A Multi-Objective Satellite Dynamic Mission Scheduling Method and Related Devices Based on Genetic Taboo Search

Abstract

This invention discloses a multi-objective reactive scheduling method and related apparatus for relay satellite systems based on genetic tabu search, comprising the following steps: performing outer-layer genetic operations to generate a progeny population through selection, crossover, and mutation operations, and performing conflict detection and repair again; performing inner-layer tabu search operations to alternately use a multi-neighborhood structure for local resource reallocation for tasks in an unscheduled state; updating the current individual and the global optimal solution according to the amnesty criterion and the quality of neighborhood solutions, and simultaneously updating the tabu table; repeating the above outer-layer genetic operations and inner-layer tabu search until the iteration stopping condition is met, and outputting the globally optimal reactive scheduling scheme. This method maximizes the total benefit of the adjusted system while effectively minimizing scheme deviation and ensuring the high stability of the underlying original scheduling plan.

Description

Technical Field

[0001] This invention belongs to the field of space information processing and intelligent task scheduling technology, and relates to a multi-target satellite dynamic task scheduling method and related devices based on genetic tabu search. Background Technology

[0002] Relay satellite systems, as the core hub of space-based information transmission, play a crucial role in ensuring the telemetry, tracking, and command (TT&C) and data transmission of various low-Earth orbit spacecraft. The actual on-orbit operating environment of satellites is highly dynamic, complex, and uncertain. During the actual operational cycle, with the increasing density of space missions, the system inevitably encounters dynamic disturbances such as the random arrival of sudden emergency missions or temporary satellite equipment failures. These new resource requests, with extremely high timeliness and priority, inevitably overlap and conflict significantly with the regular tasks to be executed in the initial scheduling plan, given the limited time and physical resources. Faced with such a highly uncertain operating environment, simply relying on the static pre-scheduling plan generated before the execution phase can easily lead to chaos in the communication order of the entire relay system and serious waste of resources due to plan rigidity. Therefore, breaking through the limitations of static planning and researching a reactive scheduling mechanism for relay satellites that adapts to dynamic and complex environments has become an inevitable requirement for further improving system performance.

[0003] To effectively address the aforementioned dynamic disturbances and improve system resource utilization and overall robustness, existing research largely focuses on designing reactive scheduling mechanisms that adapt to dynamic and complex environments. When dealing with sudden mission intrusions or resource conflicts, current strategies primarily rely on replanning the affected missions. By introducing a dynamic adjustment mechanism during the execution phase, the system aims to find a balance between accepting high-reward new missions and maintaining the existing mission order, thereby ensuring the continuity of relay satellite operations in orbit.

[0004] However, existing reactive scheduling methods still have certain shortcomings: First, while traditional global rescheduling strategies may yield theoretically optimal solutions, relay satellite resource scheduling is essentially an NP-hard problem. The massive computational overhead of global planning easily leads to combinatorial explosion, making it difficult to meet the strong real-time requirements of online scheduling. Second, simple immediate preemption strategies often overemphasize single efficiency indicators such as task completion benefits, easily triggering large-scale task instruction changes, thus severely disrupting the original operational order of the relay system and the stability of the baseline plan. Finally, efficiency and stability are inherently contradictory, and existing work often relies on single heuristic algorithms, which are prone to getting stuck in local optima or premature convergence when facing complex conflicts of strong coupling between multiple tasks. Existing methods struggle to quantitatively and efficiently coordinate the optimization of these two inherently contradictory core objectives of scheduling efficiency and plan stability within a very short timeframe. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and related apparatus for dynamic scheduling of multi-target satellites based on genetic tabu search. This method and related apparatus can ensure the efficiency and stability of the underlying original scheduling plan while steadily improving the overall benefits of the local scheduling scheme.

[0006] To achieve the above objectives, this invention discloses a multi-objective satellite dynamic mission scheduling method based on genetic taboo search, comprising the following steps: 1) Initialize the basic parameters of the task dynamic scheduling system to obtain the initial local scheduling scheme; 2) Employ a locally independent integer encoding mechanism to encode the task set TC to be planned, and construct a chromosome; 3) Based on the initial local scheduling scheme, an initial population is generated using a heuristic method, conflict resolution is performed, and then the overall fitness of individuals is calculated; 4) Perform outer-layer genetic operations, generate offspring populations through selection, crossover, and mutation operations, and perform conflict repair again; 5) Perform inner tabu search operations, and for tasks in an unscheduled state, alternately use a multi-neighborhood structure to perform local resource reallocation; 6) Update the current individual and global optimal solutions based on the amnesty criterion and the quality of neighborhood solutions, and update the tabu list simultaneously; 7) Repeat steps 4) to 6) until the iteration stopping condition is met, and finally decode to obtain and output the optimal local scheduling scheme.

[0007] Furthermore, the basic parameters of the dynamic task scheduling system include the relay satellite set RS, the low-Earth orbit user satellites S, and the task set to be planned TC. Any task in Represented as a six-dimensional tuple ,in, and Representing tasks In the reactive scheduling phase, the effective earliest start time and latest end time, constrained by the current resource allocation, satisfy... ; Indicates task Processing time; Indicates task Task rewards; Indicates the first i Each mission corresponds to a low-Earth orbit user satellite service. Indicates the first i The index number of the satellite serving low-Earth orbit users for each mission. This indicates a set of routine tasks that have already begun execution or whose scheduling arrangements have been determined. For fixed routine tasks f The start time, For fixed routine tasks f Processing time, This is a state indication constant; For the set of tasks to be planned TC With fixed routine task set The logical relationship between them introduces decision variables. When planning tasks Arranged in fixed routine tasks f When executed previously, Otherwise, =0; Introduction and As a reference benchmark, they represent the routine tasks. The planned start time and resource occupancy status in a local scheduling scheme; routine tasks in a local scheduling scheme. Occupy relay satellite window ,but Otherwise, Set the decision variable to 0. For 0-1 variables, Indicates that in relay satellite On the task Immediately following the mission Then execute; decision variables For 0-1 variables, Indicates task Assigned to relay satellite window Otherwise, =0, Indicates an opportunity task The blank time, As a continuous variable, representing the task The actual start time; Low Earth Orbit User Satellites Visible windows for all relay satellites ,in, , For user satellites With relay satellite Between the first k One visible window, The start time of the window. The end time of the window, the scheduling cycle .

[0008] Furthermore, the specific operation of step 3) is as follows: 3a) Generate the first batch of initial population individuals using the initial scheduling results before the occurrence of dynamic events. In the first batch of initial population individuals, randomly assign resource numbers to newly arrived sudden tasks and overflowing opportunity tasks within the available resource range. 3b) For the remaining part of the initial population size, satellite resource numbers are generated completely randomly for all planned tasks within the set of relay satellite physical numbers to construct a second batch of random individuals. The second batch of random individuals is then merged with some individuals from the first batch of the initial population to obtain a complete initial population. 3c) Perform conflict detection and repair on all individuals, decode the tasks on the chromosome and map them to the time axis of the corresponding satellite, and perform legality verification based on resource allocation constraints. When multiple tasks are assigned to the same relay satellite in the same time period, compare the benefit weight of conflicting tasks, retain the task with the highest benefit, and reset the gene value corresponding to the failed task to 0 and mark it as unscheduled. 3d) Construct a comprehensive fitness function based on the linear weighted method of extreme value normalization, and calculate the comprehensive fitness value of each current individual. , and These are the weighting coefficients. The goal is the normalized total return. The normalized scheme bias cost objective is used, and the globally optimal individual is recorded. .

[0009] Furthermore, the specific operation of step 3c) is as follows: 3c1) Decode the task gene sequence on the chromosome, map the set of tasks to be planned to the time axis of the corresponding relay satellite according to the relay satellite number specified by the gene value, and overlay it with the time window of the fixed regular tasks that have been locked in the original scheduling scheme. 3c2) Establish resource allocation constraint one: each task is served by a visible window at any given time, and the allocation of visible windows is unique:

[0010] 3c3) Establish resource allocation constraint two: each relay satellite can only handle one task at a time, and the arrangement of tasks in the scheduling sequence must follow a unique order:

[0011]

[0012] Among them, 0 and +1 represents two virtual tasks, indicating the start and end of the task sequence on each relay satellite; 3c4) Establish resource allocation constraint three: when executing missions on the same relay satellite, ensure that the time windows of two adjacent missions do not overlap.

[0013] 3c5) Establish resource allocation constraints. Fourthly, in task planning, for each task, ensure that... The scope begins and ends within a certain range to meet task requirements and ensure a reasonable allocation of system resources:

[0014]

[0015] 3c6) Establish resource allocation constraint five: In task scheduling, each task must be executed within the visible time window to ensure that tasks are carried out within the time available for scheduling between the satellite and the target.

[0016]

[0017] 3c7) Establish resource allocation constraint six: the start time of all scheduled tasks must not be earlier than the earliest time when reactive scheduling instructions can take effect.

[0018] 3c8) Establish resource allocation constraint seven to prevent resource allocation overlap and ensure that when the visible window of the task to be planned is reallocated, there is no time window conflict with the regular tasks already locked in the original initial scheduling scheme:

[0019]

[0020] 3c9) Based on the preset resource allocation constraints, scan and verify the validity of the mapped scheduling sequence; 3c10) When the legality check finds that the same relay satellite is assigned multiple tasks in the same time period, thus causing the time window for violating the resource allocation constraint to overlap, compare the benefit weight of the conflicting tasks, retain the task with the highest benefit to legally occupy the satellite resource; for low-benefit tasks that fail to compete, reset their corresponding gene value to 0, deprive them of the allocation qualification in the current round and mark them as unscheduled, until all tasks strictly meet the resource allocation constraint.

[0021] Furthermore, the specific operation of step 4) is as follows: 4a) Using the tournament selection algorithm, several individuals are randomly selected from the current population to form a competition group, and the best individual with the highest fitness value is selected for replication and added to the mating pool; 4b) Employ a two-point crossover mechanism, based on the crossover probability. Two crossover sites are randomly generated on two selected parent chromosomes. The gene segments contained in the crossover sites are then exchanged between the two parents to generate offspring chromosomes. 4c) Use a single-point mutation method, based on the mutation probability. A new value is randomly selected from the set of valid values ​​to replace the original gene at the mutation site; 4d) Perform heuristic conflict detection and repair on all newly generated offspring chromosomes again, and recalculate the overall fitness to form a new generation of population.

[0022] Furthermore, the specific operation of step 5) is as follows: 5a) Use each individual in the new population as the initial solution for the inner tabu search algorithm; 5b) For the 0 value in the current individual gene sequence, call the greedy swap neighborhood, roulette swap neighborhood, or random swap neighborhood in a random alternation manner; 5c) Combine the insertion operator and the replacement operator to perform local resource reallocation and generate a candidate solution set.

[0023] Furthermore, the specific operation of step 5c) is as follows: 5c1) For an unscheduled task with a designated target relay satellite, if there is no time overlap between the target satellite's available time window and all currently scheduled tasks, the insertion operator is triggered to add the task to the local scheduling scheme and update the corresponding gene value from 0 to the target relay satellite's number. 5c2) If the allocation of an unscheduled task causes a local time conflict, the replacement operator is triggered to calculate the benefit of the task to be allocated and the total benefit of all affected conflicting tasks. 5c3) When the benefit of the task to be assigned is greater than the total benefit of all affected conflict tasks, the visible window of the original conflict task is forcibly removed and resources are allocated to the new task. The gene value of the original conflict task is reset to 0, and the target satellite number is assigned to the new task to generate a candidate solution set.

[0024] Furthermore, the specific operation of step 6) is as follows: 6a) Evaluate the overall fitness of the candidate solution set, where the total reward of any candidate solution exceeds the global historical best. If this occurs, the amnesty criterion is triggered directly, the resource allocation change is accepted, and the global optimal solution record is updated simultaneously. ; 6b) If the amnesty criterion is not triggered, the optimal neighborhood solution with the highest fitness is selected from the non-taboo candidate solutions to update the current individual; 6c) Push tasks whose genes are reduced to 0 due to replacement operations into a tabu list based on a first-in-first-out queue; 6d) Determine if the tabu list is full. If the tabu list reaches a fixed maximum capacity threshold, remove the earliest enqueued task and completely remove the tabu restriction.

[0025] This invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the multi-target satellite dynamic mission scheduling method based on genetic tabu search.

[0026] This invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-target satellite dynamic mission scheduling method based on genetic tabu search.

[0027] The present invention has the following beneficial effects: The multi-target satellite dynamic task scheduling method and related device based on genetic tabu search described in this invention employs a local reactive scheduling strategy. It reconstructs resources only for task sequences within the local time domain affected by conflicts. Compared to traditional global reactive scheduling or immediate preemption strategies, this approach ensures high stability of the initial scheduling plan while avoiding large-scale task changes and system instability. Furthermore, this invention innovatively designs a local independent integer encoding mechanism, encoding only newly planned tasks and affected tasks. It uses fixed, regular tasks as the resource allocation constraint background within a time window, significantly reducing the search space for the algorithm and lowering the probability of generating illegal solutions and computational complexity. Simultaneously, this invention effectively integrates the global parallel search advantages of genetic algorithms with the multi-neighborhood local deep development capabilities of tabu search algorithms. It ensures the absolute satisfaction of resource allocation constraints through a heuristic conflict repair strategy and combines various neighborhood structures such as greedy, roulette wheel, and random methods to deeply insert and replace unscheduled tasks. Thus, in dynamic and complex environments such as sudden task and opportunistic task overflows, it maximizes the overall system benefit and minimizes the scheme deviation with extremely high solution efficiency. Attached Figure Description

[0028] Figure 1 This is a schematic diagram illustrating a usage scenario of the present invention; Figure 2 This is an overall flowchart of the present invention; Figure 3 This is a schematic diagram of the coding of the task to be planned in this invention; Figure 4 This is a schematic diagram of the crossover operator in this invention; Figure 5 This is a schematic diagram of the mutation operator in this invention; Figure 6 This is a schematic diagram of the task conflict detection and repair in the invention; Figure 7 This is a flowchart of taboo search in the invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0031] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0032] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0033] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0034] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0036] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0037] Example 1 refer to Figure 7 The multi-objective satellite dynamic mission scheduling method based on genetic taboo search described in this invention includes the following steps: 1) Initialize the basic parameters of the task dynamic scheduling system; The system's relay satellite set is Low Earth Orbit User Satellites The set of tasks to be planned is ,in, The new set of tasks has been added. For the overflow of opportunity tasks, the time domain The set of routine tasks affected by internal conflicts is defined as follows: ,for Any task in It can be represented as a six-dimensional tuple, that is ,in, and Representing tasks In the reactive scheduling phase, the effective earliest start time and latest end time are constrained by the current resource allocation, and satisfy the following conditions: ; Indicates task Processing time; Indicates task Task rewards; Indicates the first i Each mission corresponds to a low-Earth orbit user satellite service. Indicates the first i The index number of the satellite serving low-Earth orbit users for each mission. Used to distinguish tasks from different sources, when This indicates that the current task belongs to the set of emergency tasks. ET ;when This indicates that the current task belongs to the overflow opportunity task set. ;when This indicates that the current task belongs to the set of regular tasks affected by the conflict. , This indicates a set of regular tasks that have already started execution or whose scheduling arrangements have been determined. Their state within the original scheduling scheme is locked; that is, a fixed set of regular tasks. For fixed routine tasks f The start time, For fixed routine tasks f Processing time, This is a status indicator constant; if a regular task is fixed... f Assigned to relay satellite Execute, then Otherwise, it is 0, for the set of tasks to be planned. TC With fixed routine task set The logical relationship between them introduces decision variables. When planning tasks Arranged in fixed routine tasks f When executed previously Otherwise, it is 0.

[0038] To measure the degree of disturbance that reactive scheduling causes to the original scheme, we introduce... and As a reference benchmark, they represent the routine tasks. In the original plan, the scheduled start time and resource usage status, if the original plan includes routine tasks... Occupy relay satellite window but Otherwise, it is 0. Based on this, the decision variable is defined. For 0-1 variables, Indicates that in relay satellite On the task Immediately following the mission Then execute; decision variables For 0-1 variables, Indicates task Assigned to relay satellite window Otherwise, it is 0. Indicates an opportunity task The blank time. As a continuous variable, representing the task The actual start time.

[0039] Introducing state indicator variables and Describe the affected routine tasks respectively. ( Whether the low-Earth orbit user satellite was cancelled and whether its resource or time allocation changed, thus providing a mathematical basis for subsequently constructing a minimum deviation objective function, is considered. Visible windows for all relay satellites ,in, , For user satellites With relay satellite Between the first k One visible window, The start time of the window. This refers to the end time of the window and the scheduling cycle. ; 2) Employ a locally independent integer encoding mechanism, referring to... Figure 3 The task set TC to be planned is encoded to construct a chromosome. Tasks within the local reactive scheduling time domain are divided into fixed, regular tasks with locked states and task sets TC to be planned that require resource reallocation. The chromosome only encodes task sets TC to be planned; fixed, regular tasks are not included in the encoding and only serve as the background for resource allocation constraints within the time window. Regarding gene values, when a gene value is a positive integer, the integer directly represents the physical number of the relay satellite, explicitly specifying that the task is assigned to the corresponding relay satellite for execution; when a gene value is 0, it indicates that the task has not yet been allocated any relay satellite resources and is in an unscheduled state.

[0040] 3) Based on the initial scheduling scheme, an initial population is generated using a heuristic method, and after conflict repair, the overall fitness of individuals is calculated; The specific operation of step 3) is as follows: 3a) The first batch of initial population individuals is generated using the initial scheduling results before the occurrence of dynamic events. In the first batch of initial population individuals, the regular tasks affected on the entire chromosome will strictly inherit their satellite allocation resource numbers in the original scheme; newly arrived emergency tasks and overflow opportunity tasks will be randomly allocated resource numbers within their available resource range. 3b) For the remaining individuals in the population, ignoring the historical allocation status of the tasks, satellite resource numbers are generated completely randomly for all tasks to be planned within the set of physical numbers of relay satellites, generating random individuals to expand population diversity and prevent the population from falling into premature convergence in the early stages of evolution. 3c) Decode the tasks on the generated chromosomes and map them onto the timelines of the corresponding satellites, then overlay them with the time windows of fixed routine tasks and scan them one by one. When multiple tasks are assigned to the same relay satellite within the same time period, compare the benefit weights of the conflicting tasks, retain the task with the highest benefit to legally occupy the satellite resources; for low-benefit tasks that fail to compete, reset their corresponding gene values ​​to 0, that is, deprive them of their allocation eligibility in the current round and mark them as unscheduled. 3c1) Decode the task gene sequence on the chromosome, map the set of tasks to be planned to the time axis of the corresponding relay satellite according to the relay satellite number specified by the gene value, and overlay it with the time window of the fixed regular tasks that have been locked in the original scheduling scheme. 3c2) Establish resource allocation constraint one: each task can only be served by one visible window at any given time, and the allocation of visible windows is unique:

[0041] 3c3) Establish resource allocation constraint two: each relay satellite can only process one task at a time to ensure the exclusivity of task processing. At the same time, the arrangement of tasks in the scheduling sequence must follow a unique order to avoid resource conflicts.

[0042]

[0043] Among them, 0 and +1 represents two virtual tasks, indicating the start and end of the task sequence on each relay satellite.

[0044] 3c4) Establish resource allocation constraint three: when executing missions on the same relay satellite, ensure that the time windows of two adjacent missions do not overlap.

[0045] 3c5) Establish resource allocation constraints. Fourthly, in task planning, for each task, ensure that... The scope begins and ends within a certain range to meet task requirements and ensure a reasonable allocation of system resources:

[0046]

[0047] 3c6) Establish resource allocation constraint five: In task scheduling, each task must be executed within the visible time window to ensure that tasks are carried out within the time available for scheduling between the satellite and the target.

[0048]

[0049] 3c7) Establish resource allocation constraint six: To ensure the executability of the generated scheduling scheme, the start time of all scheduled tasks must not be earlier than the earliest time when the reactive scheduling instruction can take effect.

[0050] 3c8) Establish resource allocation constraint seven to prevent resource allocation overlap and ensure that when the visible window of the task to be planned is reallocated, there is no time window conflict with the regular tasks already locked in the original initial scheduling scheme:

[0051]

[0052] 3c9) Based on the preset resource allocation constraints, scan and verify the validity of the mapped scheduling sequence; 3c10) When the legality check finds that the same relay satellite is assigned multiple tasks in the same time period, thus causing the time window for violating the resource allocation constraint to overlap, the benefit weight of the conflicting tasks is compared, and the task with the highest benefit is retained to legally occupy the satellite resource; for low-benefit tasks that fail to compete, their corresponding gene value is reset to 0, their allocation qualification in the current round is revoked and they are marked as unscheduled, until all tasks strictly meet the resource allocation constraint.

[0053] 3d) For the repaired legitimate individuals, calculate the overall fitness using a linear weighted method based on extreme value normalization. First, set the system's total revenue objective... Deviation from the plan cost target Each value was compared with its theoretical maximum value and then normalized to obtain the results. and The specific formula is as follows:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059] The objective of minimizing deviation is transformed into a maximization form to unify the optimization direction, combined with weighting coefficients. and Calculate the overall fitness value Fitness Finally, the individual with the highest fitness value in the current population is recorded as the globally optimal individual. The fitness function is expressed as:

[0060] 3e) Complete the construction of the initial population; 4) Perform outer-layer genetic operations, generate offspring populations through selection, crossover, and mutation operations, and perform conflict repair again; The specific operation of step 4) is as follows: 4a) A tournament selection algorithm is used to select superior individuals. To retain high-fitness scheduling schemes in the next generation, several individuals are randomly selected from the current population to form a competition group. The individuals in the competition group are evaluated and ranked based on the comprehensive fitness function. The optimal individual with the highest fitness value is selected, copied, and added to the new generation population. The above random sampling and selection process is repeated until the size of the newly generated population reaches the same size as the original population. 4b) Reference Figure 4 Gene recombination is performed using a two-point crossover mechanism, based on a set crossover probability. On two selected parent chromosomes, two identical crossover sites are randomly generated. Then, the gene segments contained in these two crossover sites are completely interchanged between the two parents, thereby generating two completely new offspring chromosomes. In this way, the local satellite allocation scheme contained in the parent chromosomes can be recombinated and inherited in the offspring. 4c) Refer to Figure 5 A single-point mutation strategy is used to randomly perturb genes, based on a set mutation probability. If a mutation operation is performed on an offspring individual, then at a randomly determined mutation site, a new value different from the current value is randomly selected from the set of valid values ​​consisting of the available relay satellite physical number and 0 to replace the original gene at that location, thereby quickly generating new individuals with entirely new mapping relationships in the population.

[0061] 4d) Refer to Figure 6 Heuristic conflict detection and repair are performed, and fitness is updated. Since crossover and mutation operations are blind and can easily destroy existing legitimate gene sequences and generate resource conflicts, heuristic conflict detection must be performed again on all newly generated offspring chromosomes. The reward weights of conflicting tasks with overlapping resources are compared, high-reward tasks are retained, and the gene values ​​corresponding to low-reward tasks that fail to compete are forcibly reset to 0. Finally, the overall fitness of all legitimate offspring individuals after repair is recalculated to form a new generation population to enter the subsequent inner taboo search stage. 5) Reference Figure 7 Perform inner tabu search operations, and for tasks in an unscheduled state, alternately use a multi-neighborhood structure to perform local resource reallocation; The specific operation of step 5) is as follows: 5a) Use each individual in the new generation population as the initial solution of the inner tabu search algorithm, and initialize the number of local search iterations for the current individual.

[0062] 5b) Invoking the multi-neighborhood structure. For the 0 value in the current individual gene sequence, the target mission and relay satellite combination is selected by randomly alternating between greedy exchange neighborhoods, roulette wheel exchange neighborhoods, or random exchange neighborhoods. The three exchange neighborhoods are as follows: 5b1) Greedy swap neighborhood priority selects the target task with the highest priority or task benefit, and matches it with the satellite resources with the highest current fitness or the most execution opportunities; 5b2) The roulette wheel exchange neighborhood is extracted based on a probability distribution constructed using task reward weights and satellite fitness. 5b3) Randomly swapping neighborhoods involves uniformly and completely randomly combining the unscheduled task set and the available satellite set, injecting random perturbations into the local search.

[0063] 5c) Combine insertion and replacement operators to perform local resource reallocation and generate a set of candidate solutions; 5c1) Attempt to specify a target relay satellite for an unscheduled task. If there is no time overlap between the available time window of the target satellite and all currently scheduled tasks, trigger the insertion operator to add the task to the scheduling scheme and update the corresponding gene value from 0 to the number of the target relay satellite. 5c2) If the allocation of an unscheduled task causes a local time conflict, the replacement operator is triggered to calculate the benefit of the task to be allocated and the total benefit of all affected conflicting tasks. 5c3) When the benefit of the task to be assigned is significantly greater than the total benefit of all affected conflict tasks, the visible window of the original conflict task is forcibly removed and resources are allocated to the new task. The gene value of the original conflict task is reset to 0, and the target satellite number is assigned to the new task, thereby generating a set of candidate solutions.

[0064] 6) Update the current individual and global optimal solutions based on the amnesty criterion and the quality of neighborhood solutions, and update the tabu list simultaneously; The specific operation of step 6) is as follows: 6a) Determine the amnesty criterion and update the global optimal solution. Evaluate the overall fitness of the candidate solution set. If the total reward of a candidate solution strictly breaks the global historical optimal level recorded so far, the amnesty criterion is triggered directly, ignoring the current taboo attribute of the task and forcibly allowing it, accepting the resource allocation change and simultaneously updating the global optimal solution record; 6b) Update the current individual by selecting the best one. If the amnesty criterion is not triggered, select the best neighborhood solution with the highest comprehensive fitness from the set of non-taboo candidate solutions to replace and update the current individual. 6c) Updating the taboo list and removing taboos: Tasks that were forcibly stripped of their visible window and had their genes reset to 0 during the replacement operation in step 5c) due to low returns are pushed into the taboo list based on a first-in-first-out queue. Within a fixed number of iterations, their participation in the preemption and allocation of target satellite resources is strictly restricted. At the same time, it is determined whether the taboo list is full. If the taboo list reaches the preset fixed maximum capacity threshold, the earliest enqueued task is automatically removed from the queue and the taboo restriction is completely removed, allowing it to regain the qualification for resource allocation. 6d) Determine the inner layer termination condition: Determine whether the current inner layer tabu search iteration count has reached the maximum iteration count limit. If not, increase the iteration count and return to step 5b) to continue executing neighborhood search and resource reconstruction; if it has, end the local fine-tuning of the current individual and replace the original individual with the optimized solution.

[0065] 7) Repeat steps 4) to 6) until the iteration stopping conditions such as the maximum number of generations are met, and finally decode and output the optimal local scheduling scheme.

[0066] Example 2 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the multi-target satellite dynamic task scheduling method based on genetic tabu search. For example, the steps include: 1) initializing the basic parameters of the task dynamic scheduling system to obtain an initial local scheduling scheme; 2) encoding the task set TC to be planned using a local independent integer encoding mechanism to construct chromosomes; 3) generating an initial population based on the initial local scheduling scheme using a heuristic method, performing conflict repair, and then calculating the overall fitness of individuals; 4) performing outer-layer genetic operations, generating offspring populations through selection, crossover, and mutation operations, and performing conflict repair again; 5) performing inner-layer tabu search operations, alternately using a multi-neighborhood structure for local resource reallocation for tasks in an unscheduled state; 6) updating the current individual and the global optimal solution according to the amnesty criterion and the quality of neighborhood solutions, and synchronously updating the tabu list; 7) repeating steps 4) to 6) until the iteration stopping condition is met, finally decoding to obtain and output the optimal local scheduling scheme. The memory may include main memory, such as high-speed random access memory (RAM), or non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, or an extended industry-standard architecture bus. The bus can be categorized as an address bus, data bus, or control bus. The memory stores programs; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0067] Example 3 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-target satellite dynamic task scheduling method based on genetic tabu search, for example including: 1) initializing the basic parameters of the task dynamic scheduling system to obtain an initial local scheduling scheme; 2) encoding the task set TC to be planned using a local independent integer encoding mechanism to construct chromosomes; 3) generating an initial population based on the initial local scheduling scheme using a heuristic method, performing conflict repair, and then calculating the overall fitness of individuals; 4) performing outer-layer genetic operations, generating offspring populations through selection, crossover, and mutation operations, and performing conflict repair again; 5) performing inner-layer tabu search operations, alternately using a multi-neighborhood structure to locally reallocate resources for tasks in an unscheduled state; 6) updating the current individual and the global optimal solution according to the amnesty criterion and the quality of neighborhood solutions, and synchronously updating the tabu list; 7) repeating steps 4) to 6) until the iteration stopping condition is met, finally decoding, obtaining and outputting the optimal local scheduling scheme. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0073] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0074] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-objective satellite dynamic mission scheduling method based on genetic tabu search, characterized in that, Includes the following steps: 1) Initialize the basic parameters of the task dynamic scheduling system to obtain the initial local scheduling scheme; 2) Employ a locally independent integer encoding mechanism to encode the task set TC to be planned, and construct a chromosome; 3) Based on the initial local scheduling scheme, an initial population is generated using a heuristic method, conflict resolution is performed, and then the overall fitness of individuals is calculated; 4) Perform outer-layer genetic operations, generate offspring populations through selection, crossover, and mutation operations, and perform conflict repair again; 5) Perform inner tabu search operations, and for tasks in an unscheduled state, alternately use a multi-neighborhood structure to perform local resource reallocation; 6) Update the current individual and global optimal solutions based on the amnesty criterion and the quality of neighborhood solutions, and update the tabu list simultaneously; 7) Repeat steps 4) to 6) until the iteration stopping condition is met, and finally decode to obtain and output the optimal local scheduling scheme.

2. The multi-objective satellite dynamic task scheduling method based on genetic taboo search according to claim 1, characterized in that, The basic parameters of the mission dynamic scheduling system include the relay satellite set RS, the low-Earth orbit user satellites S, and the set of missions to be planned TC. Any task in Represented as a six-dimensional tuple ,in, and Representing tasks In the reactive scheduling phase, the effective earliest start time and latest end time, constrained by the current resource allocation, satisfy... ; Indicates task Processing time; Indicates task Task rewards; Indicates the first i Each mission corresponds to a low-Earth orbit user satellite service. Indicates the first i The index number of the satellite serving low-Earth orbit users for each mission. This indicates a set of routine tasks that have already begun execution or whose scheduling arrangements have been determined. For fixed routine tasks f The start time, For fixed routine tasks f Processing time, This is a state indication constant; For the set of tasks to be planned TC With fixed routine task set The logical relationship between them introduces decision variables. When planning tasks Arranged in fixed routine tasks f When executed previously, Otherwise, =0; Introduction and As a reference benchmark, they represent the routine tasks. The planned start time and resource occupancy status in a local scheduling scheme; routine tasks in a local scheduling scheme. Occupy relay satellite window ,but Otherwise, Set the decision variable to 0. For 0-1 variables, Indicates that in relay satellite On the task Immediately following the mission Then execute; decision variables For 0-1 variables, Indicates task Assigned to relay satellite window Otherwise, =0, Indicates an opportunity task The blank time, As continuous variables, representing tasks The actual start time; Low Earth Orbit User Satellites Visible windows for all relay satellites ,in, , For user satellites With relay satellite Between k One visible window, The start time of the window. The end time of the window, the scheduling cycle .

3. The multi-objective satellite dynamic task scheduling method based on genetic taboo search according to claim 2, characterized in that, The specific operation of step 3) is as follows: 3a) Generate the first batch of initial population individuals using the initial scheduling results before the occurrence of dynamic events. In the first batch of initial population individuals, randomly assign resource numbers to newly arrived sudden tasks and overflowing opportunity tasks within the available resource range. 3b) For the remaining part of the initial population size, satellite resource numbers are generated completely randomly for all planned tasks within the set of relay satellite physical numbers to construct a second batch of random individuals. The second batch of random individuals is then merged with some individuals from the first batch of the initial population to obtain a complete initial population. 3c) Perform conflict detection and repair on all individuals, decode the tasks on the chromosome and map them to the time axis of the corresponding satellite, and perform legality verification based on resource allocation constraints. When multiple tasks are assigned to the same relay satellite in the same time period, compare the benefit weight of conflicting tasks, retain the task with the highest benefit, and reset the gene value corresponding to the failed task to 0 and mark it as unscheduled. 3d) Construct a comprehensive fitness function based on the linear weighted method of extreme value normalization, and calculate the comprehensive fitness value of each current individual. , and These are the weighting coefficients. The goal is the normalized total return. The normalized scheme bias cost objective is used, and the globally optimal individual is recorded. .

4. The multi-objective satellite dynamic mission scheduling method based on genetic taboo search according to claim 3, characterized in that, The specific operation of step 3c) is as follows: 3c1) Decode the task gene sequence on the chromosome, map the set of tasks to be planned to the time axis of the corresponding relay satellite according to the relay satellite number specified by the gene value, and overlay it with the time window of the fixed regular tasks that have been locked in the original scheduling scheme. 3c2) Establish resource allocation constraint one: each task is served by a visible window at any given time, and the allocation of visible windows is unique: 3c3) Establish resource allocation constraint two: each relay satellite can only handle one task at a time, and the arrangement of tasks in the scheduling sequence must follow a unique order: Among them, 0 and +1 represents two virtual tasks, indicating the start and end of the task sequence on each relay satellite; 3c4) Establish resource allocation constraint three: when executing missions on the same relay satellite, ensure that the time windows of two adjacent missions do not overlap. 3c5) Establish resource allocation constraints. Fourthly, in task planning, for each task, ensure that... The scope begins and ends within a certain range to meet task requirements and ensure a reasonable allocation of system resources: 3c6) Establish resource allocation constraint five: In task scheduling, each task must be executed within the visible time window to ensure that tasks are carried out within the time available for scheduling between the satellite and the target. 3c7) Establish resource allocation constraint six: the start time of all scheduled tasks must not be earlier than the earliest time when reactive scheduling instructions can take effect. 3c8) Establish resource allocation constraint seven to prevent resource allocation overlap and ensure that when the visible window of the task to be planned is reallocated, there is no time window conflict with the regular tasks already locked in the original initial scheduling scheme: 3c9) Based on the preset resource allocation constraints, scan and verify the validity of the mapped scheduling sequence; 3c10) When the legality check finds that the same relay satellite is assigned multiple tasks in the same time period, thus causing the time window for violating the resource allocation constraint to overlap, compare the benefit weight of the conflicting tasks, retain the task with the highest benefit to legally occupy the satellite resource; for low-benefit tasks that fail to compete, reset their corresponding gene value to 0, deprive them of the allocation qualification in the current round and mark them as unscheduled, until all tasks strictly meet the resource allocation constraint.

5. The multi-objective satellite dynamic mission scheduling method based on genetic taboo search according to claim 1, characterized in that, The specific operation of step 4) is as follows: 4a) Using the tournament selection algorithm, several individuals are randomly selected from the current population to form a competition group. The optimal individual with the highest fitness value is selected for replication and added to the mating pool. 4b) Employ a two-point crossover mechanism, based on the crossover probability. Two crossover sites are randomly generated on two selected parent chromosomes. The gene segments contained in the crossover sites are then exchanged between the two parents to generate offspring chromosomes. 4c) Use a single-point mutation method, based on the mutation probability. A new value is randomly selected from the set of valid values ​​to replace the original gene at the mutation site; 4d) Perform heuristic conflict detection and repair on all newly generated offspring chromosomes again, and recalculate the overall fitness to form a new generation of population.

6. The multi-objective satellite dynamic task scheduling method based on genetic taboo search according to claim 1, characterized in that, The specific operation of step 5) is as follows: 5a) Use each individual in the new population as the initial solution for the inner tabu search algorithm; 5b) For the 0 value in the current individual gene sequence, call the greedy swap neighborhood, roulette swap neighborhood, or random swap neighborhood in a random alternation manner; 5c) Combine the insertion operator and the replacement operator to perform local resource reallocation and generate a candidate solution set.

7. The multi-objective satellite dynamic mission scheduling method based on genetic taboo search according to claim 6, characterized in that, The specific operation of step 5c) is as follows: 5c1) For an unscheduled task with a designated target relay satellite, if there is no time overlap between the target satellite's available time window and all currently scheduled tasks, the insertion operator is triggered to add the task to the local scheduling scheme and update the corresponding gene value from 0 to the target relay satellite's number. 5c2) If the allocation of an unscheduled task causes a local time conflict, the replacement operator is triggered to calculate the benefit of the task to be allocated and the total benefit of all affected conflicting tasks. 5c3) When the benefit of the task to be assigned is greater than the total benefit of all affected conflict tasks, the visible window of the original conflict task is forcibly removed and resources are allocated to the new task. The gene value of the original conflict task is reset to 0, and the target satellite number is assigned to the new task to generate a candidate solution set.

8. The multi-objective satellite dynamic mission scheduling method based on genetic taboo search according to claim 1, characterized in that, The specific operation of step 6) is as follows: 6a) Evaluate the overall fitness of the candidate solution set, where the total reward of any candidate solution exceeds the global historical best. If this occurs, the amnesty criterion is triggered directly, the resource allocation change is accepted, and the global optimal solution record is updated simultaneously. ; 6b) If the amnesty criterion is not triggered, the optimal neighborhood solution with the highest fitness is selected from the non-taboo candidate solutions to update the current individual; 6c) Push tasks whose genes are reduced to 0 due to replacement operations into a tabu list based on a first-in-first-out queue; 6d) Determine if the tabu list is full. If the tabu list reaches a fixed maximum capacity threshold, remove the earliest enqueued task and completely remove the tabu restriction.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the multi-target satellite dynamic mission scheduling method based on genetic tabu search as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-target satellite dynamic mission scheduling method based on genetic tabu search as described in any one of claims 1-8.

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