Unmanned cluster combat network global efficiency analysis method and device, medium and product

Abstract

The invention discloses an unmanned cluster combat network global efficiency analysis method and device, a medium and a product, and relates to the technical field of unmanned clusters. The method comprises the following steps: forming a relation matrix based on an intra-layer connection relation and an inter-layer connection relation among nodes of a sensing class, a decision class, an influence class and a target class in an unmanned cluster combat network architecture; determining a path length upper bound based on the number of the sensing type nodes and the number of the decision type nodes; determining the shortest path length of an effective combat closed loop starting from the target class node based on the number of the sensing class nodes and the number of the decision class nodes; determining the number of effective combat closed loops passing through the target class node and corresponding to the shortest path length based on the relation matrix; and under the constraint of the upper bound of the path length, determining the global efficiency of the unmanned cluster combat network based on the shortest path length and the number of effective combat closed loops. According to the method, the global efficiency of the unmanned cluster combat network can be analyzed, and a data basis is provided for improving the overall combat effectiveness of the unmanned cluster.

Description

Technical Field

[0001] The present application relates to the field of unmanned swarm technology, and in particular to a method, device, medium and product for analyzing the global efficiency of an unmanned swarm combat network. Background Art

[0002] Unmanned swarms are large-scale complex systems composed of numerous unmanned intelligent devices, and their internal interaction methods are complex and diverse. Unmanned swarms rely on advanced communication technologies for information exchange and feedback. Through dynamic interaction schemes and collaborative strategies, each unmanned intelligent device can coordinate and cooperate to adapt to the ever-changing external environment, and jointly achieve the established multi-dimensional mission objectives within a limited time and space domain. It has shown broad application potential and significant practical value in many fields such as military and civilian.

[0003] In known research, complex networks are often used to characterize the internal correlation of unmanned clusters. However, unmanned clusters involve unmanned intelligent devices of different types, and the internal communication relationships are orderly and diverse, which makes the unmanned cluster combat network show the characteristics of heterogeneous nodes and directed links. Traditional network efficiency can quantify the speed of information transmission between nodes in the network, and information timeliness is the basis for the unmanned units in the cluster to maximize their efficiency and synchronously perform combat tasks. Therefore, network efficiency is a key indicator for measuring cluster performance. In the unmanned cluster combat network, the level of network efficiency can directly reflect the ability of information coordination and rapid response between unmanned units. A highly efficient combat network can ensure that instructions and information are quickly transmitted between unmanned units, thereby improving the overall combat effectiveness of the unmanned cluster.

[0004] However, there is no known way to analyze the global efficiency of unmanned swarm combat networks. Summary of the invention

[0005] The purpose of this application is to provide a method, equipment, medium and product for analyzing the global efficiency of an unmanned swarm combat network, which can analyze the global efficiency of an unmanned swarm combat network and provide a data basis for improving the overall combat effectiveness of the unmanned swarm.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for analyzing the global efficiency of an unmanned swarm combat network, comprising:

[0008] In the unmanned swarm combat network architecture, the friendly nodes are classified according to the functions of each unit and the role played by each unit in the actual combat environment, and perception nodes, decision-making nodes and influence nodes are obtained;

[0009] The target tasks of the unmanned swarm combat network are considered as target class nodes;

[0010] Based on the intra-layer connection relationship and inter-layer connection relationship among the perception nodes, the decision nodes, the influence nodes and the target nodes in the unmanned swarm combat network architecture, a relationship matrix is ​​formed;

[0011] Determine an upper limit of the path length based on the number of the perception nodes and the number of the decision nodes;

[0012] Determine the shortest path length of an effective combat closed loop starting from the target node based on the number of the perception nodes and the number of the decision nodes; an effective combat closed loop refers to the use of nodes in the unmanned swarm combat network to complete the target task through observation, adjustment, decision-making and action; the number of nodes involved in the effective combat closed loop is used as the path length of the effective combat closed loop;

[0013] Determine the number of effective combat closed loops passing through the target class node corresponding to the shortest path length based on the relationship matrix;

[0014] Under the constraint of the upper limit of the path length, the global efficiency of the unmanned swarm combat network is determined based on the shortest path length and the number of effective combat closed loops.

[0015] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for analyzing the global efficiency of an unmanned swarm combat network.

[0016] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for analyzing the global efficiency of an unmanned swarm combat network.

[0017] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for analyzing the global efficiency of an unmanned swarm combat network.

[0018] According to the specific embodiments provided in this application, this application has the following technical effects:

[0019] The present application provides a method, device, equipment, medium and product for analyzing the global efficiency of an unmanned cluster combat network. By determining the upper bound of the path length of the shortest path length of an effective combat closed loop starting from a target class node, a solid theoretical foundation can be laid for determining the global efficiency of the unmanned cluster combat network. On this basis, the global efficiency of the unmanned cluster combat network can be determined based on the shortest path length and the number of effective combat closed loops, so as to realize the analysis of the global efficiency of the unmanned cluster combat network, and then provide a data basis for improving the overall combat effectiveness of the unmanned cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is an application environment diagram of a method for analyzing the global efficiency of an unmanned swarm combat network in one embodiment of the present application;

[0022] Figure 2 A flowchart of a method for analyzing the global efficiency of an unmanned swarm combat network provided in one embodiment of the present application;

[0023] Figure 3 A schematic diagram showing the results of the unmanned swarm combat network global efficiency (referred to as the combat network global efficiency) and the traditional network global efficiency changing over time under the random attack mode provided in one embodiment of the present application;

[0024] Figure 4 A schematic diagram showing the result of the number of combat rings changing over time in a random attack mode provided in an embodiment of the present application;

[0025] Figure 5 A schematic diagram showing the change in the number of connected node pairs over time in a random attack mode provided in an embodiment of the present application;

[0026] Figure 6 A schematic diagram showing the results of the unmanned swarm combat network global efficiency (referred to as the combat network global efficiency) and the traditional network global efficiency changing over time in the maximum attack mode provided in one embodiment of the present application;

[0027] Figure 7 A schematic diagram showing the variation of the number of combat rings over time in the maximum attack mode provided in an embodiment of the present application;

[0028] Figure 8A schematic diagram showing the change in the number of connected node pairs over time in a maximum attack mode provided in an embodiment of the present application;

[0029] Fig. 9 A schematic diagram showing the results of the unmanned cluster combat network global efficiency (referred to as the combat network global efficiency) and the traditional network global efficiency changing over time under the maximum betweenness attack mode provided in one embodiment of the present application;

[0030] Fig.10 A schematic diagram showing the result of the number of combat rings changing over time in the maximum betweenness attack mode provided in one embodiment of the present application;

[0031] Fig.11 A schematic diagram showing the change in the number of connected node pairs over time in a maximum betweenness attack mode provided in an embodiment of the present application;

[0032] Fig.12 A schematic diagram showing the result of the average combat network efficiency determined by the present application changing over time in the TA mode provided in one embodiment of the present application;

[0033] Fig.13 A schematic diagram showing the results of the average combat network efficiency determined by the present application changing over time in the SA mode provided in one embodiment of the present application;

[0034] Fig.14 A schematic diagram showing the result of the average combat network efficiency determined by the present application changing over time under the DA mode provided in one embodiment of the present application;

[0035] Fig.15 A schematic diagram showing the result of the average combat network efficiency determined by the present application changing over time in the WA mode provided in one embodiment of the present application;

[0036] Fig.16 A schematic diagram showing the result of the average traditional network efficiency determined by the present application changing over time in the TA mode provided in an embodiment of the present application;

[0037] Fig.17 A schematic diagram showing the result of the average traditional network efficiency determined by the present application changing over time in the SA mode provided in one embodiment of the present application;

[0038] Fig.18 A schematic diagram showing the result of the average traditional network efficiency determined by the present application changing over time in the DA mode provided in one embodiment of the present application;

[0039] Fig.19 A schematic diagram showing the result of the average traditional network efficiency determined by the present application changing over time in the WA mode provided in an embodiment of the present application;

[0040] Fig. 20A schematic diagram showing the result of the average number of combat rings determined by the present application changing over time in the TA mode provided in one embodiment of the present application;

[0041] Fig.21 A schematic diagram of the result of the average number of combat rings determined by the present application changing over time in the SA mode provided in one embodiment of the present application;

[0042] Fig. 22 A schematic diagram showing the result of the average number of combat rings determined by the present application changing over time under the DA mode provided in one embodiment of the present application;

[0043] Fig.23 A schematic diagram showing the result of the average number of combat rings determined by the present application changing over time in the WA mode provided in an embodiment of the present application;

[0044] Fig.24 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0047] The technical solutions provided by this application include:

[0048] (1) Definition of the global efficiency of combat networks in heterogeneous directed networks.

[0049] Unmanned cluster networks are different from ordinary homogeneous networks or single-layer networks. They are interwoven with multiple heterogeneous nodes and a large number of heterogeneous relationships. This application relies on the interactive network of unmanned clusters and regards each unit in the cluster as a single node in the network. In the unmanned cluster combat network architecture, according to the functions of each unit and the role it plays in the actual environment, the own nodes can be divided into perception nodes S, decision-making nodes D, and influence nodes W, and the target task is set as the target node T.

[0050] In the framework of complex network theory, the network efficiency between two nodes is defined as the inverse of the shortest distance between the two points, reflecting the speed of information transmission between nodes. The global efficiency of the entire network is defined as the mean of the efficiency between all pairs of nodes on the network, providing a comprehensive evaluation of the overall performance of the network. However, in the specific heterogeneous directed network of the unmanned swarm combat network, the traditional network efficiency indicators need to be adjusted accordingly to adapt to its inherent characteristics. Considering that the node association pattern of the unmanned swarm combat network is fixed in a limited scope, this application proposes the definition of the combat network efficiency of the unmanned swarm combat network and the global efficiency of the combat network based on the OODA loop theory and the unmanned swarm combat process. Among them, OODA is the abbreviation of observation (Oberve), adjustment (Orient), decision (Decide) and action (Act).

[0051] Definition 1: Operation network efficiency refers to the efficiency of an effective operation closed loop of a target class node in the operation network, that is, the reciprocal of the shortest path distance from a target class node to form an effective operation closed loop in the topological structure of the operation network, as shown in the following formula (1):

[0052]

[0053] in, is the number of target class nodes n in the combat network at time t T To form an effective closed-loop network efficiency, It is the shortest path length of the node OODA loop in the combat network at time t.

[0054] The combat network efficiency reflects the transmission efficiency of the network when achieving a closed loop between target nodes, and provides a direct assessment of the transmission and response capabilities of the unmanned swarm combat network. For a typical unmanned swarm effective combat network, that is, the average value of the maximum efficiency of the combat network to form an effective combat closed loop (T→S→D→W→T) between target nodes, perception nodes, decision nodes, influence nodes, and target nodes. In a general combat network under the conditions of "resource sharing and information fusion", any t n Indirectly, all nodes need to pass through the perception class s i , decision node d j , influence class node w m Three types of nodes, therefore, in a conventional effective combat network A general unmanned swarm combat network includes seven types of OODA loops, namely, the typical OODA loop, the OODA loop with information sharing, the OODA loop with collaborative decision-making, the OODA loop with information sharing and collaborative decision-making, the OODA loop with information feedback, the OODA loop with information sharing and feedback, and the OODA loop with collaborative decision-making and information feedback.

[0055] Definition 2: Operation network global efficiency is the average efficiency of the effective operation closed loop formed by all target nodes in the cluster operation network, that is, the average value of the maximum efficiency of the operation network starting from the target node and returning to the target node through the operation network to form an effective operation closed loop. The global real-time efficiency of the cluster operation network can be expressed as:

[0056]

[0057] Among them, N T is the total number of target nodes T in the combat network, and N is the total number of effective combat closed loops of all target nodes in the combat network.

[0058] In the unmanned swarm combat network, an effective combat closed loop for a target node represents a complete process from mission initiation to target achievement. The global efficiency of the combat network quantifies the efficiency of the unmanned swarm combat network in achieving the closed loop of each target node as a whole, reflecting the global performance and collaborative combat capability of the unmanned swarm combat network.

[0059] (2) Calculation of the global efficiency of combat networks in heterogeneous directed networks.

[0060] According to the definition of the global efficiency of the combat network given in the above step (1), the matrix is ​​used to represent the intra-layer connection relationship and inter-layer connection relationship of the cluster combat network detection layer, decision layer, attack layer and target layer. Based on this, the global efficiency of the combat network is calculated.

[0061] Global Efficiency of Operational Network on The total number of effective combat closed loops of all target nodes in the combat network N and the number of nodes in the combat network about node t n The shortest path length of a valid OODA loop According to the definition of combat network In a directed network such as a swarm combat network, when the number of nodes is fixed, the shortest path length of an effective combat closed loop formed on each target node will be limited to a maximum theoretical value, that is, there is an upper bound. Therefore, the following theorem needs to be constructed:

[0062] When the number of nodes is determined, the shortest path length of the effective combat closed loop starting from the target node in the cluster combat network has an upper bound M, and:

[0063] M=2+N S +N D (3)

[0064] Among them, N S represents the number of perception nodes S, N D Represents the number of decision nodes D.

[0065] The proof process of the above theorem is as follows: According to the combat network node association pattern, observing the structure of the OODA loop, it is not difficult to find that any valid OODA loop contains and only contains one target node T and one influence node W. In other words, there is only one edge of each of the three types of T→S, D→W, and W→T in the valid OODA loop. Therefore, the length of the shortest path of an effective combat closed loop is determined by the number of detection nodes S and the number of decision nodes D in the closed loop. At this time, if there is an upper bound of the valid OODA loop, it should contain all the detection nodes S and decision nodes D. Assume that the OODA loop is in, Represents 1 to N S +N D The shortest path length of this effective combat closed loop is If you want to build a longer OODA loop, you need to select two nodes from the detection node S and the decision node D (let’s set them as and )Add an edge However, due to the properties of the shortest path, M The effective OODA loop shortest path length of the outgoing path remains unchanged.

[0066] The existence of the above theorem reveals the following key characteristics of unmanned swarm combat networks from the two dimensions of application and calculation:

[0067] First, the above theorem means that in the actual application of unmanned swarm combat networks, no matter how the scale of the network expands, the effective information transmission in unmanned swarm combat will eventually reach a clear end point, thus eliminating the possibility of infinite information loops and ensuring that combat commands and information can be transmitted efficiently and orderly in the ever-changing battlefield environment.

[0068] Second, the above theorem is of great significance in calculating the efficiency of unmanned swarm combat networks. The problem of the rapid increase in computational complexity of N when it tends to infinity prevents the high complexity of link analysis and lays a theoretical foundation for building a global efficiency algorithm for unmanned swarm combat networks.

[0069] To calculate the total number N of effective combat closed loops of target nodes, let the number of closed loops with the shortest path length l of effective combat closed loops of target node T be Then the calculation formula of N is:

[0070]

[0071] In the actual application process, based on the above-mentioned definitions and determination methods, the conception and principle of the unmanned swarm combat network global efficiency analysis method provided by this application are as follows:

[0072] Firstly, based on the OODA loop theory and cluster combat process, a definition of the efficiency of the unmanned cluster combat network and the global efficiency of the combat network under the framework of complex network theory is proposed. Then, the boundedness theorem of the shortest path length of the effective combat closed loop starting from the target class node in the unmanned cluster combat network is proved, which lays the theoretical foundation for the calculation method of the global efficiency of the unmanned cluster combat network.

[0073] Based on this, the embodiment of the present application provides a method for analyzing the global efficiency of an unmanned cluster combat network. The method can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the unmanned cluster combat network architecture to the server 104. After the server 104 receives the unmanned cluster combat network architecture, for the unmanned cluster combat network architecture, the server 104 classifies its own nodes according to the functions of each unit and the role played by each unit in the actual combat environment, and obtains perception nodes, decision nodes and influence nodes. The target task of the unmanned cluster combat network is considered as a target node. Based on the intra-layer connection relationship and inter-layer connection relationship between the perception nodes, decision nodes, influence nodes and target nodes in the unmanned cluster combat network architecture, a relationship matrix is ​​formed. The upper limit of the path length is determined based on the number of perception nodes and the number of decision nodes. The shortest path length of the effective combat closed loop starting from the target node is determined based on the number of perception nodes and the number of decision nodes. The number of effective combat closed loops passing through the target node corresponding to the shortest path length is determined based on the relationship matrix. Under the constraint of the upper limit of the path length, the global efficiency of the unmanned cluster combat network is determined based on the shortest path length and the number of effective combat closed loops. The server 104 can feed back the obtained global efficiency analysis results of the unmanned cluster combat network to the terminal 102. In addition, in some embodiments, the global efficiency analysis method of the unmanned cluster combat network can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform the global efficiency analysis of the unmanned cluster combat network for the unmanned cluster combat network architecture, or the server 104 can obtain the unmanned cluster combat network architecture from the data storage system and perform the global efficiency analysis of the unmanned cluster combat network for the unmanned cluster combat network architecture.

[0074] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0075] In an exemplary embodiment, Figure 2 As shown, a method for analyzing the global efficiency of an unmanned swarm combat network is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1The server 104 in the example is used for explanation, and the steps include the following steps 200 to 206.

[0076] in:

[0077] Step 200: In the unmanned swarm combat network architecture, the own nodes are classified according to the functions of each unit and the role played by each unit in the actual combat environment to obtain perception nodes, decision nodes and influence nodes.

[0078] Step 201: Consider the target task of the unmanned swarm combat network as a target class node.

[0079] Step 202: Based on the intra-layer connection relationship and inter-layer connection relationship among the perception nodes, decision nodes, influence nodes and target nodes in the unmanned swarm combat network architecture, a relationship matrix is ​​formed.

[0080] Step 203: Determine the upper limit of the path length based on the number of perception nodes and the number of decision nodes.

[0081] Step 204: Determine the shortest path length of an effective combat closed loop starting from the target node based on the number of perception nodes and the number of decision nodes. An effective combat closed loop refers to the use of nodes in an unmanned swarm combat network to complete a target task through observation, adjustment, decision-making, and action. The number of nodes involved in the effective combat closed loop is used as the path length of the effective combat closed loop.

[0082] Step 205: Determine the number of effective combat closed loops passing through the target class node corresponding to the shortest path length based on the relationship matrix.

[0083] Step 206: Under the constraint of the upper limit of the path length, determine the global efficiency of the unmanned swarm combat network based on the shortest path length and the number of effective combat closed loops.

[0084] Implementing the above steps 200 to 206 can analyze the global efficiency of the unmanned swarm combat network to provide a data basis for improving the overall combat effectiveness of the unmanned swarm.

[0085] In another exemplary embodiment of the present application, the implementation process of step 205 can be described as follows:

[0086] (11) Based on the relationship matrix, the effective combat closed loop length starting from the target class node is obtained as the matrix eigenvalue of the shortest path length.

[0087] (12) Determine the number of effective combat closed loops based on the matrix eigenvalues.

[0088] In another exemplary embodiment of the present application, the method for determining the global efficiency of the unmanned swarm combat network provided by the present application may also be:

[0089] (21) Based on the number of effective combat closed loops, the total number of all effective combat closed loops in the unmanned swarm combat network is determined under the constraint of the upper limit of the path length.

[0090] (22) The reciprocal of the shortest path length of the effective combat closed loop starting from the target class node is taken as the efficiency of the effective combat closed loop of the corresponding target class node.

[0091] (23) Based on the total number of target nodes, the efficiency of the effective combat closed loop of each target node is weighted and summed to obtain the initial efficiency.

[0092] (24) The ratio of the initial efficiency to the total number of all effective combat closed loops in the unmanned swarm combat network is taken as the global efficiency of the unmanned swarm combat network.

[0093] In the actual application process, the implementation process of the above steps (21) to (24) can be: in the unmanned swarm combat network, the effective combat closed loop length starting from the target class node is the shortest path length, or the number of nodes passing is the matrix of the shortest path length l is set as A l , matrix A l The diagonal elements of Indicates passing through node t n The number of combat closed loops with a path length of l.

[0094] When l = 4, A 4 =A TS A SD A DW A WT When l = 5, ...and so on.

[0095] In summary, according to the physical meaning of the product of the correlation matrix:

[0096]

[0097] in, Represents the matrix A l The tth n eigenvalues, n l represents the number of effective combat closed loops. Substituting into formula (4), we can get:

[0098]

[0099] Substituting into formula (2), the global efficiency of the unmanned swarm combat network can be obtained as:

[0100]

[0101] In the formula, NE on(t) represents the global efficiency of the unmanned swarm combat network at time t.

[0102] In another exemplary embodiment of the present application, by establishing a 105-node unmanned cluster network including 20 target-type nodes, 45 perception-type nodes, 10 decision-type nodes and 30 influence-type nodes as an example, the global efficiency of the combat network is compared with the global efficiency of the traditional network under a variety of different simulation scenarios to verify the effectiveness of the resilience assessment model based on the global efficiency of the combat network.

[0103] Analysis 1: Figures 3 to 5 The global efficiency of the combat network under random attack mode, the global efficiency of the traditional network, the number of effective combat rings in the combat network, and the number of connected node pairs change over time. Figures 3 to 5 It can be seen that in the random attack mode, due to the redundancy and synergy of cluster combat network resources, the global efficiency of the combat network remains stable in the early stage. However, as more and more nodes fail, the global efficiency of the combat network begins to slowly decline. When the number of failed nodes exceeds a certain threshold, the number of effective OODA loops in the cluster combat network decreases sharply, and the global efficiency of the combat network begins to rapidly decline to 0. In contrast, the global efficiency of the traditional network shows a trend of first rising and then falling during the initial node failure process. This phenomenon is because there are many connected node pairs with long shortest paths in the multi-layer directed network. As the nodes in the combat network gradually fail, the number of connected node pairs in the network decreases rapidly, the network structure is gradually streamlined, and the efficiency of information transmission is improved. Finally, as the number of connected node pairs decreases to 0, the global efficiency of the traditional network also rapidly decreases to 0.

[0104] Analysis 2: Figures 6 to 8 The global efficiency of the combat network under the maximum attack mode, the global efficiency of the traditional network, the number of effective combat rings in the combat network, and the number of connected node pairs change over time. Figures 6 to 8It can be seen that the trend of the global efficiency change of the combat network in the maximum attack mode is similar to that of the random failure scenario. Although it remains in the initial stable state for a longer time, this phenomenon is because the number of effective OODA loops remains stable in the initial stage of maximum failure. However, the global efficiency of the traditional network slowly decreases in the early stage of node failure, drops to the initial low point and then gradually rises, and then rapidly decreases after the number of connected node pairs in the network decreases to 0. This phenomenon is because in the early stage of node failure, first, the number of connected node pairs in the network decreases more slowly than the number of connected node pairs in the random failure mode, the number is relatively more stable, and the change in the network structure is not large. Second, the node with the largest degree in the combat network fails, and the number of connected node pairs with shorter shortest paths decreases, resulting in a decrease in network efficiency. As the failure process proceeds, the rate of decrease in the number of connected node pairs in the network gradually increases, the combat network gradually simplifies, and the global efficiency of the traditional network begins to rise. Finally, as the number of connected node pairs decreases to 0, the global efficiency of the traditional network also rapidly decreases to 0.

[0105] Analysis 3: Figures 9 to 11 The global efficiency of the combat network in the maximum betweenness attack mode, the global efficiency of the traditional network, the number of effective combat rings in the combat network, and the number of connected node pairs change over time. Figures 9 to 11 It can be seen that the change trend of the global efficiency of the combat network in the maximum betweenness attack mode is similar to the maximum degree failure scenario, and it begins to decline with the number of effective OODA loops. Compared with the maximum degree failure scenario, the global efficiency of the traditional network has a less obvious decline trend in the early stage of node failure in the maximum betweenness attack mode, and it almost remains stable, and then still shows a trend of first rising and then falling.

[0106] Analysis 4: Figures 12 to 23 The global efficiency of the combat network with a specified type of node attack mode, the global efficiency of the traditional network, the number of effective combat rings in the combat network, and the number of connected node pairs change over time. Figures 12 to 23In the scenario of node failure of the specified type, the corresponding node failure modes are set according to the node types of the unmanned cluster network: target node failure (TA) mode, perception node failure (SA) mode, decision node failure (DA) mode, and influence node failure (WA) mode. Among them, the TA mode means that the enemy quickly changes and hides the relevant activities after obtaining relevant information from us, making the target lose its strategic value. In the attack mode of the specified type of nodes, the failure of target nodes and influence nodes will not affect the global efficiency of the combat network before the OODA loop disappears in the cluster network, and the detection nodes and decision nodes will only have a slight impact on the global efficiency of the combat network before the OODA loop disappears in the cluster network. However, with the failure of target nodes and influence nodes, the global efficiency of the traditional network gradually increases. With the disappearance of detection nodes and decision nodes, the global efficiency of the traditional network first gradually decreases. When the number of detection nodes and decision nodes is lower than a certain threshold, the global efficiency of the traditional network increases rapidly. With the failure of target nodes and influence nodes, the number of effective OODA loops decreases linearly. As detection nodes and decision nodes disappear, the number of effective OODA loops remains stable in the early stage, and then gradually decreases. Therefore, in order to maintain the global efficiency of the unmanned swarm combat network, more resources should be invested in maintaining detection nodes and decision nodes.

[0107] In various attack modes, the global efficiency of the unmanned swarm combat network should generally decrease over time. However, the case results show that the global efficiency of the traditional network shows a trend of first increasing and then decreasing, which is difficult to reflect the changes in the global efficiency of the unmanned swarm combat network. In contrast, the global efficiency of the combat network can better reflect the decline in the global efficiency of the unmanned swarm combat network, further confirming the effectiveness and superiority of the global efficiency of the combat network in characterizing the global efficiency of the unmanned swarm combat network.

[0108] This application provides a method for calculating the global efficiency of an unmanned swarm combat network based on the boundedness theorem of the shortest path length of an effective combat closed loop in an unmanned swarm combat network. By simulating the global efficiency of the combat network under a variety of different attack scenarios and comparing it with the global efficiency changes of the traditional network, the effectiveness and excellence of the global efficiency of the unmanned swarm combat network are analyzed and verified.

[0109] In summary, this application is based on the OODA loop theory and the unmanned cluster combat process, and proposes the definition of the efficiency of the unmanned cluster combat network and its global efficiency within the framework of complex network theory. Subsequently, this application proves the boundedness theorem of the shortest path length of the effective combat closed loop starting from the target class node in the unmanned cluster combat network, laying a solid theoretical foundation for the global efficiency calculation method of the unmanned cluster combat network. And on this basis, this application further proposes a method for calculating the global efficiency of the unmanned cluster combat network. Finally, through case analysis, the difference between the global efficiency improvement scheme of the unmanned cluster combat network and the efficiency of the traditional network is compared. It is found that under various attack modes, the global efficiency of the traditional network shows a trend of first rising and then falling, while the global efficiency of the combat network can better reflect the decline in the global efficiency of the unmanned cluster combat network, further confirming the effectiveness and superiority of the global efficiency of the combat network in characterizing the global efficiency of the unmanned cluster combat network. This application provides an innovative analysis method based on the global efficiency of the combat network for the unmanned cluster combat network, revealing the important role of the combat network efficiency in the unmanned cluster combat network.

[0110] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.24 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store global efficiency analysis data of unmanned cluster combat networks. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for global efficiency analysis of unmanned cluster combat networks is implemented.

[0111] Those skilled in the art will understand that Fig.24The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0112] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0113] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0114] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0115] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0116] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0117] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for analyzing the global efficiency of an unmanned swarm combat network, characterized in that: The unmanned swarm combat network global efficiency analysis method comprises: In the unmanned swarm combat network architecture, the friendly nodes are classified according to the functions of each unit and the role played by each unit in the actual combat environment, and perception nodes, decision-making nodes and influence nodes are obtained; The target tasks of the unmanned swarm combat network are considered as target class nodes; Based on the intra-layer connection relationship and inter-layer connection relationship among the perception nodes, the decision nodes, the influence nodes and the target nodes in the unmanned swarm combat network architecture, a relationship matrix is ​​formed; Determine an upper bound on the path length based on the number of the perception nodes and the number of the decision nodes; Determine the shortest path length of an effective combat closed loop starting from the target node based on the number of the perception nodes and the number of the decision nodes; an effective combat closed loop refers to the use of nodes in the unmanned swarm combat network to complete the target task through observation, adjustment, decision-making and action; the number of nodes involved in the effective combat closed loop is used as the path length of the effective combat closed loop; Determine the number of effective combat closed loops passing through the target class node corresponding to the shortest path length based on the relationship matrix; Under the constraint of the upper limit of the path length, the global efficiency of the unmanned swarm combat network is determined based on the shortest path length and the number of effective combat closed loops.

2. The method for analyzing the global efficiency of unmanned swarm combat network according to claim 1 is characterized in that: Based on the number of the perception nodes and the number of the decision nodes, the formula M=2+N is used. S +N D Determine the upper bound of the path length; Among them, M represents the upper limit of the path length, N S Represents the number of perception nodes, N D Indicates the number of decision nodes.

3. The method for analyzing the global efficiency of unmanned swarm combat network according to claim 1 is characterized in that: Determining the number of effective combat closed loops passing through the target node corresponding to the shortest path length based on the relationship matrix includes: Based on the relationship matrix, the effective combat closed loop length from the target class node is obtained as the matrix eigenvalue of the shortest path length; The number of effective combat closed loops is determined based on the matrix eigenvalues.

4. The method for analyzing the global efficiency of unmanned swarm combat network according to claim 1, characterized in that: The formula for determining the global efficiency of the unmanned swarm combat network is expressed as: In the formula, NE on (t) represents the global efficiency of the unmanned swarm combat network at time t, M represents the upper limit of the path length, l represents the shortest path length, and n l Indicates the number of effective combat closed loops.

5. The method for analyzing the global efficiency of unmanned swarm combat network according to claim 1, characterized in that: The unmanned swarm combat network global efficiency analysis method further includes: Based on the number of effective combat closed loops, the total number of all effective combat closed loops in the unmanned swarm combat network is determined under the constraint of the upper limit of the path length.

6. The method for analyzing the global efficiency of unmanned swarm combat network according to claim 5 is characterized in that: The unmanned swarm combat network global efficiency analysis method further includes: The reciprocal of the shortest path length of the effective combat closed loop starting from the target class node is used as the efficiency of the effective combat closed loop of the corresponding target class node; Based on the total number of target nodes, the efficiency of the effective combat closed loop of each target node is weighted and summed to obtain the initial efficiency; The ratio of the initial efficiency to the total number of all effective combat closed loops in the unmanned swarm combat network is taken as the global efficiency of the unmanned swarm combat network.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the global efficiency analysis method for an unmanned swarm combat network as claimed in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for analyzing the global efficiency of an unmanned swarm combat network described in any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for analyzing the global efficiency of an unmanned swarm combat network described in any one of claims 1 to 6 is implemented.

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