Methods, systems, and drones for determining the deployment location of drone hangars

Abstract

This invention provides a method, system, and drone for determining the deployment location of a drone hangar, relating to the field of drone deployment control. The method, after determining the inspection nodes corresponding to the inspection area, determines the inspection zone corresponding to the inspection target based on the location parameters of the inspection target, and divides the inspection zone into multiple sub-regions based on the order parameters of the inspection target. Subsequently, it acquires the inspection nodes and their location parameters contained in each sub-region, and determines the initial deployment point corresponding to the sub-region based on the clustering results of the location parameters. Finally, it adjusts the position of the initial deployment point based on the drone's operating radius parameters and power-accessible location parameters to obtain the final deployment position of the drone. This method fully utilizes the drone's operating radius parameters and power-accessible location parameters to dynamically adjust the gridded deployment points, achieving high-precision automatic acquisition of deployment points while ensuring normal drone operation, thereby improving the inspection execution efficiency of the drone.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) deployment and control, and in particular to a method, system, and UAV for determining the deployment location of a UAV hangar. Background Technology

[0002] When inspecting power facilities or other industrial equipment, grid-based deployment of drones can improve inspection efficiency. Grid deployment divides the inspection area into multiple inspection nodes, deploying several drone nests and drones at the center of each node. When performing an inspection task, the task is broken down and distributed among the nodes along the route, allowing the inspection to proceed in a grid-based manner. However, due to the presence of multiple inspection nodes, each involving multiple drone nests and drones, and numerous variables, grid deployment is generally more complex, and it's difficult to guarantee that the deployment location is at the optimal level for inspection efficiency, thus affecting the effectiveness of drone inspections. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, system and drone for determining the deployment location of a drone hangar. The method makes full use of the drone's operating radius parameters and power supply location parameters to dynamically adjust the gridded deployment points, and achieves high-precision automatic acquisition of deployment points while ensuring the normal operation of the drone, thereby improving the inspection execution efficiency of the drone.

[0004] In a first aspect, embodiments of the present invention provide a method for determining the deployment location of a drone hangar, the method comprising:

[0005] The inspection targets are determined based on the inspection area, and the inspection nodes corresponding to the inspection area are determined using the type parameters of the inspection targets.

[0006] The inspection zone corresponding to the inspection target is determined based on the location parameters of the inspection target, and the inspection zone is divided based on the sequence parameters of the inspection target to obtain multiple sub-regions;

[0007] Identify the inspection nodes contained in each sub-region and obtain the location parameters corresponding to the inspection nodes. Based on the clustering results of the location parameters, determine the initial deployment point corresponding to the sub-region.

[0008] The initial deployment point is adjusted based on the drone's operating radius parameters and available power source parameters corresponding to the hangar type to be deployed, in order to obtain the final deployment location of the drone.

[0009] In one implementation, the steps of determining the inspection target based on the inspection area and determining the inspection node corresponding to the inspection area using the type parameter of the inspection target include:

[0010] Based on the operating range of the UAV, the boundary parameters of the inspection area are determined, and the inspection boundary range of the inspection area is determined using the boundary parameters;

[0011] Obtain the inspection targets within the inspection boundary range, and determine the location coordinates of the inspection targets based on the coordinate system corresponding to the inspection area;

[0012] Multiple inspection nodes corresponding to the inspection area are determined based on the type parameters and location coordinates of the inspection target.

[0013] In one implementation, the step of determining multiple inspection nodes corresponding to the inspection area based on the type parameters and location coordinates of the inspection target includes:

[0014] The power transmission equipment included in the inspection target is determined based on the type parameters corresponding to the inspection target.

[0015] Obtain the location coordinates of the power transmission equipment, and determine the height data of the power transmission equipment based on the location coordinates;

[0016] Multiple inspection nodes corresponding to the inspection area are determined by using the operating radius parameters of the drone, the altitude data of the power transmission equipment, and the location coordinates.

[0017] In one implementation, determining the inspection zone corresponding to the inspection target based on the location parameters of the inspection target includes:

[0018] Determine the distribution characteristic data of the inspection targets based on their location parameters;

[0019] Based on the distribution characteristic data, determine the linear distribution area, point distribution area, target-dense area and route direction area corresponding to the inspection target.

[0020] Inspection zones corresponding to inspection targets are constructed by utilizing linear distribution areas, point distribution areas, dense target areas, and route alignment areas.

[0021] In one implementation, after segmenting the inspection zone based on the sequence parameters of the inspection targets, multiple sub-regions are obtained, including:

[0022] The flight direction of the UAV is determined by the starting and ending points of the UAV in the inspection zone, and the sequence parameters of the inspection targets are determined by the flight direction.

[0023] The segmentation strategy of the inspection zone is determined by using the corresponding operating radius parameters and power supply location parameters of the UAV;

[0024] After dividing the inspection zone based on the sequence parameters and according to the segmentation strategy, multiple sub-regions are obtained.

[0025] In one implementation, the steps of determining the inspection nodes contained in each sub-region and obtaining the location parameters corresponding to the inspection nodes, and determining the initial deployment point corresponding to the sub-region based on the clustering results of the location parameters, include:

[0026] The coordinate system corresponding to the inspection node is determined based on the location parameters corresponding to the inspection target, and the boundary coordinates of the sub-regions under the coordinate system are obtained.

[0027] The inspection nodes contained in each sub-region are determined using boundary coordinates, and the position parameters corresponding to the inspection nodes are obtained based on the coordinate system.

[0028] After performing clustering calculations on the inspection nodes using location parameters, the cluster center points corresponding to the inspection nodes are obtained, and the clustering results of the location parameters are determined based on the cluster center points.

[0029] The initial deployment points corresponding to the sub-regions in the coordinate system are determined based on the clustering results.

[0030] In one implementation, the step of adjusting the initial deployment point based on the drone's operating radius parameters and available power location parameters corresponding to the hangar type to obtain the final deployment location of the drone includes:

[0031] The coverage area of ​​the drone is determined by the drone's operating radius parameter, the obstacle area included in the coverage area is determined based on the location parameter of the inspection target, and the first adjustment strategy and the first deployment position corresponding to the initial deployment point are determined based on the obstacle area.

[0032] The power supply range of the UAV is determined by using the power-accessible location parameters, and the second adjustment strategy and second deployment location corresponding to the initial deployment point are determined based on the power supply range.

[0033] The final deployment location of the drone is determined based on the first and second deployment locations;

[0034] The initial deployment point is adjusted to the final deployment location based on the first and second adjustment strategies.

[0035] In one implementation, the step of adjusting the initial deployment point to the final deployment location according to a first adjustment strategy and a second adjustment strategy includes:

[0036] After adjusting the initial deployment point using the first adjustment strategy, the adjustment position corresponding to the initial deployment point is determined; wherein, the operating area of ​​the UAV at the adjustment position covers a sub-area.

[0037] After adjusting the position using the second adjustment strategy, the final deployment position of the drone is determined; where the power supply distance of the drone is minimized at the final deployment position.

[0038] Secondly, embodiments of the present invention provide a system for determining the deployment location of a drone hangar, the system comprising:

[0039] The inspection node determination module is used to determine the inspection target based on the inspection area, and to determine the inspection node corresponding to the inspection area using the type parameter of the inspection target.

[0040] The sub-region segmentation module is used to determine the inspection zone corresponding to the inspection target based on the location parameters of the inspection target, and to segment the inspection zone based on the order parameters of the inspection target to obtain multiple sub-regions;

[0041] The initial deployment point determination module is used to determine the inspection nodes contained in each sub-region and obtain the location parameters corresponding to the inspection nodes. Based on the clustering results of the location parameters, the initial deployment point corresponding to the sub-region is determined.

[0042] The final deployment point determination module is used to adjust the position of the initial deployment point based on the drone's operating radius parameters and available power location parameters corresponding to the type of hangar to be deployed, so as to obtain the final deployment position of the drone.

[0043] Thirdly, embodiments of the present invention also provide a drone that, during the process of grid-based deployment, employs the steps of the drone hangar deployment location determination method mentioned in the first aspect.

[0044] Fourthly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the steps of the method for determining the deployment location of a drone hangar provided in the first aspect.

[0045] Fifthly, embodiments of the present invention also provide a storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the steps of the method for determining the deployment location of a drone hangar provided in the first aspect.

[0046] This invention provides a method, system, and drone for determining the deployment location of a drone hangar. During the gridded deployment of drones, the method first determines inspection targets based on the inspection area and uses the type parameters of the inspection targets to determine the corresponding inspection nodes within the inspection area. Then, it determines the inspection zone corresponding to the inspection targets based on their position parameters, and divides the inspection zone into multiple sub-regions based on the order parameters of the inspection targets. Subsequently, it determines the inspection nodes contained in each sub-region and obtains the position parameters corresponding to the inspection nodes. Based on the clustering results of the position parameters, it determines the initial deployment point corresponding to the sub-region. Finally, it adjusts the position of the initial deployment point based on the drone's operating radius parameters corresponding to the hangar type to be deployed and the available power supply location parameters to obtain the final deployment location of the drone. This method fully utilizes the drone's operating radius parameters and power supply location parameters to dynamically adjust the gridded deployment points, achieving high-precision automatic acquisition of deployment points while ensuring normal drone operation, thereby improving the inspection execution efficiency of the drone.

[0047] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0049] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating a method for determining the deployment location of a drone hangar, as provided in an embodiment of the present invention;

[0051] Figure 2 The flowchart of a method for determining the deployment location of a drone hangar provided in an embodiment of the present invention includes determining the inspection target based on the inspection area and determining the inspection node corresponding to the inspection area using the type parameter of the inspection target;

[0052] Figure 3 A flowchart illustrating a method for determining the deployment location of a drone hangar provided in this embodiment of the invention, which includes determining multiple inspection nodes corresponding to an inspection area based on the type parameters and location coordinates of the inspection target;

[0053] Figure 4 A flowchart illustrating the method for determining the deployment location of a drone hangar provided in this embodiment of the invention, which includes determining the inspection zone corresponding to the inspection target based on the location parameters of the inspection target;

[0054] Figure 5 In a method for determining the deployment location of a drone hangar provided in an embodiment of the present invention, a flowchart is used to obtain multiple sub-regions after dividing the inspection zone based on the sequence parameters of the inspection target.

[0055] Figure 6 The flowchart of a method for determining the deployment location of a drone hangar provided in an embodiment of the present invention includes determining the inspection nodes contained in each sub-region and obtaining the position parameters corresponding to the inspection nodes, and determining the initial deployment point corresponding to the sub-region based on the clustering results of the position parameters.

[0056] Figure 7 A flowchart illustrating a method for determining the deployment location of a drone hangar provided in this embodiment of the invention, which involves adjusting the position of the initial deployment point based on the drone's operating radius parameters and available power location parameters corresponding to the type of hangar to be deployed, in order to obtain the final deployment location of the drone;

[0057] Figure 8 A flowchart illustrating the method for determining the deployment location of a drone hangar provided in this embodiment of the invention, which involves adjusting an initial deployment point to a final deployment location based on a first adjustment strategy and a second adjustment strategy;

[0058] Figure 9 This is a schematic diagram illustrating the effect of a method for determining the deployment location of a drone hangar provided in an embodiment of the present invention;

[0059] Figure 10 This is a schematic diagram of a system for determining the deployment location of a drone hangar, provided in an embodiment of the present invention.

[0060] Figure 11 A schematic diagram of the structure of a drone provided in an embodiment of the present invention;

[0061] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0062] icon:

[0063] 1010 - Inspection Node Determination Module; 1020 - Sub-region Segmentation Module; 1030 - Initial Deployment Point Determination Module; 1040 - Final Deployment Point Determination Module;

[0064] 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

[0066] With the large-scale application of drones in power facility inspection, grid-based deployment of drones for inspecting power facilities or other industrial equipment can improve inspection efficiency and control convenience. Specifically, grid-based deployment divides the inspection area into multiple inspection nodes, deploying several drone nests and drones at the center of each node. When performing an inspection task, the task is broken down into the inspection nodes along the route, allowing the inspection to proceed in a grid-based deployment mode. Due to the existence of multiple inspection nodes, and each node involving multiple drone nests and drones, and multiple variables, grid-based deployment is generally difficult, and it is hard to guarantee that the deployment location is at the optimal inspection efficiency, thus affecting the drone inspection effect. Based on this, this invention provides a method, system, and drone for determining the deployment location of drone hangars. This method fully utilizes the drone's operating radius parameters and power supply location parameters to dynamically adjust the grid-based deployment points, achieving high-precision automatic acquisition of deployment points while ensuring normal drone operation, thereby improving the drone's inspection efficiency.

[0067] To facilitate understanding of this embodiment, a method for determining the deployment location of a drone hangar, as disclosed in this embodiment of the invention, will first be described in detail, such as... Figure 1 As shown, the method includes:

[0068] Step S101: Determine the inspection target based on the inspection area, and determine the inspection node corresponding to the inspection area using the type parameter of the inspection target.

[0069] This step forms the foundation of grid-based deployment, with the core being the identification of inspection targets and nodes. Determining inspection targets first requires defining the drone's inspection area (e.g., a 100-kilometer power transmission line, an industrial park containing five substations, etc.), and then extracting all power facilities or industrial equipment to be inspected from this area as inspection targets. For example, in a power transmission line scenario, inspection targets might include towers, insulators, conductors, and vibration dampers; in a substation scenario, they might include transformers, circuit breakers, and disconnect switches.

[0070] Inspection nodes are defined using target type parameters. Different types of inspection targets have different inspection requirements (such as inspection frequency, accuracy requirements, and risk levels), necessitating the division of inspection nodes accordingly, which serve as the basic units for grid-based deployment. Specifically, type parameters may include: functional importance parameters of the target (specifically representing equipment in hub substations versus ordinary branch line towers), structural complexity parameters (specifically representing special towers crossing mountains and rivers versus conventional towers in plains), and environmental risk parameters (specifically representing towers in icy areas versus equipment in normal climate zones). For example, for highly important and structurally complex targets (such as the main transformer of a 500kV substation), a smaller surrounding area can be designated as an independent inspection node to ensure refined management; for densely distributed but structurally simple targets (such as 110kV transmission towers in plains areas), a continuous area can be merged into a single inspection node to reduce management costs.

[0071] Step S102: Determine the inspection zone corresponding to the inspection target based on the location parameters of the inspection target, and divide the inspection zone based on the sequence parameters of the inspection target to obtain multiple sub-regions.

[0072] This step aims to further refine the inspection nodes into operable sub-regions, providing a more precise range for determining subsequent deployment points.

[0073] In determining the inspection zone, the inspection targets are typically distributed along a specific path (e.g., transmission lines extend sequentially along towers, and distribution lines are distributed along streets). Based on the location parameters of these targets (e.g., latitude, longitude, and altitude coordinates), an inspection zone can be fitted, which is a strip-shaped area covering all targets (the width is usually determined by the drone's operating radius and the target distribution density). For example, the inspection zone for transmission lines can be formed along the line's direction, with a width twice the maximum single-side inspection radius of the drone, ensuring that equipment on both sides of the line is covered.

[0074] In the process of dividing the inspection area into sub-regions based on sequence parameters, because the inspection area may be too long (such as a 100-kilometer power transmission line inspection area), a single drone or a single drone nest cannot efficiently cover it. Therefore, it is necessary to divide it into multiple sub-regions according to sequence parameters. Sequence parameters may include: the spatial arrangement order of the targets (such as tower numbers from 1# to 100#), the segmentation of the line route (such as the line from town A to town B being divided into 3 segments), and the separation of geographical obstacles (such as rivers and mountains naturally dividing the line). For example, a power transmission line inspection area containing 50 towers can be divided into 5 sub-regions according to the sequence parameters of 10 towers per group. Each sub-region corresponds to the inspection range of 10 towers, avoiding the decrease in drone flight efficiency due to an excessively large single region.

[0075] Step S103: Determine the inspection nodes contained in each sub-region and obtain the location parameters corresponding to the inspection nodes. Determine the initial deployment point corresponding to the sub-region based on the clustering results of the location parameters.

[0076] This step uses spatial clustering algorithms to initially determine the deployment locations of the drone nests and drones, providing a foundation for subsequent optimization. In the process of extracting the coordinate parameters of the inspection nodes within the sub-region, each sub-region contains several inspection nodes (i.e., the basic units divided in step S101). It is necessary to collect the precise coordinate parameters (such as latitude, longitude, and altitude) of these nodes to form a spatial point set (for example, a sub-region contains 8 towers with coordinates (X1, Y1), (X2, Y2)...(X8, Y8)).

[0077] In determining the initial deployment point through coordinate clustering, clustering algorithms (such as K-means clustering and density clustering) can be used to analyze the aforementioned set of coordinate points to find the spatial center of the sub-region, i.e., the location that minimizes the average distance from all inspection nodes, and this location is used as the initial deployment point. The core logic of clustering is to make the initial deployment point as close as possible to the majority of inspection nodes in the sub-region, reducing the invalid distance of the drone's round-trip flight. For example, if the towers in the sub-region are distributed along a straight line, the clustering result may be the midpoint of that line; if the towers are distributed in an L-shape, the clustering result may be the center location near the corner. In practical scenarios, the initial deployment point must meet the following requirements: it must cover at least all inspection nodes in the sub-region (i.e., all nodes are within the drone's operating radius), and the location must be relatively flat and free of obvious obstacles (such as tall buildings or dense forests) to allow space for subsequent adjustments.

[0078] Step S104: Adjust the position of the initial deployment point based on the drone's operating radius parameters and available power location parameters corresponding to the type of hangar to be deployed, so as to obtain the final deployment position of the drone.

[0079] This step is the core of optimized deployment. It eliminates potential flaws in the initial deployment point through dynamic adjustments, ensuring the final location balances efficiency and feasibility. Specifically, the operational radius parameter involved in determining the deployment process—the maximum safe flight distance of the drone under full charge (considering round-trip distance and redundant power)—determines the coverage area of ​​the deployment point. If the distance from the initial deployment point to a certain inspection node exceeds the operational radius, the deployment point needs to be adjusted towards that node to avoid blind spots. Since the drone nest requires a stable power supply (for drone charging and equipment operation), the power source is usually a nearby power facility (such as a substation, distribution box, or utility pole). The initial deployment point must be close to the power source (e.g., ≤50 meters away); otherwise, it needs to be adjusted to an area with convenient power access to reduce the cost of laying power lines.

[0080] During the process of adjusting the initial deployment point and determining the final deployment location, if the initial deployment point simultaneously meets the requirements of "covering all sub-region nodes (within the operating radius)" and "being close to the power source location," it can be directly used as the final location. If there are conflicts (such as the initial point meeting the coverage requirements but being far from the power source location, or being close to the power source location but with some nodes exceeding the operating radius), readjustment is required. For example, prioritize ensuring coverage by moving towards the power source location within the allowable range of the operating radius; or select the optimal location around the power source location that covers the most nodes (solved through geometric calculations or heuristic algorithms). The final deployment location needs to be verified in the field (such as drone test flights to test coverage effects and on-site surveys to assess the feasibility of power source access) to ensure its feasibility.

[0081] Through the above four steps, progressing step by step from "clarifying objectives, refining the scope, initial positioning, and optimization and adjustment," the deployment location of drones can be determined with high precision and automation, laying the foundation for the efficient execution of grid-based inspections.

[0082] In one implementation, step S101, which involves determining the inspection target based on the inspection area and using the type parameter of the inspection target to determine the inspection node corresponding to the inspection area, is as follows: Figure 2 As shown, it includes:

[0083] Step S201: Determine boundary parameters from the inspection area based on the operation range corresponding to the UAV, and use the boundary parameters to determine the inspection boundary range of the inspection area.

[0084] This method first uses the operational capability range of the UAV itself as a basis to extract key boundary parameters from the overall area to be inspected to define the inspection range, and then uses these boundary parameters to finally determine the inspection boundary range that the UAV can actually cover and perform inspection tasks (i.e. the actual effective area of ​​the inspection task).

[0085] The operational range of a drone is not a single indicator, but a comprehensive coverage capability determined by its core performance parameters, mainly including three dimensions: First, the maximum flight radius (limited by power endurance and battery capacity; for example, the common operational radius of multi-rotor drones is 5-20 kilometers, while that of fixed-wing drones can reach more than 50 kilometers); second, the signal control range (if relying on ground station remote control, it is necessary to ensure that the inspection area is within the stable coverage area of ​​radio or satellite signals to avoid signal interruption leading to drone loss of control); and third, the effective working range of the operational payload (such as high-definition cameras and infrared thermal imagers used for inspection, which need to be within the effective shooting distance; if they are too far away, the images will be blurry and the equipment defects cannot be identified, so this also needs to be taken into account in the operational range).

[0086] The boundary parameters here are key data that quantify the geometric boundaries of the inspection area. They are usually in the form of geographic spatial coordinates, such as latitude and longitude boundaries that clearly define the easternmost / westernmost longitude and the northernmost / southernmost latitude of the inspection area. Geometric boundary point set: If the inspection area is irregularly shaped (such as a narrow area along a power transmission line or a polygonal area surrounding a factory), then a closed boundary is formed by connecting multiple discrete coordinate points (such as (X1,Y1), (X2,Y2)...(Xn,Yn)). These coordinate points are the core components of the boundary parameters.

[0087] The essential purpose of determining boundary parameters is to prevent the inspection area from exceeding the actual operational capabilities of the drone. If the boundary is not defined based on the operational range, some areas may be inaccessible to the drone, or even if it does reach them, it may be unable to effectively complete the inspection (e.g., signal loss, payload failure). Therefore, this step is the spatial prerequisite for all subsequent inspection tasks. Ultimately, the inspection boundary range locked by the boundary parameters is the specific area that the drone needs to focus on covering and can effectively operate in.

[0088] Step S202: Obtain the inspection targets within the inspection boundary range, and determine the location coordinates of the inspection targets based on the coordinate system corresponding to the inspection area.

[0089] Within the inspection boundary range determined in step S201, all target objects that need to be inspected (i.e., inspection targets) are screened and obtained. At the same time, based on the unified spatial coordinate system corresponding to the inspection area, the precise position coordinates of each inspection target in the coordinate system are calculated and determined.

[0090] Inspection targets refer to core equipment or facilities within the inspection boundary that need to be inspected by drones. Their type needs to be determined based on the specific inspection scenario, for example:

[0091] Power inspection scenarios: transmission towers, substation transformers, cable terminations, insulator strings, etc.

[0092] Industrial equipment inspection scenarios: chemical storage tanks, reaction vessels, pipe welding joints, fan blades, photovoltaic panel arrays, etc.

[0093] There are generally three ways to acquire these targets: First, importing basic data in the early stage (such as exporting the registered equipment location information from the power GIS system or the factory equipment management system); second, conducting on-site preliminary surveys (marking the target location by manual reconnaissance or using drones for low-precision rough scanning, combined with AI image recognition to initially locate the target); and third, reusing historical inspection data (if there have been inspection records in the area, the current list of targets to be inspected can be updated based on historical data, removing dismantled equipment and adding new equipment).

[0094] The coordinate system corresponding to the inspection area must be a unified spatial reference standard to avoid confusion in position coordinates due to inconsistencies in the coordinate system. Determining the position coordinates of the inspection targets is the core basis for subsequent division of inspection nodes. Only by clarifying the precise spatial location of each target can the distance relationship between targets be determined, and thus the inspection nodes can be reasonably grouped.

[0095] Step S203: Determine multiple inspection nodes corresponding to the inspection area based on the type parameters and location coordinates of the inspection target.

[0096] After clarifying the type attributes (type parameters) and precise spatial location (location coordinates) of all inspection targets, the inspection targets within the inspection boundary are divided into multiple independent inspection nodes (i.e., the basic execution units of inspection tasks) through type association and spatial clustering.

[0097] Type parameters are key indicators for distinguishing the attributes of inspection targets and determining the differences in inspection requirements. They directly affect the logic of dividing inspection nodes. For example:

[0098] In power scenarios, the type parameters of transmission towers can include: straight-line towers, tension towers, and angle towers. Tension towers require special attention to the conductor anchor points and the tower's load-bearing structure, resulting in longer and more frequent inspections. Therefore, tension towers are usually assigned separate nodes from straight-line towers (even if they are located close to each other, they may be set up as a separate node to avoid task confusion due to different inspection requirements). Simply put, the type parameter determines which targets are suitable for grouping together. Targets of the same type or with highly similar inspection requirements are more suitable for inclusion in the same node, facilitating the subsequent unified allocation of drones (matching the corresponding inspection payload) and the development of inspection procedures.

[0099] Inspection nodes are the smallest spatial units in a gridded deployment, with each node containing several inspection targets that are related in type and geographically adjacent. The purpose of dividing the area into nodes is to provide a foundation for subsequent hangar deployment and task allocation. In subsequent steps, hangars will be deployed around inspection nodes (ensuring coverage of all targets within a node), and inspection tasks will be decomposed by node (the UAV starts from the hangar and completes the inspection of all targets within a node in one go, reducing round-trip distance and improving efficiency). In one implementation, step S203, which determines multiple inspection nodes corresponding to the inspection area based on the type parameters and location coordinates of the inspection targets, is as follows: Figure 3 As shown, it includes:

[0100] Step S301: Determine the power transmission equipment included in the inspection target based on the type parameters corresponding to the inspection target.

[0101] The core of this step is to focus on critical facilities of the power system from all inspection targets, clearly defining the core control objects for the grid-based deployment. Specifically, the type parameter of the inspection target covers the functional attributes of the equipment (such as "transmission," "distribution," and "auxiliary facilities"). This parameter allows for the precise extraction of objects belonging to transmission equipment from all targets within the inspection area. The specific scope of transmission equipment, based on the composition of the power system, includes at least four types of core facilities:

[0102] Substation: A key facility responsible for voltage transformation and power distribution (such as 500kV substation and 220kV substation).

[0103] Towers: Structures that support power transmission lines (such as straight-line towers, tension towers, angle towers, etc.);

[0104] Transmission lines: High-voltage power transmission conductors (such as overhead lines of 110kV and above).

[0105] Power distribution lines: medium and low voltage lines (such as 10kV power distribution lines) that distribute power to users.

[0106] Step S302: Obtain the location coordinates of the power transmission equipment and determine the height data of the power transmission equipment based on the location coordinates.

[0107] This step aims to supplement the three-dimensional spatial information of the power transmission equipment, providing more comprehensive geographic parameter support for subsequent node division. The acquisition of location coordinates can be based on the inspection target location coordinates determined in step S202, extracting the precise coordinates (such as latitude and longitude) of all power transmission equipment to form a spatial distribution dataset of the equipment. Height data can be combined with location coordinates and obtained through Geographic Information System (GIS), UAV LiDAR scanning, or equipment ledger records to acquire the height information of each power transmission device. For example:

[0108] For towers, the height data is the vertical distance from the top of the tower to the ground (e.g., a 50-meter-high tension tower).

[0109] For substations, height data can be referenced from the installation height of their core equipment (such as transformers and outgoing line structures);

[0110] For transmission / distribution lines, the height data is the vertical distance from the conductor suspension point to the ground (for example, the conductor height of a mountain crossing line may reach 80 meters, while the height of an urban distribution line is about 10-15 meters).

[0111] The purpose of altitude data is that drones need to consider flight altitude limitations when inspecting equipment at high altitudes or with large drops (such as avoiding collisions with lines and ensuring shooting angles), and its value will directly affect the calculation of the coverage range of subsequent nodes.

[0112] Step S303: Use the operating radius parameters of the UAV, the altitude data of the power transmission equipment, and the position coordinates to determine multiple inspection nodes corresponding to the inspection area.

[0113] This step is the core of the inspection node division. By integrating the spatial characteristics of the equipment with the performance parameters of the drones, the nodes can be scientifically divided. The drone's operating radius determines the maximum horizontal range that a single drone can cover from a certain center point; the height data of the power transmission equipment determines the actual effective coverage range of the drone. For high towers or high conductors, the drone needs to increase its flight altitude to avoid collisions, which may lead to a reduction in the actual horizontal operating radius; the location coordinates of the power transmission equipment reflect the spatial distribution density of the equipment.

[0114] Specifically, the logic for node division can be as follows: Calculate the spatial distance between power transmission equipment based on their location coordinates; correct the actual effective operating radius of the drone using altitude data; and group power transmission equipment that is close in distance and within the same corrected operating radius into one inspection node, based on equipment density and effective operating radius. For example, in urban areas with dense power distribution lines (small equipment spacing) and low conductor height (minimal impact on operating radius), power distribution lines and surrounding towers within a 1-square-kilometer area can be grouped into one node; in mountainous areas with sparse power transmission lines (large tower spacing) and high tower height (requiring adjustment of operating radius), towers and power transmission lines within a 5-8 kilometer range can be grouped into one node, ensuring that a single drone flight can cover all equipment within the node. The resulting multiple inspection nodes meet the requirement that the power transmission equipment within each node can be efficiently covered by drones from the same cluster, providing a reasonable unit division for subsequent deployment point determination.

[0115] Through steps S201-S203, the preliminary preparations for gridded deployment were completed in a progressive manner, from delineating boundaries to locking targets and then dividing nodes. This ensures that subsequent steps are carried out based on precise spatial range and target attributes, laying the foundation for improving inspection efficiency.

[0116] In one implementation, the inspection zone corresponding to the inspection target is determined based on the location parameters of the inspection target, such as... Figure 4 As shown, it includes:

[0117] Step S401: Determine the distribution characteristic data of the inspection target based on the location parameters of the inspection target.

[0118] This step analyzes the spatial location of the inspection targets and extracts their distribution patterns, providing a data foundation for subsequent area division. Specifically, the location parameters of the inspection targets include latitude and longitude, altitude, and relative distance (such as the coordinates of towers and the geographical location of substations). Distribution characteristic data are the statistical and analytical results of these location parameters, such as: the density of targets (number of towers per square kilometer), spatial distribution pattern (whether they are arranged in a straight line or clustered), and their relationship with the terrain (whether they are distributed along ridges or avoid rivers).

[0119] Step S402: Based on the distribution characteristic data, determine the linear distribution area, point distribution area, target dense area, and route direction area corresponding to the inspection target.

[0120] This step refines the distribution pattern of inspection targets into specific area types, clarifying the core components of the inspection zone. Specifically, linear distribution areas refer to areas where inspection targets are continuously arranged along straight lines or curves, such as power transmission line towers distributed sequentially along ridgelines in mountainous areas, or power distribution lines extending linearly along streets in urban areas; point distribution areas refer to areas where inspection targets are scattered and isolated, without obvious continuous arrangement, such as scattered transformers and independent surge arresters in remote areas; densely distributed target areas refer to concentrated areas with a large number of inspection targets and small spatial spacing, such as the dense distribution of various equipment (transformers, circuit breakers, busbars, etc.) in substations, or the dense arrangement of power distribution towers in the core urban area; line alignment areas refer to the areas formed by the extension direction of inspection targets (mainly line-type equipment), such as a power transmission line running from town A to town B in a northeast-southwest direction, and this direction and a certain range on both sides constitute the line alignment area.

[0121] For example, within a certain inspection area, the substation is a densely populated area, the transmission lines extending from it are linearly distributed, the isolated towers in the middle of the line are point-like distributed, and the entire line's extension path from south to north constitutes the line's orientation area.

[0122] Step S403: Construct inspection zones corresponding to inspection targets using linear distribution areas, point distribution areas, dense target areas, and route alignment areas.

[0123] This step integrates the four types of areas mentioned above to form a strip-shaped area covering all inspection targets, providing a clear operational path framework for UAV inspections. The construction of the inspection strip must ensure that all four types of areas are completely covered, and the boundaries must be expanded according to the UAV's operational range (such as the maximum observation distance on one side).

[0124] For example: the inspection zone for linear distribution areas extends the effective observation width of the UAV to both sides of the line (e.g., by 200 meters on each side); the inspection zone for densely distributed targets needs to cover the outer boundary of the entire dense area; the inspection zone for point-distributed areas needs to include isolated targets within the coverage of the surrounding linear or directional areas; the directional area of ​​the line determines the overall extension direction of the inspection zone. The final inspection zone is a continuous spatial range that includes all inspection targets and conforms to the flight path logic of the UAV, providing a unified basic range for subsequent sub-regional division.

[0125] In one implementation, the inspection zone is segmented based on the sequence parameters of the inspection targets to obtain multiple sub-regions, such as... Figure 5 As shown, it includes:

[0126] Step S501: Determine the flight direction of the UAV based on the starting and ending points of the UAV in the inspection zone, and use the flight direction to determine the sequence parameters of the inspection targets.

[0127] This step clarifies the drone's operational path, providing a basis for the sequence of inspection zone divisions. Specifically, the drone's starting point is typically the initial deployment point or the starting point of the inspection task (e.g., a substation), and the ending point is the end point of the task (e.g., another substation or the end of a line). Connecting the two points determines the flight direction (e.g., from west to east). The sequence parameter of the inspection targets refers to the order in which the targets are arranged along the flight direction. For example, along a west-to-east flight direction, poles are arranged sequentially as numbered 1#, 2#, 3#…, with the sequence parameter being 1, 2, 3…; if the substation is the starting point, the sequence parameter can be set to 0. The purpose of the sequence parameter is to ensure that the divided sub-areas are distributed sequentially along the flight direction, avoiding drone back-and-forth maneuvers and improving operational efficiency.

[0128] Step S502: Determine the segmentation strategy of the inspection zone using the corresponding operating radius parameters and power supply location parameters of the UAV.

[0129] This step combines drone performance with actual deployment conditions to formulate reasonable segmentation rules, ensuring that the size of the sub-regions is adapted to the drone's capabilities. The operating radius parameter determines the maximum coverage area that a single drone can cover from a certain point (e.g., an operating radius of 5 kilometers means that a single drone flight can cover an area with a radius of 5 kilometers centered on that point). The segmentation strategy must ensure that the length of each sub-region does not exceed twice the operating radius (round-trip distance). The power supply location parameter, representing the power supply location (e.g., substation, distribution box), is crucial for the drone's power supply. The segmentation strategy must consider whether the center of the sub-region is close to the power supply location (e.g., setting a sub-region boundary between every 2-3 power supply locations) to reduce the cost of laying power supply lines to the drone's nest.

[0130] For example, if the drone's operating radius is 5 kilometers and there is a power source every 10 kilometers along the inspection route, the segmentation strategy can be set to "divide into a sub-region every 8 kilometers" (less than twice the operating radius), and the center of the sub-region should be as close as possible to the power source.

[0131] Step S503: After dividing the inspection zone based on the sequence parameters and according to the segmentation strategy, multiple sub-regions are obtained.

[0132] This step divides the inspection zone into several independent sub-regions according to a predetermined sequence and rules, enabling refined management. Specifically, the zone can be divided according to the sequence parameters of the inspection targets (e.g., towers #1 to #100) and a segmentation strategy (e.g., every 20 towers or 8 kilometers as a unit). For example, if an inspection zone contains 50 towers with sequence parameters 1-50, and the segmentation strategy is "every 10 towers as a sub-region," it can be divided into 5 sub-regions, containing towers #1-10, #11-20, #21-30, #31-40, and #41-50 respectively. The divided sub-regions must meet the following requirements: targets within each sub-region can be efficiently covered by drones from the same nest, and the boundaries between sub-regions are clear, facilitating task allocation and control.

[0133] Through the above steps, the inspection zone is refined from an overall area into orderly sub-regions, which not only adapts to the operational capabilities of drones but also takes into account actual deployment conditions, laying the foundation for the accurate determination of subsequent deployment points.

[0134] In one implementation, step S103 involves determining the inspection nodes contained in each sub-region and obtaining the location parameters corresponding to the inspection nodes. Based on the clustering results of the location parameters, the initial deployment point corresponding to the sub-region is determined. Figure 6 As shown, it includes:

[0135] Step S601: Determine the coordinate system corresponding to the inspection node based on the position parameters corresponding to the inspection target, and obtain the boundary coordinates of the sub-regions under the coordinate system.

[0136] This step is fundamental to spatial positioning, providing a standardized spatial reference for subsequent coordinate parameter calculations and clustering by establishing a unified coordinate system. Specifically, in determining the coordinate system, the location parameters of the inspected target (such as latitude and longitude, relative distance) need to be converted into a unified coordinate system (such as the Gauss-Kruger Cartesian coordinate system, UTM coordinate system, etc.) to avoid calculation errors caused by inconsistent coordinate systems.

[0137] When obtaining the boundary coordinates of a sub-region, it is necessary to define the spatial range of each sub-region (i.e., the boundary of the sub-region after segmentation in step S102) within a defined coordinate system and extract the coordinate parameters of the boundary. For example, if a sub-region is a rectangular area, its boundary coordinates can be represented as the upper left corner (x1, y1), upper right corner (x2, y2), lower right corner (x3, y3), and lower left corner (x4, y4). These coordinates can accurately define the spatial boundary of the sub-region, ensuring that the subsequently selected inspection nodes are all within this range.

[0138] Step S602: Use boundary coordinates to determine the inspection nodes contained in each sub-region, and obtain the position parameters corresponding to the inspection nodes based on the coordinate system.

[0139] This step aims to identify the core controlled objects (inspection nodes) within a sub-region and collect their precise spatial coordinates, providing a data foundation for clustering calculations. When filtering inspection nodes within a sub-region, nodes that fall entirely within that sub-region can be selected by comparing their coordinates with the sub-region's boundary coordinates. For example, if an inspection node's coordinates (x0, y0) satisfy x1≤x0≤x2 and y4≤y0≤y1 (meeting the boundary conditions of a rectangular sub-region), then that node is considered to belong to that sub-region. During the acquisition of the inspection node's coordinate parameters, the precise coordinates of the selected inspection nodes can be extracted using a unified coordinate system. These coordinates contain sufficient precision to ensure the accuracy of the clustering results.

[0140] Step S603: After performing clustering calculation on the inspection nodes using location parameters, obtain the cluster center points corresponding to the inspection nodes, and determine the clustering results of the location parameters based on the cluster center points.

[0141] This step analyzes the spatial distribution patterns of nodes using clustering algorithms to find the spatial center within a sub-region, providing a core reference for initial deployment points. The clustering calculation process can be implemented using correlational clustering algorithms. The core of clustering algorithms (such as K-means) is to group inspection nodes that are spatially close into the same cluster and calculate the center point of each cluster (i.e., the average coordinate of all nodes in that cluster). For example, if there are 10 towers in a sub-region, and their coordinate distribution presents two dense clusters, clustering will yield two center points, representing the center positions of the two dense clusters respectively.

[0142] In the process of determining coordinate parameters based on cluster centroids, the clustering results include not only the coordinates of the cluster centroids, but also the coverage area corresponding to each centroid (i.e., the inspection nodes included in the cluster). If the inspection nodes in a sub-region are relatively concentrated (such as equipment in a substation), they may be clustered into one cluster, resulting in one centroid; if the nodes are scattered (such as scattered poles in mountainous areas), they may be clustered into multiple clusters, resulting in multiple centroids.

[0143] Step S604: Determine the initial deployment points corresponding to the sub-regions in the coordinate system based on the clustering results.

[0144] This step, based on clustering results, transforms cluster centers into initial deployment points, providing a foundation for subsequent location adjustments. In determining the initial deployment point, if the clustering result is a single center, that point is directly used as the initial deployment point for the sub-region (i.e., the initial location of the drone nest); if the clustering result is multiple centers, the location with the widest coverage and balanced distance from each center point must be selected as the initial deployment point, taking into account the sub-region's area and the drone's operational capabilities (e.g., taking the geometric center of multiple centers).

[0145] The initial deployment point should be as close as possible to the majority of inspection nodes to reduce the flight distance of the UAV. Through steps S601-S604, this method goes from establishing a coordinate system to selecting nodes, and then to cluster calculation, finally obtaining the initial deployment point of the sub-region. This ensures that the point can theoretically and efficiently cover the inspection nodes in the region, providing a scientific starting point for subsequent adjustments based on actual conditions (such as operating radius and power supply location).

[0146] In one implementation, step S104 involves adjusting the initial deployment point based on the drone's operating radius parameters and available power location parameters corresponding to the hangar type to obtain the final deployment location for the drone. Figure 7 As shown, it includes:

[0147] Step S701: Determine the coverage area of ​​the UAV using the UAV operation radius parameter, determine the obstacle area included in the coverage area based on the location parameter of the inspection target, and determine the first adjustment strategy and first deployment position corresponding to the initial deployment point based on the obstacle area.

[0148] This step focuses on the spatial feasibility of drone operations, ensuring the integrity of inspection coverage by avoiding obstacles. Specifically, in determining the coverage area, based on the drone's operating radius parameters, a circular coverage area that the drone can reach can be drawn with the initial deployment point as the center, ensuring that all inspection targets within the sub-area are theoretically within this range.

[0149] When identifying obstacle areas, the location parameters of the inspection target (such as tower coordinates and terrain data) can be combined to check for obstacles within the coverage area (such as tall buildings, dense forests, high-voltage power line towers, mountains, etc.). These obstacles may obstruct the drone's flight path or block the signal, forming an "obstacle area". For example, if there is a mountain with an altitude of 500 meters 3 kilometers north of the initial deployment point, it will block the inspection towers within a 1-kilometer radius to the north, and this area will be marked as an obstacle area.

[0150] In determining the first adjustment strategy, the direction and distance of the initial deployment point are adjusted for the obstacle area to ensure that the coverage range of the drone can avoid the obstacle and fully cover the sub-area after adjustment. For example, if the obstacle area prevents the north tower from being covered, the first adjustment strategy is set to "move the initial deployment point 500 meters south to avoid the mountain's obstruction."

[0151] Step S702: Determine the power supply range corresponding to the UAV using the available power location parameters, and determine the second adjustment strategy and second deployment location corresponding to the initial deployment point based on the power supply range.

[0152] This step focuses on the power supply feasibility of the storage facility, reducing power supply costs and risks by being located close to the power source. In determining the power supply range, the power source location parameters include the coordinates of nearby available power sources (such as substation distribution boxes or dedicated power supply piles) and the maximum laying distance of the power supply line (e.g., a maximum cable length of 300 meters), and based on this, the power supply range centered on the power source is defined. When assessing the power supply adaptability of the initial deployment point, if the initial deployment point is within the power supply range (e.g., 200 meters from the power source, less than the 300-meter upper limit), no adjustment is needed; if it exceeds the range (e.g., 400 meters from the power source), an adjustment strategy must be developed.

[0153] In determining the second adjustment strategy, the goal is to shorten the power supply distance, and the adjustment path from the initial deployment point to the power collection point is planned. For example, the second adjustment strategy can be set as "moving 150 meters towards the power collection point, reducing the power supply distance to 250 meters, and placing it within a 300-meter power supply range".

[0154] Step S703: Determine the final deployment location of the UAV based on the first deployment location and the second deployment location.

[0155] This step balances coverage and power supply requirements to create a feasible adjustment plan. If the first and second adjustment strategies are aligned (e.g., both require moving south), the adjustment distances can be combined (e.g., if the first strategy requires moving 500 meters south and the second strategy requires moving 150 meters south, the final strategy is to move 500 meters south, satisfying both coverage and power supply needs). If the strategies conflict (e.g., the first strategy requires moving east to avoid obstacles, and the second strategy requires moving west to be closer to the power source), priorities must be weighed; for example, prioritizing coverage integrity (obstacle avoidance), and then shortening the power supply distance as much as possible while still ensuring coverage (e.g., moving 300 meters east to avoid obstacles, and then fine-tuning to the location closest to the power source). The final deployment location must clearly define the adjustment direction, distance, and constraints.

[0156] Step S704: Adjust the initial deployment point to the final deployment location according to the first adjustment strategy and the second adjustment strategy.

[0157] In real-world scenarios, the first adjustment strategy can be used to perform initial adjustments: starting from the initial deployment point, move in the direction that covers the inspection target and avoids obstacles to reach the first deployment position; then, the second adjustment strategy can be used to perform secondary fine adjustments: starting from the first deployment position, move in the direction that enters the power supply range and is close to the power-accessible location to reach the final deployment position (if the first and second deployment positions are very close, they can be combined into one adjustment step).

[0158] In one implementation, step S704, which adjusts the initial deployment point to the final deployment location according to the first adjustment strategy and the second adjustment strategy, is as follows: Figure 8 As shown, it includes:

[0159] Step S801: After adjusting the initial deployment point using the first adjustment strategy, determine the adjustment position corresponding to the initial deployment point; wherein, the operating area of ​​the UAV at the adjustment position covers a sub-area.

[0160] This step prioritizes fulfilling coverage requirements to obtain an initial adjusted location that meets the spatial operational conditions. The initial deployment point can be moved according to the direction and distance of the first adjustment strategy, for example, moving 300 meters eastward from (x0, y0) to (x1, y1). In a real-world scenario, the coverage effect of the adjusted location can be verified by simulating the drone's flight path to confirm that all inspection targets within the sub-area are within the operational radius of the adjusted location and are unobstructed. For example, the coverage area of ​​the adjusted location can completely encompass 10 poles and avoid the original mountain obstacles. If the verification is successful, (x1, y1) is the adjusted location; if there are still blind spots, a second fine-tuning is required based on the actual situation (such as moving another 50 meters eastward) until complete coverage is achieved.

[0161] Step S802: After adjusting the position using the second adjustment strategy, determine the final deployment position of the drone; where the power supply distance of the drone is minimized at the final deployment position.

[0162] This step, based on achieving the coverage target, optimizes power supply efficiency to determine the final deployment location. Specifically, starting from the initial adjustment position (x1, y1), fine-tuning is performed towards the power source point according to the second adjustment strategy, for example, moving 50 meters south from (x1, y1) to (x2, y2). The power supply distance at the fine-tuned location is calculated and compared. If (x2, y2) is 280 meters from the power source point, shorter than (x1, y1)'s 330 meters, and still meets the coverage requirements, then (x2, y2) is a candidate location. This fine-tuning and verification process is repeated to find the location with complete coverage and the shortest power supply distance, which is the final deployment location. For example, the final location (x2, y2) is 280 meters from the power source point, covering all targets and unaffected by obstacles, making it the final installation point for the drone nest.

[0163] like Figure 9The diagram shown illustrates the effect of the method for determining the deployment location of drone hangars. Figure 9 The circle contains multiple inspection nodes, which are formed by the inspection targets (substations, transmission line towers, distribution line towers, etc.) within the inspection area. Figure 9 The continuous straight lines in the diagram represent the inspection zone. From the starting area to the ending area of ​​the inspection zone, it is divided into multiple sub-regions according to the flight sequence of the UAVs. Then, based on the multiple inspection nodes contained in each sub-region, the corresponding position coordinates are determined. After clustering these position coordinates, the initial deployment point D is determined based on the cluster center point. Subsequently, the initial deployment point D is adjusted according to the flight operation radius of the UAV and the nearest power source location. Under the premise that the flight operation radius can cover the sub-regions of the inspection zone, the initial deployment point D is updated to D'. Then, the nearest power source location D'' of D' is determined, and this deployment point is used as the final deployment location of the gridded area.

[0164] As can be seen from the method for determining the deployment location of the drone hangar in the above embodiments, this method makes full use of the drone's operating radius parameters and power supply location parameters to dynamically adjust the gridded deployment points. Under the premise of ensuring the normal operation of the drone, it achieves high-precision automatic acquisition of deployment points, thereby improving the inspection execution efficiency of the drone.

[0165] Regarding the method for determining the deployment location of drone hangars provided in the foregoing embodiments, this invention provides a system for determining the deployment location of drone hangars, such as... Figure 10 As shown, the system includes:

[0166] The patrol node determination module 1010 is used to determine the patrol target based on the patrol area and to determine the patrol node corresponding to the patrol area using the type parameter of the patrol target.

[0167] The sub-region segmentation module 1020 is used to determine the inspection zone corresponding to the inspection target based on the location parameters of the inspection target, and to segment the inspection zone based on the order parameters of the inspection target to obtain multiple sub-regions.

[0168] The initial deployment point determination module 1030 is used to determine the inspection nodes contained in each sub-region and obtain the location parameters corresponding to the inspection nodes, and determine the initial deployment point corresponding to the sub-region based on the clustering results of the location parameters.

[0169] The final deployment point determination module 1040 is used to adjust the position of the initial deployment point based on the drone's operating radius parameters and available power location parameters corresponding to the type of hangar to be deployed, so as to obtain the final deployment position of the drone.

[0170] As can be seen from the UAV hangar deployment location determination system mentioned in the above embodiments, the system makes full use of the UAV's operating radius parameters and power supply location parameters to dynamically adjust the gridded deployment points. Under the premise of ensuring the normal operation of the UAV, it achieves high-precision automatic acquisition of deployment points, thereby improving the inspection execution efficiency of the UAV.

[0171] The drone hangar deployment location determination system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned drone hangar deployment location determination method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned drone hangar deployment location determination method embodiment.

[0172] This embodiment also provides a drone, such as Figure 11 As shown, the drone employs the steps of the drone hangar deployment location determination method mentioned in the above embodiments during the gridded deployment process.

[0173] This embodiment also provides an electronic device, the structural schematic diagram of which is shown below. Figure 12 As shown, the device includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer instructions, which are executed by the processor to implement the steps of the above-described method for determining the deployment location of the UAV hangar.

[0174] Figure 12 The electronic device shown also includes a bus 103 and a communication interface 104, with the processor 101, communication interface 104 and memory 102 connected via the bus 103.

[0175] The memory 102 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 12 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0176] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the user terminal through the network interface.

[0177] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. The processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0178] This invention also provides a storage medium storing a computer program, which, when run by a processor, executes the steps of the method for determining the deployment location of the UAV hangar in the foregoing embodiments.

[0179] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0180] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0181] In addition, 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.

[0182] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion 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 this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0183] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for determining the deployment location of a drone hangar, characterized in that, The method includes: The inspection targets are determined based on the inspection area, and the inspection nodes corresponding to the inspection area are determined using the type parameters of the inspection targets. Based on the location parameters of the inspection target, the inspection zone corresponding to the inspection target is determined, and the inspection zone is divided based on the sequence parameters of the inspection target to obtain multiple sub-regions; The inspection nodes contained in each sub-region are determined and the location parameters corresponding to the inspection nodes are obtained. Based on the clustering results of the location parameters, the initial deployment point corresponding to the sub-region is determined. The position of the initial deployment point is adjusted based on the drone's operating radius parameters and available power location parameters corresponding to the type of hangar to be deployed, so as to obtain the final deployment position of the drone.

2. The method for determining the deployment location of a drone hangar according to claim 1, characterized in that, The steps of determining inspection targets based on the inspection area and determining the inspection nodes corresponding to the inspection area using the type parameters of the inspection targets include: Based on the operating range corresponding to the UAV, boundary parameters are determined from the inspection area, and the inspection boundary range of the inspection area is determined using the boundary parameters; The inspection targets within the inspection boundary range are obtained, and the position coordinates of the inspection targets are determined based on the coordinate system corresponding to the inspection area. Based on the type parameter corresponding to the inspection target and the location coordinates, multiple inspection nodes corresponding to the inspection area are determined.

3. The method for determining the deployment location of a drone hangar according to claim 2, characterized in that, The step of determining multiple inspection nodes corresponding to the inspection area based on the type parameter corresponding to the inspection target and the location coordinates includes: The power transmission equipment included in the inspection target is determined based on the type parameter corresponding to the inspection target; Obtain the position coordinates corresponding to the power transmission equipment, and determine the height data corresponding to the power transmission equipment based on the position coordinates; The multiple inspection nodes corresponding to the inspection area are determined by using the operating radius parameter of the UAV, the altitude data of the power transmission equipment, and the position coordinates.

4. The method for determining the deployment location of a drone hangar according to claim 1, characterized in that, Determining the inspection zone corresponding to the inspection target based on the location parameters of the inspection target includes: The distribution characteristic data of the inspection target are determined based on the location parameters of the inspection target; Based on the distribution feature data, determine the linear distribution area, point distribution area, target-dense area, and route direction area corresponding to the inspection target. The inspection zone corresponding to the inspection target is constructed by utilizing the linear distribution area, the dotted distribution area, the target-dense area, and the route direction area.

5. The method for determining the deployment location of a drone hangar according to claim 1, characterized in that, After segmenting the inspection zone based on the sequence parameters of the inspection targets, multiple sub-regions are obtained, including: The flight direction of the UAV is determined based on the start and end points of the UAV in the inspection zone, and the sequence parameters of the inspection target are determined using the flight direction. The segmentation strategy of the inspection zone is determined by using the operating radius parameter and the power supply location parameter corresponding to the UAV; After dividing the inspection zone according to the sequence parameters and the segmentation strategy, multiple sub-regions are obtained.

6. The method for determining the deployment location of a drone hangar according to claim 1, characterized in that, The steps of determining the inspection nodes contained in each sub-region and obtaining the location parameters corresponding to the inspection nodes, and determining the initial deployment point corresponding to the sub-region based on the clustering results of the location parameters, include: Based on the location parameters corresponding to the inspection target, determine the coordinate system corresponding to the inspection node, and obtain the boundary coordinates of the sub-region under the coordinate system; The inspection nodes contained in each sub-region are determined using the boundary coordinates, and the position parameters corresponding to the inspection nodes are obtained based on the coordinate system. After performing clustering calculations on the inspection nodes using the location parameters, the cluster center points corresponding to the inspection nodes are obtained, and the clustering results of the location parameters are determined based on the cluster center points. The initial deployment point corresponding to the sub-region in the coordinate system is determined based on the clustering results.

7. The method for determining the deployment location of a drone hangar according to claim 1, characterized in that, The step of adjusting the position of the initial deployment point based on the drone's operating radius parameters and available power location parameters corresponding to the hangar type to obtain the final deployment position of the drone includes: The coverage area corresponding to the UAV is determined by using the UAV's operating radius parameter, the obstacle area included in the coverage area is determined based on the location parameter of the inspection target, and the first adjustment strategy and the first deployment position corresponding to the initial deployment point are determined based on the obstacle area. The power supply range corresponding to the UAV is determined using the available power location parameters, and a second adjustment strategy and a second deployment location corresponding to the initial deployment point are determined based on the power supply range. The final deployment location of the UAV is determined based on the first deployment location and the second deployment location; The initial deployment point is adjusted to the final deployment location according to the first adjustment strategy and the second adjustment strategy.

8. The method for determining the deployment location of a drone hangar according to claim 7, characterized in that, The step of adjusting the initial deployment point to the final deployment location according to the first adjustment strategy and the second adjustment strategy includes: After adjusting the initial deployment point using the first adjustment strategy, the adjusted position corresponding to the initial deployment point is determined; wherein, at the adjusted position, the operating area of ​​the UAV covers the sub-area; After adjusting the adjustment position using the second adjustment strategy, the final deployment position corresponding to the UAV is determined; wherein the power supply distance of the UAV is minimized at the final deployment position.

9. A system for determining the deployment location of a drone hangar, characterized in that, The system includes: The inspection node determination module is used to determine the inspection target based on the inspection area, and to determine the inspection node corresponding to the inspection area using the type parameter of the inspection target; The sub-region segmentation module is used to determine the inspection zone corresponding to the inspection target based on the location parameters of the inspection target, and to segment the inspection zone based on the order parameters of the inspection target to obtain multiple sub-regions; The initial deployment point determination module is used to determine the inspection nodes contained in each of the sub-regions and obtain the location parameters corresponding to the inspection nodes, and determine the initial deployment point corresponding to the sub-region based on the clustering results of the location parameters. The final deployment point determination module is used to adjust the position of the initial deployment point based on the drone's operating radius parameters and available power location parameters corresponding to the type of hangar to be deployed, so as to obtain the final deployment position of the drone.

10. A drone, characterized in that, During the grid-based deployment of the drone, the method for determining the drone hangar deployment location as described in any one of claims 1 to 8 is employed.

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