A multi-launch site space launch mission planning and scheduling method based on resource residual amount

Abstract

The present application relates to the technical field of electric data processing, and especially relates to a multi-launch site space launch task planning and scheduling method based on resource residual quantity, which comprises the following steps: S1, constructing a launch task set based on launch tasks and constructing an available resource set based on existing resources; S2, constructing a resource requirement set for each launch task; S3, according to the launch task set, the available resource set and the resource requirement set, determining a global optimal solution of a planning and scheduling model based on an improved ant colony algorithm, and performing launch task planning and available resource scheduling according to the global optimal solution. The present application fully considers the relationship among launch tasks, task requirements and available resources, and can improve the reliability of space launch task planning and scheduling.

Description

Technical Field

[0001] This invention relates to the field of aerospace launch technology, and in particular to a multi-launch site aerospace launch mission planning and scheduling method based on resource surplus. Background Technology

[0002] The demand for space launches is growing rapidly, while the construction of launch site resources involves high investment and long cycles, highlighting the increasingly prominent contradiction between the growing demand and the shortage of launch site resources. Multi-launch site space launch mission planning and scheduling involves selecting a subset of feasible launch missions from a group of missions with proposed launch requests, while simultaneously optimizing test times, launch windows, and test resource allocation for these missions within the subset, all to maximize the overall benefits of multi-launch site space launches, under various constraints such as launch site resource capacity limitations, time window limitations, and launch location limitations.

[0003] Currently, the space launch mission planning and scheduling problem is generally transformed into an integer optimization problem, employing methods such as greedy algorithms, branch and bound methods, genetic algorithms, and multi-agent methods. However, in research on space launch mission planning and scheduling, foreign literature mainly focuses on the matching and scheduling of rockets and payloads, giving less consideration to the role of launch site resources in launch mission planning and scheduling; while domestic literature often makes assumptions that differ significantly from launch site-related conditions, such as failing to consider the launch site's ability to execute tasks concurrently, the multi-object service capabilities of resources, or the selectivity and differences of launch time windows. Therefore, the reliability and feasibility of current space launch mission planning and scheduling methods are not strong. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-launch site space launch mission planning and scheduling method based on resource surplus, so as to improve the reliability of space launch mission planning and scheduling.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a multi-launch site space launch mission planning and scheduling method based on resource surplus, comprising the following steps:

[0007] S1, construct a set of launch missions based on launch missions, and construct a set of available resources based on existing resources;

[0008] S2, constructs a resource requirement set for each launch mission;

[0009] S3. Based on the launch mission set, the available resource set, and the resource demand set, the global optimal solution of the planning and scheduling model is determined iteratively using the improved ant colony algorithm, and launch mission planning and available resource scheduling are performed based on the global optimal solution.

[0010] In step S3, the planning and scheduling model consists of decision variables, objective function, and constraints.

[0011] Decision variables include x i y i,w z i,d,j x i Indicates launch mission m i Whether ∈M is selected to be inserted into a feasible solution is a 0-1 variable; y i,w Indicates launch mission m i Whether the w-th emission time window ∈M is selected is a 0-1 variable; z i,d,j Indicates launch mission m i Should the d-th resource requirement in ∈M select resource r? j ∈R to meet the requirements, which are 0-1 variables; M represents the launch task set, and R represents the available resource set;

[0012] The objective function is F1 represents the total launch revenue, and F2 represents the resource balance.

[0013] The constraints include the maximum resource usage per unit time, the maximum storage of consumable resources, the consistency of launch site location for resources used by the mission, the resource state transition time, and the uniqueness of the launch site time window.

[0014] In the above scheme, the planning and scheduling model consists of decision variables, objective functions, and constraints. This model fully considers the role of resources in launch mission planning and scheduling, such as the launch site's ability to execute tasks in parallel, the multi-object service capability of resources, and the selectivity of launch time windows, which are reflected through constraints and the objective function. It also considers the storage and computational complexity of different types of resource measurement data, using a percentage-based resource surplus to measure all resources. This significantly enhances the reliability and feasibility of the aerospace launch mission planning and scheduling method, and improves the efficiency of planning, scheduling, and result interpretation. The improved ant colony algorithm overcomes the limitations of the basic ant colony algorithm on ant path length and node position. Furthermore, it uses three pheromone matrices to simultaneously record and transmit task, launch window, and resource information contained in the feasible solution of the launch mission planning and scheduling problem, improving the algorithm's adaptability to the problem and achieving multi-objective optimization of complex, multi-dimensional constraints on problems with uncertain feasible solutions and dynamic coupling of mission planning and resource scheduling.

[0015] Secondly, the present invention provides a computer program product including computer-readable instructions, characterized in that the computer-readable instructions, when executed by a processor, implement the steps in the multi-launch site aerospace launch mission planning and scheduling method based on resource surplus of the present invention.

[0016] Thirdly, the present invention provides a computer-readable storage medium including computer-readable instructions, characterized in that the computer-readable instructions, when executed by a processor, implement the steps in the multi-launch site space launch mission planning and scheduling method based on resource surplus of the present invention.

[0017] Fourthly, the present invention provides an electronic device, comprising: a memory storing program instructions; and a processor connected to the memory, executing the program instructions in the memory to implement the steps in the multi-launch site space launch mission planning and scheduling method based on resource surplus of the present invention.

[0018] Compared with existing technologies, this invention fully considers the relationship between launch missions, mission requirements and available resources, and fully extracts the role of launch site resources, which can improve the reliability of space launch mission planning and scheduling.

[0019] Other advantages of this invention are described in the embodiments section. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a multi-launch site space launch mission planning and scheduling method based on resource surplus provided in the embodiments.

[0022] Figure 2 This is a diagram illustrating how remaining resources are stored in a data file.

[0023] Figure 3 for Figure 1 Detailed flowchart of step S3.

[0024] Figure 4 This is a schematic diagram of the structure of the window-non-consumable resource combination available in the embodiment.

[0025] Figure 5 This is a graph showing the change in the objective function value during the iterative process of the solution algorithm in the simulation test example.

[0026] Figure 6 This is a Gantt chart of the launch mission planning scheme for the globally optimal solution in the simulation test case.

[0027] Figure 7This is a schematic diagram showing the usage time of the seven main non-consumable resources in the global optimal solution for each launch mission in the simulation test case.

[0028] Figure 8 This is a usage curve of 7 main non-consumable resources (resources 1 to 7) and 2 main consumable resources (resources 8 to 9) during the planning period in the simulation test case.

[0029] Figure 9 This is a block diagram of the components of an electronic device. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0031] Please see Figure 1 This embodiment provides a multi-launch site space launch mission planning and scheduling method based on resource surplus, which includes the following steps:

[0032] S1, construct a launch mission set M based on the launch mission, and construct an available resource set R based on existing resources.

[0033] Each resource r j ∈R is defined as in It is a resource r j Location, G j Indicates resource r j The resource type (consumable or non-consumable), E j It is a resource r j List of available quantities on the planning and scheduling timeline.

[0034] Resources used for space launches are divided into three main categories: fixed resources, consumable resources, and human resources. Both human resources and fixed resources are non-consumable resources. Fixed resources include test facilities, launch pads, and other specialized facilities and equipment. Consumable resources include power sources such as liquid propellants and other specific liquids and gases.

[0035] The remaining resource quantity can be stored in the data file in the following ways: Figure 2 As shown, the horizontal axis represents the planning and scheduling timeline, the vertical axis represents resources, and the numerical list represents the remaining amount of the resource at the corresponding time (i.e., the available amount, which can be expressed as a percentage of the actual remaining amount of the resource to the total amount).

[0036] Launch mission m in mission set M i ∈M is defined as Where T iRepresents task m i Total test duration, Represents task m i Designated launch site location, O i Represents task m i The set of objects (referring to spacecraft or launch vehicles), W i Represents task m i The set of launch time windows, D i Represents task m i The set of resource requirements.

[0037] S2, for each launch mission m i Construct resource requirement set D i .

[0038] Resource demand refers to the demand for a specified resource or a specified type of resource, including the remaining amount of the specified resource or a specified type of resource occupied and the duration of its continuous occupation during the scheduling cycle.

[0039] A launch mission may have multiple requirements for the same type of resources. This is mainly because there are multiple objects involved in the launch mission. For example, both spacecraft and rockets require launch pads and their times overlap to some extent. In this case, the resource requirements are merged into one, and the union of the occupied time in the requirements is calculated (that is, from the earliest time to the latest time).

[0040] Specifically, regarding task m i In constructing the resource demand set D i At that time, first, task m i Each required resource requirement is recorded in the resource requirement set D. i Then, it will be used to determine whether the spacecraft or launch vehicle is simultaneously occupied or has partially overlapping time slots. ( The demand for resources (representing a set of resources of the same type) is organized into a single demand, that is, all the demands for resources are combined into one demand. The demand for resources is merged, where the demand time is unioned, and the maximum value of the resource demand for each unit of time is taken.

[0041] S3, based on the launch mission set M, the available resource set R, and the resource requirement set D i The global optimal solution of the planning and scheduling model is determined iteratively based on the improved ant colony algorithm, and the launch mission planning and available resource scheduling are carried out according to the global optimal solution.

[0042] The planning and scheduling model consists of decision variables, objective function, and constraints.

[0043] Decision variables include x i yi,w z i,d,j x i Represents task m i Whether ∈M is selected to be inserted into a feasible solution is a 0-1 variable; y i,w Represents task m i Whether the w-th emission time window ∈M is selected is a 0-1 variable; z i,d,j Represents task m i Should the d-th resource requirement in ∈M select resource r? j ∈R to meet the requirements, and are 0-1 variables.

[0044] The objective function is F1 represents the total launch revenue, and F2 represents the resource balance.

[0045] Where, ω i It is task m i The total weight of the object (referring to spacecraft or launch vehicle). It is task m i The weight of object o; I O It is m i The total number of objects o in the object set O; ω i,w It is task m i The weight of the w-th launch time window; I M This represents the total number of launch missions to be planned and scheduled. During launch mission planning and scheduling, launch mission m... i The more objects with larger weights included in ∈M, the greater the benefit of the launch task. When a launch task has multiple launch time windows to choose from, the launch time window that best matches the user's expectations has a larger weight, and the greater the benefit of launching through that launch time window. Therefore, the planning and scheduling scheme should aim to maximize the total launch benefit F1.

[0046] in, Indicates resource r j The initial remaining quantity S at any unit time t j,t This indicates that the resource r j The final actual remaining amount per unit time t; z i,k,j Represents task m i Should the k-th resource requirement in ∈M select resource r? j ∈R to satisfy; Represents task m i The relative occupancy time of the kth resource requirement is equivalent to the resource usage at the planned time t (which can be deduced from the launch time window); T g It is the total duration of the planned scheduling; f jIndicates resource r j Average usage rate; J represents the average utilization rate of similar resources; CON Is related to resource r j The quantity of the same type of resources, J CONs It is the total number of all resource types; Indicates resource r j The degree of balanced use of similar resources is equal to 1 minus the resource r. j The mean square deviation of the utilization rate of resources of the same type. If multiple resources of the same type exist within the same launch site, efforts should be made to ensure that all individuals of that type of resource are used evenly, thereby avoiding overuse of any single resource, meeting daily maintenance needs, and extending the lifespan of individual resources. Therefore, the planning and scheduling scheme should maximize the mean F² of the utilization balance of various resource types.

[0047] The constraints include the maximum resource usage per unit time, the maximum storage of consumable resources, the consistency of launch site location for resources used by the mission, the resource state transition time, and the uniqueness of the launch site time window.

[0048] Resources have a maximum capacity limit within each unit of time. Therefore, the total usage of the same resource by all tasks in the planning and scheduling scheme at the same time should be less than or equal to the maximum capacity limit of that resource at that time. Thus, the maximum resource usage constraint per unit of time refers to... Among them, u j,t,i Indicates resource r j At time t, task m i The amount used It is a resource r j The maximum remaining amount at time t. This constraint applies to both fixed resources and human resources.

[0049] The total replenishment and remaining amount of consumable resources cannot exceed the maximum storage limit of that resource, and the usage of that resource by all tasks at the same time cannot exceed the maximum storage limit. Therefore, the maximum storage limit constraint of consumable resources refers to... in, It is a resource r j (This resource is a consumable resource) The maximum storage capacity at any time t (the maximum storage capacity takes into account the time factor and is a measure to cope with changes in the quantity of storage devices when they are added or under maintenance), S j,t B represents the remaining amount of the resource at any time t. j,t u represents the amount of the resource replenished at any time t. j,t,i This indicates that the resource was used by task m at time t. i The amount used.

[0050] Resources that meet the resource requirements of the same mission should be located at the same launch site. Therefore, the constraint that the launch site locations of resources used in a mission must be consistent means that... in, Indicates the launch site location of a certain resource; and All belong to the planning and scheduling schemes that satisfy task m i The set of all resources R required for the resource i .

[0051] Some resources, after being used and released and before being used again, require state restoration or a new state setting; therefore, they cannot be occupied during state transitions. Thus, the resource state transition time constraint refers to... in, It is a resource r j State transition time; It is a resource r j Any two occupied and adjacent tasks on the planning and scheduling timeline The arbitrary execution (test) time.

[0052] At a launch site, only one launch window can be executed within the same unit of time. Therefore, the uniqueness constraint of the launch site time window means that... in, These are any two tasks in the planning and scheduling process. The absolute time on the timeline of the planning and scheduling cycle corresponding to the selected launch time window.

[0053] The problem of planning and scheduling space launch missions at multiple launch sites, that is, within a time range T... start ,T end Within this framework, a launch mission planning and scheduling scheme is developed, selecting an executable subset M of launch missions from the launch mission set M. sel And for each task m i ∈M sel From its launch time window set W i Select a launch time window and assign it to mission m. i Resource demand set D i Each demand selects an available resource from the resource set R, so that the total launch revenue and the balance of resource utilization of the same type and launch site are optimal while satisfying the constraints.

[0054] See also Figure 3 In step S3 above, the process of iteratively determining the global optimal solution based on the improved ant colony algorithm includes the following steps:

[0055] S31, Initialize parameters, including the number of ants A in the ant colony. ant Maximum pheromone quantity Q, pheromone evaporation factor ρ, maximum number of iterations The pheromone importance factor α and the heuristic function importance factor β are set, and the initial number of iterations is set to n = 1.

[0056] S32, for a single ant in the ant colony, for each candidate task, construct a set of available window-resource combinations for each resource requirement of the candidate task. An available window-resource combination set refers to a collection of several available window-resource combinations that simultaneously satisfy one of the resource requirements of the candidate task. An available window-resource combination is a combination of an available time window and an available resource.

[0057] Candidate tasks refer to launch tasks that have not yet been inserted into a feasible solution and are waiting to be inserted when an ant selects a path node. These tasks are stored in the candidate task list.

[0058] The available window-resource combination set for each resource requirement of a candidate task consists of either available window-resource combinations that satisfy non-consumable resource requirements or available window-resource combinations that satisfy consumable resource requirements. Non-consumable resources and available time windows together constitute available window-resource combinations that satisfy the task's non-consumable resource requirements, while consumable resources and available time windows together constitute available window-resource combinations that satisfy the task's consumable resource requirements. The selection methods for available window-resource combinations for non-consumable resource requirements and available window-resource combinations for consumable resource requirements differ, and will be explained separately below.

[0059] The process of constructing the available window-resource combination set for the non-consumable resource requirements of candidate tasks is as follows:

[0060] ①Based on resource demand d i,k (Task m) i The k-th resource requirement (non-consumable) requires the resource type and task m. i Given the required launch site location, establish a resource set that meets the resource type requirements;

[0061] ② Select a resource r from the resource set that meets the resource type requirements. j Based on task m i For each launch time window and the total duration of the mission test cycle, calculate the mission test time range and determine the resource r j List of remaining resources (resources) j The remaining quantity list is a list of the remaining quantity of the resource in each unit of time within the entire planning and scheduling cycle. For example, if the planning cycle is 365 days and the unit of time is days, then the list contains the remaining quantity of resource r. j For the daily remaining amount, please refer to... Figure 2 Does the remaining resource quantity within the test time range meet the resource requirement d? i,k Record the usage of all required resources, and if satisfied, record the time window and resource r. j The information is presented as a combination of available windows and resources;

[0062] ③ Regarding resource demand d i,k For all available window-resource combinations, determine whether resources other than telemetry and control related resources (including various telemetry and control equipment, such as radar and optical equipment, which belong to "other dedicated facilities and equipment" in fixed resources) meet the launch site location consistency constraint. Delete available window-resource combinations that do not meet the launch site location consistency constraint. If no resource requirement d is met after deletion... i,k If the available window-resource combination is given, then the non-consumable resource requirement d i,k The available window-resource set is set to empty, indicating that the candidate task m i A feasible solution cannot be inserted; if at least one solution exists that satisfies resource requirement d after deletion. i,k If the available window-resource combinations are a given set, then these available window-resource combinations constitute a set that satisfies resource requirement d. i,k Available window - resource set.

[0063] An available window-resource combination includes a task, a time window, an object, a requirement, and a resource. To facilitate computer recording and processing, tasks, time windows, objects, requirements, and resources can be numbered. The available window-resource combination then consists of the task number, window number, object number, requirement number, and resource number, such as... Figure 4 As shown.

[0064] The processing of the available window for resource combinations of candidate tasks' consumable resource requirements is as follows:

[0065] Assuming all consumable resources are replenished at their own fixed rates, the replenishment rate is defined as follows: Among them, v x,j Represents the j-th resource r j (At this point, the resource is a consumable resource) replenishment rate. It is the shortest replenishment time for this consumable resource, b x,j This represents the maximum amount that can be replenished within the shortest replenishment time. Although the above formula is in fractional form, the replenishment rate cannot be directly calculated and used. x,j Instead of using the value, the shortest replenishment time and the maximum replenishable amount should be measured separately.

[0066] When inserting a task into a feasible solution, the method for determining whether a consumable resource meets the requirements of the task is as follows:

[0067] ①Based on the current insertion task m i Within a window of time, determine if a task has been inserted into the task list, and the previous task m on the timeline. i-1 The remaining amount after using the consumable resource, plus m i-1 The time the task uses this consumable resource up to the current task m i Does the replenishment amount of the consumable resource between the times it is used meet the requirements of the current task m? i The required amount of this consumable resource. If the requirement is not met, task m cannot be inserted. i If satisfied, it means that the resources on the front end can satisfy task m. i Proceed to step ②. Consumable resource replenishment amount B j,i The formula for calculation is: Where [*] is the floor symbol, t j,i Is it using resource r j Task m i Usage time.

[0068] ② Using the same method as ①, determine if task m is inserted. i Then, the current task m i The time of using this consumable resource is up to the next task m on the timeline of the inserted task list. i+1 Does the replenishment amount of the consumable resource between the time it is used satisfy the requirements of task m? i+1 The required amount of this consumable resource. If this requirement is not met, task m cannot be inserted. i The available window-resource set for this consumable resource requirement is set to empty; if satisfied, proceed to step ②, which means repeatedly executing this step until the last task in the inserted task list is reached. If the remaining amount plus the replenishment amount of this consumable resource satisfies the requirements of all subsequent tasks, then task m can be inserted. i Proceed to step ③; if the demand for any subsequent task cannot be met, task m cannot be inserted. i .

[0069] ③ Record task m i The available window-resource combination set for each consumable resource requirement (there are no multiple consumable resources of the same type in the same launch site, so there is only one available window-resource combination in this set) and the remaining amount of resources for all times that use the consumable resource after insertion are updated, to be used when the finally selected task uses the consumable resource.

[0070] S33, based on the available window-resource combination set, construct an executable combination of available window-resource combinations for each candidate task, and calculate the selection probability of each executable combination.

[0071] Candidate task m i The available window-resource combination set for all resource requirements (including non-consumable and consumable resource requirements) is relative to m. i For each launch window, find the intersection. If every intersection is empty, it means that the task has no time window and no remaining resources that can simultaneously satisfy all resource requirements, i.e., task m... i Unable to insert a feasible solution, task m i Move the resource from the candidate task list to the forbidden task list. If there is a non-empty intersection, delete all available window-resource combinations corresponding to the launch windows when the intersection is empty, and retain the available window-resource combinations corresponding to the launch windows when the intersection is not empty, thus obtaining the set of available window-resource combinations corresponding to each resource requirement of the candidate task.

[0072] An executable combination of available window-resource combinations for a candidate task consists of available window-resource combinations that satisfy all resource requirements of the candidate task, wherein each resource requirement has one and only one available window-resource combination corresponding to it.

[0073] For a candidate task, multiple types of resources are required, resulting in multiple resource requirements. A resource requirement can specify either a resource or a resource type. When specifying a resource type requirement, such as needing a test facility of a certain type, there may be multiple test facilities that meet that type requirement. Therefore, when selecting the same time window, the resources that satisfy all the resource requirements of this candidate task may form multiple combinations.

[0074] For each candidate task m i When selecting a window from the intersection of available windows among all the above resource requirements, some resource requirements of the task may have multiple available window-resource combinations that coincide with the selected time window. However, when inserting the task into the feasible solution, each resource requirement only needs one available window-resource combination to be satisfied. Therefore, there may be multiple executable combinations of available window-resource combinations that satisfy all resource requirements of the task. Each executable combination uniquely determines the three decision variables: all executable tasks, launch time windows, and resources that satisfy the resource requirements.

[0075] like Figure 4 As shown, assuming a candidate task has three resource requirements, there are also three available window-resource combinations that can satisfy all resource requirements of the task. These three window-resource combinations correspond to the three resource requirements and constitute an executable combination of available window-resource combinations for the candidate task. However, since the number of available window-resource combinations that can satisfy each non-consumable resource requirement is greater than or equal to one, the number of executable combinations formed by the three available window-resource combinations corresponding to the three resource requirements is also greater than or equal to one.

[0076] Satisfying candidate task m i The probability P of selecting the executable combination of the l-th available window-resource combination i,l for: Among them, M CAN It is a set of candidate tasks, m i ∈M CAN ; The candidate task set M CAN The number of tasks in L; i It is M CAN Candidate task m i The total number of executable combinations of available window-resource combinations, α is the pheromone importance factor, β is the heuristic function importance factor; τ i,l It is candidate task m i When selecting the pheromone for the executable combination of the l-th available window - resource combination. It is the task that was previously inserted into the feasible solution and the current candidate task m. i The pheromones between them It is task m i The pheromone between the time windows in the executable combination of its l-th available window-resource combination, It is task m i Pheromones between all resources in the executable combination of its l-th available window-resource combination The average value of J i,l Candidate task m i Select the total number of resources (including consumable and non-consumable resources) for the executable combination of the l-th available window - resource combination; η i,l It is candidate task m i The heuristic function (i.e., the objective function value of a single task) for selecting the l-th available window - resource combination executable combination. Then it is the candidate task m i Select the l-th available window - the total number of all resource types in the executable combination of resource combinations; The calculation method and the objective function Consistent; ω i,w This refers to the weight of the time window selected above.

[0077] S34. Following the same operation as step S33, obtain the set of executable combinations of available window-resource combinations for all candidate tasks, and select one of the executable combinations of available window-resource combinations to be added to the feasible solution (as part of the feasible solution) using the roulette wheel method according to the selection probability. Update the taboo task list, candidate task list, resource remaining list and ant position.

[0078] Each executable combination of available window-resource combinations requires calculating a selection probability. Based on this probability and the roulette wheel algorithm, one can be randomly selected to satisfy all resource requirements of a candidate task. However, each time the ant faces its next choice (the next candidate task to insert a feasible solution into), there are multiple candidate tasks (launch tasks that haven't yet had a feasible solution inserted and satisfy the constraints). Each candidate task also has multiple resource requirements and a set of executable combinations of available window-resource combinations that satisfy these requirements. Therefore, the ant not only needs to select a candidate task but also an executable combination of available window-resource combinations that satisfies all resource requirements of that candidate task. Figure 4 As can be seen, the executable combinations of available window-resource combinations already contain task numbers. All executable combinations of available window-resource combinations for all candidate tasks in the candidate task list are gathered into one set. Then, based on selection probability and roulette wheel selection, one executable combination of available window-resource combinations is randomly selected and inserted into the feasible solution. The taboo task list, candidate task list, resource remaining list, and ant position are updated, thus completing the ant's path node position selection in this step. If any resource requirement of a candidate task cannot be met, i.e., there is no available window-resource combination that satisfies that resource requirement, then there will be no available window-resource combination that can satisfy all the resource requirements of that candidate task, and in this case, the task cannot be inserted into a feasible solution.

[0079] S35. For a single ant, repeat steps S32 to S34 until the candidate task list or the set of executable combinations of available window-resource combinations of all candidate tasks is empty, thus completing the construction of a feasible solution.

[0080] An ant can construct a feasible solution, which includes a subset of all tasks to be planned. Steps S32 to S34 are repeated. If the candidate task list is empty, it means all candidate tasks have been inserted into a feasible solution or moved to the taboo task list. If the candidate task list is not empty, but the set of executable combinations of available windows and resources for all candidate tasks is empty, it means none of the candidate tasks in the list can be inserted into a feasible solution. At this point, the executable combinations of available windows and resources for all candidate tasks that have been inserted into a feasible solution constitute the feasible solution constructed by the ant.

[0081] S36. Calculate the objective function value of the feasible solution constructed by each ant, update the global optimal solution, and update the pheromone matrix.

[0082] Calculate the objective function value of the feasible solution constructed by each ant in the current iteration of the ant colony, and take the maximum objective function value. Compare it with the objective function value of the global optimal solution. If the maximum objective function value in this iteration is greater than the objective function value of the global optimal solution, then the feasible solution corresponding to the maximum objective function value in this iteration is taken as the global optimal solution, and its objective function value is recorded.

[0083] In each feasible solution, task m i-1 With m i The pheromone update formula between them is:

[0084] Task m i The pheromone update formula between it and the emission time window used is:

[0085] Task m i Compared to the resources r used j The pheromone update formula between them is:

[0086] in, These are the pheromone increments between tasks, between a task and its launch time window, and between a task and the resources it uses. Their values ​​are the average of the objective functions of all ants' tasks, i.e., the ant cyclesystem update model is used.

[0087] S36, increment the iteration count by 1, and determine whether the current iteration count has reached the set maximum iteration count. If so, output the global optimal solution obtained when the objective function value is maximized in the historical iterations, and formulate a launch mission plan and resource scheduling scheme based on the global optimal solution; otherwise, return to step S32 until the iteration ends.

[0088] The launch mission plan includes the set of executable launches scheduled within the planned scheduling period, and their order by launch time window. The resource scheduling scheme includes the resource requirements of each mission in the launch mission plan, that is, the specified resources and time intervals for each resource requirement of each mission.

[0089] The improvements to the ant colony algorithm in this invention are mainly reflected in the different methods of solution construction and pheromone update. In the basic ant colony algorithm, the node positions in all paths are fixed during path construction. However, in this algorithm, although the set of candidate tasks is fixed each time an ant chooses its next step, the selectable range includes multiple identical candidate tasks (because it may contain multiple executable combinations of available window-resource combinations for that candidate task). Furthermore, the set of executable combinations of available window-resource combinations for each candidate task changes depending on the preceding task selection in the path. Also, each path in the basic ant colony algorithm contains all nodes to be planned, while the feasible solution of this algorithm contains only a subset of the set of all tasks to be planned. The basic ant colony algorithm only needs to update one pheromone matrix, but this algorithm plans and schedules based on three decision variables, thus updating three pheromone matrices.

[0090] In one simulation case, there are 46 main available resources across 41 categories. The remaining amount of each resource per day within the planning period is determined based on its status and maintenance plan. There are 32 launch missions that need to be planned and scheduled. Each mission includes at least one rocket object and one spacecraft object, as well as launch time windows of varying lengths (a total of 522 days) and resource requirements of varying lengths (a total of 440 requirements). The planning period is one year, and a launch mission planning scheme needs to be developed to maximize the annual launch revenue and the balance of resource utilization.

[0091] The above simulation case is solved using the planning and scheduling model of this invention and the method based on the improved ant colony algorithm. The initial parameter value of the algorithm is: number of ants A. ant =40, pheromone evaporation rate factor ρ = 0.4, maximum pheromone quantity Q = 1, pheromone to heuristic function weight ratio Maximum number of iterations

[0092] The curve of the objective function value change during the iterative process of the solution algorithm is shown below. Figure 5 As shown.

[0093] The Gantt chart of the launch mission planning scheme with the global optimal solution is shown below. Figure 6 As shown, there are a total of 21 launch missions. The missions are sorted on the timeline according to the launch window of each mission, and the occupancy of 7 major non-consumable resources is marked with different colors in the mission bars.

[0094] Figure 7The diagram shows the usage time of seven main non-consumable resources by each launch task in the globally optimal solution. The numbers in the boxes represent task numbers, and the length of the box on the time axis represents the duration of its occupation of the resource. Resources 4 and 5 are of the same type, as are resources 6 and 7. It is evident that resource 3 has the shortest occupation time due to its limited applicability to fewer tasks and its lower user selection frequency. Resources 4 and 5, and resources 6 and 7 are all resources with fast turnover and short single-use durations. Furthermore, these two types of resources are upstream and downstream resources in the process, exhibiting a significant and minimal time overlap. This indicates that upstream resources 4 and 5 are not fully utilized to improve efficiency. The reasons may include: the time when the test object enters upstream resources 4 and 5 is not sufficiently aligned with its available time; constraints imposed by other resources closely related to resources 4 and 5; or the time distribution of launch tasks in the simulation case (tasks spend less time on resources 4 and 5 than on resources 6 and 7).

[0095] Figure 8 The figure shows the remaining resource curves for seven main non-consumable resources (resources 1-7) and the usage curves for two main consumable resources (resources 8-9) during the planning period. The bottom axis represents the time within the planning period; the left axis represents resources; the right axis represents the remaining or usage of each resource; and the top axis represents the launch window time, with "Window-X" indicating the launch window time for the planned task X. It is evident that the usage of resources 4 and 5 is generally less than 50% during the period they are occupied by tasks. This indicates that resources 4 and 5 also have low utilization rates in terms of remaining resources, significantly lower than those of resources 6 and 7. This is because the launch task uses resources 4 and 5 less than it uses resources 6 and 7. Considering only the time and remaining resource utilization of the seven non-consumable resources, the launch site in the simulation case still has spare launch capacity. However, due to launch task window settings, resource demand conflicts, or constraints from other related resources, these seven types of resources are not fully utilized. Furthermore, from... Figure 7 and Figure 5 This shows that the same task uses different types of resources in rotation (e.g., Figure 8 From resource 1 or resource 2 or resource 3, to resource 4 or resource 5, then to resource 6 or resource 7, and then to resource 8 or resource 9, a resource scheduling plan is formed for each task. The test task arrangement and resource scheduling plan of multiple tasks within the planning period together constitute the resource scheduling plan. Figure 6 The entire planning and scheduling scheme is shown below.

[0096] Calculations show that the utilization balance of a group of similar resources 4 and 5 is 0.9860, while that of another group of similar resources 6 and 7 is 0.9764, indicating that the utilization balance of both types of resources is relatively high. Meanwhile, the remaining amount of each resource is greater than or equal to 0%, and the utilization is less than or equal to 100%, indicating that the constraints of "maximum resource utilization constraint per unit time" and "maximum storage constraint for consumable resources" are both applied. Furthermore, resources 6 and 7, which require state transitions, have sufficient intervals for state transitions after each release, meaning the "resource state transition time constraint" is applied. The launch time windows of all tasks do not overlap, indicating that the "uniqueness constraint of launch site time window" is applied.

[0097] like Figure 9 As shown, this embodiment also provides an electronic device that may include a processor 41 and a memory 42, wherein the memory 42 is coupled to the processor 41. It is worth noting that this figure is exemplary, and other types of structures can be used to supplement or replace this structure to achieve data extraction, report generation, communication, or other functions.

[0098] like Figure 9 As shown, the electronic device may also include an input unit 43, a display unit 44, and a power supply 45. It is worth noting that the electronic device is not necessarily required to include these components. Figure 9 All components shown in the image. Furthermore, electronic devices may also include... Figure 9 For components not shown, please refer to existing technologies.

[0099] Processor 41, sometimes also called controller or operation control, may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the electronic device.

[0100] The memory 42 may be one or more of the following: a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store configuration information of the processor 41, instructions executed by the processor 41, and other information. The processor 41 can execute programs stored in the memory 42 to perform information storage or processing. In one embodiment, the memory 42 further includes a buffer memory, or buffer, to store intermediate information.

[0101] This invention also provides a computer program product including computer-readable instructions. When the computer-readable instructions are executed in an electronic device, the program product causes the electronic device to perform the operation steps included in the method of this invention.

[0102] This invention also provides a storage medium storing computer-readable instructions that cause an electronic device to perform the operation steps included in the method of this invention.

[0103] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0105] The embodiments described above are merely specific implementations of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications, substitutions, and improvements within the technical scope disclosed in the present invention, and these modifications, substitutions, and improvements should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for resource surplus-based multi-launch site space launch mission planning and scheduling, characterized in that, The method comprises the following steps: S1, constructing a launch task set based on launch tasks and constructing an available resource set based on existing resources; S2, constructs a resource requirements set for each launch mission; for mission-specific... In constructing resource demand sets At that time, the task will be completed first. Each required resource requirement is recorded in the resource requirement set. In the middle, then all pairs The demand for resources is merged into a single demand, the union of demand times is calculated, and the maximum resource demand is taken for each unit of time. Represents a set of resources of the same type; S3, determining a global optimal solution of a planning and scheduling model based on an improved ant colony algorithm according to the launch task set, the available resource set and the resource requirement set, and performing launch task planning and available resource scheduling according to the global optimal solution; The planning and scheduling model comprises decision variables, an objective function and constraint conditions; The decision variables include , , , representing whether a task is selected to be inserted into a feasible solution, 0-1 variable; representing whether the first launch time window of a task is selected, 0-1 variable; representing whether the first launch time window of a task is selected, 0-1 variable; representing whether the first launch time window of a task is selected, 0-1 variable; representing whether the first launch time window of a task is selected, 0-1 variable; representing whether the first launch time window of a task is selected, 0-1 variable; representing whether the first launch time window of a task is selected, 0-1 variable; representing whether the first launch time window of a task is selected, 0-1 variable; representing whether the first launch time window of a task is selected, 0-1 variable; representing a set of launched tasks, representing a set of available resources; the objective function is , representing total launch revenue, representing resource balance; the constraint conditions include maximum usage amount of resources per unit time, maximum storage amount of consumable resources, consistency of resource usage of a task with a launch site, resource state conversion time, and uniqueness of a launch site time window. In the objective function, , wherein, is the total weight of the objects of the task , is the weight of the object of the task of the object ; is the total number of objects in the object set of the task ; is the weight of the wth time window of the task ; is the total number of launch tasks in the set of launch tasks to be planned for scheduling ; ​ , , , wherein, denotes the resource initial remaining amount at any unit time t, denotes the resource final actual remaining amount at unit time ; denotes whether the kth resource requirement of the task selects the resource to meet the requirement; denotes the relative occupation time equivalent to the planning time of the kth resource requirement of the task ; denotes the resource usage amount at the planning time ; denotes the average usage rate of the resource ; denotes the average value of the average usage rate of the same type of resource; is the number of resources of the same type as the resource , is the total number of all resource types; denotes the usage balance degree of the resource of the same type of resource; The maximum usage amount constraint condition of the resource in a unit of time is that wherein, represents the resource used by the task in the time t, ; is the maximum remaining amount of the resource in the time t; The consumable resource maximum storage constraint is defined as , where is the consumable resource maximum storage at any time t, represents the remaining amount of the resource at any time t, represents the replenishment amount of the resource at any time t, represents the amount of the resource used by a task at time t; The emission field location consistency constraint of the resources used by a task is that wherein denotes the emission field location of a certain resource; and both belong to the set of all resources that satisfy the resource requirements of the task m i ;​ Resource state transition time constraints refer to ,in, It is a resource State transition time; It is a resource Any two occupied and adjacent tasks on the planning and scheduling timeline , At any time during execution; The emission field time window uniqueness constraint condition refers to wherein, respectively are any two tasks in the planning schedule , The absolute time on the planning schedule period time axis corresponding to the selected emission time window. The S3 comprises the following steps: S31, initializing parameters, including the number of ants in the ant colony , maximum pheromone amount , pheromone evaporation factor , maximum iteration number , pheromone importance factor , heuristic function importance factor , and setting the initial iteration number n = 1; S32, for one ant, constructing an available window-resource combination set of each resource requirement of a candidate task; S33, based on the available window-resource combination set, assembling an executable combination of the available window-resource combination of each candidate task, and calculating a selection probability of each executable combination; candidate task m i the set of available window-resource combinations of all resource requirements of m i intersection of each launch window, if each intersection is empty, then task m i move the candidate task m into the taboo task list, if there is an intersection that is not empty, then delete all available window-resource combinations corresponding to the launch window whose intersection is empty, keep the available window-resource combinations corresponding to the launch window whose intersection is not empty, obtain the set of available window-resource combinations corresponding to each resource requirement of the candidate task, the executable combination of the available window-resource combinations of the candidate task is composed of the available window-resource combinations that meet all resource requirements of the candidate task, and each resource requirement has and only has one available window-resource combination corresponding thereto; The selection probability of the executable combination of the lth available window-resource combination of the candidate task m i is: wherein, is the candidate task set, ; is the number of tasks in the candidate task set ; is the total number of executable combinations of the available window-resource combination of the candidate task m i , is the pheromone of the executable combination of the lth available window-resource combination when the candidate task m i is selected, , , is the pheromone between the task that has been inserted into the feasible solution and the current candidate task m i , is the pheromone between the task m i and the time window in the executable combination of the lth available window-resource combination, is the average of the pheromone between the task m i and all resources in the executable combination of the lth available window-resource combination , is the total number of resources of the executable combination of the lth available window-resource combination when the candidate task m i is selected; is the heuristic function when the candidate task m i is selected, , is the total number of all resource types when the candidate task m i is selected the executable combination of the lth available window-resource combination; is the weight of the wth time window of the task .​​ S34, obtaining a set of executable combinations of the available window-resource combination of all candidate tasks according to the same operation of step S33, and selecting one executable combination of the available window-resource combination according to the selection probability by using a roulette method to add the executable combination to a feasible solution, and updating a taboo task list, a candidate task list, a resource remaining amount list and an ant position; S35, for one ant, repeating steps S32 to S34 until the set of executable combinations of the available window-resource combination of all candidate tasks is empty, that is, the construction of one feasible solution is completed; S36, calculate the objective function value of each ant constructed feasible solution, update the global optimal solution, and update the pheromone matrix; specifically: calculate the objective function value of each ant constructed feasible solution in the current iteration of the ant colony, and take the maximum objective function value, compare it with the objective function value of the global optimal solution, if the maximum objective function value of the current iteration is greater than the objective function value of the global optimal solution, then the feasible solution corresponding to the maximum objective function value of the current iteration is taken as the global optimal solution, and its objective function value is recorded; the task m i-1 and the pheromone update formula between m i and the transmission time window used by m is: i and the pheromone update formula between m i and the resource used by m is: The method comprises the following steps: ; wherein, , are the pheromone increments between tasks, between tasks and their transmission time windows, and between tasks and the resources used by them respectively, and their values are the average of the average objective function of all ants. S37, increasing the iteration number by 1, and determining whether the current iteration number reaches a set maximum iteration number, if yes, outputting a global optimal solution obtained when the objective function value is maximum in historical iterations, and formulating a launch task plan and a resource scheduling scheme according to the global optimal solution; otherwise, returning to step S32 until the iteration ends.

2. The method of claim 1, wherein, In the S32, the processing of constructing the available window-resource combination set of the candidate task comprises the following operations: constructing an available window-resource combination set of non-consumable resource requirements of the candidate task; screening available window-resource combinations of consumable resource requirements of the candidate task; the available window-resource combination set of the non-consumable resource requirements and the available window-resource combinations of the consumable resource requirements constitute the available window-resource combination set of the candidate task.

3. A computer program product comprising computer readable instructions, characterized in that, The computer readable instructions, when executed by the processor, implement the steps in the resource-remaining-amount-based multi-launch-site space launch task planning and scheduling method according to any one of claims 1-2.

4. An electronic device, comprising: comprise: a memory storing program instructions; a processor connected with the memory, executing the program instructions in the memory, and implementing the steps in the resource-remaining-amount-based multi-launch-site space launch task planning and scheduling method according to any one of claims 1-2.

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