A method and device for on-demand weighted clustering for multi-service joint operation

Abstract

The application provides a multi-service joint operation demand-oriented weighted clustering method and device, and relates to the technical field of mobile communication. The method comprises the following steps: obtaining the coordinates of multi-service joint operation nodes and the attributes of the multi-service joint operation nodes; adopting a Cartesian coordinate system to construct a multi-service joint operation cluster head election model according to the coordinates of the multi-service joint operation nodes and the attributes of the multi-service joint operation nodes; and selecting a cluster head in a joint operation scene by a dynamic ring particle swarm optimization method to obtain a cluster head selection result by using a demand-oriented weighted clustering method according to the multi-service joint operation cluster head election model. The application can effectively prolong the cluster structure maintenance time and improve the battlefield communication quality.

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 infrastructure support to operate properly. Centralized networks are unable to adapt to the rapidly changing dynamics of modern warfare. Wireless ad hoc networks (WANs) self-organize, eliminating the need for fixed infrastructure and enabling rapid networking in harsh battlefield environments. They have been widely adopted in military communications. Due to the mobility, heterogeneity, and mission-driven nature of combat nodes, the topology of WANs frequently changes on the battlefield, severely impacting information transmission within the combat network. Therefore, topology control is necessary to improve network performance. In multi-service joint operations, communication networks are large in scale. A planar network structure would require each node to maintain a massive routing table, consuming significant computational and memory resources and reducing communication efficiency. Clustering, however, addresses this issue through network hierarchies.

[0003] In multi-service joint combat scenarios, individual nodes vary in parameters such as mobility, data processing capabilities, power consumption, and communication range. Excessive cluster head mobility can reduce the stability of the connection between the cluster head and its members. When the number of cluster members exceeds the cluster head's optimal connection threshold, network congestion can occur. Excessive communication distance between the cluster head and network members can accelerate energy consumption. Current clustering methods consider relatively single factors, making them inappropriate for different combat scenarios and difficult to select appropriate cluster heads based on battlefield needs. To effectively improve the reliability of cluster structures, clustering requires comprehensive consideration of multiple factors. Determining the weights for each factor to minimize the number of re-clustering operations is a major challenge facing weighted clustering methods. Summary of the Invention

[0004] To address the technical issues of existing technologies, such as incompatibility with multi-service joint operations and difficulty selecting appropriate cluster heads based on battlefield requirements, which leads to frequent changes in network clustering structures, the present invention provides a method and apparatus for on-demand weighted clustering for multi-service joint operations. 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 is implemented by an on-demand weighted clustering device for multi-service joint operations. The method includes:

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

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

[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 operations cluster head election model is expressed by the following formula (1):

[0011] (1)

[0012] in, Represents weight; Indicates the 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 transmit power among 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 step 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:

[0014] S31. Obtain the combat mission, 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: 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 number of connections 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 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 time of 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 based on the normalized processing result;

[0021] S38. According to the weight corresponding to each operational 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 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 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 weights 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 The influencing factors of a factor.

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

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

[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] According to the set fixed time interval, the combat network performs self-inspection at every fixed time interval; if it is detected that a cluster head node is lost or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, the 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 lost 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 obtain the coordinates and attributes of a multi-service joint operations 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 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.

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

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

[0040] (1)

[0041] in, Represents weight; Indicates the 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 transmit power among 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 configured to:

[0043] (1) Obtain the combat mission, 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) Normalize 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 the normalized processing results; based on the normalized processing results, calculate the weight corresponding to each combat node of the unassigned cluster head;

[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)-(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 The influencing factors of a factor.

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

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

[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] According to the set fixed time interval, the combat network performs self-inspection at every fixed time interval; if it is detected that a cluster head node is lost or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, the 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 lost 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. The device comprises: a processor; and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, any one of the above-mentioned on-demand weighted clustering methods for multi-service joint operations is implemented.

[0064] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, 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 and attributes of the multi-service joint combat nodes; secondly, according to the coordinates and attributes of the multi-service joint combat nodes, a Cartesian coordinate system is adopted 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 cluster heads in the joint combat scenario to obtain a cluster head selection result.

[0067] The embodiment of the present invention comprehensively considers multiple influencing factors and assigns different weights according to their importance to obtain the ideal cluster head selection and cluster network structure in the current combat scenario. 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 factors through dynamic ring particle swarm optimization, ensuring the timeliness of the solution while selecting cluster head nodes suitable for the current battlefield situation. After 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 effectively extending the maintenance time of the cluster structure. In response to the problem of frequent changes in the cluster structure of traditional communication networks, the embodiment of the present invention can enable the cluster structure to adapt to multi-service joint combat scenarios and improve the quality of battlefield communications by reducing the update frequency of cluster heads. 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 This 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 the distribution of combat nodes 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 cluster head selection results 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 This 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 This is a structural 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 an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0078] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey 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 clearer, 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. The method can be implemented by an on-demand weighted clustering device for multi-service joint operations. 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. The processing flow of this 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 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.

[0084] S2. Based on 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 one feasible implementation, the Cartesian coordinate system is used to describe the position of each combat node, such as Figure 2The figure shows a schematic diagram of the distribution of combat nodes provided by an embodiment of the present invention, where 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 cluster heads forming a secondary network to maintain the network cluster heads. During the cluster head election process, each node corresponds to a weight. The smaller the weight, the more suitable the node is to become a cluster head. 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 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 transmit power among 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. Based on 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 heads in the joint combat scenario to obtain the cluster head selection results.

[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, and the communication range, optimal number of connections, maximum moving speed, energy, command authority level and influence factor are set; the number of friendly forces of the unassigned cluster heads within the transmission range of the combat node of the unassigned cluster head is calculated; the absolute value of the difference between the optimal number of connections of the combat node of the unassigned cluster head and the number of nodes of the unassigned cluster heads within its transmission range is calculated; the sum of the distances between the combat node of the unassigned cluster head 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 node of the unassigned cluster head and the position at the time of the last cluster head election is calculated; the energy consumption ratio of the combat node of the unassigned cluster head is calculated; the above five factors are normalized to obtain the normalization processing result; according to the normalization processing result, the weight of the unassigned node is calculated; according to the weight obtained by calculation, the weight of the unassigned node is selected The node with the smallest weight is the cluster head node, and the cluster head node forms a cluster with the unassigned nodes within its transmission range; 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, it continues 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, the number of friendly forces within the transmission range of the combat node with the unassigned cluster head is recalculated.

[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 out of connection, and will not move after it is permanently damaged.

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

[0094] S31. Obtain the combat mission, 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: 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 battle on the battlefield are different, this application sets an impact 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 used to search for the optimal solution of the impact factor.

[0099] Among them, the dynamic ring particle swarm is based on the traditional particle swarm algorithm and integrates 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 information utilization of 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 those skilled in the art and will not be further elaborated in this application.

[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 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; represents connectivity; Indicates 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 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 time of the last cluster head election;

[0118] In a feasible implementation, the Euclidean distance of a 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 has not been 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; Indicates the maximum number of cluster heads set by different nodes before the battle; 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 based on 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; Indicates the result of normalization processing.

[0129] S38. According to the weight corresponding to each unassigned cluster head node, the node with the smallest weight is selected as the cluster head node; wherein, the selected cluster head node and the combat nodes that are not assigned cluster heads within the transmission range form a cluster, and the nodes in the cluster withdraw from this cluster head election. The remaining nodes repeat steps S32-S38 until all nodes are assigned and the cluster head election result is obtained.

[0130] Among them, such as Figure 4 FIG2 is a schematic diagram of a cluster head selection result provided by an embodiment of the present invention.

[0131] Optionally, after the step of obtaining the cluster head selection result, S3 further includes: selecting cluster heads in a joint combat scenario by using an on-demand weighted clustering method based on a multi-service joint combat cluster head election model and a dynamic ring particle swarm optimization method.

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

[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] According to the set fixed time interval, the combat network performs self-inspection at every fixed time interval; if it is detected that a cluster head node is lost or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, the 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 lost 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 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 disconnected or damaged, there is no need to re-elect the cluster head, thereby minimizing the number of clustering times and maintaining the stability of the cluster structure.

[0137] The embodiment of the present invention first obtains the coordinates and attributes of the multi-service joint combat nodes; secondly, according to the coordinates and attributes of the multi-service joint combat nodes, a Cartesian coordinate system is adopted 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 cluster heads in the joint combat scenario to obtain a cluster head selection result.

[0138] The embodiment of the present invention comprehensively considers multiple influencing factors and assigns different weights according to their importance to obtain the ideal cluster head selection and cluster network structure in the current combat scenario. 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 factors through dynamic ring particle swarm optimization, ensuring the timeliness of the solution while selecting cluster head nodes suitable for the current battlefield situation. After 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 effectively extending the maintenance time of the cluster structure. In response to the problem of frequent changes in the cluster structure of traditional communication networks, the embodiment of the present invention can enable the cluster structure to adapt to multi-service joint combat scenarios and improve the quality of battlefield communications by reducing the update frequency of cluster heads.

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

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

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

[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 movement speed, communication range, combat area, command authority level, optimal number of connections and energy of each combat node.

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

[0145] (1)

[0146] in, Represents weight; Indicates the 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 transmit power among 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 configured to:

[0148] (1) Obtain the combat mission, 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) Normalize 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 the normalized processing results; based on the normalized processing results, calculate the weight corresponding to each combat node of the unassigned cluster head;

[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)-(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 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 a joint combat scenario through a dynamic ring particle swarm optimization method. After the step of obtaining the cluster head selection result, the step further includes:

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

[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] According to the set fixed time interval, the combat network performs self-inspection at every fixed time interval; if it is detected that a cluster head node is lost or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, the 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 lost or damaged, there is no need to re-elect the cluster head.

[0168] The embodiment of the present invention first obtains the coordinates and attributes of the multi-service joint combat nodes; secondly, according to the coordinates and attributes of the multi-service joint combat nodes, a Cartesian coordinate system is adopted 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 cluster heads in the joint combat scenario to obtain a cluster head selection result.

[0169] The embodiment of the present invention comprehensively considers multiple influencing factors and assigns different weights according to their importance to obtain the ideal cluster head selection and cluster network structure in the current combat scenario. 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 factors through dynamic ring particle swarm optimization, ensuring the timeliness of the solution while selecting cluster head nodes suitable for the current battlefield situation. After 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 effectively extending the maintenance time of the cluster structure. In response to the problem of frequent changes in the cluster structure of traditional communication networks, the embodiment of the present invention can enable the cluster structure to adapt to multi-service joint combat scenarios and improve the quality of battlefield communications by reducing the update frequency of cluster heads.

[0170] Figure 7 FIG is a structural diagram 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 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 further 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] The following describes the application in detail Figure 7 The various components of the multi-service joint operation demand-weighted clustering device 710 are described in detail as follows:

[0174] The first processor 2001 is the control center of the multi-service joint operation demand-weighted clustering device 710, which can be one processor or a plurality of processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present application, such as one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0175] Optionally, the first processor 2001 can execute various functions of the multi-service joint operation demand-weighted clustering device 710 by running or executing software programs 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 can include one or more CPUs, such as the CPU0 and CPU1 shown in FIG. 1. Figure 7

[0177] In a specific implementation, as an embodiment, the multi-service joint operation demand-weighted clustering device 710 can also include a plurality of processors, such as the first processor 2001 and the second processor 2004 shown in FIG. 1. Each of these processors can be a single-CPU or a multi-CPU. The processor here can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions). Figure 7

[0178] The memory 2002 is used to store software programs for implementing the schemes of the present application and is controlled by the first processor 2001 for execution. The specific implementation manner can refer to the above method embodiments, which will not be described here.

[0179] ​​Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact 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 capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed 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 communicate with the first processor 2001 through the interface circuit of the on-demand weighted clustering device 710 for multi-service joint operations ( 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, and 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), or may 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 be any conventional processor, etc.

[0186] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be 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 SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0187] The above-described embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented 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 processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired (such as infrared, wireless, microwave, etc.) manner. 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, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0188] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the associated objects before and after it are in an "or" relationship, but it can also represent an "and / or" relationship, which can be understood according to the context before and after it.

[0189] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0190] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined according to their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[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. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0192] Those skilled in the art will 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 merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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 interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

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

[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 as 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 portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

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

Claims

1. A method of on-demand weighted clustering for multi-service joint operations, characterized by: The method comprises: S1. Obtain the coordinates and attributes of the multi-service joint combat node; S2. Constructing a multi-service joint operations cluster head election model using a Cartesian coordinate system according to the coordinates and attributes of the multi-service joint operations nodes; S3. Based on the multi-service joint combat cluster head election model, adopt an on-demand weighted clustering method and a dynamic ring particle swarm optimization method to select cluster heads in the joint combat scenario to obtain a cluster head selection result; Among them, the S3 adopts the on-demand weighted clustering method and the dynamic ring particle swarm optimization method to select the cluster head in the joint combat scenario, and obtains the cluster head selection result, including: S31. Obtain the combat mission, 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: 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 number of connections 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 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 time of 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 based on the normalized processing result; S38. According to the weight corresponding to each operational 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 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 S32-S38 until all nodes are assigned and the cluster head election result is obtained.

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 node of S1 include: the maximum movement 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 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 transmit power among 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 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 adopting 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 The influencing factors of a factor.

5. 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 by using a dynamic ring particle swarm optimization method based on the multi-service joint combat cluster head election model in S3 and obtaining a cluster head selection result, the method further includes: According to the obtained cluster heads, an adaptive cluster dynamic update method driven by backup cluster heads is adopted to detect abnormal situations in the combat network, adjust the cluster structure, and update the cluster heads.

6. The on-demand weighted clustering method for multi-service joint operations according to claim 5 is characterized in that: The adaptive cluster dynamic update method driven by the backup cluster head detects abnormal conditions in the combat network, adjusts the cluster structure, and updates the cluster head, including: According to the set fixed time interval, the combat network performs self-inspection at every fixed time interval; if it is detected that a cluster head node is lost or a cluster member leaves the communication range of the original cluster and does not enter the communication range of any other cluster, the 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 lost or damaged, there is no need to re-elect the cluster head.

7. A device for on-demand weighted clustering for multi-service joint operations, the device for on-demand weighted clustering for multi-service joint operations being used to implement the method for on-demand weighted clustering for multi-service joint operations as claimed in any one of claims 1 to 6, characterized in that: The device comprises: An acquisition unit, used to obtain the coordinates and attributes of a multi-service joint operations 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 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.

8. An on-demand weighted clustering device for multi-service joint operations, characterized by: The on-demand weighted clustering equipment 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 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 6.

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