CN114040358A - High-stability clustering method for unmanned aerial vehicle cluster network - Google Patents
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Abstract
The invention discloses a high-stability clustering method for an unmanned aerial vehicle cluster network, which comprises the following steps: clustering nodes of the unmanned aerial vehicle cluster network according to the speed and distance similarity of the nodes, namely, dividing the unmanned aerial vehicle nodes which are relatively static within a certain distance range into a cluster; selecting a cluster head by adopting an improved graying algorithm according to joint measurement indexes of four influencing factors of node residual energy, highest node degree, communication condition and task type; and a periodic maintenance mechanism is adopted when the cluster network structure of the unmanned aerial vehicle cluster is maintained, so that the node with the highest joint measurement index can be used as a cluster head in a cluster head election period. The method of the invention enables the network structure to be more stable, and simultaneously enables the cluster heads to be uniformly distributed, balances the node energy consumption and prolongs the network life cycle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle cluster clustering networks, in particular to a high-stability clustering method for an unmanned aerial vehicle cluster network.
Background
The unmanned aerial vehicle continuously expands airborne functions by loading various components, simultaneously, the unmanned aerial vehicle has more and more powerful performances, and can realize various complex functions of target attack, local communication relay, firepower guidance, target damage assessment, early warning reconnaissance, electronic countermeasure and the like. And then the position of unmanned aerial vehicle cluster also receives more and more attention, then also puts forward higher requirement to unmanned aerial vehicle cluster network clustering structure's stability to adapt to complicated environment.
The classical clustering method has certain disadvantages, such as no consideration of factors influencing network performance, no consideration of energy consumption of nodes, huge dynamic network overhead and the like, and is not suitable for being directly used in the unmanned aerial vehicle cluster network. However, both domestic and foreign scholars improve the classical clustering method according to the target to be realized, but have problems, such as too large calculation amount, increased node energy consumption, reduced network life and the like.
Disclosure of Invention
Aiming at the problems of unstable network structure and short network life cycle of unmanned aerial vehicle cluster, the invention provides a High Stability Clustering Optimization (HSCOA) method, which enables the network structure to be more stable, enables cluster heads to be uniformly distributed, balances node energy consumption and prolongs the network life cycle.
In order to achieve the above object, the present application provides a high-stability clustering method for an unmanned aerial vehicle cluster network, including:
clustering nodes of the unmanned aerial vehicle cluster network according to the speed and distance similarity of the nodes, namely, dividing the unmanned aerial vehicle nodes which are relatively static within a certain distance range into a cluster;
selecting a cluster head by adopting an improved graying algorithm according to joint measurement indexes of four influencing factors of node residual energy, highest node degree, communication condition and task type;
and a periodic maintenance mechanism is adopted when the cluster network structure of the unmanned aerial vehicle cluster is maintained, so that the node with the highest joint measurement index can be used as a cluster head in a cluster head election period.
Further, the nodes of the unmanned aerial vehicle cluster network are clustered according to the speed and distance similarity of the nodes, that is, the nodes of the unmanned aerial vehicle which is relatively static within a certain distance range are divided into a cluster, specifically:
if the node i is an arbitrary node of the node j in the next-hop communication range, and the speed and the moving direction of the node are calculated in the three-dimensional coordinate system, the speed differences of the node i and the node j on the x axis, the y axis and the z axis are as follows:
wherein, Vjx、Vjy、VjzIs the velocity of node j on the x, y, z axes; vix、ViyViz is the speed of the node i on the x axis, the y axis and the z axis; alpha is alphaj、αiRespectively are included angles between the nodes j and i and the x axis; beta is aj、βiRespectively are included angles between the nodes j and i and the y axis; gamma rayj、γiRespectively are included angles between the nodes j and i and the z axis;
then the average speed difference between the node j and the N neighboring nodes i on the x axis, the y axis and the z axis is:
the standard deviation of the speed difference between the node j and the neighboring node i on the x axis, the y axis and the z axis is as follows:
as known from the pythagorean theorem, the standard deviation of the speed difference between the node j and the neighboring node i is as follows:
δjv 2＝δjx 2+δjy 2+δjz 2 (10)
setting a standard deviation threshold value of the speed difference as q, and when the standard deviation delta of the speed difference isjvWhen the speed threshold q is smaller than the speed threshold q, the node j and the neighbor node i have similarity in the movement speed and the movement direction, that is, have speed similarity.
Further, if the node i is an arbitrary node of the node j within the next-hop communication range, the distance difference between the node j and the neighboring node i is:
wherein, PtTransmitting for a nodePower; gtGain for the transmit antenna; h istIs the transmit antenna height; h isrIs the receive antenna height; prReceiving power for a node;
the standard deviation of the average distance difference between the node j and the N neighboring nodes i is:
wherein the content of the first and second substances,
the average distance difference between the node j and N neighbor nodes i is obtained;
setting the standard deviation threshold of the distance difference as p, and when the standard deviation delta of the distance difference isjdWhen the distance is smaller than the distance threshold p, the node j has similarity with the neighbor node i, that is, has distance similarity.
Further, when the standard deviation of the node speed difference and the standard deviation of the distance difference are both smaller than the corresponding speed threshold q and the distance threshold p, the node j and the neighbor node i thereof are considered to have the speed similarity and the distance similarity at the same time, namely the node j and the neighbor node i thereof accord with the clustering condition; the clustering condition has transitivity, namely the node j and the node i accord with the clustering condition, the node j and the node k accord with the clustering condition, and then the node i and the node k also accord with the clustering condition, namely the node j, the node i and the node k form a cluster;
after the primary clustering is finished, detecting the number of nodes in each cluster, and setting the maximum allowable number of nodes in each cluster as nmaxTo ensure the balance of clustering.
Further, according to joint measurement indexes of four influence factors of node residual energy, highest node degree, communication condition and task type, a cluster head is selected by adopting an improved wolf algorithm, and the method specifically comprises the following steps:
when head of cluster election is performed, e.g.Residual energy E of a certain noderesLess than the average energy E of all neighboring nodesavg(the sum of the residual energy of all the neighbor nodes is divided by the number of the neighbor nodes), exiting the election of the cluster head; wherein, the residual energy is obtained by a ground control station; normalizing the residual energy, i.e. residual energy EresDivided by the initial energy E0Normalized residual energy EτComprises the following steps:
further, the node degree refers to the number of neighbor nodes of the node in the communication range; in the same network, the higher the node degree is, the fewer the cluster heads are, and the less the network delay is; node degree normalization processing, namely, the number N of neighbor nodesiDivided by the total number of nodes in the cluster NsNormalized node degree NτComprises the following steps:
further, the probability fs (i) of successful communication of the node depends on the number y of successful communication and the number n of failed communication in M independent repeated tests, and then the probability fs (i) of successful communication of the node is:
wherein, the successful communication is that the HELLO message of the neighbor node is received in one period; the communication failure is that no message of a neighbor node or error information of link fracture is received in one period;
the unmanned aerial vehicles have different combat missions and different performances; the main tasks of the unmanned aerial vehicle for executing the detection task are to collect information, observe a target area and enhance the display and early warning capability; the unmanned aerial vehicle for executing the reconnaissance task goes deep into the local defense depth for monitoring, target indication, damage assessment and the like(ii) a The unmanned aerial vehicle executing the rescue task replaces other unmanned aerial vehicles to continue executing the task when the other unmanned aerial vehicles fail; the unmanned aerial vehicle executing the striking task needs to carry an attack weapon to strike the target accurately. Therefore, the influence of the unmanned aerial vehicle on the task type selection needs to be considered, and the priority T of the node cluster head selection is set according to the danger degree of the unmanned aerial vehicle task typeτAs shown in table 1:
TABLE 1 task categories and priority weights
Further, 4 influence factors are weighted and summed according to the residual energy of the node i, the number of neighbor nodes, the communication condition and the task type; the composite weight obtained by comprehensively considering the 4 factors through the weighting clustering algorithm is as follows:
Mi＝ω1Eτ+ω2Nτ+ω3fs(i)+ω4Tτ (18)
the value ranges of ω 1, ω 2, ω 3, and ω 4 are [0, 1], and ω 1+ ω 2+ ω 3+ ω 4 is 1, and the specific size may be set according to actual conditions.
Further, a gray wolf optimization algorithm (GWO) simulates a level system and a hunting behavior of a gray wolf in nature, divides a gray wolf group into three levels of an α wolf, a β wolf and a γ wolf, and judges the approximate position of a hunting object by using the α wolf, the β wolf and the γ wolf in the search process of the gray wolf group as follows:
wherein D isα、Dβ、DγThe distances between the alpha wolf, the beta wolf and the gamma wolf and the prey are respectively; c1、C2、C3Is a random vector, which is the search range of alpha wolf, beta wolf and gamma wolf respectively; xα(t)、Xβ(t)、Xγ(t) current positions of the alpha wolf, the beta wolf, and the gamma wolf, respectively; x is the current gray wolf position vector; a. the1、A2、A3Is an attack range with adaptive vector of alpha wolf, beta wolf and gamma wolf.
Further, since α wolf, β wolf, γ wolf of the gray wolf optimization algorithm affect the position of the prey together, a weight distribution strategy needs to be adopted for it; the improved weight proportion of the invention is as follows:
wherein M is1、M2、M3Respectively improving the weight influence factors of alpha wolf, beta wolf and gamma wolf on the position of the prey in the grey wolf algorithm, namely the learning rates of the alpha wolf, the beta wolf and the gamma wolf on the position of the prey;
the prey positions are calculated according to the improved grayling optimization algorithm as follows:
X'(t+1)＝M1X1+M2X2+M3X3 (26)
the method adopts a wolf algorithm to optimize the election of the cluster heads of the unmanned aerial vehicle network, the wolf cluster is regarded as the unmanned aerial vehicle cluster network, the wolf is regarded as a node in the unmanned aerial vehicle cluster network, and the prey is regarded as the cluster head of each cluster. In each cluster, three nodes with the maximum M value are selected as M1、M2、M3The hunting object found in the cluster is calculated according to the formula (26), i.e. the cluster head.
Further, a periodic maintenance mechanism is adopted when the cluster network cluster structure of the unmanned aerial vehicle is maintained, so that a node with the highest joint measurement index serves as a cluster head in a cluster head election period, and the method specifically comprises the following steps:
the cluster head election period is determined according to the topology change degree of the unmanned aerial vehicle cluster network, when the topology change of the unmanned aerial vehicle cluster network is slow, the election period is properly prolonged, and the problem that a large amount of expenses are generated due to frequent re-election of the cluster head is avoided; when the topology change of the unmanned aerial vehicle cluster network is fast, the cluster head election time is properly shortened, and the cluster structure is prevented from being separated from the whole network due to the failure of the cluster head.
Compared with the prior art, the technical scheme adopted by the invention has the advantages that:
(1) the invention increases the number detection of the nodes in the cluster in the forming stage of the cluster, and ensures the moderate number of cluster heads and the balance degree of clustering.
(2) The invention adds the node energy detection before adopting the gray wolf algorithm to elect the cluster head, thereby greatly reducing the network overhead when electing the best cluster head.
(3) The cluster heads are elected by adopting the improved wolf optimization algorithm, so that the cluster heads are uniformly distributed, the problem of unstable network structure of the unmanned aerial vehicle cluster network caused by node movement is solved, and the life cycle of the network is prolonged.
Drawings
FIG. 1 is a graph of velocity differentials at node i and node j;
FIG. 2 is a flow chart of the present invention;
FIG. 3 is a graph illustrating the effect of node transmission distance on cluster head number;
FIG. 4 is a graph showing the influence of node transmission distance on the number of changes in cluster dependency;
FIG. 5 is a graph of the impact of node transmission distance on cluster balance;
FIG. 6 is a graph showing a relationship between simulation time and update times of the domination set;
fig. 7 is a graph showing a relationship between the number of simulation rounds and the number of surviving nodes.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the application, i.e., the embodiments described are only a subset of, and not all embodiments of the application.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Example 1
As shown in fig. 2, the present application provides a high-stability clustering method for an unmanned aerial vehicle cluster network, which specifically includes the following steps:
step 1: initializing a network, and defining the total number of nodes in the unmanned aerial vehicle cluster network as N;
step 2: clustering nodes in the network according to the speed similarity and the distance similarity of the nodes;
step 3: judging whether the number of members in the cluster is larger than a maximum threshold value nmax；
step 4: when the number of the members in all the clusters is smaller than the maximum threshold value, clustering is preliminarily completed;
step 5: judging the residual energy E of each node in the clusterresWhether or not it is greater than the mean energy E in the clusteravg；
step 6: when the node energy is less than the average energy EavgThe node quits electionClustering heads;
step 7: and selecting the optimal cluster head based on an improved wolf optimization algorithm according to joint measurement indexes of four influence factors of node residual energy, the highest node degree, communication conditions and task types.
Wherein the cluster formation steps are as follows:
step 1: judging whether the speed standard deviation is greater than a speed threshold q;
step 2: when the speed standard deviation is larger than a speed threshold value q, discarding the neighbor node i with the maximum speed difference with the node j, and executing step 1; otherwise step3 is executed;
step 3: judging whether the distance standard deviation is greater than a distance threshold value p;
step 4: when the distance standard deviation is larger than the distance threshold value p, discarding the neighbor node i with the largest distance difference with the node j, and executing step 3; otherwise step5 is executed;
step 5: judging whether the node smaller than the speed threshold and the node smaller than the distance threshold are the same node or not;
step 6: when the nodes meeting the two conditions are the same node, executing step 7;
step 7: detecting the number of nodes, and judging whether the number of member nodes in each cluster is less than or equal to the maximum allowed number n of nodesmax；
step 8: when the number of member nodes in each cluster is more than nmaxWhen the node j is not the node with the maximum sum of the speed difference and the distance difference, step7 is executed;
step 9: when the number of member nodes in each cluster is less than or equal to nmaxAnd then finishing clustering.
The above method was simulated using the parameters shown in table 2.
TABLE 2 simulation parameters
In order to embody the advantages of the invention, the invention is compared with four Clustering algorithms, namely a Low ID Clustering Algorithm (LID), a Highest Node Degree Clustering Algorithm (HD), a Weighted Clustering Algorithm (WCA) and an Improved multi-parameter combined Weighted Clustering Algorithm (IWCA), in a simulation mode, and comparison results are embodied from five aspects of cluster head number, cluster attachment change times, cluster balance, control set update times and Node survival numbers.
The simulation results of the number of cluster heads are shown in fig. 3. For a network, when the number of the cluster heads is too small, the cluster head burden is too large, so that the survival time is too short, and the life cycle of the network is short; when the number of cluster heads is too large, inter-cluster communication is frequent, and network communication overhead and end-to-end delay are increased. Therefore, in clustering, to ensure a moderate number of cluster structures, the requirement of document Sa u L Z M, Abel G N, Antonio L J]Expert Systems With Applications,2019,120(9):357 and 371
N is the number of nodes in the network, and the dotted line in the figure represents the number of cluster heads required by the optimal node degree. As can be seen from fig. 3, the cluster head numbers of the 5 algorithms decrease with the increasing transmission distance of the nodes, and the trend gradually becomes slower, that is, the cluster structure decreases with the increasing transmission distance. The HSCOA algorithm detects the number of nodes in the cluster during the initial formation of the cluster, so that the number of cluster heads is moderate. In summary, compared with the LID, HD and WCA algorithms, the HSCOA algorithm has the cluster head number closer to the optimal value, but is slightly worse than the IWCA algorithm after the node transmission distance exceeds 20 cm.
The simulation results of the number of times of changes in cluster attachment relationship are shown in fig. 4. The more stable the cluster structure, the fewer the cluster attachment changes. As can be seen from fig. 4, the cluster dependency change of the 5-clustering algorithm is decreased after the transmission distance of the node is increased. When the transmission distance is smaller, the probability that the node is out of the cluster is smaller, and increases as the transmission distance of the node increases. At this time, the network has more cluster structures and fewer member nodes in each cluster, but as the transmission distance increases, the cluster structures decrease, the number of member nodes in each cluster increases, and the probability that a node leaves a cluster gradually decreases as the transmission distance increases. Because the HSCOA algorithm adopts the improved grey wolf optimization algorithm when electing the cluster heads, the cluster heads are ensured to be uniformly distributed, the change times of the cluster attachment relation of the HSCOA algorithm are smaller than those of other four clustering algorithms, and the network structure is more stable.
The simulation results of the degree of balance of clustering are shown in fig. 5. The cluster balance degree means that the number of nodes of each cluster is basically equal, so that the clusters in the network are distributed relatively uniformly. The balance degree of the experimental clustering algorithm is measured by adopting a standard deviation mode, namely the smaller the value of the standard deviation of the number of nodes contained in each cluster, the more excellent the clustering algorithm is. As can be seen from fig. 5, the cluster balance of the 5 algorithms increases as the transmission distance of the node increases. As the transmission distance of the nodes increases, the cluster structure decreases, and the average number of nodes in each cluster increases, thereby causing an increase in the standard deviation of the number of nodes included in each cluster, i.e., an increase in the degree of balance. The HSCOA algorithm detects the number of nodes after primary clustering, so that the number of nodes in each cluster is approximately the same, the clusters are distributed relatively uniformly, and the clustering balance degree of the HSCOA algorithm is obviously superior to that of other clustering algorithms.
The simulation result of the update times of the domination set is shown in fig. 6. The update times of the dominating set are the update times of the cluster head in unit time, the more the update times of the dominating set are, the more unstable the cluster structure is, and the more huge calculation amount and communication overhead are generated in the clustering process by the clustering algorithm. As can be seen from fig. 6, the update times of the dominating set of 5 algorithms all increase with the increase of the simulation time. Because the HSCOA algorithm firstly clusters the network and then elects the cluster head, when the member node fails, the clustering and electing are not needed, and the updating times of the dominating set are reduced, so that the updating times of the dominating set are obviously lower than those of other four clustering algorithms, the stability of the whole network is improved, the calculated amount is reduced, and the network overhead is reduced.
The simulation result of the number of nodes to live is shown in fig. 7. For a network, as the number of simulation rounds increases, the number of survival nodes increases, and the life cycle of the network is longer. As can be seen from fig. 7, the number of the survival nodes of the 5 algorithms decreases as the number of simulation rounds increases. The WCA, the IWCA and the HSCOA take residual energy of nodes into consideration, so that the survival number of the nodes passing through the three algorithms with the same round number is obviously more than that of the other two algorithms, but the weight setting of the residual energy of the nodes in the IWCA algorithm is larger and changes along with the number of neighbor nodes, so that the IWCA algorithm is superior to the WCA algorithm. The HSCOA algorithm not only considers the residual energy of the nodes, but also adopts a wolf optimization algorithm with small calculation amount to select the cluster heads, and also adopts a periodic maintenance mechanism to maintain the clusters, so the HSCOA algorithm is optimal, and the network life cycle is longest.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (9)
1. A high-stability clustering method for an unmanned aerial vehicle cluster network is characterized by comprising the following steps:
clustering nodes of the unmanned aerial vehicle cluster network according to the speed and distance similarity of the nodes, namely, dividing the unmanned aerial vehicle nodes which are relatively static within a certain distance range into a cluster;
selecting a cluster head by adopting an improved graying algorithm according to joint measurement indexes of four influencing factors of node residual energy, highest node degree, communication condition and task type;
and a periodic maintenance mechanism is adopted when the cluster network structure of the unmanned aerial vehicle cluster is maintained, so that the node with the highest joint measurement index can be used as a cluster head in a cluster head election period.
2. The high-stability clustering method for the unmanned aerial vehicle cluster network according to claim 1, wherein the nodes of the unmanned aerial vehicle cluster network are clustered according to the speed and distance similarity of the nodes, that is, the nodes of the unmanned aerial vehicle which are relatively static within a certain distance range are divided into a cluster, specifically:
if the node i is an arbitrary node of the node j in the next-hop communication range, and the speed and the moving direction of the node are calculated in the three-dimensional coordinate system, the speed differences of the node i and the node j on the x axis, the y axis and the z axis are as follows:
wherein, Vjx、Vjy、VjzIs the velocity of node j on the x, y, z axes; vix、ViyViz is the speed of the node i on the x axis, the y axis and the z axis; alpha is alphaj、αiRespectively are included angles between the nodes j and i and the x axis; beta is aj、βiRespectively are included angles between the nodes j and i and the y axis; gamma rayj、γiRespectively are included angles between the nodes j and i and the z axis;
then the average speed difference between the node j and the N neighboring nodes i on the x axis, the y axis and the z axis is:
the standard deviation of the speed difference between the node j and the neighboring node i on the x axis, the y axis and the z axis is as follows:
according to the Pythagorean theorem, the standard deviation of the speed difference between the node j and the neighbor node i is as follows:
δjv 2＝δjx 2+δjy 2+δjz 2 (10)
setting a standard deviation threshold value of the speed difference as q, and when the standard deviation delta of the speed difference isjvWhen the speed threshold q is smaller than the speed threshold q, the node j and the neighbor node i have similarity in the movement speed and the movement direction, that is, have speed similarity.
3. The high-stability clustering method for the unmanned aerial vehicle cluster network according to claim 2, wherein if a node i is an arbitrary node of a node j in a next-hop communication range, the distance difference between the node j and a neighboring node i is:
wherein, PtTransmitting power for the node; gtGain for the transmit antenna; h istIs the transmit antenna height; h isrIs the receive antenna height; prReceiving power for a node;
the standard deviation of the average distance difference between the node j and the N neighboring nodes i is:
wherein the content of the first and second substances,
the average distance difference between the node j and N neighbor nodes i is obtained;
setting the standard deviation threshold of the distance difference as p, and when the standard deviation delta of the distance difference isjdWhen the distance is smaller than the distance threshold p, the node j has similarity with the neighbor node i, that is, has distance similarity.
4. The high-stability clustering method for the unmanned aerial vehicle cluster network according to claim 2 or 3, wherein when the standard deviation of the node speed difference and the standard deviation of the distance difference are both smaller than the corresponding speed threshold q and distance threshold p, the node j and the neighboring node i thereof are considered to have the speed similarity and the distance similarity at the same time, namely the node j and the neighboring node i thereof accord with the clustering condition; the clustering condition has transitivity, namely the node j and the node i accord with the clustering condition, the node j and the node k accord with the clustering condition, and then the node i and the node k also accord with the clustering condition, namely the node j, the node i and the node k form a cluster;
after the primary clustering is finished, detecting the number of nodes in each cluster, and setting the maximum allowable number of nodes in each cluster as nmaxTo ensure the balance of clustering.
5. The high-stability clustering method for the unmanned aerial vehicle cluster network according to claim 1, wherein a cluster head is selected by adopting an improved wolf algorithm according to joint measurement indexes of four influencing factors, namely node residual energy, highest node degree, communication condition and task type, and specifically the method comprises the following steps:
when the cluster head election is carried out, if the residual energy E of a certain noderesLess than the average energy E of all neighboring nodesavgQuitting the election of the cluster head; wherein, the residual energy is obtained by a ground control station; normalizing the residual energy, i.e. residual energy EresDivided by the initial energy E0Normalized residual energy EτComprises the following steps:
the node degree refers to the number of neighbor nodes of the node in a communication range; node degree normalization processing, namely, the number N of neighbor nodesiDivided by the total number of nodes in the cluster NsNormalized node degree NτComprises the following steps:
the probability fs (i) of successful communication of the node depends on the number y of successful communication and the number n of failed communication in the M independent repeated tests, and the probability fs (i) of successful communication of the node is as follows:
wherein, the successful communication is that the HELLO message of the neighbor node is received in one period; the communication failure is that no message of a neighbor node or error information of link fracture is received in one period;
setting priority T of node cluster head election according to danger degree of unmanned aerial vehicle task typesτThe detection task > the scratch detection task > the rescue task > the attack task.
6. The high-stability clustering method for the unmanned aerial vehicle cluster network according to claim 5, wherein 4 influencing factors are weighted and summed according to the residual energy of a node i, the number of neighbor nodes, the communication condition and the task type; the composite weight obtained by comprehensively considering the 4 factors through the weighting clustering algorithm is as follows:
Mi＝ω1Eτ+ω2Nτ+ω3fs(i)+ω4Tτ (18)
the value ranges of ω 1, ω 2, ω 3, and ω 4 are [0, 1], and ω 1+ ω 2+ ω 3+ ω 4 is 1, and the specific size may be set according to actual conditions.
7. The method as claimed in claim 1, wherein the gray wolf optimization algorithm simulates the level system and hunting behavior of the gray wolf in nature, and divides the gray wolf population into three levels of α wolf, β wolf and γ wolf, and during the search of the gray wolf population, the α wolf, β wolf and γ wolf are used to determine the approximate positions of the hunting objects as follows:
wherein D isα、Dβ、DγRespectively are alpha wolf, beta wolf,Distance between the y wolf and the prey; c1、C2、C3Is a random vector, which is the search range of alpha wolf, beta wolf and gamma wolf respectively; xα(t)、Xβ(t)、Xγ(t) current positions of the alpha wolf, the beta wolf, and the gamma wolf, respectively; x is the current gray wolf position vector; a. the1、A2、A3Is an attack range with adaptive vector of alpha wolf, beta wolf and gamma wolf.
8. The high-stability clustering method for the unmanned aerial vehicle cluster network according to claim 7, wherein since α wolf, β wolf and γ wolf of the gray wolf optimization algorithm affect the position of the prey together, a weight distribution strategy needs to be adopted, and the improved weight ratio is:
wherein M is1、M2、M3Respectively improving the weight influence factors of alpha wolf, beta wolf and gamma wolf on the position of the prey in the grey wolf algorithm, namely the learning rates of the alpha wolf, the beta wolf and the gamma wolf on the position of the prey;
the prey positions are calculated according to the improved grayling optimization algorithm as follows:
X'(t+1)＝M1X1+M2X2+M3X3 (26)
optimizing the election of the cluster heads of the unmanned aerial vehicle network by adopting a wolf algorithm, wherein a wolf cluster is regarded as an unmanned aerial vehicle cluster network, wolfs are regarded as nodes in the unmanned aerial vehicle cluster network, and preys are regarded as the cluster heads of each cluster; in each cluster, three nodes with the maximum M value are selected as M1、M2、M3The hunting object found in the cluster is calculated according to the formula (26), i.e. the cluster head.
9. The high-stability clustering method for the unmanned aerial vehicle cluster network according to claim 1, wherein a periodic maintenance mechanism is adopted when maintaining the unmanned aerial vehicle cluster network cluster structure, so as to ensure that a node with the highest joint metric index serves as a cluster head in a cluster head election period, and specifically comprises:
the election period of the cluster head is determined according to the topology change degree of the unmanned aerial vehicle cluster network, and when the topology change of the unmanned aerial vehicle cluster network is slow, the election period is prolonged; when the topology change of the unmanned aerial vehicle cluster network is fast, the cluster head election time is shortened.
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