Task-driven unmanned cluster toughness topology control method and device

Abstract

The invention discloses a task-driven unmanned cluster tough topology control method, which mainly solves the problems that topology adjustment is limited by node positions and the generated topology is poorer in toughness in the prior art, and the implementation scheme of the method comprises the following steps: a node broadcasts a Hello packet containing own position information at the maximum power; the non-central node broadcasts the Hello packets again after receiving the Hello packets of other nodes; the center node collects a Hello packet of a non-center node, records position information in the Hello packet, constructs an optimization problem and a constraint condition according to the recorded whole network position information and solves the optimization problem and the constraint condition to obtain a target position of each node; and the center node issues the solved target position of each node to the corresponding non-center node, and the non-center node moves the position of the non-center node according to the received target position to complete the topological structure adjustment of the network. According to the method, the task execution capability of the unmanned cluster can be improved, the influence of node position limitation on topology control is reduced, the network toughness is improved, and the method can be used for controlling the topological structure of the unmanned cluster.

Description

Technical Field

[0001] This invention belongs to the field of unmanned swarm technology, and specifically relates to an unmanned swarm resilient topology control method and device, which can be used to control the topology of unmanned swarms and improve the resilience and task completion capability of the network under different task scenarios. Background Technology

[0002] Topology control in unmanned swarms refers to controlling the connections between nodes in the network to establish and maintain the network's topology. Since unmanned swarms face a wide range of mission scenarios, different scenarios have significantly different network capability requirements. The network capability of an unmanned swarm is limited by its topology; an unreasonable network topology will severely affect the unmanned swarm's ability to complete tasks. Furthermore, due to the complexity of the unmanned swarm's operating environment, nodes are prone to communication failures due to damage or interference. This places higher demands on the resilience of the unmanned swarm network topology. Existing topology control methods mainly ensure network resilience by constructing K-connectivity topologies. However, K-connectivity only describes the network's connectivity at the current moment and cannot describe the connectivity of the remaining parts after network damage. Therefore, while algorithms using K-connectivity as the control objective can guarantee the network's resilience to some extent, they cannot guarantee the communication capability of the remaining parts of the network after damage. In addition, existing topology control methods are limited to adjusting the network topology by controlling single parameters such as transmission power and transmission frequency. This results in the control outcome being limited by the initial network topology configuration, i.e., the initial geographical location of the nodes, leading to significant limitations in the control effect.

[0003] The invention patent application CN202510682392.2 discloses an improved optimized link-state routing protocol for UAV swarms under a hybrid communication architecture. Its implementation scheme includes: 1) transmitting control messages and service data messages during the routing process through omnidirectional RF transceiver antennas and directional FSO transceivers equipped on each UAV node; 2) designing a multi-index, multi-point relay node comprehensive selection algorithm to optimize control message propagation; 3) establishing a real-time link quality prediction mechanism based on unscented Kalman filtering to generate stability-aware routing paths; and 4) developing a routing compression method adapted to heterogeneous link propagation characteristics to eliminate redundant hops. This method addresses the inefficiency of heterogeneous networks to some extent, but it is limited by the geographical location of UAV nodes and does not consider the differences in UAV swarm network capability requirements for different tasks, making it difficult to adapt to different task scenarios. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by proposing a task-driven unmanned cluster resilient topology control method and apparatus to improve the task execution capability of unmanned clusters in different scenarios, reduce the impact of node spatial location limitations on unmanned cluster topology control, and enhance the resilience of unmanned cluster networks.

[0005] The technical approach to achieving the objective of this invention is to control the network topology by solving the position of each network node in the unmanned cluster through the central node of the unmanned cluster: by modeling the unmanned cluster topology control problem as an optimization problem of solving the optimal position of each network node in the unmanned cluster, the network task execution capability and resilience are maximized.

[0006] Based on the above ideas, the technical solution of the present invention includes:

[0007] (1) All nodes in the network broadcast Hello packets containing their own location information at maximum power. Non-central nodes rebroadcast the Hello packets broadcast by other nodes after receiving them.

[0008] (2) The central node collects Hello packets from all non-central nodes in the network and records their location information;

[0009] (3) The central node constructs the following optimization problem based on the recorded location information of all nodes in the network:

[0010] ;

[0011] in, Indicators of resilience This indicates the task coverage capability metric. Indicates network communication capability indicators. Indicates the cost of execution. This represents the toughness index coefficient. This represents the coefficient of the task coverage capability index. This represents a coefficient indicating network communication capability. , representing the set of node positions after node adjustment, is the decision variable;

[0012] (4) Set the constraints of the optimization problem for the central node and solve it to obtain the target position of each node;

[0013] (5) The central node sends the target positions of each node obtained by the solution to the corresponding non-central nodes. After receiving their respective target positions, the non-central nodes move to the target positions to complete the adjustment of the network topology.

[0014] Furthermore, the central node in step (3) constructs an optimization problem based on the recorded location information of all nodes in the network, and its implementation includes:

[0015] (3a) The central node is based on the current set of all network nodes. Constructing a network topology graph And according to the diagram Constructing network resilience index cohesion :

[0016] (3b) The central node is based on the task coverage radius of the network nodes. Build task coverage capability metrics :

[0017] (3c) The central node is based on the current set of all network nodes. Build network communication capability indicators :

[0018] (3d) The central node is based on the current set of all network node positions. and the initial node position set Build execution cost metrics :

[0019] (3e) The central node constructs an optimization problem based on the four network metrics described in (3a)-(3d):

[0020] .

[0021] 2. A task-driven unmanned swarm resilient topology control device, characterized in that it comprises:

[0022] The location information sharing module is used by non-central nodes to obtain their own location information and encapsulate it into a hello package for broadcasting.

[0023] The location information processing module is used by the central node to process the received hello packets and record the location information of the nodes in the hello packets;

[0024] The optimization problem construction and solution module is used by the central node to construct an optimization problem based on the node position information recorded by the position information processing module, set the relevant parameters of the optimization problem, and search and solve it to obtain the final set of positions.

[0025] The location result distribution module is used by the central node to encapsulate the final location set obtained by the optimization problem construction and solution module into a specified data frame format for broadcasting.

[0026] The location result receiving module is used by non-central nodes to receive location results sent by the central node and move themselves to the specified location based on the location results.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] Firstly, this invention controls the network topology by adjusting the node positions, which largely avoids the problem that traditional topology control methods are limited by the initial position of the unmanned cluster when adjusting the topology.

[0029] Secondly, when constructing the optimization problem, this invention comprehensively considers resilience, task coverage, and network communication capabilities, so that the network topology adjusted according to the solution results has stronger task execution capabilities. Furthermore, by introducing cohesion as a resilience index, it can ensure that the adjusted network topology has stronger fault tolerance. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the implementation of the task-driven resilient topology control method for unmanned swarms according to the present invention.

[0031] Figure 2 This is a schematic diagram of the frame format of the Hello packet in the method of the present invention;

[0032] Figure 3 This is a schematic diagram of the frame format of the data packets sent in the method of the present invention;

[0033] Figure 4 This is a block diagram of the task-driven unmanned swarm resilient topology control device of the present invention. Detailed Implementation

[0034] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0035] Example 1: A task-driven resilient topology control method for unmanned clusters.

[0036] Reference Figure 1 The implementation steps of this example include:

[0037] Step 1: All nodes in the network exchange location information.

[0038] (1.1) All nodes in the network broadcast Hello packets containing their own location information at maximum power. The frame format of the Hello packet is as follows: Figure 2 As shown, it includes message type, message sequence number, source node ID, and node location information. The message type occupies 1 byte, the message sequence number and source node ID each occupy 2 bytes, and the node location information occupies 12 bytes. The message sequence number starts from 0 and increments as the number of Hello packets is sent.

[0039] (1.2) After receiving a Hello packet broadcast by other nodes, a non-central node determines whether the location information of that node exists in its neighbor table based on the node ID in the received Hello packet:

[0040] If it does not exist, add the node's ID, message sequence number, and location information to its own neighbor table;

[0041] If it exists, proceed to step (1.3).

[0042] (1.3) Compare the message sequence number in the Hello packet with the sequence number information of the corresponding node in the neighbor table:

[0043] If the sequence number in the Hello packet is greater than the sequence number of the corresponding node in the neighbor table, then the sequence number in the Hello packet is used to update the sequence number of the corresponding node in the neighbor table, and the position information of the corresponding node is updated according to the position information in the Hello packet. Then the Hello packet is rebroadcast.

[0044] Otherwise, no action will be taken.

[0045] Step 2: Collection of central node location information.

[0046] The central node determines whether the location information of the node exists in its neighbor table based on the node ID in the received Hello packet.

[0047] If it does not exist, add the node's ID, message sequence number, and location information to its own neighbor table;

[0048] If it exists, compare the message sequence number in the Hello packet with the sequence number information of the corresponding node in the neighbor table:

[0049] If the sequence number in the Hello packet is greater than the sequence number of the node in the neighbor table, then the sequence number in the Hello packet is used to update the sequence number of the corresponding node in the neighbor table, and the position information of the corresponding node is updated according to the position information in the Hello packet.

[0050] Otherwise, no action will be taken.

[0051] Step 3: Construct the optimization problem.

[0052] This method controls the network topology by adjusting node positions. The goal is to improve the resilience and task execution capabilities of the unmanned swarm while maintaining a relatively unchanged overall boundary shape. Therefore, it is necessary to solve for the optimal position of each node within the swarm. To ensure that the solution improves the network's resilience and task execution capabilities, this method comprehensively considers the unmanned swarm's resilience, task capability coverage, network communication capability, and execution cost metrics, constructing the following optimization problem. The specific implementation includes the following:

[0053] (3.1) Resilience indicators of central node construction :

[0054] The central node is based on the current set of all nodes in the network. Constructing a network topology graph That is, when the relative distance between two nodes is less than the maximum transmission radius of the nodes, a connection exists between the two nodes, and according to the diagram... Constructing network resilience index cohesion :

[0055] ;

[0056] This cohesion, as one of the vulnerability parameters of network topology, not only reflects the extent to which the network has been attacked, but also the connectivity of the remaining parts of the network after the attack. This represents the set of nodes that were deleted. This indicates the number of nodes that were deleted. It is a picture The order of the maximum connected component. It is a picture The number of connected components;

[0057] (3.2) Central node construction task coverage capability indicators :

[0058] The central node models the task execution capability of each unmanned node as a circular task coverage area centered on the node. This is achieved by adjusting the radius of the circular area. By configuring settings, the task execution capabilities of different types of unmanned nodes can be adjusted, as well as the task coverage capabilities of the unmanned cluster. Then, it is defined as the effective area of ​​the circular task coverage region of all unmanned nodes, that is, the area of ​​the union of the coverage circles of all nodes, which can be expressed as:

[0059]

[0060] in, Indicates the first The independent coverage area of ​​each node, Indicates the coverage area of ​​the first The area of ​​the overlapping coverage area. Indicates the number of nodes in the network;

[0061] (3.3) Network communication capability indicators of central nodes :

[0062] Network communication capacity metrics are used to describe the information carrying capacity of unmanned swarm networks. Network capacity is typically used as a metric, but since unmanned swarms are self-organizing networks, their network capacity is generally difficult to describe accurately. Therefore, different methods need to be selected to model this metric depending on the application scenario's focus. In this embodiment, network capacity is modeled using link aggregation, as shown below:

[0063] ;

[0064] in, This represents the set of links at the current node location in the entire network. Represents a node and nodes Link speed between For signal bandwidth, For node power transmission, For noise power, , This is the path loss factor. For nodes Location coordinates, For nodes Location coordinates, For nodes and nodes The relative distance;

[0065] (3.4) Central node construction execution cost indicators :

[0066] Execution cost indicators Used to limit the movement distance of each node and prevent excessive node position offset, it is defined as:

[0067] ;

[0068] in, Represents a node The current position coordinates, Represents a node The initial position coordinates, Indicates the number of nodes in the network;

[0069] (3.5) Based on the four network metrics described in (3.1)-(3.4), the central node constructs the following optimization problem:

[0070] ;

[0071] in, This represents the current set of all node locations in the network, which serves as the decision variable for this optimization problem. This represents the toughness index coefficient; This represents the coefficient of the task coverage capability index; This represents a coefficient indicating network communication capability. , , The network capability requirements can be adjusted according to the actual task scenario.

[0072] Step 4: Set constraints for the central node and solve the optimization problem.

[0073] Since the lack of constraints when solving optimization problems can lead to unbounded solutions or solutions that do not conform to reality, and since network nodes in unmanned swarm networks usually cannot move indefinitely due to the initial formation configuration, it is necessary to design reasonable constraints for the optimization problem.

[0074] In this method, the constraints of the optimization problem are used to maintain the overall shape of the unmanned swarm formation when nodes adjust their positions, limit the adjustment range of node positions, and ensure a safe distance between nodes to prevent overlapping or collisions during the position adjustment process. The specific implementation is as follows:

[0075] (4.1) Constructing formation constraints:

[0076] Formation constraints are primarily used to ensure that nodes maintain the overall shape of the unmanned cluster formation when adjusting their positions. In this method, the formation constraints are set based on support function theory and are expressed as follows:

[0077] ;

[0078] in, Indicates the current location of the unmanned swarm. The farthest position projected in the direction.

[0079] Indicates the selected first Unit direction vector,

[0080] This indicates the number of unit direction vectors selected. The more unit direction vectors selected, the greater the number of vectors. The larger the value, the more precise the control over the formation shape.

[0081] Represents a node The current position coordinates,

[0082] Indicates the initial location of the unmanned cluster. The farthest position in the direction,

[0083] Represents a node The initial position coordinates, For the formation shape in the first The allowable amount of change in each direction;

[0084] (4.2) Constructing amplitude constraints:

[0085] Amplitude constraints are used to limit the movement range of nodes, and take the following form:

[0086]

[0087] in, Represents a node The distance from the current position to the initial position.

[0088] Represents a node The current position coordinates,

[0089] Represents a node The initial position coordinates,

[0090] Represents a node Adjustable maximum displacement amplitude

[0091] Indicates the number of nodes in the network;

[0092] (4.3) Constructing security constraints:

[0093] Safety constraints are used to ensure safe distances between nodes, preventing overlap or collisions during repositioning. They take the form shown below:

[0094] ;

[0095] in, Represents a node With nodes Distance between current locations

[0096] Represents a node The current position coordinates,

[0097] Represents a node The current position coordinates, Indicates the safe distance between network nodes;

[0098] (4.4) The central node solves for the final position set according to the constraints set in steps (4.1)-(4.3). Since the optimization problem proposed in this invention has the characteristics of a typical non-convex optimization problem, a heuristic algorithm can be used to solve it. In this embodiment, the particle swarm optimization algorithm in this heuristic algorithm is used, but not limited to, to solve the problem and obtain the final position set. .

[0099] Step 5: Result location distribution and node location adjustment.

[0100] (5.1) Set the frame format:

[0101] like Figure 3 As shown, the frame format set in this step includes: message type, message sequence number, source node ID, and node location list, wherein: the message type occupies 1 byte, the message sequence number and the source node ID each occupy 2 bytes, and the node location list includes the ID of non-central nodes in the network and the corresponding node target location coordinates. The ID of a single node occupies 2 bytes, and its corresponding location information occupies 12 bytes.

[0102] (5.2) The central node encapsulates the obtained coordinates of each node into a set data frame format and broadcasts it at maximum power;

[0103] (5.3) After receiving a broadcast message, a non-central node determines whether it has already received the data packet based on the message type and message sequence number in the data packet:

[0104] If it has not been received before, extract the location information of this node from the data packet and rebroadcast the data packet at maximum power;

[0105] If it has been received, no further processing is required;

[0106] (5.4) Non-central nodes move to their target locations to complete the network topology adjustment.

[0107] It should be noted that the present invention is not limited to the disclosed preferred embodiments, and its implementation may not follow the order shown or discussed. That is, the step numbers in the specification and claims of the present invention are only for the purpose of clearly describing the embodiments of the present invention and facilitating understanding, and their order is not limited.

[0108] Example 2: Task-driven unmanned swarm resilient topology control device

[0109] Reference Figure 4This example includes: a location information sharing module 1, a location information processing module 2, an optimization problem construction and solution module 3, a location result distribution module 4, and a location result receiving module 5. The optimization problem construction and solution module 3 includes: an initial parameter setting submodule 31, an iterative search submodule 32, an objective function calculation submodule 33, and a solution termination determination submodule 34. The working principle of the entire device is as follows:

[0110] The location information sharing module 1 is used to obtain the location information of the non-central node itself and encapsulate it into a Hello package for broadcasting;

[0111] The location information processing module 2 is used by the central node to process the Hello packets sent by the location information sharing module 1 of the non-central node, record the location information of the nodes in the Hello packets, and transmit the information to the optimization problem construction and solution module 3.

[0112] The optimization problem construction and solution module 3 is used by the central node to construct an optimization problem based on the node position information recorded by the position information processing module 2, set the relevant parameters of the optimization problem, and search and solve it to obtain the final position set. The initial parameter setting submodule 31 is used by the central node to construct the optimization problem based on the position information sent by the position information processing module 2, and to set the iteration parameters and objective function parameters of the optimization problem according to task requirements, and then transmit them to the iterative search submodule 32 and the objective function calculation submodule 33 respectively. The iterative search submodule 32 is used by the central node to receive the iteration parameters sent by the initial parameter setting submodule 31, search for feasible solutions to the optimization problem within the constraints, and transmit the feasible solutions to the objective function calculation submodule. Block 33; Objective function calculation submodule 33 is used by the central node to receive the objective function parameters sent by the initial parameter setting submodule 31 and the feasible solution sent by the iterative search submodule 32, calculate the resilience index, task coverage index, network communication index, and execution cost index, obtain the objective function value under the current feasible solution, and transmit the objective function value to the solution termination determination submodule 34; Solution termination determination submodule 34 is used by the central node to receive the objective function value calculated by the objective function calculation submodule 33 and determine the current feasible solution according to the iterative termination condition. If the termination condition is not met, the iterative search submodule 32 is notified to continue the iterative search; otherwise, the final position set is output and the position set is transmitted to the position result distribution module 4;

[0113] The location result distribution module 4 is used by the central node to encapsulate the final location set obtained by the optimization problem construction and solution module 3 into a specified data frame format for broadcasting.

[0114] The location result receiving module 5 is used for non-central nodes to receive location results sent by the central node location result sending module 4, and to move itself to a designated location according to the location results, thereby completing the network topology adjustment.

[0115] It should be noted that the above functional modules can be implemented, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, as program instruction products. A program instruction product includes one or a set of program instructions. When the program instructions are loaded and executed on a computer, the described process or function is generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The program instructions can be stored in a computer-readable and writable storage medium, or transferred from one computer's readable and writable storage medium to another.

[0116] The direct coupling or communication connections between the modules shown or discussed in this embodiment can be achieved through indirect coupling or communication connections via interfaces, devices, or modules. The various functional modules and sub-modules in this embodiment can dynamically reside within a single processing unit, or each module can exist physically independently, or two or more modules can dynamically reside within a single processing unit. When these dynamic components are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable and writable storage medium. This storage medium can be a memory, disk, or optical disc, etc.

[0117] The above descriptions are merely two specific examples of the present invention and do not constitute any limitation on the present invention. Obviously, those skilled in the art, after understanding the content and principles of the present invention, may make various modifications and changes in form and detail without departing from the principles and structure of the present invention. For example, when constructing the objective function of the optimization problem, in addition to the modeling method described in this example, the resilience index, task capability coverage index, network communication capability index, and execution cost index can also be modeled using other mathematical forms. However, these modifications and changes based on the ideas of the present invention are still within the scope of protection of the claims of the present invention.

Claims

1. A mission-driven unmanned swarm resiliency topology control method, characterized in that, Comprise: (1) All nodes in the network broadcast Hello packet containing their own position information with maximum power, non-central node receives Hello packet broadcasted by other nodes and rebroadcasts it; (2) The central node collects Hello packet of all non-central nodes in the network and records the position information therein; (3) The central node constructs the following optimization problem according to the recorded position information of all nodes in the network; ； wherein, denotes a resilience index, denotes a task coverage index, denotes a network communication index, denotes an execution cost index, denotes a resilience index coefficient, denotes a task coverage index coefficient, denotes a network communication index coefficient, is a decision variable, representing an adjusted node position set of the node; (4) The central node sets the constraint condition of the optimization problem and solves it to obtain the target position of each node; (5) The central node sends the target position of each node obtained by solving to the corresponding non-central node, and the non-central node moves to the target position after receiving the target position, completing the topology adjustment of the network.

2. The method of claim 1, wherein, The Hello packet in the (1) comprises: message type, message sequence number, source node id and node position information, wherein the message type occupies 1 byte, the message sequence number and the source node id each occupy 2 bytes, and the node position information occupies 12 bytes.

3. The method of claim 1, wherein, In the (1), the non-central node receives the Hello packet broadcasted by other nodes and rebroadcasts it according to the node id in the received Hello packet to judge whether the position information of the node exists in its neighbor table: If not, the id, message sequence number and position information of the node are added to its neighbor table; If yes, the message sequence number in the Hello packet is compared with the sequence number information of the corresponding node in the neighbor table: If the sequence number in the Hello packet is greater than the sequence number of the node in the neighbor table, the sequence number of the corresponding node in the neighbor table is updated with the sequence number in the Hello packet, and the position information of the corresponding node is updated according to the position information in the Hello packet, and then the Hello packet is rebroadcasted; Otherwise, no processing is done.

4. The method of claim 1, wherein, In the (2), the central node collects Hello packet of all non-central nodes in the network and records the position information therein, which is according to the node id in the received Hello packet to judge whether the position information of the node exists in its neighbor table: If not, the id, message sequence number and position information of the node are added to its neighbor table; If yes, the message sequence number in the Hello packet is compared with the sequence number information of the corresponding node in the neighbor table: If the sequence number in the Hello packet is greater than the sequence number of the node in the neighbor table, the sequence number of the corresponding node in the neighbor table is updated with the sequence number in the Hello packet, and the position information of the corresponding node is updated according to the position information in the Hello packet; Otherwise, no processing is done.

5. The method of claim 1, wherein, The central node constructs the optimization problem according to the recorded position information of all nodes in the network in step (3), which comprises: (3a) the central node constructs a current set of node positions in the network constructs a network topology graph and constructs a network resilience indicator, stickiness, from the graph constructs a network resilience indicator, stickiness, from the graph : ； wherein, denotes the set of nodes that are deleted, denotes the number of nodes that are deleted, is the graph the order of the largest connected component, is the graph the number of connected components; (3b) the central node is configured to determine the task coverage radius of the network node based on the network node's task coverage capability constructing a task coverage capability indicator : ， wherein, denotes the individual coverage area of the th node, denotes the area of the coverage region in which the th coverage region has an overlap, denotes the number of nodes in the network; (3c) the central node constructs a current set of network node positions constructing network communication capability indicators : ， wherein, represents the set of links under the current global node position, representing a node and a link rate between the node , a signal bandwidth, a node transmission power, a noise power, , a path loss factor, a position coordinate of the node , a position coordinate of the node , a relative distance between the node and the node ; (3d) the central node constructs an execution cost indicator based on the current set of node positions across the network and the initial set of node positions :​ ， wherein, represents the current position coordinates of a node , represents the initial position coordinates of a node , represents the number of nodes in the network; (3e) The central node constructs the optimization problem according to the four network indicators in (3a)-(3d): ， wherein, represents the current set of network node locations as decision variables of the optimization problem, represents a resilience indicator coefficient, represents a task coverage capability indicator coefficient, represents a network communication capability indicator coefficient.

6. The method of claim 1, wherein, The central node sets the constraint condition of the optimization problem and solves it in step (4), which comprises: (4a) The central node constructs the formation constraint condition according to the initial position set of the unmanned cluster: , wherein the support function represents the farthest position of the current unmanned cluster position in the direction, represents the selected first unit direction vector, represents the number of selected unit direction vectors, represents the current position coordinates of the node , represents the farthest position of the initial unmanned cluster position in the direction, represents the initial position coordinates of the node , is the allowed variation of the formation shape in the first direction; (4b) The central node constructs the amplitude constraint condition according to the initial position set of the unmanned cluster: ， wherein, representing a node a distance of the current position from the initial position, representing a node a current position coordinate of the node representing a node an initial position coordinate of the node representing a node an adjustable maximum displacement amplitude, representing a number of nodes in the network; (4c) The central node constructs the safety constraint condition according to the current position set of the unmanned cluster: , wherein, representing a node with a node a distance between a current position, representing a node a current position coordinate of a node representing a node a current position coordinate of a node representing a secure distance between network nodes; (4d) The final set of positions is obtained by heuristic search subject to the constraints of (4a)-(4c) .

7. The method of claim 1, wherein, Step (5) The central node issues the target position of each node obtained by solving to the corresponding non-central node, and the implementation includes: (5a) The central node encapsulates the position coordinates of each node obtained by solving into a set data frame format, and broadcasts with maximum power; (5b) After receiving the broadcast message, the non-central node judges whether the data packet has been accepted according to the message type and message sequence number in the data packet: If not, take out the position information of the node in the data packet and rebroadcast the data packet with maximum power; If received, do not process.

8. The method of claim 7, wherein, The data frame format of step (5a) includes: message type, message sequence number, source node id and node position list, wherein: The message type occupies 1 byte, The message sequence number and source node id each occupy 2 bytes, The node position list includes the non-central node id and the corresponding node target position coordinates in the network, and the id of a single node occupies 2 bytes, and the corresponding position information occupies 12 bytes.

9. A mission-driven unmanned swarm resiliency topology control apparatus, comprising: Including: The position information sharing module is used for the non-central node to obtain its own position information and encapsulate it into the Hello packet for broadcast; The position information processing module is used for the central node to process the received Hello packet and record the position information of the node in the Hello packet; The optimization problem construction and solving module is used for the central node to construct the optimization problem according to the node position information recorded by the position information processing module, and set the related parameters of the optimization problem to search and solve to obtain the final position set; The position result issuing module is used for the central node to encapsulate the final position set obtained by the optimization problem construction and solving module into a specified data frame format for broadcast; The position result receiving module is used for the non-central node to receive the position result issued by the central node, and to move to the specified position according to the position result.

10. The apparatus of claim 9, wherein, The optimization problem construction and solving module includes: The initial parameter setting submodule is used for the central node to set the related parameter values of the optimization problem according to the task demand; The iterative search submodule is used for the central node to search for feasible solutions of the optimization problem within the constraint condition range; The objective function calculation submodule is used for the central node to calculate the resilience index, task coverage ability index, network communication ability index and execution cost index according to the feasible solution obtained by searching, and to obtain the objective function value under the current feasible solution; The solving termination judgment submodule is used for the central node to judge the current feasible solution according to the iteration termination, and output the final position set.

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