Unmanned aerial vehicle target locking method and system

Abstract

The invention provides an unmanned aerial vehicle target locking method and system, and relates to the technical field of unmanned aerial vehicle control, and the main points of the technical scheme are that the monitoring separation probability of a target is calculated based on the motion vector of the target, the field of view of the current unmanned aerial vehicle and a three-dimensional map; when the monitoring separation probability of the target is greater than a preset value, entering a monitoring handover mode for calculating and searching a second unmanned aerial vehicle for monitoring handover; when a second unmanned aerial vehicle is found through calculation, monitoring handover information is sent to the second unmanned aerial vehicle, and the monitoring handover information comprises a monitoring separation reason of the current unmanned aerial vehicle and a target prediction position obtained through calculation based on the motion vector of the target and the three-dimensional map; and the second unmanned aerial vehicle locks the target according to the monitoring handover information. The unmanned aerial vehicle target locking method and system provided by the invention have the advantages.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and more specifically, to a drone target locking method and system. Background Art

[0002] In a scenario where a drone formation is performing a continuous surveillance mission of a ground target, when the primary tracking drone locks onto the target, it may predict that the target is about to be lost due to reasons such as line of sight obstruction or physical limitations of its sensors. In existing technologies, when the primary tracking drone predicts that the target is about to be lost, it typically sends a general takeover request to other drones in the formation. However, this general request cannot effectively guide the takeover drone to quickly and accurately take over the mission. For example, the takeover drone may attempt to obtain the primary tracking drone's sensor view, attempting to observe the area that is about to be lost, which wastes time. If the primary tracking drone's lock is completely lost, the entire formation system may experience a brief surveillance blackout period until the takeover drone re-searches and acquires the target, which may result in the target being lost during this period. Existing technologies lack a mechanism that allows the primary tracking drone to transmit key information such as the cause of loss and the target's predicted location to potential takeover drones when it predicts target loss. This allows the takeover drone to quickly determine an effective lock strategy based on this information, achieving seamless handover.

[0003] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0004] The purpose of this application is to provide a drone target locking method and system, which has the advantages of being able to predict the risk of target loss and initiate a targeted handover process, avoiding monitoring blank periods, and improving the continuity and success rate of target locking.

[0005] In the first aspect, the present application provides a method and system for drone target locking, the technical solution of which is as follows: include: Calculating the probability of the target being out of surveillance based on the target's motion vector, the current UAV's field of view, and the three-dimensional map; When the probability of the target being out of surveillance is greater than a preset value, the surveillance handover mode is entered to calculate and find a second drone for surveillance handover; When the second drone is found by calculation, monitoring handover information is sent to the second drone. The monitoring handover information includes the reason for the current drone's monitoring separation and the target predicted position calculated based on the target's motion vector and the three-dimensional map, so that the second drone can lock the target according to the monitoring handover information.

[0006] Furthermore, in the present application, the step of calculating the probability of the target being out of monitoring based on the target's motion vector, the current UAV's field of view, and the three-dimensional map includes: generating a set of predicted trajectories of the target within a preset time based on the motion vector of the target; For each predicted trajectory in the set of predicted trajectories, determining whether line of sight obstruction occurs by combining the current field of view of the drone and the three-dimensional map, and obtaining a line of sight obstruction determination result; For each predicted trajectory in the set of predicted trajectories, determining whether a posture required by a sensor of the current drone to maintain lock on a target on the predicted trajectory exceeds a preset physical limit, and obtaining a physical limit determination result; Based on the line of sight obstruction judgment result and the physical limitation judgment result, the number of predicted trajectories that trigger line of sight obstruction or exceed the physical limitation is counted, and the monitoring escape probability is determined according to the ratio of the number to the total number of the set of predicted trajectories.

[0007] Furthermore, in the present application, for each predicted trajectory in the set of predicted trajectories, determining whether a posture required by a sensor of the current drone to maintain lock on a target on the predicted trajectory exceeds a preset physical limit, and obtaining a physical limit determination result includes: Obtaining a predicted body posture sequence of the current UAV that is synchronized with the time points on the predicted trajectory; For each target point on the predicted trajectory, determine the target sight vector required by the current UAV's sensor in the world coordinate system to maintain lock on the target point; Based on the predicted body posture in the predicted body posture sequence that is time-synchronized with the target point, converting the target sight line vector in the world coordinate system into a target pointing vector in the body coordinate system of the current UAV; The target pointing vector of the current UAV in the body coordinate system is compared with the sensor physical limit preset in the body coordinate system to generate the physical limit judgment result.

[0008] Furthermore, in the present application, the step of entering the monitoring handover mode for calculating and searching for the second drone for monitoring handover includes: After entering the monitoring handover mode, based on the target predicted position and the position information of other UAVs in the formation, a group of candidate UAVs that can observe the target predicted position are screened; For each candidate drone in the group of candidate drones, obtaining its preset operating status parameters, the operating status parameters including observation effectiveness parameters, communication link parameters, and own platform parameters; Calculating a handover adaptability score for each candidate UAV based on the operating status parameters of the candidate UAV; The candidate drone with the highest handover adaptability score is determined as the second drone.

[0009] Furthermore, in the present application, the step of calculating a handover adaptability score of each candidate UAV based on the operating status parameters of the candidate UAV includes: Obtaining current mission information of the candidate UAV, wherein the current mission information includes a mission priority; Searching among the drones in the formation other than the candidate drone and the current drone whether there is an alternative drone that can take over the current mission, and obtaining a search result of an alternative drone; determining a task transfer cost based on the search results of the alternative drones and the task priority; The handover adaptability score of the candidate UAV is calculated based on the operating state parameters of the candidate UAV and the task transfer cost.

[0010] Furthermore, in the present application, the step of determining the task transfer cost based on the alternative drone search results and the task priority includes: Obtaining capability information of each alternative drone included in the alternative drone search results; evaluating the adaptability of each of the alternative UAVs to the current mission based on the current mission information of the candidate UAV and the capability information of each of the alternative UAVs; The task transfer cost is calculated according to the task priority and the adaptability of each replacement UAV to the current task.

[0011] Furthermore, in the present application, the step of causing the second UAV to lock onto the target according to the monitoring handover information includes: determining a target behavior uncertainty level based on the monitoring separation reason included in the monitoring handover information; generating a set of execution parameters for the locking action according to the uncertainty level of the target behavior; Based on the generated execution parameters and the target predicted position included in the monitoring handover information, the second UAV is controlled to perform a locking action.

[0012] Furthermore, in the present application, the step of causing the second UAV to lock onto the target according to the monitoring handover information includes: receiving the monitoring handover information; determining a target locking logic corresponding to the monitoring separation reason based on the monitoring separation reason included in the monitoring handover information; According to the determined target locking logic and the target predicted position included in the monitoring handover information, the second UAV is controlled to perform a locking action.

[0013] Furthermore, in the present application, the step of determining the target locking logic corresponding to the monitoring separation reason included in the monitoring handover information includes: When the monitoring separation reason is a physical limitation of the sensor, determining the target locking logic to predict the target exit position and monitor it; When the monitoring separation reason is long-term line of sight obstruction, determining the target locking logic to adjust the position or altitude of the second UAV to bypass the obstacle; When the monitoring separation reason is that the target maneuvers at high speed beyond the tracking capability, determining the target locking logic to perform interception observation based on the target's final velocity vector; When the monitoring separation reason is that the communication link is about to be interrupted, the target locking logic is determined to immediately take over the locking of the target.

[0014] In a second aspect, the present application also proposes a drone target lock system, which includes: A first calculation module is used to calculate the probability of the target being out of monitoring based on the target's motion vector, the current UAV's field of view, and the three-dimensional map; A second calculation module is configured to enter a monitoring handover mode to calculate and search for a second drone for monitoring handover when the probability of the target being out of monitoring is greater than a preset value; The control module is configured to send monitoring handover information to the second drone when the second drone is found through calculation. The monitoring handover information includes the reason why the current drone was out of monitoring and the target predicted position calculated based on the motion vector of the target and the three-dimensional map, so that the second drone can lock onto the target according to the monitoring handover information.

[0015] From the above, it can be seen that the drone target locking method and system provided by the present application solves the problem in the prior art that general requests cannot effectively guide the successor drone by predicting the risk of target loss and sending monitoring handover information containing the reason for separation and the predicted target position to the successor drone, avoiding the monitoring blank period, and has the advantages of being able to predict the risk of target loss and initiate a targeted handover process, avoiding the monitoring blank period, and improving the continuity and success rate of target locking. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1A flowchart of a drone target locking method provided in this application.

[0017] Figure 2 A schematic diagram of the structure of a drone target locking system provided in this application.

[0018] In the figure: 210, a first calculation module; 220, a second calculation module; 230, a control module. DETAILED DESCRIPTION

[0019] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0021] When conventional drones perform target monitoring missions, if the primary tracking drone predicts it is about to lose the target due to line of sight obstruction or physical sensor limitations, it must hand over the monitoring mission to other drones in the formation. However, existing handover methods often only send general handover instructions, failing to fully inform the replacement drone of the specific cause of target loss and predicted location information. This can lead to the replacement drone attempting ineffective observations, resulting in brief periods of lag in monitoring and the risk of losing the target.

[0022] For example, imagine a drone formation continuously monitoring a moving ground target in an urban environment. The primary tracking drone, Drone A, is positioned above and behind the target, using an electro-optical pod to lock onto it. The target suddenly turns into a narrow alleyway. Based on the three-dimensional map information stored in Drone A, it predicts that the target will emerge at the alleyway exit. Simultaneously, to maintain lock on the target entering the alleyway, Drone A's electro-optical pod needs to rotate rapidly. Calculations indicate that the pod's rotation angle will reach its mechanical limit within seconds. At this point, Drone A predicts that it will lose the target due to physical limitations of its sensors, and this event occurs earlier than predicted based on line-of-sight obstruction. Drone B in the formation, located on the other side of the city, has visibility into the alleyway exit area. If Drone A only sends a general takeover command to Drone B, Drone B may first attempt to observe the alleyway entrance, which will soon be lost. This information asymmetry and ineffective attempts result in Drone B being unable to immediately lock onto the target emerging from the alleyway exit after Drone A loses the target, resulting in a surveillance interruption.

[0023] In this regard, refer to Figure 1 , this application proposes a drone target locking method, including: S110, calculating the target's probability of being out of monitoring based on the target's motion vector, the current UAV's field of view, and the three-dimensional map; S120: When the probability of the target being out of surveillance is greater than a preset value, entering a surveillance handover mode to calculate and search for a second drone for surveillance handover; S130. When the second UAV is found by calculation, monitoring handover information is sent to the second UAV. The monitoring handover information includes the reason why the current UAV is out of monitoring and the target predicted position calculated based on the target's motion vector and the three-dimensional map, so that the second UAV can lock the target according to the monitoring handover information.

[0024] This application combines the monitoring separation probability calculated based on the target's motion vector, the current drone's field of view, and the three-dimensional map with a monitoring handover mechanism triggered based on this probability, and includes the monitoring separation cause and the target's predicted position in the handover information, thereby achieving early warning of potential monitoring separation risks and intelligent handover based on the cause, thereby achieving the effect of ensuring continuous lock on the target.

[0025] The solution of this application quantitatively assesses potential monitoring risks by comprehensively calculating the target's monitoring separation probability based on the target's motion vector, the current drone's field of view, and a three-dimensional map. When the assessed monitoring separation probability exceeds a preset threshold, the system will actively enter the monitoring handover mode and start the process of finding a suitable second drone. Once the second drone for monitoring handover is determined, the current drone will send monitoring handover information containing the monitoring separation reason and the target's predicted position to the second drone. After receiving this information, the second drone can adjust its locking strategy according to the specific reason for monitoring separation and use the target's predicted position to quickly locate the target, thereby achieving effective takeover and continuous locking of the target. The entire process embodies a complete logical chain from risk assessment to intelligent decision-making to information transmission and strategy adjustment, ensuring the continuity of monitoring tasks in complex environments.

[0026] In one specific embodiment, the drone system continuously acquires real-time motion vector data of the target and combines it with the field of view of the current drone sensor (e.g., an electro-optical pod) and pre-loaded 3D city map data. The system's internal computing unit periodically runs an algorithm to estimate the probability of the target leaving the field of view or being obscured by obstacles within a future period (e.g., the next 10 seconds), given the target's current motion trend, the drone's flight status, and the distribution of obstacles in the map. It also assesses whether the sensor pose required to maintain lock will exceed its physical limitations. If the calculated probability of disengagement exceeds a system-set threshold of 0.8, the system triggers a surveillance handover process. The system queries the position and status information of other drones in the formation, screens candidate drones that can effectively observe the target's predicted position from their current position, and selects the optimal candidate as the second drone based on factors such as mission priority and communication status. The current drone then sends a surveillance handover command to the selected second drone via the data link. This command includes the primary reason for the handover (e.g., "expected sensor horizontal rotation angle limit reached") and the target's predicted coordinates at the time of disengagement, calculated based on the target's last known state and map information. After the second drone receives this information, its control system will analyze the cause of the separation. For example, if the separation is caused by the physical limitation of the sensor, the second drone may prioritize the area near the predicted target location and adjust its sensor pointing according to the predicted location, preparing to take over the lock.

[0027] Through this technical solution, the drone system can proactively predict potential surveillance disengagement risks and initiate surveillance handover based on a quantitative risk assessment. By explicitly including the specific cause of the disengagement and the predicted target location in the handover information, the second drone receiving the handover can quickly understand the current situation and adjust its lock strategy and search range accordingly, avoiding ineffective operations. This makes the handover of surveillance tasks between drones more efficient and intelligent, significantly reducing the monitoring interruption time caused by surveillance disengagement and ensuring continuous and effective target lock in complex and dynamic environments.

[0028] In some of the above-mentioned embodiments of the present application, it is proposed to calculate the monitoring separation probability of the target based on the target's motion vector, the current field of view of the drone and the three-dimensional map. The monitoring separation probability of the target calculated based on the target's motion vector, the current field of view of the drone and the three-dimensional map can be specifically calculated by analyzing the current speed and direction of the target, combining the current position and field of view of the drone, and the known obstacle information in the three-dimensional map, and performing a simple line of sight occlusion judgment to estimate the monitoring risk of the target in a short period of time. This can provide a preliminary risk assessment. However, in its implementation process, simply relying on the target's motion vector may not fully consider environmental factors (such as occlusion) and the limitations of the drone itself, thereby resulting in inaccurate calculation of the monitoring separation probability.

[0029] In this regard, the present application further proposes that the steps of calculating the probability of a target being out of monitoring based on the target's motion vector, the current UAV's field of view, and the three-dimensional map include: Based on the target's motion vector, a set of predicted trajectories of the target within a preset time is generated; For each predicted trajectory in a set of predicted trajectories, the current UAV's field of view and the three-dimensional map are combined to determine whether line of sight is blocked and obtain a line of sight blockage judgment result; For each predicted trajectory in a set of predicted trajectories, determining whether a posture required by the current UAV sensor to maintain lock on the target on the predicted trajectory exceeds a preset physical limit, and obtaining a physical limit determination result; Based on the line of sight obstruction judgment results and the physical limitation judgment results, the number of predicted trajectories that trigger line of sight obstruction or exceed physical limitations is counted, and the probability of monitoring separation is determined based on the ratio of this number to the total number of predicted trajectories in a group.

[0030] This solution generates a set of predicted trajectories for the target within a preset time based on the target's motion vector. This takes into account multiple possibilities for the target's future movement rather than a single prediction, thereby more comprehensively reflecting the uncertainty of the target's behavior. For these predicted trajectories, the current drone's field of view and three-dimensional map are further combined to determine whether there is any line of sight obstruction on each trajectory. At the same time, it is determined whether the drone's sensors need to exceed the preset physical limitations to lock on the target. This approach comprehensively considers environmental factors (occlusion) and the drone's own capability limitations, making up for the shortcomings of relying solely on the target's motion vector for judgment.

[0031] Finally, the probability of escape is calculated by counting the number of predicted trajectories that trigger line-of-sight obstruction or exceed physical limitations and calculating their proportion of the total predicted trajectories. This probability calculation method, based on multi-trajectory simulation and multi-factor judgment, can provide a more accurate and comprehensive risk assessment of escape. Applying this more accurate probability of escape to subsequent surveillance handover decisions enables the UAV system to more promptly and reliably determine when to initiate the handover process, providing a more reliable basis for handover, effectively avoiding target loss and ensuring the successful execution of continuous surveillance missions.

[0032] In some preferred embodiments, a set of predicted trajectories of the target within a preset time is generated based on the target's motion vector, and on this basis, multiple possible future trajectories are generated by adding random noise or using the Monte Carlo method.

[0033] For each predicted trajectory in a set of predicted trajectories, the current UAV's field of view and the three-dimensional map are combined to determine whether the line of sight is blocked. Specifically, a ray can be emitted from the UAV's predicted position to a point on the target predicted trajectory, and whether the ray intersects with the obstacle model in the three-dimensional map.

[0034] For each predicted trajectory in a set of predicted trajectories, determine whether the attitude the current drone's sensor needs to achieve to maintain lock on the target on the predicted trajectory exceeds the preset physical limitations. Specifically, the sensor gimbal angle required to lock on the target at each time point on the predicted trajectory can be calculated and compared with the sensor's preset maximum pitch and azimuth angle ranges.

[0035] Based on the line of sight occlusion judgment results and the physical limitation judgment results, the number of predicted trajectories that trigger line of sight occlusion or exceed physical limitations is counted, and the probability of monitoring separation is determined based on the ratio of this number to the total number of predicted trajectories in a group. Specifically, the occurrence of line of sight occlusion or exceeding physical limitations on any predicted trajectory can be marked as a "failure", and the number of failures in all predicted trajectories is counted and divided by the total number of predicted trajectories.

[0036] Through the above technical solution, the probability of a drone losing surveillance of a target can be evaluated more accurately and comprehensively. By comprehensively considering the target's motion trajectory prediction, environmental occlusion, and the drone's own physical limitations, the reliability of the drone's target lock can be improved, providing a more reliable basis for subsequent surveillance handover decisions.

[0037] In some of the aforementioned embodiments of this application, a method is proposed for determining whether the posture required by the current drone's sensor to maintain lock on a target on a predicted trajectory exceeds preset physical limits, thereby obtaining a physical limit determination result. However, in this implementation, the determination of whether the required sensor posture exceeds the physical limit is made solely without fully considering the impact of temporal changes in the drone's posture on the determination. Furthermore, the method lacks the step of converting the target sight line vector in the world coordinate system into the target pointing vector in the drone's body coordinate system for comparison with the sensor's physical limits in the body coordinate system, potentially resulting in inaccurate determination results.

[0038] In this regard, the present application further proposes determining, for each predicted trajectory in a set of predicted trajectories, whether the posture required by the current drone's sensor to maintain lock on a target on the predicted trajectory exceeds a preset physical limit. The steps of obtaining the physical limit determination result include: Obtaining a predicted body posture sequence of the current UAV that is synchronized with the time points on the predicted trajectory; For each target point on the predicted trajectory, determine the target sight vector required by the current UAV's sensor in the world coordinate system to maintain lock on the target point; Based on the predicted body posture in the predicted body posture sequence that is synchronized with the target point, the target sight vector in the world coordinate system is converted into the target pointing vector in the body coordinate system of the current UAV; The target pointing vector in the current drone's body coordinate system is compared with the sensor physical limit preset in the body coordinate system to generate a physical limit judgment result.

[0039] By obtaining a predicted body attitude sequence synchronized with the predicted trajectory time, and converting the target sight vector in the world coordinate system into the target pointing vector in the body coordinate system for comparison with the physical limitations of the sensor, the impact of the UAV attitude change on the sensor pointing capability can be fully considered, achieving a more accurate physical limitation judgment.

[0040] Because the sensor's physical limitations are defined relative to the drone's body, simply knowing the target's direction in the world coordinate system is insufficient to determine whether the limitations have been exceeded. Therefore, based on the predicted drone pose at the corresponding point in time, the target's sight line vector in the world coordinate system is accurately converted to the drone's body coordinate system to obtain the target pointing vector. This target pointing vector directly reflects the angle the sensor needs to rotate relative to the drone's body.

[0041] Finally, the target pointing vector in this body coordinate system is compared with the sensor's physical limitations preset in the body coordinate system, so that it can accurately determine whether the sensor can maintain lock on the target without exceeding its own physical boundaries when the drone is in this predicted attitude.

[0042] In some preferred embodiments, the drone's flight control system can predict the drone's flight path and corresponding body posture over a period of time based on the current flight state and control instructions, generating a predicted body posture sequence. For example, the drone's roll, pitch, and yaw angles can be predicted every 0.1 seconds for the next 5 seconds.

[0043] Next, for each target point on the predicted trajectory, such as the target position 3 seconds into the future on the predicted trajectory, the target sight vector in the world coordinate system from the predicted position of the drone at 3 seconds to the target position can be calculated.

[0044] Then, the target sight line vector in the world coordinate system is converted to the drone body coordinate system using the predicted body posture synchronized with the 3rd second in the predicted body posture sequence, such as the corresponding rotation matrix, to obtain the target pointing vector.

[0045] Finally, the angles corresponding to the target pointing vector in the aircraft coordinate system, such as the horizontal and vertical angles relative to the front of the aircraft, are compared with the sensor's physical limitations, such as the horizontal and vertical rotation ranges of the electro-optical pod, preset in the aircraft coordinate system. If the target pointing vector exceeds the sensor's physical limitations, it is determined to have exceeded the physical limitations, and a corresponding physical limitation determination result is generated.

[0046] The above technical solution, by acquiring a predicted aircraft posture sequence synchronized with the predicted trajectory time, and converting the target sight line vector in the world coordinate system into a target pointing vector in the aircraft coordinate system for comparison with the sensor's physical limitations, fully considers the impact of the drone's posture changes on the sensor's pointing capability, achieving a more accurate physical limitation judgment. This helps improve the accuracy of the monitoring disengagement probability calculation, allowing the drone to initiate the monitoring handover process at a more appropriate time, avoiding accidental target loss due to sensor physical limitations, and ensuring the ability to continuously lock on the target.

[0047] In some of the above-mentioned embodiments of the present application, it is proposed that when the probability of the target being out of monitoring is greater than a preset value, a monitoring handover mode is entered to calculate and find a second drone for monitoring handover. The entering of the monitoring handover mode to calculate and find a second drone for monitoring handover can be specifically performed by simply selecting the drone closest to the predicted target position as the second drone, so that a potential handover object can be quickly determined. However, in its implementation process, selection based solely on distance may not guarantee that the drone has good observation conditions, communication capabilities or suitable mission status, thereby resulting in handover failure or inefficiency. How to quickly and accurately find a suitable second drone for monitoring handover to avoid delays and target loss during the handover process is a problem that needs to be solved.

[0048] In this regard, the present application further proposes that the steps of entering the monitoring handover mode for calculating and finding the second drone for monitoring handover include: After entering the monitoring handover mode, a group of candidate drones that can observe the predicted target position are screened based on the target's predicted position and the position information of other drones in the formation; For each candidate UAV in a group of candidate UAVs, obtain its preset operating state parameters, the operating state parameters including observation effectiveness parameters, communication link parameters and own platform parameters; Based on the operating status parameters of each candidate UAV, a handover adaptability score of the candidate UAV is calculated; The candidate UAV with the highest handover fitness score is determined as the second UAV.

[0049] When the current drone predicts that it is about to lose the target and enters the monitoring handover mode, it no longer simply selects a replacement drone. Instead, it first conducts preliminary screening based on the target's predicted position and the position information of other drones in the formation to determine a group of candidate drones that can theoretically observe the target's predicted position.

[0050] Next, detailed operational status parameters are obtained for each candidate drone, covering multiple dimensions such as observation capability, communication capabilities, and platform status. Based on these multi-dimensional operational status parameters, a comprehensive handover suitability score is calculated for each candidate drone. This score quantifies the drone's overall capability and readiness to perform the takeover mission. Ultimately, the drone with the highest handover suitability score in the candidate drone group is selected as the second drone.

[0051] By combining location screening with multi-dimensional operational status assessment and quantitative scoring, we can more comprehensively and accurately identify the most suitable drone for surveillance handover, avoiding the potential blindness of selection based on a single factor. Combined with a mechanism to trigger handovers, this solution intelligently selects the optimal successor when a handover is necessary, ensuring a smooth and efficient handover process. This effectively solves the problem of quickly and accurately finding the right drone and ensures continuous target lock.

[0052] In some preferred embodiments, when the current drone predicts it is about to lose lock on a target and enters surveillance handover mode, the system first obtains the target's predicted location area for a period of time. Simultaneously, the system obtains the real-time location information of all other drones in the formation. The system then checks each other drone to determine whether its current position covers the target's predicted location area within its sensor's field of view.

[0053] For example, if the predicted target location is at a narrow street exit, the system checks which drones are currently positioned with unobstructed view of the exit. All drones that meet the criteria are grouped into a candidate set. The system then queries each candidate drone for its current operating status parameters. For example, information such as remaining battery life, the current operating mode and health status of the optoelectronic pod, and the signal strength and bandwidth of communications with the current drone or other key nodes may be obtained.

[0054] The system calculates a handover suitability score based on these operational status parameters, combined with pre-set weights or an evaluation model. For example, a drone with sufficient battery life, healthy sensors, a stable communication link, and a low-priority mission might receive a higher score. The system compares the scores of all candidate drones and selects the one with the highest score as the secondary drone, preparing to send the monitoring handover information to it.

[0055] In some of the aforementioned embodiments of the present application, after entering the monitoring handover mode, a group of candidate drones capable of observing the predicted target location is selected based on the predicted target location and the location information of other drones in the formation. For each candidate drone in this group, preset operating status parameters are obtained, including observation efficiency parameters, communication link parameters, and platform parameters. Based on the operating status parameters of each candidate drone, a handover adaptability score is calculated for that candidate drone. The candidate drone with the highest handover adaptability score is then determined as the second drone. This allows for preliminary selection based on the drone's own status and capabilities. However, in its implementation, considering only the operating status parameters is not comprehensive, as the importance of the candidate drone's current mission and whether other drones can replace it can influence the handover decision. If a candidate drone is performing a high-priority mission and no other drone can replace it, it should not be easily selected for handover, even if its operating status parameters indicate favorable conditions, as this can result in greater losses.

[0056] In this regard, the present application further proposes that the steps of calculating a handover adaptability score of each candidate UAV based on its operating status parameters include: Obtain the current mission information of the candidate drone, including the mission priority; Searching among the drones in the formation other than the candidate drone and the current drone whether there is an alternative drone that can take over the current mission, and obtaining a search result of an alternative drone; Determine the task transfer cost based on the search results of alternative drones and the task priority; Based on the operating status parameters and task transfer cost of the candidate UAV, the handover adaptability score of the candidate UAV is calculated.

[0057] The task transfer cost is a quantitative representation of the cost or impact of transferring the task currently being performed by the candidate UAV to other UAVs. It can comprehensively consider factors such as task priority, availability and capabilities of alternative UAVs, and its purpose is to measure the difficulty and potential risks of task transfer. The handover adaptability score refers to a quantitative assessment of the overall suitability of candidate drones for monitoring handover. It can comprehensively consider the drone's own status, capabilities, and the cost of task transfer. Its purpose is to provide a basis for selecting the most suitable drone for handover.

[0058] By taking into account not only the operating status parameters of the UAV but also the task transfer cost as an evaluation factor when calculating the candidate UAV handover adaptability score, whether a UAV is suitable for handover depends not only on whether it has the ability and status to perform the new task, but also on whether its current task can be transferred smoothly and at low cost.

[0059] First, we obtain the current mission information of the candidate UAVs and clarify the mission priority, which provides a basis for the subsequent evaluation of the mission transfer cost. The higher the mission priority, the more important the mission is and the greater the potential impact of the transfer.

[0060] Next, the fleet searches for alternative drones capable of taking over the current mission, generating search results for alternative drones. This step assesses the mission's substitutability; if a suitable alternative drone exists, the difficulty and risk of task transfer are reduced. Then, based on the search results and the mission's priority, the mission transfer cost is determined. This comprehensive reflection of the mission's importance and substitutability is a key metric for assessing the cost of a transfer. Finally, the mission transfer cost is combined with the candidate drone's own operational parameters to calculate the candidate's transfer suitability score.

[0061] In this way, the score reflects not only the drone's own operational status but also the impact of the task transfer on the overall mission. This comprehensive assessment avoids selecting a drone for handover simply because it's in good condition, which can lead to important mission interruptions or difficulties in the transfer. This mechanism allows for a more comprehensive assessment of the cost of handovers, allowing drone handover decisions to be based on a wider range of factors, ultimately selecting the drone with the lowest impact on the overall mission and the highest success rate.

[0062] For example, when Drone A needs to conduct a surveillance handover, the system screens a group of candidate drones, such as Drone B and Drone C. For candidate Drone B, the system obtains its current mission information, such as a high-priority reconnaissance mission. The system searches for drones in the formation other than Drone A and Drone B. If no other drones are capable of performing the high-priority reconnaissance mission, the system's search results for alternative drones indicate low substitutability.

[0063] Based on its low substitutability and high task priority, UAV B is determined to have a high task transfer cost. Then, combining UAV B's operating status parameters (e.g., sufficient fuel, good sensor status) and the high task transfer cost, the system calculates UAV B's transfer suitability score. For candidate UAV C, the system obtains its current mission information, such as a low-priority area patrol mission. The system searches and finds other UAVs in the formation that can take over this patrol mission. The search results for alternative UAVs indicate high substitutability.

[0064] Based on its high substitutability and low task priority, Drone C is determined to have a low task transfer cost. Then, combining Drone C's operating parameters (e.g., adequate fuel level, normal sensor status) with its low task transfer cost, the system calculates Drone C's handover suitability score. Even though Drone B's operating parameters may be slightly better than Drone C's, its higher task transfer cost may result in a lower handover suitability score than Drone C. The system then selects Drone C, with its higher handover suitability score, as the second Drone for the handover.

[0065] The above technical solution not only considers the operational parameters of drones but also the cost of task transfer when calculating the suitability score of candidate drones for handover. This makes handover decisions more comprehensive and rational, avoiding the risk of mission interruption caused by selecting drones that are already performing important tasks and are difficult to replace. This helps improve the mission completion efficiency and robustness of the entire drone formation.

[0066] In some of the above-mentioned embodiments of the present application, it is proposed to calculate a handover adaptability score of the candidate UAV based on the operating status parameters and the task transfer cost of the candidate UAV. The handover adaptability score of the candidate UAV calculated based on the operating status parameters and the task transfer cost can be specifically calculated by taking a weighted sum of the operating status parameters and the task transfer cost or adopting methods such as fuzzy reasoning to comprehensively evaluate the overall suitability of the candidate UAV in receiving tasks. This can provide a quantitative basis for selecting a suitable successor UAV. However, in its implementation process, in the process of calculating the task transfer cost, only considering whether there are alternative UAVs that can take over the current task, without fully evaluating the specific capabilities of these alternative UAVs and the importance of the task itself, may lead to inaccurate assessment of the task transfer cost, and fail to accurately reflect the actual difficulty and potential risks of task transfer, thereby affecting the rationality of the final handover adaptability score.

[0067] In this regard, this application further proposes that the steps for determining the task transfer cost based on the search results of alternative drones and the task priority include: Obtain capability information for each alternative drone included in the alternative drone search results; Based on the current mission information of the candidate UAVs and the capability information of each alternative UAV, the adaptability of each alternative UAV to the current mission is evaluated; The task transfer cost is calculated based on the task priority and the adaptability of each alternative UAV to the current task.

[0068] The search results for alternative drones refer to the result set obtained by searching for drones in the formation other than the candidate drones and the current drone to see if there are any drones that can take over the current mission. The search results may include information on one or more drones that meet the requirements. The purpose is to identify potential mission undertakers. Capability information refers to the technical attributes and performance indicators of alternative drones related to performing missions. Specifically, it may include sensor type, communication bandwidth, computing processing power, flight time, etc. Its purpose is to quantitatively assess the potential of drones to perform specific missions. By obtaining the capability information of each alternative drone included in the search results for alternative drones and combining it with the candidate drone's current mission information, the adaptability of each alternative drone to the current mission is evaluated. The task transfer cost is then calculated based on the mission priority and each alternative drone's adaptability to the current mission. This fully considers the specific capabilities of the alternative drone, the mission priority itself, and the degree of match between the capabilities and the mission, allowing the calculated task transfer cost to more accurately reflect the actual difficulty and potential risks of task transfer. This more accurate task transfer cost assessment can provide more reliable input for the subsequent calculation of the candidate drone's handover adaptability score, which in turn enables the handover adaptability score to more reasonably measure the candidate drone's suitability for taking over the mission.

[0069] In some preferred embodiments, assume that the suitability of candidate drone C for handover needs to be evaluated, and one step involves determining the mission transfer cost. Drone A is currently executing a high-priority target lock mission. A search within the formation reveals drones D and E as potential replacement drones. First, the capabilities of drones D and E are obtained. For example, drone D is equipped with a high-resolution zoom camera and a high-speed data link, while drone E is equipped with a wide-angle camera and a standard data link. Next, based on drone A's current target lock mission (e.g., requiring continuous high-precision tracking of a fast-moving target and real-time HD video transmission) and the capabilities of drones D and E, their suitability for the mission is evaluated. The evaluation results may indicate that drone D has a high suitability for the mission (its high-resolution camera is suitable for high-precision tracking, and its high-speed data link is suitable for HD video transmission), while drone E has a low suitability (its wide-angle camera lacks accuracy, and its standard data link has limited bandwidth). The mission transfer cost is then calculated based on the high priority of the target lock mission and the respective suitabilities of drones D and E. Due to the high priority of the mission, any transfer carries a high risk. The cost of transferring tasks to the highly adaptable drone D is relatively low, while the cost of transferring tasks to the less adaptable drone E is relatively high. Ultimately, based on these evaluation results, the total task transfer cost of transferring tasks from candidate drone C (to either D or E) is determined. For example, the task transfer cost of candidate drone C can be determined by taking the lowest transfer cost among all the alternative drones found, or by using some weighted average.

[0070] Through the above technical solution, the difficulty and cost of task transfer can be evaluated more accurately, thereby more reasonably determining the handover adaptability score, and improving the accuracy and reliability of UAV task handover decisions.

[0071] In some of the above-mentioned embodiments of the present application, it is proposed to enable the second UAV to lock the target based on the monitoring handover information. Specifically, the second UAV can lock the target based on the monitoring handover information. After receiving the monitoring handover information containing the predicted target position, the second UAV directly goes to the predicted position area to search and lock. In this way, the preliminary position information provided by the first UAV can be used to quickly approach the target area. However, in its implementation process, the monitoring handover information may only include the reason for the monitoring separation and the predicted target position, and lacks specific locking action guidance, which makes it difficult for the second UAV to perform locking quickly and effectively, and may cause locking delays or failures.

[0072] In this regard, the present application further proposes that the steps of enabling the second UAV to lock onto the target according to the monitoring handover information include: Determine a target behavior uncertainty level based on the monitoring separation reasons contained in the monitoring handover information; Generate a set of execution parameters for the locking action based on the uncertainty level of the target behavior; Based on the generated execution parameters and the target predicted position included in the monitoring handover information, the second UAV is controlled to perform the locking action.

[0073] By analyzing the cause of the disengagement contained in the surveillance handover information, the predictability of the target's behavior is inferred and converted into a target behavior uncertainty level. Because different disengagement causes correspond to different target behavior patterns or environmental constraints, the uncertainty of the target's behavior can be assessed based on the disengagement cause. For example, a disengagement due to sensor physical limitations may still indicate a predictable target path, while a disengagement due to high-speed maneuvers indicates a high degree of uncertainty in the target's behavior. Based on the determined target behavior uncertainty level, the system generates a set of lock-on action execution parameters that match that level. This is because different uncertainty levels require different search and lock-on strategies. For example, for low uncertainty levels, refined, narrow-range search parameters can be employed, while for high uncertainty levels, wide-range, rapid scanning parameters are required. Ultimately, the second drone executes specific search and lock-on actions based on these generated execution parameters and the predicted target position provided in the surveillance handover information. The predicted target position provides an initial search center or area, while the execution parameters guide the drone in effectively searching and locking on the target within that area. This locking strategy based on cause analysis, uncertainty assessment and parameter generation enables the second UAV to receive more targeted locking guidance, thus overcoming the shortcomings of blind search relying solely on predicted positions and improving the locking efficiency and success rate.

[0074] In some of the above-mentioned embodiments of the present application, it is proposed to enable the second UAV to lock the target according to the monitoring handover information. The specific method of enabling the second UAV to lock the target according to the monitoring handover information can be to start a preset general search and lock program directly according to the target predicted position contained in the information after receiving the monitoring handover information. In this way, the second UAV can start the locking attempt immediately after receiving the handover information. However, in its implementation process, the target is only locked according to the monitoring handover information, and there is a lack of analysis of the cause of the monitoring separation, which may cause the second UAV to be unable to quickly and effectively lock the target. Especially when the cause of the monitoring separation is complex or urgent, a simple locking strategy may not be able to cope with it, resulting in locking failure or delay.

[0075] In this regard, the present application further proposes that the steps of enabling the second UAV to lock onto the target according to the monitoring handover information include: Receive monitoring handover information; Determining a target locking logic corresponding to the monitoring separation reason contained in the monitoring handover information; According to the determined target locking logic and the target predicted position contained in the monitoring handover information, the second UAV is controlled to perform the locking action.

[0076] The second drone receives monitoring handover information, which includes the reason for the first drone's disengagement and the target's predicted location. Based on the received disengagement reason, the second drone can intelligently determine a target lock logic appropriate to that reason. For example, different disengagement reasons require different response strategies. By identifying the specific cause, the second drone can avoid using generic, inefficient lock methods and instead select the strategy most appropriate for the situation.

[0077] Subsequently, based on the determined target locking logic and the target predicted position contained in the monitoring handover information, the second UAV is controlled to perform a specific locking action. The target predicted position provides a preliminary search area or direction for the second UAV, while the determined locking logic guides the second UAV on how to search, identify and lock within this area. This method of selecting the locking logic based on the cause enables the second UAV to quickly adapt to different handover scenarios, significantly improving the efficiency and success rate of target locking. This solution ensures that when the first UAV is about to lose the target, the handover information can be promptly and accurately transmitted to the second UAV that is most suitable to take over, and the second UAV can use this information to adopt the optimal locking strategy according to the specific reason, thereby achieving seamless and efficient handover of the monitoring task, and effectively avoiding the risk of target loss due to simple handover or improper strategy.

[0078] Specifically, based on the monitoring separation reason included in the monitoring handover information, the step of determining the target locking logic corresponding to the monitoring separation reason includes: When the reason for monitoring separation is the physical limitation of the sensor, the target locking logic is determined to predict the target exit position and monitor it; When the cause of the monitoring separation is long-term line of sight obstruction, the target locking logic is determined to adjust the position or altitude of the second UAV to bypass the obstacle; When the reason for the monitoring separation is that the target's high-speed maneuver exceeds the tracking capability, the target locking logic is determined to be interception observation based on the target's final velocity vector; When the reason for disengagement from monitoring is that the communication link is about to be interrupted, the target locking logic is determined to immediately take over the locking of the target.

[0079] By receiving the monitoring handover information including the monitoring separation reason and the target predicted position, and based on the monitoring separation reason, determining a corresponding target locking logic, and then controlling the second UAV to perform the locking action according to the determined target locking logic and the target predicted position.

[0080] Specifically, the system analyzes the cause of the disengagement indicated in the surveillance handover message, such as sensor physical limitations, long-term line of sight obstruction, high-speed target maneuvering, or impending communication link loss. Depending on the cause, the system selects or generates a specific target lock logic. For example, if the cause is sensor physical limitations, the system focuses on predicting the target's possible exit locations and monitoring them; if the cause is line of sight obstruction, the system directs the second drone to adjust its position to circumvent the obstacle; if the cause is high-speed target maneuvering, the system employs interception observation based on the final velocity vector; and if the cause is a communication link issue, the system requires immediate lock takeover.

[0081] This approach of selecting a targeted strategy based on the specific cause allows the second drone to take the action most appropriate for the current situation, avoiding the ineffective attempts or delays that may result from using a general strategy. In this way, the solution can more effectively utilize monitoring handover information, making the second drone's lock control more intelligent and efficient, thereby improving the success rate of target lock and mission continuity.

[0082] In some preferred embodiments, when the second drone receives the surveillance handover information and identifies that the cause of the loss of surveillance is a physical limitation of the sensor, the system determines the target lock logic to predict the target's exit location and monitor it. At this point, the system can use the predicted target location contained in the surveillance handover information (for example, the location where the current drone last observed the target entering the narrow area) and pre-stored three-dimensional map information to calculate or predict the target's possible exit location or area from that area. The control system then instructs the second drone to adjust its flight path and attitude to fly toward the predicted exit area, and adjusts the pointing direction of its onboard sensors (such as the electro-optical pod) to continuously search and monitor the area, waiting for the target to appear and re-establish lock.

[0083] If the target vehicle enters a narrow lane due to physical limitations of the sensors, the second drone will predict its exit location based on the lane's geometry and fly to the vicinity of the exit for monitoring. As another specific implementation, if monitoring is disengaged due to an impending communication link loss, the system determines that the target lock logic immediately takes over the target lock. At this point, the second drone will prioritize the target's predicted position in the monitoring handover information, rapidly adjusting its own position and sensor pointing, attempting to capture the target as quickly as possible. It will then immediately begin autonomous tracking to minimize information loss and lock-on periods that may result from communication interruptions.

[0084] Through the above technical solution, based on the cause of the monitoring loss contained in the monitoring handover information, the target lock logic corresponding to the cause is determined. Based on the determined target lock logic and the target's predicted position contained in the monitoring handover information, the second drone is controlled to execute the lock action. This approach of using different target lock logics based on different monitoring loss causes can specifically address monitoring loss issues caused by different reasons, avoiding the shortcomings of simple, general strategies. This allows the second drone to take the optimal action based on the specific situation, thereby improving the success rate and efficiency of target lock and ensuring the continuity of the monitoring mission.

[0085] Reference Figure 2 , this application further proposes a drone target locking system, the system comprising: A first calculation module 210 is configured to calculate a probability of the target being out of surveillance based on a motion vector of the target, the current field of view of the UAV, and a three-dimensional map; The second calculation module 220 is configured to enter a monitoring handover mode to calculate and find a second drone for monitoring handover when the probability of the target being out of monitoring is greater than a preset value; The control module 230 is used to send monitoring handover information to the second drone when the second drone is found by calculation. The monitoring handover information includes the reason for the current drone's monitoring separation and the target predicted position calculated based on the target's motion vector and the three-dimensional map, so that the second drone can lock the target according to the monitoring handover information.

[0086] By predicting the risk of target loss and sending monitoring handover information including the reason for separation and the predicted target position to the replacement UAV, the problem in the existing technology that general requests cannot effectively guide the replacement UAV is solved, and the monitoring blank period is avoided. It has the advantages of being able to predict the risk of target loss and initiate a targeted handover process, avoiding the monitoring blank period, and improving the continuity and success rate of target locking.

[0087] In addition, in some preferred embodiments, the drone target locking system proposed in this application can perform any one of the steps in the above method.

[0088] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for locking a target of an unmanned aerial vehicle, characterized in that: include: Calculating the probability of the target being out of surveillance based on the target's motion vector, the current UAV's field of view, and the three-dimensional map; When the probability of the target being out of surveillance is greater than a preset value, the surveillance handover mode is entered to calculate and find a second drone for surveillance handover; When the second drone is found by calculation, monitoring handover information is sent to the second drone. The monitoring handover information includes the reason for the current drone's monitoring separation and the target predicted position calculated based on the target's motion vector and the three-dimensional map, so that the second drone can lock the target according to the monitoring handover information.

2. The method for locking a target of an unmanned aerial vehicle according to claim 1, wherein: The step of calculating the target's probability of escaping from monitoring based on the target's motion vector, the current UAV's field of view, and the three-dimensional map includes: generating a set of predicted trajectories of the target within a preset time based on the motion vector of the target; For each predicted trajectory in the set of predicted trajectories, determining whether line of sight obstruction occurs by combining the current field of view of the drone and the three-dimensional map, and obtaining a line of sight obstruction determination result; For each predicted trajectory in the set of predicted trajectories, determining whether a posture required by a sensor of the current drone to maintain lock on a target on the predicted trajectory exceeds a preset physical limit, and obtaining a physical limit determination result; Based on the line of sight obstruction judgment result and the physical limitation judgment result, the number of predicted trajectories that trigger line of sight obstruction or exceed the physical limitation is counted, and the monitoring escape probability is determined according to the ratio of the number to the total number of the set of predicted trajectories.

3. The method for locking a target of an unmanned aerial vehicle according to claim 2, wherein: The step of determining, for each predicted trajectory in the set of predicted trajectories, whether a posture required by a sensor of the current drone to maintain lock on a target on the predicted trajectory exceeds a preset physical limit, and obtaining a physical limit determination result includes: Obtaining a predicted body posture sequence of the current UAV that is synchronized with the time points on the predicted trajectory; For each target point on the predicted trajectory, determine the target sight vector required by the current UAV's sensor in the world coordinate system to maintain lock on the target point; Based on the predicted body posture in the predicted body posture sequence that is time-synchronized with the target point, converting the target sight line vector in the world coordinate system into a target pointing vector in the body coordinate system of the current UAV; The target pointing vector of the current UAV in the body coordinate system is compared with the sensor physical limit preset in the body coordinate system to generate the physical limit judgment result.

4. The method for locking a target of an unmanned aerial vehicle according to claim 1, wherein: The step of entering the monitoring handover mode to calculate and find the second drone for monitoring handover includes: After entering the monitoring handover mode, based on the target predicted position and the position information of other UAVs in the formation, a group of candidate UAVs that can observe the target predicted position are screened; For each candidate drone in the group of candidate drones, obtaining its preset operating status parameters, the operating status parameters including observation effectiveness parameters, communication link parameters, and own platform parameters; Calculating a handover adaptability score for each candidate UAV based on the operating status parameters of the candidate UAV; The candidate drone with the highest handover adaptability score is determined as the second drone.

5. The method for locking a target of an unmanned aerial vehicle according to claim 4, characterized in that: The step of calculating a handover adaptability score of each candidate UAV based on the operating status parameters of the candidate UAV includes: Obtaining current mission information of the candidate UAV, wherein the current mission information includes a mission priority; Searching among the drones in the formation other than the candidate drone and the current drone whether there is an alternative drone that can take over the current mission, and obtaining a search result of an alternative drone; determining a task transfer cost based on the search results of the alternative drones and the task priority; The handover adaptability score of the candidate UAV is calculated based on the operating state parameters of the candidate UAV and the task transfer cost.

6. The method for locking a target of an unmanned aerial vehicle according to claim 5, characterized in that: The step of determining the task transfer cost based on the search results of the alternative drone and the task priority includes: Obtaining capability information of each alternative drone included in the alternative drone search results; evaluating the adaptability of each of the alternative UAVs to the current mission based on the current mission information of the candidate UAV and the capability information of each of the alternative UAVs; The task transfer cost is calculated according to the task priority and the adaptability of each replacement UAV to the current task.

7. The method for locking a target of an unmanned aerial vehicle according to claim 1, wherein: The step of causing the second UAV to lock onto the target according to the monitoring handover information includes: determining a target behavior uncertainty level based on the monitoring separation reason included in the monitoring handover information; generating a set of execution parameters for the locking action according to the uncertainty level of the target behavior; Based on the generated execution parameters and the target predicted position included in the monitoring handover information, the second UAV is controlled to perform a locking action.

8. The method for locking a target of an unmanned aerial vehicle according to claim 1, wherein: The step of causing the second UAV to lock onto the target according to the monitoring handover information includes: receiving the monitoring handover information; determining a target locking logic corresponding to the monitoring separation reason based on the monitoring separation reason included in the monitoring handover information; According to the determined target locking logic and the target predicted position included in the monitoring handover information, the second UAV is controlled to perform a locking action.

9. The method for locking a target of an unmanned aerial vehicle according to claim 7, wherein: The step of determining the target locking logic corresponding to the monitoring separation reason contained in the monitoring handover information includes: When the monitoring separation reason is a physical limitation of the sensor, determining the target locking logic to predict the target exit position and monitor it; When the monitoring separation reason is long-term line of sight obstruction, determining the target locking logic to adjust the position or altitude of the second UAV to bypass the obstacle; When the monitoring separation reason is that the target maneuvers at high speed beyond the tracking capability, determining the target locking logic to perform interception observation based on the target's final velocity vector; When the monitoring separation reason is that the communication link is about to be interrupted, the target locking logic is determined to immediately take over the locking of the target.

10. A drone target lock system, characterized in that: The system includes: A first calculation module is used to calculate the probability of the target being out of monitoring based on the target's motion vector, the current UAV's field of view, and the three-dimensional map; A second calculation module is configured to enter a monitoring handover mode to calculate and search for a second drone for monitoring handover when the probability of the target being out of monitoring is greater than a preset value; The control module is configured to send monitoring handover information to the second drone when the second drone is found through calculation. The monitoring handover information includes the reason why the current drone was out of monitoring and the target predicted position calculated based on the motion vector of the target and the three-dimensional map, so that the second drone can lock onto the target according to the monitoring handover information.

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