CN116227856A - Task planning method, device and storage medium for multi-star multi-task - Google Patents
Task planning method, device and storage medium for multi-star multi-task Download PDFInfo
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Abstract
The embodiment of the specification provides a task planning method, a device and a storage medium for multi-star multi-task. The method comprises the following steps: constructing at least one window combination based on the requirements of the observation task on the observation window of the satellite; determining an objective function according to the completion conditions of observation tasks with different priorities; synthesizing conflict relations and objective functions among window calling conditions, task planning conditions, satellite capacity constraints and different window combinations, and constructing a multi-star task planning model; calculating objective function values of all neighborhood sequences corresponding to the current window combination sequence by using a multi-star task planning model; selecting a neighborhood sequence which does not exist in the tabu list as a next window combination sequence based on the magnitude of the objective function value; and repeatedly executing the steps of calculating the objective function values of different neighborhood sequences and selecting the next window combination sequence to complete task planning. The method ensures the overall execution efficiency of the task, ensures the priority processing of the high-priority task, and is beneficial to practical application.
Description
Technical Field
The embodiment of the specification relates to the technical field of satellite task planning, in particular to a task planning method, device and storage medium for multi-satellite and multi-task.
Background
Earth observation satellites can use various observation devices to observe or track objects on the earth surface on earth orbit so as to acquire influence and electromagnetic parameter information, and play an important role in various fields. Although the number of in-orbit satellites is increasing, the need for earth observation is increasing, and for limited satellite observation resources, there are a large number of observation tasks that need to be performed. Therefore, efficient planning is required for the currently existing observation tasks to efficiently utilize satellite resources.
Currently, when satellite observation tasks are planned, the execution sequence of the tasks is often determined by considering the priority of the observation tasks. However, simply considering the priority of tasks easily causes that the high-priority tasks occupy satellite resources fixedly, cannot be adjusted based on the global, and cannot guarantee efficient utilization of the satellite resources. And the task priority is quantized into a specific value, and the task planning is performed in a linear weighting mode, so that the overall benefit is ensured, but the situation that a small number of high-priority tasks are abandoned to complete more low-priority tasks exists, and the urgency of executing the high-priority tasks in practical application is not met. Therefore, a technical solution that can achieve both the overall utilization of satellite resources and the priority processing of high-priority tasks is needed.
Disclosure of Invention
The embodiment of the specification aims to provide a task planning method, device and storage medium for multi-satellite multi-task, so as to solve the problem of how to consider the overall satellite utilization condition and the priority processing of high-priority tasks during task planning.
In order to solve the above technical problems, an embodiment of the present disclosure provides a task planning method for multi-star and multi-task, including: constructing at least one window combination based on the requirements of the observation task on the observation window of the satellite; the satellite includes at least one observation window; determining an objective function according to the completion conditions of observation tasks with different priorities; the function value of the objective function has a size relation with respect to the number of the completion of the observation tasks with high priority; synthesizing a conflict relation among window calling conditions, task planning conditions, satellite capacity constraints and different window combinations and the objective function, and constructing a multi-star task planning model; calculating objective function values corresponding to all neighborhood sequences corresponding to the current window combination sequence by using the multi-star task planning model; the neighborhood sequence represents a window combination sequence obtained after the current window combination sequence is adjusted; the current window combination sequence is used for covering corresponding tasks; selecting a neighborhood sequence which does not exist in the tabu list as a next window combination sequence based on the magnitude of the objective function value; the tabu list is used for representing window combination sequences selected in a fixed period; and repeatedly executing the steps of calculating the objective function values of different neighborhood sequences and selecting the next window combination sequence to complete task planning.
The embodiment of the specification also provides a task planning device for multi-star and multi-task, which comprises: the window combination construction module is used for constructing at least one window combination based on the requirement of an observation task on an observation window of the satellite; the satellite includes at least one observation window; the objective function determining module is used for determining an objective function according to the completion conditions of the observation tasks with different priorities; the function value of the objective function has a size relation with respect to the number of the completion of the observation tasks with high priority; the model construction module is used for integrating window calling conditions, task planning conditions, satellite capacity constraints, conflict relations among different window combinations and the objective function to construct a multi-star task planning model; the objective function value calculation module is used for calculating objective function values corresponding to all neighborhood sequences corresponding to the current window combination sequence by utilizing the multi-star task planning model; the neighborhood sequence represents a window combination sequence obtained after the current window combination sequence is adjusted; the current window combination sequence is used for covering corresponding tasks; the window combination sequence selection module is used for selecting a neighborhood sequence which does not exist in the tabu list as a next window combination sequence based on the size of the objective function value; the tabu list is used for representing window combination sequences selected in a fixed period; and the task planning completion module is used for repeatedly executing the steps of calculating the objective function values of different neighborhood sequences and selecting the next window combination sequence to complete task planning.
The embodiments of the present specification also propose a computer storage medium on which a computer program is stored, which when executed implements the steps of the task planning method for multi-star multi-tasking described above.
As can be seen from the technical solutions provided in the embodiments of the present disclosure, a window combination is constructed based on requirements of observation tasks on observation windows of satellites, and an objective function is determined according to completion conditions of observation tasks with different priorities, so that the calculated objective function value can represent the completion condition of the observation task with high priority. And then, integrating the window calling condition, the task planning condition, conflict relations among different window combinations and the objective function, and constructing a multi-star task planning model, so that the model can integrate the window utilization condition, the task execution condition, the conflict condition and the task execution condition. After the objective function value is calculated by using the multi-star task planning model, the change condition of the window combination sequence which is most suitable at present can be selected based on the size of the objective function value, and then task planning is completed according to the determined execution sequence of different window combination sequences. By the method, the utilization condition of satellite resources is considered, the overall execution efficiency is ensured, the tasks with high priority can be processed preferentially, the requirements of practical application are met, meanwhile, the logic of the method is clear and simple, the time consumed by task planning is shortened, and the rapid and effective planning of satellite tasks is realized.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present description, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a task planning method for multi-star multi-task according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a tabu search algorithm according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram showing a comparison of the total task plan number with the number of iterations according to the embodiment of the present disclosure;
FIG. 4 is a schematic diagram showing a comparison of task planning situations with highest priority according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating a comparison of a next-highest priority mission planning scenario according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram showing a comparison of 1000 task optimization durations according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram showing a comparison of a 3000 task optimization duration according to an embodiment of the present disclosure;
fig. 8 is a block diagram of a task planning apparatus for multi-star multi-task according to an embodiment of the present disclosure.
Detailed Description
The technical solutions of the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is apparent that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
In order to solve the above technical problems, an embodiment of the present disclosure provides a task planning method for multi-star and multi-task. As shown in fig. 1, the task planning method for multi-star multi-task includes the following specific implementation steps.
S110: at least one window combination is constructed based on the observation window requirements of the satellite for the observation task.
Firstly, in order to ensure the effective implementation of the subsequent mission planning process, to prevent the calculation process from being interfered by other external factors, the following assumptions are defined:
1) The communication resources are sufficient and the data transmission process is not considered.
2) All satellites have discretized the visible time window of the task, and the discrete interval is the shortest time length of task observation. The discretized satellite-to-task visible time window is hereinafter collectively referred to as an observation window.
3) The observation windows to be planned all meet the imaging resolution requirements required by the observation tasks covered by the observation windows.
Firstly, corresponding sensing devices are arranged on satellites, correspondingly, different satellites have different observation windows based on the positions of the satellites and the arranged sensing devices, and the different observation windows can observe different observation targets.
The observation task may include a point target, a region target, and a hybrid target. An observation task may require the use of an observation window or the observation may be performed by a combination of observation windows. To facilitate scheduling of observation windows in mission planning, at least one window combination may be constructed according to the observation window requirements of the satellite for the observation mission. In order to facilitate calculation in the subsequent process, vectorization processing is carried out on the observation window and the observation task.
First, assume that the task index set to be observed is
The available satellite index set is +.>
Further, set the observation window index set of satellite-to-task as +.>
Establishing a relation matrix of visible windows and satellites>
Wherein the method comprises the steps of
Since observation tasks may require joint coverage of multiple observation windows, window combinations may be derived from different combinations of observation windows, each window combination containing one or more observation windows, and one or more tasks may be observed simultaneously. Make window combination set as 
Establishing the inclusion relation moment of window combination and observation window>
Wherein the method comprises the steps of
Establishing a coverage relation matrix of window combination to observation tasks
Wherein the method comprises the steps of
In order to effectively describe the window occupation situation and the execution situation of the observation task, decision variables can be further defined: xg ε {0,1}, g=1, …, N X When x is g When=1, the window combination g is used, otherwise, it is not used;
when w is j When=1, the observation window j is used, otherwise, the observation window j is not used; t is t i ∈{0,1},i＝1,…,N T When t i When=1, it means that the observation task i has scheduled the observation, and otherwise, the observation is not completed.
S120: determining an objective function according to the completion conditions of observation tasks with different priorities; the function value of the objective function has a size relation with respect to the number of completed observation tasks with high priority.
Different observation tasks are preset with priority attributes, and in order to meet practical application, high-priority observation tasks generally need to be processed preferentially.
To achieve the above effect, the priority set of observation tasks is set to
The different values in the priority set are used to represent all possible priorities, wherein the smaller the value, the higher the corresponding task priority. The priority of the ith task is recorded as p i For task i, define vector +.>
One-hot (one-hot) coding for task priority, i.e.
Wherein the method comprises the steps of
Represents o i Is the kth component of (c). The priorities of different tasks are reflected in a 0-1 coding mode through a single-heat coding mode, and the subsequent comparison of the magnitude of the objective function values is facilitated.
Further, the objective function is set as:
wherein the vector is
Representing the planning quantity of each priority task, t i When=1, it indicates that observation task i has arranged observation, t i =0 indicates that observation task i did not complete the observation. The kth component of f represents the number of tasks for which priority k has been planned.
The preemptive priority (preemptive priority) mode is used in this embodiment of the present description, i.e., the number of completed high priority tasks cannot be affected by low priority tasks and even a small number of high priority tasks cannot be sacrificed in order to complete more low priority tasks. To achieve this effect, in
The word order is defined as follows: f (f) 1 ≤f 2 If and only if f 1 And f 2 Front of (2) n The same in each component and f on the (n+1) th component 1 Smaller or n=k. Maximizing f according to the dictionary sequence can achieve maximization of preemptive priority, namely, processing of tasks with high priority can be completed preferentially by comparing the sizes of different objective functions.
S130: and integrating the conflict relation among window calling conditions, task planning conditions, satellite capacity constraints, different window combinations and the objective function to construct a multi-star task planning model.
In practical applications, the capacity of the satellite itself has a certain limit to task planning, for example, the capacity and energy of the satellite itself limit the number of task execution. Therefore, the constraints imposed by satellite storage and energy on the mission planning process need to be considered. The satellite capacity constraint comprises a satellite storage constraint and an energy constraint, wherein the satellite storage constraint is used for representing the constraint of the maximum storage capacity of a satellite on the storage capacity occupied by observed data; the energy constraint is used to represent the constraint of the maximum energy supported by the satellite on the energy consumed by the satellite to observe with the observation window. Specifically, the satellite storage constraints and energy constraints are constructed as follows:
wherein equation (6) stores constraints for the satellite, m js Is a satellite s Acquisition of storage capacity, M, occupied by observed data on window j s Is a satellite s Maximum storage capacity of (2); equation (7) is a satellite energy constraint, e js Is a satellite s Observing the consumed energy using window j, E s Is a satellite s Maximum energy supported.
The satellite storage constraint and the energy constraint can be applied as termination conditions of task planning, so that an iteration process can be jumped out in a subsequent iteration process.
In addition, when the same satellite completes two observations in succession, the start time of the next observation must be greater than or equal to the sum of the current observation completion time and the transition time, i.e., the two observation windows cannot collide in time. Suppose a satellite s The start and end moments of the observation window j of (a) are respectively tws j And twe j ；d ju Is a satellite s Adjusting from window j observation state to window u All transition time required to observe the state. Order the
h ju ＝tws j -twe u -d ju (8)
Wherein h is ju For redundancy time, tws j To observe the start time of window j, we u For observing the ending time of window u, d ju Is a satellite s From observation window j toObservation window u The required transition time.
The above formula (8) represents a satellite s The difference between the interval time of the observation window of the two continuous tasks and the transition time of the observation state of the satellite, namely the starting time of the window j and the window u Subtracting the satellite from the difference in end times of (2) s Adjusting from window j to window u Is a transition time d of (2) ju . If h ju < 0, the window cannot be used u The observation is continued using window j after the observation. Thus, a continuous window time collision indication can be defined
Wherein, the liquid crystal display device comprises a liquid crystal display device,
to indicate a function. When z ju When=1, window j and window u Collisions may occur due to too close or overlapping. Thus, window conflict constraints are
Indicating that the total number of allowed conflict window pairs in the satellite mission planning scheme is 0. In the event that the window conflict constraint is satisfied, the determined mission plan does not have a conflict in converting the window.
Further, in satellite mission planning, decision variable relationships may also be constructed to define the execution of a mission, the decision variable relationships being used to represent the planned observations of a mission if and only if the mission is covered by one or more selected window combinations. Specifically, the decision variable relationship may be as follows:
wherein formulas (12), (13) indicate that a window is selected for use if and only if the window is contained by one or more selected window combinations; formulas (14), (15) represent that a task may plan for observation if and only if the task is covered by one or more selected window combinations.
In summary, in combination with the above description of different constraint relationships and objective functions, a corresponding multi-star mission planning model can be constructed as follows:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
representing window combination decision variables; />
Representing window decision variables;
representing the mission planning decision variables.
Through the multi-star task planning model, the calculation of the objective function value of the corresponding window combination can be completed under the condition that different constraint conditions are met.
S140: calculating objective function values corresponding to all neighborhood sequences corresponding to the current window combination sequence by using the multi-star task planning model; the neighborhood sequence represents a window combination sequence obtained after the current window combination sequence is adjusted; the current window combination sequence is used for covering corresponding tasks.
In order to perform different observation tasks, it is generally necessary to select different window combinations, i.e. the process of task planning is equivalent to the selection process of window combinations. The use state of each window combination is formed into N X Dimension 0-1 vector
With each bit corresponding to a window-combined decision variable. The neighborhood is constructed by inserting new window combinations in the list of selected window combinations or deleting selected window combinations. Since each operation changes the state of one window combination at most, i.e. a change from one window combination to another window combination is achieved, the neighborhood contains at most N X Adjacent states that can act as corresponding neighbor sequences.
Decision variables during task planning w And t can be determined by x; from formulae (12) - (15)
Thus the state only needs to be encoded with x, updated according to equations (16) and (17) each time x is updated w And t.
In the calculation process, a better optimization effect can be achieved only by increasing the neighborhood scale, and the full neighborhood greedy search strategy according to the embodiment of the specification breaks through the limitation of the neighborhood scale by maintaining the benefit list of all non-conflict window combinationsSearching within a large possible neighborhood for maximizing target gain Δf g Is a neighbor solution of (a). Under the condition of determining different neighborhood sequences, objective function values corresponding to all neighborhood combinations can be calculated in sequence and applied in the subsequent process.
S150: selecting a neighborhood sequence which does not exist in the tabu list as a next window combination sequence based on the magnitude of the objective function value; the tabu list is used for representing window combination sequences selected within a fixed period.
After the objective function value is calculated, the objective function change value generated by the transformation window combination sequence can be determined; wherein, include: using the formula 
Calculating the transformation value of the objective function, wherein +.>
Representing the objective function value before the operation on window combination g,/->
The objective function value of the neighborhood sequence generated after the window combination g is operated on is shown.
Based on the size relation of the objective function defined above based on the word order, in the subsequent search strategy, Δf is maximized in each operation g The window combination g to be operated upon can be determined for the target.
To find the operation with the maximum gain quickly, the target gains of all non-conflict window combinations are calculated each iteration, and are ordered based on the word order and recorded as a list k . The target gain and conflict state of most window combinations are the same as those of the previous iteration, so that only a small window combination affected by operation needs to be updated, and the calculation efficiency is ensured.
In addition, to prevent resources from being occupied by fixed high priority tasks for a long period of time, a list of contraindications may be set. The tabu list is used for recording the recently accessed state, and forbids continuous repeated searching of the same neighborhood solution so as to prevent search circulation and local optimum.
The selection of the length of the tabu list is closely related to the actual problem, and too short results in circulation, too long results in slow convergence, and in some examples, the tabu length can be in the range of 
After the objective function change value is obtained through calculation, a corresponding candidate window combination sequence can be selected based on the magnitude of the objective function change value, and the specific magnitude relation can be determined based on the setting, so that the method is not limited.
And if the selected candidate window combination sequence does not belong to the tabu list, adding the candidate window combination sequence into the tabu list, and determining the candidate window combination sequence as the next window combination sequence.
If the candidate window combination sequence belongs to the tabu list, the candidate window combination sequence is selected in a near term, the candidate window combination sequence is reselected based on the magnitude of the change value of the objective function, and whether the candidate window combination sequence belongs to the tabu list is judged until the position of the candidate window combination sequence which does not exist in the tabu list is selected.
The selected next window combination sequence can be used as a schedule in task planning, and the task execution flow is determined based on the current window combination sequence and the next window combination sequence.
S160: and repeatedly executing the steps of calculating the objective function values of different neighborhood sequences and selecting the next window combination sequence to complete task planning.
The calculation process is an iteration process, and each observation task in the task planning is sequentially determined in an iteration mode. For a better description, the iterative process, taking the kth iteration as an example, has the following operations:
step 1: traversing χ from the operation with the highest target gain k Stopping when finding the first operation which does not trigger the tabu, and obtaining the iterationIs a neighbor solution of (a).
Step 2: let χ k+1 ＝χ k 。
Step 3: the traversal operation involves all window combinations on the satellite, checking if their conflict state changes. If a window combination changes from undershoot to conflict due to the current operation, the slave is needed
Deleting the window combination; conversely, if a window combination is changed from conflict to non-conflict due to the present operation, the target gain of the window combination needs to be recalculated and added to χ k+1 Is a kind of medium.
Step 4: all window combinations corresponding to a task involved in traversing an operation if the task is
The target gain is updated and reinserted into the correct position to maintain the ordering.
Wherein, store list χ k When using red and black trees as data structures, this can be used
Time to complete insertion and deletion of >
Representation list +.>
The total number of elements in (a) is determined. Meanwhile, each window combination needs to record the conflict state, so that the inquiry and update of the conflict state can be completed only by O (1) time.
A complete iterative process is described in connection with fig. 2. Firstly, an initial feasible scheme is obtained, and a tabu table is initialized; then, generating a current scheme alternative neighborhood scheme list, and sequencing according to a dictionary sequence; based on the ranking, the best alternative in the list is selected and deleted. Judging whether the selected scheme is in a tabu list, and if so, re-selecting and deleting the optimal alternative scheme in the list; if not, the current solution is replaced with the selected alternative and added to the tabu table to update it. Judging whether the termination condition is reached at the moment, ending the iteration process if the termination condition is reached, otherwise, generating a current scheme alternative neighborhood scheme list again, sequencing according to a dictionary sequence, and continuing the iteration.
The termination condition includes one of all observation tasks to complete the plan, satellite storage resource exhaustion, satellite energy exhaustion, time length of current task plan reaching a defined time length. In practical application, the setting can be performed based on the requirement, and the setting is not limited.
The simulation task planning is performed based on the mode in the embodiment of the specification, and the task planning time range is set to 2022-04-12:00:00 to 2022-04-13:00:00:00. Under the condition that the number of tasks to be observed is 100, 200 and 500, simulation experiments with the number of satellites being 3, 4, 5 and 6 are used, and all experimental results are obtained by taking average values at random for 10 times. In the experiment, the algorithm iteration number is set to 1000, and the tabu list length is set to 500.
Fig. 3 shows the situation that the total number of task plans varies with iteration under a 4-star 200 task scenario for three different algorithms. As can be seen from fig. 3, the full neighborhood tabu search algorithm in the implementation of the present description in the three types of algorithms has the greatest number of tasks and the highest convergence speed.
Fig. 4 illustrates the variation of the highest priority mission plan number in the iterative process in a 4-star 200 mission scenario. As can be seen from fig. 4, the full neighborhood tabu search algorithm and the hierarchical planning algorithm in the implementation of the present disclosure plan the same number of tasks with the highest priority, and the number of task plans with the highest priority is better than that of the conventional tabu search algorithm.
Fig. 5 illustrates the variation of the next highest priority mission plan number in the iterative process in a 4-star 200 mission scenario. As can be seen from fig. 5, the full neighborhood tabu search algorithm in the implementation of the present specification plans the most next highest priority tasks, and the convergence speed is leading. The hierarchical planning algorithm can not give consideration to tasks with different priorities due to the fact that the problem is decomposed into a series of sub-optimization problems, and the convergence speed is obviously slower than that of other two algorithms.
Therefore, the full neighborhood tabu search algorithm can plan high-priority tasks and simultaneously comprehensively consider low-priority tasks, satellite resources are utilized to the greatest extent, and the algorithm optimization effect is optimal.
The statistics of the task planning quantity of the three algorithms on the same test case are shown in table 1.
TABLE 1
Therefore, the full neighborhood tabu search algorithm obtains the best optimization result in all instance data, which shows that the full neighborhood greedy search strategy can be well combined with the tabu search algorithm, and a better effect is obtained in solving the multi-star multi-task planning problem.
In order to verify the solving effect and performance of the algorithm to the large-scale task planning problem, simulation experiments with the number of satellites of 20, 30, 50 and 100 are used under the condition that the number of tasks to be observed is 1000 and 3000 respectively. The algorithm effect is shown in table 2, and the solving performance is shown in fig. 6 and 7. All experimental results were averaged over 10 randomizations.
TABLE 2
Table 2 counts task planning cases of three methods, namely a hierarchical planning algorithm, a traditional tabu search algorithm and a full neighborhood tabu search algorithm under a large-scale task scene. Fig. 6 and fig. 7 respectively show curves of time consumption calculated according to the number of available satellites under the scale of 1000 and 3000 tasks to be planned for the full neighborhood tabu search algorithm and the other two types of comparison algorithms according to the embodiment of the present disclosure. The results shown in the table 2, the fig. 6 and the fig. 7 are synthesized, and on the premise that the total number of the planning tasks of the three algorithms is equivalent or the proposed algorithm is slightly higher than that of the other two algorithms, the calculation time of the full neighborhood tabu search algorithm is far less than that of the other two algorithms. And with the increase of the task planning problem scale, the performance advantages of the full-neighborhood tabu search algorithm are more prominent, namely 1/4 and 1/5 of the calculation time of the traditional tabu search algorithm and the hierarchical planning algorithm, so that the superiority of the full-neighborhood tabu search algorithm is reflected.
By introducing the above embodiment, it can be seen that, according to the method, a window combination is constructed based on the requirements of the observation tasks on the observation windows of the satellite, and the objective function is determined according to the completion conditions of the observation tasks with different priorities, so that the calculated objective function value can embody the completion conditions of the observation tasks with high priorities. And then, integrating the window calling condition, the task planning condition, conflict relations among different window combinations and the objective function, and constructing a multi-star task planning model, so that the model can integrate the window utilization condition, the task execution condition, the conflict condition and the task execution condition. After the objective function value is calculated by using the multi-star task planning model, the change condition of the window combination sequence which is most suitable at present can be selected based on the size of the objective function value, and then task planning is completed according to the determined execution sequence of different window combination sequences. By the method, the utilization condition of satellite resources is considered, the overall execution efficiency is ensured, the tasks with high priority can be processed preferentially, the requirements of practical application are met, meanwhile, the logic of the method is clear and simple, the time consumed by task planning is shortened, and the rapid and effective planning of satellite tasks is realized.
Based on the task planning method for multi-star multi-task, the embodiment of the specification also provides a task planning device for multi-star multi-task. As shown in fig. 8, the task planning device for multi-star multi-task may include the following specific modules.
A window combination construction module 810 for constructing at least one window combination based on the observation window requirements of the satellite for the observation task.
An objective function determining module 820, configured to determine an objective function according to the completion conditions of the observation tasks with different priorities; the function value of the objective function has a size relation with respect to the number of completed observation tasks with high priority.
The model construction module 830 is configured to synthesize a window call condition, a task planning condition, a satellite capability constraint, a conflict relationship between different window combinations, and the objective function, and construct a multi-star task planning model.
The objective function value calculation module 840 is configured to calculate objective function values corresponding to all neighborhood sequences corresponding to the current window combination sequence by using the multi-star task planning model; the neighborhood sequence represents a window combination sequence obtained after the current window combination sequence is adjusted; the current window combination sequence is used for covering corresponding tasks.
A window combination sequence selection module 850, configured to select, based on the magnitude of the objective function value, a neighborhood sequence that does not exist in the tabu list as a next window combination sequence; the tabu list is used for representing window combination sequences selected within a fixed period.
The task planning completion module 860 is configured to repeatedly execute the steps of calculating objective function values of different neighborhood sequences and selecting a next window combination sequence to complete task planning.
Based on the task planning method for multi-star multi-task, the embodiment of the specification also provides task planning equipment for multi-star multi-task. The task planning device for multi-star multi-tasking may include a memory and a processor.
In this embodiment, the memory may be implemented in any suitable manner. For example, the memory may be a read-only memory, a mechanical hard disk, a solid state hard disk, or a usb disk. The memory may be used to store computer program instructions.
In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor, and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable logic controller, and an embedded microcontroller, among others. The processor may execute the computer program instructions to implement the steps of the task planning method for multi-star multi-task corresponding to fig. 1.
The present description also provides one embodiment of a computer storage medium. The computer storage medium includes, but is not limited to, random access Memory (Random Access Memory, RAM), read-Only Memory (ROM), cache (Cache), hard Disk (HDD), memory Card (Memory Card), and the like. The computer storage medium stores computer program instructions. The computer program instructions in the corresponding embodiment of fig. 1 of the present specification are realized when said computer program is executed.
The task planning method for multi-satellite and multi-task described in the above embodiment can be applied to the technical field of satellite task planning, and can also be applied to other technical fields, which is not limited.
While the process flows described above include a plurality of operations occurring in a particular order, it should be apparent that the processes may include more or fewer operations, which may be performed sequentially or in parallel (e.g., using a parallel processor or a multi-threaded environment).
While the process flows described above include a plurality of operations occurring in a particular order, it should be apparent that the processes may include more or fewer operations, which may be performed sequentially or in parallel (e.g., using a parallel processor or a multi-threaded environment).
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present embodiments may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. The task planning method for multi-star and multi-task is characterized by comprising the following steps:
constructing at least one window combination based on the requirements of the observation task on the observation window of the satellite; the satellite includes at least one observation window;
determining an objective function according to the completion conditions of observation tasks with different priorities; the function value of the objective function has a size relation with respect to the number of the completion of the observation tasks with high priority;
synthesizing a conflict relation among window calling conditions, task planning conditions, satellite capacity constraints and different window combinations and the objective function, and constructing a multi-star task planning model;
calculating objective function values corresponding to all neighborhood sequences corresponding to the current window combination sequence by using the multi-star task planning model; the neighborhood sequence represents a window combination sequence obtained after the current window combination sequence is adjusted; the current window combination sequence is used for covering corresponding tasks;
Selecting a neighborhood sequence which does not exist in the tabu list as a next window combination sequence based on the magnitude of the objective function value; the tabu list is used for representing window combination sequences selected in a fixed period;
and repeatedly executing the steps of calculating the objective function values of different neighborhood sequences and selecting the next window combination sequence to complete task planning.
2. The method of claim 1, wherein the constructing at least one window combination based on the observation window requirements of the satellite for the observation task comprises:
vectorizing different window combinations; wherein, include: building a matrix of inclusion relationships between window combinations and observation windows
Determining the coverage condition of different window combinations on the observation task; wherein, include: constructing a coverage relation matrix of window combination and observation task
3. The method of claim 1, wherein determining the objective function based on completion of observation tasks of different priorities comprises:
determining priorities of different observation tasks; the priority is expressed by a single-hot coding mode;
constructing an objective function as
In (1) the->
For task priority, ++>
p i For the ith priority, t i ∈{0,1},i＝1,…,N T ，t i When=1, it indicates that observation task i has arranged observation, t i =0 indicates that observation task i did not complete the observation; objective function->
The objective function is based on +.>
The corresponding dictionary sequence has a size relation of f 1 ≤f 2 If and only if f 1 And f 2 Front of (2) n The same in each component and f on the (n+1) th component 1 Smaller or n=k.
4. The method of claim 1, wherein the conflicting relationship between the different window combinations is used to indicate whether there is a time conflict in transitioning between the different window combinations; the conflicting relationship between the different window combinations is determined by:
using formula h ju ＝tws j -twe u -d ju Calculating redundancy time, wherein, h ju For redundancy time, tws j To observe the start time of window j, we u For observing the ending time of window u, d ju Is a satellite s Adjusting from observation window j to observation window u The required transition time;
defining a window conflict relation indication quantity according to the redundant time; wherein the window conflict relation indication quantity is
In (1) the->
Determining window conflict constraints according to the window conflict relation indication quantity; wherein the window conflict constraint is that
The window conflict constraint is used to define that the determined mission plan does not have a conflict in converting the window.
5. The method of claim 1, wherein the satellite capability constraints comprise satellite storage constraints and energy constraints; the satellite storage constraint is used for representing the constraint of the maximum storage capacity of the satellite on the storage capacity occupied by the observed data; the energy constraint is used for representing the constraint of the maximum energy supported by the satellite on the energy consumed by the satellite for observing by using an observation window; wherein the satellite storage constraint is expressed as 
m js Is a satellite s Acquisition of storage capacity occupied by observed data on window j, w j ∈{0,1},j＝1,…,N W When w is j When=1, it means that window j is used, w j When =0 indicates that window j is not used, +.>
The multi-star task planning model also comprises a decision variable relation; the decision variable relationship is used to represent that an observation window is selected for use if and only if the observation window is contained by one or more selected window combinations, and that an observation task is listed in a task plan if and only if the observation task is covered by one or more selected window combinations; the decision variable relationship comprises w j ≥x g b gj ,
t i ≥x g c gi ,
Wherein x is g ∈{0,1},g＝1,…,N X When x is g When=1, it means that window combination g is used, x g When=0, it means window combination g is not used, +.>
t i ∈{0,1},i＝1,…,N T ，t i When=1, it indicates that observation task i has arranged observation, t i =0 indicates that the observation task i has not completed the observation,
6. the method of claim 1, wherein selecting a neighborhood sequence that is not in the tabu list as a next window combination sequence based on the magnitude of the objective function value comprises:
determining an objective function change value generated by the transformation window combination sequence; wherein, include: using the formula
Calculating the transformation value of the objective function, wherein +. >
Representing the objective function value before the operation on window combination g,/->
Representing an objective function value of a neighborhood sequence generated after the window combination g is operated;
selecting a candidate window combination sequence based on the magnitude of the objective function change value;
adding the candidate window combination sequence into a tabu list under the condition that the candidate window combination sequence is not attributed to the tabu list;
and determining the candidate window combination sequence as a next window combination sequence.
7. The method of claim 6, wherein after selecting a candidate window combination sequence based on the magnitude of the objective function change value, further comprising:
and under the condition that the candidate window combination sequence belongs to the tabu list, re-selecting the candidate window combination sequence based on the magnitude of the change value of the objective function, and judging whether the candidate window combination sequence belongs to the tabu list or not until the position of the candidate window combination sequence which does not exist in the tabu list is selected.
8. The method of claim 6, wherein the repeatedly performing the steps of calculating objective function values for different neighborhood sequences, selecting a next window combination sequence to complete the task planning comprises:
Repeatedly executing the steps of calculating the objective function values of different neighborhood sequences and selecting the next window combination sequence until reaching the termination condition; the termination condition includes one of all observation tasks to complete the plan, satellite storage resource exhaustion, satellite energy exhaustion, time length of current task plan reaching a defined time length.
9. A mission planning apparatus for multi-star multi-tasking, comprising:
the window combination construction module is used for constructing at least one window combination based on the requirement of an observation task on an observation window of the satellite; the satellite includes at least one observation window;
the objective function determining module is used for determining an objective function according to the completion conditions of the observation tasks with different priorities; the function value of the objective function has a size relation with respect to the number of the completion of the observation tasks with high priority;
the model construction module is used for integrating window calling conditions, task planning conditions, satellite capacity constraints, conflict relations among different window combinations and the objective function to construct a multi-star task planning model;
the objective function value calculation module is used for calculating objective function values corresponding to all neighborhood sequences corresponding to the current window combination sequence by utilizing the multi-star task planning model; the neighborhood sequence represents a window combination sequence obtained after the current window combination sequence is adjusted; the current window combination sequence is used for covering corresponding tasks;
The window combination sequence selection module is used for selecting a neighborhood sequence which does not exist in the tabu list as a next window combination sequence based on the size of the objective function value; the tabu list is used for representing window combination sequences selected in a fixed period;
and the task planning completion module is used for repeatedly executing the steps of calculating the objective function values of different neighborhood sequences and selecting the next window combination sequence to complete task planning.
10. A computer storage medium having stored thereon computer program instructions, which when executed implement the steps of a task planning method for multi-star multi-tasking according to any of the claims 1-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116599575A (en) * | 2023-07-17 | 2023-08-15 | 数字太空(北京)科技股份公司 | Simulation environment construction method and device for large-scale remote sensing task system |
| CN116629463A (en) * | 2023-07-25 | 2023-08-22 | 数字太空(北京)科技股份公司 | Multi-star remote sensing task dynamic programming method and device based on greedy strategy |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116599575A (en) * | 2023-07-17 | 2023-08-15 | 数字太空(北京)科技股份公司 | Simulation environment construction method and device for large-scale remote sensing task system |
| CN116599575B (en) * | 2023-07-17 | 2023-10-13 | 数字太空(北京)科技股份公司 | Simulation environment construction method and device for large-scale remote sensing task system |
| CN116629463A (en) * | 2023-07-25 | 2023-08-22 | 数字太空(北京)科技股份公司 | Multi-star remote sensing task dynamic programming method and device based on greedy strategy |
| CN116629463B (en) * | 2023-07-25 | 2023-10-13 | 数字太空(北京)科技股份公司 | Multi-star remote sensing task dynamic programming method and device based on greedy strategy |
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