On-demand weighted clustering method and device for multi-military-type joint combat

Abstract

The invention provides an on-demand weighted clustering method and device for multi-military-species joint combat, and relates to the technical field of mobile communication. The method comprises the following steps: acquiring coordinates of a multi-military-type joint combat node and attributes of the multi-military-type joint combat node; according to the coordinates of the multi-military-type joint combat nodes and the attributes of the multi-military-type joint combat nodes, adopting a Cartesian coordinate system to construct a multi-military-type joint combat cluster head election model; and according to the multi-military-type joint combat cluster head election model, selecting a cluster head in a joint combat scene by adopting an on-demand weighted clustering method and a dynamic annular particle swarm optimization method to obtain a cluster head selection result. The cluster structure maintenance time can be effectively prolonged, and the battlefield communication quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to an on-demand weighted clustering method and device for multi-service joint operations. Background Art

[0002] Traditional communication networks require the support of infrastructure to operate normally. Centralized networks cannot adapt to the ever-changing modern warfare. Wireless ad hoc networks are self-organizing and do not require fixed infrastructure. They can be quickly networked in harsh battlefield environments and have been widely used in military communications. Due to the mobility, heterogeneity, and task-driven nature of combat nodes, the topology of wireless ad hoc networks changes frequently on the battlefield, which seriously affects the transmission of information in the combat network. Therefore, topology control is needed to improve network performance. In the scenario of multi-service joint operations, the communication network is large in scale. At this time, if the network structure is a planar structure, each node needs to maintain a huge routing table, which consumes a lot of computing and memory resources and reduces communication efficiency. The clustering structure solves this problem better through network grading.

[0003] In the multi-service joint combat scenario, the mobility, data processing capability, power supply energy and communication range of each node are different; if the mobility of the cluster head is too strong, the connection stability between the cluster head and the members in the cluster may be reduced; when the number of members in the cluster exceeds the optimal connection threshold of the cluster head, it may cause network congestion; if the communication distance between the cluster head and the network members is too large, it may accelerate energy consumption. The current clustering method considers relatively single factors, which is not suitable for different combat scenarios and it is difficult to select a suitable cluster head according to battlefield needs. In order to effectively improve the reliability of the cluster structure, many factors need to be considered comprehensively when clustering. How to determine the weight of each factor so that the number of re-clustering is less is a major difficulty faced by the weighted clustering method. Summary of the invention

[0004] In order to solve the technical problems that the existing technology is not compatible with the multi-service joint combat scenario, it is difficult to select appropriate cluster heads according to battlefield requirements, resulting in frequent changes in network clustering structure, the embodiment of the present invention provides an on-demand weighted clustering method and device for multi-service joint combat. The technical solution is as follows:

[0005] On the one hand, a method for on-demand weighted clustering for multi-service joint operations is provided, the method being implemented by an on-demand weighted clustering device for multi-service joint operations, the method comprising:

[0006] S1. Obtain the coordinates and attributes of the multi-service joint combat node;

[0007] S2. According to the coordinates of the multi-service joint operations nodes and the attributes of the multi-service joint operations nodes, a multi-service joint operations cluster head election model is constructed using a Cartesian coordinate system;

[0008] S3. According to the multi-service joint combat cluster head election model, an on-demand weighted clustering method is adopted, and a dynamic ring particle swarm optimization method is used to select cluster heads in the joint combat scenario to obtain cluster head selection results.

[0009] Optionally, the attributes of the multi-service joint combat node of S1 include: the maximum moving speed, communication range, combat area, command authority level, optimal number of connections and energy of each combat node.

[0010] Optionally, the multi-service joint combat cluster head election model is expressed by the following formula (1):

[0011] (1)

[0012] in, represents weight; Indicates The influencing factors of each factor; Indicates the load gap, Indicates the signal transmission power, represents the displacement distance, Indicates the energy consumption ratio, Indicates the level of command authority; Indicates the maximum load gap among all combat nodes in the unassigned state; Indicates the maximum transmission power of all combat nodes in the unassigned state; Indicates the maximum displacement distance among all combat nodes in the unassigned state; Indicates the maximum energy consumption ratio among all combat nodes in the unallocated state; Indicates the maximum command authority level among all combat nodes in the unassigned state.

[0013] Optionally, the S3 adopts an on-demand weighted clustering method, selects a cluster head in a joint combat scenario through a dynamic ring particle swarm optimization method, and obtains a cluster head selection result, including:

[0014] S31. Obtain combat missions, initialize the coordinates and combat parameters of our combat nodes, and use the dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of five factors; the five factors include: load gap, signal transmission power, displacement distance, energy consumption ratio, and command authority level;

[0015] S32, each combat node that is not assigned a cluster head detects the number of nodes that are not assigned a cluster head within the transmission range;

[0016] S33, each combat node that is not assigned a cluster head calculates the absolute value of the difference between its optimal connection number and the number of nodes that are not assigned a cluster head within the transmission range; wherein the absolute value is the load gap;

[0017] S34, each combat node that is not assigned a cluster head calculates the sum of the distances to the friendly forces that are not assigned a cluster head within the transmission range; wherein the sum of the distances approximately represents the signal transmission power required by the combat node;

[0018] S35, calculating the Euclidean distance between the current position of each combat node that is not assigned a cluster head and the position at the last cluster head election;

[0019] S36, calculating the current energy consumption ratio of each combat node that is not assigned a cluster head;

[0020] S37, normalizing the five factors of the combat node load difference, signal transmission power, displacement distance, energy consumption ratio and command authority level of each unassigned cluster head to obtain a normalized processing result; and calculating the weight corresponding to each combat node of the unassigned cluster head according to the normalized processing result;

[0021] S38. According to the weight corresponding to each combat node that is not assigned a cluster head, select the node with the smallest weight as the cluster head node; wherein the selected cluster head node and the combat nodes that are not assigned a cluster head within the transmission range form a cluster, the nodes in the cluster withdraw from this cluster head election, and the remaining nodes repeat steps S32-S38 until all nodes are assigned, and the cluster head election result is obtained.

[0022] Optionally, the adopting of a dynamic ring particle swarm optimization method to optimize the influencing factors of the five factors comprises: adopting a dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of the five factors by updating the dynamic inertia weight and adopting a particle ring topology;

[0023] The process of updating the dynamic inertia weight is expressed by the following formula (2):

[0024] (2)

[0025] in, represents the initial value of inertia weight, represents the minimum inertia weight, Indicates the current iteration number, Indicates the maximum number of iterations; represents the dynamic inertia weight;

[0026] Among them, the influencing factors of the five factors satisfy the constraints and are expressed by formula (3)-formula (4):

[0027] (3)

[0028] (4)

[0029] in, Indicates The influencing factors of a factor.

[0030] Optionally, after the step of obtaining the cluster head selection result by adopting an on-demand weighted clustering method and a dynamic ring particle swarm optimization method according to the multi-service joint combat cluster head election model in S3, the step further includes:

[0031] According to the obtained cluster heads, the adaptive cluster dynamic update method driven by the backup cluster head is adopted to detect the abnormal situation of the combat network, adjust the cluster structure and update the cluster head.

[0032] Optionally, the adaptive cluster dynamic update method driven by a backup cluster head detects abnormal conditions in the combat network, adjusts the cluster structure, and updates the cluster head, including.

[0033] The combat network performs self-inspection at fixed time intervals according to the settings. If it is detected that a cluster head node is disconnected or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, a cluster head election is performed again. If it is detected that a cluster member node leaves the communication range of the original cluster and enters the communication range of another cluster, the cluster head of the entered cluster is set as the backup cluster head of the node and the cluster head is switched. When an ordinary node is disconnected or damaged, there is no need to re-elect the cluster head.

[0034] On the other hand, a device for on-demand weighted clustering for multi-service joint operations is provided. The device is applied to a method for on-demand weighted clustering for multi-service joint operations. The device includes:

[0035] An acquisition unit, used to acquire the coordinates of a multi-service joint combat node and the attributes of a multi-service joint combat node;

[0036] A construction unit, configured to construct a multi-service joint operations cluster head election model using a Cartesian coordinate system according to the coordinates of the multi-service joint operations node and the attributes of the multi-service joint operations node;

[0037] The selection unit is used to select the cluster head in the joint combat scenario according to the multi-service joint combat cluster head election model, adopt an on-demand weighted clustering method, and obtain a cluster head selection result through a dynamic ring particle swarm optimization method.

[0038] Optionally, the attributes of the multi-service joint combat node include: maximum moving speed, communication range, combat area, command authority level, optimal number of connections and energy of each combat node.

[0039] Optionally, the multi-service joint combat cluster head election model is expressed by the following formula (1):

[0040] (1)

[0041] in, represents weight; Indicates The influencing factors of each factor; Indicates the load gap, Indicates the signal transmission power, represents the displacement distance, Indicates the energy consumption ratio, Indicates the level of command authority; Indicates the maximum load gap among all combat nodes in the unassigned state; Indicates the maximum transmission power of all combat nodes in the unassigned state; Indicates the maximum displacement distance among all combat nodes in the unassigned state; Indicates the maximum energy consumption ratio among all combat nodes in the unallocated state; Indicates the maximum command authority level among all combat nodes in the unassigned state.

[0042] Optionally, the selection unit is used to:

[0043] (1) Obtain combat missions, initialize the coordinates and combat parameters of our combat nodes, and use the dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of five factors; the five factors include: load gap, signal transmission power, displacement distance, energy consumption ratio, and command authority level;

[0044] (2) Each combat node that is not assigned a cluster head detects the number of nodes that are not assigned a cluster head within its transmission range;

[0045] (3) Each combat node that is not assigned a cluster head calculates the absolute value of the difference between its optimal number of connections and the number of nodes that are not assigned a cluster head within the transmission range; the absolute value is the load gap;

[0046] (4) Each combat node that is not assigned a cluster head calculates the sum of the distances to the friendly nodes that are not assigned a cluster head within the transmission range; the sum of the distances approximately represents the signal transmission power required by the combat node;

[0047] (5) Calculate the Euclidean distance between the current position of each combat node that is not assigned a cluster head and its position at the time of the last cluster head election;

[0048] (6) Calculate the current energy consumption ratio of each combat node that is not assigned a cluster head;

[0049] (7) Normalizing the five factors of the combat node load difference, signal transmission power, displacement distance, energy consumption ratio, and command authority level of each unassigned cluster head to obtain a normalized processing result; and calculating the weight corresponding to each combat node of the unassigned cluster head based on the normalized processing result;

[0050] (8) According to the weight corresponding to each operational node that is not assigned a cluster head, the node with the smallest weight is selected as the cluster head node; the selected cluster head node and the operational nodes that are not assigned a cluster head within the transmission range form a cluster, and the nodes in the cluster withdraw from this cluster head election. The remaining nodes repeat steps (2) to (8) until all nodes are assigned and the cluster head election result is obtained.

[0051] Optionally, the adopting of a dynamic ring particle swarm optimization method to optimize the influencing factors of the five factors comprises: adopting a dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of the five factors by calculating dynamic inertia weights and adopting a particle ring topology;

[0052] The process of updating the dynamic inertia weight is expressed by the following formula (2):

[0053] (2)

[0054] in, represents the initial value of inertia weight, represents the minimum inertia weight, Indicates the current iteration number, Indicates the maximum number of iterations; represents the dynamic inertia weight;

[0055] Among them, the influencing factors of the five factors satisfy the constraints and are expressed by formula (3)-formula (4):

[0056] (3)

[0057] (4)

[0058] in, Indicates The influencing factors of a factor.

[0059] Optionally, after the step of selecting a cluster head in a joint combat scenario according to the multi-service joint combat cluster head election model, using an on-demand weighted clustering method and a dynamic ring particle swarm optimization method to obtain a cluster head selection result, the step further includes:

[0060] According to the obtained cluster heads, the adaptive cluster dynamic update method driven by the backup cluster head is adopted to detect the abnormal situation of the combat network, adjust the cluster structure and update the cluster head.

[0061] Optionally, the adaptive cluster dynamic update method driven by a backup cluster head detects abnormal conditions in the combat network, adjusts the cluster structure, and updates the cluster head, including.

[0062] The combat network performs self-inspection at fixed time intervals according to the settings. If it is detected that a cluster head node is disconnected or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, a cluster head election is performed again. If it is detected that a cluster member node leaves the communication range of the original cluster and enters the communication range of another cluster, the cluster head of the entered cluster is set as the backup cluster head of the node and the cluster head is switched. When an ordinary node is disconnected or damaged, there is no need to re-elect the cluster head.

[0063] On the other hand, a device for on-demand weighted clustering for multi-service joint operations is provided, and the device for on-demand weighted clustering for multi-service joint operations comprises: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the on-demand weighted clustering methods for multi-service joint operations described above is implemented.

[0064] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned on-demand weighted clustering methods for multi-service joint operations.

[0065] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0066] The embodiment of the present invention first obtains the coordinates of the multi-service joint combat nodes and the attributes of the multi-service joint combat nodes; secondly, according to the coordinates of the multi-service joint combat nodes and the attributes of the multi-service joint combat nodes, a Cartesian coordinate system is used to construct a multi-service joint combat cluster head election model; finally, according to the multi-service joint combat cluster head election model, an on-demand weighted clustering method is adopted, and a dynamic ring particle swarm optimization method is used to select the cluster head in the joint combat scenario to obtain a cluster head selection result.

[0067] The embodiment of the present invention comprehensively considers multiple influencing factors, assigns different weights according to the importance, obtains the ideal cluster head selection and clustering network structure in the current combat scenario, and when the battlefield situation changes, the embodiment of the present invention can adaptively adjust the timing of re-clustering to maintain the stability of the cluster structure. The embodiment of the present invention explores the optimal solution of the influencing factor through dynamic ring particle swarm optimization, ensures the timeliness of the solution, and can select the cluster head node suitable for the current battlefield situation; when the cluster structure is formed, the adaptive cluster dynamic update method driven by the backup cluster head is used to handle battlefield emergencies, reducing the need for re-clustering, and can effectively extend the maintenance time of the cluster structure; in view of the problem of frequent changes in the clustering structure of the traditional communication network, the embodiment of the present invention can make the clustering structure adaptive to the multi-service joint combat scenario, and improve the battlefield communication quality by reducing the update frequency of the cluster head. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0069] Figure 1 It is a flow chart of an on-demand weighted clustering method for multi-service joint operations provided by an embodiment of the present invention;

[0070] Figure 2 This is a schematic diagram of combat node distribution provided by an embodiment of the present invention;

[0071] Figure 3 This is a specific method implementation process of an on-demand weighted scoring method for multi-service joint operations provided by an embodiment of the present invention;

[0072] Figure 4 This is a schematic diagram of a cluster head selection result provided by an embodiment of the present invention;

[0073] Figure 5 This is a schematic diagram of the end of a cluster maintenance phase provided by an embodiment of the present invention;

[0074] Figure 6 It is a block diagram of an on-demand weighted clustering device for multi-service joint operations provided by an embodiment of the present invention;

[0075] Figure 7 It is a structural schematic diagram of an on-demand weighted clustering device for multi-service joint operations provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0077] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0078] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0079] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0080] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0081] The embodiment of the present invention provides an on-demand weighted clustering method for multi-service joint operations, which can be implemented by an on-demand weighted clustering device for multi-service joint operations, and the on-demand weighted clustering device for multi-service joint operations can be a terminal or a server. Figure 1 The flowchart of the on-demand weighted clustering method for multi-service joint operations is shown in FIG. The processing flow of the method may include the following steps:

[0082] S1. Obtain the coordinates and attributes of the multi-service joint combat node.

[0083] Optionally, the attributes of the multi-service joint combat nodes of S1 include: the maximum moving speed, communication range, combat area, command authority level, optimal number of connections and energy of each combat node.

[0084] S2. According to the coordinates and attributes of the multi-service joint combat nodes, a multi-service joint combat cluster head election model is constructed using the Cartesian coordinate system.

[0085] In a feasible implementation, the position of each combat node is described using a Cartesian coordinate system, such as Figure 2The figure shows a schematic diagram of the distribution of combat nodes provided by an embodiment of the present invention, in which the triangles represent army nodes, the squares represent navy nodes, and the circles represent air force nodes; wherein different combat nodes have different communication ranges, optimal number of connections, maximum moving speeds, energy, and command authority levels.

[0086] Among them, the combat network adopts a two-level structure, with a secondary network composed of cluster heads to maintain the network cluster heads. In the process of cluster head election, each node corresponds to a weight. The smaller the weight, the more suitable the node is to become a cluster head. Among them, the weight is composed of five parts: load gap, signal transmission power, displacement distance, energy consumption ratio and command authority level. Each part corresponds to a different weight, indicating the degree of influence of each part on the weight.

[0087] Optionally, the multi-service joint combat cluster head election model is expressed by the following formula (1):

[0088] (1)

[0089] in, represents weight; Indicates The influencing factors of each factor; Indicates the load gap, Indicates the signal transmission power, represents the displacement distance, Indicates the energy consumption ratio, Indicates command authority; Indicates the maximum load gap among all combat nodes in the unassigned state; Indicates the maximum transmission power of all combat nodes in the unassigned state; Indicates the maximum displacement distance among all combat nodes in the unassigned state; Indicates the maximum energy consumption ratio among all combat nodes in the unallocated state; Indicates the maximum command authority level among all combat nodes in the unassigned state.

[0090] S3. According to the multi-service joint combat cluster head election model, the on-demand weighted clustering method is adopted, and the dynamic ring particle swarm optimization method is used to select the cluster head in the joint combat scenario to obtain the cluster head selection result.

[0091] like Figure 3The figure shows a specific method implementation process of an on-demand weighted distribution method for multi-service joint operations provided by an embodiment of the present invention; in a feasible implementation method, the coordinates of the combat nodes are initialized, the communication range, the optimal number of connections, the maximum moving speed, the energy, the command authority level and the influencing factor are set; the number of friendly forces of the unassigned cluster heads within the transmission range of the combat nodes of the unassigned cluster heads is calculated; the absolute value of the difference between the optimal number of connections of the combat nodes of the unassigned cluster heads and the number of nodes of the unassigned cluster heads within its transmission range is calculated; the sum of the distances between the combat nodes of the unassigned cluster heads and the friendly forces of the unassigned cluster heads within its transmission range is calculated; the Euclidean distance between the current position of the combat nodes of the unassigned cluster heads and the position at the last cluster head election is calculated; the energy consumption ratio of the combat nodes of the unassigned cluster heads is calculated; the above five factors are normalized to obtain the normalized processing result; according to the normalized processing result, the weights of the unassigned nodes are calculated; according to the weights obtained by calculation, the The node with the smallest weight is the cluster head node, and the cluster head node and the unassigned nodes within its transmission range form a cluster; determine whether all nodes are assigned. If so, periodically check whether there are any nodes that are unassigned, lost, or destroyed by attack, and determine whether the cluster head is lost or the node is unassigned. If it is judged as yes, all nodes are marked as unassigned, and the number of friendly forces within the transmission range of the unassigned node is recalculated; if it is judged as no, the nodes that exceed the meta-cluster range and enter other clusters are reallocated, and the backup cluster head of the node is enabled to become its new cluster head, and continue to determine whether all nodes are invalid. If it is judged as yes, it ends and outputs the total number of updates to the combat network cluster structure. If it is judged as no, continue to check whether there are any nodes that are unassigned, lost, or destroyed by attack until all nodes are invalid; when it is judged as no whether all nodes are assigned, recalculate from the step of calculating the number of friendly forces within the transmission range of the combat node of the unassigned cluster head.

[0092] Among them, the probability of each combat node temporarily losing connection within the time interval can be expressed as , the probability of permanent damage can be expressed as ; The probability of restoring communication after loss of connection can be expressed as , the node will still keep moving when it is temporarily disconnected, and will no longer move after it is permanently damaged.

[0093] Optionally, the specific implementation process of S3 may include S31-S38:

[0094] S31. Obtain combat missions, initialize the coordinates and combat parameters of our combat nodes, and use the dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of five factors; the five factors include: load gap, signal transmission power, displacement distance, energy consumption ratio, and command authority level;

[0095] Among them, the lower the command authority level of the combat node, the more important the information it sends.

[0096] Among them, the influencing factors of the five factors are the corresponding weights of the five factors.

[0097] Optionally, a dynamic ring particle swarm optimization method is used to optimize the influencing factors of the five factors, including: using the dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of the five factors by updating the dynamic inertia weights;

[0098] In a feasible implementation, since the troop deployment, combat mission and node characteristics of each operation on the battlefield are different, the present application sets an influencing factor for each factor in different scenarios; since the dynamic circular particle swarm optimization can converge quickly and smoothly, the dynamic circular particle swarm optimization method is selected to search for the optimal solution of the influencing factor.

[0099] Among them, the dynamic ring particle swarm is based on the traditional particle swarm algorithm, integrating the advantages of the firefly algorithm. By defining the selection logic, the particles are allowed to independently perform global search and local exploration alternately during the iteration process, and the adaptive particle speed and position constraint method is used to improve the utilization of information in the algorithm during the iteration process. The dynamic ring particle swarm optimization method improves the particle swarm topology structure, and adopts a dynamic multi-neighborhood ring topology structure to improve the search space coverage capability. The balanced convergence speed is a method mastered by technical personnel in this field, and this application will not be further elaborated here.

[0100] The process of updating the dynamic inertia weight is expressed by the following formula (2):

[0101] (2)

[0102] in, represents the initial value of inertia weight, represents the minimum inertia weight, Indicates the current iteration number, Indicates the maximum number of iterations; represents the dynamic inertia weight;

[0103] Among them, the influencing factors of the five factors satisfy the constraints and are expressed by formula (3)-formula (4):

[0104] (3)

[0105] (4)

[0106] in, Indicates The influencing factors of a factor.

[0107] S32, each combat node that is not assigned a cluster head detects the number of nodes that are not assigned a cluster head within the transmission range;

[0108] In a feasible implementation, each unassigned cluster head combat node in the combat network sends out a "Hello" message to detect friendly forces that are not assigned cluster heads within the transmission range, and the number of detected friendly forces is the connectivity of the combat node.

[0109] S33, each combat node that is not assigned a cluster head calculates the absolute value of the difference between its optimal number of connections and its connectivity; wherein the absolute value is the load gap;

[0110] In a feasible implementation, the load gap can be expressed by the following formula (5):

[0111] (5)

[0112] in, Indicates the load gap; Indicates connectivity; Represents the optimal number of connections for combat nodes that are not assigned cluster heads.

[0113] S34, each combat node that is not assigned a cluster head calculates the sum of the distances to the friendly forces that are not assigned a cluster head within the transmission range; wherein the sum of the distances approximately represents the signal transmission power required by the combat node;

[0114] In a feasible implementation, the sum of the distances between the combat nodes that are not assigned cluster heads and the friendly nodes that are not assigned cluster heads within the transmission range can be expressed by the following formula (6):

[0115] (6)

[0116] in, Indicates the signal transmission power; It represents the set of friendly forces that are not assigned cluster heads within the transmission range; Indicates the node ID.

[0117] S35, calculating the Euclidean distance between the current position of each combat node that is not assigned a cluster head and the position at the last cluster head election;

[0118] In a feasible implementation, the Euclidean distance of the node before and after clustering is used to represent the node's mobility, which can be expressed by the following formula (7):

[0119] (7)

[0120] in, Indicates the node position at the current moment; Indicates the node position during the last cluster head election; It represents the Euclidean distance between the current position of each node that is not assigned a cluster head and its position at the time of the last cluster head election.

[0121] S36, calculating the current energy consumption ratio of each combat node that is not assigned a cluster head;

[0122] In a feasible implementation, the energy consumption ratio can be expressed by the following formula (8):

[0123] (8)

[0124] in, Indicates the energy consumption ratio; It indicates the maximum number of cluster heads set by different nodes before the operation; Indicates the total duration of the cluster head.

[0125] S37, normalizing the five factors of the combat node load difference, signal transmission power, displacement distance, energy consumption ratio and command authority level of each unassigned cluster head to obtain a normalized processing result; and calculating the weight corresponding to each combat node of the unassigned cluster head according to the normalized processing result;

[0126] In a feasible implementation, the result of the normalization process can be expressed by the following formula (9):

[0127] (9)

[0128] in, Represents a certain weight factor; Indicates the maximum value of a factor; Represents the result of normalization processing.

[0129] S38. According to the weight corresponding to each unassigned cluster head node, select the node with the smallest weight as the cluster head node; wherein the selected cluster head node and the combat nodes without assigned cluster heads within the transmission range form a cluster, the nodes in the cluster withdraw from this cluster head election, and the remaining nodes repeat steps S32-S38 until all nodes are assigned, and the cluster head election result is obtained.

[0130] Among them, Figure 4 A schematic diagram of cluster head selection results provided by an embodiment of the present invention is shown.

[0131] Optionally, after the step of obtaining the cluster head selection result, S3 further includes:

[0132] According to the obtained cluster heads, the adaptive cluster dynamic update method driven by the backup cluster head is adopted to detect the abnormal situation of the combat network, adjust the cluster structure and update the cluster head.

[0133] Among them, abnormal situations in the combat network include: combat nodes are permanently lost due to damage by enemy attacks, combat nodes are temporarily lost, and cluster member nodes leave the original cluster range.

[0134] Optionally, an adaptive cluster dynamic update method driven by a backup cluster head is used to detect abnormal conditions in the combat network, adjust the cluster structure, and update the cluster head, including:

[0135] The combat network performs self-inspection at fixed time intervals according to the settings. If it is detected that a cluster head node is disconnected or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, a cluster head election is performed again. If it is detected that a cluster member node leaves the communication range of the original cluster and enters the communication range of another cluster, the cluster head of the entered cluster is set as the backup cluster head of the node and the cluster head is switched. When an ordinary node is disconnected or damaged, there is no need to re-elect the cluster head.

[0136] In a feasible implementation, after the cluster head election phase ends, the cluster maintenance phase begins. During the cluster maintenance phase, the cluster will perform a dynamic update at fixed time intervals. During the dynamic update, if it is detected that a cluster head node is disconnected or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, the maintenance phase ends and the cluster head election is performed again. Figure 5 The figure shows a schematic diagram of the end of the cluster maintenance phase provided by an embodiment of the present invention; if it is detected that a cluster member node leaves the communication range of the original cluster and enters the communication range of another cluster, the cluster head of the entered cluster is set as the backup cluster head of the node and the cluster head is switched; when an ordinary node is lost or damaged, there is no need to re-elect the cluster head, so the number of clustering times can be reduced as much as possible to maintain the stability of the clustering structure.

[0137] The embodiment of the present invention first obtains the coordinates of the multi-service joint combat nodes and the attributes of the multi-service joint combat nodes; secondly, according to the coordinates of the multi-service joint combat nodes and the attributes of the multi-service joint combat nodes, a Cartesian coordinate system is used to construct a multi-service joint combat cluster head election model; finally, according to the multi-service joint combat cluster head election model, an on-demand weighted clustering method is adopted, and a dynamic ring particle swarm optimization method is used to select the cluster head in the joint combat scenario to obtain a cluster head selection result.

[0138] The embodiment of the present invention comprehensively considers multiple influencing factors, assigns different weights according to the importance, obtains the ideal cluster head selection and clustering network structure in the current combat scenario, and when the battlefield situation changes, the embodiment of the present invention can adaptively adjust the timing of re-clustering to maintain the stability of the cluster structure. The embodiment of the present invention explores the optimal solution of the influencing factor through dynamic ring particle swarm optimization, ensures the timeliness of the solution, and can select the cluster head node suitable for the current battlefield situation; when the cluster structure is formed, the adaptive cluster dynamic update method driven by the backup cluster head is used to handle battlefield emergencies, reducing the need for re-clustering, and can effectively extend the maintenance time of the cluster structure; in view of the problem of frequent changes in the clustering structure of the traditional communication network, the embodiment of the present invention can make the clustering structure adaptive to the multi-service joint combat scenario, and improve the battlefield communication quality by reducing the update frequency of the cluster head.

[0139] Figure 6 The block diagram of a device for weighted clustering on demand for multi-service joint operations according to an exemplary embodiment is shown. The device is used for a method for weighted clustering on demand for multi-service joint operations. Figure 6 The device includes an acquisition unit 610, a construction unit 620 and a selection unit 630. Among them:

[0140] An acquisition unit 610 is used to acquire the coordinates of the multi-service joint combat node and the attributes of the multi-service joint combat node;

[0141] A construction unit 620 is used to construct a multi-service joint operations cluster head election model using a Cartesian coordinate system according to the coordinates of the multi-service joint operations node and the attributes of the multi-service joint operations node;

[0142] The selection unit 630 is used to select cluster heads in the joint combat scenario according to the multi-service joint combat cluster head election model, adopt an on-demand weighted clustering method, and use a dynamic ring particle swarm optimization method to obtain a cluster head selection result.

[0143] Optionally, the attributes of the multi-service joint combat node include: maximum moving speed, communication range, combat area, command authority level, optimal number of connections and energy of each combat node.

[0144] Optionally, the multi-service joint combat cluster head election model is expressed by the following formula (1):

[0145] (1)

[0146] in, represents weight; Indicates The influencing factors of each factor; Indicates the load gap, Indicates the signal transmission power, represents the displacement distance, Indicates the energy consumption ratio, Indicates command authority; Indicates the maximum load gap among all combat nodes in the unassigned state; Indicates the maximum transmission power of all combat nodes in the unassigned state; Indicates the maximum displacement distance among all combat nodes in the unassigned state; Indicates the maximum energy consumption ratio among all combat nodes in the unallocated state; Indicates the maximum command authority level among all combat nodes in the unassigned state.

[0147] Optionally, the selection unit 630 is used to:

[0148] (1) Obtain combat missions, initialize the coordinates and combat parameters of our combat nodes, and use the dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of five factors; the five factors include: load gap, signal transmission power, displacement distance, energy consumption ratio, and command authority level;

[0149] (2) Each combat node that is not assigned a cluster head detects the number of nodes that are not assigned a cluster head within its transmission range;

[0150] (3) Each combat node that is not assigned a cluster head calculates the absolute value of the difference between its optimal number of connections and its connectivity; the absolute value is the load gap;

[0151] (4) Each combat node that is not assigned a cluster head calculates the sum of the distances to the friendly nodes that are not assigned a cluster head within the transmission range; the sum of the distances approximately represents the signal transmission power required by the combat node;

[0152] (5) Calculate the Euclidean distance between the current position of each combat node that is not assigned a cluster head and its position at the time of the last cluster head election;

[0153] (6) Calculate the current energy consumption ratio of each combat node that is not assigned a cluster head;

[0154] (7) Normalizing the five factors of the combat node load difference, signal transmission power, displacement distance, energy consumption ratio, and command authority level of each unassigned cluster head to obtain a normalized processing result; and calculating the weight corresponding to each combat node of the unassigned cluster head based on the normalized processing result;

[0155] (8) According to the weight corresponding to each operational node that is not assigned a cluster head, the node with the smallest weight is selected as the cluster head node; the selected cluster head node and the operational nodes that are not assigned a cluster head within the transmission range form a cluster, and the nodes in the cluster withdraw from this cluster head election. The remaining nodes repeat steps (2) to (8) until all nodes are assigned and the cluster head election result is obtained.

[0156] Optionally, the adopting of a dynamic ring particle swarm optimization method to optimize the influencing factors of the five factors comprises: adopting a dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of the five factors by calculating dynamic inertia weights and adopting a particle ring topology;

[0157] The process of updating the dynamic inertia weight is expressed by the following formula (2):

[0158] (2)

[0159] in, represents the initial value of inertia weight, represents the minimum inertia weight, Indicates the current iteration number, Indicates the maximum number of iterations; represents the dynamic inertia weight;

[0160] Among them, the influencing factors of the five factors satisfy the constraints and are expressed by formula (3)-formula (4):

[0161] (3)

[0162] (4)

[0163] in, Indicates The influencing factors of a factor.

[0164] Optionally, according to the multi-service joint combat cluster head election model, an on-demand weighted clustering method is adopted to select cluster heads in the joint combat scenario through a dynamic ring particle swarm optimization method, and after the step of obtaining the cluster head selection result, the step further includes:

[0165] According to the obtained cluster heads, the adaptive cluster dynamic update method driven by the backup cluster head is adopted to detect the abnormal situation of the combat network, adjust the cluster structure and update the cluster head.

[0166] Optionally, the adaptive cluster dynamic update method driven by a backup cluster head detects abnormal conditions in the combat network, adjusts the cluster structure, and updates the cluster head, including.

[0167] The combat network performs self-inspection at fixed time intervals according to the settings. If it is detected that a cluster head node is disconnected or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, a cluster head election is performed again. If it is detected that a cluster member node leaves the communication range of the original cluster and enters the communication range of another cluster, the cluster head of the entered cluster is set as the backup cluster head of the node and the cluster head is switched. When an ordinary node is disconnected or damaged, there is no need to re-elect the cluster head.

[0168] The embodiment of the present invention first obtains the coordinates of the multi-service joint combat nodes and the attributes of the multi-service joint combat nodes; secondly, according to the coordinates of the multi-service joint combat nodes and the attributes of the multi-service joint combat nodes, a Cartesian coordinate system is used to construct a multi-service joint combat cluster head election model; finally, according to the multi-service joint combat cluster head election model, an on-demand weighted clustering method is adopted, and a dynamic ring particle swarm optimization method is used to select the cluster head in the joint combat scenario to obtain a cluster head selection result.

[0169] The embodiment of the present invention comprehensively considers multiple influencing factors, assigns different weights according to the importance, obtains the ideal cluster head selection and clustering network structure in the current combat scenario, and when the battlefield situation changes, the embodiment of the present invention can adaptively adjust the timing of re-clustering to maintain the stability of the cluster structure. The embodiment of the present invention explores the optimal solution of the influencing factor through dynamic ring particle swarm optimization, ensures the timeliness of the solution, and can select the cluster head node suitable for the current battlefield situation; when the cluster structure is formed, the adaptive cluster dynamic update method driven by the backup cluster head is used to handle battlefield emergencies, reducing the need for re-clustering, and can effectively extend the maintenance time of the cluster structure; in view of the problem of frequent changes in the clustering structure of the traditional communication network, the embodiment of the present invention can make the clustering structure adaptive to the multi-service joint combat scenario, and improve the battlefield communication quality by reducing the update frequency of the cluster head.

[0170] Figure 7 FIG. 1 is a schematic diagram of a structure of an on-demand weighted clustering device for multi-service joint operations provided by an embodiment of the present invention. Figure 7 As shown, the on-demand weighted clustering equipment for multi-service joint operations may include the above Figure 6 The on-demand weighted clustering device for multi-service joint operations shown. Optionally, the on-demand weighted clustering device 710 for multi-service joint operations may include a first processor 2001.

[0171] Optionally, the on-demand weighted clustering device 710 for multi-service joint operations may also include a memory 2002 and a transceiver 2003 .

[0172] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0173] Combine the following Figure 7 The components of the on-demand weighted clustering device 710 for multi-service joint operations are introduced in detail:

[0174] The first processor 2001 is the control center of the on-demand weighted clustering device 710 for multi-service joint operations, and can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0175] Optionally, the first processor 2001 can perform various functions of the on-demand weighted clustering device 710 for multi-service joint operations by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002.

[0176] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 7 CPU0 and CPU1 are shown in FIG.

[0177] In a specific implementation, as an embodiment, the on-demand weighted clustering device 710 for multi-service joint operations may also include multiple processors, such as Figure 7 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0178] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0179] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be connected to the memory 2002 through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0180] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0181] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 7 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0182] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0183] It should be noted that Figure 7 The structure of the on-demand weighted clustering device 710 for multi-service joint operations shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0184] In addition, the technical effects of the on-demand weighted clustering device 710 for multi-service joint operations can refer to the technical effects of the on-demand weighted clustering method for multi-service joint operations described in the above method embodiment, which will not be repeated here.

[0185] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0186] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0187] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0188] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0189] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0190] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0191] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0192] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0193] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0194] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0196] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0197] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method of on-demand weighted clustering for multi-service joint operations, characterized in that: The method comprises: S1. Obtain the coordinates and attributes of the multi-service joint combat node; S2. According to the coordinates of the multi-service joint operations nodes and the attributes of the multi-service joint operations nodes, a multi-service joint operations cluster head election model is constructed using a Cartesian coordinate system; S3. According to the multi-service joint combat cluster head election model, an on-demand weighted clustering method is adopted, and a dynamic ring particle swarm optimization method is used to select cluster heads in the joint combat scenario to obtain cluster head selection results.

2. The on-demand weighted clustering method for multi-service joint operations according to claim 1 is characterized in that: The attributes of the multi-service joint combat nodes of S1 include: the maximum moving speed, communication range, combat area, command authority level, optimal number of connections and energy of each combat node.

3. The on-demand weighted clustering method for multi-service joint operations according to claim 1 is characterized in that: The multi-service joint combat cluster head election model is expressed by the following formula (1): （1） in, represents weight; Indicates The influencing factors of each factor; Indicates the load gap, Indicates the signal transmission power, represents the displacement distance, Indicates the energy consumption ratio, Indicates the level of command authority; Indicates the maximum load gap among all combat nodes in the unassigned state; Indicates the maximum transmission power of all combat nodes in the unassigned state; Indicates the maximum displacement distance among all combat nodes in the unassigned state; Indicates the maximum energy consumption ratio among all combat nodes in the unallocated state; Indicates the maximum command authority level among all combat nodes in the unassigned state.

4. The on-demand weighted clustering method for multi-service joint operations according to claim 1 is characterized in that: The S3 adopts an on-demand weighted clustering method and a dynamic ring particle swarm optimization method to select cluster heads in a joint combat scenario, and obtains cluster head selection results, including: S31. Obtain combat missions, initialize the coordinates and combat parameters of our combat nodes, and use the dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of five factors; the five factors include: load gap, signal transmission power, displacement distance, energy consumption ratio, and command authority level; S32, each combat node that is not assigned a cluster head detects the number of nodes that are not assigned a cluster head within the transmission range; S33, each combat node that is not assigned a cluster head calculates the absolute value of the difference between its optimal connection number and the number of nodes that are not assigned a cluster head within the transmission range; wherein the absolute value is the load gap; S34, each combat node that is not assigned a cluster head calculates the sum of the distances to the friendly forces that are not assigned a cluster head within the transmission range; wherein the sum of the distances approximately represents the signal transmission power required by the combat node; S35, calculating the Euclidean distance between the current position of each combat node that is not assigned a cluster head and the position at the last cluster head election; S36, calculating the current energy consumption ratio of each combat node that is not assigned a cluster head; S37, normalizing the five factors of the combat node load difference, signal transmission power, displacement distance, energy consumption ratio and command authority level of each unassigned cluster head to obtain a normalized processing result; and calculating the weight corresponding to each combat node of the unassigned cluster head according to the normalized processing result; S38. According to the weight corresponding to each combat node that is not assigned a cluster head, select the node with the smallest weight as the cluster head node; wherein the selected cluster head node and the combat nodes that are not assigned a cluster head within the transmission range form a cluster, the nodes in the cluster withdraw from this cluster head election, and the remaining nodes repeat steps S32-S38 until all nodes are assigned, and the cluster head election result is obtained.

5. The on-demand weighted clustering method for multi-service joint operations according to claim 4 is characterized in that: The method of using a dynamic ring particle swarm optimization method to optimize the influencing factors of the five factors includes: using a dynamic ring particle swarm optimization method to optimize the optimal values ​​of the influencing factors of the five factors by updating the dynamic inertia weight and using a particle ring topology; The process of updating the dynamic inertia weight is expressed by the following formula (2): （2） in, represents the initial value of inertia weight, represents the minimum inertia weight, Indicates the current iteration number, Indicates the maximum number of iterations; represents the dynamic inertia weight; Among them, the influencing factors of the five factors satisfy the constraints and are expressed by formula (3)-formula (4): （3） （4） in, Indicates The influencing factors of a factor.

6. The on-demand weighted clustering method for multi-service joint operations according to claim 1 is characterized in that: After the step of selecting cluster heads in a joint combat scenario according to the multi-service joint combat cluster head election model by using an on-demand weighted clustering method and a dynamic ring particle swarm optimization method to obtain a cluster head selection result in S3, the step further includes: According to the obtained cluster heads, the adaptive cluster dynamic update method driven by the backup cluster head is adopted to detect the abnormal situation of the combat network, adjust the cluster structure and update the cluster head.

7. The on-demand weighted clustering method for multi-service joint operations according to claim 6 is characterized in that: The adaptive cluster dynamic update method driven by the backup cluster head detects abnormal conditions of the combat network, adjusts the cluster structure, and updates the cluster head, including: The combat network performs self-inspection at fixed time intervals according to the settings. If it is detected that a cluster head node is disconnected or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, a cluster head election is performed again. If it is detected that a cluster member node leaves the communication range of the original cluster and enters the communication range of another cluster, the cluster head of the entered cluster is set as the backup cluster head of the node and the cluster head is switched. When an ordinary node is disconnected or damaged, there is no need to re-elect the cluster head.

8. An on-demand weighted clustering device for multi-service joint operations, the on-demand weighted clustering device for multi-service joint operations is used to implement the on-demand weighted clustering method for multi-service joint operations as claimed in any one of claims 1 to 7, characterized in that: The device comprises: An acquisition unit, used to acquire the coordinates of a multi-service joint combat node and the attributes of a multi-service joint combat node; A construction unit, configured to construct a multi-service joint operations cluster head election model using a Cartesian coordinate system according to the coordinates of the multi-service joint operations node and the attributes of the multi-service joint operations node; The selection unit is used to select the cluster head in the joint combat scenario according to the multi-service joint combat cluster head election model, adopt an on-demand weighted clustering method, and obtain a cluster head selection result through a dynamic ring particle swarm optimization method.

9. An on-demand weighted clustering device for multi-service joint operations, characterized in that: The on-demand weighted clustering device for multi-service joint operations includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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