Unmanned aerial vehicle persistent formation communication topology optimization method and system considering energy consumption balance

Abstract

The application provides a kind of method and system for optimizing persistent formation communication topology of unmanned aerial vehicle considering energy consumption balance, and relates to the field of communication technology.The application obtains the shortest path from each point to any other point by calculating all nodes with in-degree of 0, 1 or 2 in the third persistent graph G3, selects the shortest path with negative distance, reverses the path in the original graph, obtains a new weighted directed graph and continuously performs the above operation until there is no path meeting the condition, thereby reducing the total weight of the first persistent graph, i.e., reducing the communication energy consumption of three-dimensional persistent formation.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and specifically to a method and system for optimizing persistent UAV formation communication topology considering energy consumption balance. Background Technology

[0002] With the development of technology, the application of intelligent agents is becoming increasingly popular in fields such as military, intelligent transportation, maritime patrol, and logistics vehicles. When utilizing intelligent agents, controlling the formation of agents to maintain a specific formation is a crucial aspect. Intelligent agents exchange information point-to-point through communication links to maintain formation throughout the mission.

[0003] Information exchange between intelligent agents is asymmetrical because the sender needs an amplifier to amplify the power for the receiver to receive the information. Since the sender consumes more power than the receiver, the communication connection between the sender and receiver is asymmetrical, and therefore the energy consumed in their communication is also asymmetrical. Optimizing the communication topology of intelligent agent formations is of practical value in order to better extend the time it takes for the formation to maintain its alignment.

[0004] Persistent formation is a control method for maintaining the formation of multi-agent formations. Optimizing the communication topology of persistent formation aims to minimize the energy consumption of communication between agents while ensuring that the multi-agent formation control method maintains the formation.

[0005] However, existing optimization methods for agent formation communication topologies cannot provide an optimal topology, and there is still considerable room for optimization to further reduce the communication energy consumption of agent formations. For example, the paper "Energy-Balanced Generation of 3D Optimal Persistent Formation Communication Topology" published in the Acta Aeronautica Sinica uses arc addition to construct the communication topology of persistent formations. This method is based on a greedy approach, leaving room for optimization in the overall communication energy consumption of the formation.

[0006] Therefore, there is an urgent need for an optimal persistent graph generation method to reduce the communication energy consumption of three-dimensional persistent formations. Summary of the Invention

[0007] (a) Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing persistent UAV formation communication topology that considers energy consumption balance, in order to reduce the communication energy consumption of three-dimensional persistent formations.

[0009] (II) Technical Solution

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] In a first aspect, the present invention provides a method for optimizing persistent formation communication topology for unmanned aerial vehicles (UAVs) that considers energy consumption balance, the method comprising:

[0012] S1. Obtain the initial weighted directed graph D of the three-dimensional persistent formation, and obtain the corresponding first persistent graph G1 based on the initial weighted directed graph D;

[0013] S2. Construct a second persistent graph G2 based on the first persistent graph G1; wherein the set of nodes and the set of directed edges in the second persistent graph G2 are the same as those in the first persistent graph G1.

[0014] The process of obtaining the arc weight set in the second persistent graph G2 includes: calculating the difference between the reverse arc weight and the forward arc weight of each arc in the first persistent graph G1, and using this difference as the weight of the corresponding arc in the second persistent graph G2 to obtain the arc weight set; wherein, the forward arc weight represents the arc weight of the directed edge in the first persistent graph G1; the reverse arc weight represents the arc weight after reversing the direction of the directed edge in the first persistent graph G1, and is obtained through the initial weighted directed graph D;

[0015] S3. Perform a path reversal operation on the negative loops in the second persistent graph G2 to obtain a three-dimensional persistent graph with no negative loops, which will be used as the third persistent graph G3.

[0016] S4. Select all nodes with in-degree of 0, 1, and 2 from the third persistent graph G3 as the first node set; each node in the first node set is used as the starting point in turn, and the shortest path from the current starting point to any other node in the third persistent graph G3 is calculated. Select and retain the path with the smallest total arc weight and a negative value among all shortest paths as the path to be reversed.

[0017] S5. Reverse the path to be reversed to obtain the updated third persistent graph;

[0018] S6. Repeat steps S4-S5 on the updated third persistent graph until the minimum total arc weight in the shortest path is positive, and use the corresponding updated third persistent graph as the fourth persistent graph G4.

[0019] S7. Change the arc weight of each arc in the fourth persistent graph G4 to the arc weight of the same arc in the initial weighted directed graph D, so as to update the arc weight set of the fourth persistent graph G4 and obtain the fifth persistent graph G5 as the three-dimensional optimal persistent graph.

[0020] Optionally, the initial weighted directed graph D = (V, A, W);

[0021] Where V represents the set of nodes in the initial weighted directed graph, v i ∈V, i∈[1,n],vi Let represent the i-th node in the array, and n represent the total number of nodes in the two-dimensional persistent array.

[0022] A represents the set of directed edges in the initial weighted directed graph, a ij ∈A, and i,j∈[1,n],i≠j,a ij In a weighted directed graph, node v i To node v j Directed edges for information transmission;

[0023] W represents the set of arc weights in the initial weighted directed graph. And i,j∈[1,n],i≠j, Represents a directed edge ij The arc weight;

[0024] The first persistent graph G1 = (V1, A1, W1); where V1 represents the set of nodes in the first persistent graph, A1 represents the set of directed edges in the first persistent graph, and W1 represents the set of arc weights in the first persistent graph.

[0025] The second persistent graph G2 = (V2, A2, W2); where V2 represents the set of nodes in the second persistent graph and V2 = V1, A2 represents the set of directed edges in the second persistent graph and A2 = A1, and W2 represents the set of arc weights in the second persistent graph.

[0026] Optionally, step S5 involves reversing the path to be reversed to obtain an updated third persistent graph, including:

[0027] S501. Reverse the directed edges corresponding to the path to be reversed;

[0028] S502. Replace the path to be reversed with the reversed path to obtain the updated third persistent graph.

[0029] Secondly, the present invention provides a persistent UAV formation communication topology optimization system considering energy consumption balance, the system comprising:

[0030] The first acquisition module is used to execute S1, acquire the initial weighted directed graph D of the three-dimensional persistent formation, and acquire the corresponding first persistent graph G1 based on the initial weighted directed graph D;

[0031] The first construction module is used to execute S2 and construct a second persistent graph G2 based on the first persistent graph G1; wherein the set of nodes and the set of directed edges in the second persistent graph G2 are the same as those in the first persistent graph G1.

[0032] The process of obtaining the arc weight set in the second persistent graph G2 includes: calculating the difference between the reverse arc weight and the forward arc weight of each arc in the first persistent graph G1, and using this difference as the weight of the corresponding arc in the second persistent graph G2 to obtain the arc weight set; wherein, the forward arc weight represents the arc weight of the directed edge in the first persistent graph G1; the reverse arc weight represents the arc weight after reversing the direction of the directed edge in the first persistent graph G1, and is obtained through the initial weighted directed graph D;

[0033] The first inversion module is used to execute S3 to reverse the path of the negative loop in the second persistent graph G2 to obtain a three-dimensional persistent graph with no negative loops, which is used as the third persistent graph G3.

[0034] The path selection module is used to execute S4, select all nodes with in-degree of 0, 1, and 2 from the third persistent graph G3 as the first node set; each node in the first node set is used as the starting point in turn, calculate the shortest path from the current starting point to any other node in the third persistent graph G3, and select and retain the path with the smallest total arc weight and a negative value among all shortest paths as the path to be reversed;

[0035] The second reversal module is used to execute S5, perform a path reversal operation on the path to be reversed, and obtain an updated third persistent graph.

[0036] The first update module is used to execute S6, repeat steps S4-S5 on the updated third persistent graph until the minimum total arc weight in the shortest path is a positive value, and use the corresponding updated third persistent graph as the fourth persistent graph G4.

[0037] The weight change module is used to execute S7, changing the arc weight of each arc in the fourth persistent graph G4 to the arc weight of the same arc in the initial weighted directed graph D, so as to update the arc weight set of the fourth persistent graph G4 and obtain the fifth persistent graph G5 as the three-dimensional optimal persistent graph.

[0038] Optionally, the initial weighted directed graph D = (V, A, W);

[0039] Where V represents the set of nodes in the initial weighted directed graph, v i ∈V, i∈[1,n],v i Let represent the i-th node in the array, and n represent the total number of nodes in the two-dimensional persistent array.

[0040] A represents the set of directed edges in the initial weighted directed graph, a ij ∈A, and i,j∈[1,n],i≠j,a ij In a weighted directed graph, node v i To node v jDirected edges for information transmission;

[0041] W represents the set of arc weights in the initial weighted directed graph. And i,j∈[1,n],i≠j, Represents a directed edge ij The arc weight;

[0042] The first persistent graph G1 = (V1, A1, W1); where V1 represents the set of nodes in the first persistent graph, A1 represents the set of directed edges in the first persistent graph, and W1 represents the set of arc weights in the first persistent graph.

[0043] The second persistent graph G2 = (V2, A2, W2); where V2 represents the set of nodes in the second persistent graph and V2 = V1, A2 represents the set of directed edges in the second persistent graph and A2 = A1, and W2 represents the set of arc weights in the second persistent graph.

[0044] Optionally, the second inversion module includes:

[0045] The direction reversal unit is used to execute S501, reversing the directed edge corresponding to the path to be reversed;

[0046] The first substitution unit is used to execute S502, replacing the path to be reversed with the reversed path to obtain the updated second persistent graph.

[0047] Thirdly, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to implement any of the steps of the UAV persistent formation communication topology optimization method considering energy consumption balance when executing the program stored in the memory.

[0048] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of any of the aforementioned UAV persistent formation communication topology optimization methods considering energy consumption balance.

[0049] (III) Beneficial Effects

[0050] This invention provides a method and system for optimizing persistent UAV formation communication topology considering energy consumption balance. Compared with existing technologies, it has the following advantages:

[0051] This invention calculates the shortest path from each node to any other node in the third persistent graph G3 for all nodes with in-degrees of 0, 1, and 2, selects the shortest path with a negative distance, reverses the shortest path in the original graph, obtains a new weighted directed graph, and continues the above operation until no path that meets the conditions exists, thereby reducing the total weight of the first persistent graph, that is, reducing the communication energy consumption of the three-dimensional persistent formation. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart of a persistent UAV formation communication topology optimization method considering energy consumption balance is provided in an embodiment of the present invention;

[0054] Figure 2 A flowchart illustrating another method for optimizing persistent UAV formation communication topology considering energy consumption balance, provided in an embodiment of the present invention;

[0055] Figure 3 A flowchart of a persistent UAV formation communication topology optimization algorithm considering energy consumption balance is provided for this invention;

[0056] Figure 4a This is a schematic diagram of a three-dimensional directed graph provided in an embodiment of the present invention;

[0057] Figure 4b A schematic diagram of another three-dimensional directed graph provided in an embodiment of the present invention;

[0058] Figure 4c A schematic diagram of another three-dimensional directed graph provided in an embodiment of the present invention;

[0059] Figure 4d A schematic diagram of another three-dimensional directed graph provided in an embodiment of the present invention;

[0060] Figure 4e A schematic diagram of another three-dimensional directed graph provided in an embodiment of the present invention;

[0061] Figure 4f A schematic diagram of another three-dimensional directed graph provided in an embodiment of the present invention;

[0062] Figure 4g A schematic diagram of another three-dimensional directed graph provided in an embodiment of the present invention;

[0063] Figure 5a A D provided as an embodiment of the present invention R The experimental results for the upper limit of the value are shown in the figure;

[0064] Figure 5b A D provided as an embodiment of the present invention R The experimental results for the lower limit of the value are shown in the figure;

[0065] Figure 5c A D provided by the present invention R A graph showing the experimental results of the average value;

[0066] Figure 5d An experimental result graph showing the average computation time of various comparison algorithms provided in an embodiment of the present invention;

[0067] Figure 6 A structural diagram of a persistent UAV formation communication topology optimization system considering energy consumption balance is provided in an embodiment of the present invention;

[0068] Figure 7 This is a structural diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0069] 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 are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0070] This application provides a method and system for optimizing the communication topology of persistent UAV formations by considering energy consumption balance, which reduces the communication energy consumption of three-dimensional persistent formations and reduces the total weight of the first persistent graph, thereby reducing the communication energy consumption of the three-dimensional persistent formations.

[0071] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0072] First, the relevant basic concepts in the embodiments of the present invention will be introduced.

[0073] The three-dimensional formation in this embodiment of the invention can be understood as a multi-agent formation applied in a three-dimensional planar scene. Compared to a single agent, agents in a multi-agent formation can interact with other agents to complete complex tasks. For example, in the field of autonomous driving in vehicle platooning, following vehicles can follow the instructions of the lead vehicle, reducing the energy consumption from environmental perception and route planning, thereby extending the platoon's travel distance.

[0074] In maintaining a cooperative formation, communication links serve as the medium for communication between agents; these communication links are called the communication topology. When a formation of multiple agents executes a pre-defined task, it typically employs overall formation control techniques to ensure that each agent is functioning correctly. Therefore, point-to-point communication is necessary for information transmission between agents. The communication links between agents constitute this point-to-point communication, and the set of all communication links in the entire agent formation is called the multi-agent formation communication topology for formation maintenance.

[0075] In persistent formations, each agent needs to maintain a pre-defined formation through one-way communication. This often leads to uneven energy consumption among the agents, mainly for the following reasons: First, the energy required for receiving and sending communications between agents is not equal; second, the communication distances between different agents are also unequal.

[0076] Therefore, communication topology optimization under asymmetric connectivity is a very effective method for reducing the communication energy consumption of agent formations.

[0077] To address the asymmetric communication weights between two agents, selecting links with relatively smaller weights can further reduce the overall weight. In existing generation methods, some links between nodes inevitably use paths with relatively larger weights in order to generate a persistent graph. In this method, while maintaining the persistence of the graph, the overall weight is reduced by flipping the links with relatively larger weights between two nodes in the original persistent graph.

[0078] See Figure 1 , Figure 1 A flowchart illustrating a persistent UAV formation communication topology optimization method considering energy consumption balance, as provided in an embodiment of the present invention, is shown below. Figure 1 As shown, the method includes:

[0079] S1. Obtain the initial weighted directed graph D of the three-dimensional persistent formation, and obtain the corresponding first persistent graph G1 based on the initial weighted directed graph D;

[0080] S2. Construct a second persistent graph G2 based on the first persistent graph G1; wherein the set of nodes and the set of directed edges in the second persistent graph G2 are the same as those in the first persistent graph G1.

[0081] The process of obtaining the arc weight set in the second persistent graph G2 includes: calculating the difference between the reverse arc weight and the forward arc weight of each arc in the first persistent graph G1, and using this difference as the weight of the corresponding arc in the second persistent graph G2 to obtain the arc weight set; wherein, the forward arc weight represents the arc weight of the directed edge in the first persistent graph G1; the reverse arc weight represents the arc weight after reversing the direction of the directed edge in the first persistent graph G1, and is obtained through the initial weighted directed graph D;

[0082] S3. Perform a path reversal operation on the negative loops in the second persistent graph G2 to obtain a three-dimensional persistent graph with no negative loops, which will be used as the third persistent graph G3.

[0083] S4. Select all nodes with in-degrees of 0, 1, and 2 from the third persistent graph G3 to form the first node set; each node in the first node set is used as a starting point in turn, and the shortest path from the current starting point to any other node in the third persistent graph G3 is calculated. Select and retain the path with the smallest total arc weight and a negative value among all shortest paths as the path to be reversed; S5. Reverse the path to be reversed to obtain the updated third persistent graph.

[0084] S6. Repeat steps S4-S5 on the updated third persistent graph until the minimum total arc weight in the shortest path is positive, and use the corresponding updated third persistent graph as the fourth persistent graph G4.

[0085] S7. Change the arc weight of each arc in the fourth persistent graph G4 to the arc weight of the same arc in the initial weighted directed graph D, so as to update the arc weight set of the fourth persistent graph G4 and obtain the fifth persistent graph G5 as the three-dimensional optimal persistent graph.

[0086] Based on the above processing, this embodiment of the invention calculates the shortest path from each node to any other node in the third persistent graph G3 for all nodes with in-degrees of 0, 1, and 2, selects the shortest path with a negative distance, reverses the path in the original graph, obtains a new weighted directed graph, and continues to perform the above operation until no path that meets the conditions exists, thereby reducing the total weight of the first persistent graph, that is, reducing the communication energy consumption of the three-dimensional persistent formation.

[0087] For step S1, the initial weighted directed graph is D = (V, A, W);

[0088] Where V represents the set of nodes in the initial weighted directed graph, v i ∈V, i∈[1,n],v i Let represent the i-th node in the array, and n represent the total number of nodes in the two-dimensional persistent array.

[0089] A represents the set of directed edges in the initial weighted directed graph, a ij ∈A, and i,j∈[1,n],i≠j,a ij In a weighted directed graph, node v i To node v j Directed edges for information transmission;

[0090] W represents the set of arc weights in the initial weighted directed graph. And i,j∈[1,n],i≠j, Represents a directed edge ij The arc weight. In this embodiment of the invention, the distance between two nodes can be approximated as the arc weight.

[0091] The first persistent graph G1 = (V1, A1, W1); where V1 represents the set of nodes in the first persistent graph, A1 represents the set of directed edges in the first persistent graph, and W1 represents the set of arc weights in the first persistent graph. The first persistent graph represents the optimal three-dimensional persistent graph obtained by processing the initial weighted directed graph D using existing techniques. For example, see the technical solution in the paper "Energy-Balanced Generation of Optimal Three-Dimensional Persistent Formation Communication Topology".

[0092] For step S2, the second persistent graph G2 = (V2, A2, W2); where V2 represents the set of nodes in the second persistent graph and V2 = V1, A2 represents the set of directed edges in the second persistent graph and A2 = A1, and W2 represents the set of arc weights in the second persistent graph.

[0093] In practical operation, the second persistent graph G2 is obtained by initializing the first persistent graph G1. Specifically, the difference between the reverse arc weight and the forward arc weight in the initial weighted directed graph of the first persistent graph G1 is used as the arc weight of the same arc in the second persistent graph G2.

[0094] For example, arc a in the first persistent graph ij Add to the second persistent graph and obtain arc a from the initial weighted directed graph D. ij reverse arc a ji And the corresponding arc weights. Accordingly, in the second persistent graph, arc a ij The arc weight is |a ji |-|a ij |。 Where, |a ji | indicates the reverse arc a ji The arc weight, i.e., arc a ij The reverse arc weights. |a ij | represents arc a ij The arc weights are the positive arc weights in this embodiment of the invention.

[0095] The second persistent graph G2 = (V2, A2, W2); where V2 represents the set of nodes in the second persistent graph and V2 = V1, A2 represents the set of directed edges in the second persistent graph and A2 = A1, and W2 represents the set of arc weights in the second persistent graph.

[0096] Regarding step S3, a negative cycle refers to a cycle in the directed graph with a negative total weight, also known as a negative weight loop or negative weight cycle. In practice, the Bellman-Ford algorithm based on queue optimization can be used to find negative cycles in the first persistent graph G2 and save them. Specifically, a negative cycle can be viewed as a path with its beginning and end connected. This path (i.e., the negative cycle) is reversed in the original directed graph (i.e., the first directed graph in this embodiment) to obtain a new weighted directed graph. This process is continued until no negative cycles exist, resulting in the third persistent graph G3. This provides the foundation for subsequent steps and also reduces the total weight of the original weighted directed graph (i.e., the first persistent graph in this embodiment).

[0097] The path reversal operation involves flipping the direction of a directed edge and obtaining the arc weight of the reversed directed edge based on the initial weighted directed graph.

[0098] In one implementation, all negative loops in the second persistent graph G2 are obtained and treated as a ring set, loop. While the ring set loop is not empty, the following steps are performed iteratively:

[0099] Step a: Perform a path reversal operation on the negative loops in the second persistent graph G2 to obtain the updated second persistent graph;

[0100] Step b: Obtain the loop set corresponding to the updated second persistent graph, and determine whether the loop set is empty. If not, proceed to step a. If yes, use the updated second persistent graph as the third persistent graph G3.

[0101] For step S4, the single-source shortest path algorithm can be used to traverse each node as the starting point, thereby calculating the distance from each node with an in-degree of 0, 1, or 2 to any other node. (The distance is also the sum of the weights of each directed arc on the shortest path).

[0102] In some embodiments, step S5 includes the following:

[0103] S501. Reverse the directed edges corresponding to the path to be reversed;

[0104] S502. Replace the path to be reversed with the reversed path to obtain the updated second persistent graph.

[0105] Furthermore, for a persistent UAV formation communication topology optimization method that considers energy consumption balance, the inventors designed a three-dimensional communication topology optimization algorithm, such as... Figure 2 As shown. For the optimal persistent graph generation process, please refer to... Figure 3 .

[0106] In practical work, the specific processing procedure of the UAV persistent formation communication topology optimization method considering energy consumption balance provided by the embodiments of the present invention is as follows:

[0107] A weighted directed graph G containing 5 nodes is defined (i.e., the initial weighted directed graph D in this embodiment of the invention). Wherein, G = (V, A, W): V = {v1, v2, v3, v4, v5}, A = {a12, a13, a14, a15, a21, a23, a24, a25, a31, a32, a34, a35, a41, a42, a43, a45, a51, a52, a53, a54}, W = {0.53, 0.87, 0.08, 0.27, 0.54, 0.64, 0.29, 0.77, 0.8, 0.08, 0.04, 0.35, 0.22, 0.31, 0.74, 0.68, 0.1, 0.29, 0.4, 0.29}. The initial weighted directed graph D and the weighted directed graph of the persistent formation calculated using the 3DOPG-RA algorithm (i.e., the first persistent graph G1 in this embodiment of the invention) are both used as inputs.

[0108] Where G1 = (V1, A1, W1): V1 = {v1, v2, v3, v4, v5}, A1 = {a34, a32, a14, a51, a24, a52, a35, a12, a45}, W1 = {0.04, 0.08, 0.08, 0.1, 0.29, 0.29, 0.35, 0.53, 0.68}. The total weight of the weighted directed graph G1 is 2.44. For persistent array G1, please refer to [link to documentation]. Figure 4a .

[0109] First, the weighted directed graph (i.e., the first persistent graph in this embodiment) undergoes a weight initialization transformation operation. Based on each directed arc in the weighted directed graph G1, the corresponding forward and reverse arcs in the initial weighted directed graph G are found. The weight of the reverse arc is subtracted from the weight of the forward arc, and the resulting weight is used as the weight of the arc corresponding to G1 in the new weighted directed graph G2. This yields G2 = (V2, A2, W2): V2 = {v1, v2, v3, v4, v5}, A2 = {a34, a32, a14, a51, a24, a52, a35, a12, a45}, W2 = {0.7, 0.56, 0.14, 0.17, 0.02, 0.48, 0.05, 0.01, -0.39}. G2 at this point is as follows: Figure 4b As shown, directed edge a 45 The arc weight of the first directed edge is negative, while the arc weights of the other directed edges are positive.

[0110] Secondly, a negative cycle search operation is performed on the weighted directed graph G2, and a negative cycle v1-v2-v4-v5-v1 is found, denoted as loop, as follows. Figure 4c As shown.

[0111] Perform a negative cycle reversal operation on the weighted directed graph G2, treating the negative cycle as a path with the same starting and ending points. Since the sum of the weights of the edges on the path loop is negative, after reversal, the total weight of the path loop multiplied by the total weight equals the negative of the total weight of the loop. This results in an increase of 0.19 in the total weight of the weighted directed graph G3. At this point, the weighted directed graph G3 is as follows: Figure 4d As shown.

[0112] The negative cycle search operation is performed again on the weighted directed graph G3. Since there are no negative cycles in the weighted directed graph G3 at this time, the negative cycle obtained after this step is empty. Since the negative cycle found is empty, the search then turns to finding a reversible path in the weighted directed graph G3.

[0113] Starting from the first node with an in-degree of 2, v5, we use the Bellman-Ford algorithm with queue optimization to find the shortest path from v5 to all other nodes with a negative distance. At this point, no such path exists. Next, starting from the first node with an in-degree of 1, v1, we search for the shortest path from v1 to all other nodes with a negative distance. The shortest path from v1 to v5 with a negative distance is v1-v5, with a length of -0.17. This path is retained as Path. Finally, starting from the first node with an in-degree of 0, v3, we search for the shortest path from v3 to all other nodes with a negative distance. Again, no such path exists. The path finding operation is now complete. The reversible path Path found at this point is as follows: Figure 4e As shown in v1-v5.

[0114] Reversing the directed path Path in the weighted directed graph G3, since the total weight of Path is negative, the reversed path * total weight is the inverse of the total weight of Path, resulting in an increase of 0.34 in the total weight of the weighted directed graph G4. At this point, the weighted directed graph G4 is as follows: Figure 4f As shown.

[0115] The reversible path search operation is performed again on the weighted directed graph G4 (i.e., the fourth persistent graph in this embodiment of the invention). At this point, no new reversible path can be found in the weighted directed graph G4. Because no reversible path can be found, the directed arc directions of the resulting weighted directed graph G4 remain unchanged, and the arc weights are changed to the weights of the corresponding directed arcs in the original weighted directed graph G, resulting in the weighted directed graph G5 (i.e., the fifth persistent graph in this embodiment of the invention). Figure 4gAs shown. At this point, the total weight of the weighted directed graph G5 is 2.08, which is 0.36 less than the total weight of the input weighted directed graph G1.

[0116] Based on the above processing, this embodiment of the invention calculates the shortest path from each node to any other node in the third persistent graph G3 for all nodes with in-degrees of 0, 1, and 2, selects the shortest path with a negative distance, reverses the path in the original graph, obtains a new weighted directed graph, and continues to perform the above operation until no path that meets the conditions exists, thereby reducing the total weight of the first persistent graph, that is, reducing the communication energy consumption of the three-dimensional persistent formation.

[0117] To verify that the technical solution provided in the embodiments of the present invention has superior solution quality, the following verification experiment was conducted.

[0118] In the randomized experiment, six different numbers of nodes (5, 8, 10, 15, 20, and 25) were tested, with a size of 10... 3 ×10 3 The coordinates of nodes are randomly generated in the three-dimensional space, and the weights of each arc are randomly generated in the interval [0,1]. 100 sets of experiments are conducted, and the computation time and the sum of the calculated weights W(C) for each algorithm are statistically analyzed. The difference ratio D is then calculated using a pre-defined formula. R , used to calculate the percentage difference between the calculation results of each comparison algorithm and the calculation results of the embodiments of the present invention. Wherein, D R The larger the value, the worse the calculation result of the comparison algorithm is compared with the calculation result of the technical solution provided in the embodiment of the present invention.

[0119] The preset calculation formula is as follows: W(C) 对比算法 This indicates 3DOPG-SPPR, W(C) 参照算法 It indicates 3DOPG-RA.

[0120] In this embodiment of the invention, the 3DOPG-RA algorithm (referring to the algorithm in "Luo He, Li Xiaoduo, Wang Guoqiang. Energy-Constrained Three-Dimensional Optimal Persistent Formation Communication Topology Generation [J]. Acta Aeronautica Sinica, 2021, 42(10): 324922") and the 3DOPG-SPPR algorithm (another three-dimensional optimal persistent graph generation algorithm proposed by the inventors to reduce communication energy consumption, and filed on the same day as this invention) are selected as reference algorithms for randomized experimental comparison. Under six different node counts, the upper and lower limits and average values ​​of the DR values ​​corresponding to each comparison algorithm in 100 sets of experiments are as follows: Figure 5a , 5b As shown in 5c.

[0121] Meanwhile, to verify the average computation time of each comparative algorithm in 100 sets of randomized experiments under six different node scenarios, Figure 5d As shown.

[0122] Depend on Figure 6 It can be seen that the average computation time of the 3DOPG-NLSPPR algorithm (i.e., the technical solution provided in this embodiment of the invention) is similar to that of the 3DOPG-SPPR algorithm, and the former is superior to the latter. The unit of computation time is ms. The average computation time of the 3DOPG-NLSPPR algorithm is similar to that of the 3DOPG-RA algorithm. As the number of nodes increases, the difference in average computation time between the two becomes negligible compared to their average computation time, indicating that their time complexities are similar.

[0123] As can be seen from the experimental results, the generation results of the UAV persistent formation communication topology optimization method considering energy consumption balance proposed in this embodiment are on par with the calculation results of each algorithm without the improved method, and in some cases are better than the original calculation results, with better solution quality; at the same time, the improved algorithm has lower time complexity and higher solution efficiency.

[0124] like Figure 6 As shown, this embodiment of the invention also provides a persistent UAV formation communication topology optimization system that considers energy consumption balance. The system includes:

[0125] The first acquisition module 601 is used to execute S1, acquire the initial weighted directed graph D of the three-dimensional persistent formation, and acquire the corresponding first persistent graph G1 based on the initial weighted directed graph D;

[0126] The first construction module 602 is used to execute S2 and construct a second persistent graph G2 based on the first persistent graph G1; wherein the node set and the directed edge set in the second persistent graph G2 are the same as those in the first persistent graph G1.

[0127] The process of obtaining the arc weight set in the second persistent graph G2 includes: calculating the difference between the reverse arc weight and the forward arc weight of each arc in the first persistent graph G1, and using this difference as the weight of the corresponding arc in the second persistent graph G2 to obtain the arc weight set; wherein, the forward arc weight represents the arc weight of the directed edge in the first persistent graph G1; the reverse arc weight represents the arc weight after reversing the direction of the directed edge in the first persistent graph G1, and is obtained through the initial weighted directed graph D;

[0128] The first inversion module 603 is used to execute S3, which reverses the path of the negative loop in the second persistent graph G2 to obtain a three-dimensional persistent graph with no negative loop, which is used as the third persistent graph G3.

[0129] The path selection module 604 is used to execute S4, select all nodes with in-degree of 0, 1, and 2 from the third persistent graph G3 as the first node set; each node in the first node set is used as the starting point in turn, calculate the shortest path from the current starting point to any other node in the third persistent graph G3, and select and retain the path with the smallest total arc weight and a negative value among all shortest paths as the path to be reversed;

[0130] The second reversal module 605 is used to execute S5, perform a path reversal operation on the path to be reversed, and obtain an updated third persistent graph.

[0131] The first update module 606 is used to execute S6, repeat steps S4-S5 on the updated third persistent graph until the minimum total arc weight in the shortest path is positive, and use the corresponding updated third persistent graph as the fourth persistent graph G4.

[0132] The weight change module 607 is used to execute S7, changing the arc weight of each arc in the fourth persistent graph G4 to the arc weight of the same arc in the initial weighted directed graph D, so as to update the arc weight set of the fourth persistent graph G4 and obtain the fifth persistent graph G5 as the three-dimensional optimal persistent graph.

[0133] It is understood that the UAV persistent formation communication topology optimization system considering energy consumption balance provided in the embodiments of the present invention corresponds to the above-mentioned UAV persistent formation communication topology optimization method considering energy consumption balance. The explanation, examples, and beneficial effects of the relevant content can be referred to the corresponding content in the UAV persistent formation communication topology optimization method considering energy consumption balance, and will not be repeated here.

[0134] This invention also provides an electronic device, such as... Figure 7 As shown, it includes a processor 701, a communication interface 702, a memory 703, and a communication bus 704, wherein the processor 701, the communication interface 702, and the memory 703 communicate with each other through the communication bus 704.

[0135] Memory 703 is used to store computer programs;

[0136] When the processor 701 executes the program stored in the memory 703, it implements any of the above-mentioned methods for optimizing persistent UAV formation communication topology that considers energy consumption balance.

[0137] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0138] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0139] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0140] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0141] In another embodiment of the present invention, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the steps of any of the above-described methods for optimizing persistent UAV formation communication topology considering energy consumption balance.

[0142] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the above-described methods for optimizing persistent UAV formation communication topology that considers energy consumption balance.

[0143] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated.

[0144] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0145] In summary, compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0146] In this embodiment of the invention, for all nodes with in-degrees of 0, 1, and 2 in the third persistent graph G3, the shortest path from each node to any other node is calculated, the shortest path with a negative distance is selected, and the path is reversed in the original graph to obtain a new weighted directed graph. The above operation is continued until no path that meets the conditions exists, thereby reducing the total weight of the first persistent graph, that is, reducing the communication energy consumption of the three-dimensional persistent formation.

[0147] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0148] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing persistent UAV formation communication topology considering energy consumption balance, characterized in that, include: S1. Obtain the initial weighted directed graph D of the three-dimensional persistent formation, and obtain the corresponding first persistent graph G1 based on the initial weighted directed graph D; S2. Construct a second persistent graph G2 based on the first persistent graph G1; wherein the set of nodes and the set of directed edges in the second persistent graph G2 are the same as those in the first persistent graph G1. The process of obtaining the arc weight set in the second persistent graph G2 includes: calculating the difference between the reverse arc weight and the forward arc weight of each arc in the first persistent graph G1, and using this difference as the weight of the corresponding arc in the second persistent graph G2 to obtain the arc weight set; wherein, the forward arc weight represents the arc weight of the directed edge in the first persistent graph G1; the reverse arc weight represents the arc weight after reversing the direction of the directed edge in the first persistent graph G1, and is obtained through the initial weighted directed graph D; S3. Perform a path reversal operation on the negative loops in the second persistent graph G2 to obtain a three-dimensional persistent graph with no negative loops, which will be used as the third persistent graph G3. S4. Select all nodes with in-degree of 0, 1, and 2 from the third persistent graph G3 as the first node set; each node in the first node set is used as the starting point in turn, and the shortest path from the current starting point to any other node in the third persistent graph G3 is calculated. Select and retain the path with the smallest total arc weight and a negative value among all shortest paths as the path to be reversed. S5. Reverse the path to be reversed to obtain the updated third persistent graph; S6. Repeat steps S4-S5 on the updated third persistent graph until the minimum total arc weight in the shortest path is positive, and use the corresponding updated third persistent graph as the fourth persistent graph G4. S7. Change the arc weight of each arc in the fourth persistent graph G4 to the arc weight of the same arc in the initial weighted directed graph D, so as to update the arc weight set of the fourth persistent graph G4 and obtain the fifth persistent graph G5 as the three-dimensional optimal persistent graph.

2. The method for optimizing persistent UAV formation communication topology considering energy consumption balance according to claim 1, characterized in that, The initial weighted directed graph ; in, This represents the set of nodes in the initial weighted directed graph. , , Indicates the first in the formation 1 node This represents the total number of nodes in a two-dimensional persistent array; Let represent the set of directed edges in the initial weighted directed graph. ,and , In a weighted directed graph, the node... To the node Directed edges for information transmission; This represents the set of arc weights in the initial weighted directed graph. ,and , Represents a directed edge The arc weight; First Persistent Graph ;in, Represents the set of nodes in the first persistent graph. Represents the set of directed edges in the first persistent graph. This represents the set of arc weights in the first persistent graph; Second Persistent Graph ;in, Let represent the set of nodes in the second persistent graph, and , Let represent the set of directed edges in the second persistent graph, and , This represents the set of arc weights in the second persistent graph.

3. The method for optimizing persistent UAV formation communication topology considering energy consumption balance according to claim 1, characterized in that, Step S5 involves reversing the path to be reversed to obtain an updated third persistent graph, including: S501. Reverse the directed edges corresponding to the path to be reversed; S502. Replace the path to be reversed with the reversed path to obtain the updated third persistent graph.

4. A persistent UAV formation communication topology optimization system considering energy consumption balance, characterized in that, include: The first acquisition module is used to execute S1, acquire the initial weighted directed graph D of the three-dimensional persistent formation, and acquire the corresponding first persistent graph G1 based on the initial weighted directed graph D; The first construction module is used to execute S2 and construct a second persistent graph G2 based on the first persistent graph G1; wherein the set of nodes and the set of directed edges in the second persistent graph G2 are the same as those in the first persistent graph G1. The process of obtaining the arc weight set in the second persistent graph G2 includes: calculating the difference between the reverse arc weight and the forward arc weight of each arc in the first persistent graph G1, and using this difference as the weight of the corresponding arc in the second persistent graph G2 to obtain the arc weight set; wherein, the forward arc weight represents the arc weight of the directed edge in the first persistent graph G1; the reverse arc weight represents the arc weight after reversing the direction of the directed edge in the first persistent graph G1, and is obtained through the initial weighted directed graph D; The first inversion module is used to execute S3 to perform a path inversion operation on the negative loops in the second persistent graph G2 to obtain a three-dimensional persistent graph with empty negative loops, which serves as the third persistent graph G3. The path selection module is used to execute S4, select all nodes with in-degree of 0, 1, and 2 from the third persistent graph G3 as the first node set; each node in the first node set is used as the starting point in turn, calculate the shortest path from the current starting point to any other node in the third persistent graph G3, and select and retain the path with the smallest total arc weight and a negative value among all shortest paths as the path to be reversed; The second reversal module is used to execute S5, perform a path reversal operation on the path to be reversed, and obtain an updated third persistent graph. The first update module is used to execute S6, repeat steps S4-S5 on the updated third persistent graph until the minimum total arc weight in the shortest path is a positive value, and use the corresponding updated third persistent graph as the fourth persistent graph G4. The weight change module is used to execute S7, changing the arc weight of each arc in the fourth persistent graph G4 to the arc weight of the same arc in the initial weighted directed graph D, so as to update the arc weight set of the fourth persistent graph G4 and obtain the fifth persistent graph G5 as the three-dimensional optimal persistent graph.

5. The UAV persistent formation communication topology optimization system considering energy consumption balance according to claim 4, characterized in that, The initial weighted directed graph ; in, This represents the set of nodes in the initial weighted directed graph. , , Indicates the first in the formation 1 node This represents the total number of nodes in a two-dimensional persistent array; Let represent the set of directed edges in the initial weighted directed graph. ,and , In a weighted directed graph, the node... To the node Directed edges for information transmission; This represents the set of arc weights in the initial weighted directed graph. ,and , Represents a directed edge The arc weight; First Persistent Graph ;in, Represents the set of nodes in the first persistent graph. Represents the set of directed edges in the first persistent graph. This represents the set of arc weights in the first persistent graph; Second Persistent Graph ;in, Let represent the set of nodes in the second persistent graph, and , Let represent the set of directed edges in the second persistent graph, and , This represents the set of arc weights in the second persistent graph.

6. The UAV persistent formation communication topology optimization system considering energy consumption balance according to claim 4, characterized in that, The second inversion module includes: The direction reversal unit is used to execute S501, reversing the directed edge corresponding to the path to be reversed; The first substitution unit is used to execute S502, replacing the path to be reversed with the reversed path to obtain the updated second persistent graph.

7. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor, when executing the program stored in the memory, implements the steps of the UAV persistent formation communication topology optimization method considering energy consumption balance as described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the UAV persistent formation communication topology optimization method considering energy consumption balance as described in any one of claims 1-3.

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