Task scheduling method, system and equipment suitable for low-orbit constellation to space dynamic target under fully-distributed satellite system

Abstract

The invention discloses a task scheduling method, system and equipment suitable for a low-orbit constellation to a space dynamic target under a fully-distributed satellite system, belongs to the technical field of spacecraft on-orbit task planning, and solves the problems of high cost and low flexibility and stability of a low-orbit constellation task scheduling technology. The method comprises the following steps: solving by adopting an autonomous distributed task scheduling method based on CCGA, decomposing a multi-satellite scheduling problem into a single-satellite scheduling problem, and respectively solving an optimal scheduling scheme of each satellite to form an overall optimal scheduling scheme. The scheduling process is composed of local optimization and global negotiation, each satellite independently explores the scheduling of the satellite to carry out local optimization according to problems, meanwhile, an annular topological structure is adopted to form an inter-satellite communication network to carry out global negotiation in a constellation, and comprehensive evaluation is carried out through weighting of satellite contribution degrees, switching times and satellite loads and the observation effect of a scheduling scheme. And an evaluation index of task income is introduced to assist in optimization of a single-satellite scheduling scheme. The method is suitable for low-orbit constellation task scheduling.

Description

Technical Field

[0001] The present application relates to the technical field of on-orbit mission planning for spacecraft, and in particular to the task scheduling of dynamic space targets by low-orbit constellations in a fully distributed satellite system. Background Art

[0002] Status information of low-orbit constellations on dynamic targets in space. With its wide coverage and strong detection capabilities, low-orbit constellations can effectively monitor and track dynamic targets in space. As the number of satellites in orbit continues to increase, constellation systems are becoming more complex, and the high-speed motion characteristics of space targets have also brought new challenges to constellation scheduling. Within a limited time frame and specific constraints, the reasonable allocation and optimal scheduling of satellite resources are important guarantees for achieving accurate positioning and tracking of space targets.

[0003] The key to task scheduling is to optimize the complex allocation relationship between targets and resources and solve the problem of target conflicts and resource competition. The mission information of low-orbit constellations is recorded by ground stations or generated on the satellite. Since space targets are dynamic and real-time, and the area covered during flight is very large, a satellite has a limited field of view to observe the target. In order to continuously observe the target for a period of time, multiple satellites are required to relay detection.

[0004] Traditional satellite mission scheduling relies on centralized ground management, and is planned by ground personnel in combination with satellite status information and mission requirements. This approach requires sufficient communication time between the satellite and the ground and generates high communication costs, consuming a lot of resources and time. With the continuous advancement of aerospace technology, the capabilities of onboard processors are constantly improving, showing a trend of onboard autonomy. Transferring the task scheduling algorithm to the onboard computer for execution can improve the ability to respond to emergencies and reduce the satellite's dependence on the ground during operation.

[0005] In order to effectively realize the autonomous scheduling of on-board tasks, it is necessary to establish a time-sensitive rapid response mechanism based on limited resource capabilities and design a multi-satellite autonomous collaborative task scheduling algorithm. Currently, most of the hierarchical structures are used, and satellites in the constellation are divided into two types according to their functions: master and slave. As the core of the entire collaborative planning, the master satellite first analyzes the urgency of the task and the visibility of the observation target, decomposes the task, and then presides over the inter-satellite information exchange to ensure that each slave satellite can receive the task information in a timely and accurate manner. The slave satellite receives the task information from the master satellite, independently plans the plan based on its own resources and environmental conditions, and then the master satellite summarizes and evaluates it, and optimizes the scheduling plan through continuous information exchange.

[0006] This method has high requirements on the main satellite's capabilities. Each satellite needs to communicate with the main satellite, which results in excessive communication overhead and low system stability. Once the main satellite fails, the entire system will fail. Currently, the fully distributed structure without a central node is becoming the future trend of the development of multi-satellite collaborative systems. This structure not only ensures the successful execution of on-board task scheduling, but also plays an important role in improving the overall stability of the system. Summary of the invention

[0007] The purpose of the present invention is to solve the problems of high cost, low flexibility and low stability of existing low-orbit constellation task scheduling technology, and to provide a task scheduling method, system and equipment for low-orbit constellations to space dynamic targets in a fully distributed satellite system.

[0008] The present invention is implemented by the following technical solutions. On the one hand, the present invention provides a task scheduling method for a low-orbit constellation to a space dynamic target in a fully distributed satellite system. The resource allocation in the constellation is determined by negotiation among multiple satellites, and each satellite only plans the scheduling scheme of the satellite. The method includes:

[0009] The satellite that receives the mission information is determined to be the mission initiator. Based on the ephemeris and target status information, the visible conditions of all satellites and targets in the constellation are determined to obtain the visible time window.

[0010] According to the visible time window, the task cycle is decomposed into a series of subtasks to form task set information; each subtask represents the observation of a target within the time period and determines the corresponding visible satellite resources;

[0011] The mission initiating satellite broadcasts the mission set information to other satellites in the constellation that are visible to the target. Each satellite organizes the subtasks that it can perform into its own mission set.

[0012] According to the local satellite task set, each satellite selects subtasks with non-overlapping time windows to form the local satellite scheduling plan, and generates a series of scheduling plans to form the local satellite scheduling plan set during the task initialization process;

[0013] By integrating the scheduling plans of all satellites, satellites that perform the same subtask are selected and rejected. The weighted expectation of the subtask to be performed by these satellites is calculated according to the subtask priority and duration, and the two satellites that are most suitable for performing the subtask are determined to obtain an overall constellation scheduling plan within the mission cycle.

[0014] The overall constellation scheduling plan is optimized through local optimization and global negotiation within the constellation, including:

[0015] The co-evolutionary genetic algorithm is used to locally optimize the single-satellite scheduling scheme, including:

[0016] Subpopulations representing different satellite solutions are distributed on corresponding satellites, including a set of local satellite scheduling solutions for the corresponding satellites. The initial solution of the local satellite scheduling solution set is randomly generated according to a time window and is continuously updated by using iteration and optimization of the algorithm.

[0017] Evaluate each scheme in the subpopulation, and calculate the fitness value of the overall constellation scheduling scheme obtained by integrating the scheme with other satellite schemes. The fitness value is obtained according to the mission benefit MP and the observation effect EP. The mission benefit MP is used to measure the number of successfully scheduled subtasks, and the observation effect EP is used to measure the comprehensive effect of the scheduling scheme on the target observation accuracy, sensor working efficiency and satellite load.

[0018] According to the fitness value, the individual with the highest fitness value in each satellite subpopulation is used as the latest scheduling plan for the satellite;

[0019] Global negotiation and communication are carried out within the constellation. Each satellite continuously sends its latest plan to its neighboring satellites, and optimizes and updates its own plan based on the latest plans of other satellites received from neighboring satellites.

[0020] When all satellite plans are no longer updated, the plan optimization phase ends and a multi-satellite task scheduling plan is obtained; each satellite performs its mission according to its own scheduling plan and observes the target.

[0021] Furthermore, a sun-synchronous near-circular orbit is adopted, and the constellation is uniformly deployed at the same orbital altitude in a Walker-δ configuration.

[0022] Furthermore, the calculation formula of the fitness value is:

[0023] maxf=v1·MP+v2·EP

[0024] Among them, v1 and v2 represent weight coefficients, v1+v2=1.

[0025] Furthermore, the calculation formula for task income MP is:

[0026]

[0027] Among them, STNum_do represents the number of successfully executed subtasks, that is, within the time window of this subtask, there are two satellites observing the target at the same time, and STNum_all represents the total number of subtasks.

[0028] Furthermore, the calculation formula of the observation effect EP is:

[0029] EP=ω1·sumDegree+ω2·SwitchNum+ω3·sumLoad

[0030] ω1+ω2+ω3=1

[0031] Among them, sumDegree represents the satellite contribution, SwitchNum represents the number of switches, and sumLoad represents the satellite load.

[0032] Furthermore, the global negotiation communication link adopts a ring topology structure and an inter-satellite negotiation strategy based on receiving, updating and sending.

[0033] Furthermore, the determining of the two satellites most suitable for performing the subtask is specifically:

[0034] When a satellite has multiple consecutive subtasks corresponding to each target, the subtasks of different targets are defined as conflicting subtasks. The expected weight of each conflicting subtask is calculated by the priority and duration of the subtask, which represents the benefit of arranging other tasks to be executed in this time window.

[0035] When there are multiple satellites that can perform the same subtask, the satellite with the lowest expected weight is selected to perform the subtask.

[0036] In a second aspect, the present invention provides a task scheduling system for low-orbit constellations to space dynamic targets in a fully distributed satellite system, the system comprising:

[0037] The mission initiation module is used to determine the satellite that receives the mission information as the mission initiator, and determine the visible conditions of all satellites and targets in the constellation according to the ephemeris and target status information to obtain the visible time window; according to the visible time window, the mission cycle is decomposed to obtain a series of subtasks to form the mission set information; each subtask represents the observation of a target within the time period, and determines the corresponding visible satellite resources;

[0038] The task broadcast module is used by the task initiating satellite to broadcast the task set information to other satellites in the constellation that are visible to the target. Each satellite organizes the subtasks that it can perform into its own task set;

[0039] The satellite scheduling plan generation module is used to select subtasks with non-overlapping time windows for each satellite to form a satellite scheduling plan according to the satellite task set, and generate a series of scheduling plans to form a satellite scheduling plan set during the task initialization process;

[0040] The scheme integration and execution determination module is used to integrate the scheduling schemes of all satellites, select satellites that perform the same subtask, calculate the weight expectation of the subtask to be performed by these satellites according to the subtask priority and duration, determine the two satellites that are most suitable for performing the subtask, and obtain an overall constellation scheduling scheme within the task cycle;

[0041] The solution optimization module is used to optimize the overall scheduling solution of the constellation through local optimization and global negotiation within the constellation, including:

[0042] The co-evolutionary genetic algorithm is used to locally optimize the single-satellite scheduling scheme, including:

[0043] Subpopulations representing different satellite solutions are distributed on corresponding satellites, including a set of local satellite scheduling solutions for the corresponding satellites. The initial solution of the local satellite scheduling solution set is randomly generated according to a time window and is continuously updated by using iteration and optimization of the algorithm.

[0044] Evaluate each scheme in the subpopulation, and calculate the fitness value of the overall constellation scheduling scheme obtained by integrating the scheme with other satellite schemes. The fitness value is obtained according to the mission benefit MP and the observation effect EP. The mission benefit MP is used to measure the number of successfully scheduled subtasks, and the observation effect EP is used to measure the comprehensive effect of the scheduling scheme on the target observation accuracy, sensor working efficiency and satellite load.

[0045] According to the fitness value, the individual with the highest fitness value in each satellite subpopulation is used as the latest scheduling plan for the satellite;

[0046] The global negotiation module is used for global negotiation and communication within the constellation. Each satellite continuously sends its latest plan to its neighboring satellites, and optimizes and updates its own plan based on the latest plans of other satellites received from neighboring satellites.

[0047] The multi-satellite task scheduling scheme acquisition module is used to obtain the multi-satellite task scheduling scheme when all satellite schemes are no longer updated and the scheme optimization phase ends; each satellite performs its task according to its own scheduling scheme and observes the target.

[0048] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the steps of a task scheduling method for low-orbit constellations to dynamic targets in space in a fully distributed satellite system as described above are executed.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein a plurality of computer instructions are stored in the computer-readable storage medium, and the plurality of computer instructions are used to enable a computer to execute a task scheduling method for space dynamic targets by a low-orbit constellation in a fully distributed satellite system as described above.

[0050] Beneficial effects of the present invention:

[0051] The present invention provides an optimization method for solving multi-satellite multi-objective scheduling problems in a distributed satellite system based on a co-evolutionary genetic algorithm. A fully distributed structure with a non-hierarchical structure is adopted, so that each satellite has the same decision-making ability, and the consistency of information cognition between nodes is guaranteed through inter-satellite negotiation. By mobilizing all nodes to cooperate with each other, a global plan is jointly made for the entire system. This structure has higher flexibility and stability, is more reliable in actual operation, and will not cause the entire system to be paralyzed due to the failure of a certain satellite, thereby greatly improving the flexibility, stability and scalability of the system.

[0052] For the observation mission of low-orbit constellations on dynamic targets in space, this paper uses a fully distributed method with decentralized nodes to optimize the task scheduling problem, which can effectively realize on-board autonomous scheduling for rapid response tasks. The basic idea is to transform the multi-satellite multi-target task scheduling problem into a series of single-satellite scheduling problems, and each satellite independently explores the scheduling plan of its own satellite and then integrates it into an overall scheduling plan. An optimization model with the number of subtasks completed, satellite contribution, number of switching and satellite load as evaluation indicators under constraints is established, and the collaborative evolution genetic algorithm (CCGA) is used to solve the optimization problem to obtain the optimal scheduling plan.

[0053] The present invention solves the problem that the centralized method is too dependent on the central node and the system stability is poor. The present invention adopts an autonomous distributed task scheduling method based on CCGA to solve the problem. The multi-satellite scheduling problem is decomposed into a series of single-satellite scheduling problems, and the optimal scheduling solution for each satellite is solved separately to form an overall optimal scheduling solution.

[0054] The scheduling process consists of local optimization and global negotiation. Each satellite independently explores its own scheduling problem for local optimization. At the same time, a ring topology is used to form an intersatellite communication network for global negotiation within the constellation to maintain the consistency of information cognition. The observation effect of the scheduling scheme is comprehensively evaluated by the weighted sum of satellite contribution, number of switching times and satellite load. In addition, due to the limitations of a single satellite's understanding of global information, which leads to the failure of successful scheduling of individual subtasks, the task benefit evaluation index is introduced to assist in the optimization of the single satellite scheduling scheme, and the optimal solution to the optimization problem is finally obtained through continuous iteration. The algorithm makes full use of the characteristics of the fully distributed satellite system, performs distributed optimization on the scheduling problem, improves system stability, and effectively reduces the number of satellite switching times.

[0055] The present invention is applicable to the task scheduling of low-orbit constellations to dynamic space targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0057] Figure 1 This is a flowchart for multi-satellite autonomous distributed task scheduling;

[0058] Figure 2 This is the flow chart of inter-satellite negotiation;

[0059] Figure 3 is the change in fitness value corresponding to the centralized method;

[0060] Figure 4 The change of satellite contribution corresponding to the centralized method;

[0061] Figure 5 The change of satellite switching times corresponding to the centralized method;

[0062] Figure 6 The satellite load variation corresponding to the centralized method;

[0063] Figure 7 The fitness value change corresponding to the task scheduling method of the low-orbit constellation for space dynamic targets in the fully distributed satellite system of the present invention;

[0064] Figure 8 The task benefit changes corresponding to the task scheduling method of the low-orbit constellation for space dynamic targets in a fully distributed satellite system applicable to the present invention;

[0065] Fig. 9 The observation effect changes corresponding to the task scheduling method of the low-orbit constellation for space dynamic targets in the fully distributed satellite system applicable to the present invention;

[0066] Fig.10 The satellite contribution changes corresponding to the task scheduling method of the low-orbit constellation for space dynamic targets in the fully distributed satellite system applicable to the present invention;

[0067] Fig.11 The number of switching times corresponding to the task scheduling method of the low-orbit constellation for space dynamic targets in a fully distributed satellite system applicable to the present invention;

[0068] Fig.12 The satellite load change corresponding to the task scheduling method of the low-orbit constellation for space dynamic targets in a fully distributed satellite system applicable to the present invention;

[0069] Fig.13 Scheduling Gantt charts for tasks. DETAILED DESCRIPTION

[0070] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0071] Embodiment 1: A task scheduling method for a low-orbit constellation to a dynamic target in space under a fully distributed satellite system, wherein resource allocation within the constellation is determined by negotiation among multiple satellites, and each satellite only plans its own scheduling scheme, the method comprising:

[0072] The satellite that receives the mission information is determined to be the mission initiator. Based on the ephemeris and target status information, the visible conditions of all satellites and targets in the constellation are determined to obtain the visible time window.

[0073] According to the visible time window, the task cycle is decomposed into a series of subtasks to form task set information; each subtask represents the observation of a target within the time period and determines the corresponding visible satellite resources;

[0074] The mission initiating satellite broadcasts the mission set information to the remaining satellites in the constellation that are visible to the target. Each satellite organizes the subtasks that it can execute into its own mission set; the local mission set is the mission set of each satellite.

[0075] According to the local satellite task set, each satellite selects subtasks with non-overlapping time windows to form the local satellite scheduling plan, and generates a series of scheduling plans to form the local satellite scheduling plan set during the task initialization process;

[0076] By integrating the scheduling plans of all satellites, satellites that perform the same subtask are selected and rejected. The weighted expectation of the subtask to be performed by these satellites is calculated according to the subtask priority and duration, and the two satellites that are most suitable for performing the subtask are determined to obtain an overall constellation scheduling plan within the mission cycle.

[0077] The overall constellation scheduling plan is optimized through local optimization and global negotiation within the constellation, including:

[0078] The co-evolutionary genetic algorithm is used to locally optimize the single-satellite scheduling scheme, including:

[0079] The subpopulations representing different star plans are distributed on the corresponding stars, including the set of local scheduling plans for the corresponding stars. This set is randomly generated according to the time window at the beginning of the algorithm, that is, the initial scheduling plan set (i.e., the initial solution), but it is continuously updated with the iteration and optimization of the algorithm.

[0080] Evaluate each scheme in the subpopulation, and calculate the fitness value of the overall constellation scheduling scheme obtained by integrating the scheme with other satellite schemes. The fitness value is obtained according to the mission benefit MP and the observation effect EP. The mission benefit MP is used to measure the number of successfully scheduled subtasks, and the observation effect EP is used to measure the comprehensive effect of the scheduling scheme on the target observation accuracy, sensor working efficiency and satellite load.

[0081] According to the fitness value, the individual with the highest fitness value in each satellite subpopulation is used as the latest scheduling plan for the satellite;

[0082] In this implementation, before evaluating the fitness value, the satellites in the constellation need to communicate to obtain the optimal scheduling plan of other satellites (the first round of communication obtains the initial scheduling plan of other satellites) to form the current cognition. The higher the fitness value, the better the overall plan formed by the other satellite plans. By comparing the fitness values ​​of each plan in the corresponding subpopulation, the optimal scheduling plan for each satellite under the current cognition can be obtained.

[0083] Global negotiation and communication are carried out within the constellation. Each satellite continuously sends its latest plan to its neighboring satellites, and optimizes and updates its own plan based on the latest plans of other satellites received from neighboring satellites.

[0084] When all satellite plans are no longer updated, the plan optimization phase ends and a multi-satellite task scheduling plan is obtained; each satellite performs its mission according to its own scheduling plan and observes the target.

[0085] This implementation method adopts a divide-and-conquer strategy to reduce the complexity of global optimization. It solves the problem that the centralized method is too dependent on the central node and the system stability is poor, and adopts an autonomous distributed task scheduling method based on CCGA to solve it. The multi-satellite scheduling problem is decomposed into a series of single-satellite scheduling problems, and the optimal scheduling scheme of each satellite is solved separately to form an overall optimal scheduling scheme. The scheduling process consists of local optimization and global negotiation. Each satellite independently explores the scheduling sub-problem of the satellite for local optimization. The observation effect of the scheduling scheme can be comprehensively evaluated by the weighted sum of the satellite contribution, the number of switching times and the satellite load. In addition, due to the limitations of a single satellite's understanding of global information, which leads to the failure of successful scheduling of individual subtasks, the task benefit evaluation index is introduced to assist the optimization of the single-satellite scheduling scheme, and the optimal solution to the optimization problem is finally obtained through continuous iteration. The algorithm makes full use of the characteristics of the fully distributed satellite system, performs distributed optimization on the scheduling problem, improves system stability, and effectively reduces the number of satellite switching times.

[0086] Implementation method 2: This implementation method further limits the task scheduling method for low-orbit constellations to space dynamic targets in a fully distributed satellite system as described above. In this implementation method, the low-orbit constellation to space dynamic targets is further limited, specifically including:

[0087] The constellation adopts a sun-synchronous near-circular orbit and is evenly deployed at the same orbital altitude in a Walker_δ configuration to achieve successive observation of multiple ballistic missile targets in mid-flight.

[0088] Implementation method three: This implementation method is a further limitation on the task scheduling method for low-orbit constellations to space dynamic targets in a fully distributed satellite system as described above. In this implementation method, the fitness value is further limited, specifically including:

[0089] The calculation formula of the fitness value is:

[0090] maxf=v1·MP+v2·EP

[0091] Among them, v1 and v2 represent weight coefficients, v1+v2=1.

[0092] This implementation provides a method for calculating a fitness value, which can effectively evaluate the fitness of an overall constellation scheduling solution obtained by integrating the solution with other satellite solutions.

[0093] Embodiment 4: This embodiment further defines the task scheduling method for a low-orbit constellation to a dynamic target in a space under a fully distributed satellite system as described above. In this embodiment, the task benefit MP is further defined, specifically including:

[0094] The calculation formula for task income MP is:

[0095]

[0096] Among them, STNum_do represents the number of successfully executed subtasks, that is, within the time window of this subtask, there are two satellites observing the target at the same time, and STNum_all represents the total number of subtasks.

[0097] This embodiment provides a method for calculating task profit MP, which is used to improve the accuracy and rationality of fitness value.

[0098] Embodiment 5: This embodiment further limits the task scheduling method for low-orbit constellations to space dynamic targets in a fully distributed satellite system as described above. In this embodiment, the observation effect EP is further limited, specifically including:

[0099] The calculation formula of observation effect EP is:

[0100] EP=ω1·sumDegree+ω2·SwitchNum+ω3·sumLoad

[0101] ω1+ω2+ω3=1

[0102] Among them, sumDegree represents the satellite contribution, SwitchNum represents the number of switches, and sumLoad represents the satellite load.

[0103] This embodiment provides a calculation method of the observation effect EP, which is used to improve the accuracy and rationality of the fitness value.

[0104] Implementation method 6: This implementation method is a further limitation on the task scheduling method for a low-orbit constellation to a space dynamic target in a fully distributed satellite system as described above. In this implementation method, the global negotiation communication is further limited, specifically including:

[0105] The global negotiation communication link adopts a ring topology structure and an inter-satellite negotiation strategy based on receiving, updating and sending.

[0106] This implementation adopts a ring topology structure to form an inter-satellite communication network for global consultation within the constellation to maintain consistency in information cognition.

[0107] Embodiment 7: This embodiment further defines the task scheduling method for a low-orbit constellation to a space dynamic target in a fully distributed satellite system as described above. In this embodiment, the method for determining the two satellites most suitable for executing the subtask is further defined, specifically including:

[0108] The two satellites most suitable for performing the subtask are determined as follows:

[0109] When a satellite has multiple consecutive subtasks corresponding to each target, the subtasks of different targets are defined as conflicting subtasks. The expected weight of each conflicting subtask is calculated by the priority and duration of the subtask, which represents the benefit of arranging other tasks to be executed in this time window.

[0110] When there are multiple satellites that can perform the same subtask, the satellite with the lowest expected weight is selected to perform the subtask.

[0111] In this embodiment, satellite Si may have a continuous subtasks corresponding to target Tj, and there are also a series of corresponding subtasks for other targets. There will be time conflicts between subtasks of different targets. Then, among these time-conflicting subtasks, satellite Si can only choose one to execute. Satellite Si will calculate the weight expectation for each conflicting subtask by the priority of the subtask and the duration of the subtask, which represents the benefit of arranging other tasks to execute in this time window. Since a subtask can be executed by different satellites, when multiple satellites are willing to execute the subtask (that is, the single-satellite scheduling plan of these satellites contains the subtask), they can be selected by weight expectation. The lower the weight expectation, the more appropriate it is.

[0112] Embodiment 8: This embodiment is an embodiment of a task scheduling method for a low-orbit constellation to a space dynamic target in a fully distributed satellite system as described above, and specifically includes:

[0113] 1) The present invention is aimed at mission scenarios: the constellation adopts a sun-synchronous near-circular orbit and is evenly deployed at the same orbital altitude in a Walker-δ configuration to achieve successive observation of multiple ballistic missile targets in mid-flight.

[0114] 2) The satellite that receives the mission information is used as the mission initiator. It combines the ephemeris and target status information to determine the visible conditions of all satellites and targets in the constellation and obtain the visible time window. According to the visible time window, the mission cycle is decomposed into a series of subtasks to form the task set information. Each subtask represents the observation of a target within the time period and determines the corresponding visible satellite resources. Subsequently, the mission initiator broadcasts the task set information to the remaining satellites in the constellation that are visible to the target (working satellite set). Each satellite organizes the executable subtasks into its own task set.

[0115] 3) According to the mission set of the satellite, each satellite selects subtasks with non-overlapping time windows to form the initial scheduling plan of the satellite. By integrating the scheduling plans of all satellites, selecting satellites that are willing to perform the same subtask, and retaining the two satellites that are most suitable for performing the subtask, an overall scheduling plan for the constellation within the mission cycle can be obtained.

[0116] 4) The overall scheduling scheme of the constellation is optimized through local optimization and global negotiation within the constellation. The task scheduling process is as follows: Figure 1 The number on the subtask represents the sequence of the observation target and its subtask, such as 103 is the third subtask of the first observation target, S1, S2, ..., S n are the satellite numbers respectively.

[0117] 5) The multi-satellite scheduling problem is transformed into a series of single-satellite scheduling problems, and CCGA is used to locally optimize the single-satellite scheduling solutions. Each subpopulation corresponds to a set of single-satellite scheduling solutions for a satellite, and each subpopulation evolves relatively independently to explore the scheduling problem of the satellite. To evaluate each individual solution in the subpopulation, it is necessary to calculate the fitness value of the overall constellation scheduling solution obtained by integrating the solution with other satellite solutions. The fitness value is composed of the mission benefit MP and the observation effect EP.

[0118] maxf=v1·MP+v2·EP

[0119] Mission revenue MP is used to measure the number of successfully scheduled subtasks. STNum_do represents the number of successfully executed subtasks, that is, within the subtask time window, there are two satellites observing the target at the same time. STNum_all represents the total number of subtasks.

[0120]

[0121] The observed effect EP is used to comprehensively measure the overall effect of the scheduling scheme. It is the weighted sum of the satellite contribution sumDegree, the number of switches SwitchNum and the satellite load sumLoad.

[0122] EP=ω1·sumDegree+ω2·SwitchNum+ω3·sumLoad

[0123] ω1+ω2+ω3=1

[0124] 6) In the solution optimization stage, in order to assist the fitness value evaluation process, global negotiation communication is required within the constellation. The specific process is as follows: Figure 2 As shown in the figure. The communication link adopts a ring topology and an inter-satellite negotiation strategy based on receiving, updating, and sending. That is, each satellite continuously sends its latest plan to its neighboring satellites, and optimizes and updates its own plan based on the latest plans of other satellites received from neighboring satellites. When all satellite plans are no longer updated, the plan optimization phase ends, and a task scheduling plan unanimously recognized by multiple satellites is obtained. Each satellite performs tasks according to its own satellite scheduling plan and observes the target.

[0125] Finally, the technical effects of the present invention are given in conjunction with the accompanying drawings:

[0126] The initial UTCG time of the mission is: 2024-07-11 05:10:00; the Walker-δ constellation with a configuration of 24 / 3 / 2 is adopted, and the six orbit numbers of the first satellite are shown in Table 1.

[0127] Table 1 Six numbers of the first satellite orbit

[0128]

[0129] Three targets to be observed are generated using STK software, and the specific parameters are shown in Table 2.

[0130] Table 2 Target missile take-off and landing point parameters

[0131] Starting point latitude, longitude / (°) Latitude and longitude of landing point / (°) Goal 1 (42,124) (47,-122) Goal 2 (43,122) (37,-118) Goal 3 (45,121) (44,-119)

[0132] The centralized method is used for optimization. The changes of fitness value and optimization indexes are as follows: Figures 3 to 6 As shown. The optimization scheme of this application is used for simulation and solution. All satellites communicate with each other once as one round. The maximum number of rounds is g = 100. The number of generations updated by genetic operations for each satellite subpopulation in each round is Gen = 10. The weights of the objective function are: ω1 = 0.4, ω2 = 0.2, ω3 = 0.4, v1 = 0.7, v2 = 0.3. The optimization process is shown in Figures 7 to 12 The Gantt chart of the optimal scheduling solution obtained by the algorithm after 100 rounds of negotiation is shown in Fig.13 As shown in the figure. Each block in the figure represents a subtask, and the row it is in corresponds to the satellite that executes the subtask. For example, 301 is the first subtask of the third observation target, which is observed by the 11th and 12th satellites. The simulation results show that the distributed optimization method can reach convergence at the 60th iteration, and all tasks are successfully executed. Compared with the centralized optimization method, the number of sensor switching is effectively reduced.

Claims

1. A task scheduling method for low-orbit constellations to space dynamic targets in a fully distributed satellite system, characterized in that: The resource allocation within the constellation is determined by negotiation among multiple satellites, and each satellite only plans the scheduling plan for its own satellite. The method includes: The satellite that receives the mission information is determined to be the mission initiator. Based on the ephemeris and target status information, the visible conditions of all satellites and targets in the constellation are determined to obtain the visible time window. According to the visible time window, the task cycle is decomposed into a series of subtasks to form task set information; each subtask represents the observation of a target within the time period and determines the corresponding visible satellite resources; The mission initiating satellite broadcasts the mission set information to other satellites in the constellation that are visible to the target. Each satellite organizes the subtasks that it can perform into its own mission set. According to the local satellite task set, each satellite selects subtasks with non-overlapping time windows to form the local satellite scheduling plan, and generates a series of scheduling plans to form the local satellite scheduling plan set during the task initialization process; By integrating the scheduling plans of all satellites, satellites that perform the same subtask are selected and rejected. The weighted expectation of the subtask to be performed by these satellites is calculated according to the subtask priority and duration, and the two satellites that are most suitable for performing the subtask are determined to obtain an overall constellation scheduling plan within the mission cycle. The overall constellation scheduling plan is optimized through local optimization and global negotiation within the constellation, including: The co-evolutionary genetic algorithm is used to locally optimize the single-satellite scheduling scheme, including: Subpopulations representing different satellite solutions are distributed on corresponding satellites, including a set of local satellite scheduling solutions for the corresponding satellites. The initial solution of the local satellite scheduling solution set is randomly generated according to a time window and is continuously updated by using iteration and optimization of the algorithm. Evaluate each scheme in the subpopulation, and calculate the fitness value of the overall constellation scheduling scheme obtained by integrating the scheme with other satellite schemes. The fitness value is obtained according to the mission benefit MP and the observation effect EP. The mission benefit MP is used to measure the number of successfully scheduled subtasks, and the observation effect EP is used to measure the comprehensive effect of the scheduling scheme on the target observation accuracy, sensor working efficiency and satellite load. According to the fitness value, the individual with the highest fitness value in each satellite subpopulation is used as the latest scheduling plan for the satellite; Global negotiation and communication are carried out within the constellation. Each satellite continuously sends its latest plan to its neighboring satellites, and optimizes and updates its own plan based on the latest plans of other satellites received from neighboring satellites. When all satellite plans are no longer updated, the plan optimization phase ends and a multi-satellite task scheduling plan is obtained; each satellite performs its mission according to its own scheduling plan and observes the target.

2. According to claim 1, a task scheduling method for low-orbit constellations to space dynamic targets in a fully distributed satellite system is characterized in that: Using a sun-synchronous near-circular orbit, the constellation is evenly deployed at the same orbital altitude in a Walker-δ configuration.

3. The task scheduling method for low-orbit constellations to space dynamic targets in a fully distributed satellite system according to claim 1 is characterized in that: The calculation formula of the fitness value is: maxf=v1·MP+v2·EP Among them, v1 and v2 represent weight coefficients, v1+v2=1.

4. The method for scheduling tasks of low-orbit constellations for dynamic space targets in a fully distributed satellite system according to claim 3, characterized in that: The calculation formula for task income MP is: Among them, STNum_do represents the number of successfully executed subtasks, that is, within the time window of this subtask, there are two satellites observing the target at the same time, and STNum_all represents the total number of subtasks.

5. The method for scheduling tasks of low-orbit constellations for dynamic space targets in a fully distributed satellite system according to claim 4, characterized in that: The calculation formula of observation effect EP is: EP=ω1·sumDegree+ω2·SwitchNum+ω3·sumLoad ω1+ω2+ω3=1 Among them, sumDegree represents the satellite contribution, SwitchNum represents the number of switches, and sumLoad represents the satellite load.

6. The method for scheduling tasks of low-orbit constellations for dynamic space targets in a fully distributed satellite system according to claim 1, characterized in that: The global negotiation communication link adopts a ring topology structure and an inter-satellite negotiation strategy based on receiving, updating and sending.

7. The method for scheduling tasks of low-orbit constellations for dynamic space targets in a fully distributed satellite system according to claim 1, characterized in that: The two satellites most suitable for performing the subtask are determined as follows: When a satellite has multiple consecutive subtasks corresponding to each target, the subtasks of different targets are defined as conflicting subtasks. The expected weight of each conflicting subtask is calculated by the priority and duration of the subtask, which represents the benefit of arranging other tasks to be executed in this time window. When there are multiple satellites that can perform the same subtask, the satellite with the lowest expected weight is selected to perform the subtask.

8. A task scheduling system for low-orbit constellations to dynamic space targets in a fully distributed satellite system, characterized in that: The system comprises: The mission initiation module is used to determine the satellite that receives the mission information as the mission initiator, and determine the visible conditions of all satellites and targets in the constellation according to the ephemeris and target status information to obtain the visible time window; according to the visible time window, the mission cycle is decomposed to obtain a series of subtasks to form the mission set information; each subtask represents the observation of a target within the time period, and determines the corresponding visible satellite resources; The task broadcast module is used by the task initiating satellite to broadcast the task set information to other satellites in the constellation that are visible to the target. Each satellite organizes the subtasks that it can perform into its own task set; The satellite scheduling plan generation module is used to select subtasks with non-overlapping time windows for each satellite to form a satellite scheduling plan according to the satellite task set, and generate a series of scheduling plans to form a satellite scheduling plan set during the task initialization process; The scheme integration and execution determination module is used to integrate the scheduling schemes of all satellites, select satellites that perform the same subtask, calculate the weight expectation of the subtask to be performed by these satellites according to the subtask priority and duration, determine the two satellites that are most suitable for performing the subtask, and obtain an overall constellation scheduling scheme within the task cycle; The solution optimization module is used to optimize the overall scheduling solution of the constellation through local optimization and global negotiation within the constellation, including: The co-evolutionary genetic algorithm is used to locally optimize the single-satellite scheduling scheme, including: Subpopulations representing different satellite solutions are distributed on corresponding satellites, including a set of local satellite scheduling solutions for the corresponding satellites. The initial solution of the local satellite scheduling solution set is randomly generated according to a time window and is continuously updated by using iteration and optimization of the algorithm. Evaluate each scheme in the subpopulation, and calculate the fitness value of the overall constellation scheduling scheme obtained by integrating the scheme with other satellite schemes. The fitness value is obtained according to the mission benefit MP and the observation effect EP. The mission benefit MP is used to measure the number of successfully scheduled subtasks, and the observation effect EP is used to measure the comprehensive effect of the scheduling scheme on the target observation accuracy, sensor working efficiency and satellite load. According to the fitness value, the individual with the highest fitness value in each satellite subpopulation is used as the latest scheduling plan for the satellite; The global negotiation module is used for global negotiation and communication within the constellation. Each satellite continuously sends its latest plan to its neighboring satellites, and optimizes and updates its own plan based on the latest plans of other satellites received from neighboring satellites. The multi-satellite task scheduling scheme acquisition module is used to obtain the multi-satellite task scheduling scheme when all satellite schemes are no longer updated and the scheme optimization phase ends; each satellite performs its task according to its own scheduling scheme and observes the target.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor runs the computer program stored in the memory, the steps of the method according to any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of computer instructions, and the plurality of computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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