Satellite ground station resource scheduling-oriented strategy generation method

Abstract

The invention discloses a strategy generation method for satellite ground station resource scheduling, and relates to the technical field of spaceflight. Measurement and control tasks of multiple satellites are optimized and scheduled through the improved particle swarm optimization, and an optimal scheduling scheme is generated according to factors such as satellite states, measurement and control resources and task priorities; by monitoring satellite and ground station states and environment changes in real time, a scheduling strategy is dynamically adjusted in combination with an intelligent algorithm and historical experience data, and manual intervention and revision of the strategy are supported at the same time, so that the scheduling scheme has relatively high flexibility and adaptability; the distribution problem of measurement and control resources is converted into a multi-objective optimization problem; and an editable and extensible data reading module is designed, so that a data access mode can be flexibly selected. The automation level of satellite measurement and control scheduling can be effectively improved, and the error rate is reduced; the method has a wide application prospect in multi-satellite networking measurement and control scheduling, and has remarkable technical advantages and economic benefits.

Description

Technical Field

[0001] The present invention relates to the field of aerospace technology, and in particular to a strategy generation method for satellite ground station resource scheduling. Background Art

[0002] With the rapid development of my country's aerospace technology, the number of satellites in orbit has seen explosive growth. In recent years, the number of satellite constellations has increased dramatically, reaching unprecedented scale. In particular, the planned launch of mega-constellations will pose significant challenges to the existing ground station resource scheduling and management model. Traditional resource scheduling relies on a pre-allocation model, an active satellite management method driven by measurement and control plans, which requires significant manual intervention during the scheduling process. However, this model struggles to meet the satellite measurement and control scheduling requirements of future mega-constellations in terms of efficiency, scheduling timeliness, and flexibility.

[0003] Traditional resource scheduling is suitable for scenarios with a small number of satellites, a limited number of equipment sets, and single-function equipment. This model requires significant manual effort, and operators can manage satellite resource scheduling based on their experience. However, with the rapid increase in the number of satellites, the diversification of satellite user needs, and the expansion of equipment functionality, this pre-allocation model is no longer suitable for future TT&C resource scheduling. Key issues include: The resource scheduling center's extensive management lacks systematic planning for user applications. For example, applications from satellite users are not consolidated, allowing users to apply arbitrarily, resulting in resource waste. The scheduling algorithm is not intelligent enough, requiring full-segment tracking for missions, which also reduces resource utilization. For example, there may be situations where a satellite does not require full station tracking. In such cases, intelligent segmentation by the algorithm allows equipment to directly transition to the next mission's preparation or idle state, improving resource utilization. The scheduling algorithm is inflexible. The proactive management model, driven by ground station plans, requires users to plan their work in advance. Equipment is driven by plans, which is inflexible and time-consuming.

[0004] The current scheduling of satellite ground station measurement and control resources has problems such as low scheduling strategy efficiency, weak dynamic adaptability, insufficient use of historical data, and insufficient system correction capabilities.

[0005] Therefore, those skilled in the art have devoted themselves to developing a strategy generation method for satellite ground station resource scheduling, which has become a major technical means to address the above-mentioned problems. This present invention addresses the resource scheduling needs of satellite ground stations by designing a strategy generation method for satellite ground station resource scheduling. This method effectively addresses issues such as low scheduling strategy efficiency, weak dynamic adaptability, insufficient utilization of historical data, and insufficient system error correction capabilities. It enables efficient scheduling and management of large-scale, multi-type measurement and control resources, and flexibly and rapidly responds to the measurement and control needs of numerous satellite users. Summary of the Invention

[0006] In view of the above-mentioned defects of the prior art, the technical problems to be solved by the present invention are the low efficiency of the satellite ground station measurement and control resource scheduling strategy, weak dynamic adaptability, insufficient utilization of historical data, and insufficient system correction capability.

[0007] To achieve the above objectives, the present invention provides a strategy generation method for satellite ground station resource scheduling, which optimizes and schedules satellite measurement and control tasks by improving the particle swarm optimization algorithm.

[0008] Furthermore, the improved particle swarm algorithm generates an optimal scheduling solution according to satellite status, measurement and control resources, and task priority.

[0009] Furthermore, by real-time monitoring of satellite and ground station status and environmental changes, the scheduling strategy is dynamically adjusted in combination with historical experience data.

[0010] Furthermore, manual intervention to revise the scheduling strategy is supported.

[0011] Furthermore, the measurement and control resource allocation problem is transformed into a multi-objective optimization problem.

[0012] Furthermore, by solving the multi-objective optimization problem, an optimal resource allocation solution is obtained.

[0013] Furthermore, it includes an editable and expandable satellite ground station measurement and control status reading module.

[0014] Furthermore, the satellite ground station measurement and control status reading module includes real-time access and manual revision of measurement and control resource configuration status, real-time access and manual revision of measurement and control resource operation status, and real-time access and manual revision of measurement and control resource boundary parameters.

[0015] Further, the following steps are included:

[0016] Step 1: Data reading: Read satellite status information and ground station measurement and control resource information;

[0017] Step 2: Data analysis: Comprehensively analyze and organize the data;

[0018] Step 3: Strategy generation: Generate measurement and control scheduling strategy based on scheduling requirements.

[0019] Furthermore, the strategy generation includes matching call patterns and autonomous generation patterns.

[0020] Existing satellite ground station TT&C scheduling methods rely primarily on manual experience and are ineffective in addressing the complexities of multiple satellites and multiple tasks. This paper proposes a method for generating TT&C scheduling strategies for satellite ground stations based on an intelligent algorithm. This method automatically generates scheduling plans, reduces manual intervention, and minimizes scheduling errors. This method optimizes and schedules TT&C tasks for multiple satellites by introducing an improved particle swarm optimization algorithm. The algorithm generates an optimal scheduling plan based on factors such as satellite status, TT&C resources, and task priority.

[0021] Traditional scheduling methods lack dynamic adaptability and are unable to adjust scheduling strategies in real time based on satellite status and environmental changes. This paper develops a dynamic scheduling adjustment mechanism that enables the scheduling system to adjust strategies based on real-time changes in satellite status and mission requirements. By monitoring satellite and ground station status and environmental changes in real time, and combining intelligent algorithms with historical empirical data, the scheduling strategy is dynamically adjusted. Furthermore, manual intervention and revision of strategies are supported, making the scheduling solution highly flexible and adaptable.

[0022] In existing technologies, measurement and control (MTC) resource utilization is low, leading to some resources being idle or wasted. This paper designs an optimized allocation mechanism for MTC resources, achieving optimal resource utilization through rational scheduling of these resources. Based on a resource allocation optimization model, the MTC resource allocation problem is transformed into a multi-objective optimization problem, which is solved to obtain the optimal resource allocation solution.

[0023] Existing satellite ground stations have limited access to measurement and control data, preventing flexible real-time access or manual revision. This invention designs a satellite ground station measurement and control status reading module, enabling real-time access and manual revision of measurement and control resource configuration, measurement and control resource operational status, and measurement and control resource boundary parameters. This editable and extensible data reading module allows for flexible data access options.

[0024] This invention has broad application prospects in multi-satellite network measurement, control, and scheduling, offering significant technical advantages and economic benefits. Technically, this method can effectively enhance the automation level of satellite measurement, control, and scheduling, reducing error rates. Compared with traditional methods, it significantly improves solution speed, problem-solving scale, scalability, and environmental adaptability. Performance-wise, it significantly enhances scheduling efficiency and resource utilization, making it adaptable to complex multi-satellite network measurement, control, and scheduling scenarios. In terms of production implementation, this invention is easily integrated into existing satellite measurement and control systems, exhibiting excellent scalability and portability, making it suitable for large-scale application and promotion.

[0025] Compared with the prior art, the present invention has the following obvious substantial features and significant advantages:

[0026] 1. This invention significantly improves the efficiency and accuracy of scheduling, can effectively cope with complex scenarios where multiple satellites are simultaneously monitored and controlled, reduces the scheduling error rate, and improves the utilization efficiency of measurement and control resources.

[0027] 2. The present invention enables the scheduling system to be more flexible in responding to emergencies, thereby improving the completion rate of satellite missions and the stability of the system.

[0028] 3. The present invention significantly improves the utilization rate of measurement and control resources, reduces resource waste, and lowers operating costs.

[0029] 4. The present invention effectively improves the flexibility of data access mode, provides a powerful function of manual intervention in the system at any time, and ensures the validity of the data.

[0030] 5. This invention has broad application prospects in multi-satellite networked measurement, control, and scheduling, offering significant technical advantages and economic benefits. It can effectively enhance the automation level of satellite measurement, control, and scheduling, reducing the error rate. Compared with traditional methods, it significantly improves solution speed, problem-solving scale, scalability, and environmental adaptability. Scheduling efficiency and resource utilization are significantly enhanced, making it adaptable to complex multi-satellite networked measurement, control, and scheduling scenarios. The invention is easily integrated into existing satellite measurement and control systems, exhibits excellent scalability and portability, and is suitable for large-scale application and promotion.

[0031] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a strategy generation flow chart of a preferred embodiment of the present invention;

[0033] Figure 2 This is a strategy generation architecture diagram of a preferred embodiment of the present invention;

[0034] Figure 3 This is a flow chart of an improved particle swarm algorithm combined with heuristic rules according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0036] like Figure 1As shown in the figure, it is a flow chart of the intelligent strategy generation method for satellite measurement, control and scheduling of the present invention. Its typical process includes three processes: data reading, data analysis, and strategy generation. At the same time, databases such as resource status library, scheduling plan library, and scheduling rule library are also called. Figure 2 It is a strategy generation architecture diagram of the present invention;

[0037] 1. Data reading

[0038] For a group of satellites to be tracked and controlled, the system reads the status information of each satellite and the ground station's tracking and control resource information, including orbital parameters, mission requirements, available tracking and control resources, etc. A satellite ground station tracking and control status reading module was designed, which includes real-time access and manual revision of tracking and control resource configuration, real-time access and manual revision of tracking and control resource operation status, and real-time access and manual revision of tracking and control resource boundary parameters.

[0039] 2. Data Analysis

[0040] Comprehensively analyze and organize the relevant data read.

[0041] 3. Strategy Generation

[0042] Depending on the specific situation, choose matching call or autonomous generation to generate measurement and control scheduling strategy.

[0043] Matching call pattern:

[0044] The matching call mode calls TT&C scheduling plans from the scheduling plan library based on satellite status, resource status, and TT&C resource analysis. Based on scheduling rules and current relevant information, the plan is searched and matched with a TT&C scheduling strategy that meets the constraints. The scheduling strategy is determined based on factors such as satellite status, scheduling task requirements, and operator characteristics. This mode supports manual intervention and revision.

[0045] This module also monitors execution in real time, making adjustments based on actual results and status changes. The matching call mode is suitable for scenarios with clear circumstances and existing historical experience or plans, enabling rapid response and strategy execution.

[0046] Self-generation mode:

[0047] The autonomous generation mode generates a TT&C scheduling strategy based on satellite status, resource status, and TT&C resource analysis. This mode supports manual intervention and revision.

[0048] Firstly, a constraint satisfaction model is established for the ground station resource scheduling problem of satellite missions, the variables of the problem are described, and then the optimization objectives and constraints of the problem are analyzed.

[0049] The variables involved in the ground station resource scheduling problem are as follows:

[0050] (1) Satellite mission set J = {j1, j2, j3, ..., j i ,…,j n}

[0051] (2) Ground station set G = {g1, g2, g3, ..., g q ,…,g m}

[0052] (3) Satellite set S = {s1, s2, s3, ..., s p ,…,s z}, Task j i Satellite express

[0053] (4) Antenna set A = {a1, a2, a3, ..., a k ,…,a x}, Task j i Antenna used express

[0054] (5) Recorder set R = {r1, r2, r3, ..., r l ,…,r y}, Task j i The recorder used express

[0055] Ground station resource scheduling for satellite missions mainly involves arranging ground station equipment for satellite missions. Depending on the actual scheduling scenario, this problem is often a multi-objective optimization problem. The following optimization objectives are mainly designed for the resource scheduling problem of satellite missions:

[0056] (1) Maximum task execution rate

[0057] Use C ji Represents task j i Whether to execute, if task j i The equipment that meets the constraints is arranged, then Otherwise C ji =0.

[0058]

[0059] (2) The total arc length of all tasks in the scheduling scheme is the largest

[0060]

[0061]

[0062]

[0063] In the above formula: They represent the actual data transmission start time, actual measurement and control start time, actual data transmission end time, and actual measurement and control end time of the task respectively. Indicates the actual task start time, represents the actual task end time, and f2 represents the total task arc length of the scheduling plan.

[0064] (3) Try to arrange the best equipment for the task

[0065]

[0066] f3 represents the product of the actual duration of each task in the scheduling plan and the degree of preference of its equipment. The larger its value is, the more preferred the equipment in the scheduling plan is. Represents task j i The satellite's preference value for its antenna, Represents task j i The preference value of the satellite for its logger. Since the device preference value is negatively correlated with its preference level, the reciprocal of the device preference value represents the device preference level.

[0067] Execute satellite data transmission and measurement and control tasks in descending order of priority: Tasks have different priorities based on their urgency and importance. During the resource scheduling process, urgent and important tasks should be arranged first.

[0068] The present invention considers two types of device resources, antenna and recorder, and is described according to the device's capability constraints, time occupancy constraints, and antenna-recorder association constraints.

[0069] (1) Equipment capability constraints

[0070] 1) The antenna is available to the satellite

[0071]

[0072] 2) Antenna capabilities match the mission type

[0073] That is, task j i Antenna Ability To meet the task type

[0074] 3) The logger is available to the task

[0075]

[0076] 4) Only assign recorders to tasks that contain data transmission

[0077] That is, if task j i Type For a single measurement and control task, there is no need to arrange a recorder for the task.

[0078] 5) Number of logical recorders of the recorder Number of downlink channels that meet satellite mission requirements

[0079]

[0080] 6) Each logical code rate of the recorder Meet the downlink code rate of each channel of the satellite mission

[0081]

[0082] 7) Recorder physical bit rate Maximum code rate to meet satellite mission requirements:

[0083]

[0084] (2) Equipment time occupancy constraints

[0085] 1) At a certain moment, one antenna can only arrange the mission of one satellite: During the time period, satellite mission j i Antenna Cannot be used by other tasks.

[0086] 2) When an antenna completes a task, it takes a certain switching time t to be ready for the next task:

[0087] That is During the time period, satellite mission j i Antenna Cannot be used by other satellites.

[0088] 3) If the load (i.e., number of logical recorders, logical bit rate, and physical bit rate) is met, a recorder can be shared by multiple tasks. However, sharing a recorder with multiple data transmission tasks can lead to unstable execution. Therefore, if resources are sufficient, multiple tasks should not share a recorder. Sharing a recorder among multiple tasks should only be considered when the available recorder resources are insufficient for the current task to use individually.

[0089] When the recorder resources are sufficient, During the time period, satellite mission j i Logger Cannot be used by other tasks. When the recorder resources are insufficient, consider The number of remaining logical recorders, logical code rate, and remaining physical code rate of the recorder within the time period can be determined. If they can meet the needs of the current task, the recorder can be arranged.

[0090] 4) When a recorder completes a task, a certain switching time t1 is required before it can be used for the next task:

[0091] That is, within the time period, the recorder of satellite mission j i is occupying a channel that cannot be used by other satellites.

[0092] (3) Antenna recorder association constraint

[0093] Only antenna resources need to be arranged for a single TT&C mission. For missions with data transmission, missions that only arrange antennas or only arrange recorders are invalid.

[0094] The ground station resource scheduling problem of satellite missions has characteristics such as complex constraints and large scale. Therefore, the divide-and-conquer idea is used to decompose the problem, that is, the missions are divided into multiple non-related sets according to equipment and time, which can effectively avoid traversing the irrelevant parts during the calculation process and reduce the time complexity of the problem. The ground station resource scheduling problem of satellite missions is a multi-objective optimization problem. For optimization objective (1) the maximum mission execution rate and optimization objective (2) the maximum total mission duration, its evolution range is very limited. If evolution or search is carried out through algorithms, not only will the time cost of calculation increase, but the effect will not be obvious either. Therefore, it is considered to first randomly generate a certain number of initial solutions, and then screen them according to optimization objective (1) and optimization objective (2), and use the better initial population to participate in the evolution and search of the algorithm.

[0095] After obtaining the task plan file to be input, group the tasks. The tasks in the same set belong to the same ground station, there are time conflicts between tasks, and the resource scheduling between different conflict task sets is not related to each other. Constraints and solutions are carried out for each group separately.

[0096] Since the initial solutions of the algorithm are randomly generated, in order to reduce the number of calculations in the iteration process, the initial solutions can be preliminarily screened according to the objective function values of optimization objective (1) and (2), and the better initial solutions are selected to evolve for optimization objective (3). First, randomly generate M initial solutions, calculate the mission execution rate of each initial solution, and select the top N (N < M) initial solutions with the highest execution rate.

[0097] For each conflict task set, when there are irreconcilable device resource usage conflicts within the group, the task time also needs to be reasonably trimmed in arcs, and try to arrange devices for each task. For those tasks that do not have devices arranged due to resource conflicts, arc trimming is carried out according to the following principles to resolve the conflicts:

[0098] 1) Pruning is only performed on the data transmission missions of remote sensing imaging satellites. For other missions, incomplete execution is likely to lead to mission failure, so arc segment pruning is not applicable to all missions.

[0099] 2) Only prune tasks of normal priority, ensuring the complete execution of important and urgent tasks.

[0100] 3) The remaining data transmission time after arc segment trimming must not be less than the specified time t2. Tasks with too short data transmission time will be invalid.

[0101] 4) Based on the optimization goal of maximizing the total task duration, in order to maximize the overall task duration, if there are multiple pruning schemes that can resolve conflicts, the scheme with the shortest pruning time is selected.

[0102] 5) Ensure that equipment is arranged for all tasks, except for tasks whose time is almost completely covered by other tasks.

[0103] The particle swarm algorithm is an evolutionary algorithm that starts from the initial solution and seeks the optimal solution through iteration. The optimization goals mentioned in the present invention (1) maximizing the task execution rate and (2) maximizing the total arc length of all tasks in the scheduling scheme have been screened in the initial solution generation step. The particle swarm algorithm optimizes the optimization goal (3) arranging the optimal equipment for the task as much as possible. The input is the N selected better initial solutions, and the evolutionary decision variable is the antenna and loggers The traditional particle swarm optimization process is as follows:

[0104] 1) Input the initial solution consisting of N particles to initialize the particle swarm: determine the population position pop[i] and velocity Vel[i].

[0105] 2) Calculate the fitness value of each particle.

[0106] 3) Find the individual historical optimal P of the particle best , find the global historical optimal G of the particle best .

[0107] 4) Update the particle position according to the following formula:

[0108] Vel[i]=Vel[i]+r1×c1×(P best -pop[i])+r2×c2×(G best -pop[i])

[0109] pop[i]=Vel[i]+pop[i]

[0110] Where r1 and r2 are random numbers between 0 and 1, and c1 and c2 are learning factor parameters, typically set to 2. A particle determines its next evolutionary direction based on its own experience and the experience of the entire population. In this invention, since the satellite mission ground station resource scheduling problem is a discrete integer problem, the calculated value of the velocity evolution formula is rounded before updating the particle's position.

[0111] 5) Determine whether the updated particles meet the constraints: if so, the evolution is successful; otherwise, they return to their original position and remain stationary.

[0112] 6) If the termination condition is not met, repeat steps 2) to 6), otherwise end and output the global optimal value.

[0113] In traditional particle swarm algorithms, after each iterative evolution, it is determined whether the particles meet the constraints. Only particles that meet the constraints evolve, while those that do not meet the constraints do not evolve. This greatly limits the search space of the particle swarm algorithm and causes it to fall into a local optimum. Therefore, for those particles that do not meet the constraints after evolution, heuristic rules are used to correct them so that they meet the constraints. This approach can effectively expand the solution space and find better solutions. Figure 3 As shown in the figure, according to the constraints of satellite mission resource scheduling, the following heuristic rules are defined:

[0114] 1) For each task in the population that has no time conflict with any other task, directly schedule its optimal device.

[0115] 2) If the evolved task does not meet the device time occupancy constraint, search for tasks in its conflict set that have device occupancy conflicts with the task, and replace the devices that meet the constraints for these conflicting tasks.

[0116] 3) When replacing equipment, prioritize replacing equipment that is more preferred than the current equipment. If the constraints are not met, consider replacing the less preferred equipment.

[0117] Through simulation tests, this method demonstrates excellent scheduling capabilities in complex measurement and control environments, and both the scheduling success rate and measurement and control resource utilization are improved.

[0118] The preferred embodiments of the present invention have been described in detail above. It should be understood that numerous modifications and variations based on the concepts of the present invention are possible without inventive effort by those skilled in the art. Therefore, any technical solution that can be derived by one skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A strategy generation method for satellite ground station resource scheduling, characterized in that: The satellite's measurement and control tasks are optimized and scheduled by improving the particle swarm algorithm.

2. The strategy generation method for satellite ground station resource scheduling according to claim 1, characterized in that: The improved particle swarm algorithm generates an optimal scheduling solution according to satellite status, measurement and control resources, and task priority.

3. The strategy generation method for satellite ground station resource scheduling according to claim 1, characterized in that: By real-time monitoring of satellite and ground station status and environmental changes, the scheduling strategy is dynamically adjusted based on historical experience data.

4. The strategy generation method for satellite ground station resource scheduling according to claim 3, characterized in that: Supports manual intervention to revise scheduling strategies.

5. The strategy generation method for satellite ground station resource scheduling according to claim 1, characterized in that: The measurement and control resource allocation problem is transformed into a multi-objective optimization problem.

6. The strategy generation method for satellite ground station resource scheduling according to claim 5, characterized in that: By solving the multi-objective optimization problem, an optimal resource allocation solution is obtained.

7. The strategy generation method for satellite ground station resource scheduling according to claim 1, characterized in that: Includes an editable and expandable satellite ground station measurement and control status reading module.

8. The strategy generation method for satellite ground station resource scheduling according to claim 7, characterized in that: The satellite ground station measurement and control status reading module includes real-time access and manual revision of measurement and control resource configuration status, real-time access and manual revision of measurement and control resource operation status, and real-time access and manual revision of measurement and control resource boundary parameters.

9. The strategy generation method for satellite ground station resource scheduling according to claim 1, characterized in that: The following steps are involved: Step 1: Data reading: read satellite status information and ground station measurement and control resource information; Step 2: Data analysis: Comprehensively analyze and organize the data; Step 3: Strategy generation: Generate measurement and control scheduling strategy based on scheduling requirements.

10. The strategy generation method for satellite ground station resource scheduling according to claim 1, characterized in that: The strategy generation includes matching call mode and autonomous generation mode.

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