A multi-aircraft relay flight task cooperative control method and system

Abstract

The application discloses a kind of multi-machine relay flight task coordination control method and system, it is related to multi-machine relay flight task coordination control field, for improving the safety of multi-machine relay flight task under extreme condition, including: according to flight risk assessment result and communication condition, whether the current preset task execution mode meets preset switching condition is judged;If the current preset task execution mode meets preset switching condition, then abort the current preset task execution mode, and start non-contact task continuation and material support process;In non-contact task continuation and material support process, guide accompanying unmanned aerial vehicle and main transport unmanned aerial vehicle keep specific relative position and accompany, obtain the visual information of main transport unmanned aerial vehicle, and according to visual information, judge the flight state and material carrying state of main transport unmanned aerial vehicle;According to flight state and material carrying state, flight guidance information is sent to main transport unmanned aerial vehicle, to assist main transport unmanned aerial vehicle adjustment flight.

Description

Technical Field

[0001] This invention relates to the field of collaborative control for multi-aircraft relay flight missions, and in particular to a method and system for collaborative control of multi-aircraft relay flight missions. Background Technology

[0002] In the field of drone operations, particularly for long-distance logistics and emergency supply transport missions, multi-drone relay flight is widely adopted to overcome the limitations of a single aircraft's endurance. These missions require sophisticated coordination and control systems to ensure seamless handover and safe delivery of supplies. However, in practice, these complex missions are often challenged by unpredictable environmental factors and communication limitations, especially when operating in remote or terrain-challenged areas. Ensuring mission safety and continuity, especially when critical events occur far from pre-designated safe areas and communication with ground control is disrupted, remains a significant technical challenge. Summary of the Invention

[0003] This application discloses a collaborative control method and system for multi-drone relay flight missions, which aims to solve the technical problems of high risks faced by traditional material handover methods in complex environments and under limited communication conditions, as well as the difficulty of drone swarms in autonomously responding to emergencies.

[0004] In a first aspect, this application discloses a cooperative control method for multi-aircraft relay flight missions, comprising the following steps:

[0005] In response to a mission adjustment request from the main transport drone, the system receives local environmental information perceived by the escort drone. This local environmental information describes the airspace conditions within a preset range of the main transport drone. The mission adjustment request is used to indicate that the main transport drone's battery level is below a threshold.

[0006] Assess flight risks in the current airspace based on local environmental information and monitor communication with external control centers;

[0007] Based on the flight risk assessment results and communication status, determine whether the current preset mission execution mode meets the preset switching conditions; the preset switching conditions include flight risks exceeding the preset safety range and communication status being limited;

[0008] If the current preset task execution mode meets the preset switching conditions, the current preset task execution mode will be terminated, and a contactless task continuation and material support process will be initiated.

[0009] In the non-contact mission continuation and material support process, guide the escort drone to maintain a specific relative position with the main transport drone to escort it, obtain the visual information of the main transport drone, and judge the flight status and material carrying status of the main transport drone based on the visual information.

[0010] Based on the flight status and cargo carrying status, flight guidance information is sent to the main transport drone to assist the main transport drone in adjusting its flight.

[0011] When it is determined that the main transport drone cannot continue flying or the cargo carrying status is abnormal, a target escort drone is designated to guide the main transport drone to a safe landing.

[0012] Optionally, the visual information includes image sequences; guiding the escort drone to maintain a specific relative position with the main transport drone for escort flight, acquiring the visual information of the main transport drone, and determining the flight status and cargo carrying status of the main transport drone based on the visual information, including:

[0013] The accompanying drone acquires its own flight attitude data and image sequences from the main transport drone;

[0014] Based on the flight attitude data of the accompanying drone, calculate the drone's trajectory and attitude change rate in space.

[0015] Image processing was performed on the image sequence to identify key feature points on the main transport drone; key feature points included wingtips and rotor fairings.

[0016] Motion compensation is performed on the image sequence based on the motion trajectory, the rate of change of posture, and the positional changes of key feature points in the image sequence.

[0017] Based on the compensated image sequence, the flight status and cargo carrying status of the main transport drone are determined.

[0018] Optionally, a target escort drone can be designated to guide the main transport drone to a safe landing, including:

[0019] The accompanying drone scans the terrain within a preset range to obtain the terrain features of potential landing areas;

[0020] Assess the safety of each potential landing area based on terrain features and the flight status of the main transport drone;

[0021] Based on the safety assessment results, the optimal temporary landing plan is determined;

[0022] Based on the optimal temporary landing plan, a target escort drone is designated, which guides the main transport drone to a safe landing through visual recognition, radio signals, or light indication.

[0023] Optionally, the method also includes:

[0024] Acquire data on the airframe vibration and impact of the main transport drone, as well as the fluctuation range of the rotor motor power output;

[0025] The load risk index is calculated based on the vibration and impact data of the airframe and the fluctuation range of the rotor motor power output; the load risk index is used to reflect the risk of damage to the loading point of the materials.

[0026] Adjust the damping characteristics of the material mounting points according to the mounting risk index;

[0027] The accompanying drone acquires the structural characteristics of the cargo loading point of the main transport drone;

[0028] Based on structural characteristics, determine signs of loosening, deformation, or damage at the material mounting points;

[0029] Assess whether the load-bearing status of the materials is abnormal based on the load risk index, damping characteristics, and signs of loosening, deformation, or damage.

[0030] Optionally, after adjusting the damping characteristics of the material mounting point, the method also includes:

[0031] The main transport drone acquires real-time force data and vibration spectrum data at the cargo mounting points;

[0032] The real-time force data and vibration spectrum data are compared with preset thresholds;

[0033] Based on the comparison results, evaluate the effect of the damping characteristic adjustment;

[0034] If the adjustment does not achieve the expected results, a correction instruction will be generated;

[0035] According to the revised instructions, the damping characteristics of the material mounting points were readjusted.

[0036] Optionally, generate correction instructions, including:

[0037] Obtain real-time status information of the communication links within the current cluster. The real-time status information includes the available bandwidth and signal strength of the links.

[0038] Based on real-time status information, a transmission strategy is determined; and a correction instruction is generated based on the transmission strategy; the transmission strategy includes the instruction encoding method, the number of retransmissions, and the transmission interval.

[0039] Optionally, a correction instruction is generated based on the transmission strategy, including:

[0040] When the available bandwidth is less than the bandwidth threshold or the signal strength is less than the strength threshold, a high redundancy coding method is used to encode the correction command, increasing the number of retransmissions of the correction command and extending the transmission interval.

[0041] When the available bandwidth is greater than or equal to the bandwidth threshold and the signal strength is greater than or equal to the strength threshold, a low-redundancy coding method is used to encode the correction command, reducing the number of retransmissions of the correction command and shortening the transmission interval.

[0042] Optionally, the method also includes:

[0043] When extending the transmission interval, acquire the mechanical data of the main transport drone, including the vibration, impact and force values ​​of the cargo mounting points;

[0044] Based on mechanical data, determine the rate of deterioration of the main transport drone's condition;

[0045] When the deterioration rate exceeds a preset threshold, the number of retransmissions of the correction command is stopped, the transmission interval is stopped, and an emergency correction command is generated. The emergency correction command uses the maximum number of retransmissions and the minimum transmission interval.

[0046] The main transport drone adjusted the damping characteristics of the cargo mounting points according to the emergency correction instructions.

[0047] Optional local environmental information includes wind speed, wind direction, turbulence intensity, and distance to obstacles.

[0048] Secondly, this application also discloses a multi-aircraft relay flight mission cooperative control system, which includes:

[0049] The information receiving module is used to respond to the mission adjustment request issued by the main transport drone and receive local environmental information perceived by the escort drone. The local environmental information is used to describe the airspace conditions within a preset range of the main transport drone. The mission adjustment request is used to indicate that the main transport drone's battery level is less than the battery threshold.

[0050] The risk assessment module is used to assess the flight risks in the current airspace based on local environmental information and to monitor the communication status with the external control center.

[0051] The condition judgment module is used to determine whether the current preset mission execution mode meets the preset switching conditions based on the flight risk assessment results and communication status. The preset switching conditions include the flight risk exceeding the preset safety range and the communication status being restricted.

[0052] The mode switching module is used to stop the current preset task execution mode and start a non-contact task continuation and material support process if the current preset task execution mode meets the preset switching conditions.

[0053] The escort guidance and information judgment module is used to guide the escort drone to maintain a specific relative position with the main transport drone during non-contact mission continuation and material support processes, acquire visual information of the main transport drone, and judge the flight status and material carrying status of the main transport drone based on the visual information.

[0054] The flight guidance module is used to send flight guidance information to the main transport drone based on the flight status and cargo carrying status, so as to assist the main transport drone in adjusting its flight.

[0055] The landing scheme and guidance module is used to designate a target escort drone to guide the main transport drone to a safe landing when it is determined that the main transport drone cannot continue flying or the cargo carrying status is abnormal.

[0056] Beneficial effects

[0057] The multi-aircraft relay flight mission collaborative control method disclosed in this application responds to the mission adjustment request of the main transport UAV when its battery is low, and assesses airspace flight risks and communication conditions by combining the local environmental information perceived by the escort UAV. When the flight risk exceeds the preset safety range and communication is restricted, the system can intelligently determine and terminate the current mission execution mode, and initiate a non-contact mission continuation and material support process. In this process, the escort UAV can maintain a specific relative position with the main transport UAV and acquire visual information to determine the flight status and material carrying status of the main transport UAV, and then send flight guidance information to assist it in adjusting its flight. When the main transport UAV cannot continue flying or its material carrying status is abnormal, the system can designate a target escort UAV to guide it to a safe landing.

[0058] This technical solution effectively addresses the significant collision risks inherent in traditional multi-drone relay missions under complex environments and limited communication conditions, as well as the technical challenges of drone swarms autonomously generating and executing unpreset emergency response plans. By introducing a non-contact mission continuation and material support process, the collision risks associated with close-range escort and precise alignment operations are avoided. Simultaneously, the system possesses autonomous assessment, judgment, and decision-making capabilities. Even with poor communication with the ground, it can rapidly generate and execute emergency response plans based on drone status, environmental perception data, and mission objectives. These plans may include changing the relay method or temporarily finding a sheltered platform for emergency landing, thereby ensuring the safe transport of medical supplies and preventing drone crashes. This method significantly improves the safety, reliability, and autonomous adaptability of multi-drone relay missions under extreme conditions, demonstrating significant practical application value. Attached Figure Description

[0059] Figure 1 This is a schematic flowchart of a collaborative control method for multi-aircraft relay flight missions provided by an embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of another collaborative control method for multi-aircraft relay flight missions provided by an embodiment of the present invention;

[0061] Figure 3This is a schematic diagram of a collaborative control system for multi-aircraft relay flight missions provided in an embodiment of the present invention. Detailed Implementation

[0062] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0063] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0064] To better understand the cooperative control method for multi-aircraft relay flight missions proposed in this application, the following will elaborate on some key terms and implementation environments involved.

[0065] "Main transport drones" refer to drones that are primarily responsible for carrying and transporting supplies in multi-drone relay missions. Their core function is to complete the predetermined transport task, but they may face challenges such as power consumption and environmental changes during flight.

[0066] "Escort drones" refer to drones that fly in coordination with the main transport drone in multi-drone relay missions, providing auxiliary support. The main responsibilities of escort drones include environmental perception, information gathering, risk assessment, flight guidance, and assisting the main transport drone in making a safe landing in emergency situations.

[0067] "Local environmental information" refers to data collected by the accompanying drone within its preset perception range, used to describe the airspace conditions around the main transport drone. This information is crucial for assessing flight risks and may include, for example, wind speed, wind direction, turbulence intensity, and obstacle distances.

[0068] A “mission adjustment request” is a signal sent by the main transport drone to the escort drone under specific conditions (such as when the battery level is below a preset threshold), indicating that it needs to adjust its current mission execution status or seek assistance.

[0069] "Battery threshold" refers to the preset lower limit of battery power. When the battery power of the main transport drone falls below this threshold, a mission adjustment request is triggered, indicating that emergency measures may need to be taken.

[0070] "Flight risk" refers to the degree of danger that the main transport drone may face if it continues to fly under the current airspace conditions, such as collision risk and crash risk.

[0071] "Communication status" refers to the quality and availability of the communication link between the main transport drone or escort drone and the external control center, including signal strength, latency, bandwidth, etc.

[0072] "Preset mission execution mode" refers to the established operating procedures and strategies followed by multi-aircraft relay flight missions under normal circumstances.

[0073] "Preset switching conditions" refer to a specific combination of conditions that trigger a switch from the current preset task execution mode to a non-contact task continuation and material support process, such as when flight risks exceed the preset safety range and communication conditions are limited.

[0074] "Contactless mission continuity and material support process" refers to a process in which, under special circumstances, the escort drone and the main transport drone can achieve continuous mission execution and secure material support without physical contact.

[0075] "Visual information" refers to the image or video data of the main transport drone acquired by the accompanying drone through its onboard visual sensors, which is used to analyze the flight status and cargo carrying status of the main transport drone.

[0076] "Flight status" refers to the current flight attitude, speed, altitude, heading, and other parameters of the main transport drone.

[0077] "Material carrying status" refers to the stability and safety of the materials carried by the main transport drone, such as whether there are any abnormalities such as loosening, deformation or damage.

[0078] "Flight guidance information" refers to the instructions or suggestions sent by the escort drone to the main transport drone based on its assessment of the main transport drone's status, in order to assist in adjusting its flight.

[0079] The following specific embodiments will provide a detailed introduction and explanation of the cooperative control method for multi-aircraft relay flight missions provided in this application.

[0080] Reference Figure 1 This invention provides a collaborative control method for multi-aircraft relay flight missions, comprising the following steps:

[0081] S1, in response to the mission adjustment request issued by the main transport drone, receives local environmental information perceived by the accompanying drone.

[0082] Among them, local environmental information is used to describe the airspace conditions within the preset range of the main transport drone; mission adjustment request is used to indicate that the main transport drone's battery level is less than the battery threshold.

[0083] Specifically, when the main transport drone's battery level falls below a preset threshold, it will proactively send a mission adjustment request. Upon receiving this request, the accompanying drone will immediately initiate subsequent collaborative control procedures. For example, the main transport drone can periodically monitor its own battery level, and once it detects that the battery level is below a certain threshold (e.g., enough to sustain flight for only 5 minutes), it will broadcast a mission adjustment request to the accompanying drone via radio signal.

[0084] S2. Assess the flight risks in the current airspace based on local environmental information and monitor communication with external control centers.

[0085] This local environmental information is used to describe the airspace conditions within a pre-defined range of the main transport drone. For example, the accompanying drone can use its onboard lidar, millimeter-wave radar, or visual sensors to scan the airspace around the main transport drone in real time, acquiring data including wind speed, wind direction, turbulence intensity, and obstacle distance. This data can be processed into 3D point cloud maps or environmental parameter lists for subsequent risk assessment.

[0086] Flight risk assessment can be based on parameters such as wind speed, wind direction, turbulence intensity, and obstacle distance from local environmental information. For example, when wind speed is too high, turbulence intensity is strong, or obstacles are dense, the flight risk will be assessed as high. Communication status monitoring can be accomplished by detecting indicators such as signal strength, data transmission rate, and latency between the drone and an external control center. For example, an escort drone can periodically send heartbeat packets to an external control center, and the quality of the communication link can be judged based on the received response time.

[0087] S3. Based on the flight risk assessment results and communication status, determine whether the current preset mission execution mode meets the preset switching conditions.

[0088] The preset switching conditions include flight risks exceeding the preset safety range and communication conditions being restricted.

[0089] For example, if the flight risk assessment results show that the current airspace is at high risk (e.g., wind speed exceeds the safety threshold and there are potential collision obstacles), and the communication signal strength with the external control center is lower than the preset threshold or the data transmission delay is too high, then the preset switching conditions are met.

[0090] S4. If the current preset task execution mode meets the preset switching conditions, the current preset task execution mode is terminated, and a contactless task continuation and material support process is initiated.

[0091] For example, if the switching conditions are met, the system will immediately stop the original relay point flight plan and initiate a pre-set emergency procedure designed to ensure mission continuity and the safety of supplies without physical contact.

[0092] S5. In the non-contact mission continuation and material support process, guide the escort drone to maintain a specific relative position with the main transport drone to escort it, obtain the visual information of the main transport drone, and judge the flight status and material carrying status of the main transport drone based on the visual information.

[0093] For example, a companion drone can continuously track the main transport drone using its vision system (such as a high-definition camera) and acquire real-time images or video streams of the main transport drone through image recognition technology. Simultaneously, the companion drone adjusts its flight attitude and position using its own navigation system to ensure a safe relative position with the main transport drone, conducive to visual information acquisition. Analysis of this visual information can determine whether the main transport drone's flight attitude is stable, whether there are any abnormal vibrations, and whether the cargo it carries is loose, tilted, or damaged.

[0094] S6. Based on the flight status and cargo carrying status, send flight guidance information to the main transport drone to assist the main transport drone in adjusting its flight.

[0095] For example, if visual information analysis results show that the main transport drone has slight attitude instability or slight shaking of the cargo, the escort drone can calculate the corresponding correction instructions and send flight guidance information to the main transport drone via radio link, such as suggesting that it adjust its flight speed, altitude or attitude to restore stable flight.

[0096] S7. When it is determined that the main transport drone cannot continue to fly, or the cargo carrying status is abnormal, designate a target escort drone to guide the main transport drone to a safe landing.

[0097] For example, if visual information analysis indicates that the main transport drone is experiencing severe attitude instability, structural damage, or cargo detachment, the system will determine that it cannot continue flying or that the cargo-bearing status is abnormal. In this case, the system will select the most suitable escort drone from the escort drone swarm as the target escort drone, which will be responsible for guiding the main transport drone to find and perform a safe landing.

[0098] The overall working principle of this application lies in constructing an intelligent, adaptive multi-drone collaborative control framework to cope with complex and ever-changing mission environments. When the main transport drone issues a mission adjustment request due to insufficient power, the escort drone swarm no longer passively waits for external instructions but actively intervenes. The escort drones first utilize their environmental perception capabilities to acquire local environmental information around the main transport drone, and combine this with communication status with the external control center to conduct a comprehensive risk assessment of the current mission environment. This assessment process is real-time and dynamic, accurately reflecting the degree of danger in the current airspace and the feasibility of external support.

[0099] Based on risk assessment results and communication status, the system intelligently determines whether the currently preset mission execution mode can continue to be executed safely. If the assessment results indicate that the flight risk exceeds the safe range and external communication is restricted, it means that the traditional mission mode can no longer cope with the current emergency. The system will then decisively terminate the current mode and immediately initiate a non-contact mission continuation and logistical support process. This mode switching is one of the core innovations of this application, enabling the drone swarm to make autonomous decisions and responses even when external control is lost.

[0100] In this non-contact process, the accompanying drone maintains a specific relative position with the main transport drone, continuously monitoring its flight status and cargo carrying status via visual information. This non-contact monitoring method avoids the collision risks associated with traditional close-range handovers, while ensuring real-time monitoring of the main transport drone's critical status. Based on the visual information analysis, the accompanying drone can send precise flight guidance information to the main transport drone, assisting it in adjusting its flight and thus extending its safe flight time or improving its flight attitude to some extent.

[0101] Furthermore, when it is determined that the main transport drone is unable to continue flying or that the cargo-carrying status is severely abnormal, the system will designate a target escort drone to guide the main transport drone to a safe landing. This mechanism ensures that, even in the most critical situations, the mission can end in the safest manner, maximizing the protection of both the drone and the cargo. The entire process demonstrates the autonomous perception, assessment, decision-making, and execution capabilities of the escort drone swarm, forming a closed-loop emergency response system that effectively solves the challenge of coordinated control in multi-drone relay flight missions in complex environments.

[0102] Compared to existing technologies, this application demonstrates significant progress and innovation in the collaborative control of multi-aircraft relay flight missions. Traditional multi-aircraft relay flight modes often struggle to provide effective autonomous emergency response when faced with multiple challenges, such as the main transport drone's battery depletion, harsh local environments, and limited communication with external control centers. For example, when transporting emergency medical supplies in remote mountainous areas, if the main transport drone prematurely triggers a relay request in dangerous airspace and communication is interrupted, existing systems may become stuck due to the inability to obtain external instructions in a timely manner or a lack of autonomous decision-making capabilities, leading to mission interruption or even drone crashes.

[0103] In some embodiments described above, a method is proposed to guide a companion drone to maintain a specific relative position with the main transport drone during flight, acquire visual information from the main transport drone, and determine the flight status and cargo carrying status of the main transport drone based on this visual information. However, in practical applications, the movement and attitude changes of the companion drone itself may interfere with the acquired visual information, such as causing image jitter or target blurring, thereby affecting the accuracy and reliability of the judgment of the flight status and cargo carrying status of the main transport drone.

[0104] In this regard, such as Figure 2 As shown, this application further proposes that the aforementioned visual information includes image sequences; the aforementioned guiding drone maintains a specific relative position with the main transport drone to acquire the visual information of the main transport drone, and determines the flight status and cargo carrying status of the main transport drone based on the visual information, specifically including:

[0105] S101, the accompanying drone acquires its own flight attitude data and image sequences from the main transport drone.

[0106] Specifically, visual information can be understood as a series of continuous image frames, i.e., an image sequence, which provides a flow of visual information that changes over time, facilitating dynamic analysis. The accompanying drone, through its onboard sensors such as the inertial measurement unit (IMU) and global positioning system (GPS), can acquire its own flight attitude data in real time, including pitch angle, roll angle, yaw angle, and three-axis acceleration and angular velocity. Simultaneously, it continuously captures image sequences from the main transport drone via its onboard camera.

[0107] S102. Based on the flight attitude data of the accompanying drone, calculate the trajectory and attitude change rate of the accompanying drone in space.

[0108] Based on the flight attitude data acquired by the accompanying drone itself, the drone's trajectory in three-dimensional space can be accurately calculated, including its position, velocity, and acceleration, as well as its rate of attitude change, i.e., the rate of change of its attitude angle over time. This data provides essential reference for subsequent image processing and motion compensation.

[0109] S103. Perform image processing on the image sequence to identify key feature points on the main transport drone.

[0110] Key features include the wingtip and rotor fairing.

[0111] Identifying these feature points can be achieved through various image processing algorithms, such as deep learning-based object detection algorithms or traditional feature extraction algorithms, with the aim of providing accurate tracking targets for subsequent motion compensation.

[0112] S104. Perform motion compensation on the image sequence based on the motion trajectory, the rate of change of posture, and the positional changes of key feature points in the image sequence.

[0113] Motion compensation refers to the use of algorithms to eliminate or reduce the jitter and blurring caused by the movement of the escort drone in the image sequence, so that the main transport drone presents a relatively stable visual effect in the compensated image sequence. This can be achieved by estimating the relative motion between the escort drone and the main transport drone and performing geometric transformations on the images.

[0114] S105. Based on the compensated image sequence, determine the flight status and cargo carrying status of the main transport drone.

[0115] For example, by analyzing the relative positions, shape changes, or vibration patterns of key feature points of the main transport drone in the compensated image, it is possible to assess whether its flight attitude is stable and whether there is any abnormal shaking or tilting. At the same time, by examining the visual characteristics of the cargo mounting area, such as the integrity of the mounting points, the fixation of the cargo, or whether there are signs of loosening or deformation, it is possible to determine whether the cargo loading status is abnormal.

[0116] This application's solution effectively solves the problem of interference caused by the movement of the accompanying drone itself on the accuracy of visual information judgment by introducing the acquisition and calculation of the accompanying drone's own flight attitude data, combined with key feature point recognition and motion compensation of the image sequence. Specifically, the accompanying drone's own flight attitude and trajectory are dynamically changing during the accompanying flight. If this is not considered and the original image sequence is used directly to judge the state of the main transport drone, the main transport drone in the image may appear unstable due to the shaking or attitude changes of the accompanying drone, leading to misjudgment. By acquiring the accompanying drone's own flight attitude data and calculating its trajectory and attitude change rate, the motion state of the accompanying drone can be accurately quantified. Subsequently, using this motion data to perform motion compensation on the image sequence can "stabilize" the main transport drone in the image, eliminating the visual noise caused by the accompanying drone's own movement. As a result, in the compensated image sequence, the subtle changes in the actual flight state of the main transport drone and the cargo carrying state are clearly presented, thus ensuring the accuracy and reliability of the judgment.

[0117] In some preferred embodiments, it is assumed that the escort drone is equipped with a high-precision inertial navigation system (INS) and a high-definition visible light camera. When the escort drone maintains a specific relative position with the main transport drone, the INS outputs the three-axis attitude angles, angular velocities, and position information of the escort drone in real time. Simultaneously, the camera continuously captures image sequences of the main transport drone at a rate of 30 frames per second. In the data processing unit, the precise motion trajectory and attitude change rate of the escort drone in the world coordinate system are first calculated using the INS data. Next, each frame in the image sequence is preprocessed, and a deep learning-based object detection model (e.g., a trained YOLO model) is used to identify key feature points on the main transport drone, such as wingtips and rotor fairings, and track the pixel coordinate changes of these feature points in consecutive frames. Subsequently, combining the escort drone's own motion data and the pixel displacements of the key feature points, a motion compensation algorithm based on Kalman filtering or optical flow is applied to perform frame-by-frame correction of the image sequence to eliminate image jitter caused by the escort drone's own motion. The compensated image sequence will present a relatively stable view of the main transport drone. Finally, by analyzing minute changes in the main transport drone's attitude, the uniformity of its rotor speed, and the visual integrity of the cargo attachment points (e.g., using image recognition to determine if hooks are loose or connectors are cracked) in the compensated image sequence, it is possible to accurately determine whether the main transport drone's flight is stable, whether there are abnormal vibrations, and whether the cargo is securely supported or if any abnormalities have occurred. For example, if the wingtips of the main transport drone exhibit continuous, non-periodic, large-amplitude oscillations in the compensated image, it may be judged as an unstable flight state; if the cargo attachment points show obvious deformation or cracks, it is judged as an abnormal cargo-bearing condition.

[0118] In some embodiments of this application, when it is determined that the main transport drone cannot continue flying or that the cargo-carrying status is abnormal, it is necessary to designate a target escort drone to guide the main transport drone to a safe landing. Specifically, the step of designating a target escort drone to guide the main transport drone to a safe landing includes:

[0119] S201, the accompanying drone scans the terrain within a preset range to obtain the terrain features of potential landing areas.

[0120] Specifically, the escort drone is configured to use its onboard sensors (such as lidar, high-resolution cameras, or synthetic aperture radar) to scan the ground area around the main transport drone's preset flight path or current location. This scanning acquires terrain feature data, including topographic relief, vegetation cover, water distribution, buildings, and obstacles, thereby identifying multiple potential landing areas.

[0121] S202. Assess the safety of each potential landing area based on terrain features and the flight status of the main transport drone.

[0122] For example, the assessment will consider the flatness of the landing area, the density of obstacles, wind conditions, and the difficulty and risks of the main transport drone landing under the current conditions.

[0123] S203. Based on the safety assessment results, determine the optimal temporary landing plan.

[0124] The optimal temporary landing plan includes the specific landing location.

[0125] In some embodiments, the optimal temporary landing scheme may also include a landing path, landing attitude, and necessary auxiliary operation instructions.

[0126] S204. Based on the optimal temporary landing plan, a target escort drone is designated, which guides the main transport drone to a safe landing through visual recognition, radio signals, or light indication.

[0127] For example, the target escort drone can identify the main transport drone through its vision system and use its own flight attitude, position changes, or projected light signals to indicate the landing direction and position of the main transport drone; or, it can send precise flight commands and landing coordinates through radio signals to assist the main transport drone in landing autonomously.

[0128] This application's solution, through a systematic approach, ensures efficient and safe landing guidance for the main transport UAV in emergency situations. First, a companion UAV scans the terrain and acquires its features, providing a comprehensive environmental data foundation for subsequent landing area assessment. Second, by combining the real-time flight status of the main transport UAV with a safety assessment of potential landing areas, the landing plan is made more targeted and reliable, avoiding the risks associated with blindly selecting landing points. It is precisely this comprehensive consideration of the environment and the main transport UAV's status that ensures the optimal temporary landing plan maximizes landing safety. Finally, by designating a target companion UAV and employing multiple guidance methods, the solution ensures that the main transport UAV receives clear and effective guidance when it loses autonomous flight capability or needs to land urgently, thus enabling a safe and successful landing.

[0129] In some embodiments described above, the flight status and cargo-carrying status of the main transport UAV are determined by acquiring visual information from the UAV. However, relying solely on visual information may not be sufficient to comprehensively and accurately assess potential risks within or to the structure of the cargo attachment points, especially during high-intensity flights, complex airflow environments, or long-duration missions. In these situations, the cargo attachment points may experience subtle loosening, deformation, or internal damage, affecting the safety of the cargo load. If these issues are not addressed, cargo may accidentally detach or be damaged during flight, severely impacting mission success and safety. Therefore, this application proposes a more refined method for assessing cargo-carrying status. By comprehensively considering the mechanical data of the main transport UAV, the risks associated with its attachment, and the structural integrity of the cargo attachment points, this method enables comprehensive, real-time monitoring and risk warning of the cargo-carrying status.

[0130] In response, the methods also include:

[0131] S301. Acquire the vibration and impact data of the main transport UAV and the fluctuation range of the rotor motor power output.

[0132] Specifically, vibration and shock data refer to the vibration frequency and amplitude of various parts of the main transport UAV during flight, collected in real time by its onboard sensors (such as accelerometers and gyroscopes), as well as the instantaneous acceleration changes caused by external impacts (such as airflow turbulence or minor collisions). Rotor motor power output fluctuation refers to the instantaneous range and stability of the power output of each rotor motor of the main transport UAV while maintaining flight or performing specific maneuvers. This fluctuation reflects the motor load, rotor balance, and the health of the transmission system. These data are used to calculate the payload risk index, which aims to quantify the potential risk of damage to cargo mounting points due to long-term vibration, impact, or structural fatigue.

[0133] S302. Calculate the load risk index based on the airframe vibration and impact data and the fluctuation range of rotor motor power output.

[0134] Among them, the mounting risk index is used to reflect the risk of damage to the material mounting point.

[0135] The load risk index can be calculated based on a preset mathematical model or machine learning algorithm, taking airframe vibration and impact data, as well as rotor motor power output fluctuations, as inputs, and outputting a value reflecting the risk of damage. For example, when the vibration frequency is close to the natural frequency of the load point, the impact intensity exceeds a threshold, or the motor power output fluctuation increases abnormally, the load risk index will be increased accordingly.

[0136] S303. Adjust the damping characteristics of the material mounting point according to the mounting risk index.

[0137] Damping characteristics refer to the ability of the mounting point to absorb and dissipate vibration energy. For example, when the mounting risk index is high, the damping of the mounting point can be increased by activating smart materials (such as piezoelectric materials and magnetorheological fluids) or adjusting mechanical dampers, thereby effectively suppressing vibration transmission and reducing the risk of damage to materials and mounting points.

[0138] S304. Accompanying drones acquire structural characteristics of the cargo loading points of the main transport drone.

[0139] This can be achieved using high-resolution cameras, LiDAR, or structured light scanners carried by the accompanying drone to obtain three-dimensional geometric information, surface texture, and material integrity data of the mounting point.

[0140] S305. Based on structural characteristics, determine signs of loosening, deformation, or damage at the material mounting points.

[0141] Based on these structural features, image processing and pattern recognition technologies can be used to determine whether there are any signs of looseness (e.g., loose bolts, increased gaps between connectors), deformation (e.g., bent supports, dented materials), or damage (e.g., cracks, wear, corrosion) that are difficult to detect with the naked eye at the material mounting points.

[0142] S306. Assess whether the load-bearing status of the materials is abnormal based on the load risk index, damping characteristics, and signs of loosening, deformation, or damage.

[0143] For example, when the load risk index is high, the damping characteristic adjustment effect is poor, and the accompanying drone detects obvious signs of structural damage, it can be determined that the load-bearing status of the materials is abnormal.

[0144] This application's solution overcomes the limitations of relying solely on visual information to assess the load-bearing status of materials by introducing a multi-dimensional data acquisition and analysis mechanism. Specifically, by acquiring vibration and impact data of the main transport UAV and the fluctuation range of rotor motor power output, the system can perceive dynamic stresses that may damage the material mounting points in real time from the perspective of internal mechanical operation. This data is used to calculate a mounting risk index, providing a quantitative and forward-looking risk assessment indicator that allows the system to issue early warnings before potential damage occurs. Based on this, the damping characteristics of the material mounting points are adjusted according to the mounting risk index, enabling the system to proactively intervene by changing the physical properties of the mounting points to absorb and dissipate excessive vibration energy, thereby effectively reducing the probability of damage to materials and mounting points under harsh flight conditions. This is a proactive risk management strategy that significantly improves the stability of material carrying. Simultaneously, the accompanying UAV acquires the structural characteristics of the material mounting points and identifies signs of loosening, deformation, or damage, providing direct verification of the integrity of the mounting points from the perspective of external physical structure. This combination of non-contact external inspection and internal mechanical data analysis forms a complementary assessment system. Ultimately, by comprehensively assessing the load risk index, damping characteristics, and signs of structural damage, the system can make a comprehensive and accurate judgment on the load-bearing status of materials. This multi-source information fusion assessment method can detect potential problems earlier and more accurately, avoiding misjudgments or omissions that may occur from a single information source, thereby ensuring the reliability of material transportation.

[0145] In some preferred embodiments, this application is implemented as follows: Assume the main transport drone is performing a long-distance, high-value cargo transport mission, and its battery level is approaching its threshold, triggering a mission adjustment request. In the contactless mission continuation and cargo support process, in addition to the accompanying drone monitoring the main transport drone via visual information, multiple sensors on the main transport drone continuously collect vibration data from various parts of its body, impact data during turbulent airflow, and real-time power output fluctuations of each rotor motor.

[0146] This data is transmitted to the processing unit of the main transport drone or the escort drone. The processing unit analyzes this data using a pre-trained model, such as a model based on support vector machines (SVM) or neural networks, and calculates a real-time load risk index. For example, if an abnormally large fluctuation in the power output of a rotor motor is detected, and the vibration frequency of a specific part of the aircraft resonates with the natural frequency of the cargo loading point, the load risk index will be judged as high.

[0147] Once the risk index exceeds the preset safety threshold, the system will immediately send a command to the material mounting point to adjust its damping characteristics. For example, if the material mounting point integrates a magnetorheological damper, the viscosity of the damping fluid can be adjusted by changing the magnetic field strength, thereby increasing the vibration reduction capacity of the mounting point, effectively absorbing and dissipating vibration energy, and reducing the risk of material loosening or damage.

[0148] Meanwhile, the accompanying drone uses its onboard high-precision stereo camera or laser scanner to perform a detailed scan of the cargo mounting points beneath the main transport drone, acquiring its three-dimensional structural feature data. By comparing this data with a pre-stored normal structural model, the accompanying drone can identify signs such as slight loosening of mounting bolts, minute cracks in connectors, or slight deformation of the support structure.

[0149] Ultimately, the system will comprehensively assess the load risk index, the adjustment status of damping characteristics (e.g., whether damping has been successfully increased), and structural anomalies detected by the escort drone. If the load risk index remains high, the damping adjustment effect is not significant, and the escort drone detects obvious structural loosening or damage, the system will determine that the cargo-bearing status is abnormal and immediately trigger an emergency landing procedure. The designated target escort drone will guide the main transport drone to a safe landing to avoid cargo loss or further flight risks.

[0150] In some embodiments described above, this application proposes adjusting the damping characteristics of the material mounting point based on the mounting risk index. However, in practice, a one-time adjustment may not ensure optimal damping characteristics or adapt to subsequent environmental changes and load fluctuations, potentially leading to continued damage risks at the material mounting point and hindering mission execution. Without addressing these issues, the stability of the material's load-bearing state cannot be consistently guaranteed, and improper damping characteristic adjustment may even exacerbate the risk. Therefore, this application further proposes a scheme to evaluate the damping characteristic adjustment effect and make further adjustments to ensure continuous optimization of the material mounting point's damping characteristics, thereby effectively guaranteeing the safety and stability of the material load.

[0151] After adjusting the damping characteristics of the material mounting points, the above method also includes:

[0152] S401, the main transport drone acquires real-time force data and vibration spectrum data of the cargo mounting point.

[0153] Real-time force data can be understood as the instantaneous force value experienced by the attachment point during flight, reflecting the immediate stress condition of the attachment point. Vibration spectrum data refers to the frequency distribution information obtained after processing the vibration signal of the attachment point through Fourier transform and other methods. Its purpose is to reveal the vibration energy distribution of the attachment point at different frequencies, thereby identifying potential resonance or abnormal vibration modes. In practical applications, these data can be collected in real time by various sensors such as force sensors, accelerometers, or strain gauges installed on the material attachment points.

[0154] S402. Compare the real-time force data and vibration spectrum data with the preset threshold.

[0155] Preset thresholds are safety ranges or performance indicators pre-defined based on factors such as the type of material, mounting method, UAV flight envelope, and safety standards. For example, the stress threshold can be set as a percentage of the yield strength or fatigue limit of the material at the mounting point, and the vibration spectrum threshold can be set as the maximum permissible vibration amplitude within a specific frequency range. The purpose of the comparison is to determine whether the current stress and vibration conditions at the material mounting point are within acceptable safety and stability ranges.

[0156] S403. Based on the comparison results, evaluate the adjustment effect of the damping characteristics.

[0157] If both the real-time force data and vibration spectrum data are within the preset threshold range, it indicates that the current damping characteristic adjustment is effective and can effectively suppress excessive force and abnormal vibration at the mounting point. Conversely, if either data exceeds the preset threshold, it indicates that the current damping characteristic adjustment has not met expectations and further optimization is needed.

[0158] S404. If the adjustment effect does not meet expectations, a correction instruction will be generated.

[0159] Correction commands are control signals or sets of parameters used to guide the readjustment of the damping characteristics at the material mounting points. For example, correction commands can instruct the increase or decrease of the damping coefficient, change the stiffness of the damping material, or adjust the control parameters of the active damping system.

[0160] S405. According to the correction instruction, the damping characteristics of the material mounting point are readjusted again.

[0161] This can be achieved through mechanical structural adjustments (such as changing the preload of the damper), intelligent material responses (such as changing the viscosity of an electromagnetic rheotropic fluid damper), or active control systems (such as applying a counterforce via an actuator). The aim is to optimize the damping characteristics of the cargo mounting point more precisely to adapt to the actual flight environment and cargo carrying requirements, thereby minimizing the risks associated with the cargo.

[0162] This application's solution effectively addresses the limitations of a one-time damping characteristic adjustment by introducing a closed-loop feedback mechanism. Specifically, after the initial adjustment of the damping characteristics at the cargo mounting point, the main transport UAV can acquire real-time force and vibration spectrum data of the mounting point. These data directly reflect the actual effect of the damping adjustment. By comparing this real-time data with preset safety thresholds, it is possible to objectively assess whether the current damping characteristics can effectively suppress abnormal forces and vibrations at the mounting point. It is precisely this real-time monitoring and evaluation that enables the system to promptly identify deficiencies in the damping adjustment. When the evaluation results show that the adjustment effect does not meet expectations, the system can intelligently generate correction commands and readjust the damping characteristics based on these commands. This iterative optimization process ensures that the damping characteristics of the cargo mounting point can continuously adapt to the constantly changing flight environment and cargo load status, thereby avoiding potential risks caused by improper one-time adjustments.

[0163] In some preferred embodiments, it is assumed that when the main transport UAV is performing a long-distance cargo transport mission, airflow disturbances or a slight shift in the cargo's center of gravity cause periodic vibrations exceeding a preset threshold at the cargo attachment point during flight. According to the above scheme, after initially adjusting the damping characteristics of the cargo attachment point, sensors on the main transport UAV continuously acquire real-time force data and vibration spectrum data of the attachment point. For example, if the vibration spectrum data shows an abnormally high vibration amplitude at a specific frequency, and this amplitude exceeds a preset safety threshold, the system will determine that the current damping characteristic adjustment effect has not met expectations. At this time, the system will automatically generate a correction command, which may instruct the active damping system to increase the suppression force of the vibration at that specific frequency, or adjust the response parameters of the smart material damper. After receiving the correction command, the main transport UAV will immediately readjust the damping characteristics of the cargo attachment point. After readjustment, the system will continue to monitor and evaluate until the force data and vibration spectrum data of the attachment point are stable within the preset safety range, thereby ensuring that the cargo is safely and stably carried throughout the entire flight.

[0164] In some of the embodiments described above in this application, a scheme is proposed to generate a correction command and perform a second adjustment if the adjustment effect does not meet expectations after adjusting the damping characteristics of the cargo loading point. However, in actual multi-aircraft relay flight missions, the communication link status within the fleet may dynamically change due to environmental factors (such as distance, obstacles, electromagnetic interference, etc.). If the generation and transmission of correction commands do not take these real-time communication conditions into account, the commands may experience delays, loss, or errors during transmission, thereby affecting the timely and effective adjustment of the damping characteristics of the cargo loading point by the main transport UAV, and potentially exacerbating the risk of damage to the cargo loading point.

[0165] In this regard, this application further proposes that the steps for generating the above-mentioned amendment instructions include:

[0166] S501. Obtain the real-time status information of the current internal communication links of the cluster.

[0167] The real-time status information includes the available bandwidth and signal strength of the link.

[0168] Specifically, obtaining real-time status information of the communication links within the current drone cluster refers to the continuous monitoring and collection of various performance indicators of the communication links used for data exchange between drones within the cluster via their built-in communication modules. Among these, the available bandwidth of the link refers to the maximum data transmission rate that the current communication channel can carry, reflecting the data transmission capacity; signal strength refers to the power of the received radio signal, reflecting the quality and stability of the communication link. This real-time status information is crucial for assessing the quality of the communication environment.

[0169] S502. Determine the transmission strategy based on the real-time status information; and generate correction instructions based on the transmission strategy.

[0170] The transmission strategy includes the encoding method of the instruction, the number of retransmissions, and the transmission interval.

[0171] This transmission strategy aims to optimize the transmission efficiency and reliability of correction commands. The strategy includes the command encoding method, retransmission count, and transmission interval. The command encoding method refers to the method used to encode the correction command data; for example, different error correction codes or redundant codes can be used to enhance the command's anti-interference capability during transmission. The retransmission count refers to the number of times the system attempts to retransmit the command when transmission fails or no acknowledgment is received; increasing the retransmission count can improve the transmission success rate. The transmission interval is the time interval between consecutive command transmissions; a reasonable transmission interval can avoid network congestion and allow time for the receiver to process the command.

[0172] Therefore, generating correction instructions based on transmission strategies means that after determining a specific transmission strategy, the system applies the parameters of that strategy (such as encoding method, number of retransmissions, and transmission interval) to the generation process of correction instructions. This ensures that the final generated correction instructions not only contain the specific content of adjusting damping characteristics, but also embed or associate the transmission parameters that should be used, so as to ensure that the instructions can be transmitted in the way most suitable for the current communication environment.

[0173] This application's solution addresses the reliability and efficiency issues that may arise in transmitting correction commands in complex and ever-changing flight environments by acquiring real-time status information of the communication links within the fleet and dynamically determining transmission strategies based on this information. Specifically, when the available bandwidth of the communication link is low or the signal strength is weak, the system can adopt a transmission strategy that employs high-redundancy coding, increases the number of retransmissions, and extends the transmission interval. This improves the success rate and anti-interference capability of command transmission under adverse communication conditions, preventing correction commands from failing to be delivered in a timely manner due to communication interruptions or data loss. Conversely, when the communication link is in good condition, the system can adopt a strategy that employs low-redundancy coding, reduces the number of retransmissions, and shortens the transmission interval. This improves the transmission efficiency and real-time performance of commands, ensuring that the main transport UAV can quickly respond and adjust the damping characteristics of the cargo loading point. This dynamic adjustment mechanism allows the generation and transmission of correction commands to fully adapt to the current communication environment, thereby guaranteeing the timeliness and effectiveness of adjusting the damping characteristics of the cargo loading point.

[0174] In some preferred embodiments, assuming that during a multi-aircraft relay flight mission, after adjusting the damping characteristics of the cargo mounting point, the main transport UAV assesses that the adjustment effect is not as expected and a correction command needs to be generated. At this time, either the escort UAV or the main transport UAV first acquires the real-time status information of the current communication link within the fleet. For example, if the available bandwidth of the current communication link is detected to be 5 Mbps and the signal strength to be -80 dBm, the system will determine, based on this real-time data and a preset communication quality assessment model, that the current communication environment is at a below-average level. Based on this determination, the system will determine a transmission strategy, which may include: encoding the correction command using a moderately redundant error correction coding method, setting the retransmission count to 3, and setting the transmission interval to 500 milliseconds. Subsequently, the system will generate the correction command according to this transmission strategy and send it to the main transport UAV. In this way, even if the communication environment is not ideal, the correction command can be transmitted in a relatively reliable manner, thereby assisting the main transport UAV in effectively adjusting its damping characteristics.

[0175] In some embodiments described above, a transmission strategy is proposed to be determined based on the real-time status information of the internal communication links of the aircraft cluster, and a correction command is generated based on the transmission strategy to adjust the damping characteristics of the cargo mounting point. However, in practical applications, the real-time status of the internal communication links of the aircraft cluster may fluctuate; for example, available bandwidth or signal strength may decrease due to environmental interference, distance changes, or other factors. If the transmission strategy fails to fully consider these dynamic changes and simply generates and sends the correction command, the transmission efficiency and reliability of the correction command may be insufficient, thereby affecting the timely and effective adjustment of the damping characteristics of the cargo mounting point by the main transport UAV, which may further exacerbate the risk of cargo loading.

[0176] In this regard, this application further proposes that the steps for generating the correction instruction based on the transmission strategy include:

[0177] S601. When the available bandwidth is less than the bandwidth threshold or the signal strength is less than the strength threshold, a high redundancy coding method is used to encode the correction command, increasing the number of retransmissions of the correction command and extending the transmission interval.

[0178] Specifically, available bandwidth refers to the maximum data transmission rate a communication link can achieve at a given moment, and the bandwidth threshold is a preset standard value for judging the quality of the communication link. Signal strength refers to the power of the received radio signal, and the strength threshold is a preset standard for judging signal quality. When the communication link quality is poor, manifested as available bandwidth being less than the bandwidth threshold or signal strength being less than the strength threshold, the system employs a high-redundancy coding method to encode the correction command to ensure reliable transmission. High-redundancy coding adds extra redundant information to the original data, allowing the receiver to recover the original command even if some data is lost or corrupted during transmission, thereby improving data transmission fault tolerance. Simultaneously, to further enhance reliability, the system increases the number of retransmissions of the correction command; that is, the sender retransmits the command multiple times if the receiver fails to acknowledge receipt or if acknowledgment fails. Furthermore, the transmission interval is extended to avoid further packet loss due to continuous command transmission during communication link congestion or instability, allowing more time for link recovery or data processing.

[0179] S602. When the available bandwidth is greater than or equal to the bandwidth threshold and the signal strength is greater than or equal to the strength threshold, the correction instruction is encoded using a low-redundancy coding method to reduce the number of retransmissions of the correction instruction and shorten the transmission interval.

[0180] Conversely, when the communication link quality is good, with available bandwidth greater than or equal to the bandwidth threshold and signal strength greater than or equal to the strength threshold, the system employs a low-redundancy coding method to encode correction commands to improve transmission efficiency. This low-redundancy coding method reduces the addition of redundant information while ensuring a certain level of reliability, thereby reducing data volume and improving transmission efficiency. In this case, the system reduces the number of retransmissions of correction commands because the communication environment is good, and the probability of successful transmission on the first attempt is high; reducing retransmissions saves communication resources. Simultaneously, shortening the transmission interval allows correction commands to reach the main transport drone more quickly, enabling more timely response and adjustments.

[0181] The proposed solution effectively addresses the balance between reliability and efficiency in transmitting correction commands under varying communication link conditions by dynamically adjusting the transmission strategy. When communication link quality is poor, employing high-redundancy coding, increasing retransmission frequency, and extending transmission intervals significantly improves the success rate of correction command transmission in harsh communication environments. This ensures that critical adjustment commands are received and executed by the main transport UAV, allowing for timely adjustment of the damping characteristics of the cargo loading point and preventing increased cargo carrying risks due to communication interruptions or command loss. Conversely, when communication link quality is good, employing low-redundancy coding, reducing retransmission frequency, and shortening transmission intervals maximizes transmission efficiency, enabling the main transport UAV to respond and adjust quickly, avoiding unnecessary delays and resource waste. It is precisely this adaptive transmission strategy that ensures the efficient and reliable transmission of correction commands in various complex flight environments.

[0182] In some preferred embodiments, a specific example is given below. Suppose that in a multi-aircraft relay flight mission, the main transport UAV needs to adjust the damping characteristics of its cargo hardpoints and generates correction commands.

[0183] Specifically, when the accompanying drone detects real-time status information of the communication link within the fleet, showing an available bandwidth of 500kbps (while the preset bandwidth threshold is 1Mbps) and a signal strength of -90dBm (while the preset strength threshold is -80dBm), the system determines the current communication link quality to be poor due to both available bandwidth and signal strength being below the threshold. In this case, the system will employ a high-redundancy encoding method such as RS(255, 223) to encode the correction command, setting the retransmission count to 5 times and extending the transmission interval to 500 milliseconds. This strategy ensures that even under poor communication conditions, the correction command can be successfully transmitted to the main transport drone with a high probability.

[0184] On the other hand, if the accompanying drone detects an improvement in the communication link status during flight, with available bandwidth reaching 2 Mbps and signal strength at -70 dBm, and the available bandwidth is greater than or equal to the bandwidth threshold, and the signal strength is greater than or equal to the strength threshold, the system determines that the current communication link quality is good. The system will then switch to using low-redundancy encoding methods such as CRC checksum to encode the correction command, reducing the number of retransmissions to one and shortening the transmission interval to 100 milliseconds. This strategy maximizes transmission efficiency while ensuring command reliability, enabling the main transport drone to quickly receive and execute the correction command and promptly adjust the damping characteristics of the cargo loading point.

[0185] In some embodiments described above, this application proposes a strategy for adjusting the transmission of correction commands based on real-time status information of the communication link. For example, when available bandwidth or signal strength is limited, high-redundancy coding, increasing the number of retransmissions, and extending the transmission interval are employed to improve the reliability of command transmission. However, in practical applications, if the mechanical condition of the main transport drone is rapidly deteriorating, simply extending the transmission interval may lead to delays in critical correction commands, resulting in an inability to respond promptly to emergencies and potentially exacerbating the risk of drone damage or loss of supplies.

[0186] In response, this application further proposes an optimization scheme to ensure that emergency correction commands can be sent in a timely manner when the mechanical condition of the main transport drone deteriorates.

[0187] When extending the transmission interval, the method also includes:

[0188] S701. When extending the transmission interval, acquire the mechanical data of the main transport drone.

[0189] The mechanical data includes vibrations, impacts, and stress values ​​at the cargo attachment points. This data can be collected in real time using various sensors (such as accelerometers, strain gauges, and force sensors) installed on the main transport drone.

[0190] S702. Based on mechanical data, determine the rate of deterioration of the main transport drone's condition.

[0191] The rate of deterioration can be determined by analyzing the trends in mechanical data over time. For example, the growth rate of vibration amplitude per unit time, changes in the frequency or intensity of impact events, and the fluctuation range of stress values ​​at material mounting points can be calculated. By comparing these values ​​with preset normal operating baselines or safety thresholds, the degree and speed of deterioration in the current state can be quantified.

[0192] S703. When the deterioration rate exceeds a preset threshold, stop increasing the number of retransmissions of the correction command, stop extending the transmission interval, and generate an emergency correction command.

[0193] The emergency correction command employs a maximum number of retransmissions and a minimum transmission interval. The maximum number of retransmissions aims to maximize the delivery probability of the command in adverse communication environments, while the minimum transmission interval ensures that the command can be sent and received as quickly as possible to gain valuable response time.

[0194] The S704 main transport UAV adjusts the damping characteristics of the cargo mounting points according to the emergency correction command.

[0195] The solution proposed in this application monitors the mechanical data of the main transport drone in real time and determines its rate of deterioration. This allows the system to intelligently adjust the transmission strategy of corrective commands when potential structural risks or abnormal cargo loading are detected. Because the system can immediately stop extending the transmission interval and generate emergency corrective commands using the maximum retransmission count and minimum transmission interval in emergency situations, the main transport drone can receive and execute critical damping characteristic adjustments at the fastest speed. This effectively avoids command delays caused by communication strategies, thus enabling timely responses to the risks of mechanical failure or cargo detachment.

[0196] Through the above technical solution, this application can significantly improve the safety and reliability of multi-aircraft relay flight missions in complex environments. Especially when the communication link is unstable and the mechanical condition of the main transport UAV deteriorates rapidly, this solution can ensure the timely delivery and execution of critical correction commands, effectively avoiding potential catastrophic consequences, thereby guaranteeing the successful completion of the mission and the safe transportation of supplies.

[0197] In some preferred embodiments, it is assumed that the main transport UAV is performing a long-distance transport mission and enters an area with unstable communication signals. Based on the real-time status of the communication link, the system decides to adopt a transmission strategy of extending the transmission interval to send correction commands for the damping characteristics of the cargo attachment points. However, during flight, the main transport UAV suddenly encounters strong airflow, causing severe vibration and impact to the airframe, and the force values ​​at the cargo attachment points also fluctuate abnormally. Sensors on the main transport UAV acquire these mechanical data in real time and calculate the rate of deterioration of its condition. When this rate of deterioration exceeds a preset safety threshold, the system immediately determines that the current situation is an emergency. At this time, the system will stop the previous strategy of extending the transmission interval and immediately generate an emergency correction command. This emergency correction command is configured to use the maximum number of retransmissions and the minimum transmission interval to ensure that it can be sent to the main transport UAV at the fastest speed and with the highest reliability. After receiving the emergency correction command, the main transport UAV will immediately adjust the damping characteristics of the cargo attachment points, such as increasing damping to absorb vibration energy, thereby effectively preventing cargo from falling off or further structural damage.

[0198] like Figure 3 As shown, this embodiment of the invention also provides a multi-aircraft relay flight mission collaborative control system. The system includes:

[0199] The information receiving module is used to respond to the mission adjustment request issued by the main transport drone and receive local environmental information perceived by the escort drone. The local environmental information is used to describe the airspace conditions within a preset range of the main transport drone. The mission adjustment request is used to indicate that the main transport drone's battery level is less than the battery threshold.

[0200] The risk assessment module is used to assess the flight risks in the current airspace based on local environmental information and to monitor the communication status with the external control center.

[0201] The condition judgment module is used to determine whether the current preset mission execution mode meets the preset switching conditions based on the flight risk assessment results and communication status. The preset switching conditions include the flight risk exceeding the preset safety range and the communication status being restricted.

[0202] The mode switching module is used to stop the current preset task execution mode and start a non-contact task continuation and material support process if the current preset task execution mode meets the preset switching conditions.

[0203] The escort guidance and information judgment module is used to guide the escort drone to maintain a specific relative position with the main transport drone during non-contact mission continuation and material support processes, acquire visual information of the main transport drone, and judge the flight status and material carrying status of the main transport drone based on the visual information.

[0204] The flight guidance module is used to send flight guidance information to the main transport drone based on the flight status and the cargo carrying status, so as to assist the main transport drone in adjusting its flight.

[0205] The landing scheme and guidance module is used to designate a target escort drone to guide the main transport drone to a safe landing when it is determined that the main transport drone cannot continue flying or the cargo carrying status is abnormal.

[0206] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0207] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0208] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0209] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for cooperative control of multi-aircraft relay flight missions, characterized in that, include: In response to a mission adjustment request from the main transport drone, the system receives local environmental information perceived by the escort drone, which describes the airspace conditions within a preset range of the main transport drone; the mission adjustment request is used to indicate that the main transport drone's battery level is below a battery threshold. Assess the flight risks in the current airspace based on the local environmental information, and monitor the communication status with the external control center; Based on the flight risk assessment results and the communication status, it is determined whether the current preset mission execution mode meets the preset switching conditions; the preset switching conditions include the flight risk exceeding the preset safety range and the communication status being restricted; If the current preset task execution mode meets the preset switching conditions, the current preset task execution mode is terminated, and a non-contact task continuation and material support process is initiated. In the non-contact mission continuation and material support process, the escort drone is guided to maintain a specific relative position with the main transport drone to escort it, obtain the visual information of the main transport drone, and judge the flight status and material carrying status of the main transport drone based on the visual information. Based on the flight status and the cargo carrying status, flight guidance information is sent to the main transport drone to assist the main transport drone in adjusting its flight. When it is determined that the main transport drone cannot continue to fly, or the cargo carrying status is abnormal, a target escort drone is designated to guide the main transport drone to a safe landing. The visual information includes image sequences; the guiding drone maintains a specific relative position with the main transport drone to acquire the visual information of the main transport drone, and determines the flight status and cargo carrying status of the main transport drone based on the visual information, including: The accompanying drone acquires its own flight attitude data and image sequences from the main transport drone; Based on the flight attitude data of the accompanying drone, the trajectory and attitude change rate of the accompanying drone in space are calculated; The image sequence is processed to identify key feature points on the main transport drone; the key feature points include wingtips and rotor fairings. Motion compensation is performed on the image sequence based on the motion trajectory, the rate of change of posture, and the positional changes of the key feature points in the image sequence. Based on the compensated image sequence, the flight status and cargo carrying status of the main transport drone are determined; The accompanying drone calculates the corresponding correction instructions and sends the flight guidance information to the main transport drone via a radio link to suggest that it adjust its flight speed, altitude or attitude. The method further includes: Acquire data on the airframe vibration and impact of the main transport drone, as well as the fluctuation range of the rotor motor power output; Based on the vibration and impact data of the aircraft body and the power output fluctuation of the rotor motor, the load risk index is calculated; the load risk index is used to reflect the risk of damage to the material loading point. Adjust the damping characteristics of the material mounting point according to the aforementioned mounting risk index; The accompanying drone acquires the structural characteristics of the cargo loading point of the main transport drone; Based on the structural features, determine whether the material mounting point shows signs of loosening, deformation, or damage; Assess whether the material load-bearing status is abnormal based on the load risk index, the damping characteristics, and the signs of loosening, deformation, or damage.

2. The multi-aircraft relay flight task cooperative control method according to claim 1, characterized in that, The step of designating a target escort drone to guide the main transport drone to a safe landing includes: The accompanying drone scans the terrain within a preset range to obtain the terrain features of potential landing areas; The safety of each potential landing area is assessed based on the terrain features and the flight status of the main transport drone. Based on the safety assessment results, the optimal temporary landing plan is determined; Based on the optimal temporary landing scheme, a target escort drone is designated, which guides the main transport drone to a safe landing through visual recognition, radio signals, or light indication.

3. The method of claim 1, wherein, After adjusting the damping characteristics of the material mounting points, the method further includes: The main transport drone acquires real-time force data and vibration spectrum data at the cargo mounting points; The real-time force data and vibration spectrum data are compared with preset thresholds; Based on the comparison results, evaluate the effect of the damping characteristic adjustment; If the adjustment does not achieve the expected results, a correction instruction will be generated; According to the aforementioned correction instruction, the damping characteristics of the material mounting point are readjusted.

4. The multi-aircraft relay flight task cooperative control method according to claim 3, characterized in that, The generation of correction instructions includes: Obtain real-time status information of the communication links within the current cluster, including the available bandwidth and signal strength of the links; Based on the real-time status information, a transmission strategy is determined; and a correction instruction is generated based on the transmission strategy; the transmission strategy includes the instruction encoding method, the number of retransmissions, and the transmission interval.

5. The method for coordinated control of multi-aircraft relay flight missions according to claim 4, characterized in that, The generation of correction instructions based on the transmission strategy includes: When the available bandwidth is less than the bandwidth threshold or the signal strength is less than the strength threshold, the correction instruction is encoded using a high redundancy coding method to increase the number of retransmissions of the correction instruction and extend the transmission interval. When the available bandwidth is greater than or equal to the bandwidth threshold and the signal strength is greater than or equal to the strength threshold, the correction instruction is encoded using a low-redundancy coding method to reduce the number of retransmissions of the correction instruction and shorten the transmission interval.

6. The method for coordinated control of multi-aircraft relay flight missions according to claim 5, characterized in that, The method further includes: When extending the transmission interval, mechanical data of the main transport drone is acquired, including body vibration, impact, and force values ​​at the cargo mounting points; Based on the mechanical data, determine the rate of deterioration of the main transport drone's condition; When the deterioration rate exceeds a preset threshold, the number of retransmissions of the correction instruction is stopped, the transmission interval is stopped, and an emergency correction instruction is generated; the emergency correction instruction uses the maximum number of retransmissions and the minimum transmission interval. The main transport drone adjusts the damping characteristics of the cargo mounting points according to the emergency correction command.

7. The method for coordinated control of multi-aircraft relay flight missions according to claim 1, characterized in that, The local environmental information includes wind speed, wind direction, turbulence intensity, and distance to obstacles.

8. A collaborative control system for multi-aircraft relay flight missions, characterized in that, The system includes: The information receiving module is used to respond to the task adjustment request issued by the main transport drone and receive local environmental information sensed by the escort drone. The local environmental information is used to describe the airspace conditions within a preset range of the main transport drone. The task adjustment request is used to indicate that the main transport drone's battery level is less than a battery threshold. The risk assessment module is used to assess the flight risks in the current airspace based on the local environmental information and to monitor the communication status with the external control center. The condition judgment module is used to determine whether the current preset task execution mode meets the preset switching conditions based on the flight risk assessment results and the communication status; the preset switching conditions include the flight risk exceeding the preset safety range and the communication status being restricted; The mode switching module is used to stop the current preset task execution mode and start a non-contact task continuation and material support process if the current preset task execution mode meets the preset switching conditions. The escort guidance and information judgment module is used to guide the escort drone to maintain a specific relative position with the main transport drone during non-contact mission continuation and material support processes, acquire visual information of the main transport drone, and judge the flight status and material carrying status of the main transport drone based on the visual information. The flight guidance module is used to send flight guidance information to the main transport drone based on the flight status and the cargo carrying status, so as to assist the main transport drone in adjusting its flight. The landing scheme and guidance module is used to designate a target escort drone to guide the main transport drone to a safe landing when it is determined that the main transport drone cannot continue to fly or the cargo carrying status is abnormal. The visual information includes image sequences; the guiding drone maintains a specific relative position with the main transport drone to acquire the visual information of the main transport drone, and determines the flight status and cargo carrying status of the main transport drone based on the visual information, including: The accompanying drone acquires its own flight attitude data and image sequences from the main transport drone; Based on the flight attitude data of the accompanying drone, the trajectory and attitude change rate of the accompanying drone in space are calculated; The image sequence is processed to identify key feature points on the main transport drone; the key feature points include wingtips and rotor fairings. Motion compensation is performed on the image sequence based on the motion trajectory, the rate of change of posture, and the positional changes of the key feature points in the image sequence. Based on the compensated image sequence, the flight status and cargo carrying status of the main transport drone are determined; The accompanying drone calculates the corresponding correction instructions and sends the flight guidance information to the main transport drone via a radio link to suggest that it adjust its flight speed, altitude or attitude. The system also includes: Acquire data on the airframe vibration and impact of the main transport drone, as well as the fluctuation range of the rotor motor power output; Based on the vibration and impact data of the aircraft body and the power output fluctuation of the rotor motor, the load risk index is calculated; the load risk index is used to reflect the risk of damage to the material loading point. Adjust the damping characteristics of the material mounting point according to the aforementioned mounting risk index; The accompanying drone acquires the structural characteristics of the cargo loading point of the main transport drone; Based on the structural features, determine whether the material mounting point shows signs of loosening, deformation, or damage; Assess whether the material load-bearing status is abnormal based on the load risk index, the damping characteristics, and the signs of loosening, deformation, or damage.

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