Satellite-borne resource scheduling method under multi-task cooperation scene

Abstract

The invention relates to the technical field of satellite scheduling, in particular to a satellite-borne resource scheduling method in a multi-task coordination scene, which comprises the following steps: S1, constructing a single-satellite multi-task coordination scene; s2, judging the priority of a to-be-completed task of a satellite based on a multi-task cooperation scene, and analyzing evaluation indexes of different tasks and satellite-borne resources required by the evaluation indexes; and S3, designing a spaceborne resource scheduling algorithm based on spaceborne resources required by different tasks. According to the satellite-borne resource scheduling method in the multi-task collaborative scene, an evolution model based on a dynamic environment and a grey correlation analysis method are adopted, the diversity of satellite-borne resource types in the modeling process can be kept, meanwhile, the importance degrees of the tasks are ranked, and the task priorities are determined; and designing a corresponding spaceborne resource scheduling algorithm, and finally constructing a spaceborne resource scheduling method in a multi-task collaborative scene, so that reasonable allocation, efficient management and dynamic adjustment of spaceborne resources can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite scheduling, and specifically to an on-board resource scheduling method in a multi-task collaboration scenario. Background Art

[0002] With the increasingly complex electromagnetic environment, during the autonomous operation of a satellite system, there may be a situation where multiple tasks compete for the same time, energy, or hardware resources. How to reasonably schedule on-board resources when multiple tasks need to be executed to meet the combat effectiveness of the system is an issue that needs to be taken seriously.

[0003] Since the application mode of the satellite system is more flexible, and the priorities, service requirements, etc. of different tasks (remote sensing, measurement, etc.) are all different, the task scheduling is highly complex and difficult. For the on-board array resources, channel resources, and computing and processing resources, research on resource virtualization configuration and management services driven by tasks is carried out to improve the usage efficiency and meet the requirements of on-demand services. Summary of the Invention

[0004] The purpose of the present invention is to provide an on-board resource scheduling method in a multi-task collaboration scenario to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: An on-board resource scheduling method in a multi-task collaboration scenario, characterized by including the following steps: Step S1: Construct a multi-task collaboration scenario for a single satellite; Step S2: Based on the multi-task collaboration scenario, judge the priority of the tasks to be completed by the satellite, and analyze the evaluation indicators of different tasks and the on-board resources required by them; Step S3: Design an on-board resource scheduling algorithm based on the on-board resources required by different tasks.

[0006] Preferably, the multi-task collaboration working scenario of a single satellite described in step S1 refers to that a single satellite simultaneously executes search, tracking, interference, guidance, and collaboration tasks during a certain period of time.

[0007] Preferably, step S2 includes using the grey relational analysis method to establish the relationship between the importance of tasks and task indicators, and then judge the task priorities.

[0008] Preferably, the grey relational analysis method specifically includes: Step S21: Determine the mother index: The task indicator that has the greatest impact on the importance of the task i is used as the mother index, i ∈{1, 2, …, m}, where mIndicates the number of task indicators; Step S22, raw data processing: The raw data is processed using the mean normalization method, that is, the average value of the raw data of each task indicator is calculated respectively, and then each data of the corresponding indicator is divided by the mean value to obtain dimensionless data; Step S23, calculate the correlation coefficient: (1); In formula (1): Indicates the correlation coefficient, k Indicates the serial number of the task, j Indicates the serial number of the task indicator, Indicates the k th task, the sequence difference between the jth and the ith task indicators, a Indicates the minimum value of the sequence difference, b Indicates the maximum value of the sequence difference, n Indicates the number of tasks; Among them, the sequence difference is calculated by the following formula: (2); In formula (2): x ' kj Indicates the data after mean normalization for the k th task and the j th task indicator, x ' ki Indicates the data after mean normalization for the task indicator k in the th task that has the greatest impact on the task importance; i Step S24, calculate the correlation degree: (3); In formula (3): r j Indicates the correlation degree; Step S25, calculate the weights corresponding to each task indicator: (4); In formula (4): r ' j Indicates the j th weight corresponding to the task indicator; Step S26, construct a comprehensive evaluation model: (5); In formula (5): Zk Indicates the task importance level of the k th task, r ' m Indicates the weight of the m th task indicator, x km Indicates the threat value of the k th task's m th task indicator.

[0009] Preferably, the on-board resources described in step S2 include: On-board array resources: beam width, number of beams, antenna transmit power, transmit waveform, channel bandwidth; Channel resources: number of channels, channel bandwidth, channel combination mode; Processing resources: computing resources of the boards of the digital single unit; Time resources: period of the working frame, beam dwell time.

[0010] Preferably, the on-board resource scheduling algorithm described in step S3 is implemented through a population diversity maintenance strategy based on a dynamic environment evolution model.

[0011] Preferably, the on-board resource scheduling algorithm specifically includes: Step S31: Randomly obtain an initial population P 0, number of environment changes t = 0, number of iterations g t = 0; Step S32: Detect environmental changes. If there are no changes, go to step 7; if there are changes, construct a dynamic environment; Step S33: Evaluate the dynamic environment and generate guiding individuals; Step S34: Divide the population after environmental changes P t into three subpopulations; Step S35: Perform recombination operations on the three subpopulations respectively to generate three new subpopulations; Step S36: Merge the three new subpopulations to obtain a new population P ’ t ; Step S37: Use a multi-objective estimation of distribution algorithm to optimize the population merged in step S36; Step S38: If g t > gmax , then output the result population P ’’t , otherwise g t = g t + 1, go to step 2; Among them, the finally output result population P ’’ t is the result of resource allocation.

[0012] Preferably, the formula for constructing the dynamic environment in step S32 is as follows: (6); In formula (6): represents an individual, and respectively represent the unit domains to which the individual indiv belongs on the r-th dimensional objective before and after the environmental change, and respectively represent the r-th r dimensional objective values of the individual before and after the environmental change, lb r represents the bottom boundary of the environment and the r-th r dimensional objective, area_size r represents the r-th r dimensional size of the unit domain on the objective.

[0013] Compared with the prior art, the beneficial effects of the present invention are: The on-board resource scheduling method provided by the present invention in a multi-task collaborative scenario adopts an evolutionary model based on a dynamic environment and a grey relational analysis method, which can maintain the diversity of on-board resource types during the modeling process, while sorting the importance of tasks and determining task priorities, and designing a corresponding on-board resource scheduling algorithm. Finally, an on-board resource scheduling method in a multi-task collaborative scenario is constructed, which can realize the reasonable allocation, efficient management and dynamic adjustment of on-board resources. Brief Description of the Drawings

[0014] Figure 1 is a flowchart of an on-board resource scheduling method provided by the present invention in a multi-task collaborative scenario; Figure 2 is a schematic diagram of a multi-task collaborative scenario in an on-board resource scheduling method provided by the present invention; Figure 3 is a schematic diagram of task importance evaluation in an on-board resource scheduling method provided by the present invention in a multi-task collaborative scenario; Figure 4 is a schematic diagram of time resource division for executing multiple tasks in the same time period in an on-board resource scheduling method provided by the present invention in a multi-task collaborative scenario. Specific implementation manners

[0015] 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 a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0016] Figure 1 It is a flowchart of a on-board resource scheduling method in a multi-task collaborative scenario provided by the present invention. As Figure 1 shown, the embodiments of the present invention provide a on-board resource scheduling method in a multi-task collaborative scenario, including the following steps: Step S1: Construct a multi-task collaborative scenario for a single satellite; Step S2: Based on the multi-task collaborative scenario, judge the priority of the tasks to be completed by the satellite, and analyze the evaluation indicators of different tasks and the on-board resources required by them; Step S3: Design an on-board resource scheduling algorithm based on the on-board resources required by different tasks.

[0017] The on-board resource scheduling method provided by the present invention in a multi-task collaborative scenario sorts the importance of tasks and determines the task priorities, and finally constructs an on-board resource scheduling method in a multi-task collaborative scenario, which can realize the reasonable allocation, efficient management and dynamic adjustment of on-board resources.

[0018] Figure 2 It is a schematic diagram of a multi-task collaborative scenario in the on-board resource scheduling method provided by the present invention. As Figure 2 shown, in an embodiment of the present invention, the multi-task collaborative working scenario of a single satellite described in step S1 refers to that a single satellite simultaneously executes search, tracking, interference, guidance, and coordination tasks in a certain time period.

[0019] Figure 3 It is a schematic diagram of the evaluation of the importance of tasks in the on-board resource scheduling method provided by the present invention. As Figure 3 shown, in an embodiment of the present invention, step S2 includes using the grey relational analysis method to establish the relationship between the importance of tasks and task indicators, and then judging the task priorities.

[0020] Specifically, in an embodiment of the present invention, the grey relational analysis method specifically includes: Step S21: Determine the mother index: Take the task indicator that has the greatest impact on the importance of the task i as the mother index,i ∈{1, 2, …, m}, where m represents the number of task indicators; Step S22, Original data processing: The original data is processed using the mean normalization method, that is, the average value of the original data of each task indicator is calculated respectively, and then each data of the corresponding indicator is divided by the mean value to obtain dimensionless data; Step S23, Calculate the correlation coefficient: (1); In formula (1): represents the correlation coefficient, k represents the serial number of the task, j represents the serial number of the task indicator, represents the k th task, the sequence difference between the jth and ith task indicators of the task, a represents the minimum value of the sequence difference, b represents the maximum value of the sequence difference, n represents the number of tasks; Among them, the sequence difference is calculated through the following formula: (2); In formula (2): x ' kj represents the data after mean normalization for the k th task, the j th task indicator, x ' ki represents the data after mean normalization for the task indicator k that has the greatest impact on the task importance among the i th task; Step S24, Calculate the correlation degree: (3); In formula (3): r j represents the correlation degree; Step S25, Calculate the weights corresponding to each task indicator: (4); In formula (4): r ' j represents the weight corresponding to the j th task indicator; Step S26, Construct a comprehensive evaluation model: (5); In formula (5): Z k represents the task importance of the k th task, r ' m represents the weight of the m th task indicator, x km represents the threat value of the k th task for the m th task indicator.

[0021] Specifically, in an embodiment of the present invention, examples of task indicators may include command system control ability, ability to receive control information, action range, accuracy and real-time performance, operator decision-making ability, maximum effective range, maximum miss distance, kill probability, attack mode, environmental adaptability, maximum available overload, anti-interference ability, etc.

[0022] By integrating steps S23 to S26, the task importance of each task can be obtained Z k The comprehensive score, the higher the score, the higher the importance, and the higher the priority in the resource allocation process. Conversely, the lower the score, the lower the importance and the lower the priority in the resource allocation process.

[0023] Figure 4 FIG. is a schematic diagram of the time resource division for executing multiple tasks in the same time period in a spaceborne resource scheduling method provided by the present invention for a multi-task cooperation scenario. In an embodiment of the present invention, the spaceborne resources described in step S2 include: On-board array resources: beam width, number of beams, antenna transmit power, transmit waveform, channel bandwidth; Channel resources: number of channels, channel bandwidth, channel combination mode; Processing resources: computing resources of the boards of the digital single machine; Time resources: period of the working frame, beam dwell time. An example of the time resource division for executing multiple tasks in the same time period in the present invention is as Figure 4 shown.

[0024] Based on the above resources, a resource pool is constructed. According to the task importance information and resource pool information, and combined with user requirements, resources are reasonably allocated to provide on-demand and opportunistic dynamic access services to meet the flexible use of spaceborne resources.

[0025] In an embodiment of the present invention, the spaceborne resource scheduling algorithm described in step S3 is implemented through a population diversity preservation strategy based on a dynamic environment evolution model.

[0026] Specifically, in an embodiment of the present invention, the on-board resource scheduling algorithm specifically includes: Step S31: Randomly obtain the initial population P 0, the number of environmental changes t = 0, the number of iterations g t = 0; Step S32: Detect environmental changes. If there is no change, go to step 7; if there is a change, construct a dynamic environment; Step S33: Evaluate the dynamic environment to generate guiding individuals; Step S34: Divide the population after environmental change P t into three sub-populations; Step S35: Perform recombination operations on the three sub-populations respectively to generate three new sub-populations; Step S36: Merge the three new sub-populations to obtain a new population P ’ t ; Step S37: Optimize the population merged in step S36 using the multi-objective estimation of distribution algorithm; Step S38: If g t > gmax , then output the result population P ’’ t , otherwise g t = g t + 1, go to step 2; Among them, the finally output result population P ’’ t is the result of resource allocation.

[0027] Furthermore, in an embodiment of the present invention, the formula for constructing the dynamic environment in step S32 is as follows: (6); In formula (6): represents an individual, and respectively represent the unit domains to which the individual indiv belongs on the r-th dimensional objective before and after environmental change, and respectively represent the r-th r dimensional objective values of the individual before and after environmental change, lb r represents the environment and ther The bottom boundary of the area_size r representation of the r size of the upper unit domain of the

[0028] A on - orbit resource scheduling method in a multi - task collaboration scenario provided by the present invention adopts an evolutionary model based on a dynamic environment and a grey relational analysis method, which can maintain the diversity of on - orbit resource types during the modeling process, while sorting the importance of tasks and determining task priorities, and designing a corresponding on - orbit resource scheduling algorithm. Finally, an on - orbit resource scheduling method in a multi - task collaboration scenario is constructed, which can realize the reasonable allocation, efficient management and dynamic adjustment of on - orbit resources.

[0029] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A on-orbit resource scheduling method in a multi-task collaborative scenario, characterized in that, It includes the following steps: Step S1: Construct a multi - task collaborative scenario for a single satellite; Step S2: Based on the multi - task collaborative scenario, determine the priority of the tasks to be completed by the satellite, and analyze the evaluation indicators of different tasks and the on - board resources required; Step S3: Based on the on - board resources required for different tasks, design an on - board resource scheduling algorithm.

2. The on-board resource scheduling method in a multi-task collaborative scenario according to claim 1, wherein The multi - task collaborative working scenario of a single satellite described in Step S1 refers to that within a certain time period, a single satellite simultaneously executes multiple tasks such as search, tracking, interference, guidance, and collaboration.

3. The on-board resource scheduling method in a multi-task collaborative scenario according to claim 2, wherein Step S2 includes using the grey relational analysis method to establish the relationship between the importance of tasks and task indicators, and then determine the task priority.

4. The on-board resource scheduling method in a multi-task collaborative scenario according to claim 3, wherein, The specific grey relational analysis method includes: Step S21, determine the parent indicator: Use the task indicator that has the greatest impact on the importance of the task i as the parent indicator, i ∈{1, 2, …, m}, where m represents the number of task indicators; Step S22: Original data processing: Process the original data using the mean - normalization method, that is, calculate the average value of the original data of each task indicator respectively, and then divide each data of the corresponding indicator by the mean value to obtain dimensionless data; Step S23: Calculate the correlation coefficient; （1）； In formula (1): represents the correlation coefficient k represents the serial number of the task j represents the serial number of the task index represents the k sequence difference between the j-th and the i-th task indices of the k-th task a represents the minimum value of the sequence differences b represents the maximum value of the sequence differences n represents the number of tasks; Among them, the sequence difference is calculated by the following formula: （2）； In formula (2): x ' kj represents the data after equalization processing for the k th task index of the j th task; x ' ki represents the data after equalization processing for the task index k that has the greatest impact on the importance of the task among the i th task; Step S24: Calculate the correlation degree; （3）； In formula (3): r j represents the degree of association; Step S25: Calculate the weights corresponding to each task indicator; （4）； In formula (4): r ' j represents the weight corresponding to the j th task index; Step S26: Construct a comprehensive evaluation model; （5）； In formula (5): Z k represents the importance of the k th task, r ' m represents the weight of the m th task indicator, x km represents the threat value of the k th task for the m th task indicator.

5. The on-board resource scheduling method in a multi-task collaborative scenario according to claim 4, wherein The on - board resources described in Step S2 include: On - board array resources: beam width, number of beams, antenna transmit power, transmit waveform, channel bandwidth; Channel resources: number of channels, channel bandwidth, channel combination mode; Processing resources: computing resources of the boards of the digital single - machine; Time resources: period of the working frame, beam dwell time.

6. The on-board resource scheduling method in a multi-task collaborative scenario according to claim 5, wherein The on - board resource scheduling algorithm described in Step S3 is realized through a population diversity maintenance strategy based on a dynamic environment evolution model.

7. The on-board resource scheduling method in a multi-task collaborative scenario according to claim 6, characterized in that, The specific on - board resource scheduling algorithm includes: Step S31: Randomly obtain the initial population P 0, number of environmental changes t = 0, number of iterations g t = 0; Step S32: Detect environmental changes. If there is no change, go to Step 7; if there is a change, construct a dynamic environment; Step S33: Evaluate the dynamic environment and generate guiding individuals; Step S34: Divide the population after environmental change P t into three sub-populations; Step S35: Recombine the three sub - populations respectively to generate three new sub - populations; Step S36: Combine the three new sub-populations to obtain a new population P ’ t ; Step S37: Use the multi - objective estimation of distribution algorithm to optimize the population merged in Step S36; Step S38. If g t > gmax , then output the result population P ’’ t . Otherwise g t = g t + 1, and go to Step 2; Among them, the finally output result population P ’’ t is the result of resource allocation.

8. The on-board resource scheduling method in a multi-task collaborative scenario according to claim 7, wherein The formula for constructing the dynamic environment in Step S32 is as follows: （6）； In formula (6): represents an individual, and respectively represent the unit domains to which the individual belongs on the r-th dimensional objective indiv before and after the environmental change, and respectively represent the r-th r dimensional objective values of the individual before and after the environmental change, lb r represents the bottom boundary of the environment and the r-th r dimensional objective, area_size r represents the size of the unit domain on the r-th r dimensional objective.

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