Satellite electric propulsion orbital transfer control method and system based on propulsion strategy optimization

Abstract

The invention relates to the technical field of satellite orbital transfer, in particular to a satellite electric propulsion orbital transfer control method and system based on propulsion strategy optimization, and the method comprises the steps: determining a satellite to be subjected to orbital transfer, target orbital parameters and total orbital transfer duration, calculating initial orbital parameters, constructing an initial particle swarm, and according to the target orbital parameters and the initial orbital parameters, constructing an initial particle swarm; setting an initial individual optimal solution set and an initial group optimal solution, iterating the initial particle swarm to obtain a local individual optimal solution set and a local group optimal solution, calculating particle dispersion, judging whether the particle dispersion is greater than standard dispersion or not, if not, performing hyper-parameter adjustment on the initial particle swarm to obtain an updated particle swarm, and updating the updated particle swarm; and repeating the particle swarm iteration step to obtain the optimal solution of the target group, and determining the optimal individual position and the optimal individual speed of the optimal solution of the target group. According to the method, the accuracy in the satellite orbital transfer process can be improved, and the energy consumption in the orbital transfer process is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite orbit change, and in particular to a satellite electric propulsion orbit change control method and system based on propulsion strategy optimization. Background Art

[0002] Satellite orbit change is a key technology in modern space missions. Its main purpose is to ensure that the satellite can smoothly transfer from the initial orbit to the target orbit by adjusting the satellite's orbital parameters. With the increasing frequency of space activities, the efficiency and reliability of satellite orbit change technology are directly related to the success of space missions, and also have a profound impact on the safety of satellites.

[0003] Among the traditional methods of satellite orbit change, chemical propulsion systems are mainly used to achieve orbit transfer. Although this strategy is simple, it requires a large amount of fuel to achieve orbit change, which results in high energy consumption. In addition, since the thrust generated by the chemical propulsion system is usually large, it is difficult to control accurately, resulting in low accuracy of this method. Summary of the invention

[0004] The present invention provides a satellite electric propulsion orbit change control method and system based on propulsion strategy optimization, the main purpose of which is to improve the accuracy of the satellite orbit change process and reduce the energy consumption during the orbit change process.

[0005] To achieve the above-mentioned purpose, the present invention provides a satellite electric propulsion orbit change control method based on propulsion strategy optimization, comprising: receiving a satellite orbit change instruction, determining the satellite to be changed, target orbit parameters and the total orbit change time based on the satellite orbit change instruction, wherein the target orbit parameters include: target orbit radius and target orbit speed; calculating the initial orbit parameters of the satellite to be changed, wherein the initial orbit parameters include: initial orbit radius and initial orbit speed; constructing an initial particle swarm based on the total orbit change time, wherein the initial particle swarm presets hyperparameters, wherein the hyperparameters include: inertia factor and learning factor, and the initial particle swarm includes a plurality of initial particles, wherein the initial particles pre-set initial individual positions and initial individual speeds; setting the initial individual optimal solution set and the initial group optimal solution of the initial particle swarm according to the target orbit parameters and the initial orbit parameters; iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution The method comprises the following steps: generating a local individual optimal solution set and a local group optimal solution, calculating a particle discreteness according to the local individual optimal solution set and the local group optimal solution, and determining whether the particle discreteness is greater than a preset standard discreteness; if it is determined that the particle discreteness is not greater than the standard discreteness, adjusting the hyperparameters of the initial particle swarm by using a preset reinforcement learning model to obtain an updated particle swarm; taking the local individual optimal solution set, the local group optimal solution and the updated particle swarm as the initial individual optimal solution set, the initial group optimal solution and the initial particle swarm, respectively, and returning to the step of iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution until the particle discreteness is greater than the standard discreteness, and recording the local group optimal solution at this time as the target group optimal solution; determining the optimal individual position and the optimal individual speed of the target group optimal solution, and completing the satellite electric propulsion orbit change control based on propulsion strategy optimization based on the optimal individual position and the optimal individual speed.

[0006] Optionally, calculating the initial orbital parameters of the satellite to be changed orbit includes: determining an initial orbit of the satellite to be changed orbit, acquiring an initial orbital radius of the initial orbit, and calculating an initial orbital velocity of the satellite to be changed orbit on the initial orbit based on the initial orbital radius, wherein the initial orbital velocity is expressed as: ,in, represents the initial orbital velocity, represents the preset gravitational constant, represents the assumed mass of the Earth, represents the initial orbit radius.

[0007] Optionally, constructing an initial particle swarm based on the total track change duration includes: constructing a particle swarm, wherein the particle swarm includes a plurality of particles; extracting particles from the particle swarm in sequence, and generating initial individual positions of the particles based on the total track change duration, wherein the initial individual positions are expressed as: , , ,in, represents the initial individual position, represents the first electrical propulsion moment in the initial individual position, Indicates 0 to A random number between represents the random number generation function, Indicates the total track change time. Represents the second electric propulsion moment in the initial individual position; generates the initial individual speed according to the initial individual position, wherein the initial individual speed includes: a first electric propulsion speed and a second electric propulsion speed, and the first electric propulsion speed and the second electric propulsion speed correspond to the first electric propulsion moment and the second electric propulsion moment in the initial individual position, respectively; takes the particles after the initial individual position and the initial individual speed are generated as the initial particles, summarizes the initial particles, and obtains an initial particle group.

[0008] Optionally, generating the initial individual velocity according to the initial individual position includes: calculating the orbital semi-major axis based on the target orbital velocity and the initial orbital velocity, wherein the orbital semi-major axis is an average of the target orbital velocity and the initial orbital velocity; and calculating the orbital eccentricity based on the orbital semi-major axis, wherein the orbital eccentricity represents: ,in, is the orbital eccentricity, represents the initial orbital velocity, represents the target orbital velocity, represents the orbital semi-major axis; according to the target orbital radius, the initial orbital radius and the orbital semi-major axis, the first electric propulsion speed and the second electric propulsion speed are calculated using the following formula: , ,in, represents the first electric propulsion speed, represents the second electric propulsion speed; the first electric propulsion speed and the second electric propulsion speed are summed up to obtain the initial individual speed.

[0009] Optionally, setting the initial individual optimal solution set and the initial group optimal solution of the initial particle swarm according to the target orbital parameters and the initial orbital parameters includes: extracting initial particles in the initial particle swarm in sequence, and determining the first electric propulsion speed and the second electric propulsion speed of the initial particles; and calculating the speed optimization target based on the first electric propulsion speed, the second electric propulsion speed, the target orbital parameters and the initial orbital parameters, wherein the speed optimization target is expressed as: ,in, represents the speed optimization goal, represents the first electric propulsion speed, represents the initial orbital velocity in the initial orbital parameters, represents the second electric propulsion speed, represents the target orbit speed in the target orbit parameters; the speed optimization target is recorded as the initial individual optimal solution of the initial particle, the initial individual optimal solutions are summarized to obtain an initial individual optimal solution set, the minimum individual optimal solution in the initial individual optimal solution set is identified, and the minimum individual optimal solution is recorded as the initial group optimal solution.

[0010] Optionally, the method iterates the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution to obtain a local individual optimal solution set and a local group optimal solution, including: extracting initial particles in the initial particle swarm in sequence, identifying the current individual optimal solution corresponding to the initial particle in the initial individual optimal solution set, and obtaining the historical individual optimal solution of the initial particle; respectively determining the current individual position and the current individual speed corresponding to the current individual optimal solution, and the historical optimal position corresponding to the historical individual optimal solution; determining the initial group optimal position corresponding to the initial group optimal solution, and calculating the group change rate of the initial group optimal position; based on the current individual position, the historical optimal position, the current individual speed and the initial group optimal solution, position, using a preset update formula to respectively update the initial individual speed and the initial individual position of the initial particle to obtain an updated individual speed and an updated individual position; using the updated individual speed and the updated individual position to update the initial particle to obtain an updated particle, summarizing the updated particles to obtain an updated particle group, and determining an updated group optimal solution in the updated particle group; using the updated particle group and the updated group optimal solution as the initial particle group and the initial group optimal solution, respectively, and returning to the step of sequentially extracting the initial particles from the initial particle group until the group change rate is no greater than the preset minimum change rate, and recording the initial individual optimal solution set and the initial group optimal solution of the initial particle group as the local individual optimal solution set and the local group optimal solution, respectively.

[0011] Optionally, obtaining the historical individual optimal solution of the initial particle includes: determining whether the initial particle has been updated; if the initial particle has been updated, obtaining the historical optimal solution set of the initial particle, identifying the minimum historical optimal solution in the historical optimal solution set, and recording the minimum historical optimal solution as the historical individual optimal solution; if the initial particle has not been updated, recording the current individual optimal solution as the historical individual optimal solution.

[0012] Optionally, the update formula includes: ,in, represents the update individual speed, and They respectively represent the first electric propulsion speed and the second electric propulsion speed in the updated individual speed, represents the inertia factor, Indicates the current individual speed, and They represent the first electric propulsion speed and the second electric propulsion speed in the current individual speed respectively, and They represent the individual learning factor and group learning factor in the learning factor respectively. represents the best historical position, and They represent the first electric propulsion moment and the second electric propulsion moment in the historical optimal position, respectively. Indicates the current individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the current individual position, represents the optimal position of the initial group, and They represent the first electric propulsion moment and the second electric propulsion moment in the initial group optimal position, Indicates updating individual positions. and They respectively represent the first electric propulsion time and the second electric propulsion time in updating the individual position.

[0013] Optionally, the particle discreteness is calculated according to the local individual optimal solution set and the local group optimal solution, including: respectively determining the local individual position set corresponding to the local individual optimal solution set and the local group optimal position corresponding to the local group optimal solution; calculating the average individual position of the local individual position set; sequentially extracting local individual positions from the local individual position set, calculating the position deviation between the local individual position and the average individual position, summarizing the position deviation, and obtaining a position deviation set; identifying the maximum position deviation and the minimum position deviation in the position deviation set, and respectively determining the maximum individual position and the minimum individual position corresponding to the maximum position deviation and the minimum position deviation in the local individual position set; calculating the particle discreteness according to the maximum individual position, the minimum individual position and the local group optimal position, wherein the particle discreteness is expressed as: ,in, represents the particle dispersion, represents the number of local individual positions in the local individual position set, represents the first local individual positions, and Respectively represent The first electric propulsion moment and the second electric propulsion moment in each local individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the local group optimal position, represents the maximum individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the maximum individual position, represents the minimum individual position, and They respectively represent the first electric propulsion moment and the second electric propulsion moment in the minimum individual position.

[0014] To achieve the above-mentioned purpose, the present invention also provides a satellite electric propulsion orbit change control system based on propulsion strategy optimization, including: an orbit parameter acquisition module, used to receive a satellite orbit change instruction, determine the satellite to be changed, the target orbit parameters and the total orbit change time based on the satellite orbit change instruction, wherein the target orbit parameters include: the target orbit radius and the target orbit speed, calculate the initial orbit parameters of the satellite to be changed, wherein the initial orbit parameters include: the initial orbit radius and the initial orbit speed; an initial particle construction module, used to construct an initial particle group based on the total orbit change time, wherein the initial particle group presets hyperparameters, wherein the hyperparameters include: an inertia factor and a learning factor, and the initial particle group includes a plurality of initial particles, wherein the initial particles pre-set an initial individual position and an initial individual speed, and set the initial individual optimal solution set and the initial group optimal solution of the initial particle group according to the target orbit parameters and the initial orbit parameters; an initial particle update module, used to According to the initial individual optimal solution set and the initial group optimal solution, the initial particle swarm is iterated to obtain the local individual optimal solution set and the local group optimal solution, the particle discreteness is calculated according to the local individual optimal solution set and the local group optimal solution, and it is determined whether the particle discreteness is greater than the preset standard discreteness. If it is confirmed that the particle discreteness is not greater than the standard discreteness, the initial particle swarm is hyperparameter adjusted by using the preset reinforcement learning model to obtain an updated particle swarm; the optimal parameter determination module is used to use the local individual optimal solution set, the local group optimal solution and the updated particle swarm as the initial individual optimal solution set, the initial group optimal solution and the initial particle swarm respectively, and return to the step of iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution until the particle discreteness is greater than the standard discreteness, and the local group optimal solution at this time is recorded as the target group optimal solution, and the optimal individual position and the optimal individual speed of the target group optimal solution are determined.

[0015] In order to solve the above problems, the present invention also provides an electronic device, which includes: a memory, storing at least one instruction; and a processor, executing the instructions stored in the memory to implement the above-mentioned satellite electric propulsion orbit change control method based on propulsion strategy optimization.

[0016] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned satellite electric propulsion orbit change control method based on propulsion strategy optimization.

[0017] In order to solve the problem described in the background technology, the present invention first determines the satellite to be changed in orbit, the target orbit parameters and the total orbit change time based on the satellite orbit change instruction. This step provides clear instructions and target parameters for the satellite orbit change task, ensuring that the orbit change process has clear direction and time constraints. Then, an initial particle swarm is constructed based on the total orbit change time. This step utilizes the swarm intelligence characteristics of the particle swarm optimization algorithm, and can quickly explore the optimal solution in the complex orbit transfer solution space, that is, the moment of two electric propulsions, so as to find the most energy-saving orbit change strategy under the constraint of satisfying the total orbit change time. Further, according to the target orbit parameters and the initial orbit parameters, the initial individual optimal solution set and the initial group optimal solution of the initial particle swarm are set. This step provides a clear optimization starting point for the particle swarm optimization algorithm, ensuring that the orbit change optimization process can be carried out around the target orbit parameters. By setting the initial individual optimal solution set and the initial group optimal solution, the particle swarm can be guided to quickly approach the target orbit in the initial stage to avoid blind search, which not only improves the optimization efficiency, but also can significantly reduce unnecessary fuel consumption in the orbit change process. Then, according to the initial individual optimal solution set and the initial group optimal solution, the initial The particle swarm is iterated to obtain the local individual optimal solution set and the local group optimal solution. Based on the local individual optimal solution set and the local group optimal solution, the particle discreteness is calculated to determine whether the particle discreteness is greater than the standard discreteness. This step gradually approaches the optimal orbit change path by continuously updating the position and velocity of the particles. By calculating the particle discreteness, the distribution state of the particle swarm can be monitored in real time. When the particle discreteness is greater than the standard discreteness, it indicates that the distribution state of the particle swarm is relatively dispersed, and the hyperparameters need to be further adjusted to optimize the search behavior, which helps to avoid the particle swarm from converging prematurely or falling into the local optimum, thereby improving the global optimization ability of the orbit change strategy. When the particle discreteness is not greater than the standard discreteness, it indicates that the distribution state of the particle swarm is too concentrated, and the hyperparameters need to be dynamically adjusted through the reinforcement learning model to enhance the diversity of particles. This step can intelligently adjust the hyperparameters according to the current optimization state by introducing reinforcement learning, so that the particle swarm has appropriate search capabilities in different search stages. This dynamic adjustment mechanism can significantly improve the optimization performance of the orbit change strategy, avoid insufficient optimization due to fixed hyperparameters, and further improve the efficiency and accuracy of satellite orbit change. Therefore, the present invention can improve the accuracy of satellite orbit change and reduce energy consumption during the orbit change process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A schematic flow chart of a satellite electric propulsion orbit change control method based on propulsion strategy optimization provided by an embodiment of the present invention; Figure 2 A functional module diagram of a satellite electric propulsion orbit change control system based on propulsion strategy optimization provided by one embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device for implementing the satellite electric propulsion orbit change control method based on propulsion strategy optimization provided in one embodiment of the present invention.

[0019] Explanation of the reference numerals: 1. electronic device; 10. processor; 11. memory; 12. bus.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] An embodiment of the present application provides a satellite electric propulsion orbit change control method based on propulsion strategy optimization. The execution subject of the satellite electric propulsion orbit change control method based on propulsion strategy optimization includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the satellite electric propulsion orbit change control method based on propulsion strategy optimization can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0023] Reference Figure 1 As shown, it is a flow chart of a satellite electric propulsion orbit change control method based on propulsion strategy optimization provided by one embodiment of the present invention. In this embodiment, the satellite electric propulsion orbit change control method based on propulsion strategy optimization includes: S1, receiving a satellite orbit change instruction, and determining the satellite to be changed, the target orbit parameters and the total orbit change time based on the satellite orbit change instruction, wherein the target orbit parameters include: target orbit radius and target orbit speed.

[0024] It is understandable that the satellite orbit change instruction refers to an instruction initiated manually to change the orbit of a specific satellite, and the satellite to be changed refers to the specific satellite referred to by the satellite orbit change instruction. The target orbit parameters refer to the parameters of the target orbit, wherein the target orbit refers to the orbit of the satellite to be changed that needs to complete the orbit change task here, and the target orbit parameters include: target orbit radius and target orbit speed, wherein the target orbit radius refers to the radius of the target orbit, and the target orbit speed refers to the speed of the satellite on the target orbit. The total orbit change duration refers to the total duration set manually to complete the satellite orbit change this time, and the duration of the subsequent satellite orbit change process cannot exceed this total orbit change duration.

[0025] It needs to be explained that the satellite orbit change instruction at this time is intended to allow the satellite to change its orbit from the initial orbit to the target orbit. During this orbit change process, two electric propulsion moments need to be determined. At the first electric propulsion moment, the satellite to be changed is accelerated (or decelerated) by electric propulsion, thereby leaving the initial orbit and entering the transfer orbit. The transfer orbit is the transition orbit of the satellite to be changed from the initial orbit to the target orbit. After entering the transfer orbit, at the second electric propulsion moment, the satellite to be changed is decelerated (or accelerated) by electric propulsion to enter the target orbit. Therefore, in the above process, determining the two electric propulsion moments and the speed at each electric propulsion moment are important parameters for this orbit change.

[0026] S2. Calculate the initial orbital parameters of the satellite to be changed orbit, wherein the initial orbital parameters include: initial orbital radius and initial orbital velocity.

[0027] It can be understood that the initial orbit parameters refer to the parameters of the initial orbit, wherein the initial orbit refers to the orbit where the satellite to be changed is located at the current moment, wherein the current moment refers to the moment when the satellite orbit change instruction is received, the initial orbit radius refers to the radius of the initial orbit, and the initial orbit speed refers to the speed of the satellite to be changed in the initial orbit at the current moment.

[0028] In detail, the calculating the initial orbital parameters of the satellite to be changed orbit includes: determining the initial orbit of the satellite to be changed orbit, obtaining the initial orbital radius of the initial orbit, and calculating the initial orbital velocity of the satellite to be changed orbit on the initial orbit based on the initial orbital radius, wherein the initial orbital velocity is expressed as: ,in, represents the initial orbital velocity, represents the preset gravitational constant, represents the assumed mass of the Earth, represents the initial orbit radius.

[0029] S3. constructing an initial particle swarm based on the total track change duration, wherein the initial particle swarm presets hyper parameters, and the hyper parameters include: an inertia factor and a learning factor, and the initial particle swarm includes a plurality of initial particles, wherein the initial individual positions and initial individual velocities are preset for the initial particles.

[0030] It can be understood that the initial particle swarm refers to a group composed of multiple particles at the beginning of the particle swarm optimization algorithm. These particles are randomly distributed in the solution space, and each particle represents a potential solution. The solution of each initial particle in the initial particle set represents two electric propulsion moments, namely the first propulsion moment and the second propulsion moment. The initial particle swarm is the starting point of the algorithm. Through iterative optimization, the initial particle swarm gradually approaches the optimal solution, wherein the optimal solution refers to the absolute sum of the electric propulsion speeds of the satellite to be changed at the two propulsion moments during the entire orbit change process.

[0031] It should be explained that the hyperparameters refer to the parameters set manually before running the initial particle swarm, which have an important influence on the behavior and performance of the initial particle swarm. The hyperparameters include: inertia factor and learning factor, wherein the inertia factor refers to a coefficient set manually between 0 and 1, which is used to balance the current speed and historical speed of the initial particles. When the inertia factor is larger, it means that the current speed direction of the initial particles accounts for a higher proportion in the subsequent update formula, which is conducive to the overall search, but may cause the particles to deviate from the optimal solution. When the inertia factor is smaller, it means that the particles are more easily affected by individual experience and group experience, which is conducive to local search, but may fall into local optimality. Therefore, by adjusting the inertia factor in the future, the ability of the initial particle swarm to retrieve the global optimal solution can be enhanced.

[0032] Furthermore, the learning factor includes an individual learning factor and a group learning factor, wherein the individual learning factor refers to the value of the degree of dependence of the initial particle on its own historical optimal position, and the historical optimal position of the initial particle is recorded as the subsequent historical optimal position, and the group learning factor refers to the value of the degree of dependence of the initial particle on the historical optimal position of the group, and the historical optimal position of the group refers to the subsequent group optimal position. The initial individual position refers to the initial coordinates of the initial particle set artificially before the iteration of the initial particle group begins. In this scheme, the initial individual position is the moment of two electric propulsions. The initial individual speed refers to the initial speed of the initial particle set artificially before the iteration of the initial particle group begins. In this scheme, the initial individual speed refers to the speed of two electric propulsions.

[0033] In detail, the constructing of the initial particle swarm based on the total track change duration includes: constructing a particle swarm, wherein the particle swarm includes a plurality of particles; extracting particles in the particle swarm in sequence, and generating initial individual positions of the particles based on the total track change duration, wherein the initial individual positions are expressed as: , , ,in, represents the initial individual position, represents the first electrical propulsion moment in the initial individual position, Indicates 0 to A random number between represents the random number generation function, Indicates the total track change time. Represents the second electric propulsion moment in the initial individual position; generates the initial individual speed according to the initial individual position, wherein the initial individual speed includes: a first electric propulsion speed and a second electric propulsion speed, and the first electric propulsion speed and the second electric propulsion speed correspond to the first electric propulsion moment and the second electric propulsion moment in the initial individual position, respectively; takes the particles after the initial individual position and the initial individual speed are generated as the initial particles, summarizes the initial particles, and obtains an initial particle group.

[0034] It can be understood that the first electric propulsion time refers to the time when the orbit-changing satellite is electrically propulsed for the first time, and the second electric propulsion time refers to the time when the orbit-changing satellite is electrically propulsed for the second time. The first electric propulsion speed refers to the speed of electric propulsion at the first electric propulsion time, and the second electric propulsion speed refers to the speed of electric propulsion at the second electric propulsion time.

[0035] In detail, generating the initial individual velocity according to the initial individual position includes: calculating the orbital semi-major axis based on the target orbital velocity and the initial orbital velocity, wherein the orbital semi-major axis is the average of the target orbital velocity and the initial orbital velocity; calculating the orbital eccentricity based on the orbital semi-major axis, wherein the orbital eccentricity represents: ,in, is the orbital eccentricity, represents the initial orbital velocity, represents the target orbital velocity, represents the orbital semi-major axis; according to the target orbital radius, the initial orbital radius and the orbital semi-major axis, the first electric propulsion speed and the second electric propulsion speed are calculated using the following formula: , ,in, represents the first electric propulsion speed, represents the second electric propulsion speed; the first electric propulsion speed and the second electric propulsion speed are summed up to obtain the initial individual speed.

[0036] It can be understood that the orbital semi-major axis refers to the semi-major axis of the transfer orbit of the satellite to be changed, wherein the transfer orbit refers to the intermediate orbit of the satellite to be changed from the initial orbit to the target orbit, which can be expressed as the path of the satellite to be changed from the initial orbit to the target orbit. The orbital eccentricity refers to the eccentricity of the transfer orbit.

[0037] S4. According to the target orbit parameters and the initial orbit parameters, an initial individual optimal solution set and an initial group optimal solution of the initial particle swarm are set.

[0038] It should be explained that the initial individual optimal solution set refers to a set of multiple initial individual optimal solutions, the initial individual optimal solution refers to the optimal solution of the initial particle itself before the iteration starts, wherein the optimal solution represents two electric propulsion moments, and the initial group optimal solution refers to the optimal solution of all initial particles in the initial particle group.

[0039] In detail, the setting of the initial individual optimal solution set and the initial group optimal solution of the initial particle group according to the target orbit parameter and the initial orbit parameter includes: extracting initial particles in the initial particle group in sequence, and confirming the first electric propulsion speed and the second electric propulsion speed of the initial particles; and calculating the speed optimization target based on the first electric propulsion speed, the second electric propulsion speed, the target orbit parameter and the initial orbit parameter, wherein the speed optimization target is expressed as: ,in, represents the speed optimization goal, represents the first electric propulsion speed, represents the initial orbital velocity in the initial orbital parameters, represents the second electric propulsion speed, represents the target orbit speed in the target orbit parameters; the speed optimization target is recorded as the initial individual optimal solution of the initial particle, the initial individual optimal solutions are summarized to obtain an initial individual optimal solution set, the minimum individual optimal solution in the initial individual optimal solution set is identified, and the minimum individual optimal solution is recorded as the initial group optimal solution.

[0040] It can be understood that the speed optimization target refers to a value that measures the difference between the first electric propulsion speed and the second electric propulsion speed and the initial orbital speed and the target orbital speed. The greater the difference between the first electric propulsion speed and the second electric propulsion speed and the initial orbital speed and the target orbital speed, the smaller the speed optimization target. The smaller the speed optimization target, the more energy the satellite to be changed can save by using the minimum electric propulsion power. The minimum individual optimal solution refers to the initial individual optimal solution with the smallest value in the initial individual optimal solution set.

[0041] S5. According to the initial individual optimal solution set and the initial group optimal solution, the initial particle swarm is iterated to obtain the local individual optimal solution set and the local group optimal solution, and the particle discreteness is calculated according to the local individual optimal solution set and the local group optimal solution, and it is determined whether the particle discreteness is greater than a preset standard discreteness.

[0042] It can be understood that the local individual optimal solution set refers to a set of multiple local individual optimal solutions, the local individual optimal solution refers to the optimal solution of a single initial particle in the initial particle swarm, which represents the speed optimization target of the single initial particle when the iteration is completed, and the local group optimal solution refers to the optimal solution of all particles in the initial particle swarm, which represents the minimum speed optimization target of all initial particles in the initial particle swarm after the iteration is completed. The particle discreteness refers to the value that measures the distribution state of the particle swarm in the solution space. The more dispersed the particle swarm is in the search space, the greater the particle discreteness, and the more concentrated the particle swarm is in the search space, the smaller the particle discreteness.

[0043] It should be explained that the introduction of particle discreteness can quantify the distribution state of the particle swarm, solve the premature convergence problem caused by fixed parameters in traditional particle swarm optimization, and at the same time, particle discreteness can monitor the concentration or dispersion of the particle swarm in real time: when the particles are too concentrated (low particle discreteness), the algorithm is prone to fall into the local optimum, and the inertia factor needs to be increased to enhance the global search capability. When the particles are too dispersed (high particle discreteness), the inertia factor needs to be reduced to improve the retrieval efficiency of the local optimal value. Through this particle discreteness, it can avoid missing the potential optimal solution and significantly improve the convergence speed and accuracy of the position optimal solution. The standard discreteness refers to the artificially set discreteness.

[0044] In detail, the method iterates the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution to obtain the local individual optimal solution set and the local group optimal solution, including: extracting initial particles in the initial particle swarm in sequence, identifying the current individual optimal solution corresponding to the initial particle in the initial individual optimal solution set, and obtaining the historical individual optimal solution of the initial particle; respectively determining the current individual position and the current individual speed corresponding to the current individual optimal solution, and the historical optimal position corresponding to the historical individual optimal solution; determining the initial group optimal position corresponding to the initial group optimal solution, and calculating the group change rate of the initial group optimal position; based on the current individual position, the historical optimal position, the current individual speed and the initial group optimal solution, position, using a preset update formula to respectively update the initial individual speed and the initial individual position of the initial particle to obtain an updated individual speed and an updated individual position; using the updated individual speed and the updated individual position to update the initial particle to obtain an updated particle, summarizing the updated particles to obtain an updated particle group, and determining an updated group optimal solution in the updated particle group; using the updated particle group and the updated group optimal solution as the initial particle group and the initial group optimal solution, respectively, and returning to the step of sequentially extracting the initial particles from the initial particle group until the group change rate is no greater than the preset minimum change rate, and recording the initial individual optimal solution set and the initial group optimal solution of the initial particle group as the local individual optimal solution set and the local group optimal solution, respectively.

[0045] It can be understood that the current individual optimal solution refers to the initial individual optimal solution corresponding to the initial particle in the initial individual optimal solution set, and the historical individual optimal solution refers to the minimum value of the current individual optimal solutions of all the initial particles that appear in the iteration process. The current individual position and the current individual speed refer to the individual position and individual speed corresponding to the current individual optimal solution, respectively, and the historical optimal position refers to the individual position corresponding to the historical individual optimal solution. The initial group optimal position refers to the individual position corresponding to the initial group optimal solution. The group change rate refers to a value that measures the degree of change of the initial group optimal position. The smaller the degree of change of the initial group optimal position, the smaller the group change rate. When the group change rate is not greater than the minimum change rate, it means that the search process of the initial particle group tends to be stable at this time, the algorithm converges, and the local individual optimal solution set and the local group optimal solution obtained can be used as the approximate optimal solution of the current optimization problem. The calculation process of the group change rate can be expressed as: group change rate = (initial group optimal value of the current iteration step - initial group optimal value of the previous iteration step) / initial group optimal value of the previous iteration step.

[0046] Furthermore, the updated individual speed and the updated individual position refer to the updated current individual position and the current individual speed respectively, the updated particle refers to the updated initial particle, wherein updating refers to replacing the initial individual speed and the initial individual position in the initial particle respectively with the updated individual speed and the updated individual position, and the updated group optimal solution refers to the initial group optimal solution in the updated particle group. The minimum change rate refers to an artificially set constant.

[0047] In detail, the obtaining of the historical individual optimal solution of the initial particle includes: determining whether the initial particle has been updated; if the initial particle has been updated, obtaining the historical optimal solution set of the initial particle, identifying the minimum historical optimal solution in the historical optimal solution set, and recording the minimum historical optimal solution as the historical individual optimal solution; if the initial particle has not been updated, recording the current individual optimal solution as the historical individual optimal solution.

[0048] It should be explained that the determination of whether the initial particle has been updated refers to determining whether the initial particle has gone through the steps of updating the initial particle by updating the individual velocity and updating the individual position. If the initial particle has gone through the steps, it is considered that the initial particle has been updated. If the initial particle has not gone through the steps, it is considered that the initial particle has not been updated. The historical optimal solution set refers to the set of all current individual optimal solutions of the initial particle that appear in the iterative process, and the minimum historical optimal solution refers to the historical optimal solution with the smallest value in the historical optimal solution set.

[0049] In detail, the update formula includes: ,in, represents the update individual speed, and They respectively represent the first electric propulsion speed and the second electric propulsion speed in the updated individual speed, represents the inertia factor, Indicates the current individual speed, and They represent the first electric propulsion speed and the second electric propulsion speed in the current individual speed respectively, and They represent the individual learning factor and group learning factor in the learning factor respectively. represents the best historical position, and They represent the first electric propulsion moment and the second electric propulsion moment in the historical optimal position, respectively. Indicates the current individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the current individual position, represents the optimal position of the initial group, and They represent the first electric propulsion moment and the second electric propulsion moment in the initial group optimal position, Indicates updating individual positions. and They respectively represent the first electric propulsion time and the second electric propulsion time in updating the individual position.

[0050] It can be understood that the updated individual speed and the updated individual position can be calculated through the update formula.

[0051] In detail, the particle discreteness is calculated according to the local individual optimal solution set and the local group optimal solution, including: respectively determining the local individual position set corresponding to the local individual optimal solution set and the local group optimal position corresponding to the local group optimal solution; calculating the average individual position of the local individual position set; sequentially extracting the local individual positions in the local individual position set, calculating the position deviation between the local individual position and the average individual position, summarizing the position deviation, and obtaining the position deviation set; identifying the maximum position deviation and the minimum position deviation in the position deviation set, and respectively determining the maximum individual position and the minimum individual position corresponding to the maximum position deviation and the minimum position deviation in the local individual position set; calculating the particle discreteness according to the maximum individual position, the minimum individual position and the local group optimal position, wherein the particle discreteness is expressed as: ,in, represents the particle dispersion, represents the number of local individual positions in the local individual position set, represents the first local individual positions, and Respectively represent The first electric propulsion moment and the second electric propulsion moment in each local individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the local group optimal position, represents the maximum individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the maximum individual position, represents the minimum individual position, and They respectively represent the first electric propulsion moment and the second electric propulsion moment in the minimum individual position.

[0052] It can be understood that the local individual position set refers to the set of individual positions corresponding to each local individual optimal solution in the local individual optimal solution set, the local group optimal position refers to the individual position corresponding to the local group optimal solution, and the average individual position refers to the average value of all local individual positions in the local individual position set, wherein the average individual position includes: a first average moment and a second average moment, wherein the first average moment is the average value of all first electric propulsion moments of all local individual positions, and the second average moment is the average value of all second electric propulsion moments of all local individual positions. The position deviation refers to the deviation value between the local individual position and the average individual position, and its calculation process is expressed as: ,in, Indicates position deviation, represents the local individual position, represents the average individual position. The maximum position deviation and the minimum position deviation refer to the maximum value and the minimum value in the position deviation set, respectively. The maximum individual position and the minimum individual position refer to the individual position corresponding to the maximum position deviation and the minimum position deviation, respectively.

[0053] S6. If it is confirmed that the particle discreteness is not greater than the standard discreteness, the preset reinforcement learning model is used to adjust the hyperparameters of the initial particle swarm to obtain an updated particle swarm.

[0054] Understandably, when the particle discreteness is not greater than the standard discreteness, it means that although the iteration of the initial particle group is completed at this time, the initial particle group dispersion is low at this time, and the result of the iteration is more inclined to the local optimal value rather than the global optimal value. Therefore, it is necessary to adjust the hyperparameters in the initial particle group so that the initial particle group is more inclined to the global optimal value in the subsequent iteration process. The reinforcement learning model is a model for adjusting the hyperparameters, and the updated particle group is the initial particle group after the hyperparameter adjustment. Further, the reinforcement learning model can be selected as: Q-learning, REINFORCE, etc.

[0055] S7, taking the local individual optimal solution set, the local group optimal solution and the updated particle swarm as the initial individual optimal solution set, the initial group optimal solution and the initial particle swarm respectively, and returning to the step of iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution until the particle discreteness is greater than the standard discreteness, and recording the local group optimal solution at this time as the target group optimal solution.

[0056] It should be explained that when the particle discreteness is not greater than the standard discreteness, it means that the local group optimal solution obtained at this time, that is, the local group optimal value obtained in the most recent iteration step, can express the global optimal solution, so the local group optimal solution at this time can be recorded as the target group optimal solution.

[0057] S8. Determine the optimal individual position and optimal individual speed of the optimal solution for the target group, and complete the satellite electric propulsion orbit change control based on propulsion strategy optimization based on the optimal individual position and optimal individual speed.

[0058] It can be understood that the optimal individual position refers to the individual position corresponding to the optimal solution of the target group, which includes the moments of two electric propulsions, and the optimal individual speed refers to the individual speed corresponding to the optimal solution of the target group, which includes the speeds of two electric propulsions.

[0059] Exemplarily, for the orbit change process of a certain satellite, when the moment is the first electric propulsion moment in the optimal individual position, electric propulsion is performed, wherein the speed of the electric propulsion is the first electric propulsion speed in the optimal individual speed, and then when the moment is the second electric propulsion moment in the optimal individual position, electric propulsion is performed, wherein the speed of the electric propulsion is the second electric propulsion speed in the optimal individual speed.

[0060] In order to solve the problem described in the background technology, the present invention first determines the satellite to be changed in orbit, the target orbit parameters and the total orbit change time based on the satellite orbit change instruction. This step provides clear instructions and target parameters for the satellite orbit change task, ensuring that the orbit change process has clear direction and time constraints. Then, an initial particle swarm is constructed based on the total orbit change time. This step utilizes the swarm intelligence characteristics of the particle swarm optimization algorithm, and can quickly explore the optimal solution in the complex orbit transfer solution space, that is, the moment of two electric propulsions, so as to find the most energy-saving orbit change strategy under the constraint of satisfying the total orbit change time. Further, according to the target orbit parameters and the initial orbit parameters, the initial individual optimal solution set and the initial group optimal solution of the initial particle swarm are set. This step provides a clear optimization starting point for the particle swarm optimization algorithm, ensuring that the orbit change optimization process can be carried out around the target orbit parameters. By setting the initial individual optimal solution set and the initial group optimal solution, the particle swarm can be guided to quickly approach the target orbit in the initial stage to avoid blind search, which not only improves the optimization efficiency, but also can significantly reduce unnecessary fuel consumption in the orbit change process. Then, according to the initial individual optimal solution set and the initial group optimal solution, the initial The particle swarm is iterated to obtain the local individual optimal solution set and the local group optimal solution. Based on the local individual optimal solution set and the local group optimal solution, the particle discreteness is calculated to determine whether the particle discreteness is greater than the standard discreteness. This step gradually approaches the optimal orbit change path by continuously updating the position and velocity of the particles. By calculating the particle discreteness, the distribution state of the particle swarm can be monitored in real time. When the particle discreteness is greater than the standard discreteness, it indicates that the distribution state of the particle swarm is relatively dispersed, and the hyperparameters need to be further adjusted to optimize the search behavior, which helps to avoid the particle swarm from converging prematurely or falling into the local optimum, thereby improving the global optimization ability of the orbit change strategy. When the particle discreteness is not greater than the standard discreteness, it indicates that the distribution state of the particle swarm is too concentrated, and the hyperparameters need to be dynamically adjusted through the reinforcement learning model to enhance the diversity of particles. This step can intelligently adjust the hyperparameters according to the current optimization state by introducing reinforcement learning, so that the particle swarm has appropriate search capabilities in different search stages. This dynamic adjustment mechanism can significantly improve the optimization performance of the orbit change strategy, avoid insufficient optimization due to fixed hyperparameters, and further improve the efficiency and accuracy of satellite orbit change. Therefore, the present invention can improve the accuracy of satellite orbit change and reduce energy consumption during the orbit change process.

[0061] like Figure 2 , which is a functional module diagram of a satellite electric propulsion orbit change control system based on propulsion strategy optimization provided by one embodiment of the present invention.

[0062] The satellite electric propulsion orbit change control system 100 based on propulsion strategy optimization of the present invention can be installed in an electronic device. According to the functions to be implemented, the satellite electric propulsion orbit change control system 100 based on propulsion strategy optimization can include an orbit parameter acquisition module 101, an initial particle construction module 102, an initial particle update module 103 and an optimal parameter determination module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0063] The orbital parameter acquisition module 101 is used to receive a satellite orbit change instruction, determine the satellite to be changed, the target orbital parameters and the total orbit change time based on the satellite orbit change instruction, wherein the target orbital parameters include: target orbital radius and target orbital speed, calculate the initial orbital parameters of the satellite to be changed, wherein the initial orbital parameters include: initial orbital radius and initial orbital speed; the initial particle construction module 102 is used to construct an initial particle swarm based on the total orbit change time, wherein the initial particle swarm presets hyperparameters, wherein the hyperparameters include: inertia factor and learning factor, and the initial particle swarm includes a plurality of initial particles, wherein the initial particles pre-set initial individual positions and initial individual speeds, and set the initial individual optimal solution set and the initial group optimal solution of the initial particle swarm according to the target orbital parameters and the initial orbital parameters; the initial particle updating module 103 is used to update the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution. The optimal solution of the whole body is obtained, the initial particle swarm is iterated to obtain the local individual optimal solution set and the local group optimal solution, the particle discreteness is calculated according to the local individual optimal solution set and the local group optimal solution, and it is determined whether the particle discreteness is greater than the preset standard discreteness. If it is confirmed that the particle discreteness is not greater than the standard discreteness, the initial particle swarm is hyperparameter adjusted by using the preset reinforcement learning model to obtain an updated particle swarm; the optimal parameter determination module 104 is used to use the local individual optimal solution set, the local group optimal solution and the updated particle swarm as the initial individual optimal solution set, the initial group optimal solution and the initial particle swarm respectively, and return to the step of iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution until the particle discreteness is greater than the standard discreteness, and the local group optimal solution at this time is recorded as the target group optimal solution, and the optimal individual position and the optimal individual speed of the target group optimal solution are determined.

[0064] In detail, the modules in the satellite electric propulsion orbit change control system 100 based on propulsion strategy optimization described in the embodiment of the present invention are used in the same manner as described above. Figure 1 The satellite electric propulsion orbit change control method based on propulsion strategy optimization described in the invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0065] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a satellite electric propulsion orbit change control method based on propulsion strategy optimization provided by an embodiment of the present invention.

[0066] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a satellite electric propulsion orbit change control method program based on propulsion strategy optimization.

[0067] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the satellite electric propulsion orbit change control method program based on propulsion strategy optimization, but also can be used to temporarily store data that has been output or is to be output.

[0068] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a satellite electric propulsion orbit change control method program based on propulsion strategy optimization, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0069] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0070] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0071] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0072] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0073] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0074] The satellite electric propulsion orbit change control method program based on propulsion strategy optimization stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: receiving a satellite orbit change instruction, and determining the satellite to be changed, the target orbit parameters and the total orbit change time based on the satellite orbit change instruction, wherein the target orbit parameters include: the target orbit radius and the target orbit speed; calculating the initial orbit parameters of the satellite to be changed, wherein the initial orbit parameters include: the initial orbit radius and the initial orbit speed; constructing an initial particle swarm based on the total orbit change time, wherein the initial particle swarm presets hyperparameters, wherein the hyperparameters include: an inertia factor and a learning factor, and the initial particle swarm includes multiple initial particles, wherein the initial particles pre-set initial individual positions and initial individual speeds; setting an initial individual optimal solution set and an initial group optimal solution of the initial particle swarm according to the target orbit parameters and the initial orbit parameters; and The optimal solution of the whole body is obtained, the initial particle swarm is iterated to obtain the local individual optimal solution set and the local group optimal solution, the particle discreteness is calculated according to the local individual optimal solution set and the local group optimal solution, and it is determined whether the particle discreteness is greater than the preset standard discreteness; if it is confirmed that the particle discreteness is not greater than the standard discreteness, the initial particle swarm is hyperparameter adjusted by using the preset reinforcement learning model to obtain an updated particle swarm; the local individual optimal solution set, the local group optimal solution and the updated particle swarm are respectively used as the initial individual optimal solution set, the initial group optimal solution and the initial particle swarm, and the step of iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution is returned until the particle discreteness is greater than the standard discreteness, and the local group optimal solution at this time is recorded as the target group optimal solution; the optimal individual position and the optimal individual speed of the target group optimal solution are determined, and the satellite electric propulsion orbit change control based on the propulsion strategy optimization is completed based on the optimal individual position and the optimal individual speed.

[0075] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0076] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0077] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can achieve: receiving a satellite orbit change instruction, determining the satellite to be changed, target orbit parameters and the total orbit change time based on the satellite orbit change instruction, wherein the target orbit parameters include: target orbit radius and target orbit speed; calculating the initial orbit parameters of the satellite to be changed, wherein the initial orbit parameters include: initial orbit radius and initial orbit speed; constructing an initial particle swarm based on the total orbit change time, wherein the initial particle swarm presets hyperparameters, wherein the hyperparameters include: inertia factor and learning factor, and the initial particle swarm includes multiple initial particles, wherein the initial particles pre-set initial individual positions and initial individual speeds; setting the initial individual optimal solution set and the initial group optimal solution of the initial particle swarm according to the target orbit parameters and the initial orbit parameters; and calculating the initial individual optimal solution set and the initial group optimal solution according to the initial individual optimal solution set and the initial group optimal solution. The method comprises the following steps: first, iterating the initial particle swarm to obtain a local individual optimal solution set and a local group optimal solution, calculating the particle discreteness according to the local individual optimal solution set and the local group optimal solution, and judging whether the particle discreteness is greater than a preset standard discreteness; if it is confirmed that the particle discreteness is not greater than the standard discreteness, adjusting the hyperparameters of the initial particle swarm by using a preset reinforcement learning model to obtain an updated particle swarm; taking the local individual optimal solution set, the local group optimal solution and the updated particle swarm as the initial individual optimal solution set, the initial group optimal solution and the initial particle swarm respectively, and returning to the step of iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution until the particle discreteness is greater than the standard discreteness, and recording the local group optimal solution at this time as the target group optimal solution; determining the optimal individual position and the optimal individual speed of the target group optimal solution, and completing the satellite electric propulsion orbit change control based on the propulsion strategy optimization based on the optimal individual position and the optimal individual speed.

[0078] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0079] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0081] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A satellite electric propulsion orbit change control method based on propulsion strategy optimization, characterized in that: The method comprises: receiving a satellite orbit change instruction, determining the satellite to be orbit-changed, target orbit parameters and total orbit change duration based on the satellite orbit change instruction, wherein the target orbit parameters comprise target orbit radius and target orbit velocity; calculating initial orbit parameters of the satellite to be orbit-changed, wherein the initial orbit parameters comprise initial orbit radius and initial orbit velocity; constructing an initial particle swarm based on the total orbit change duration, wherein the initial particle swarm presets hyperparameters, wherein the hyperparameters comprise inertia factor and learning factor, and the initial particle swarm comprises a plurality of initial particles, wherein the initial particles pre-set initial individual positions and initial individual velocities; setting an initial individual optimal solution set and an initial group optimal solution of the initial particle swarm according to the target orbit parameters and the initial orbit parameters; iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution to obtain a local individual optimal solution set and a local group optimal solution. The optimal solution of the whole body is obtained, and the particle discreteness is calculated according to the local individual optimal solution set and the local group optimal solution, and it is determined whether the particle discreteness is greater than the preset standard discreteness; if it is confirmed that the particle discreteness is not greater than the standard discreteness, the initial particle swarm is adjusted by using the preset reinforcement learning model to obtain an updated particle swarm; the local individual optimal solution set, the local group optimal solution and the updated particle swarm are respectively used as the initial individual optimal solution set, the initial group optimal solution and the initial particle swarm, and the step of iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution is returned until the particle discreteness is greater than the standard discreteness, and the local group optimal solution at this time is recorded as the target group optimal solution; the optimal individual position and the optimal individual speed of the target group optimal solution are determined, and the satellite electric propulsion orbit change control based on the propulsion strategy optimization is completed based on the optimal individual position and the optimal individual speed.

2. The satellite electric propulsion orbit change control method based on propulsion strategy optimization according to claim 1, characterized in that: The calculating the initial orbital parameters of the satellite to be changed in orbit includes: determining the initial orbit of the satellite to be changed in orbit, obtaining the initial orbital radius of the initial orbit, and calculating the initial orbital velocity of the satellite to be changed in the initial orbit based on the initial orbital radius, wherein the initial orbital velocity is expressed as: ,in, represents the initial orbital velocity, represents the preset gravitational constant, represents the assumed mass of the Earth, represents the initial orbit radius.

3. The satellite electric propulsion orbit change control method based on propulsion strategy optimization according to claim 2, characterized in that: The constructing of an initial particle swarm based on the total track change duration includes: constructing a particle swarm, wherein the particle swarm includes a plurality of particles; extracting particles from the particle swarm in sequence, and generating initial individual positions of the particles based on the total track change duration, wherein the initial individual positions are expressed as: , , ,in, represents the initial individual position, represents the first electrical propulsion moment in the initial individual position, Indicates 0 to A random number between represents the random number generation function, Indicates the total track change time. Represents the second electric propulsion moment in the initial individual position; generates the initial individual speed according to the initial individual position, wherein the initial individual speed includes: a first electric propulsion speed and a second electric propulsion speed, and the first electric propulsion speed and the second electric propulsion speed correspond to the first electric propulsion moment and the second electric propulsion moment in the initial individual position, respectively; takes the particles after the initial individual position and the initial individual speed are generated as the initial particles, summarizes the initial particles, and obtains an initial particle group.

4. The satellite electric propulsion orbit change control method based on propulsion strategy optimization according to claim 3, characterized in that: The generating the initial individual velocity according to the initial individual position includes: calculating the orbital semi-major axis based on the target orbital velocity and the initial orbital velocity, wherein the orbital semi-major axis is an average value of the target orbital velocity and the initial orbital velocity; and calculating the orbital eccentricity based on the orbital semi-major axis, wherein the orbital eccentricity represents: ,in, is the orbital eccentricity, represents the initial orbital velocity, represents the target orbital velocity, represents the orbital semi-major axis; according to the target orbital radius, the initial orbital radius and the orbital semi-major axis, the first electric propulsion speed and the second electric propulsion speed are calculated using the following formula: , ,in, represents the first electric propulsion speed, represents the second electric propulsion speed; the first electric propulsion speed and the second electric propulsion speed are summed up to obtain the initial individual speed.

5. The satellite electric propulsion orbit change control method based on propulsion strategy optimization according to claim 4, characterized in that: The step of setting the initial individual optimal solution set and the initial group optimal solution of the initial particle group according to the target orbit parameter and the initial orbit parameter includes: extracting initial particles in the initial particle group in sequence, and determining the first electric propulsion speed and the second electric propulsion speed of the initial particles; and calculating the speed optimization target based on the first electric propulsion speed, the second electric propulsion speed, the target orbit parameter and the initial orbit parameter, wherein the speed optimization target is expressed as: ,in, represents the speed optimization goal, represents the first electric propulsion speed, represents the initial orbital velocity in the initial orbital parameters, represents the second electric propulsion speed, represents the target orbit speed in the target orbit parameters; the speed optimization target is recorded as the initial individual optimal solution of the initial particle, the initial individual optimal solutions are summarized to obtain an initial individual optimal solution set, the minimum individual optimal solution in the initial individual optimal solution set is identified, and the minimum individual optimal solution is recorded as the initial group optimal solution.

6. The satellite electric propulsion orbit change control method based on propulsion strategy optimization according to claim 5, characterized in that: The method iterates the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution to obtain a local individual optimal solution set and a local group optimal solution, including: extracting initial particles in the initial particle swarm in sequence, identifying the current individual optimal solution corresponding to the initial particle in the initial individual optimal solution set, and obtaining the historical individual optimal solution of the initial particle; respectively determining the current individual position and the current individual speed corresponding to the current individual optimal solution, and the historical optimal position corresponding to the historical individual optimal solution; determining the initial group optimal position corresponding to the initial group optimal solution, and calculating the group change rate of the initial group optimal position; based on the current individual position, the historical optimal position, the current individual speed and the initial group optimal position , using a preset update formula, respectively updating the initial individual speed and the initial individual position of the initial particle to obtain an updated individual speed and an updated individual position; using the updated individual speed and the updated individual position to update the initial particle to obtain an updated particle, summarizing the updated particles to obtain an updated particle group, and determining an updated group optimal solution in the updated particle group; using the updated particle group and the updated group optimal solution as the initial particle group and the initial group optimal solution, respectively, and returning to the step of sequentially extracting initial particles from the initial particle group until the group change rate is not greater than the preset minimum change rate, and recording the initial individual optimal solution set and the initial group optimal solution of the initial particle group as the local individual optimal solution set and the local group optimal solution, respectively.

7. The satellite electric propulsion orbit change control method based on propulsion strategy optimization according to claim 6, characterized in that: The obtaining of the historical individual optimal solution of the initial particle comprises: determining whether the initial particle has been updated; if the initial particle has been updated, obtaining the historical optimal solution set of the initial particle, identifying the minimum historical optimal solution in the historical optimal solution set, and recording the minimum historical optimal solution as the historical individual optimal solution; if the initial particle has not been updated, recording the current individual optimal solution as the historical individual optimal solution.

8. The satellite electric propulsion orbit change control method based on propulsion strategy optimization according to claim 7, characterized in that: The update formula includes: ,in, represents the update individual speed, and They respectively represent the first electric propulsion speed and the second electric propulsion speed in the updated individual speed, represents the inertia factor, Indicates the current individual speed, and They represent the first electric propulsion speed and the second electric propulsion speed in the current individual speed respectively, and They represent the individual learning factor and group learning factor in the learning factor respectively. represents the best historical position, and They represent the first electric propulsion moment and the second electric propulsion moment in the historical optimal position, respectively. Indicates the current individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the current individual position, represents the optimal position of the initial group, and They represent the first electric propulsion moment and the second electric propulsion moment in the initial group optimal position, Indicates updating individual positions. and They respectively represent the first electric propulsion time and the second electric propulsion time in updating the individual position.

9. The satellite electric propulsion orbit change control method based on propulsion strategy optimization according to claim 8, characterized in that: The particle discreteness is calculated according to the local individual optimal solution set and the local group optimal solution, including: respectively determining the local individual position set corresponding to the local individual optimal solution set and the local group optimal position corresponding to the local group optimal solution; calculating the average individual position of the local individual position set; sequentially extracting the local individual positions in the local individual position set, calculating the position deviation between the local individual position and the average individual position, summarizing the position deviation, and obtaining the position deviation set; identifying the maximum position deviation and the minimum position deviation in the position deviation set, and respectively determining the maximum individual position and the minimum individual position corresponding to the maximum position deviation and the minimum position deviation in the local individual position set; calculating the particle discreteness according to the maximum individual position, the minimum individual position and the local group optimal position, wherein the particle discreteness is expressed as: ,in, represents the particle dispersion, represents the number of local individual positions in the local individual position set, represents the first local individual positions, and Respectively represent The first electric propulsion moment and the second electric propulsion moment in each local individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the local group optimal position, represents the maximum individual position, and They represent the first electric propulsion moment and the second electric propulsion moment in the maximum individual position, represents the minimum individual position, and They respectively represent the first electric propulsion moment and the second electric propulsion moment in the minimum individual position.

10. A satellite electric propulsion orbit change control system based on propulsion strategy optimization, characterized in that: The system includes: an orbit parameter acquisition module, which is used to receive a satellite orbit change instruction, determine the satellite to be changed, the target orbit parameters and the total orbit change time based on the satellite orbit change instruction, wherein the target orbit parameters include: the target orbit radius and the target orbit speed, calculate the initial orbit parameters of the satellite to be changed, wherein the initial orbit parameters include: the initial orbit radius and the initial orbit speed; an initial particle construction module, which is used to construct an initial particle group based on the total orbit change time, wherein the initial particle group presets hyperparameters, wherein the hyperparameters include: an inertia factor and a learning factor, and the initial particle group includes a plurality of initial particles, wherein the initial particles pre-set an initial individual position and an initial individual speed, and set an initial individual optimal solution set and an initial group optimal solution of the initial particle group according to the target orbit parameters and the initial orbit parameters; an initial particle update module, which is used to update the initial individual optimal solution set and the initial group optimal solution according to the initial individual optimal solution set and the initial group optimal solution. The optimal solution is to iterate the initial particle swarm to obtain the local individual optimal solution set and the local group optimal solution. According to the local individual optimal solution set and the local group optimal solution, the particle discreteness is calculated to determine whether the particle discreteness is greater than the preset standard discreteness. If it is confirmed that the particle discreteness is not greater than the standard discreteness, the initial particle swarm is hyperparameter-adjusted using the preset reinforcement learning model to obtain an updated particle swarm; the optimal parameter determination module is used to use the local individual optimal solution set, the local group optimal solution and the updated particle swarm as the initial individual optimal solution set, the initial group optimal solution and the initial particle swarm respectively, and return to the step of iterating the initial particle swarm according to the initial individual optimal solution set and the initial group optimal solution until the particle discreteness is greater than the standard discreteness, and the local group optimal solution at this time is recorded as the target group optimal solution, and the optimal individual position and the optimal individual speed of the target group optimal solution are determined.

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