Unmanned aerial vehicle group coordination control system and method based on data analysis

Abstract

The present invention discloses an unmanned aerial vehicle (UAV) coordinated control system and method based on data analysis, belonging to the technical field of UAV tracking control. The system includes a mission planning and control module, a data acquisition module, a three-dimensional model management module, an intelligent judgment module, an intelligent analysis and calculation module, and an intelligent tracking control module; the mission planning and control module is used to determine the tracking targets and control a number of UAVs to perform real-time tracking on each target; the data acquisition module is used to acquire image data and point cloud data; the three-dimensional model management module is used to determine the flight paths of each UAV and the movement paths of each target; the intelligent judgment module is used to judge whether to re-dynamically allocate the targets corresponding to each UAV; the intelligent analysis and calculation module is used to re-determine the target sets of each UAV and the regions corresponding to the maximum tracking ranges of each UAV; the intelligent tracking control module is used to control each UAV to go to the corresponding tracking position.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV tracking control, and particularly to a coordinated control system and method for a UAV group based on data analysis. Background Art

[0002] UAV tracking control is a comprehensive technology integrating computer vision, automatic control, artificial intelligence, and UAV technology; it is mainly used to achieve precise tracking and dynamic following of specific targets by UAVs, and is widely applied in many fields of life; in a UAV tracking control system, a 3D model plays a crucial role. Through the fine construction of its grid and texture, it can realistically simulate the shape and surface characteristics of real or fictional objects, providing rich visual information and an accurate spatial positioning basis for UAV tracking. It is not only used for target recognition and positioning, but also provides prediction and decision-making support for tracking algorithms by simulating the movement trajectory and attitude of the target object.

[0003] When a UAV group executes a multi-target tracking task, when there are too many targets to be tracked and the number of UAVs is insufficient, one UAV needs to track multiple targets; due to the uncertainty of target movement and change, during the tracking process of the UAV, the target often goes out of the monitoring range. In the prior art, the cooperation ability between UAV groups is insufficient, resulting in low tracking efficiency; and in the tracking control of UAV groups, there is a lack of an effective fault tolerance mechanism, which easily leads to target loss. Summary of the Invention

[0004] The purpose of the present invention is to provide a coordinated control system and method for a UAV group based on data analysis to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A coordinated control method for a UAV group based on data analysis, the method comprising the following steps:

[0006] S10. Establish a 3D space model, determine the targets to be tracked and the target sets of the UAVs, and control several UAVs to respectively perform real-time tracking on the targets in their respective target sets; collect the image data and point cloud data during the tracking process of each UAV.

[0007] S20. Determine the maximum field of view coverage range of the UAV according to the clarity of the collected image data; determine the maximum tracking range of the UAV according to the moving speed of the target and the response time of the UAV; wherein, the maximum tracking range is within the maximum field of view coverage range; determine the change of the flight path of each UAV over time according to the UAV positioning, analyze the collected image data and point cloud data, and respectively determine the change of the moving path of each target over time.

[0008] S30. Determine the position information of each current target according to the movement paths of the respective targets; determine whether it is necessary to re-dynamically allocate the target sets corresponding to each UAV according to the current position information of each target, the target sets corresponding to the current UAVs, and the maximum tracking ranges corresponding to the image data collected by the current UAVs; if necessary, execute step S40 at this time; if not, execute step S50 at this time;

[0009] S40. Re-determine the target sets of each UAV and the image position areas corresponding to the maximum tracking ranges of each UAV according to the current position information of each target and the maximum field-of-view coverage ranges corresponding to the image data collected by each UAV, and re-determine the number of UAVs. When the current number of UAVs is insufficient, increase the number of UAVs; when the current number of UAVs is excessive, reduce the number of UAVs; determine the position information of each current UAV according to the flight paths of each UAV; control each UAV to go to the corresponding tracking position according to the re-determined image position areas corresponding to the maximum tracking ranges of each UAV and the current position information of each UAV, and execute step S60;

[0010] S50. Control each UAV to hover, and execute step S60;

[0011] S60. Repeat steps S30 - S50 until the target tracking ends, and control each UAV to return.

[0012] An unmanned aerial vehicle group coordination control system based on data analysis, the system includes a mission planning and control module, a data acquisition module, a three-dimensional model management module, an intelligent judgment module, an intelligent analysis and calculation module, and an intelligent tracking control module;

[0013] The mission planning and control module is used to determine the targets to be tracked, determine the targets to be tracked and the target sets of the UAVs, control several UAVs to respectively track the targets in their respective target sets in real time, and send a data acquisition signal to the data acquisition module;

[0014] The data acquisition module is used to collect image data and point cloud data during the UAV tracking process, and send the collected data to the three-dimensional model management module;

[0015] The three-dimensional model management module is used to establish a three-dimensional space model, determine the maximum field-of-view coverage range of the UAV according to the clarity of the collected image data; determine the maximum tracking range of the UAV according to the moving speed of the target and the response time of the UAV; wherein, the maximum tracking range is within the maximum field-of-view coverage range; determine the change of the flight path of each UAV over time according to the UAV positioning, analyze the collected image data and point cloud data, and respectively determine the change of the movement path of each target over time;

[0016] The intelligent judgment module is used to respectively determine the position information of each current target according to the movement paths of the targets; judge whether it is necessary to re-dynamically allocate the target sets corresponding to each UAV according to the position information of each current target, the target sets corresponding to each current UAV, and the maximum tracking range corresponding to the image data collected by each current UAV; if it is necessary to re-dynamically allocate the targets corresponding to each UAV, send the signal of re-dynamic allocation to the intelligent analysis and calculation module; if it is not necessary to re-dynamically allocate the targets corresponding to each UAV, send the signal of not requiring re-dynamic allocation to the intelligent tracking control module;

[0017] The intelligent analysis and calculation module is used to re-determine the target sets of each UAV and the image position areas corresponding to the maximum tracking ranges of each UAV according to the position information of each current target and the maximum field of view coverage range corresponding to the image data collected by each UAV, and re-determine the number of UAVs. When the current number of UAVs is insufficient, increase the number of UAVs; when the current number of UAVs is excessive, reduce the number of UAVs, and send it to the intelligent tracking control module;

[0018] The intelligent tracking control module is used to respectively determine the position information of each current UAV according to the flight paths of each UAV when it is necessary to re-dynamically allocate the target sets corresponding to each UAV; control each UAV to go to the corresponding tracking position according to the image position areas corresponding to the re-determined maximum tracking ranges of each UAV and the position information of each current UAV; when it is not necessary to re-dynamically allocate the target sets corresponding to each UAV, control each UAV to be in a hovering state.

[0019] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: by establishing a three-dimensional space model, determining the maximum field of view coverage range and the maximum tracking range corresponding to the image data collected by the UAVs, analyzing the movement paths of the tracking targets, determining the position information that the UAVs need to go to in the three-dimensional space model, and dynamically coordinating the UAV group according to the analysis results, the target loss caused by the uncertainty of the target movement change is avoided, and the robustness of the UAV tracking system is improved; by determining the tracking targets corresponding to each UAV and allocating the UAV tracking tasks, the collaborative operation ability of the UAV group is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is the pixel resolution of the image data collected in the embodiment of the coordinated control method of the UAV group based on data analysis of the present invention;

[0021] Figure 2 is a schematic structural diagram of the coordinated control system of the UAV group based on data analysis of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1-2 , the present invention provides a technical solution:

[0024] Please refer to Figure 1 , in the first embodiment: a coordinated control method for a drone group based on data analysis is provided. This method is based on the existing drone tracking technology. When analyzing a drone corresponding to multiple tracked targets, when there is a target beyond the tracking range, it is a process of automatically allocating the targets and tracking ranges corresponding to the drones; through a three-dimensional space model, the position information of each target and each drone is determined respectively. By the clarity of the image data, the moving speed of the target, and the response time of the drone, the maximum field of view coverage range and the maximum tracking range are introduced. According to the position information of each target in the three-dimensional space model, the targets corresponding to each drone are dynamically allocated, and the drone group is dynamically coordinated according to the dynamic allocation result; fewer drones are used to track the targets. At the same time, when the number of tracking drones is insufficient, the number of drones is increased, avoiding the loss of targets caused by the uncertainty of target movement changes, and improving the robustness of the drone tracking system; the method includes the following steps:

[0025] S10. Establish a three-dimensional space model, determine the set of targets to be tracked and the target set of the drones, and control several drones to respectively track the targets in their respective target sets in real time; collect the image data and point cloud data during the tracking process of each drone.

[0026] In this embodiment, the targets to be tracked are wild animals. Initially, the wild animals gather together, and the number of targets is m. The targets to be tracked include several targets. At this time, 2 drones are controlled to monitor the targets. Initially, the targets corresponding to the 2 drones are determined respectively, and the target sets corresponding to each drone are manually determined by the management personnel; as time changes, the movement of the targets causes them to exceed the monitoring range of the drones. At this time, it is necessary to dynamically allocate the tracking ranges of the drones and the targets to be tracked.

[0027] Furthermore, the number of the targets is m; the number of the drones during the tracking process is n; where m > n ≥ 2; the drones are equipped with high-definition cameras, lidars, and positioning systems; the high-definition cameras are used to collect image data during the tracking process of the drones; the lidars are used to collect point cloud data during the tracking process of the drones; the positioning systems are used to determine the position information of the drones; where the image data and the point cloud data contain the tracked targets and the scenery around the targets.

[0028] In this embodiment, the drones capture image data with different bands and resolutions through high-definition cameras, emit laser beams through lidars and receive the reflected signals, and determine the distance and position of the target objects by calculating the round-trip time of the signals, thereby generating point cloud data; according to the collected image data, extract the features of the tracked targets and the scenery around the targets, and use the CNN neural network to classify and identify the extracted features to distinguish the tracked targets and the scenery around the targets, and then perform real-time tracking on the targets; where m > n ≥ 2, that is, in this method, the situation where there is a single drone corresponding to multiple targets is considered.

[0029] S20. Determine the maximum field of view coverage range of the drones according to the clarity of the collected image data; determine the maximum tracking range of the drones according to the moving speed of the targets and the response time of the drones; where the maximum tracking range is within the maximum field of view coverage range; respectively determine the change of the flight path of each drone over time according to the positioning of the drones, and analyze the collected image data and point cloud data to respectively determine the change of the moving path of each target over time.

[0030] Specifically, the method steps are as follows:

[0031] Determine the maximum field of view coverage range S of the drones according to the clarity of the collected image data max , determine the maximum tracking range s of the drones according to the moving speed of the targets and the response time of the drones max ; where s max < S max ; v max represents the maximum moving speed of the targets; T represents the response time of the drones; represents the length of the maximum field of view coverage range; represents the length of the maximum tracking range; the width of the maximum field of view coverage range; the width of the maximum tracking range;

[0032] Respectively determine the change of the flight path of each drone over time t according to the positioning system of the drones, and obtain Analyze the collected image data and point cloud data, respectively determine the change of the movement path of each target over time t, and obtain Among them, and The change situation is expressed as the change of the spatial coordinate points in the three-dimensional space model over time t; t represents the timestamp.

[0033] It should be noted that when the number of targets is too large and there are target position offsets, at this time, it is necessary to adjust the UAV so that each target is within the tracking range. Therefore, through the three-dimensional space model established by the UAV positioning system, the position information of each target and each UAV is determined respectively, so as to provide data support for the dynamic coordination of the UAV group and improve the accuracy of data analysis.

[0034] It should be noted that the maximum field of view coverage S max represents the maximum range that the high-definition camera on the UAV can capture to ensure the clarity of the target images collected; by determining the minimum image clarity of the target, the farthest image data collection distance between the UAV and the target is determined, and according to the farthest image data collection distance, the maximum field of view coverage S max of the UAV for collecting images is obtained; among them, in the embodiment, the influence of other variable conditions such as changes in high-definition camera parameters and obstacles is not considered; the image clarity is judged according to whether the CNN neural network can recognize the target, and according to the maximum image data collection distance threshold at which the CNN neural network can recognize the target, the maximum field of view coverage is determined.

[0035] It should be noted that the maximum tracking range s max is used as a trigger condition for judging whether it is necessary to perform dynamic adjustment on the UAV group, and is determined according to the maximum moving speed of the target; for example, if the target is a wild animal and the maximum moving speed of the wild animal is 10 m / s, at this time, the maximum field of view coverage of the UAV is 30 m × 18 m, and the process of the UAV for one dynamic allocation is 0.2 s. Therefore, the maximum tracking range of the UAV is determined to be 26 m × 14 m; since the movement path of the target is often difficult to predict, the maximum tracking range s max is added. When there is a target exceeding the tracking range, in order to ensure the clarity of the target image, it is necessary to allocate other UAVs to track the target in real time. Therefore, the condition for re-dynamically allocating the UAV group is triggered, and the targets corresponding to each UAV are dynamically allocated; among them, the maximum tracking range s max can be manually determined by the administrator or automatically changed by adding a training model. When the target moving speed is too fast, the maximum tracking range s max can be reduced to prevent the target from being lost, thereby improving the robustness of the UAV tracking system.

[0036] S30. Determine the position information of each current target according to the movement paths of the respective targets; based on the position information of each current target, the target sets corresponding to the current respective drones, and the maximum tracking ranges corresponding to the image data collected by the current respective drones, determine whether it is necessary to re-dynamically allocate the target sets corresponding to the respective drones; if so, execute step S40 at this time; if not, execute step S50 at this time.

[0037] Specifically, the method steps are as follows:

[0038] According to respectively determine the position information of each current target Determine the target set P of each current drone 1 、P 2 、...、P n ; among them, each target has a corresponding drone; according to s max respectively determine the image position areas corresponding to the maximum tracking ranges of the current respective drones

[0039] Determine that after connecting the spatial coordinate points corresponding to the target position information in the set P i the outer edge line forms the image position area corresponding to the figure According to and judge whether it is necessary to re-dynamically allocate the target sets corresponding to the respective drones: when there is it is necessary to re-dynamically allocate the target sets corresponding to the respective drones; when there is no it is not necessary to re-dynamically allocate the target sets corresponding to the respective drones; among them, P i represents the target set of the current i-th drone; represents the image position area corresponding to the maximum tracking range of the current i-th drone.

[0040] S40. According to the position information of each current target and the maximum field of view coverage ranges corresponding to the image data collected by the respective drones, re-determine the target sets of the respective drones and the image position areas corresponding to the maximum tracking ranges of the respective drones, and re-determine the number of drones. When the current number of drones is insufficient, increase the number of drones; when the current number of drones is excessive, reduce the number of drones; according to the flight paths of the respective drones, respectively determine the position information of the current respective drones; according to the re-determined image position areas corresponding to the maximum tracking ranges of the respective drones and the position information of the current respective drones, control the respective drones to go to the corresponding tracking positions and execute step S60.

[0041] Specifically, the method steps are as follows:

[0042] According to s max 、s max and respectively determine the image position areas corresponding to the maximum field of view coverage ranges of the current UAVs According to and re-determine the target sets P' of each UAV 1 、P' 2 、...、P' h and the image position areas corresponding to the maximum tracking ranges of each UAV, so as to satisfy the conditions:

[0043]

[0044] where h represents the number of UAVs that satisfy the conditions after re-determination; respectively represent the number of targets in the target sets corresponding to different UAVs after re-determination; represents the empty set; represents the position information of the current a-th target; represents the position information of the current b-th target; a = 1, 2,..., m, b = 1, 2,..., m, and a ≠ b. When calculating at this time, a and b are in the same target set; L max represents the maximum image data distance of the maximum tracking range of the UAV in the corresponding image position area; P' i represents the target set corresponding to the i-th UAV after re-determination; represents the image position area corresponding to the figure formed by connecting the spatial coordinate points corresponding to the target position information in the set P' i after re-determination; represents the image position area corresponding to the maximum tracking range of the i-th UAV after re-determination; i = 1, 2,..., n;

[0045] Among them, re-determine the number of UAVs: when the current number of UAVs cannot meet the conditions, at this time, it is judged that the number of UAVs is insufficient, let h + 1, and then judge whether the conditions are met again. If not, continue to increase the number of UAVs until the conditions are met; when the current number of UAVs meets the conditions, let h - 1, and then judge whether the conditions are met again. If so, continue to reduce the number of UAVs until the conditions are not met; so that the number of UAVs that meet the conditions after re-determination reaches the minimum;

[0046] According to respectively determine the image position areas corresponding to the maximum tracking ranges of each UAV after re-determination According to P' 1 、P'2 ..., P' h , respectively determine the targets corresponding to each UAV after re - determination;

[0047] According to the image position areas corresponding to the maximum tracking ranges of the re - determined UAVs, determine the position information that each UAV needs to go to; according to the position information that each UAV needs to go to and the current position information of each UAV, control each UAV to go to the corresponding tracking position: according to the position information that each UAV needs to go to and the current position information of each UAV, calculate the moving distance of each UAV from the current position information to the position information it needs to go to respectively and make meet the conditional formula: where, L z represents the total moving distance of n UAVs from the current position to the position they need to go to; represents the moving distance of the i - th UAV from the current position to the position it needs to go to.

[0048] It should be noted that the states of UAVs are divided into mission UAVs that are executing tracking tasks and idle UAVs without tracking tasks. All UAVs mentioned in this method are mission UAVs; through h, continuously calculate the target sets of each UAV that meet the conditions and the regions corresponding to the maximum tracking ranges of each UAV; when h does not meet the conditions, let h + 1, that is, change the state of an idle UAV to a mission UAV, and then judge again whether it meets the conditions, so that the image position area corresponding to the re - determined figure is within the maximum tracking range; when h meets the conditions, let h - 1, that is, change the state of a mission UAV to an idle UAV, and then judge again whether it meets the conditions, so that the image position area corresponding to the re - determined figure is within the maximum tracking range; until the minimum number of mission UAVs that meet the conditions is found, at this time determine the corresponding h, and obtain the target sets of each UAV and the regions corresponding to the maximum tracking ranges of each UAV.

[0049] It should be noted that when and L max are compared, a and b need to be in the same target set. By determining the region corresponding to the maximum field - of - view coverage range of the UAV and determining the maximum image data distance according to this region; the maximum image data distance represents the distance between the two farthest spatial coordinate points within the maximum tracking range.

[0050] It should be noted that through the above conditions, fewer UAVs are used to perform real - time tracking of targets. When the number of UAVs in the current tracking state is insufficient, the number of UAVs is added one by one, and the targets corresponding to each UAV are dynamically allocated, so as to use fewer UAVs to track the targets and improve the UAV tracking management efficiency.

[0051] In this embodiment, when each UAV tracks a target in real time, the collected image data always remains within the maximum field of view coverage range; the maximum field of view coverage ranges of each UAV for collecting image data are the same and fixed; the maximum tracking ranges of each UAV in the three-dimensional space model are the same and fixed; as Figure 1 shown, the image pixel size corresponding to the maximum field of view coverage range S max is 1920×1080 pixels, and the image pixel size corresponding to the maximum tracking range s max is 1620×780 pixels; at this time, the distance of L max is s max The diagonal connection distance of the image corresponds to the distance in the maximum tracking range of the three-dimensional space model.

[0052] It should be noted that since this method considers the UAV to ensure image clarity, that is, the image data collected by the UAV within the maximum field of view coverage range. When the maximum field of view coverage range and the maximum tracking range of the UAV are determined, there is corresponding three-dimensional space model position information for the maximum tracking range of each UAV, that is, the spatial coordinate points corresponding to the positions. At this time, according to the determined position information that the UAV needs to go to, combined with the current UAV position information, the UAV is controlled to go to the corresponding position.

[0053] It should be noted that by determining the target set of each UAV and the area corresponding to the maximum tracking range of each UAV, according to the position information that each UAV needs to go to and the current position information of each UAV, the tracking movement of each UAV is dynamically allocated, so that the total movement distance of each UAV from the current position to the position it needs to go to is the shortest, thereby improving the tracking efficiency.

[0054] S50. Control each UAV to be in a hovering state and execute step S60.

[0055] S60. Repeat steps S30 - S50 until the target tracking is completed, and control each UAV to return.

[0056] In this embodiment, if there is no then the UAV remains in a hovering state all the time. When there is a trigger condition for the UAV group to re-dynamically allocate is triggered. At this time, according to the collected image data is re-analyzed, and each UAV is controlled to go to the corresponding tracking area until the management personnel determine that the UAV tracking task is completed. At this time, each UAV is controlled to return, and the status of each UAV is changed to an idle UAV.

[0057] It should be noted that when the target exceeds the range of the image data collected by the UAV, it is easy to cause the loss of the target. In the prior art, it is difficult to plan the UAV tracking in advance. By introducing s max when the target exceeds s maxWhen it is within the range, the condition for the UAV group to be re-dynamically allocated is triggered. At this time, the target is still within the S max corresponding image range. Therefore, the UAV group can be tracked and controlled in advance according to the position information of the target, thereby improving the robustness of the system.

[0058] Please refer to Figure 2 In the second embodiment: A coordinated control system for a UAV group based on data analysis is provided. The system includes a mission planning and control module, a data acquisition module, a three-dimensional model management module, an intelligent judgment module, an intelligent analysis and calculation module, and an intelligent tracking and control module;

[0059] The mission planning and control module is used to determine the target to be tracked, determine the target to be tracked and the target set of the UAVs, control several UAVs to respectively track the targets in their respective target sets in real time, and send a data acquisition signal to the data acquisition module;

[0060] The data acquisition module is used to collect image data and point cloud data during the UAV tracking process, and send the collected data to the three-dimensional model management module;

[0061] The three-dimensional model management module is used to establish a three-dimensional space model, determine the maximum field of view coverage range of the UAVs according to the clarity of the collected image data; determine the maximum tracking range of the UAVs according to the moving speed of the target and the response time of the UAVs; wherein, the maximum tracking range is within the maximum field of view coverage range; determine the change of the flight path of each UAV over time according to the UAV positioning, analyze the collected image data and point cloud data, and respectively determine the change of the moving path of each target over time;

[0062] The intelligent judgment module is used to respectively determine the position information of each current target according to the moving paths of each target; according to the position information of each current target, the target set corresponding to each current UAV, and the maximum tracking range corresponding to the image data collected by each current UAV, judge whether it is necessary to re-dynamically allocate the target sets corresponding to each UAV; if it is necessary to re-dynamically allocate the targets corresponding to each UAV, send a re-dynamically allocated signal to the intelligent analysis and calculation module; if it is not necessary to re-dynamically allocate the targets corresponding to each UAV, send a signal indicating that re-dynamic allocation is not required to the intelligent tracking and control module;

[0063] The intelligent analysis and calculation module is used to re-determine the target set of each UAV and the image position area corresponding to the maximum tracking range of each UAV according to the position information of each current target and the maximum field of view coverage range corresponding to the image data collected by each UAV, and re-determine the number of UAVs. When the current number of UAVs is insufficient, increase the number of UAVs; when the current number of UAVs is excessive, reduce the number of UAVs, and send it to the intelligent tracking and control module;

[0064] The intelligent tracking control module is used to, when it is necessary to re-dynamically allocate the target sets corresponding to each UAV, determine the position information of each UAV at present according to the flight paths of the UAVs; control each UAV to go to the corresponding tracking position according to the image position areas corresponding to the maximum tracking ranges of each UAV determined again and the position information of each UAV at present; when it is not necessary to re-dynamically allocate the target sets corresponding to each UAV, control each UAV to be in a hovering state.

[0065] In this embodiment:

[0066] The mission planning control module is connected to the data acquisition module; the data acquisition module is connected to the three-dimensional model management module; the three-dimensional model management module is connected to the intelligent judgment module; the intelligent judgment module is connected to the intelligent analysis and calculation module; the intelligent judgment module is connected to the intelligent tracking control module; the intelligent analysis and calculation module is connected to the intelligent tracking control module.

[0067] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A coordinated control method for a drone group based on data analysis, characterized in that: The method comprises the following steps: S10, establishing a three-dimensional space model, determining the target to be tracked and the target set of the drone, controlling several drones to respectively track the targets of their respective target sets in real time; collecting image data and point cloud data of each drone during the tracking process; S20, determining the maximum field of view coverage of the drone according to the clarity of the collected image data; determining the maximum tracking range of the drone according to the moving speed of the target and the response time of the drone; wherein the maximum tracking range is within the maximum field of view coverage; determining the change of the flight path of each drone over time according to the positioning of the drone, analyzing the collected image data and point cloud data, and determining the change of the moving path of each target over time; S30, according to the moving paths of the targets, respectively determine the current position information of the targets; according to the current position information of the targets, the target sets corresponding to the current drones and the maximum tracking range corresponding to the image data collected by the current drones, determine whether it is necessary to dynamically reallocate the target sets corresponding to the drones; if necessary, execute step S40; if not, execute step S50; S40, based on the current position information of each target and the maximum field of view coverage corresponding to the image data collected by each drone, re-determine the target set of each drone and the image position area corresponding to the maximum tracking range of each drone, and re-determine the number of drones. When the current number of drones is insufficient, increase the number of drones; when the current number of drones is too large, reduce the number of drones; determine the current position information of each drone according to the flight path of each drone; control each drone to go to the corresponding tracking position according to the re-determined image position area corresponding to the maximum tracking range of each drone and the current position information of each drone, and execute step S60; The method steps of step S40 are: S401, based on the maximum field of view coverage of the drone S max , the maximum tracking range of the drone max The image location area corresponding to the current maximum tracking range of each drone Determine the image location area corresponding to the maximum field of view coverage of each drone According to the current location information of each target and Re-determine the target set P'1, P'2, ..., P' of each drone h The image location area corresponding to the maximum tracking range of each drone satisfies the following conditions: Wherein, h represents the number of drones that meet the conditions after redetermination; They respectively represent the number of targets in the target set corresponding to different UAVs after re-determination; represents the empty set; Indicates the location information of the current a-th target; represents the current position information of the bth target; a=1, 2, ..., m, b=1, 2, ..., m, and a≠b. When calculating When a and b are in the same target set; L max Indicates the maximum image data distance of the maximum tracking range of the drone in the corresponding image location area; P' i represents the target set corresponding to the i-th UAV after redetermination; Re-determine the set P' i After the spatial coordinate points corresponding to the target position information are connected to each other, the outer edge lines form the image position area corresponding to the graphic; represents the image position area corresponding to the maximum tracking range of the i-th drone after re-determination; i = 1, 2, ..., n; m represents the number of targets; n represents the number of drones in the tracking process; Among them, the number of drones is re-determined: when the current number of drones cannot meet the conditions, it is determined that the number of drones is insufficient, and h+1 is set to determine whether the conditions are met again. If not, the number of drones is continuously increased until the conditions are met; when the current number of drones meets the conditions, h-1 is set to determine whether the conditions are met again. If so, the number of drones is continuously reduced until the conditions are not met; thereby, the number of drones that meet the conditions after re-determination is minimized; S402, according to Determine the image location area corresponding to the maximum tracking range of each drone after redefinition According to P'1, P'2, ..., P' h , respectively determine the targets corresponding to each UAV after redetermination; S403, determine the location information that each drone needs to go to based on the image position area corresponding to the re-determined maximum tracking range of each drone; control each drone to go to the corresponding tracking position based on the location information that each drone needs to go to and the current location information of each drone; calculate the moving distance of each drone from the current location information to the location information that it needs to go to based on the location information that each drone needs to go to and the current location information of each drone and make Satisfying the condition formula: Among them, L z It represents the total moving distance of n drones from their current location to the desired location; represents the moving distance of the i-th UAV from its current position to the desired position; Among them, the maximum field of view coverage range S max Indicates the maximum range that the high-definition camera on the drone can capture to ensure the clarity of the collected target image; the maximum tracking range s max Used as a trigger condition to determine whether the drone group needs to be dynamically adjusted, determined according to the maximum moving speed of the target; S50, control each drone to be in a hovering state, and execute step S60; S60, repeating steps S30-S50 until the target tracking is completed and each UAV is controlled to return.

2. The coordinated control method of a drone group based on data analysis according to claim 1 is characterized in that: The number of targets is m; the number of drones in the tracking process is n; wherein m>n≥2; the drone is equipped with a high-definition camera, a laser radar and a positioning system; the high-definition camera is used to collect image data during the drone tracking process; the laser radar is used to collect point cloud data during the drone tracking process; the positioning system is used to determine the location information of the drone; wherein the image data and point cloud data contain the tracked target and the scenery around the target.

3. The coordinated control method of a drone group based on data analysis according to claim 2 is characterized in that: The method steps of step S20 are: S201. Determine the maximum field of view coverage of the drone based on the clarity of the collected image data S max , according to the target's moving speed and the drone's response time, determine the drone's maximum tracking range s max ; Among them, s max max ; v max Indicates the maximum moving speed of the target; T indicates the response time of the drone; Indicates the maximum field of view coverage length; Indicates the maximum tracking range length; Maximum field of view coverage width; Maximum tracking range width;​ S202: According to the positioning system of the UAV, the flight path of each UAV is determined as a function of time t, and the obtained The collected image data and point cloud data are analyzed to determine the change of the moving path of each target over time t, and the in, and The change of is expressed as the change of the spatial coordinate point in the three-dimensional space model over time t; t represents the timestamp.

4. The coordinated control method of a drone group based on data analysis according to claim 3 is characterized in that: The method steps of step S30 are: S301, according to Determine the current location information of each target Determine the current target set P1, P2, ..., P of each drone n ; Each target has a corresponding drone; according to s max , respectively determine the image location area corresponding to the maximum tracking range of each drone S302, determine the set P i After the spatial coordinate points corresponding to the target position information are connected to each other, the outer edge lines form the image position area corresponding to the graphic according to and Determine whether it is necessary to dynamically redistribute the target sets corresponding to each drone: When , the target set corresponding to each UAV needs to be dynamically reallocated; when there is no When P i represents the target set of the current i-th UAV; Indicates the image location area corresponding to the maximum tracking range of the current i-th drone.

5. A coordinated control system for a drone group based on data analysis, applying the coordinated control method for a drone group based on data analysis as described in any one of claims 1 to 4, characterized in that: The system includes a task planning control module, a data acquisition module, a three-dimensional model management module, an intelligent judgment module, an intelligent analysis and calculation module, and an intelligent tracking control module; The mission planning control module is used to determine the target to be tracked, determine the target to be tracked and the target set of the drone, control several drones to respectively track the targets of their respective target sets in real time, and send data acquisition signals to the data acquisition module; The data acquisition module is used to collect image data and point cloud data during the UAV tracking process, and send the collected data to the three-dimensional model management module; The three-dimensional model management module is used to establish a three-dimensional space model, determine the maximum field of view coverage of the drone according to the clarity of the collected image data; determine the maximum tracking range of the drone according to the moving speed of the target and the response time of the drone; wherein the maximum tracking range is within the maximum field of view coverage; determine the change of the flight path of each drone over time according to the positioning of the drone, analyze the collected image data and point cloud data, and determine the change of the moving path of each target over time; The intelligent judgment module is used to determine the current position information of each target according to the moving path of each target; determine whether it is necessary to dynamically reallocate the target set corresponding to each drone according to the current position information of each target, the target set corresponding to each drone and the maximum tracking range corresponding to the current image data collected by each drone; if it is necessary to dynamically reallocate the targets corresponding to each drone, send the dynamic reallocation signal to the intelligent analysis and calculation module; if it is not necessary to dynamically reallocate the targets corresponding to each drone, send the signal that does not need to be dynamically reallocated to the intelligent tracking control module; The intelligent analysis and calculation module is used to re-determine the target set of each drone and the image position area corresponding to the maximum tracking range of each drone according to the current position information of each target and the maximum field of view coverage corresponding to the image data collected by each drone, and re-determine the number of drones. When the current number of drones is insufficient, the number of drones is increased; when the current number of drones is too large, the number of drones is reduced and sent to the intelligent tracking control module; The intelligent tracking control module is used to determine the current position information of each drone according to the flight path of each drone when it is necessary to dynamically redistribute the target set corresponding to each drone; control each drone to go to the corresponding tracking position according to the image position area corresponding to the redetermined maximum tracking range of each drone and the current position information of each drone; and control each drone to be in a hovering state when it is not necessary to dynamically redistribute the target set corresponding to each drone.

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