Universal satellite parametric modeling method

Abstract

The invention discloses a universal satellite parametric modeling method, and relates to the technical field of satellite modeling. The modeling method comprises the steps of satellite platform parametric modeling and satellite load parametric modeling. In the satellite platform parametric modeling stage, typical characteristic parameter spaces are constructed for a sun-synchronous orbit satellite platform and a geosynchronous orbit satellite platform respectively; configuring a platform parameter selection principle, a platform model range, platform model composition, a platform model interface and an algorithm related to the platform model; in a satellite load parametric modeling stage, a typical characteristic parameter space is constructed for a satellite remote sensing load; and configuring functions, composition and related algorithms of the load model. The invention provides a universal satellite parametric modeling method which is wide in application.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite modeling, and in particular to a universal satellite parameterized modeling method. Background Art

[0002] In the field of satellite modeling and simulation, the main goal of research is to establish models with uniform granularity, parameterization, and reusability, thereby forming the technical capabilities and means for satellite simulation analysis. However, in the existing technology, there is no universal satellite parametric modeling solution. Summary of the Invention

[0003] In view of the above technical status, the present invention proposes a universal satellite parametric modeling method, including satellite platform parametric modeling and satellite payload parametric modeling.

[0004] The universal satellite parametric modeling method provided by the present invention includes: satellite platform parametric modeling and satellite payload parametric modeling.

[0005] In the satellite platform parametric modeling stage: construct typical characteristic parameter spaces for sun-synchronous orbit satellite platforms and geosynchronous orbit satellite platforms respectively; configure platform parameter selection principles, platform model range, platform model composition, platform model interface, and algorithms involved in the platform model.

[0006] In the satellite payload parameter modeling stage: construct a typical characteristic parameter space for satellite remote sensing payloads; configure the functions, composition, and involved algorithms of the payload model.

[0007] During the satellite platform parametric modeling phase:

[0008] The parameters involved in the typical characteristic parameter space of a sun-synchronous orbit satellite platform include: platform size, launch mass, platform mass, symmetry, agile maneuverability, end-of-life power, operating time, orbit altitude, orbit inclination, operating speed, ground speed, flight cycle, descending node local time, and revisit frequency;

[0009] The parameters involved in the typical characteristic parameter space of geosynchronous orbit satellite platforms include: platform size, solar array wingspan, typical launch envelope, average launch mass, average orbital mass, design life, thruster, apogee engine thrust, propellant, and end-of-life power.

[0010] During the satellite platform parametric modeling phase:

[0011] The principles for selecting platform parameters include: selecting parameters related to on-orbit status, selecting parameters related to application effectiveness analysis, and selecting parameters related to countermeasure effectiveness calculation;

[0012] The platform model range includes: orbit prediction simulation model, orbit maneuver autonomous planning model, orbit control simulation model, attitude maneuver simulation model;

[0013] The platform model consists of: orbit dynamics model, attitude control model, data balance model, measurement and control data transmission model, temperature balance model, power balance model, and ephemeris environment model;

[0014] The platform model interface is used to realize information interaction between the satellite platform model, satellite payload model and ground system model, including the satellite platform model external input interface and the satellite platform model external output interface.

[0015] During the satellite platform parametric modeling phase, the algorithms involved in the platform model are used to perform the following: predict the satellite's orbit and attitude, calculate the relative relationship between the satellite and the Earth, and calculate the relative relationship between the satellite and the sun; among them:

[0016] The orbit prediction simulation model based on orbital dynamics is used to realize orbit prediction and simulation. Based on the perturbation effects of the Earth's gravity, the Sun's gravity, the Moon's gravity, atmospheric drag, and solar radiation pressure, the satellite motion differential equation is established:

[0017]

[0018] Among them, f represents the perturbation acceleration, which consists of six parts, namely the engine thrust acceleration fp, the earth's gravitational perturbation acceleration Δg, the air resistance perturbation acceleration d, the solar gravitational perturbation acceleration f h , lunar gravitational perturbation acceleration f l and the solar pressure perturbation acceleration f sr ;and:

[0019] f=f p +Δg+d+f l +f h +f sr

[0020] The orbit prediction simulation model uses the Cowell method to solve the satellite motion differential equations. The three-body gravity is calculated using the JPL DE405 model, the Earth's gravitational field uses the JGM3 model, the atmospheric perturbation part uses the standard atmosphere model, the light pressure uses the standard light pressure cross-section algorithm, and the integrator uses RKF78. The orbit control part uses the finite thrust method, and selects two types of orbit control analysis results: inertial holding and orbit holding.

[0021] In the satellite payload parametric modeling stage, the parameters involved in the typical characteristic parameter space of satellite remote sensing payload include: observation width, payload, detector, aperture, field of view angle, and spatial resolution.

[0022] During the satellite payload parameter modeling phase:

[0023] The functions of the payload model are: to calculate and simulate the satellite's imaging capabilities based on the camera's focal length, photographic resolution, camera aperture, field of view, and photographic period;

[0024] The payload model consists of: onboard camera module, solar cell array thermal module, and payload cabin temperature control loop module;

[0025] The onboard camera module involves the camera's focal length, photographic resolution, camera aperture, field of view, and photographic cycle algorithm; the solar cell array thermal module involves the solar cell array thermal algorithm, and the payload cabin temperature control loop module involves the payload cabin temperature control loop algorithm.

[0026] During the satellite payload parameter modeling phase:

[0027] For the camera focal length f, the camera ground photography resolution depends on the camera photography resolution, orbit altitude, and camera focal length:

[0028]

[0029] Wherein, GRD is the ground photography resolution; Rs is the camera photography resolution; H is the photography height; f is the camera focal length;

[0030] For the camera photography resolution Rs, we have:

[0031]

[0032] The dynamic and static resolutions of the camera laboratory are R and R0 respectively; the camera photography resolution is determined by the camera laboratory resolution. The satellite's photographic window, attitude control accuracy, satellite attitude jitter, temperature and pressure changes, and the contrast between the atmosphere and the scenery all affect the resolution.

[0033] During the satellite payload parameter modeling phase, the camera aperture D / f is calculated, where D is the lens aperture. The relative aperture directly affects the still photography resolution, modulation transfer function, and light intensity of the space camera. When photographing at the lowest solar altitude, the relative aperture meets the minimum exposure requirements for film photography.

[0034] According to the selected film resolution, the relative aperture of the lens optical system is calculated. After the optical system form is selected, the modulation transfer function of the ideal optical system is calculated. The modulation transfer function and the film threshold model curve are plotted respectively. The resolution corresponding to the intersection of the two curves is the combined still photography resolution of the optical system and the film.

[0035] The camera image motion compensation error, film flattening error, focus error, shutter vibration, and film determine the camera laboratory dynamic photography resolution. The resolution corresponding to the intersection of the camera laboratory dynamic modulation transfer function curve and the film threshold curve is the camera laboratory dynamic photography resolution.

[0036] During the satellite payload parameter modeling phase, the field of view angle (2w) is calculated based on the ground photography coverage area or image size:

[0037]

[0038] Among them, a is the image size; f is the focal length of the camera.

[0039] In the satellite payload parameter modeling stage, for the photography period T:

[0040]

[0041] Where l is the phase length; v / H is the velocity-to-height ratio of the satellite; and k is the longitudinal overlap ratio.

[0042] The method provided by the present invention follows the system-module-algorithm modeling structure and simultaneously formulates object analysis-overall design-main algorithm object modeling parameterized description specifications. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 Schematic diagram of a satellite interface according to an embodiment of the present invention.

[0045] Figure 2 4 is a flowchart of satellite orbit attitude prediction according to an embodiment of the present invention.

[0046] Figure 3 Schematic diagram of the satellite payload simulation system model according to an embodiment of the present invention.

[0047] Figure 4 Graph showing threshold modulus of an optical system and a film according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0049] 1. Parametric modeling of satellite platforms

[0050] 1.1 Object Analysis

[0051] The satellite platform represents the satellite's primary structure and generally includes subsystems such as structure, power supply, thermal control, measurement and control, data management, attitude and orbit control, and propulsion. Its rationality directly impacts the performance of its payload in space. Remote sensing satellites primarily utilize sun-synchronous and geosynchronous orbits, so we prioritize the common platforms used for these two types of satellites. Their typical characteristic parameter spaces are shown in the table below.

[0052] Table 1: Typical characteristic parameter space of sun-synchronous orbit satellite platform

[0053]

[0054] Table 2: Typical characteristic parameter space of geosynchronous orbit satellite platform

[0055]

[0056]

[0057] 1.2 Overall Design

[0058] 1.2.1 Model Function

[0059] (1) Platform parameter selection principles

[0060] Not all satellite platform-related information can be applied to satellite platform parametric modeling. Here we summarize the principles for selecting key parameters for modeling as follows:

[0061] (1-1) Select parameters related to on-orbit status

[0062] The main parameters selected are those of the satellite in the on-orbit operating state, and the launch state parameters are not considered. For example, the typical launch envelope and launch mass are not included in the model metadata, while the orbital mass and solar array wingspan are included as model metadata.

[0063] At the same time, parameters that can reflect or calculate the satellite's on-orbit operating status should be selected, such as satellite orbit information, attitude information, battery charge and discharge status, and other information contained in satellite telemetry parameters.

[0064] (1-2) Select parameters related to application performance analysis

[0065] In system simulation, the focus should be on selecting subsystem parameters relevant to satellite application performance analysis and calculation. For remote sensing satellites, performance indicators such as temporal and spatial resolution should be analyzed. Therefore, specific technical indicators of the satellite's remote sensing payload should be selected, such as bandwidth, nominal resolution, and positioning accuracy. Other parameters related to performance indicators should also be selected, such as orbital altitude, storage capacity, and downlink data rate.

[0066] (1-3) Select parameters related to the calculation of the effectiveness of the confrontation

[0067] In the system confrontation environment, target characteristic parameters that can be used for confrontation effectiveness evaluation should be selected, including the satellite's optical characteristic parameters, radar characteristic parameters, and electromagnetic signal characteristics, specifically involving the satellite's geometric shape, outline scale, structural composition, temperature, radiation / scattering information, brightness characteristics, RCS, electromagnetic signal frequency band, electromagnetic signal power, etc.

[0068] (2) Platform model scope

[0069] Based on the selection of typical satellite platform parameters, we sorted out typical functional models of satellite platforms that can be established using these parameters. Since satellite model construction can be based on a limited basis of characteristic algorithms and the parameter inputs that can be used for model calculations are limited, satellite mathematical models are primarily constructed based on classical physical principles. The resulting models are universally applicable to different types of satellites, have low input parameter requirements, and are highly reusable. The satellite platforms formed through preliminary analysis specifically include the following models:

[0070] (2-1) Orbit Prediction Simulation Model: This model treats the satellite as a point mass and simulates the motion of its center of mass under the influence of celestial gravity and other factors. The perturbations typically considered include the gravitational forces of celestial bodies (such as the Earth, Moon, and other celestial bodies), Earth's non-spherical perturbations, solar pressure, and atmospheric drag. The spectral input parameters of the orbit prediction simulation model can take various forms, including: 1) initial time, satellite position, and velocity; 2) orbital epoch time and orbital element number; and output parameters include satellite position, velocity, acceleration, and other parameters.

[0071] (2-2) Autonomous Orbital Maneuver Planning Model: Based on the specific requirements of the satellite orbit for a specific flight mission and using different orbital control strategies, the orbital control thrust that meets the mission requirements is calculated. Spectral parameters of the orbital control model include orbit change requirements and engine parameters. Commonly used orbital maneuver planning models are divided into two categories: long-range orbital maneuvers and short-range orbital maneuvers. 1) The long-range orbital maneuver planning model can autonomously calculate the long-range control strategy and, based on the long-range guidance real-time correction algorithm, calculate the new control force to control the orbit. 2) The short-range orbital maneuver planning model mainly includes the relative short-range relative orbit element maneuver simulation model and the short-range orbit line-of-sight guidance simulation model. The former formulates the control strategy based on the relative orbit elements of the tracking satellite's relative motion to the target satellite. The latter generates control torque based on the line-of-sight guidance control law to achieve short-range guidance of the orbit. Spectral parameters of the autonomous orbital maneuver planning model include orbital control strategy and engine parameters. In light of the characteristics of this model, a behavior tree-based autonomous task behavior model for satellite orbital maneuver planning should be established.

[0072] (2-3) Orbital control simulation model: When the satellite is in orbit, while considering the perturbation factors involved in the orbit prediction simulation model, the thrust factor of the orbit change engine is added to establish an orbital maneuvering dynamics simulation model. It can calculate the changes in the satellite center of mass motion according to the orbit change strategy given by the command (including thrust pulse, continuous thrust, etc.). The spectral input parameters of the orbit control simulation model can include different forms, including: 1) pulse action time, thrust size, 2) thrust action time period, thrust size, etc.; output parameters include satellite position, velocity, acceleration, etc.

[0073] (2-4) Attitude maneuver simulation model: This model simulates the attitude changes of a satellite rotating around its center of mass under the action of different forms of control torque, providing satellite attitude information for the design and simulation of other subsystems. Since different satellites have different configurations and attitude control methods, and the specific configuration parameters and control scheme parameters are difficult to obtain, the blue satellite platform attitude maneuver simulation problem is considered as a single rigid body attitude maneuver dynamic characteristics planning problem. That is, under the premise of satisfying the satellite attitude maneuver angle amplitude, angular velocity and angular acceleration constraints, the state quantities of the satellite attitude maneuver dynamic process are planned and calculated based on the initial attitude state and attitude maneuver instructions. The spectral input parameters of the attitude maneuver simulation model include: the initial attitude of the satellite center body, the target attitude, and the maneuver time; the output parameters include: the dynamic characteristics of the satellite attitude angle, angular velocity, and angular acceleration during the maneuver time period.

[0074] In addition, the satellite platform subsystem should also include power supply models, thermal control models, configuration models, measurement and control data transmission models, target characteristic models, etc. The above models are all related to the application and confrontation of space systems. The modeling granularity needs to be determined according to the level of detail of the intelligence to form a parameterized model.

[0075] 1.2.2 Model composition

[0076] With reference to the main structural features of typical foreign satellite platforms, the designed satellite platform model consists of orbital dynamics model, attitude control model, data balance model, measurement and control data transmission model, temperature balance model, power balance model, and ephemeris environment.

[0077] Orbital dynamics model: Realizes real-time extrapolation calculation of satellite orbits. Orbital extrapolation algorithms include two-body, J2, J4, HPOP, SGP4, etc., and completes various space visibility simulations at the same time.

[0078] Attitude control model: Calculates the attitude of the satellite entity according to the attitude command and solves the attitude quaternion in different coordinate systems.

[0079] Measurement and control data transmission model: simulates the satellite telemetry, remote control and data transmission receiving and sending functions, simulates measurement and control data transmission link establishment, data transmission delay, etc.

[0080] Data balance model: Calculates the corresponding energy and data constraints and sends them to the payload system and measurement and control data transmission system, affecting the latter's functions.

[0081] Temperature balance model: simulates the external and internal heat flows of the star, establishes the temperature field state equation, and solves the temperature state of the device nodes of the entire star.

[0082] Power balance model: simulates the power subsystem battery array, storage battery, and charge and discharge logic control functions to achieve the load balance status calculation of the entire satellite.

[0083] Ephemeris environment model: provides time services such as UTC time and Beijing time, and sidereal ephemeris position information.

[0084] 1.3 Main interface design

[0085] like Figure 1 As shown in the figure, the satellite platform model mainly has information interaction interfaces with the satellite payload model and the ground system model.

[0086] The satellite platform model takes as external input: time information, ephemeris information, ground system remote control instructions, ground system status information (including ground station location information, antenna pointing information, target motion information, target attitude information, etc.), payload working status and working mode, and payload imaging data volume.

[0087] The satellite platform model outputs: satellite platform position, attitude and pointing information, link availability status and link margin, payload and telemetry downlink data.

[0088] 1.4 Main Algorithm

[0089] 1.4.1 Overview of Orbital Dynamics Algorithm

[0090] The orbit prediction model treats the satellite as a point mass and calculates its motion under the influence of the earth's gravity and other perturbations (such as solar pressure, atmospheric resistance, etc.). Specifically, it includes simulation of normal orbit motion, illumination, and measurement and control visibility. The orbit extrapolation calculation supports two-body, J2, J234, HPOP, SGP4 and other methods. At the same time, it completes various types of space visibility simulations and supports maneuvering simulation functions with velocity pulses as input. It can receive external maneuvering commands and process the commands sequentially. Figure 2 As shown:

[0091] (1) Predicting the satellite's orbit and attitude

[0092] After one system time step, the satellite's position and velocity information at the next moment are calculated and predicted based on the satellite orbit dynamics and kinematics algorithm model; after receiving the attitude maneuver information, the satellite attitude information at the next moment is calculated based on the satellite attitude dynamics and kinematics algorithm model.

[0093] (2) Calculate the relative relationship between the satellite and the Earth

[0094] After one system time step, the latitude, longitude and altitude of the satellite in the fixed coordinates at the next moment are calculated.

[0095] (3) Calculate the relative relationship between the satellite and the sun

[0096] After one system time step, the position of the sun is calculated, and the satellite's visibility of the sun and the angle between the satellite's orbital plane and sunlight are calculated.

[0097] 1.4.2 Algorithm Input

[0098] The orbit prediction model supports TLE, six orbit elements, ECI coordinate system position and velocity as inputs. Other inputs include orbit change time and velocity pulse.

[0099] 1.4.3 Algorithm Output

[0100] The orbit prediction model outputs high-precision orbit prediction results, including the satellite's position and velocity in different coordinate systems, the longitude and latitude of the sub-satellite point, the six elements of the real-time orbit, and other information. It can also calculate and output the satellite's visibility to sunlight, ground stations, etc.

[0101] 1.4.4 Mathematical Description

[0102] The orbit simulation model performs orbit prediction and simulation functions. Taking into account the effects of various perturbations such as the Earth's gravity, the Sun's gravity, the Moon's gravity, atmospheric drag, and solar radiation pressure, the following satellite motion differential equation can be established:

[0103]

[0104] Among them: f represents the perturbation acceleration, which consists of six parts, namely the engine thrust acceleration fp, the earth's gravitational perturbation acceleration Δg, the air resistance perturbation acceleration d, the solar gravitational perturbation acceleration f h , lunar gravitational perturbation acceleration f l and the solar pressure perturbation acceleration f sr :

[0105] f=f p +Δg+d+f l +f h +f sr

[0106] The orbit simulation model performs orbit prediction and simulation functions. It employs the Cowell method to solve the satellite's differential equations of motion. The three-body gravity calculations utilize JPL's DE405 model, the Earth's gravitational field calculations utilize the JGM3 model, the atmospheric perturbations utilize the US Standard Atmosphere Model, the light pressure calculations utilize the Standard Light Pressure Cross Section algorithm, and the RKF78 integrator for high accuracy. The orbit control system utilizes a finite thrust approach, with two types of orbit control analysis options: inertial hold and orbit hold.

[0107] 2. Satellite payload parameter modeling

[0108] 2.1 Object Analysis

[0109] With reference to typical satellite data publicly available abroad, the satellite remote sensing payload is parametrically modeled, and its main characteristic parameters are shown in the following table.

[0110] Table 3: Main characteristic parameters of satellite remote sensing payload

[0111]

[0112]

[0113] 2.2 Overall Design

[0114] 2.2.1 Model Function

[0115] The satellite remote sensing payload model calculates and simulates the satellite's imaging capabilities based on camera focal length, photographic resolution, camera aperture, field of view, photographic period, etc.

[0116] 2.2.2 Model composition

[0117] like Figure 3As shown in Figure 1, the satellite model primarily consists of an onboard camera module, a solar array thermal module, and a payload cabin temperature control loop module. The onboard camera module includes the camera's focal length, imaging resolution, aperture, field of view, and imaging cycle algorithm. The solar array thermal module is implemented by the solar array thermal algorithm, and the payload cabin temperature control loop module is implemented by the payload cabin temperature control loop algorithm.

[0118] 2.3 Main Algorithm

[0119] 2.3.1 Spaceborne Camera Model Algorithm

[0120] Calculate the focal length, photographic resolution, camera aperture, field of view, and photographic period of a satellite camera using mathematical formulas.

[0121] Algorithm input:

[0122] Table 4: Input parameters of the spaceborne camera model algorithm

[0123]

[0124] Algorithm output:

[0125] Table 5: Output parameters of the spaceborne camera model algorithm

[0126]

[0127] Mathematical description:

[0128] (1) Camera focal length (f)

[0129] The camera's ground photography resolution depends on the camera's photography resolution, orbital altitude, and the camera's focal length, and can be expressed as follows:

[0130]

[0131] Where GRD is the ground photography resolution (m); Rs is the camera photography resolution (lines / mm); H is the photography altitude (km); and f is the camera focal length (m).

[0132] (2) Camera photography resolution (Rs)

[0133]

[0134] Camera laboratory dynamic and static resolution (R, R0). Camera photography resolution is primarily determined by the camera laboratory resolution, followed by the satellite's photographic window, attitude control accuracy, satellite attitude jitter, temperature and pressure changes, and the contrast between the atmosphere and the scene.

[0135] (3) Camera aperture D / f

[0136] Optical elements or systems can be described using the relative aperture D / f, where D is the lens aperture. This relative aperture directly affects the still photography resolution, modulation transfer function (MTF), and light output of a space camera. Specifically, when photographing at the lowest solar altitude, the relative aperture should ensure the minimum exposure required for film photography.

[0137] According to the selected film resolution, the relative aperture of the lens optical system is preliminarily calculated. After the optical system form is selected, the MTF of the ideal optical system is calculated, and the MTF and film threshold model curves are drawn respectively (such as Figure 4 The resolution corresponding to the intersection of the two curves is the combined static photography resolution of the optical system and film.

[0138] The camera's laboratory dynamic photography resolution is determined by factors such as image motion compensation error, film flattening error, focus error, shutter vibration, and film. The resolution corresponding to the intersection of the camera's laboratory dynamic MTF curve and the film threshold curve is the camera's laboratory dynamic photography resolution.

[0139] (4) Field of view

[0140] The lens field of view (2w) is calculated based on the ground photography coverage area or image size, as shown in the following formula:

[0141]

[0142] Where a is the image size; f is the focal length of the camera.

[0143] (5) Photography cycle

[0144] The photographic cycle is calculated using the following formula:

[0145]

[0146] Where: T is the photography period (s); l is the phase length (effective length) (mm); v / H is the satellite's velocity-to-height ratio; k is the longitudinal overlap ratio.

[0147] 2.3.2 Satellite Solar Array Thermal Algorithm

[0148] The heat transfer boundary conditions of the satellite solar cell panels' surface, back and sides receiving solar radiation, earth reflected radiation and earth infrared radiation are calculated using mathematical formulas.

[0149] Algorithm input:

[0150] Table 6: Solar array thermal algorithm input parameters

[0151]

[0152]

[0153] Algorithm output:

[0154] Table 7: Solar array thermal algorithm output parameters

[0155]

[0156] Mathematical description:

[0157] The solar cells are evenly arranged along the surface of the panel. The solar panel adopts a honeycomb sandwich structure and is mainly composed of the following parts: solar cells, outer panels, honeycomb core, and inner surface.

[0158] The temperature control equation of the solar cell shell adopts the three-dimensional heat conduction differential equation:

[0159]

[0160] Where: ρc p The heat capacity of the material, k is the thermal conductivity of the material.

[0161] The surface of the solar cell receives solar radiation, earth reflected radiation and earth infrared radiation. Therefore, the boundary conditions are:

[0162]

[0163] Where: a s is the solar absorptivity of the outer surface of the star's skin, S is the solar constant; E r is the density of solar radiation on the earth's surface; E e is the average infrared radiation density on the earth's surface; η is the photoelectric conversion efficiency of the solar cell; ε e is the emissivity of the surface material, φ1, φ2, φ3 are the solar radiation angular coefficient, the earth's albedo angular coefficient, and the earth's infrared radiation angular coefficient, respectively.

[0164] The back and sides of the solar panel receive solar radiation, earth reflected radiation and earth infrared radiation, so the heat transfer boundary conditions are:

[0165]

[0166] 2.3.3 Satellite Payload Cabin Temperature Control Loop Algorithm

[0167] The temperature control levels of the inner and outer loops of the satellite payload cabin temperature control system are calculated using mathematical formulas.

[0168] Algorithm input:

[0169] Table 8: Temperature control loop algorithm input parameters

[0170] serial number Parameter name Data Type unit Remark 1 Working fluid density double 2 Fluid circuit cross-sectional area double <![CDATA[m 2 ]]> 3 Time in orbit double s 4 Liquid working medium temperature double 。 5 Convective heat transfer coefficient double 6 Flow heat transfer area double <![CDATA[m 2 ]]> 7 Total number of pipelines double root 8 Pipeline length double m 9 Static pressure on upstream and downstream of pipeline double <![CDATA[p a ]]>

[0171] Algorithm output:

[0172] Table 9: Temperature control loop algorithm output parameters

[0173] serial number Parameter name Data Type unit Remark 1 External circuit temperature control double 。 2 Low temperature inner loop control temperature double 。 3 Medium temperature inner loop control temperature double 。

[0174] Mathematical description:

[0175] The satellite payload cabin temperature control system includes an inner loop and an outer loop temperature control circuit system, and the inner loop includes a low-temperature inner loop and a medium-temperature inner loop.

[0176] The low-temperature inner loop provides a cold source for cold-dry components and some on-orbit experimental equipment with low-temperature requirements. The temperature of the low-temperature inner loop is directly controlled by the outer loop, and the working fluid is set to pure water.

[0177] A temperature control valve and a branch are set in the medium-temperature inner circuit to control the temperature level of the circuit by adjusting the flow of the working fluid flowing into the medium-temperature intermediate heat exchanger. The temperature control point is set at the working fluid outlet on the inner side of the medium-temperature intermediate heat exchanger, and the working fluid is pure water.

[0178] The outer circuit collects heat from the inner circuit and transfers it to the body-mounted radiator. A temperature control valve and branch circuit are installed at the inlet of the body-mounted radiator to control the temperature level of the circuit by adjusting the flow of the working fluid into the radiator. The temperature control point is set at the working fluid outlet of the radiator.

[0179] The main control equations are as follows:

[0180] The mass equation is:

[0181]

[0182] Where: ρ is the working fluid density; S is the cross-sectional area of ​​the fluid circuit; u is the flow velocity; t is the on-orbit flight time; x is the process flow.

[0183] The momentum equation is:

[0184]

[0185] Where: p is pressure; local resistance f a is the local resistance coefficient; the friction resistance along the wall f m is the resistance coefficient along the way; s M is the momentum source.

[0186] The energy equation is:

[0187]

[0188] Where: U is internal energy; H is enthalpy; λ is thermal conductivity; T is liquid working medium temperature; h is convective heat transfer coefficient; TW is the tube wall temperature; S W is the convective heat transfer area; Q i For heat input.

[0189] In order to make the model closed, the physical relationship of flow and heat transfer must be introduced. The spatial terms in equations (4)-(6) are discretized, the time terms are kept continuous, and the distributed parameter problem is transformed into a lumped parameter problem to obtain a discrete model.

[0190]

[0191] Where: M is the mass of the working fluid node; e n is the flow rate correction coefficient θ of the nth pipeline r,n is the mass flow rate of the working fluid in the nth pipeline; N is the total number of pipelines.

[0192]

[0193] Where: S f is the flow cross-sectional area of ​​the pipeline; L is the length of the pipeline; p u is the static pressure upstream of the pipeline; p d is the static pressure downstream of the pipeline; K c is the pressure head coefficient; f ng is the non-obtainable loss coefficient; Z is the flow rate index; f g is the available loss coefficient; f d is the interface resistance coefficient.

[0194]

[0195] Where: I n is the nth pipe connection offset factor; Q d is the energy source or energy sink of the working fluid node; p1 is the static pressure of the working fluid; V d is the node volume change rate; V0 is the volume flow rate; C0 is the node outer wall compatibility coefficient.

[0196] Please note that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above-mentioned embodiments only express several implementation methods of the present application. The description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of this application, several variations and improvements can be made, which all fall within the scope of protection of this application. Therefore, the scope of protection of the patent in this application shall be based on the attached claims.

Claims

1. A universal satellite parameter modeling method, characterized in that: The modeling method includes satellite platform parameterized modeling and satellite payload parameterized modeling; wherein: During the satellite platform parametric modeling phase: construct typical characteristic parameter spaces for both the sun-synchronous orbit satellite platform and the geosynchronous orbit satellite platform; configure the platform parameter selection principles, platform model range, platform model composition, platform model interface, and algorithms involved in the platform model; In the satellite payload parameter modeling stage: construct a typical characteristic parameter space for satellite remote sensing payloads; configure the functions, composition, and involved algorithms of the payload model.

2. A universal satellite parameterized modeling method according to claim 1, characterized in that: During the satellite platform parametric modeling phase: The parameters involved in the typical characteristic parameter space of a sun-synchronous orbit satellite platform include: platform size, launch mass, platform mass, symmetry, agile maneuverability, end-of-life power, operating time, orbit altitude, orbit inclination, operating speed, ground speed, flight cycle, descending node local time, and revisit frequency; The parameters involved in the typical characteristic parameter space of geosynchronous orbit satellite platforms include: platform size, solar array wingspan, typical launch envelope, average launch mass, average orbital mass, design life, thruster, apogee engine thrust, propellant, and end-of-life power.

3. A universal satellite parameterized modeling method according to claim 2, characterized in that: During the satellite platform parametric modeling phase: The principles for selecting platform parameters include: selecting parameters related to on-orbit status, selecting parameters related to application effectiveness analysis, and selecting parameters related to countermeasure effectiveness calculation; The platform model range includes: orbit prediction simulation model, orbit maneuver autonomous planning model, orbit control simulation model, attitude maneuver simulation model; The platform model consists of: orbit dynamics model, attitude control model, data balance model, measurement and control data transmission model, temperature balance model, power balance model, and ephemeris environment model; The platform model interface is used to realize information interaction between the satellite platform model, satellite payload model and ground system model, including the satellite platform model external input interface and the satellite platform model external output interface.

4. A universal satellite parameterized modeling method according to claim 3, characterized in that: During the satellite platform parametric modeling phase, the algorithms involved in the platform model are used to perform the following: predict the satellite's orbit and attitude, calculate the relative relationship between the satellite and the Earth, and calculate the relative relationship between the satellite and the sun; among them: The orbit prediction simulation model based on orbital dynamics is used to realize orbit prediction and simulation. Based on the perturbation effects of the Earth's gravity, the Sun's gravity, the Moon's gravity, atmospheric drag, and solar radiation pressure, the satellite motion differential equation is established: Among them, f represents the perturbation acceleration, which consists of six parts, namely the engine thrust acceleration fp, the earth's gravitational perturbation acceleration Δg, the air resistance perturbation acceleration d, the solar gravitational perturbation acceleration f h , lunar gravitational perturbation acceleration f l and the solar pressure perturbation acceleration f sr ;and: f=f p +Δg+d+f l +f h +f sr The orbit prediction simulation model uses the Cowell method to solve the satellite motion differential equations. The three-body gravity is calculated using the JPL DE405 model, the Earth's gravitational field uses the JGM3 model, the atmospheric perturbation part uses the standard atmosphere model, the light pressure uses the standard light pressure cross-section algorithm, and the integrator uses RKF78. The orbit control part uses the finite thrust method, and selects two types of orbit control analysis results: inertial holding and orbit holding.

5. A universal satellite parameterized modeling method according to claim 1, characterized in that: In the satellite payload parametric modeling stage, the parameters involved in the typical characteristic parameter space of satellite remote sensing payload include: observation width, payload, detector, aperture, field of view angle, and spatial resolution.

6. A universal satellite parameter modeling method according to claim 5, characterized in that: During the satellite payload parameter modeling phase: The functions of the payload model are: to calculate and simulate the satellite's imaging capabilities based on the camera's focal length, photographic resolution, camera aperture, field of view, and photographic period; The payload model consists of: onboard camera module, solar cell array thermal module, and payload cabin temperature control loop module; The onboard camera module involves the camera's focal length, photographic resolution, camera aperture, field of view, and photographic cycle algorithm; the solar cell array thermal module involves the solar cell array thermal algorithm, and the payload cabin temperature control loop module involves the payload cabin temperature control loop algorithm.

7. A universal satellite parameterized modeling method according to claim 6, characterized in that: During the satellite payload parameter modeling phase: For the camera focal length f, the camera ground photography resolution depends on the camera photography resolution, orbit altitude, and camera focal length: Wherein, GRD is the ground photography resolution; Rs is the camera photography resolution; H is the photography height; f is the camera focal length; For the camera photography resolution Rs, we have: The dynamic and static resolutions of the camera laboratory are R and R0 respectively; the camera photography resolution is determined by the camera laboratory resolution. The satellite's photographic window, attitude control accuracy, satellite attitude jitter, temperature and pressure changes, and the contrast between the atmosphere and the scenery all affect the resolution.

8. A universal satellite parameterized modeling method according to claim 7, characterized in that: During the satellite payload parameter modeling phase, the camera aperture D / f is calculated, where D is the lens aperture. The relative aperture directly affects the still photography resolution, modulation transfer function, and light intensity of the space camera. When photographing at the lowest solar altitude, the relative aperture meets the minimum exposure requirements for film photography. According to the selected film resolution, the relative aperture of the lens optical system is calculated. After the optical system form is selected, the modulation transfer function of the ideal optical system is calculated. The modulation transfer function and the film threshold model curve are plotted respectively. The resolution corresponding to the intersection of the two curves is the combined still photography resolution of the optical system and the film. The camera image motion compensation error, film flattening error, focus error, shutter vibration, and film determine the camera laboratory dynamic photography resolution. The resolution corresponding to the intersection of the camera laboratory dynamic modulation transfer function curve and the film threshold curve is the camera laboratory dynamic photography resolution.

9. A universal satellite parameterized modeling method according to claim 8, characterized in that: During the satellite payload parameter modeling phase, the field of view angle (2w) is calculated based on the ground photography coverage area or image size: Among them, a is the image size; f is the focal length of the camera.

10. A universal satellite parameterized modeling method according to claim 9, characterized in that: In the satellite payload parameter modeling stage, for the photography period T: Where l is the phase length; v / H is the velocity-to-height ratio of the satellite; and k is the longitudinal overlap ratio.

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