Drone rotation strategy method for drone swarm

Abstract

The application discloses a kind of unmanned plane rotation strategy method for unmanned plane cluster, belong to unmanned plane cluster scheduling technical field.The application jointly optimizes unmanned plane access distribution, network topology selection and unmanned plane flight trajectory to minimize the number of unmanned plane, continuously dynamically optimizes the rotation scheduling mode of unmanned plane cluster.The application solves replacement problem by constructing the working path of rotation unmanned plane, and periodically adjusts the position of relay point on the path according to the network connection relationship to minimize the number of unmanned plane required by the path, thereby improving the stability and resource utilization efficiency of unmanned plane cluster rotation scheduling.Dynamic relay point will cause the change of network topology, and even make some unmanned plane nodes in the network disconnected.The application uses the dynamic tree method of graph theory to guide the change of relay point position on the unmanned plane path, and affects the construction of rotation unmanned plane working path, to always ensure the connectivity of the network between the target area and the remote base station.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle cluster scheduling, and particularly relates to a method for unmanned aerial vehicle rotation strategy of unmanned aerial vehicle cluster. BACKGROUND

[0002] In recent years, the unmanned aerial vehicle technology develops rapidly and matures, and its related applications gradually attract widespread attention in various fields. Compared with the traditional ground base station, the unmanned aerial vehicle base station has unique and irreplaceable advantages in cooperative communication due to its low cost, fast deployment, wide coverage, high mobility and strong variability, so that it has been widely used in wireless communication, emergency communication and other fields. As an important part of the rapid emergency communication system, the unmanned aerial vehicle base station can build a temporary communication network at the first time after natural disasters or public emergencies occur, and provide basic communication guarantee for the disaster area or communication interruption area.

[0003] In emergency situations such as earthquakes, landslides and hurricanes, the original ground cellular network often fails partially or even completely. In order to restore communication services in time, multiple unmanned aerial vehicle base stations can be deployed according to the distribution of information service points to build a multi-hop communication network connected with the remote base station. Although it is mature in technology to build such an unmanned aerial vehicle network, it still faces many challenges to maintain its continuous and stable operation. One of the main problems is that the airborne energy of the unmanned aerial vehicle is limited, and the flight and communication tasks must be reasonably allocated to improve the execution efficiency of the overall task; at the same time, the number of unmanned aerial vehicles available in emergency situations is usually limited, so how to efficiently schedule limited unmanned aerial vehicle resources to meet the continuity of network coverage and service quality requirements has become one of the key problems of current research. SUMMARY

[0004] The present application proposes a method for unmanned aerial vehicle rotation strategy of unmanned aerial vehicle cluster, which aims to jointly optimize the unmanned aerial vehicle access allocation matrix, the rotation trajectory of the unmanned aerial vehicle and the communication topology to minimize the number of unmanned aerial vehicles, and continuously and dynamically optimize the rotation scheduling mode of the unmanned aerial vehicle cluster.

[0005] The technical scheme adopted by the present application is as follows: a method for unmanned aerial vehicle rotation strategy of unmanned aerial vehicle cluster, which comprises the following steps:

[0006] Step 1, input the position information, including the position of the base station and the center position of several target areas;

[0007] Step 2, the base station and all target areas form a first node set, and the base station is taken as a root node to build an initial multi-hop communication network about the first node set;

[0008] For the initial multi-hop communication network, it is detected whether the positional distance between adjacent nodes is greater than the maximum relay distance of the UAV. If so, at least one relay point is added between the current adjacent nodes so that the UAV can communicate between the adjacent nodes based on the added relay point.

[0009] The final multi-hop communication network is obtained based on all first nodes and all relay points; then, the set of task points is obtained based on all target areas and all relay points in this multi-hop communication network.

[0010] Step 3: Divide the multi-hop communication network into several periodic rotation paths, with each task point existing on only one periodic rotation path. Each periodic rotation path is a drone replacement loop. Drones depart from the base station and arrive at each task point one by one. After completing the service at the current task point, each drone moves along the periodic rotation path to the next task point to replace the drone at that task point. The replaced drone then continues to its next task point until the drone that initially departed completes a round of replacement and returns to the base station, forming an ordered closed loop with the base station as the beginning and end. Furthermore, the departure time interval of drones at adjacent task points is the same, and the target area in the task point is connected to the drone for one-to-one access service.

[0011] For each relay point on the periodic rotation path, the UAV currently performing the relay task advances ahead of the path in advance and always maintains the maximum relay distance that can be communicated with the follow-up UAV.

[0012] Step 4: Under the constraint that the remaining energy of the UAV at any time can support it to fly back to the base station at a constant speed, and on the premise that all periodic rotation paths share the same task cycle, the optimization objective is to minimize the cumulative number of UAVs required to perform one round of replacement on all periodic rotation paths. The rotation decision information for each periodic rotation path is obtained, including: the number of UAVs, the access allocation matrix between the UAVs and the target area, the rotation trajectory of the UAVs, and the communication topology.

[0013] Furthermore, the drones on the periodically rotating path move horizontally at the same altitude.

[0014] Furthermore, access each element of the allocation matrix. Used to characterize the current moment drones With the target area The connection relationship between them is expressed as:

[0015] ;

[0016] in, On a two-dimensional horizontal plane Time Drone Location, is the center position of the target region.

[0017] Further, in step 4, the turning trajectory of the UAV is: wherein, is the position of the UAV at time t in a two-dimensional horizontal plane; and the communication topology is: wherein, is a binary decision variable indicating whether a network connection is established between any two task points and in the multi-hop communication network at time t, if established, ; otherwise, . Further, the residual energy of the UAV at any time t is set as: wherein, is the total energy capacity of the UAV,

[0018] is the time from the last time the UAV leaves the base station to time t, is the total power of the UAV at time t; and wherein, is the state of the UAV when performing a task at time t, if , it is in a sleep state, i.e. ; otherwise, it is in a working state, wherein is the position of the base station; is the communication energy consumption, is the motion energy consumption, and wherein, is the position of the base station; is the communication energy consumption, is the motion energy consumption, and

[0019]

[0020] wherein, , are the parasitic drag coefficient and the induced drag coefficient, respectively, is the gravitational acceleration, and are the speed and acceleration of the UAV at time t, respectively, is the mass of the UAV.

[0021] ​​​​​​​​​Furthermore, the task points in the task point set are divided into two categories: fixed points and non-fixed points. Fixed points include all task points corresponding to the target area and fixed relay points; non-fixed points refer to non-fixed relay points. Furthermore, drones on non-fixed points cannot advance until their tasks at the previous task point are completed. Therefore, any periodic rotation path... On the task cycle and time interval Set to:

[0022]

[0023] And according to the task cycle and time interval Calculate any periodic rotation path The number of drones required to perform one round of replacement: ;in, This indicates the drone's periodic rotation path. Total flight time during the non-hovering phase; , These are the periodic rotation paths of the drones. The hovering time at the i-th fixed task point and the j-th non-fixed task point. , These are periodic rotation paths. The set of fixed task points and the set of non-fixed task points corresponding to the target area.

[0024] Furthermore, for each periodic rotation path The relay points on the network are dynamically adjusted:

[0025] Determine whether the current relay point, as a disconnected task point, can restore the connection by changing the link. If so, set the current relay point as a non-fixed task point; otherwise, set it as a fixed task point.

[0026] The specific methods for determining whether the current relay point, as a disconnected task point, can restore the connection by changing the link include:

[0027] The return links formed by multiple UAVs on the currently determined periodic rotation path are defined as stable return links; a link set is obtained based on all stable return links;

[0028] Determine if the current relay point is on a stable backhaul link in the link set. If so, determine if the current relay point can establish a connection with a stable backhaul link located in circular region C (with the current relay point as the center and the maximum relay distance of the drone as the radius). That is, determine if there is at least one stable backhaul link in circular region C. If so, continue to confirm the path length of each stable backhaul link in circular region C. If the path length is greater than or equal to the product of the time interval and flight speed of the drone on the stable backhaul link it is on, then the current relay point can re-establish a connection with the current stable backhaul link, that is, the connection can be restored by changing the link.

[0029] Furthermore, the specific method for determining whether the current relay point, as a disconnection task point, can restore the connection by changing the link is replaced with:

[0030] The exclusion node set is composed of the base station, the previous task point of the current relay point, and the next task point;

[0031] Based on the adjacency matrix H of the multi-hop communication network, the candidate node set is obtained by determining the task points that are connected to the current relay point and do not belong to the excluded node set.

[0032] Connectivity analysis is performed on the task points in the candidate node set to divide them into several connected subsets. For each connected subset, it is determined whether the current relay point has a connected task point during the synchronization push process. If all connected subsets exist, it indicates that the current relay point can restore the connection by changing the link. The connected task point is defined as either a task point in the current connected subset that always maintains a connection with the current relay point, or a task point in the current connected subset that is connected to other relay points.

[0033] The technical solution provided by this invention brings at least the following beneficial effects:

[0034] This invention addresses the replacement problem by constructing rotating drone work paths and periodically adjusting the positions of relay points along the paths based on network connectivity to minimize the number of drones required for each path, thereby improving the stability and resource utilization efficiency of drone swarm rotation scheduling. Dynamic relay points can alter network topology and even cause some drone nodes to disconnect. This invention utilizes a dynamic tree method based on graph theory to guide changes in the positions of relay points along the drone paths, influencing the construction of rotating drone work paths to ensure network connectivity between the target area and remote base stations at all times. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in detail and completely below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments.

[0036] In one embodiment, this invention provides a drone rotation strategy method for drone swarms, which aims to jointly optimize drone access allocation, network topology selection, and drone flight trajectory to minimize the number of drones and continuously and dynamically optimize the rotation scheduling method of the drone swarm.

[0037] In this embodiment of the invention, each population cluster is considered a target area requiring communication services, and a multi-hop communication network is constructed by deploying M isomorphic UAVs to meet the communication access needs of all target areas. In this embodiment, the M UAVs depart from a base station (with a charging station) and fly sequentially to each task point (including relay points and access points corresponding to the target areas) according to a deployed periodic rotation path. For ease of description, in this embodiment, all UAVs move horizontally at the same altitude and provide communication access services to ground users within each target area by hovering above the center of that target area. The access relationship between UAV m and target area k at time t is defined as follows: The specific quantification method is as follows: ;in, On a two-dimensional horizontal plane Time Drone Location, For target area The central position. Therefore, based on Construct the corresponding access allocation matrix between the UAV and the target area. .

[0038] To reduce the number of drones required, this embodiment sets the following: at any given time, each target area can only be served by one drone, and each drone can serve at most one target area. That is, for each target area k, its corresponding access relationship... The cumulative sum of all target regions k does not exceed 1; and for each drone m, its corresponding access relationship... The sum of all drones m does not exceed 1.

[0039] To achieve data transmission between target areas and remote base stations, information from each target area needs to be relayed multiple times by drones within the network before reaching the base station. This relaying process relies on communication links between drones and between drones and base stations. To uniformly represent all nodes in the network, the network node set is defined as the set of nodes composed of all drones and base stations. At any time t, the condition for any two nodes in the network to establish a communication link is that the distance between the two nodes does not exceed the maximum relay distance D. Available variables... Characterized by the fact that if the positional distance does not exceed D, then The value is set to 1; otherwise, it is set to 0, where p and q represent any two nodes.

[0040] In this embodiment of the invention, the total power of any UAV m at any time t is set as follows:

[0041]

[0042] in, Indicates drone At any moment The state during task execution, if Then it is in a dormant state, that is Otherwise, it is in working status. ,in Indicates the location of the base station; Indicates communication energy consumption. Indicates energy consumption during exercise, and

[0043]

[0044] in, , These represent the parasitic drag coefficient and the induced drag coefficient, respectively. It is the acceleration due to gravity. and The drone at time velocity and acceleration For the quality of drones.

[0045] Furthermore, based on the total energy capacity of the drone The remaining energy of the drone at any given time can be calculated as follows: ,in, Indicates drone At any moment The time since it last left the base station.

[0046] To ensure energy security during the mission, the drone should have sufficient remaining energy to support it at a constant speed at all times. The aircraft flies back to the base station. This energy constraint can be expressed as: .

[0047] The goal of the round-robin strategy proposed in this invention is to rationally plan the drone swarm based on an existing multi-hop network, enabling it to provide continuous communication services to all target areas while minimizing the total number of drones required and reducing system costs. Essentially, this is a joint optimization problem involving three key decision variables: the access allocation matrix between drones and target areas. UAV trajectory planning The communication topology is That is, the tree topology selected from the communication network. express Two arbitrary task points in a time-limited multi-hop communication network and The binary decision variable for whether to establish a network connection between them; if so, then... ;otherwise The optimization objective can then be expressed as: ,in A collection of drones.

[0048] The constraints required for solving the optimization objective include:

[0049] (1) At any given time, the target area and the UAV are one-to-one;

[0050] (2) Feasibility constraints of tree network topology, i.e. ,in, This is a set of network nodes, including base stations and all drones;

[0051] (2) The network topology must include base stations and drones providing communication services to the target area at all times, that is: , ;in Indicates a base station; among which For the target region set;

[0052] (3) The tree network has good connectivity and no loops in its topology. ; ; ;

[0053] (4) The drone returns to the base station before its energy is depleted, i.e. .

[0054] Based on the above analysis, in one embodiment, the drone rotation strategy method for drone swarms provided by this invention includes the following steps:

[0055] Step S1: Input location information, including the location of the base station and the center location of several target areas;

[0056] Step S2, a first node set is constituted by the base station and all target areas, and an initial multi-hop communication network about the first node set is constructed taking the base station as a root node; for the constructed initial multi-hop communication network, it is detected whether the position distance between adjacent nodes is greater than the maximum relay distance of the unmanned aerial vehicle, if yes, at least one relay point is added between the current adjacent nodes to enable the unmanned aerial vehicle communication to be reachable between the adjacent nodes based on the added relay point; a final multi-hop communication network is obtained based on all first nodes and all relay points; and a task point set is obtained based on all target areas and all relay points in the multi-hop communication network;

[0057] Step S3, the multi-hop communication network is divided into a plurality of periodic rotation paths, and each task point exists only in one periodic rotation path; each periodic rotation path is a unmanned aerial vehicle replacement loop, and the unmanned aerial vehicle reaches each task point in turn from the base station, and after completing the service of the current task point, the unmanned aerial vehicle goes to the next task point to replace the unmanned aerial vehicle of the task point, and the replaced unmanned aerial vehicle continues to go to the next task point, until the originally starting unmanned aerial vehicle returns to the base station after completing a round of replacement, forming an ordered closed loop with the base station as the first and last; and the starting time intervals of the unmanned aerial vehicles of adjacent task points are the same, and the target areas in the task points are one-to-one accessed and served by the unmanned aerial vehicles; for each relay point on the periodic rotation path, the unmanned aerial vehicle currently performing the relay task pushes forward to the front of the path, and always keeps the maximum relay distance of the communication reachable with the subsequent unmanned aerial vehicle;

[0058] Step S4, under the constraint that the residual energy of the unmanned aerial vehicle at any time can support it to fly back to the base station at a constant speed, and under the premise that all periodic rotation paths share the same task period, the cumulative value of the number of unmanned aerial vehicles required to perform a round of replacement on all periodic rotation paths is minimized as the optimization target, that is, , , The rotation decision information of each periodic rotation path is obtained, including: the number of unmanned aerial vehicles M, the access allocation matrix of the unmanned aerial vehicles and the target areas , the rotation trajectory of the unmanned aerial vehicles , and the communication topology .

[0059] In one embodiment, the task points in the task point set are divided into two categories: fixed points and non-fixed points; wherein the fixed points include the task points corresponding to all target areas and fixed relay points; the non-fixed points refer to non-fixed relay points; and the unmanned aerial vehicle on the non-fixed point cannot push forward when the task of the previous task point has not been completed; then the task period and the time interval and on any periodic rotation path

[0060] wherein, denotes the total flight time of the UAVs on the non-hovering section of the periodic round-path; denotes the hovering time of the UAVs on the first fixed task point and the first non-fixed task point of the periodic round-path, respectively, denotes the fixed task point set and the non-fixed task point set corresponding to the target area on the periodic round-path, respectively.

[0061] In one embodiment, the relay points on each periodic round-path are dynamically adjusted:

[0062] determining whether the current relay point as a disconnection task point can recover connection by changing the link, if yes, setting the current relay point as a non-fixed task point; otherwise, setting as a fixed task point;

[0063] wherein, the specific way of determining whether the current relay point as a disconnection task point can recover connection by changing the link can be selected from the following two ways:

[0064] Way one:

[0065] defining the backhaul link composed of multiple UAVs on the currently determined periodic round-path as a stable backhaul link; obtaining a link set based on all stable backhaul links; determining whether the current relay point is on a stable backhaul link in the link set, if yes, then determining whether the current relay point can establish connection with a certain stable backhaul link located within a circular area C (the current relay point as the center and the maximum relay distance of the UAV as the radius), i.e., determining whether there is at least one stable backhaul link in the circular area C; if yes, then continuing to confirm the path length of each stable backhaul link within the circular area C, if the path length is greater than or equal to the product of the time interval and the flight speed of the UAV on the stable backhaul link, then the current relay point can reestablish connection with the current stable backhaul link, i.e., can recover connection by changing the link.

[0066] Way two:

[0067] ​​​​​​​​The exclusion node set is formed based on the base station, the last task point and the next task point of the current relay point; the candidate node set is determined based on the adjacency matrix H of the multi-hop communication network, and the task points connected to the current relay point and not belonging to the exclusion node set are obtained; the task points in the candidate node set are subjected to connectivity analysis, and a plurality of connected subsets are divided; for each connected subset, it is judged whether there is a task point in the connected subset that always maintains connection with the current relay point in the synchronization forward propagation process, or whether there is a task point connected to other relay points; if all connected subsets satisfy any of the above conditions, it is indicated that the current relay point can restore connection by changing the link.

[0068] In order to maintain the communication link connection, the maximum distance between the non-fixed point and the adjacent task point is Therefore, the maximum flight time of the unmanned aerial vehicle between the adjacent task points with the communication link is If the time required for the unmanned aerial vehicle at the non-fixed point to fly to the next task point just meets the time interval , the unmanned aerial vehicle at the non-fixed point does not need to additionally hover (the hovering time is 0); otherwise, the flight time is less than , and the excess time needs to be hovered at a certain position to wait, and at this time, the hovering time of the non-fixed point is . Therefore, the periodic rotation path The hovering time of all non-fixed points on the periodic rotation path can be expressed as: In order to solve , it can be further converted to: that is, the optimization of the interval time, and then based on the hovering time of the non-fixed point and the total flight time of the non-hoovering section on the path , the time interval on the periodic rotation path can be solved by the following optimization problem:

[0069]

[0070] Wherein, is a minimum time interval set artificially to prevent the unmanned aerial vehicle on the path from replacing too frequently and affecting the service stability. , are the fixed task point set and the non-fixed task point set corresponding to the target area on the periodic rotation path . The constraints , are derived from the linearization rewriting of the non-smooth function , which is convenient for directly using a linear programming solver to solve.

[0071] Based on the periodic rotation path the time interval on the path Optimization solution, the number of UAVs required to maintain a rotating path can be calculated. To verify the rationality and effectiveness of the method proposed in the embodiment of the application, the following is specified:

[0072] First, an arbitrary periodic rotating path the time interval on the path can be rewritten as: ; thus, the number of UAVs required on the path can be further expressed as:

[0073] .

[0074] And in the direct replacement method, the lower limit of the number of UAVs required is usually: where the total number of task points , is the charging time, represents the one-way flight time from the th task point to the charging station.

[0075] After obtaining the number of UAVs required by the method of the embodiment and the direct replacement method, it is proved that:

[0076]

[0077] By ignoring the energy supplement time of the UAV , the right side of the inequality is tightened, and the inequality becomes:

[0078]

[0079] According to the definition of Jensen's inequality, the right side of the transformed inequality satisfies: , the right side of the inequality can be further tightened to obtain: .

[0080] Therefore, by proving that the inequality obtained after two condition tightening is still established, it can be proved that . Its transformation obtains: , and let , , , be a constant value related only to the selected UAV.

[0081] Since there are two cases for the value of

[0082] When , it is obtained​​ , then the transformed inequality can be converted to: ; at this time, as long as the path satisfies and , then holds.

[0083] When , , get , that is: , then the transformed inequality can be converted to: ; similarly, in this case, as long as the path satisfies formula and , then holds.

[0084] In summary, as long as the path design satisfies the above conditions, the number of unmanned aerial vehicles required under the rotation replacement strategy of the present application does not exceed the lower limit of the direct replacement method , that is , thereby theoretically proving the superiority of the method proposed in the present application in terms of resource utilization efficiency.

[0085] In the method of the present application, the total number of unmanned aerial vehicles of the cluster is represented as the accumulation of the number of unmanned aerial vehicles required on multiple paths, and a specific time interval is allocated to each path under the premise that all paths share the same task cycle. The optimization goal is to find a set of optimal path configurations to minimize the sum of the reciprocals of all path time intervals under the premise of meeting the communication persistence and energy constraints, thereby effectively reducing the overall resource consumption of the system.

[0086] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

[0087] The above only describes some embodiments of the present application. For those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application.​

Claims

1. A UAV rotation strategy method for a UAV swarm, characterized in that, The method comprises the following steps: Step 1, inputting position information, including the position of a base station and the center positions of a plurality of target areas; Step 2, forming a first node set with the base station and all the target areas, and constructing an initial multi-hop communication network with the base station as a root node; For the constructed initial multi-hop communication network, it is detected whether the position distance between adjacent nodes is greater than the maximum relay distance of the unmanned aerial vehicle, and if so, at least one relay point is added between the adjacent nodes to enable the unmanned aerial vehicle communication to be achieved between the adjacent nodes based on the added relay point; Based on all the first nodes and all the relay points, a final multi-hop communication network is obtained; Based on all the target areas and all the relay points in the multi-hop communication network, a task point set is obtained; Step 3, dividing the multi-hop communication network into a plurality of periodic rotation paths, and each task point only exists in one periodic rotation path; each periodic rotation path is a replacement ring for the unmanned aerial vehicle, and the unmanned aerial vehicle reaches each task point from the base station; after completing the service of the current task point, the unmanned aerial vehicle replaces the unmanned aerial vehicle at the next task point along the periodic rotation path, and the replaced unmanned aerial vehicle continues to the next task point, until the initially launched unmanned aerial vehicle completes a round of replacement and returns to the base station, forming an ordered closed loop with the base station as the first and last; the starting time intervals of the unmanned aerial vehicles at adjacent task points are the same, and the target areas in the task points are one-to-one connected to the service of the unmanned aerial vehicle; For each relay point on the periodic rotation path, the unmanned aerial vehicle currently performing the relay task advances to the front of the path, and always maintains a maximum relay distance from the following unmanned aerial vehicle; Step 4, under the constraint that the remaining energy of the unmanned aerial vehicle at any time can support it to fly back to the base station at a constant speed, and under the premise that all the periodic rotation paths share the same task cycle, the optimization objective is to minimize the cumulative value of the number of unmanned aerial vehicles required to perform a round of replacement on all the periodic rotation paths, and the rotation decision information of each periodic rotation path is obtained, including: the number of unmanned aerial vehicles, the access allocation matrix of the unmanned aerial vehicle and the target area, the rotation trajectory of the unmanned aerial vehicle and the communication topology; Divide the task points in the task point set into two categories: fixed points and non-fixed points; wherein the fixed points include all the task points corresponding to the target areas and fixed relay points; the non-fixed points refer to non-fixed relay points; and the unmanned aerial vehicle on the non-fixed points cannot advance when the task at its previous task point has not been completed; then the task period on any periodic rotation path and the time interval are set as: ;​ and according to the mission cycle and time intervals calculating an arbitrary periodic rotation path the number of drones required to perform a rotation of replacements ; wherein, denotes the total flight time of the UAV on the periodic round-robin path denotes the total flight time of the UAV on the periodic round-robin path , denote the hovering time of the UAV at the i-th fixed waypoint and the j-th non-fixed waypoint on the periodic round-robin path denote the hovering time of the UAV at the i-th fixed waypoint and the j-th non-fixed waypoint on the periodic round-robin path , denote the fixed waypoint set and the non-fixed waypoint set corresponding to the target region on the periodic round-robin path denote the fixed waypoint set and the non-fixed waypoint set corresponding to the target region on the periodic round-robin path Step 4 further comprises: dynamically adjusting the relay points on each periodic rotation path : judging whether the current relay point can restore connection by changing the link as a disconnected task point, if yes, setting the current relay point as a non-fixed task point; otherwise, setting it as a fixed task point; The specific way of judging whether the current relay point as a disconnected task point can restore the connection by changing the link comprises: The backhaul link formed between a plurality of unmanned aerial vehicles on the currently determined periodic rotation path is defined as a stable backhaul link; a link set is obtained based on all the stable backhaul links; It is judged whether the current relay point is on a stable backhaul link in the link set, and if so, it is judged whether there is at least one stable backhaul link in the circular area C; if so, the path length of each stable backhaul link in the circular area C is further confirmed, and if the path length is greater than or equal to the product of the time interval and the flight speed of the unmanned aerial vehicle on the stable backhaul link, the current relay point can restore the connection by changing the link; wherein the circular area C has the current relay point as the center and the maximum relay distance of the unmanned aerial vehicle as the radius.

2. The method of claim 1, wherein, The unmanned aerial vehicles on the periodic rotation path move at the same height level. 3.The method for UAV rotation strategy of UAV swarm of claim 1, wherein, Each element of the access allocation matrix for characterizing at a current time a drone an access relationship between the target region , which is expressed as: ； wherein, is the position of the UAV in a two-dimensional horizontal plane at the moment, is the position of the UAV, is the center position of the target area .

4. The method of claim 1, wherein, In step 4, the drone's rotation trajectory is as follows: ,in, On a two-dimensional horizontal plane Time Drone Location; Communication topology is: ,in, express Two arbitrary task points in a time-limited multi-hop communication network and The binary decision variable for whether to establish a network connection between them; if so, then... ;otherwise .

5. The method of claim 1, wherein, The remaining energy of the UAV at any time t is set as: wherein, denotes the total energy capacity of the UAV, denotes the total power of the UAV at time t, the time since its last departure from the base station, denotes the total energy capacity of the UAV, at time t, the total power of the UAV; and wherein, denotes the total energy capacity of the UAV, at time t, the state of the UAV while performing the task, if it is in the sleep state, i.e. ; otherwise, it is in the active state, wherein denotes the location of the base station; denotes the communication energy consumption, denotes the motion energy consumption, and ； in, , These represent the parasitic drag coefficient and the induced drag coefficient, respectively. It is the acceleration due to gravity. and The drone at time velocity and acceleration For the quality of drones.

6. The method of claim 1, wherein, The specific way of judging whether the current relay point can recover the connection by changing the link as the disconnection task point is replaced by: forming an exclusion node set based on the base station, the previous task point and the next task point of the current relay point; determining a candidate node set from the task points connected to the current relay point and not belonging to the exclusion node set based on the adjacency matrix H of the multi-hop communication network; performing connectivity analysis on the task points in the candidate node set and dividing them into several connected subsets; for each connected subset, judging whether there is a connection task point in the synchronization forward process of the current relay point; if all connected subsets exist, it indicates that the current relay point can recover the connection by changing the link; wherein the connection task point is: a task point that always maintains connection with the current relay point in the current connected subset, or a task point connected to other relay points in the current connected subset.

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