Search

CN-122019941-A - Space survey operation control resource scheduling method oriented to fragment optimization

CN122019941ACN 122019941 ACN122019941 ACN 122019941ACN-122019941-A

Abstract

The application discloses a space measurement and operation control resource scheduling method for fragment optimization, which comprises the steps of firstly, carrying out data preprocessing on task demands and feasible solutions thereof, constructing demand-feasible solution mapping, carrying out time standardization and establishing a time slice index, secondly, fusing task demand satisfaction rate, equipment load balancing rate and resource fragment rate, constructing a comprehensive optimization objective function, then, based on the objective function, adopting a greedy iterative algorithm, establishing and resolving through iterative conflict blocks to generate an initial resource utilization plan, and finally, carrying out overall resource arrangement on the initial plan, and merging resource fragments by detecting and transferring adjacent tasks to generate a final scheduling plan. By means of the combination of active optimization and post-scheduling finishing in the scheduling process, the fragmentation degree of the measurement, transportation and control resources is effectively reduced, the task demand satisfaction rate and the equipment load balance are ensured, and the overall utilization rate of the system resources is remarkably improved.

Inventors

  • DING HENG
  • WANG JUNHUI
  • ZHANG FENGGUI
  • GU XI
  • LUO YINING

Assignees

  • 中国电子科技集团公司第十研究所

Dates

Publication Date
20260512
Application Date
20260128

Claims (10)

  1. 1. A space survey operation control resource scheduling method oriented to fragment optimization is characterized by comprising the following steps: Performing data preprocessing operation aiming at a plurality of task demands and a plurality of corresponding feasible solutions in a scheduling scene, wherein the preprocessing operation comprises the steps of constructing an association mapping between the demands and the feasible solutions, converting absolute time into relative time and establishing an index based on a time slice; designing a comprehensive optimization objective function based on the task demand satisfaction rate, the equipment load balancing rate and the resource fragment rate; Based on the comprehensive optimization objective function, adopting a greedy iterative algorithm, and generating an initial measurement and control resource use plan by iteratively establishing conflict blocks, resolving conflicts and updating a feasible solution space; And carrying out global resource arrangement on the initial measurement and operation control resource usage plan, wherein the global resource arrangement comprises the steps of detecting the fragmented idle time period, judging and executing the transfer of the peripheral task plan, and merging fragments to generate a final plan.
  2. 2. The method for scheduling space survey operations resource of claim 1, wherein the step of constructing an associative mapping between demand and feasible solution comprises: building a first mapping structure Wherein Taking task requirements as keys and taking a list of all feasible solutions corresponding to the requirements as values; Building a second mapping structure Wherein The feasible solution is used as a key, and the task requirement corresponding to the feasible solution is used as a value.
  3. 3. The method for scheduling space survey operations and control resources of claim 1, wherein the step of converting absolute time to relative time comprises: setting a time base line ; For any absolute time Calculate the relative time - And stores all time parameters as seconds in integer form.
  4. 4. The method for scheduling space measurement and control resources of claim 3, wherein the step of establishing a time slice based index comprises: At a fixed time length Dividing a time axis for a time slice unit, the first The time interval corresponding to each time slice is , Wherein , In order to schedule the end time of the cycle, Is an upward rounding function; For each feasible solution time interval Judgment and time slice Whether or not there is overlap, if Then feasible solution is associated to the time slice The overlapping condition is judged as follows: And is also provided with , Representing the relative points in time at which it is possible to unbind the initially occupied resource device, Indicating the relative point in time at which a feasible solution is ending to occupy the resource device. ; constructing a hash table index, wherein the key is a time slice index The value is a list of all possible solutions associated within the time slice.
  5. 5. The space survey operation control resource scheduling method of claim 1, wherein the comprehensive optimization objective function is: , 、 、 As the weight coefficient of the light-emitting diode, For the user's demand to meet the rate, For the purpose of device load balancing rate, Resource fragmentation rate; The task demand satisfaction rate is: , To use the total number of task demands actually met by the plan, The total task demand number; The equipment load balancing rate is as follows: , As a total number of resource devices, Representing resource devices Is used for the total duration of the available time period, Indicating the total duration of the task assigned on the resource device, Representing resource devices The number of tasks allocated; The resource fragment rate is: wherein Representing an idle time window for the resource device, Represents a fragmentation window that is determined to be unavailable, Is a super parameter.
  6. 6. The space survey operation control resource scheduling method of claim 1, wherein the establishing process of the conflict blocks is as follows: grouping the feasible solution spaces according to the devices, and sorting the feasible solutions under each device group according to the ascending order of the starting time; Acquiring a feasible solution with earliest starting time in the global feasible solutions; And searching other feasible solution sets with intersections with the feasible solution with the earliest starting time in time through the association index to form the conflict block.
  7. 7. The method for scheduling space survey operations and control resources of claim 6, wherein the step of resolving conflicts comprises: Calculating the acceptance degree of each feasible solution in the conflict block; The feasible solution with the highest acceptance degree is selected as the selected plan of the current iteration, wherein the acceptance degree is calculated based on the optimization objective function.
  8. 8. The method for scheduling space measurement and control resources of claim 1, wherein the step of updating the feasible solution space comprises: Deleting the feasible solution which conflicts with the current and the historical selected plans in time from the residual tasks to be scheduled; And deleting the rest feasible solutions which are not selected from the feasible solutions corresponding to the current allocated requirements.
  9. 9. The method for scheduling space measurement and control resources of claim 1, wherein the step of detecting the fragmented idle period comprises: after the usage plan is generated, calculating an idle time period of each resource device; if the duration of the idle time period is smaller than the preset threshold value, judging that the idle time period is a fragment unit.
  10. 10. The method for scheduling space survey operations and control resources of claim 1, wherein the fragment extension comprises: For the idle time period determined to be the fragmentation unit, judging whether the adjacent allocated mission plan can be transferred to other resource equipment for execution; If mission planning Transfer from original device A to device B for execution, then on device A the task plans the time window that was originally occupied Idle interval with front and back And Merging to form new continuous idle window Continuous idle window Length of (2) , Representing the interval length.

Description

Space survey operation control resource scheduling method oriented to fragment optimization Technical Field The application relates to the technical field of aerospace measurement and control, in particular to a fragment optimization-oriented aerospace measurement and control resource scheduling method. Background With the rapid development of aerospace industry, the scale of an on-orbit spacecraft is continuously enlarged, task types are increasingly diversified and complex, and higher requirements are provided for the scheduling capability of measurement, transportation and control resources. Efficient and reliable resource scheduling is a fundamental guarantee for smooth execution of space missions. Currently, research and practice in the field at home and abroad focuses on improving task completion rate, shortening total scheduling time, optimizing resource load balance and the like, and series of achievements are achieved in the directions. However, resource fragmentation is increasingly prominent during long-run and multitasking alternating scheduling. The problem is characterized in that measurement and control resources have a large number of scattered and discontinuous available fragments in the dimensions of time, frequency bands, antennas and the like, so that the overall utilization rate of the resources is reduced, and the overall efficiency of the system is limited although local load balancing possibly reaches the standard, so that the system is difficult to adapt to the requirements of modern aerospace tasks with high density and high dynamic. Therefore, in order to overcome the defect of resource fragmentation in the existing scheduling method, there is a need for a space measurement operation control resource scheduling method capable of systematically reducing fragments and improving the resource integration and utilization efficiency so as to enhance the overall service capability and robustness of the measurement and control network. Disclosure of Invention The application aims to overcome the defects of the prior art, provides a space survey operation control resource scheduling method oriented to fragment optimization, which is implemented by constructing a comprehensive optimization objective function fusing resource fragment rate, and the technical means of combining greedy iteration generation plans and global defragmentation is adopted, so that the technical problem of low overall utilization rate caused by serious resource fragmentation in space measurement operation and control resource scheduling is solved. The aim of the application is achieved by the following technical scheme: In a first aspect, the application provides a space measurement operation control resource scheduling method oriented to fragment optimization, which comprises the following steps: Performing data preprocessing operation aiming at a plurality of task demands and a plurality of corresponding feasible solutions in a scheduling scene, wherein the preprocessing operation comprises the steps of constructing an association mapping between the demands and the feasible solutions, converting absolute time into relative time and establishing an index based on a time slice; designing a comprehensive optimization objective function based on the task demand satisfaction rate, the equipment load balancing rate and the resource fragment rate; Based on the comprehensive optimization objective function, adopting a greedy iterative algorithm, and generating an initial measurement and control resource use plan by iteratively establishing conflict blocks, resolving conflicts and updating a feasible solution space; And carrying out global resource arrangement on the initial measurement and operation control resource usage plan, wherein the global resource arrangement comprises the steps of detecting the fragmented idle time period, judging and executing the transfer of the peripheral task plan, and merging fragments to generate a final plan. In one possible implementation, the step of constructing an associative mapping between requirements and feasible solutions includes: building a first mapping structure WhereinTaking task requirements as keys and taking a list of all feasible solutions corresponding to the requirements as values; Building a second mapping structure WhereinThe feasible solution is used as a key, and the task requirement corresponding to the feasible solution is used as a value. In one possible embodiment, the step of converting the absolute time into the relative time includes: setting a time base line ; For any absolute timeCalculate the relative time-And stores all time parameters as seconds in integer form. In one possible implementation, the step of establishing the time-slice based index includes: At a fixed time length Dividing a time axis for a time slice unit, the firstThe time interval corresponding to each time slice is,Wherein,In order to schedule the end time of the cycle,Is an upward roundi