Unmanned aerial vehicle formation reconstruction method and system based on minimum cost maximum flow algorithm

Abstract

The application provides a UAV formation reconstruction method and system based on a minimum cost maximum flow algorithm, wherein the method comprises the following steps: acquiring the residual energy of each UAV participating in reconstruction; calculating the neighbor number change amount of a target UAV after the target UAV reaches a target position; calculating the position density of the target position; calculating the energy consumption of the target UAV to the target position according to the residual energy of each UAV and the position density of the target position; calculating the cost value of the target UAV to the target position according to the neighbor number change amount and the energy consumption of the target UAV to the target position; and performing position allocation according to the cost value of the target UAV to the target position. In the application, the residual energy and the topological relationship among UAVs are taken as a value function with minimum cost constraints, and the maximum flow minimum cost algorithm is used to allocate new and old positions; the residual cluster members are recombined into a new cluster with other UAV clusters to continue to perform tasks.

Description

Technical Field

[0001] This invention belongs to the field of UAV formation control technology, and particularly relates to a UAV formation reconstruction method and system based on the minimum cost maximum flow algorithm. Background Technology

[0002] With the continuous development of UAV collaborative technology, UAV swarms have demonstrated powerful mission execution capabilities on the battlefield. UAVs carrying different payloads cooperate, complementing each other's payloads and coordinating their missions to form multi-functional UAV swarms integrating reconnaissance, strike, and jamming, achieving system-level capability enhancement. UAV swarms have demonstrated excellent tactical effectiveness in various fields such as reconnaissance, surveillance, emergency communications, and combat.

[0003] Missions in the battlefield environment are characterized by high adversarial nature, high uncertainty, and high dynamism. Faced with the pressure of enemy air defense mechanisms, drone swarms urgently need corresponding mechanisms for collaborative survival. In this highly adversarial context, ensuring the integrity of the drone formation's mission capabilities is a prerequisite for achieving the objective. Maintaining the integrity of the drone formation is a crucial issue for the swarm. If a drone squadron carrying out a mission is interfered with or destroyed, and the remaining drones are unable to complete the objective, the ground station will instruct the remaining drones to stand by or return to the ground station. The mission efficiency of an attacked drone swarm is also significantly reduced. Therefore, timely drone reconfiguration on the battlefield is of great importance for the timely completion of combat missions.

[0004] However, there is little research, both domestic and international, on scenarios where damaged drones can reassemble and form new swarms with other drones to continue their combat missions. Therefore, for multi-swarm scenarios in drone swarm operations, this study considers parameters such as formation, communication topology, and remaining energy among different mission swarms to reorganize and redistribute damaged and incomplete drone swarms on the battlefield. This aims to address the problem in large-scale combat scenarios where drone formations suffer from the destruction of basic swarm integrity due to damage to too many nodes, resulting in the loss of critical nodes and the lack of mission-critical payloads, thus hindering mission completion. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing a method and system for reconstructing drone formations based on a minimum cost maximum flow algorithm.

[0006] In a first aspect, the present invention provides a method for reconstructing UAV formations based on a minimum cost maximum flow algorithm, comprising:

[0007] Obtain the remaining energy of each drone participating in the reconstruction;

[0008] Calculate the change in the number of neighbors of the target drone after it reaches the target location;

[0009] Calculate the position density at the target location;

[0010] Calculate the energy consumption of the target drone to the target location based on the remaining energy of each drone and the location density of the target location;

[0011] Calculate the cost of the target drone to reach the target location based on the change in the number of neighbors and energy consumption.

[0012] Location allocation is based on the cost of transporting the target drone to the target location.

[0013] Furthermore, the calculation of the change in the number of neighbors of the target drone after it reaches the target location includes:

[0014] The change in the number of neighbors of the target drone is calculated using the following formula:

[0015]

[0016] Among them, S ij N represents the change in the number of neighbors of drone i after drone i moves to location j; i N is the set of neighbors of drone i; j Let be the set of neighbors of position j; η is the topology change factor, used to determine the magnitude of the change in the number of neighbors.

[0017] Further, the calculation of the position density of the target location includes:

[0018] Calculate the location density of the target position using the following formula:

[0019]

[0020] Among them, dens j Let N be the position density at position j; α be the position density coefficient; N j ={k|||l ki ||≤R comm k≠i};R comn The maximum communication distance between drones; ki Let be the Euclidean distance between drone i and drone k; n is the number of drones at position j.

[0021] Furthermore, the calculation of the energy consumption of the target drone to the target location based on the remaining energy of each drone and the location density of the target location includes:

[0022] The energy consumption of the target drone to the target location can be calculated using the following formula:

[0023] P ij =densj *(k1Pathcost ij -k2Energyfactor i );

[0024] Among them, P ij Energy consumption for drone i to travel to location j; Pathcost ij k1 is the estimated path cost from drone i to location j; k2 is the path cost. ij Impact factor; Energy factor i k1 represents the remaining energy value of drone i; k2 represents the Energy Factor. i Influencing factors.

[0025] Furthermore, the step of calculating the cost value of the target drone to the target location based on the change in the number of neighbors of the target drone to the target location and energy consumption includes:

[0026] Calculate the cost of transporting the target drone to the target location using the following formula:

[0027] c ij =ω1P ij +ω2S ij ;

[0028] Among them, c ij Let ω1 be the cost value from drone i to location j; ω1 is P ij Influence factor; ω2 is S ij Influencing factors.

[0029] Furthermore, the location allocation based on the cost value from the target drone to the target location includes:

[0030] Construct the cost matrix C m×n , where m is the initial number of drones;

[0031] Select m values ​​of c from different rows and columns in the cost matrix. ij The value that minimizes the total sum of the target allocation is:

[0032]

[0033] Where χ∈(0,1) m×n , for containing x ij The set of variables; x ij The value of indicates whether there is an allocation relationship between UAV i and target position j; the allocation process includes the following constraints:

[0034]

[0035] The goal is to minimize the total allocation using the minimum cost maximum flow algorithm.

[0036]

[0037] Construct a bipartite graph G = (V, E) based on the initial and target positions of the UAV; V is the vertex set of the bipartite graph; E is the edge set; v i Let v be the i-th node in the bipartite graph; add a source node v to the bipartite graph. s With Huidian v t And at the source point v s With the initial position set S, the target position set D, and the sink v t Establish edges (v) s ,s) and (d,v t );ω ij f is the cost of pointing from the initial set of positions to the target set of positions in a bipartite graph; ij Let the flow values ​​on the corresponding edges in the constructed bipartite graph be denoted as v; let the edge (v) be denoted as v. s ,s), (d,v t The flow value f on (s, d) ij All are 1; c from drone i to target position j ij Let ω be the cost value of the corresponding edge (s, d). ij This is used to construct a maximum flow graph and allocate targets based on the minimum cost.

[0038] Secondly, the present invention provides a UAV formation reconfiguration system based on a minimum cost maximum flow algorithm, comprising:

[0039] The acquisition module is used to acquire the remaining energy of each drone participating in the reconstruction.

[0040] The first calculation module is used to calculate the change in the number of neighbors of the target drone after it reaches the target location.

[0041] The second calculation module is used to calculate the position density of the target location;

[0042] The third calculation module is used to calculate the energy consumption of the target drone to the target location based on the remaining energy of each drone and the location density of the target location.

[0043] The fourth calculation module is used to calculate the cost of the target drone to the target location based on the change in the number of neighbors of the target drone to the target location and the energy consumption.

[0044] The location allocation module is used to allocate locations based on the cost value from the target drone to the target location.

[0045] Thirdly, the present invention provides a computer device including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the steps of the UAV formation reconstruction method based on the minimum cost maximum flow algorithm described in the first aspect.

[0046] Fourthly, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, it implements the steps of the UAV formation reconstruction method based on the minimum cost maximum flow algorithm described in the first aspect.

[0047] This invention provides a method and system for drone formation reconfiguration based on a minimum cost maximum flow algorithm. The method includes: obtaining the remaining energy of each drone participating in the reconfiguration; calculating the change in the number of neighbors of the target drone after it reaches the target location; calculating the location density of the target location; calculating the energy consumption of the target drone to reach the target location based on the remaining energy of each drone and the location density of the target location; calculating the cost value of the target drone to reach the target location based on the change in the number of neighbors and the energy consumption; and performing location allocation based on the cost value of the target drone to reach the target location.

[0048] This invention employs the aforementioned scheme, allowing remaining drone swarm members to continue their mission by regrouping with other drone swarms to form a new swarm. When designing the reconstruction of multiple drone swarms, considering the different remaining energy and topologies among the swarms, the remaining energy and topological relationships between drones are used as value functions with minimum cost constraints. A maximum flow minimum cost algorithm is then used to allocate old and new positions. When a new swarm is formed, the old drones fly to the new swarm, minimizing both the total remaining energy and the change in swarm topology. Attached Figure Description

[0049] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is an application scenario diagram of a UAV formation reconstruction method based on the minimum cost maximum flow algorithm provided in an embodiment of the present invention;

[0051] Figure 2 for Figure 1 Flowchart of drone formation reconfiguration in application scenarios;

[0052] Figure 3A flowchart of a UAV formation reconstruction method based on the minimum cost maximum flow algorithm provided in this embodiment of the invention;

[0053] Figure 4 This is a schematic diagram illustrating the application of a drone node to the minimum cost maximum flow algorithm provided in an embodiment of the present invention;

[0054] Figure 5 This is a structural diagram of a UAV formation reconfiguration system based on a minimum cost maximum flow algorithm, provided in an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] like Figure 1 and Figure 2 As shown, when a drone swarm formation is damaged and scattered, the remaining swarm members can be reorganized into a new swarm by matching with other drone swarms, thereby improving the efficiency of swarm operation.

[0057] A damaged drone cluster W sends a reconstruction request, seeking to interact with drone cluster R. Drone cluster R determines whether to agree to the reconstruction request. If it agrees, drone cluster R enters reconstruction state, maintaining its current state, and sends a reconstruction confirmation message to the damaged drone cluster W. Upon receiving the confirmation message, the damaged drone cluster W enters reconstruction state, maintaining its current state.

[0058] The drone swarm W and R jointly transmit the current formation's position information and derive a candidate drone set for the new formation's target layer. Considering the drone formation's requirements, and based on the value function of remaining energy and drone topology changes, the allocation of candidate drones to the target location set T is evaluated. Based on the evaluated value of each drone at each target location, a minimum-cost maximum-flow algorithm is used to allocate the target layer drones' positions. After allocation, unselected drones automatically join the candidate drone set for the next layer and participate in the next layer's position allocation.

[0059] After being assigned positions, the drone swarm at the initial location moves to the target location in hierarchical order. For example, when moving, the drones in the target layer first move along the Z-axis to the corresponding height, and then move along the XY plane to the target location. Once the last drone in the layer reaches its position, it sends a confirmation message to notify the drones in the next layer to begin moving.

[0060] In one embodiment, such as Figure 3 As shown, this embodiment of the invention provides a UAV formation reconstruction method based on the minimum cost maximum flow algorithm, including:

[0061] Step 101: Obtain the remaining energy of each drone participating in the reconstruction.

[0062] Step 102: Calculate the change in the number of neighbors of the target drone after it reaches the target location.

[0063] For example, the change in the number of neighbors of the target drone is calculated according to the following formula:

[0064]

[0065] Among them, S ij N represents the change in the number of neighbors of drone i after drone i moves to location j; i N is the set of neighbors of drone i; j Let be the set of neighbors of position j; η is the topology change factor, used to determine the magnitude of the change in the number of neighbors.

[0066] Step 103: Calculate the position density of the target location.

[0067] For example, the location density of the target location is calculated according to the following formula:

[0068]

[0069] Among them, dens j Let N be the position density at position j; α be the position density coefficient; N j ={k|||l ki ||≤R comm k≠i};R comn The maximum communication distance between drones; ki Let be the Euclidean distance between drone i and drone k; n is the number of drones at position j. Drone positions in dense areas will be given higher allocation priority, while target positions at the edge will receive lower allocation priority.

[0070] Step 104: Calculate the energy consumption of the target drone to the target location based on the remaining energy of each drone and the location density of the target location.

[0071] For example, the energy consumption of the target drone to the target location is calculated according to the following formula:

[0072] P ij =dens j *(k1Pathcost ij-k2Energyfactor i ).

[0073] Among them, P ij Energy consumption for drone i to travel to location j; Pathcost ij k1 is the estimated path cost from drone i to location j; k2 is the path cost. ij Impact factor; Energy factor i k1 represents the remaining energy value of drone i; k2 represents the Energy Factor. i Influencing factors.

[0074] Step 105: Calculate the cost of the target drone traveling to the target location based on the change in the number of neighbors and energy consumption.

[0075] For example, the cost of transporting the target drone to the target location is calculated using the following formula:

[0076] c ij =ω1P ij +ω2S ij .

[0077] Among them, c ij Let ω1 be the cost value from drone i to location j; ω1 is P ij Influence factor; ω2 is S ij The influencing factors are ω1 and ω2, which are used to adjust the degree of influence of the energy consumption component and the topology component on the cost value, respectively.

[0078] Step 106: Allocate a location based on the cost value from the target drone to the target location.

[0079] For example, this step includes:

[0080] Construct the cost matrix C m×n , where m is the initial number of drones.

[0081] Select m values ​​of c from different rows and columns in the cost matrix. ij The value that minimizes the total sum of the target allocation is:

[0082]

[0083] Where χ∈(0,1) m×n , for containing x ij The set of variables; x ij The value of indicates whether there is an allocation relationship between UAV i and target position j; the allocation process includes the following constraints:

[0084]

[0085] The constraints indicate that during the group transformation process, each target location can only be assigned to one drone, and not all drones in the initial drone set will be selected; some will be discarded.

[0086] The goal is to minimize the total allocation using the minimum cost maximum flow algorithm.

[0087]

[0088] like Figure 4 As shown, a bipartite graph G = (V, E) is constructed based on the initial position of the UAV and the target position; V is the vertex set of the bipartite graph; E is the edge set; v i Let v be the i-th node in the bipartite graph; add a source node v to the bipartite graph. s With Huidian v t And at the source point v s With the initial position set S, the target position set D, and the sink v t Establish edges (v) s ,s) and (d,v t );ω ij f is the cost of pointing from the initial set of positions to the target set of positions in a bipartite graph; ij Let the flow values ​​on the corresponding edges in the constructed bipartite graph be denoted as v; let the edge (v) be denoted as v. s ,s), (d,v t The flow value f on (s, d) ij All are 1; c from drone i to target position j ij Let ω be the cost value of the corresponding edge (s, d). ij This is used to construct a maximum flow graph and allocate targets based on the minimum cost.

[0089] This invention employs the aforementioned scheme, allowing remaining drone swarm members to continue their mission by regrouping with other drone swarms to form a new swarm. When designing the reconstruction of multiple drone swarms, considering the different remaining energy and topologies among the swarms, the remaining energy and topological relationships between drones are used as value functions with minimum cost constraints. A maximum flow minimum cost algorithm is then used to allocate old and new positions. When a new swarm is formed, the old drones fly to the new swarm, minimizing both the total remaining energy and the change in swarm topology.

[0090] Based on the same inventive concept, this invention also provides a UAV formation reconstruction system based on the minimum cost maximum flow algorithm. Since the principle of this system in solving the problem is similar to the aforementioned UAV formation reconstruction method based on the minimum cost maximum flow algorithm, the implementation of this system can refer to the implementation of the UAV formation reconstruction method based on the minimum cost maximum flow algorithm, and the repeated parts will not be described again.

[0091] In another embodiment, the UAV formation reconfiguration system based on the minimum cost maximum flow algorithm provided in this invention is, for example... Figure 5 As shown, it includes:

[0092] Acquisition module 10 is used to acquire the remaining energy of each drone participating in the reconfiguration.

[0093] The first calculation module 20 is used to calculate the change in the number of neighbors of the target drone after the target drone arrives at the target location.

[0094] The second calculation module 30 is used to calculate the position density of the target location.

[0095] The third calculation module 40 is used to calculate the energy consumption of the target drone to the target location based on the remaining energy of each drone and the location density of the target location.

[0096] The fourth calculation module 50 is used to calculate the cost value of the target drone to the target location based on the change in the number of neighbors of the target drone to the target location and the energy consumption.

[0097] The location allocation module 60 is used to allocate a location based on the cost value from the target drone to the target location.

[0098] For example, the first computing module includes:

[0099] The first calculation unit is used to calculate the change in the number of neighbors of the target drone according to the following formula:

[0100]

[0101] Among them, S ij N represents the change in the number of neighbors of drone i after drone i moves to location j; i N is the set of neighbors of drone i; j Let be the set of neighbors of position j; η is the topology change factor, used to determine the magnitude of the change in the number of neighbors.

[0102] For example, the second computing module includes:

[0103] The second calculation unit is used to calculate the position density of the target location according to the following formula:

[0104]

[0105] Among them, dens j Let N be the position density at position j; α be the position density coefficient; N j ={k|||l ki ||≤R comm k≠i};R comn The maximum communication distance between drones; ki Let be the Euclidean distance between drone i and drone k; n is the number of drones at position j.

[0106] For example, the third computing module includes:

[0107] The third calculation unit is used to calculate the energy consumption of the target drone to the target location according to the following formula:

[0108] P ij =dens j *(k1Pathcost ij -k2Energyfactor i ).

[0109] Among them, P ij Energy consumption for drone i to travel to location j; Pathcost ij k1 is the estimated path cost from drone i to location j; k2 is the path cost. ij Impact factor; Energy factor i k1 represents the remaining energy value of drone i; k2 represents the Energy Factor. i Influencing factors.

[0110] For example, the fourth computing module includes:

[0111] The fourth calculation unit is used to calculate the cost of the target drone to the target location according to the following formula:

[0112] c ij =ω1P ij +ω2S ij .

[0113] Among them, c ij Let ω1 be the cost value from drone i to location j; ω1 is P ij Influence factor; ω2 is S ij Influencing factors.

[0114] For example, the location allocation module includes:

[0115] Construction unit, used to construct cost matrix C m×n, where m is the initial number of drones.

[0116] The selection unit is used to select m items from different rows and columns of the cost matrix. ij The value that minimizes the total sum of the target allocation is:

[0117]

[0118] Where χ∈(0,1) m×n , for containing x ij The set of variables; x ij The value of indicates whether there is an allocation relationship between UAV i and target position j; the allocation process includes the following constraints:

[0119]

[0120] The fifth computational unit is used to achieve the goal of minimizing the total allocation using the minimum-cost maximum flow algorithm.

[0121]

[0122] Construct a bipartite graph G = (V, E) based on the initial and target positions of the UAV; V is the vertex set of the bipartite graph; E is the edge set; v i Let v be the i-th node in the bipartite graph; add a source node v to the bipartite graph. s With Huidian v t And at the source point v s With the initial position set S, the target position set D, and the sink v t Establish edges (v) s ,s) and (d,v t );ω ij f is the cost of pointing from the initial set of positions to the target set of positions in a bipartite graph; ij Let the flow values ​​on the corresponding edges in the constructed bipartite graph be denoted as v; let the edge (v) be denoted as v. s ,s), (d,v t The flow value f on (s, d) ij All are 1; c from drone i to target position j ij Let ω be the cost value of the corresponding edge (s, d). ij This is used to construct a maximum flow graph and allocate targets based on the minimum cost.

[0123] For more detailed information on the working process of each of the above modules, please refer to the relevant content disclosed in the foregoing method embodiments, which will not be repeated here.

[0124] In another embodiment, the present invention provides a computer device including a processor and a memory; wherein the processor executes a computer program stored in the memory to implement the steps of the above-described UAV formation reconstruction method based on the minimum cost maximum flow algorithm.

[0125] For more detailed information on the above methods, please refer to the relevant content disclosed in the foregoing method embodiments, which will not be repeated here.

[0126] In another embodiment, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, it implements the steps of the above-described UAV formation reconstruction method based on the minimum cost maximum flow algorithm.

[0127] For more detailed information on the above methods, please refer to the relevant content disclosed in the foregoing method embodiments, which will not be repeated here.

[0128] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The systems, devices, and storage media disclosed in the embodiments are described simply because they correspond to the methods disclosed in the embodiments; relevant details can be found in the method section.

[0129] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0130] The present invention has been described in detail above with reference to specific embodiments and exemplary examples; however, these descriptions should not be construed as limiting the present invention. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and embodiments of the present invention without departing from the spirit and scope of the invention, and all such modifications and improvements fall within the scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

Claims

1. A method for reconstructing UAV formations based on a minimum-cost maximum-flow algorithm, characterized in that, include: Obtain the remaining energy of each drone participating in the reconstruction; Calculate the change in the number of neighbors of the target drone after it reaches the target location; Calculate the position density at the target location; Calculate the energy consumption of the target drone to the target location based on the remaining energy of each drone and the location density of the target location; Calculate the cost of the target drone traveling to the target location based on the change in the number of neighbors and energy consumption. Location allocation is based on the cost of transporting the target drone to the target location.

2. The UAV formation reconfiguration method according to claim 1, characterized in that, The calculation of the change in the number of neighbors of the target drone after it reaches the target location includes: The change in the number of neighbors of the target drone is calculated using the following formula: Among them, S ij N represents the change in the number of neighbors of drone i after drone i moves to location j; i N is the set of neighbors of drone i; j Let be the set of neighbors of position j; η is the topology change factor, used to determine the magnitude of the change in the number of neighbors.

3. The UAV formation reconfiguration method according to claim 2, characterized in that, The calculation of the location density of the target location includes: Calculate the location density of the target position using the following formula: Among them, dens j Let N be the position density at position j; α be the position density coefficient; N j ={k|||l ki ||≤R comm k≠i};R comn The maximum communication distance between drones; ki Let be the Euclidean distance between drone i and drone k; n is the number of drones at position j.

4. The UAV formation reconfiguration method according to claim 3, characterized in that, The calculation of energy consumption from the target drone to the target location based on the remaining energy of each drone and the location density of the target location includes: Calculate the energy consumption of the target drone to the target location using the following formula: P ij =dens j *(k1Pathcost ij -k2Energyfactor i ); Among them, P ij Energy consumption for drone i to travel to location j; Pathcost ij k1 is the estimated path cost from drone i to location j; k2 is the path cost. ij Impact factor; Energy factor i k1 represents the remaining energy value of drone i; k2 represents the Energy Factor. i Influencing factors.

5. The UAV formation reconfiguration method according to claim 4, characterized in that, The calculation of the cost value for the target drone to reach the target location based on the change in the number of neighbors and energy consumption includes: Calculate the cost of transporting the target drone to the target location using the following formula: c ij =ω1P ij +ω2S ij ; Among them, c ij Let ω1 be the cost value from drone i to location j; ω1 is P. ij Influence factor; ω2 is S ij Influencing factors.

6. The UAV formation reconfiguration method according to claim 5, characterized in that, The location allocation based on the cost value from the target drone to the target location includes: Construct the cost matrix C m×n , where m is the initial number of drones; Select m values ​​of c from different rows and columns in the cost matrix. ij The value that minimizes the total sum of the target allocation is: Where χ∈(0,1) m×n , for containing x ij The set of variables; x ij The value of indicates whether there is an allocation relationship between UAV i and target position j; the allocation process includes the following constraints: The goal is to minimize the total allocation using the minimum cost maximum flow algorithm. Construct a bipartite graph G = (V, E) based on the initial and target positions of the UAV; V is the vertex set of the bipartite graph; E is the edge set; v i Let v be the i-th node in the bipartite graph; add a source node v to the bipartite graph. s With Huidian v t And at the source point v s With the initial position set S, the target position set D, and the sink v t Establish edges (v) s ,s) and (d,v) t );ω ij f is the cost of pointing from the initial set of positions to the target set of positions in a bipartite graph; ij Let the flow values ​​on the corresponding edges in the constructed bipartite graph be denoted as v; let the edge (v) be denoted as v. s ,s), (d,v t The flow values ​​f on (s, d) ij All are 1; c from drone i to target position j ij Let ω be the cost value on the corresponding edge (s, d). ij This is used to construct a maximum flow graph and allocate targets based on the minimum cost.

7. A UAV formation reconfiguration system based on a minimum-cost maximum-flow algorithm, characterized in that, include: The acquisition module is used to acquire the remaining energy of each drone participating in the reconstruction. The first calculation module is used to calculate the change in the number of neighbors of the target drone after it reaches the target location. The second calculation module is used to calculate the position density of the target location; The third calculation module is used to calculate the energy consumption of the target drone to the target location based on the remaining energy of each drone and the location density of the target location. The fourth calculation module is used to calculate the cost of the target drone to the target location based on the change in the number of neighbors of the target drone to the target location and the energy consumption. The location allocation module is used to allocate locations based on the cost value from the target drone to the target location.

8. A computer device, characterized in that, It includes a processor and a memory; wherein, when the processor executes the computer program stored in the memory, it implements the steps of the UAV formation reconstruction method based on the minimum cost maximum flow algorithm as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer programs; when the computer programs are executed by a processor, they implement the steps of the UAV formation reconstruction method based on the minimum cost maximum flow algorithm as described in any one of claims 1-6.

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