Unmanned aerial vehicle hangar deployment position determination method and system and unmanned aerial vehicle

Abstract

The invention provides an unmanned aerial vehicle hangar deployment position determination method and system and an unmanned aerial vehicle, and relates to the field of unmanned aerial vehicle deployment control, and the method comprises the steps: determining an inspection zone corresponding to an inspection target according to a position parameter of the inspection target after determining an inspection node corresponding to an inspection region; dividing the inspection zone into a plurality of sub-regions based on the sequence parameters of the inspection target; then, acquiring inspection nodes contained in each sub-region and position parameters of the inspection nodes, and determining initial deployment points corresponding to the sub-regions based on a clustering result of the position parameters; and finally, adjusting the position of the initial deployment point based on the operation radius parameter and the power-taking position parameter of the unmanned aerial vehicle to obtain a final deployment position corresponding to the unmanned aerial vehicle. According to the method, the operation radius parameter and the power-taking position parameter of the unmanned aerial vehicle are fully utilized to realize dynamic adjustment on the gridding deployment point; on the premise of ensuring normal operation of the unmanned aerial vehicle, high-precision automatic acquisition of the deployment points is realized, so that the inspection execution efficiency of the unmanned aerial vehicle is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle deployment control, and particularly relates to an unmanned aerial vehicle hangar deployment position determination method and system. BACKGROUND

[0002] When performing inspection on power facilities or other industrial equipment, grid deployment of unmanned aerial vehicles can improve the efficiency of inspection execution. Grid deployment divides the inspection area into multiple inspection nodes, and deploys several nests and unmanned aerial vehicles in the center of each node. When performing an inspection task, the inspection task is divided into the inspection nodes along the way, so that the inspection is performed in a grid deployment mode. Since there are multiple inspection nodes, and each node involves multiple nests and unmanned aerial vehicles, multiple variables are involved, which makes the overall difficulty of grid deployment high, and it is difficult to ensure that the deployment position is at the best inspection execution efficiency, thereby affecting the effect of unmanned aerial vehicle inspection. SUMMARY

[0003] Therefore, the present application aims to provide an unmanned aerial vehicle hangar deployment position determination method and system, which fully utilizes the work radius parameter and power supply position parameter of the unmanned aerial vehicle to dynamically adjust the grid deployment point, and realizes high-precision automatic acquisition of the deployment point under the premise of ensuring normal operation of the unmanned aerial vehicle, thereby improving the efficiency of unmanned aerial vehicle inspection execution.

[0004] In a first aspect, the present application provides an unmanned aerial vehicle hangar deployment position determination method, which comprises: determining an inspection target based on a survey area, and determining an inspection node corresponding to the survey area based on a type parameter of the inspection target; determining an inspection zone corresponding to the inspection target according to a position parameter of the inspection target, and obtaining a plurality of sub-areas by segmenting the inspection zone based on a sequence parameter of the inspection target; determining an inspection node contained in each sub-area and obtaining a position parameter corresponding to the inspection node, determining an initial deployment point corresponding to the sub-area based on a clustering result of the position parameter; adjusting the position of the initial deployment point based on the work radius parameter and the power supply position parameter corresponding to the type of the to-be-deployed hangar, to obtain a final deployment position corresponding to the unmanned aerial vehicle.

[0005] In one embodiment, the step of determining an inspection target based on a survey area, and determining an inspection node corresponding to the survey area based on a type parameter of the inspection target comprises: determining a boundary parameter from the survey area based on a work range corresponding to the unmanned aerial vehicle, and determining a survey boundary range of the survey area based on the boundary parameter; obtaining an inspection target contained in the survey boundary range, and determining a position coordinate corresponding to the inspection target based on a coordinate system corresponding to the survey area. determine the plurality of inspection nodes corresponding to the inspection area according to the type parameter and the position coordinate corresponding to the inspection target.

[0006] In an implementation, the step of determining the plurality of inspection nodes corresponding to the inspection area according to the type parameter and the position coordinate corresponding to the inspection target comprises: determining the power transmission equipment contained in the inspection target according to the type parameter corresponding to the inspection target; obtaining the position coordinate corresponding to the power transmission equipment, and determining the height data corresponding to the power transmission equipment based on the position coordinate; determining the plurality of inspection nodes corresponding to the inspection area by using the operation radius parameter corresponding to the unmanned aerial vehicle, the height data corresponding to the power transmission equipment, and the position coordinate.

[0007] In an implementation, the step of determining the inspection belt corresponding to the inspection target according to the position parameter of the inspection target comprises: determining the distribution feature data of the inspection target according to the position parameter of the inspection target; determining the linear distribution area, the point distribution area, the target dense area, and the line direction area corresponding to the inspection target based on the distribution feature data; constructing the inspection belt corresponding to the inspection target by using the linear distribution area, the point distribution area, the target dense area, and the line direction area.

[0008] In an implementation, after the inspection belt is segmented based on the sequence parameter of the inspection target, a plurality of sub-areas are obtained, comprising: determining the flight direction of the unmanned aerial vehicle according to the starting point and the ending point corresponding to the unmanned aerial vehicle in the inspection belt, and determining the sequence parameter of the inspection target by using the flight direction; determining the segmentation strategy of the inspection belt by using the operation radius parameter corresponding to the unmanned aerial vehicle and the power supply position parameter; obtaining the plurality of sub-areas after the inspection belt is segmented based on the sequence parameter and according to the segmentation strategy.

[0009] In an implementation, the step of determining the inspection node contained in each sub-area, obtaining the position parameter corresponding to the inspection node, and determining the initial deployment point corresponding to the sub-area based on the clustering result of the position parameter comprises: determining the coordinate system corresponding to the inspection node based on the position parameter corresponding to the inspection target, and obtaining the boundary coordinate of the sub-area under the coordinate system; determining the inspection node contained in each sub-area by using the boundary coordinate, and obtaining the position parameter corresponding to the inspection node based on the coordinate system; obtaining the clustering center point corresponding to the inspection node after the inspection node is clustered by using the position parameter, and determining the clustering result of the position parameter based on the clustering center point; Determine the initial deployment point of the sub-region in the coordinate system according to the clustering result.

[0010] In an embodiment, the step of adjusting the position of the initial deployment point based on the UAV operation radius parameter and the available power position parameter corresponding to the type of the hangar to be deployed to obtain the final deployment position of the UAV includes: Determine the coverage range of the UAV by using the UAV operation radius parameter, determine the obstacle region contained in the coverage range based on the position parameter of the inspection target, and determine the first adjustment strategy and the first deployment position corresponding to the initial deployment point according to the obstacle region; Determine the power supply range of the UAV by using the available power position parameter, determine the second adjustment strategy and the second deployment position corresponding to the initial deployment point based on the power supply range; Determine the final deployment position of the UAV based on the first deployment position and the second deployment position; Adjust the initial deployment point to the final deployment position according to the first adjustment strategy and the second adjustment strategy.

[0011] In an embodiment, the step of adjusting the initial deployment point to the final deployment position according to the first adjustment strategy and the second adjustment strategy includes: After adjusting the initial deployment point by using the first adjustment strategy, determine the adjustment position corresponding to the initial deployment point; wherein the operation region of the UAV covers the sub-region at the adjustment position; After adjusting the adjustment position by using the second adjustment strategy, determine the final deployment position of the UAV; wherein the power supply distance of the UAV is the smallest at the final deployment position.

[0012] In a second aspect, an embodiment of the present application provides a system for determining the deployment position of a UAV hangar, which includes: A patrol node determination module for determining an inspection target based on a patrol area and determining a patrol node corresponding to the patrol area by using the type parameter of the inspection target; A sub-region segmentation module for determining an inspection belt corresponding to the inspection target according to the position parameter of the inspection target, and obtaining a plurality of sub-regions by segmenting the inspection belt based on the sequence parameter of the inspection target; An initial deployment point determination module for determining the patrol node contained in each sub-region and obtaining the position parameter corresponding to the patrol node, and determining the initial deployment point of the sub-region based on the clustering result of the position parameter; A final deployment point determination module for adjusting the position of the initial deployment point based on the UAV operation radius parameter and the available power position parameter corresponding to the type of the hangar to be deployed to obtain the final deployment position of the UAV.

[0013] In a third aspect, the embodiments of the present application further provide a UAV, which adopts the steps of the UAV hangar deployment position determination method of the first aspect during the process of grid deployment.

[0014] In a fourth aspect, the embodiments of the present application further provide an electronic device, which comprises a processor and a memory, the memory stores computer executable instructions capable of being executed by the processor, and the processor executes the computer executable instructions to implement the steps of the UAV hangar deployment position determination method of the first aspect.

[0015] In a fifth aspect, the embodiments of the present application further provide a storage medium, which stores computer executable instructions, and the computer executable instructions, when invoked and executed by a processor, cause the processor to implement the steps of the UAV hangar deployment position determination method of the first aspect.

[0016] The UAV hangar deployment position determination method, system and UAV provided by the embodiments of the present application, in the process of grid deployment of the UAV, first determine the inspection target based on the patrol area, and determine the inspection node corresponding to the patrol area by using the type parameter of the inspection target; then determine the inspection belt corresponding to the inspection target according to the position parameter of the inspection target, and after segmenting the inspection belt based on the sequence parameter of the inspection target, obtain a plurality of sub-areas; subsequently determine the inspection node contained in each sub-area and obtain the position parameter corresponding to the inspection node, determine the initial deployment point corresponding to the sub-area based on the clustering result of the position parameter; finally, adjust the position of the initial deployment point based on the UAV operation radius parameter and the available power supply position parameter corresponding to the type of the to-be-deployed hangar, to obtain the final deployment position of the UAV. This method fully utilizes the operation radius parameter and the power supply position parameter of the UAV to dynamically adjust the grid deployment point, realizes high-precision automatic acquisition of the deployment point on the premise of ensuring normal operation of the UAV, and thus improves the inspection execution efficiency of the UAV.

[0017] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by the structure particularly pointed out in the description, claims and drawings.

[0018] In order to make the above-mentioned objects, features and advantages of the present application more apparent, clear and easy to understand, the following preferred embodiments are specifically described with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the accompanying drawings required by the specific embodiments or the prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 2 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 3 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 4 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 5 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 6 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 7 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 8 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 9 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 10 A flowchart of a method for determining the deployment position of a UAV hangar according to an embodiment of the present application is shown in FIG. 1. Figure 11 A structural schematic diagram of a UAV provided by an embodiment of the present application; Figure 12 A structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0021] Icon: 1010 - patrol node determination module; 1020 - sub-region segmentation module; 1030 - initial deployment point determination module; 1040 - final deployment point determination module; 101 - processor; 102 - memory; 103 - bus; 104 - communication interface. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described clearly and completely below in conjunction with embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0023] With the large-scale application of UAVs in power facility inspection, the UAVs are deployed in a grid manner to inspect power facilities or other industrial equipment, which can improve the inspection execution efficiency and the control convenience. Specifically, the grid deployment divides the inspection area into multiple inspection nodes, and deploys a plurality of nests and UAVs in the center of each node. When performing an inspection task, the inspection task is decomposed into the inspection nodes along the way, so that the inspection is performed in the grid deployment mode. Since there are multiple inspection nodes, and each node involves multiple nests and UAVs, and multiple variables, the overall difficulty of the grid deployment is high, and the deployment position is difficult to ensure to be at the best inspection execution efficiency, thereby affecting the UAV inspection effect. Based on this, the present application provides a UAV hangar deployment position determination method, system and UAV, which fully utilizes the work radius parameter and power supply position parameter of the UAV to dynamically adjust the grid deployment point, realizes high-precision automatic acquisition of the deployment point on the premise of ensuring normal operation of the UAV, and thereby improves the inspection execution efficiency of the UAV.

[0024] In order to facilitate the understanding of the present embodiment, first, a UAV hangar deployment position determination method disclosed by the present embodiment will be described in detail, as shown in Figure 1 The method comprises: Step S101, determining an inspection target based on a patrol area, and determining an inspection node corresponding to the patrol area by using a type parameter of the inspection target.

[0025] This step is the basis of grid deployment, and the core is to determine the inspection target and determine the inspection node. In the process of determining the inspection target, first of all, the inspection area of the unmanned aerial vehicle (such as a 100-kilometer power transmission line, an industrial park containing five substations, etc.) needs to be determined, and all power facilities or industrial equipment to be inspected in the area are extracted as the inspection target. For example, the inspection target in the power transmission line scene can include towers, insulators, conductors, and shock absorbers; the substation scene may include transformers, circuit breakers, and disconnectors.

[0026] The inspection node is obtained by dividing the target type parameter. Different types of inspection targets have different inspection requirements (such as inspection frequency, accuracy requirement, risk level), which need to be divided into inspection nodes accordingly, and used as the basic unit of grid deployment. Specifically, the type parameter can include: target functional importance parameter (which can specifically represent hub substation equipment and ordinary branch tower), structural complexity parameter (which can specifically represent special towers across mountains and rivers and plain conventional towers), environmental risk parameter (which can specifically represent icing area towers and normal climate area equipment), etc. For example, for important and structurally complex targets (such as the main transformer of a 500kV substation), a small range around them can be divided into independent inspection nodes to ensure fine control; for densely distributed but structurally simple targets (such as 110kV power transmission towers in the plains), a continuous area can be combined into one inspection node to reduce management costs.

[0027] In step S102, the inspection target corresponding to the inspection belt is determined according to the position parameter of the inspection target, and the inspection belt is segmented based on the sequence parameter of the inspection target, and a plurality of sub-regions are obtained.

[0028] This step aims to further refine the inspection node into an operable sub-region, providing a more accurate range for subsequent deployment point determination.

[0029] In the process of determining the inspection belt, the inspection targets involved are usually distributed along a specific path (such as power transmission lines extending along towers in sequence, and distribution lines distributed along streets), and based on the position parameters (such as latitude, longitude, and altitude coordinates) of these targets, an inspection belt can be fitted, which is a belt-shaped area covering all targets (the width is usually determined by the radius of the unmanned aerial vehicle operation and the target distribution density). For example, the inspection belt of the power transmission line can be formed along the line direction, with a width of twice the maximum inspection radius of the unmanned aerial vehicle on one side, ensuring that the equipment on both sides of the line is covered.

[0030] In the process of dividing into sub-regions based on sequence parameters, due to the long inspection belt (such as a 100-kilometer power transmission line inspection belt), a single unmanned aerial vehicle or a single nest cannot efficiently cover it, and it needs to be divided into multiple sub-regions according to sequence parameters. The sequence parameters can include: the spatial arrangement order of the target (such as the tower number from 1# to 100#), the segmentation of the line direction (such as the line from A town to B town is divided into 3 segments), the separation of geographical obstacles (such as rivers, mountains naturally divide the line), etc. For example, a certain power transmission line inspection belt containing 50 towers can be divided into 5 sub-regions according to the sequence parameter of every 10 towers, each sub-region corresponds to the inspection range of 10 towers, avoiding the decrease of unmanned aerial vehicle flight efficiency caused by the too large single region.

[0031] Step S103, determine the inspection nodes contained in each sub-region and obtain the position parameters corresponding to the inspection nodes, and determine the initial deployment point corresponding to the sub-region based on the clustering results of the position parameters.

[0032] This step can preliminarily determine the deployment position of the nest and the unmanned aerial vehicle through the spatial clustering algorithm, providing a basis for subsequent optimization. In the process of extracting the coordinate parameters of the inspection nodes in the sub-region, each sub-region contains a number of inspection nodes (i.e. the basic unit divided in step S101), and the accurate coordinate parameters (such as latitude and longitude, altitude) of these nodes need to be collected to form a spatial point set (for example, a sub-region contains 8 towers, whose coordinates are (X1, Y1), (X2, Y2)…(X8, Y8)).

[0033] In the process of determining the initial deployment point through coordinate clustering, the clustering algorithm (such as K-means clustering, density clustering) can be used to analyze the above coordinate point set to find the spatial center in the sub-region, that is, the position that can minimize the average distance to all inspection nodes, 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 unmanned aerial vehicle'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 the straight line; if the towers are distributed in L shape, the clustering result may be the center position near the corner. In actual scenarios, the initial deployment point needs to satisfy at least covering all inspection nodes in the sub-region (i.e. the nodes are within the radius of the unmanned aerial vehicle operation), and the position is relatively flat and has no obvious obstacles (such as high-rise buildings, dense forests), leaving space for subsequent adjustment.

[0034] Step S104, adjust the position of the initial deployment point based on the unmanned aerial vehicle operation radius parameter and the available power position parameter corresponding to the type of the to-be-deployed hangar, to obtain the final deployment position of the unmanned aerial vehicle.

[0035] This step is the core of optimizing deployment, which eliminates the potential defects of the initial deployment point through dynamic adjustment, ensuring that the final location balances efficiency and feasibility. Specifically, in the deployment process, the involved job radius parameter, which is the maximum safe flight distance of the UAV under full battery (considering the round trip range and redundant power), determines the coverage range of the deployment point. If the distance from the initial deployment point to a certain inspection node exceeds the job radius, the node needs to be adjusted in the direction to avoid the inspection blind area. Since the nest needs stable power supply (for charging the UAV and running the equipment), the power supply location is usually near the power supply facility (such as a substation, distribution box, or power pole). The initial deployment point needs to be close to the power supply location (such as within 50 meters), otherwise it needs to be adjusted to an area where power supply is convenient to reduce the cost of power supply line laying.

[0036] In the process of adjusting the initial deployment point and determining the final deployment location, if the initial deployment point meets both "cover all sub-area nodes (within the job radius)" and "be close to the power supply location", it can be directly used as the final location; if there is a conflict (such as the initial point meets the coverage range but is far from the power supply point, or is close to the power supply point but some nodes exceed the job radius), it needs to be adjusted again: for example, prioritize coverage range and move to the power supply location within the job radius; or select the optimal location that can cover the most nodes around the power supply location (solved by geometric calculation or heuristic algorithm). The final deployment location needs to be verified in the field (such as UAV test flight to test coverage effect, on-site survey to verify power supply feasibility) to ensure the feasibility of landing.

[0037] Through the above four steps, from "clear target - refine range - preliminary positioning - optimization adjustment", the high-precision and automated determination of the UAV deployment location is finally achieved, laying the foundation for efficient execution of grid inspection.

[0038] In one embodiment, the step S101 of determining the inspection target based on the inspection area and determining the inspection nodes corresponding to the inspection area based on the type parameters of the inspection target includes: Figure 2 as shown, comprising: Step S201, determining the boundary parameters from the inspection area based on the working range of the UAV, and determining the inspection boundary range of the inspection area based on the boundary parameters.

[0039] This method first extracts the key boundary parameters for defining the inspection range from the overall area to be inspected based on the working capacity range of the UAV itself, and then determines the inspection boundary range (i.e. the actual effective area of the inspection task) that the UAV can actually cover and execute the inspection task through these boundary parameters.

[0040] The operation range of a UAV 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 operation radius of multi-rotor UAV is 5-20 kilometers, and the fixed-wing UAV can reach more than 50 kilometers); second, the signal control range (if relying on ground station remote control, it needs to ensure that the patrol area is within the stable coverage area of radio signal or satellite signal to avoid signal interruption leading to UAV loss of control); third, the effective working range of operation load (such as high-definition cameras and infrared thermal imagers used for inspection need to be within the effective shooting distance, otherwise the image will be blurred and the equipment defects cannot be identified, so the effective working range of operation load also needs to be considered in the operation range).

[0041] The boundary parameter here is the key data for quantitatively describing the geometric boundary of the patrol area, usually in the form of geographic spatial coordinates, such as longitude and latitude boundaries to clearly define the easternmost / westernmost longitude and northernmost / southernmost latitude of the patrol area; geometric boundary point set: if the patrol area is irregularly shaped (such as a narrow area along the power transmission line or a polygonal area around the factory site), a closed boundary is formed by connecting multiple discrete coordinate points (such as (X1, Y1), (X2, Y2)…(Xn, Yn)), and these coordinate points are the core components of the boundary parameter.

[0042] The essential purpose of determining the boundary parameter is to avoid the patrol area exceeding the actual operation capability of the UAV. If the boundary is not defined based on the operation range, it may lead to the UAV being unable to reach some areas or being unable to effectively complete the inspection (such as signal loss or load failure) after reaching, so this step is a spatial prerequisite for the subsequent landing of all inspection tasks. The patrol boundary range locked by the boundary parameter is the specific area that the UAV needs to focus on covering and can effectively operate.

[0043] In step S201, the patrol boundary range is determined, and the patrol boundary range is obtained. The patrol boundary range is determined based on the operation range of the UAV and the boundary parameter of the patrol area.

[0044] In step S201, the patrol boundary range is determined, and the patrol boundary range is obtained. The patrol boundary range is determined based on the operation range of the UAV and the boundary parameter of the patrol area.

[0045] The inspection target refers to the core equipment or facility within the patrol boundary that needs to be inspected by the UAV, and its type needs to be determined according to the specific inspection scene, for example: Power inspection scene: power transmission tower, transformer in substation, cable terminal, insulator string, etc. Industrial equipment inspection scene: chemical storage tank, reaction kettle, pipeline welding interface, fan blade, photovoltaic panel array, etc.

[0046] The way to obtain these targets usually includes three categories: one is the pre-basic data import (such as exporting the registered equipment location information from the power GIS system, factory equipment management system); two is the field pre-survey (marking the target location by artificial reconnaissance, or using a low-precision rough scan by a drone, combined with AI image recognition to preliminarily locate the target); three is the reuse of historical inspection data (if there are inspection records in this area, the target list for current inspection can be updated based on historical data, and the removed equipment and added equipment are supplemented).

[0047] The coordinate system corresponding to the inspection area needs to be a unified spatial reference standard to avoid confusion of position coordinates due to non-uniform coordinate systems. Determining the position coordinates of the inspection target is the core basis for subsequent division of the inspection node. Only the accurate spatial position of each target can determine the distance correlation between targets, and then reasonably group to form an inspection node.

[0048] Step S203, determining a plurality of inspection nodes corresponding to the inspection area according to the type parameter and the position coordinates of the inspection target.

[0049] After the type attribute (type parameter) and accurate spatial position (position coordinates) of all inspection targets are determined, the inspection targets within the inspection boundary range are divided into a plurality of independent inspection nodes (i.e. the basic execution unit of the inspection task) by type association + spatial clustering.

[0050] The type parameter is a key indicator to distinguish the attributes of the inspection target and determine the difference of the inspection demand, which directly affects the division logic of the inspection node, for example: In the power scenario, the type parameters of the power transmission tower can include: straight tower, strain tower, corner tower. The strain tower needs to be checked for wire anchor points and tower body load-bearing structure, which takes longer and has higher frequency, so the strain tower and the straight tower are usually divided into nodes (even if the location is close, it may also be set as a node to avoid task confusion due to different inspection requirements). Simply put, the type parameter determines which targets are suitable for grouping. Targets with the same type or similar inspection requirements are more suitable for inclusion in the same node, which facilitates subsequent deployment of unmanned aerial vehicles (matching corresponding inspection load) and development of inspection processes.

[0051] The inspection node is the smallest spatial unit of grid deployment, and each node contains a plurality of type-associated and position-adjacent inspection targets. The purpose of dividing the node is to provide a basis for subsequent hangar deployment and task allocation. In the subsequent steps, the hangar will be deployed around the inspection node (to ensure coverage of all targets in the node), and the inspection task will also be divided by node (the unmanned aerial vehicle departs from the hangar and completes all target inspection in the node at one time, reducing the round trip distance and improving efficiency). In one implementation, step S203 of determining a plurality of inspection nodes corresponding to the inspection area according to the type parameter and the position coordinates of the inspection target is as follows:Figure 3 As shown, comprising: Step S301, determining the power transmission equipment contained in the inspection target according to the type parameter corresponding to the inspection target.

[0052] The core of this step is to focus on the key facilities of the power system from all inspection targets, and to clearly define the core management objects of the grid deployment. Specifically, the type parameter of the inspection target covers the functional attributes of the equipment (such as "power transmission", "power distribution", "auxiliary facilities", etc.), and through this parameter, the objects belonging to the power transmission equipment can be accurately extracted from all targets in the inspection area. The specific range of power transmission equipment includes at least four types of core facilities according to the composition of the power system: Substation: pivotal facility for voltage conversion and power distribution (such as 500kV substation, 220kV substation); Pole tower: structure supporting the transmission line (such as straight pole tower, strain tower, corner pole tower, etc.); Transmission line: high-voltage level power transmission conductor (such as 110kV and above overhead line); Distribution line: medium and low voltage line for distributing power to user side (such as 10kV distribution line).

[0053] Step S302, obtaining the position coordinates corresponding to the power transmission equipment, and determining the height data corresponding to the power transmission equipment based on the position coordinates.

[0054] 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 position coordinates can be obtained based on the position coordinates of the inspection targets determined in step S202, and the accurate coordinates (such as latitude and longitude) of all power transmission equipment can be extracted to form a spatial distribution data set of the power transmission equipment. The height data can be combined with the position coordinates to obtain the height information of each power transmission equipment through geographic information system (GIS), unmanned aerial vehicle laser radar scanning or equipment account records. For example: For pole towers, the height data is the vertical distance from the top of the pole tower to the ground (such as a 50-meter-high strain tower); For substations, the height data can refer to the installation height of its core equipment (such as transformers, outgoing line structures); For transmission lines / distribution lines, the height data is the vertical distance from the conductor suspension point to the ground (such as the conductor height of a cross-mountain line may reach 80 meters, while the height of a city distribution line is about 10-15 meters).

[0055] The role of height data is that the flight height limit needs to be considered for the inspection of high-altitude or high-fall equipment by unmanned aerial vehicles (such as avoiding line collision and ensuring shooting angle), and its value will directly affect the calculation of the subsequent node coverage range.

[0056] Step S303, using the corresponding operation radius parameter of the unmanned aerial vehicle, the height data corresponding to the power transmission equipment, and the position coordinates to determine a plurality of inspection nodes corresponding to the inspection area.

[0057] This step is the core of the inspection node division. By fusing the device spatial features and the unmanned aerial vehicle performance parameters, the scientific division of the node is realized. The unmanned aerial vehicle operation radius determines the maximum horizontal range that a single unmanned aerial vehicle can cover from a certain center point. The height data of the power transmission equipment determines the actual effective coverage range of the unmanned aerial vehicle. For high towers or high conductors, the unmanned aerial vehicle needs to increase the flight height to avoid collision, which may result in the actual horizontal operation radius being shortened. The position coordinates of the power transmission equipment reflect the spatial distribution density of the equipment.

[0058] Specifically, the specific logic of node division can be: based on the position coordinates of the power transmission equipment, the spatial distance between the equipment is calculated; the actual effective operation radius of the unmanned aerial vehicle is corrected in combination with the height data; based on the equipment density and the effective operation radius, the power transmission equipment with similar distance and within the same corrected operation radius is classified into an inspection node. For example: the distribution line in the urban area is dense (the distance between the equipment is small), and the conductor height is low (the influence on the operation radius is small), so the distribution line and the surrounding towers within a 1 square kilometer range can be divided into a node; the transmission line in the mountainous area is sparse (the distance between the towers is large), and the tower height is high (the operation radius needs to be corrected), so the towers and the transmission line within a range of 5-8 kilometers can be divided into a node, ensuring that the unmanned aerial vehicle can cover all equipment in the node in a single flight. The plurality of inspection nodes formed finally meet the requirement that the power transmission equipment in each node can be efficiently covered by the unmanned aerial vehicle of the same nest, providing reasonable unit division for subsequent deployment point determination.

[0059] Through steps S201-S203, the pre-preparation of the grid deployment is completed from delimiting the boundary to locking the target and then dividing the node, layer by layer, ensuring that the subsequent steps are based on accurate spatial range and target attributes, laying the foundation for improving the inspection efficiency.

[0060] In one embodiment, the inspection belt corresponding to the inspection target is determined according to the position parameter of the inspection target, as shown in Figure 4 The method comprises the following steps: Step S401, determining the distribution feature data of the inspection target according to the position parameter of the inspection target.

[0061] This step refines the distribution rule of the inspection target by analyzing the spatial position of the inspection target, providing a data basis for subsequent regional division. Specifically, the position parameters of the inspection target include latitude and longitude, altitude, relative distance, etc. (such as the coordinates of the tower and the geographical orientation of the transformer substation). The distribution characteristic data is the statistical and analysis result of these position parameters, for example: the density of the target (the number of towers per square kilometer), the spatial distribution form (whether arranged along a straight line, whether gathered in a cluster), and the association with the terrain (whether distributed along the ridge, whether to avoid rivers), etc.

[0062] In step S402, the linear distribution region, the point distribution region, the target dense region, and the line direction region corresponding to the inspection target are determined based on the distribution characteristic data.

[0063] This step refines the distribution form of the inspection target into specific regional types, and clearly defines the core components of the inspection belt. Specifically, the linear distribution region refers to the region where the inspection target is arranged continuously along a straight line or a curve, such as the towers of the transmission line in the mountainous area distributed along the ridge line, or the distribution line in the urban area extending linearly along the street; the point distribution region refers to the region where the inspection target is scattered and isolated without obvious continuous arrangement, such as the scattered transformers in remote areas, independent lightning arresters, etc.; the target dense region refers to the region where the number of inspection targets is large and the spatial distance is small, such as the dense distribution of various equipment (transformers, circuit breakers, busbars, etc.) in the transformer substation, or the dense arrangement of distribution towers in the core area of the city; the line direction region refers to the region formed by the extension direction of the inspection target (mainly the line type equipment), such as the northeast-southwest direction of the transmission line from A town to B town, which constitutes the line direction region along with the direction and both sides within a certain range.

[0064] For example, in a certain inspection area, the transformer substation belongs to the target dense region, the transmission line extending out of the transformer substation belongs to the linear distribution region, and the several isolated towers in the middle of the line belong to the point distribution region. The extension path of the entire line from south to north constitutes the line direction region.

[0065] In step S403, the inspection belt corresponding to the inspection target is constructed using the linear distribution region, the point distribution region, the target dense region, and the line direction region.

[0066] This step integrates the above four types of regions to form a belt-shaped range covering all inspection targets, providing a clear operation path framework for unmanned aerial vehicle inspection. The construction of the inspection belt needs to ensure that the four types of regions are completely covered, and the boundary needs to be expanded according to the operation range of the unmanned aerial vehicle (such as the maximum one-sided observation distance).

[0067] For example, the inspection belt of the linear distribution area is centered on the line and extends to both sides by the effective observation width of the UAV (such as each extension of 200 meters); the inspection belt of the target dense area needs to cover the entire peripheral boundary of the dense area; the inspection belt of the point distribution area needs to include the isolated target into the coverage range of the peripheral linear or strike area; the strike area determines the overall extension direction of the inspection belt. The final inspection belt is a continuous spatial range, which contains all the inspection targets and meets the flight path logic of the UAV, providing a unified basic range for subsequent segmentation into sub-areas.

[0068] In an embodiment, after the inspection belt is segmented based on the sequence parameters of the inspection targets, a plurality of sub-areas are obtained, such as Figure 5 As shown in the figure, it comprises: Step S501, determining the flight direction of the UAV according to the corresponding starting point and ending point of the UAV in the inspection belt, and determining the sequence parameters of the inspection targets by using the flight direction.

[0069] This step provides the basis for the sequence of segmentation of the inspection belt by clearly defining the operation path direction of the UAV. Specifically, the starting point of the UAV is usually the initial deployment point or the starting point of the inspection task (such as a certain transformer substation), and the ending point is the end point of the task (such as another transformer substation or the end of the line), and the line connecting the two determines the flight direction (such as from west to east). The sequence parameter of the inspection target refers to the arrangement order of the target along the flight direction, for example: along the flight direction from west to east, the towers are arranged in the order of 1#, 2#, 3#…, and the sequence parameter is 1, 2, 3…; the transformer substation is set as the starting point, and the sequence parameter can be set as 0. The role of the sequence parameter is to ensure that the sub-areas are distributed in sequence along the flight direction, avoiding the UAV to return and turn back, and improving the operation efficiency.

[0070] Step S502, determining the segmentation strategy of the inspection belt by using the operation radius parameter and the power supply position parameter corresponding to the UAV.

[0071] This step formulates reasonable segmentation rules by combining the UAV performance and the actual deployment conditions, and ensures that the size of the sub-area is adapted to the UAV capability. The operation radius parameter determines the maximum range that a single UAV can cover from a certain point (such as an operation radius of 5 kilometers, which means that a single flight of the UAV can cover an area with a center point of the point and a radius of 5 kilometers), and the segmentation strategy needs to ensure that the length of each sub-area is not more than twice the operation radius (round-trip distance). The power supply position represented by the power supply position parameter (such as a transformer substation or a distribution box) is the key to the UAV nest power supply, and the segmentation strategy needs to consider whether the center of the sub-area is close to the power supply position (such as setting a sub-area boundary between every 2-3 power supply positions), so as to reduce the laying cost of the UAV nest power supply line.

[0072] For example, if the operation radius of the UAV is 5 kilometers, and there is a power pickup point every 10 kilometers along the inspection belt, the division strategy can be set as "divide a sub-region every 8 kilometers" (less than 2 times the operation radius), and the center of the sub-region is as close to the power pickup point as possible.

[0073] In step S503, after the inspection belt is divided based on the sequence parameter and according to the division strategy, a plurality of sub-regions are obtained.

[0074] This step divides the inspection belt into a plurality of independent sub-regions according to the established sequence and rules, and realizes fine management. Specifically, the inspection belt can be divided according to the sequence parameter (such as 1# to 100# tower) of the inspection target and the division strategy (such as every 20 towers or 8 kilometers as a unit). For example, a certain inspection belt contains 50 towers, the sequence parameter is 1-50, and the division strategy is "every 10 towers as a sub-region". Therefore, the inspection belt can be divided into 5 sub-regions, which respectively contain 1-10#, 11-20#, 21-30#, 31-40#, and 41-50# towers. The divided sub-regions need to meet the following conditions: the targets in each sub-region can be efficiently covered by the UAV of the same nest, and the boundaries between the sub-regions are clear, which is convenient for task allocation and management.

[0075] Through the above steps, the inspection belt is refined from the overall range to the ordered sub-regions, which not only adapts to the operation ability of the UAV, but also takes into account the actual deployment conditions, and lays a foundation for the accurate determination of subsequent deployment points.

[0076] In one embodiment, the step S103 of determining the initial deployment point corresponding to each sub-region based on the clustering result of the position parameters of the inspection nodes includes: Figure 6 as shown, comprising: In step S601, the coordinate system corresponding to the inspection nodes is determined based on the position parameters of the inspection targets, and the boundary coordinates of the sub-regions in the coordinate system are obtained.

[0077] This step is the basis of spatial positioning, and provides a standardized spatial reference for the calculation and clustering of subsequent coordinate parameters by establishing a unified coordinate system. Specifically, in the process of determining the coordinate system, the position parameters (such as latitude and longitude, relative distance) of the inspection targets need to be converted into a unified coordinate system (such as Gauss-Kruger plane rectangular coordinate system, UTM coordinate system, etc.), so as to avoid calculation errors caused by non-uniform coordinate systems.

[0078] When obtaining the boundary coordinates of the sub-regions, the spatial range of each sub-region (i.e., the boundary of the sub-region after segmentation in step S102) needs to be determined in a determined coordinate system, and the coordinate parameters of the boundary are extracted. For example, a certain sub-region is a rectangular range, and the boundary coordinates can be represented as the upper-left corner (x1, y1), the upper-right corner (x2, y2), the lower-right corner (x3, y3), and the lower-left corner (x4, y4). Through these coordinates, the spatial boundary of the sub-region can be accurately framed, and it is ensured that the subsequent screening of the inspection nodes is within the range.

[0079] In step S602, the inspection nodes included in each sub-region are determined by using the boundary coordinates, and the position parameters corresponding to the inspection nodes are obtained based on the coordinate system.

[0080] This step aims to lock the core control object (inspection node) in the sub-region and collect its accurate spatial coordinates to provide a data basis for clustering calculation. When screening the inspection nodes in the sub-region, the inspection nodes completely falling within the range of the sub-region can be screened out by comparing the coordinates of the inspection nodes with the boundary coordinates of the sub-region. For example, if the coordinates (x0, y0) of a certain inspection node satisfy x1≤x0≤x2 and y4≤y0≤y1 (satisfy the boundary conditions of the rectangular sub-region), it is determined that the node belongs to the sub-region. In the process of obtaining the coordinate parameters of the inspection nodes, the accurate coordinates of the screened inspection nodes can be extracted in a unified coordinate system. These coordinates contain sufficient accuracy to ensure the accuracy of the clustering results.

[0081] In step S603, the clustering center points corresponding to the inspection nodes are obtained by clustering calculation of the inspection nodes based on the position parameters, and the clustering results of the position parameters are determined based on the clustering center points.

[0082] This step analyzes the spatial distribution law of the nodes by using a clustering algorithm, so as to find the spatial center in the sub-region and provide a core reference for the initial deployment point. The process of clustering calculation can be realized by using a related clustering algorithm. The core of the clustering algorithm (such as the K-means algorithm) is to classify the inspection nodes with close spatial distances into the same class, and to calculate the center point of each class (i.e., the average value of all node coordinates in the class). For example, there are 10 towers in a sub-region, and the coordinate distribution presents two dense clusters. After clustering, two center points are obtained, which represent the center positions of the two dense clusters.

[0083] In the process of determining the clustering results of the coordinate parameters based on the clustering center points, the clustering results not only include the coordinates of the clustering center points, but also include the coverage range corresponding to each center point (i.e., the inspection nodes included in the class). If the inspection nodes in the sub-region are concentrated (such as the equipment in a substation), they can be clustered into one class to obtain one center point. If the nodes are dispersed (such as the dispersed towers in a mountainous area), they can be clustered into multiple classes to obtain multiple center points.

[0084] Step S604, determining the initial deployment point corresponding to the sub-region in the coordinate system according to the clustering result.

[0085] This step is based on the clustering result, and the clustering center point is converted into the initial deployment point, which provides a basic scheme for subsequent position adjustment. In the process of determining the initial deployment point, if the clustering result is a single center point, the point is directly taken as the initial deployment point of the sub-region (i.e. the preliminary site selection of the nest); if the clustering result is multiple center points, the position with the widest coverage and balanced distance from each center point is selected as the initial deployment point (for example, the geometric center of multiple center points) in combination with the range of the sub-region and the unmanned aerial vehicle operation capability.

[0086] The initial deployment point needs to meet the principle of being as close as possible to most of the inspection nodes to reduce the flight distance of the unmanned aerial vehicle. Through steps S601-S604, this method establishes a coordinate system, filters nodes, and finally obtains the initial deployment point of the sub-region, which ensures that the point can theoretically efficiently cover the inspection nodes in the region and provides a scientific starting point for subsequent adjustment in combination with actual conditions (such as operation radius and power supply location).

[0087] In one embodiment, the step S104 of adjusting the position of the initial deployment point based on the unmanned aerial vehicle operation radius parameter and the power supply location parameter corresponding to the type of the machine warehouse to be deployed to obtain the final deployment position of the unmanned aerial vehicle, as shown in Figure 7 includes: Step S701, determining the coverage range of the unmanned aerial vehicle using the unmanned aerial vehicle operation radius parameter, determining the obstacle region contained in the coverage range based on the position parameter of the inspection target, and determining the first adjustment strategy and the first deployment position corresponding to the initial deployment point according to the obstacle region.

[0088] This step focuses on the spatial feasibility of the unmanned aerial vehicle operation, and ensures the integrity of the inspection coverage by avoiding obstacles. Specifically, in the process of determining the coverage range, the circular coverage range that the unmanned aerial vehicle can reach can be determined with the initial deployment point as the center according to the operation radius parameter of the unmanned aerial vehicle, to ensure that all inspection targets in the sub-region are theoretically within the range.

[0089] When identifying the obstacle region, the position parameter of the inspection target (such as the tower coordinate and terrain data) can be combined to investigate the obstacles (such as tall buildings, dense forests, high-voltage towers, and mountains) in the coverage range. These obstacles may cause the flight path of the unmanned aerial vehicle to be blocked or the signal to be blocked, forming an "obstacle region". For example, there is a mountain with an altitude of 500 meters 3 kilometers north of the initial deployment point, which will block the inspection towers within a range of 1 kilometer to the north. This region is marked as an obstacle region.

[0090] In the determination process of the first adjustment strategy, the adjustment direction and distance of the initial deployment point in the obstacle region are planned to ensure that the coverage range of the adjusted unmanned aerial vehicle can avoid the obstacle and completely cover the sub-region. For example, if the obstacle region causes the north tower to be unable to be covered, the first adjustment strategy is set to "move the initial deployment point 500 meters south to make the coverage range avoid the mountain shelter".

[0091] In step S702, the power supply range corresponding to the unmanned aerial vehicle is determined by using the power taking position parameter, and the second adjustment strategy and the second deployment position corresponding to the initial deployment point are determined based on the power supply range.

[0092] This step focuses on the power supply feasibility of the nest, and reduces the power supply cost and risk by being close to the power taking point. In the process of determining the power supply range, the power taking position parameter includes the coordinates of the nearby available power supply point (such as a transformer distribution box or a dedicated power supply pile) and the maximum laying distance of the power supply line (such as the longest cable laying distance of 300 meters), and the power supply range is determined based on the power taking point. In the process of evaluating the power supply adaptability of the initial deployment point, if the initial deployment point is within the power supply range (such as 200 meters away from the power taking point, less than the upper limit of 300 meters), adjustment is not required; if it is out of range (such as 400 meters away from the power taking point), an adjustment strategy needs to be developed.

[0093] In the determination process of the second adjustment strategy, the adjustment path of the initial deployment point to the power taking point direction is planned to shorten the power supply distance. For example, the second adjustment strategy can be set to "move 150 meters to the power taking point direction to reduce the power supply distance to 250 meters within the 300-meter power supply range".

[0094] In step S703, the final deployment position of the unmanned aerial vehicle is determined based on the first deployment position and the second deployment position.

[0095] This step balances the coverage demand and the power supply demand to form a feasible adjustment scheme. If the first and second adjustment strategies are consistent (such as both need to move south), the adjustment distance can be combined (such as the first strategy needs to move south by 500 meters, the second strategy needs to move south by 150 meters, and the final strategy is to move south by 500 meters to meet the coverage and power supply). If the strategy direction conflicts (such as the first strategy needs to move east to avoid obstacles, and the second strategy needs to move west to be close to the power taking point), the priority needs to be weighed; for example, the coverage integrity (avoiding obstacles) is ensured first, and then the power supply distance is shortened as much as possible (such as moving 300 meters east to avoid obstacles, and then fine-tuning to the nearest position to the power taking point) on the premise of meeting the coverage. The final deployment position needs to clearly indicate the adjustment direction, distance and constraint conditions.

[0096] In step S704, the initial deployment point is adjusted to the final deployment position according to the first adjustment strategy and the second adjustment strategy.

[0097] 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).

[0098] 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: 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] like Figure 9 The diagram shown illustrates the effect of the method for determining the deployment location of drone hangars.Figure 9 The multiple inspection nodes in the circle are formed by inspection targets (transformer substations, transmission line towers, distribution line towers, etc.) in the inspection area. Figure 9 The continuous straight line in the grid represents an inspection belt, and the inspection belt is divided into multiple sub-regions in the order of UAV flight from the starting region to the ending region. Then, the position coordinates of the multiple inspection nodes in each sub-region are determined, and the initial deployment point D is determined based on the clustering center point after clustering the position coordinates. Then, the initial deployment point D is adjusted based on the flight operation radius of the UAV and the nearest power supply location, so that the initial deployment point D is updated to D` under the premise that the flight operation radius can cover the sub-region of the inspection belt. Then, the nearest power supply location D`` of D` is further determined, and the deployment point is taken as the final deployment location of the grid region.

[0103] It can be known from the UAV hangar deployment position determination method in the above embodiments that the method fully utilizes the operation radius parameter and the power supply location parameter of the UAV to dynamically adjust the grid deployment point, realizes high-precision automatic acquisition of the deployment point under the premise of ensuring normal operation of the UAV, and thus improves the inspection execution efficiency of the UAV.

[0104] For the UAV hangar deployment position determination method provided in the above embodiments, the embodiment of the present application provides a UAV hangar deployment position determination system, as shown in Figure 10 The system comprises: The inspection node determination module 1010 is configured to determine the inspection target based on the inspection area, and determine the inspection node corresponding to the inspection area based on the type parameter of the inspection target. The sub-region division module 1020 is configured to determine the inspection belt corresponding to the inspection target based on the position parameter of the inspection target, divide the inspection belt based on the sequence parameter of the inspection target, and obtain multiple sub-regions. The initial deployment point determination module 1030 is configured to determine the inspection node contained in each sub-region and obtain the position parameter corresponding to the inspection node, and determine the initial deployment point corresponding to the sub-region based on the clustering result of the position parameter. The final deployment point determination module 1040 is configured to adjust the position of the initial deployment point based on the UAV operation radius parameter and the power supply location parameter corresponding to the type of the UAV to be deployed, so as to obtain the final deployment position corresponding to the UAV.

[0105] It can be known from the UAV hangar deployment position determination system mentioned in the above embodiments that the system fully utilizes the operation radius parameter and the power supply location parameter of the UAV to dynamically adjust the grid deployment point, realizes high-precision automatic acquisition of the deployment point under the premise of ensuring normal operation of the UAV, and thus improves the inspection execution efficiency of the UAV.

[0106] The unmanned aerial vehicle hangar deployment position determination system provided by the embodiments of the present application has the same implementation principle, technical effects and the above-mentioned unmanned aerial vehicle hangar deployment position determination method embodiments. For brief description, the part of the device embodiments not mentioned can refer to the corresponding content in the above-mentioned unmanned aerial vehicle hangar deployment position determination method embodiments.

[0107] The embodiments also provide an unmanned aerial vehicle, as shown in the figure. Figure 11 The unmanned aerial vehicle adopts the steps of the unmanned aerial vehicle hangar deployment position determination method mentioned in the above-mentioned embodiments in the process of grid deployment.

[0108] The embodiments also provide an electronic device, as shown in the figure. Figure 12 The device includes a processor 101 and a memory 102; wherein the memory 102 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor to implement the steps of the above-mentioned unmanned aerial vehicle hangar deployment position determination method.

[0109] Figure 12 The electronic device shown in the figure also includes a bus 103 and a communication interface 104, and the processor 101, the communication interface 104 and the memory 102 are connected through the bus 103.

[0110] The memory 102 can include a high-speed random access memory (RAM), and can also include a non-volatile memory, for example, at least one disk memory. The bus 103 can be an ISA bus, a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 12 In the figure, only one bidirectional arrow is used to represent, but it does not mean that there is only one bus or one type of bus.

[0111] The communication interface 104 is used to connect with at least one user terminal and other network units through a network interface, and send the encapsulated IPv4 packet or IPv4 packet to the user terminal through the network interface.

[0112] The processor 101 can be an integrated circuit chip with a processing capability of signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of hardware in the processor 101 or the instruction in the form of software. The processor 101 described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; 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 gates or transistor logic devices, discrete hardware components. Each method, step and logic block diagram disclosed in the embodiment of the present disclosure can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiment of the present disclosure can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the memory 102, and the processor 101 reads the information in the memory 102 and combines the hardware to complete the steps of the method of the above embodiment.

[0113] The embodiment of the present application further provides a storage medium, and the storage medium stores a computer program. When the computer program is run by a processor, the steps of the method for determining the deployment position of the UAV hangar in the above embodiment are executed.

[0114] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented by other ways. The above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and other division manners can be used in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interfaces, devices or units, and can be electrical, mechanical or other forms.

[0115] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0116] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0117] If the functions are realized in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various program code storage media.

[0118] Finally, it should be noted that: the above-described embodiments are only specific embodiments of the present application, used to illustrate the technical solutions of the present application, and not to limit them, the protection scope of the present application is not limited thereto, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: any skilled person in the art within the technical range disclosed by the present application, they can still modify or easily think of changes to the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications, changes or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and all should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for determining the deployment location of a drone hangar, characterized in that, The method comprises: determining an inspection target based on a patrol area, and determining an inspection node corresponding to the patrol area based on a type parameter of the inspection target; determining an inspection belt corresponding to the inspection target based on a position parameter of the inspection target, and obtaining a plurality of sub-areas after segmenting the inspection belt based on a sequence parameter of the inspection target; determining the inspection node contained in each of the sub-areas and obtaining a position parameter corresponding to the inspection node, determining an initial deployment point corresponding to the sub-area based on a clustering result of the position parameter; adjusting the position of the initial deployment point based on an unmanned aerial vehicle operation radius parameter and a power obtainable position parameter corresponding to a type of a to-be-deployed warehouse, to obtain a final deployment position corresponding to the unmanned aerial vehicle. 2.The UAV hangar deployment position determination method of claim 1, wherein, The step of determining an inspection target based on a patrol area and determining an inspection node corresponding to the patrol area based on a type parameter of the inspection target comprises: determining a boundary parameter from the patrol area based on an operation range corresponding to the unmanned aerial vehicle, and determining a patrol boundary range of the patrol area based on the boundary parameter; obtaining the inspection target contained in the patrol boundary range, and determining a position coordinate corresponding to the inspection target based on a coordinate system corresponding to the patrol area; determining a plurality of the inspection nodes corresponding to the patrol area based on the type parameter and the position coordinate corresponding to the inspection target. 3.The UAV hangar deployment position determination method of claim 2, wherein, The step of determining a plurality of the inspection nodes corresponding to the patrol area based on the type parameter and the position coordinate corresponding to the inspection target comprises: determining a power transmission device contained in the inspection target based on the type parameter corresponding to the inspection target; obtaining the position coordinate corresponding to the power transmission device, and determining height data corresponding to the power transmission device based on the position coordinate; determining a plurality of the inspection nodes corresponding to the patrol area based on the operation radius parameter corresponding to the unmanned aerial vehicle, the height data corresponding to the power transmission device, and the position coordinate. 4.The UAV hangar deployment position determination method of claim 1, wherein, The step of determining an inspection belt corresponding to the inspection target based on a position parameter of the inspection target comprises: determining distribution feature data of the inspection target based on the position parameter of the inspection target; determining a linear distribution area, a point distribution area, a target dense area, and a line direction area corresponding to the inspection target based on the distribution feature data; constructing the inspection belt corresponding to the inspection target based on the linear distribution area, the point distribution area, the target dense area, and the line direction area. 5.The UAV hangar deployment position determination method of claim 1, wherein, The step of obtaining a plurality of sub-areas after segmenting the inspection belt based on a sequence parameter of the inspection target comprises: determining a flight direction of the unmanned aerial vehicle based on a starting point and an ending point corresponding to the unmanned aerial vehicle in the inspection belt, and determining the sequence parameter of the inspection target based on the flight direction; determining a segmentation strategy of the inspection belt based on the operation radius parameter corresponding to the unmanned aerial vehicle and the power obtainable position parameter; obtaining a plurality of the sub-areas after segmenting the inspection belt based on the sequence parameter and according to the segmentation strategy. 6.The UAV hangar deployment position determination method of claim 1, wherein, The step of determining the inspection nodes contained in each of the sub-regions and obtaining the position parameters corresponding to the inspection nodes, and determining the initial deployment point corresponding to the sub-region based on a clustering result of the position parameters, comprises: determining a coordinate system corresponding to the inspection nodes based on the position parameters corresponding to the inspection target, and obtaining boundary coordinates of the sub-region in the coordinate system; determining the inspection nodes contained in each of the sub-regions by using the boundary coordinates, and obtaining the position parameters corresponding to the inspection nodes based on the coordinate system; obtaining a clustering center point corresponding to the inspection nodes after clustering calculation of the inspection nodes by using the position parameters, and determining the clustering result of the position parameters based on the clustering center point; determining the initial deployment point corresponding to the sub-region in the coordinate system according to the clustering result. 7.The UAV hangar deployment position determination method of claim 1, wherein, The step of adjusting the position of the initial deployment point based on the unmanned aerial vehicle operation radius parameter and the available power supply position parameter corresponding to the type of the to-be-deployed machine warehouse, so as to obtain the final deployment position of the unmanned aerial vehicle, comprises: determining a coverage range corresponding to the unmanned aerial vehicle by using the unmanned aerial vehicle operation radius parameter, determining an obstacle region contained in the coverage range based on the position parameters of the inspection target, and determining a first adjustment strategy and a first deployment position corresponding to the initial deployment point according to the obstacle region; determining a power supply range corresponding to the unmanned aerial vehicle by using the available power supply position parameter, determining a second adjustment strategy and a second deployment position corresponding to the initial deployment point based on the power supply range; determining the final deployment position of the unmanned aerial vehicle based on the first deployment position and the second deployment position; adjusting the initial deployment point to the final deployment position according to the first adjustment strategy and the second adjustment strategy. 8.The UAV hangar deployment position determination method of claim 7, wherein, The step of adjusting the initial deployment point to the final deployment position according to the first adjustment strategy and the second adjustment strategy, comprises: after adjusting the initial deployment point by using the first adjustment strategy, determining an adjusted position corresponding to the initial deployment point; wherein a work area of the unmanned aerial vehicle in the adjusted position covers the sub-region; after adjusting the adjusted position by using the second adjustment strategy, determining the final deployment position corresponding to the unmanned aerial vehicle; wherein a power supply distance of the unmanned aerial vehicle in the final deployment position is the minimum.

9. An unmanned aircraft hangar deployment location determination system, characterized by, The system comprises: a patrol node determination module configured to determine an inspection target based on a patrol region, and determine an inspection node corresponding to the patrol region by using a type parameter of the inspection target; a sub-region segmentation module configured to determine an inspection belt corresponding to the inspection target according to a position parameter of the inspection target, and obtain a plurality of sub-regions by segmenting the inspection belt based on a sequence parameter of the inspection target; an initial deployment point determination module configured to determine the inspection nodes contained in each of the sub-regions and obtain position parameters corresponding to the inspection nodes, and determine an initial deployment point corresponding to the sub-region based on a clustering result of the position parameters. A final deployment point determination module is configured to adjust the position of the initial deployment point based on the UAV operation radius parameter and the available power supply location parameter corresponding to the type of the UAV hangar to be deployed, so as to obtain the final deployment position of the UAV.

10. A drone, characterized in that, In the process of grid deployment, the UAV adopts the steps of the UAV hangar deployment position determination method in any one of claims 1 to 8.

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