An unmanned aerial vehicle outdoor charging platform and a charging method

Abstract

The application discloses an unmanned aerial vehicle outdoor charging platform and a charging method, relates to the technical field of unmanned aerial vehicle charging management and control, and improves the unmanned aerial vehicle outdoor charging efficiency.The application fills a path environment analysis space according to real-time dynamic and static environment data, simultaneously sets base station charging points in the path environment analysis space, sets photovoltaic space points in the path environment analysis space, marks each photovoltaic space point as a photovoltaic charging space point by analyzing real-time dynamic environment data of each photovoltaic space point, sets dynamic marks of time and space dimensions for each photovoltaic charging space point, sets corresponding unmanned aerial vehicle nodes in the path environment analysis space, and then charges each unmanned aerial vehicle node by taking the next inspection target of the unmanned aerial vehicle as an end point, and selects photovoltaic charging space points and base station charging points in the path environment analysis space according to a simulation result to generate a short-term flight path.

Description

TECHNICAL FIELD

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

[0002] In today's wave of technological development, unmanned aerial vehicles (UAVs) play a key role in many fields, such as geographic mapping, agricultural monitoring, and power inspection. As the application scenarios of UAVs continue to expand, the demand for long-time operation of UAVs in outdoor environments is increasing. However, the endurance of UAVs has always been a major bottleneck limiting their widespread application.

[0003] Traditional UAV charging methods are relatively single, usually relying on fixed base station charging. In complex outdoor environments, this fixed charging mode has many limitations. On the one hand, due to the uncertainty of outdoor environments, such as complex terrain and variable weather conditions, UAVs may encounter various obstacles during flight, making it difficult to reach the fixed base station charging point, resulting in a failure to charge in time and affecting the completion of the operation task. On the other hand, the distribution of fixed base station charging points is limited, which cannot meet the continuous operation needs of UAVs in large outdoor areas, greatly reducing the operation efficiency and flexibility of UAVs.

[0004] In addition, existing UAV charging planning often lacks comprehensive consideration of the flight environment. When planning the charging path, real-time static environmental data (such as terrain, obstacle distribution, etc.) and dynamic environmental data (such as weather changes, activities of other aircraft, etc.) are not fully combined, making the charging planning less accurate and easily leading to unnecessary risks for UAVs during flight, and even the possibility of crashing due to low battery.

[0005] Therefore, how to solve the charging problem of UAVs in outdoor environments, improve their endurance and operation efficiency, and at the same time ensure the safety and accuracy of the charging planning, has become a problem to be solved in the current UAV technology field, and therefore an outdoor UAV charging platform and a charging method are provided. SUMMARY

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

[0007] In order to achieve the above purpose, the present application provides the following technical solutions:

[0008] An outdoor UAV charging platform, comprising a cloud control terminal, wherein the cloud control terminal is communicatively connected with a patrol path environment analysis module, a charging planning module, and a UAV management and control module.

[0009] The path environment analysis module is used to demarcate a flight inspection area and a path environment analysis space according to a UAV inspection path, set a sensor network in the flight inspection area, collect real-time static environment data and real-time dynamic environment data in the flight inspection area through the sensor network, and fill data in the path environment analysis space according to the real-time static and dynamic environment data, while setting a base station charging point in the path environment analysis space;

[0010] 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 real-time dynamic environment data of each photovoltaic space point, and set a dynamic mark of a time-space dimension for each photovoltaic charging space point;

[0011] The UAV control module is provided with an environment perception unit and an edge charging planning unit;

[0012] The environment perception unit is in communication connection with the sensor network of the path environment analysis module, and is used to collect real-time static and dynamic environment data of a space position around the UAV;

[0013] The edge charging planning unit is used to obtain current residual power and a real-time space position of all UAVs, set corresponding UAV nodes in the path environment analysis space, and then simulate charging of each UAV node with a next inspection target of the UAV as a terminal point, select photovoltaic charging space points and base station charging points in the path environment analysis space according to a simulation result, and generate a short-term flight path.

[0014] Further, the process of demarcating a flight inspection area and a path environment analysis space according to a UAV inspection path includes:

[0015] A worker uploads a UAV inspection path to the path environment analysis module, and then demarcates a preset inspection range of the UAV inspection path as a flight inspection area through GIS technology, and labels a plurality of inspection targets in the flight inspection area;

[0016] A polygon analysis boundary is formed by expanding 5 to 10 kilometers around each inspection target as a regional core, the analysis boundary is anchored through high-precision satellite positioning data, the coverage range of the flight inspection area is determined, and a path environment analysis space is established.

[0017] Further, the path environment analysis module deploys three kinds of sensor networks, including a fixed sensor network, a mobile sensor network, and a remote sensing sensor network;

[0018] The fixed sensor network is distributed in the flight inspection area at the same spatial distance by a plurality of meteorological monitoring stations, and is used to collect macro-weather data in the flight inspection area;

[0019] The mobile sensor network is composed of sensors on the unmanned aerial vehicle performing the unmanned aerial vehicle inspection path in the flight inspection area, including a laser radar, an infrared thermal imager and a gas sensor, for collecting micro-environmental data in the unmanned aerial vehicle inspection path;

[0020] The remote sensing sensor network is used to acquire wide-area environmental data of the flight inspection area through a satellite;

[0021] According to the starting positions of the meteorological monitoring stations, the unmanned aerial vehicles and the spatial position distribution of the base station charging piles in the flight inspection area, fixed collection points, unmanned aerial vehicle flight points and base station charging points are set in the path environment analysis space.

[0022] Further, the process of data filling in the path environment analysis space includes:

[0023] A plurality of three-dimensional unit grids are divided in the path environment analysis space, and a photovoltaic space point is set at the center position of each three-dimensional unit grid;

[0024] The micro-environmental data, the macro-environmental data and the wide-area environmental data are classified into real-time static environmental data and real-time dynamic environmental data;

[0025] The real-time static environmental data is loaded as an environmental framework in an environmental three-dimensional coordinate system, and the real-time dynamic environmental data is superimposed on the environmental framework in the form of a visual layer;

[0026] The longitude, latitude and altitude of all base station charging piles are located by GPS, marked with a cylindrical icon in the path environment analysis space, and the working state of the base station charging pile is updated in real time.

[0027] Further, the process of marking each photovoltaic space point as a photovoltaic charging space point according to the real-time dynamic environmental data includes:

[0028] A unit flight speed is set for each unmanned aerial vehicle, and the longest time required for each unmanned aerial vehicle to pass through a three-dimensional unit grid is obtained according to the unit flight speed, which is recorded as a space update period;

[0029] Further, when a space update period starts, the charging planning module marks each photovoltaic space point as a photovoltaic charging space point according to the real-time dynamic environmental data of each three-dimensional unit grid:

[0030] The light intensity data in the real-time dynamic environmental data is filtered to remove transient interference and retain effective light values, and then the average light intensity in a space update period is obtained;

[0031] According to the environmental temperature, the output power of the photovoltaic panel is obtained, a plurality of photovoltaic point restriction rules are set, and each real-time dynamic environmental data of each photovoltaic space point is compared. If any one of the real-time dynamic environmental data does not meet the corresponding photovoltaic point restriction rule within the space update period, it is judged that the corresponding photovoltaic space point cannot be used, otherwise the corresponding photovoltaic space point is recorded as a photovoltaic charging space point.

[0032] Further, a dynamic marker of space-time dimension is set for the photovoltaic space point recorded as a photovoltaic charging space point, and the dynamic marker content includes:

[0033] Space marker: displayed in the path environment analysis space with a luminous sphere icon, and the sphere brightness is positively correlated with the real-time output power;

[0034] Time marker: generate a charging capacity prediction curve of the future space update period time length, divide the time into m time sections, and mark the corresponding predicted charging efficiency for each time section, where m is a natural number greater than 10;

[0035] State marker: distinguish the current state by color.

[0036] Further, the process of charging simulation for each unmanned aerial vehicle node includes:

[0037] Whenever the unmanned aerial vehicle completes a patrol task of a patrol target, the next patrol target coordinate of the unmanned aerial vehicle is taken as a short-term endpoint, and the next patrol target coordinate of the unmanned aerial vehicle is taken as a long-term endpoint, and then it is judged whether the unmanned aerial vehicle node can reach the short-term endpoint and the long-term endpoint along a straight line according to the current remaining power of the unmanned aerial vehicle, and the predicted required power is obtained;

[0038] If it is judged that it can be reached, the unmanned aerial vehicle patrol path is continued to be executed;

[0039] If it is judged that it cannot be reached, a charging response area is generated with the current position of the unmanned aerial vehicle and the short-term endpoint as boundaries, and the photovoltaic charging space points and the base station charging points in the charging response area are traversed.

[0040] Further, the generation process of the short-term flight path includes:

[0041] According to the dynamic marker content of the photovoltaic charging space point, the required additional power and the predicted charging power of the unmanned aerial vehicle passing through each photovoltaic charging space point are judged, and the required additional power and the waiting time of the unmanned aerial vehicle passing through the base station charging point are judged according to the spatial position of the base station charging point and each real-time state data;

[0042] Further, a plurality of short-term flight paths are traversed in the charging response area with the current position of the unmanned aerial vehicle as the starting point, each photovoltaic charging space point and base station charging point as the way node, and the next patrol target coordinate as the endpoint, and the predicted required power as the target.

[0043] The sum cost value C of each short-term flight path is obtained, the short-term flight path with the minimum sum cost value C is selected and sent to the corresponding unmanned aerial vehicle for execution, and the above process of generating the short-term flight path is repeated until all unmanned aerial vehicles complete the unmanned aerial vehicle flight path.

[0044] An unmanned aerial vehicle outdoor charging method comprises the following steps:

[0045] In step S1, a flight inspection area is demarcated according to an unmanned aerial vehicle inspection path, a sensor network is arranged in the flight inspection area, real-time static and dynamic environment data in the flight inspection area are collected through the sensor network, and a path environment analysis space is established according to the real-time static and dynamic environment data;

[0046] In step S2, photovoltaic space points are arranged in the path environment analysis space, each photovoltaic space point is marked as a photovoltaic charging space point by analyzing the real-time dynamic environment data of each photovoltaic space point, and a dynamic mark in the time-space dimension is arranged for each photovoltaic charging space point;

[0047] In step S3, corresponding unmanned aerial vehicle nodes are arranged in the path environment analysis space, and then a next inspection target of the unmanned aerial vehicle is taken as an end point to simulate charging for each unmanned aerial vehicle node, and a photovoltaic charging space point and a base station charging point are selected in the path environment analysis space to generate a short-term flight path according to a simulation result.

[0048] Compared with the prior art, the present application has the following advantages:

[0049] 1. Precise environment analysis and path planning: the present application demarcates a flight inspection area according to an unmanned aerial vehicle inspection path through a path environment analysis module, arranges a sensor network to collect real-time static and dynamic environment data, and establishes a path environment analysis space, thereby comprehensively understanding the flight environment, providing accurate basis for subsequent charging planning and unmanned aerial vehicle control, and making charging simulation and path planning more in line with actual conditions, greatly improving the safety and reliability of unmanned aerial vehicle flight.

[0050] 2. Diversified charging mode and flexible charging planning: photovoltaic space points are arranged in the path environment analysis space and marked as photovoltaic charging space points, and a dynamic mark in the time-space dimension is arranged, thereby fully utilizing solar energy as a clean energy source, increasing the charging options of the unmanned aerial vehicle, and improving the endurance of the unmanned aerial vehicle outdoors.

[0051] According to the current remaining power of the unmanned aerial vehicle, the real-time spatial position and the next inspection target, a short-term flight path is generated by selecting appropriate photovoltaic charging space points and base station charging points in the path environment analysis space, so that flexible adjustment is realized according to the actual situation of the unmanned aerial vehicle, and the unmanned aerial vehicle can be charged in time and efficiently while completing the work task. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A platform structure diagram of the unmanned aerial vehicle outdoor charging platform is provided.

[0053] Figure 2 A structure diagram of the path environment analysis space is provided.

[0054] Figure 3 A method flowchart of the unmanned aerial vehicle outdoor charging method is provided. DETAILED DESCRIPTION

[0055] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purpose, the specific embodiments, structures, features and effects of the present application will be described in detail below in combination with the drawings and preferred embodiments.

[0056] As shown in Figure 1 An unmanned aerial vehicle outdoor charging platform includes a cloud control terminal, the cloud control terminal is communicatively connected with a path environment analysis module, a charging planning module and an unmanned aerial vehicle management and control module;

[0057] The path environment analysis module is used for dividing a flight inspection area and a path environment analysis space according to an unmanned aerial vehicle inspection path, setting a sensor network in the flight inspection area, collecting real-time static environment data and real-time dynamic environment data in the flight inspection area through the sensor network, filling data in the path environment analysis space according to the real-time static and dynamic environment data, and setting a base station charging point in the path environment analysis space;

[0058] The charging planning module is used for setting photovoltaic space points in the path environment analysis space, 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 dynamic labels of each photovoltaic charging space point in time and space dimensions;

[0059] The unmanned aerial vehicle management and control module is provided with an environment perception unit and an edge charging planning unit;

[0060] The environment perception unit is communicatively connected with the sensor network of the path environment analysis module, and is used for collecting real-time static and dynamic environment data of a surrounding space position of the unmanned aerial vehicle;

[0061] The edge charging planning unit is configured to acquire current residual power of all unmanned aerial vehicles and real-time spatial positions, set corresponding unmanned aerial vehicle nodes in the path environment analysis space, simulate charging of the unmanned aerial vehicle nodes with next inspection targets of the unmanned aerial vehicles as terminal points, and select photovoltaic charging space points and base station charging points in the path environment analysis space according to simulation results to generate a short-term flight path.

[0062] The working principle of the application is explained below by way of examples:

[0063] The staff uploads the unmanned aerial vehicle inspection path to the path environment analysis module, and then delimits the preset inspection range of the unmanned aerial vehicle inspection path as a flight inspection area by means of GIS technology, and labels a plurality of inspection targets in the flight inspection area, such as power transmission lines, oil and gas pipelines, forest belts, etc.

[0064] A polygon analysis boundary is formed by extending 5 to 10 kilometers from each inspection target as a regional core, the analysis boundary is anchored by high-precision satellite positioning data to determine the coverage range of the flight inspection area, and a path environment analysis space is established;

[0065] The path environment analysis module deploys three kinds of sensor networks, including a fixed sensor network, a mobile sensor network and a remote sensing sensor network;

[0066] The fixed sensor network is distributed in the flight inspection area by a plurality of meteorological monitoring stations at the same spatial distance, and is configured to collect macro-weather data such as temperature, humidity, wind speed, wind direction and precipitation in the flight inspection area;

[0067] The mobile sensor network is composed of sensors on the unmanned aerial vehicles performing the unmanned aerial vehicle inspection path in the flight inspection area, including a laser radar, an infrared thermal imager and a gas sensor, and is configured to collect micro-environmental data such as obstacle coordinates (such as trees, buildings and bird flocks) in the unmanned aerial vehicle inspection path, local temperature field distribution (such as power transmission line joint temperature), harmful gas concentration (such as oil and gas pipeline leakage detection), etc.

[0068] The remote sensing sensor network is configured to acquire satellite remote sensing data of the flight inspection area by satellite, including wide-area environmental data such as illumination intensity, cloud coverage and ground vegetation height;

[0069] According to the spatial position distribution of each meteorological monitoring station, the starting position of the unmanned aerial vehicle and the spatial position of the base station charging pile in the flight inspection area, fixed collection points, unmanned aerial vehicle flight points and base station charging points are set in the path environment analysis space.

[0070] Further, a command generation frequency is set, and the time length of the command generation frequency is generally 30 to 60 seconds. In the flight inspection area, n unmanned aerial vehicles are simultaneously called to execute the unmanned aerial vehicle inspection path from different starting positions along the same flight direction, and n is an integer greater than 1.

[0071] Before the unmanned aerial vehicle inspection path is executed, an edge charging planning unit in the unmanned aerial vehicle management and control module sends a global inspection instruction to each unmanned aerial vehicle according to the starting position of each unmanned aerial vehicle. The global inspection instruction includes the starting point coordinates, the end point coordinates, and the inspection target coordinates of the inspection target.

[0072] The path environment analysis module divides a plurality of three-dimensional unit grids in the path environment analysis space. Each three-dimensional unit grid corresponds to a space position of 10m×10m×5m (length×width×height) in the flight inspection area.

[0073] A photovoltaic space point is set at the center position of each three-dimensional unit grid. During the execution of the unmanned aerial vehicle flight path by the unmanned aerial vehicle according to the global inspection instruction, the path environment analysis module sends a data sensing instruction to the environment sensing unit, the fixed sensor network, and the remote sensing sensor network in the unmanned aerial vehicle management and control module.

[0074] The environment sensing unit collects micro-environment data of the surrounding space position of each unmanned aerial vehicle, and the fixed sensor network and the remote sensing sensor network collect macro-weather data and wide-area environment data, and are synchronized with the path environment analysis module.

[0075] Further, the path environment analysis module fills data in each three-dimensional unit grid in the path environment analysis space according to the received micro-environment data, macro-weather data, and wide-area environment data.

[0076] A central reference point is selected in the path environment analysis space, and an environment three-dimensional coordinate system is established with the central reference point as the origin, the X-axis pointing to the east direction, the Y-axis pointing to the north direction, and the Z-axis pointing vertically upward. The starting point coordinates, the end point coordinates, and the inspection target coordinates in the global inspection instruction of each unmanned aerial vehicle are mapped on the environment three-dimensional coordinate system.

[0077] The micro-environment data, the macro-environment data, and the wide-area environment data are classified into real-time static environment data (such as trees, buildings, power transmission lines, oil and gas pipelines, etc.) and real-time dynamic environment data (temperature, light intensity, wind speed, etc.).

[0078] The real-time static environment data is loaded as an environment framework in an environment three-dimensional coordinate system, and the real-time dynamic environment data is superimposed on the environment framework in the form of a visual layer, such as marking temperature intervals with different colors (red represents ≥ 35℃, blue represents ≤ 0℃), and using line density to represent wind speed (the denser the line, the greater the wind speed);

[0079] The longitude and latitude and altitude of all base station charging piles are located by GPS, and are marked in the path environment analysis space in the form of a cylindrical icon, the radius of the cylindrical icon represents the service radius of the charging pile (such as 50 meters), and the working state of the base station charging pile (such as the number of available charging ports, the remaining available power, and whether there is a fault) is updated in real time;

[0080] When the path environment analysis space is completed, the path environment analysis module synchronizes the path environment analysis space to the charging planning module.

[0081] Further, the unit flight speed of each unmanned aerial vehicle is set, and the longest time required for each unmanned aerial vehicle to pass through a three-dimensional unit grid is obtained according to the unit flight speed, and the longest time required is recorded as a space update period;

[0082] Further, when a space update period starts, the charging planning module records each photovoltaic space point as a photovoltaic charging space point according to the real-time dynamic environment data of each three-dimensional unit grid;

[0083] As shown in Figure 2 , the light intensity data in the real-time dynamic environment data is filtered to remove transient interference (such as light fluctuations caused by flying birds) and retain valid light values, and then the average light intensity of the photovoltaic space point in a space update period is obtained;

[0084] The output power of the photovoltaic panel is obtained according to the environmental temperature:

[0085] , wherein is the actual output power, is the standard working condition power (power at 25℃), is the real-time environmental temperature, is the best working temperature (25℃), is the temperature correction parameter, and the value range is (0, 0.01);

[0086] A plurality of photovoltaic point restriction rules are set, such as the minimum effective sunshine duration and the maximum limited wind speed, and the real-time dynamic environment data of each photovoltaic space point is compared, if any one of the real-time dynamic environment data does not meet the corresponding photovoltaic point restriction rule in the space update period, it is judged that the corresponding photovoltaic space point cannot be used, otherwise the corresponding photovoltaic space point is recorded as a photovoltaic charging space point;

[0087] For example, if the effective sunshine duration of a photovoltaic space point is less than the minimum effective sunshine duration, then the photovoltaic space point is determined to be unusable.

[0088] A dynamic marker for the spatiotemporal dimension is set for the photovoltaic spatial point, denoted as the photovoltaic charging spatial point. The dynamic marker content includes:

[0089] Spatial markers: Displayed as glowing sphere icons 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 stronger the brightness).

[0090] Time stamp: Generate a charging capacity prediction curve for the length of the future space update cycle, divide the time into m time segments, and mark the corresponding expected charging efficiency for each time segment (e.g., the expected charging efficiency for the first to second time segments is 90%, and the expected charging efficiency for the second to third time segments is 70%), where m is a natural number greater than 10.

[0091] Status markers: The current status is distinguished by color. Green indicates normal charging, yellow indicates derating charging (efficiency <50%), and red indicates non-charging (e.g., no light at night).

[0092] Furthermore, the charging planning module synchronizes the path environment analysis space with the drone management module. Then, the edge charging planning unit in the drone management module sets short-term flight paths for each drone based on its current remaining battery power and real-time spatial location.

[0093] The edge charging planning unit acquires real-time status data of each drone, including current remaining power, real-time spatial location, and coordinates of the next inspection target, and updates the drone flight points in the path environment analysis space in real time based on the drone's real-time status data.

[0094] Charging simulation was performed on each drone node:

[0095] After each drone completes the inspection task of an inspection target, the coordinates of the next inspection target of the drone are taken as the short-term endpoint and the coordinates of the next inspection target of the drone are taken as the long-term endpoint. Then, based on the current remaining power of the drone, it is determined whether the drone node can reach the short-term endpoint and the long-term endpoint in a straight line, and the expected power required is obtained.

[0096] If it is determined that the destination is reachable, continue executing the drone inspection path;

[0097] If it is determined that the destination cannot be reached, a charging response area is generated with the current position 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.

[0098] According to the dynamic marking content of the photovoltaic charging space point, the required additional power and the expected charging power of the unmanned aerial vehicle passing through each photovoltaic charging space point are determined, and according to the spatial position and the real-time state data of the base station charging point, the required additional power and the waiting time of the unmanned aerial vehicle passing through the base station charging point are determined;

[0099] It should be noted that since each unmanned aerial vehicle 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 be no multiple unmanned aerial vehicles competing for the same photovoltaic charging space point in the same space update cycle;

[0100] Further, taking the current position of the unmanned aerial vehicle as the starting point, each photovoltaic charging space point and the base station charging point as the way node, and the next inspection target coordinate as the end point, a plurality of short-term flight paths are traversed in the charging response region with the expected required power as the target;

[0101] It should be noted that each short-term flight path can enable the unmanned aerial vehicle to obtain the expected required power after passing through the short-term flight path, for example, the short-term flight path is composed of a plurality of photovoltaic charging space points, and the cumulative sum of the required additional power and the expected charging power of each photovoltaic charging space is greater than or equal to the expected required power;

[0102] The total cost value C of each short-term flight path is obtained:

[0103] , wherein represents the total cost value of the i-th short-term flight path, D, E, and H represent the total length, the flight power consumption, and the total time spent passing through all photovoltaic charging space points and base station charging points of the short-term flight path, , is an operation correction parameter, and i is a natural number greater than 0;

[0104] The short-term flight path with the minimum total cost value C is selected and sent to the corresponding unmanned aerial vehicle for execution, and the above process of generating a short-term flight path is repeated until all unmanned aerial vehicles complete the unmanned aerial vehicle flight path.

[0105] As shown in Figure 3 , the application also discloses an unmanned aerial vehicle outdoor charging method, comprising the following steps:

[0106] Step S1, according to the unmanned aerial vehicle inspection path, the flight inspection area and the path environment analysis space are demarcated, and the sensor network is set in the flight inspection area, the real-time static environment data and the real-time dynamic environment data in the flight inspection area are collected through the sensor network, and the path environment analysis space is filled with data according to the real-time dynamic and static environment data;

[0107] Step S2, setting photovoltaic space points in the path environment analysis space, 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 dynamic markers of space-time dimensions for each photovoltaic charging space point;

[0108] Step S3, setting corresponding unmanned aerial vehicle nodes in the path environment analysis space, then simulating charging of each unmanned aerial vehicle node with the next inspection target of the unmanned aerial vehicle as the terminal, selecting photovoltaic charging space points and base station charging points in the path environment analysis space according to the simulation results to generate a short-term flight path.

[0109] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the technical solution of the present application. Any indirect modification, equivalent change and modification of the above embodiments made according to the technical essence of the present application, as long as it does not deviate from the technical solution of the present application, still belongs to the scope of the technical solution of the present application.

Claims

1. An outdoor charging platform for unmanned aerial vehicles (UAVs), comprising a cloud control terminal, characterized in that, The cloud control terminal is connected to a patrol environment analysis module, a charging planning module, and a drone management and control module. The path inspection environment analysis module is used to delineate the flight inspection area and path environment analysis space according to the UAV inspection path, and set up a sensor network in the flight inspection area to collect real-time static and dynamic environmental data in the flight inspection area through the sensor network. The module also fills the path environment analysis space with data based on the real-time static and dynamic environmental data, and sets up base station charging points in the path environment analysis space. The charging planning module is used to set photovoltaic spatial points in the path environment analysis space. By analyzing the real-time dynamic environmental data of each photovoltaic spatial point, each photovoltaic spatial point is marked as a photovoltaic charging spatial point, and dynamic marking of the spatiotemporal dimension is set for each photovoltaic charging spatial point. The drone management module is equipped with an environmental perception unit and an edge charging planning unit. The environmental perception unit is communicatively connected to the sensor network of the path-following environmental analysis module, and is used to collect real-time dynamic and static environmental data of the spatial position around the UAV. The edge charging planning unit is used to obtain the current remaining power and real-time spatial location of all drones, and set the corresponding drone nodes in the path environment analysis space. Then, with the next inspection target of the drone as the endpoint, it performs charging simulation on each drone node. Based on the simulation results, it selects photovoltaic charging space points and base station charging points in the path environment analysis space to generate a short-term flight path.

2. The outdoor charging platform for unmanned aerial vehicles according to claim 1, characterized in that, The process of delineating 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 UAV inspection path is defined as the 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 extends outwards by 5 to 10 kilometers to form a polygonal analysis boundary. Then, high-precision satellite positioning data is used to anchor the analysis boundary to determine the coverage of the flight inspection area and establish a path environment analysis space.

3. The outdoor charging platform for unmanned aerial vehicles according to claim 2, characterized in that, The path exploration environment analysis module deploys three types of 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 within the flight inspection area, used to collect macroscopic meteorological data within the flight inspection area; The mobile sensor network consists of sensors on the 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 acquire wide-area environmental data of the flight inspection area via satellite; Based on the spatial distribution of various meteorological monitoring stations, drone starting positions, and 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 unmanned aerial vehicles according to claim 3, characterized in that, The process of populating the path environment analysis space with data includes: Several three-dimensional cell grids are divided within the path environment analysis space, and a photovoltaic spatial point is set at the center of each three-dimensional cell grid; Micro-environmental data, macro-environmental data, and wide-area environment data are classified into real-time static environment data and real-time dynamic environment data. Real-time static environment data is loaded as an environment framework in the path environment analysis space, and real-time dynamic environment data is overlaid on the environment framework in the form of a visualization layer. The charging piles are marked with cylindrical icons in the path environment analysis space and their working status is updated in real time.

5. The outdoor charging platform for unmanned aerial vehicles according to claim 4, characterized in that, The process of marking each photovoltaic spatial point as a photovoltaic charging spatial point based on real-time dynamic environmental data includes: Set a unit flight speed for each UAV, and obtain the maximum time required for each UAV to traverse a three-dimensional cell grid based on the unit flight speed, which is recorded as the spatial update cycle; At the start of each spatial update cycle, based on the real-time dynamic environmental data of each three-dimensional unit grid, each photovoltaic spatial point is recorded as a photovoltaic charging spatial point, and the average light intensity of the photovoltaic spatial point within each spatial update cycle is obtained. Multiple photovoltaic (PV) point restriction rules are set up, and the real-time dynamic environmental data of each PV space point are compared. If any real-time dynamic environmental data does not meet the corresponding PV point restriction rules within the space update cycle, the corresponding PV space point is determined to be unusable; otherwise, the corresponding PV space point is recorded as a PV charging space point.

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

7. The outdoor charging platform for unmanned aerial vehicles according to claim 6, characterized in that, The process of simulating charging for each drone node includes: After each drone completes the inspection task of an inspection target, the coordinates of the next inspection target of the drone are taken as the short-term endpoint and the coordinates of the next inspection target of the drone are taken as the long-term endpoint. Then, based on the current remaining power of the drone, it is determined whether the drone node can reach the short-term endpoint and the long-term endpoint in a straight line, and the expected power required is obtained. If the destination is determined to be reachable, the drone inspection path continues; otherwise, a charging response area is generated with the drone's current location and short-term endpoint as the boundary, and the photovoltaic charging space points and base station charging points within the charging response area are traversed.

8. The outdoor charging platform for unmanned aerial vehicles according to claim 7, characterized in that, The process of generating the short-term flight path includes: Based on the dynamic markings of the photovoltaic charging space points, the additional power required and the expected charging amount of the drone when passing through each photovoltaic charging space point are obtained. Based on the spatial location of the base station charging point and various real-time status data, the additional power required and the waiting time of the drone when passing through the base station charging point are determined. Starting from the current position of the drone, the photovoltaic charging space points and the base station charging point are the path nodes, and the coordinates of the next inspection target are the end point. Multiple short-term flight paths are traversed within the charging response area with the expected power required as the target. Obtain the total cost C of each short-term flight path, select the short-term flight path with the smallest total cost C, send it to the corresponding drone, and execute it.

9. A method for outdoor charging of unmanned aerial vehicles (UAVs), applied to an outdoor charging platform for UAVs as described in any one of claims 1 to 8, characterized in that, Includes the following steps: Step S1: Delineate the flight inspection area according to the UAV inspection path, set up a sensor network in the flight inspection area, collect real-time static environmental data and real-time dynamic environmental data in 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: Set up photovoltaic space points in the path environment analysis space. By analyzing the real-time dynamic environmental data of each photovoltaic space point, mark each photovoltaic space point as a photovoltaic charging space point, and set dynamic markings of spatiotemporal dimensions for each photovoltaic charging space point. Step S3: Set up the corresponding UAV nodes in the path environment analysis space, and then simulate charging for each UAV node with the next inspection target of the UAV as the endpoint. Based on the simulation results, select photovoltaic charging space points and base station charging points in the path environment analysis space to generate short-term flight paths.

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