Large-scale constellation earth observation task planning method and device

Abstract

The invention provides a large-scale constellation earth observation task planning method and device, and belongs to the technical field of satellite task planning. According to the embodiment of the invention, a large-scale constellation earth observation task problem is divided into a multi-satellite-cluster cluster allocation problem and a single-satellite-cluster cluster autonomous task planning problem, and the improved adaptive large neighborhood search algorithm fused with tabu search is adopted to allocate the large-scale constellation earth observation tasks to different satellite clusters. And then, multi-satellite task planning in a satellite cluster is realized by adopting a satellite cluster autonomous task planning algorithm based on an extended contract network mechanism. Therefore, the dimension reduction of the problem scale can be realized, the search space can be greatly reduced, the solving efficiency can be improved, the cooperative capability between satellites in the satellite cluster can be improved, and especially in the scene of satellite damage and interference, the anti-interference capability and cooperative completion efficiency of the task can be improved, and the robustness and reliability of task planning can be ensured.

Description

Technical Field

[0001] This application relates to the technical field of satellite mission planning, and particularly to a method and device for large-scale constellation earth observation mission planning. Background Art

[0002] With the development of satellite technology, the number and observation capabilities of earth observation satellites have been continuously improved. How to efficiently arrange these satellite resources to meet the growing user needs has become an important research topic.

[0003] Currently, when facing the problem of large-scale constellation earth observation mission planning, traditional methods such as exact algorithms and meta-heuristic algorithms are usually used for solving, and centralized planning of tasks is carried out on the ground. However, with the continuous increase in the scale of satellites and tasks, the solution time of the existing technology grows exponentially, resulting in difficulties in solving the large-scale constellation earth observation mission planning problem; at the same time, the centralized planning method cannot be applied to scenarios of satellite damage and interference, resulting in insufficient satellite autonomous cooperation and anti-interference capabilities. Summary of the Invention

[0004] This application provides a method and device for large-scale constellation earth observation mission planning to solve the problems of difficult solution and insufficient satellite anti-interference capabilities existing in the related technology when facing the large-scale constellation earth observation mission planning problem.

[0005] To solve the above problems, this application adopts the following technical solutions:

[0006] In a first aspect, an embodiment of this application provides a method for large-scale constellation earth observation mission planning, and the method includes:

[0007] Based on an improved adaptive large neighborhood search algorithm integrated with tabu search, optimize and solve a preset large-scale constellation earth observation mission planning model to allocate large-scale constellation earth observation tasks to multiple star clusters, and obtain a target star cluster allocation plan; wherein, the target star cluster allocation plan represents that multiple star clusters are allocated with their respective corresponding sets of tasks to be planned;

[0008] For any one of the star clusters, based on a star cluster autonomous task planning algorithm based on an extended contract net mechanism, allocate the tasks in the set of tasks to be planned to the satellites in the star cluster to obtain a star cluster task planning plan for the star cluster;

[0009] Based on the star cluster task planning plans corresponding to multiple star clusters respectively, generate a large-scale constellation earth observation mission planning plan corresponding to the large-scale constellation earth observation mission.

[0010] In an embodiment of this application, the large-scale constellation earth observation mission planning model includes an objective function and a set of constraint conditions; wherein,

[0011] The objective function aims to maximize the total revenue of the observation tasks;

[0012] The set of constraint conditions includes:

[0013] The single-track fixed storage constraint condition is used to ensure that the storage space required for all tasks observed on each track of each satellite must be less than or equal to the fixed storage capacity limit of each track of the satellite;

[0014] The single-track energy constraint condition is used to ensure that the energy consumed by all tasks observed on each track of each satellite must be less than or equal to the energy limit of each track of the satellite;

[0015] The task execution times constraint condition is used to ensure that each task only needs to be successfully observed once on one track to be considered completed;

[0016] The task uniqueness constraint condition is used to ensure that each satellite can only execute one task at the same time;

[0017] The task completion time constraint condition is used to ensure that each task needs to be completed before the preset task completion time;

[0018] The observation start and end time constraint condition is used to ensure that the actual observation start time and actual observation end time of all tasks are within the observable time window of the satellite;

[0019] The satellite attitude adjustment constraint condition is used to ensure that the duration of satellite attitude adjustment and the on / off time of the satellite payload must be less than the time interval between the start time of the successor task of the current task and the completion time of the current task's observation activity.

[0020] In an embodiment of the present application, based on the improved adaptive large neighborhood search algorithm integrated with tabu search, the preset large-scale constellation earth observation task planning model is optimized and solved to allocate the large-scale constellation earth observation tasks to multiple star clusters, obtaining the target star cluster allocation scheme, including:

[0021] Generate the initial solution of the algorithm and use the initial solution as the current solution and the current optimal solution;

[0022] Repeat the iterative solution steps until the algorithm meets the preset iterative termination condition; wherein, the iterative solution steps are: among the preset at least one destruction operator and at least one repair operator, respectively determine the target destruction operator and the target repair operator, and based on the tabu attributes of each task, delete a preset number of scheduled tasks from the current solution through the target destruction operator, and insert a preset number of unscheduled tasks through the target repair operator to generate a new solution; based on the new solution, update the current solution and the current optimal solution;

[0023] When it is determined that the algorithm meets the iterative termination condition, the current optimal solution is used as the target star cluster allocation scheme.

[0024] In an embodiment of the present application, among at least one preset destruction operator and at least one repair operator, determining a target destruction operator and a target repair operator respectively includes:

[0025] Based on the current operator weights respectively corresponding to the at least one destruction operator, determining the target destruction operator among the at least one destruction operator; wherein, the at least one destruction operator includes one or more of a random deletion operator, a lowest benefit deletion operator, a maximum attitude adjustment conflict deletion operator, a maximum imaging opportunity deletion operator, and a maximum time window conflict deletion operator;

[0026] Based on the current operator weights respectively corresponding to the at least one repair operator, determining the target repair operator among the at least one repair operator; wherein, the at least one repair operator includes one or more of a maximum benefit insertion operator, a minimum time window conflict insertion operator, a minimum attitude adjustment conflict insertion operator, and a minimum imaging opportunity insertion operator.

[0027] In an embodiment of the present application, based on the new solution, updating the current solution and the current optimal solution includes:

[0028] When the benefit of the new solution is greater than the benefit of the current solution and greater than the benefit of the current optimal solution, updating the new solution to the current optimal solution and the current solution;

[0029] When the benefit of the new solution is greater than the benefit of the current solution and less than the benefit of the current optimal solution, updating the new solution to the current solution;

[0030] When the benefit of the new solution is less than the benefit of the current solution, determining a target probability based on the benefits of the new solution and the current solution and the current annealing temperature; and based on the target probability, determining whether to accept the new solution as the current solution.

[0031] In an embodiment of the present application, the taboo attribute includes a deletion taboo attribute and an insertion taboo attribute; the method further includes:

[0032] For any task, when the new solution including the task is accepted as the current solution, or when the new solution after deleting the task is not accepted as the current solution, setting the deletion taboo attribute of the task to a first attribute value; the first attribute value is used to indicate that the task is prohibited from being deleted in subsequent preset number of iterative solutions;

[0033] For any task, in the case where the new solution after deleting the task is accepted as the current solution, or, in the case where the new solution including the task is not accepted as the current solution, set the insertion taboo attribute of the task to a second attribute value; the second attribute value is used to indicate that the task is prohibited from being inserted in subsequent iterative solutions for a preset number of times.

[0034] In an embodiment of the present application, the method further includes:

[0035] For any one of the at least one destruction operator and the at least one repair operator, obtain a new solution generated based on the operator;

[0036] In the case where the new solution is updated to the current optimal solution, assign a first score to the operator;

[0037] In the case where the benefit of the new solution is greater than the benefit of the current solution and less than the benefit of the current optimal solution, assign a second score to the operator;

[0038] In the case where the benefit of the new solution is less than the benefit of the current solution and the new solution is accepted as the current solution, assign a third score to the operator;

[0039] In the case where the benefit of the new solution is less than the benefit of the current solution and the new solution is not accepted as the current solution, assign a fourth score to the operator; where the first score, the second score, the third score, and the fourth score decrease in sequence;

[0040] Every time after a preset number of iterative solutions, determine the total score of the operator in the preset number of iterative solutions, and update the current operator weight of the operator based on the total score and the historical operator weight of the operator.

[0041] In an embodiment of the present application, for any one of the star clusters, based on the star cluster autonomous task planning algorithm of the extended contract net mechanism, allocate the tasks in the task set to be planned to the satellites in the star cluster to obtain the star cluster task planning scheme of the star cluster, including:

[0042] For any one of the star clusters, determine the main star and the slave stars of the star cluster;

[0043] The main star publishes the tasks in the task set to be planned to the slave stars for bidding;

[0044] Each slave star generates a bidding scheme and submits the bidding scheme to the main star;

[0045] The main star determines the task planning scheme corresponding to the star cluster based on the bidding schemes of each slave star.

[0046] In one embodiment of the present application, generating a bidding plan by each of the slave satellites includes:

[0047] For any one of the slave satellites, perform a descending order sorting based on the priority of the tasks issued by the master satellite to obtain an observation task set;

[0048] In the order from high to low of the priority, perform task planning on the tasks in the observation task set in sequence; the task planning includes: when it is determined that there is at least one observable time window in the current observable time window set of the slave satellite that meets the task requirements of the task, determine the earliest observable time window as the target window of the task; plan the task into the task planning set, and update the current observable time window based on the target window;

[0049] When the task planning of the last task in the observation task set is completed, obtain the bidding plan based on the task planning set.

[0050] In a second aspect, based on the same inventive concept, an embodiment of the present application provides a large-scale constellation earth observation task planning device, and the device includes:

[0051] An upper-layer task allocation module, configured to optimize and solve a preset large-scale constellation earth observation task planning model based on an improved adaptive large neighborhood search algorithm integrated with taboo search, so as to allocate large-scale constellation earth observation tasks to multiple star clusters to obtain a target star cluster allocation plan; wherein, the target star cluster allocation plan represents that multiple star clusters are allocated with their respective corresponding task sets to be planned;

[0052] A lower-layer task allocation module, configured to, for any one of the star clusters, based on a star cluster autonomous task planning algorithm based on an extended contract net mechanism, allocate the tasks in the task set to be planned to the satellites in the star cluster to obtain a star cluster task planning plan for the star cluster;

[0053] A planning plan generation module, configured to generate a large-scale constellation earth observation task planning plan corresponding to the large-scale constellation earth observation task based on the star cluster task planning plans corresponding to multiple star clusters.

[0054] In one embodiment of the present application, the large-scale constellation earth observation task planning model includes an objective function and a set of constraint conditions; wherein,

[0055] The objective function aims to maximize the total revenue of the observation tasks;

[0056] The set of constraint conditions includes:

[0057] The single - track fixed - storage constraint condition is used to ensure that the storage space required for all tasks of each orbit observation of each satellite must be less than or equal to the fixed - storage capacity limit of each orbit of the satellite;

[0058] The single - track energy constraint condition is used to ensure that the energy consumed by all tasks of each orbit observation of each satellite must be less than or equal to the energy limit of each orbit of the satellite;

[0059] The task execution - times constraint condition is used to ensure that each task is considered completed as long as it is successfully observed on one orbit once;

[0060] The task uniqueness constraint condition is used to ensure that each satellite can only execute one task at the same time;

[0061] The task completion - time constraint condition is used to ensure that each task needs to be completed before the preset task completion time;

[0062] The observation start - and - end - time constraint condition is used to ensure that the actual observation start time and the actual observation end time of all tasks are within the observable time window of the satellite;

[0063] The satellite attitude - adjustment constraint condition is used to ensure that the duration for satellite attitude adjustment and the on - off duration of the satellite payload must be less than the time interval between the start time of the successor task of the current task and the completion time of the current task's observation activity.

[0064] In an embodiment of the present application, the upper - layer task - allocation module includes:

[0065] The initial - solution generation sub - module is used to generate the initial solution of the algorithm and use the initial solution as the current solution and the current optimal solution;

[0066] The iterative - solution sub - module is used to repeat the iterative - solution steps until the algorithm meets the preset iterative cut - off condition; wherein, the iterative - solution steps are: among the preset at least one destruction operator and at least one repair operator, respectively determine the target destruction operator and the target repair operator, and based on the tabu attributes of each task, use the target destruction operator to delete a preset number of scheduled tasks from the current solution, and use the target repair operator to insert a preset number of unscheduled tasks to generate a new solution; based on the new solution, update the current solution and the current optimal solution;

[0067] The star - cluster allocation - scheme determination sub - module is used to, when it is determined that the algorithm meets the iterative cut - off condition, use the current optimal solution as the target star - cluster allocation scheme.

[0068] In an embodiment of the present application, the iterative - solution sub - module includes:

[0069] A disruption operator determination unit, configured to determine the target disruption operator from the at least one disruption operator based on the current operator weights respectively corresponding to the at least one disruption operator; wherein, the at least one disruption operator includes one or more of a random deletion operator, a lowest benefit deletion operator, a maximum attitude adjustment conflict deletion operator, a maximum imaging opportunity deletion operator, and a maximum time window conflict deletion operator;

[0070] A repair operator determination unit, configured to determine the target repair operator from the at least one repair operator based on the current operator weights respectively corresponding to the at least one repair operator; wherein, the at least one repair operator includes one or more of a maximum benefit insertion operator, a minimum time window conflict insertion operator, a minimum attitude adjustment conflict insertion operator, and a minimum imaging opportunity insertion operator.

[0071] In an embodiment of the present application, the iterative solution sub-module includes:

[0072] A first update unit, configured to update the new solution to the current optimal solution and the current solution when the benefit of the new solution is greater than the benefit of the current solution and greater than the benefit of the current optimal solution;

[0073] A second update unit, configured to update the new solution to the current solution when the benefit of the new solution is greater than the benefit of the current solution and less than the benefit of the current optimal solution;

[0074] A third update unit, configured to determine a target probability based on the benefits of the new solution and the current solution and the current annealing temperature when the benefit of the new solution is less than the benefit of the current solution; and determine whether to accept the new solution as the current solution based on the target probability.

[0075] In an embodiment of the present application, the taboo attribute includes a deletion taboo attribute and an insertion taboo attribute; the large-scale constellation earth observation mission planning device further includes:

[0076] A deletion taboo attribute determination module, configured to set the deletion taboo attribute of any task to a first attribute value when a new solution including the task is accepted as the current solution, or when a new solution after deleting the task is not accepted as the current solution; the first attribute value is used to indicate that the task is prohibited from being deleted in subsequent preset iterations of the solution;

[0077] An insertion taboo attribute determination module is configured to, for any task, when the new solution after deleting the task is accepted as the current solution, or when the new solution including the task is not accepted as the current solution, set the insertion taboo attribute of the task to a second attribute value; the second attribute value is used to indicate that the task is prohibited from being inserted in the subsequent preset number of iterative solutions.

[0078] In an embodiment of the present application, the large-scale constellation earth observation mission planning device further includes:

[0079] A new solution acquisition module is configured to, for any one of the at least one destruction operator and the at least one repair operator, acquire a new solution generated based on the operator.

[0080] A first scoring module is configured to assign a first score to the operator when the new solution is updated to the current optimal solution.

[0081] A second scoring module is configured to assign a second score to the operator when the benefit of the new solution is greater than the benefit of the current solution and less than the benefit of the current optimal solution.

[0082] A third scoring module is configured to assign a third score to the operator when the benefit of the new solution is less than the benefit of the current solution and the new solution is accepted as the current solution.

[0083] A fourth scoring module is configured to assign a fourth score to the operator when the benefit of the new solution is less than the benefit of the current solution and the new solution is not accepted as the current solution; wherein, the first score, the second score, the third score, and the fourth score decrease in sequence.

[0084] An operator weight update module is configured to, every preset number of iterative solutions, determine the total score of the operator in the preset number of iterative solutions, and update the current operator weight of the operator based on the total score and the historical operator weight of the operator.

[0085] In an embodiment of the present application, the lower-layer task allocation module includes:

[0086] A satellite type determination sub-module is configured to, for any one of the star clusters, determine the main star and the slave star of the star cluster.

[0087] A tendering sub-module is configured to publish the tasks in the task set to be planned to the slave stars through the main star for tendering.

[0088] A bidding sub-module is configured to generate a bidding plan through each slave star and submit the bidding plan to the main star.

[0089] A solution determination sub-module, configured to determine a task planning solution corresponding to the satellite cluster based on the bidding solutions of each slave satellite by the master satellite.

[0090] In an embodiment of the present application, the tendering sub-module includes:

[0091] A sorting unit, configured to perform a descending order sorting on any slave satellite based on the priority of the tasks issued by the master satellite to obtain an observation task set;

[0092] A task planning unit, configured to sequentially perform task planning on the tasks in the observation task set in the order from high to low of the priority; the task planning includes: when there is at least one observable time window that meets the task requirements in the currently observable time window set of the slave satellite, determining the earliest observable time window as the target window of the task; planning the task into a task planning set, and updating the currently observable time window based on the target window;

[0093] A bidding solution generation unit, configured to obtain the bidding solution based on the task planning set when the task planning of the last task in the observation task set is completed.

[0094] Compared with the prior art, the present application has the following advantages:

[0095] A method for large-scale constellation earth observation task planning provided by an embodiment of the present application first optimally solves a preset large-scale constellation earth observation task planning model based on an improved adaptive large neighborhood search algorithm integrated with tabu search, so as to allocate large-scale constellation earth observation tasks to multiple satellite clusters to obtain a target satellite cluster allocation solution; then for any satellite cluster, based on a satellite cluster autonomous task planning algorithm based on an extended contract net mechanism, allocates the tasks in the task set to be planned to the satellites in the satellite cluster to obtain a satellite cluster task planning solution of the satellite cluster; finally, based on the satellite cluster task planning solutions corresponding to multiple satellite clusters respectively, generates a large-scale constellation earth observation task planning solution corresponding to the large-scale constellation earth observation task. In this way, on the one hand, by splitting the large-scale constellation earth observation task problem into a multi-satellite cluster cluster allocation problem and a single-satellite cluster cluster autonomous task planning problem, the problem scale can be reduced in dimension, thereby greatly reducing the search space and improving the solution efficiency; on the other hand, by allocating a large number of tasks to different satellite cluster clusters and performing collaborative autonomous task planning within the satellite cluster, the collaborative ability between the satellites within the satellite cluster can be increased. Especially in the scenario of satellite damage and interference, the anti-interference ability of the tasks and the collaborative completion efficiency can be improved, ensuring the robustness and reliability of the task planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0097] Figure 1 It is a flowchart of the steps of a method for planning large-scale constellation earth observation tasks in an embodiment of the present application.

[0098] Figure 2 It is a framework diagram showing the planning and solution of large-scale constellation resource earth observation tasks in an embodiment of the present application.

[0099] Figure 3 It is a schematic diagram of the working principle of the minimum benefit deletion operator in an embodiment of the present application.

[0100] Figure 4 It is a schematic diagram of the working principle of the maximum imaging opportunity deletion operator in an embodiment of the present application.

[0101] Figure 5 It is a schematic diagram of the working principle of the maximum time window conflict deletion operator in an embodiment of the present application.

[0102] Figure 6 It is a schematic diagram of task planning based on the contract net mechanism in an embodiment of the present application.

[0103] Figure 7 It is a module diagram of a device for planning large-scale constellation earth observation tasks in an embodiment of the present application. Detailed implementation manners

[0104] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0105] It should be noted that with the development of satellite technology, the number and observation capabilities of earth observation satellites have been continuously improved. How to efficiently arrange these satellite resources to meet the growing user needs has become an important research topic. Regarding the problem of planning large-scale constellation earth observation tasks, a large number of scholars have conducted research. The current existing solution algorithms are mainly as follows:

[0106] (1) Exact algorithms: Exact algorithms are a class of algorithms that find the exact solutions to problems, such as branch and bound algorithms, dynamic programming algorithms, etc. They are mainly used to solve problems with a small number of satellites and a small mission scale.

[0107] (2) Meta-heuristic algorithms: Meta-heuristic algorithms are based on optimization mechanisms in natural and social phenomena, as well as the combination of random algorithms and local search algorithms. They mainly include genetic algorithms, simulated annealing algorithms, large neighborhood search algorithms, and particle swarm algorithms.

[0108] (3) Heuristic algorithms: Heuristic algorithms mainly solve problems based on certain rules, such as tabu search and greedy search.

[0109] For the problem of large-scale constellation earth observation mission planning, the above existing technologies usually have the following disadvantages:

[0110] (1) It is difficult to solve the problem of large-scale constellation earth observation mission planning. With the continuous increase in the number of satellites and the mission scale, it leads to a high complexity in solving large-scale constellation mission planning under a large amount of demand. The solution time of existing technologies shows an exponential growth, and it is impossible to achieve a solution in a short time, which does not meet the requirements of lightweight deployment. At the same time, the non-linearity of the constraint conditions and the objective function, as well as the continuous expansion of the problem scale, make the constraint conditions and the objective function increasingly complex, resulting in the inability of existing technologies to apply to the problem of large-scale constellation earth observation mission planning.

[0111] (2) The satellite autonomous cooperation and anti-interference capabilities are insufficient under the centralized planning method. The existing on-board autonomous mission planning methods usually perform task allocation on the ground and then upload the tasks to a single satellite for autonomous mission planning. This solution method cannot apply to scenarios of satellite damage and interference. When satellites are damaged and interfered, tasks cannot be effectively coordinated to complete, resulting in poor robustness and reliability of mission planning.

[0112] Aiming at the problems of difficult solution and insufficient satellite anti-interference ability in the related technologies when facing the problem of large-scale constellation earth observation mission planning, this application aims to provide a large-scale constellation earth observation mission planning method. On the one hand, by splitting the large-scale constellation earth observation mission problem into multi-satellite cluster allocation problems and single-satellite cluster autonomous mission planning problems, the problem scale can be reduced in dimension, thereby greatly reducing the search space and improving the solution efficiency. On the other hand, by allocating a large number of tasks to different satellite cluster groups and conducting collaborative autonomous mission planning within the satellite clusters, the cooperation ability between satellites within the satellite clusters can be increased. Especially in the face of scenarios of satellite damage and interference, the anti-interference ability and collaborative completion efficiency of tasks can be improved, ensuring the robustness and reliability of mission planning.

[0113] Refer to Figure 1, which shows a method for planning large-scale constellation earth observation tasks in this application. This method may include the following steps:

[0114] S101: Based on an improved adaptive large neighborhood search algorithm integrated with tabu search, optimize and solve the preset large-scale constellation earth observation task planning model to allocate large-scale constellation earth observation tasks to multiple satellite clusters, and obtain a target satellite cluster allocation plan.

[0115] In this embodiment, a large-scale constellation earth observation task planning model will be established first based on agile satellite mission scheduling analysis and constraint conditions.

[0116] It should be noted that in the planning of multi-agile satellite earth observation tasks, the scenarios of agile satellites in actual observation activities are very complex, and some constraint conditions and scenarios cannot be modeled. Therefore, in order to simplify the scenarios and the difficulty of solution, the large-scale constellation earth observation task planning model can be constructed based on the following assumptions:

[0117] (1) Assume that all tasks are point targets, that is, a task can be completely covered by a satellite and can be observed within a visible time window.

[0118] (2) Assume that all tasks are observed once and no repeated observations are required.

[0119] (3) Assume that all tasks meet the requirements of optical imaging, and factors such as cloud cover, equipment failure, and electromagnetic interference are not considered.

[0120] (4) Ignore the equipment and operation differences between different satellites, and assume that all satellites have the same storage space and total power.

[0121] (5) Assume that the data transmission process is not considered in the observation task scheduling.

[0122] In this embodiment, for the convenience of subsequent model description and establishment, the variables appearing in the model are defined and symbol explanations are provided, as shown in Table 1 below.

[0123] Table 1 Symbol Explanation

[0124]

[0125]

[0126] In this embodiment, the large-scale constellation earth observation task planning model includes an objective function with the goal of maximizing the total benefit of observation tasks. Specifically, the objective function is as shown in formula (1):

[0127]

[0128] Among them, pi is the revenue value for task i; T represents the set of imaging tasks; S represents the set of satellites; O g represents the set of orbital resources of the satellites; represents a binary decision variable. When , task i is observed on the k-th orbit of the satellite. If , then task i is not observed on the k-th orbit of the satellite.

[0129] In this embodiment, the large-scale constellation earth observation task planning model further includes a set of constraint conditions; among them, the set of constraint conditions includes:

[0130] (1) Single-orbit fixed storage constraint condition.

[0131] The single-orbit fixed storage constraint condition is used to constrain that the storage space required for all tasks observed on each orbit of each satellite must be less than or equal to the fixed storage capacity limit of each orbit of the satellite. Specifically, the single-orbit fixed storage constraint condition is shown in formula (2):

[0132]

[0133] It should be noted that the storage space required for all tasks observed on each orbit of each satellite must be less than or equal to the fixed storage capacity limit of each orbit of the satellite. If, when performing the observation activity, the sum of the storage spaces occupied by the tasks already observed on this orbit has approached the fixed storage critical value of this orbit, then all the remaining tasks that have been assigned to this orbit cannot be observed.

[0134] (2) Single-orbit energy constraint condition.

[0135] The single-orbit energy constraint condition is used to constrain that the energy consumed by all tasks observed on each orbit of each satellite must be less than or equal to the energy limit of each orbit of the satellite. Specifically, the single-orbit energy constraint condition is shown in formula (3):

[0136]

[0137] It should be noted that the energy consumed by all tasks observed on each orbit of each satellite must be less than or equal to the energy limit of each orbit of the satellite. If, when performing the observation activity, the sum of the energy consumed by the tasks already observed on this orbit has approached the energy critical value of this orbit, then all the remaining tasks that have been assigned to this orbit cannot be observed.

[0138] (3) Task execution times constraint condition. The task execution times constraint condition is used to constrain that each task only needs to be successfully observed once on one orbit to be considered as completed. Specifically, the task execution times constraint condition is shown in formula (4):

[0139]

[0140] It should be noted that for each task, once it is successfully observed on one track, it is considered as the completion of the observation, and repeated observations are not required.

[0141] (4) Task uniqueness constraint condition.

[0142] The task uniqueness constraint condition is used to ensure that each satellite can only execute one task at the same time. Specifically, the task uniqueness constraint condition is shown in formula (5):

[0143]

[0144] It should be noted that formula (5) ensures that each task except the first and the last tasks has exactly one preceding task and exactly one succeeding task.

[0145] (5) Task completion time constraint condition.

[0146] The task completion time constraint condition is used to ensure that each task needs to be completed before the preset task completion time. Specifically, the task completion time constraint condition is shown in formula (6):

[0147]

[0148] It should be noted that when the user has specific time limit requirements for some tasks, the tasks need to be completed before the time limit required by the tasks. Therefore, the time window later than the time window for the required completion time is invalid.

[0149] (6) Observation start and end time constraint condition.

[0150] The observation start and end time constraint condition is used to ensure that the actual observation start time and the actual observation end time of all tasks are within the observable time window of the satellite. Specifically, the observation start and end time constraint condition is shown in formula (7):

[0151]

[0152] It should be noted that the actual observation start time and the actual observation end time of all tasks should be within the observable time window of the satellite for this target, and no observation activities can be carried out outside the observable time window.

[0153] (7) Satellite attitude adjustment constraint condition.

[0154] The satellite attitude adjustment constraint is used to constrain that the duration for the satellite to adjust its attitude and the on / off duration of the satellite payload must be less than the time interval between the start time of the successor task of the current task and the completion time of the observation activity of the current task. Specifically, the satellite attitude adjustment constraint is shown in formula (8):

[0155]

[0156] It should be noted that different from traditional earth observation satellites, agile satellites can complete observation activities in a maneuvering state, have the ability of "non-steady imaging", and do not require attitude stabilization time. When calculating the attitude adjustment amplitude of agile satellites, the degree of freedom with the longest maneuvering time for pitch and yaw is taken as the attitude adjustment time. The successor task of the current task refers to the next task closest to the current task.

[0157] It should be noted that the parameter meanings of formulas (2)-(8) refer to Table 1 and will not be elaborated here.

[0158] In this embodiment, after constructing the large-scale constellation earth observation task planning model, based on the objective function and the set of constraint conditions, the improved adaptive large neighborhood search algorithm integrating tabu search is used to optimize and solve the large-scale constellation earth observation task planning model, so as to allocate the large-scale constellation earth observation tasks to multiple satellite clusters and obtain the target satellite cluster allocation scheme. The target satellite cluster allocation scheme represents that multiple satellite clusters are allocated with their respective corresponding sets of tasks to be planned.

[0159] In this embodiment, a satellite cluster represents a set of n satellites with similar spatial distances in the same orbital plane. All satellites within the same satellite cluster share the same task information, where n is a positive integer greater than 1. It should be noted that the number of satellite clusters can be set according to actual needs. For example, a group of satellites in the same orbital plane can be combined into a satellite cluster.

[0160] It should be noted that the Adaptive Large Neighborhood Search (ALNS) can effectively jump out of the local optimal solution, explore a larger range of the solution space, and the adaptive operator update can also adjust the search strategy according to the current search process. However, the traditional adaptive large neighborhood search algorithm may also repeatedly search for recent solutions, resulting in a decrease in search efficiency. By integrating the Tabu Search (TS) algorithm, it can well avoid the algorithm from repeatedly searching for recent solutions and greatly improve the search efficiency. Therefore, in this embodiment, a tabu search strategy is added to the adaptive large neighborhood algorithm, and then an improved adaptive large neighborhood search algorithm integrating tabu search is proposed to realize the allocation of large-scale constellation observation tasks among satellite clusters, which can effectively improve the search efficiency and search quality of the solution.

[0161] S102: For any satellite cluster, based on the satellite cluster autonomous task planning algorithm of the extended contract net mechanism, allocate the tasks in the task set to be planned to the satellites in the satellite cluster to obtain the satellite cluster task planning scheme of the satellite cluster.

[0162] It should be noted that the contract net mechanism is a protocol for task allocation and resource management, mainly used for task allocation and production scheduling in distributed systems. It enables the entire system to complete task allocation at a relatively low cost and high quality through the method of "tendering - bidding - winning the bid".

[0163] In this embodiment, aiming at the problem that the traditional method cannot be applied to the scenarios of satellite damage and interference, this embodiment proposes a satellite cluster autonomous task planning algorithm based on the extended contract net mechanism, which can allocate a large number of tasks to different satellite cluster groups, and perform multi - satellite distributed collaborative autonomous task planning within the satellite cluster. This method can share the satellite resources within the same satellite cluster group. When some satellites within the same satellite cluster group cannot work properly, other satellites can cooperate to complete the task planning. Compared with the traditional method of performing task allocation on the ground and then uploading the tasks to a single satellite for autonomous task planning, this embodiment can fully adapt to the scenarios of satellite damage and interference. When satellites are damaged and interfered, the tasks can be completed by other satellites in cooperation, thereby ensuring the robustness and reliability of task planning.

[0164] S103: Based on the satellite cluster task planning schemes corresponding to multiple satellite clusters, generate the large - scale constellation earth observation task planning scheme corresponding to the large - scale constellation earth observation task.

[0165] In this embodiment, referring to Figure 2 , a planning solution framework diagram for the large - scale constellation resource earth observation task is shown. This framework is divided into an upper layer (task allocation layer) and a lower layer (satellite cluster multi - satellite autonomous task planning layer). The upper layer is a task allocation problem, and the lower layer is a multi - satellite task planning problem within the satellite cluster. The upper layer uses an improved adaptive large - neighborhood search algorithm based on the fusion tabu search to implement task allocation to different satellite clusters, splitting the large - scale constellation earth observation task planning problem into single - satellite - cluster earth observation task planning sub - problems; for the single - satellite - cluster earth observation task planning sub - problems, the lower layer uses a satellite cluster autonomous task planning algorithm based on the extended contract net mechanism to implement multi - satellite task planning within the satellite cluster. Each satellite cluster is equivalent to a sub - problem, and the satellite cluster task planning scheme of each satellite cluster is the solution to the corresponding sub - problem. By combining the solutions of all sub - problems, the solution to the original problem, that is, the large - scale constellation earth observation task planning scheme, can be obtained.

[0166] A method for large-scale constellation earth observation mission planning provided by an embodiment of the present application. On the one hand, by splitting the large-scale constellation earth observation mission problem into a multi-star cluster cluster allocation problem and a single-star cluster cluster autonomous mission planning problem, the dimensionality reduction of the problem scale can be achieved, thereby greatly reducing the search space and improving the solution efficiency. On the other hand, by allocating a large number of tasks to different star cluster clusters and performing collaborative autonomous mission planning within the star cluster, the collaborative ability between satellites within the star cluster can be increased. Especially in the scenario of satellite damage and interference, the anti-interference ability of the mission and the collaborative completion efficiency can be improved, ensuring the robustness and reliability of the mission planning.

[0167] In a feasible implementation manner, S101 may specifically include the following sub-steps:

[0168] S101-1: Generate the initial solution of the algorithm, and use the initial solution as the current solution and the current optimal solution.

[0169] In this implementation manner, all algorithm parameters of the algorithm can be initialized first, and then based on the initialized algorithm parameters, the initial solution of the algorithm is generated. Among them, the algorithm represents an improved adaptive large neighborhood search algorithm based on the fusion of tabu search; the initial solution represents the initial allocation scheme.

[0170] In this implementation manner, considering that in the search process, accepting the optimal solution unconditionally is prone to falling into the local optimum. To jump out of the local optimum, during the algorithm search process, inferior solutions will be accepted with a certain probability. Therefore, the simulated annealing algorithm can be combined for solution, so that in the process of cooling annealing and neighborhood search, both excellent solutions can be accepted, and inferior solutions can also be accepted with a certain probability according to the Metropolis criterion (a criterion for judging whether a new state is accepted). At the same time, as the temperature drops, the acceptance probability of inferior solutions also decreases.

[0171] Specifically, the algorithm parameters may specifically include the initial temperature T0, the lowest temperature T min , the temperature cooling rate τ, the initial weight ρ of the operator, the fraction w, and the maximum number of iterations Iter max .

[0172] In this implementation manner, considering that the acceptance probability of inferior solutions is related to the setting of the initial temperature. If the initial temperature is set too high, the acceptance probability of inferior solutions will be too low to jump out of the local optimum; if the initial temperature is set too low, the acceptance probability of inferior solutions will be too high and the quality of the solution will be poor. Therefore, the initial temperature T init can be calculated according to the revenue value of the initial allocation scheme, and the calculation formula is as follows:

[0173]

[0174] Among them, f(s init) represents the total revenue value of the initial allocation scheme, and the total revenue value can be calculated according to Equation (1); T init is the initial temperature.

[0175] In this embodiment, after obtaining the initial solution, the initial solution can be used as the current solution and the current optimal solution. Among them, the current solution and the current optimal solution will be continuously updated during the subsequent iterative solution process.

[0176] S101-2: Repeat the iterative solution step until the algorithm meets the preset iterative termination condition.

[0177] In this embodiment, the iterative solution step is as follows: Among the preset at least one destruction operator and at least one repair operator, determine the target destruction operator and the target repair operator respectively, and based on the tabu attributes of each task, delete a preset number of scheduled tasks from the current solution through the target destruction operator, and insert a preset number of unscheduled tasks through the target repair operator to generate a new solution; Based on the new solution, update the current solution and the current optimal solution.

[0178] It should be noted that the adaptive large neighborhood search algorithm realizes the reconstruction of the solution by dynamically selecting the destruction operator and the repair operator, and the performance of the operator directly determines the performance of the adaptive large neighborhood search algorithm.

[0179] In this embodiment, to ensure the effect of the adaptive large neighborhood search algorithm, multiple destruction operators and multiple repair operators with different functions are provided. After obtaining the destroyed solution by executing the selected target repair operator, the target repair operator can repair the destroyed solution into a feasible solution, and thus a new solution can be generated in each round of iterative solution. By updating the current solution and the current optimal solution with the new solution, when the algorithm meets the preset iterative termination condition, the current optimal solution that maximizes the total revenue of the observed tasks can be output.

[0180] In this embodiment, based on the new solution, updating the current solution and the current optimal solution specifically includes: when the revenue of the new solution s n is greater than the revenue of the current solution s c and greater than the revenue of the current optimal solution s b , update the new solution s n to the current optimal solution s b and the current solution s c ; when the revenue of the new solution s n is greater than the revenue of the current solution s c and less than the revenue of the current optimal solution s b , update the new solution s n to the current solution s c ; when the revenue of the new solution s n is less than the revenue of the current solution s cIn the case of the benefits based on the new solution s c and the current solution s c 's benefits and the current annealing temperature, determine the target probability; and based on the target probability, determine whether to accept the new solution as the current solution.

[0181] In a specific implementation, if the new solution s n is not as good as the current solution s c , then the Metropolis acceptance criterion can be used to determine whether to accept the new solution. The working principle of the Metropolis acceptance criterion is as follows:

[0182] The Metropolis acceptance criterion is used to determine whether to accept the neighborhood solution (i.e., the new solution) as the current solution. In the search process, accepting the new solution unconditionally is likely to fall into a local optimum. To jump out of the local optimum, in the algorithm search process, accept the inferior solution with a certain probability. However, as the search time increases and the search scope expands, the probability of accepting the inferior solution should gradually decrease. Since the simulated annealing algorithm can accept both the superior solution and, according to the Metropolis criterion, accept the inferior solution with the target probability during the process of cooling annealing and neighborhood search, and at the same time, as the temperature drops, the acceptance probability of the inferior solution also decreases. The calculation formula of the target probability is as follows:

[0183]

[0184] where ρ(s n ) represents the target probability of the current solution s c ; f(s c ) and f(s n ) are the task scheduling benefit values of the current solution s c and the new solution s n respectively; T is the current annealing temperature.

[0185] S101-3: When it is determined that the algorithm meets the iteration cut-off condition, take the current optimal solution as the target star cluster allocation scheme.

[0186] In this embodiment, the iteration cut-off condition can be either the first iteration cut-off condition or the second iteration cut-off condition. Among them, the first iteration cut-off condition is: the number of iterative solutions of the algorithm reaches the maximum number of iterations Iter max ; the second iteration cut-off condition is: the temperature of the algorithm is annealed to the lowest temperature T min .

[0187] In this embodiment, based on the principle of simplicity and intuitiveness, the annealing process can adopt the linear annealing method, and the temperature update is as shown in the formula:

[0188] T n+1 =τT n (11);

[0189] where τ represents the temperature cooling rate; T n represents the annealing temperature of the current round; T n+1 represents the annealing temperature of the next round of the current round.

[0190] In this embodiment, if the number of iterative solutions of the algorithm does not reach the maximum number of iterations Iter max , or the annealing temperature of the algorithm does not reach the lowest temperature T min , then repeat the iterative solution step; otherwise, output the current optimal solution as the target star cluster allocation scheme.

[0191] In a feasible embodiment, in S101-2, the steps of respectively determining the target destruction operator and the target repair operator among at least one preset destruction operator and at least one repair operator may specifically include the following sub-steps:

[0192] S101-2-1: Based on the current operator weights respectively corresponding to at least one destruction operator, determine the target destruction operator among at least one destruction operator.

[0193] In this embodiment, at least one destruction operator includes one or more of a random deletion operator, a lowest revenue deletion operator, a maximum attitude adjustment conflict deletion operator, a maximum imaging opportunity deletion operator, and a maximum time window conflict deletion operator.

[0194] In this embodiment, to ensure the quality of the solution, the above 5 destruction operators can be configured simultaneously.

[0195] Random deletion operator: Randomly delete n scheduled tasks on the orbit and put the deleted tasks into the unscheduled task set. If the number of scheduled tasks on the orbit is less than n, then delete all scheduled tasks on the orbit. The random deletion operator introduces randomness into the search process and reduces the risk of falling into local optimality.

[0196] Lowest revenue deletion operator: Prioritize deleting the n scheduled tasks with the lowest revenue on the orbit and put the deleted tasks into the unscheduled task set. The lowest revenue deletion operator selects to delete the tasks that contribute less, that is, have lower revenue, to the current scheduling scheme, minimizes the disruption to the current solution, and controls the search direction towards the direction beneficial to optimizing the scheduling scheme. Refer to Figure 3 , which shows a schematic diagram of the working principle of the lowest revenue deletion operator. There are scheduled tasks i, j, k, and m on orbit g, with revenue values of 1, 2, 9, and 7 respectively. When the lowest revenue deletion operator is executed, task i will be deleted from the original scheduling scheme and task i will be added to the unscheduled task set.

[0197] Maximum attitude adjustment conflict deletion operator: preferentially delete the n tasks with the greatest assigned attitude adjustment conflict degree on the orbit, and put the deleted tasks into the unscheduled task set. The maximum attitude adjustment conflict deletion operator improves the quality of the solution by deleting the tasks with the greatest attitude adjustment conflict in the current scheduling scheme, creating better conditions for the subsequent repair of the solution.

[0198] Maximum imaging opportunity deletion operator: preferentially delete the n tasks with the greatest assigned imaging opportunity, that is, the tasks with the longest total duration of the imaging time window, on the orbit, and put the deleted tasks into the unscheduled task set. Refer to Figure 4 , which shows a schematic diagram of the working principle of the maximum imaging opportunity deletion operator. There are scheduled tasks i, j, k, and m on orbit g, and the shadow length is the length of the observable time window. According to the principle of the maximum imaging opportunity deletion operator, the task with the greatest imaging opportunity, that is, the task with the longest observable time window duration, will be preferentially deleted. Therefore, on orbit g, the maximum imaging opportunity deletion operator will delete task k. The maximum imaging opportunity deletion operator improves the quality of the solution by deleting the task with the greatest imaging opportunity, enabling more tasks to be processed and improving the task processing efficiency.

[0199] Maximum time window conflict deletion operator: preferentially delete the n tasks with a large time window conflict degree on the orbit, and put the deleted tasks into the unscheduled task set. Refer to Figure 5 , which shows a schematic diagram of the working principle of the maximum time window conflict deletion operator. There are scheduled tasks i, j, and m on orbit g, and the observable time window durations are 15s, 10s, and 25s respectively. The conflict time between tasks i and j is 4s, the conflict time between tasks i and m is 6s, and the conflict time between tasks j and m is 10s. Then that is, the conflict coefficient of task j on orbit g is 1.4, that is, the conflict coefficient of task m on orbit g is 0.64. Then the maximum time window conflict deletion operator will preferentially delete task j with the greatest conflict coefficient.

[0200] S101-2-1: Determine the target repair operator among at least one repair operator based on the current operator weight corresponding to each repair operator.

[0201] In this embodiment, at least one repair operator includes one or more of the maximum benefit insertion operator, the minimum time window conflict insertion operator, the minimum attitude adjustment conflict insertion operator, and the minimum imaging opportunity insertion operator.

[0202] In this embodiment, to ensure the quality of the solution, the above 4 repair operators can be configured simultaneously.

[0203] Maximum Revenue Insertion Operator: Prioritize inserting the n tasks with the highest assigned revenue on the orbit selected from the unscheduled task set. The Maximum Revenue Insertion Operator will continuously insert the selected tasks until a feasible observable time window is found. If there is no feasible observable time window for a task, the insertion of that task will be abandoned, and instead, the task with the highest revenue other than that task will be selected for insertion until a task that can be inserted is found. The Maximum Revenue Insertion Operator selects the tasks that can maximize the revenue improvement of the current scheduling scheme to maximize the quality of the solution.

[0204] Minimum Time Window Conflict Insertion Operator: Prioritize inserting the n tasks with the least time window conflict degree on the orbit selected from the unscheduled task set.

[0205] Minimum Attitude Adjustment Conflict Insertion Operator: Prioritize inserting the n tasks with the least attitude adjustment conflict degree on the orbit selected from the unscheduled task set.

[0206] Minimum Imaging Opportunity Insertion Operator: Prioritize inserting the n tasks with the fewest imaging opportunities on the orbit selected from the unscheduled task set.

[0207] In this embodiment, the larger the operator weight of the operator, the greater the probability that the operator is selected. The probability calculation formula for the destruction operator or the repair operator being selected is as follows:

[0208]

[0209] Where: φ i represents the probability that the i-th operator is selected; ρ i represents the weight of the i-th operator; Ω represents the number of operators.

[0210] In this embodiment, when the operator is a destruction operator, Ω takes 5, and when the operator is a repair operator, Ω takes 4.

[0211] In this embodiment, the operator score, the operator weight, and the tabu attribute value of the task's tabu attribute can also be updated every n generations. Considering that the basic idea of the adaptive process is to assign different scores to the destruction operator and the repair operator according to their performance. Therefore, the operator score and the operator weight can be updated in the following manner:

[0212] For any operator among at least one destruction operator and at least one repair operator, obtain the new solution s generated based on the operator n ;

[0213] In the case where the new solution s n is updated to the current optimal solution s b , that is, when the new solution s n generated after the operator operates is the optimal solution sb When it is, assign the first score w1 to the operator;

[0214] In the new solution s n has a greater benefit than the current solution s c and is less than the benefit of the current optimal solution, that is, when the new solution s generated after the operator operates n is better than the current solution s c but inferior to the current optimal solution s b When it is, assign the second score w2 to the operator;

[0215] In the new solution s n has a smaller benefit than the current solution s c and the new solution s n is accepted as the current solution s c When it is, assign the third score w3 to the operator;

[0216] In the new solution s n has a smaller benefit than the current solution s c and the new solution s n is not accepted as the current solution s c When it is, assign the fourth score w4 to the operator; where w1 > w2 > w3 > w4.

[0217] Every time after a preset number of iterative solutions, determine the total score of the operator in the preset number of iterative solutions, and update the current operator weight of the operator based on the total score and the historical operator weight of the operator. Among them, the total score represents the sum of the scores assigned to the operator in the preset number of iterative solutions.

[0218] It should be noted that the higher the total score of the operator, the better the solution performance of the operator. The operator weight will be updated according to the historical weight and total score of the operator. The update formula of the operator weight is as follows:

[0219]

[0220] Among them, ρ i represents the weight of the i-th operator; ρ' i represents the historical weight of the i-th operator; n i represents the number of times the i-th operator is called; Q i represents the total score of the i-th operator in the process of iterative solution with a preset number of times; λ is a weight parameter, and its range is [0,1], indicating the importance of the total score of the operator and the historical operator weight. For example, λ can be 0.5.

[0221] In a feasible implementation manner, the taboo attribute includes a deletion taboo attribute and an insertion taboo attribute; the large-scale constellation ground observation mission planning method may further include the following steps:

[0222] S201: For any task, when a new solution containing the task is accepted as the current solution, or when the new solution after deleting the task is not accepted as the current solution, set the deletion tabu attribute of the task to the first attribute value.

[0223] In this embodiment, the first attribute value is used to indicate that the task is prohibited from being deleted in subsequent preset numbers of iterative solutions.

[0224] S202: For any task, when the new solution after deleting the task is accepted as the current solution, or when the new solution containing the task is not accepted as the current solution, set the insertion tabu attribute of the task to the second attribute value.

[0225] In this embodiment, the second attribute value is used to indicate that the task is prohibited from being inserted in subsequent preset numbers of iterative solutions.

[0226] It should be noted that in the tabu search algorithm, some simple local search operations are usually defined for solution update. To prevent short-term cycles, the most recently visited solutions or the most recently used local search operations are stored in the tabu list, and the solutions or operations in the list will not be selected again. The tabu solutions or operations will cancel the tabu status after a period of time or a certain number of iterations.

[0227] In this embodiment, a tight hybrid strategy of ALNS (Adaptive Large Neighborhood Search algorithm) and TS (Tabu Search algorithm) is proposed. This strategy sets two tabu attributes for tasks: deletion tabu attribute and insertion tabu attribute. That is to say, for each task in the large-scale constellation earth observation task, a deletion tabu attribute and an insertion tabu attribute will be generated.

[0228] The two tabu attributes are used in the solution update phase. The deletion tabu attribute and the insertion tabu attribute of each task will be updated every n iterations. For the determination of the value of n, n can be set as a random number in, where m is the number of tasks.

[0229] In this embodiment, for the large-scale constellation observation task allocation problem, by proposing an improved adaptive large neighborhood search algorithm integrating tabu search, the effective allocation of large-scale constellation observation tasks in multiple clusters can be realized. The Adaptive Large Neighborhood Search algorithm (ALNS) can effectively jump out of the local optimal solution and explore a larger range of the solution space. The adaptive operator update can also adjust the search strategy according to the current search process. At the same time, combined with the tabu search strategy, it can well avoid the algorithm from repeatedly searching recent solutions and greatly improve the search efficiency.

[0230] In a feasible implementation, S102 may specifically include the following sub-steps:

[0231] S102-1: For any star cluster, determine the primary star and the secondary stars of the star cluster.

[0232] It should be noted that the contract net mechanism is an effective method for solving distributed task planning, and the task planning is completed by imitating the "bidding - tendering - evaluation" process in the market mechanism.

[0233] Specifically, when using the contract net protocol to solve the distributed satellite task planning problem, the satellites can be divided into primary stars and secondary stars to act as the tenderer and the bidder in the bidding process respectively. Any star in the star cluster can be used as the primary star, and the other satellites are used as secondary stars. For example, the satellite that receives the task information in the star cluster can be used as the primary star of the star cluster.

[0234] In this implementation, referring to Figure 6 , a schematic diagram of task planning based on the contract net mechanism is shown. First, the primary star broadcasts the tasks in the task set to be planned as tender information to the secondary stars for tendering; then, each secondary star makes independent planning according to the information after receiving the tender information, generates a tender proposal for bidding; finally, the primary star evaluates all the returned tender proposals, determines the winning proposal, and broadcasts the winning information to the secondary stars. The entire planning process requires multiple rounds of bidding and tendering, and finally outputs a complete task planning scheme.

[0235] In this implementation, by introducing the contract net mechanism and adopting the star cluster autonomous task planning algorithm based on the extended contract net mechanism, the star cluster autonomous task planning can be realized.

[0236] S102-2: The primary star publishes the tasks in the task set to be planned to the secondary stars for tendering.

[0237] In this implementation, the traditional contract net protocol adopts the method of "single-task tendering", and the primary star and the secondary stars conduct "tendering - bidding - evaluation - winning" for each task in the task set T to be planned respectively to determine the winning secondary star for each task. This method requires continuous inter-satellite negotiation, and the communication volume is huge in large-scale task scenarios. Therefore, this embodiment will adopt a centralized tendering strategy, that is, the primary star conducts centralized tendering for all the tasks to be planned in T in each round of bidding and tendering process to reduce the number of negotiations in the bidding and tendering process, thereby reducing the inter-satellite communication volume.

[0238] S102-3: Each secondary star generates a tender proposal and submits the tender proposal to the primary star.

[0239] In this implementation, each secondary star can generate a tender proposal based on the heuristic algorithm of the precedence arrangement strategy and submit the tender proposal to the primary star.

[0240] Specifically, the heuristic algorithm based on the precedence arrangement strategy sorts tasks according to the start time of the visible time window, and then plans as many tasks as possible into the bidding scheme according to the constraints. Each task in the bidding scheme adopts the precedence arrangement method, that is, each task starts as early as possible.

[0241] S102-4: Determine the task planning scheme corresponding to the satellite cluster through the master satellite based on the bidding scheme of each slave satellite.

[0242] In this embodiment, the master satellite receives the bidding schemes of each slave satellite, directly wins the bid for tasks without duplicate planning, and needs to conduct bid evaluation for tasks with duplicate planning to determine the winning bid scheme.

[0243] It should be noted that under the centralized bidding strategy, each slave satellite may bid for multiple tasks, and there may be different tasks or the same tasks in the bidding schemes of multiple slave satellites. It is difficult to reasonably determine the winning bid slave satellite for each task using the single-agent winning bid strategy. Therefore, a task group winning bid strategy is proposed based on the centralized bidding strategy.

[0244] Specifically, the task group winning bid strategy includes: in the case where the bidding tasks of any slave satellite are non-duplicate planning tasks, determining the bidding scheme of the slave satellite as the winning bid scheme; in the case where the bidding tasks of any slave satellite are duplicate planning tasks, conducting bid evaluation for the duplicate planning tasks, and based on the bid evaluation results, correcting the bidding scheme of the slave satellite to obtain the winning bid scheme of the slave satellite. Among them, non-duplicate planning tasks refer to tasks planned by only one slave satellite; duplicate planning tasks refer to tasks planned by at least two slave satellites simultaneously.

[0245] In this embodiment, based on the task group winning bid strategy, the master satellite only needs to conduct bid evaluation for duplicate planning tasks, and non-duplicate planning tasks are directly won by the bidding slave satellites. In this way, it can not only ensure that the time window resources are not wasted due to duplicate planning tasks, but also make full use of the computing resources of the slave satellites, avoiding the situation where most slave satellites fail in the bidding and computing resources are wasted in the "single-agent winning bid" mode.

[0246] In this embodiment, based on the improved contract net protocol, the master satellite can correct the bidding scheme of the slave satellite according to the bid evaluation results, and the slave satellite will accept the winning bid scheme corrected by the master satellite based on its bidding scheme.

[0247] In this embodiment, for duplicate planning tasks, they can be planned according to the first bid evaluation strategy and the second bid evaluation strategy. The first bid evaluation strategy is: preferentially select the bidding scheme with the earliest execution time to win the bid. The second bid evaluation strategy is: preferentially select the bidding scheme with the least number of plans to win the bid. Among them, the priority of the first bid evaluation strategy is greater than that of the second bid evaluation strategy.

[0248] In a feasible implementation, S102-3 may specifically include the following sub-steps:

[0249] S102-3-1: For any slave satellite, perform a descending order sorting based on the priority of the tasks issued by the master satellite to obtain an observation task set.

[0250] In this implementation, after the slave satellite receives the to-be-planned task set T of the master satellite, it will perform a descending order sorting according to the priority of the tasks to obtain a new observation task set New_OT.

[0251] S102-3-2: Sequentially perform task planning on the tasks in the observation task set in the order from the highest priority to the lowest; the task planning includes: when it is determined that there is at least one observable time window that meets the task requirements in the current observable time window set of the slave satellite, determine the earliest observable time window as the target window of the task; plan the task into the task planning set, and update the current observable time window based on the target window.

[0252] In this implementation, for each task planning, let the first task with the highest priority in the observation task set New_OT be g i , then first find all the visible time windows time_win in the current observable time window set VAR of the slave satellite. Then, filter out the observable time windows remain_win that meet the task requirements of the observation task g i , where the task requirements may include resolution requirements, payload type requirements, imaging deadline requirements, etc. Considering that there is more than one observable time window that meets the task requirements, therefore, to ensure that high-priority tasks can be completed first, the earliest observable time window is determined as the target window of the task g i . Finally, plan the task g i into the task planning set NP, and delete the target window occupied by the task g i in the current observable time window set VAR to obtain the updated current observable time window. Furthermore, based on the updated current observable time window, perform task planning on the next task in the measurement task set New_OT for g i . In this way, by continuously repeating the above process, the final task planning set can be obtained.

[0253] It should be noted that the execution start time i and the execution end time of the current task g need to satisfy the constraints of formulas (2)-(8).

[0254] In a specific implementation, based on the following formula, the execution start time and execution end time of the current task can be calculated according to the previous task:

[0255]

[0256]

[0257] Wherein, represents the execution start time of the current task g i ; represents the execution end time of the current task g i ; represents the start time of the w i -th observable time window of the current task g i ; is the observation end time of the previous task pre i ; is the attitude conversion time between the previous task pre i and the current task g i ; dur i is the duration of the task g i ;

[0258] S102-3-3: In the case where the task planning of the last task in the observation task set is completed, a bidding plan is obtained based on the task planning set.

[0259] In this embodiment, by continuously repeating the above task planning process, the slave satellite can obtain the final task planning set after completing the task planning of the last task in the observation task set, and finally obtain the bidding plan of the slave satellite according to the tasks that have been planned in the task planning set.

[0260] In this embodiment, for the problem of multi-satellite task planning within a satellite cluster, this embodiment proposes a satellite cluster autonomous task planning algorithm based on an extended contract net mechanism. During the bidding process, an all-task centralized bidding strategy is adopted, and for multiple winners, a task group winning strategy is adopted. Among them, compared with the traditional single bidding method, the centralized bidding strategy can greatly reduce inter-satellite communication; and the task group winning strategy can not only ensure that the time window resources are not wasted due to repeated planning tasks, but also make full use of the computing resources of the slave satellite, avoiding the situation where most slave satellites bid unsuccessfully and computing resources are wasted in the "single agent winning" mode.

[0261] The large-scale constellation earth observation mission planning method provided by the embodiments of this application (hereinafter referred to as ALNS-CN) can avoid repeated search for repeated solutions, with higher search efficiency. Compared with traditional GA (Genetic Algorithm) and VNS (Variable Neighborhood Search), it can be superior to other traditional methods in terms of solution quality and solution efficiency.

[0262] To verify the superiority of the embodiments of this application in terms of solution quality and solution efficiency, experimental simulations and comparisons are carried out in two scenarios: the global uniform distribution mode and the regional local concentration mode. The simulation experiment scenarios are designed as follows.

[0263] Scenario A: The mission scale is 1500, the number of satellites is 500, and the missions are randomly generated globally.

[0264] Scenario B: The mission scale is 1500, the number of satellites is 500, and the missions are randomly generated within the range of 21.85°N - 25.33°N, 119.91°E - 122.25°E.

[0265] 20 orbital planes are designed for 500 satellites, with 25 satellites in each orbital plane. The simulation scenario time is 24 hours, and the CPU in the experimental environment is Intel(R) Core(TM) i5-12600K.

[0266] As shown in Table 2, the comparison results of different algorithms in different scenarios are shown.

[0267] Table 2 Comparison results of different algorithms

[0268]

[0269] According to the experimental simulation results, it can be seen that the embodiments of this application have higher superiority than traditional algorithms in terms of solution quality and solution efficiency.

[0270] When some satellites are damaged or there is interference in the embodiments of this application, other satellites can cooperate to complete the mission planning of the damaged satellites, and can give play to the collaborative advantages of the satellite cluster. Compared with the traditional method of performing task allocation on the ground and then uploading the tasks to a single satellite for autonomous mission planning, it can effectively avoid the problem that the tasks on the damaged satellite cannot be effectively executed when some satellites are damaged.

[0271] To verify that the embodiments of this application can adapt to the scenarios of partial satellite damage and interference compared with traditional methods, the allocation results of a satellite cluster will be selected, and 1 - 4 satellites will be randomly selected for destruction for simulation experiments. As shown in Table 3, the experimental simulation results of destroying different numbers of satellites are shown.

[0272] Table 3 Experimental results of damage tests

[0273] Number Number of damaged satellites Number of tasks Number of completed tasks Task completion rate A1 1 14 14 100％ A2 2 14 14 100％ A3 3 14 14 100％ A4 4 14 12 85.7％

[0274] According to the experimental simulation results, it can be known that the embodiments of the present application support the damage of a certain number of satellites. When satellites are damaged, other satellites can cooperate to complete the mission planning.

[0275] In a second aspect, referring to Figure 7 , the embodiments of the present application provide a large-scale constellation earth observation mission planning device 700, and the large-scale constellation earth observation mission planning device 700 includes:

[0276] An upper-layer task allocation module 701, configured to optimize and solve a preset large-scale constellation earth observation mission planning model based on an improved adaptive large neighborhood search algorithm integrating tabu search, so as to allocate the large-scale constellation earth observation mission to multiple clusters, and obtain a target cluster allocation scheme; wherein, the target cluster allocation scheme represents that multiple clusters are allocated with respective corresponding sets of tasks to be planned;

[0277] A lower-layer task allocation module 702, configured to, for any cluster, based on a cluster autonomous task planning algorithm based on an extended contract net mechanism, allocate the tasks in the set of tasks to be planned to the satellites in the cluster, and obtain a cluster task planning scheme for the cluster;

[0278] A planning scheme generation module 703, configured to generate a large-scale constellation earth observation mission planning scheme corresponding to the large-scale constellation earth observation mission based on the cluster task planning schemes respectively corresponding to multiple clusters.

[0279] In an embodiment of the present application, the large-scale constellation earth observation mission planning model includes an objective function and a set of constraint conditions; wherein,

[0280] The objective function aims to maximize the total revenue of the observation mission;

[0281] The set of constraint conditions includes:

[0282] A single-track fixed storage constraint condition, configured to constrain that the storage space required for all tasks observed on each track of each satellite must be less than or equal to the fixed storage capacity limit of each track of the satellite;

[0283] A single-track energy constraint condition, configured to constrain that the energy consumed by all tasks observed on each track of each satellite must be less than or equal to the energy limit of each track of the satellite;

[0284] A task execution times constraint condition, configured to constrain that each task only needs to be successfully observed once on one track to be regarded as completed;

[0285] The task uniqueness constraint is used to ensure that each satellite can only execute one task at the same time;

[0286] The task completion time constraint is used to ensure that each task needs to be completed before the preset task completion time;

[0287] The observation start and end time constraint is used to ensure that the actual observation start time and actual observation end time of all tasks are within the observable time window of the satellite;

[0288] The satellite attitude adjustment constraint is used to ensure that the duration of satellite attitude adjustment and the on / off duration of satellite payloads must be less than the time interval between the start time of the successor task of the current task and the completion time of the current task's observation activity.

[0289] In an embodiment of the present application, the upper-level task allocation module 701 includes:

[0290] The initial solution generation sub-module is used to generate the initial solution of the algorithm and use the initial solution as the current solution and the current optimal solution;

[0291] The iterative solution sub-module is used to repeat the iterative solution steps until the algorithm meets the preset iterative termination condition; wherein, the iterative solution steps are: among at least one destruction operator and at least one repair operator, determine the target destruction operator and the target repair operator respectively, and based on the taboo attributes of each task, delete a preset number of scheduled tasks from the current solution through the target destruction operator, and insert a preset number of unscheduled tasks through the target repair operator to generate a new solution; based on the new solution, update the current solution and the current optimal solution;

[0292] The star cluster allocation scheme determination sub-module is used to use the current optimal solution as the target star cluster allocation scheme when it is determined that the algorithm meets the iterative termination condition.

[0293] In an embodiment of the present application, the iterative solution sub-module includes:

[0294] The destruction operator determination unit is used to determine the target destruction operator among at least one destruction operator based on the current operator weight corresponding to each destruction operator; wherein, the at least one destruction operator includes one or more of a random deletion operator, a lowest benefit deletion operator, a maximum attitude adjustment conflict deletion operator, a maximum imaging opportunity deletion operator, and a maximum time window conflict deletion operator;

[0295] A repair operator determination unit, configured to determine a target repair operator from at least one repair operator based on the current operator weight corresponding to each of the at least one repair operator; wherein, the at least one repair operator includes one or more of a maximum benefit insertion operator, a minimum time window conflict insertion operator, a minimum attitude adjustment conflict insertion operator, and a minimum imaging opportunity insertion operator.

[0296] In an embodiment of the present application, the iterative solution sub-module includes:

[0297] A first update unit, configured to update the new solution to the current optimal solution and the current solution when the benefit of the new solution is greater than the benefit of the current solution and greater than the benefit of the current optimal solution;

[0298] A second update unit, configured to update the new solution to the current solution when the benefit of the new solution is greater than the benefit of the current solution and less than the benefit of the current optimal solution;

[0299] A third update unit, configured to determine a target probability based on the benefits of the new solution and the current solution and the current annealing temperature when the benefit of the new solution is less than the benefit of the current solution; and determine whether to accept the new solution as the current solution based on the target probability.

[0300] In an embodiment of the present application, the taboo attribute includes a deletion taboo attribute and an insertion taboo attribute; the large-scale constellation earth observation mission planning device further includes:

[0301] A deletion taboo attribute determination module, configured to set the deletion taboo attribute of any task to a first attribute value when the new solution including the task is accepted as the current solution, or when the new solution after deleting the task is not accepted as the current solution; the first attribute value is used to indicate that the task is prohibited from being deleted in subsequent preset iterations of the solution;

[0302] An insertion taboo attribute determination module, configured to set the insertion taboo attribute of any task to a second attribute value when the new solution after deleting the task is accepted as the current solution, or when the new solution including the task is not accepted as the current solution; the second attribute value is used to indicate that the task is prohibited from being inserted in subsequent preset iterations of the solution.

[0303] In an embodiment of the present application, the large-scale constellation earth observation mission planning device further includes:

[0304] A new solution acquisition module, configured to acquire a new solution generated based on any operator among at least one destruction operator and at least one repair operator;

[0305] A first scoring module, configured to assign a first score to the operator when the new solution is updated to the current optimal solution;

[0306] The second scoring module is used to assign a second score to an operator when the benefit of the new solution is greater than the benefit of the current solution and less than the benefit of the current optimal solution.

[0307] The third scoring module is used to assign a third score to an operator when the benefit of the new solution is less than the benefit of the current solution and the new solution is accepted as the current solution.

[0308] The fourth scoring module is used to assign a fourth score to an operator when the benefit of the new solution is less than the benefit of the current solution and the new solution is not accepted as the current solution; among them, the first score, the second score, the third score, and the fourth score decrease in sequence.

[0309] The operator weight update module is used to determine the total score of the operator in the preset number of iterative solutions every other preset number of iterative solutions, and update the current operator weight of the operator based on the total score and the historical operator weight of the operator.

[0310] In an embodiment of the present application, the lower-layer task allocation module 702 includes:

[0311] The satellite type determination sub-module is used to determine the main star and the slave star of any star cluster.

[0312] The tendering sub-module is used to publish the tasks in the to-be-planned task set to the slave stars for tendering through the main star.

[0313] The bidding sub-module is used to generate a bidding plan by each slave star and submit the bidding plan to the main star.

[0314] The plan determination sub-module is used to determine the task planning plan corresponding to the star cluster through the main star based on the bidding plans of each slave star.

[0315] In an embodiment of the present application, the tendering sub-module includes:

[0316] The sorting unit is used to perform a descending order sorting on any slave star based on the priority of the tasks published by the main star to obtain an observation task set.

[0317] The task planning unit is used to sequentially perform task planning on the tasks in the observation task set in the order from high to low priority; the task planning includes: when there is at least one observable time window that meets the task requirements in the current observable time window set of the slave star, determining the earliest observable time window as the target window of the task; planning the task into the task planning set, and updating the current observable time window based on the target window.

[0318] The tendering plan generation unit is configured to obtain a tendering plan based on the task planning set when the task planning for the last task in the set of observation tasks is completed. It should be noted that the specific implementation manner of the large-scale constellation earth observation task planning device 700 in the embodiments of the present application refers to the specific implementation manner of the large-scale constellation earth observation task planning method proposed in the first aspect of the embodiments of the present application, which will not be elaborated herein.

[0319] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, apparatus, or computer program product. Therefore, the embodiments of the present invention can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0320] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0321] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0322] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0323] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0324] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the element.

[0325] The above has introduced in detail a method and device for large-scale constellation earth observation mission planning provided by the present invention. Specific examples are used in this text to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A method for large-scale constellation earth observation mission planning, characterized in that, The method includes: Based on an improved adaptive large neighborhood search algorithm integrated with tabu search, optimizing and solving a preset large-scale constellation earth observation mission planning model to allocate large-scale constellation earth observation tasks to multiple clusters, obtaining a target cluster allocation plan; wherein, the target cluster allocation plan represents that each of the multiple clusters is allocated a corresponding set of tasks to be planned; For any one of the clusters, based on a cluster autonomous task planning algorithm based on an extended contract net mechanism, allocating the tasks in the set of tasks to be planned to the satellites in the cluster, obtaining a cluster task planning plan for the cluster; Based on the cluster task planning plans corresponding to multiple clusters respectively, generating a large-scale constellation earth observation task planning plan corresponding to the large-scale constellation earth observation tasks.

2. The method according to claim 1, characterized in that, The large-scale constellation earth observation task planning model includes an objective function and a set of constraint conditions; wherein, The objective function aims to maximize the total revenue of the observation tasks; The set of constraint conditions includes: A single-orbit fixed storage constraint condition, which is used to constrain that the storage space required for all tasks observed by each orbit of each satellite must be less than or equal to the fixed storage capacity limit of each orbit of the satellite; A single-orbit energy constraint condition, which is used to constrain that the energy consumed by all tasks observed by each orbit of each satellite must be less than or equal to the energy limit of each orbit of the satellite; A task execution times constraint condition, which is used to constrain that each task only needs to be successfully observed once on one orbit to be considered completed; A task uniqueness constraint condition, which is used to constrain that each satellite can only execute one task at the same time; A task completion time constraint condition, which is used to constrain that each task needs to be completed before a preset task completion time; An observation start and end time constraint condition, which is used to constrain that the actual observation start time and actual observation end time of all tasks are within the observable time window of the satellite; A satellite attitude adjustment constraint condition, which is used to constrain that the duration for the satellite to adjust its attitude and the on-off duration of the satellite payload must be less than the time interval between the start time of the successor task of the current task and the completion time of the current task's observation activity.

3. The method according to claim 1, characterized in that, Based on an improved adaptive large neighborhood search algorithm integrated with tabu search, optimizing and solving a preset large-scale constellation earth observation mission planning model to allocate large-scale constellation earth observation tasks to multiple clusters, obtaining a target cluster allocation plan, including: Generating an initial solution of the algorithm, and taking the initial solution as the current solution and the current optimal solution; Repeating the iterative solution steps until the algorithm meets a preset iterative termination condition; wherein, the iterative solution steps are: among at least one destruction operator and at least one repair operator preset, respectively determining a target destruction operator and a target repair operator, and based on the tabu attributes of each task, deleting a preset number of scheduled tasks from the current solution through the target destruction operator, and inserting a preset number of unscheduled tasks through the target repair operator to generate a new solution; based on the new solution, updating the current solution and the current optimal solution; When it is determined that the algorithm meets the iterative termination condition, taking the current optimal solution as the target cluster allocation plan.

4. The method according to claim 3, wherein Among at least one preset destruction operator and at least one repair operator, determine a target destruction operator and a target repair operator respectively, including: Determine the target destruction operator among the at least one destruction operator based on the current operator weights corresponding to the at least one destruction operator respectively; wherein, the at least one destruction operator includes one or more of a random deletion operator, a lowest benefit deletion operator, a maximum attitude adjustment conflict deletion operator, a maximum imaging opportunity deletion operator, and a maximum time window conflict deletion operator; Determine the target repair operator among the at least one repair operator based on the current operator weights corresponding to the at least one repair operator respectively; wherein, the at least one repair operator includes one or more of a maximum benefit insertion operator, a minimum time window conflict insertion operator, a minimum attitude adjustment conflict insertion operator, and a minimum imaging opportunity insertion operator.

5. The method according to claim 3, characterized in that, Update the current solution and the current optimal solution based on the new solution, including: In the case where the benefit of the new solution is greater than the benefit of the current solution and greater than the benefit of the current optimal solution, update the new solution to the current optimal solution and the current solution; In the case where the benefit of the new solution is greater than the benefit of the current solution and less than the benefit of the current optimal solution, update the new solution to the current solution; In the case where the benefit of the new solution is less than the benefit of the current solution, determine a target probability based on the benefits of the new solution and the current solution and the current annealing temperature; and determine whether to accept the new solution as the current solution based on the target probability.

6. The method according to claim 3, wherein The taboo attribute includes a deletion taboo attribute and an insertion taboo attribute; the method further includes: For any task, in the case where a new solution including the task is accepted as the current solution, or in the case where a new solution after deleting the task is not accepted as the current solution, set the deletion taboo attribute of the task to a first attribute value; the first attribute value is used to indicate that the task is prohibited from being deleted in subsequent preset iterations of solution seeking; For any task, in the case where a new solution after deleting the task is accepted as the current solution, or in the case where a new solution including the task is not accepted as the current solution, set the insertion taboo attribute of the task to a second attribute value; the second attribute value is used to indicate that the task is prohibited from being inserted in subsequent preset iterations of solution seeking.

7. The method according to claim 4, characterized in that The method further includes: For any operator among the at least one destruction operator and the at least one repair operator, obtain a new solution generated based on the operator; In the case where the new solution is updated to the current optimal solution, assign a first score to the operator; In the case where the benefit of the new solution is greater than the benefit of the current solution and less than the benefit of the current optimal solution, assign a second score to the operator; In the case where the benefit of the new solution is less than the benefit of the current solution and the new solution is accepted as the current solution, assign a third score to the operator; When the benefit of the new solution is less than the benefit of the current solution and the new solution is not accepted as the current solution, assign a fourth score to the operator; wherein, the first score, the second score, the third score, and the fourth score decrease in sequence. Every preset number of iterative solutions, determine the total score of the operator in the preset number of iterative solutions, and update the current operator weight of the operator based on the total score and the historical operator weight of the operator.

8. The method according to claim 1, wherein For any one of the star clusters, based on the star cluster autonomous task planning algorithm of the extended contract net mechanism, allocate the tasks in the set of tasks to be planned to the satellites in the star cluster to obtain the star cluster task planning scheme of the star cluster, including: For any one of the star clusters, determine the main star and the slave stars of the star cluster; The main star publishes the tasks in the set of tasks to be planned to the slave stars for bidding; Each slave star generates a bidding plan and submits the bidding plan to the main star; The main star determines the task planning scheme corresponding to the star cluster based on the bidding plans of each slave star.

9. The method according to claim 8, wherein Each slave star generates a bidding plan, including: For any one slave star, sort the tasks in the task published by the main star in descending order of priority to obtain a set of observation tasks; In the order from high to low of the priority, perform task planning on the tasks in the set of observation tasks in sequence; the task planning includes: when there is at least one observable time window that meets the task requirements in the current observable time window set of the slave star, determine the earliest observable time window as the target window of the task; plan the task into the task planning set, and update the current observable time window based on the target window. When the task planning of the last task in the set of observation tasks is completed, obtain the bidding plan based on the task planning set.

10. A large-scale constellation ground observation mission planning device, characterized in that, The device includes: An upper-layer task allocation module, configured to optimize and solve a preset large-scale constellation earth observation task planning model based on an improved adaptive large neighborhood search algorithm integrating tabu search, so as to allocate large-scale constellation earth observation tasks to multiple star clusters to obtain a target star cluster allocation plan; wherein, the target star cluster allocation plan represents that multiple star clusters are allocated with their respective corresponding sets of tasks to be planned. A lower-layer task allocation module, configured to, for any one of the star clusters, allocate the tasks in the set of tasks to be planned to the satellites in the star cluster based on the star cluster autonomous task planning algorithm of the extended contract net mechanism to obtain the star cluster task planning scheme of the star cluster. A planning scheme generation module, configured to generate a large-scale constellation earth observation task planning scheme corresponding to the large-scale constellation earth observation task based on the star cluster task planning schemes corresponding to multiple star clusters.

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