Constellation optimization design method and system for regional high-density scatter high-revisit task

Abstract

The invention relates to a constellation optimization design method and system for a regional high-density scatter high revisit task. The method comprises the following steps: S1, confirming a constellation optimization target; s2, constructing a revisit performance index of the low-orbit heterogeneous satellite constellation model; s3, selecting one or more of satellite constellation revisit performance indexes, and establishing an optimization design objective function; s4, determining a constellation configuration: regressing an orbit basic configuration by adopting a common sub-satellite point trajectory; s5, determining a constellation orbit optimization design variable; s6, determining constellation optimization design constraint conditions according to task requirements; and S7, a constellation optimal design strategy: constructing an optimal design environment based on a common simulation tool, establishing a constellation single-target or multi-target optimal design model, and performing iterative solution through an intelligent optimization algorithm to obtain a low-orbit heterogeneous constellation optimal design strategy. The method is wide in applicability, and the constellation obtained through optimization has the advantages of being wide in applicability, high in anti-interference performance and good in countermeasure.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite constellation design and mission planning, and in particular to a constellation optimization design method and system for regional high-density scattered-point high-revisit missions. Background Art

[0002] With the development of space technology, providing space support for regional emergency missions has become a focus of space missions. With the rapid progress and application of small satellite technology, it is possible for people to obtain information support from space to respond to and solve regional emergency space missions. At present, the development and application of small satellites have become a hot spot in the development of space science and technology at home and abroad. Compared with traditional spacecraft that mainly perform global missions, have large mass, high cost, long design life and launch cycle, small satellites have many advantages such as being able to be launched on demand, light mass, low cost, short development time, and relatively simple requirements for carriers and ground maintenance. They are more suitable for deployment in low-Earth orbit to complete specific tasks targeting regional targets.

[0003] However, due to the time limit of passing through the top, the coverage capability of a single satellite in a near-Earth circular orbit for targets in a specific area is seriously insufficient. It cannot cover targets multiple times in a long period of time, and the coverage range is also limited, making it difficult to meet application requirements. By networking multiple satellites to form a constellation, it is possible to achieve rapid response capabilities for observation efficiency in hot spots, and complete diversified space missions such as multiple coverage, effective observation, and navigation and communication of high-density scattered dynamic targets in specific areas. Therefore, how to select appropriate orbital parameters to network a certain number of small satellites to form a specific constellation is a hot issue in solving the problem of networking coverage of high-density scattered dynamic targets in hot spots. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a constellation optimization design method and system for regional high-density scattered point high revisit tasks, which has wide applicability, and the optimized constellation has the characteristics of wide applicability, strong anti-interference and good confrontation.

[0005] To achieve the above-mentioned object of the invention, the present invention provides a constellation optimization design method for regional high-density scattered point high revisit tasks, comprising the following steps:

[0006] Step S1, confirm the constellation optimization target: according to the rapid response capability enhancement task requirements of space-based observation efficiency, high revisit coverage is carried out for hot target areas and high-density scattered ground mission target points;

[0007] Step S2, constructing a satellite constellation revisit performance index of a low-orbit heterogeneous satellite constellation model;

[0008] Step S3, selecting one or more of the satellite constellation revisit performance indicators and establishing an optimization design objective function;

[0009] Step S4, determining the constellation configuration: according to the characteristics of the regional coverage mission, the constellation adopts the basic configuration of the common subsatellite point trajectory regression orbit;

[0010] Step S5, determining constellation orbit optimization design variables;

[0011] Step S6: Determine the constellation optimization design constraint conditions according to the mission requirements;

[0012] Step S7, constellation strategy optimization: construct a constellation optimization design model according to the optimization design objective function, the constellation orbit optimization design variables and the constellation optimization design constraints, and build an optimization design environment based on a simulation tool, and iteratively solve the constellation optimization design model through an intelligent optimization algorithm to obtain the optimal design strategy for the low-orbit heterogeneous constellation.

[0013] According to a technical solution of the present invention, the satellite constellation revisit performance indicators include total coverage time, coverage percentage, number of coverages, coverage multiplicity, average coverage time, maximum coverage time, average revisit period, minimum revisit interval, and maximum revisit interval.

[0014] According to a technical solution of the present invention, the ground track of each satellite in the constellation of the basic configuration of the common sub-satellite point trajectory regression orbit is the same, and the ascending nodes of the constellation of the basic configuration of the common sub-satellite point trajectory regression orbit are non-uniformly distributed along the equator, and a regression orbit is adopted, and its ground track repeats after one regression cycle.

[0015] According to a technical solution of the present invention, in step S5, it specifically includes:

[0016] According to the basic configuration of the common subsatellite point trajectory regression orbit, the orbit is an elliptical orbit. The parameter model of the eccentricity, orbit inclination and semi-major axis of the orbit is established by analytical method, and the perigee argument, mean anomaly angle and ascending node right ascension of the orbit are determined as optimization design variables.

[0017] According to a technical solution of the present invention, in step S5, establishing a parameter model of the eccentricity, orbital inclination and semi-major axis of the orbit by analytical method specifically includes:

[0018] The semi-major axis a is calculated according to the following formula (1):

[0019]

[0020] Where T is the orbital period, μ is the gravitational parameter, and for the Earth μ = 3.96012×10 5 km 3 / s2 ;

[0021] The eccentricity e is calculated according to the following formula (2):

[0022]

[0023] Where c is the semi-focal length of the track, a is the semi-major axis of the track, r a is the distance from the apogee to the center of the orbit, r p is the distance from the perigee to the center of the orbit, h a is the apogee orbital height, h p is the perigee orbit height, R E is the radius of the Earth;

[0024] The orbital inclination λ is calculated according to the following formula:

[0025]

[0026] Where λ is the orbital inclination, represents the target dimension, M represents the number of circles between satellites in the constellation; D represents the regression period of the satellites in the constellation, and N represents the number of circles that the satellites in the constellation run within the regression period;

[0027] When M is 1, equation (3.1) is expressed as

[0028]

[0029] According to a technical solution of the present invention, in step S6, the constraint condition of the common subsatellite point trajectory regression orbit basic configuration is expressed as:

[0030]

[0031] Among them, Δu represents the latitude argument difference, ΔΩ represents the right ascension difference of the ascending node, D represents the regression period of the satellite in the constellation, and N represents the number of circles that the satellite in the constellation runs in the regression period;

[0032] The range of the right ascension difference ΔΩ and the latitude angle difference Δu of the ascending node is

[0033]

[0034] Where l is the total number of satellites in the constellation.

[0035] According to a technical solution of the present invention, in step S7, the constellation optimization design model uses the orbit's perigee argument, mean anomaly and ascending node right ascension as optimization design variables, one or more of the satellite constellation revisit performance indicators as optimization design objective functions, and the constellation optimization design constraints determined in step S6 as constraints.

[0036] According to a technical solution of the present invention, in the step S7, a single-objective optimization design model is constructed for one of the satellite constellation revisit performance indicators according to the optimization design objective function;

[0037] Alternatively, a plurality of the satellite constellation revisit performance indicators are selected according to the optimization design objective function to construct a multi-objective optimization design model.

[0038] According to a technical solution of the present invention, the mathematical definition of the multi-objective optimization design model is expressed as

[0039]

[0040] Where x=(x1,x2,…,x k ,…,x n )∈Ω, x is the n-dimensional decision variable, xn is the selected optimization target, Ω is the n-dimensional decision space, L k and U k x k The upper and lower bounds of is the m-dimensional target space, F(x) is the set of target optimization functions, representing mapping relationship; g and h are the inequality constraint function and equality constraint function of the problem respectively, p and q represent the number of inequality constraint functions and the number of equality constraint functions respectively.

[0041] According to one aspect of the present invention, the present invention provides a system for implementing the above-mentioned constellation optimization design method for regional high-density scattered-point high-revisit tasks.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention proposes a constellation optimization design method and system for regional high-density scattered point high revisit tasks. On the basis of sufficient satellite resources, multiple coverage and observation performance optimization design indicators of low-orbit heterogeneous satellite constellation models are constructed, and according to the characteristics of regional coverage tasks, the constellation adopts the basic configuration of the common sub-satellite point trajectory regression orbit with overlapping sub-satellite point trajectories. The present invention establishes satellite orbit inclination, eccentricity and semi-major axis orbit parameter models of each sub-satellite of the constellation according to the analytical method, and then adopts the intelligent optimization method to use the satellite mean anomaly angle and ascending node right ascension as optimization design variables. On the basis of determining the constellation optimization design constraints (which can be designed as common sub-satellite point trajectory regression orbit constraints and / or total engineering cost constraints according to mission requirements), a single-objective or multi-objective optimization design objective function of the low-orbit heterogeneous satellite constellation is established. Finally, an optimization environment is constructed based on a simulation tool, and the single-objective or multi-objective optimization design model of the constellation design is iteratively solved by an intelligent optimization algorithm, thereby obtaining an optimal design strategy for a low-orbit heterogeneous constellation.

[0044] The present invention can not only effectively integrate a variety of different types of heterogeneous satellite payloads to ensure better anti-interference and countermeasure performance, but also more comprehensively and flexibly meet the growing mission requirements of constellation diversification and hot spot area mission targets, thereby achieving long-term stable high revisit coverage of high-density scattered mission targets in hot spot areas, greatly enhancing the rapid response capability of observation efficiency, and having high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 A strategy diagram schematically shows a constellation optimization design method for regional high-density scattered-point high-revisit missions provided by the present invention;

[0047] Figure 2 A flowchart schematically shows a constellation optimization design method for regional high-density scattered-point high-revisit tasks provided according to an embodiment of the present invention;

[0048] Figure 3 A schematic diagram schematically showing a constellation revisit performance indicator according to an embodiment of the present invention;

[0049] Figure 4 A schematic diagram schematically illustrates a constellation optimization design process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The description of the embodiments of this specification should be combined with the corresponding drawings, which should be considered as part of the complete specification. In the drawings, the shape or thickness of the embodiments may be enlarged and indicated for simplification or convenience. Furthermore, the parts of each structure in the drawings will be described separately. It is worth noting that the elements not shown in the drawings or not described in words are in a form known to ordinary technicians in the relevant technical field.

[0051] The description of the embodiments herein and any reference to directions and orientations are only for the convenience of description and are not to be construed as any limitation on the scope of protection of the present invention. The following description of the preferred embodiments may involve combinations of features, which may exist independently or in combination, and the present invention is not particularly limited to the preferred embodiments. The scope of the present invention is defined by the claims.

[0052] like Figure 1 As shown, the present invention provides a constellation optimization design method for regional high-density scattered point high revisit tasks, comprising the following steps:

[0053] Step S1, confirm the constellation optimization target: according to the rapid response capability enhancement mission requirement of space-based observation efficiency, high revisit coverage is carried out for hot target areas and high-density scattered ground mission target points.

[0054] Step S2: constructing a satellite constellation revisit performance index for a low-orbit heterogeneous satellite constellation model.

[0055] According to specific mission requirements, one or more satellite constellation revisit performance indicators can be selected as the intelligent optimization objective function to perform single-objective or multi-objective optimization. Figure 2 As shown, the satellite constellation revisit performance indicators include total coverage time, coverage percentage, number of coverage times, coverage repetition number, average revisit period, maximum revisit interval, minimum revisit interval, average coverage time, maximum coverage time, and minimum coverage time.

[0056] The definition of satellite constellation revisit performance indicators is described in detail below.

[0057] 1) Total coverage time refers to the total coverage time of satellites in the constellation over the ground mission day target within a given time.

[0058] 2) Coverage percentage refers to the coverage percentage of any point on the ground, which is the ratio of the total coverage time of satellites in the constellation to the simulation period (i.e., given time).

[0059] 3) Coverage times refers to the number of times satellites in a constellation cover the Earth mission target within a given time.

[0060] 4) Coverage multiplicity is the number of satellites in the constellation that simultaneously observe ground mission targets at a given moment.

[0061] 5) The average revisit period refers to the average of a single revisit time of satellites in a constellation.

[0062] 6) Maximum revisit interval refers to the maximum value of a single revisit time of a satellite in the constellation.

[0063] 7) Minimum revisit interval refers to the minimum single revisit time of a satellite in the constellation.

[0064] 8) Average coverage time refers to the average coverage time of each satellite in the constellation over the ground mission target.

[0065] 9) Maximum coverage time refers to the maximum value of a single coverage time of a satellite in a constellation for a ground mission.

[0066] 10) Minimum coverage time refers to the minimum single coverage time of a satellite in a constellation for a ground mission.

[0067] Step S3: Select one or more of the satellite constellation revisit performance indicators and establish an optimization design objective function, expressed as F(x)=(f1(x),f2(x),…,f m (x) T .

[0068] Step S4, determine the constellation configuration: according to the characteristics of the regional coverage mission, the constellation adopts the basic configuration of the common subsatellite point trajectory regression orbit.

[0069] In step S4, the ground track of each satellite in the constellation of the common sub-satellite point trajectory regression orbit basic configuration is the same, and the ascending node of the constellation of the common sub-satellite point trajectory regression orbit basic configuration is non-uniformly distributed along the equator, and a regression orbit is adopted, and its ground track repeats after one regression cycle.

[0070] Step S5, determining constellation orbit optimization design variables;

[0071] The orbit of the basic configuration of the determined common subsatellite point trajectory regression orbit is an elliptical orbit. The parameter model of the orbit's eccentricity, orbital inclination and semi-major axis is established through the analytical method, and the orbit's perigee argument, mean anomaly angle and ascending node right ascension are determined as optimization design variables.

[0072] The parameter model of the orbital eccentricity, orbital inclination and semi-major axis is established by analytical method, specifically including:

[0073] The semi-major axis a is calculated according to the following formula (1):

[0074]

[0075] Where T is the orbital period, μ is the gravitational parameter, and for the Earth μ = 3.96012×10 5 km 3 / s 2 .

[0076] The eccentricity e is calculated according to the following formula (2):

[0077]

[0078] Where c is the semi-focal length of the orbit, a is the semi-major axis of the elliptical orbit, and r a is the distance from the apogee to the center of the orbit, r p is the distance from the orbital perigee to the center of the earth, h a is the apogee orbital height, h p is the perigee orbit height, R E is the radius of the Earth.

[0079] The orbital inclination λ is calculated according to the following formula (3.1):

[0080]

[0081] Where λ is the orbital inclination, represents the target dimension, M represents the number of circles between satellites; D represents the regression period of the satellites in the constellation, and N represents the number of circles that the satellites in the constellation run within the regression period.

[0082] When M is 1, equation (3.1) can be expressed as

[0083]

[0084] Step S6: Determine the constellation optimization design constraints according to the mission requirements.

[0085] The constellation optimization design constraints can be selected according to the mission requirements. The constellation optimization design constraints include but are not limited to the common sub-satellite point trajectory regression orbit constraints and the total engineering cost constraints. The constellation optimization design constraints can be selected as one or a combination of several of the above constraints.

[0086] The constraints of the basic configuration of the common subsatellite point trajectory regression orbit are expressed as:

[0087]

[0088] Among them, Δu represents the latitude argument difference, ΔΩ represents the right ascension difference of the ascending node, D represents the regression period of the satellite in the constellation, and N represents the number of circles that the satellite in the constellation runs in the regression period;

[0089] The range of the right ascension difference ΔΩ and the latitude angle difference Δu of the ascending node is

[0090]

[0091] Where l is the total number of satellites in the constellation.

[0092] The constraints on the total project cost include the number of satellites, the number of payloads and the revisit period.

[0093] Step S7, constellation strategy optimization: construct a constellation optimization design model according to the optimization design objective function, the constellation orbit optimization design variables and the constellation optimization design constraints, and build an optimization design environment based on a simulation tool, and iteratively solve the constellation optimization design model through an intelligent optimization algorithm to obtain the optimal design strategy for the low-orbit heterogeneous constellation.

[0094] Optimizing the objective function is essentially an optimization problem, and its purpose is to find the best solution, that is, to maximize or minimize the value of the objective function to achieve the desired goal. According to the specific task requirements, the optimization objective function can be divided into single-objective optimization function and multi-objective optimization function. The single-objective optimization function has only one goal, which is to find the extreme value. The multi-objective optimization function refers to having multiple goals to balance the advantages of satisfying multiple goals.

[0095] Intelligent optimization methods use a random search strategy to optimize individual states by simulating the evolutionary mechanisms of nature, the behavior of organisms, or human decision-making ideas, and stably maintain a trend toward the optimal solution set during random iterations. Intelligent optimization methods have no requirements for the mathematical properties of single-objective or multi-objective optimization problems, nor do they require prior knowledge of the relevant information of single-objective or multi-objective optimization problems. They can be solved directly, and the solution process is stable, efficient, and highly versatile. They have greatly enriched optimization techniques and provided practical solutions to combinatorial optimization problems that are difficult to handle with traditional optimization methods.

[0096] An optimized design environment can be built through common simulation tools. The simulation tools can be selected from well-known software such as STK, CSTK, etc. in the industry.

[0097] In step S7, when the constellation optimization design model is a single-objective optimization design model, obtaining the optimal design strategy for the heterogeneous constellation specifically includes:

[0098] Step S711, initializing parameters of the single-objective optimization design model;

[0099] Step S712, performing iterative calculation based on orbit design indicators, wherein the orbit design indicators include orbit eccentricity, orbit inclination, semi-major axis, argument of perigee, mean anomaly and right ascension of ascending node;

[0100] Step S713, updating the optimization design variables by using an intelligent optimization algorithm until the number of iterations meets the termination condition;

[0101] Step S714: extract the iteration results to obtain the optimal design strategy for the low-orbit heterogeneous constellation.

[0102] The intelligent optimization algorithm for solving the single-objective optimization model of constellation design can be selected as gradient descent method, genetic algorithm or particle swarm algorithm.

[0103] In step S7, when the constellation optimization design model is a multi-objective optimization design model, obtaining the optimal design strategy for the heterogeneous constellation specifically includes:

[0104] Step S721, initializing parameters of the multi-objective optimization design model;

[0105] Step S722, performing iterative calculation based on orbit design indicators, wherein the orbit design indicators include orbit eccentricity, orbit inclination, semi-major axis, argument of perigee, mean anomaly and right ascension of ascending node;

[0106] Step S723, updating the optimization design variables by using an intelligent optimization algorithm until the number of iterations meets the termination condition and the iterative calculation result is a Pareto optimal solution;

[0107] Step S724: extract the iteration results to obtain the optimal design strategy for the low-orbit heterogeneous constellation.

[0108] The intelligent optimization algorithm for solving the multi-objective optimization model of constellation design can be selected as Pareto optimal solution set, weighted average method, reference point method, genetic algorithm or particle swarm algorithm.

[0109] Without loss of generality, taking the minimization problem as an example, the mathematical definition of the multi-objective optimization model for constellation design is:

[0110]

[0111] Where x=(x1,x2,…,x k ,…,x n )∈Ω, x is the n-dimensional decision variable, xn is the selected optimization target, Ω is the n-dimensional decision space, L k and U k x k The upper and lower bounds of is the m-dimensional target space, F(x) is the set of target optimization functions, representing mapping relationship; g and h are the inequality constraint function and equality constraint function of the problem respectively, p and q represent the number of inequality constraint functions and the number of equality constraint functions respectively.

[0112] In addition, it should be noted that the present invention can be provided as a method or a system. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0113] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods and systems according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing system to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing system generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing system to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded into a computer or other programmable data processing system so that a series of operation steps are executed on the computer or other programmable system to produce a computer-implemented process, so that the instructions executed on the computer or other programmable system provide the functions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] It should also be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0116] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A constellation optimization design method for regional high-density scattered point high revisit tasks, characterized in that: The following steps are involved: Step S1, confirm the constellation optimization target: according to the rapid response capability enhancement task requirements of space-based observation efficiency, high revisit coverage is carried out for hot target areas and high-density scattered ground mission target points; Step S2, constructing a satellite constellation revisit performance index of a low-orbit heterogeneous satellite constellation model; Step S3, selecting one or more of the satellite constellation revisit performance indicators and establishing an optimization design objective function; Step S4, determining the constellation configuration: according to the characteristics of the regional coverage mission, the constellation adopts the basic configuration of the common subsatellite point trajectory regression orbit; Step S5, determining constellation orbit optimization design variables; Step S6: Determine the constellation optimization design constraint conditions according to the mission requirements; Step S7, constellation strategy optimization: construct a constellation optimization design model according to the optimization design objective function, the constellation orbit optimization design variables and the constellation optimization design constraints, and build an optimization design environment based on a simulation tool, and iteratively solve the constellation optimization design model through an intelligent optimization algorithm to obtain the optimal design strategy for the low-orbit heterogeneous constellation.

2. The constellation optimization design method for regional high-density scattered point high revisit tasks according to claim 1 is characterized in that: The satellite constellation revisit performance indicators include total coverage time, coverage percentage, number of coverages, coverage multiplicity, average coverage time, maximum coverage time, average revisit period, minimum revisit interval, and maximum revisit interval.

3. The constellation optimization design method for regional high-density scattered point high revisit tasks according to claim 1 is characterized in that: The ground track of each satellite in the constellation of the common sub-satellite point trajectory regression orbit basic configuration is the same, and the ascending node of the constellation of the common sub-satellite point trajectory regression orbit basic configuration is non-uniformly distributed along the equator, and a regression orbit is adopted, and its ground track repeats after one regression cycle.

4. The constellation optimization design method for regional high-density scattered point high revisit tasks according to claim 1 is characterized in that: In the step S5, it specifically includes: According to the basic configuration of the common subsatellite point trajectory regression orbit, the orbit is an elliptical orbit. The parameter model of the eccentricity, orbit inclination and semi-major axis of the orbit is established by analytical method, and the perigee argument, mean anomaly angle and ascending node right ascension of the orbit are determined as optimization design variables.

5. The constellation optimization design method for regional high-density scattered point high revisit tasks according to claim 4 is characterized in that: In step S5, establishing the parameter model of the orbital eccentricity, orbital inclination and semi-major axis by analytical method specifically includes: The semi-major axis a is calculated according to the following formula (1): Where T is the orbital period, μ is the gravitational parameter, and for the Earth μ = 3.96012×10 5 km 3 / s 2 ; The eccentricity e is calculated according to the following formula (2): Where c is the semi-focal length of the track, a is the semi-major axis of the track, r a is the distance from the apogee to the center of the orbit, r p is the distance from the perigee to the center of the orbit, h a is the apogee orbital height, h p is the perigee orbit height, R E is the radius of the Earth; The orbital inclination λ is calculated according to the following formula: Where λ is the orbital inclination, represents the target dimension, M represents the number of circles between satellites in the constellation; D represents the regression period of the satellites in the constellation, and N represents the number of circles that the satellites in the constellation run within the regression period; When M is 1, equation (3.1) is expressed as 6. The constellation optimization design method for regional high-density scattered point high revisit tasks according to claim 5 is characterized in that: In step S6, the constraint condition of the common subsatellite point trajectory regression orbit basic configuration is expressed as: Among them, Δu represents the latitude argument difference, ΔΩ represents the right ascension difference of the ascending node, D represents the regression period of the satellite in the constellation, and N represents the number of circles that the satellite in the constellation runs in the regression period; The range of the right ascension difference ΔΩ and the latitude angle difference Δu of the ascending node is Where l is the total number of satellites in the constellation.

7. The constellation optimization design method for regional high-density scattered point high revisit tasks according to claim 5, characterized in that: In step S7, the constellation optimization design model uses the orbit's perigee argument, mean anomaly and ascending node right ascension as optimization design variables, one or more of the satellite constellation revisit performance indicators as optimization design objective functions, and the constellation optimization design constraints determined in step S6 as constraints.

8. The constellation optimization design method for regional high-density scattered point high revisit tasks according to claim 7, characterized in that: In the step S7, a single-objective optimization design model is constructed for one of the satellite constellation revisit performance indicators according to the optimization design objective function; Alternatively, a plurality of the satellite constellation revisit performance indicators are selected according to the optimization design objective function to construct a multi-objective optimization design model.

9. The constellation optimization design method for regional high-density scattered point high revisit tasks according to claim 7, characterized in that: The mathematical definition of the multi-objective optimization design model is expressed as Where x=(x1,x2,…,x k ,…,x n )∈Ω, x is the n-dimensional decision variable, xn is the selected optimization target, Ω is the n-dimensional decision space, L k and U k x k The upper and lower bounds of is the m-dimensional target space, F(x) is the set of target optimization functions, representing mapping relationship; g and h are the inequality constraint function and equality constraint function of the problem respectively, p and q represent the number of inequality constraint functions and the number of equality constraint functions respectively.

10. A system for implementing the constellation optimization design method for regional high-density scattered-point high-revisit tasks as described in any one of claims 1 to 9.

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