A multi-level low earth orbit constellation configuration design method suitable for diversified services

Abstract

The application discloses a multi-level low-orbit constellation configuration design method suitable for diversified services and belongs to the field of wireless communication; specifically comprising the following steps: firstly, a single-satellite coverage and multi-satellite global coverage geometric model is established, including a constellation configuration of P orbits and M satellites respectively arranged on each orbit; then, an aggregated QoS utility characterization model is established, the aggregated utility function of a single user is calculated, when different types of users access different levels of low-orbit satellites, the overall system optimal aggregated utility function is calculated by adjusting the constellation configuration; finally, combined with the deployment cost, a multi-objective optimization function and constraint conditions of large-scale multi-level constellation configuration design are constructed; and the adaptive particle swarm algorithm is used to solve the large-scale low-orbit satellite constellation deployment parameters, and finally, the multi-level low-orbit constellation configuration is designed according to the parameters. The application fully considers the orbit parameter design to meet the differentiated service requirements and considers the constellation deployment cost.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication, specifically a multi-level low-orbit constellation configuration design method applicable to diverse services. Background Technology

[0002] With the rapid development of wireless communication technology, terminal types and service types are becoming increasingly diversified, and the demand for large-scale user connections is growing daily. Satellite communication systems, with their advantages of seamless global coverage, large communication capacity, unaffected node deployment by geographical location, and strong resilience, have become an important component of the future integrated air-space-ground-sea network. Among them, low-Earth orbit (LEO) satellites have attracted widespread attention due to their lower manufacturing and deployment costs, lower transmission latency and path loss, and rapid and flexible networking methods. With the rapid development of satellite manufacturing and mass production technologies, satellites are becoming smaller and lower-cost, resulting in increasingly large constellations.

[0003] Constellation configuration design, as a crucial and paramount aspect of low-Earth orbit (LEO) constellation system deployment, is closely related to satellite communication missions, determining factors such as satellite constellation coverage performance, cost, and lifespan. In satellite communication systems, various service scenarios and mission requirements, including communication, remote sensing, and navigation, are complex and diverse. The design goals and methods for constellation configurations vary considerably, involving decision-making design with multiple coupled discrete and continuous parameters. Due to the practical constraints of a large number of satellites, high deployment costs, long construction cycles, and complex operation and management, constellation deployment and design are increasingly difficult. Therefore, research on large-scale, multi-layered LEO constellation configuration design schemes based on diverse service requirements is of great significance for improving coverage performance, increasing system capacity, and reducing constellation deployment costs.

[0004] Low Earth Orbit (LEO) satellite constellation design refers to determining the orbits of individual satellites and the size of the constellation by designing satellite orbital parameters, including the total number of satellites, orbital altitude, number of orbital planes, orbital plane phase factors, and orbital inclination, to achieve a network design of dozens to tens of thousands of satellites. In satellite communication systems, various service scenarios and mission requirements, such as communication, remote sensing, and navigation, are complex and diverse, leading to significant differences in the design goals and methods for satellite constellation configurations. How to effectively design large-scale, multi-layered LEO satellite constellation configurations, closely integrating constellation design with actual user needs to achieve an optimal balance between communication performance and deployment costs, has become a critical issue that urgently needs to be addressed, possessing significant theoretical and practical value.

[0005] Traditional constellation design theory research mainly focuses on single-layer LEO constellation configurations. However, with the surge in operational demands, the number of satellites in single-layer LEO constellations has increased dramatically, leading to high deployment costs. Current multi-layer LEO constellation designs mostly consider the simple superposition of constellation performance, without fully considering multi-layer collaboration within the LEO constellation. Compared to single-layer satellite constellations, multi-layer satellite constellation designs exhibit differences in coverage, communication, computing, storage, and sensing capabilities among satellites at different orbital altitudes. Summary of the Invention

[0006] This invention proposes a multi-level LEO constellation configuration design method suitable for diverse business needs and the characteristics of inter-coupling constellation orbital parameters. By analyzing the inter-coupling orbital parameters, a multi-level constellation configuration is designed to improve system capacity, increase coverage multiples, enhance system resilience, and meet diverse mission requirements. While fully considering the orbital parameter design to meet differentiated business needs, the invention also takes into account constellation deployment costs, providing an effective solution for large-scale LEO constellation configuration design.

[0007] The specific steps of the large-scale, multi-level low-Earth orbit constellation configuration design method applicable to diverse business needs are as follows:

[0008] Step 1: Establish geometric models for single-satellite coverage and multi-satellite global coverage, including P orbits and constellation configurations with M satellites deployed in each orbit;

[0009] First, the semi-geocene angle corresponding to the coverage area of ​​a single satellite. for:

[0010]

[0011] R is the Earth's radius; h is the orbital altitude of a single satellite; θ min This is the minimum visible elevation angle of the satellite from the ground terminal;

[0012] Then, M satellites are deployed in a single orbit, with the angular distance between the ground points of adjacent satellites being λ = 2π / M.

[0013] Next, determine the half-geocene angle. The angular distance between the nadir points of adjacent satellites satisfies At that time, a continuous coverage area is formed on the same orbit, namely a satellite ring:

[0014] The coverage angle Δ of the satellite ring is shown in the following formula:

[0015]

[0016] Finally, based on the coverage of a single orbital satellite ring and the distance between adjacent orbital planes, the single-satellite constellation configuration design with seamless global coverage is determined:

[0017] when At the same time, it can meet the requirement of seamless global single coverage:

[0018]

[0019] Step 2: Establish an aggregated QoS utility representation model and calculate the aggregated utility function for a single user u.

[0020] The aggregate utility function includes the data transfer rate utility function U. rate (R u and the time delay utility function U delay (T u );

[0021] The aggregation utility function of user u is characterized as follows

[0022] w1 is the weight of the user rate utility function, and w2 is the weight of the user delay utility function.

[0023] The data transmission rate utility function U rate (R u It includes the following two parts:

[0024]

[0025]

[0026] in The data transmission rate utility function representing the Soft-QoS service for user u; Let p1 represent the data transmission rate utility function corresponding to the Best-effort service for user u, p1 represent the slope of the data transmission rate utility function corresponding to the Soft-QoS service, and q1 represent the influence utility function. The range of R u R represents the data transmission rate of user u; th p2 represents the threshold requirement for data transmission rate, p2 represents the slope of the data transmission rate utility function corresponding to the Best-effort service, and q2 represents the influence utility function. The range.

[0027] Time delay utility function U delay (T u It includes the following two parts:

[0028]

[0029]

[0030] This represents the latency utility function corresponding to the Soft-QoS service for user u; T represents the latency utility function corresponding to the best-effort service for user u; u T represents the latency of user u; u,s P represents the end-to-end latency between the user and the satellite terminal. d This is the parameter for the utility function decay. This represents the maximum latency tolerance for user u's Soft-QoS service. This represents the maximum latency tolerance value corresponding to the Best-effort service for user u.

[0031] Step 3: When different types of users access different layers of low-Earth orbit satellites, the optimal aggregate utility function of the overall system is calculated by adjusting the constellation configuration;

[0032] The optimal aggregation utility function is:

[0033] N represents the total number of satellites deployed in the constellation, P represents the number of orbits, i represents the orbital inclination, and UT represents the total set of all users.

[0034] Step 4: Calculate the deployment cost of adjusting the constellation configuration, and construct a multi-objective optimization function and constraints for large-scale, multi-level constellation configuration design;

[0035] Deployment costs include space segment costs C space Satellite manufacturing cost C const Launch cost C launch Insurance premium C insurance and maintenance costs C mc The calculation is as follows:

[0036]

[0037] C launch =0.00049×W sat ×h 0.43 ×N×P α ×i γ

[0038] C insurance =β(C const +C launch )

[0039] C mc =(C const +C launch +C insurance ) / Y life

[0040]

[0041] Among them, C sat Q represents the cost of manufacturing a single satellite, and W represents the slope of the learning curve for satellite manufacturing costs. sat P represents the weight of a single satellite. α Let α represent the influence factor of the number of orbits P on launch cost; γ Y represents the impact factor γ on the deployment cost of a satellite constellation, where orbital inclination i is the influencing factor, β represents the proportion of insurance costs, and Y represents the overall cost. life Indicates the satellite's orbital lifespan.

[0042] The objective function and constraints are as follows:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] C5:g1+g2+…+g l ≥G

[0049] U represents the sum of the aggregate utility functions of all users, i.e., U = ΣU(R) u ,T u );h l This represents the orbital altitude when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and Indicates the orbital height h l The upper and lower threshold values; i l The orbital inclination angle represents the orbital inclination angle when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and Indicates the orbital inclination angle i l The upper and lower threshold values; P l This indicates the number of orbits when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and P represents the number of orbits l Upper and lower threshold values; M l This represents the number of satellites in a single orbit when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and M represents the number of satellites in a single orbit. l The upper and lower threshold values; g l This represents the satellite Earth coverage weight when the number of orbital layers in a low-Earth orbit constellation is l.

[0050] Step 5: Use the adaptive particle swarm optimization algorithm to solve the multi-objective optimization problem of large-scale multi-level constellation configuration design, and finally obtain the deployment parameters of large-scale low-Earth orbit satellite constellations; and design multi-level low-Earth orbit constellation configurations according to these parameters.

[0051] The deployment parameters include the number of orbital layers L and the number of orbits P. l Track height h l Total number of satellites N l Track inclination angle i l Phase factor α l wait.

[0052] During the iteration process, the initial position and velocity of the particles are randomly set (as input) within the search space consisting of the orbital altitude, orbital inclination, number of orbits, and number of satellites of a multi-layered constellation. The individual learning weight C1 and the group learning weight C2 of the particles are continuously modified during the iteration process, shifting from group learning to individual learning. The output serves as the orbital parameters for a large-scale, multi-layered low-Earth orbit constellation configuration with multiple objectives and constraints.

[0053] The advantages of this invention are:

[0054] A multi-level LEO constellation configuration design method applicable to diverse businesses is proposed. Based on the single-level LEO constellation configuration design, this method adds considerations for multi-level constellation configuration design by analyzing the impact of configuration designs with different orbital altitudes and parameters on coverage performance, deployment costs, and satisfaction of diverse user needs. Combined with constellation deployment cost constraints, this method has good applicability to large-scale constellation design for diverse business needs compared to other constellation configuration design schemes. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the large-scale, multi-layered low-Earth orbit constellation configuration of the present invention;

[0056] Figure 2 This invention is a large-scale, multi-level low-Earth orbit constellation configuration design scheme based on complex constraints and multiple optimization objectives;

[0057] Figure 3 This is a flowchart of a multi-level low-orbit constellation configuration design method applicable to diverse businesses according to the present invention;

[0058] Figure 4 This is a flowchart illustrating the design of the particle swarm optimization algorithm for large-scale, multi-level low-orbit constellation configurations in this invention. Detailed Implementation

[0059] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments, and in particular, the present invention does not limit the types of swarm intelligence optimization and traditional optimization algorithms in the technical solutions. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0060] This invention provides a large-scale multi-layer LEO satellite constellation design scheme suitable for diverse business requirements. Based on the characteristics of diverse business needs and the mutual coupling of constellation orbital parameters, the constellation deployment cost is considered while fully taking into account the orbital parameter design to meet differentiated business needs.

[0061] like Figure 1 As shown, considering the differences in communication, computing, and storage capabilities of satellites at different orbital altitudes, the satellites in the constellation configuration design are divided into low-layer LEO (L-LEO) and high-layer LEO (H-LEO) satellites according to their orbital altitude. The satellites of different layers work together to form a network to meet the diverse and spatiotemporally uneven ground service (communication, navigation, remote sensing, etc.) needs.

[0062] To address the challenges of limited frequency / orbit resources, a large number of satellites, diverse ground user service requirements, varied performance evaluation metrics, and high deployment costs associated with low-Earth orbit (LEO) satellite constellations, this paper proposes a large-scale, multi-level LEO satellite constellation configuration design scheme based on complex constraints and multiple optimization objectives, catering to diverse service needs. The design concept is as follows: Figure 2 As shown, based on the impact of mutually coupled continuous and discrete orbital parameters on constellation coverage performance, deployment cost, and orbital lifetime, a multi-level low-Earth orbit satellite constellation deployed on orbits of different altitudes, inclinations, and types is designed. A multi-attribute service aggregation QoS utility representation function is designed in conjunction with the characteristics of user service needs. A multi-constraint, multi-objective constellation configuration evaluation index model is designed, and the problem is solved using an adaptive particle swarm optimization algorithm.

[0063] like Figure 3 As shown, the specific process includes:

[0064] The specific steps of the large-scale, multi-level low-Earth orbit constellation configuration design method applicable to diverse business needs are as follows:

[0065] Step 1: Establish geometric models for single-satellite coverage and multi-satellite global coverage, including P orbits and constellation configurations with M satellites deployed in each orbit;

[0066] The parameter space in low Earth orbit (LEO) constellation design is vast, with continuous and discrete orbital parameters coupled to jointly determine the constellation's coverage performance, deployment cost, and orbital lifetime. Constellation coverage performance, a prerequisite for meeting mission requirements, is influenced by orbital altitude and communication angle, involving complex solid geometry. Deployment cost, a key practical factor constraining satellite constellation design, is affected by orbital altitude h, the total number of satellites N, and the number of satellite orbits P. Orbital lifetime is closely related to orbital altitude. This example establishes a coupled satellite orbital parameter correlation model to analyze the coverage of a single satellite and the continuous coverage of satellites on a single orbital plane. Based on the above analysis, it determines the number of orbital planes and the number of satellites per orbital plane in the constellation deployment. Specifically:

[0067] First, construct the geometric coverage theory of the satellite constellation: the coverage range of a single satellite is related to its orbital altitude h and minimum visible elevation angle θ. min Determined, corresponding half-geocene angle for:

[0068]

[0069] R is the Earth's radius; h is the orbital altitude of a single satellite; θ min This is the minimum visible elevation angle of the satellite from the ground terminal;

[0070] Then, M satellites are deployed in a single orbit, with the angular distance between the ground points of adjacent satellites being λ = 2π / M.

[0071] Next, determine the half-geocene angle. The angular distance between the nadir points of adjacent satellites satisfies At that time, a continuous coverage area is formed on the same orbit, namely a satellite ring:

[0072] The coverage angle Δ of the satellite ring is shown in the following formula:

[0073]

[0074] Finally, based on the coverage of a single orbital satellite ring and the distance between adjacent orbital planes, a single-satellite configuration design for seamless global coverage is determined, including the number of orbits P and the number of satellites per orbit M.

[0075] when At the same time, it can meet the requirement of seamless global single coverage:

[0076]

[0077] Step 2: Establish an aggregated QoS utility representation model and calculate the aggregated utility function for a single user u.

[0078] This invention addresses the diverse and varied needs of users with diverse mission requirements, particularly in areas such as latency sensitivity and data transmission rate. It establishes a multi-attribute service aggregation QoS (latency / rate / capacity) utility representation model to reflect these differentiated mission requirements. Based on the varying satellite coverage, user access capacity, and end-to-end latency capabilities at different orbital altitudes, this invention designs a large-scale, multi-layered low-Earth orbit satellite constellation configuration to meet diverse user needs.

[0079] This invention employs aggregated QoS utility representation modeling to represent the QoS utility functions of different services, characterizing the user's satisfaction with the service obtained. The utility function values ​​are defined within the interval [0,1]. The aggregated utility function includes the data transmission rate utility function U. rate (R u and the time delay utility function U delay (T u );

[0080] The aggregation utility function of user u is characterized as follows

[0081] w1 is the weight of the user rate utility function, and w2 is the weight of the user delay utility function.

[0082] This example categorizes user service u into two types—Soft-QoS and Best-effort—based on the latency sensitivity and data transmission rate of the user service. The data transmission rate utility function U... rate (R u It includes the following two parts:

[0083]

[0084]

[0085] Where B indicates that the service is a Best-effort service, and Q indicates that the service is a Soft-QoS service. The data transmission rate utility function representing the Soft-QoS service for user u; Let p1 represent the data transmission rate utility function corresponding to the Best-effort service for user u, p1 represent the slope of the data transmission rate utility function corresponding to the Soft-QoS service, and q1 represent the influence utility function. The range of R u R represents the data transmission rate of user u; thp2 represents the threshold requirement for data transmission rate, p2 represents the slope of the data transmission rate utility function corresponding to the Best-effort service, and q2 represents the influence utility function. The range.

[0086] Time delay utility function U delay (T u It includes the following two parts:

[0087]

[0088]

[0089] This represents the latency utility function corresponding to the Soft-QoS service for user u; T represents the latency utility function corresponding to the best-effort service for user u; u T represents the end-to-end latency of user u; u,s P represents the end-to-end latency between the user and the satellite terminal. d This is the parameter for the utility function decay. This represents the maximum latency tolerance for user u's Soft-QoS service. This represents the maximum latency tolerance value corresponding to the Best-effort service for user u.

[0090] Step 3: When different types of users access different layers of low-Earth orbit satellites, the optimal aggregate utility function of the overall system is calculated by adjusting the constellation configuration;

[0091] The end-to-end latency between satellites and ground users varies at different orbital altitudes, and the satellite coverage and the number of users that can be accommodated also differ. The average bandwidth allocated to users within the coverage area varies for satellites at different orbital altitudes, affecting the user's data transmission rate. This example demonstrates a large-scale, multi-tiered low-Earth orbit constellation designed by continuously adjusting orbital parameters, including the orbital altitudes h of different tiers of satellites. l Number of orbital planes P l Number of satellites in orbit M l Track inclination angle i l The overall utility function of a constellation network is defined as the sum of the utilities of all user services. By optimizing the orbital altitude and regional coverage multiples of the multi-level constellation, a satellite constellation design that meets QoS requirements is obtained.

[0092] The optimal aggregation utility function is:

[0093] N represents the total number of satellites deployed in the constellation, P represents the number of orbits, i represents the orbital inclination, and UT represents the total set of all users.

[0094] Step 4: Calculate the deployment cost of adjusting the constellation configuration, and construct a multi-objective optimization function and constraints for large-scale, multi-level constellation configuration design;

[0095] This example combines factors such as users' differentiated business needs, the coverage performance of LEO constellations, and objective deployment costs to characterize and evaluate the performance of constellation configuration design. It avoids the limitations and insufficiency of a single evaluation index for constellation configuration design evaluation and designs a multi-constraint, multi-objective LEO constellation configuration evaluation index model.

[0096] Deployment costs include space segment costs C space Satellite manufacturing cost C const Launch cost C launch Insurance premium C insurance and maintenance costs C mc The calculation is as follows:

[0097]

[0098] C launch =0.00049×W sat ×h 0.43 ×N×P α ×i γ

[0099] C insurance =β(C const +C launch )

[0100] C mc =(C const +C launch +C insurance ) / Y life

[0101]

[0102] Among them, C sat Q represents the cost of manufacturing a single satellite, and W represents the slope of the learning curve for satellite manufacturing costs. sat P represents the weight of a single satellite. α Let α represent the influence factor of the number of orbits P on launch cost; γ Y represents the impact factor γ on the deployment cost of a satellite constellation, where orbital inclination i is the influencing factor, β represents the proportion of insurance costs, and Y represents the overall cost. life Indicates the satellite's orbital lifespan.

[0103] Based on the constellation configuration satisfying seamless coverage, a multi-objective and multi-constraint constellation configuration design evaluation index is established, including business requirement utility function satisfaction and deployment cost; the objective function and constraints are as follows:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] C5:g1+g2+…+g l ≥G

[0110] U represents the sum of the aggregate utility functions of all users, i.e., U = ΣU(R) u ,T u );h l This represents the orbital altitude when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and Indicates the orbital height h l The upper and lower threshold values; i l The orbital inclination angle represents the orbital inclination angle when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and Indicates the orbital inclination angle i l The upper and lower threshold values; P l This indicates the number of orbits when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and P represents the number of orbits l Upper and lower threshold values; M l This represents the number of satellites in a single orbit when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and M represents the number of satellites in a single orbit. l The upper and lower threshold values; g l G represents the satellite coverage weight when the number of orbital layers in a low-Earth orbit constellation is l, and G represents the maximum value of the required ground coverage weight.

[0111] Step 5: Use the adaptive particle swarm optimization algorithm to solve the multi-objective optimization problem of large-scale multi-level constellation configuration design, and finally obtain the deployment parameters of large-scale low-Earth orbit satellite constellations; and design multi-level low-Earth orbit constellation configurations according to these parameters.

[0112] The deployment parameters include the number of orbital layers L and the number of orbits P. l Track height h l Total number of satellites N l Track inclination angle i l Phase factor α l wait.

[0113] like Figure 4 As shown, during the iteration process, the initial position and velocity of the particles are randomly set (as input) within the search space consisting of the orbital altitude, orbital inclination, number of orbits, and number of satellites of a multi-layered constellation. The individual learning weight C1 and the group learning weight C2 of the particles are continuously modified during the iteration process, shifting from group learning to individual learning. The output serves as the orbital parameters for a large-scale, multi-level low-Earth orbit constellation configuration with multiple objectives and constraints.

Claims

1. A multi-level low-orbit constellation configuration design method applicable to diversified businesses, characterized in that, The specific steps are as follows: Step 1: Establish geometric models for single-satellite coverage and multi-satellite global coverage, including P orbits and constellation configurations with M satellites deployed in each orbit; Step 2: Establish an aggregated QoS utility representation model and calculate the aggregated utility function for a single user u. The aggregate utility function includes the data transfer rate utility function U. rate (R u and the time delay utility function U delay (T u ); The aggregation utility function of user u is characterized as follows w1 is the weight of the user rate utility function, and w2 is the weight of the user delay utility function; The data transmission rate utility function U rate (R u It includes the following two parts: in The data transmission rate utility function representing the Soft-QoS service for user u; Let p1 represent the data transmission rate utility function corresponding to the Best-effort service for user u, p1 represent the slope of the data transmission rate utility function corresponding to the Soft-QoS service, and q1 represent the influence utility function. The index of the range, R u R represents the data transmission rate of user u; th p2 represents the threshold requirement for data transmission rate, p2 represents the slope of the data transmission rate utility function corresponding to the Best-effort service, and q2 represents the influence utility function. An index of the range; Time delay utility function U delay (T u It includes the following two parts: This represents the latency utility function corresponding to the Soft-QoS service for user u; T represents the latency utility function corresponding to the best-effort service for user u; u T represents the latency of user u; u,s P represents the end-to-end latency between the user and the satellite terminal. d The parameter for the utility function decay; This represents the maximum latency tolerance for user u's Soft-QoS service. This represents the maximum latency tolerance value corresponding to user u's Best-effort service; Step 3: When different types of users access different layers of low-Earth orbit satellites, the optimal aggregate utility function of the overall system is calculated by adjusting the constellation configuration; The optimal aggregation utility function is: N represents the total number of satellites deployed in the constellation, P represents the number of orbits, i represents the orbital inclination, and UT represents the total set of all users; Step 4: Calculate the deployment cost of adjusting the constellation configuration, and construct a multi-objective optimization function and constraints for large-scale, multi-level constellation configuration design; Deployment costs include space segment costs C space Satellite manufacturing cost C const Launch cost C launch Insurance premium C insurance and maintenance costs C mc The calculation is as follows: Among them, C sat Q represents the cost of manufacturing a single satellite, and W represents the slope of the learning curve for satellite manufacturing costs. sat P represents the weight of a single satellite, and h represents the orbital altitude of a single satellite; α Let α represent the influence factor of the number of orbits P on launch cost; γ Y represents the impact factor γ on the deployment cost of a satellite constellation, where orbital inclination i is the influencing factor, β represents the proportion of insurance costs, and Y represents the overall cost. life Indicates the satellite's orbital lifetime; The objective function and constraints are as follows: U represents the sum of the aggregate utility functions of all users, i.e., U = ∑U(R) u ,T u );h l This represents the orbital altitude when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and Indicates the orbital height h l The upper and lower threshold values; i l The orbital inclination angle represents the orbital inclination angle when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and Indicates the orbital inclination angle i l The upper and lower threshold values; P l This indicates the number of orbits when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and P represents the number of orbits l Upper and lower threshold values; M l This represents the number of satellites in a single orbit when the number of orbital layers in a large-scale, multi-level low-Earth orbit constellation is l. and M represents the number of satellites in a single orbit. l The upper and lower threshold values; g l G represents the satellite coverage weight when the number of orbital layers in a low Earth orbit constellation is l, and G represents the maximum value of the required ground coverage weight. Step 5: Use the adaptive particle swarm optimization algorithm to solve the multi-objective optimization problem of large-scale multi-level constellation configuration design, and finally obtain the deployment parameters of large-scale low-Earth orbit satellite constellations; and design multi-level low-Earth orbit constellation configurations according to these parameters.

2. The multi-level low-orbit constellation configuration design method applicable to diversified businesses as described in claim 1, characterized in that, Step one specifically involves: First, the semi-geocene angle corresponding to the coverage area of ​​a single satellite. for: R is the Earth's radius; θ min This is the minimum visible elevation angle of the satellite from the ground terminal; Then, M satellites are deployed in a single orbit, with the angular distance between the sub-satellite points of adjacent satellites being λ = 2π / M; Next, determine the half-geocene angle. The angular distance between the nadir points of adjacent satellites satisfies At that time, a continuous coverage area is formed on the same orbit, namely a satellite ring: The coverage angle Δ of the satellite ring is shown in the following formula: Finally, based on the coverage of a single orbital satellite ring and the distance between adjacent orbital planes, the single-satellite constellation configuration design with seamless global coverage is determined: when At the same time, it can meet the requirement of seamless global single coverage:

3. The multi-level low-orbit constellation configuration design method applicable to diversified businesses as described in claim 1, characterized in that, The deployment parameters in step five include the number of orbital layers L and the number of orbits P. l Track height h l Total number of satellites N l Track inclination angle i l and phase factor α l ; During the iteration process, the initial position and velocity of the particles are randomly set within the search space consisting of the orbital altitude, orbital inclination, number of orbits, and number of satellites of the multi-layer constellation, and are used as inputs; the individual learning weight C1 and the group learning weight C2 of the particles are continuously modified during the iteration process, tending from group learning to individual learning; The output serves as orbital parameters for a large-scale, multi-level low-Earth orbit constellation configuration with multiple objectives and constraints.

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