Dynamic incremental greedy assignment method for multi-satellite on-orbit services based on resource balancing strategy

Abstract

The present application relates to a method for dynamic incremental greedy assignment of multi-satellite on-orbit services based on a resource balancing strategy. The method comprises: recursively assigning the service satellite and target satellite to the epoch time of the mission start; constructing a circular orbit rendezvous cost model to estimate the rendezvous cost matrix between the service satellite and the target satellite; constructing the objective function of the mission planning problem model based on the overall balanced allocation strategy and the rendezvous cost matrix, and designing the constraints of the mission planning problem model based on the mission start and end times and the information of each service satellite; constructing the mission planning problem model using the objective function and the constraints; designing a dynamic incremental greedy algorithm to solve the mission planning problem model to obtain the assignment result for each service satellite; and designing the access sequence and rendezvous time for each service satellite's assignment result based on the dynamic programming algorithm to obtain the final mission planning plan for each service satellite. This method can improve the efficiency of mission planning while ensuring the accuracy of the solution.

Description

Technical Field

[0001] The present application relates to the technical field of spacecraft mission planning, and in particular to a dynamic incremental greedy assignment method for multi-satellite on-orbit services based on a resource balancing strategy. Background Art

[0002] As the number of space-based control platforms deployed in orbit increases, the on-orbit control mission model for spacecraft will evolve from the traditional "one-to-one" model to a "one-to-many" and even "many-to-many" model. This significantly increases the difficulty of on-orbit control mission planning and necessitates the development of efficient "many-to-many" space-based platform mission planning methods. The space rendezvous mission planning problem primarily involves designing the rendezvous sequence, orbital maneuvering schemes, and allocating time for each subtask. Regarding orbital maneuvers, some researchers have developed a Hohmann-Lambert four-pulse rendezvous model for GEO on-orbit servicing and employed Q-learning to solve satellite servicing sequence planning. Other researchers have compared and analyzed four orbital rendezvous strategies for GEO satellite on-orbit servicing: Hohmann orbit maneuvering, drift maneuvering, radial maneuvering, and Lambert orbit maneuvering. Genetic algorithms are then employed to optimize the many-to-many on-orbit servicing mission planning problem. Furthermore, the multi-spacecraft rendezvous sequence design problem involves multiple variables, multiple constraints, and multiple objective functions. Problems involving both discrete and continuous design variables can be considered mixed integer linear programming (MILP) problems. To address this problem, some researchers grouped target points and combined tree search and ant colony algorithms to derive a space debris removal sequence. Others proposed a greedy search and multi-round planning approach for multi-target patrol mission planning under constraints such as illumination and relative position, similarly achieving rendezvous with the target points by applying in-plane pulse maneuvers. Others used deep Q-learning to solve the on-orbit service resource allocation problem, considering "one-to-many" and "many-to-one" composite service models while balancing the comprehensive benefits of resource allocation and overall energy efficiency.

[0003] However, the current "many-to-many" mission planning method has the following disadvantages: (1) The rendezvous sequence solution and maneuver trajectory optimization in the "many-to-many" mission planning is a nested problem. The double-layer nested optimization structure is a very time-consuming and complex problem. Moreover, as the mission scale increases, the solution time will increase exponentially, making the rapid planning of "many-to-many" on-orbit control missions still challenging. It is difficult to meet the real-time requirements of on-orbit mission planning and the constraints of limited inter-satellite computing resources. (2) The current "many-to-many" mission planning mostly adopts the linear integer programming method for solution. This method can ensure that the global optimal solution is found, but the computational complexity is exponential, and it is usually only applicable to small and medium-sized problems ( If the problem is large, a more efficient solver may be required, such as Gurobi or CPLEX, which has lower solution efficiency. Summary of the Invention

[0004] Based on this, it is necessary to provide a dynamic incremental greedy assignment method for multi-satellite on-orbit services based on a resource balancing strategy, which can ensure the accuracy of the solution while improving the efficiency of mission planning, in order to address the above technical problems.

[0005] A method for dynamic incremental greedy assignment of multi-satellite on-orbit services based on a resource balancing strategy, the method comprising:

[0006] Obtain the mission start and end times, service satellite, and target satellite; recursively extrapolate the service satellite and target satellite to the epoch time of the mission start; construct a circular orbit rendezvous cost model to estimate the rendezvous cost matrix between the service satellite and the target satellite;

[0007] The objective function of the mission planning problem model is constructed based on the overall equilibrium allocation strategy and the rendezvous cost matrix. The constraints of the mission planning problem model are designed according to the mission start and end times and the information of each service satellite. The mission planning problem model is constructed using the objective function and constraints.

[0008] A dynamic incremental greedy algorithm is designed to solve the task planning problem model and obtain the assignment results of each service star. According to the dynamic programming algorithm, the access sequence and rendezvous time are designed for the assignment results of each service star to obtain the final task planning plan for each service star.

[0009] This method for multi-satellite on-orbit servicing dynamic incremental greedy assignment based on a resource-balancing strategy first constructs a circular orbit rendezvous cost model to estimate rendezvous costs. This model provides an accurate cost basis for task allocation, ensuring solution accuracy and simplifying the computational process. An objective function is constructed based on the overall balanced allocation strategy and the rendezvous cost matrix. Constraints are designed based on the mission start and end times and service satellite information to make the task planning problem model realistic and ensure solution accuracy. Reasonable constraints narrow the search space, reduce the computational effort, and improve task planning efficiency. Tasks are then allocated based on overall balance. Finally, visit sequences and rendezvous times are planned for each service satellite. This avoids repeated calls to the inner optimization module, significantly improving planning efficiency without compromising solution accuracy. A dynamic incremental greedy algorithm is designed to formulate allocation rules based on the overall balanced strategy and dynamically update the results. This allows for rapid task allocation while ensuring a certain level of accuracy, significantly improving efficiency compared to traditional linear integer programming methods. Finally, a dynamic programming algorithm is used to solve the visit sequence and rendezvous time of the service satellite, decomposing the complex problem into sub-problems. By storing the solutions to the sub-problems, repeated calculations are avoided, which not only ensures the calculation of accurate action plans, but also improves the efficiency of the algorithm. This application adopts a framework that decouples task allocation and sequence planning, avoiding repeated calls to the inner optimization module. Based on the characteristics of task planning, the maneuvering plan between adjacent target satellites is calculated according to the specified rendezvous strategy at the task level, avoiding the iterative solution of the innermost "one-to-one" maneuvering plan, which is conducive to further improving the efficiency of action plan generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 1. A flowchart of a method for dynamic incremental greedy assignment of multi-satellite on-orbit services based on a resource balancing strategy in one embodiment;

[0011] Figure 2 A schematic diagram of coplanar resection in one embodiment;

[0012] Figure 3 The figure is a flow chart of an overall balanced allocation algorithm in one embodiment. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0014] In one embodiment, Figure 1 As shown, a multi-satellite on-orbit service dynamic incremental greedy assignment method based on a resource balancing strategy is provided, which includes the following steps:

[0015] Step 102: Obtain the mission start and end time, the service satellite, and the target satellite; recursively extrapolate the service satellite and the target satellite to the epoch time when the mission starts; and construct a circular orbit rendezvous cost model to estimate the rendezvous cost matrix between the service satellite and the target satellite.

[0016] This application utilizes a mission planning framework that decouples task allocation from sequence planning, taking into account the characteristics of the "many-to-many" planning problem. Throughout the process, a circular orbit rendezvous cost model is first constructed to estimate the rendezvous cost. Tasks are then allocated based on overall equilibrium (solved using a dynamic incremental greedy algorithm). Finally, the visit sequence and rendezvous time are planned for each service satellite (using a dynamic programming algorithm). This decoupling avoids the double-layer nested structure of rendezvous sequence solution and maneuver trajectory optimization in traditional nested "many-to-many" mission planning frameworks, preventing repeated calls to inner-layer optimization modules and reducing computational complexity and time consumption. This improves mission planning efficiency without compromising solution accuracy. In traditional nested structures, solution time increases exponentially with increasing task size. Decoupling breaks this complex nested relationship, making the computational process simpler and more efficient.

[0017] The transfer cost between each service satellite and the target satellite is estimated based on the circular orbit rendezvous cost, which serves as the fuel estimation basis for the outer mission allocation. According to orbital dynamics, the circular orbit rendezvous cost model is divided into two models: coplanar rendezvous and out-of-plane rendezvous. The coplanar rendezvous model is used as an example to illustrate its detailed scheme. Figure 2 As shown, when the target and the service satellite are in the same circular orbit, and the service satellite is at a certain angle When following the target, in order to rendezvous, the service satellite must enter a lower orbit with a shorter period to eliminate the initial phase difference.

[0018] When the service satellite rotates one circle on the phase adjustment orbit, the phase of the target satellite is an integer multiple of the number of circles minus the initial phase angle , then the phase modulation time is:

[0019] (1)

[0020] Where, is the number of movement circles of the target star, and the initial phase angle With positive and negative signs, is the orbital angular velocity of the target star.

[0021] Since the service satellite needs to run an integer multiple of the phase modulation orbit when completing the rendezvous, its semi-major axis is:

[0022] (2)

[0023] Since the research object is a circular orbit with tangential pulses, the velocity increments applied are different for different orbital radii. During the first maneuver, the service satellite must slow down to enter the phase modulation orbit, and the velocity increment is

[0024] (3)

[0025] Total speed increase is twice the initial velocity pulse:

[0026] (4)

[0027] By constructing a circular orbit rendezvous cost model to estimate the rendezvous cost matrix between the service and target satellites, this model provides a more accurate cost basis for subsequent task allocation, enabling more rational task allocation and ensuring solution accuracy. Furthermore, subsequent calculations and planning based on this model avoid the increased computational effort and decreased accuracy that would result from using overly complex or inaccurate models.

[0028] Step 104: construct an objective function of the mission planning problem model based on the overall balanced allocation strategy and the rendezvous cost matrix, design constraints of the mission planning problem model according to the mission start and end time and the information of each service satellite; and construct the mission planning problem model using the objective function and constraints.

[0029] Based on the overall balanced allocation strategy and the rendezvous cost matrix, the objective function of the mission planning problem model is constructed. According to the mission start and end time and the information of each service satellite, the constraints of the mission planning problem model are designed to make the mission planning problem model more consistent with the actual situation, so that more accurate results can be obtained in the solution process, ensuring the solution accuracy. At the same time, reasonable constraints avoid unnecessary search space, reduce the amount of calculation, and improve the efficiency of mission planning. Among them, the overall balanced allocation strategy is as follows Figure 3 shown.

[0030] Step 106: Design a dynamic incremental greedy algorithm to solve the mission planning problem model to obtain the assignment result of each service star; design the access sequence and rendezvous time for each service star's assignment result according to the dynamic programming algorithm to obtain the final mission planning plan for each service star.

[0031] The rendezvous cost matrix between the service and target satellites is designed, and a dynamic incremental greedy algorithm is devised for the mixed integer programming problem of task allocation. Based on the overall balanced allocation strategy, corresponding "service-target" allocation rules are designed to generate a corresponding task allocation plan. A dynamic update mechanism is used to update the current allocation results for the next round of allocation, ultimately clarifying which target satellites each service satellite needs to control. While maintaining a certain level of solution accuracy, task allocation can be performed quickly. Compared to traditional linear integer programming methods, the computational complexity is exponential, making it only suitable for small and medium-sized problems. This greatly improves the efficiency of task allocation, and thus the efficiency of the entire mission planning.

[0032] A dynamic programming algorithm is used to solve the visit sequence and rendezvous time between different missions for each service satellite. This decomposes the complex problem into simpler subproblems, and the solutions to these subproblems are stored to avoid repeated calculations. The sequence in which a service satellite visits the target satellite is modeled as a time-varying discrete nonlinear system. The aforementioned phase-shifting dynamic calculation method is used to obtain the velocity increment and transfer time for each maneuver. Combined with the weighted coefficients input by the program, the cost of this level is represented as a dynamic function. This dynamic programming algorithm is then used to solve the visit sequence and maneuver time, ultimately forming an action plan for each service satellite. This method accurately calculates each service satellite's action plan, ensuring solution accuracy. Furthermore, avoiding repeated calculations improves the efficiency of the algorithm, enabling rapid results when designing visit sequences and rendezvous times, thereby enhancing mission planning efficiency.

[0033] This method for multi-satellite on-orbit servicing dynamic incremental greedy assignment based on a resource-balancing strategy first constructs a circular orbit rendezvous cost model to estimate rendezvous costs. This model provides an accurate cost basis for task allocation, ensuring solution accuracy and simplifying the computational process. An objective function is constructed based on the overall balanced allocation strategy and the rendezvous cost matrix. Constraints are designed based on the mission start and end times and service satellite information to make the task planning problem model realistic and ensure solution accuracy. Reasonable constraints narrow the search space, reduce the computational effort, and improve task planning efficiency. Tasks are then allocated based on overall balance. Finally, visit sequences and rendezvous times are planned for each service satellite. This avoids repeated calls to the inner optimization module, significantly improving planning efficiency without compromising solution accuracy. A dynamic incremental greedy algorithm is designed to formulate allocation rules based on the overall balanced strategy and dynamically update the results. This allows for rapid task allocation while ensuring a certain level of accuracy, significantly improving efficiency compared to traditional linear integer programming methods. Finally, a dynamic programming algorithm is used to solve the visit sequence and rendezvous time of the service satellite, decomposing the complex problem into sub-problems. By storing the solutions to the sub-problems, repeated calculations are avoided, which not only ensures the calculation of accurate action plans, but also improves the efficiency of the algorithm. This application adopts a framework that decouples task allocation and sequence planning, avoiding repeated calls to the inner optimization module. Based on the characteristics of task planning, the maneuvering plan between adjacent target satellites is calculated according to the specified rendezvous strategy at the task level, avoiding the iterative solution of the innermost "one-to-one" maneuvering plan, which is conducive to further improving the efficiency of action plan generation.

[0034] In one embodiment, the task planning problem model is constructed using the objective function and constraints:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] in, represents the decision variable, represents the intersection cost matrix, Indicates the serial number of the service star, Indicates the target star number, m Indicates the total number of service stars, n represents the total number of target stars, is the weight coefficient, represents the matrix elements of the intersection cost matrix, represents the total velocity increment, Service Star The time of the last rendezvous, Indicates the start and end time of the task.

[0040] In a specific embodiment, a greedy algorithm based on dynamic increments is designed based on the rendezvous cost matrix between the service and target satellites. Furthermore, corresponding "service-to-target" allocation rules are designed for an overall balanced allocation strategy to generate a corresponding task allocation scheme. This overall balanced allocation strategy assigns tasks to service satellites within a given start and end time, ensuring that the service satellites complete as many rendezvous tasks as possible and maintaining a balanced load across the service satellites, i.e., minimizing the mean squared deviation of the remaining fuel on each service satellite.

[0041] existing Service Stars and The target star has known orbital elements and epoch times, and the The total speed increment that can be provided by the service satellite is Each target satellite can only be assigned to one service satellite (it is allowed to be unassigned), and each service satellite can provide services to multiple target satellites in sequence. The requirement is to assign the service satellites' tasks within a given start and end time so that as many rendezvous missions as possible are completed with the minimum total speed increase.

[0042] First, the rendezvous cost matrix between the service satellite and the target satellite is constructed. Assuming that both the service satellite and the target satellite are operating near the geostationary orbit, the circular orbit rendezvous cost estimation method introduced above can be used to obtain the velocity increment cost matrix of the service satellite rendezvous with the target satellite under the current state. and the maneuver time cost matrix , both of which are Dimensional matrix. Cost matrix with speed increment For example, the matrix elements are as follows:

[0043] (5)

[0044] The matrix elements Service Star With target star The velocity increment at which the rendezvous is performed.

[0045] Secondly, the 0-1 integer programming method is used to model the task planning problem, and its decision variable is a Boolean variable. ,in: Indicates the target star Assigned to Service Star ; Indicates no allocation.

[0046] The objective function of the integer programming model includes two aspects: one is to complete as many rendezvous tasks as possible, that is,

[0047] (6)

[0048] Second, the speed increment consumed in completing the task is the smallest, that is,

[0049] (7)

[0050] It should be noted that the above-mentioned intersection costs It may be adjusted dynamically as the task allocation process progresses. Usually, the above two objective functions can be integrated into one objective function in the form of weighted sum, that is:

[0051] (8)

[0052] In the formula is the weight coefficient.

[0053] The constraints of the integer programming model include three: First, each target satellite can only be assigned to one service satellite at most, that is:

[0054] (9)

[0055] Second, the total speed increment of each service satellite is limited, namely:

[0056] (10)

[0057] Third, the final rendezvous time of each service satellite cannot exceed the mission start and end time, that is:

[0058] .

[0059] In one embodiment, a dynamic incremental greedy algorithm is designed to solve the task planning problem model to obtain the assignment result for each service star, including:

[0060] Sort the target stars, calculate the set of assignable service stars for the target stars, define the priority of the target stars, and sort the target stars by priority; design a balance indicator to traverse the sorted service stars, select the appropriate service stars to assign to the target stars in the current round, and update the remaining speed increment of the service stars and the allocation matrix between the service stars and the target stars. , let the corresponding elements , which is convenient for the next round of task assignment to the unassigned target stars;

[0061] At the beginning of the next round of allocation, it is necessary to update the orbits of the available service stars and the target stars to be allocated based on the new starting time and the results of the previous round of allocation, and then calculate a new rendezvous cost matrix for this round of allocation. The start time of this round of allocation is set to the longest rendezvous time in the "service star-target star" combination paired in the previous round. After completing each round of iteration in sequence, the assignment result of each service star is obtained.

[0062] In a specific embodiment, a heuristic method based on a greedy strategy is designed to allocate tasks while satisfying resource constraints, including several stages, as follows:

[0063] i. Phase 1: Target Star Ranking:

[0064] The main task of this stage is to prioritize the allocation of target satellites with low rendezvous costs and few optional service satellites to avoid resource competition that may lead to incompletion. The main steps include:

[0065] 1) Calculate the set of service stars that can be assigned to the target star:

[0066] For each target star , according to the speed incremental cost matrix Filter out Service Star Collection , and calculate their number .

[0067] 2) Define target star priority:

[0068] Mainly consider the number of optional service stars : The fewer the number of optional service stars, the higher the priority (to avoid no service stars to be arranged later), define the first The priority scores of the target stars are:

[0069] (11)

[0070] In the formula In case the denominator is 0.

[0071] 3) Sort target stars by priority:

[0072] according to Sort the target star list in ascending order, prioritizing low-scoring tasks.

[0073] ii. Phase 2: Service Star Allocation:

[0074] The main task of this stage is to select appropriate service stars and assign them to the current target star according to the balance index. The main steps include:

[0075] 1) Traverse the sorted target stars:

[0076] The equilibrium indicator designed in this section is to minimize the difference in remaining resources of all service stars after assignment:

[0077] (12)

[0078] Where, is the variance of the remaining resources of all service stars.

[0079] 2) Perform allocation and update the available resources of the service star:

[0080] The target star Assigned to Service Star , and update the remaining speed increment of the service star:

[0081] (13)

[0082] 3) Mark the assigned target star:

[0083] Update the allocation matrix between the service star and the target star , let the corresponding elements , which facilitates task assignment to unassigned target stars in the next round.

[0084] iii. Dynamic update mechanism:

[0085] Different from the static task allocation problem, in the task allocation problem between service stars and target stars, the rendezvous cost matrix between available service stars and unassigned target stars will change with the implementation of the assigned tasks. The target stars are allocated. Since the greedy strategy set in the above stage 2 is to "select the service star with sufficient remaining resources, the lowest cost, and no target star currently allocated" ", so there must be Service stars have been assigned tasks, and the remaining The target stars need to be allocated in the second decision-making step.

[0086] At the start of the second round of allocation, the orbits of available service satellites (remaining resources) and target satellites to be allocated are updated based on the new starting time and the results of the previous round. This allows the calculation of a new rendezvous cost matrix for the second round of allocation. For simplicity, the start time of the second round of allocation is set to the longest rendezvous time among the service satellite-target satellite pairs paired in the first round. After completing each round of iteration, the assignment results for each service satellite are obtained.

[0087] In one embodiment, calculating the set of allocatable service satellites of the target satellite includes:

[0088] For each target star , according to the speed incremental cost matrix Filter out The set of allocable service stars , and calculate their number ;in, Indicates the total velocity increment.

[0089] In one embodiment, the target star priority is defined as

[0090] ;

[0091] in, , Indicates the target star number, represents the intersection cost matrix, i Indicates the serial number of the service star.

[0092] In one embodiment, the design balance index is

[0093]

[0094] in, is the variance of the remaining resources of all service stars, i Indicates the serial number of the service star, m Indicates the total number of service stars, represents the set of assignable service stars, Indicates the average value of remaining resources of all service stars.

[0095] In one embodiment, a visit sequence and rendezvous time are designed based on the assignment result of each service star according to a dynamic programming algorithm to obtain a final mission planning plan for each service star, including:

[0096] The order in which service satellites visit target satellites is modeled as a discrete nonlinear system that varies with time. The velocity increment and transfer time of the current round of maneuvers are obtained according to the phase adjustment motion calculation method. The cost of this level is represented as a dynamic function combined with the weighted coefficient input by the program. The visit sequence and maneuvering time are solved according to the dynamic programming algorithm to obtain the final mission planning plan for each service satellite.

[0097] In one embodiment, the sequence of a service satellite visiting a target satellite is modeled as a time-varying discrete nonlinear system, including:

[0098] The order of the service satellite visiting the target satellite is modeled as a discrete nonlinear system that changes with time:

[0099] ;

[0100] Among them, define the system time For service star visit target star, system time Status For the visit The number of the target star, time The amount of control Contains the target star number for the next visit And the number of phase adjustment cycles , expressed as .

[0101] In one embodiment, the speed increment and transfer time of the current round of maneuvers are obtained according to the phase adjustment dynamic calculation method, and the cost of the current level is represented as a dynamic function in combination with the weighting coefficient input by the program, including:

[0102] According to the phase adjustment dynamic calculation method, the speed increment and transfer time of the current round of maneuvers are obtained. Combined with the weighted coefficient input by the program, the cost of this level is represented as a dynamic function:

[0103] ;

[0104] in, Indicates the target star number where the current service star is located, determined according to the state quantity. Indicates the number of the next target star to be visited by the service star determined by the control amount. Indicates the number of phase adjustment cycles.

[0105] In a specific embodiment, the access sequence and maneuvering time planning problem can be regarded as a multi-level decision-making process. In the one-to-many access sequence and maneuvering time planning problem of this application, the order in which the service star visits the target star can be regarded as the state change of a discrete nonlinear system over time. Define the system time For service star visit target star, system time Status For the visit The number of the target star. The amount of control Contains the target star number for the next visit And the number of phase adjustment cycles , expressed as Therefore, the system state equation can be expressed as

[0106] .

[0107] Since there is no requirement to visit a specific target star last, the final value term in the cost function Can be 0. The cost of each level It can be determined by the current state and control quantity, specifically: determine the target star number where the current service star is located according to the state quantity , determine the number of the next target star to be visited by the service star according to the control amount And the number of phase adjustment cycles According to the program input, the state of the corresponding numbered target star can be determined. The speed increment and transfer time of the maneuver can be obtained by the phase adjustment maneuver calculation method. Combined with the weighted coefficient of the program input, it can be converted into the cost of this level. The above process is expressed as

[0108] .

[0109] In one embodiment, the performance of the present application is verified by a numerical simulation verification method. Taking the multi-to-multi task allocation of 6 service satellites to 12 target satellites as an example, the access allocation results and access time are planned. It is known that the mission start time is 00:10:04 on March 28, 2025, and the mission end time is 00:10:04 on November 28, 2025. Other specific parameters such as the six orbital numbers of the service satellite and the target satellite at the initial time, the propellant mass, and the platform priority are shown in Tables 1 and 2 below:

[0110] Table 1

[0111]

[0112] Table 2

[0113]

[0114] Assuming that the weight coefficients of the two indicators of minimum fuel consumption and minimum time consumption are 0.8 and 0.2 respectively, the developed many-to-many task allocation program is run, and the balanced allocation strategy based on the two-stage greedy algorithm is adopted. The many-to-many task allocation results are as follows:

[0115] Table 3

[0116]

[0117] Table 3 shows the task allocation results for each service star obtained using the dynamic incremental greedy algorithm based on the overall equilibrium strategy. For example, the access order of service star 1 is target star 9 → target star 8, and the corresponding access times are 7.1342e+04s and 1.6818e+05s after the start time.

[0118] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0119] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A dynamic incremental greedy assignment method for multi-satellite on-orbit services based on resource balancing strategy, characterized by: The method comprises: Obtain the mission start and end times, service satellite, and target satellite; recursively extrapolate the service satellite and target satellite to the epoch time of the mission start; construct a circular orbit rendezvous cost model to estimate the rendezvous cost matrix between the service satellite and the target satellite; Constructing an objective function of a mission planning problem model based on an overall equilibrium allocation strategy and a rendezvous cost matrix, and designing constraints of the mission planning problem model based on the mission start and end times and information about each service satellite; and constructing a mission planning problem model using the objective function and constraints. A dynamic incremental greedy algorithm is designed to solve the mission planning problem model to obtain the assignment result of each service star; a visit sequence and rendezvous time are designed based on the assignment result of each service star according to the dynamic programming algorithm to obtain the final mission planning plan for each service star; A dynamic incremental greedy algorithm is designed to solve the task planning problem model and obtain the assignment results for each service star, including: Sort the target stars, calculate the set of assignable service stars for the target stars, define the priority of the target stars, and sort the target stars by priority; design a balance indicator to traverse the sorted target stars, select the appropriate service stars to assign to the current round of target stars, and update the remaining speed increment of the service stars and the allocation matrix between the service stars and the target stars. , let the corresponding elements , which is convenient for the next round of task assignment to the unassigned target stars; At the beginning of the next round of allocation, the orbits of the available service satellites and the target satellite to be allocated need to be updated based on the new starting time and the results of the previous round of allocation. Then, a new rendezvous cost matrix is calculated for this round of allocation. The start time of this round of allocation is set to the longest rendezvous time among the "service satellite-target satellite" pairs paired in the previous round. After completing each round of iteration, the assignment result of each service satellite is obtained.

2. The method according to claim 1, characterized in that The task planning problem model is constructed using the objective function and constraints: in, represents the decision variable, represents the intersection cost matrix, i Indicates the serial number of the service star, Indicates the target star number, m Indicates the total number of service stars, n represents the total number of target stars, is the weight coefficient, represents the matrix elements of the intersection cost matrix, represents the total velocity increment, Service Star i The time of the last rendezvous, Indicates the task termination time.

3. The method according to claim 1, characterized in that Calculate the set of service stars that can be assigned to the target star, including: For each target star , according to the speed incremental cost matrix Filter out The set of allocable service stars , and calculate their number ;in, represents the total velocity increment, Service Star With target star The velocity increment at which the rendezvous is performed.

4. The method according to claim 3, characterized in that Define the target star priority as: in, , Indicates the target star number, represents the intersection cost matrix, i Indicates the serial number of the service star.

5. The method according to claim 1, wherein The design equilibrium index is: in, is the variance of the remaining resources of all service stars, i Indicates the serial number of the service star, m Indicates the total number of service stars, represents the set of assignable service stars, Represents the average value of the remaining resources of all service stars, Indicates the total velocity increment.

6. The method according to claim 1, wherein Based on the dynamic programming algorithm, the visit sequence and rendezvous time are designed for each service satellite's assignment results, resulting in the final mission planning plan for each service satellite, including: The order in which service satellites visit target satellites is modeled as a discrete nonlinear system that varies with time. The velocity increment and transfer time of the current round of maneuvers are obtained according to the phase adjustment motion calculation method. The cost of this level is represented as a dynamic function combined with the weighted coefficient input by the program. The visit sequence and maneuvering time are solved according to the dynamic programming algorithm to obtain the final mission planning plan for each service satellite.

7. The method according to claim 6, characterized in that The order in which the service satellite visits the target satellite is modeled as a time-varying discrete nonlinear system, including: The state equation of the service satellite visiting the target satellite is modeled as a discrete nonlinear system that changes with time: Among them, define the system time For service star visit target star, system time Status For the visit The number of the target star, time The amount of control Contains the target star number for the next visit And the number of phase adjustment cycles , expressed as .

8. The method according to claim 6, characterized in that The speed increment and transfer time of the current round of maneuvers are obtained according to the phase adjustment dynamic calculation method. Combined with the weighted coefficients input by the program, the cost of this level is represented as a dynamic function, including: According to the phase adjustment dynamic calculation method, the speed increment and transfer time of the current round of maneuvers are obtained. Combined with the weighted coefficient input by the program, the cost of this level is represented as a dynamic function: in, Indicates the target star number where the current service star is located, determined according to the state quantity. Indicates the number of the next target star to be visited by the service star determined by the control amount. Indicates the number of phase adjustment cycles.

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