CN112469047B - Method for deploying space-ground integrated intelligent network satellite nodes - Google Patents
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Abstract
The invention discloses a space-ground integrated intelligent network satellite node deployment method, which comprises the following steps: establishing a mathematical model; establishing a network clustering algorithm; and establishing an artificial bee colony algorithm. The invention provides an intelligent satellite node optimization deployment algorithm based on artificial bee colony by adopting an artificial bee colony optimization algorithm and combining a network clustering algorithm. The algorithm fully considers the high dynamic property of the satellite nodes, combines the characteristics of periodicity of the satellite running track, limited satellite connectivity, limited satellite visual range and the like to deploy the minimum intelligent satellite nodes, and achieves the highest effective service coverage rate of the whole edge service. The invention can provide more comprehensive edge computing service for other satellite nodes and ground users in remote areas by deploying the minimum intelligent satellite nodes. And further, the transmission distance of data is effectively reduced, and the transmission delay of the satellite system is reduced. Certain help can be provided for the deployment of the edge service system on the satellite in the future.
Description
Technical Field
The invention relates to a satellite node deployment strategy and a mobile edge computing technology of a space-ground integrated intelligent network, in particular to an intelligent satellite node deployment method in the space-ground integrated intelligent network.
Background
With the continuous development of satellite network technology, the low-orbit satellite network with inter-satellite links can realize seamless coverage of global mobile communication with huge deployment scale and low deployment cost. However, with the continuous development and the continuous increase of the internet of things technology, the continuous increase of the constellation scale and the continuous complex topology structure in the space-based access network, the satellite computing task is increased. The traditional calculation mode of transmitting a calculation task to a high-orbit satellite for calculation or forwarding the calculation task to a ground data center through the high-orbit satellite causes extremely high transmission delay, and cannot meet the requirements of more and more real-time applications. Therefore, a new technique is urgently needed to solve the problem of too high transmission delay in the satellite network.
The core idea of the mobile edge computing is to place a computing task on a computing resource closest to a data source as much as possible for running, so that the transmission delay of a computing system is effectively reduced, and the pressure of a cloud computing center is relieved. The mobile edge computing technology has solved many problems existing in the traditional cloud computing due to its excellent real-time performance and connectivity, and is favored by more and more researchers. But the related research is directed to ground networks. The satellite nodes have high dynamic performance, and the topological structure changes almost every moment, so that an algorithm needs to be designed to solve the deployment problem of the intelligent satellite nodes in the space-ground integrated intelligent network by combining the high dynamic performance of the satellite nodes.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to design an edge-computing-based space-ground integrated intelligent network satellite node deployment method which can meet the requirements of integrated satellite networks on high dynamic performance and limited satellite node connectivity.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method for deploying a space-ground integrated intelligent network satellite node comprises the following steps:
A. establishing a mathematical model
The space-ground integrated intelligent network comprises a ground-based network, a space-based access network and a space-based backbone network, wherein the space-based access network is composed of a plurality of low-orbit satellites (LEO satellites) which work cooperatively; and selecting part of LEO satellite nodes to carry on an edge server to serve as intelligent satellite nodes so as to establish an intelligent satellite network system in a space-based access network and provide reliable edge service for other LEO satellite nodes and part of ground users. In order to better study the deployment problem of intelligent satellite nodes in a space-ground integrated intelligent network, D ═ D is adopted below1,d2,…,daRepresents all low-orbit satellite nodes in the intelligent satellite network. Let R ═ D | be the number of satellite nodes in the space-based access network, DaInitial position information of the a-th satellite node; x ═ X1,x2,…,xnExpressing intelligent satellite in the integrated intelligent network of the heaven and earthThe deployment strategy of the nodes is called deployment strategy X for short, and N is the total number of deployed intelligent satellite nodes, X1～xnAnd respectively representing the numbers of the selected N intelligent satellite nodes, wherein N is equal to N. Wherein x is more than or equal to 1nR is less than or equal to R, and represents the x-th satellite node in R satellite nodes in the heaven-earth integrated intelligent networknEach satellite node is an intelligent satellite node.
B. Network clustering algorithm establishment
The network clustering algorithm is an algorithm for calculating the fitness of the intelligent satellite node deployment strategy. The algorithm has the function of calculating the effective coverage rate of the edge service corresponding to the deployment strategy X under the condition that the deployment strategy X of the intelligent satellite node and the information of each satellite node in the low-orbit satellite layer are known. Therefore, the fitness of the corresponding deployment strategy X is evaluated, and the evaluation index of the deployment strategy X is provided for the following artificial bee colony algorithm. The satellite node information comprises specific coordinates, orbit parameters, a running period and satellite node connectivity.
Aiming at the characteristics of strong dynamic property and periodicity of satellite nodes, the complete operation period T of a satellite is divided into w determined time periods, namely [ T0＝0,t1],[t1,t2],…,[tw-1,tw＝T]And the topology of the satellite is assumed to be unchanged during each time period. Therefore, the rectangular coordinates of each satellite in each time period are calculated according to the orbit parameters. Let t0Changing the value of t results in rectangular coordinates of each satellite at different time periods, where 0 denotes the initial time.
After the specific coordinates of each satellite node are known, the spatial distance between different satellite nodes is calculated. And the non-intelligent satellite node searches the intelligent satellite node closest to the non-intelligent satellite node within the visible distance, and provides a connection request. And the intelligent satellite nodes establish connection with the nearest K satellite nodes according to the maximum connection degree K of the intelligent satellite nodes. Until all non-intelligent satellite nodes have been connected, or no suitable intelligent satellite nodes are available to provide the connection.
The situation that all satellite nodes acquire the edge service in a certain time period is obtained through the steps. With SlinktaRepresenting time tSlink, the situation that the a-th satellite node in the segment acquires edge servicetaAcquisition to edge service, Slink, is denoted 1ta0 means that edge service cannot be acquired. Thus, the effective coverage rate P of edge services within a single time periodtAnd the calculation formula of the effective coverage rate P of the edge service in one period is as follows:
at this point, the effective coverage rate P of the edge service of the intelligent satellite network system in one operation period is calculated. And effective data support is provided for judging the advantages and disadvantages of the intelligent satellite node deployment strategy.
C. Algorithm for establishing artificial bee colony
And B, iterating the intelligent satellite node deployment strategy X in the low-orbit satellite layer, calculating the effective coverage rate P of the edge service of the intelligent satellite network system under the deployment strategy X by utilizing the step B, and calculating the optimal intelligent satellite node deployment strategy X by using the coverage rate P as fitness.
C1, code representation of honey source and initialization of question
Each honeysource in the artificial bee colony is assumed to represent an intelligent satellite node deployment strategy X, namely a feasible solution of the deployment strategy X. A certain honey source XiThe code of (a) is expressed as: xi＝{xi1,xi2…xinIn which xinE {1,2, …, R }, and the values of different dimensions are different, which indicates that the corresponding number is x in the satellite nodeinThe satellite nodes are deployed as intelligent satellite nodes. i belongs to {1,2, …, M }, and M is the number of honey sources in the artificial bee colony. The task of leading bees is to find a honey source and share the honey source with following bees according to a certain probability; searching a new honey source near the honey source according to the information transmitted by the leading bees by the following bees, and performing greedy selection; the reconnaissance bee is used for avoidingTrapping into a local optimal solution; leading bees and honey sources keep one-to-one correspondence, and the number of the following bees is consistent with that of the leading bees, so the number of the following bees is M. The termination condition is set to reach the maximum iteration number MCN or the effective time of the whole edge service of the intelligent satellite network system reaches 100 percent.
C2 fitness function of honey source and generation of new solution
Calculating each honey source X through the step BiAnd takes it as the judgment honey source XiGoodness of fitness fitiThe calculation formula is as follows:
fiti＝fcluster(Xi) (6)
in the process of each iteration, a new feasible solution is searched near the honey source by leading the bees, and the specific formula is as follows:
vij＝xij+φij(xij-xkj) (7)
wherein k is equal to {1,2, …, M }, j is equal to {1,2, …, N }, and k is equal to i, φijIs [ -1,1 [ ]]A random number in between. Through calculating the fitness fit of the new honey sourceiAnd determining whether to replace the old honey source according to whether the fitness of the new honey source is greater than that of the old honey source. If the honey source is larger than the original honey source, the new honey source is used for replacing the old honey source, and otherwise, the new honey source is abandoned.
The honey source information provided by leading bees is the fitness fitiThe following bees decide which leading bee to follow in a roulette mode, and the probability that the following bee follows the leading bee i is as follows:
in the formula, piFor corresponding to the probability that the leading bee is followed, the following bee generates a new solution by the formula (7) after reaching the corresponding bee source, and the fitness fit of the following bee is calculatediAccording to fit of the new solutioniAnd judging whether the new solution is reserved or not by judging whether the new solution is larger than the old solution or not, if so, reserving the new solution, and otherwise, abandoning the new solution and reserving the old solution.
C3, avoiding falling into local optimum
Setting a threshold value Limit, and when the fitness of a solution which is not yet a new solution is larger than an old solution after the solution is iterated for the Limit times, considering that the solution space is trapped in the local optimum, wherein the honey source X isiIt is discarded. When a honey source XiAfter being abandoned, the corresponding leading bee is converted into a scout bee, and a new honey source is generated by the formula (9) to replace the scout bee. The formula for randomly generating a new honey source is as follows:
xij＝xminj+rand(0,1)(xmaxj-xminj) (9)
wherein x isminjAnd xmaxjRespectively representing the lower limit and the upper limit of a j-th dimension vector in a solution space, and rand (0,1) is a random number within 0-1.
Further, the lower limit x of the j-th vector in the solution space minj0, upper bound x of j-th dimension vector in solution spacemaxj＝R。
Further, the value of M is 5 to 30.
Further, M takes the value of 20.
Compared with the prior art, the invention has the following beneficial effects:
1. the deployment of the intelligent satellite nodes can well solve the problems of insufficient bandwidth, large transmission delay and the like in a data explosion type growth environment. However, facing a large satellite constellation, how many of the deployments, and how to deploy edge service nodes, are issues that need to be considered first. The invention provides an intelligent satellite node optimization deployment algorithm based on artificial bee colony by adopting an artificial bee colony optimization algorithm and combining a network clustering algorithm. The algorithm fully considers the high dynamic property of the satellite nodes, combines the characteristics of periodicity of the satellite running track, limited satellite connectivity, limited satellite visual range and the like to deploy the minimum intelligent satellite nodes, and achieves the highest effective service coverage rate of the whole edge service.
2. The invention can provide more comprehensive edge computing service for other satellite nodes and ground users in remote areas by deploying the minimum intelligent satellite nodes. And further, the transmission distance of data is effectively reduced, and the transmission delay of the satellite system is reduced. Certain help can be provided for the deployment of the edge service system on the satellite in the future.
Drawings
Fig. 1 is a schematic diagram comparing network clustering algorithms.
Fig. 2 is a schematic view of a satellite visibility.
Fig. 3 is a graph of the effect of edge service coverage optimization when N is 14.
Fig. 4 is a schematic diagram of smart satellite node deployment when N-14.
FIG. 5 is an optimized comparison graph of the present invention with a simulated annealing algorithm and a particle swarm algorithm.
Fig. 6 is a comparison graph of optimization results of different algorithms when the number N of nodes takes different values.
Fig. 7 is a diagram illustrating the optimization effect of the average transmission delay.
Detailed Description
The invention is further described below with reference to the accompanying drawings. Fig. 1 is a schematic diagram of the network clustering algorithm in step B. The method for deploying the satellite nodes in the space-ground integrated intelligent network comprises the following concrete implementation steps:
step 1: initializing the number M of feasible solutions, the maximum iteration number MCN, the threshold value limit and the maximum fitness fit max0, feasible solution X1～XMUpdate times trail1～trailM＝0；
Step 2: calculating the fitness fit of each feasible solution by using a network clustering algorithm according to the formula (6)1～fitM；
Step 3: initializing a maximum connectivity K-4 and P-0 of all satellite nodes, and connecting application queue
The number of the divided time slices is T, and the starting time T is 0;
step 4: calculating the rectangular coordinate D of each satellite node in the time slice according to a formulatCalculating the distance L between each unconnected non-intelligent satellite node and the intelligent satellite node in the current time slice according to the graph 2ijAnd is visibleApplying for connection to the nearest intelligent satellite node within the range;
step 5: if the application queues are all empty, jumping to Step7, otherwise, each intelligent satellite node respectively processes the own application queue LinkjEstablishing connection with the applied satellite node under the condition of ensuring the connectivity of the satellite;
step 6: if the non-intelligent satellite nodes still do not acquire the connection, jumping to Step 4;
step 7: calculating the effective coverage rate P of the edge service in the current time slice according to the formula (4)t. If the full time slice has already been calculated, the total coverage P is calculated according to equation (5) and the process moves to Step 8. Otherwise, adding 1 to the time slice, and jumping to Step 4;
step 8: leading bee activity, circulating for M times, and obtaining X by formula (7)newPerforming greedy selection to store the optimal X;
step 9: method for determining probability p of following bee by using formula (8) in roulettei；
Step 10: moving with bee, circulating for M times, and keeping with bee according to p1～pmDeciding the object X to followjJ is equal to {1,2, …, M }. The subsequent operation is similar to the leading bee activity, yielding XnewAnd decides whether to update Xj；
Step 11: detecting bee activity, repeating for M times, and determining whether trail is presentiIf the solution is more than or equal to limit, generating a new solution X by using the formula (9)iAnd update the fiti＝fcluster(Xi)，traili＝0；
Step 12: saving the optimal solution of the current loop, if the iteration times are less than or equal to MCN and fitmaxIf < 1, go to Step 8. Otherwise, the program is ended to obtain the optimal solution X and fitmax。
In order to verify the effectiveness of the intelligent satellite node deployment strategy, the invention utilizes the matlab and the STK platform to establish a simulation platform of a space-ground integrated intelligent network. The hardware environment of the simulation platform is Inter (R) core (TM) i7-6700HQCPU @2.60GHz, and the running memory is 16G. An iridium constellation (IridiumConstellation) with 66 LEO and 3 GEO satellites in the equatorial plane were used as simulation systems. The longitude of 3 GEO satellites is 100E, 140W and 20W respectively. The specific parameters of the LEO constellation are as follows:
TABLE 1 constellation simulation parameters
The value of the deployment quantity N of the intelligent satellite nodes directly determines the effective coverage rate of the edge service of the whole system. However, the high value of N causes high deployment cost. Therefore, how much N is taken is also a problem to be considered at the same time. Since the iridium satellite constellation has 66 LEO satellites, and the connectivity of each satellite is set to be 4, the effective service coverage of the system is to be 100%, and the number N of deployed intelligent satellite nodes is theoretically at least 14(66/(4+1) ═ 13.2). Therefore, the optimization results obtained when N is 14 and the number of iterations is 200 are shown in fig. 3-4.
As can be seen from the results in fig. 3, when N is 14, the present invention can obtain an intelligent satellite node deployment strategy, so that the effective coverage rate of the edge service is approximately 98%, which is very close to the theoretical value. As is also apparent from fig. 4, the dark dots representing the intelligent satellite nodes are distributed more uniformly, and can provide edge services for neighboring satellite nodes well.
In order to better embody the advantages of the algorithm of the invention. Fig. 5 is a comparison graph of the cluster deployment strategy iGSA and the PSO particle swarm algorithm after 1000 iterations after N is set to 14. As shown in fig. 5, after 1000 iterations, the optimization speed of the algorithm proposed by the present invention is significantly higher than that of the other two algorithms. In the optimization effect, the optimization is about 7.6% higher than that of a clustering deployment strategy and a PSO particle swarm algorithm, so that a better optimal solution can be obtained.
FIG. 6 is a comparison graph of optimization results obtained after 1000 iterations of each algorithm when N is 10-20. As is apparent from the figure, the algorithm provided by the present invention can obtain an intelligent node deployment strategy when N is 16, so that the effective service coverage of the edge service can reach 99.85%, and can reach 100% when N is 17. And the other two algorithms enable the effective service coverage rate to reach 100% when N is 19, and the optimization effect is improved by about 10.5%.
By deploying the edge computing server on the intelligent satellite node, the non-intelligent satellite node can transfer the complex computing task to the intelligent satellite node closest to the non-intelligent satellite node, and then the intelligent satellite node returns the computing result to the original satellite node. Compared with the traditional cloud computing model, the computing mode provided by the invention can greatly reduce the transmission distance of data. Therefore, higher edge service coverage will necessarily result in lower transmission delay. Fig. 7 illustrates the average transmission delay required from the simultaneous transmission of computation requests to the reception of computation results by all satellite nodes. The abscissa of the graph is the number N of deployed edge service nodes, and the ordinate is the average transmission delay in milliseconds (ms). The calculation formula of the average transmission delay Tra is as follows:
Trai＝2Si/C (10)
wherein, TraiData transmission delay from sending a calculation request to receiving a calculation result for the ith satellite; siThe linear distance from the ith satellite node to the target satellite node is defined, if the satellite node can request the edge service, the target satellite node is the nearest edge service node, if the satellite node can not request the edge service, the target satellite node is the nearest GEO node, if the ith satellite node is an intelligent satellite node, S is definedi0; c is the propagation speed of light in vacuum; and R is the total number of the satellite nodes.
In a traditional satellite network architecture, information is transmitted through a GEO satellite, and the transmission delay is up to over 240 ms. As can be seen from fig. 7, when the number N of intelligent satellite nodes is 10, the average transmission delay of the intelligent satellite network system is only 30 ms. When the number of the intelligent satellite nodes N is 16, the average transmission delay of the intelligent satellite network system is reduced to about 7ms, and then the number of the edge service nodes is increased again, so that the optimization effect of the average transmission delay of the intelligent satellite network system is not obvious. It can thus be derived: the addition of the edge service node can effectively reduce the average transmission delay of the intelligent satellite network system. The intelligent satellite node deployment algorithm based on the artificial bee colony can quickly find out the optimal edge service node deployment strategy according to different constellations, and provides certain help for the deployment of an edge service system on the satellite in future.
The present invention is not limited to the embodiment, and any equivalent idea or change within the technical scope of the present invention is to be regarded as the protection scope of the present invention.
Claims (4)
1. A method for deploying an integrated intelligent network satellite node is characterized by comprising the following steps: the method comprises the following steps:
A. establishing a mathematical model
The space-ground integrated intelligent network comprises a ground-based network, a space-based access network and a space-based backbone network, wherein the space-based access network is composed of a plurality of low-orbit satellites (LEO satellites) which work cooperatively; selecting part of LEO satellite nodes to carry on an edge server to serve as intelligent satellite nodes so as to establish an intelligent satellite network system in a space-based access network and provide reliable edge service for other LEO satellite nodes and part of ground users; in order to better study the deployment problem of intelligent satellite nodes in a space-ground integrated intelligent network, D ═ D is adopted below1,d2,…,daDenotes all low-orbit satellite nodes in the intelligent satellite network; let R ═ D | be the number of satellite nodes in space-based access network, DaInitial position information of the a-th satellite node; x ═ X1,x2,…,xnExpressing a deployment strategy of intelligent satellite nodes in the integrated space-ground intelligent network, namely a deployment strategy X for short, and making N be the total number of the deployed intelligent satellite nodes, X1～xnRespectively representing the numbers of the N selected intelligent satellite nodes, wherein N is equal to N; wherein x is more than or equal to 1nR is less than or equal to R, and represents the x-th satellite node in R satellite nodes in the integrated intelligent network of the heaven and earthnA satelliteThe nodes are intelligent satellite nodes;
B. network clustering algorithm establishment
The network clustering algorithm is an algorithm for calculating the fitness of the deployment strategy of the intelligent satellite nodes; the algorithm has the function that under the condition that a deployment strategy X of an intelligent satellite node and information of each satellite node in a low-orbit satellite layer are known, the effective coverage rate of the edge service corresponding to the deployment strategy X is calculated; thus, the fitness of the corresponding deployment strategy X is evaluated, and the evaluation index of the deployment strategy X is provided for the next artificial bee colony algorithm; the satellite node information comprises specific coordinates, orbit parameters, an operation cycle and satellite node connectivity;
aiming at the characteristics of strong dynamic property and periodicity of satellite nodes, the complete operation period T of a satellite is divided into w determined time periods, namely [ T0＝0,t1],[t1,t2],…,[tw-1,tw＝T]And assuming that the topology of the satellite is unchanged in each time period; therefore, the rectangular coordinates of each satellite in each time period are calculated according to the orbit parameters; let t0Changing the value of t to obtain the rectangular coordinates of each satellite in different time periods;
after the specific coordinates of each satellite node are known, the space distance between different satellite nodes is calculated; the non-intelligent satellite node searches for the intelligent satellite node closest to the non-intelligent satellite node within the visible distance, and provides a connection request; the intelligent satellite nodes establish connection with the nearest K satellite nodes according to the maximum connection degree K of the intelligent satellite nodes; until all non-intelligent satellite nodes are connected or no suitable intelligent satellite nodes can provide connection;
the situation that all satellite nodes acquire edge service in a certain time period is obtained through the steps; with SlinktaIndicating the situation that the a-th satellite node acquires the edge service in the t time period, SlinktaAcquisition to edge service, Slink, is denoted 1ta0 means that edge service cannot be obtained; thus, the effective coverage rate P of edge services within a single time periodtAnd the calculation formula of the effective coverage rate P of the edge service in one period is as follows:
calculating the effective coverage rate P of the edge service of the intelligent satellite network system in one operation period; providing effective data support for judging the advantages and disadvantages of the intelligent satellite node deployment strategy;
C. algorithm for establishing artificial bee colony
B, iterating an intelligent satellite node deployment strategy X in a low-orbit satellite layer, calculating the effective coverage rate P of the edge service of the intelligent satellite network system under the deployment strategy X by utilizing the step B, and calculating the optimal intelligent satellite node deployment strategy X by using the coverage rate P as fitness;
c1, code representation of honey source and initialization of question
Each honey source in the artificial bee colony is set to represent an intelligent satellite node deployment strategy X, namely a feasible solution of the deployment strategy X; a certain honey source XiThe code of (a) is expressed as: xi＝{xi1,xi2…xinIn which xinE {1,2, …, R }, and the values of different dimensions are different, which indicates that the corresponding number is x in the satellite nodeinThe satellite nodes are deployed as intelligent satellite nodes; i belongs to {1,2, …, M }, wherein M is the number of honey sources in the artificial bee colony; the task of leading bees is to find honey sources and share the honey sources with following bees with a certain probability; searching a new honey source near the honey source according to the information transmitted by the leading bees by the following bees, and performing greedy selection; the reconnaissance bees are used for avoiding trapping in the local optimal solution; leading bees and honey sources are in one-to-one correspondence, and the number of the following bees is consistent with that of the leading bees, so that the number of the following bees is M; setting the termination condition to reach the maximum iteration number MCN or the effective time of the whole edge service of the intelligent satellite network system to reach 100 percent;
c2 fitness function of honey source and generation of new solution
Calculating each honey source X through the step BiIs used as the judgment honey source XiGood and bad fitness fitiThe calculation formula is as follows:
fiti＝fcluster(Xi) (6)
in the process of each iteration, a new feasible solution is searched near the honey source by leading the bees, and the specific formula is as follows:
vij＝xij+φij(xij-xkj) (7)
wherein k is {1,2, …, M }, j is {1,2, …, N }, and k is not equal to i, φijIs [ -1,1 [ ]]A random number in between; through calculating the fitness fit of the new honey sourceiDetermining whether to replace the old honey source according to whether the fitness of the new honey source is greater than that of the old honey source; if the value is larger than the preset value, replacing the old honey source with the new honey source, and otherwise, abandoning the new honey source;
the honey source information provided by leading bees is the fitness fitiThe following bees decide which leading bee to follow in a roulette mode, and the probability that the following bee follows the leading bee i is as follows:
in the formula, piFor corresponding to the probability that the leading bee is followed, the following bee generates a new solution by the formula (7) after reaching the corresponding bee source, and the fitness fit of the following bee is calculatediAccording to fit of the new solutioniJudging whether the new solution is reserved or not by judging whether the new solution is larger than the old solution or not, if so, reserving the new solution, and otherwise, abandoning the new solution and reserving the old solution;
c3, avoiding falling into local optimum
Setting a threshold value Limit, and when the fitness of a solution which is not yet a new solution is larger than an old solution after the solution is iterated for the Limit times, considering that the solution space is trapped in the local optimum, wherein the honey source X isiWill be discarded; when a honey source XiAfter being abandoned, the corresponding leadingThe bees are converted into detection bees and a new honey source is generated by the formula (9) to replace the detection bees; the formula for randomly generating a new honey source is as follows:
xij＝xminj+rand(0,1)(xmaxj-xminj) (9)
wherein x isminjAnd xmaxjRespectively representing the lower limit and the upper limit of a j-th dimension vector in a solution space, and rand (0,1) is a random number within 0-1.
2. The method for deploying the integrated intelligent network satellite node in the sky and the earth according to claim 1, wherein the method comprises the following steps: the lower limit x of the j-th vector in the solution spaceminj0, upper bound x of j-th dimension vector in solution spacemaxj＝R。
3. The method for deploying the integrated intelligent network satellite node in the sky and the earth according to claim 1, wherein the method comprises the following steps: said M takes a value of 5 to 30.
4. The method for deploying the integrated intelligent network satellite node in the sky and the earth according to claim 1, wherein the method comprises the following steps: the value of M is 20.
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