Unmanned aerial vehicle inspection management system suitable for power grid infrastructure equipment

Abstract

The invention relates to the technical field of unmanned aerial vehicle inspection, and particularly discloses an unmanned aerial vehicle inspection management system suitable for power grid infrastructure equipment, which comprises a graph structure cd1 construction module, an overall path list construction module, a parameter determination module, a node division module, a graph structure cd2 construction module, a unit path list construction module and an inspection path list construction module. According to the method, the power grid facilities and the power transmission line information are modeled into the graph structure, and based on the planned whole and the unit path list, efficient and comprehensive inspection of the power grid facilities is realized, the unmanned aerial vehicle is ensured to cover all to-be-inspected areas on the optimal inspection path, and meanwhile, the inspection efficiency is improved. The system can automatically adjust and divide node surfaces according to the shooting range of the unmanned aerial vehicle, so that the inspection process is more accurate, missing inspection or repeated inspection is avoided, the efficiency and reliability of the inspection task are finally improved, and the flight time and energy consumption are reduced.

Description

Technical Field

[0001] The present application relates to the field of drone inspection technology, and in particular to a drone inspection management system suitable for power grid infrastructure equipment. Background Art

[0002] Inspection of power grid infrastructure equipment is an important part of ensuring safe, stable and efficient operation of the power system. Drone inspection mainly uses high-definition cameras and sensors to conduct real-time monitoring and data collection of power grid facilities. Drones can quickly cover large areas, adapt to complex terrain, and significantly improve inspection efficiency.

[0003] The inspection of power grid infrastructure equipment mostly relies on manually controlled drones. However, manual operation is labor-intensive and operators need to concentrate for a long time to monitor flight status and data collection, which can easily lead to fatigue. In addition, manually planned inspection routes are prone to local duplication or omission of inspection areas, thereby increasing the length of the inspection route while reducing accuracy, ultimately resulting in low overall inspection efficiency. Summary of the invention

[0004] This application solves the problem in the prior art that manually controlled drone inspections are labor-intensive and that planned inspection routes are prone to local duplication or omission of inspection areas by providing a drone inspection management system suitable for power grid infrastructure equipment. It realizes automatic planning of inspection routes based on power grid facilities and the surrounding environment, accurately plans inspection paths, effectively improves inspection efficiency, reduces costs, and brings significant economic and social benefits.

[0005] In the first aspect, the present application provides a drone inspection management method suitable for power grid infrastructure equipment, which includes: based on a pre-constructed model containing power grid facility information, transmission line information and its surrounding environment information, constructing a graph structure cd1 with power grid facilities and take-off points as parent nodes and transmission lines as edges; planning an overall path list based on the graph structure cd1; determining the shooting range of the drone, and determining the photographed surface representing the area to be inspected in each power grid facility in the model; traversing all photographed surfaces to determine whether the photographed surface exceeds the shooting range, if not, the photographed surface is used as a node surface, if so, the photographed surface is divided into several node surfaces that meet the shooting range; taking all node surfaces as child nodes, determining the path between node surfaces based on environmental information, and taking the path as an edge to construct a graph structure cd2; planning a unit path list for each power grid facility based on the graph structure cd2; and constructing an inspection path list based on the overall path list and the unit path list.

[0006] Furthermore, in the model, a surface is used to represent the area to be inspected whose features can be effectively captured by taking a photo.

[0007] Furthermore, planning the overall path list specifically includes: Sa1. Constructing the overall path list, setting the take-off point as the current mother node; Sb1. Adding the current mother node to the overall path list, and marking it as visited, calculating the distance between the current mother node and all unvisited mother nodes, updating the mother node with the smallest edge distance to the current mother node, and marking the edge as visited; Sc1. Repeating Sb1 until all mother nodes and edges are visited; Sd1. Obtaining the forward path list after all mother nodes and edges are visited; Se1. Reversing the forward path list and obtaining the return path list; Sf1. Concatenating the forward path list with the return path list and removing duplicate mother nodes.

[0008] Furthermore, in constructing a graph structure cd1 with power grid facilities and take-off points as parent nodes and transmission lines as edges, the edges are paths L that maintain a safe distance from the transmission lines.

[0009] Furthermore, the path L in the forward path list and the path L in the return path list are geometrically symmetrical about the transmission line.

[0010] Further, judging whether the photographed surface exceeds the photographing range specifically includes: determining the boundary of the photographing range and the boundary of the photographed surface, and the boundary of the photographing range includes the left boundary x min , right boundary x max , lower boundary y min and the upper boundary y max , the boundaries of the photographed surface include the left boundary x1, the right boundary x2, the lower boundary y1 and the upper boundary y2; determine whether the following four conditions are fully met: 1. The left boundary x1 of the photographed surface min On the right side of the left boundary x1 of the shooting area; 2. On the right side of the shooting area x max On the left side of the right boundary x2 of the shooting area; 3. The lower boundary y of the shooting surface min Above the lower boundary y1 of the shooting area; 4. The upper boundary y1 of the shooting surface max Below the upper boundary y2 of the shooting area; if so, the shot surface does not exceed the shooting range, otherwise the shot surface exceeds the shooting range.

[0011] Furthermore, dividing the photographed surface into a number of node surfaces that meet the photographing range specifically includes: determining the width W1 and height H1 of the photographing range, calculating the width W2 and height H2 of the photographed surface, W2 is: W2 = x2-x1, H2 is: H2 = y2-y1; calculating the number N of node surfaces to be divided in the horizontal direction x and the number N in the vertical direction y , N x for: N y for: The imaged surface is evenly divided into N parts in the horizontal direction. x The photographed surface is evenly divided into N parts in the vertical direction. y The node surface is thus obtained.

[0012] Furthermore, planning the unit path list of each power grid facility specifically includes: Sa2. Constructing the unit path list, determining the initial node, and taking the initial node as the current child node; Sb2. Adding the current child node to the unit path list and marking it as visited, calculating the distance of the edge between the current child node and all unvisited child nodes, updating the child node with the smallest edge distance as the current child node, and marking the edge as visited; Sc2. Repeating Sb2 until all child nodes and edges are visited; Sd2. Adding the initial node to the unit path list again after all parent nodes and edges are visited.

[0013] Furthermore, constructing a complete inspection path list based on the overall path list and the unit path list specifically includes: Sa3. Constructing the inspection path list, taking the take-off point as the starting point, and adding the starting point to the inspection path list; Sb3. Traversing each parent node and edge in the overall path list, for each parent node, adding the child nodes and paths in the unit path list corresponding to the parent node to the inspection path list.

[0014] Furthermore, the grid facilities and node surfaces have geometric centers as parent nodes and child nodes, respectively.

[0015] In the second aspect, the present application also provides a drone inspection management system suitable for power grid infrastructure equipment, which adopts the drone inspection management method suitable for power grid infrastructure equipment as described in the first aspect, and includes: a graph structure cd1 construction module, an overall path list construction module, a parameter determination module, a node division module, a graph structure cd2 construction module, a unit path list construction module and an inspection path list construction module.

[0016] The graph structure cd1 construction module is used to construct a graph structure cd1 with power grid facilities and take-off points as parent nodes and transmission lines as edges based on a pre-constructed model containing power grid facility information, transmission line information and its surrounding environment information; the overall path list construction module is used to plan the overall path list based on the graph structure cd1; the parameter determination module is used to determine the shooting range of the drone and determine the photographed surface representing the area to be inspected in each power grid facility in the model; the node division module is used to traverse all photographed surfaces and determine whether the photographed surface exceeds the shooting range. If not, the photographed surface is used as a node surface. If so, the photographed surface is divided into several node surfaces that meet the shooting range; the graph structure cd2 construction module is used to take all node surfaces as child nodes, determine the path between node surfaces based on environmental information, and use the path as an edge to construct the graph structure cd2; the unit path list construction module is used to plan the unit path list of each power grid facility based on the graph structure cd2; the inspection path list construction module is used to construct the inspection path list based on the overall path list and the unit path list.

[0017] In a third aspect, the present application provides a drone inspection and management device suitable for power grid infrastructure equipment, which includes a memory and a processor.

[0018] The memory is used to store computer programs; the processor is used to implement the steps of the drone inspection management method applicable to power grid infrastructure equipment as the first aspect when executing the computer program.

[0019] The technical solution provided by this application has at least the following technical effects or advantages:

[0020] Since the information of power grid facilities and transmission lines is modeled as a graph structure, and the overall and unit path lists are planned based on this, the problem of high labor intensity of manually controlled drone inspections in the existing technology and the planned inspection routes that easily cause local duplication or omission of inspection areas is solved. The automatic planning of inspection routes based on power grid facilities and the surrounding environment is realized, and the inspection paths are accurately planned to ensure that the drones cover all areas to be inspected on the optimal path, effectively improving the inspection efficiency. At the same time, it maximizes the use of flight resources, reduces inspection time and energy consumption, reduces costs, and brings significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the overall process of the drone inspection management method applicable to power grid infrastructure equipment in this application;

[0022] Figure 2 For this application Figure 1 A flowchart of building a complete inspection path list in ;

[0023] Figure 3 For this application Figure 1 Schematic diagram of graph structure cd1;

[0024] Figure 4 For this application Figure 1 Schematic diagram of the structure of CD2. DETAILED DESCRIPTION

[0025] In order to solve the problems raised in the background technology, the present application realizes automatic planning of inspection routes based on power grid facilities and surrounding environment by modeling the information of power grid facilities and transmission lines into a graph structure, and based on this, plans the overall and unit path lists, and accurately plans the inspection paths to ensure that the drone covers all areas to be inspected on the optimal path.

[0026] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0027] Embodiment 1:

[0028] Please refer to Figure 1 - Figure 4 This embodiment provides a drone inspection management method applicable to power grid infrastructure equipment, which includes the following steps:

[0029] S100. It is used to construct a graph structure cd1 with power grid facilities and take-off points as parent nodes and transmission lines as edges based on a pre-built model containing power grid facility information, power transmission line information and surrounding environment information.

[0030] In the process of building the model, a face can be used to represent the area to be inspected, whose features can be effectively captured by taking a photo. This simplified representation method can reduce the complexity of data processing, making path planning and graph structure construction more efficient. The single face representation can ensure that all important features are covered during the inspection process, thereby improving the comprehensiveness and accuracy of the data. In addition, this method also facilitates quick judgment of the shooting range, optimizes the inspection strategy, reduces redundant inspections, and thus improves inspection efficiency. Overall, this face representation method not only improves the inspection effect, but also reduces the difficulty of manual control and improves the intelligence level of drone inspection.

[0031] The constructed graph structure cd1 is based on "parent nodes" and "edges", which represent power grid facilities and transmission lines respectively. The parent nodes can include key facilities such as substations and distribution boxes. The coordinates of the center point of the power grid facilities can be used as the coordinates of the parent node, and the edges represent the transmission lines connecting these facilities. By taking the power grid facilities and take-off points as parent nodes, it can ensure that the drone focuses on important infrastructure during inspections.

[0032] S200. Plan an overall path list based on the graph structure cd1.

[0033] The overall path list refers to a list of all possible inspection paths generated by the drone when performing an inspection based on the parent nodes and edges defined in the graph structure cd1. For example, if there are multiple power grid facilities and transmission lines in the graph structure, the overall path list will provide the drone with the optimal flight route from the take-off point to each power grid facility.

[0034] By using the mathematical properties of the graph structure, the shortest or optimal path can be quickly calculated, thus significantly improving inspection efficiency. At the same time, by considering environmental information and facility locations, the overall path list can ensure that the drone's inspection coverage is extensive and avoids missing important facilities.

[0035] S300. Determine the shooting range of the drone, and determine the photographed surface representing the area to be inspected in each power grid facility in the model.

[0036] The shooting range represents the area where the drone can effectively capture the power grid facilities and their surrounding environment when performing inspections. This range is usually determined by factors such as the drone's camera viewing angle, flight altitude, and shooting angle.

[0037] S400. Traverse all the photographed surfaces and determine whether the photographed surface exceeds the photographing range. If not, the photographed surface is used as a node surface. If so, the photographed surface is divided into a number of node surfaces that meet the photographing range.

[0038] By ensuring that all photographed surfaces are within the effective shooting range, the integrity and accuracy of the inspection data can be maximized, avoiding omissions due to insufficient shooting range, thereby providing a comprehensive foundation for subsequent data analysis and fault detection. It not only improves inspection efficiency, but also enhances the monitoring capabilities of power grid facilities and ensures the safe operation of the power system.

[0039] S500. Take all node faces as child nodes, determine the paths between node faces based on environmental information, and take the paths as edges to construct a graph structure cd2.

[0040] The surface to be inspected is regarded as a geometric shape (such as a polygon), and the coordinates of its center point can be used as the coordinates of the node. By building a path based on environmental information, obstacles can be effectively avoided and the flight path of the drone can be optimized, thereby improving the efficiency and safety of the inspection. The graph structure cd2 contains the node surfaces of the power grid facilities and the connection paths between them, reflecting the actual layout and environmental information of the power grid facilities.

[0041] S600. Plan a unit path list for each power grid facility based on the graph structure cd2.

[0042] The unit path list refers to planning an optimal inspection path for each power grid facility. This path should cover all the areas to be inspected in the facility to ensure that the drone can complete the inspection task efficiently and comprehensively.

[0043] S700. Construct an inspection path list based on the overall path list and the unit path list.

[0044] By integrating the overall path list and the unit path list, a comprehensive inspection path plan is formed to guide the drone to perform tasks in actual inspections, achieve overall and local coordination, and ensure that the drone can not only efficiently cover all power grid facilities when performing inspections, but also conduct in-depth inspections on each facility.

[0045] This embodiment scheme utilizes the graph structure cd1 and the graph structure ccd2 to accurately plan the inspection paths of the UAV. These paths are constructed through pre-set power grid facility information, transmission line information and surrounding environment information to ensure that the UAV can cover the entire inspection area. By dividing the photographed surface into node surfaces that meet the shooting range and designing a reasonable path for each node surface, the comprehensiveness and efficiency of the UAV inspection are guaranteed. The path planning process adopts the structure of parent nodes and child nodes, which not only takes into account the overall inspection needs, but also carries out detailed path design for each specific power grid facility.

[0046] In this embodiment, the power grid facilities and the node surface use the geometric centers as the parent nodes and child nodes respectively.

[0047] The graph structure constructed in this embodiment can efficiently plan the inspection path of the drone, reduce the situation of repeated inspection areas, thereby improving the inspection efficiency. The reasonable matching of the photographed surface and the shooting range ensures that the inspection area of ​​each power grid facility can be effectively covered, reducing the risk of missed inspections. By combining the overall path list with the unit path list, the flight route of the drone is optimized, reducing energy consumption and inspection time. Users only need to input relevant power grid facilities and environmental information, and the system can automatically generate the optimal inspection path, reducing the complexity of operation.

[0048] In this embodiment, the graph structures cd1 and cd2 can be replaced by other types of data structures, such as tree structures or grid structures, to adapt to different power grid layouts and environmental conditions. The shooting range can be adjusted based on different drone models or camera equipment parameters to adapt to different power grid facility types and inspection requirements.

[0049] In S200, planning the overall path list specifically includes: Sa1. Constructing the overall path list, setting the take-off point as the current mother node; Sb1. Adding the current mother node to the overall path list, and marking it as visited, calculating the distance between the current mother node and all unvisited mother nodes, updating the mother node with the smallest edge distance to the current mother node, and marking the edge as visited; Sc1. Repeating Sb1 until all mother nodes and edges are visited; Sd1. Obtaining the forward path list after all mother nodes and edges are visited; Se1. Reversing the forward path list and obtaining the return path list; Sf1. Concatenating the forward path list with the return path list and removing duplicate mother nodes.

[0050] The path planning in this implementation ensures the optimality of the inspection path by gradually constructing the overall path by selecting the unvisited nodes with the shortest distance to the edge of the current node. It can quickly generate an inspection path covering all parent nodes, generate a return path by reversing the forward path list, and splice the two. The system can avoid repeated inspections and optimize the flight route of the drone.

[0051] The edges between the parent nodes represent the transmission lines, and the drone can inspect the transmission lines by moving along the transmission lines, thereby fully covering the power grid facilities and transmission lines. The optimized path in this embodiment can reduce the flight time and battery consumption of the drone, extend the flight time of the drone, and ensure that each power grid facility and transmission line can be effectively inspected without missing key areas.

[0052] In constructing a graph structure cd1 with power grid facilities and take-off points as parent nodes and transmission lines as edges, the edges are paths L that maintain a safe distance from the transmission lines.

[0053] By setting the edge in the graph structure cd1 as a path L that maintains a safe distance from the transmission line, it is ensured that the drone will not get too close to the transmission line during inspection, thereby reducing the risk of collision or interference. At the same time, the accuracy of the inspection is guaranteed, and the drone can effectively obtain the required inspection data within a safe distance.

[0054] In this embodiment, the path L in the forward path list and the path L in the return path list are geometrically symmetrical about the power transmission line.

[0055] Ensure that the drone covers the transmission line inspection in different directions completely. This symmetrical design ensures that both sides of the transmission line can be fully inspected, avoiding any blind spots. It not only optimizes the comprehensiveness of the inspection, but also effectively reduces the flight energy consumption of the drone and the complexity of path planning during the inspection, making the inspection path more concise and clear, and facilitating the implementation of the system in actual operation.

[0056] In S400, determining whether the photographed surface is beyond the photographing range specifically includes:

[0057] Determining whether the photographed surface is beyond the photographing range specifically includes:

[0058] S410. Determine the boundary of the shooting range and the boundary of the photographed surface, the boundary of the shooting range includes the left boundary x min , right boundary x max , lower boundary y min and the upper boundary y max The boundaries of the photographed surface include a left boundary x1, a right boundary x2, a lower boundary y1, and an upper boundary y2.

[0059] S420. Determine whether the following four conditions are fully satisfied: 1. The left boundary x of the photographed surface min On the right side of the left boundary x1 of the shooting area; 2. On the right side of the shooting area x max On the left side of the right boundary x2 of the shooting area; 3. The lower boundary y of the shooting surface min Above the lower boundary y1 of the shooting area; 4. The upper boundary y1 of the shooting surface max Below the upper boundary y2 of the shooting area; if so, the shot surface does not exceed the shooting range, otherwise the shot surface exceeds the shooting range.

[0060] In this embodiment, by accurately determining the shooting range of the drone and the boundary of the photographed surface, it is possible to accurately determine whether the area to be inspected exceeds the drone's shooting capability. By comparing the boundary coordinates of the photographed surface and the shooting area, their relative position relationship is determined, thereby determining whether it is necessary to adjust the shooting parameters or segment the photographed surface, thereby avoiding missed inspections due to insufficient shooting range. By pre-analyzing the boundary of the photographed surface, the system can plan the shooting strategy in advance, thereby improving the flight efficiency of the drone and reducing unnecessary returns and repeated shooting.

[0061] In addition, a real-time adjustment mechanism can be introduced to dynamically adjust the shooting range and angle based on the image data captured by the drone in real time during flight, further improving the flexibility and accuracy of inspections.

[0062] In S400, the photographed surface is divided into a plurality of node surfaces that meet the photographing range, specifically including:

[0063] S430. Determine the width W1 and height H1 of the shooting range, calculate the width W2 and height H2 of the shot surface, W2 is: W2 = x2-x1, H2 is: H2 = y2-y1; S440. Calculate the number N of node surfaces that need to be divided in the horizontal direction x and the number N in the vertical direction y , N x for: Ny for: S450. Divide the imaged surface equally into N parts in the horizontal direction. x The photographed surface is evenly divided into N parts in the vertical direction. y The node surface is thus obtained.

[0064] In this embodiment, the photographed surface that is beyond the shooting range of the drone is divided into multiple small node surfaces, ensuring that each small surface can be completely covered in a single shot of the drone. The segmentation process is based on mathematical calculations, and the large surface is decomposed according to the shooting range to generate multiple sub-node surfaces and round up, thereby providing a patrol path planning module for use, significantly improving the comprehensiveness and accuracy of the patrol, and ensuring that the area to be inspected of each power grid facility can be completely photographed. Through a reasonable segmentation strategy, the system can reduce the number of drone movements and optimize the flight path, thereby improving patrol efficiency and reducing flight energy consumption.

[0065] In S600, planning the unit path list of each power grid facility specifically includes: Sa2. Constructing the unit path list, determining the initial node, and taking the initial node as the current child node; Sb2. Adding the current child node to the unit path list and marking it as visited, calculating the edge distance between the current child node and all unvisited child nodes, updating the child node with the smallest edge distance as the current child node, and marking the edge as visited; Sc2. Repeating Sb2 until all child nodes and edges are visited; Sd2. Adding the initial node to the unit path list again after all parent nodes and edges are visited.

[0066] In this embodiment, a patrol path is constructed separately for each power grid facility through a similar overall path planning method. Through a greedy algorithm, the shortest path is selected between each sub-node, gradually covering all sub-nodes, and finally forming a closed-loop path list, thereby ensuring a comprehensive inspection of each power grid facility, avoiding missed inspections or repeated inspections, and ensuring that the drone can efficiently inspect each power grid facility within the shortest path, maximizing the use of the drone's endurance and shooting efficiency. By establishing a closed-loop path list, the system can reduce unnecessary repeated flights and further improve the overall efficiency of the inspection task.

[0067] In S700, constructing a complete inspection path list based on the overall path list and the unit path list specifically includes: Sa3. Constructing the inspection path list, taking the take-off point as the starting point, and adding the starting point to the inspection path list; Sb3. Traversing each parent node and edge in the overall path list, for each parent node, adding the child nodes and paths in the unit path list corresponding to the parent node to the inspection path list.

[0068] In this embodiment, a complete inspection path is constructed by combining the overall path list and the unit path list, ensuring that the inspection process can cover both the macro layout of power grid facilities and each area to be inspected of specific facilities at the micro level. By traversing and integrating the paths, a comprehensive and efficient inspection path is generated, which effectively integrates the inspection needs at the macro and micro levels, ensures comprehensive coverage of drone inspection tasks and optimization of paths, minimizes the idle flight time of drones during the inspection process, improves inspection efficiency, and reduces operating costs.

[0069] Embodiment 2:

[0070] The present embodiment provides a drone inspection management system suitable for power grid infrastructure equipment, which adopts the drone inspection management method suitable for power grid infrastructure equipment as in Example 1, which includes: a graph structure cd1 construction module, an overall path list construction module, a parameter determination module, a node division module, a graph structure cd2 construction module, a unit path list construction module and an inspection path list construction module.

[0071] The graph structure cd1 construction module is used to construct a graph structure cd1 with power grid facilities and take-off points as parent nodes and transmission lines as edges based on a pre-constructed model containing power grid facility information, transmission line information and its surrounding environment information; the overall path list construction module is used to plan the overall path list based on the graph structure cd1; the parameter determination module is used to determine the shooting range of the drone and determine the photographed surface representing the area to be inspected in each power grid facility in the model; the node division module is used to traverse all photographed surfaces and determine whether the photographed surface exceeds the shooting range. If not, the photographed surface is used as a node surface. If so, the photographed surface is divided into several node surfaces that meet the shooting range; the graph structure cd2 construction module is used to take all node surfaces as child nodes, determine the path between node surfaces based on environmental information, and use the path as an edge to construct the graph structure cd2; the unit path list construction module is used to plan the unit path list of each power grid facility based on the graph structure cd2; the inspection path list construction module is used to construct the inspection path list based on the overall path list and the unit path list.

[0072] This embodiment has all the advantages of the drone inspection management method applicable to power grid infrastructure equipment in Embodiment 1, and can automatically implement all steps of the drone inspection management method applicable to power grid infrastructure equipment.

[0073] Embodiment three:

[0074] This embodiment provides a drone inspection management device suitable for power grid infrastructure equipment, which includes a memory and a processor.

[0075] The memory is used to store computer programs; the processor is used to implement the steps of the drone inspection management method applicable to power grid infrastructure equipment in Example 1 when executing the computer program.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Claims

1. A drone inspection management system suitable for power grid infrastructure equipment, characterized in that: It includes: Graph structure cd1 construction module: It is used to construct a graph structure cd1 with power grid facilities and take-off points as parent nodes and transmission lines as edges based on a pre-built model containing power grid facility information, power transmission line information and surrounding environment information; Overall path list construction module: it is used to plan the overall path list based on the graph structure cd1; Parameter determination module: It is used to determine the shooting range of the drone and determine the photographed surface representing the area to be inspected in each power grid facility in the model; Node division module: It is used to traverse all the photographed surfaces and determine whether the photographed surface exceeds the shooting range. If not, the photographed surface is used as a node surface. If so, the photographed surface is divided into several node surfaces that meet the shooting range. Graph structure cd2 construction module: it is used to take all node faces as child nodes, determine the paths between node faces based on environmental information, and take the paths as edges to construct graph structure cd2; Unit path list building module: It is used to plan the unit path list of each power grid facility based on the graph structure cd2; Inspection path list construction module: it is used to construct the inspection path list based on the overall path list and the unit path list.

2. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 1, characterized in that: In the model, a surface is used to represent the area to be inspected whose features can be effectively captured by taking a photo.

3. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 1, characterized in that: The overall planning path list specifically includes: Sa1. Build the overall path list and set the take-off point as the current parent node; Sb1. Add the current parent node to the overall path list and mark it as visited, calculate the distance between the current parent node and all unvisited parent nodes, update the parent node with the smallest edge distance to the current parent node, and mark the edge as visited; Sc1. Repeat Sb1 until all parent nodes and edges are visited; Sd1. Get the forward path list after all parent nodes and edges are visited; Se1. Reverse the forward path list and get the return path list; Sf1. Concatenate the forward path list and the return path list and remove duplicate parent nodes.

4. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 3, characterized in that: In constructing a graph structure cd1 with power grid facilities and take-off points as parent nodes and transmission lines as edges, the edges are paths L that maintain a safe distance from the transmission lines.

5. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 4, characterized in that: The path L in the forward path list and the path L in the return path list are geometrically symmetrical about the transmission line.

6. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 1, characterized in that: Determining whether the photographed surface is beyond the photographing range specifically includes: Determine the boundaries of the shooting range and the boundaries of the photographed surface. The boundaries of the shooting range include the left boundary x min , right boundary x max , lower boundary y min and the upper boundary y max , the boundaries of the photographed surface include the left boundary x1, the right boundary x2, the lower boundary y1 and the upper boundary y2; Determine whether the following four conditions are fully met:

1. Left edge x of the photographed surface min To the right of the left boundary x1 of the shooting area; 2. Right edge x of the photographed surface max On the left side of the right boundary x2 of the shooting area; 3. Lower boundary y of the photographed surface min Above the lower boundary y1 of the shooting area; 4. Boundary y on the photographed surface max Below the upper boundary y2 of the shooting area; If yes, the photographed surface does not exceed the photographing range; otherwise, the photographed surface exceeds the photographing range.

7. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 6, characterized in that: The photographed surface is divided into several node surfaces that meet the shooting range, including: Determine the width W1 and height H1 of the shooting range, and calculate the width W2 and height H2 of the photographed surface, where W2 is: W2=x2-x1, and H2 is: H2=y2-y1; Calculate the number N of node faces that need to be divided in the horizontal direction x and the number N in the vertical direction y , N x for: N y for: The imaged surface is evenly divided into N parts in the horizontal direction. x The photographed surface is evenly divided into N parts in the vertical direction. y The node surface is thus obtained.

8. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 3, characterized in that: The unit path list for planning each grid facility specifically includes: Sa2. Build a cell path list, determine the initial node, and use the initial node as the current child node; Sb2. Add the current child node to the unit path list and mark it as visited, calculate the edge distance between the current child node and all unvisited child nodes, update the child node with the smallest edge distance to the current child node, and mark the edge as visited; Sc2. Repeat Sb2 until all child nodes and edges are visited; Sd2. Add the initial node to the cell path list again after all parent nodes and edges have been visited.

9. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 8, characterized in that: Constructing a complete inspection path list based on the overall path list and the unit path list specifically includes: Sa3. Build a patrol path list, take the take-off point as the starting point, and add the starting point to the patrol path list; Sb3. Traverse each parent node and edge in the overall path list, and for each parent node, add the child nodes and paths in the unit path list corresponding to the parent node to the inspection path list.

10. The unmanned aerial vehicle inspection management system applicable to power grid infrastructure equipment according to claim 1, characterized in that: The grid facilities and node surfaces take their geometric centers as parent nodes and child nodes, respectively.

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