Low-orbit satellite dynamic self-adaptive threshold orbit keeping method based on space environment parameter perception

Abstract

A low earth orbit satellite dynamic adaptive threshold orbit keeping method based on space environment parameter perception comprises the following steps: S1, off-line mapping model construction: with global fuel efficiency optimization as a target, generating a mapping model between a space environment parameter and an optimal orbit keeping threshold through a simulation and optimization algorithm; and S2, online sensing and control: sensing space environment parameters in real time, dynamically determining a self-adaptive threshold value in the current space environment based on the mapping model, monitoring the orbit attenuation of the satellite, and triggering the orbit to keep maneuvering when the orbit attenuation reaches or exceeds the self-adaptive threshold value. By constructing an intelligent mapping model of'space environment-optimal threshold ', a trigger threshold maintained by an orbit is upgraded from a fixed value to a variable dynamically adaptive to space weather, so that the optimization of fuel consumption is realized on the global level; and meanwhile, the control efficiency of the space weather calm period and the track safety under extreme events such as magnetic storm are effectively considered.

Description

TECHNICAL FIELD

[0001] The present application relates to space orbit control technology, in particular to a low-orbit satellite dynamic adaptive threshold orbit maintenance method based on space environment parameter sensing. BACKGROUND

[0002] Currently, in the orbit maintenance control of low-orbit satellites, the reactive control strategy based on fixed threshold is the mainstream technical solution widely used and publicly disclosed in the industry. This scheme sets a constant orbit height or orbit semi-major axis deviation tolerance (i.e. fixed threshold), and when the satellite orbit decay is monitored to reach the preset tolerance, an orbit maintenance maneuver is triggered to restore the satellite orbit to the nominal height.

[0003] The working principle and process of the existing low-orbit satellite orbit maintenance scheme can be summarized as the following steps: ① Set a fixed threshold: that is, in the ground planning or on-board control system, a fixed orbit maintenance threshold (upper and lower limits of the orbit semi-major axis) is preset; ② Orbit monitoring: during the satellite's in-orbit operation, the satellite's own orbit parameters, especially the semi-major axis and the mean semi-major axis, are continuously monitored by the on-board navigation system (such as GPS); ③ Threshold judgment: compare the real-time monitored orbit semi-major axis with the nominal semi-major axis, and calculate the decay amount; ④ Trigger and execute: when the decay amount reaches or exceeds the fixed threshold (or the semi-major axis decays to the lower limit of the preset semi-major axis), an orbit maneuver is triggered to restore the orbit semi-major axis to the nominal value (or the upper limit of the preset semi-major axis); ⑤ Loop: after the maneuver is completed, the system reenters the monitoring state and the above process is repeated.

[0004] Although the fixed threshold strategy is simple and easy to implement, its inherent "one-size-fits-all" static nature leads to significant defects in the dynamically changing space environment. On the one hand, it is poor in safety and fuel cost increases dramatically under severe space weather (such as geomagnetic storm), when a strong geomagnetic storm occurs, the density of the upper atmosphere will increase sharply, causing the satellite orbit decay rate to increase significantly, the fixed threshold strategy may be too large, leading to a slow response, which may cause the satellite orbit to decay excessively, even below the safe height, bringing mission risks. In order to "rescue" the satellite from the excessively low orbit, the required velocity increment will increase nonlinearly, resulting in a dramatic increase in maneuver energy consumption, which is a costly passive response; on the other hand, it is too conservative under calm space weather, causing fuel waste. In order to ensure safety under extreme space weather (such as strong geomagnetic storm), the fixed threshold set by the existing strategy is usually conservative (i.e. the threshold is small), however, in the period of calm space weather, the atmospheric drag is small and the orbit decay is slow, the small fixed threshold forces the satellite to perform too frequent orbit maintenance maneuvers, accumulating additional energy consumption and limiting the satellite's on-orbit life. In essence, the existing technology sacrifices fuel efficiency in most normal cases in order to cope with a few extreme cases.

[0005] In summary, the existing fixed threshold orbit maintenance strategy has the problem of being unable to respond to the dynamic changes of the space environment due to its static nature, resulting in excessive fuel consumption during calm periods due to excessive control and potential safety risks during extreme space weather events due to delayed response.

[0006] It should be noted that the information disclosed in the above background section is only for understanding the background of the present application, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] The main purpose of the present application is to overcome the defects existing in the background art, and to provide a low earth orbit satellite dynamic adaptive threshold orbit maintenance method based on space environment parameter perception.

[0008] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: A low earth orbit satellite dynamic adaptive threshold orbit maintenance method based on space environment parameter perception, comprising the following steps: S1, offline mapping model construction: taking global fuel efficiency optimization as the target, a mapping model between space environment parameters and optimal orbit maintenance threshold is generated through simulation and optimization algorithm; wherein the space environment parameter is a physical parameter that can represent the dominant influence of space weather state on the thermosphere atmospheric density; S2, online perception and control: real-time perception of space environment parameters, dynamic determination of adaptive threshold under current space environment based on the mapping model, monitoring of satellite orbit decay, and triggering of orbit maintenance maneuver when the orbit decay reaches or exceeds the adaptive threshold.

[0009] Further, in step S1, the global fuel efficiency optimization is to take the fuel consumption per unit of orbit decay as the optimization index, and the fuel efficiency is defined as the ratio of total orbit maneuver speed increment to total resistance decay.

[0010] Further, the speed increment calculation adopts a pulse thrust model or a continuous small thrust model. Wherein, the pulse thrust model models the orbit maintenance maneuver as an instantaneous speed impulse, and the speed increment includes a speed increment part calculated based on the orbit transfer theory and a speed increment part based on atmospheric resistance compensation, which is calculated in a dynamic environment containing an atmospheric model through high-precision orbit integration. The continuous small thrust model models the orbit maintenance maneuver as a continuous process, and the speed increment is determined by integrating the thrust acceleration over the maneuver time period, wherein the thrust acceleration is the ratio of the thrust to the satellite mass varying with time.

[0011] Further, in step S1, the optimization algorithm comprises a parameter scanning method or a Bayesian optimization method. The parameter scanning method comprises: discretely generating a candidate threshold sequence within a predefined threshold range; performing long-term orbit maintenance simulation on each candidate threshold, in which an orbit recovery maneuver is performed when the orbit decay reaches the candidate threshold, and the speed increment is recorded; when the cumulative orbit decay reaches a preset value, the fuel efficiency under the candidate threshold is calculated; after traversing all candidate thresholds, the candidate threshold with the optimal fuel efficiency is selected as the optimal threshold. The Bayesian optimization method comprises: constructing a proxy model to fit the relationship between the threshold and the fuel efficiency, iteratively selecting candidate thresholds for simulation guided by the acquisition function, updating the proxy model until convergence, and finding the global optimal threshold.

[0012] Further, the mapping model is in the form of a query table or a fitting function. The query table stores optimal thresholds corresponding to different spatial environment scenarios, and the fitting function represents the mapping relationship between the spatial environment parameters and the optimal thresholds through a mathematical function.

[0013] Further, in step S2, the real-time perception of the spatial environment parameters comprises obtaining the geomagnetic activity index and the solar activity parameter through a satellite-ground link or an on-board model. The dynamic determination of the adaptive threshold based on the mapping model comprises: matching the perceived spatial environment parameters with the parameter range in the query table to obtain the optimal threshold, or substituting the perceived spatial environment parameters into the fitting function to calculate the optimal threshold.

[0014] Further, in step S2, the monitoring of the satellite orbit decay comprises real-time calculation of the average semi-major axis of the satellite orbit, and calculation of the decay thereof relative to the nominal value.

[0015] Further, in step S2, the triggering of the orbit maintenance maneuver comprises performing orbit recovery by using a pulse thrust model or a continuous small thrust model. The pulse thrust model applies an acceleration impulse at a specific orbit point, and the continuous small thrust model turns on the thruster to work continuously until the required speed increment is accumulated.

[0016] Further, the spatial environment parameters comprise a geomagnetic activity index and a solar activity parameter.

[0017] A computer program product comprising a computer program, which, when executed by a processor, implements the low-orbit satellite dynamic adaptive threshold orbit maintenance method based on spatial environment parameter perception.

[0018] The present application has the following advantages: The application provides a low-orbit satellite dynamic self-adaptive threshold orbit keeping method based on space environment parameter sensing.

[0019] The important innovation of the application is that real-time space environment parameters are taken as decision variables to dynamically and autonomously adjust the triggering threshold of orbit keeping, so that fuel consumption in the whole life cycle is significantly reduced under the premise of ensuring satellite safety, and a fundamental leap from "static passive control" to "dynamic intelligent optimization" is realized.

[0020] Through the cooperative work of the offline modeling and optimization stage and the online sensing and control stage, the application deeply integrates space environment sensing into the control loop, realizes the intelligentization and automation of threshold decision in a modeling way, and finally achieves the effect of global fuel optimization.

[0021] Other beneficial effects in the embodiments of the application will be further described below. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 This is a flowchart illustrating the overall process of the low-Earth orbit satellite dynamic adaptive threshold orbit maintenance method based on space environment parameter sensing, as described in this invention.

[0023] Figure 2 This is a schematic diagram of the technical route of the low-orbit satellite dynamic adaptive threshold orbit maintenance method according to an embodiment of the present invention. Detailed Implementation

[0024] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0025] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] This invention aims to address the problem that fixed-threshold orbit holding strategies cannot adapt to dynamic space environments. It proposes an intelligent method that dynamically adjusts the orbit holding trigger threshold by sensing space environment parameters. By constructing a "space environment-optimal threshold" mapping model, it achieves global optimization of fuel consumption, significantly improving the adaptability and full life cycle operation efficiency of low-Earth orbit satellite orbit holding systems.

[0027] See Figure 1 and Figure 2 This invention provides a method for dynamic adaptive threshold orbit maintenance of low-Earth orbit satellites based on space environment parameter sensing, comprising the following steps: Step S1: Offline mapping model construction: With the goal of optimizing global fuel efficiency, a mapping model between space environment parameters and the optimal orbit maintenance threshold is generated through simulation and optimization algorithms; wherein, the space environment parameters are physical parameters that can characterize the dominant influence of space weather conditions on thermospheric atmospheric density.

[0028] In some embodiments, the space environment parameters include (but are not limited to) geomagnetic activity index and solar activity parameters.

[0029] In some embodiments, in step S1, the global fuel efficiency optimization uses fuel consumption per unit orbital decay as the optimization index, and the fuel efficiency is defined as the ratio of the total orbital maneuver speed increment to the total resistance decay.

[0030] In some embodiments, the velocity increment calculation employs a pulse thrust model or a continuous low-thrust model; wherein the pulse thrust model models the orbit maintenance maneuver as an instantaneous velocity impulse, the velocity increment includes a velocity increment part calculated based on orbit transfer theory and a velocity increment part compensated based on atmospheric drag, the atmospheric drag compensation is calculated by high-precision orbit integration in a dynamic environment containing an atmospheric model; the continuous low-thrust model models the orbit maintenance maneuver as a continuous process, the velocity increment is determined by integrating the thrust acceleration over the maneuver time period, wherein the thrust acceleration is the ratio of the thrust to the satellite mass varying with time.

[0031] In some embodiments, in step S1, the optimization algorithm includes a parameter scanning method or a Bayesian optimization; wherein the parameter scanning method includes: discretely generating a candidate threshold sequence within a pre-defined threshold range; performing a long-term orbit maintenance simulation for each candidate threshold, in which an orbit recovery maneuver is performed when the orbit decay reaches the candidate threshold and the velocity increment is recorded; when the cumulative orbit decay reaches a preset value, the fuel efficiency under the candidate threshold is calculated; after traversing all candidate thresholds, the candidate threshold with the optimal fuel efficiency is selected as the optimal threshold; the Bayesian optimization includes: constructing a proxy model to fit the relationship between the threshold and the fuel efficiency, guiding the iterative selection of candidate thresholds for simulation through acquisition functions, updating the proxy model until convergence, and finding the globally optimal threshold.

[0032] In some embodiments, the mapping model is in the form of a query table or a fitting function; wherein the query table stores the optimal threshold corresponding to different spatial environment scenarios, and the fitting function represents the mapping relationship between the spatial environment parameters and the optimal threshold through a mathematical function.

[0033] Step S2, online perception and control: real-time perception of spatial environment parameters, dynamic determination of the adaptive threshold under the current spatial environment based on the mapping model, monitoring of the satellite orbit decay, and triggering of the orbit maintenance maneuver when the orbit decay reaches or exceeds the adaptive threshold.

[0034] In some embodiments, in step S2, real-time perception of spatial environment parameters includes obtaining the geomagnetic activity index and solar activity parameters through a satellite-ground link or an on-board model; dynamic determination of the adaptive threshold based on the mapping model includes: matching the perceived spatial environment parameters with the parameter range in the query table to obtain the optimal threshold, or substituting the perceived spatial environment parameters into the fitting function to calculate the optimal threshold.

[0035] In some embodiments, in step S2, monitoring of the satellite orbit decay includes real-time calculation of the average semi-major axis of the satellite orbit and calculation of the decay thereof relative to the nominal value.

[0036] In some embodiments, in step S2, triggering the orbit maintenance maneuver comprises performing orbit recovery using a pulse thrust model or a continuous small thrust model; wherein the pulse thrust model applies an impulse of velocity at a specific orbit point, and the continuous small thrust model turns on a thruster to work continuously until the required velocity increment is accumulated.

[0037] The low-orbit satellite dynamic adaptive threshold orbit maintenance method based on space environment parameter perception has the following main technical advantages: in view of the inherent defects of the traditional fixed threshold orbit maintenance strategy, that is, excessive control of fuel waste in a calm period and insufficient control of safety risks in a magnetic storm period, by introducing a space environment parameter perception mechanism and combining a two-stage architecture of “offline modeling and optimization to generate a space environment-optimal threshold mapping model and online real-time calling of the model to dynamically adjust the threshold”, intelligent adaptive decision of the orbit maintenance threshold is realized; the satellite safety in extreme space weather is ensured, the fuel consumption in the whole life cycle is significantly reduced, the balance between “global fuel optimization” and “overall orbit safety” is achieved, and the intelligence, adaptability and operation reliability of the low-orbit satellite orbit maintenance system are improved.

[0038] The following further describes specific embodiments of the application and examples of algorithm implementation thereof.

[0039] A low-orbit satellite dynamic adaptive threshold orbit maintenance method based on space weather parameter perception, the core of which is to construct and apply a “space environment-optimal threshold” mapping model, which is the key to intelligent adaptive control.

[0040] The space environment parameters, as physical parameters capable of representing the dominant influence of space weather state on the thermospheric atmospheric density, include but are not limited to geomagnetic activity indexes (such as Dst index, Ap index, Kp index, etc.) and solar activity parameters (such as F10.7 index, etc.), which are the fundamental inputs driving the dynamic changes of the threshold.

[0041] The application utilizes a dynamic adaptive threshold, which can automatically adjust the orbit maintenance trigger threshold according to the changes of the space environment parameters.

[0042] As shown in Figure 1 The technical scheme of the application mainly includes the following two core stages: Stage one: offline modeling and optimization stage (ground or on-board preprocessing) This stage aims to generate an on-orbit intelligent decision mapping model of “space environment-optimal threshold”, which is the embodiment of the invention.

[0043] 1. Mapping model construction step: (1) Optimization objective determination: the optimization objective of the application is to achieve optimal fuel efficiency. The fuel consumption per unit orbit decay (i.e. fuel efficiency) As the core optimization index, the goal is to find the orbit maintenance threshold that minimizes the total fuel consumption of the task, which is mathematically defined as follows: Where, represents the fuel efficiency, and the smaller the value is, the higher the efficiency is; represents the total speed increment of orbit maneuver, is the preset total resistance decay amount.

[0044] (2) Speed increment (Δv) ) calculation: speed increment includes the potential energy part for lifting the orbit and the part for compensating for the loss of atmospheric resistance. According to the different propulsion systems, there are two models: 1) Impulse thrust model: The maneuver is modeled as an instantaneous speed impulse. The speed increment required for a single orbit maintenance maneuver is composed of two parts: one is the potential energy increase for lifting the orbit height, and the other is the energy loss for compensating for the atmospheric resistance during operation at a lower orbit. The calculation formula is as follows: Where, is the theoretical value of the speed increment calculated by the Hohmann transfer, is the Earth's gravitational constant, is the average radius of the Earth, is the satellite orbit height at the time of maneuver triggering, is the target height of orbit maintenance; is the resistance compensation term. In the simulation, this term can be automatically calculated by a high-precision orbit integrator in a dynamic environment including an atmospheric model. Specifically, when the satellite operates on a decaying orbit, the speed lost needs to be compensated by additional thrust, and this part of the cost is integrated into the total of a single maneuver.

[0045] 2) Continuous small thrust model: The maneuver is a continuous process. The total is determined by numerical integration of the thrust duration, and the calculation formula is as follows: Where is the thrust, is the satellite mass varying with time.

[0046] (3) Optimization algorithm implementation: Basic scheme (parameter scanning method): As a basic and reliable implementation method, the global parameter scanning method can be used. That is, a series of candidate thresholds are set for each scenario, and the long-term fuel efficiency corresponding to each threshold is calculated by simulation, and a "fuel efficiency-control threshold" curve is drawn. The threshold corresponding to the global optimum point in the curve is determined as the optimal threshold under the scenario. The specific implementation steps of the parameter scanning method are as follows: 1) Parameter discretization: For a given space environment scenario , within a predefined threshold range , with a step size , a candidate threshold sequence is generated ; 2) Loop simulation: for each candidate threshold in the sequence , a long-term orbit maintenance simulation is performed. In the simulation, whenever the satellite orbit semi-major axis decay reaches , an orbit recovery maneuver is performed according to the above formula, and the of this maneuver is recorded; 3) Performance index calculation: when the cumulative orbit decay reaches , the simulation is ended. The total fuel consumption under this threshold is calculated, and then the fuel efficiency is obtained; 4) Finding the optimal point: after traversing all candidate thresholds, a dataset is obtained. By comparing all , the minimum value is found, and the threshold corresponding to it is the optimal threshold under this scenario .

[0047] Preferred solution (Bayesian optimization): To improve efficiency, Bayesian optimization can be used. The steps include: 1) Construct a Gaussian process surrogate model to fit the "threshold-efficiency" function; 2) Define the acquisition function (such as expected improvement); 3) Iterative optimization: select candidate points for simulation under the guidance of the surrogate model, update the model, until convergence, find the global optimal threshold. It should be understood that the present application does not limit the optimization algorithm itself, other global optimization algorithms such as genetic algorithm, particle swarm algorithm are also applicable.

[0048] (4) Model formation: traverse all preset spatial environment scenarios , repeat step (3) for each scenario to obtain its optimal threshold . Finally, a "spatial environment-optimal threshold" mapping model is constructed. This model can be a query table (such as Table 1 example) or a fitted mathematical function stored in the on-board computer for low-orbit satellites to call during the on-orbit phase.

[0049] Table 1 "Spatial environment-optimal threshold" mapping query example Phase two: online perception and autonomous control phase (real-time execution on board) This stage uses the intelligent mapping model generated in phase one to achieve autonomous control of the satellite on orbit.

[0050] 2. Parameter sensing step: The satellite obtains one or more space environment parameters in real time or quasi-real time through the satellite-ground link or the on-board model when the satellite is in orbit.

[0051] 3. Threshold decision step: The space environment parameters sensed in step 2 are taken as input quantities to query or call the pre-stored “space environment-optimal threshold” mapping model in phase one, and the corresponding dynamic adaptive threshold under the current environment is output in real time: If the mapping model is in the form of a query table, the “query” operation is to match the currently sensed space environment parameters with the parameter ranges in the query table, find the corresponding scene row through logical judgment, and return the “optimal threshold” field value of the row ; If the mapping model is in the form of a fitting function, the “query” operation is to directly substitute as input variables into the function for calculation, and the output value of the function is the dynamic adaptive threshold.

[0052] 4. State monitoring and judgment step: The average orbit semi-major axis of the satellite is calculated in real time, and it is judged whether the decay amount thereof relative to the nominal value reaches or exceeds the dynamic adaptive threshold determined in step 3.

[0053] 5. Orbit keeping execution step: When the judgment result is “yes”, the orbit keeping maneuver is triggered to restore the satellite orbit to the nominal state: If the impulsive thrust model is used, the calculated velocity impulse is applied at a specific orbit point (such as the perigee); If the continuous small thrust model is used, the thruster is turned on to work continuously until the required is accumulated.

[0054] Through the cooperative work of the above two stages, the space environment sensing depth is integrated into the control loop, the intelligentization and automation of the threshold decision are realized through the modeling method, and the global fuel optimization is ultimately achieved.

[0055] In summary, the present application proposes a low-orbit satellite dynamic adaptive threshold orbit keeping method based on space environment parameter sensing. The important innovation contributions and technical points of the present application include: (1) Dynamic threshold orbit keeping decision mechanism design driven by space environment sensing The application proposes a decision mechanism that changes the triggering condition of orbit maintenance from static presetting to dynamic calculation driven by real-time space environment, taking space environment parameters as direct input and dynamic adaptive threshold as decision output. The mechanism is the basis for realizing intelligent adaptation.

[0056] (2) Mapping model generation method based on offline optimization The application takes global fuel efficiency optimization as the target, generates a model construction method of mapping relationship between space environment and optimal threshold through simulation and optimization algorithm (such as parameter scanning and Bayesian optimization). The offline optimization mapping model is the intelligent source of the dynamic threshold orbit maintenance decision mechanism.

[0057] (3) Two-stage control architecture design of offline / on-line separation The application proposes a system architecture that separates computationally intensive tasks from real-time control tasks, clearly defines the independent division and cooperative relationship of the two stages of offline modeling and online control, and guarantees the efficient and reliable operation of the dynamic adaptive threshold orbit maintenance control based on space environment perception on the satellite.

[0058] Compared with the prior art, the application has the following significant technical advantages: compared with the traditional fixed threshold orbit maintenance strategy, the application realizes the leap from static control to dynamic optimization by introducing space environment perception and dynamic decision mechanism, solves the inherent contradiction between global fuel efficiency and overall orbit safety of the fixed threshold strategy, realizes global optimization of fuel consumption, and relies on the intelligent closed-loop control system of perception-decision-execution to improve the intelligence, adaptability and safety of the low-orbit satellite orbit maintenance system.

[0059] The embodiment of the application also provides a storage medium for storing a computer program, which is executed to perform at least the method described above.

[0060] The embodiment of the application also provides a control device, which includes a processor and a storage medium for storing a computer program; wherein the processor is used to execute the computer program to perform at least the method described above.

[0061] The embodiment of the application also provides a processor, which executes a computer program to perform at least the method described above.

[0062] The storage medium can be implemented by any type of nonvolatile storage device, or a combination thereof. The nonvolatile memory can be a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Ferromagnetic Random Access Memory (FRAM), a Flash memory, a magnetic surface storage, an optical disc or a Compact Disc Read-Only Memory (CD-ROM). The magnetic surface storage can be a disk memory or a tape memory. The storage medium described in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable type of memory.

[0063] In several embodiments provided by the present application, it should be understood that the disclosed system and method can be implemented in other manners. The described device embodiments are merely schematic, and the division of the units is merely a logical function division. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the components can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0064] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0065] In addition, each functional unit in the embodiments of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0066] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium, and the program performs the steps of the above-mentioned method embodiments when executed; and the foregoing storage medium includes a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disc or an optical disc and various storage medium capable of storing program codes.

[0067] Alternatively, the integrated unit of the present application can be stored in a computer readable storage medium if it is realized in the form of a software function module and sold or used as an independent product. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes a mobile storage device, a ROM, a RAM, a magnetic disc or an optical disc and various storage medium capable of storing program codes.

[0068] The methods disclosed in the several method embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments.

[0069] The features disclosed in the several product embodiments of the present application can be combined arbitrarily without conflict to obtain new product embodiments.

[0070] The features disclosed in the several method or device embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments or device embodiments.

[0071] The above is a further detailed description of the present application in combination with specific preferred embodiments, and the specific implementation of the present application cannot be limited to these descriptions. For those skilled in the art, without departing from the concept of the present application, a number of equivalent substitutions or obvious modifications can be made, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present application.

Claims

1. A method for dynamic adaptive threshold orbit maintenance of low-Earth orbit satellites based on space environment parameter sensing, characterized in that, Includes the following steps: S1. Offline mapping model construction: With the goal of optimizing global fuel efficiency, a mapping model between space environment parameters and the optimal orbit maintenance threshold is generated through simulation and optimization algorithms; wherein, the space environment parameters are physical parameters that can characterize the dominant influence of space weather conditions on thermospheric atmospheric density; S2. Online Sensing and Control: Real-time sensing of space environment parameters, dynamic determination of adaptive thresholds in the current space environment based on the mapping model, monitoring of satellite orbit decay, and triggering orbit holding maneuvers when the orbit decay reaches or exceeds the adaptive threshold.

2. The method as described in claim 1, characterized in that, In step S1, the global fuel efficiency optimization uses fuel consumption per unit orbital decay as the optimization index, and the fuel efficiency is defined as the ratio of the total orbital maneuver speed increment to the total resistance decay.

3. The method as described in claim 2, characterized in that, The velocity increment calculation adopts either an pulse thrust model or a continuous small thrust model; Among them, the pulse thrust model models the orbital holding maneuver as an instantaneous velocity impulse. The velocity increment includes a velocity increment calculated based on orbital transfer theory and a velocity increment based on atmospheric drag compensation. The atmospheric drag compensation is calculated by high-precision orbital integration in a dynamic environment containing an atmospheric model. The continuous low-thrust model models the orbital maneuver as a continuous process, and the velocity increment is determined by integrating the thrust acceleration over the maneuver period, where the thrust acceleration is the ratio of thrust to the time-varying satellite mass.

4. The method as described in claim 1, characterized in that, In step S1, the optimization algorithm includes parameter scanning or Bayesian optimization; The parameter scanning method includes: generating a candidate threshold sequence discretely within a predefined threshold range; performing a long-term orbital maintenance simulation for each candidate threshold, and performing an orbital recovery maneuver and recording the velocity increment when the orbital decay reaches the candidate threshold during the simulation; calculating the fuel efficiency under the candidate threshold when the cumulative orbital decay reaches a preset value; and selecting the candidate threshold with the best fuel efficiency as the optimal threshold after traversing all candidate thresholds. Bayesian optimization includes: constructing a surrogate model to fit the relationship between the threshold and fuel efficiency, using a sampling function to guide iterative selection of candidate thresholds for simulation, updating the surrogate model until convergence, and finding the globally optimal threshold.

5. The method as described in claim 1, characterized in that, The mapping model is in the form of a lookup table or a fitting function; The lookup table stores the optimal thresholds for different spatial environment scenarios, and the fitting function uses mathematical functions to represent the mapping relationship between spatial environment parameters and the optimal thresholds.

6. The method as described in claim 1, characterized in that, In step S2, real-time sensing of space environment parameters includes obtaining geomagnetic activity index and solar activity parameters through satellite-to-ground links or on-board models; Dynamically determining the adaptive threshold based on the mapping model includes: matching the perceived spatial environment parameters with the parameter range in the lookup table to obtain the optimal threshold, or substituting the perceived spatial environment parameters into the fitting function to calculate the optimal threshold.

7. The method as described in claim 1, characterized in that, In step S2, monitoring the satellite orbital decay includes calculating the average semi-major axis of the satellite's orbit in real time and calculating its decay relative to the nominal value.

8. The method as described in claim 1, characterized in that, In step S2, triggering the orbit holding maneuver includes performing orbit recovery using a pulse thrust model or a continuous small thrust model; Among them, the pulse thrust model applies a velocity impulse at a specific trajectory point, while the continuous small thrust model activates the thruster and continues to work until the required velocity increment is accumulated.

9. The method as described in claim 1, characterized in that, The space environment parameters include the geomagnetic activity index and solar activity parameters.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the low-orbit satellite dynamic adaptive threshold orbit maintenance method based on space environment parameter awareness as described in any one of claims 1 to 9.

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