Space-ground combined space debris batch rendezvous elimination method

Abstract

The application relates to a space-ground combined space debris batch rendezvous elimination method and relates to the technical field of space-ground mixed multi-space debris elimination. In order to solve the technical problem that the existing several relatively mature space debris cleaning technologies verified in orbit are based on single satellite striking multiple satellites and have an optimization upper limit, the technical scheme provided by the application comprises the following steps: a step of collecting an optimization target of an algorithm; a step of setting simulation annealing algorithm parameters according to the optimization target; a step of collecting a randomly generated initial solution as a starting point of the simulation annealing algorithm; a step of iteratively optimizing the initial solution until a preset condition of a variable orbit parameter combination is obtained. On the basis of each satellite eliminating only one space debris, a step of matching satellite individuals with space debris individuals according to the variable orbit parameter combination. The application is suitable for application in space-ground mixed multi-space debris elimination.

Description

Technical Field

[0001] This relates to the field of air-ground hybrid multi-space debris removal technology, specifically to air-ground hybrid multi-space debris removal task planning based on simulated annealing algorithm. Background Technology

[0002] With the development of science and technology and the progress of the times, large-scale satellite constellations are receiving increasing attention from countries around the world. Large-scale satellite constellations in planning and operation are constantly emerging, and more and more small satellites are being launched into space. Among the more prominent large-scale constellations are Starlink and BeiDou. Space debris, generated by human activities, is space junk in outer space. Space debris can collide with satellites in orbit, interfering with the normal operation of satellite constellations. As more and more satellites are launched into outer space, the space debris generated by these satellites is also constantly increasing, significantly impacting their normal operation. Suitable orbital resources are therefore becoming increasingly precious. Therefore, using appropriate methods to remove space debris is extremely important.

[0003] Extensive research has been conducted on space debris removal technologies, and several relatively mature, in-orbit validated technologies exist, such as space rope-net capture technology and robotic arm capture technology. These technologies require satellites carrying removal devices to be launched into space. To conserve resources, each satellite can perform multiple orbital maneuvers and capture missions until its fuel is depleted. Therefore, conserving satellite fuel and enabling a single satellite to remove multiple pieces of space debris is paramount for clearing more space debris. Many scholars have explored the issue of fuel conservation for single satellites. However, there are optimization limits to a single satellite targeting multiple satellites. When the dihedral angles of two orbits differ significantly or their initial positions are far apart, even the best optimized result will consume a considerable amount of fuel. Summary of the Invention

[0004] To address the existing problems in space debris removal technologies, which are generally mature and verified in orbit, all of which rely on a single satellite targeting multiple satellites, this invention provides the following technical solution: A method for calculating trajectory change parameters for air-to-ground hybrid debris removal based on the annealing algorithm includes: Steps for a space debris collection and strike mission; The steps for simulating satellites in a constellation; Based on the space debris strike mission and the satellites in the constellation, the steps for obtaining the optimization target of the algorithm are as follows; The steps for setting the simulated annealing algorithm parameters according to the optimization objective; The step of collecting a randomly generated initial solution as the starting point of the simulated annealing algorithm; The step of iteratively optimizing the initial solution until a combination of trajectory change parameters under preset conditions is obtained; Based on the principle that each satellite removes only one piece of space debris, the step involves matching individual satellites with individual pieces of space debris according to the aforementioned combination of orbit change parameters.

[0005] Furthermore, a preferred implementation is provided, wherein the algorithm is the Lambert algorithm.

[0006] Furthermore, a preferred implementation is provided, wherein the optimization objectives include orbit change time, rendezvous time, and number of rendezvous circles.

[0007] Furthermore, a preferred embodiment is provided, wherein the parameters include initial temperature, termination temperature, cooling rate, and number of iterations at each temperature.

[0008] Based on the same inventive concept, this invention also provides a device for calculating orbit change parameters for air-to-ground hybrid space debris removal based on an annealing algorithm, comprising: Module for collecting space debris and striking targets; A module for simulating satellites in a constellation; Based on the space debris strike mission and the satellites in the constellation, a module for optimizing the algorithm's target is obtained; A module for setting the parameters of the simulated annealing algorithm based on the optimization objective; The module that collects randomly generated initial solutions as the starting point of the simulated annealing algorithm; The module iteratively optimizes the initial solution until a combination of trajectory change parameters under preset conditions is obtained; This module, based on the principle of clearing only one piece of space debris per satellite, matches individual satellites with individual pieces of space debris according to the aforementioned combination of orbit change parameters.

[0009] Based on the same inventive concept, this invention also provides a method for bulk rendezvous and elimination of space debris using a combined air-to-ground approach, comprising: The steps for matching the collected satellite individuals with space debris individuals; The step of clearing space debris based on the matching results.

[0010] Based on the same inventive concept, the present invention also provides a combined air-to-ground space debris batch rendezvous and elimination device, comprising: A module for matching the collected data between individual satellites and individual space debris. A module for clearing space debris based on the matching results.

[0011] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program, wherein when the computer program is read by a computer, the computer executes the method described thereon.

[0012] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium, wherein when the processor reads a computer program stored in the storage medium, the computer executes the method described thereon.

[0013] Based on the same inventive concept, the present invention also provides a computer program product, which, when executed, implements the method described.

[0014] Compared with the prior art, the advantages of the technical solution provided by the present invention are as follows: The introduction of simulated annealing effectively solves the global optimization problem of the Lambert orbital change problem. Specifically, by gradually reducing the temperature and accepting suboptimal solutions, the simulated annealing algorithm avoids the limitation of traditional Lambert orbital change methods that are prone to getting trapped in local optima. This improvement significantly reduces velocity pulsations during the orbital change process, thereby reducing fuel consumption. Compared to directly using the Lambert method, the optimized effect is more significant.

[0015] Dynamic programming algorithms optimize the matching problem between satellites and space debris. By rationally designing the state transition equations and boundary conditions, dynamic programming ensures that the total removal cost is minimized under the constraint that each satellite can only remove one piece of debris. Compared to simple heuristic matching methods, this optimization approach is more systematic and efficient, ultimately significantly reducing the overall removal cost of the mission.

[0016] By combining simulated annealing and dynamic programming, the entire technical solution not only solves the dual problems of trajectory changing and debris removal, but also achieves optimal resource allocation. Compared with existing single optimization methods, this combined approach provides stronger global optimization capabilities, especially when facing multiple variables and complex constraints, offering higher-quality solutions and significantly improving task execution efficiency and success rate.

[0017] Suitable for use in the removal of debris in mixed open and ground spaces. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a satellite removal system. Figure 2 This is a schematic diagram simulating a space debris constellation. Figure 3 This is a flowchart of the simulation algorithm; Figure 4 This is a schematic diagram of the average velocity pulse size; Figure 5A diagram illustrating the number of emergency satellites that need to be launched; Figure 6 A schematic diagram showing a pre-positioned satellite approaching target space debris; Figure 7 A schematic diagram illustrating the approach of an emergency satellite to target debris; Figure 8 This is a schematic diagram illustrating the removal of target debris by an emergency satellite. Detailed Implementation

[0019] To make the advantages and benefits of the technical solution provided by the present invention clearer, the technical solution provided by the present invention will now be described in further detail with reference to the accompanying drawings, specifically: Implementation Method 1: This implementation method provides a method for matching space debris removal schemes using a hybrid air-ground approach, including: Brief Step 1: Select the spacecraft orbit change method and determine the optimization strategy.

[0020] Step 2: Initialize the simulated annealing algorithm parameters.

[0021] Step 3: Generate an initial solution, calculate and evaluate the objective function value.

[0022] Step 4: Iteratively optimize the trajectory change parameters to find the optimal solution.

[0023] Step 5: Use dynamic programming to optimize the matching scheme between satellites and space debris.

[0024] Step 6: Integrate and optimize the results, and execute the spacecraft orbit change and debris removal mission.

[0025] Detailed description Step 1: Select the spacecraft orbit change method and determine the optimization strategy Input: Orbit change method options (Hohmann orbit change, three-pulse orbit change, Lambert orbit change) Solution: Among numerous orbit change methods, the Lambert orbit change method was selected for spacecraft orbit change. Since directly using the Lambert algorithm is unlikely to yield optimal results, a simulated annealing algorithm was employed for global optimization. The simulated annealing algorithm optimizes the three key variables of the Lambert problem (orbit change time, rendezvous time, and number of rendezvous orbits) to minimize velocity impulses and achieve fuel savings.

[0026] Output: The selected optimization strategy and objective function (minimizing velocity impulses) will serve as the basis for subsequent steps.

[0027] Step 2: Initialize the simulated annealing algorithm parameters Input: Optimization strategy and objective function Processing: Determine the initial parameters of the simulated annealing algorithm, including the initial temperature (100.0), the termination temperature (0.001), the cooling rate, and the number of iterations at each temperature (100). The settings of these parameters will directly affect the convergence speed of the algorithm and the quality of the final solution.

[0028] Output: The simulated annealing algorithm after initialization is ready for optimization.

[0029] Step 3: Generate an initial solution, calculate and evaluate the objective function value. Input: The initialized simulated annealing algorithm Processing: An initial solution is generated randomly or obtained heuristically. This solution includes initial values ​​for the orbit change time, rendezvous time, and number of rendezvous orbits. Then, the objective function value of this solution, i.e., the velocity pulse size, is calculated, and its quality is evaluated.

[0030] Output: The initial solution and its objective function value will serve as the starting point for the simulated annealing algorithm.

[0031] Step 4: Iteratively optimize the trajectory change parameters to find the optimal solution. Input: Initial solution and its objective function value Process: Iterative optimization is performed using simulated annealing. In each iteration, new candidate solutions are generated and their objective function values ​​are calculated. Based on the Metropolis criterion, poorer solutions are accepted with a certain probability to prevent getting trapped in local optima. As the temperature gradually decreases, the optimization process gradually converges, eventually finding the optimal combination of trajectory change parameters.

[0032] Output: Optimal orbit change parameters (orbit change time, rendezvous time, number of rendezvous circles) and minimized velocity pulse values.

[0033] Step 5: Optimize the matching scheme between satellites and space debris using dynamic programming. Input: Optimized trajectory change parameters Solution: The optimized orbital parameters are applied to the satellite-space debris matching problem. A dynamic programming algorithm is used to construct a two-dimensional array to represent the states and determine the state transition equations to ensure that each satellite can only remove one piece of space debris and that the total removal cost is minimized. Based on the state transition equations and boundary conditions, the satellite-debris matching scheme is progressively optimized.

[0034] Output: The optimal satellite and space debris matching scheme will be used to carry out the actual cleanup mission.

[0035] Step 6: Integrate and optimize the results, and execute the spacecraft orbit change and debris removal mission. Input: Optimal orbital adjustment parameters and satellite debris matching scheme Process: Integrate orbit change parameters and debris matching schemes to plan the actual orbit change and debris removal missions for the spacecraft. Ensure that orbit change and debris removal are executed as planned during actual operations to achieve optimal fuel consumption and mission efficiency.

[0036] Output: The spacecraft successfully completed its orbit change and space debris removal mission, ultimately achieving safe and efficient mission execution.

[0037] This summary method ensures the integrity and logical order of the technical solution, which helps in understanding and executing each step in practical applications.

[0038] Implementation Method Two: Combination Figure 1-8 This embodiment describes the technical solution provided above in further detail through specific examples. Specifically: The technical solution provided in this embodiment uses the optimization algorithm module of SpaceSim software, developed by the School of Aerospace Engineering at Harbin Institute of Technology, to calculate the velocity pulses for the orbit change of the service satellite and directly displays the orbit change process in the software, making the optimization results of the debris removal task clear and concise. For satellite disintegration scenarios, the multi-loop Lambert algorithm is optimized using simulated annealing to obtain the velocity pulse size required for each service satellite to remove each target space debris, thus establishing a velocity pulse matrix. This matrix is ​​then processed using a dynamic programming algorithm, and when some space debris is located in unusual positions, an emergency launch of a satellite to the target space debris location is used to remove the debris.

[0039] This implementation proposes a novel approach: establishing a constellation of space debris removal satellites and supplementing it with ad-hoc launches. It studies the problem of multiple satellites clearing space debris in a cluster setting. For each piece of space debris, dynamic programming and other methods are used to find the most suitable satellite for removal, thus saving fuel for individual satellites and clearing more debris. When some space debris is located in unusual positions, an emergency launch of satellites to the target debris location is employed to remove it, ensuring minimal fuel consumption. Much research has been conducted on space debris removal technologies, and several mature, in-orbit verified technologies exist, such as space rope net capture technology. These mechanisms can effectively capture obsolete space debris.

[0040] Simulated annealing is a heuristic algorithm that simulates the cooling process of metal in real life. As the temperature decreases, the state of the algorithm's solution gradually stabilizes, eventually ceasing calculation and outputting the optimal solution when the temperature reaches its lowest point. During the search process, the algorithm decides whether to accept a new solution based on the difference between the current solution and a new solution, as well as the current temperature. Initially, the algorithm is set at a higher temperature, allowing for the acceptance of poorer solutions. As iterations progress and the temperature gradually decreases, the acceptance probability also decreases, leading the algorithm to gradually find a better solution globally. Simulated annealing has been widely used in aerospace planning research. For example, Ding Yinan et al. from the Beijing Institute of Control Engineering established an agile imaging satellite mission planning model with multiple satellites and multiple observation targets. Further, using the imaging time as the optimization object, they proposed a multi-target variable neighborhood simulated annealing algorithm that adjusts the search range using the cooling process, achieving good results.

[0041] Dynamic programming is a method for solving optimization problems in multi-stage decision-making processes. It effectively reduces redundant computation and improves algorithm efficiency. For highly complex problems, dynamic programming can effectively reduce computational complexity through recursion and memoization, making the problem solvable. Due to its wide applicability and efficiency, dynamic programming is widely used in algorithm design and practical problem solving.

[0042] This implementation study investigates the mission planning problem of multi-satellite clearance of multiple space debris involving both air and ground. A complete mission planning module for multi-space debris clearance was established in the domestic space simulation software SpaceSim, enabling clear visualization of the results. A simulated annealing algorithm was applied to the multi-cycle Lambert algorithm to establish the velocity pulse matrix. When some space debris locations are unusual, an emergency satellite launch to the target debris location was employed for clearance. Results show that the mission planning for multi-space debris clearance yields good results, effectively reducing the total mission pulse and fuel consumption per satellite, thus saving satellite costs while eliminating more space debris.

[0043] Specifically: Space debris strike mission planning issues The scenario is that a satellite suddenly disintegrates, and the debris is distributed across multiple orbital planes, forming a debris cluster. It is urgent to remove this debris cluster in orbit to prevent it from colliding with other satellites and causing greater impact.

[0044] The parameters and variables are defined as follows: Pre-set satellite array in The number of satellites is preset.

[0045] Emergency satellite collection, in This refers to the number of emergency satellites.

[0046] Collection of spatial fragments in This represents the number of space fragments.

[0047] Our satellite's orbit change time.

[0048] The time when a satellite rendezvous with space debris.

[0049] Number of orbits our satellite completed before rendezvous.

[0050] Space debris strike mission modeling: For space debris removal missions, an equivalent simulation model for debris removal is established as a design constraint for both pre-deployed and emergency constellations. Deployment methods for debris removal satellites include emergency deployment and pre-deployment using dedicated constellations. Co-orbital deployment is considered the primary form of emergency deployment.

[0051] Clearance Model: Clearance systems are simplified using beams, such as weapon systems... Figure 1 As shown, the beam direction represents the removal direction of our satellite. When our satellite's beam is pointed at a space debris, it means that our satellite will remove that space debris. It is assumed that the removal will be successful, and a single satellite can only remove a single piece of space debris.

[0052] Because the satellites in the Walker constellation are located in different orbital planes and are relatively evenly distributed, the display effect is good, and it can effectively play a simulation role. This implementation uses the Walker constellation as space debris to simulate the space debris group that we want to clear. The specific orbital numbers of the generated space debris are calculated by SpaceSim software and displayed within the software. Figure 2 Show.

[0053] Emergency constellations are designed for rapid deployment when the number of pre-installed satellites or the available fuel is insufficient to strike all space debris in a debris cluster. This requires careful planning of launch sites, the number of satellites to be launched, and other details. The orbital design principle for emergency satellites prioritizes the rapid achievement of strike targets.

[0054] At the initial moment, the satellite begins its erection process, reaching its designated position within Chinese territory. After erection, the satellite first travels to one of the seven aggregation points located within Chinese territory closest to its intended launch location. Once erected, the satellite maneuvers at a constant speed towards the target launch point based on its geographical location. After reaching the launch point, the satellite remains there for a period of time to prepare for launch.

[0055] After a period of simulation, our satellites will be launched successively. Satellite launches are based on the relative positions of our satellites and space debris. When the relative positions reach a minimum within a certain timeframe, the satellite will be launched immediately. If the minimum value does not appear within 3000 seconds, the satellite will not wait for the launch window and will be launched immediately. The time it takes for the satellite to reach its expected position is related to the distance between our satellite and the space debris at launch time. The shorter the distance between our satellite and the space debris at launch time, the shorter the flight time after launch.

[0056] Spacecraft orbit change model: Common orbit-changing methods for spacecraft include Hohmann orbit change, three-pulse orbit change, and Lambert orbit change. This implementation uses the Lambert algorithm as the orbit-changing algorithm and optimizes the Lambert problem using simulated annealing. Directly using the Lambert method often yields poor results; therefore, an optimized Lambert orbit change method is typically employed to conserve fuel. This implementation uses simulated annealing to optimize the Lambert problem. Simulated annealing is a global optimization algorithm based on physics principles, initially used to solve crystal structure optimization problems during metal annealing. This algorithm finds the minimum value of a function by simulating the process of solid materials becoming more homogeneous and stable at high temperatures. For the multi-loop Lambert problem, the variables to be optimized are the orbit change time, rendezvous time, and number of rendezvous loops. The minimum value of the function refers to the minimum value of the velocity pulses.

[0057] In simulated annealing, the system is initially at a high temperature, accepting poor solutions at this state. As the temperature gradually decreases, the system becomes increasingly inclined to accept solutions better than the current one. This avoids getting trapped in local optima.

[0058] Specifically, the simulated annealing algorithm includes the following steps: 1. Initialization: Determine parameters such as initial temperature, ending temperature, and cooling rate.

[0059] 2. Randomly generate an initial solution or obtain an initial approximate solution from a known heuristic method.

[0060] 3. In each iteration step, a small perturbation is applied to the current solution to generate a new candidate solution. This perturbation decreases as the temperature decreases, and the objective function value is calculated.

[0061] 4. Based on the Metropolis criterion (probabilistic acceptance of inferior solutions), choose with a certain probability whether to accept the new candidate solution as the next search point or update the current search point.

[0062] 5. Repeat steps 3-4 until the termination condition is met (such as reaching the maximum number of iterations).

[0063] For the Lambert problem, there are three variables: the time of our satellite's orbit change. Satellite rendezvous time with space debris And the number of orbits our satellite made before the rendezvous. The initial conditions are set as follows: , , .

[0064] in, This is the semi-major axis of our satellite's original orbit. That is, the flight time of our satellite after changing its orbit. That is, assuming our satellite does not change its orbit... Time's up The number of flight circles at any given time is rounded up.

[0065] The main limitations are as follows: Regarding time The restrictions are

[0066] For the number of laps The restrictions are

[0067] The orbit cannot cross the Earth during the orbital change process. For the annealing algorithm, the initial temperature is set to 100.0, the final temperature is set to 0.001, and the number of iterations at each temperature is set to 100. After every 100 iterations, the temperature is decreased. The simulation algorithm flowchart is as follows. Figure 3 As shown.

[0068] For our side Pre-positioned satellites cleared The problem of space debris clustering assumes that each satellite can only remove one piece of space debris, and each piece of space debris can only be removed by one satellite. The correspondence between satellites removing space debris can affect the final removal plan. Using the annealing algorithm, each pre-set satellite can obtain an optimized result for each piece of space debris. Ultimately, a... The problem can be simplified to taking one value from each row, with no duplicate column values, and finding the final matrix. The goal is to minimize the sum of all the numbers. Dynamic programming can effectively solve this problem.

[0069] The dynamic programming algorithm is used to solve this problem as follows: 1. Define the state: Design a state representation, which is usually related to the solution to the problem. In this problem, a two-dimensional array can be designed. With the order of selection and the final selection order . middle, The stored number is the first The array starting with column passes through The minimum total obtained after planning the rows. It stores the selection order for each row. for The best choice order among the options.

[0070] 2. State Transition Equation: Determine the state transition equation, i.e., how to update the current state based on the previous states. In this problem, the state transition equation is expressed as: ,in And satisfy the constraint conditions (i.e., the first one) The number of columns selected in a row cannot be the same as the number of columns selected in a row. (The same number of columns were selected previously).

[0071] 3. Boundary Conditions: Determine the boundary conditions, i.e., the initial state. In this problem, the state of the first row can be directly determined by the values ​​in the matrix.

[0072] Finally, the results are analyzed: Classification of mixed space debris removal tasks For our side Pre-positioned satellites cleared The problem of space debris clustering assumes that each satellite can only remove one piece of space debris, and each piece of space debris can only be removed by one satellite. The correspondence between satellites removing space debris can affect the final removal plan. Using the annealing algorithm, each pre-set satellite can obtain an optimized result for each piece of space debris. Ultimately, a... The matrix. There are three cases here. Scenario 1:

[0073] At this point, the number of our satellites equals the number of space debris. The matrix is ​​calculated using the original dynamic programming algorithm, which yields the selection order. Assuming The value is This represents our plan numbered as follows: The satellite will be numbered Space debris is cleared by maneuvering its orbit. Using a set of parameters, taking the example of 5 satellites clearing 5 pieces of space debris, the resulting matrix is ​​shown in Table 1. for The total velocity pulse size was 1189.8 m / s, and none of the individual velocity pulses exceeded 300 m / s. The total velocity pulse size was relatively small, resulting in a good cleaning effect.

[0074]

[0075] Scenario 2:

[0076] At this point, the number of our satellites exceeds the amount of space debris, and some of our satellites will not be assigned to any clearing targets. Matrix flipped The matrix is ​​still calculated using the original dynamic programming algorithm. The original algorithm aims to prioritize finding the space debris that our satellites can best clear. After flipping, since the number of columns is greater than the number of rows, and the number of columns represents the number of our satellites, this allows for full utilization of our satellite resources. Taking the example of our 5 satellites clearing 4 pieces of space debris, the resulting matrix is ​​shown in Table 2. for The total velocity pulse size was 958.4 m / s, and none of the individual velocity pulses exceeded 300 m / s. The total velocity pulse size was relatively small, resulting in a good cleaning effect.

[0077]

[0078] Scenario 3:

[0079] At this time, the number of our satellites is less than the number of space debris. The matrix is ​​calculated using the original dynamic programming algorithm, which yields the selection order. Assuming The value is This represents our first step in the planning. The satellite will be numbered Space debris is cleared by maneuvering its orbit. Taking the example of our four satellites clearing five pieces of space debris, the resulting matrix is ​​shown in Table 3. for The total velocity pulse size was 918.8 m / s, with no single velocity pulse exceeding 300 m / s. The overall velocity pulse was relatively small, resulting in good debris removal. However, one piece of space debris remained unremoved. In this situation, an emergency satellite will be launched to remove the space debris not assigned to a satellite. The emergency satellite's set velocity is 400 m / s. Furthermore, if the velocity pulse for pre-assigned satellite debris removal exceeds 400 m / s, an emergency satellite will also be launched to remove the debris. Therefore, the total velocity pulse in this case is 1318.8 m / s.

[0080]

[0081] The effect of the number of satellites on the total velocity pulse size: Taking scenario one as an example, we continuously increase the number of our satellites and plot the curve showing the change in the average velocity pulse magnitude as the number of our satellites increases, as shown below. Figure 4 As shown in the analysis, the average velocity pulse size decreases with the increase of the number of satellites, and drops sharply at some points. This is because adding new satellites at these points significantly impacts the overall dynamic programming path selection, while adding satellites at most points has little impact, thus causing the average velocity pulse to increase. When the number of satellites is small, the average velocity pulse decreases sharply with the increase of the number of satellites. After exceeding 10, a smoother dynamic programming path can be selected, causing the decrease in velocity pulse size to slow down. Therefore, it is recommended to pre-deploy at least 10 satellites, preferably located in different orbital planes, to reduce the average velocity pulse.

[0082] The curve showing the change in the number of emergency satellites required for clearance (i.e., the amount of space debris requiring clearance with velocity pulses exceeding 400 m / s) versus the number of pre-set satellites is as follows: Figure 5 As shown, the number of emergency satellites that need to be launched is generally small, and emergency satellites are mainly used when the number of our satellites is less than the number of space debris.

[0083] The results in Spacesim show: The calculation results are visualized in Spacesim. Taking a scenario where our satellites are fewer than space debris as an example, the Spacesim visualization window is shown below. Our pre-positioned satellites are green, space debris that has not been cleared is represented by white spheres, which turn red after being cleared. Our emergency launch satellite is represented by a blue sphere. Pre-positioned satellites approaching target space debris... Figure 6 As shown, our pre-positioned satellites have approached and overlapped with the target space debris and cleared it. Since the number of our pre-positioned satellites is less than the number of space debris, there are still small white balls in the scene. Our satellites aimed their beams at the small white balls in an attempt to clear them, but failed because the distance was too far.

[0084] After a period of time, emergency satellites are launched to intercept the remaining space debris. These emergency satellites approach the target debris, such as... Figure 7 As shown, the emergency satellite successfully cleared the target debris. Figure 8 As shown, our emergency satellite successfully approached and cleared the target debris. The scenario demonstration was successful, and Spacesim can demonstrate space debris removal scenarios in a relatively intuitive way.

[0085] This implementation study investigates the planning problem of space debris removal missions using a multi-service satellite system based on simulated annealing. Simulation results show that establishing a large-scale pre-positioned constellation can effectively remove space debris swarms. The average velocity pulse size decreases with the number of satellites, and launching emergency satellites can effectively supplement the pre-positioned satellites. Spacesim software has strong analytical and visualization capabilities, supporting simulations of large-scale scenarios.

[0086] The above description of several specific embodiments further details the technical solution provided by the present invention in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, combinations of embodiments, and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calculating trajectory change parameters for air-to-ground hybrid debris removal based on annealing algorithm, characterized in that, include: Steps for a space debris collection and strike mission; The steps for simulating satellites in a constellation include a pre-deployed constellation and an emergency constellation. The satellite deployment method includes pre-deployment of a dedicated constellation and emergency deployment. The emergency constellation is used to supplement the deployment when the number of satellites or fuel in the pre-deployed constellation is insufficient to strike all space debris in the debris cluster. Based on the space debris strike mission and the satellites in the constellation, the steps for obtaining the optimization target of the orbit change algorithm are as follows; The steps for setting the simulated annealing algorithm parameters according to the optimization objective; The step of collecting a randomly generated initial solution as the starting point of the simulated annealing algorithm; The initial solution is iteratively optimized until a combination of orbit change parameters under preset conditions is obtained. For each preset satellite and each space debris, the corresponding velocity pulse magnitude is obtained based on the combination of orbit change parameters to establish a velocity pulse matrix. Under the condition that each pre-positioned satellite removes only one piece of space debris and each piece of space debris is removed by only one pre-positioned satellite, the velocity pulse matrix is ​​processed by a dynamic programming algorithm to construct a two-dimensional array to represent the state and determine the state transition equation. Based on the state transition equation and boundary conditions, the matching result of the satellite individual and the space debris individual that minimizes the sum of the velocity pulse sizes corresponding to the matching items is obtained.

2. The method for calculating orbit change parameters for air-to-ground hybrid debris removal based on annealing algorithm according to claim 1, characterized in that, The orbit-changing algorithm is the Lambert algorithm.

3. The method for calculating orbit change parameters for air-to-ground hybrid debris removal based on annealing algorithm according to claim 1, characterized in that, The optimization objectives include orbit change time, rendezvous time, and number of rendezvous cycles.

4. The method for calculating orbit change parameters for air-to-ground hybrid debris removal based on annealing algorithm according to claim 1, characterized in that, The simulated annealing algorithm parameters include the initial temperature, the termination temperature, the cooling rate, and the number of iterations at each temperature.

5. A device for calculating orbit change parameters for air-to-ground hybrid space debris removal based on annealing algorithm, characterized in that, To implement the method of claim 1, the method comprises: Module for collecting space debris and striking targets; A module for simulating satellites in a constellation; Based on the space debris strike mission and the satellites in the constellation, a module is derived to optimize the target of the orbit change algorithm. A module for setting the parameters of the simulated annealing algorithm based on the optimization objective; The module that collects randomly generated initial solutions as the starting point of the simulated annealing algorithm; The module iteratively optimizes the initial solution until a combination of trajectory change parameters under preset conditions is obtained; This module, based on the principle of clearing only one piece of space debris per satellite, matches individual satellites with individual pieces of space debris according to the aforementioned combination of orbit change parameters.

6. A method for bulk rendezvous and elimination of space debris using a combined air-to-ground approach, characterized in that, include: The steps for collecting the matching results of individual satellites and individual space debris obtained in claim 1; The step of clearing space debris based on the matching results.

7. A combined air-ground space debris batch rendezvous and elimination device, characterized in that, include: A module for collecting the matching results of individual satellites and individual space debris obtained in claim 1; A module for clearing space debris based on the matching results.

8. A computer storage medium for storing computer programs, characterized in that, When the computer program is read by the computer, the computer executes the method of claim 1.

9. A computer, comprising a processor and a storage medium, characterized in that, When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1.

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