A multi-parameter fuzzy constraint remote sensing satellite orbit design method and system

Abstract

This invention proposes a method and system for remote sensing satellite orbit design based on multi-parameter fuzzy constraints. The method includes: receiving input orbit boundary constraints and design constraints; calculating a series of information about the corresponding orbit based on the altitude range, pixel resolution, revisit constraints, and number of mosaic operations for the sun-synchronous return circular orbit; calculating relevant information that can be directly filtered by subsequent design constraints based on the candidate orbit parameters; filtering the directly filtered relevant information using imaging angle constraints, swath width constraints, and integration time constraints; and filtering the orbit using latitude constraints, marking results that meet and do not meet the constraints. This invention integrates and coordinates multi-domain knowledge related to orbit design and the fragmented steps of orbit design, significantly improving the overall efficiency of satellite design.

Description

Technical Field

[0001] This invention belongs to the field of aerospace technology, specifically relating to a method and system for designing remote sensing satellite orbits with multi-parameter fuzzy constraints. Background Technology

[0002] Orbit design is the first step in the overall design of a remote sensing satellite. For optical remote sensing satellites, the sun-synchronous circular orbit is the most commonly used orbit type. However, the final determination of the parameters for this type of orbit is still related to many factors. In the early stages of satellite overall demonstration, relevant indicators and parameters are not clear, and orbit design can only be completed based on engineering experience and assumptions of some input values. Influence analysis is then performed based on the obtained orbit, and further adjustments are made to the orbit to finally obtain reasonable parameter values. This process involves many iterations, is very labor-intensive, and requires redesign if the input changes significantly. This is because the orbit design process lacks comprehensiveness and is difficult to consider in conjunction with the design of other satellite parameters. To solve these two problems, the satellite orbit design system should meet the following requirements:

[0003] 1. Improve comprehensiveness: Indicators and parameters from various fields that interact with the orbit in satellite design can be used as inputs, without needing to be converted into direct constraints on orbital elements.

[0004] 2. Constraint fuzzification allows you to specify the feasible range of constraints, and the system will automatically filter out results that satisfy all constraints for selection.

[0005] 3. Easy to use and highly operable.

[0006] The challenge of this system lies in coordinating and integrating knowledge from multiple orbit-related fields and the fragmented steps of orbit design. The system features multidisciplinary integration, modular design methods, integrated design processes, and user interface display. This system is significant for improving the overall efficiency of satellite design. Summary of the Invention

[0007] In view of this, the present invention provides a remote sensing satellite orbit design method with multi-parameter fuzzy constraints, comprising:

[0008] Step S1: Receive the input orbital boundary constraints and design constraints. The boundary constraints include the altitude range of the sun-synchronous return circular orbit and the number of return days. The design constraints include pixel resolution, number of stitches, imaging angle constraints, swath width constraints, integration time constraints, revisit constraints, and latitude constraints.

[0009] Step S2: Calculate the series of information of the corresponding orbit based on the orbital height range, pixel resolution, revisit constraints and number of stitches of the sun-synchronous return circular orbit to generate candidate orbital parameters;

[0010] Step S3: Based on the candidate trajectory parameters, calculate and generate relevant information that can be directly filtered by subsequent design constraints;

[0011] Step S4: Filter the relevant information directly by means of the imaging angle constraint, swath width constraint, and integration time constraint; filter the orbit by means of the latitude constraint, and mark the results that meet the constraints and the results that do not meet the constraints.

[0012] Specifically, step S2 includes:

[0013] S201: Utilize the formula

[0014]

[0015] Based on the input range of the sun-synchronous circular orbit altitude [h] min ,h max ], thus obtaining the corresponding regression coefficient range [Q] max Q min ]; where ΔΩ=0.9856° / d represents the rate at which the Earth revolves around the Sun annually, r e =6378.14km represents the average radius of the Earth, a = h + r e T represents the orbital altitude. N Let μ be the nodal period of the orbital motion, μ = 3.986005 × 10⁻⁶. 5 km 3 / s 2 Where J is the gravitational constant, J² = 0.001802 is the J² perturbation term, and n is the average speed of the satellite orbiting in a circular orbit. This is the rate of change of the perigee angle;

[0016] S202: Based on the range of regression coefficients and the number of regression days n1, obtain the regression coefficient sequence that meets the requirements for the regression coefficient Q value.

[0017] S203: Based on the regression coefficient sequence The corresponding orbital altitude sequence is obtained.

[0018] Specifically, step S202 includes: the regression coefficient Q can be decomposed into I, N, and C; where I is the integer part of Q, representing the number of orbital revolutions in approximately one day; N is the denominator of the fractional part of Q, representing the orbit's regression after N days; C is the numerator of the fractional part of Q, representing the orbit's offset after one day; the range of I can be obtained by rounding down the upper and lower limits of Q. The value of C is equal to the number of days of regression n1, where N = n1; the range of C is equal to the integer interval [1,…,N-1], and we obtain the result by finding integer multiples of prime numbers for N. Finally, the values ​​of I, N, and C are combined to obtain the specific values ​​of the regression coefficient Q, resulting in the regression coefficient sequence.

[0019] Specifically, the relevant information directly filtered by subsequent design constraints includes one or more of the following: orbital inclination, ground speed, flight speed, orbital period, distance between adjacent trajectories, integration time, swath width, maximum imaging angle, and maximum imaging latitude.

[0020] Step S3 specifically includes: S301: According to the formula,

[0021]

[0022] Calculate the orbital inclination i, satellite ground velocity v1, satellite flight speed v2, and orbital period T using the orbital altitude a. N The equatorial distance d1 between adjacent time trajectories;

[0023] S302: Utilize formula

[0024] Δt=Pi / v1

[0025] Calculate the integration time Δt based on the ground velocity and pixel resolution Pi;

[0026] S303: Calculate the maximum equatorial distance d2 of spatially adjacent trajectories within the coverage days and the farthest distance between the imaging point at the equator and the intersection of the nadir point trajectory and the equator, based on the equatorial distance d1 of the temporally adjacent trajectories, the regression coefficient Q, the number of regression days n1, and the coverage day constraint n2.

[0027] S304: Utilize formula

[0028] L d = d² / n³*sin(i)

[0029] Based on the maximum equatorial distance between spatially adjacent trajectories within the coverage days, the orbital inclination, the number of splicing strips, and the required swath width to meet the input load requirements;

[0030] S305: Utilizing track height h and width L d The corresponding load field of view angle is obtained by calculating using the bisection method;

[0031] S306: Using the formula,

[0032]

[0033] The maximum imaging angle α required to satisfy the input is calculated based on the farthest distance d3 between the imaging point at the equator and the intersection of the trajectory of the nadir point and the equator, and the orbital height h.

[0034] Specifically, step S4 includes:

[0035] S401, using imaging angle constraint α max Width constraint L dmax Integral time constraint ΔT min The parameter calculation results are directly filtered, and orbits whose calculated imaging angle, swath width, and integration time are greater than, greater than, and less than the corresponding constraints are considered to meet the constraints.

[0036] S402, using latitudinal constraint L1 max The designed orbits are screened, and orbits whose calculated maximum imaging latitude is less than the latitude constraint are considered to meet the constraint; the latitude coverage limit L1 of the orbit is determined by the inclination angle based on...

[0037] L1 = 180 - i

[0038] Perform calculations;

[0039] S403: Mark "√" for orbits that satisfy all of the above constraints, and mark "×" for orbits that do not satisfy any of the above constraints.

[0040] This invention also proposes a remote sensing satellite orbit design system with multi-parameter fuzzy constraints, comprising:

[0041] The constraint input module is used to receive input orbital boundary constraints and design constraints. The boundary constraints include the altitude range of the sun-synchronous return circular orbit and the number of return days. The design constraints include pixel resolution, number of stitches, imaging angle constraints, swath width constraints, integration time constraints, revisit constraints, and latitude constraints.

[0042] The alternative orbit generation module is used to calculate and generate alternative orbit parameters based on a series of information about the corresponding orbit, such as the orbital height range, pixel resolution, revisit constraints, and number of frame stitches of the sun-synchronous return circular orbit.

[0043] The track parameter calculation module is used to calculate and generate relevant information that can be directly filtered by subsequent design constraints based on the candidate track parameters.

[0044] The orbit filtering module filters the relevant information for direct filtering based on the imaging angle constraint, swath width constraint, and integration time constraint, respectively; it also filters the orbits based on the latitude constraint and marks the results that meet the constraints and the results that do not meet the constraints.

[0045] The results display page module is used to display the constraint inputs, orbital parameter calculation results, and filtering results on a webpage.

[0046] Beneficial effects:

[0047] 1) This invention can use indicators and parameters from various fields that interact with the orbit in satellite design as inputs, without having to convert them into direct constraints on orbital elements;

[0048] 2) In this invention, constraint fuzzification allows for the definition of a feasible range of constraints, with the system automatically filtering out results that satisfy all constraints for selection.

[0049] 3) In this invention, orbits whose imaging angle, swath width, and integration time calculated by orbit are greater than, greater than, and less than the corresponding constraints are considered to meet the constraints, and are displayed through the interface, which is convenient to use and highly operable. Attached Figure Description

[0050] Figure 1 This is a flowchart of the remote sensing satellite orbit design method with multi-parameter fuzzy constraints in this invention;

[0051] Figure 2 This is the default display page for the track design method and system provided by the present invention;

[0052] Figure 3 The display page for the track design method and system provided by the present invention in the embodiment calculations;

[0053] Figure 4 A module relationship diagram of the track design system provided by this invention;

[0054] Figure 5 A diagram showing the relationship between the track design method and the track design system provided by this invention. Detailed Implementation

[0055] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0056] This invention provides a method for designing remote sensing satellite orbits with multi-parameter fuzzy constraints, the flowchart of which is shown below. Figure 1 As shown, it includes:

[0057] Step S1: Receive the input orbital boundary constraints and design constraints. The boundary constraints include the altitude range of the sun-synchronous return circular orbit and the number of return days. The design constraints include pixel resolution, number of stitches, imaging angle constraints, swath width constraints, integration time constraints, revisit constraints, and latitude constraints.

[0058] Step S2: Based on the orbital altitude range, pixel resolution, revisit constraints, and frame count of the sun-synchronous return circular orbit, calculate the series of information corresponding to the orbit to generate candidate orbital parameters; this step involves the initial orbital traversal method, specifically including:

[0059] Step S201: Based on the input range of the sun-synchronous circular orbit altitude [h] min ,hmax ], thus obtaining the corresponding regression coefficient range [Q] max Q min ].

[0060] The direction and rate of precession of a sun-synchronous orbit are equal to the direction and rate of Earth's annual motion around the sun. Therefore, the relationship between the orbital inclination i and the orbital altitude h can be obtained:

[0061]

[0062] Where ΔΩ=0.9856° / d represents the rate at which the Earth revolves around the Sun in a year, r e =6378.14km represents the average radius of the Earth, a = h + r e Indicates the orbital altitude.

[0063] The precession period T of the orbital nodes can be calculated using the orbital inclination angle i. N :

[0064]

[0065] Where μ = 3.986005 × 10 5 km 3 / s 2 J is the gravitational constant, and J2 = 0.001802 is the J2 perturbation term.

[0066] The average speed of a satellite orbiting in a circular orbit can also be calculated from the orbital altitude:

[0067]

[0068] Furthermore, the rate of change of the perigee angle can be calculated using the average speed n and the orbital inclination i.

[0069]

[0070] Furthermore, utilizing the precession period T N Perimeter angle change rate and the Earth's rotation rate ω e The regression coefficients of satellites can be calculated:

[0071]

[0072] Based on the regression coefficient calculation process described above, the orbital height range [h] can be obtained. min ,h max ], thus obtaining the corresponding regression coefficient range [Q] max Q min ].

[0073] Step 202: Based on the range of regression coefficients and the number of regression days n1, obtain the Q-value sequence that meets the requirements.

[0074] The regression coefficient Q can be decomposed into I, N, and C. I is the integer part of Q, representing the number of orbital revolutions within one day; N is the denominator of the fractional part of Q, representing the orbit's regression after N days; and C is the numerator of the fractional part of Q, representing the orbit's offset within one day.

[0075] The range of values ​​for I can be obtained by rounding down the upper and lower limits of the value of Q:

[0076]

[0077] The value of N is equal to the number of days of regression, n1:

[0078] N = n1

[0079] The range of C is equal to the integer interval [1,…,N-1], and we obtain the integer multiples of prime numbers of N:

[0080]

[0081] Finally, the specific values ​​of the regression coefficient Q are obtained by combining I, N, and C:

[0082]

[0083] Step 203: Based on the regression coefficient sequence Obtain the corresponding orbital altitude sequence

[0084] This step enables the forward calculation from the orbital height h to the regression coefficient Q, and the reverse calculation from the regression coefficient Q to the orbital height h can be achieved using the bisection method.

[0085]

[0086] Where f(h1 / 2+h2 / 2) represents the regression coefficient Q calculated based on the height h1 / 2+h2 / 2.

[0087] Step S3: Based on the candidate orbit parameters, calculate and generate relevant information that can be directly filtered by subsequent design constraints; the relevant information that can be directly filtered by constraints includes orbit inclination, ground speed, flight speed, orbit period, distance between adjacent trajectories, integration time, swath width, maximum imaging angle, and maximum imaging latitude.

[0088] The specific steps involved in this parameter calculation process are as follows:

[0089] Step S301: Based on the calculated orbital altitude sequence The system calculates a series of orbital information based on the input pixel resolution Pi, revisit days constraint n2, and satellite mosaic number n3, including orbital inclination i, satellite ground velocity v1, satellite flight speed v2, and orbital period T. n The equatorial distance d1 between adjacent time trajectories, the load integration time Δt, and the required swath width L to satisfy the input. d The required load field of view β and the required maximum imaging angle α must satisfy the input requirements.

[0090] Calculate the orbital inclination i, satellite ground speed v1, satellite flight speed v2, and orbital period T using the orbital altitude. n The equatorial distance d1 between adjacent time trajectories:

[0091]

[0092] S302: Utilize formula

[0093] Δt=Pi / v1

[0094] Calculate the integration time Δt based on the ground velocity and pixel resolution Pi:

[0095] S303: Calculate the maximum equatorial distance d2 between spatially adjacent trajectories within the coverage days and the farthest distance d3 from the imaging point at the equator to the intersection of the nadir point trajectory and the equator, based on the equatorial distance d1 between temporally adjacent trajectories, regression coefficient Q, regression days n1, and coverage days constraint n2.

[0096] if n1≤n2, d2=d1 / n1, d3=d2 / 2, end

[0097] if n2=1,d2=d1,d3=d2 / 2,end

[0098] if 1 <n2<n1

[0099]

[0100] d2 = max(S(3:end) - S(1:end-2))

[0101] d3=max(S(2:end-1)-S(1:end-2))

[0102] end

[0103] Here, sort(X) represents sorting the elements in the discrete set X in ascending order, max(X) represents finding the maximum value in the discrete set X, and S(x:end-x) represents the set of elements in the discrete set S from the x-th position to the last element minus x.

[0104] S304: Calculate the required swath width L to satisfy the input by using the maximum equatorial distance d2 of spatially adjacent trajectories within the coverage days, the orbital inclination i, and the number of swath stripes n3. d :

[0105] L d = d² / n³*sin(i)

[0106] S305: Utilizing track height h and width L d The corresponding load field of view angle is obtained by calculating using the bisection method:

[0107]

[0108] S306: The maximum imaging angle α required by the input is calculated using the farthest distance d3 between the imaging point at the equator and the intersection of the nadir trajectory and the equator, and the orbital height h.

[0109]

[0110] Step S4: The relevant information for direct screening is filtered using the imaging angle constraint, swath width constraint, and integration time constraint, respectively; the orbit is filtered using the latitude constraint, and the results that meet the constraints and those that do not are labeled. This step provides a parameter filtering method, specifically including the following steps:

[0111] Step S401, using the imaging angle constraint α max Width constraint L dmax Integral time constraint ΔT min The parameter calculation results are directly filtered, and orbits whose calculated imaging angle, swath width, and integration time are greater than, greater than, and less than the corresponding constraints are considered to meet the above constraints.

[0112] Step S402: Utilize latitude constraint L1 max The designed orbits are screened, and orbits whose calculated maximum imaging latitude is less than the latitude constraint are considered to meet the above constraints. The latitude coverage limit L1 of the orbit is given by the formula...

[0113] L1 = 180 - i

[0114] The calculation is performed using the inclination angle; where i represents the orbital inclination angle.

[0115] Step S403: Mark the orbital that satisfies all of the above constraints with “√”, and mark the orbital that does not satisfy any of the above constraints with “×”.

[0116] Specifically, the results display page shows the calculated and filtered results of the orbital parameters. The interface is a large table. The table header information includes orbital altitude, orbital inclination, regression coefficient, ground speed, flight speed, orbital period, distance between adjacent tracks, integration time, swath width, maximum imaging angle, and whether the constraints are met. Apart from the header, each row in the large table represents all the display information for a single orbital.

[0117] Below, we will use a specific design scenario as an example for illustration. Assume the input track boundary constraints include a height range of h. min =500,h min =4000, with a return period requirement of 4 days. Other design constraints include achieving full coverage in the longitude direction in 1 day, a camera swath width not exceeding 1000km, a maximum camera imaging angle not exceeding 35°, a payload integration time of not less than 0.2ms under a 2km pixel resolution, and a maximum of four whole-satellite stitching operations.

[0118] Based on the input range of the sun-synchronous circular orbit altitude [h] min ,h max ], thus obtaining the corresponding regression coefficient range [Q] max Q min = [8.2369, 15.2663].

[0119] The integer part I of the regression coefficient is determined by the integer part of the regression coefficient as I = {8, ..., 15}.

[0120] Given the number of regression days input as n1 = 4, the denominator N of the decimal part of the regression coefficient is determined by the number of regression days as N = n1 = 4.

[0121] The range of values ​​for the decimal part of the regression coefficient is determined by removing integer multiples of the prime numbers of N from the integer interval [1,…,N-1], resulting in C = {1,3}.

[0122] The specific values ​​of the regression coefficient Q obtained by combining I, N, and C are:

[0123] Serial Number Q value 1 8.2500 2 8.7500 3 9.2500 4 9.7500 5 10.2500 6 10.7500 7 11.2500 8 11.7500 9 12.2500 10 12.7500 11 13.2500 12 13.7500 13 14.2500 14 14.7500 15 15.2500

[0124] Based on the algorithm described above, a forward calculation from orbital height h to regression coefficient Q can be achieved, and based on the bisection method, it is possible to... The corresponding height sequence is obtained by reverse engineering:

[0125]

[0126]

[0127] Calculate the orbital inclination i, satellite ground speed v1, satellite flight speed v2, and orbital period T using the orbital altitude.n The equatorial distance d1 between adjacent time trajectories:

[0128]

[0129] The integration time Δt is calculated using the ground velocity and pixel resolution Pi, thus obtaining the integration time series.

[0130]

[0131]

[0132] Furthermore, based on the equatorial distance d1 of temporally adjacent trajectories, the regression coefficient Q, the number of regression days n1, and the coverage day constraint n2, the maximum equatorial distance d2 of spatially adjacent trajectories within the coverage days and the farthest distance d3 of the imaging point at the equator from the intersection of the nadir point trajectory and the equator are calculated.

[0133] Furthermore, based on the maximum equatorial distance d2 between spatially adjacent trajectories within the coverage days, the orbital inclination i, and the number of patchwork strips n3, the required swath width L to satisfy the input is calculated. d .

[0134] Furthermore, utilizing the orbital height h and swath width L d The corresponding load field of view angle is obtained by calculating using the bisection method.

[0135] Furthermore, the maximum imaging angle α required to satisfy the input is calculated using the farthest distance d3 between the imaging point at the equator and the intersection of the trajectory of the sub-satellite point and the equator, and the orbital height h.

[0136] Results obtained:

[0137]

[0138]

[0139] Based on the input imaging angle constraint α′, swath width constraint d2′, integration time constraint Δt′, and latitude constraint L1, each orbital is filtered to obtain the results.

[0140] Serial Number Does the constraint satisfy? 1 × 2 × 3 × 4 × 5 × 6 × 7 × 8 × 9 × 10 × 11 √ 12 √ 13 √ 14 × 15 ×

[0141] For details on the track design methodology and the system's default display page, please refer to [link / reference]. Figure 2 As shown, the results display page displays the calculation results and filtering results of the orbital parameters in the example. See details below. Figure 3 .

[0142] Furthermore, this invention proposes a remote sensing satellite orbit design system with multi-parameter fuzzy constraints, comprising: a constraint input module, a candidate orbit generation module, an orbit parameter calculation module, an orbit filtering module, and a result display page module. The module relationship diagram of the orbit design system is shown below. Figure 4 As shown in the diagram, the relationship between the track design method and the track design system is as follows: Figure 5 As shown.

[0143] The constraint input module is used to receive input orbital boundary constraints and design constraints. The boundary constraints include the altitude range of the sun-synchronous return circular orbit and the number of return days. The design constraints include pixel resolution, number of stitches, imaging angle constraints, swath width constraints, integration time constraints, revisit constraints, and latitude constraints.

[0144] The alternative orbit generation module is used to calculate and generate alternative orbit parameters based on a series of information about the corresponding orbit, such as the orbital height range, pixel resolution, revisit constraints, and number of frame stitches of the sun-synchronous return circular orbit.

[0145] The track parameter calculation module is used to calculate and generate relevant information that can be directly filtered by subsequent design constraints based on the candidate track parameters.

[0146] The orbit filtering module filters the relevant information for direct filtering based on the imaging angle constraint, swath width constraint, and integration time constraint, respectively; it also filters the orbits based on the latitude constraint and marks the results that meet the constraints and the results that do not meet the constraints.

[0147] The results display page module is used to display the constraint inputs, orbital parameter calculation results, and filtering results on a webpage.

[0148] The content defined by this system is similar to that defined in the method embodiments, so it will not be repeated here.

[0149] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0150] It will be apparent to those skilled in the art that the embodiments of the present invention are not limited to the details of the exemplary embodiments described above, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the embodiments of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the embodiments of the present invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be encompassed within the embodiments of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules, or devices recited in the system, apparatus, or terminal claims may also be implemented by the same unit, module, or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the embodiments of the present invention should not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for designing remote sensing satellite orbits with multi-parameter fuzzy constraints, characterized in that, include: Step S1: Receive the input orbital boundary constraints and design constraints. The boundary constraints include the altitude range of the sun-synchronous return circular orbit and the number of return days. The design constraints include pixel resolution, number of stitches, imaging angle constraints, swath width constraints, integration time constraints, revisit constraints, and latitude constraints. Step S2: Calculate a series of information about the corresponding orbit based on the orbital height range, pixel resolution, revisit constraints, and number of stitches of the sun-synchronous return circular orbit, and generate candidate orbital parameters; Step S2 specifically includes: S201: Utilize the formula Indicates the orbital inclination angle. This represents the Earth's rotation rate, based on the orbital altitude range of the input sun-synchronous circular orbit. The corresponding regression coefficient range is obtained. ;in, This represents the rate at which the Earth revolves around the sun in a year. Represents the Earth's average radius. This represents the distance from the Earth's center to the satellite. Indicates the distance from the Earth's surface to the satellite. For the node period of the orbital motion, The gravitational constant, for Perturbation item, The average speed at which the satellite orbits in a circular orbit. This is the rate of change of the perigee angle; S202: Based on the range of regression coefficients and the number of regression days To obtain the regression coefficients that meet the requirements. regression coefficient sequence of values ; S203: Based on the regression coefficient sequence The corresponding orbital height sequence is obtained by using the binary search method; Step S202 specifically includes: regression coefficients It can be broken down into , , ;in for The integer part represents the number of orbital revolutions that the orbit takes approximately one day; for The denominator of the decimal part indicates the distance the track passes through. The heavens return once; for The numerator of the decimal part represents the orbital offset over one day; The range of values ​​is determined by The value is obtained by rounding down the upper and lower limits. , The value of is equal to the number of days of regression. , It is a positive integer; ; The range of values ​​is equal to the integer interval. And proposed To obtain integer multiples of prime numbers, Finally by , , Merging to obtain regression coefficients The specific values ​​are used to obtain the regression coefficient sequence. ; Step S3: Based on the candidate trajectory parameters, calculate and generate relevant information that can be directly filtered by subsequent design constraints; Step S4: Filter the relevant information directly by means of the imaging angle constraint, swath width constraint, and integration time constraint; filter the orbit by means of the latitude constraint, and mark the results that meet the constraints and the results that do not meet the constraints.

2. The remote sensing satellite orbit design method with multi-parameter fuzzy constraints as described in claim 1, characterized in that, The relevant information that is directly filtered by subsequent design constraints includes one or more of the following: orbital inclination, ground speed, flight speed, orbital period, distance between adjacent trajectories, integration time, swath width, maximum imaging angle, and maximum imaging latitude. Step S3 specifically includes: S301: According to the formula, Utilizing the distance from the Earth's center to the satellite Calculate the orbital inclination angle Satellite ground speed Satellite flight speed orbital period Equatorial distance between adjacent time trajectories ; S302: Utilize formula Based on ground speed and pixel resolution Calculation time of integration ; S303: Equatorial spacing based on temporally adjacent trajectories Regression coefficient Return days Coverage days constraint Calculate the maximum equatorial distance between spatially adjacent trajectories within the coverage days. The farthest distance between the imaging point at the equator and the intersection of the trajectory of the sub-satellite point and the equator; S304: Utilize formula Based on the maximum equatorial distance between spatially adjacent trajectories within the coverage period, the orbital inclination, and the number of patchwork stripes. The calculated load must meet the required input width. S305: Utilization and width The corresponding load field of view angle is obtained by calculating using the bisection method; S306: Using the formula, Based on the farthest distance between the imaging point at the equator and the intersection of the trajectory of the sub-satellite point and the equator. , The maximum imaging angle required to satisfy the input is calculated. .

3. The remote sensing satellite orbit design method with multi-parameter fuzzy constraints as described in claim 2, characterized in that, Step S4 specifically includes: S401, utilizing imaging angle constraints Width constraints Integration time constraints The parameter calculation results are directly filtered, and orbits whose calculated imaging angle, swath width, and integration time are greater than, greater than, and less than the corresponding constraints are considered to meet the constraints. S402, utilizing latitudinal constraints The designed orbits are screened, and orbits whose calculated maximum imaging latitude is less than the latitude constraint are considered to meet the constraint; among which, the latitude coverage limit of the orbit is... Based on the angle of inclination Perform calculations; S403: Mark "√" for orbits that satisfy all of the above constraints, and mark "×" for orbits that do not satisfy any of the above constraints.

4. A remote sensing satellite orbit design system with multi-parameter fuzzy constraints applying the method described in claim 1, characterized in that, include: The constraint input module is used to receive input orbital boundary constraints and design constraints. The boundary constraints include the altitude range of the sun-synchronous return circular orbit and the number of return days. The design constraints include pixel resolution, number of stitches, imaging angle constraints, swath width constraints, integration time constraints, revisit constraints, and latitude constraints. The alternative orbit generation module is used to calculate and generate alternative orbit parameters based on a series of information about the corresponding orbit, such as the orbital height range, pixel resolution, revisit constraints, and number of frame stitches of the sun-synchronous return circular orbit. The track parameter calculation module is used to calculate and generate relevant information that can be directly filtered by subsequent design constraints based on the candidate track parameters. The orbit filtering module filters the relevant information for direct filtering based on the imaging angle constraint, swath width constraint, and integration time constraint, respectively; it also filters the orbits based on the latitude constraint and marks the results that meet the constraints and the results that do not meet the constraints. The results display page module is used to display the constraint inputs, orbital parameter calculation results, and filtering results on a webpage.

Images