Satellite ground station optimization layout method and device based on improved NSGAII

Abstract

The invention discloses an improved NSGAII-based satellite ground station optimization layout method and device, and relates to the technical field of satellite ground station optimization layout, and the improved NSGAII-based satellite ground station optimization layout method mainly comprises the steps: generating an initial population according to a satellite ground station optimization layout problem parameter, and an optimization evaluation system is constructed, an improved non-dominated sorting genetic algorithm II is utilized to obtain a preliminary offspring population, a dynamic congestion degree selection mechanism is utilized to obtain an offspring population until the maximum number of iterations is reached, and a final optimization result is obtained. By implementing the satellite ground station optimization layout method and equipment based on the improved NSGAII provided by the invention, the problem of ground station layout optimization under double indexes of satellite-ground connectivity and ground station flow load balance can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite ground station layout, and more specifically, to a satellite ground station optimization layout method and device based on improved NSGAII. Background Art

[0002] Satellite ground stations are an important part of satellite communication systems. It is very important to develop reliable methods to evaluate the communication and data transmission support effect of ground station clusters on satellite networks, as well as to explore their deployment rules within a feasible range. As countries around the world have invested in the construction of giant constellation networks in recent years, the number and scale of satellites in communication satellite systems have also expanded dramatically, the complexity of satellite space systems has gradually increased, and the application of satellite constellation networks has become more diversified and complex. Therefore, facing the application needs of large-scale satellite constellation systems, studying how to use limited ground station resources to accommodate large-capacity communication and data transmission needs as much as possible and enhance the communication effect of ground stations on satellite constellations is an important problem in the future construction of satellite communication constellation systems in my country. Researchers have proposed some classic evaluation methods for the Optimal ground station placement (OGSP) scheme from multiple aspects. For example: aiming to minimize the number of intersatellite link hops during data transmission; maximizing the quality of the communication link between the ground station and the satellite under the influence of distance, weather, and region, and maximizing the total traffic revenue generated in the target area. However, most studies evaluate the ground station layout strategy from a single aspect, or introduce the number of ground stations as an optimization target into the model by introducing the deployment cost. Few studies consider the effect of ground station deployment strategy on the quality of data transmission from multiple aspects. For the OGSP problem, the evaluation benefit or quality of a ground station cluster is composed of multiple factors, and it is difficult to make an effective evaluation of the deployment method of the ground station cluster from a single aspect. In the satellite communication system, the ground station communicates data via the feeder link established with the satellite. Since the relative position of the satellite and the ground target point continues to change, for a specific satellite constellation, the location of the ground station will greatly affect the communication quality of the ground station and the satellite in the time dimension. Therefore, it is necessary to measure the optimization of the ground station layout from the perspective of satellite-to-ground connectivity. In addition, as the traffic hub in the satellite communication system, almost all the traffic generated by the communication satellite system will be transmitted through the ground station. When studying OGSP, unreasonable layout schemes will cause some ground stations to bear too heavy a traffic transmission burden, resulting in load imbalance. This problem will greatly affect the data transmission quality and speed provided to users by the satellite communication system. Therefore, when evaluating the ground station deployment plan, the traffic load balancing status of each ground station should be regarded as a reasonable evaluation indicator, which will effectively evaluate a ground station layout strategy in terms of information transmission and data quality in the system.Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a multi-objective optimization algorithm based on Pareto sorting. It inherits the global search capability of traditional genetic algorithms for solution space, and through fast non-dominated sorting and elite selection strategies, the algorithm has good performance in convergence and diversity. However, there is still room for improvement in the search speed, individual exploration method of the population, and the judgment of individual crowding. When faced with some problems that require high search capabilities and have discontinuous feasible spaces, the performance of the algorithm will decrease. Therefore, if the NSGA-II algorithm is to be used for OGSP solution, necessary improvements should be made to the above-mentioned defects. Summary of the invention

[0003] The purpose of the present invention is to provide a satellite ground station optimization layout method and device based on improved NSGAII, which can solve the ground station layout optimization problem under the dual indicators of satellite-to-ground connectivity and ground station traffic load balancing.

[0004] The present invention provides a satellite ground station optimization layout method based on improved NSGAII, comprising the following steps: S1: determining satellite ground station optimization layout problem parameters according to ground stations involved in deployment and target communication satellites; S2: generating an initial population according to the satellite ground station optimization layout problem parameters; S3: constructing an optimization evaluation system according to the initial population and the satellite ground station optimization layout problem parameters; S4: obtaining a preliminary offspring population according to the optimization evaluation system by using an improved non-dominated sorting genetic algorithm II; S5: obtaining a offspring population according to the preliminary offspring population by using a dynamic crowding degree selection mechanism; S6: when the number of iterations is less than the maximum number of iterations, returning to step S3; otherwise, taking all ground station layout schemes of the final offspring population as the final optimization result.

[0005] Furthermore, the parameters of the above-mentioned satellite ground station optimization layout problem include satellite ground station clusters, target communication satellite constellations, minimum communication belief angles of ground stations, minimum transmission delays for single communications, ground station layout areas, sub-area constraint sets, scenario time, maximum number of iterations and population size.

[0006] Furthermore, the above optimization evaluation system includes satellite-ground connectivity, traffic load balancing measurement, and constraint violation degree.

[0007] Furthermore, the above satellite-to-ground connectivity is as follows: , , , , ,

[0008] , .

[0009] in, Represents a collection of ground station resources. Indicates the number of ground station resources, Indicates i Ground station resources, represents a collection of satellite resources, Indicates the number of satellite resources, Indicates j Satellite resources, Ground station within the scenario simulation cycle Satellite The set of visible windows, Indicates ground station Satellite The number of visible time windows, Indicates ground station Satellite No. k Visible windows, and Respectively represent ground stations Satellite No. k The start and end time of the visible window; For all ground stations to satellites during the scenario simulation cycle The set of visible windows, Indicates all ground stations to the satellite The number of visible time windows, Indicates all ground stations to the satellite No. l Visible windows, and Represents the ground station to the satellite No. l The start and end time of the visible window; Satellite resource collection within the scenario simulation cycle All ground station resources are collected The average visible duration of the satellite resource collection Ground station resource collection The degree of satellite-to-ground connectivity.

[0010] Furthermore, the above traffic load balancing metric is as follows: , , , , , , in, i Represents a single node in a region node, j Represents a single node in the ground station node, Indicates from the region i generated in and flows to the ground station j The total flow size, From the regional node i The sum of user traffic of represents its exclusion factor, To send to the ground station node j The total flow rate represents its attraction factor, For regional nodes i and ground station nodes j The spherical distance between . For influence and The index factor of the decay rate between It is a constant coefficient that is adjusted according to different scenarios; For Region i The communication traffic demand of a single user in is the weight factor of the repulsion factor, used to adjust the attraction factor The proportion in the whole gravity model; For ground station The weight factor of the network switching node, For ground station The number of network switching nodes in the effective area, For ground station The total population in the effective area; Represents user area The spherical distance to the ground station, that is, the shortest spherical distance to all ground stations; For ground station The communication traffic load, Indicates ground station service area, Provides load balancing measure for traffic between various ground stations; The average traffic transmitted by all ground stations.

[0011] Furthermore, step S4 specifically includes: S41: according to the optimization evaluation system, using the non-dominated sorting genetic algorithm II, Pareto sorting the individuals to obtain the Pareto level of each individual; S42: according to the Pareto level of each individual, determining the proportion of the elite population; S43: according to the proportion of the elite population and the Pareto non-dominated sorting result, dividing the population to obtain an elite population and an ordinary population; S44: according to the elite population and the ordinary population, using the crossover operator to perform crossover and mutation to obtain an elite sub-population and an ordinary sub-population; S45: merging and Pareto sorting the elite sub-population and the ordinary sub-population to obtain a preliminary offspring population.

[0012] Furthermore, step S42 specifically includes: determining the proportion of the elite population according to the Pareto level of each individual, such as the formula: , in, It indicates the proportion of the elite population size in the nth generation population to the total population size. is the adjustment coefficient, is the total population of the population, is the total number of individuals whose Pareto ranking result is 1 in the nth generation population.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned satellite ground station optimization layout method based on improved NSGAII are implemented.

[0014] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned satellite ground station optimization layout method based on improved NSGAII are implemented.

[0015] The present invention also provides a computer program product, including a computer program, which implements the steps of the above-mentioned satellite ground station optimization layout method based on improved NSGAII when executed by a processor.

[0016] The satellite ground station optimization layout method and device based on improved NSGAII provided by the present invention have the following beneficial effects: The ground station layout optimization problem proposed in the present invention is a multi-objective optimization problem that comprehensively considers satellite-to-ground connectivity and traffic load balancing in the layout plan; most existing ground station layout-related studies only consider single aspects such as feeder link quality and traffic revenue when evaluating the advantages and disadvantages of ground station layout plans; the present invention evaluates the communication quality between the ground station and the airspace satellite through the satellite-to-ground connectivity objective function, and evaluates the information transmission quality between the ground station and the ground network through traffic load balancing, and jointly evaluates the rationality of the ground station layout plan from multiple aspects, which is more suitable for the accuracy of application in actual engineering; Compared with the traditional NSGA-II algorithm, the improved dual-population coevolution non-dominated sorting genetic algorithm-II (DPC-NSGA-II) provided by the present invention can dynamically adjust the algorithm's control mechanism for diversity and convergence at different stages according to the evolution of the population. By adaptively allocating the sizes of the common population and the elite population, the DPC-NSGA-II algorithm can strengthen the population search capability in the early stage and improve the population convergence speed in the later stage. In addition, DPC-NSGA-II uses a dynamic crowding degree update mechanism to make up for the loss of population diversity in densely populated areas caused by the fixed crowding degree screening strategy of the traditional NSGA-II. By comparing DPC-NSGA-II with NSGA-II, ICDA-NSGA-II, ToP, CTAEA and other algorithms, the results show that DPC-NSGA-II has good performance in solving the multi-objective optimization model of ground station layout proposed by the present invention. In summary, in order to overcome the shortcomings of the existing ground station site selection scheme, the present invention adopts the DPC-NSGA-II algorithm to optimize the satellite ground station layout scheme, which can solve the ground station layout optimization problem under the dual indicators of satellite-to-ground connectivity and ground station traffic load balance. When applied to OGSP, it can effectively calculate the Pareto solution set based on satellite-to-ground connectivity and traffic load balance, which is beneficial for decision makers to choose the optimal solution; it can provide an effective solution for the construction of my country's ground network system in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 It is a flow chart of the satellite ground station optimization layout method based on improved NSGAII provided by the present invention; Figure 2 It is a flow chart of the DPC-NSGA-II algorithm provided by the present invention; Figure 3It is a schematic diagram comparing the experimental optimization algorithm results of the second-generation Iridium constellation provided by the present invention; Figure 4 It is a structural block diagram of the computer device provided by the present invention. DETAILED DESCRIPTION

[0018] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0019] Figure 1 A schematic diagram of the satellite ground station optimization layout method based on the improved NSGAII of this embodiment is shown. In this embodiment, the satellite ground station optimization layout method based on the improved NSGAII includes the following steps: S1: Determine the parameters of the satellite ground station optimization layout problem based on the ground stations involved in the deployment and the target communication satellite; In an exemplary embodiment, the satellite ground station optimization layout problem parameters include a satellite ground station group, a target communication satellite constellation, a minimum communication belief angle of a ground station, a minimum transmission delay of a single communication, a ground station layout area, a sub-area constraint set, a scenario time, a maximum number of iterations, and a population size; As an exemplary embodiment, in step S1, a ground station group consisting of ground stations participating in the deployment is determined. , target communications satellite constellation , minimum communication angle of the ground station , the minimum transmission delay of a single communication , longitude interval of ground station layout area and latitude range , sub-region constraint set , scene time , the maximum number of iterations of the algorithm , population size ; S2: generating an initial population according to the parameters of the satellite ground station optimization layout problem; As an exemplary embodiment, in step S2, let the number of iterations be , within the range of longitude and latitude, that is, within the range of longitude and latitude and Randomly generated ground station cluster layout schemes as the initial population of the algorithm; each ground station layout scheme, that is, each individual in the population, contains a satellite ground station cluster The latitude and longitude coordinates of all ground stations in Represents the current population of the algorithm, then: , in, Indicates the current population individuals, which store the The layout plan of the group ground station is as follows: , That is, each individual contains the longitude and latitude coordinates of n ground stations; S3: constructing an optimization evaluation system according to the initial population and the parameters of the satellite ground station optimization layout problem; In an exemplary embodiment, the optimization evaluation system includes satellite-to-ground connectivity, traffic load balancing measurement, and constraint violation degree; In an exemplary embodiment, the satellite-to-ground connectivity is as follows: , , , , ,

[0020] , .

[0021] in, Represents a collection of ground station resources. Indicates the number of ground station resources, Indicates i Ground station resources, represents a collection of satellite resources, Indicates the number of satellite resources, Indicates j Satellite resources, Ground station within the scenario simulation cycle Satellite The set of visible windows, Indicates ground station Satellite The number of visible time windows, Indicates ground station Satellite No. k Visible windows, and Respectively represent ground stations Satellite No. k The start and end time of the visible window; For all ground stations to satellites during the scenario simulation cycle The set of visible windows, Indicates all ground stations to the satellite The number of visible time windows, Indicates all ground stations to the satellite No. l Visible windows, and Represents the ground station to the satellite No. l The start and end time of the visible window; Satellite resource collection within the scenario simulation cycle All ground station resources are collected The average visible duration of the satellite resource collection Ground station resource collection The degree of satellite-to-ground connectivity; In an exemplary embodiment, the traffic load balancing metric is as follows: , , , , , , in, i Represents a single node in a region node, j Represents a single node in the ground station node, Indicates from the region i generated in and flows to the ground station j The total flow size, From the regional node i The sum of user traffic of represents its exclusion factor, To send to the ground station node j The total flow rate represents its attraction factor, For regional nodes i and ground station nodes j The spherical distance between . For influence and The index factor of the decay rate between It is a constant coefficient that is adjusted according to different scenarios; For Region i The communication traffic demand of a single user in is the weight factor of the repulsion factor, used to adjust the attraction factor The proportion in the whole gravity model; For ground station The weight factor of the network switching node, For ground station The number of network switching nodes in the effective area, For ground station The total population in the effective area; Represents user area The spherical distance to the ground station, that is, the shortest spherical distance to all ground stations; For ground station The communication traffic load, Indicates ground station service area, Provides load balancing measure for traffic between various ground stations; The average traffic volume transmitted for all ground stations; As an exemplary embodiment, in step S3, based on In A ground station cluster layout plan is proposed, and an optimization evaluation system based on satellite-ground connectivity and traffic load balancing is established. The optimization evaluation system includes indicators such as satellite-ground connectivity, traffic load balancing measurement, communication angle constraint, regional constraint, satellite transmission delay constraint, etc. Among them, satellite-ground connectivity and traffic load balancing measurement are used to evaluate the advantages and disadvantages of the configuration plan, and communication angle constraint, regional constraint, and satellite transmission delay constraint are used to evaluate the constraint satisfaction of the plan; the first Take the layout scheme as an example and calculate each satellite For ground station cluster Visible time window: Since the satellite can transmit when it is visible to any ground station, the visible time window of the satellite to the ground station group refers to the union of the visible time windows of the satellite to each ground station in the ground station group within a certain scene time; if Indicates the ground station within the scenario simulation cycle Satellite The visible window set of , then: , in, Indicates ground station Satellite No. k Visible windows, and Respectively represent ground stations Satellite No. k The start and end time of the visible window; therefore, all ground stations in the scenario simulation cycle have The set of visible windows , will take With all ground stations Produced The union of: , Getting satellite For ground station cluster Visible time window After that, the visible time of the satellite and the earth can be calculated by the following formula Calculation: ; Then, to reflect the constellations All satellites in the ground station group The numerical measurement on this indicator is right The average visible duration of each satellite in represents Pair of constellations For the final value of the visible duration indicator, that is, to reflect the constellation Overall ground station group To improve the connectivity between the satellite and the ground, it is necessary to coordinate the The visible duration of each star is The average visible duration of Pair of constellations For the final value of the visible duration indicator; For constellations During the entire scenario simulation cycle The average visible time of The calculation is as follows: , The transmission quality between the ground station group and each satellite in the constellation in the time dimension can be measured in the time dimension. Therefore, this embodiment will The resulting values ​​are considered as constellations With ground station cluster The satellite-to-ground connectivity between One of the evaluation criteria.

[0022] Calculation of ground station clusters based on gravity model Traffic load balancing measurement: In order to evaluate the traffic load in the network node, this embodiment uses the gravity model to estimate the traffic of each ground station. The calculation formula of the classic gravity model is: , in, Is the flow out i and traffic inflow jThe flow accessibility, yes i The magnitude of the repulsive force, yes j The attraction of G is the constant coefficient of scale. yes i and j The mobility cost between people can be measured by distance, time, travel cost, etc. is the distance constant coefficient; in this embodiment, all nodes in the traffic network can be divided into two independent parts, namely user nodes and ground station nodes. Therefore, for the traffic matrix in the satellite communication network system, i and j will belong to two different sets, where i Represents a single node in the user node, j Represents a single node in the ground station node; in the satellite communication system, user traffic is generated from the user node and transmitted to the ground station through the satellite link. Therefore, analogous to the traffic matrix in the gravity model, the user node in the satellite communication system will serve as the traffic output end and the ground station node as the traffic input end; use Indicates from the region i generated in and flows to the ground station j The total flow size of the satellite communication network system can be expressed as follows: , in, From the regional node i The sum of user traffic of represents its exclusion factor, To send to the ground station node j The total flow rate represents its attraction factor, For regional nodes i and ground station nodes j The spherical distance between . For influence and The index factor of the decay rate between It is a constant coefficient that is adjusted according to different scenarios; In an exemplary embodiment, the earth's surface is discretized into 360*180 area units with an accuracy of 1°, totaling 64,800 area units, and Indicates; then the 64800 regions represent the user flow output nodes in the gravity model, and the scheme In The ground stations will constitute the ground station flow input nodes in the gravity model; and The calculation method is as follows: , in, is the communication traffic demand of a single user in area i, is the weight factor of the repulsion factor, used to adjust the attraction factor The proportion in the whole gravity model; is the weight factor of IXPs, is the number of Internet Exchange Points (IXPs) within the effective area of ​​ground station j, is the total population in the effective area of ​​ground station j; in the above formula, It can be replaced by the average value of user traffic usage, which ranges from 2.4Kbps to 2Mbps according to the traffic usage frequency of different area units; It can be adjusted according to the actual scene; in order to obtain the gravity model , The data needs to be The effective area is divided into In ground stations, the division will be based on the shortest spherical distance. Each regional unit is divided into ground station sets different ground stations in the ground station set M; that is, all user nodes need to be divided into different ground stations in the ground station set M by the minimum spherical distance determination method; that is, if the area Allocated to ground station , if and only if the following formula holds: , in, Represents user area The spherical distance between the ground station and the ground station is the shortest spherical distance to all ground stations. After the above flow calculation and regional division, for the ground station Traffic load , can be calculated by the following formula: , Will Represented as passing through each ground station The estimated amount of traffic transmitted, where , and It is expressed as a measure of traffic load balancing between various ground stations; It can be calculated by the following formula: , , in, The average traffic transmitted by all ground stations.

[0023] Calculate the degree of constraint violation: The longitude and latitude of each ground station in the If the solution falls within these areas, The degree of constraint violation Add 1.

[0024] S4: according to the optimization evaluation system, using the improved non-dominated sorting genetic algorithm II, obtaining a preliminary offspring population; As an exemplary embodiment, in step S4, after obtaining the current population After calculating the two objective functions of each individual and the degree of constraint violation, the dual population division and offspring generation process of the DPC-NSGA-II algorithm are carried out, that is, according to the fitness of the individuals calculated in the above steps, the individuals are Pareto sorted and divided into populations. According to the Pareto sorting results, the number of individuals in the elite population and the ordinary population is calculated using the proportion of the population occupied by individuals with a Pareto ranking of 1. Subsequently, the individuals with a high Pareto ranking are divided into the elite population, and the individuals are divided into the ordinary population. The crossover and mutation processes of the population are performed using the Normal Distribution Crossover (NDX) operator and the Simulated Binary Crossover (SBX) operator respectively. In an exemplary embodiment, step S4 specifically includes: S41: According to the optimization evaluation system, using the non-dominated sorting genetic algorithm II, Pareto sorting is performed on the individuals to obtain the Pareto level of each individual; As an exemplary embodiment, in step S41, a population Pareto ranking of individuals in the population: For each individual in the current population, the Pareto level of each individual is determined based on its performance on the two objective functions and the degree of constraint violation; the Pareto ranking calculation is consistent with NSGA-II; S42: Determine the proportion of the elite population according to the Pareto level of each individual; In an exemplary embodiment, step S42 specifically includes: determining the proportion of the elite population according to the Pareto level of each individual, such as the formula: , in, It indicates the proportion of the elite population size in the nth generation population to the total population size. is the adjustment coefficient, is the total population of the population, is the total number of individuals with a Pareto ranking result of 1 in the nth generation population; As an exemplary embodiment, in step S42, the parent populations of the common population and the elite population in the algorithm are determined. At the same time, in order to prevent the elite population from being too small due to the excessive classification of Pareto sorting results in the early stage of evolution, thus losing the local search ability of the simulated binary crossover (SBX) operator, and the elite population from accounting for too high a proportion in the late stage of evolution, thus causing the normal distribution crossover (NDX) operator to fail and causing the algorithm search to stagnate, that is, in order to prevent the search from stagnation, Use the maximum and minimum values ​​to limit, for Minimum value, for The maximum value, Satisfies the following formula: ; S43: dividing the population according to the proportion of the elite population and the Pareto non-dominated sorting result to obtain an elite population and a common population; As an exemplary embodiment, in step S43, after determining the size of the common population and the elite population, the population with the highest Pareto ranking is sorted according to the Pareto non-dominated sorting result. Individuals are divided into the nth generation elite population until the elite population size is reached, and the remaining individuals are divided into the common population middle; S44: performing crossover and mutation using a crossover operator according to the elite population and the common population to obtain an elite sub-population and a common sub-population; As an exemplary embodiment, in step S44, the elite population and common population The individuals in the algorithm use different crossover operators to select, cross, and mutate, and generate population individuals that are 1 times their own size. In order to improve its local search ability and speed up the population convergence, The individuals in use the simulated binary crossover (SBX) operator for crossover operation; in order to enhance its global search capability, The individuals in are crossovered using the Normal Distribution Crossover (NDX) operator; It should be noted that compared with the traditional SBX operator, the NDX operator is widely used in various problems that require strong algorithm search performance. Its calculation formula is as follows: , in, and There are two parents in the The value of the variable in dimension, is a random variable in the range (0,1), and The two offspring produced are The value of the variable on the dimension; S45: merging and Pareto sorting the elite sub-population and the common sub-population to obtain a preliminary offspring population; As an exemplary embodiment, in step S45, the individuals in the elite sub-population and the common sub-population are merged, and all the individuals are Pareto sorted again; according to the sorting result, the individuals with the highest ranking are selected in turn to enter the next generation population; that is, after obtaining the offspring of the two populations, the populations are merged, and Pareto sorting is performed again, and the set of individuals with the highest ranking are selected in turn to be incorporated into the next generation population; S5: according to the preliminary offspring population, using a dynamic crowding selection mechanism to obtain an offspring population; As an exemplary embodiment, in step S5, during the selection of the individual set, if the number of individuals required for the next generation population is less than the number of individuals at the current level, a sufficient number of individuals are selected and incorporated into the next generation population according to the dynamic crowding selection mechanism; that is, when the individual level with Pareto ranking a is selected in sequence, if the number of individuals required for the remaining level is less than the number of individuals at level a, all individuals at level a are incorporated into the next generation population, and this iteration ends, and the next iteration is performed; if the number of individuals required for the remaining level is less than the number of individuals at level a, a dynamic crowding screening mechanism is required, and the main steps are as follows: (1) Assume that the Pareto level of this layer is a, there are m individuals in this level, and the number of remaining individuals in the population is required to be k; first calculate the crowding distance of the m individuals, and remove the individual with the smallest crowding; (2) Subsequently, the crowding values ​​of the individuals adjacent to the left and right sides of the individual will change: let the order subscript of the eliminated individual be x, then after individual x is eliminated, update the crowding values ​​of the two individuals at positions x-1 and x+1 (if any); (3) Based on the two individuals x-1 and x+1 (if any) whose crowding has changed, update their positions in the crowding ranking of the m-1 individuals in the current Pareto level; (4) Eliminate the individual with the smallest crowding again and repeat step (2) until the number of eliminated individuals reaches mk; (5) Incorporate the remaining k individuals into the new population to participate in the next evolution process of the algorithm; S6: When the number of iterations is less than the maximum number of iterations, return to step S3; otherwise, all ground station layout plans of the last child population are taken as the final optimization result; As an exemplary embodiment, in step S6, if the number of iterations is Less than the maximum number of iterations , then repeat steps S3 to S5, and then Otherwise, if , then exit the loop and output the current population as the solution of the model, that is, output the last generation of population All ground station layout plans are taken as the final optimization results; it should be noted that the purpose of the algorithm is to continuously repeat steps S3 to S5 to update the population. During the iteration process, the new population obtained by each generation of the algorithm will be better than the parent population in terms of two indicators.

[0025] In some embodiments, the above satellite ground station optimization layout method based on improved NSGAII can also be implemented in the following ways: Figure 2 FIG. 1 is a flow chart of the DPC-NSGA-II algorithm. In this embodiment, the satellite ground station optimization layout method based on the improved NSGAII includes: Step 1: Determine the satellite ground station cluster composed of the ground stations involved in the deployment , the number of ground stations involved in the implementation of this embodiment is set to 3, then ; Target communication constellation It is the second-generation Iridium constellation, consisting of 66 basic satellites + 9 spare satellites, namely ; Minimum communication angle of ground station , the minimum transmission delay of a single communication , the latitude and longitude interval of the ground station layout is selected as the global, the sub-region constraint set is the land area except Antarctica and Arctic (excluding islands with too small volume), the scenario time is from January 1, 2023 to January 2, 2023, and the maximum number of algorithm iterations is =200, population size ; Step 2: Set the number of iterations , in the latitude and longitude interval and 50 ground station group layout schemes are randomly generated as the initial population of the algorithm; then for the Ground station layout plan , which contains the following information: , Step 3: Based on In A ground station cluster layout plan is developed to establish an optimization evaluation system based on satellite-ground connectivity and traffic load balancing; the evaluation system calculation includes the following steps: (1) Calculate each satellite Visible time window for ground station group; In order to demonstrate the calculation process of the present invention, it is assumed that: , There is a single satellite at this time , by calculating its effect on The time windows of the three ground stations are as follows: , , , The above three groups of ground station time windows are combined to obtain the satellite For ground station clusters Time window: , In getting middle For ground station clusters After obtaining the visible information, the total visible time of the satellite is calculated by the following formula: ; So far, we can get For ground station clusters The total visible time of each satellite For ground station clusters Visible duration, get the constellation During the scenario simulation cycle Average viewable time: .

[0026] (2) Calculation of ground station clusters Traffic load balancing measurement: The world population density and population distribution data used for calculation in this embodiment are derived from the 2020 population density data statistics version of gpwv4 (Gridded population of the world, version 4) (https: / / doi.org / 10.7927 / H4JW8BX5), and the world IXPs distribution coordinate data are derived from PCH (Packet Clearing House). The data used in this embodiment is the January 2023 version (https: / / www.pch.net / ixp / data); In order to demonstrate the calculation process of this embodiment, the ground station layout solution is still used. To describe the calculation process; the earth's surface is discretized into 360 * 180, a total of 64800 regional units with an accuracy of 1°, and Indicates; According to the following formula, the size of the rejection factor generated by each regional unit is calculated: , in, is the communication traffic demand of a single user in area i, which is set to 100KB / s here. is the weight factor of the repulsion factor, used to adjust the attraction factor The proportion in the entire gravity model is set to 1 here; is the population density of region i, obtained from gpwv4 data; then, according to The latitude and longitude coordinates in the 3 ground stations are obtained, and the division areas of the 3 ground stations are obtained; that is, if the area Allocated to ground station , if and only if the following formula holds: , in, Represents user area The spherical distance between the ground station and the ground station is the shortest spherical distance to all ground stations. After the divided area is obtained, the attraction factor of each ground station can be calculated according to the following formula: , in is the weight factor of IXPs, that is, the proportion of IXPs nodes at the same longitude and latitude, For ground station The number of IXP (Internet Exchange Point) nodes in the effective area, For ground station The total population in the effective area; then for the ground station Traffic load , can be calculated by the following formula: , If you will Represented as passing through each ground station The estimated amount of traffic to be transmitted and It is expressed as a measure of traffic load balancing between various ground stations; It can be calculated by the following formula: ; (3) Calculate the degree of constraint violation: The longitude and latitude of each ground station in the If the solution falls within these areas, The degree of constraint violation Add 1; according to The longitude and latitude coordinates of the three ground stations in =2; Step 4: Get After the objective function value and constraint violation degree of each individual in the DPC-NSGA-II algorithm are obtained, the dual population division and offspring generation process of the DPC-NSGA-II algorithm are carried out. The specific flow chart of the algorithm is as follows Figure 2 As shown, the specific steps are as follows: (1) Conduct population The Pareto ranking of individuals in the , determines the Pareto level of each individual; (2) Calculate the proportion of the parent population occupied by the common population and the elite population by the following formula: Scale: In this embodiment, set =0.2; if the number of individuals with Pareto hierarchy ranking of 1 in this sorting result is 24, then according to the above formula, the proportion of the elite population is calculated as:

[0027] Then the proportion of elite population is 16, and the proportion of ordinary population is 34; (3) According to the Pareto sorting results, individuals are placed in the elite population from front to back. , and then put into the common population ; (4) and The individuals in use different crossover operators to perform the selection, crossover, and mutation processes of the algorithm, and generate population individuals that are 1 times their own size; The individuals in are crossovered using the SBX operator; The individuals in use the NDX operator for crossover operation; after selection, crossover, and mutation, The number of individuals contained in the population is 32, and the number contained in the common population is 68; (5) and Merge into a parent population of size 100, and perform Pareto sorting again; from front to back, give priority to selecting individuals with higher Pareto sorting to enter the next generation population; Step 5: If the number of individuals remaining in the sequential selection process is less than the number of individuals at the current level a, a dynamic crowding degree update operation is performed; assuming that there are 6 individuals at the current level a: ( ); The remaining number of individuals required is 4, and the steps of the dynamic crowding update strategy are as follows: (1) Calculate the crowding distance of the six individuals. Assume that their crowding degrees are: (∞, 1.23, 4.11, 0.35, 3.51, ∞). The current crowding degree ranking is: , then the individual that needs to be eliminated is , then Remove from the current collective; (2) After elimination, recalculate and The crowding degree of each individual after update is (∞, 1.23, 4.56, 5.41, ∞); (3) Update ) sorting: The current sorting is: , then we know that the next individual to be deleted is ; (4) The current number of individuals is 5, which is still greater than the required number of 4, so delete ,renew and The degree of crowding and sorting; (5) The current number of individuals is 4, which meets the required number condition, and the operation of selecting individuals by congestion degree ends; Step 6: If the number of iterations Less than the maximum number of iterations , then return to step 3 to step 5, then ; Otherwise, output the last generation population All ground station layout schemes are taken as the final optimization results; The method proposed in this embodiment is applied to the optimization and scheduling of ground station layout. For an experiment with a scale of 5 ground stations and a global layout area, targeting the second-generation Iridium constellation, Figure 3 It can be seen from the above that the Pareto solution set obtained by this method is obviously better than that of other algorithms.

[0028] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the satellite ground station optimization layout method based on the improved NSGAII are implemented. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0029] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned satellite ground station optimization layout method based on improved NSGAII are implemented.

[0030] like Figure 4 As shown, the computer device 120 may include: at least one processor 121, such as a central processing unit (CPU), at least one communication interface 123, a memory 124, and at least one communication bus 122. Among them, the communication bus 122 is used to realize the connection and communication between these components. Among them, the communication interface 123 may include a display screen (Display), a keyboard (Keyboard), and the optional communication interface 123 may also include a standard wired interface and a wireless interface. The memory 124 may be a high-speed random access memory (Random Access Memory, RAM), or a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 124 may also be at least one storage device located away from the aforementioned processor 121. Among them, the memory 124 stores an application program, and the processor 121 calls the program code stored in the memory 124 to perform any of the above method steps. Among them, the communication bus 122 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 122 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4Only one line is used to represent it, but it does not mean that there is only one bus or one type of bus. Among them, the memory 124 may include a volatile memory (volatile memory), such as a random-access memory (random-access memory, RAM); the memory may also include a non-volatile memory (non-volatile memory), such as a flash memory (flash memory), a hard disk drive (hard disk drive, HDD) or a solid-state drive (solid-state drive, SSD); the memory 124 may also include a combination of the above-mentioned types of memory. Among them, the processor 121 may be a central processing unit (central processing unit, CPU), a network processor (network processor, NP) or a combination of CPU and NP. Among them, the processor 121 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit (application-specific integrated circuit, ASIC), a programmable logic device (programmable logic device, PLD) or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (field-programmable gate array, FPGA), a generic array logic (generic array logic, GAL) or any combination thereof. Optionally, the memory 124 is also used to store program instructions. The processor 121 can call the program instructions to implement the satellite ground station optimization layout method based on the improved NSGAII as in this embodiment.

[0031] This embodiment provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned satellite ground station optimization layout method based on improved NSGAII.

[0032] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A satellite ground station optimization layout method based on improved NSGAII, characterized in that: The following steps are involved: S1: Determine the parameters of the satellite ground station optimization layout problem based on the ground stations involved in the deployment and the target communication satellite; S2: generating an initial population according to the parameters of the satellite ground station optimization layout problem; S3: constructing an optimization evaluation system according to the initial population and the parameters of the satellite ground station optimization layout problem; S4: according to the optimization evaluation system, using the improved non-dominated sorting genetic algorithm II, obtaining a preliminary offspring population; S5: according to the preliminary offspring population, using a dynamic crowding selection mechanism to obtain an offspring population; S6: When the number of iterations is less than the maximum number of iterations, return to step S3; otherwise, all ground station layout plans of the last child population are taken as the final optimization result.

2. The satellite ground station optimization layout method based on improved NSGAII according to claim 1 is characterized in that: The parameters of the satellite ground station optimization layout problem include satellite ground station group, target communication satellite constellation, minimum communication belief angle of ground station, minimum transmission delay of single communication, ground station layout area, sub-area constraint set, scenario time, maximum number of iterations and population size.

3. The satellite ground station optimization layout method based on improved NSGAII according to claim 1, characterized in that: The optimization evaluation system includes satellite-to-ground connectivity, traffic load balancing measurement, and constraint violation degree.

4. The satellite ground station optimization layout method based on improved NSGAII according to claim 3 is characterized in that: The satellite-to-ground connectivity is as follows: ， ， ， ， ， ， ， ， in, Represents a collection of ground station resources. Indicates the number of ground station resources, Indicates i Ground station resources, represents a collection of satellite resources, Indicates the number of satellite resources, Indicates j Satellite resources, Ground station within the scenario simulation cycle Satellite The set of visible windows, Indicates ground station Satellite The number of visible time windows, Indicates ground station Satellite No. k Visible windows, and Represents ground station Satellite No. k The start and end time of the visible window; For all ground stations to satellites during the scenario simulation cycle The set of visible windows, Indicates all ground stations to the satellite The number of visible time windows, Indicates all ground stations to the satellite No. l Visible windows, and Represents the ground station to the satellite No. l The start and end time of the visible window; Satellite resource collection within the scenario simulation cycle All ground station resources are collected The average visible duration of the satellite resource collection Ground station resource collection The degree of satellite-to-ground connectivity.

5. The satellite ground station optimization layout method based on improved NSGAII according to claim 3 is characterized in that: The traffic load balancing metric is as follows: ， ， ， ， ， ， in, i Represents a single node in a region node, j Represents a single node in the ground station node, Indicates from the region i generated in and flows to the ground station j The total flow size, From the regional node i The sum of user traffic of is the exclusion factor. To send to the ground station node j The total flow rate represents its attraction factor, For regional nodes i and ground station nodes j The spherical distance between For influence and The index factor of the decay rate between It is a constant coefficient that is adjusted according to different scenarios; For Region i The communication traffic demand of a single user in is the weight factor of the repulsion factor, used to adjust the attraction factor The proportion in the whole gravity model; For ground station The weight factor of the network switching node, For ground station The number of network switching nodes in the effective area, For ground station The total population in the effective area; Represents user area The spherical distance to the ground station, that is, the shortest spherical distance to all ground stations; For ground station The communication traffic load, Indicates ground station service area, Provides load balancing measure for traffic between various ground stations; is the average traffic transmitted by all ground stations.

6. The satellite ground station optimization layout method based on improved NSGAII according to claim 1, characterized in that: Step S4 specifically includes: S41: According to the optimization evaluation system, using the non-dominated sorting genetic algorithm II, Pareto sorting is performed on the individuals to obtain the Pareto level of each individual; S42: Determine the proportion of the elite population according to the Pareto level of each individual; S43: dividing the population according to the proportion of the elite population and the Pareto non-dominated sorting result to obtain an elite population and a common population; S44: performing crossover and mutation using a crossover operator according to the elite population and the common population to obtain an elite sub-population and a common sub-population; S45: Merge and Pareto sort the elite sub-population and the ordinary sub-population to obtain a preliminary offspring population.

7. The satellite ground station optimization layout method based on improved NSGAII according to claim 6 is characterized in that: Step S42 specifically includes: determining the proportion of the elite population according to the Pareto level of each individual, such as the formula: in, It indicates the proportion of the elite population size in the nth generation population to the total population size. is the adjustment coefficient, is the total population of the population, is the total number of individuals whose Pareto ranking result is 1 in the nth generation population.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the satellite ground station optimization layout method based on improved NSGAII are implemented as described in any one of claims 1 to 7.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the satellite ground station optimization layout method based on improved NSGAII are implemented as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the satellite ground station optimization layout method based on improved NSGAII described in any one of claims 1-7 are implemented.

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