Route planning method for unmanned aerial vehicle power equipment transportation based on complex terrain

Abstract

The invention discloses a route planning method for transporting power equipment by an unmanned aerial vehicle based on a complex terrain, and the method comprises the steps: planning a transportation route through the unmanned aerial vehicle, carrying the power equipment for material transportation, and constructing a real-time GIS geographic model through a construction position terrain; the method comprises the following steps: analyzing a construction drawing of a power line project, marking a target tower construction position in a real-time GIS geographic model, constructing a power construction GIS model in combination with a power line topological structure, marking and obtaining a flight area between the position of a construction material and the target tower construction position on the power construction GIS model, and dividing the flight area into a plurality of passable grids; and planning a flight path of the unmanned aerial vehicle carrying the construction material through a preset algorithm, and constraining the planned flight path based on a preset constraint condition to generate an optimal flight path of the unmanned aerial vehicle. By reasonably planning the transportation route of the unmanned aerial vehicle, the power grid equipment is efficiently and safely transported to a specified point, and the effect of improving the construction speed of power line engineering is achieved.

Description

Technical Field

[0001] This invention relates to the technical field of transporting construction materials for power engineering, and in particular to a route planning method for transporting power equipment by unmanned aerial vehicles (UAVs) based on complex terrain. Background Technology

[0002] With the rapid development of economic construction and the increasing demand for electricity in mountainous and remote areas, power engineering infrastructure projects are being carried out in various places. The transportation of power infrastructure materials has become a key factor restricting the progress of construction.

[0003] The foundation towers of power transmission lines are mostly located in complex and inconvenient areas such as mountains and hills. Due to the limitations of the terrain, construction materials mainly rely on manual transportation or a combination of manual and cableway transportation, which seriously restricts the construction period of power line projects and even causes delays, affecting the timeliness of power transmission. There are methods of transporting power equipment and tower materials by manual or manual-cableway transportation in complex terrain or areas with weak infrastructure, which consumes a lot of manpower and material resources, has low transportation efficiency, and is prone to damaging equipment. Summary of the Invention

[0004] To address the inconvenience of transporting power infrastructure materials in complex areas such as mountainous and hilly regions in existing technologies, this application provides a route planning method for transporting power equipment by drones based on complex terrain. This method can replace manual transportation with drone transportation, and efficiently and safely transport power grid equipment to designated points by rationally planning drone transportation routes, thereby improving the construction speed of power line projects.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution: A route planning method for transporting power equipment by unmanned aerial vehicles (UAVs) in complex terrain, wherein the method involves planning a transport route using UAVs to carry power equipment for material transport, and the method includes: The terrain data of the construction site is obtained to construct a real-time GIS geographic model. The construction drawings of the power line project are parsed and the construction points are located in the real-time GIS geographic model to obtain the target tower construction location for each power pole. The power line topology is obtained based on the construction drawings of the power line project. The power line topology is then integrated with the real-time GIS geographic model based on the construction location of the target tower to obtain a power construction GIS model. The flight area between the location of construction materials and the construction location of the target tower is marked and obtained on the power construction GIS model. Passable area analysis is performed to obtain multiple passable grids. Within the passable grid, a flight path for a drone carrying construction materials is planned using a preset algorithm, and the planned flight path is constrained based on preset constraints to generate the optimal flight path for the drone.

[0006] In a preferred embodiment, this application can be further configured as follows: Within the passable grid, the step of planning the flight path of a drone carrying construction materials using a preset algorithm, and constraining the planned flight path based on preset constraints to generate the optimal flight path for the drone, further includes: Based on the flight route planning results, key flight sections of the curved flight route are marked, and turning plans are made in combination with the UAV's payload weight and attitude to obtain the turning flight route of the curved flight route. The curved flight route includes a detour route to avoid obstacles and a turning position flight route. Based on the pre-planned flight route, the starting and ending points of the turning flight route are respectively matched with the pre-planned flight route to obtain the optimal flight path of the UAV that meets the constraints of payload weight and attitude.

[0007] In a preferred embodiment, this application can be further configured as follows: based on the flight route planning results, key flight segments are marked on the curved flight route, and turning planning is performed in conjunction with the UAV's payload weight and attitude, resulting in a turning flight route for the curved flight route, which includes: The B-spline curve algorithm is used to plan the turning route of the curved flight path.

[0008] In a preferred embodiment, this application can be further configured as follows: Within the passable grid, the step of planning the flight path of a drone carrying construction materials using a preset algorithm, and constraining the planned flight path based on preset constraints to generate the optimal flight path for the drone, further includes: During the flight of the drone, monitoring images within a preset range of the drone are acquired in real time, and dynamic obstacle recognition is performed on the monitoring images through a neural convolutional network to obtain dynamic obstacle information; Based on the dynamic obstacle information, the drone's obstacle avoidance route is pre-planned, and the avoidance route is adjusted in combination with the flight speed and direction of the dynamic obstacle to obtain the drone's dynamic obstacle avoidance route.

[0009] In a preferred embodiment, this application can be further configured as follows: Within the passable grid, a flight path for a drone carrying construction materials is planned using a preset algorithm, and the planned flight path is constrained based on preset constraints to generate route constraints in the drone's optimal flight path. Specifically, this includes: By acquiring real-time environmental data and adjusting the drone's flight speed and acceleration based on its payload weight, and generating flight constraints that conform to the drone's payload weight, attitude, and current environmental influences under preset dynamic constraints, the system can achieve the desired flight conditions.

[0010] In a preferred embodiment, this application can be further configured as follows: after planning the flight path of the UAV carrying construction materials in the passable grid using a preset algorithm, and constraining the planned flight path based on preset constraints to generate the optimal flight path of the UAV, the application further includes: The drone is simulated to fly along the optimal flight path, and the deviation between the actual simulated flight path and the optimal flight path is obtained. Based on the deviation, the optimal flight path is optimized by deviation path.

[0011] In a preferred embodiment, this application can be further configured such that the method also includes: Contour lines are drawn in the power construction GIS model, and the UAV is controlled to perform contour flight according to the drawn contour lines and the optimal flight path.

[0012] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions: A route planning system for unmanned aerial vehicle (UAV) transportation of power equipment based on complex terrain, the system being applied to the aforementioned route planning method for unmanned aerial vehicle (UAV) transportation of power equipment based on complex terrain, the system comprising: The construction positioning module is used to acquire terrain data of the construction location to build a real-time GIS geographic model, parse the construction drawings of the power line project, locate the construction point in the real-time GIS geographic model, and obtain the target tower construction location for each power pole. The model building module is used to obtain the power line topology based on the construction drawings of the power line project, and to fuse the power line topology with the real-time GIS geographic model based on the construction location of the target tower to obtain a power construction GIS model. The flight area analysis module is used to mark and obtain the flight area between the location of the construction materials and the construction location of the target tower on the power construction GIS model, perform passable area analysis, and obtain multiple passable grids. The path planning module is used to plan the flight route of a drone carrying construction materials in the passable grid using a preset algorithm, and to constrain the planned flight route based on preset constraints to generate the optimal flight path for the drone.

[0013] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described route planning method for transporting power equipment by unmanned aerial vehicles based on complex terrain.

[0014] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described route planning method for transporting power equipment by unmanned aerial vehicles (UAVs) based on complex terrain.

[0015] In summary, the beneficial technical effects of this application are as follows: This application locates the construction sites based on the construction drawings of the power line project. It then constructs a real-time GIS geographic model by combining the GIS topography of the region / mountainous area where the construction sites are located. Each construction site is located on the GIS geographic map, and a power GIS model is formed by combining the power line topology. The application analyzes the passable areas by obtaining topography, obstacle locations, etc., between the construction sites and the locations of construction materials or power equipment. The Dijkstra algorithm is used to plan the passageway, and B-spline curves are used for turning points. The algorithm combines the weight and attitude of the drone and the power equipment it carries to perform turning planning. It also optimizes or constrains the planned route by taking into account the drone's flight speed, acceleration, turning radius, and external environmental factors such as wind speed. This results in a safe flight path that meets the dynamic constraints of the drone and the weight and attitude constraints of the equipment it carries. By using drones to transport power grid equipment instead of human transport, and by rationally planning drone transport routes, the algorithm can efficiently and safely transport power grid equipment to designated points, thereby improving the construction speed of power line projects. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart illustrating the implementation of the route planning method for transporting power equipment by unmanned aerial vehicles (UAVs) based on complex terrain in this embodiment.

[0018] Figure 2 This is a flowchart illustrating the implementation of dynamic obstacle avoidance in this embodiment.

[0019] Figure 3This is a flowchart illustrating the implementation of turning flight planning in this embodiment.

[0020] Figure 4 This is a structural block diagram of the route planning system for transporting power equipment by unmanned aerial vehicles based on complex terrain, as described in this embodiment.

[0021] Figure 5 This is a schematic diagram of the internal structure of a computer device used to implement route planning methods for transporting power equipment by drones. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0024] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] In one embodiment, such as Figure 1 As shown, this application discloses a route planning method for transporting power equipment by unmanned aerial vehicles (UAVs) in complex terrain. This method involves planning a transport route using UAVs to carry power equipment for material transportation, and specifically includes the following steps: S10: Obtain terrain data of the construction location to build a real-time GIS geographic model, parse the construction drawings of the power line project, locate the construction point in the real-time GIS geographic model, and obtain the target tower construction location for each power pole.

[0027] Specifically, by having construction personnel conduct on-site surveys of the terrain data of the power line project construction location, and combining this with satellite data of the corresponding construction location, a real-time GIS geographic model that conforms to the actual geographical parameters of the construction location is constructed. The construction location is marked on the construction drawings of the power line project, and the construction point is located in the GIS geographic model to obtain the target tower construction location for each power tower to be constructed.

[0028] S20: Obtain the power line topology based on the construction drawings of the power line project, and integrate the power line topology with the real-time GIS geographic model according to the construction location of the target tower to obtain the power construction GIS model.

[0029] Specifically, based on the construction drawings of the power line project, the power line topology between different power poles is obtained. Taking the construction location of the target pole as the correlation point, the power line topology is coupled with the real-time GIS geographic model to obtain the power construction GIS model.

[0030] S30: Mark and obtain the flight area between the location of construction materials and the construction location of the target tower on the power construction GIS model, perform passable area analysis, and obtain multiple passable grids.

[0031] Specifically, the locations of construction materials and target tower construction sites are marked on the power construction GIS model, and the area between them is designated as the UAV's flight area. By removing obstacles or obstructed areas that affect the UAV's flight within the flight area, a passable area analysis and division are performed, resulting in multiple passable grids. In this embodiment, the passable area division is based on the shortest distance and shortest flight time as the maximum flight objectives.

[0032] S40: In a passable grid, the flight path of a drone carrying construction materials is planned using a preset algorithm, and the planned flight path is constrained based on preset constraints to generate the optimal flight path for the drone.

[0033] Specifically, within the traversable grid, the Dijkstra algorithm is used to plan the flight path of the UAV in all traversable grids, and real-time environmental data is acquired. Combined with the UAV's payload weight, the flight speed and acceleration of the UAV are adjusted. Under preset dynamic constraints, flight constraints that meet the UAV's payload weight, attitude, and the influence of the current environment are generated. The planned flight path is constrained according to the flight constraints, and finally the optimal flight path of the UAV is generated.

[0034] In this embodiment, A can also be used. The algorithm or the RRT series of algorithms is used for flight path planning.

[0035] This embodiment also includes: The drone is simulated to fly along the optimal flight path. The deviation between the actual simulated flight path and the optimal flight path is obtained. Based on the deviation, the optimal flight path is optimized by deviation path.

[0036] Specifically, path optimization is achieved through simulated flight practice. The parameters of the UAV are set according to the optimal flight route to conduct simulated flight, the actual simulated flight path is obtained, and the deviation position is found by comparing it with the optimal flight path. The actual simulated parameters are then used to replace the optimal flight route parameters at the deviation position to optimize the path.

[0037] In this embodiment, contour lines are drawn in the power construction GIS model, and the UAV is controlled to perform contour flight according to the drawn contour lines and the optimal flight path.

[0038] like Figure 2 As shown, the dynamic obstacle avoidance process in this embodiment includes: S401: During the flight of the UAV, real-time monitoring images within a preset range of the UAV are acquired, and dynamic obstacle recognition is performed on the monitoring images through a neural convolutional network to obtain dynamic obstacle information.

[0039] Specifically, during drone flight, high-definition cameras capture real-time monitoring images of the drone's surroundings within a preset safety range, including its forward, backward, left, and right positions. By comparing monitoring images captured at adjacent times, changes in the drone's surrounding environment are identified. A pre-trained neural convolutional network then performs dynamic obstacle recognition on the monitoring images to obtain dynamic obstacle information, such as birds. The neural convolutional network is trained using images of common birds.

[0040] S402: Based on dynamic obstacle information, pre-plan the drone's obstacle avoidance route, and adjust the avoidance route according to the flight speed and direction of the dynamic obstacle to obtain the drone's dynamic obstacle avoidance route.

[0041] Specifically, based on dynamic obstacle information, including obstacle size, flight speed, and flight direction, and combined with the drone's current flight path and speed, it is determined whether the obstacle will affect the drone's flight. If so, an avoidance route for the drone is pre-planned, and the avoidance route is adjusted based on changes in the dynamic obstacle's flight speed and flight direction to generate an avoidance route with the highest probability of avoiding the dynamic obstacle.

[0042] like Figure 3 As shown, in this embodiment, it also includes: S50: Based on the flight route planning results, key flight sections of the curved flight route are marked, and turning plans are made in combination with the UAV's payload weight and attitude to obtain the turning flight route of the curved flight route. The curved flight route includes the detour route to avoid obstacles and the flight route at the turning position.

[0043] Specifically, based on the flight path planning results, non-straight flight paths are highlighted, including obstacle avoidance routes, turning points, and upward or downward flight paths. Turning plans are then developed based on the UAV's payload and flight attitude, including turning radius, turning speed and acceleration, and turning direction, resulting in the turning flight path of the curved flight path. In this embodiment, a B-spline curve algorithm is used for turning planning of the curved flight path.

[0044] S60: Based on the pre-planned flight route, the starting point and ending point of the turning flight route are respectively matched with the pre-planned flight route to obtain the optimal flight path of the UAV that meets the constraints of payload weight and attitude.

[0045] Specifically, based on the planned flight route, the starting and ending positions of the turning flight route are respectively matched with the pre-planned flight route. For example, at the starting position, the uniform flight speed of the pre-planned flight route is used as the starting speed, and the turning acceleration is set according to the turning radius of the turning flight route to adjust the starting speed. At the ending position, the uniform flight speed is used as the target speed for speed adjustment, so that the turning flight route is matched with the pre-planned flight route, and the optimal flight path of the UAV that meets the constraints of payload weight and flight attitude is obtained.

[0046] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0047] In one embodiment, a route planning system for transporting power equipment by unmanned aerial vehicles (UAVs) based on complex terrain is provided. This route planning system corresponds one-to-one with the route planning method for transporting power equipment by unmanned aerial vehicles based on complex terrain described in the above embodiments. Figure 4 As shown, this route planning system for transporting power equipment using unmanned aerial vehicles (UAVs) in complex terrain includes a construction positioning module, a model building module, a flight area analysis module, and a path planning module. Detailed descriptions of each functional module are as follows: The construction positioning module is used to acquire terrain data of the construction location to build a real-time GIS geographic model, parse the construction drawings of the power line project, locate the construction point in the real-time GIS geographic model, and obtain the target tower construction location for each power pole.

[0048] The model building module is used to obtain the power line topology based on the construction drawings of the power line project, and to integrate the power line topology with the real-time GIS geographic model according to the construction location of the target tower to obtain the power construction GIS model.

[0049] The Flight Area Analysis module is used to mark and obtain the flight area between the location of construction materials and the construction location of the target tower on the power construction GIS model, perform passable area analysis, and obtain multiple passable grids.

[0050] The path planning module is used to plan the flight path of a drone carrying construction materials in a passable grid using a preset algorithm, and to constrain the planned flight path based on preset constraints to generate the optimal flight path for the drone.

[0051] Preferably, the path planning module also includes: The turning route planning submodule is used to mark key flight segments of the curved flight route based on the flight route planning results, and to plan turns in combination with the drone's payload weight and attitude to obtain the turning flight route of the curved flight route. The curved flight route includes the detour route to avoid obstacles and the flight route at the turning position.

[0052] The path fitting submodule is used to perform path fitting between the starting point and the ending point of the turning flight path and the pre-planned flight path, respectively, to obtain the optimal flight path of the UAV that meets the constraints of payload weight and attitude.

[0053] Preferably, the turning route planning submodule includes: planning the turning of the curved flight route using a B-spline curve algorithm.

[0054] Preferably, the path planning module also includes: The dynamic obstacle recognition submodule is used to acquire monitoring images within a preset range of the drone in real time during drone flight, and to perform dynamic obstacle recognition on the monitoring images through a neural convolutional network to obtain dynamic obstacle information.

[0055] The dynamic obstacle avoidance submodule is used to pre-plan the drone's avoidance route based on dynamic obstacle information, and adjust the avoidance route in combination with the flight speed and direction of the dynamic obstacle to obtain the drone's dynamic obstacle avoidance route.

[0056] Preferably, the route constraints in the path planning module specifically include: acquiring real-time environmental data, adjusting the flight speed and acceleration of the UAV based on its payload weight, and generating flight constraints that conform to the UAV's payload weight, attitude, and current environmental influences under preset dynamic constraints.

[0057] Preferably, after the path planning module, the system further includes: simulating flight of the UAV according to the optimal flight route, obtaining the deviation between the actual simulated flight path and the optimal flight path, and performing deviation path optimization on the optimal flight path based on the deviation.

[0058] Preferably, this embodiment also includes: Contour lines are drawn in the GIS model of power construction, and the UAV is controlled to perform contour flight according to the drawn contour lines and the optimal flight path.

[0059] Specific limitations regarding the route planning system for UAVs transporting power equipment in complex terrain can be found in the limitations of the route planning method for UAVs transporting power equipment in complex terrain described above, and will not be repeated here. Each module in the aforementioned route planning system for UAVs transporting power equipment in complex terrain can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0060] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores route planning data for UAV-based power transportation equipment. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a route planning method for UAV-based power transportation equipment in complex terrain.

[0061] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a route planning method for transporting power equipment by unmanned aerial vehicles (UAVs) based on complex terrain.

[0062] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application of the technical solution and the constraints involved. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0063] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0064] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0065] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for route planning of unmanned aerial vehicles for transporting power equipment based on complex terrain, characterized by, The method involves using drones to plan transport routes and carry power equipment for material transportation. The method includes: The terrain data of the construction site is obtained to construct a real-time GIS geographic model. The construction drawings of the power line project are parsed and the construction points are located in the real-time GIS geographic model to obtain the target tower construction location for each power pole. The power line topology is obtained based on the construction drawings of the power line project. The power line topology is then integrated with the real-time GIS geographic model based on the construction location of the target tower to obtain a power construction GIS model. The flight area between the location of construction materials and the construction location of the target tower is marked and obtained on the power construction GIS model. Passable area analysis is performed to obtain multiple passable grids. Within the passable grid, a flight path for a drone carrying construction materials is planned using a preset algorithm, and the planned flight path is constrained based on preset constraints to generate the optimal flight path for the drone. 2.The method of claim 1, wherein, The process of planning the flight path of a drone carrying construction materials within the passable grid using a preset algorithm, and constraining the planned flight path based on preset constraints to generate the optimal flight path for the drone, further includes: Based on the flight route planning results, key flight sections of the curved flight route are marked, and turning plans are made in combination with the UAV's payload weight and attitude to obtain the turning flight route of the curved flight route. The curved flight route includes a detour route to avoid obstacles and a turning position flight route. Based on the pre-planned flight route, the starting and ending points of the turning flight route are respectively matched with the pre-planned flight route to obtain the optimal flight path of the UAV that meets the constraints of payload weight and attitude.

3. The route planning method for transporting power equipment by unmanned aerial vehicles based on complex terrain according to claim 2, characterized in that, Based on the flight route planning results, key flight segments of the curved flight route are marked. Turning plans are then performed in conjunction with the UAV's payload weight and attitude, resulting in the following turning flight routes of the curved flight route: The B-spline curve algorithm is used to plan the turning route of the curved flight path.

4. The route planning method for transporting power equipment by unmanned aerial vehicles based on complex terrain according to claim 1, characterized in that, The process of planning the flight path of a drone carrying construction materials within the passable grid using a preset algorithm, and constraining the planned flight path based on preset constraints to generate the optimal flight path for the drone, further includes: During the flight of the drone, monitoring images within a preset range of the drone are acquired in real time, and dynamic obstacle recognition is performed on the monitoring images through a neural convolutional network to obtain dynamic obstacle information; Based on the dynamic obstacle information, the drone's obstacle avoidance route is pre-planned, and the avoidance route is adjusted in combination with the flight speed and direction of the dynamic obstacle to obtain the drone's dynamic obstacle avoidance route.

5. The route planning method for transporting power equipment by unmanned aerial vehicles (UAVs) based on complex terrain according to claim 1, characterized in that, Within the passable grid, a flight path for a drone carrying construction materials is planned using a preset algorithm. The planned flight path is then constrained based on preset constraints to generate the optimal flight path for the drone. Specifically, this includes: By acquiring real-time environmental data and adjusting the drone's flight speed and acceleration based on its payload weight, and generating flight constraints that conform to the drone's payload weight, attitude, and current environmental influences under preset dynamic constraints, the system can achieve the desired flight conditions.

6. The route planning method for transporting power equipment by unmanned aerial vehicles based on complex terrain according to claim 1, characterized in that, After planning the flight path of the UAV carrying construction materials within the passable grid using a preset algorithm, and constraining the planned flight path based on preset constraints to generate the optimal flight path for the UAV, the process further includes: The drone is simulated to fly along the optimal flight path, and the deviation between the actual simulated flight path and the optimal flight path is obtained. Based on the deviation, the optimal flight path is optimized by deviation path.

7. The route planning method for transporting power equipment by unmanned aerial vehicles (UAVs) based on complex terrain according to claim 1, characterized in that, The method further includes: Contour lines are drawn in the power construction GIS model, and the UAV is controlled to perform contour flight according to the drawn contour lines and the optimal flight path.

8. A route planning system for transporting power equipment by unmanned aerial vehicles (UAVs) in complex terrain, characterized in that, The system is applied to the route planning method for transporting power equipment by unmanned aerial vehicles based on complex terrain as described in any one of claims 1-7, and the system comprises: The construction positioning module is used to acquire terrain data of the construction location to build a real-time GIS geographic model, parse the construction drawings of the power line project, locate the construction point in the real-time GIS geographic model, and obtain the target tower construction location for each power pole. The model building module is used to obtain the power line topology based on the construction drawings of the power line project, and to fuse the power line topology with the real-time GIS geographic model based on the construction location of the target tower to obtain a power construction GIS model. The flight area analysis module is used to mark and obtain the flight area between the location of the construction materials and the construction location of the target tower on the power construction GIS model, perform passable area analysis, and obtain multiple passable grids. The path planning module is used to plan the flight route of a drone carrying construction materials in the passable grid using a preset algorithm, and to constrain the planned flight route based on preset constraints to generate the optimal flight path for the drone.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the route planning method for transporting power equipment by unmanned aerial vehicles based on complex terrain as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the route planning method for transporting power equipment by unmanned aerial vehicles based on complex terrain as described in any one of claims 1 to 7.

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