A phased deployment optimization method and system for giant constellations

Abstract

A phased deployment optimization method and system for giant constellations, including: determining the magnitude of giant constellations, determining the scale of giant constellations according to the ideal number of satellites accommodated by low-orbit satellite orbits and satellite application requirements, and formulating short-term, medium-term, and long-term construction goals for giant constellations; establishing a coverage model for key coverage areas of giant constellations; establishing a comprehensive evaluation index J for giant constellations; optimizing the configuration parameters of "backbone constellations" to be deployed in short-term construction goals; optimizing the configuration parameters of "backbone constellations" to be deployed in medium-term construction goals; optimizing the configuration parameters of "backbone constellations" to be deployed in long-term construction goals, and configuring corresponding "enhanced satellites". The method of the present invention can be oriented to multiple application needs, and in view of the characteristics of large-scale, multi-satellite types, and large differences in orbital heights of low-orbit giant constellations, it can realize the maintenance and optimization of self-organized giant constellations with nodes and multiple scales using genetic algorithms.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite constellation configuration design, and in particular to a constellation phased deployment optimization method and system. Background Art

[0002] A satellite constellation is a collection of satellites that work together to complete specific tasks in a certain spatial geometric configuration. After entering the 21st century, due to the rapid development of highly integrated and automated technologies, the gradual reduction of launch costs and the continuous expansion of market demand, it has become possible to build a giant constellation consisting of thousands of tiny satellites. A giant constellation can provide global real-time coverage, effectively improve the constellation's in-orbit communication, remote sensing and other capabilities, and provide low-latency service quality in terms of communication broadband. Applying it to global navigation satellite system signal enhancement can achieve fast and accurate positioning. Foreign commercial companies such as SpaceX, Boeing, and OneWeb have successively proposed low-orbit giant constellation plans. The most representative of these is SpaceX's provision of broadband communication services through the low-orbit giant satellite constellation "Starlink", which will deploy the constellation in three phases.

[0003] Satellite constellation configuration includes the orbit type, spatial distribution and inter-satellite relationship of satellites. As a complex space system, the configuration design of giant constellations is a very complex problem. A large number of optimization variables and constraints must be considered, and on-orbit operation management faces many challenges. Due to its large scale, multiple satellite types, and large differences in orbital altitudes, the overall configuration stability under mission conditions is required to be high. In order to eliminate the influence of perturbation forces on the orbit of constellation satellites, it is necessary to find a dynamic stable configuration for giant constellations and perform low-frequency hybrid self-organization maintenance of constellation configurations. Due to the limitations of the satellite's own life and reliability, as well as the influence of the complex and harsh space environment, giant constellation satellites will experience short-term / long-term failures. In addition, it is necessary to combine the application service capabilities of giant constellations to evaluate the simulation performance of low-orbit giant constellations. Accelerating the construction and deployment of low-orbit giant constellation systems is, on the one hand, to seize limited orbital resources as soon as possible, and on the other hand, to seize the initiative in space and enhance my country's space dominance. No effective solution has been proposed for the phased optimization deployment of giant constellations. Summary of the invention

[0004] The technical problem solved by the present invention is: in view of the characteristics of giant constellations, such as large scale, multiple satellite types and large differences in orbital altitudes, to provide a giant constellation phased deployment optimization method and system that takes into account the mission requirements at different stages.

[0005] The technical solution adopted by the present invention is: a phased deployment optimization method for a giant constellation, comprising:

[0006] Determine the scale of the mega-constellation based on the ideal number of satellites that can be accommodated in the low-orbit satellite orbit and the satellite application requirements, and formulate short-term, mid-term and long-term construction goals for the mega-constellation;

[0007] Establish a coverage model for the key coverage areas of the mega-constellation;

[0008] Establish comprehensive evaluation index J for giant constellations;

[0009] According to the recent construction goals of the giant constellation, optimize the configuration parameters of the "backbone constellation" to be deployed in the recent construction goals;

[0010] According to the mid-term construction goals of the giant constellation, optimize the configuration parameters of the "backbone constellation" to be deployed in the mid-term construction goals;

[0011] According to the long-term construction goals of giant constellations, optimize the configuration parameters of the "backbone constellation" to be deployed in the long-term construction goals, and configure corresponding "enhanced satellites".

[0012] Determining the scale of the mega-constellation based on the ideal number of satellites that can be accommodated in the low-orbit satellite orbit and satellite application requirements includes:

[0013] Take 100km as the interval within the orbital altitude of 500km~2000km, calculate the interval constellation distribution density, and the orbital altitude is h km~(h+100)km. The ideal number of satellites N that can be accommodated in the orbital altitude interval is max :

[0014]

[0015] Among them, R1 and R2 are the orbital semi-major axes of the upper and lower limits of the orbital altitude range, that is, R1 = R e +h, R1 = R e +h+100km, R e is the radius of the earth; P is the percentage of available space; R s is the relative safety distance; r is the satellite relative control accuracy, ε m is the measurement error.

[0016] The short-term, mid-term and long-term construction goals of the mega-constellation include:

[0017] Short-term construction goals: Deploy a "backbone constellation" with an orbit altitude of 850km to 700km, including the Walker constellation composed of sun-synchronous orbits, to meet the short-term requirements of revisit time ≤ 1 hour and coverage period ≤ 10 hours for key coverage areas;

[0018] Mid-term construction goals: Deploy a "backbone constellation" with an orbit altitude of 650km to 600km: Continue to deploy the Walker constellation including satellites in sun-synchronous orbits, low-inclination orbits, and highly elliptical orbits;

[0019] Long-term construction goals: Deploy a "backbone constellation" with an orbital altitude of 550km to 500km, and configure corresponding "enhanced satellites": continue to deploy the Walker constellation including satellites in sun-synchronous orbit, low-inclination orbit, and large elliptical orbit, and on the basis of the original constellation, release a set number of micro-nano satellite "enhanced satellites" through the main satellite of the constellation "backbone constellation", so that the "enhanced satellites" are scattered near the main satellite.

[0020] The establishing of the coverage model of the mega-constellation for the key coverage area includes:

[0021] Divide the earth's spherical surface into regular polyhedrons, use the vertices of the polyhedrons as sampling points, and calculate the coverage of each sampling point κ;

[0022] Based on geocentric latitude Longitude λ, right ascension α, declination δ, spacecraft position r, half field of view angle γ, minimum solar altitude angle β min , Earth's flattening The satellite coverage of any sampling point at any time is calculated as:

[0023] If the inequality (V1 T ×V2)≥cos(γ), then κ1=1. If not, then κ1=0. If the inequality (V2 T ×V3)≥sin(β min ), then κ2=1, if not satisfied, then κ2=0; finally, the satellite coverage at any time is κ=κ1×κ2, where κ=1 in the coverage area and κ=0 outside the coverage area;

[0024] Among them, the unit vector in the direction of the geocenter-satellite Unit vector of sub-satellite direction: Local Earth radius

[0025] Unit vector in the direction from the center of the Earth to the subsatellite point:

[0026] The establishment of the giant constellation comprehensive evaluation index J includes:

[0027] According to different usage requirements, different evaluation indicators are assigned different weight factors α1, α2, α3, α4, α5, and the comprehensive evaluation index J of the giant constellation is obtained as follows:

[0028] J=μ t ×(α1ωcover +α2ω PDOP +α3ω C +α4ω GSD -α5ω Link );

[0029] Where 0≤α j ≤1, j=1,2,3,4,5; μ t represents the mega-constellation service availability performance parameter; ω cover represents the average response time considering the statistical characteristics of constellation coverage and gap statistics; ω PDOP Indicates the average PDOP of all ground target points in the time period that meets the basic navigation requirements, PDOP<10; ω C represents the expected value of constellation communication capacity; ω GSD Represents the mean ground sampling distance of each ground grid point; ω Lin k represents the average value of the sum of the link azimuth amplitude, elevation amplitude, and ratio of the range difference to the average distance between all satellites and the four satellites closest to them.

[0030] The optimization of the configuration parameters of the “backbone constellation” to be deployed in the near-term construction goal of the mega-constellation includes:

[0031] Genetic algorithms are used to optimize the configuration parameters of the mega-constellation for near-term construction goals. The mega-constellation configuration parameters to be optimized include the total number of satellites in sun-synchronous orbits, the number of satellites per orbit, and the orbital altitude.

[0032] The optimization target is the comprehensive evaluation index J of the giant constellation, and the optimized configuration parameters of the giant constellation that meet the short-term construction goals are output.

[0033] The configuration parameters of the “backbone constellation” to be deployed in the near-term construction goals are optimized according to the mid-term construction goals of the giant constellation, including:

[0034] Genetic algorithms are used to optimize the configuration parameters of the mega-constellation for the mid-term construction goal. The mega-constellation configuration parameters to be optimized include the total number of satellites in sun-synchronous orbits, the number of satellites per orbit, and the orbital altitude; the total number of satellites in low-inclination orbits, the number of satellites per orbit, and the orbital altitude; and the total number of satellites in highly elliptical orbits;

[0035] The optimization target is the comprehensive evaluation index J of the giant constellation, and the output is the optimized giant constellation configuration parameters that meet the mid-term construction goals.

[0036] According to the long-term construction goal of the giant constellation, the configuration parameters of the "backbone constellation" to be deployed in the long-term construction goal are optimized, and the corresponding "enhanced satellites" are configured, including:

[0037] Genetic algorithms are used to optimize the configuration parameters of the giant constellation for long-term construction goals. The giant constellation configuration parameters to be optimized include the total number of satellites in sun-synchronous orbits, the number of satellites per orbit, and the orbital altitude; the total number of satellites in low-inclination orbits, the number of satellites per orbit, and the orbital altitude; the total number of satellites in highly elliptical orbits, the number of "enhanced satellites", the relative ascending node right ascension difference, and the relative phase difference;

[0038] The optimization target is the comprehensive evaluation index J of the giant constellation, and the optimized configuration parameters of the giant constellation that meet the long-term construction goals are output.

[0039] Taking the “backbone constellation” satellite as the reference satellite, the right ascension difference of the relative ascending node of the “enhanced satellite” relative to the reference satellite is ΔΩ k and relative phase difference Δu k :

[0040] ΔΩ k =Ω k -Ω j

[0041] Δu k =u k -u j ;

[0042] Among them, Ω k is the right ascension of the ascending node of satellite k in the “enhanced satellite”, Ω j u is the right ascension of the ascending node of satellite j in the “backbone constellation”; k is the phase of satellite k in the “enhanced satellite”, u j is the phase of satellite j in the “backbone constellation”.

[0043] An optimization system using the above-mentioned phased deployment optimization method for a mega-constellation includes:

[0044] The first module is used to determine the scale of the giant constellation, based on the ideal number of satellites that can be accommodated in the low-orbit satellite orbit and the satellite application requirements, and to formulate the short-term, medium-term and long-term construction goals of the giant constellation;

[0045] The second module is used to establish a coverage model of the giant constellation for key coverage areas and establish a comprehensive evaluation index J for the giant constellation;

[0046] The third module is used to optimize the configuration parameters of the "backbone constellation" to be deployed in the short-term construction goals according to the short-term construction goals of the giant constellation; optimize the configuration parameters of the "backbone constellation" to be deployed in the medium-term construction goals according to the medium-term construction goals of the giant constellation; optimize the configuration parameters of the "backbone constellation" to be deployed in the long-term construction goals according to the long-term construction goals of the giant constellation, and configure corresponding "enhanced satellites".

[0047] The advantages of the present invention compared with the prior art are:

[0048] (1) The present invention clarifies the magnitude and scale of the giant LEO constellation from the perspective of the ideal number of satellites to be accommodated in the LEO satellite orbit and satellite applications, with the characteristics of the giant LEO constellation being large in scale, having multiple satellite types, and having large differences in orbital altitudes.

[0049] (2) Aiming at the needs of multiple applications, the present invention proposes a giant constellation deployment method that combines a "backbone constellation" and a "reinforced satellite", so that the giant constellation can be deployed in stages according to the short-term, medium-term, and long-term constellation development requirements, thereby improving the long-term stability of the constellation.

[0050] (3) The present invention combines the application service capabilities of giant constellations and considers static and dynamic performance indicators at the same time, and constructs a giant constellation evaluation index that integrates dynamic and static indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0052] The present invention will be further described in detail below with reference to the accompanying drawings.

[0053] The present invention proposes a method for optimizing the deployment of a giant constellation consisting of a "backbone constellation" + "enhanced satellite", taking into account the efficiency progressive configuration optimization design of phased deployment, formulating short-term, medium-term and long-term construction goals, and realizing the phased deployment and optimization of the giant constellation. Aiming at the short-term and medium-term development needs, firstly based on the Walker constellation, by optimizing the constellation configuration parameters, the orbital elements of the "backbone constellation" in the giant constellation are determined, and then according to the long-term mission requirements, with the "backbone constellation" as the center, the relative orbital elements of the "enhanced satellite" satellite are optimized, thereby determining the long-term complete configuration of the giant constellation and realizing the deployment of the giant constellation.

[0054] like Figure 1 As shown, a phased deployment optimization method for a giant constellation of the present invention comprises the following steps:

[0055] Step 1: Clarify the scale of the mega-constellation, determine the scale of the mega-constellation from the perspective of the ideal number of satellites that can be accommodated in low-orbit satellite orbits and the perspective of satellite applications, and formulate short-term, mid-term and long-term construction goals for the mega-constellation;

[0056] Mega-constellations are large in scale and require the definition of specific magnitudes, in order to clarify the scale of the mega-constellations from the perspective of the ideal number of satellites that can be accommodated in low-orbit satellite orbits and from the perspective of satellite applications.

[0057] It is known that low-orbit constellations with an orbital altitude of 500km-2000km are the most common. Taking 100km within this altitude as an interval, the distribution density of constellations in this interval is studied. In practice, relative distances between spacecraft are maintained through autonomous control, and there is a certain control accuracy due to the limitation of control capabilities. Satellites within giant constellations may also collide with each other, including: collisions caused by the interaction of perturbation forces and initial orbit errors, and collisions caused by uncertainty in satellite positions during maneuvers such as orbit entry, orbit maintenance, collision avoidance, and deorbit disposal, as well as observation errors. The relative safety distance R between satellites must be considered. s In addition, the absolute position measurement accuracy of the satellite restricts the overall migration and evolution of the constellation, and is limited by the constellation deployment capability and space environment protection factors, and the percentage of available space P is limited. Therefore, the ideal number of satellites that can be accommodated in the orbital altitude interval of h km–(h+100) km is determined by the relative safety distance R s 、Satellite relative control accuracy ε r and measurement error ε m Joint decision:

[0058]

[0059] Where R1 and R2 are the semi-major axes of the orbital altitude range, that is, R1 = R e +h, R1 = R e +h+100km, R e is the radius of the Earth.

[0060] Mega-constellations are deployed in three phases: near-term, mid-term, and long-term. Different satellite types are used in different phases to meet different needs. Therefore, mega-constellations are mainly deployed in a combination of "backbone constellations" and "enhanced constellations" according to the near-term, mid-term, and long-term constellation development positioning. The following are the near-term, mid-term, and long-term constellation deployment plans.

[0061] In the near term: Deployment of 850km-700km "backbone constellation". It is mainly composed of the Walker constellation composed of sun-synchronous orbits, meeting the recent requirements of revisit time ≤ 1 hour and coverage period ≤ 10 hours for key coverage areas;

[0062] Mid-term: Deploy a 650km-600km “backbone constellation”. Continue to deploy the Walker constellation consisting of sun-synchronous orbits, low-inclination orbits, and highly elliptical orbits to further enhance the constellation’s coverage and revisit capabilities, and increase global coverage areas;

[0063] Long-term: Deploy a 550km-500km "backbone constellation" and configure corresponding "enhanced satellites". Continue to deploy the Walker constellation consisting of sun-synchronous orbits, low-inclination orbits, and large elliptical orbits. On the basis of the original constellation, release a certain number of micro-nano satellite "enhanced satellites" through the main satellite of the constellation "backbone constellation" to spread them around the main satellite, improve the observation capability of a single satellite, and further improve the flexibility and scalability of the constellation.

[0064] Step 2: Establish a coverage model for key areas using a giant constellation.

[0065] Divide the earth's spherical surface into regular polyhedrons, use the vertices of the polyhedrons as sampling points, and calculate the coverage of each sampling point κ;

[0066] Known geocentric latitude Longitude λ, right ascension α, declination δ, spacecraft position r, half field of view angle γ, minimum solar altitude angle β min , Earth's flattening Get the satellite coverage situation κ at any time.

[0067] Local Earth radius

[0068]

[0069] Unit vector in the direction from the center of the Earth to the satellite:

[0070]

[0071] Unit vector of sub-satellite direction:

[0072]

[0073]

[0074] Unit vector in the direction from the center of the Earth to the subsatellite point:

[0075]

[0076] Substitute the above formula into the following equations and respectively calculate the half field angle γ and the minimum solar altitude angle β min Compare:

[0077] If the inequality (V1 T ×V2)≥cos(γ), then κ1=1. If not, then κ1=0. If the inequality (V2 T ×V3)≥sin(β min ), then κ2=1, if not satisfied, then κ2=0; finally, the satellite coverage at any time is κ=κ1×κ2 (κ=1 in the coverage area, κ=0 outside the coverage area).

[0078] The coverage characteristics of the giant constellation introduce a sampling point scheme based on regular polyhedron partitioning. This scheme has uniform partitioning area, small deformation, uniform distance between sampling points, and a good spherical point distance ratio at each partitioning level. The polyhedron uses a regular icosahedron with relatively small spherical grid deformation and uniform spherical distance between grid points. When all target points are covered by the satellite once, it can be considered that the coverage mission is completed.

[0079] Step 3: Establish a comprehensive evaluation index that considers the static and dynamic performance of the giant constellation;

[0080] For the evaluation index system of giant constellations, a constellation evaluation index system function that combines static and dynamic indicators is constructed by integrating static performance indicators (including navigation, mobile communication, and remote sensing characteristics) and dynamic performance indicators (availability of intersatellite links and constellation services).

[0081] The six evaluation parameters are evaluated and called during the entire simulation cycle. Different evaluation indicators are given different weight factors α1, α2, α3, α4, α5 (0≤α j ≤1), the comprehensive evaluation index J of the giant constellation is obtained as:

[0082] J=μ t ×(α1ω cover +α2ω PDOP +α3ω C +α4ω GSD -α5ω Link )

[0083] Among them, μ t represents the mega-constellation service availability performance parameter; ω cover represents the average response time considering the statistical characteristics of constellation coverage and gap statistics; ω PDOP Represents the average PDOP value of all ground target points in the time period that meets the basic navigation requirements (PDOP<10); ω C represents the expected value of constellation communication capacity; ω GSD represents the mean ground sampling distance (GSD) of each ground grid point; ω Link It represents the average value of the sum of the link azimuth amplitude, elevation amplitude, and the ratio of the range difference to the average distance between all satellites and the four satellites closest to them. PDOP ,ω cover ,ω C ,ω GSD is the static performance index, μ t ,ωL ink It is a dynamic performance indicator.

[0084] Step 4: According to the short-term construction goals of the giant constellation, optimize the configuration parameters of the "backbone constellation" based on the sun-synchronous orbit;

[0085] In order to obtain the long-term stable performance of the constellation, the necessary height and orbit type unification are the prerequisites for configuration optimization. According to the recent construction goals of the giant constellation, the "backbone constellation" consists of the Walker constellation composed of 850km-700km sun-synchronous orbits. The Walker constellation only needs three parameters (T / P / F) to describe the distribution of the entire constellation when the orbital height and orbital inclination are determined: T represents the total number of constellation satellites; P represents the number of orbital planes; F represents the relative position of satellites in different orbits, and the value range is (0~P-1).

[0086] In the present invention, a genetic algorithm is used to optimize the configuration parameters of a giant constellation, and the specific steps are as follows.

[0087] S041: The optimized giant constellation configuration parameters include the total number of sun-synchronous orbit satellites, the number of satellites per orbit, and the orbital altitude in the near-term construction goals. Determine the range, accuracy, initial mutation probability, maximum genetic generation and other parameters of the optimized satellite constellation configuration parameters, and use random methods to generate the initial population;

[0088] S042: Calculate the comprehensive evaluation index of each individual in the initial population according to the giant constellation comprehensive evaluation method described in step 3, and perform individual evaluation and sorting to form parent individuals;

[0089] S043: Select and retain the remaining individuals for crossover mutation to generate new offspring individuals, which form a new population with the parent individuals. Calculate individual fitness and adjust the mutation probability.

[0090] S044: Determine whether the comprehensive evaluation index of the constellation of the first individual in the population is converged, if so, execute step S045, otherwise, execute S042 of this step;

[0091] S045: Output optimized giant constellation configuration parameters that meet short-term construction goals.

[0092] Step 5: According to the mid-term construction goals of the giant constellation, optimize the configuration parameters of the "backbone constellation" based on sun-synchronous orbits, low-inclination orbits, and highly elliptical orbits;

[0093] Compared with the recent deployment plan, the mid-term construction has added a low-inclination orbit with an orbital inclination of 60° and a large elliptical orbit with a perigee height of 300km and an apogee height of 2000km. The low-inclination orbit has a shorter period, better sub-satellite point track interval, and better repeated coverage characteristics, which helps to improve the coverage performance at low latitudes and meet the complete continuous coverage of mid- and low-latitude regions. In addition, the addition of a large elliptical orbit in the mid-term construction is conducive to achieving real-time coverage in China.

[0094] According to the mid-term construction goal of the giant constellation, the mid-term "backbone constellation" consists of a Walker constellation with a 650km-600km sun-synchronous orbit, a low-inclination orbit, and a large elliptical orbit. The genetic algorithm is used to optimize the configuration parameters of the giant constellation. The specific steps are as follows.

[0095] S051: The optimized giant constellation configuration parameters include the total number of satellites in the sun-synchronous orbit, the number of satellites per orbit, the orbital altitude, the total number of satellites in the low-inclination orbit, the number of satellites per orbit, the orbital altitude, and the total number of satellites in the highly elliptical orbit in the mid-term construction goal, a total of 7 variables. Determine the range, accuracy, initial mutation probability, maximum genetic generation and other parameters of the optimized satellite constellation configuration parameters, and use a random method to generate the initial population;

[0096] S052: Calculate the comprehensive evaluation index of each individual in the initial population according to the giant constellation comprehensive evaluation method described in step 3, and perform individual evaluation and sorting to form parent individuals;

[0097] S053: Select and retain the remaining individuals for crossover mutation to generate new offspring individuals, which form a new population with the parent individuals. Calculate individual fitness and adjust the mutation probability.

[0098] S054: Determine whether the comprehensive evaluation index of the constellation of the first individual in the population is converged, if so, execute step S055, otherwise, execute S052 of this step;

[0099] S055: Output optimized giant constellation configuration parameters that meet short-term construction goals.

[0100] Step six: According to the long-term construction goals of the giant constellation, optimize the configuration parameters of the "backbone constellation" based on sun-synchronous orbit, low-inclination orbit, and large elliptical orbit, and configure corresponding "enhanced satellites".

[0101] According to the long-term construction goal of the giant constellation, the long-term "backbone constellation" will be deployed first, consisting of a 550km-500km sun-synchronous orbit, a low-inclination orbit, and a Walker constellation with a large elliptical orbit. When the "backbone constellation" satellite is deployed, the "enhanced satellite" satellite will be deployed. The main purpose of the "enhanced satellite" satellite is to increase the constellation's ground coverage time, reduce the coverage interval and revisit time, so as to achieve the real-time coverage requirements of the low-orbit high-density constellation.

[0102] In order to reduce the number of variables, the "enhanced satellite" satellite is released by the "backbone constellation" satellite to complete the configuration. Therefore, the "backbone constellation" satellite is used as the reference satellite, and the main difference between the "enhanced satellite" and the reference satellite is the relative ascending node right ascension Ω and relative phase u:

[0103] ΔΩ k =Ω k -Ω j

[0104] Δu k =u k -u j

[0105] The subscript j represents the satellite number of the backbone constellation, and k represents the satellite number of the enhanced constellation. The genetic algorithm is used to optimize the configuration parameters of the giant constellation. The specific steps are as follows.

[0106] S061: The optimized giant constellation configuration parameters include the total number of satellites in the sun-synchronous orbit, the number of satellites per orbit, and the orbital altitude in the long-term construction goals; the total number of satellites in the low-inclination orbit, the number of satellites per orbit, and the orbital altitude; the total number of satellites in the highly elliptical orbit; the number of "enhanced satellites", the right ascension difference of the relative ascending node, and the relative phase difference, for a total of 10 variables. Determine the range, accuracy, initial mutation probability, maximum genetic generation number and other parameters of the optimized satellite constellation configuration parameters, and use a random method to generate the initial population;

[0107] S062: Calculate the comprehensive evaluation index of each individual in the initial population according to the giant constellation comprehensive evaluation method described in step 3, and perform individual evaluation and sorting to form parent individuals;

[0108] S063: Select and retain the remaining individuals for crossover mutation to generate new offspring individuals, which form a new population with the parent individuals. Calculate individual fitness and adjust the mutation probability.

[0109] S064: Determine whether the comprehensive evaluation index of the constellation of the first individual in the population is converged, if so, execute step S065, otherwise, execute S062 of this step;

[0110] S065: Output optimized giant constellation configuration parameters that meet short-term construction goals.

[0111] At this point, the giant constellation configuration is completed.

[0112] An optimization system using the above-mentioned phased deployment optimization method for a mega-constellation includes:

[0113] The first module is used to determine the scale of the giant constellation, based on the ideal number of satellites that can be accommodated in the low-orbit satellite orbit and the satellite application requirements, and to formulate the short-term, medium-term and long-term construction goals of the giant constellation;

[0114] The second module is used to establish a coverage model of the giant constellation for key coverage areas and establish a comprehensive evaluation index J for the giant constellation;

[0115] The third module is used to optimize the configuration parameters of the "backbone constellation" to be deployed in the short-term construction goals according to the short-term construction goals of the giant constellation; optimize the configuration parameters of the "backbone constellation" to be deployed in the medium-term construction goals according to the medium-term construction goals of the giant constellation; optimize the configuration parameters of the "backbone constellation" to be deployed in the long-term construction goals according to the long-term construction goals of the giant constellation, and configure corresponding "enhanced satellites".

[0116] In view of the requirements of stability, coverage and revisit characteristics, the present invention combines the spatial aggregation characteristics of the constellation and the structural characteristics of long-term evolution. The giant constellation deployment solution adopts a dynamic hybrid strategy of absolute configuration and relative configuration, and uses a genetic algorithm to maintain and optimize the node-based, multi-scale self-organizing configuration.

[0117] Parts of the present invention that are not described in detail belong to the well-known technologies of those skilled in the art.

Claims

1. A phased deployment optimization method for a giant constellation, characterized in that: include: Determine the scale of the mega-constellation based on the ideal number of satellites that can be accommodated in the low-orbit satellite orbit and the satellite application requirements, and formulate short-term, mid-term and long-term construction goals for the mega-constellation; Establish a coverage model for the key coverage areas of the mega-constellation; Establish comprehensive evaluation index J for giant constellations; According to the recent construction goals of the giant constellation, optimize the configuration parameters of the "backbone constellation" to be deployed in the recent construction goals; According to the mid-term construction goals of the giant constellation, optimize the configuration parameters of the "backbone constellation" to be deployed in the mid-term construction goals; According to the long-term construction goals of the giant constellation, optimize the configuration parameters of the "backbone constellation" to be deployed in the long-term construction goals, and configure the corresponding "enhanced satellites"; Determining the scale of the mega-constellation based on the ideal number of satellites that can be accommodated in the low-orbit satellite orbit and satellite application requirements includes: Taking 100km as the interval within the orbital altitude of 500km~2000km, calculate the interval constellation distribution density, and the orbital altitude is hkm~(h+100)km. The ideal number of satellites N accommodated in the orbital altitude interval max : Among them, R1 and R2 are the orbital semi-major axes of the upper and lower limits of the orbital altitude range, that is, R1 = R e +h, R1 = R e +h+100km, R e is the radius of the earth; P is the percentage of available space; R s is the relative safety distance; r is the satellite relative control accuracy, ε m is the measurement error; The short-term, mid-term and long-term construction goals of the mega-constellation include: Short-term construction goals: Deploy a "backbone constellation" with an orbit altitude of 850km to 700km, including the Walker constellation composed of sun-synchronous orbits, to meet the short-term requirements of revisit time ≤ 1 hour and coverage period ≤ 10 hours in key coverage areas; Mid-term construction goals: Deployment of a "backbone constellation" with an orbit altitude of 650km to 600km: Continue to deploy the Walker constellation including satellites in sun-synchronous orbits, low-inclination orbits, and highly elliptical orbits; Long-term construction goals: deploy a "backbone constellation" with an orbital altitude of 550km to 500km, and configure corresponding "enhanced satellites": continue to deploy the Walker constellation including satellites in sun-synchronous orbit, low-inclination orbit, and large elliptical orbit, and on the basis of the original constellation, release a set number of micro-nano satellite "enhanced satellites" through the main satellite of the constellation "backbone constellation", so that the "enhanced satellites" are scattered near the main satellite; According to the long-term construction goal of the giant constellation, the configuration parameters of the "backbone constellation" to be deployed in the long-term construction goal are optimized, and the corresponding "enhanced satellites" are configured, including: Genetic algorithm is used to optimize the configuration parameters of the giant constellation for long-term construction goals. The giant constellation configuration parameters to be optimized include the total number of satellites in sun-synchronous orbit, the number of satellites in each orbit, and the orbital altitude; the total number of satellites in low-inclination orbit, the number of satellites in each orbit, and the orbital altitude; the total number of satellites in highly elliptical orbits, the number of "enhanced satellites", and the right ascension difference ΔΩ relative to the ascending node. k and relative phase difference Δu k ; The optimization target is the comprehensive evaluation index J of the giant constellation, and the optimized configuration parameters of the giant constellation that meet the long-term construction goals are output.

2. The phased deployment optimization method for a giant constellation according to claim 1, characterized in that: The establishing of the coverage model of the mega-constellation for the key coverage area includes: Divide the earth's spherical surface into regular polyhedrons, use the vertices of the polyhedrons as sampling points, and calculate the coverage of each sampling point κ; Based on geocentric latitude Longitude λ, right ascension α, declination δ, spacecraft position r, half field of view angle γ, minimum solar altitude angle β min , Earth's flattening Calculate the coverage of satellites at any sampling point at any time κ: If the inequality (V1 T ×V2)≥cos(γ), then κ1=1. If not, then κ1=0. If the inequality (V2 T ×V3)≥sin(β min ), then κ2=1, if not satisfied, then κ2=0; finally, the satellite coverage at any time is κ=κ1×κ2, where κ=1 in the coverage area and κ=0 outside the coverage area; Among them, the unit vector in the direction of the geocenter-satellite Unit vector of sub-satellite direction: Local Earth radius Unit vector in the direction from the center of the Earth to the subsatellite point:

3. The phased deployment optimization method for a giant constellation according to claim 1, characterized in that: The establishment of the giant constellation comprehensive evaluation index J includes: According to different usage requirements, different evaluation indicators are assigned different weight factors α1, α2, α3, α4, α5, and the comprehensive evaluation index J of the giant constellation is obtained as follows: J=μ t ×(α1ω cover +a2w PDOP +a3h C +a4w GSD -a5h Link ); Where 0≤α j ≤1, j=1,2,3,4,5; μ t represents the mega-constellation service availability performance parameter; ω cover represents the average response time considering the statistical characteristics of constellation coverage and gap statistics; ω PDOP Indicates the average PDOP of all ground target points in the time period that meets the basic navigation requirements, PDOP<10; ω C represents the expected value of constellation communication capacity; ω GSD Represents the mean ground sampling distance of each ground grid point; ω Link It represents the average value of the sum of the ratios of the link azimuth amplitude, elevation amplitude, and range extreme difference to the average distance between all satellites and the four nearest satellites.

4. The phased deployment optimization method for a giant constellation according to claim 1, characterized in that: The optimization of the configuration parameters of the "backbone constellation" to be deployed in the near-term construction goal of the mega-constellation includes: Genetic algorithms are used to optimize the configuration parameters of the mega-constellation for near-term construction goals. The mega-constellation configuration parameters to be optimized include the total number of satellites in sun-synchronous orbits, the number of satellites per orbit, and the orbital altitude. The optimization target is the comprehensive evaluation index J of the giant constellation, and the optimized configuration parameters of the giant constellation that meet the short-term construction goals are output.

5. The phased deployment optimization method for a giant constellation according to claim 1, characterized in that: The configuration parameters of the "backbone constellation" to be deployed in the near-term construction goals are optimized according to the mid-term construction goals of the giant constellation, including: Genetic algorithms are used to optimize the configuration parameters of the mega-constellation for the mid-term construction goal. The mega-constellation configuration parameters to be optimized include the total number of satellites in sun-synchronous orbits, the number of satellites per orbit, and the orbital altitude; the total number of satellites in low-inclination orbits, the number of satellites per orbit, and the orbital altitude; and the total number of satellites in highly elliptical orbits; The optimization target is the comprehensive evaluation index J of the giant constellation, and the output is the optimized giant constellation configuration parameters that meet the mid-term construction goals.

6. The phased deployment optimization method for a giant constellation according to claim 1, characterized in that: The relative ascending node right ascension difference ΔΩ k and the relative phase difference Δu k : DO k =Oh k -Oh j Δu k =u k -u j ; Among them, Ω k is the right ascension of the ascending node of satellite k in the "enhanced satellite", Ω j is the right ascension of the ascending node of satellite j in the "backbone constellation"; u k is the phase of satellite k in the "enhanced satellite", u j is the phase of satellite j in the "backbone constellation".

7. The optimization system of the phased deployment optimization method for giant constellations according to any one of claims 1 to 6, characterized in that: include: The first module is used to determine the scale of the giant constellation, based on the ideal number of satellites that can be accommodated in the low-orbit satellite orbit and the satellite application requirements, and to formulate the short-term, medium-term and long-term construction goals of the giant constellation; The second module is used to establish a coverage model of the giant constellation for key coverage areas and establish a comprehensive evaluation index J for the giant constellation; The third module is used to optimize the configuration parameters of the "backbone constellation" to be deployed in the short-term construction goals according to the short-term construction goals of the giant constellation; optimize the configuration parameters of the "backbone constellation" to be deployed in the medium-term construction goals according to the medium-term construction goals of the giant constellation; optimize the configuration parameters of the "backbone constellation" to be deployed in the long-term construction goals according to the long-term construction goals of the giant constellation, and configure corresponding "reinforcement satellites".

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