Network construction and destroy-resistant reconstruction method for high-dynamic intelligent ammunition cooperative combat

Abstract

The invention discloses a network construction and destroy-resistant reconstruction method for high-dynamic intelligent ammunition cooperative combat, and the method comprises the steps: S1, distributing all nodes in a high-dynamic intelligent ammunition network to different node regions according to a designed division rule, and dividing the nodes into different clusters; s2, selecting a high-dynamic intelligent ammunition node best matched with an election requirement as a cluster head in each divided cluster node through a designed cluster head election algorithm; s3, generating a network topology according to the divided partitions and the selected cluster head; and S4, dynamically updating and maintaining the cluster structure. According to the method, efficient network topology construction is realized in the intelligent ammunition ad hoc network through an improved clustering strategy, and network reconstruction and recovery can be quickly and stably performed when encountering enemy interference or node failure, so that the combat effectiveness in cooperative combat is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication network topology, in particular to a network construction and anti-destroying reconstruction method for high-dynamic intelligent ammunition cooperative combat. BACKGROUND

[0002] As a new type of efficient weapon system, high-dynamic intelligent ammunition is increasingly applied to complex battlefield environments. Due to its strong autonomous decision-making ability, high-precision strike and strong adaptability, intelligent ammunition can achieve accurate target strike in high-dynamic, high-speed and variable battlefield environments, greatly improving combat effectiveness. However, in the face of today's complex combat requirements, a single intelligent ammunition often cannot complete the task independently, especially in the environment of multiple combat platforms and weapon systems operating in parallel. How to efficiently and stably coordinate the cooperation between these platforms and systems is the key to improving combat effectiveness.

[0003] In cooperative combat, high-dynamic intelligent ammunition ad hoc networks have the characteristics of strong node mobility and dynamic changes in network topology. In order to maximize the cluster advantage, the number of nodes in intelligent ammunition ad hoc networks is usually large, and the distribution range is wide, and the network scale is large. In real battlefield environment, intelligent ammunition ad hoc network also faces the threat of enemy interference attack. Such interference attack usually involves multiple nodes or links, which may cause changes in network topology, thereby reducing the stability and anti-attack ability of the network.

[0004] In order to solve the above problems, clustering strategy is considered as an effective method to improve network performance. By dividing the nodes in the network into several clusters, each cluster can be regarded as a relatively stable small sub-network. Compared with flat structure, the clustering network structure can reduce the influence of local topology changes on the whole network. In the process of routing calculation and generation, only part of the nodes need to participate, effectively reducing the routing and control overhead. In addition, the clustering structure can realize effective control and management of the network, and improve the stability of the network. However, the existing clustering method still has certain limitations, especially in dealing with external interference attacks, the anti-interference and network reconstruction problems in high-dynamic environment have not been fully considered.

[0005] Therefore, a network construction and anti-destroying reconstruction method for high-dynamic intelligent ammunition cooperative combat is provided. SUMMARY

[0006] The purpose of the present application is to overcome the existing defects and provide a network construction and anti-destroying reconstruction method for high-dynamic intelligent ammunition cooperative combat. Through the improved clustering strategy, efficient network topology construction is realized in intelligent ammunition ad hoc network, and when encountering enemy interference or node failure, network reconstruction and recovery can be quickly and stably carried out, thereby improving the combat effectiveness in cooperative combat.

[0007] The technical scheme for achieving the above object is: A network construction and anti-destroying reconstruction method for high-dynamic intelligent ammunition cooperative combat, comprising: Step S1, all nodes in the high-dynamic intelligent ammunition network are distributed into different node areas according to the designed distribution rules, and then divided into different clusters; Step S2, the cluster head election algorithm is designed to select the high-dynamic intelligent ammunition node that best matches the election requirements as the cluster head in each divided cluster node; Step S3, the network topology is generated according to the divided partitions and selected cluster heads; Step S4, dynamic update and maintenance of cluster structure.

[0008] Preferably, in step S1, the K-means++ algorithm (an algorithm for selecting better initial clustering centers) is used to pre-cluster the high-dynamic intelligent ammunition network, specifically including: Step S11, a node in the network is randomly selected as a seed node; Step S12, the Euclidean distance between other nodes and the seed node is calculated; Step S13, a node with a larger distance from the seed node is randomly selected as a new seed node; Step S14, repeat the above steps until K seed nodes are selected; Step S15, the Euclidean distance between other nodes in the network and all seed nodes is calculated, and the node with the closest distance is added to the cluster, completing the division of this round of cluster, and the network is further divided into different clusters; Wherein, K represents the number of clusters to be divided, which is pre-set according to the scene needs, or calculated through the throughput balancing principle.

[0009] Preferably, in step S2, the remaining nodes in the cluster are cluster member nodes, and a backup cluster head is selected among the cluster member nodes.

[0010] Preferably, in step S2, the key factors affecting the performance of the high-dynamic intelligent ammunition network are converted into cluster head influence factors suitable for the cluster topology structure, wherein the cluster head influence factors include: residual energy factor, moving similarity factor, average distance factor and external interference factor; Residual energy factor: in the high-dynamic intelligent ammunition cluster network, the cluster head is responsible for data transmission between nodes in the cluster and data forwarding between nodes in different clusters. Assuming that the energy storage capacity of nodes in the ad hoc network is the same, the residual energy factor of the candidate node is normalized as: ; In the formula, The maximum energy of the node, The current residual energy of the node; Mobile similarity factor: in the high dynamic smart ammunition cluster network, the cluster head needs to maintain stable communication link with all cluster members, the relative mobility of nodes in three-dimensional coordinate system is calculated by using speed information, the projection of speed in XY plane can be expressed as: ; ; In the formula, The speed of the node in the three-dimensional coordinate system, The speed The angle between the speed and Z axis, The speed of the node in the three-dimensional coordinate system, The speed The angle between the speed and Z axis; Therefore, the speed difference of the nodes and is expressed as: ; ; ; In the formula, The speed difference of the nodes and in Z axis direction, The speed difference of the nodes and in X axis direction, The speed difference of the nodes and in Y axis direction, The angle between the speed and X axis, The angle between the speed and X axis; The average speed difference between the nodes and its adjacent nodes in the same cluster is expressed as: ; ; ; ; In the formula, The number of nodes in the cluster; After normalization processing, the mobile similarity factor is expressed as: ; wherein, is the maximum moving speed of the node; External interference factor: the external interference factor of the node is normalized by the packet error rate of the hello message, and the specific formula is as follows: ; wherein, is the number of error data packets of the hello message received by the node ; is the total number of hello message data packets received by the node ; Average distance factor: assuming that the nodes have the same transmission power and the same maximum communication distance, the average distance factor of the node is normalized as follows: ; wherein, is the maximum communication distance of the node, and are the three-dimensional position information of the nodes and , respectively; After the four cluster head selection factors are calculated, the clustering weight value of the cluster head candidate node is obtained by a weighting method, the node with the smallest weight value is elected as the cluster head node, the second smallest is the backup cluster head node, and the weight value calculation method is as follows: ; wherein, , , and are weight value coefficients of the four cluster head selection factors, respectively. .

[0011] Preferably, in the step S3, the network topology is generated, including: Step S31, the cluster head node sends a C-Clustering (C-Clustering, used to inform other nodes in the partition to enter the cluster) clustering notification message; Step S32, after the other nodes receive the clustering information of the cluster head of the cluster, the Role flag bit in the Hello message is modified, the nodes are listed as cluster member nodes, and a C-Replay (C-Replay) is returned to notify the cluster head node to join the cluster; Step S33, the cluster head node updates the cluster member list after receiving the C-Replay, until all nodes join the corresponding cluster, and the network topology construction is completed.

[0012] Preferably, in the step S4, the dynamic updating and maintaining of the cluster structure comprises dynamic updating and maintaining of the cluster member nodes and dynamic updating and maintaining of the cluster head nodes.

[0013] Preferably, in the step S4, the dynamic updating and maintaining of the cluster member nodes comprises: The cluster head node monitors the connectivity of the nodes by continuously listening to the Hello messages sent by the cluster member nodes. If the Hello message of a cluster member node is not received for three times in succession, the cluster head node judges that the link of the node has been interrupted, and immediately updates the internal data and removes the failed node from the member list of the current cluster.

[0014] Preferably, in the step S4, the dynamic updating and maintaining of the cluster head nodes comprises: The backup cluster head node continuously listens to the Hello message of the current cluster head node. If the Hello message of the cluster head node is not received for three times in succession, the backup cluster head node judges that the cluster head node has failed. The backup cluster head node will automatically become a new cluster head and broadcast a C-Clustering notification cluster message to notify all member nodes in the original cluster to join the new cluster structure, thereby updating the network topology of the entire cluster.

[0015] The present application has the following advantages: 1) In the network topology construction, the existing clustering algorithm is improved, the K-means++ algorithm is used for node region division, and the cluster head selection algorithm is combined to optimize the cluster head selection, thereby improving the stability and performance of the network and ensuring the efficient operation of the intelligent ammunition ad hoc network in a high dynamic environment. 2) By introducing an external interference factor in the cluster head selection process, the degree of interference received by the node is accurately evaluated, and dynamic adjustment is made in the cluster head election and node selection, thereby effectively improving the stability and communication quality of the network in a complex interference environment. 3) Through the dynamic updating and maintaining mechanism of the cluster member nodes and the cluster head nodes, the network can be quickly detected and automatically reconstructed and recovered when the network is disturbed or the nodes fail, thereby ensuring the high robustness and stability of the network and improving the anti-destroying ability. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of a network construction and anti-destroying reconstruction method for high dynamic intelligent ammunition cooperative combat of the present application; Figure 2 is a specific flowchart of the K-means++ algorithm used in the present application to pre-cluster the high dynamic intelligent ammunition network; Figure 3is a specific flow chart of generating a network topology according to the divided zones and selected cluster heads in the application; Figure 4 is a node motion state diagram in the application; Figure 5 is an initial distribution situation diagram of nodes when there is no clustering in the specific embodiment of the application; Figure 6 is a partition situation diagram after considering the geographical positions of nodes using K-means++ in the specific embodiment of the application; Figure 7 is a network topology diagram formed after selecting cluster heads and backup cluster heads in the specific embodiment of the application; Figure 8 is a cluster updating and maintaining process diagram of 24 cluster head nodes after 3s of simulation running in the specific embodiment of the application, and the communication links of the nodes are interrupted; Figure 9 is a cluster updating and maintaining process diagram of 10 cluster member nodes after 5s of simulation running in the specific embodiment of the application, and the communication links of the nodes are interrupted. DETAILED DESCRIPTION

[0017] The technical solutions of the application will be described clearly and completely below in combination with the drawings. In the description of the application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the application. In addition, the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying the importance of the opposite.

[0018] The application will be further described below in combination with the drawings.

[0019] As shown in Figure 1 , a network construction and anti-destroying reconstruction method for high-dynamic intelligent ammunition cooperative combat includes: Step S1, all nodes in the high-dynamic intelligent ammunition network are distributed into different node areas according to the designed division rule, and then divided into different clusters.

[0020] In the embodiment, the K-means++ algorithm is used to pre-cluster the high-dynamic intelligent ammunition network, which considers the distance between data points when selecting initial cluster centers, so that the initial center points are more dispersed, thereby making the cluster distribution uniform and reducing the interference between clusters, as shown in Figure 2As shown, specifically includes: Step S11, randomly select a node in the network as a seed node.

[0021] Step S12, calculate the Euclidean distance between other nodes and the seed node.

[0022] Step S13, randomly select a node with a larger distance from the seed node as a new seed node.

[0023] Step S14, repeat the above steps until K seed nodes are selected.

[0024] Step S15, calculate the Euclidean distance between other nodes and all seed nodes in the network, and add it to the nearest cluster to complete the cluster division this time. At this time, the network is further divided into different clusters.

[0025] Where K represents the number of clusters to be divided, which is pre-set according to the scene needs, or calculated through the throughput balancing principle.

[0026] Step S2, in each cluster node divided by the designed cluster head election algorithm, the most matching high dynamic intelligent ammunition node meeting the election requirements is selected as the cluster head.

[0027] In the embodiment, the remaining nodes in the cluster are cluster member nodes, and a backup cluster head is selected among the cluster member nodes.

[0028] In the embodiment, the key factor affecting the performance of the high dynamic intelligent ammunition network is converted into a cluster head influence factor suitable for the cluster topology, wherein the cluster head influence factor includes: a residual energy factor, a moving similarity factor, an average distance factor and an external interference factor. Residual energy factor: In the high dynamic intelligent ammunition cluster network, the cluster head is responsible for data transmission between nodes in the cluster and data forwarding between nodes in different clusters, which leads to the energy consumption of the cluster head far greater than that of the cluster member. Therefore, the initial residual energy of the node must be considered in the cluster head selection process. Assuming that the energy storage capacity of the nodes in the ad hoc network is the same, the residual energy factor of the candidate node is normalized as: ; In the formula, is the maximum energy of the node, is the current residual energy of the node; Mobile similarity factor: In the high dynamic smart ammunition cluster network, the cluster head needs to maintain stable communication link with all cluster members. If the motion mode of cluster head and cluster members is similar, the relative position change between them is small, which helps to maintain stable connection. Similar moving track reduces the distance fluctuation between nodes, thus reducing the communication interruption caused by rapid distance change or signal attenuation. The relative mobility of nodes in three-dimensional coordinate system is calculated by using speed information. The motion state of nodes in cluster is shown in and as shown in Figure 4 . The projection of speed in XY plane can be expressed as: ; ; In the formula, is the speed of node in three-dimensional coordinate system, is the angle between speed and Z axis, is the speed of node in three-dimensional coordinate system, is the angle between speed and Z axis; Therefore, the speed difference of nodes and is expressed as: ; ; ; In the formula, is the speed difference of nodes and in Z axis direction, is the speed difference of nodes and in X axis direction, is the speed difference of nodes and in Y axis direction, is the angle between speed and X axis, is the angle between speed and X axis; The average speed difference between node and its adjacent node in the same cluster is expressed as: ; ; ; ; where, is the number of nodes in the cluster; After normalization, the moving similarity factor is expressed as: ; where, is the maximum moving speed of the node; External interference factor: In the real battlefield environment, the enemy will launch interference attacks on our side, which can be mainly divided into energy-type interference and information-type interference. For energy-type interference, when the interference power is small, the communication quality between nodes will decrease; when the interference power is large, it may lead to complete interruption of the communication link of the node. In contrast, information-type interference usually does not lead to communication link interruption, but by introducing erroneous data, it leads to the decrease of node communication quality, the deviation of information transmission, and then affects the accuracy of data and communication efficiency. When the interference leads to the interruption of communication between nodes, the node cannot broadcast hello messages to other nodes, nor can it receive messages from other nodes, so it will not be able to participate in the clustering process, nor will it be able to participate in the cluster head competition. When the interference only leads to the decrease of node communication quality, in order to ensure the fairness of the network, the interference degree can be measured by the error packet rate of the hello message periodically broadcasted by the node. The node with a higher error packet rate indicates that the interference is more serious, and the communication quality is poorer; Therefore, the external interference factor of the node is normalized by the error packet rate of the hello message, and the specific formula is as follows: ; where, is the number of error data packets of the hello message received by the node , and is the total number of hello message data packets received by the node ; Average distance factor: In the cluster network, the cluster head serves as the center node of each cluster and is responsible for communication with other cluster heads or external nodes. The initial average distance between cluster members and the cluster head has a direct impact on communication delay and energy consumption. A closer cluster head can exchange data with cluster nodes faster, avoiding long-distance communication, thereby improving network efficiency. Assuming that the node transmission power is the same and the maximum communication distance is the same, the average distance factor of the node is normalized as follows: ; where, is the maximum communication distance of the node, and are the distances between the node and three-dimensional position information of the cluster head; After the four cluster head selection factors are calculated, the cluster head candidate nodes are obtained by a weighting method The cluster head node with the minimum weight value is elected as the cluster head node, and the second smallest is the backup cluster head node. The weight value calculation method is: ; In the formula, , , and are weight coefficients of the four cluster head selection factors, which can be dynamically selected according to the network environment.

[0029] Step S3, generating a network topology according to the divided partitions and the selected cluster heads.

[0030] As shown in Figure 3 , generating a network topology includes: Step S31, the cluster head node sends a C-Clustering clustering notification message.

[0031] Step S32, after the other nodes receive the clustering information of the cluster head of the cluster, the Role flag bit in the Hello message is modified, the nodes are listed as cluster member nodes, and a C-Replay is returned to notify the cluster head node to join the cluster.

[0032] Step S33, the cluster head node updates the cluster member list after receiving the C-Replay, until all nodes join the corresponding cluster, and the network topology construction is completed.

[0033] Step S4, dynamic updating and maintaining of the cluster structure.

[0034] In the embodiment, the dynamic updating and maintaining of the cluster structure includes dynamic updating and maintaining of the cluster member nodes and dynamic updating and maintaining of the cluster head nodes.

[0035] In the embodiment, the dynamic updating and maintaining of the cluster member nodes include: The cluster head node monitors the connectivity of the nodes by continuously listening to the Hello message sent by the cluster member nodes; If the Hello message of a cluster member node is not received for three consecutive times, the cluster head node will judge that the link of the node has been interrupted, and immediately update the internal data to remove the failed node from the member list of the current cluster.

[0036] In the embodiment, the dynamic updating and maintaining of the cluster head nodes include: The backup cluster head node continuously listens to the Hello message of the current cluster head node; If the backup cluster head node does not receive the hello message of the cluster head node for three times continuously, it is determined that the cluster head node has failed; The backup cluster head node will automatically become a new cluster head and broadcast a C-Clustering notification cluster message to notify all member nodes in the original cluster to rejoin the new cluster structure, thereby updating the network topology of the entire cluster and ensuring the normal operation and connectivity of the cluster.

[0037] In the simulation, the area is set to 1000*1000*1000 meters, and there are 30 nodes. The moving speed of the nodes is 20-30 meters / second, and the initial energy of each node is 100J. The maximum communication distance of the node is limited to 200 meters. Considering the influence of external interference and average distance on the stability of the network topology, the weight coefficients of the cluster head selection are .

[0038] (1) Cluster networking function verification According to the network situation, 30 nodes are divided into 5 clusters. The clustering process can be roughly divided into two parts. The first part is to use the K-means++ algorithm to partition the nodes, and the second part is to use the weighted algorithm to select the cluster head and backup cluster head in each partition, and finally form the network topology. Figure 5 is the initial distribution of the nodes without clustering. Figure 6 is the partitioning situation after considering the geographical location of the nodes using K-means++. It can be seen from Figure 6 that the K-means++ algorithm can cluster the network relatively evenly in terms of location, and the interference between clusters is relatively small. Figure 7 is the network topology formed after the selection of cluster heads and backup cluster heads. The selected cluster head node numbers of each cluster are: 4, 14, 24, 2, 18, and the backup cluster head node numbers are: 20, 21, 3, 28, 12. Through the proposed clustering algorithm, all nodes are connected to each other through the cluster head nodes in the clustering cluster structure, realizing efficient deployment and stable communication.

[0039] (2) Cluster update and maintenance verification under interference 1) Cluster head node offline After 3s of simulation, the communication link of the 24 cluster head node is interrupted by interference simulation, and the cluster update and maintenance process is as shown in Figure 8 After the communication of the 24 cluster head node is interrupted, the backup cluster head 3 node in the cluster will become a new cluster head node, automatically taking over the cluster and reorganizing other cluster members to join the cluster, thereby updating the network topology of the entire cluster and ensuring the normal operation and connectivity of the cluster.

[0040] 2) Cluster member node offline After the simulation continues for 5s, the communication link of the member node of No. 10 cluster is interrupted by interference simulation, and the update and maintenance process of the cluster is as shown in Figure 9 After the communication of the cluster head node of No. 10 cluster is interrupted, the cluster head 4 node in the cluster removes the failed node from the member list of the current cluster, and completes the update of the network topology.

[0041] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for network construction and anti-destruction reconstruction in highly dynamic intelligent munitions cooperative operations, characterized in that, include: Step S1: All nodes in the high-dynamic smart munition network are assigned to different node regions according to the designed partitioning rules, thereby dividing them into different clusters; Step S2: Within each of the divided cluster nodes, the highly dynamic intelligent ammunition node that best matches the election requirements is selected as the cluster head using the designed cluster head election algorithm. Step S3: Generate the network topology based on the partitions and selected cluster heads; Step S4: Dynamic update and maintenance of the cluster structure.

2. The network construction and anti-destruction reconstruction method for highly dynamic intelligent munitions cooperative combat according to claim 1, characterized in that, In step S1, the K-means++ algorithm is used to pre-cluster the highly dynamic smart munition network, specifically including: Step S11: Randomly select a node in the network as a seed node; Step S12: Calculate the Euclidean distance between other nodes and the seed node; Step S13: Randomly select a node that is far from the seed node as the new seed node; Step S14: Repeat the above steps until K seed nodes are selected; Step S15: Calculate the Euclidean distance between other nodes in the network and all seed nodes, and add them to the nearest cluster to complete this round of cluster partitioning. At this point, the network is further divided into different clusters. Where K represents the number of clusters to be divided, which can be preset according to the needs of the scenario or calculated based on the throughput balancing principle.

3. The network construction and anti-destruction reconstruction method for highly dynamic intelligent munitions cooperative combat according to claim 1, characterized in that, In step S2, the remaining nodes in the cluster are cluster member nodes, and a backup cluster head is selected from the cluster member nodes.

4. The network construction and anti-destruction reconstruction method for highly dynamic intelligent munitions cooperative combat according to claim 1, characterized in that, In step S2, the key factors affecting the performance of the high-dynamic smart munition network are transformed into cluster head influence factors adapted to the cluster topology. The cluster head influence factors include: residual energy factor, moving similarity factor, average distance factor and external interference factor. Remaining Energy Factor: In a highly dynamic intelligent munitions swarm network, the cluster head is responsible for both data transmission between nodes within the cluster and data forwarding between nodes in different clusters. Assuming that nodes in the ad hoc network have the same energy reserve capacity, the normalized remaining energy factor of a candidate node is expressed as: ； In the formula, The maximum energy of the node. This represents the node's current remaining energy. Mobility similarity factor: In a highly dynamic intelligent munitions swarm network, the cluster head needs to maintain a stable communication link with all cluster members. The relative mobility of nodes in a three-dimensional coordinate system is calculated using velocity information. The projection of velocity onto the XY plane can be expressed as: ； ； In the formula, For nodes Velocity in a three-dimensional coordinate system For speed The angle between the Z-axis and the Z-axis For nodes Velocity in a three-dimensional coordinate system For speed The angle between the Z-axis and the Z-axis; Therefore, nodes and The speed difference is expressed as: ； ； ； In the formula, For nodes and The velocity difference in the Z-axis direction For nodes and The velocity difference in the X-axis direction For nodes and The velocity difference in the Y-axis direction For speed The angle between the X-axis and the X-axis For speed The angle between the X-axis and the X-axis; Nodes in the same cluster Its neighboring nodes The average speed difference between them is expressed as: ； ； ； ； In the formula, This represents the number of nodes within the cluster. After normalization, the moving similarity factor Represented as: ； In the formula, This represents the maximum movement speed of the node. External interference factors: nodes The external interference factor is normalized using the packet error rate of the hello message, as shown in the following formula: ； In the formula, For nodes Number of erroneous hello message packets received. For nodes Total number of hello message packets received; Average distance factor: Assuming nodes have the same transmit power and the same maximum communication distance, then the average distance factor is calculated based on the node's transmission power and maximum communication distance. The normalized average distance factor is expressed as: ； In the formula, This represents the maximum communication distance between nodes. and They are nodes and Three-dimensional position information; After calculating the four cluster head selection factors, candidate cluster head nodes are obtained through a weighted method. The cluster weights are determined by assigning cluster heads to the nodes with the smallest weights. The node with the second smallest weight is elected as the backup cluster head. The weights are calculated as follows: ； In the formula, , , and The weight coefficients for the factors selected for the four cluster heads are respectively. .

5. The network construction and anti-destruction reconstruction method for highly dynamic intelligent munitions cooperative combat according to claim 1, characterized in that, In step S3, generating the network topology includes: Step S31: The cluster head node sends a C-Clustering clustering notification message; In step S32, after other nodes receive the clustering information from the cluster head, they modify the Role flag in the Hello message, mark themselves as cluster member nodes, and return C-Replay to notify the cluster head node to join the cluster. In step S33, after receiving C-Replay, the cluster head node updates the cluster member list until all nodes have joined the corresponding cluster, and the network topology construction is completed.

6. The network construction and anti-destruction reconstruction method for highly dynamic intelligent munitions cooperative combat according to claim 1, characterized in that, In step S4, the dynamic update and maintenance of the cluster structure includes the dynamic update and maintenance of cluster member nodes and the dynamic update and maintenance of cluster head nodes.

7. The network construction and anti-destruction reconstruction method for highly dynamic intelligent munitions cooperative combat according to claim 1, characterized in that, In step S4, the dynamic updating and maintenance of cluster member nodes includes: The cluster head node monitors node connectivity by continuously listening to Hello messages sent by cluster member nodes; If a cluster head node fails to receive a Hello message from a cluster member node three times in a row, it will determine that the node's link has been interrupted and immediately update its internal data to remove the failed node from the current cluster's member list.

8. The network construction and anti-destruction reconstruction method for highly dynamic intelligent munitions cooperative combat according to claim 1, characterized in that, In step S4, the dynamic updating and maintenance of the cluster head node includes: The standby cluster head node will continuously listen for the Hello message from the current cluster head node; If the backup cluster head node fails to receive the Hello message from the cluster head node three times in a row, the cluster head node is deemed to have failed. The standby cluster head node will automatically become the new cluster head and broadcast a C-Clustering notification to all member nodes in the original cluster to rejoin the new cluster structure, thereby updating the network topology of the entire cluster.

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