Multi-satellite collaborative mission planning method, device, equipment and storage medium

Abstract

The present application relates to a method, device, equipment and storage medium for multi-satellite collaborative task planning, and relates to the field of satellite technology. The multi-satellite collaborative task planning method includes: determining the task allocation order of each task to be assigned according to the task allocation weight factor, and determining the current task to be assigned according to the task allocation order; determining the available orbit of the current task to be assigned according to the orbital taboo factor; obtaining the allocation probability between the current task to be assigned and the available orbit of the current task to be assigned; assigning the current task to be assigned to the available orbit with the highest allocation probability; selecting the next task to be assigned according to the task allocation order, and taking the next task to be assigned as the current task to be assigned, and returning to execute the step of obtaining the orbital taboo factor between the current task to be assigned and each orbit, until all tasks to be assigned have been assigned. The present application is used to solve the problem of insufficient optimization of multi-satellite large-scale collaborative task planning.

Description

Technical Field

[0001] The present application relates to the field of satellite technology, and in particular to a multi-satellite collaborative mission planning method, device, equipment and storage medium. Background Art

[0002] Satellites are a scarce observation resource. Although the number and types of satellites in orbit have increased and their observation capabilities have improved, it is still difficult to meet the large number of observation needs. With the rapid development of satellite technology, using computers instead of manual work to prepare satellite scheduling plans meets the actual application requirements. There have been many research results on emergency multi-satellite collaborative observation mission planning methods, but there are still the following difficulties and challenges:

[0003] 1) The number of emergency observation tasks is large and the timeliness requirements are high, so it is necessary to design a more effective multi-satellite collaborative scheduling algorithm. Existing satellite collaborative scheduling algorithms mostly use heuristic algorithms, meta-heuristic algorithms, and a combination of multiple algorithms, but no researchers have combined the exact algorithm with other types of algorithms to improve the algorithm's solving ability;

[0004] 2) There are many types of on-orbit observation resource platforms and the types of payloads are complex. It is necessary to design a multi-source resource collaborative scheduling method to reasonably allocate satellite resources to the tasks to be observed without conflict, achieve balanced and scientific satellite loads, and improve the overall observation quality of the constellation;

[0005] 3) There is a lack of collaborative observation mission planning methods for large models and multiple parameters. In previous studies, there is still potential for observation to be explored in multi-satellite collaborative scheduling, and the problem of multi-satellite large-scale collaborative mission planning still needs further breakthroughs. Summary of the invention

[0006] The present application provides a multi-satellite collaborative mission planning method, device, equipment and storage medium to solve the problem of insufficient optimization of multi-satellite large-scale collaborative mission planning.

[0007] In a first aspect, an embodiment of the present application provides a multi-satellite collaborative mission planning method, including:

[0008] Obtaining task allocation weight factors of each task to be allocated, determining a task allocation order of each task to be allocated according to the task allocation weight factors, and determining a current task to be allocated according to the task allocation order;

[0009] Acquire the track taboo factor between the current task to be assigned and each track, and determine the available track of the current task to be assigned according to the track taboo factor;

[0010] Obtaining an allocation probability between the current task to be allocated and the available tracks of the current task to be allocated;

[0011] Allocate the current task to be assigned to the available track with the highest assignment probability;

[0012] According to the task allocation order, the next task to be allocated is selected, and the next task to be allocated is used as the current task to be allocated, and the step of obtaining the track taboo factor between the current task to be allocated and each track is returned to be executed until all the tasks to be allocated have been allocated.

[0013] Optionally, the task allocation weight factor, the track taboo factor and the allocation probability are dynamically adjusted during each iteration, and the number of iterations is less than or equal to an iteration number threshold.

[0014] Optionally, obtaining the task allocation weight factor of each task to be allocated includes:

[0015] At the end of each iteration, determine whether the task to be assigned has been successfully observed;

[0016] If the task to be assigned is successfully observed, then in the next iteration, the task assignment weight factor of the task to be assigned remains unchanged;

[0017] If the task to be assigned is not successfully observed, the task assignment weight factor of the task to be assigned is reduced in the next iteration.

[0018] Optionally, the obtaining of the track taboo factors between the current task to be assigned and each track includes:

[0019] At the end of each iteration, determining whether the current task to be assigned is successfully observed on the available track of the current task to be assigned;

[0020] If the current task to be assigned is not successfully observed on the available orbit of the current task to be assigned, the available orbit of the current task to be assigned is tabooed, the tabooed orbit is removed from the available orbit set, and the preset taboo step is set as the orbit taboo factor.

[0021] Optionally, the acquiring the allocation probability between the current task to be allocated and the available track of the current task to be allocated includes:

[0022] Acquire a heuristic factor between the current task to be assigned and the available track of the current task to be assigned, wherein the heuristic factor is used to characterize the task load degree and task conflict degree of the track;

[0023] Acquire a pheromone factor between the current task to be assigned and the available track of the current task to be assigned, wherein the pheromone factor is used to characterize a historical observation situation of the current task to be assigned on the available track of the current task to be assigned;

[0024] The allocation probability is obtained according to the heuristic factor and the pheromone factor.

[0025] Optionally, the obtaining a heuristic factor between the current task to be assigned and an available track of the current task to be assigned includes:

[0026] Obtaining a set of assigned tasks that have been assigned to the available track of the current task to be assigned before assigning the current task to be assigned;

[0027] Acquire the number of tasks in the assigned task set, and use the number of tasks in the assigned task set as the task load degree;

[0028] Obtaining the conflict between the current task to be assigned and each task in the assigned task set, and obtaining the task conflict degree according to each conflict situation;

[0029] The heuristic factor is obtained according to the task load degree and the task conflict degree.

[0030] Optionally, the obtaining of the pheromone factor between the current task to be assigned and the available track of the current task to be assigned includes:

[0031] At the end of each iteration, determining whether the current task to be assigned is successfully observed on the available track of the current task to be assigned;

[0032] If the current task to be assigned is successfully observed on the available track of the current task to be assigned, the concentration of the pheromone factor is increased and volatilized;

[0033] If the current task to be assigned is not successfully observed on the available track of the current task to be assigned, the pheromone factor is volatilized.

[0034] In a second aspect, an embodiment of the present application provides a multi-satellite collaborative mission planning device, including:

[0035] A first acquisition module is used to acquire a task allocation weight factor of each task to be allocated, determine a task allocation order of each task to be allocated according to the task allocation weight factor, and determine a current task to be allocated according to the task allocation order;

[0036] A second acquisition module is used to acquire the orbit taboo factor between the current task to be assigned and each orbit, and determine the available orbit of the current task to be assigned according to the orbit taboo factor;

[0037] A third acquisition module is used to acquire the allocation probability between the current task to be assigned and the available track of the current task to be assigned;

[0038] An allocation module, configured to allocate the current task to be allocated to the available track with the highest allocation probability;

[0039] A processing module is used to select the next task to be assigned according to the task assignment order, and use the next task to be assigned as the current task to be assigned, and return to execute the step of obtaining the track taboo factor between the current task to be assigned and each track until all the tasks to be assigned have been assigned.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a communication bus, wherein the processor and the memory communicate with each other via the communication bus;

[0041] The memory is used to store computer programs;

[0042] The processor is used to execute the program stored in the memory to implement the multi-satellite collaborative mission planning method described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the multi-satellite collaborative mission planning method described in the first aspect.

[0044] The above technical solution provided by the embodiment of the present application has the following advantages over the prior art: In the embodiment of the present application, the current task to be assigned is determined according to the task assignment order of each task to be assigned. In the process of assigning tasks to satellite orbits, the assignment order of the tasks to be assigned is fully considered. According to the orbital taboo factor, the available orbit of the current task to be assigned is determined. The observation benefit value is improved by performing taboo operations, and the assignment probability between the current task to be assigned and the available orbit of the current task to be assigned is obtained. The current task to be assigned is assigned to the available orbit with the highest assignment probability, thereby improving the probability of successful observation after task assignment. In the present application, the task assignment order of each task to be assigned is considered, and the observation benefit value is improved by performing taboo operations. Moreover, by assigning the current task to be assigned to the available orbit with the highest assignment probability, the probability of successful observation after task assignment is improved, thereby solving the problem of insufficient optimization of multi-satellite large-scale collaborative task planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0047] Figure 1 A schematic diagram of a method flow chart for multi-satellite collaborative mission planning in an embodiment of the present application;

[0048] Figure 2 This is a schematic diagram of adjusting the order of task allocation in a specific embodiment of the present application;

[0049] Figure 3 This is a flow chart of a method for obtaining allocation probability in a specific embodiment of the present application;

[0050] Figure 4 This is a schematic diagram of task non-conflict in a specific embodiment of the present application;

[0051] Figure 5 This is a schematic diagram of task conflict in a specific embodiment of the present application;

[0052] Figure 6 A schematic diagram of the task load level and task conflict level of a track in a specific embodiment of the present application;

[0053] Figure 7 This is a schematic diagram of updating pheromone factors in a specific embodiment of the present application;

[0054] Figure 8 This is a schematic diagram of the structure of a multi-satellite collaborative mission planning device in an embodiment of the present application;

[0055] Fig. 9 Schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0057] In an embodiment of the present application, a multi-satellite collaborative mission planning method is provided, which can be applied to a server, and of course, can also be applied to other electronic devices, such as terminals (mobile phones, tablet computers, etc.). In an embodiment of the present application, the method is applied to a server as an example for description.

[0058] In the present application embodiment, Figure 1 As shown in FIG. 1 , the method flow of multi-satellite collaborative mission planning mainly includes:

[0059] Step 101 , obtaining task allocation weight factors of each task to be allocated, determining the task allocation sequence of each task to be allocated according to the task allocation weight factors, and determining the current task to be allocated according to the task allocation sequence.

[0060] In a specific embodiment, the task allocation weight factor, the track taboo factor and the allocation probability are dynamically adjusted during each iteration, and the number of iterations is less than or equal to the iteration number threshold.

[0061] A meta-heuristic allocation iteration method is proposed, and n∈[1,G1] is defined as the current iteration number of the algorithm, where G1 represents the maximum iteration number of the algorithm, that is, the iteration number threshold. The task allocation weight factor, track taboo factor and allocation probability are dynamically adjusted during each iteration, and the task allocation scheme can be dynamically adjusted during each iteration.

[0062] In a specific embodiment, the task allocation weight factor of each task to be assigned is obtained, including: at the end of each iteration, determining whether the task to be assigned is successfully observed; if the task to be assigned is successfully observed, then in the next iteration, the task allocation weight factor of the task to be assigned remains unchanged; if the task to be assigned is not successfully observed, then in the next iteration, the task allocation weight factor of the task to be assigned is reduced.

[0063] Use w i (n) represents the task allocation weight factor of task i in the nth iteration.

[0064] The order of task assignment has an important impact on the final scheduling result. The earlier the task is assigned, the more tracks are available, the lower the task load on the track is, and the smaller the potential task conflict is. The opposite is true for the tasks assigned later. These factors will affect the combination of tasks and tracks, and thus affect the final scheduling result.

[0065] In order to scientifically adjust the order of task allocation and dynamically and scientifically adjust the order of allocation, the study sets the task allocation weight factor w i(n). Task allocation weight factor is an important factor set based on the completion of the task. Its main function is to adjust the allocation order of the tasks to be allocated. The basic idea is: i The tasks with larger (n) values ​​are assigned first. For tasks that have not been successfully observed, w i The value of (n) should be reduced accordingly.

[0066] The initial value of the task allocation weight factor is the observed benefit value of the task, that is, w i (1) = ω i , which directly reflects that tasks with higher observed benefit values ​​are assigned first, and in the subsequent iteration process, the main factor is the completion of the task. When task i is successfully observed on track k, the task allocation weight factor does not change according to formula (1). Otherwise, it is reduced according to formula (2), where c is the reduction parameter. T is the task set.

[0067]

[0068]

[0069] If task i is successfully scheduled during the nth iteration, its task allocation weight factor remains unchanged. If it is not successfully scheduled, its task allocation weight factor is reduced during the n+1th iteration, thereby realizing the backward shift of its allocation order during the n+1th iteration. This setting can ensure that the allocation order of tasks with low observation success rates is adjusted backward, and tasks with high observation success rates are allocated first, thereby improving the observation success rate of the overall task.

[0070] In a specific embodiment, Figure 2 As shown, it is a schematic diagram of adjusting the task allocation sequence. Figure 2 In the example, since Task 2 and Task 4 were not completed in the nth iteration, the task allocation weight factors of Task 2 and Task 4 were reduced in the n+1th iteration. In the n+1th iteration, the new task allocation order is determined in descending order according to the updated task allocation weight factors. The allocation order of Task 2 and Task 4 is adjusted backwards so that other tasks with higher task allocation weight factors are allocated first. It should be noted that when there are multiple tasks with the same task allocation weight factors, the allocation order of these tasks is determined randomly.

[0071] Step 102: Obtain the orbit taboo factor between the current task to be assigned and each orbit, and determine the available orbit for the current task to be assigned according to the orbit taboo factor.

[0072] In a specific embodiment, obtaining the orbital taboo factor between the current task to be assigned and each orbit includes: at the end of each iteration, determining whether the current task to be assigned is successfully observed on the available orbit of the current task to be assigned; if the current task to be assigned is not successfully observed on the available orbit of the current task to be assigned, tabooing the available orbit of the current task to be assigned, removing the tabooed orbit from the available orbit set, and setting a preset taboo step as the orbital taboo factor.

[0073] Use σ ik (n) represents the track taboo factor between task i and track k during the nth iteration.

[0074] This application draws on the taboo mechanism in the taboo search algorithm and performs taboo operations on some orbits to increase the diversity of solutions and enhance the algorithm's search capabilities. The basic idea of ​​orbital taboo operations is: for task i assigned to orbit k, if it is not successfully observed, it proves that the possibility of completion of this allocation scheme is low, and a new allocation scheme should be tried. Therefore, the allocation scheme of task i and orbit k is tabooed to generate a new allocation scheme. For tasks, the number of available orbits corresponding to different tasks is different. There are two specific situations:

[0075] Case 1: The task has multiple visible orbits, that is, the task can be observed by multiple orbits;

[0076] Case 2: The task has only one visible track (invisible tasks have been removed during preprocessing).

[0077] Defining CO i is the set of visible orbits of task i, and all orbits in this set have the ability to observe task i. Definition is the set of available orbits for task i at the nth iteration. The orbits in this set are all orbits that are not forbidden and have observation capabilities for task i.

[0078] For a task i with only one visible track, i.e. |CO i For tasks with |=1, taboo operations cannot be performed because this does not increase the observed benefit value. However, for tasks with multiple visible tracks, taboo operations can generate new solutions and improve the global convergence ability of the algorithm. ik (n) to determine whether track k visible to task i is disabled, and then adjust CO′ i (n).

[0079] σ ik (n) is an integer in the range [0, l], where l is an integer greater than 1, indicating the taboo step size. If track k is taboo, then 0<σ ik(n)≤l, otherwise σ ik (n) = 0; σ ik The initial value of (n) is 0, that is, σ ik (1)=0. In the nth iteration, if task i is not successfully observed on orbit k, orbit k is tabooed, and the corresponding taboo factor is calculated according to formula (3). In each iteration after the taboo, orbit k is gradually unbanned according to formula (4) until the orbit taboo factor value is 0 again, where Δl is a number that can divide l, indicating the unbanning step length. k is the set of tasks on track k.

[0080]

[0081]

[0082] The available orbital set of task i can be adjusted according to the value of the orbital taboo factor. The specific adjustment rules are as follows:

[0083] Rule 1: In the initial case, the set of visible orbits of task i is the set of available orbits, i.e., CO′ i (1) = CO i , no tracks are disabled;

[0084] Rule 2: For a task i with only one visible track, its set of available tracks is always the visible track if |CO i |=1, then

[0085] Rule 3: For tasks with multiple visible tracks, i |>1, if the orbital taboo factor σ ik (n)>0, then in the next iteration, the forbidden orbit k is removed from the available orbit set CO′ i (n) removed;

[0086] Rule 4: If task i has multiple visible tracks, i.e. |CO i |>1, but all orbits are forbidden, i.e. When the available orbital set is initialized, i.e., CO′ i (n+1)=CO i , all visible tracks are available for task assignment;

[0087] Rule 5: If the forbidden orbit is completely unbanned, that is, σ ik When (n+1)=0, the unblocked track k is added to the available track set CO′ i (n), i.e. CO′ i (n+1)=CO′i (n)UO k .

[0088] Step 103: Obtain the allocation probability between the current task to be allocated and the available tracks of the current task to be allocated.

[0089] In a specific embodiment, Figure 3 As shown, obtaining the allocation probability between the current task to be assigned and the available tracks of the current task to be assigned includes:

[0090] Step 301 : obtaining a heuristic factor between a current task to be assigned and an available track of the current task to be assigned. The heuristic factor is used to characterize the task load degree and task conflict degree of the track.

[0091] In a specific embodiment, obtaining a heuristic factor between the current task to be assigned and the available track of the current task to be assigned includes: obtaining a set of assigned tasks that have been assigned to the available track of the current task to be assigned before the current task to be assigned is assigned; obtaining the number of tasks in the assigned task set, and using the number of tasks in the assigned task set as the task load degree; obtaining the conflict situation between the current task to be assigned and each task in the assigned task set, and obtaining the task conflict degree based on each conflict situation; obtaining the heuristic factor based on the task load degree and the task conflict degree.

[0092] Use η ik (n) represents the heuristic factor between task i and track k during the nth iteration.

[0093] Heuristic factor η ik (n) is designed based on the mission load and mission conflict of the orbit. Mission load and mission conflict are important indicators of orbital capacity utilization, which can directly reflect the possibility of successful observation of mission i on orbit k. k is the number of tasks assigned to satellite orbit k before assigning task i. In general, the more tasks a satellite orbit carries, the lower the success rate of new tasks being successfully observed. Assume that the set of tasks on orbit k is T k , the set of tasks previously assigned to track k by task i is T′ k , then ε k =|T k ′|, that is, the task set T′ k The number of tasks in .

[0094] When assigning task i, track k has the following possible states:

[0095] State 1: There is no task on track k, i.e. ε k =0;

[0096] State 2: Track k already has a task assigned to it before task i, i.e., ε k >0.

[0097] In state 2, task i may conflict with an existing task on track k. Figure 4 As shown in , it is a schematic diagram of task conflict-free. Figure 4 As shown in , if the time window interval between two adjacent tasks meets the conversion time, there is no conflict between the two tasks. Figure 4 In the example, there is no conflict between Task 1 and Task 2, and there is no conflict between Task 2 and Task 3. Figure 5 As shown in Figure 1, it is a schematic diagram of task conflict. Figure 5 As shown in , if the time window interval between two adjacent tasks does not meet the conversion duration requirement, the two tasks conflict with each other. Figure 5 In the example, Task 1 and Task 2 conflict with each other, but Task 2 and Task 3 do not conflict. The existence of a conflict also means that the probability of the new task being successfully observed in this orbit is low.

[0098] Use s to represent T′ k Any assigned task in , using binary variables Indicates the conflict between task i and task s. If two tasks conflict, then otherwise

[0099] Use ξ ik represents the conflict between the task i to be assigned and the existing tasks on the satellite orbit k, and the overlap of the visible time window, ξ ik Calculate according to formula (5), where O is the orbital set.

[0100]

[0101] In a specific embodiment, Figure 6 As shown in Figure 1, it is a schematic diagram of the mission load and mission conflict of the track. Figure 6 As shown, the task load level of track 1 is 2, and the task conflict levels of task 1 and task 2 on track 1 are 2 and 0 respectively, that is, ε1 = 2, ξ 11 =2,ξ 21 = 0. The task load level and task conflict level of the track will be updated every time a task is assigned, which is very dynamic, so as to ensure that the track capacity and the conflict between the task to be assigned and the existing tasks of the track are reflected in real time during the task assignment process, thereby ensuring the scientific nature of the task assignment plan.

[0102] ε k and ikThese two parameters need to be normalized and defined as and are the normalized values ​​of these two parameters, calculated according to formulas (6) and (7) respectively. ik (n) is calculated according to formula (8), where a and b are and The weight parameter of .

[0103]

[0104]

[0105]

[0106] Step 302: obtaining a pheromone factor between the current task to be assigned and the available track of the current task to be assigned. The pheromone factor is used to characterize the historical observation situation of the current task to be assigned on the available track of the current task to be assigned.

[0107] In a specific embodiment, obtaining the pheromone factor between the current task to be assigned and the available track of the current task to be assigned includes: at the end of each iteration, determining whether the current task to be assigned is successfully observed on the available track of the current task to be assigned; if the current task to be assigned is successfully observed on the available track of the current task to be assigned, increasing the concentration and volatilizing the pheromone factor; if the current task to be assigned is not successfully observed on the available track of the current task to be assigned, volatilizing the pheromone factor.

[0108] Use τ ik (n) represents the pheromone factor between task i and track k during the nth iteration.

[0109] Pheromone factor τ ik (n) is based on the idea of ​​ants using pheromones to search for the shortest path in the ant colony algorithm, and sets pheromones between tasks and tracks that can observe them. Its basic idea is: if task i is successfully observed on track k, it proves that the success rate of this matching method is high, so the pheromone concentration between them should be strengthened to ensure that in the next iteration, the probability of task i being assigned to track k is further increased. On the contrary, if task i is not successfully observed on track k, it proves that the success rate of this matching method is low, so the pheromone concentration between them does not increase. At the same time, the pheromone concentration will be uniformly volatilized during the iteration process to achieve the retention and screening of high-quality matching solutions.

[0110] In a specific embodiment, Figure 7 Shown is a schematic diagram of pheromone factor updating. Figure 7In the example, task i is assigned to tracks 1, 6, and 13 in the 1st, 7th, and 9th iterations, respectively. Task i is not successfully scheduled on track 6. At the end of the 7th iteration, the pheromone between task i and track 6 is only volatilized, while the pheromone concentration is increased in the other two iterations, indicating that the possibility of successful scheduling of task i increases under this allocation method. Using this feedback adjustment method to correct the allocation plan of tasks and tracks can achieve continuous optimization of scheduling results.

[0111] With τ ik (n) Several parameters related to the calculation are the pheromone volatility coefficient ρ∈(0,1), the pheromone dilution coefficient λ and the pheromone increment Δτ ik Using γ(n) and Num(n) to represent the total observation benefit value and the number of observation tasks at the nth iteration, the pheromone increment Δτ between task i and satellite orbit k is ik Calculate according to formula (9).

[0112]

[0113] The initial concentration values ​​of the pheromone factors are all set to 1, that is, τ ik (1) = 1; for task i that is successfully observed in orbit k for the nth time, concentration increase and volatilization are performed according to formula (10); if task i is not successfully observed, only volatilization operation is performed according to formula (11).

[0114]

[0115]

[0116] Step 303, obtaining the allocation probability according to the heuristic factor and the pheromone factor.

[0117] Since the matching probability between tasks and tracks changes with the iteration of the algorithm, we use p ik (n) represents the matching probability between task i and track k at the nth iteration, p ik (n) is calculated according to formula (12), where α and β are the weights of the heuristic factor and the pheromone factor, respectively.

[0118]

[0119] Step 104: Allocate the current task to be assigned to the available track with the highest assignment probability.

[0120] Step 105, according to the task allocation order, select the next task to be allocated, and use the next task to be allocated as the current task to be allocated, and return to execute the step of obtaining the track taboo factor between the current task to be allocated and each track until all the tasks to be allocated have been allocated.

[0121] In summary, in the embodiment of the present application, the current task to be assigned is determined according to the task assignment order of each task to be assigned. In the process of assigning tasks to satellite orbits, the assignment order of the tasks to be assigned is fully considered. According to the orbital taboo factor, the available orbit of the current task to be assigned is determined. The observation benefit value is improved by performing taboo operations, and the assignment probability between the current task to be assigned and the available orbit of the current task to be assigned is obtained. The current task to be assigned is assigned to the available orbit with the highest assignment probability, thereby improving the probability of successful observation after task assignment. In the present application, the task assignment order of each task to be assigned is considered, and the observation benefit value is improved by performing taboo operations. Moreover, by assigning the current task to be assigned to the available orbit with the highest assignment probability, the probability of successful observation after task assignment is improved, thereby solving the problem of insufficient optimization of multi-satellite large-scale collaborative task planning.

[0122] Based on the same concept, a multi-satellite collaborative mission planning device is provided in the embodiment of the present application. The specific implementation of the device can be found in the description of the method embodiment part, and the repeated parts will not be repeated. Figure 8 As shown, the device mainly includes:

[0123] The first acquisition module 801 is used to acquire the task allocation weight factor of each task to be allocated, determine the task allocation order of each task to be allocated according to the task allocation weight factor, and determine the current task to be allocated according to the task allocation order;

[0124] The second acquisition module 802 is used to acquire the orbit taboo factor between the current task to be assigned and each orbit, and determine the available orbit of the current task to be assigned according to the orbit taboo factor;

[0125] The third acquisition module 803 is used to acquire the allocation probability between the current task to be allocated and the available track of the current task to be allocated;

[0126] An allocation module 804 is used to allocate the current task to be allocated to the available track with the highest allocation probability;

[0127] The processing module 805 is used to select the next task to be assigned according to the task assignment order, and use the next task to be assigned as the current task to be assigned, and return to execute the step of obtaining the track taboo factor between the current task to be assigned and each track until all the tasks to be assigned have been assigned.

[0128] Based on the same concept, an electronic device is also provided in the embodiment of the present application, such as Fig. 9 As shown, the electronic device mainly includes: a processor 901, a memory 902 and a communication bus 903, wherein the processor 901 and the memory 902 communicate with each other through the communication bus 903. The memory 902 stores a program executable by the processor 901, and the processor 901 executes the program stored in the memory 902 to implement the following steps:

[0129] Obtain the task allocation weight factors of each task to be assigned, determine the task allocation order of each task to be assigned according to the task allocation weight factors, and determine the current task to be assigned according to the task allocation order; obtain the orbital taboo factors between the current task to be assigned and each orbit, and determine the available orbit of the current task to be assigned according to the orbital taboo factors; obtain the allocation probability between the current task to be assigned and the available orbit of the current task to be assigned; assign the current task to be assigned to the available orbit with the highest allocation probability; select the next task to be assigned according to the task allocation order, and use the next task to be assigned as the current task to be assigned, and return to execute the step of obtaining the orbital taboo factors between the current task to be assigned and each orbit, until all tasks to be assigned have been assigned.

[0130] The communication bus 903 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 903 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0131] The memory 902 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Alternatively, the memory may also be at least one storage device located away from the processor 901.

[0132] The above-mentioned processor 901 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., and can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0133] In another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is run on a computer, the computer executes the multi-satellite collaborative mission planning method described in the above embodiment.

[0134] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instruction is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instruction is transmitted from a website site, a computer, a server or a data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integration. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape, etc.), an optical medium (such as a DVD) or a semiconductor medium (such as a solid-state hard disk), etc.

[0135] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0136] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A multi-satellite collaborative mission planning method, characterized in that: include: Obtaining task allocation weight factors of each task to be allocated, determining a task allocation order of each task to be allocated according to the task allocation weight factors, and determining a current task to be allocated according to the task allocation order; The obtaining of the task allocation weight factor of each task to be allocated includes: at the end of each iteration, determining whether the task to be allocated is successfully observed; if the task to be allocated is successfully observed, then in the next iteration, the task allocation weight factor of the task to be allocated remains unchanged; if the task to be allocated is not successfully observed, then in the next iteration, the task allocation weight factor of the task to be allocated is reduced; Acquire the orbital taboo factor between the current task to be assigned and each orbit, and determine the available orbit of the current task to be assigned according to the orbital taboo factor; the acquiring of the orbital taboo factor between the current task to be assigned and each orbit comprises: at the end of each iteration, judging whether the current task to be assigned is successfully observed on the available orbit of the current task to be assigned; if the current task to be assigned is not successfully observed on the available orbit of the current task to be assigned, tabooing the available orbit of the current task to be assigned, removing the tabooed orbit from the available orbit set, and setting the preset taboo step length as the orbital taboo factor; Obtaining the allocation probability between the current task to be assigned and the available track of the current task to be assigned; the obtaining the allocation probability between the current task to be assigned and the available track of the current task to be assigned, including: obtaining a heuristic factor between the current task to be assigned and the available track of the current task to be assigned, the heuristic factor is used to characterize the task load degree and task conflict degree of the track; obtaining a pheromone factor between the current task to be assigned and the available track of the current task to be assigned, the pheromone factor is used to characterize the historical observation situation of the current task to be assigned on the available track of the current task to be assigned; obtaining the allocation probability according to the heuristic factor and the pheromone factor; The step of obtaining the heuristic factor between the current task to be assigned and the available track of the current task to be assigned includes: obtaining a set of assigned tasks that have been assigned to the available track of the current task to be assigned before the current task to be assigned is assigned; obtaining the number of tasks in the assigned task set, and using the number of tasks in the assigned task set as the task load degree; obtaining the conflict between the current task to be assigned and each task in the assigned task set, and obtaining the task conflict degree according to each conflict situation; obtaining the heuristic factor according to the task load degree and the task conflict degree; The obtaining of the pheromone factor between the current task to be assigned and the available track of the current task to be assigned comprises: at the end of each iteration, determining whether the current task to be assigned is successfully observed on the available track of the current task to be assigned; if the current task to be assigned is successfully observed on the available track of the current task to be assigned, increasing the concentration of the pheromone factor and volatilizing it; if the current task to be assigned is not successfully observed on the available track of the current task to be assigned, volatilizing the pheromone factor; Allocate the current task to be assigned to the available track with the highest assignment probability; According to the task allocation order, the next task to be allocated is selected, and the next task to be allocated is used as the current task to be allocated, and the step of obtaining the track taboo factor between the current task to be allocated and each track is returned to be executed until all the tasks to be allocated have been allocated.

2. The multi-satellite collaborative mission planning method according to claim 1, characterized in that: The task allocation weight factor, the track taboo factor and the allocation probability are dynamically adjusted during each iteration, and the number of iterations is less than or equal to the iteration number threshold.

3. A multi-satellite collaborative mission planning device, characterized in that: include: A first acquisition module is used to acquire a task allocation weight factor of each task to be allocated, determine a task allocation order of each task to be allocated according to the task allocation weight factor, and determine a current task to be allocated according to the task allocation order; The obtaining of the task allocation weight factor of each task to be allocated includes: at the end of each iteration, determining whether the task to be allocated is successfully observed; if the task to be allocated is successfully observed, then in the next iteration, the task allocation weight factor of the task to be allocated remains unchanged; if the task to be allocated is not successfully observed, then in the next iteration, the task allocation weight factor of the task to be allocated is reduced; The second acquisition module is used to acquire the orbital taboo factor between the current task to be assigned and each orbit, and determine the available orbit of the current task to be assigned according to the orbital taboo factor; the acquisition of the orbital taboo factor between the current task to be assigned and each orbit includes: at the end of each iteration, judging whether the current task to be assigned is successfully observed on the available orbit of the current task to be assigned; if the current task to be assigned is not successfully observed on the available orbit of the current task to be assigned, then tabooing the available orbit of the current task to be assigned, removing the tabooed orbit from the available orbit set, and setting the preset taboo step length as the orbital taboo factor; The third acquisition module is used to acquire the allocation probability between the current task to be assigned and the available track of the current task to be assigned; the acquisition of the allocation probability between the current task to be assigned and the available track of the current task to be assigned includes: acquiring a heuristic factor between the current task to be assigned and the available track of the current task to be assigned, the heuristic factor is used to characterize the task load degree and task conflict degree of the track; acquiring a pheromone factor between the current task to be assigned and the available track of the current task to be assigned, the pheromone factor is used to characterize the historical observation of the current task to be assigned on the available track of the current task to be assigned; according to the heuristic factor and the pheromone factor, the allocation probability is obtained; The third acquisition module is further used to acquire a set of assigned tasks that have been assigned to the available track of the current task to be assigned before the current task to be assigned is assigned; acquire the number of tasks in the assigned task set, and use the number of tasks in the assigned task set as the task load degree; acquire the conflict between the current task to be assigned and each task in the assigned task set, and acquire the task conflict degree according to each conflict situation; acquire the heuristic factor according to the task load degree and the task conflict degree; The third acquisition module is also used for acquiring the pheromone factor between the current task to be assigned and the available track of the current task to be assigned, including: at the end of each iteration, judging whether the current task to be assigned is successfully observed on the available track of the current task to be assigned; if the current task to be assigned is successfully observed on the available track of the current task to be assigned, increasing the concentration of the pheromone factor and volatilizing it; if the current task to be assigned is not successfully observed on the available track of the current task to be assigned, volatilizing the pheromone factor; An allocation module, configured to allocate the current task to be allocated to the available track with the highest allocation probability; A processing module is used to select the next task to be assigned according to the task assignment order, and use the next task to be assigned as the current task to be assigned, and return to execute the step of obtaining the track taboo factor between the current task to be assigned and each track until all the tasks to be assigned have been assigned.

4. An electronic device, characterized in that: include: A processor, a memory and a communication bus, wherein the processor and the memory communicate with each other via the communication bus; The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the multi-satellite collaborative mission planning method described in any one of claims 1 to 2.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multi-satellite collaborative mission planning method described in any one of claims 1 to 2 is implemented.

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