An unmanned aerial vehicle intelligent flight control method and an unmanned aerial vehicle intelligent flight control device

Abstract

This application discloses an intelligent flight control method and device for unmanned aerial vehicles (UAVs), relating to the field of UAV technology. The intelligent flight control method includes the following steps: receiving a task list, which includes a task set and the task type, task priority, task area coordinates, estimated flight distance, and estimated flight time for each task subset within the task set; acquiring real-time status data of UAVs in all airport subsystems; calculating an allocation scheme and initial flight path for the task subsets based on the task list and the real-time status data; and sending the task subsets and the initial flight path to the corresponding target UAVs according to the allocation scheme. These steps improve the overall efficiency and reliability of UAV mission execution.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to an intelligent flight control method and intelligent flight control device for UAVs. Background Technology

[0002] Currently, UAV control systems mainly fall into two categories: single-unit flight control systems and centralized multi-unit scheduling systems. Single-unit flight control primarily focuses on the attitude control and trajectory tracking of a single UAV, such as quadrotor control methods based on pole placement and fuzzy active disturbance rejection control (ADRC). These methods estimate system model uncertainties and external disturbances by designing extended state observers to achieve position tracking and attitude adjustment. However, these methods mainly address stability issues at the single-unit level and cannot meet the needs of multi-unit collaborative operations. Furthermore, in multi-airport, multi-UAV control schemes, the overall efficiency and reliability of UAV mission execution are relatively low. Summary of the Invention

[0003] The purpose of this application is to provide an intelligent flight control method and device for unmanned aerial vehicles (UAVs), which improves the overall efficiency and reliability of UAVs in performing tasks.

[0004] This application discloses an intelligent flight control method for unmanned aerial vehicles (UAVs), the method comprising the following steps:

[0005] S1: Receive a task list, which includes a task set and the task type, task priority, task area coordinates, estimated flight distance and estimated flight time for each task subset in the corresponding task set;

[0006] S2: Obtain real-time status data of drones in all airport subsystems;

[0007] S3: Based on the task list and the real-time status data, calculate the task subset allocation scheme and initial flight path;

[0008] S4: According to the task subset allocation scheme, send the task subset and the initial flight path to the corresponding target UAV.

[0009] Optionally, step S4: sending the task subset and the initial flight path to the corresponding target UAV according to the task subset allocation scheme further includes:

[0010] S5: The target UAV executes a subset of tasks according to the initial flight path;

[0011] S61: During the flight of the target UAV, the collision detection and avoidance module of the target UAV calculates the relative distance and relative speed with other UAVs;

[0012] S62: The collision detection and avoidance module of the target UAV calculates the collision risk in real time based on the relative distance and relative speed, and obtains the collision risk value;

[0013] S63: When the collision risk value exceeds the preset collision risk value, the collision detection and avoidance module of the target UAV generates an avoidance command;

[0014] S64: The collision detection and avoidance module of the target UAV adjusts the initial flight path according to the avoidance command to obtain the adjusted flight path.

[0015] Optionally, step S4: sending the task subset and the initial flight path to the corresponding target UAV according to the task subset allocation scheme further includes:

[0016] S5: The target UAV executes a subset of tasks according to the initial flight path;

[0017] S71: The drone communication unit of the target drone creates a public network link to establish communication with the data communication server of the central control server subsystem; the drone communication unit of the target drone creates a self-organizing network link to establish communication with the drone communication units of other drones or the airport communication unit of the airport subsystem; the public network link is set as the primary link, and the self-organizing network link is set as the backup link.

[0018] S72: The UAV communication unit detects the signal strength and packet loss rate of the currently used primary link in real time;

[0019] S73: When it is detected that the signal strength of the currently used primary link is lower than the first preset signal strength threshold or the packet loss rate is higher than the second preset packet loss rate threshold, traverse all available links of the public network links and select the link with the highest signal strength and the lowest packet loss rate as the new primary link.

[0020] S74: When all public network links are unavailable, enable self-organizing network links to communicate with the drone communication units of other drones or the airport communication units of the airport subsystem.

[0021] Optionally, after step S74: when all public network links are unavailable, enabling communication between the self-organizing network link and the drone communication unit of other drones or the airport communication unit of the airport subsystem, the method further includes:

[0022] S81: When the self-organizing network link also cannot be established, and the communication between the target UAV and the data communication server of the central control server subsystem is completely interrupted for a duration exceeding the third preset duration threshold;

[0023] S82: The target UAV's flight control unit acquires the pre-stored airport coordinates of all airport subsystems, as well as the target UAV's flight coordinates and remaining battery power;

[0024] S83: Based on the airport coordinates of all airport subsystems, as well as the flight coordinates and remaining battery power of the target UAV, calculate the scoring function for each airport subsystem to obtain the scoring set;

[0025] S84: Select the airport subsystem with the highest score in the score set as the emergency landing target;

[0026] S85: If the emergency landing target is the airport subsystem at which the target UAV took off, the flight control unit of the target UAV shall fly according to the preset return route.

[0027] Optionally, S85: If the emergency landing target is the airport subsystem where the target UAV took off, after the step of the target UAV's flight control unit flying according to the preset return route, the following is also included:

[0028] S86: If the emergency landing target is not the airport subsystem at the time of takeoff of the target UAV, the flight control unit of the target UAV generates a new flight path from the flight position to the emergency landing target.

[0029] Optionally, step S3: calculating the allocation scheme and initial flight path of the task subset based on the task list and the real-time status data includes:

[0030] S31: Based on the task list and real-time status data, construct a multi-objective optimization model, with the core optimization objectives being the shortest total task completion time, the lowest total UAV energy consumption, and the highest task coverage.

[0031] S32: The particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the output is the allocation scheme of the task subset and the initial flight path that satisfies all core optimization objectives.

[0032] This application also discloses an intelligent flight control device for unmanned aerial vehicles (UAVs), which includes multiple UAV cluster subsystems, multiple airport subsystems, and a central control server subsystem.

[0033] The drone swarm subsystem includes multiple drones; the airport subsystem is used to accommodate multiple drones; the central control server subsystem is used to receive a task list and obtain real-time status data of drones in all airport subsystems, and calculate the task subset allocation scheme and initial flight path according to the task list and the real-time status data; then, according to the task subset allocation scheme, the task subset and the initial flight path are sent to the corresponding target drones.

[0034] Optionally, the drone includes a drone body, a collision detection and avoidance module, and a drone communication unit, wherein the collision detection and avoidance module and the drone communication unit are both disposed on the drone body;

[0035] The drone communication unit is used by other drones' drone communication units to create ad hoc network links to share location and speed;

[0036] The collision detection and avoidance module is used to calculate the relative distance and relative speed with other drones; and calculate the collision risk in real time based on the relative distance and relative speed to obtain the collision risk value; when the collision risk value exceeds the preset collision risk value, an avoidance command is generated; and the initial flight path is adjusted according to the avoidance command to obtain the adjusted flight path.

[0037] Optionally, the airport subsystem includes an airport housing structure, an automatic charging and swapping mechanism, an airport control unit, and an airport communication unit, wherein the automatic charging and swapping mechanism, the airport control unit, and the airport communication unit are all mounted on the airport housing structure.

[0038] The automatic charging and battery swapping mechanism is used to charge and swap batteries for the drone.

[0039] The airport communication unit is used to establish a communication connection with the UAV communication unit and the data communication server of the central control server subsystem of the UAV.

[0040] The airport control unit is used to receive a task subset and an initial flight path, and then send the task subset and the initial flight path to the corresponding target UAV.

[0041] Optionally, the UAV intelligent flight control device further includes an intelligent mobile terminal subsystem, which includes a terminal communication module and a task planning module. The task planning module is used to generate a task list, and the terminal communication module is used to send the task list to the central control server subsystem.

[0042] Compared to existing intelligent flight control methods for UAVs, this application calculates the allocation scheme and initial flight path of task subsets based on the task list and the real-time status data. Then, according to the allocation scheme, the task subsets and the initial flight path are sent to the corresponding target UAVs. This fully considers the task type, priority, area coordinates, expected flight distance, and expected flight time of each task subset, and combines the real-time position, battery level, endurance, and current status of each UAV to match the most suitable target UAV and initial flight path for each task subset. This avoids problems such as unreasonable task allocation, resource waste, or unbalanced UAV load in traditional flight control methods, and improves the overall efficiency and reliability of UAV mission execution. Attached Figure Description

[0043] The accompanying drawings, which form part of the specification, are used to provide a further understanding of the embodiments of this application and illustrate the implementation methods of this application, together with the textual description, to explain the principles of this application. Obviously, the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings:

[0044] Figure 1 This is a schematic diagram of an intelligent flight control method for a drone according to an embodiment of this application;

[0045] Figure 2 This is a schematic diagram of a link switching method according to an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of an intelligent flight control device for a drone according to an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of a central control server subsystem according to an embodiment of this application;

[0048] Figure 5 This is a schematic diagram of an embodiment of a drone according to this application;

[0049] Figure 6 This is a schematic diagram of an airport subsystem according to an embodiment of this application.

[0050] Among them, 10. UAV intelligent flight control equipment; 100. UAV swarm subsystem; 110. UAV; 120. UAV main body; 130. Collision detection and avoidance module; 140. UAV communication unit; 150. Flight control unit; 161. Positioning and navigation unit; 162. Perception and obstacle avoidance unit; 163. Edge computing unit; 180. Beidou short message communication module; 200. Airport subsystem; 210. Airport enclosure structure; 220. Automatic charging and swapping mechanism; 230. Airport control unit; 240. Airport communication unit; 300. Central control server subsystem; 310. Central control server main body; 320. Data communication server; 330. Task scheduling server; 340. Database server; 350. Web application server; 400. Intelligent mobile terminal subsystem; 410. Terminal communication module; 420. Task planning module. Detailed Implementation

[0051] It should be understood that the terminology, specific structural and functional details used herein are merely for describing particular embodiments and are representative. However, this application may be implemented in many alternative forms and should not be construed as being limited to the embodiments set forth herein.

[0052] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating relative importance or implying the number of technical features indicated. Therefore, unless otherwise stated, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; "multiple" means two or more. The term "comprising" and any variations thereof mean non-exclusive inclusion, where one or more other features, integers, steps, operations, units, components, and / or combinations thereof may be present or added.

[0053] In addition, terms such as “center,” “horizontal,” “up,” “down,” “left,” “right,” “vertical,” “horizontal,” “top,” “bottom,” “inner,” and “outer” that indicate orientation or positional relationships are based on the orientation or relative distance relationships shown in the accompanying drawings. They are only for the purpose of simplifying the description of this application and do not indicate that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0054] Furthermore, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0055] The present application will now be described in detail with reference to the accompanying drawings and optional embodiments.

[0056] Figure 1 This is a schematic diagram of an intelligent flight control method for a drone according to an embodiment of this application, as shown below. Figure 1 As shown, this application discloses an intelligent flight control method for unmanned aerial vehicles (UAVs), which includes the following steps:

[0057] S1: Receive a task list, which includes a task set and the task type, task priority, task area coordinates, estimated flight distance and estimated flight time for each task subset in the corresponding task set;

[0058] It is understood that the types of tasks mentioned include inspection, reconnaissance, surveying, etc.

[0059] S2: Obtain real-time status data of drones in all airport subsystems;

[0060] For example, the real-time status data includes current location, current battery level, maximum battery life, and current status, which includes idle, performing a task, returning to home, and charging.

[0061] S3: Based on the task list and the real-time status data, calculate the task subset allocation scheme and initial flight path;

[0062] A task list typically contains multiple task subsets, so it is understandable that the calculation will yield the allocation schemes for all task subsets and their corresponding initial flight paths.

[0063] S4: According to the task subset allocation scheme, send the task subset and the initial flight path to the corresponding target UAV.

[0064] Compared to existing intelligent flight control methods for unmanned aerial vehicles (UAVs), this application calculates the allocation scheme and initial flight path of task subsets based on the task list and the real-time status data. Then, according to the allocation scheme, the task subsets and the initial flight path are sent to the corresponding target UAVs 110. Considering the task type, priority, area coordinates, expected flight distance, and expected flight time of each task subset, and combining the real-time position, battery level, endurance, and current status of each UAV 110, the most suitable target UAV 110 and initial flight path are matched for each task subset. This avoids problems such as unreasonable task allocation, resource waste, or unbalanced load of UAVs 110 that exist in traditional flight control methods, and improves the overall efficiency and reliability of UAVs 110 in performing tasks.

[0065] In this application, the central control server subsystem 300 simultaneously controls multiple airport subsystems 200. After the task scheduling server 330 of the central control server subsystem 300 receives the task list, the task scheduling server 330 reads the real-time status data of the UAV 110 stored in the database server 340 of the central control server subsystem 300. Then, based on the task list and the real-time status data of the UAV 110, it assigns task subsets and initial flight paths to the UAV 110. This can be directly sent to the corresponding UAV 110 through the data communication server 320 of the central control server subsystem 300. Alternatively, it can be sent first to the airport subsystem 200 where the corresponding UAV 110 is located, and then the airport subsystem 200 sends it to the corresponding UAV 110. Specifically:

[0066] In step S4: sending the task subset and the initial flight path to the corresponding target UAV according to the task subset allocation scheme:

[0067] According to the task subset allocation scheme, the central control server subsystem 300 first sends the task subset and the initial flight path to the airport subsystem 200 where the UAV 110 is located; then the airport subsystem 200 wakes up the UAV 110 and sends the corresponding task subset and flight path to the flight control unit 150 of the target UAV 110 in the form of instructions.

[0068] Compared to the existing scheme that directly sends the task subset and the initial flight path to the target UAV 110 through the central control server subsystem 300, this scheme enables each airport subsystem 200 to have autonomous control capabilities. The central control server subsystem 300 is only responsible for task allocation and UAV 110 status monitoring. After the task is issued, the task subset and the initial flight path are stored in each airport subsystem 200. The failure of the central control server subsystem 300 will not affect the execution of the task, thus reducing the risk of failure.

[0069] Furthermore, after step S4: sending the task subset and the initial flight path to the corresponding target UAV according to the task subset allocation scheme, the following is also included:

[0070] S5: The target UAV executes a subset of tasks according to the initial flight path;

[0071] S61: During the flight of the target UAV, the collision detection and avoidance module of the target UAV calculates the relative distance and relative speed with other UAVs;

[0072] For example, the drone 110 of this application includes a drone communication unit 140. Then, through the drone communication unit 140, all drones 110 can form an ad hoc network link to share coordinates and speeds. By sharing coordinates, the relative distance between different drones 110 can be obtained, and by sharing speeds, the relative speed can be obtained.

[0073] S62: The collision detection and avoidance module of the target UAV calculates the collision risk in real time based on the relative distance and relative speed, and obtains the collision risk value;

[0074] S63: When the collision risk value exceeds the preset collision risk value, the collision detection and avoidance module of the target UAV generates an avoidance command;

[0075] For example, when it is predicted that the distance between two drones 110 is less than 10 meters and the relative speed is greater than 5 m / s, the collision risk value exceeds the preset collision risk value, and then an avoidance command is generated.

[0076] S64: The collision detection and avoidance module of the target UAV adjusts the initial flight path according to the avoidance command to obtain the adjusted flight path.

[0077] Then, the flight control unit 150 controls the drone 110 to fly according to the adjusted flight path.

[0078] The collision detection and avoidance module 130 calculates the collision risk in real time based on the relative distance and relative speed to obtain the collision risk value. When the collision risk value exceeds the preset collision risk value, the collision detection and avoidance module 130 of the target UAV 110 generates an avoidance command, which can improve the dynamic adaptability of mission planning and avoid concentrating all the computing pressure on the central control server subsystem 300, thereby improving the reaction speed of the UAV 110.

[0079] Furthermore, when the target UAV 110 is performing a subset of tasks, it can perceive the environment in real time through the perception and obstacle avoidance unit, identify it through the edge computing unit, obtain the identification results, and send the identification results to the central control server subsystem 300; the identification results include target type, GPS coordinates, and captured images.

[0080] When the dynamic replanning module in the central control server subsystem 300 determines, based on the identification results, that environmental changes or sudden obstacles will cause the UAV to malfunction and be unable to continue performing the mission, it triggers mission replanning and jumps to step S3: based on the mission list and the real-time status data, it calculates the allocation scheme of the mission subset and the initial flight path and reassigns the mission.

[0081] Furthermore, it is understandable that when jumping back to S3: when the task allocation scheme and initial flight path are calculated based on the task list and the real-time status data and the tasks are reallocated, only the task subset corresponding to the target UAV that cannot continue to perform the task is reallocated.

[0082] Figure 2 This is a schematic diagram of a link switching method according to an embodiment of this application, as shown below. Figure 2 As shown, step S4: sending the task subset and the initial flight path to the corresponding target UAV according to the task subset allocation scheme further includes:

[0083] S5: The target UAV executes a subset of tasks according to the initial flight path;

[0084] S71: The drone communication unit of the target drone creates a public network link to establish communication with the data communication server of the central control server subsystem; the drone communication unit of the target drone creates a self-organizing network link to establish communication with the drone communication units of other drones or the airport communication unit of the airport subsystem; the public network link is set as the primary link, and the self-organizing network link is set as the backup link.

[0085] The public network link mentioned therein is a 4G / 5G public network link, and the public network link includes multiple available links.

[0086] S72: The UAV communication unit detects the signal strength and packet loss rate of the currently used primary link in real time;

[0087] For example, the signal strength can be sampled every 100ms; the packet loss rate can be calculated every 1 second.

[0088] S73: When it is detected that the signal strength of the currently used primary link is lower than the first preset signal strength threshold or the packet loss rate is higher than the second preset packet loss rate threshold, traverse all available links of the public network links and select the link with the highest signal strength and the lowest packet loss rate as the new primary link.

[0089] If a signal strength is detected to be below -110dBm or a packet loss rate is above 20%, then all available public network links are traversed.

[0090] S74: When all public network links are unavailable, enable self-organizing network links to communicate with the drone communication units of other drones or the airport communication units of the airport subsystem.

[0091] For example, scan the self-organizing network nodes of the surrounding UAVs 110 and airport subsystem 200, that is, the self-organizing network links established by the target UAV 110 and other UAVs 110 or airport subsystem 200, and then select the node with the strongest signal as a relay node. In this way, indirect communication can be established with the data communication server 320 of the central control server subsystem 300 through the relay node.

[0092] Furthermore, when the self-organizing network link cannot be established, the Beidou short message communication module 180 inside the UAV 110 can be activated. The Beidou short message communication module 180 sends location information and status information to the central control server subsystem 300. For example, location information including longitude, latitude, altitude, timestamp, device ID and battery level is sent every 60 seconds.

[0093] This solves the problem that existing control methods rely on a single communication link and are prone to disconnection in scenarios where signals are disturbed, such as mountainous areas and high-rise buildings in cities. This allows the UAV intelligent flight control method of this application to maintain reliable communication even in extremely weak network environments.

[0094] Furthermore, after step S74: when all public network links are unavailable, enabling communication between the self-organizing network link and the drone communication unit of other drones or the airport communication unit of the airport subsystem, the following is also included:

[0095] S81: When the self-organizing network link also cannot be established, and the communication between the target UAV and the data communication server of the central control server subsystem is completely interrupted for a duration exceeding the third preset duration threshold;

[0096] For example, the third preset duration threshold described in this application is 30 seconds.

[0097] S82: The target UAV's flight control unit acquires the pre-stored airport coordinates of all airport subsystems, as well as the target UAV's flight coordinates and remaining battery power;

[0098] It is understandable that the flight coordinates are the coordinates of the moment of disconnection recorded by the flight control unit 150.

[0099] S83: Based on the airport coordinates of all airport subsystems, as well as the flight coordinates and remaining battery power of the target UAV, calculate the scoring function for each airport subsystem to obtain the scoring set;

[0100] The scoring function is Sj = w1 × (1 − dj × dmax) + w2 × Bj × B0 + w3 × Aj + w4 × Mj; where: dj is the distance between the flight coordinates and the airport subsystem 200, dmax is the maximum allowable return distance of the UAV 110; Bj is the estimated remaining battery power of the UAV 110 after arriving at the airport subsystem 200, B0 is the current battery power of the UAV 110; Aj is the availability coefficient of the airport subsystem 200 (for example, 1 indicates availability, 0 indicates unavailability); Mj is the matching coefficient between the UAV 110 and the airport subsystem 200 (for example, 1 indicates matching, 0 indicates mismatch); for example, in this application, w1 = 0.4, w2 = 0.3, w3 = 0.2, and w4 = 0.1.

[0101] S84: Select the airport subsystem with the highest score in the score set as the emergency landing target;

[0102] S85: If the emergency landing target is the airport subsystem at which the target UAV took off, the flight control unit of the target UAV shall fly according to the preset return route.

[0103] Furthermore, S85: If the emergency landing target is the airport subsystem where the target UAV took off, after the flight control unit of the target UAV flies according to the preset return route, it also includes:

[0104] S86: If the emergency landing target is not the airport subsystem at the time of takeoff of the target UAV, the flight control unit of the target UAV generates a new flight path from the flight coordinates to the emergency landing target.

[0105] The UAV 110 flies along a new route to the airport subsystem 200, which is the emergency landing target. The airport subsystem 200 guides the UAV 110 to land using its laser centering sensor. After landing, the airport subsystem 200 performs charging and battery swapping operations on the UAV 110.

[0106] Compared to existing flight control methods where the UAV 110 can only blindly return to its home base or hover in place when communication is completely interrupted, this application reduces the risk of crash and ensures the safe recovery of the UAV 110 by obtaining the coordinates of all airport subsystems 200 and then selecting the most suitable airport subsystem 200 for recovery through a scoring function.

[0107] Step S3: The step of calculating the allocation scheme and initial flight path of the task subset based on the task list and the real-time status data includes:

[0108] S31: Based on the task list and real-time status data, construct a multi-objective optimization model, with the core optimization objectives being the shortest total task completion time, the lowest total UAV energy consumption, and the highest task coverage.

[0109] S32: The particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the output is the allocation scheme of the task subset and the initial flight path that satisfies all core optimization objectives.

[0110] In the particle swarm optimization algorithm, each particle represents a task allocation scheme. The particle dimension is equal to the number of task subsets, and each dimension is a value representing the number of drones assigned to execute that task subset.

[0111] For each particle, tasks are sorted by priority, with higher priority tasks assigned first; then for each UAV 110, flight paths are generated by sorting by the geographical location of the task subset; then the total task completion time and total task completion energy consumption are calculated (based on flight distance and hovering time); then the task coverage is calculated.

[0112] After obtaining the total task completion time, total task completion energy consumption, and task coverage for each particle, substitute these values ​​into the formula minF = α1 × Ttotal + α2 × Etotal + α3 × (1 − Coverage); where: Ttotal is the total task completion time; Etotal is the total energy consumption for the UAV 110 task completion; Coverage is the task coverage rate, i.e., the proportion of completed tasks; α1, α2, and α3 are weighting coefficients. In this embodiment, α1 = 0.4, α2 = 0.3, and α3 = 0.3 are used, and the particle with the highest score is selected as the preferred choice.

[0113] In simple terms, the process involves first generating multiple task allocation schemes and their corresponding initial flight paths. Then, it calculates the total task completion time, total energy consumption of the UAV 110, and task coverage for each task allocation scheme and sorts them. Different weights are then used to calculate the task allocation scheme with the highest score, and the scheme is selected by substituting minF=α1×Ttotal+α2×Etotal+α3×(1−Coverage).

[0114] By matching the most suitable target UAV 110 and initial flight path to each task subset, problems such as unreasonable task allocation, resource waste, or unbalanced load on the UAV 110 that exist in traditional flight control methods are avoided, effectively improving the overall efficiency and reliability of the UAV 110 in performing tasks.

[0115] Figure 3 This is a schematic diagram of an intelligent flight control device for a drone according to an embodiment of this application, as shown below. Figure 3 As shown, this application also discloses an intelligent flight control device 10 for unmanned aerial vehicles (UAVs). The intelligent flight control device 10 is used to execute the above-described intelligent flight control method for UAVs. The intelligent flight control device 10 includes multiple UAV cluster subsystems 100, multiple airport subsystems 200, and a central control server subsystem 300.

[0116] The UAV swarm subsystem 100 includes multiple UAVs 110; the airport subsystem 200 is used to accommodate multiple UAVs 110; the central control server subsystem 300 is used to receive a task list and obtain real-time status data of all UAVs 110 in the airport subsystem 200, and calculate the task subset allocation scheme and initial flight path according to the task list and the real-time status data; then, according to the task subset allocation scheme, the task subset and the initial flight path are sent to the corresponding target UAV 110; it is understood that UAVs 110, airport subsystem 200 and central control server subsystem 300 can communicate with each other.

[0117] The central control server subsystem 300 calculates the task subset allocation scheme and initial flight path based on the obtained task list and real-time status data. Then, according to the task subset allocation scheme, it sends the task subset and the initial flight path to the corresponding target UAV 110. It fully considers the task type, priority, area coordinates, expected flight distance and expected flight time of each task subset, and combines the real-time position, battery level, endurance and current status of each UAV 110 to match the most suitable target UAV 110 and initial flight path for each task subset. This avoids the problems of unreasonable task allocation, resource waste or unbalanced load of UAV 110 in traditional flight control methods, and effectively improves the overall efficiency and reliability of UAV 110 in performing tasks.

[0118] The UAV intelligent flight control device 10 also includes an intelligent mobile terminal subsystem 400, which includes a terminal communication module 410 and a task planning module 420. The task planning module 420 is used to generate a task list, and the terminal communication module 410 is used to send the task list to the central control server subsystem 300.

[0119] Figure 4 This is a schematic diagram of a central control server subsystem according to an embodiment of this application, as shown below. Figure 4 As shown, the central control server subsystem 300 includes a central control server main body 310, a communication server 320, a task scheduling server 330, a database server 340, and a web application server 350.

[0120] The data communication server 320 is used to establish communication connections with the machine communication unit 140 of the UAV 110 and the airport communication unit 240 of the airport control unit 230. The task scheduling server 330 is used to receive a task list and obtain real-time status data of all UAVs 110 in the airport subsystem 200, and calculate the task subset allocation scheme and initial flight path based on the task list and the real-time status data; then, according to the task subset allocation scheme, it sends the task subset and the initial flight path to the corresponding target UAV 110. The database server 340 is used to store the real-time status data of the UAVs 110. The web application server 350 is used to support user access via a browser.

[0121] Figure 5 This is a schematic diagram of a drone according to an embodiment of this application, as shown below. Figure 5As shown, the drone 110 includes a drone body 120, a collision detection and avoidance module 130, and a drone communication unit 140. The collision detection and avoidance module 130 and the drone communication unit 140 are both mounted on the drone body 120.

[0122] The UAV communication unit 140 can also be used to create an ad hoc network link to share position and speed with the UAV communication units 140 of other UAVs 110; the collision detection and avoidance module 130 is used to calculate the relative distance and relative speed with other UAVs 110; and calculate the collision risk in real time based on the relative distance and relative speed to obtain the collision risk value; when the collision risk value exceeds the preset collision risk value, an avoidance command is generated; and the initial flight path is adjusted according to the avoidance command to obtain the adjusted flight path.

[0123] The UAV 110 also includes a flight control unit 150 for operating flight control. A positioning and navigation unit 161 is used to acquire the UAV 110's location. A perception and obstacle avoidance unit 162 is used to acquire images of the front and identify obstacles; an edge computing unit 163 is used to process the image data acquired by the perception and obstacle avoidance unit 162 and identify targets such as people, vehicles, and flames. A BeiDou short message communication module 180 is used to send emergency location information and status commands.

[0124] Figure 6 This is a schematic diagram of an airport subsystem according to an embodiment of this application, as shown below. Figure 6 As shown, the airport subsystem 200 includes an airport housing structure 210, an automatic charging and swapping mechanism 220, an airport control unit 230, and an airport communication unit 240. The automatic charging and swapping mechanism 220, the airport control unit 230, and the airport communication unit 240 are all mounted on the airport housing structure 210.

[0125] The automatic charging and swapping mechanism 220 is used to charge and swap batteries for the UAV 110; the airport communication unit 240 is used to establish a communication connection between the UAV communication unit 140 of the UAV 110 and the data communication server 320 of the central control server subsystem 300. The airport control unit 230 is used to receive the task subset and the initial flight path, and send the task subset and the initial flight path to the corresponding target UAV 110. The airport subsystem 200 also includes a UAV 110 landing guidance mechanism, which guides the UAV 110 to land using a laser alignment sensor.

[0126] It should be noted that the limitations on each step involved in this solution are not considered as limiting the order of steps, provided that they do not affect the implementation of the specific solution. The steps listed first can be executed first, later, or even simultaneously. As long as this solution can be implemented, it should be considered to fall within the scope of protection of this application.

[0127] It should be noted that the inventive concept of this application can form many embodiments, but due to the limited space of the application documents, they cannot all be listed. Therefore, without conflict, the embodiments described above or the technical features can be arbitrarily combined to form new embodiments. After the embodiments or technical features are combined, the original technical effect will be enhanced.

[0128] The above description, in conjunction with specific optional embodiments, provides a further detailed explanation of this application and should not be construed as limiting the specific implementation of this application to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of this application, and all such modifications or substitutions should be considered within the scope of protection of this application.

Claims

1. A method for intelligent flight control of an unmanned aerial vehicle (UAV), characterized in that, The intelligent flight control method for unmanned aerial vehicles includes the following steps: S1: Receive a task list, which includes a task set and the task type, task priority, task area coordinates, estimated flight distance and estimated flight time for each task subset in the corresponding task set; S2: Obtain real-time status data of drones in all airport subsystems; S3: Based on the task list and the real-time status data, calculate the task subset allocation scheme and initial flight path; S4: According to the task subset allocation scheme, send the task subset and the initial flight path to the corresponding target UAV.

2. The intelligent flight control method for unmanned aerial vehicles according to claim 1, characterized in that, Step S4: After sending the task subset and the initial flight path to the corresponding target UAV according to the task subset allocation scheme, the step further includes: S5: The target UAV executes a subset of tasks according to the initial flight path; S61: During the flight of the target UAV, the collision detection and avoidance module of the target UAV calculates the relative distance and relative speed with other UAVs; S62: The collision detection and avoidance module of the target UAV calculates the collision risk in real time based on the relative distance and relative speed, and obtains the collision risk value; S63: When the collision risk value exceeds the preset collision risk value, the collision detection and avoidance module of the target UAV generates an avoidance command; S64: The collision detection and avoidance module of the target UAV adjusts the initial flight path according to the avoidance command to obtain the adjusted flight path.

3. The intelligent flight control method for unmanned aerial vehicles according to claim 1, characterized in that, Step S4: After sending the task subset and the initial flight path to the corresponding target UAV according to the task subset allocation scheme, the step further includes: S5: The target UAV executes a subset of tasks according to the initial flight path; S71: The drone communication unit of the target drone creates a public network link to establish communication with the data communication server of the central control server subsystem; the drone communication unit of the target drone creates a self-organizing network link to establish communication with the drone communication units of other drones or the airport communication unit of the airport subsystem; the public network link is set as the primary link, and the self-organizing network link is set as the backup link. S72: The UAV communication unit detects the signal strength and packet loss rate of the currently used primary link in real time; S73: When it is detected that the signal strength of the currently used primary link is lower than the first preset signal strength threshold or the packet loss rate is higher than the second preset packet loss rate threshold, traverse all available links of the public network links and select the link with the highest signal strength and the lowest packet loss rate as the new primary link. S74: When all public network links are unavailable, enable self-organizing network links to communicate with the drone communication units of other drones or the airport communication units of the airport subsystem.

4. The intelligent flight control method for unmanned aerial vehicles according to claim 3, characterized in that, S74: When all public network links are unavailable, after the step of enabling the self-organizing network link to communicate with the drone communication unit of other drones or the airport communication unit of the airport subsystem, the following is also included: S81: When the self-organizing network link also cannot be established, and the communication between the target UAV and the data communication server of the central control server subsystem is completely interrupted for a duration exceeding the third preset duration threshold; S82: The target UAV's flight control unit acquires the pre-stored airport coordinates of all airport subsystems, as well as the target UAV's flight coordinates and remaining battery power; S83: Based on the airport coordinates of all airport subsystems, as well as the flight coordinates and remaining battery power of the target UAV, calculate the scoring function for each airport subsystem to obtain the scoring set; S84: Select the airport subsystem with the highest score in the score set as the emergency landing target; S85: If the emergency landing target is the airport subsystem at which the target UAV took off, the flight control unit of the target UAV shall fly according to the preset return route.

5. The intelligent flight control method for unmanned aerial vehicles according to claim 4, characterized in that, S85: If the emergency landing target is the airport subsystem where the target UAV took off, after the flight control unit of the target UAV flies according to the preset return route, the following steps are also included: S86: If the emergency landing target is not the airport subsystem at the time of takeoff of the target UAV, the flight control unit of the target UAV generates a new flight path from the flight position to the emergency landing target.

6. The intelligent flight control method for unmanned aerial vehicles according to claim 1, characterized in that, Step S3: The step of calculating the allocation scheme and initial flight path of the task subset based on the task list and the real-time status data includes: S31: Based on the task list and real-time status data, construct a multi-objective optimization model, with the core optimization objectives being the shortest total task completion time, the lowest total UAV energy consumption, and the highest task coverage. S32: The particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the output is the allocation scheme of the task subset and the initial flight path that satisfies all core optimization objectives.

7. An intelligent flight control device for unmanned aerial vehicles (UAVs), characterized in that, The intelligent flight control device for unmanned aerial vehicles (UAVs) is used to execute the intelligent flight control method for UAVs according to any one of claims 1-6. The intelligent flight control device for UAVs includes multiple UAV cluster subsystems, multiple airport subsystems, and a central control server subsystem. The drone swarm subsystem includes multiple drones; the airport subsystem is used to accommodate multiple drones. The central control server subsystem is used to receive the task list and obtain real-time status data of UAVs in all airport subsystems, and calculate the task subset allocation scheme and initial flight path according to the task list and the real-time status data; then, according to the task subset allocation scheme, the task subset and the initial flight path are sent to the corresponding target UAV.

8. The intelligent flight control device for unmanned aerial vehicles according to claim 7, characterized in that, The drone includes a drone body, a collision detection and avoidance module, and a drone communication unit, wherein the collision detection and avoidance module and the drone communication unit are both mounted on the drone body. The drone communication unit is used by other drones' drone communication units to create ad hoc network links to share location and speed; The collision detection and avoidance module is used to calculate the relative distance and relative speed with other drones; The collision risk is calculated in real time based on the relative distance and relative speed to obtain the collision risk value; When the collision risk value exceeds the preset collision risk value, an avoidance command is generated; the initial flight path is adjusted according to the avoidance command to obtain the adjusted flight path.

9. The intelligent flight control device for unmanned aerial vehicles according to claim 8, characterized in that, The airport subsystem includes an airport housing structure, an automatic charging and swapping mechanism, an airport control unit, and an airport communication unit. The automatic charging and swapping mechanism, the airport control unit, and the airport communication unit are all mounted on the airport housing structure. The automatic charging and battery swapping mechanism is used to charge and swap batteries for the drone. The airport communication unit is used to establish a communication connection with the UAV communication unit and the data communication server of the central control server subsystem of the UAV. The airport control unit is used to receive a task subset and an initial flight path, and then send the task subset and the initial flight path to the corresponding target UAV.

10. The intelligent flight control device for unmanned aerial vehicles according to claim 7, characterized in that, The UAV intelligent flight control equipment also includes an intelligent mobile terminal subsystem, which includes a terminal communication module and a task planning module. The task planning module is used to generate a task list, and the terminal communication module is used to send the task list to the central control server subsystem.

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