A low earth orbit satellite dynamic adaptive threshold orbit maintenance method based on spatial environment parameter perception

Abstract

A method for dynamic adaptive threshold orbit maintenance of low-Earth orbit satellites based on space environment parameter sensing 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; S2, Online sensing and control: Space environment parameters are sensed in real time, and the adaptive threshold under the current space environment is dynamically determined based on the mapping model. The satellite orbit decay is monitored, and when the orbit decay reaches or exceeds the adaptive threshold, an orbit maintenance maneuver is triggered. By constructing an intelligent mapping model of "space environment - optimal threshold", the trigger threshold for orbit maintenance is upgraded from a fixed value to a variable that dynamically adapts to space weather, thereby achieving optimization of fuel consumption at the global level, while effectively balancing control efficiency during calm space weather periods and orbit safety under extreme events such as geomagnetic storms.

Description

Technical Field

[0001] This invention relates to aerospace orbit control technology, and in particular to a dynamic adaptive threshold orbit maintenance method for low-Earth orbit satellites based on space environment parameter sensing. Background Technology

[0002] Currently, in the orbital maintenance control of low-Earth orbit satellites, the reactive control strategy based on a fixed threshold is the mainstream and publicly available technical solution widely adopted in the industry. This solution sets a constant orbital altitude or a tolerance for the semi-major axis deviation (i.e., a fixed threshold). When the satellite's orbital decay is detected to reach the preset tolerance, an orbital maintenance maneuver is triggered to restore the satellite's orbit to its nominal altitude.

[0003] The working principle and process of existing low-Earth orbit satellite orbit maintenance schemes can be summarized as follows: ① Setting a fixed threshold: that is, in the ground planning or on-board control system, a fixed orbit maintenance threshold (the upper and lower limits of the orbit's semi-major axis) is preset; ② Orbit monitoring: during the satellite's operation in orbit, the satellite's own orbital parameters, especially the semi-major axis and the mean semi-major axis, are continuously monitored through the on-board navigation system (such as GPS); ③ Threshold judgment: the real-time monitored orbital semi-major axis is compared with the nominal semi-major axis to calculate the attenuation; ④ Triggering and execution: when the attenuation reaches or exceeds the fixed threshold (or the semi-major axis attenuates to the preset lower limit of the semi-major axis), an orbital maneuver is triggered to restore the orbital semi-major axis to the nominal value (or the preset upper limit of the semi-major axis); ⑤ Cycle: after the maneuver is completed, the system re-enters the monitoring state and repeats the above process.

[0004] Although the fixed threshold strategy is simple and easy to implement, its inherent "one-size-fits-all" static nature leads to significant drawbacks in dynamically changing spatial environments. On the one hand, under severe space weather conditions (such as geomagnetic storms), safety is poor and fuel costs increase dramatically. When a strong geomagnetic storm occurs, the density of the upper atmosphere increases sharply, causing the satellite orbit decay rate to increase significantly. Fixed threshold strategies may be too large, resulting in a slow response. This may lead to excessive orbit decay, or even below the safe altitude, posing mission risks. To "rescue" the satellite from the excessively low orbit, the required velocity increment will increase non-linearly, leading to a surge in maneuvering energy consumption, which is a costly passive response. On the other hand, under calm space weather conditions, the strategy is too conservative and wastes fuel. To ensure safety under extreme space weather conditions (such as strong geomagnetic storms), the fixed threshold is usually set conservatively (i.e., the threshold is too small). However, during calm space weather periods, atmospheric drag is small and orbit decay is slow. An excessively small fixed threshold forces the satellite to perform orbit maintenance maneuvers too frequently, accumulating additional energy consumption and limiting the satellite's on-orbit lifespan. In essence, existing technologies sacrifice fuel efficiency under most normal conditions in order to cope with a few extreme situations.

[0005] In summary, existing fixed-threshold orbit maintenance strategies, due to their static nature, are unable to respond to dynamic changes in the space environment. This leads to fuel waste due to overly frequent control during calm periods and potential safety risks due to slow response during extreme space weather events.

[0006] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a dynamic adaptive threshold orbit maintenance method for low-Earth orbit satellites based on space environment parameter sensing.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for dynamic adaptive threshold orbit maintenance of low-Earth orbit satellites based on space environment parameter sensing includes the following steps:

[0010] 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;

[0011] 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.

[0012] Furthermore, in step S1, the global fuel efficiency optimization uses fuel consumption per unit of orbital decay as the optimization index, and the fuel efficiency is defined as the ratio of the total velocity increment of orbital maneuvers to the total resistance decay.

[0013] Furthermore, the velocity increment calculation adopts an pulse thrust model or a continuous small thrust model;

[0014] 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.

[0015] 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.

[0016] Furthermore, in step S1, the optimization algorithm includes parameter scanning or Bayesian optimization;

[0017] 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.

[0018] 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.

[0019] Furthermore, the mapping model is in the form of a lookup table or a fitting function;

[0020] 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.

[0021] Furthermore, 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;

[0022] 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.

[0023] Furthermore, in step S2, monitoring the satellite orbital attenuation includes calculating the average semi-major axis of the satellite's orbit in real time and calculating its attenuation relative to the nominal value.

[0024] Furthermore, in step S2, triggering the orbit holding maneuver includes performing orbit recovery using a pulse thrust model or a continuous small thrust model;

[0025] 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.

[0026] Furthermore, the space environment parameters include the geomagnetic activity index and solar activity parameters.

[0027] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned method for dynamic adaptive threshold orbit maintenance of low-Earth orbit satellites based on space environment parameter awareness.

[0028] The present invention has the following beneficial effects:

[0029] This invention proposes a dynamic adaptive threshold orbit-keeping method for low-Earth orbit satellites based on space environment parameter sensing. By constructing and applying a "space environment-optimal threshold" mapping model, intelligent adaptive control is achieved. This method aims to address the prominent problem caused by low-Earth orbit satellites' inability to respond to dynamic space weather changes due to fixed threshold strategies: "over-control" during calm space weather periods and "under-control" during geomagnetic storm periods, resulting in global fuel non-optimality. By sensing space environment parameters in real time and dynamically and adaptively adjusting the orbit-keeping threshold, this invention achieves global optimization of fuel consumption.

[0030] The key innovation of this invention lies in using real-time space environment parameters as decision variables to dynamically and autonomously adjust the trigger threshold for orbit maintenance. This significantly reduces fuel consumption throughout the entire life cycle while ensuring satellite safety, achieving a fundamental leap from "static passive control" to "dynamic intelligent optimization". Specifically, the important contributions of this invention are reflected in three aspects: First, a dynamic threshold orbit maintenance decision-making mechanism driven by space environment perception is established, transforming the triggering conditions for orbit maintenance from static presets to dynamic calculations driven by the space environment in real time. Space environment parameters are used as direct inputs, and a dynamic adaptive threshold is used as the decision output. This mechanism is the core foundation for achieving intelligent adaptation. Second, an offline optimization-based mapping model generation method is proposed. With global fuel efficiency optimization as the goal, a "space environment-optimal threshold" mapping relationship is generated through simulation and optimization algorithms. This offline optimized mapping model provides an intelligent source for dynamic threshold orbit maintenance decisions. Third, a dual-stage control architecture with offline / online separation is designed, effectively separating computationally intensive tasks from real-time control tasks. An independent division of labor and collaborative relationship is established between the "offline modeling" and "online control" stages, ensuring the efficient and reliable operation of space environment-aware dynamic adaptive threshold orbit maintenance on the satellite.

[0031] By coordinating the offline modeling and optimization phase and the online sensing and control phase, this invention deeply integrates space environment perception into the control loop, achieving intelligent and automated threshold decision-making in a model-based manner, ultimately achieving global fuel optimization. Compared with traditional fixed-threshold orbit maintenance strategies, this invention successfully transitions from "static control" to "dynamic optimization" by introducing space environment perception and dynamic decision-making mechanisms. It effectively resolves the inherent contradiction between "global fuel efficiency" and "end-to-end orbital safety" in fixed-threshold strategies. Relying on an intelligent closed-loop control system of "perception-decision-execution," it significantly improves the intelligence, adaptability, and safety of the low-Earth orbit satellite orbit maintenance system.

[0032] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0033] 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.

[0034] 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

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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:

[0039] 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.

[0040] In some embodiments, the space environment parameters include (but are not limited to) geomagnetic activity index and solar activity parameters.

[0041] 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.

[0042] In some embodiments, the velocity increment calculation employs an impulsive thrust model or a continuous small thrust model; wherein, the impulsive thrust model models the orbit-holding maneuver as an instantaneous velocity impulse, and the velocity increment includes a velocity increment calculated based on orbit transfer theory and a velocity increment based on atmospheric drag compensation, wherein the atmospheric drag compensation is calculated through high-precision orbit integration in a dynamic environment containing an atmospheric model; the continuous small thrust model models the orbit-holding maneuver as a continuous process, and the velocity increment is determined by integrating the thrust acceleration over the maneuver time period, wherein the thrust acceleration is the ratio of thrust to the time-varying satellite mass.

[0043] In some embodiments, in step S1, the optimization algorithm includes a parameter scanning method or Bayesian optimization; wherein, the parameter scanning method includes: discretizing and generating a candidate threshold sequence 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 the fuel efficiency, using a data acquisition function to guide iterative selection of candidate thresholds for simulation, updating the surrogate model until convergence, and finding the globally optimal threshold.

[0044] In some embodiments, the mapping model is in the form of a lookup table or a fitting function; wherein the lookup table stores the optimal thresholds corresponding to different spatial environment scenarios, and the fitting function represents the mapping relationship between spatial environment parameters and the optimal thresholds through mathematical functions.

[0045] Step 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.

[0046] In some embodiments, step S2, real-time sensing of space environment parameters includes obtaining geomagnetic activity index and solar activity parameters through a satellite-to-ground link or an on-board model; dynamically determining an adaptive threshold based on the mapping model includes: matching the sensed space environment parameters with the parameter range in a lookup table to obtain the optimal threshold, or substituting the sensed space environment parameters into a fitting function to calculate the optimal threshold.

[0047] In some embodiments, step S2, monitoring satellite orbit 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.

[0048] In some embodiments, in step S2, triggering the orbit-keeping maneuver includes performing orbit recovery using a pulse thrust model or a continuous small thrust model; wherein, the pulse thrust model applies a velocity impulse at a specific orbit point, and the continuous small thrust model activates the thruster and continues to work until the required velocity increment is accumulated.

[0049] The proposed method for dynamic adaptive threshold orbit maintenance of low-Earth orbit satellites based on space environment parameter perception has the following main technical advantages: Addressing the inherent shortcomings of traditional fixed-threshold orbit maintenance strategies—namely, excessive control during calm periods leading to fuel waste and insufficient control during geomagnetic storms posing safety risks—this method introduces a space environment parameter perception mechanism. It combines this with a two-stage architecture of "offline modeling and optimization to generate a 'space environment-optimal threshold' mapping model, and online real-time model invocation to dynamically adjust the threshold," thereby achieving intelligent adaptive decision-making for the orbit maintenance threshold. This ensures satellite safety under extreme space weather conditions while significantly reducing fuel consumption throughout its lifecycle, achieving a balance between "global fuel optimization" and "end-to-end orbit safety." Simultaneously, it enhances the intelligence, adaptability, and operational reliability of the low-Earth orbit satellite orbit maintenance system.

[0050] The following further describes specific embodiments of the present invention and examples of its algorithm implementation.

[0051] A dynamic adaptive threshold orbit maintenance method for low-Earth orbit satellites based on space weather parameter sensing is proposed. The core of this method lies in constructing and applying a "space environment-optimal threshold" mapping model, which is the key to achieving intelligent adaptive control.

[0052] Space environment parameters, as physical parameters that can characterize the dominant influence of space weather conditions on thermospheric atmospheric density, include, but are not limited to: geomagnetic activity indices (such as the Dst index, Ap index, Kp index, etc.) and solar activity parameters (such as the F10.7 index, etc.). These parameters are the fundamental inputs driving the dynamic changes of thresholds.

[0053] This invention utilizes a dynamic adaptive threshold, which can automatically adjust the orbital holding trigger threshold according to changes in the space environment parameters.

[0054] like Figure 1 As shown, the technical solution of the present invention mainly includes the following two core stages:

[0055] Phase 1: Offline Modeling and Optimization Phase (Ground or Onboard Preprocessing)

[0056] This stage aims to generate an on-orbit intelligent decision-making mapping model of "space environment - optimal threshold", which is the core of the invention's creativity.

[0057] 1. Mapping model construction steps:

[0058] (1) Optimization Objective: The optimization objective of this invention is to achieve optimal fuel efficiency. This is defined by fuel consumption per unit orbital decay (i.e., fuel efficiency). As a core optimization metric, the goal is to find the orbital maintenance threshold that minimizes the total fuel consumption of the mission, and its mathematical definition is as follows: in, This indicates fuel efficiency; the smaller the value, the higher the efficiency. Indicates the total velocity increment of the orbital maneuver. This is the preset total resistance attenuation.

[0059] (2) Speed ​​increment ( Calculation: Velocity increment This includes the potential energy component used to raise the orbit and the component used to compensate for atmospheric drag losses. Depending on the propulsion system, there are two models:

[0060] 1) Impulse Thrust Model: Maneuvering is modeled as instantaneous velocity impulse. The velocity increment required to maintain a single trajectory maneuver. It consists of two parts: first, the increase in potential energy used to raise the orbital altitude; and second, the energy loss due to atmospheric drag during operation at lower orbits. The calculation formula is as follows: in, The theoretical value of the velocity increment calculated for the Hohmann transfer. The gravitational constant of Earth, The average radius of the Earth The satellite orbital altitude at the moment the maneuver is triggered. The target altitude to be maintained for the track; As a drag compensation term, this can be automatically calculated in the simulation using a high-precision orbit integrator within a dynamic environment that includes an atmospheric model. Specifically, the velocity lost by the satellite while orbiting in a decaying orbit needs to be compensated for by additional thrust; this cost is integrated into the total thrust of a single maneuver. middle.

[0061] 2) Continuous small thrust model: The maneuver is a continuous process. Total The thruster's start-up time period is determined by numerical integration, and the calculation formula is as follows: in For thrust, This refers to the satellite's mass as it changes over time.

[0062] (3) Optimization of algorithm implementation:

[0063] Basic Solution (Parameter Scanning Method): As a basic and reliable implementation method, the global parameter scanning method can be adopted. This involves setting a series of candidate thresholds for each scenario, calculating the long-term fuel efficiency corresponding to each threshold through simulation, and plotting a "fuel efficiency - control threshold" curve. The threshold corresponding to the global optimum on the curve is determined as the optimal threshold for that scenario. The specific implementation steps of the parameter scanning method are as follows:

[0064] 1) Parameter discretization: For a given spatial environment scenario Within the predefined threshold range Inside, with step length Generate a candidate threshold sequence ;

[0065] 2) Loop simulation: For each candidate threshold in the sequence A long-term orbit-keeping simulation is performed. In the simulation, whenever the satellite's semi-major axis decay reaches a certain value... At that time, that is, according to the above The calculation formula performs one orbital recovery maneuver and records the results of this maneuver. ;

[0066] 3) Performance index calculation: When the cumulative orbital attenuation reaches The simulation ends when the threshold is reached. The total fuel consumption at that threshold is then calculated. Thus, fuel efficiency is obtained. ;

[0067] 4) Finding the optimal point: After iterating through all candidate thresholds, the dataset is obtained. By comparing all Find the minimum value among them. The corresponding threshold This is the scene. The optimal threshold is determined by the following conditions.

[0068] Preferred Solution (Bayesian Optimization): To improve efficiency, Bayesian optimization can be used. The steps include: 1) Constructing a Gaussian process surrogate model to fit the "threshold-efficiency" function; 2) Defining the acquisition function (e.g., the desired improvement); 3) Iterative optimization: Under the guidance of the surrogate model, candidate points are selected for simulation, the model is updated, and convergence is achieved to find the globally optimal threshold. It should be understood that this invention does not limit the optimization algorithm itself; other global optimization algorithms such as genetic algorithms and particle swarm optimization are also applicable.

[0069] (4) Model formation: Traverse all preset spatial environment scenes For each scenario Repeat step (3) to obtain its optimal threshold. Ultimately, a "space environment-optimal threshold" mapping model is constructed. This model can be a lookup table (as shown in Table 1) or a fitted mathematical function, stored in the onboard computer for use by low-Earth orbit satellites during their on-orbit phase.

[0070] Table 1. Example of "Spatial Environment - Optimal Threshold" mapping query

[0071]

[0072] Phase Two: Online Sensing and Autonomous Control Phase (Real-time Execution on Satellite)

[0073] This phase utilizes the intelligent mapping model generated in Phase 1 to achieve autonomous control of the satellite in orbit.

[0074] 2. Parameter sensing steps: When the satellite is in orbit, it acquires one or more current space environment parameters in real time or near real time through the satellite-to-ground link or on-board model.

[0075] 3. Threshold Decision Step: Using the spatial environment parameters sensed in step 2 as input, the pre-stored "spatial environment-optimal threshold" mapping model from stage 1 is queried or invoked to output the corresponding dynamic adaptive threshold in real time under the current environment.

[0076] If the mapping model is in the form of a lookup table, then the "query" operation is: to retrieve the currently perceived spatial environment parameters. The algorithm matches the parameter range in the query table, uses logical judgment to find the corresponding scenario row, and returns the "optimal threshold" field value for that row. ;

[0077] If the mapping model is in the form of a fitting function, then the "query" operation is: to... As input variables, directly substitute them into the function. The calculation is performed in the function, and the output value of the function is the desired dynamic adaptive threshold.

[0078] 4. Status monitoring and judgment steps: Calculate the average orbital semi-major axis of the satellite in real time and determine whether its attenuation relative to the nominal value reaches or exceeds the dynamic adaptive threshold determined in step 3.

[0079] 5. Orbit Maintenance Execution Steps: When the judgment result is "yes", the orbit maintenance maneuver is triggered to restore the satellite orbit to its nominal state.

[0080] If the pulse thrust model is used, the calculated velocity impulse is applied at a specific orbital point (such as perigee). ;

[0081] If a continuous low-thrust model is used, the thruster will be activated and continue to operate until the required thrust is accumulated. .

[0082] Through the collaborative work of the two stages mentioned above, this invention deeply integrates space environment perception into the control loop, realizes intelligent and automated threshold decision-making through modeling, and ultimately achieves the goal of global fuel optimization.

[0083] In summary, this invention proposes a dynamic adaptive threshold orbit maintenance method for low-Earth orbit satellites based on space environment parameter sensing. The key innovative contributions and technical points of this invention include:

[0084] (1) Design of a dynamic threshold orbit-keeping decision-making mechanism driven by space environment perception

[0085] This invention proposes a decision-making mechanism that transforms the triggering conditions for orbit holding from static presets to dynamic calculations driven by the space environment in real time. Space environment parameters serve as direct input, and a dynamic adaptive threshold is used as the decision output. This mechanism is fundamental to achieving intelligent adaptation.

[0086] (2) Mapping model generation method based on offline optimization

[0087] This invention aims to achieve optimal global fuel efficiency and employs a method for constructing a model that generates a mapping relationship between the space environment and the optimal threshold through simulation and optimization algorithms (such as parameter scanning and Bayesian optimization). This offline-optimized mapping model serves as the intelligent source for the dynamic threshold orbit-holding decision-making mechanism.

[0088] (3) Design of a two-stage control architecture with offline / online separation

[0089] This invention proposes a system architecture that separates computationally intensive tasks from real-time control tasks, clarifies the independent division of labor and collaborative relationship between the two stages of "offline modeling" and "online control", and ensures the efficient and reliable operation of dynamic adaptive threshold orbit maintenance control based on space environment perception on the satellite.

[0090] Compared with existing technologies, this invention has significant technical advantages: Compared with traditional fixed threshold orbit maintenance strategies, this invention achieves a leap from "static control" to "dynamic optimization" by introducing space environment perception and dynamic decision-making mechanisms. It solves the inherent contradiction between "global fuel efficiency" and "end-to-end orbit safety" in fixed threshold strategies, realizes global optimization of fuel consumption, and improves the intelligence, adaptability and safety of low-Earth orbit satellite orbit maintenance systems by relying on an intelligent closed-loop control system of "perception-decision-execution".

[0091] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0092] This invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor executes the computer program by performing at least the method described above.

[0093] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0094] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc or CD-ROM; magnetic surface memory can be disk storage or magnetic tape storage. The storage media described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0095] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0096] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0097] In addition, in the various embodiments of the present invention, each functional unit 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 hardware or in the form of hardware plus software functional units.

[0098] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0099] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0100] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0101] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0102] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0103] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

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; wherein, the optimization of global fuel efficiency uses fuel consumption per unit orbit decay as the optimization index, and the fuel efficiency is defined as the ratio of the total velocity increment of orbital maneuvers to the total resistance decay. 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, 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

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

9. 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 8.

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