A mission planning method, device and storage medium for multiple satellites and multiple missions

Abstract

The embodiments of this specification provide a task planning method, device and storage medium for multiple satellites and multiple tasks. The method includes: constructing at least one window combination based on the observation task's requirements for the satellite's observation window; determining the objective function according to the completion status of observation tasks of different priorities; comprehensively considering the window call status, task planning status, satellite capability constraints, conflict relationships between different window combinations and the objective function to construct a multi-satellite task planning model; using the multi-satellite task planning model, calculating the objective function values ​​of all neighborhood sequences corresponding to the current window combination sequence; selecting a neighborhood sequence that does not exist in the taboo list as the next window combination sequence based on the size 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 the task planning. The above method ensures the overall execution efficiency of the task, ensures the priority processing of high-priority tasks, and is conducive to practical application.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of satellite mission planning, and more particularly to a mission planning method, device, and storage medium for multiple satellites and multiple missions. Background Art

[0002] Earth observation satellites, utilizing a variety of observation instruments, can observe or track targets on the Earth's surface while in orbit, acquiring information on impacts and electromagnetic parameters. They play a vital role in a wide range of fields. Despite the increasing number of satellites in orbit, the demand for Earth observation is also increasing. With limited satellite observation resources, a large number of observation missions need to be performed. Therefore, effective planning is required for existing observation missions to efficiently utilize satellite resources.

[0003] Currently, when planning satellite observation missions, the priority of the observation tasks is often considered to determine the order in which they are executed. However, simply considering the priority of tasks can easily lead to high-priority tasks occupying a fixed amount of satellite resources, making it impossible to adjust them on a global basis, and making it difficult to ensure the efficient use of satellite resources. Quantifying task priorities into specific values ​​and performing task planning using a linear weighting method, while ensuring overall benefits, can lead to the abandonment of a small number of high-priority tasks in order to complete more low-priority tasks, which does not meet the urgency of executing high-priority tasks in actual applications. Therefore, there is an urgent need for a technical solution that can balance the overall utilization of satellite resources and the prioritization of high-priority tasks. Summary of the Invention

[0004] The purpose of the embodiments of this specification is to provide a multi-satellite multi-task mission planning method, device and storage medium to solve the problem of how to take into account the overall utilization of satellites and the priority processing of high-priority tasks during mission planning.

[0005] To solve the above technical problems, an embodiment of this specification proposes a task planning method for multiple satellites and multiple tasks, including: constructing at least one window combination based on the observation task's requirements for the observation window of the satellite; the satellite includes at least one observation window; determining an objective function based on the completion status of observation tasks of different priorities; the function value of the objective function has a size relationship with the number of completed high-priority observation tasks; comprehensively considering the window call status, task planning status, satellite capability constraints, conflicts between different window combinations, and the objective function to construct a multi-satellite task planning model; using the multi-satellite task planning model, calculating the objective function values ​​corresponding to all neighboring sequences corresponding to the current window combination sequence; the neighboring sequence represents a window combination sequence obtained by adjusting the current window combination sequence; the current window combination sequence is used to cover the corresponding task; based on the size of the objective function value, selecting a neighboring sequence that does not exist in a taboo list as the next window combination sequence; the taboo list is used to represent window combination sequences that have been selected within a fixed period; and repeatedly performing the steps of calculating the objective function values ​​of different neighboring sequences and selecting the next window combination sequence to complete task planning.

[0006] The embodiment of this specification also proposes a task planning device for multiple satellites and multiple tasks, including: a window combination construction module, used to construct at least one window combination based on the observation task's requirements for the satellite's observation window; the satellite includes at least one observation window; an objective function determination module, used to determine the objective function according to the completion status of observation tasks of different priorities; the function value of the objective function has a large-small relationship with the number of completed observation tasks of high priority; a model construction module, used to comprehensively integrate the window call status, task planning status, satellite capability constraints, the conflict relationship between different window combinations and the objective function to construct a multi-satellite task planning model; an objective function value calculation module, Used to calculate the objective function values ​​corresponding to all neighborhood sequences corresponding to the current window combination sequence using the multi-satellite mission planning model; the neighborhood sequence represents the window combination sequence obtained after adjusting the current window combination sequence; the current window combination sequence is used to cover the corresponding task; the window combination sequence selection module is used to select a neighborhood sequence that does not exist in the taboo list as the next window combination sequence based on the size of the objective function value; the taboo list is used to represent the window combination sequence that has been selected within a fixed period; the mission planning completion module is used to repeatedly execute the steps of calculating the objective function values ​​of different neighborhood sequences and selecting the next window combination sequence to complete the mission planning.

[0007] The embodiments of this specification also provide a computer storage medium having a computer program stored thereon, which implements the steps of the above-mentioned method for multi-satellite multi-mission mission planning when executed.

[0008] As can be seen from the technical solutions provided by the above embodiments of this specification, this embodiment of the specification constructs a window combination based on the observation task's demand for the satellite's observation window, and determines the objective function based on the completion of observation tasks of different priorities, so that the calculated objective function value can reflect the completion of high-priority observation tasks. Afterwards, the window call situation, task planning situation, the conflict relationship between different window combinations, and the objective function are integrated to construct a multi-satellite mission planning model, so that the model can integrate the utilization of windows, the execution of tasks, conflicts, and the execution of tasks. After calculating the objective function value using the multi-satellite mission planning model, the most suitable window combination sequence change can be selected based on the magnitude of the objective function value, and then the mission planning is completed according to the execution order of the different window combination sequences determined. Through the above method, not only the utilization of satellite resources is taken into account, the overall execution efficiency is guaranteed, but also high-priority tasks can be processed first, meeting the needs of practical applications. At the same time, the method has clear and simple logic, shortens the time consumed by mission planning, and realizes fast and effective planning of satellite missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 This is a flowchart of a multi-satellite multi-mission mission planning method according to an embodiment of this specification;

[0011] Figure 2 A schematic diagram of a tabu search algorithm according to an embodiment of this specification;

[0012] Figure 3 This is a comparative diagram of the total number of task plans versus the number of iterations in an embodiment of this specification;

[0013] Figure 4 This is a comparative diagram of the highest priority task planning situation in an embodiment of this specification;

[0014] Figure 5 A comparative diagram of a second-highest priority task planning situation according to an embodiment of this specification;

[0015] Figure 6 This is a comparative diagram of the optimized duration of 1000 tasks according to an embodiment of this specification;

[0016] Figure 7This is a comparative diagram of the optimized duration of 3000 tasks according to an embodiment of this specification;

[0017] Figure 8 This is a module diagram of a multi-satellite multi-task mission planning device according to an embodiment of this specification. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this specification.

[0019] In order to solve the above technical problems, the embodiments of this specification propose a mission planning method for multiple satellites and multiple missions. Figure 1 As shown, the mission planning method for multiple satellites and multiple missions includes the following specific implementation steps.

[0020] S110: Construct at least one window combination based on the observation window requirements of the satellite for the observation mission.

[0021] First, to ensure the effective implementation of the subsequent task planning process and prevent interference from other external factors in the calculation process, the following assumptions are made:

[0022] 1) Communication resources are sufficient and the data transmission process is not considered.

[0023] 2) The visibility time windows of all satellites for the mission have been discretized, with the discretization interval being the minimum duration of mission observation. The discretized satellite visibility time windows for the mission are hereafter collectively referred to as observation windows.

[0024] 3) The observation windows to be planned all meet the imaging resolution requirements of the observation tasks they cover.

[0025] First, the satellite is equipped with corresponding sensor equipment. Accordingly, different satellites have different observation windows based on their own positions and the installed sensor equipment. Different observation windows can be used to observe different observation targets.

[0026] Observation missions can include point targets, area targets, and mixed targets. An observation mission may utilize a single observation window or combine multiple observation windows for observation. To facilitate observation window arrangement during mission planning, at least one window combination can be constructed based on the observation window requirements of the satellite. To facilitate subsequent calculations, observation windows and observation missions are vectorized.

[0027] First, assume that the set of task indexes to be observed is The set of available satellite indexes is Furthermore, the satellite observation window index set for the mission is set to Establish the relationship matrix between visible windows and satellites in

[0028]

[0029] Since an observation task may require multiple observation windows to jointly cover, we can obtain window combinations based on different observation window combinations. Each window combination contains one or more observation windows and can observe one or more tasks at the same time. Let the window combination set be Establish the inclusion relationship matrix between the window combination and the observation window in

[0030]

[0031] Establish the coverage relationship matrix of window combination for observation tasks in

[0032]

[0033] In order to effectively describe the window occupancy and the execution of the observation task, we can further define the decision variables: xg∈{0,1},g=1,…,N X , when x g =1 indicates that the window combination g is used, otherwise it is not used; When w j =1 indicates that the observation window j is used, otherwise it is not used; i ∈{0,1},i=1,…,N T , when t i =1, it means that observation task i has been scheduled for observation; otherwise, it means that the observation has not been completed.

[0034] S120: determining an objective function according to the completion status of observation tasks of different priorities; a function value of the objective function has a magnitude relationship with the number of completed observation tasks of high priority.

[0035] Different observation tasks are pre-set with priority attributes. In order to meet the actual application requirements, high-priority observation tasks generally need to be processed first.

[0036] In order to achieve the above effect, the priority set of the observation task is set as The different values ​​in this priority set are used to represent all possible priorities, where the smaller the value, the higher the priority of the corresponding task. Let the priority of the i-th task be pi , for task i, define the vector is the one-hot encoding of the task priority, i.e.

[0037]

[0038] in Indicates o i The kth component of . Through the form of one-hot encoding, the priorities of different tasks are expressed in the form of 0-1 encoding, which is conducive to the subsequent comparison of the size of the objective function value.

[0039] Furthermore, the objective function is set as:

[0040]

[0041] where the vector Indicates the planned number of tasks of each priority level, t i =1 indicates that observation task i has been scheduled for observation, t i = 0 means that observation task i has not completed the observation. The kth component of f represents the number of tasks with priority k that have been planned.

[0042] The preemptive priority mode is adopted in the embodiments of this specification, that is, the number of high-priority tasks completed 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. The lexicographic order is defined as follows: f1≤f2 if and only if f1 and f2 are preceded by n components are the same, and f1 is smaller on the n+1th component or n = K. Maximizing f according to the above lexicographic order can maximize the preemptive priority, that is, by comparing the sizes of different objective functions, the high-priority task can be processed first.

[0043] S130: Constructing a multi-satellite mission planning model by integrating the window calling situation, the mission planning situation, the satellite capability constraints, the conflict relationship between different window combinations and the objective function.

[0044] In practical applications, the satellite's own capabilities also impose certain limitations on mission planning. For example, the satellite's own storage capacity and energy constrain the number of missions that can be executed. Therefore, it is also necessary to consider the constraints imposed by satellite storage and energy on the mission planning process. Satellite capability constraints include satellite storage constraints and energy constraints. Satellite storage constraints represent the constraints imposed by the satellite's maximum storage capacity on the storage capacity occupied by observation data; energy constraints represent the constraints imposed by the satellite's maximum energy support on the energy consumed by the satellite during observations within the observation window. Specifically, the satellite storage and energy constraints are constructed as follows:

[0045]

[0046]

[0047] Where formula (6) is the satellite storage constraint, m js For satellite s Get the storage capacity occupied by the observation data in window j, M s For satellite s The maximum storage capacity; Formula (7) is the satellite energy constraint, e js For satellite s The energy consumed by observing window j, E s For satellite s The maximum energy supported.

[0048] The above satellite storage constraints and energy constraints can be applied as termination conditions for mission planning, so that the iteration process can be skipped in subsequent iterations.

[0049] In addition, when the same satellite completes two consecutive observations, the start time of the next observation must be greater than or equal to the sum of the current observation completion time and the conversion time, that is, the two observation windows cannot conflict in time. s The start and end times of the observation window j are tws j and twe j ;d ju For satellite s Adjust the observation state from window j to window j u The total transition time required to observe the state.

[0050] h ju =tws j -twe u -d ju (8)

[0051] Among them, h ju is the redundant time, tws j is the starting time of observation window j, tweu is the end time of observation window u, d ju For satellite s Adjust from observation window j to observation window u The required transition time.

[0052] The above formula (8) expresses the satellite s The difference between the interval time of two consecutive mission observation windows and the transition time of satellite observation state, that is, the start time of window j and the start time of window u The difference between the end time of the satellite s Adjust from window j to window u The transition time d ju If h ju <0, you cannot use the window u After the observation, continue to use window j for observation. Therefore, the continuous window time conflict indicator can be defined as

[0053]

[0054] in,

[0055]

[0056] is the indicator function. ju = 1, window j and window u Conflicts will occur due to being too close or overlapping. Therefore, the window conflict constraint is

[0057]

[0058] It indicates that the total number of conflicting window pairs allowed in the satellite mission planning scheme is 0. When the window conflict constraint is satisfied, the determined mission plan does not have any conflict when switching windows.

[0059] Furthermore, when planning satellite missions, a decision variable relationship can be constructed to limit the execution of the mission. The decision variable relationship is used to indicate that a window is selected only when it is included in one or more selected window combinations, and that a mission can be planned for observation only when it is covered by one or more selected window combinations. Specifically, the decision variable relationship can be as follows:

[0060]

[0061]

[0062]

[0063]

[0064] Formulas (12) and (13) indicate that a window is selected if and only if it is included in one or more selected window combinations; Formulas (14) and (15) indicate that a task can be planned for observation if and only if it is covered by one or more selected window combinations.

[0065] In summary, combining the above different constraints and the expression of the objective function, the corresponding multi-satellite mission planning model can be constructed as follows:

[0066]

[0067] in, represents the window combination decision variable; represents the window decision variable; represents the mission planning decision variable.

[0068] Through the above multi-satellite mission planning model, the objective function value of the corresponding window combination can be calculated while meeting different constraints.

[0069] S140: Calculate the objective function values ​​corresponding to all neighboring sequences corresponding to the current window combination sequence using the multi-satellite mission planning model; the neighboring sequence represents a window combination sequence obtained by adjusting the current window combination sequence; the current window combination sequence is used to cover the corresponding task.

[0070] In order to perform different observation tasks, it is generally necessary to select different window combinations, that is, the task planning process is equivalent to the window combination selection process. The usage status of each window combination constitutes an N X dimensional 0-1 vector Each bit corresponds to a decision variable of a window combination. The neighborhood is constructed by inserting a new window combination into the list of selected window combinations or deleting the selected window combination. Since each operation changes the state of at most one window combination, that is, it realizes the change from one window combination to another, the neighborhood contains at most N X adjacent states, which can be used as corresponding neighborhood sequences.

[0071] When planning a task, the decision variables w and t can be determined by x; from formulas (12)-(15) we can get

[0072]

[0073]

[0074] Therefore, the state only needs to be encoded using x, and each time x is updated, it is updated according to formulas (16) and (17) w Just use t.

[0075] In the calculation process, increasing the neighborhood size can achieve better optimization results. The full neighborhood greedy search strategy involved in the embodiment of this specification breaks through the limitation of neighborhood size by maintaining the benefit list of all non-conflicting window combinations, and realizes the search for the maximum target gain Δf within the largest possible neighborhood range. g When different neighborhood sequences are determined, the objective function values ​​corresponding to all neighborhood combinations can be calculated in sequence and applied in subsequent processes.

[0076] S150: selecting a neighborhood sequence that does not exist in the taboo list as the next window combination sequence based on the magnitude of the objective function value; the taboo list is used to represent the window combination sequences that have been selected within a fixed period.

[0077] After the objective function value is calculated, the objective function change value generated by the transformation window combination sequence can be determined first; which includes: using the formula Calculate the transformation value of the objective function, where Represents the objective function value before operating on the window combination g, Represents the objective function value of the neighborhood sequence generated after operating the window combination g.

[0078] Based on the objective function defined above and the size relationship of the lexicographic order, in the subsequent search strategy, Δf is maximized in each step of the operation. g The window combination g to be operated can be determined by taking it as the target.

[0079] In order to quickly find the operation with the largest gain, each iteration calculates the target gain of all non-conflicting window combinations, sorts them based on the lexicographic order and records them as a list k Among them, the target gain and conflict status of most window combinations are the same as those in the previous iteration, so only a small part of the window combinations affected by the operation need to be updated, which also ensures computational efficiency.

[0080] In addition, to prevent resources from being occupied by fixed high-priority tasks for a long time, a taboo list can be set. The taboo list is used to record recently accessed states and prohibit continuous repeated searches for the same neighborhood solution, thereby preventing search loops and falling into local optimality.

[0081] The selection of the length of the taboo list is closely related to the actual problem. If it is too short, it will cause loops to appear, and if it is too long, it will lead to slower convergence. In some examples, the taboo length can range from

[0082] After the objective function change value is calculated, a corresponding candidate window combination sequence can be selected based on the size of the objective function change value. The specific size relationship can be determined based on the above settings and is not limited to this.

[0083] If the selected candidate window combination sequence does not belong to the taboo list, the candidate window combination sequence is added to the taboo list, and the candidate window combination sequence is determined to be the next window combination sequence.

[0084] If the candidate window combination sequence belongs to the taboo list, it means that the candidate window combination sequence has been selected recently. In this case, the candidate window combination sequence is reselected based on the size of the objective function change value, and it is determined whether the candidate window combination sequence belongs to the taboo list until the candidate window combination sequence position that does not exist in the taboo list is selected.

[0085] The selected next window combination sequence can be used as the arrangement in the task planning, and the task execution process is determined based on the current window combination sequence and the next window combination sequence.

[0086] S160: Repeat the steps of calculating the objective function values ​​of different neighborhood sequences and selecting the next window combination sequence to complete the task planning.

[0087] The above calculation process is an iterative process, which determines the various observation tasks in the mission planning in turn through iteration. To better describe the iterative process, taking the k-th iteration as an example, the following operations are performed:

[0088] Step 1: Traverse χ starting from the operation with the highest target gain k , stop when the first operation that does not trigger the taboo is found, and get the neighborhood solution of this iteration.

[0089] Step 2: Let χ k+1 =χ k .

[0090] Step 3: Traverse all window combinations on the satellite involved in the operation and check whether their conflict status has changed. If a window combination changes from non-conflicting to conflicting due to this operation, it is necessary to Delete the window combination; on the contrary, if a window combination changes from conflict to non-conflict due to this operation, the target gain of the window combination needs to be recalculated and added to χ k+1 middle.

[0091] Step 4: Traverse all window combinations corresponding to the tasks involved in the operation. If the task is , its target gain is updated and it is reinserted into the correct position to maintain the order.

[0092] Among them, the storage list χ k When using red-black tree as data structure, you can use time to complete the insertion and deletion, where Representation list At the same time, each window combination needs to record its conflict status, so it only takes O(1) time to complete the query and update of the conflict status.

[0093] Combine Figure 2 , describing the complete iterative process. First, the initial feasible solution is obtained and the tabu table is initialized. Next, a list of alternative neighborhood solutions for the current solution is generated and sorted lexicographically. Based on the sorting, the best alternative in the list is selected and deleted. Next, a determination is made as to whether the selected solution is in the tabu table. If so, the best alternative in the list is reselected and deleted. If not, the selected alternative replaces the current solution and is added to the tabu table to update it. A determination is then made as to whether the termination condition has been met. If so, the iteration ends. Otherwise, a list of alternative neighborhood solutions for the current solution is generated again, sorted lexicographically, and the iteration continues.

[0094] The termination condition includes one of the following: all observation tasks have been completed, satellite storage resources are exhausted, satellite energy is exhausted, or the duration of the current mission plan reaches a specified time length. In actual applications, it can also be set based on demand and is not limited to this.

[0095] Simulated mission planning was performed based on the methods described in the examples of this specification, with a time range of 00:00:00 on April 12, 2022, to 00:00:00 on April 13, 2022. Simulations were conducted with 3, 4, 5, and 6 satellites, considering 100, 200, and 500 missions to be observed, respectively. All experimental results were averaged from 10 random runs. The number of algorithm iterations was set to 1000, and the length of the taboo list was set to 500.

[0096] Figure 3 The total number of task plans for three different algorithms in a 4-star 200-task scenario is shown as the number of tasks changes with iteration. Figure 3 It can be seen that among the three types of algorithms, the full neighborhood tabu search algorithm implemented in this specification has the largest number of planning tasks and the fastest convergence speed.

[0097] Figure 4 The figure shows the change of the number of highest priority tasks in the iterative process in the 4-star 200-task scenario. Figure 4It can be seen that the full-neighborhood tabu search algorithm and the hierarchical planning algorithm implemented in this specification plan the same number of highest priority tasks, and the number of highest priority tasks planned by both is better than that of the traditional tabu search algorithm.

[0098] Figure 5 The figure shows how the number of second-highest priority tasks changes during the iteration process in the 4-star 200-task scenario. Figure 5 As can be seen, the full-neighborhood tabu search algorithm implemented in this specification plans the most sub-highest priority tasks and converges faster. The hierarchical planning algorithm, because it decomposes the problem into a series of sub-optimization problems, cannot take into account tasks of different priorities and converges significantly slower than the other two algorithms.

[0099] Therefore, the full-neighborhood tabu search algorithm can plan high-priority tasks while taking into account low-priority tasks, maximize the use of satellite resources, and achieve the best algorithm optimization effect.

[0100] The statistics of the number of task plans of the three algorithms on the same test case are shown in Table 1.

[0101] Table 1

[0102]

[0103] It can be seen that the full-neighborhood tabu search algorithm obtains the best optimization results in all instance data, indicating that the full-neighborhood greedy search strategy can be well combined with the tabu search algorithm and achieve better results in solving multi-satellite multi-task planning problems.

[0104] In order to verify the effectiveness and performance of the proposed algorithm in solving large-scale mission planning problems, simulation experiments were conducted with 20, 30, 50 and 100 satellites, considering the number of missions to be observed being 1000 and 3000 respectively. The algorithm effect is shown in Table 2, and the solution performance is shown in Table 2. Figure 6 、 Figure 7 All experimental results are the average of 10 random experiments.

[0105] Table 2

[0106]

[0107]

[0108] Table 2 summarizes the task planning status of three methods: hierarchical planning algorithm, traditional tabu search algorithm and full neighborhood tabu search algorithm in large-scale task scenarios. Figure 6 、 Figure 7The curves showing the change of computation time with the number of available satellites for the full neighborhood tabu search algorithm involved in the embodiment of this specification and the other two comparison algorithms under the scale of 1000 and 3000 tasks to be planned are shown respectively. Figure 6 、 Figure 7 The results show that, assuming the total number of planning tasks for the three algorithms is comparable or the proposed algorithm is slightly faster than the other two comparison algorithms, the full-neighborhood tabu search algorithm takes significantly less time to compute than the other two algorithms. Furthermore, as the scale of the task planning problem increases, the performance advantage of the full-neighborhood tabu search algorithm becomes increasingly pronounced, reaching computational times of 1 / 4 and 1 / 5 of the time required by the traditional tabu search algorithm and the hierarchical planning algorithm, respectively, demonstrating the superiority of the full-neighborhood tabu search algorithm.

[0109] Through the introduction of the above embodiments, it can be seen that the method constructs window combinations based on the observation task's requirements for satellite observation windows and determines an objective function based on the completion status of observation tasks of different priorities, so that the calculated objective function value can reflect the completion status of high-priority observation tasks. Subsequently, a multi-satellite mission planning model is constructed by integrating the window call status, task planning status, conflicts between different window combinations, and the objective function. The model can integrate window utilization, task execution status, conflicts, and task execution status. After calculating the objective function value using the multi-satellite mission planning model, the most suitable window combination sequence variation is selected based on the magnitude of the objective function value. Then, mission planning is completed according to the execution order of the determined different window combination sequences. This method not only takes into account the utilization of satellite resources and ensures overall execution efficiency, but also prioritizes high-priority tasks, meeting the needs of practical applications. At the same time, the method's clear and simple logic shortens the time consumed in mission planning, achieving rapid and efficient planning of satellite missions.

[0110] Based on the above-mentioned multi-satellite multi-task task planning method, the embodiment of this specification also proposes a multi-satellite multi-task task planning device. Figure 8 As shown, the mission planning device for multiple satellites and multiple missions may include the following specific modules.

[0111] The window combination building module 810 is configured to build at least one window combination based on the observation window requirements of the satellites imposed by the observation mission.

[0112] The objective function determination module 820 is used to determine the objective function according to the completion status of observation tasks of different priorities; the function value of the objective function has a large-small relationship with the number of completed observation tasks of high priority.

[0113] The model building module 830 is used to build a multi-satellite mission planning model by integrating the window calling situation, mission planning situation, satellite capability constraints, conflict relationships between different window combinations and the objective function.

[0114] The objective function value calculation module 840 is used to calculate the objective function values ​​corresponding to all neighboring sequences corresponding to the current window combination sequence using the multi-satellite mission planning model; the neighboring sequence represents the window combination sequence obtained by adjusting the current window combination sequence; the current window combination sequence is used to cover the corresponding mission.

[0115] The window combination sequence selection module 850 is used to select a neighborhood sequence that does not exist in the taboo list as the next window combination sequence based on the magnitude of the objective function value; the taboo list is used to represent the window combination sequences that have been selected within a fixed period.

[0116] The task planning completion module 860 is used to repeatedly execute the steps of calculating the objective function values ​​of different neighborhood sequences and selecting the next window combination sequence to complete the task planning.

[0117] Based on the above-mentioned multi-satellite multi-mission mission planning method, the embodiment of this specification further provides a multi-satellite multi-mission mission planning device. The multi-satellite multi-mission mission planning device may include a memory and a processor.

[0118] 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 drive, or a USB flash drive. The memory may be used to store computer program instructions.

[0119] In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. The processor may execute the computer program instructions to implement Figure 1 The corresponding steps of the mission planning method for multiple satellites and multiple tasks.

[0120] This specification also provides an embodiment of a computer storage medium. The computer storage medium includes, but is not limited to, random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), memory card, etc. The computer storage medium stores computer program instructions. When the computer program is executed, the computer program performs the functions of this specification. Figure 1 The computer program instructions in the corresponding embodiment.

[0121] The multi-satellite multi-task mission planning method described in the above embodiment can be applied to the field of satellite mission planning technology, and can also be applied to other technical fields without limitation.

[0122] Although the process flows described above include multiple operations occurring in a particular order, it should be understood that these processes may include more or fewer operations, which may be performed sequentially or in parallel (eg, using parallel processors or a multi-threaded environment).

[0123] Although the process flows described above include multiple operations occurring in a particular order, it should be understood that these processes may include more or fewer operations, which may be performed sequentially or in parallel (eg, using parallel processors or a multi-threaded environment).

[0124] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0127] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0128] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0129] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media 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 disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0130] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] Embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. Embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In distributed computing environments, program modules may be located in local and remote computer storage media, including storage devices.

[0132] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between the various embodiments can be referenced across them. Each embodiment focuses on the differences from the other embodiments. In particular, since the system embodiments are generally similar to the method embodiments, their description is relatively simple. For relevant parts, reference can be made to the description of the method embodiments. Throughout this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the embodiments in this specification. In this specification, the schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate the different embodiments or examples, and features of different embodiments or examples, described in this specification, without conflict.

[0133] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A mission planning method for multiple satellites and multiple missions, characterized in that: include: Constructing at least one window combination based on the observation task's requirement for a satellite's observation window; the satellite includes at least one observation window; Determine an objective function based on the completion status of observation tasks of different priorities; the function value of the objective function has a magnitude relationship with the number of completed observation tasks of high priority; A multi-satellite mission planning model is constructed by integrating window calling conditions, mission planning conditions, satellite capability constraints, conflict relationships between different window combinations, and the objective function; Utilizing the multi-satellite mission planning model, calculating objective function values ​​corresponding to all neighboring sequences corresponding to a current window combination sequence; the neighboring sequence represents a window combination sequence obtained by adjusting the current window combination sequence; the current window combination sequence is used to cover a corresponding mission; Based on the magnitude of the objective function value, a neighborhood sequence that does not exist in the taboo list is selected as the next window combination sequence; the taboo list is used to represent the window combination sequences that have been selected within a fixed period; Repeat the steps of calculating the objective function values ​​of different neighborhood sequences and selecting the next window combination sequence to complete the task planning; The step of constructing at least one window combination based on the observation window requirements of the satellite by the observation mission includes: Vectorize different window combinations; including: constructing the inclusion relationship matrix B of the window combination and the observation window = (b gj ) g∈X,j∈W , Determine the coverage of different window combinations on the observation task; which includes: constructing the coverage relationship matrix C between the window combination and the observation task = (c gi ) g∈X,i∈T , Determining the objective function according to the completion status of observation tasks of different priorities includes: Determine the priorities of different observation tasks; the priorities are represented by one-hot encoding; The objective function is constructed as Where, is the task priority, p i is the priority of item i, t i ∈{0,1},i=1,L,N T , t i =1 indicates that observation task i has been scheduled for observation, t i =0 means that observation task i has not completed the observation; objective function The objective function is based on The corresponding lexicographic order has a size relationship of f1≤f2 if and only if the first n components of f1 and f2 are the same, and f1 is smaller in the n+1th component or n=K.

2. The method according to claim 1, wherein The conflict relationship between different window combinations is used to indicate whether there is a time conflict when switching between different window combinations. The conflict relationship between different window combinations is determined in the following manner: Using the formula h ju =tws j -twe u -d ju Calculate the redundant time, where h ju is the redundant time, tws j is the starting time of observation window j, twe u is the end time of observation window u, d ju is the transition time required for satellite s to adjust from observation window j to observation window u; The window conflict relationship indicator is defined according to the redundant time; wherein the window conflict relationship indicator is Where, Determine the window conflict constraint according to the window conflict relationship indicator; wherein the window conflict constraint is The window conflict constraint is used to limit the determined task plan to have no conflicts when switching windows.

3. The method according to claim 1, wherein The satellite capability constraint includes satellite storage constraint and energy constraint; the satellite storage constraint is used to represent the constraint of the satellite's maximum storage capacity on the storage capacity occupied by observation 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 for observation using the observation window; wherein, the satellite storage constraint is expressed as m js is the storage capacity occupied by satellite s to obtain observation data in window j, w j ∈{0,1},j=1,L,N W , when w j =1 indicates that window j is used, w j =0 means window j is not used, The multi-satellite mission planning model also includes a decision variable relationship; the decision variable relationship is used to indicate that an observation window is selected when and only when it is included in one or more selected window combinations, and that an observation task is included in the mission planning when and only when it is covered by one or more selected window combinations; the decision variable relationship includes w j ≥x g b gj , t i ≥x g c gi , Where x g ∈{0,1},g=1,L,N X , when x g =1 indicates that the window combination g is used, x g =0 means window combination g is not used. t i ∈{0,1},i=1,L,N T , t i =1 indicates that observation task i has been scheduled for observation, t i =0 means that observation task i has not completed the observation, 4. The method according to claim 1, wherein The step of selecting a neighborhood sequence that does not exist in the taboo list as the next window combination sequence based on the magnitude of the objective function value includes: Determine the target function change value generated by the transformation window combination sequence; including: using the formula Calculate the transformation value of the objective function, where Represents the objective function value before operating on the window combination g, Represents the objective function value of the neighborhood sequence generated after operating the window combination g; Selecting a candidate window combination sequence based on the magnitude of the objective function change value; In a case where the candidate window combination sequence does not belong to the taboo list, adding the candidate window combination sequence to the taboo list; The candidate window combination sequence is determined as the next window combination sequence.

5. The method according to claim 4, wherein After selecting a candidate window combination sequence based on the magnitude of the objective function change value, the method further includes: In the case that the candidate window combination sequence belongs to the taboo list, the candidate window combination sequence is reselected based on the size of the objective function change value, and it is determined whether the candidate window combination sequence belongs to the taboo list until a candidate window combination sequence position that does not exist in the taboo list is selected.

6. The method according to claim 4, wherein The step of repeatedly calculating the objective function values ​​of different neighborhood sequences and selecting the next window combination sequence to complete the task planning includes: The steps of calculating the objective function values ​​of different neighborhood sequences and selecting the next window combination sequence are repeatedly performed until a termination condition is reached; the termination condition includes one of the following: all observation tasks are completed, satellite storage resources are exhausted, satellite energy is exhausted, and the time length of the current mission plan reaches a limited time length.

7. A mission planning device for multiple satellites and multiple missions, characterized in that: include: A window combination building module, configured to build at least one window combination based on the observation task's requirements for a satellite's observation window; the satellite includes at least one observation window; An objective function determination module is used to determine an objective function according to the completion status of observation tasks of different priorities; the function value of the objective function has a large-small relationship with the number of completed observation tasks of high priority; A model building module is used to integrate the window calling situation, mission planning situation, satellite capability constraints, conflict relationships between different window combinations and the objective function to build a multi-satellite mission planning model; an objective function value calculation module, configured to calculate, using the multi-satellite mission planning model, objective function values ​​corresponding to all neighboring sequences corresponding to a current window combination sequence; the neighboring sequence represents a window combination sequence obtained by adjusting the current window combination sequence; the current window combination sequence is used to cover the corresponding mission; A window combination sequence selection module is used to select a neighborhood sequence that does not exist in the taboo list as the next window combination sequence based on the magnitude of the objective function value; the taboo list is used to represent the window combination sequences that have been selected within a fixed period; A task planning completion module is used to repeatedly execute the steps of calculating the objective function value of different neighborhood sequences and selecting the next window combination sequence to complete the task planning; The window combination construction module is specifically used to: vectorize different window combinations; including: constructing an inclusion relationship matrix between the window combination and the observation window Determine the coverage of different window combinations on the observation task; which includes: constructing the coverage relationship matrix C between the window combination and the observation task = (c gi ) g∈X,i∈T , The objective function determination module is specifically used to: determine the priorities of different observation tasks; the priorities are represented by one-hot encoding; construct the objective function as Where, is the task priority, p i is the priority of item i, t i ∈{0,1},i=1,L,N T , t i =1 indicates that observation task i has been scheduled for observation, t i =0 means that observation task i has not completed the observation; objective function The objective function is based on The corresponding lexicographic order has a size relationship of f1≤f2 if and only if the first n components of f1 and f2 are the same, and f1 is smaller in the n+1th component or n=K.

8. A computer storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed, the computer program instructions implement the steps of the multi-satellite multi-mission mission planning method according to any one of claims 1 to 6.

Images