Unmanned aerial vehicle outdoor charging platform and charging method

Abstract

The invention discloses an unmanned aerial vehicle outdoor charging platform and charging method, relates to the technical field of unmanned aerial vehicle charging management and control, and improves the outdoor charging efficiency of an unmanned aerial vehicle. The method comprises the following steps: filling a path environment analysis space according to real-time dynamic and static environment data, setting a base station charging point position in the path environment analysis space, setting a photovoltaic space point in the path environment analysis space, and marking each photovoltaic space point as a photovoltaic charging space point by analyzing real-time dynamic environment data of each photovoltaic space point; and setting a dynamic mark of a space-time dimension for each photovoltaic charging space point, setting a corresponding unmanned aerial vehicle node in the path environment analysis space, and carrying out charging simulation on each unmanned aerial vehicle node by taking the next inspection target of the unmanned aerial vehicle as an end point. And selecting a photovoltaic charging space point and a base station charging point in the path environment analysis space according to a simulation result to generate a short-term flight path.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) charging control, and in particular to an outdoor UAV charging platform and a charging method. Background Art

[0002] In today's wave of technological advancement, drones are playing a key role in numerous fields, such as geographic surveying and mapping, agricultural monitoring, and power inspection. As drone applications continue to expand, the demand for them to operate outdoors for extended periods of time is growing. However, drone endurance has been a major bottleneck limiting their widespread adoption.

[0003] Traditional drone charging methods are relatively simple, typically relying on fixed base station charging. This fixed charging model presents numerous limitations in complex outdoor environments. Firstly, due to the uncertainties of the outdoor environment, such as complex terrain and changing weather conditions, drones may encounter various obstacles during flight, making it difficult for them to reach fixed base station charging points. This can result in delayed charging and hinder the completion of operational tasks. Secondly, the limited distribution of fixed base station charging points makes it impossible for drones to operate continuously in large outdoor areas, significantly reducing their operational efficiency and flexibility.

[0004] Furthermore, existing drone charging plans often lack comprehensive consideration of the flight environment. When planning charging routes, they fail to fully incorporate real-time static environmental data (such as topography and obstacle distribution) and dynamic environmental data (such as weather changes and other aircraft activity). This results in inaccurate charging plans, which can easily expose drones to unnecessary risks during flight and even lead to crashes due to battery depletion.

[0005] Therefore, how to solve the problem of charging drones outdoors, improve their endurance and operating efficiency, and ensure the safety and accuracy of charging planning has become an urgent problem to be solved in the current field of drone technology. To this end, a drone outdoor charging platform and charging method are provided. Summary of the Invention

[0006] In order to solve the above technical problems, the purpose of the present invention is to provide an outdoor charging platform and charging method for drones.

[0007] In order to achieve the above object, the present invention provides the following technical solutions: A drone outdoor charging platform includes a cloud control terminal, wherein the cloud control terminal is communicatively connected to a patrol path environment analysis module, a charging planning module, and a drone control module; The patrol path environment analysis module is used to delineate a flight inspection area and a path environment analysis space according to the drone patrol path, and set up a sensor network in the flight inspection area to collect real-time static and dynamic environmental data within the flight inspection area through the sensor network. The path environment analysis space is filled with data based on the real-time static and dynamic environmental data, and a base station charging point is set in the path environment analysis space. The charging planning module is used to set photovoltaic space points in the path environment analysis space, mark each photovoltaic space point as a photovoltaic charging space point by analyzing the real-time dynamic environmental data of each photovoltaic space point, and set dynamic labels in the time and space dimensions for each photovoltaic charging space point; The drone control module is provided with an environmental perception unit and an edge charging planning unit; The environmental perception unit is communicatively connected to the sensor network of the patrol environment analysis module, and is used to collect real-time dynamic and static environmental data of the spatial position around the drone; The edge charging planning unit is used to obtain the current remaining power and real-time spatial position of all drones, and set corresponding drone nodes in the path environment analysis space. Then, with the next inspection target of the drone as the end point, charging simulation is performed on each drone node. Based on the simulation results, photovoltaic charging space points and base station charging points are selected in the path environment analysis space to generate a short-term flight path.

[0008] Furthermore, the process of defining the flight inspection area and the path environment analysis space based on the UAV inspection path includes: Staff upload the drone inspection route to the patrol path environment analysis module, and then use GIS technology to demarcate the preset inspection range of the drone inspection route into a flight inspection area, and mark multiple inspection targets within the flight inspection area; Taking each inspection target as the regional core, a polygonal analysis boundary is formed by expanding 5 to 10 kilometers to the surrounding area. The analysis boundary is then anchored through high-precision satellite positioning data to determine the coverage of the flight inspection area and establish a path environment analysis space.

[0009] Furthermore, the patrol environment analysis module deploys three sensor networks, including a fixed sensor network, a mobile sensor network, and a remote sensing sensor network; The fixed sensor network consists of multiple meteorological monitoring stations distributed at the same spatial distance in the flight inspection area, and is used to collect macro-meteorological data in the flight inspection area; The mobile sensor network consists of sensors on drones that execute drone inspection paths in the flight inspection area, including lidars, infrared thermal imagers, and gas sensors, which are used to collect micro-environmental data along the drone inspection paths; The remote sensing sensor network is used to obtain wide-area environmental data of the flight inspection area via satellite; According to the spatial distribution of each meteorological monitoring station, the starting position of the drone, and the base station charging piles in the flight inspection area, fixed collection points, drone flight points, and base station charging points are set in the path environment analysis space.

[0010] Furthermore, the process of filling the path environment analysis space with data includes: A number of three-dimensional unit grids are divided in the path environment analysis space, and a photovoltaic space point is set at the center of each three-dimensional unit grid; Classify micro-environmental data, macro-environmental data and wide-area environmental data into real-time static environmental data and real-time dynamic environmental data; Loading real-time static environmental data as an environmental framework into the environmental three-dimensional coordinate system, and overlaying real-time dynamic environmental data on the environmental framework in the form of a visual layer; The latitude, longitude and altitude of all base station charging piles are located by GPS, marked with cylindrical icons in the path environment analysis space, and the working status of the base station charging piles is updated in real time.

[0011] Furthermore, the process of marking each photovoltaic space point as a photovoltaic charging space point based on the real-time dynamic environmental data includes: Set a unit flight speed for each drone, and obtain the maximum time required for each drone to pass through a three-dimensional unit grid based on the unit flight speed, which is recorded as the spatial update period; Then, every time a spatial update cycle begins, the charging planning module records each photovoltaic space point as a photovoltaic charging space point based on the real-time dynamic environmental data of each three-dimensional unit grid: Filter the light intensity data in the real-time dynamic environment data to remove instantaneous interference and retain the effective light value, thereby obtaining the average light intensity within a spatial update cycle; The output power of the photovoltaic panel is obtained according to the ambient temperature, and multiple photovoltaic point restriction rules are set. The real-time dynamic environmental data of each photovoltaic space point are compared. If any real-time dynamic environmental data does not meet the corresponding photovoltaic point restriction rule within the space update cycle, the corresponding photovoltaic space point is judged to be unusable. Otherwise, the corresponding photovoltaic space point is recorded as a photovoltaic charging space point.

[0012] Furthermore, a dynamic tag of the time and space dimension is set for the photovoltaic space point recorded as the photovoltaic charging space point, and the dynamic tag content includes: Spatial marker: Displayed as a glowing sphere icon in the path environment analysis space. The brightness of the sphere is positively correlated with the real-time output power. Time marking: Generate a charging capacity prediction curve for the length of the future space update cycle, divide the time into m time segments, and mark each time segment with the corresponding expected charging efficiency, where m is a natural number greater than 10; Status mark: Use color to distinguish the current status.

[0013] Furthermore, the process of simulating charging of each drone node includes: Whenever a drone completes an inspection mission for a target, it uses the coordinates of the next inspection target as the short-term endpoint and the coordinates of the next inspection target as the long-term endpoint. Based on the current remaining power of the drone, it determines whether the drone node can continuously reach the short-term and long-term endpoints along a straight line, and obtains the estimated power required. If it is determined to be reachable, the drone inspection route will continue to be executed; If it is judged that it is unreachable, a charging response area is generated with the current position of the UAV and the short-term end point as the boundary, and the photovoltaic charging space points and base station charging points within the charging response area are traversed.

[0014] Furthermore, the process of generating the short-term flight path includes: Based on the dynamic marking content of the photovoltaic charging space point, the additional power required and the estimated charging amount of the drone passing through each photovoltaic charging space point are determined. Based on the spatial location of the base station charging point and various real-time status data, the additional power required and waiting time of the drone passing through the base station charging point are determined. Then, with the current location of the drone as the starting point, each photovoltaic charging space point and base station charging point as the path node, and the next inspection target coordinates as the end point, multiple short-term flight paths are traversed within the charging response area with the estimated required power as the target; The total cost value C of each short-term flight path is obtained, and the short-term flight path with the smallest total cost value C is selected and sent to the corresponding UAV for execution. The above process of generating short-term flight paths is repeated until all UAVs complete the UAV flight path.

[0015] A method for charging a drone outdoors comprises the following steps: Step S1: Delineate a flight inspection area based on the drone inspection path, set up a sensor network in the flight inspection area, collect real-time static and dynamic environmental data within the flight inspection area through the sensor network, and establish a path environment analysis space based on the real-time static and dynamic environmental data; Step S2: setting photovoltaic space points in the path environment analysis space, and by analyzing the real-time dynamic environmental data of each photovoltaic space point, marking each photovoltaic space point as a photovoltaic charging space point, and setting a dynamic label of the spatiotemporal dimension for each photovoltaic charging space point; Step S3: Set the corresponding drone node in the path environment analysis space, and then use the next inspection target of the drone as the end point to perform charging simulation on each drone node. Based on the simulation results, select photovoltaic charging space points and base station charging points in the path environment analysis space to generate a short-term flight path.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Accurate environmental analysis and path planning: The present invention uses the patrol path environment analysis module to delineate the flight inspection area according to the UAV inspection path, and sets up a sensor network to collect real-time static and dynamic environmental data to establish a path environment analysis space, thereby achieving a comprehensive understanding of the flight environment, providing an accurate basis for subsequent charging planning and UAV control, and making charging simulation and path planning more in line with actual conditions, greatly improving the safety and reliability of UAV flight.

[0017] 2. Diverse charging methods and flexible charging planning: By setting photovoltaic space points in the path environment analysis space and marking them as photovoltaic charging space points, and setting dynamic markings in the time and space dimensions, this fully utilizes solar energy, a clean energy source, increases the charging options for drones, and improves the drone's outdoor endurance. Based on the drone's current remaining power, real-time spatial position, and next inspection target, suitable photovoltaic charging space points and base station charging points are selected in the path environment analysis space to generate a short-term flight path. This enables flexible adjustments based on the drone's actual situation, ensuring that the drone can be charged in a timely and efficient manner while completing its mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a structural diagram of an outdoor charging platform for a drone according to the present invention; Figure 2 A structural diagram of the path environment analysis space of the present invention; Figure 3 A flow chart of a method for outdoor charging of a drone according to the present invention. DETAILED DESCRIPTION

[0019] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0020] like Figure 1 As shown, a drone outdoor charging platform includes a cloud control terminal, which is communicatively connected to a patrol path environment analysis module, a charging planning module, and a drone control module; The patrol path environment analysis module is used to delineate a flight inspection area and a path environment analysis space according to the drone patrol path, and set up a sensor network in the flight inspection area to collect real-time static and dynamic environmental data within the flight inspection area through the sensor network. The path environment analysis space is filled with data based on the real-time static and dynamic environmental data, and a base station charging point is set in the path environment analysis space. The charging planning module is used to set photovoltaic space points in the path environment analysis space, mark each photovoltaic space point as a photovoltaic charging space point by analyzing the real-time dynamic environmental data of each photovoltaic space point, and set dynamic labels in the time and space dimensions for each photovoltaic charging space point; The drone control module is provided with an environmental perception unit and an edge charging planning unit; The environmental perception unit is communicatively connected to the sensor network of the patrol environment analysis module, and is used to collect real-time dynamic and static environmental data of the spatial position around the drone; The edge charging planning unit is used to obtain the current remaining power and real-time spatial position of all drones, and set corresponding drone nodes in the path environment analysis space. Then, with the next inspection target of the drone as the end point, charging simulation is performed on each drone node. Based on the simulation results, photovoltaic charging space points and base station charging points are selected in the path environment analysis space to generate a short-term flight path.

[0021] The working principle of the present invention is described below by way of examples: Staff upload the drone inspection route to the patrol path environment analysis module, and then use GIS technology to demarcate the preset inspection range of the drone inspection route into a flight inspection area. Multiple inspection targets are marked within the flight inspection area, such as power transmission lines, oil and gas pipelines, and forest belts. A polygonal analysis boundary is formed by expanding 5 to 10 kilometers from each inspection target area. High-precision satellite positioning data is then used to anchor the analysis boundary, determine the coverage of the flight inspection area, and establish a path environment analysis space. The patrol environment analysis module deploys three sensor networks, including a fixed sensor network, a mobile sensor network, and a remote sensing sensor network; The fixed sensor network consists of multiple meteorological monitoring stations distributed at equal spatial distances in the flight inspection area, and is used to collect macro-meteorological data such as temperature, humidity, wind speed, wind direction, and precipitation in the flight inspection area; The mobile sensor network is composed of sensors on drones that fly along the inspection route within the inspection area. These sensors include lidars, infrared thermal imagers, and gas sensors. These sensors are used to collect micro-environmental data such as the coordinates of obstacles in the inspection route (e.g., trees, buildings, flocks of birds), local temperature distribution (e.g., power line joint temperature), and hazardous gas concentrations (e.g., oil and gas pipeline leak detection). The remote sensing sensor network is used to obtain satellite remote sensing data of the flight inspection area through satellites, including wide-area environmental data such as light intensity, cloud coverage, and surface vegetation height; According to the spatial distribution of each meteorological monitoring station, the starting position of the drone, and the base station charging piles in the flight inspection area, fixed collection points, drone flight points, and base station charging points are set in the path environment analysis space.

[0022] Furthermore, a command generation frequency is set. The duration of the command generation frequency is generally 30 to 60 seconds. In the flight inspection area, n drones are simultaneously called from different starting positions along the same flight direction to execute the drone inspection path, where n is an integer greater than 1. Before the drone inspection path is executed, the edge charging planning unit in the drone control module sends a global inspection instruction to each drone based on the starting position of each drone. The global inspection instruction includes the starting coordinates, end coordinates and inspection target coordinates of the inspection target; At the same time, the patrol path environment analysis module divides the path environment analysis space into several three-dimensional unit grids. Each three-dimensional unit grid corresponds to a spatial location of 10 meters × 10 meters × 5 meters (length × width × height) in the flight inspection area. A photovoltaic space point is set at the center of each three-dimensional unit grid. When the UAV executes the UAV flight path according to the global inspection command, the patrol path environment analysis module sends data perception instructions to the environmental perception unit, fixed sensor network and remote sensing sensor network in the UAV control module; The environmental perception unit then calls on each drone to collect micro-environmental data of its surrounding spatial location. At the same time, the fixed sensor network and the remote sensing sensor network collect macro-meteorological data and wide-area environmental data, and synchronize them with the patrol environment analysis module.

[0023] Furthermore, the patrol path environment analysis module fills the data of each three-dimensional unit grid in the path environment analysis space according to the received micro-environmental data, macro-meteorological data and wide-area environmental data: A central reference point is selected in the path environment analysis space, and with the central reference point as the origin, the X-axis points to the east, the Y-axis points to the north, and the Z-axis points vertically upward to establish an environmental three-dimensional coordinate system. The starting coordinates, end coordinates, and inspection target coordinates in the global inspection instructions of each drone are mapped to the environmental three-dimensional coordinate system. Classify micro-environmental data, macro-environmental data, and wide-area environmental data into real-time static environmental data (such as trees, buildings, power transmission lines, oil and gas pipelines, etc.) and real-time dynamic environmental data (temperature, light intensity, wind speed, etc.); Loading real-time static environmental data as an environmental framework into the environmental three-dimensional coordinate system, and overlaying real-time dynamic environmental data on the environmental framework in the form of a visual layer, such as marking temperature ranges with different colors (red represents ≥35°C, blue represents ≤0°C), and using line density to represent wind speed (the denser the lines, the greater the wind speed); The latitude, longitude, and altitude of all base station charging piles are located using GPS, and marked with cylindrical icons in the path environment analysis space. The radius of the cylindrical icon represents the service radius of the charging pile (e.g., 50 meters). The working status of the base station charging piles (such as the number of available charging ports, remaining available power, and whether there are any faults) is updated in real time. When the path environment analysis space is filled with data, the patrol environment analysis module synchronizes the path environment analysis space with the charging planning module.

[0024] Furthermore, a unit flight speed is set for each UAV, and the maximum time required for each UAV to pass through a three-dimensional unit grid is obtained based on the unit flight speed. The maximum time required is recorded as the spatial update period; Then, every time a spatial update cycle begins, the charging planning module records each photovoltaic space point as a photovoltaic charging space point based on the real-time dynamic environmental data of each three-dimensional unit grid: like Figure 2 As shown in the figure, the light intensity data in the real-time dynamic environment data is filtered to remove instantaneous interference (such as light fluctuations caused by flying birds) and retain the effective light value, thereby obtaining the average light intensity of the photovoltaic space point within a spatial update cycle; Get the output power of the photovoltaic panel according to the ambient temperature: ,in is the actual output power, is the standard operating power (power at 25°C), is the real-time ambient temperature, For the best working temperature (25℃), is the temperature correction parameter, and its value range is (0, 0.01); Set multiple photovoltaic point restriction rules, such as the minimum effective sunshine duration, the maximum wind speed limit, etc., and compare the real-time dynamic environmental data of each photovoltaic space point. If any real-time dynamic environmental data does not meet the corresponding photovoltaic point restriction rule within the space update cycle, the corresponding photovoltaic space point is judged to be unusable. Otherwise, the corresponding photovoltaic space point is recorded as a photovoltaic charging space point; For example, if the effective sunshine duration of a photovoltaic space point is less than the minimum effective sunshine duration, the photovoltaic space point is judged to be unusable; A dynamic tag of the time and space dimension is set for the photovoltaic space point recorded as the photovoltaic charging space point. The dynamic tag content includes: Spatial marker: Displayed as a glowing sphere icon in the path environment analysis space. The brightness of the sphere is positively correlated with the real-time output power (the higher the power, the brighter the brightness). Time tagging: Generate a charging capacity prediction curve for the length of the future spatial update cycle, dividing the time into m time segments, and marking each time segment with the corresponding expected charging efficiency (for example, the expected charging efficiency of the 1st to 2nd time segment is 90%, and the expected charging efficiency of the 2nd to 3rd time segment is 70%). m is a natural number greater than 10. Status Mark: Use color to distinguish the current status. Green indicates normal charging, yellow indicates reduced charging (efficiency < 50%), and red indicates non-charging (such as at night without light).

[0025] Furthermore, the charging planning module synchronizes the path environment analysis space with the drone control module, and the edge charging planning unit in the drone control module sets a short-term flight path for each drone based on its current remaining power and real-time spatial position: The edge charging planning unit obtains the real-time status data of each drone in real time, including the current remaining power, real-time spatial location, and the coordinates of the next inspection target. It also synchronously updates the drone flight point in the path environment analysis space based on the real-time status data of the drone. Perform charging simulation on each drone node: Whenever a drone completes an inspection mission for a target, it uses the coordinates of the next inspection target as the short-term endpoint and the coordinates of the next inspection target as the long-term endpoint. Based on the current remaining power of the drone, it determines whether the drone node can continuously reach the short-term and long-term endpoints along a straight line, and obtains the estimated power required. If it is determined to be reachable, the drone inspection route will continue to be executed; If it is determined that it is not reachable, a charging response area is generated with the current location of the drone and the short-term destination as the boundary, and the photovoltaic charging space points and base station charging points within the charging response area are traversed; Based on the dynamic marking content of the photovoltaic charging space point, the additional power required and the estimated charging amount of the drone passing through each photovoltaic charging space point are determined. Based on the spatial location of the base station charging point and various real-time status data, the additional power required and waiting time of the drone passing through the base station charging point are determined. It should be noted that since each drone starts from a different starting position and flies at the same unit flight speed, the total length of the flight path is also equal, so there will not be multiple drones competing for the same photovoltaic charging space point in the same space update cycle; Then, with the current location of the drone as the starting point, each photovoltaic charging space point and base station charging point as the path node, and the next inspection target coordinates as the end point, multiple short-term flight paths are traversed within the charging response area with the estimated required power as the target; It should be noted that each short-term flight path allows the drone to obtain the estimated power required after passing through the short-term flight path, excluding the power required for flight. For example, if the short-term flight path consists of multiple photovoltaic charging spaces, the cumulative sum of the additional power required by each photovoltaic charging space and the estimated charging amount is greater than or equal to the estimated power required; Get the total cost value C of each short-term flight path: ,in represents the total cost value of the i-th short-term flight path, D, E, and H represent the total length of the short-term flight path, flight power consumption, and the total time spent passing through all photovoltaic charging points and base station charging points, respectively. 、 is the calculation correction parameter, i is a natural number greater than 0; The short-term flight path with the smallest total cost value C is selected and sent to the corresponding UAV for execution. The above process of generating short-term flight paths is repeated until all UAVs complete the UAV flight path.

[0026] like Figure 3 As shown, the present invention also discloses a method for outdoor charging of a drone, comprising the following steps: Step S1: Delineate a flight inspection area and a path environment analysis space based on the drone inspection path, set up a sensor network in the flight inspection area, collect real-time static and dynamic environmental data within the flight inspection area through the sensor network, and fill the path environment analysis space with data based on the real-time static and dynamic environmental data; Step S2: setting photovoltaic space points in the path environment analysis space, and by analyzing the real-time dynamic environmental data of each photovoltaic space point, marking each photovoltaic space point as a photovoltaic charging space point, and setting a dynamic label of the spatiotemporal dimension for each photovoltaic charging space point; Step S3: Set the corresponding drone node in the path environment analysis space, and then use the next inspection target of the drone as the end point to perform charging simulation on each drone node. Based on the simulation results, select photovoltaic charging space points and base station charging points in the path environment analysis space to generate a short-term flight path.

[0027] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any indirect modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An outdoor charging platform for drones, including a cloud control terminal, characterized in that: The cloud control terminal is communicatively connected to a patrol environment analysis module, a charging planning module, and a drone control module; The patrol path environment analysis module is used to delineate a flight inspection area and a path environment analysis space according to the drone patrol path, and set up a sensor network in the flight inspection area to collect real-time static and dynamic environmental data within the flight inspection area through the sensor network. The path environment analysis space is filled with data based on the real-time static and dynamic environmental data, and a base station charging point is set in the path environment analysis space. The charging planning module is used to set photovoltaic space points in the path environment analysis space, mark each photovoltaic space point as a photovoltaic charging space point by analyzing the real-time dynamic environmental data of each photovoltaic space point, and set dynamic labels in the time and space dimensions for each photovoltaic charging space point; The drone control module is provided with an environmental perception unit and an edge charging planning unit; The environmental perception unit is communicatively connected to the sensor network of the patrol environment analysis module, and is used to collect real-time dynamic and static environmental data of the spatial position around the drone; The edge charging planning unit is used to obtain the current remaining power and real-time spatial position of all drones, and set corresponding drone nodes in the path environment analysis space. Then, with the next inspection target of the drone as the end point, charging simulation is performed on each drone node. Based on the simulation results, photovoltaic charging space points and base station charging points are selected in the path environment analysis space to generate a short-term flight path.

2. The UAV outdoor charging platform according to claim 1, characterized in that: The process of demarcating the flight inspection area and path environment analysis space based on the UAV inspection path includes: Using GIS technology, the preset inspection range of the drone inspection route is delineated as a flight inspection area. Multiple inspection targets are marked within the flight inspection area. Each inspection target is used as the core of the area and expanded 5 to 10 kilometers to the surrounding area to form a polygonal analysis boundary. The analysis boundary is then anchored using high-precision satellite positioning data to determine the coverage of the flight inspection area and establish a path environment analysis space.

3. The outdoor charging platform for UAVs according to claim 2, characterized in that: The patrol environment analysis module deploys three sensor networks, including a fixed sensor network, a mobile sensor network, and a remote sensing sensor network; The fixed sensor network consists of multiple meteorological monitoring stations distributed at the same spatial distance in the flight inspection area, and is used to collect macro-meteorological data in the flight inspection area; The mobile sensor network is composed of sensors on drones that execute drone inspection paths in the flight inspection area, and is used to collect micro-environmental data in the drone inspection paths; The remote sensing sensor network is used to obtain wide-area environmental data of the flight inspection area via satellite; According to the spatial distribution of each meteorological monitoring station, the starting position of the drone, and the base station charging piles in the flight inspection area, fixed collection points, drone flight points, and base station charging points are set in the path environment analysis space.

4. The outdoor charging platform for UAVs according to claim 3, characterized in that: The process of populating the route environment analysis space with data includes the following: A number of three-dimensional unit grids are divided in the path environment analysis space, and a photovoltaic space point is set at the center of each three-dimensional unit grid; Micro-environmental data, macro-environmental data and wide-area environmental data are classified into real-time static environmental data and real-time dynamic environmental data. Real-time static environmental data is loaded into the path environmental analysis space as an environmental framework, and real-time dynamic environmental data is superimposed on the environmental framework in the form of a visual layer. It is marked with a cylindrical icon in the path environment analysis space, and the working status of the base station charging pile is updated in real time.

5. The outdoor charging platform for UAVs according to claim 4, characterized in that: The process of marking each photovoltaic space point as a photovoltaic charging space point based on real-time dynamic environmental data includes: Set a unit flight speed for each drone, and obtain the maximum time required for each drone to pass through a three-dimensional unit grid based on the unit flight speed, which is recorded as the spatial update period; Every time a spatial update cycle begins, each photovoltaic space point is recorded as a photovoltaic charging space point based on the real-time dynamic environmental data of each three-dimensional unit grid, and the average light intensity of the photovoltaic space point in each spatial update cycle is obtained; Set multiple photovoltaic point restriction rules and compare the real-time dynamic environmental data of each photovoltaic space point. If any real-time dynamic environmental data does not meet the corresponding photovoltaic point restriction rule within the space update cycle, the corresponding photovoltaic space point is judged to be unusable. Otherwise, the corresponding photovoltaic space point is recorded as a photovoltaic charging space point.

6. The outdoor charging platform for UAVs according to claim 5, characterized in that: A dynamic tag of the time and space dimension is set for the photovoltaic space point recorded as the photovoltaic charging space point, and the dynamic tag content includes: Spatial marker: Displayed as a glowing sphere icon in the path environment analysis space. The brightness of the sphere is positively correlated with the real-time output power. Time marking: Generate a charging capacity prediction curve for the length of the future space update cycle, divide the time into m time segments, and mark each time segment with the corresponding expected charging efficiency, where m is a natural number greater than 10; Status mark: Use color to distinguish the current status.

7. The outdoor charging platform for UAVs according to claim 6, characterized in that: The process of charging simulation for each drone node includes: Whenever a drone completes an inspection mission for a target, it uses the coordinates of the next inspection target as the short-term endpoint and the coordinates of the next inspection target as the long-term endpoint. Based on the current remaining power of the drone, it determines whether the drone node can continuously reach the short-term and long-term endpoints along a straight line, and obtains the estimated power required. If it is determined that the route can be reached, the drone inspection route will continue to be executed. Otherwise, a charging response area will be generated with the drone's current location and the short-term end point as the boundary, and the photovoltaic charging space points and base station charging points within the charging response area will be traversed.

8. The outdoor charging platform for UAVs according to claim 7, characterized in that: The process of generating the short-term flight path includes: Based on the dynamic marking content of the photovoltaic charging space point, the additional power required and the estimated charging amount of the drone passing through each photovoltaic charging space point are obtained. The additional power required and the waiting time of the drone passing through the base station charging point are determined based on the spatial location of the base station charging point and various real-time status data. With the current position of the drone as the starting point, the photovoltaic charging space points and the base station charging point as the path nodes, and the next inspection target coordinates as the end point, multiple short-term flight paths are traversed within the charging response area with the estimated required power as the target; Obtain the total cost value C of each short-term flight path, select the short-term flight path with the smallest total cost value C, and send it to the corresponding drone for execution.

9. A method for outdoor charging of a drone, applied to a drone outdoor charging platform according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step S1: Delineate a flight inspection area based on the drone inspection path, set up a sensor network in the flight inspection area, collect real-time static and dynamic environmental data within the flight inspection area through the sensor network, and establish a path environment analysis space based on the real-time static and dynamic environmental data; Step S2: setting photovoltaic space points in the path environment analysis space, and by analyzing the real-time dynamic environmental data of each photovoltaic space point, marking each photovoltaic space point as a photovoltaic charging space point, and setting a dynamic label of the spatiotemporal dimension for each photovoltaic charging space point; Step S3: Set the corresponding drone node in the path environment analysis space, and then use the next inspection target of the drone as the end point to perform charging simulation on each drone node. Based on the simulation results, select the photovoltaic charging space point and the base station charging point in the path environment analysis space to generate a short-term flight path.

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