Satellite compressed task time allocation method and system based on thermal equivalent constraint

Abstract

A satellite compressed task time allocation method and system based on energy-thermal equivalence constraints is proposed, relating to the field of spacecraft mission management and on-orbit scheduling technology. This method offers a simple and efficient compressed task scheduling approach that balances time window constraints, energy constraints, and thermal control constraints. The process involves: acquiring task information and obtaining non-overlapping idle regions from the scheduling window; calculating the energy-thermal equivalence scaling factor of the task based on the task information; converting the basic execution time of the task into an energy-thermal equivalence execution time based on the energy-thermal equivalence scaling factor; constructing a candidate task set based on the energy-thermal equivalence execution time; prioritizing the selection of tasks with the shortest execution time windows from the candidate set; searching for the smallest interval within the execution time window that meets the execution requirements and placing the task accordingly; updating the task status and the number of non-overlapping idle regions, and repeating the above steps until no task can be scheduled.

Description

Technical Field

[0001] This invention relates to the field of spacecraft mission management and on-orbit scheduling technology, specifically to a satellite compressed mission time allocation method and system based on energy and thermal equivalence constraints. Background Technology

[0002] With the development of high-resolution remote sensing satellites, the amount of data generated by onboard payloads has increased dramatically. To reduce downlink bandwidth pressure and ground storage costs, satellites typically perform lossy or lossless compression on-board. Onboard compression is a computationally intensive task, usually requiring continuous use of payload resources. In practical engineering, satellites need to perform not only compression tasks but also imaging and data transmission tasks during their on-orbit operation, resulting in short, discrete, and discontinuous time windows available for compression. Therefore, how to schedule as many compression tasks as possible within these limited executable time windows has become a critical issue.

[0003] Furthermore, compression missions increase satellite energy consumption and thermal load. For small satellites, microsatellites, or spacecraft constrained by onboard power systems, energy margins are often insufficient; simultaneously, onboard equipment has limited heat dissipation capabilities, and continuous high-load operation may cause equipment temperatures to exceed safe thresholds, thus requiring strict control over the cumulative execution duration of compression missions. Therefore, compression mission scheduling is not only constrained by time windows but must also simultaneously meet energy and thermal control constraints.

[0004] In existing technologies, on-board compression task scheduling often employs simple sequential execution and fixed-rule scheduling (such as sorting by the start time of a time window), which makes it difficult to fully utilize fragmented time windows and may lead to a reduction in the number of executable tasks. Therefore, there is an urgent need for a compression task scheduling method that can take into account time window constraints, energy constraints, and thermal control constraints, and is simple and efficient. Summary of the Invention

[0005] This invention proposes a compression task scheduling method that can take into account time window constraints, energy constraints, and thermal control constraints, and has a simple and efficient algorithm.

[0006] The satellite compression mission time allocation method based on energy-thermal equivalence constraints described in this invention includes the following steps: Step S1: Obtain task information and retrieve non-overlapping free areas from the scheduling window; Step S2: Calculate the energy-thermal equivalent ratio factor of the task based on the task information, and convert the basic execution time of the task in the task information into the energy-thermal equivalent execution time based on the energy-thermal equivalent ratio factor of the task. Step S3: Construct a candidate task set based on the energy and thermal equivalent execution time; Step S4: Select the task with the shortest execution time window from the candidate set; Step S5: Search for the smallest interval within the execution time window that meets the execution requirements, and place the task accordingly; Step S6: After updating the task status and non-overlapping free areas respectively, repeat steps S4 to S6 until no task can be scheduled.

[0007] Furthermore, in one embodiment of the present invention, the task information further includes an execution time window, a unit time energy consumption coefficient, a unit time heat load coefficient, an energy weight coefficient, and a thermal control weight coefficient.

[0008] Furthermore, in one embodiment of the present invention, in step S2, the energy-thermal equivalence factor of the task is: ; in, The energy-thermal equivalence factor for the mission. Energy weighting coefficient, The energy consumption coefficient per unit time. This is the thermal control weighting coefficient. The heat load coefficient per unit time; The energy-thermal equivalent execution time is as follows: ; in, To ensure the thermal equivalent execution time, The basic execution time of the task.

[0009] Furthermore, in one embodiment of the present invention, in step S3, the construction of the candidate task set satisfies: ; like If so, the task cannot be scheduled; in, For the task The size of the executable time window, For the task Executable time window end time, For the task Executable time window start time, The thermal equivalent execution time.

[0010] Furthermore, in one embodiment of the present invention, in step S4, the step of preferentially selecting the task with the shortest execution time window from the candidate set is as follows: ; in, For the task The size of the executable time window, To execute the task with the shortest execution time window, Tasks to be selected , For a set of tasks.

[0011] Furthermore, in one embodiment of the present invention, in step S5, the minimum interval within the search execution time window that satisfies the execution requirements is: The search execution time window overlaps with the task window during the time period. ; in, For the execution time window, For the task window; Find satisfaction The minimum range of execution requirements; in, Select task for the current task Thermal equivalent execution time, and It is the overlapping interval between the executable window and the idle window, that is, the difference between the minimum end time of the two windows and the maximum end time of the two windows.

[0012] The present invention discloses a satellite compression mission time allocation system based on energy-thermal equivalence constraints. The system is implemented based on the aforementioned method for satellite compression mission time allocation based on energy-thermal equivalence constraints, and includes the following modules: Module S1 obtains task information and retrieves non-overlapping free areas from the scheduling window; Module S2 calculates the energy-thermal equivalent ratio factor of the task based on the task information, and converts the basic execution time of the task in the task information into the energy-thermal equivalent execution time based on the energy-thermal equivalent ratio factor of the task. Module S3 constructs a set of candidate tasks based on the energy and thermal equivalent execution time; Module S4 prioritizes the task with the shortest execution time window from the candidate set; Module S5 searches for the smallest interval within the execution time window that meets the execution requirements and then places the task. Module S6 updates the task status and non-overlapping free areas respectively, and then repeats the execution of modules S4 to S6 until no task can be scheduled.

[0013] This invention proposes a simple and efficient compression task scheduling method that can simultaneously address time window constraints, energy constraints, and thermal control constraints. Specific benefits include: The present invention discloses a satellite compression task time allocation method based on energy and thermal equivalence constraints. Through unified constraint modeling and a dual greedy strategy, the present invention can achieve efficient scheduling of multiple tasks while taking into account energy constraints, thermal control constraints and time window constraints, and significantly improve the number of successful scheduling of compression tasks and satellite resource utilization efficiency. The satellite compression mission time allocation method based on energy and thermal equivalence constraints described in this invention is applicable to scenarios with discrete executable time windows, complex resource constraints, and strict requirements for energy and thermal security. Attached Figure Description

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a diagram of the satellite compression mission time allocation method based on energy and thermal equivalence constraints as described in Implementation Method 1. Detailed Implementation

[0015] Various embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. The embodiments described with reference to the drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0016] Implementation Method 1: This implementation method aims to provide a greedy scheduling method for compressed tasks that can quickly generate feasible scheduling schemes under the conditions of discrete executable time windows and the need to meet energy and thermal control safety requirements. By constructing a unified energy and thermal equivalent duration scaling factor, the originally dispersed energy constraints and thermal control constraints are transformed into a unified correction of task execution time, effectively simplifying the constraint modeling and calculation process, so that complex resource constraints can be directly processed in a unified equivalent time form during the scheduling process.

[0017] Building upon this, a dual greedy scheduling strategy combining the characteristics of the task's executable window and the distribution of available time periods is further proposed: First, the task with the shortest executable window is selected from the candidate task set to ensure that tasks with tight time windows get priority scheduling opportunities, thereby improving the overall task schedulability; Second, after the task is determined, the available interval with the shortest length that can accommodate the task is selected within its executable time window for scheduling, thereby minimizing time fragmentation, improving the utilization rate of scheduling space, and reserving more available scheduling space for subsequent tasks.

[0018] By employing the unified constraint modeling and dual greedy strategy described above, efficient scheduling of multiple tasks can be achieved while considering energy constraints, thermal control constraints, and time window constraints, significantly improving the number of successful scheduling of compressed tasks and the efficiency of satellite resource utilization. For example... Figure 1 As shown, the method includes the following steps: Step S1, Information Acquisition: Step S11, Task Information: Get task set Each task Features: Fixed baseline execution time Executable time window Energy consumption coefficient per unit time Heat load coefficient per unit time Energy weighting coefficient Thermal control weighting coefficient ; Step S12, Obtaining the free interval: From the scheduling window Obtain non-overlapping free areas: ,

[0019] Step S2, Calculation of thermal equivalent duration: Energy and thermal equivalence scaling factor: Thermal equivalent execution time: ; Step S3: Select a task based on the "shortest executable window" strategy: Step S31, construct a candidate task set: Candidate set satisfy: ; like If so, the task cannot be scheduled; in, For the task The size of the executable time window, For the task Executable time window end time, For the task Executable time window start time; Step S32: Select the task with the shortest executable window from the candidate tasks: Select from the candidate task set: ; in, Tasks to be selected , For a set of tasks; That is, tasks with the shortest executable window are scheduled first. The shorter the window, the less likely they are to be scheduled.

[0020] Step S4: Select the "Minimum Available Interval" within the task time window to place the task: Task The scheduling principle is: within its executable window Within, search for all available time periods that overlap with the task window. ; Find satisfaction The shortest available interval.

[0021] in, Select task for the current task Thermal equivalent execution time, and It is the overlapping interval between the executable window and the idle window, that is, the difference between the minimum end time of the two windows and the maximum end time of the two windows.

[0022] Right now: ; The final arrangement is as follows: ; in, Select task for the current task Compress the task start time. Select task for the current task Compress task completion time; If no suitable interval can be found, the task cannot be scheduled.

[0023] Step S5: State Update and Iteration renew: ; like Then the interval is completely occupied and removed from the set. Remove.

[0024] Repeat steps S32 to S5 until no tasks are available for scheduling.

[0025] To better illustrate the satellite compression mission time allocation method based on energy-thermal equivalence constraints described in this embodiment, the following examples are provided in detail, including the following steps: Step S1: Obtain task information and available idle time intervals: For step S1, the satellite planning system first uploads the compressed task list for the day to the scheduling module. Each task includes a fixed baseline execution duration. Executable time window The system obtains the energy consumption coefficient per unit time. Heat load coefficient per unit time Energy weighting coefficient Thermal control weighting coefficient The above information originates from the satellite mission planning system and satellite hardware characteristic modeling. Then, based on the satellite's existing mission sequences, such as imaging, compression, and data transmission, a set of non-overlapping idle intervals is extracted. , These intervals are time periods that are not occupied by existing tasks.

[0026] Step S2: Calculate the thermally equivalent execution time for each compression task. For step S2, firstly, based on the energy consumption coefficient per unit time of the task... Heat load coefficient per unit time Energy weighting coefficient Thermal control weighting coefficient The energy-thermal equivalence factor for this task was calculated. Then the basic execution time of the task will be... Converted to thermal equivalent execution time This equivalent duration comprehensively reflects the energy and thermal control resource consumption of the task execution, enabling subsequent scheduling steps to simultaneously satisfy both types of constraints under a single dimension. Through this equivalence mechanism, this invention transforms the complex energy and thermal control coupling constraints into a unified correction of the task execution duration, eliminating the need for separate handling of energy budgets and thermal load limits during the scheduling process, thus significantly reducing the computational complexity of the scheduling strategy.

[0027] Step S3: Prioritize the task with the shortest executable time window from the candidate set: The goal of step S3 is to prioritize the most "urgent" task among all unscheduled tasks, i.e., the task with the shortest executable time window and the highest likelihood of losing its scheduling opportunity. First, consider the current set of remaining unscheduled tasks. It is necessary to filter the energy-thermal equivalent execution time of the task. It has exceeded its executable window length. If the condition is met, the task is considered unschedulable. Then, the candidate set is sorted in ascending order of executable window length. Ultimately, the task with the shortest executable window is selected: This strategy embodies the principle of "shorter window, higher priority", which allows tasks with extremely short windows and minimal scheduling margins to be placed first, thereby improving the overall task scheduling success rate.

[0028] Step S4: Search for the smallest available interval within the window that meets the execution requirements and place the task accordingly. In defining the task Then, step S4 needs to be performed within its executable time window. Inside, we need to find a thermal equivalent duration that can accommodate its energy. First, iterate through all available intervals and select the shortest feasible interval for task scheduling. Take the overlapping part with the task window. If the interval length is insufficient to accommodate the task: If a task cannot be placed in that interval, then the set of all overlapping intervals that satisfy the placement requirements is considered as follows: Choose the interval with the shortest length: Finally, the task will be scheduled at the earliest executable position within that interval. This strategy reduces wasted fragmented time, allowing more subsequent tasks to be placed.

[0029] Step S5: Update the free space and task status, repeat until no tasks can be scheduled: After placing the task, step S5 needs to update the task set and the free interval set to form the next scheduling cycle. Assume the task is placed in the interval... sub-intervals within The rule for updating free intervals is: if an interval is completely occupied, then from the set... Delete interval When the task occupies the middle of the interval: If only the front or back portion is occupied, the remaining unoccupied portion is retained. (If the task...) Once an execution interval is found, it is marked as "scheduled" and not included in the unscheduled set. Removed from: Otherwise, the task is considered unschedulable. If the candidate set... If the result is not empty, continue iterating; otherwise, terminate the scheduling process and generate the final task scheduling plan.

[0030] Implementation Method 2: This implementation method provides a satellite compression mission time allocation system based on energy-thermal equivalence constraints. The system is implemented based on the satellite compression mission time allocation method based on energy-thermal equivalence constraints described in Implementation Method 1, and includes the following modules: Module S1 obtains task information and retrieves non-overlapping free areas from the scheduling window; Module S2 calculates the energy-thermal equivalent ratio factor of the task based on the task information, and converts the basic execution time of the task in the task information into the energy-thermal equivalent execution time based on the energy-thermal equivalent ratio factor of the task. Module S3 constructs a set of candidate tasks based on the energy and thermal equivalent execution time; Module S4 prioritizes the task with the shortest execution time window from the candidate set; Module S5 searches for the smallest interval within the execution time window that meets the execution requirements and then places the task. Module S6 updates the task status and non-overlapping free areas respectively, and then repeats the execution of modules S4 to S6 until no task can be scheduled.

[0031] The above provides a detailed description of the satellite compression mission time allocation method and system based on energy and thermal equivalence constraints proposed in this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A satellite compression mission time allocation method based on energy-thermal equivalence constraints, characterized in that, Includes the following steps: Step S1: Obtain task information and retrieve non-overlapping free areas from the scheduling window; Step S2: Calculate the energy-thermal equivalent ratio factor of the task based on the task information, and convert the basic execution time of the task in the task information into the energy-thermal equivalent execution time based on the energy-thermal equivalent ratio factor of the task. Step S3: Construct a candidate task set based on the energy and thermal equivalent execution time; Step S4: Select the task with the shortest execution time window from the candidate set; Step S5: Search for the smallest interval within the execution time window that meets the execution requirements, and place the task accordingly; Step S6: After updating the task status and non-overlapping free areas respectively, repeat steps S4 to S6 until no task can be scheduled.

2. The satellite compression mission time allocation method based on energy-thermal equivalence constraints according to claim 1, characterized in that, The task information also includes the execution time window, energy consumption coefficient per unit time, heat load coefficient per unit time, energy weight coefficient, and thermal control weight coefficient.

3. The satellite compression mission time allocation method based on energy-thermal equivalence constraints according to claim 1, characterized in that, In step S2, the energy-thermal equivalence scaling factor for the task is: ； in, The energy-thermal equivalence factor for the mission. Energy weighting coefficient, The energy consumption coefficient per unit time. This is the thermal control weighting coefficient. The heat load coefficient per unit time; The energy-thermal equivalent execution time is as follows: ； in, For the thermal equivalent execution time, The basic execution time of the task.

4. The satellite compression mission time allocation method based on energy-thermal equivalence constraints according to claim 1, characterized in that, In step S3, the construction of the candidate task set satisfies: ； like If so, the task cannot be scheduled; in, For the task The size of the executable time window, For the task Executable time window end time, For the task Executable time window start time, The thermal equivalent execution time.

5. The satellite compression mission time allocation method based on energy-thermal equivalence constraints according to claim 1, characterized in that, In step S4, the step of prioritizing the selection of the task with the shortest execution time window from the candidate set is as follows: ； in, For the task The size of the executable time window, To execute the task with the shortest execution time window, Tasks to be selected , For a set of tasks.

6. The satellite compression mission time allocation method based on energy-thermal equivalence constraints according to claim 1, characterized in that, In step S5, the minimum interval within the search execution time window that satisfies the execution requirements is: The search execution time window overlaps with the task window during the time period. ; in, For the execution time window, For the task window; Find satisfaction The minimum range of execution requirements; in, Select task for the current task Thermal equivalent execution time, and It is the overlapping interval between the executable window and the idle window, that is, the difference between the minimum end time of the two windows and the maximum end time of the two windows.

7. A satellite compression mission time allocation system based on energy-thermal equivalence constraints, wherein the system is implemented based on the satellite compression mission time allocation method based on energy-thermal equivalence constraints as described in claim 1, characterized in that, Includes the following modules: Module S1 obtains task information and retrieves non-overlapping free areas from the scheduling window; Module S2 calculates the energy-thermal equivalent ratio factor of the task based on the task information, and converts the basic execution time of the task in the task information into the energy-thermal equivalent execution time based on the energy-thermal equivalent ratio factor of the task. Module S3 constructs a set of candidate tasks based on the energy and thermal equivalent execution time; Module S4 prioritizes the task with the shortest execution time window from the candidate set; Module S5 searches for the smallest interval within the execution time window that meets the execution requirements and then places the task. Module S6 updates the task status and non-overlapping free areas respectively, and then repeats the execution of modules S4 to S6 until no task can be scheduled.

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