific steps of determining the terrain type of the inspection area based on the DEM elevation model in step S2 include:

[0015] Extract the spatial data layer of electric tower and power line data based on the standard spatial coordinate system database;

[0016] The spatial location of the power tower to be inspected is obtained by spatial query, and the research area is delineated with the tower location as the center and the longest cruising distance of the power inspection drone as the radius to obtain the basic layer.

[0017] The base layer and the DEM elevation model layer are spatially superimposed, the highest point elevation of the study area is extracted, and the highest point elevation is compared with the set parameter elevation. If the highest point elevation is less than the set parameter elevation, the terrain type is a plain area; if the highest point elevation is greater than or equal to the set parameter elevation, the terrain type is a mountainous area.

[0018] Furthermore, in step S2, if the terrain type is a mountainous area, the specific steps of performing regional high point analysis to obtain a high point set layer and using it as a point analysis base map include:

[0019] Generate contour lines based on the elevation information of the study area, convert the contour lines into a surface elevation zone map, and extract independent self-enclosed circles that meet the conditions;

[0020] The number of contour circles that meet the conditions is determined. If the number is not met, the contour generation parameters are modified to meet the conditions. The elevation information of the areas within each circle is traversed and scanned again. The highest point position within each circle is extracted as the high point within the area. The Janus visibility analysis algorithm is used to analyze and obtain the high point set within the area that meets the visibility conditions.

[0021] Use the high point collection layer as the point analysis basemap.

[0022] Furthermore, in step S4, the surface analysis base map is obtained as the alternative take-off and landing points of the power inspection UAV, and the point analysis base map is obtained as the alternative take-off and landing areas of the power inspection UAV.

[0023] Furthermore, the step of determining the take-off and landing point / area according to the alternative take-off and landing points or areas in step S4 is:

[0024] If it is a single tower inspection, then: the distance between the alternative take-off and landing point or area and the target tower to be inspected is used as a reference. The closer the distance, the higher the priority.

[0025] If multiple towers are inspected, then: Assume that the candidate take-off and landing point / area layers corresponding to each tower are L 1 ,L 2 ,…,L i The alternative take-off and landing points / areas in the layer are P 11 ,P 12 ,…,P 1j ;P 21 ,P 22 ,…,P 2j ;P i1 ,P i2 ,…,P ij ;

[0026] Perform spatial overlay analysis on all candidate take-off and landing points / areas to obtain a new layer L(i+1) and the spatial position P of each layer;

[0027] Give the new layer a point set P (P 1 ,P 2 ,…,P n ) weights are assigned, points are extracted one by one according to the weight of the P point from high to low, and the towers covered by the extracted points are marked until all the P points with a weight greater than 1 are traversed and extracted. The set of P points with a weight greater than 1 is set M, and the remaining unmarked towers have no superimposed alternative take-off and landing points;

[0028] For towers without overlapping take-off and landing points, the nearest distance method to the center point is used, including: finding the geometric center point of the spatial position of all towers that need to be inspected, traversing the alternative take-off and landing points of the remaining towers one by one, and extracting the take-off and landing point position closest to the center point as the optimal take-off and landing point, and setting the set as set N

[0029] If the weights of the P point set are all 1, then the set M is an empty set, and the high point set N closest to the center point is taken as the optimal take-off and landing point set, and the road line connecting all the points in the set (M+N) is the optimal inspection route.

[0030] The present invention also provides a system for automatically generating take-off and landing points of the power inspection UAV according to the above-mentioned method, comprising:

[0031] The import module is used to import the location data of the tower to be inspected, basic terrain and geographic data, airspace traffic data, and RTK signal coverage area data, and unify them into a standard spatial coordinate system database;

[0032] The inspection area judgment module is used to judge the terrain type of the inspection area based on the DEM elevation model. The terrain types include plain areas and mountainous areas. If the terrain type is a plain area, the layer of the area is obtained and used as the surface analysis base map; if the terrain type is a mountainous area, the regional high point analysis is performed to obtain the high point set layer and use it as the point analysis base map;

[0033] An extraction module is used to obtain the passable area for personnel at the take-off and landing point based on the basic terrain and geographic data in the standard spatial coordinate system database, and to extract the geological areas that meet the take-off and landing conditions in the passable area for personnel at the take-off and landing point based on the remote sensing image ground feature analysis and recognition method, so as to obtain a passable geological area layer;

[0034] The spatial overlay analysis module is used to perform spatial overlay analysis on the analysis base map, the passable geological area layer, the RTK strong network signal layer, and the airspace non-restricted area layer to obtain alternative take-off and landing points / areas for power inspection drones.

[0035] Compared with the prior art, the advantages of the present invention are: the method and system for automatically generating take-off and landing points for electric power inspection drones provided by the present invention, by importing the location of power towers and local DEM, road traffic and other basic terrain data, airspace restrictions, RTK network positioning signal strength distribution and other industry geographical data, and vegetation and landform recognition results based on remote sensing images, different terrain analyses are performed to automatically set the best take-off point, solving the problem that inspection personnel need to go through long-term training and practice and long-term evaluation to find the take-off point, laying the foundation for fully automated inspection of drones. The present invention not only greatly shortens the working hours of power grid staff and reduces the difficulty of power grid inspection operations, but can also be widely used in drone operations in other industries other than power grids, and has significant practical application value in the field of drone use. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 It is a flow chart of the method for automatically generating take-off and landing points of a power inspection UAV of the present invention.

[0038] Figure 2 It is a schematic diagram for selecting the best take-off and landing point in the present invention.

[0039] Figure 3 It is a schematic diagram of space coordinate transformation in the present invention.

[0040] Figure 4 It is a flow chart for obtaining a higher terrain area in the present invention.

[0041] Figure 5 This is a flow chart for obtaining alternative take-off and landing points / areas in the present invention.

[0042] Figure 6 This is a rendering of the optimal take-off point for inspection of a single tower in the present invention.

[0043] Figure 7 This is a flow chart for obtaining the set of take-off and landing points of the optimal path in the present invention.

[0044] Figure 8 It is a rendering of the optimal path for multi-tower operation in the present invention.

[0045] Fig. 9 It is a schematic diagram of the automatic generation system of take-off and landing points of the electric power inspection UAV of the present invention.

[0046] Fig.10 It is the overall architecture diagram of the program in the present invention.

[0047] Fig.11 It is a diagram showing the main functional diagram of the system of the present invention.

[0048] Fig.12 This is the inspection task management diagram of the present invention.

[0049] Fig.13 It is a selection diagram of the inspection content of the present invention.

[0050] Fig.14 This is a diagram showing the best take-off point of the present invention.

[0051] Fig.15 It is a display diagram of the inspection route of the present invention.

[0052] Fig.16 It is a detailed diagram of the route work order information of the present invention.

[0053] Fig.17 The before and after operation process of using the system of the present invention. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0055] Combination Figure 2 As shown, the optimal take-off and landing point selection concept of the present invention is: Figure 2 The middle triangle is the electric tower that needs to be inspected during the operation, and the solid circle is the flight signal range of the target electric tower inspection drone. The basic geographic data is read in the flight signal range, and the DEM analysis method is used to extract the high point position that meets the visibility requirements within the flight signal range of each electric tower to obtain the analysis base map of the high point position distribution. The analysis base map is spatially superimposed with the passable geological area layer of the road buffer, the airspace non-restricted area layer, and the RTK strong signal area layer. The high point position within all layers is the optimal take-off point set, and the path connected by the most take-off points is the optimal operation path.

[0056] Moreover, in order to meet the requirements of real-time fully autonomous UAV power inspection, the selection of the UAV take-off and landing point location not only needs to consider the distance between the UAV flight and the inspection target, the regional elevation and the visibility, but also needs to meet the following conditions:

[0057] ① It is convenient for parking and meets certain road and personnel accessibility requirements.

[0058] ② The location is flat and is not covered with vegetation or water, and is not an area where operations can be carried out.

[0059] ③In the non-restricted area of ​​the airspace.

[0060] ④In the area with strong coverage of RTK network model.

[0061] See also Figure 1 and Figure 2 As shown, this embodiment discloses a method for automatically generating take-off and landing points of a power inspection drone, comprising the following steps:

[0062] Step S1, import the location data of the tower to be inspected (including the spatial data of the tower and the wire), basic terrain and geographic data (including DEM, road traffic vector data), airspace traffic data, RTK signal coverage area data, and unify them into a standard spatial coordinate system database (this is because the coordinate system of each data may be inconsistent, for example, the tower and wire data use the 84 coordinate system, and the road traffic data base map uses the 2000 coordinate system, so all the data need to be converted into the same coordinate system first), such as Figure 3 shown.

[0063] Step S2: Determine the terrain type of the inspection area based on the DEM elevation model. The terrain types include plains and mountainous areas. If the terrain type is a plain area, obtain the layer of the area and use it as the surface analysis base map; if the terrain type is a mountainous area, perform regional high point analysis to obtain a high point set layer and use it as the point analysis base map.

[0064] The terrain type of the inspection area is determined mainly by using different take-off point generation algorithms for different scenarios.

[0065] like Figure 4 As shown in the figure, the specific steps of determining the terrain type of the inspection area based on the DEM elevation model include:

[0066] Extract the spatial data layer of the electric tower and wire data based on the standard spatial coordinate system database; use the spatial query method to obtain the spatial position of the electric tower to be inspected, and use the electric tower position as the center and the maximum cruising distance r of the power inspection drone as the radius to delineate the study area and obtain the basic layer; spatially superimpose the basic layer with the DEM elevation model layer, extract the highest point elevation of the study area, and compare the highest point elevation with the set parameter elevation. If the highest point elevation is less than the set parameter elevation (the tower point elevation + 20m, the specific value of the parameter is to be determined), the terrain type is a plain area, and the parameter elevation is the highest elevation value of the area. Therefore, there is no need to extract regional high points. When only considering the influence of elevation, any point in the area can be used as the take-off and landing point of the drone inspection. Therefore, the layer is directly obtained as the surface analysis base map.

[0067] If the highest point elevation is greater than or equal to the set parameter elevation, the terrain type is mountainous, and it is necessary to perform regional high point analysis to obtain a high point set layer and use it as a point analysis base map. The specific steps include:

[0068] Contour lines are generated based on the elevation information of the study area, and the contour lines are converted into a surface elevation zone map to extract independent self-closed circles that meet the conditions (in the contour topographic map, if a parameter threshold has been set as the contour interval, the high points in the area are distributed in independent self-closed contour circles, and there are no other contour lines in the circle).

[0069] The number of contour circles that meet the conditions is judged, taking 5 as an example. If the number is not met, return to modify the contour generation parameters to meet the conditions, then traverse and scan the elevation information of the areas within each circle, extract the highest elevation point position in each circle as the high point within the regional range, and analyze it through the Janus visibility analysis algorithm to obtain the set of high points in the regional range that meet the visibility conditions.

[0070] When only considering the influence of elevation, the regional high point set obtained through the above elevation analysis can be used as the take-off and landing points for drone inspection. Therefore, this high point set layer is used as the base map for point analysis.

[0071] Step S3, obtaining the passable area for personnel at the take-off and landing point based on the basic topographic and geographic data in the standard spatial coordinate system database, and extracting the geological areas that meet the take-off and landing conditions in the passable area for personnel at the take-off and landing point based on the remote sensing image feature analysis and recognition method, to obtain a passable geological area layer.

[0072] like Figure 5 As shown in the figure, based on the take-off and landing conditions ① and ②, the take-off and landing point is within 400m from the road (the specific value of the parameter is to be determined). Therefore, the road information of the study area is extracted from the preprocessed database, and the buffer zone analysis is performed with the road as the benchmark and 400m as the parameter to obtain the passable area for personnel at the take-off and landing point; the remote sensing image feature analysis and recognition method is used to extract the geological areas such as hard ground and sand in the area that meet the take-off and landing conditions, and obtain the passable geological area layer. The analysis base map, the passable geological area layer, the RTK strong network signal layer, and the airspace non-restricted area layer are overlaid and analyzed by the spatial overlay analysis method to obtain the alternative take-off point / area (the mountain analysis base map is obtained as the alternative take-off and landing point, and the plain analysis base map is obtained as the alternative take-off and landing area).

[0073] S4. Perform spatial overlay analysis on the analysis base map, accessible geological area layer, RTK strong network signal layer, and airspace non-restricted area layer to obtain alternative take-off and landing points / areas for power inspection drones.

[0074] Specifically, the alternative take-off and landing points / take-off and landing areas can all be used as the take-off and landing points for real-time fully autonomous inspection of the target towers in actual operations. Since each operation may be an inspection of a single or multiple towers, a comprehensive analysis is conducted on multiple tower alternative take-off and landing point / area groups to achieve the following effects: cover all towers that need to be inspected with the fewest possible take-offs; complete the day's operations with the shortest possible distance.

[0075] The process of obtaining the optimal take-off and landing point / take-off and landing path is as follows:

[0076] For a single tower inspection, the distance between the candidate take-off and landing point / area and the target tower to be inspected is used as a reference. The closer the distance, the higher the priority. Finally, one of the top three positions can be selected as the take-off and landing point for this operation according to the operator's wishes, or the default optimal take-off and landing point can be directly used. The results are shown as follows Figure 6 shown.

[0077] For multiple tower inspections, the candidate take-off and landing point / area layers corresponding to each tower are L 1 ,L 2 ,…,L i The alternative take-off and landing points / areas in the layer are P 11 ,P 12 ,…,P 1j ;P 21 ,P 22 ,…,P 2j ;P i1 ,P i2 ,…,P ij ; Perform spatial overlay analysis on all candidate take-off and landing points / areas to obtain a new layer L(i+1) and the spatial position P of each layer.

[0078] Give the new layer a point set P (P 1 ,P 2 ,…,P n ) are assigned weights (the weight of non-overlapping points is 1, the weight of two overlapping points is 2, and so on), and points are extracted one by one according to the weight of the P point from high to low, and the towers covered by the extracted points are marked until all P points with weights above 1 are traversed and extracted. Let the set of P points with weights greater than 1 be set M, and the remaining unmarked towers have no overlapping alternative take-off and landing points. For towers without overlapping take-off and landing points, the center point closest distance method is used, that is: find the geometric center point of the spatial position of all towers that need to be inspected at the time, traverse the alternative take-off and landing points of the remaining towers one by one, and extract the take-off and landing point positions closest to the center point as the optimal take-off and landing points, and set the set to set N. If the weights of the P point set are all 1, then set M is an empty set, and the high point set N closest to the center point is taken as the optimal take-off and landing point set, and the road line connecting all points in the set (M+N) in series is taken as the optimal inspection route, such as Figure 7 shown.

[0079] The inspection results of multiple power towers are as follows: Figure 8 As shown, there are 6 pole towers that need to be inspected during this operation. After calculating the optimal path, only 3 drone take-off and landing points are needed to fully cover all the operating towers. The path formed by the three take-off and landing points in series is the shortest path for the operation.

[0080] like Fig. 9 As shown, the present invention also provides a system for automatically generating take-off and landing points of power inspection drones according to the above-mentioned method, including: an import module 1, used to import the location data of the power tower to be inspected, basic terrain and geographical data, airspace traffic data, and RTK signal coverage area data, and unify them into a standard spatial coordinate system database; an inspection area judgment module 2, used to judge the terrain type of the inspection area based on the DEM elevation model, the terrain type includes plain areas and mountainous areas, if the terrain type is a plain area, the layer of the area is obtained and used as the surface analysis base map; if the terrain type is a mountainous area Then, regional high point analysis is performed to obtain a high point set layer and use it as a point analysis base map; an extraction module 3 is used to obtain the passable area for personnel at the take-off and landing point based on the basic topographic and geographical data in the standard spatial coordinate system database, and to extract the geological areas that meet the take-off and landing conditions in the passable area for personnel at the take-off and landing point based on the remote sensing image feature analysis and recognition method, and obtain a passable geological area layer; a spatial overlay analysis module 4 is used to perform spatial overlay analysis on the analysis base map, the passable geological area layer, the RTK strong network signal layer, and the airspace non-restricted area layer to obtain alternative take-off and landing points / areas for power inspection drones.

[0081] like Fig.10 As shown, the automatic generation system of take-off and landing points of power inspection drones of the present invention adopts the idea of ​​front-end and back-end separation for architecture. The front-end application is developed based on the react-native framework using JavaScript, and can be deployed on both Apple and Android phones for use. The back-end program uses the Node.JS framework for fast and lightweight development.

[0082] The front-end application of the App is implemented using the MVC architecture. The presentation layer (View) displays the functions provided by the program through the user interface. Users can use the three-dimensional display and editing of inspection routes, inspection content selection (single tower or multiple towers), inspection task management (assigned task viewing) and optimal take-off point recommendation functions provided by the program. The logic layer (Controller) is the core layer of the system, connecting the data layer (Model) and the presentation layer. It is necessary to perform necessary processing on the existing data and render the processed data to the presentation layer. The logic layer includes a route planning module, which is used to automatically identify the tower type and generate photo point data; a take-off point algorithm module, which is used to calculate the optimal take-off point based on the data provided by the data layer; a data service request module, which is used to request data in the data layer and distribute the data to the presentation layer; a data cache module, which is used to cache the processed data and the requested data, reducing the number of requests from the logic layer to the data layer and speeding up the speed of the logic layer rendering the presentation layer.

[0083] The App backend program uses the Express middleware framework in NodeJS for rapid construction, and uses Passport.JS as a security framework for security authentication of interface access. Data stored in the database on the server cannot be obtained through unconventional paths. The database is developed using the relational database mysql, which is safe and stable. Four modules are set up in the backend program, namely the data response module, which mainly distributes the access request routes so that the corresponding backend data processing module processes the data; the data processing module is mainly used to perform secondary processing on the data after receiving it, and finally obtain business-related data and use the database operation module to store it, or return it to the front-end program; the database operation module is mainly used for database storage, so that the backend program can easily add, delete, modify and query the mysql database. The data security module performs one-way encryption operations on the processed data. All sensitive data such as user passwords are one-way encrypted using the SHA-2 hash algorithm, and the correctness of the data is verified later by comparing the hashed strings.

[0084] According to the design of the presentation layer in the overall system structure, the present invention includes four front-end functional modules: inspection task management, inspection content selection, drone take-off point recommendation and inspection route display, such as Fig.11 shown.

[0085] Among them, inspection task management: is mainly used to display relevant information of inspection tasks that users need to perform, and provides related functions for submitting completed task information. It contains several content modules such as inspected towers, towers to be inspected, completed work orders, work orders to be completed, and newly created work orders. Inspected towers and towers to be inspected are mainly used to display whether the towers in the power grid have been inspected; completed work orders display detailed information on each inspection operation; work orders to be completed are displayed as created work tasks, which need to be completed according to the requirements of the work order; new work orders are new work task work orders. Expand as follows Fig.12 shown.

[0086] Inspection content selection: mainly used to define the real-time operation content. Click to select the tower that needs to be inspected for this operation, and double-click to cancel the selected tower. The inspection content selection can choose a single or multiple towers combined inspection and other different operation content selections. Functional design such as Fig.13 shown.

[0087] Recommendation of the best take-off point: mainly displays the calculation results of the best take-off point algorithm module in the logic layer. Through systematic calculation of the work content of the selected tower to be inspected, a single or multiple tower inspection alternative take-off points are finally generated, and the alternative take-off points are comprehensively sorted. The system can provide area zooming and zooming display functions to facilitate operators to view the details of the tower location. After checking and confirming, the operator saves the work order by clicking the Save button, and a single tower inspection work order is automatically generated. Fig.14 An example diagram of the best takeoff point recommendation for a single tower.

[0088] Inspection route display: mainly displays the best inspection route map planned according to the system algorithm when multiple towers are inspected. After the generated route map is manually confirmed, you can directly click to automatically generate a work order. The work order contains the work order information automatically generated by the system. It contains relevant information of the current operation, such as the line name, the name of the tower included in the inspection, the number of drone take-off and landing points for the current operation, and the length of the operation route of the operator. Click the edit button of the work order to modify the work order information, and click the OK button to save the work order. Fig.15 This is a display diagram of the inspection routes for multiple power tower inspections. Fig.16 Details of the automatically generated job order.

[0089] Through the design of the system of the present invention, the automatic search for the take-off and landing points of the drone and the planning of the inspection route of multiple power towers are realized. Since the drone is used for power inspection, the take-off and landing points are all surveyed by manpower on the spot, and can only be determined after multiple tests. Therefore, in the past, each power inspection could not predict in advance how many towers can be inspected in this operation, nor could it clearly understand how many times the inspection of multiple towers requires take-off and landing, and which towers can really use the same take-off and landing to complete the inspection task. Through the implementation of the present invention, the tower can be selected through the system, and the position of the tower required for inspection is intuitively checked at which position the drone is taken off and landed, and how to plan the work route of the operation to complete the most tower inspections with the least number of take-offs and landings. Finally, the length of the operation route of this operation and the number of take-off and landing points are displayed in the form of work order information, and the time required for the out-of-town inspection operation can be estimated, which is convenient for the arrangement of personnel and work. It avoids unnecessary waste of time, and directly shortens the two large blocks of time required for finding the take-off and landing points and drone flight inspection in the past power inspection to only need to inspect at the designated location, saving at least 50% of the working time and workload of the operator. The difference before and after using the system is as follows Fig.17 shown.

[0090] After using the present invention, the inspection of a single tower can save at least 45 minutes. In the power system, the number of towers in a power line ranges from dozens to thousands. Taking the inspection of a power line containing 100 towers as an example, without considering the situation of multiple towers with the same take-off and landing point implemented by the system, 4500 minutes, or 9.4 working days, can be saved. In addition, the system provides the optimal path and optimal take-off and landing point planning for multi-tower inspections. In the above case, the number of take-offs and landings of drones can be greatly reduced, saving the inspection time of operators and the time spent on the operation journey.

[0091] The present invention can realize an algorithm for automatically generating the best take-off point. The user can select the tower to be flown in the inspection App ground station, see the signs of the optional flight points, and navigate to the location of the flight point.

[0092] The present invention can realize the function of automatic route planning. Users can select the towers that need to be inspected in the inspection App ground station, and adjust the automatically generated photo points and routes in the three-dimensional model, inspect the towers with one click and submit the inspection results.

[0093] The present invention can realize the real-time defect front-end identification function. During the autonomous inspection of the drone, the edge computing AI acceleration module transmits back defective pictures, and the identification accuracy rate is not less than 80%.

[0094] Although the embodiments of the present invention are described in conjunction with the accompanying drawings, the patent owner may make various variations or modifications within the scope of the appended claims. As long as they do not exceed the protection scope described in the claims of the present invention, they should be within the protection scope of the present invention.

Claims

1. A method for automatically generating take-off and landing points for a power inspection drone, characterized in that: The following steps are involved: S1. Import the location data of the tower to be inspected, basic terrain and geographic data, airspace traffic data, and RTK signal coverage area data, and unify them into a standard spatial coordinate system database; S2. Determine the terrain type of the inspection area based on the DEM elevation model. The terrain types include plains and mountains. If the terrain type is a plain area, obtain the layer of the area and use it as the surface analysis base map; if the terrain type is a mountainous area, perform regional high point analysis to obtain a high point set layer and use it as the point analysis base map; S3. Obtain the passable area for personnel at the take-off and landing point based on the basic topographic data in the standard spatial coordinate system database, and extract the geological area that meets the take-off and landing conditions in the passable area for personnel at the take-off and landing point based on the remote sensing image ground feature analysis and recognition method to obtain a passable geological area layer; S4. Perform spatial overlay analysis on the analysis base map, the passable geological area layer, the RTK strong network signal layer, and the airspace non-restricted area layer to obtain the alternative take-off and landing points / areas for the power inspection drone; The step of determining the take-off and landing point / area according to the alternative take-off and landing point or area in step S4 is: If it is a single tower inspection, then: the distance between the alternative take-off and landing point or area and the target tower to be inspected is used as a reference. The closer the distance, the higher the priority. If multiple towers are inspected, then: Let the candidate take-off and landing point / area layers corresponding to each tower be L1, L2, …, L i The alternative take-off and landing points / areas in the layer are P 11 ,P 12 ,…,P 1j ;P 21 ,P 22 ,…,P 2j ;P i1 ,P i2 ,…,P ij ; Perform spatial overlay analysis on all candidate take-off and landing points / areas to obtain a new layer L(i+1) and the spatial position P of each layer; Give the new layer a set of points P (P1, P2, ..., P n ) to assign weights, extract points one by one according to the weight of the P point from high to low, and mark the towers covered by the extracted points, until all P points with weights greater than 1 are traversed and extracted, and the set of P points with weights greater than 1 is set M, and the remaining unmarked towers have no superimposed alternative take-off and landing points; For the towers without overlapping take-off and landing points, the nearest distance method to the center point is adopted, including: finding the geometric center point of the spatial position of all the towers that need to be inspected, traversing the candidate take-off and landing points of the remaining towers one by one, and extracting the take-off and landing point position closest to the center point as the optimal take-off and landing point, and setting the set as set N; If the weights of the P point set are all 1, then the set M is an empty set, and the high point set N closest to the center point is taken as the optimal take-off and landing point set, and the road line connecting all the points in the set (M+N) is the optimal inspection route.

2. The method for automatically generating take-off and landing points of a power inspection drone according to claim 1 is characterized in that: The location data of the electric tower to be inspected in step S1 includes the spatial data of the electric tower and the electric wires, and the basic topographic data includes DEM and road traffic vector data.

3. The method for automatically generating take-off and landing points of a power inspection drone according to claim 1 is characterized in that: The specific steps of determining the terrain type of the inspection area based on the DEM elevation model in step S2 include: Extract the spatial data layer of electric tower and power line data based on the standard spatial coordinate system database; The spatial location of the power tower to be inspected is obtained by spatial query, and the research area is delineated with the tower location as the center and the longest cruising distance of the power inspection drone as the radius to obtain the basic layer. The base layer and the DEM elevation model layer are spatially superimposed, the highest point elevation of the study area is extracted, and the highest point elevation is compared with the set parameter elevation. If the highest point elevation is less than the set parameter elevation, the terrain type is a plain area; if the highest point elevation is greater than or equal to the set parameter elevation, the terrain type is a mountainous area.

4. The method for automatically generating take-off and landing points of a power inspection drone according to claim 3 is characterized in that: In step S2, if the terrain type is a mountainous area, the specific steps of performing regional high point analysis to obtain a high point set layer and using it as a point analysis base map include: Generate contour lines based on the elevation information of the study area, convert the contour lines into a surface elevation zone map, and extract independent self-enclosed circles that meet the conditions; The number of contour circles that meet the conditions is determined. If the number is not met, the contour generation parameters are modified to meet the conditions. The elevation information of the areas within each circle is traversed and scanned again. The highest point position within each circle is extracted as the high point within the area. The Janus visibility analysis algorithm is used to analyze and obtain the high point set within the area that meets the visibility conditions. Use the high point collection layer as the point analysis basemap.

5. The method for automatically generating take-off and landing points of a power inspection drone according to claim 1 is characterized in that: In step S4, the surface analysis base map is obtained as the alternative take-off and landing points of the power inspection UAV, and the point analysis base map is obtained as the alternative take-off and landing areas of the power inspection UAV.

6. A system for automatically generating take-off and landing points of a power inspection drone according to any one of claims 1 to 5, characterized in that: include: The import module is used to import the location data of the tower to be inspected, basic terrain and geographic data, airspace traffic data, and RTK signal coverage area data, and unify them into a standard spatial coordinate system database; The inspection area judgment module is used to judge the terrain type of the inspection area based on the DEM elevation model. The terrain types include plain areas and mountainous areas. If the terrain type is a plain area, the layer of the area is obtained and used as the surface analysis base map; If the terrain type is a mountainous area, a regional high point analysis is performed to obtain a high point set layer and used as a point analysis base map; An extraction module is used to obtain the passable area for personnel at the take-off and landing point based on the basic terrain and geographic data in the standard spatial coordinate system database, and to extract the geological areas that meet the take-off and landing conditions in the passable area for personnel at the take-off and landing point based on the remote sensing image ground feature analysis and recognition method, so as to obtain a passable geological area layer; The spatial overlay analysis module is used to perform spatial overlay analysis on the analysis base map, the passable geological area layer, the RTK strong network signal layer, and the airspace non-restricted area layer to obtain alternative take-off and landing points / areas for power inspection drones.

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