Multi-launching-site spaceflight launching task planning and scheduling method based on resource surplus

Abstract

The invention relates to the technical field of electric data processing, in particular to a multi-launching-site spaceflight launching task planning and scheduling method based on resource surplus, and the method comprises the following steps: S1, constructing a launching task set based on launching tasks, and constructing an available resource set based on existing resources; s2, constructing a resource demand set for each launching task; and S3, according to the launch task set, the available resource set and the resource demand set, iteratively determining a global optimal solution of the planning and scheduling model based on an improved ant colony algorithm, and according to the global optimal solution, carrying out launch task planning and available resource scheduling. According to the method, the relationship among the launch task, the task demand and the available resource is fully considered, and the reliability of aerospace launch task planning and scheduling can be improved.

Description

Technical Field

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

[0002] The demand for space launches is rapidly increasing, while the construction of launch site resources is expensive and time-consuming. This contradiction between the growing demand and the shortage of launch site resources is becoming increasingly prominent. Multi-site launch mission planning and scheduling involves selecting a subset of practically executable launch missions from a set of proposed launch missions, while meeting multiple constraints such as launch site capacity, time window, and launch location. The process then rationally plans test times, launch windows, and schedules test resources for these missions to maximize the overall benefits of multi-site launches.

[0003] Currently, space launch mission planning and scheduling problems are generally formulated as integer optimization problems, employing methods such as greedy algorithms, branch-and-bound methods, genetic algorithms, and multi-agent approaches. However, in research on space launch mission planning and scheduling, international literature primarily focuses on matching rockets and payloads, with less consideration of the role of launch site resources in mission planning and scheduling. Furthermore, the assumptions used in domestic literature differ significantly from those relevant to launch sites. For example, they fail to consider the launch site's ability to execute missions concurrently, the multi-object service capabilities of resources, or the selectivity and variability of launch time windows. Consequently, current space launch mission planning and scheduling methods lack reliability and feasibility. Summary of the Invention

[0004] The purpose of the present 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] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for planning and scheduling space launch missions at multiple launch sites based on remaining resources, comprising the following steps:

[0007] S1, build a launch mission set based on the launch mission, and build an available resource set based on the existing resources;

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

[0009] S3, according to the launch task set, the available resource set, and the resource demand set, iteratively determine the global optimal solution of the planning and scheduling model based on the improved ant colony algorithm, and perform launch task planning and available resource scheduling according to the global optimal solution.

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

[0011] The decision variables include x i 、y i,w 、z i,d,j , x i Indicates the launch mission m i ∈M is selected to be inserted into the feasible solution, which is a 0-1 variable; y i,w Indicates the launch mission m i Whether the wth emission time window ∈M is selected is a 0-1 variable; z i,d,j Indicates the launch mission m i ∈M, whether the dth resource demand chooses resource r j ∈R to meet the demand, which is a 0-1 variable; 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 capacity of consumable resources, the launch site location consistency constraint of the resources used in the mission, the resource state transition time constraint, and the launch site time window uniqueness constraint.

[0014] In the above scheme, the planning and scheduling model consists of decision variables, objective functions, and constraints. This planning and scheduling 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, its ability to serve multiple objects, and the selectivity of launch time windows, all of which are reflected through constraints and objective functions. 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 space launch mission planning and scheduling methods, 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 location, and uses three pheromone matrices to simultaneously record and transmit the task, launch window, and resource information contained in the feasible solution of the launch mission planning and scheduling problem. This improves the algorithm's adaptability to the problem and enables multi-objective optimization for complex, multi-dimensional constraints, where the feasible solution is uncertain and mission planning and resource scheduling are dynamically coupled.

[0015] In a second aspect, the present invention provides a computer program product comprising computer-readable instructions, characterized in that when the computer-readable instructions are executed by a processor, the computer-readable instructions implement the steps of the method for planning and scheduling multi-launch site space launch missions based on resource surplus of the present invention.

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

[0017] In a fourth aspect, the present invention provides an electronic device comprising: a memory for storing program instructions; a processor connected to the memory for executing the program instructions in the memory to implement the steps of the present invention's method for planning and scheduling multi-launch site space launch missions based on remaining resources.

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

[0019] For other advantages of the present invention, please refer to the relevant description in the embodiment section. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 The present invention is a flowchart of a method for planning and scheduling multi-launch site space launch missions based on remaining resources provided in an embodiment.

[0022] Figure 2 This is a diagram showing how the remaining amount of resources is stored in a data file.

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

[0024] Figure 4 Schematic diagram of the structure of the available window-non-consumable resource combination in the embodiment.

[0025] Figure 5 This is a curve diagram of the objective function value change during the algorithm iteration process in the simulation test example.

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

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

[0028] Figure 8 The figure shows the usage curve of seven major non-consumable resources (resources 1 to 7) and two major consumable resources (resources 8 to 9) in the simulation test case during the planning cycle.

[0029] Figure 9 This is a block diagram of the composition of an electronic device. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present 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 only used to explain the present invention and are not intended to limit the present invention.

[0031] See also Figure 1 In this embodiment, a multi-launch site space launch mission planning and scheduling method based on remaining resources is provided, comprising the following steps:

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

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

[0034] Resources used for space launches fall into three categories: fixed resources, consumable resources, and human resources. Both human resources and fixed resources are considered non-consumable resources. Fixed resources include test facilities, launch stations, and other specialized facilities and equipment. Consumable resources include power sources like liquid propellant and other specific liquids and gases.

[0035] The remaining amount of resources can be stored in the data file as follows Figure 2 As shown, the horizontal axis is the planning and scheduling time axis, the vertical axis is the resources, and the digital list is the remaining amount of the resources at the corresponding time (that is, the available amount, which can be expressed as the percentage of the actual remaining amount of the resources to the total amount).

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

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

[0038] Resource demand refers to the demand for the use of 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 continuous occupation time within the scheduling cycle.

[0039] A launch mission may have multiple requirements for the same type of resources. This is mainly because there are multiple launch mission objects. For example, both the spacecraft and the rocket have requirements for the launch station, and there is a certain overlap in time. 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, for task m i , in constructing the resource requirement set D i When the task m is first i Each resource requirement required is recorded in the resource requirement set D i Then, the space occupied by multiple types of spacecraft or launch vehicle objects at the same time or with partial overlap in time will be ( The resource requirements in the resource collection of the same resource type are organized into one requirement, that is, all the requirements The resource demands in are merged, where the demand times are combined and the resource demand in each unit time is maximized.

[0041] S3, based on the launch task set M, the available resource set R, and the resource requirement set D i , based on the improved ant colony algorithm, the global optimal solution of the planning and scheduling model is iteratively determined, and 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 functions and constraints.

[0043] The decision variables include x i 、yi,w 、z i,d,j , x i Represents task m i ∈M is selected to be inserted into the feasible solution, which is a 0-1 variable; y i,w Represents task m i Whether the wth emission time window ∈M is selected is a 0-1 variable; z i,d,j Represents task m i ∈M, whether the dth resource demand chooses resource r j ∈R to meet the demand, which is a 0-1 variable.

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

[0045] Among them, ω i It is task m i The total weight of the object (referring to the 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 emission time window; I M is the total number of launch missions to be planned and scheduled. i The greater the total number of objects with high weights included in ∈M, the greater the revenue of the launch mission. When a launch mission has multiple launch time windows to choose from, the launch time window that best meets the user's expectations has a greater weight, and the revenue from launching in that launch time window is also greater. Therefore, the planning and scheduling plan should strive to maximize the total launch revenue F1.

[0046] in, Represents resource r j The initial remaining amount at any unit time t, S j,t Indicates that the resource j The final actual remaining amount in unit time t; z i,k,j Represents task m i ∈M, whether the kth resource demand chooses 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 based on the launch time window); T g is the total duration of planning and scheduling; f jRepresents resource r j Average usage rate; represents the average utilization rate of the same type of resources; J CON Is with resources r j The number of resources of the same type, J CONs is the total number of all resource types; Represents resource r j The balance of use of resources of the same type, which is equal to 1 minus the resource r j The mean squared deviation of resource utilization for the same type. If multiple resources of the same type exist at the same launch site, balanced utilization of all resources of that type should be achieved to avoid overuse of individual resources, meet routine maintenance needs, and extend the lifespan of individual resources. Therefore, planning and scheduling should maximize the mean F2 of resource utilization balance.

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

[0048] Resources have a maximum capacity limit in each unit time, so 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 the resource at that time. Therefore, the maximum usage constraint of the resource per unit time is Among them, u j,t,i Represents resource r j At time t, task m i The amount used, It is a resource j The maximum remaining quantity at time t. This constraint applies to both fixed and human resources.

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

[0050] The resources that meet all resource requirements in the same mission should be located at the same launch site. Therefore, the launch site location consistency constraint for the resources used in the mission is in, Indicates the launch site location of a resource; and All of them belong to the planning and scheduling scheme that meets task m i The set R of all resources required by i .

[0051] Some resources need to be restored to a new state or set to a new state after being used and released before the next use, so they cannot be occupied during the state transition. Therefore, the resource state transition time constraint is in, It is a resource j State transition time; It is a resource j Any two occupied and adjacent tasks on the planning and scheduling timeline Any execution (test) time of .

[0052] In the same unit time, only one mission launch time window can be executed at a launch site. Therefore, the launch site time window uniqueness constraint is in, Are any two tasks in the planning and scheduling The absolute time on the planning scheduling period time axis corresponding to the selected launch time window.

[0053] The multi-launch site space launch mission planning and scheduling problem is to start ,T end A launch mission planning and scheduling scheme is formulated, and an executable launch mission subset M is selected from the launch mission set M. sel , and for each task m i ∈M sel From its emission time window set W i Select a launch time window and set a time window for task m i The 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 optimized while satisfying the constraints.

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

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

[0056] S32: For each candidate task, an ant in the ant colony constructs a set of available window-resource combinations for each resource requirement of the candidate task. An available window-resource combination set is a set of several available window-resource combinations that simultaneously meet 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 the tasks that have not yet been inserted into the feasible solution and are waiting to be inserted into the launch task each time the ant selects a path node, and are stored in the candidate task list.

[0058] The set of available window-resource combinations for each candidate task's resource requirement 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 consumable resource requirements differ, and are explained separately below.

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

[0060] ① According to resource demand i,k (Task m i The resource type and task m required in the kth resource requirement (non-consumable) i The required launch site location establishes a resource set that meets the resource type requirements;

[0061] ②Select a resource from the resource set that meets the resource type requirements j , based on task m i Each launch time window and the total duration of the mission test cycle are used to calculate the test time range of the mission and determine the resource r j The remaining amount list (resource r j The remaining quantity list is a list of the remaining quantity of the resource in each unit time during the entire planning and scheduling cycle. For example, if the planning cycle is 365 days and the time unit is day, then the list is the remaining quantity of the resource r j The remaining amount per day can be referred to Figure 2 ) Whether the remaining resources within the test time range meet the resource requirements d i,k All required usage in the, if satisfied then record the time window and resource r j Information is an available window-resource combination;

[0062] ③For resource needs i,k For all available window-resource combinations, determine whether other resources except measurement and control related resources (measurement and control related resources include various measurement and control equipment, including radar, optical equipment, etc., which belong to the "other special facilities and equipment" in fixed resources) meet the launch site location consistency constraint condition, and delete the available window-resource combinations that do not meet the launch site location consistency constraint condition. If there is no resource requirement after deletion, i,k Available window-resource combination, then the non-consumable resource demand d i,k The available window-resource combination set is directly set to 0, indicating that the candidate task m i A feasible solution cannot be inserted; if after deletion there is at least one solution that satisfies the resource requirement d i,k Available window-resource combinations, then these available window-resource combinations constitute the resource requirements d i,k The set of available window-resource combinations.

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

[0064] The available window-resource combination for filtering consumable resource requirements of candidate tasks is processed as follows:

[0065] Assuming that all consumable resources are replenished at their own fixed rates, the replenishment rate is defined as: Among them, v x,j Represents the jth resource r j (This resource is a consumable resource) replenishment rate, is the shortest replenishment time of the consumable resource, b x,j Indicates the maximum amount that can be replenished in 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 the value of the minimum replenishment time and the maximum replenishable amount, the minimum 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 task requirements is as follows:

[0067] ① According to the current insertion task m i A window of time is used to determine whether the 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 from when the task uses the consumable resource to the current task m i Whether the replenishment amount of the consumable resource during the time of using the consumable resource meets the current task m i The demand for this consumable resource. If it is not satisfied, task m cannot be inserted i If satisfied, it means that the front-side resources can meet the task m i , transfer to ②. Consumable resource replenishment amount B j,i The calculation formula is: Among them, [*] is the floor rounding symbol, t j,i Is the use of resources j Task m i usage time.

[0068] ②Use the same method as ① to determine if task m is inserted i After that, the current task m i Use the time of this consumable resource to insert the next task m on the timeline in the task list i+1 Whether the replenishment amount of the consumable resource during the time of using the consumable resource meets the task m i+1 The demand for this consumable resource. If it is not satisfied, task m cannot be inserted i , the available window-resource combination set of the consumable resource demand is set to empty; if it is satisfied, go to ②, that is, execute this step repeatedly, and judge it repeatedly until the last task in the task list has been inserted. If the remaining amount plus the replenishment amount of the consumable resource meets the demand of all the tasks behind, it means that task m can be inserted. i , go to ③; if the demand of any task on the back side cannot be met, task m cannot be inserted i .

[0069] ③Record task m i The available window-resource combination set for each consumable resource requirement (there are not multiple consumable resources of the same type at the same launch site, so there is only one available window-resource combination in the set) and the resource remaining amount of all the times that the consumable resource is used after insertion, waiting to be used when the consumable resource is used by the finally selected task.

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

[0071] The candidate task m i The available window of all resource requirements (including non-consumable resource requirements and consumable resource requirements) - the resource combination set is m i Find the intersection of each launch window. If each intersection is empty, it means that the task does not have a time window and resource surplus that can meet all resource requirements at the same time, that is, task m i Unable to insert a feasible solution, task m i Move tasks from the candidate task list to the taboo task list. If there is a non-empty intersection, remove all available window-resource combinations corresponding to the launch window when the intersection is empty, and retain the available window-resource combinations corresponding to the launch window when the intersection is not empty, to obtain the set of available window-resource combinations corresponding to each resource requirement of the candidate task.

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

[0073] For a candidate task that requires multiple resource types, there are multiple resource requirements. A resource requirement can specify either a resource or a resource type. When a resource requirement specifies a resource type, such as specifying a specific test facility, there may be multiple test facilities that meet that requirement. Therefore, when selecting the same time window, there may be multiple combinations of resources that meet all resource requirements for the candidate task.

[0074] For each candidate task m i When selecting a window from the intersection of available windows between all resource requirements, some of the task's resource requirements may have multiple available window-resource combinations that are consistent with the selected time window. However, when inserting tasks into a feasible solution, each resource requirement can be satisfied by only one available window-resource combination. Therefore, there may be multiple executable combinations of available window-resource combinations that meet all the task's resource requirements. Each executable combination uniquely determines the three decision variables: all available tasks, the launch time window, and the resources that meet 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 satisfy all of the task's resource requirements, corresponding to the three resource requirements, and forming 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 consisting of three available window-resource combinations corresponding to the three resource requirements is also greater than or equal to one.

[0076] Satisfy candidate task m i The probability P of selecting the executable combination of the lth available window-resource combination i,l for: Among them, M CAN is the candidate task set, m i ∈M CAN ; is the candidate task set M CAN The number of tasks in L i It's M CAN Candidate tasks 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 is the candidate task m i The pheromone when selecting the executable combination of the lth available window-resource combination, is the previous task that has been inserted into the feasible solution and the current candidate task m i The pheromones between It is task m i The pheromone between the time windows in the executable combination of its lth available window-resource combination, It is task m i Pheromones between all resources in the executable combination of its lth available window-resource combination The average value of J i,l Candidate task m i Select the total number of resources (including consumable resources and non-consumable resources) that can be executed in the lth available window-resource combination; i,l is the candidate task m i The heuristic function for selecting the executable combination of the lth available window-resource combination (i.e., the objective function value of a single task), is the candidate task m i Select the total number of all resource types in the executable combination of the lth available window-resource combination; The calculation method of the objective function Consistent; ω i,w is the weight of the time window selected above.

[0077] S34, following the same operation as step S33, obtain a set of executable combinations of available window-resource combinations of all candidate tasks, and select one of the executable combinations of available window-resource combinations using the roulette wheel method based on the selection probability to add to the feasible solution (as part of the feasible solution), and update the taboo task list, candidate task list, resource remaining amount list and ant position.

[0078] Each executable combination of the available window-resource combination mentioned above must be calculated with a selection probability. Based on the selection probability and the roulette wheel method, one of them can be randomly selected to meet all the resource requirements of the candidate task. However, every time the ant faces the next choice (the next candidate task to be inserted with a feasible solution), there are multiple candidate tasks (launch tasks that have not yet been inserted with a feasible solution and meet the constraints). Each candidate task also has multiple resource requirements and a set of executable combinations of multiple available window-resource combinations that meet these resource requirements. Therefore, the ant not only has to select a candidate task, but also selects an executable combination of available window-resource combinations that meets all the resource requirements of the candidate task. By Figure 4 As can be seen, the executable combinations of available window-resource combinations already include the task sequence number. All executable combinations of available window-resource combinations for all candidate tasks in the candidate task list are aggregated into a single set. Then, based on the selection probability and the roulette wheel method, one of these executable combinations is randomly selected and inserted into the feasible solution. The taboo task list, candidate task list, remaining resource list, and ant position are updated, completing the ant's path node position selection for this step. If any resource requirement of a candidate task cannot be met (that is, no available window-resource combination exists that satisfies that resource requirement), then no executable combination of available window-resource combinations for that candidate task can satisfy all of its resource requirements, and the task cannot be inserted into the feasible solution.

[0079] S35, for one 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, that is, the construction of a feasible solution is completed.

[0080] A single ant can construct a feasible solution, where all tasks included are a subset of the set of all tasks to be planned. Repeat steps S32 through S34. If the candidate task list is empty, this indicates that all candidate tasks have been inserted into the 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 window-resource combinations for all candidate tasks is empty, this indicates that no candidate tasks in the candidate task list can be inserted into the feasible solution. At this point, the executable combinations of available window-resource combinations for all candidate tasks that have been inserted into the feasible solution constitute the feasible solution constructed by the ant.

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

[0082] Calculate the objective function value of the feasible solution constructed by each ant in the ant colony in this round of iteration, take the maximum objective function value, and compare it with the objective function value of the global optimal solution. If the maximum objective function value of this round of 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 this round of iteration is taken as the global optimal solution, and its objective function value is recorded.

[0083] Each feasible solution has task m i-1 With m i The pheromone update formula between is:

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

[0085] Task m i The resources it uses j The pheromone update formula between is:

[0086] in, They are the pheromone increments between tasks, between tasks and their launch time windows, and between tasks and the resources they use. Their values ​​are the mean of the average objective functions of all ants’ tasks, that is, the ant cycle system update model is adopted.

[0087] S36, add 1 to the number of iterations, and determine whether the current number of iterations reaches the set maximum number of iterations. If so, output the global optimal solution obtained when the objective function value in the historical iteration is the largest, and formulate the launch task plan and resource scheduling plan based on the global optimal solution; otherwise, return to step S32 until the iteration ends.

[0088] The launch mission plan includes the set of missions that can be launched within the planned scheduling period and their order by launch time window. The resource scheduling plan includes the satisfaction of the resource requirements of each mission in the launch mission plan, that is, the specified resources and their occupied time intervals for each resource requirement of each mission.

[0089] The improvements of the improved ant colony algorithm in this invention are mainly reflected in the different solution construction methods and pheromone update methods. In the path construction of the basic ant colony algorithm, the node positions in all paths are fixed. In this algorithm, each time the ants choose the next step, although the candidate task set is fixed, the selectable range contains multiple identical candidate tasks (because it may contain multiple executable combinations of available window-resource combinations for the candidate task). Moreover, due to different selections of the preceding tasks in the path, the executable combination set of available window-resource combinations for each candidate task will also change. In addition, each path of the basic ant colony algorithm contains all the nodes to be planned, while the feasible solution of this algorithm contains only a subset of the total set of tasks to be planned. The basic ant colony algorithm only needs to update one pheromone matrix, but this algorithm performs planning and scheduling based on three decision variables, so it updates three pheromone matrices.

[0090] In a simulation case, there are 46 major available resources in 41 categories, and the daily remaining quantity of each resource within the planning cycle is determined based on the resource status and maintenance plan; there are 32 launch missions that need to be planned and scheduled, each of which contains at least one rocket object and one spacecraft object, as well as launch time windows of varying lengths (a total of 522 days in total) and varying numbers of resource requirements (a total of 440 requirements in total); the planning cycle is one year, and a launch mission planning plan needs to be formulated to maximize the launch revenue throughout the year and maximize the balanced use of available resources.

[0091] The above simulation case is solved by using the planning and scheduling model of the present invention and the method based on the improved ant colony algorithm. The initial parameter values ​​of the algorithm are: the number of ants A ant =40, pheromone volatility factor ρ = 0.4, maximum pheromone amount Q = 1, pheromone and heuristic function weight ratio Maximum number of iterations

[0092] The curve of the objective function value change during the algorithm iteration process is as follows: Figure 5 shown.

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

[0094] Figure 7This figure shows the duration of seven major non-consumable resources used by each launch mission in the global optimal solution. The numbers in the boxes represent the mission numbers, and the length of the box on the timeline represents the duration of the resource's occupation. Resources 4 and 5 are of the same type, as are resources 6 and 7. Resource 3, due to its limited application to fewer missions and its low user selection, is occupied the least. Resources 4 and 5, as well as resources 6 and 7, all have fast turnover and short single-use durations. Furthermore, these two resource types are upstream and downstream resources in the process, with minimal temporal overlap between them. This suggests that upstream resources 4 and 5 are not fully utilized to improve efficiency. This may be due to the test subject's entry into upstream resources 4 and 5 not being fully aligned with their availability, or to the subject being subject to strong constraints from other resources closely associated with them, or to the temporal distribution of launch missions in the simulation (missions spend less time on resources 4 and 5 than on resources 6 and 7).

[0095] Figure 8 The figure shows the remaining quantity curves of 7 major non-consumable resources (resources 1 to 7) during the planning cycle and the usage curves of 2 major consumable resources (resources 8 to 9) during the planning cycle. The bottom axis in the figure is the time axis during the planning cycle; the left axis is the resource; the right axis is the remaining quantity or usage of each resource; the top axis is the launch window time, and "window-X" represents the launch window time of the planned task X. It can be seen that the usage of resources 4 and 5 during the period of being occupied by the task is generally less than 50%. It can be seen that the utilization rate of resources 4 and 5 in terms of remaining quantity is also low, and is much lower than the utilization rate of resources 6 and 7. The reason is that the usage of resources 4 or 5 by the launch mission is less than that of resources 6 and 7. Judging from the time and remaining quantity utilization of the 7 non-consumable resources alone, the launch capacity of the launch site in the simulation case still has margin, but due to the launch task window setting and resource demand conflicts or other related resource constraints, these 7 types of resources are not fully utilized. At the same time, from Figure 7 and Figure 5 It can be seen that the same task uses different types of resources in turn (such as Figure 8 From resource 1 or resource 2 or resource 3, to resource 4 or resource 5, to resource 6 or resource 7, to resource 8 or resource 9), a resource scheduling plan for each task is formed, and the test task arrangement and resource scheduling plan of multiple tasks in the planning cycle together constitute Figure 6 The entire planning and scheduling scheme is shown.

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

[0097] like Figure 9 As shown, this embodiment also provides an electronic device, which 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 may be used to supplement or replace this structure to implement data extraction, report generation, communication or other functions.

[0098] like Figure 9 As shown, the electronic device may further include: an input unit 43, a display unit 44 and a power supply 45. It is worth noting that the electronic device does not necessarily have to include Figure 9 In addition, electronic devices may also include Figure 9 For components not shown, reference may be made to the prior art.

[0099] The processor 41 is sometimes also called a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The processor 41 receives inputs and controls the operations of various components of the electronic device.

[0100] The memory 42 may be, for example, one or more of a cache, flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices, and may store information such as configuration information of the processor 41 and instructions executed by the processor 41. The processor 41 may execute programs stored in the memory 42 to implement information storage or processing. In one embodiment, the memory 42 also includes a buffer memory to store intermediate information.

[0101] An embodiment of the present invention further provides a computer program product, comprising computer-readable instructions. When the computer-readable instructions are executed in an electronic device, the program product enables the electronic device to perform the operating steps included in the method of the present invention.

[0102] An embodiment of the present invention further provides a storage medium storing computer-readable instructions, wherein the computer-readable instructions enable an electronic device to execute the operation steps included in the method of the present invention.

[0103] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0104] If the integrated unit is implemented in the form of 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 is essentially 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0105] The above-described embodiments 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 such modifications, substitutions, and improvements are intended to be encompassed within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for planning and scheduling space launch missions at multiple launch sites based on resource surplus, characterized in that: The following steps are involved: S1, build a launch mission set based on the launch mission and build an available resource set based on the existing resources; S2, builds a resource requirement set for each launch mission; S3, according to the launch task set, the available resource set, and the resource demand set, iteratively determine the global optimal solution of the planning and scheduling model based on the improved ant colony algorithm, and perform launch task planning and available resource scheduling according to the global optimal solution.

2. The method for planning and scheduling multi-launch site space launch missions based on resource surplus according to claim 1, characterized in that: In step S2, for task m i , in constructing the resource requirement set D i When the task m is i Each resource requirement required is recorded in the resource requirement set D i Then, all pairs The resource demands in the system are combined into one demand, the demand time is combined, and the resource demand in each unit time is maximized. Represents a collection of resources of the same resource type.

3. The method for planning and scheduling multi-launch site space launch missions based on resource surplus according to claim 1, characterized in that: In step S3, the planning and scheduling model consists of decision variables, objective functions and constraints; The decision variables include x i 、y i,w 、z i,d,j , x i Represents task m i ∈M is selected to be inserted into the feasible solution, which is a 0-1 variable; y i,w Represents task m i Whether the wth emission time window ∈M is selected is a 0-1 variable; z i,d,j Represents task m i ∈M, whether the dth resource demand chooses resource r j ∈R to meet the demand, which is a 0-1 variable; M represents the launch task set, and R represents the available resource set; The objective function is F1 represents the total launch revenue, and F2 represents the resource balance; The constraints include the maximum resource usage per unit time, the maximum storage capacity of consumable resources, the launch site location consistency constraint of the resources used in the mission, the resource state transition time constraint, and the launch site time window uniqueness constraint.

4. The method for planning and scheduling multi-launch site space launch missions based on resource surplus according to claim 3, characterized in that: In the objective function, Among them, ω i It is task m i The total weight of the object, 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 time window; I M is the total number of launch tasks in the launch task set M to be planned and scheduled; in, Represents resource r j The initial remaining amount at any unit time t, S j,t Indicates that the resource j The final actual remaining amount in unit time t; z i,k,j Represents task m i ∈M, whether the kth resource demand chooses resource r j ∈R to meet the demand; Represents task m i The relative occupancy time of the kth resource requirement is equivalent to the resource usage at planning time t; T g is the total duration of planning and scheduling; f j Represents resource r j Average usage rate; represents the average utilization rate of the same type of resources; J CON Is with resources r j The number of resources of the same type, J CONs is the total number of all resource types; Represents resource r j The balance of usage of resources of the same type.

5. The method for planning and scheduling multi-launch site space launch missions based on resource surplus according to claim 3, characterized in that: The maximum resource usage constraint per unit time is Among them, u j,t,i Represents resource r j At time t, task m i The amount used, It is a resource j The maximum remaining amount at time t; The maximum storage capacity constraint of consumable resources refers to in, It is a consumable resource j The maximum storage capacity at any time t, S j,t represents the remaining amount of the resource at any time t, B j,t represents the replenishment amount of the resource at any time t, u j,t,i Indicates that the resource is occupied by task m at time t i the amount used; The launch site location consistency constraint condition for the resources used in the mission is in, Indicates the launch site location of a resource; and All of them belong to the planning and scheduling scheme that meets task m i The set R of all resources required by i ; Resource state transition time constraints refer to in, It is a resource j State transition time; It is a resource j Any two occupied and adjacent tasks on the planning and scheduling timeline Any execution time of The launch site time window uniqueness constraint condition is in, Are any two tasks in the planning and scheduling The absolute time on the planning scheduling period time axis corresponding to the selected launch time window.

6. The method for planning and scheduling multi-launch site space launch missions based on resource surplus according to claim 3, characterized in that: The S3 includes the following steps: S31, initializing parameters, including the number of ants in the ant colony A ant , maximum pheromone amount Q, pheromone volatility factor ρ, maximum number of iterations Pheromone importance factor α, heuristic function importance factor β, and set the initial number of iterations to n = 1; S32, for one ant and one candidate task, construct an available window-resource combination set for each resource requirement of the candidate task; S33, based on the available window-resource combination set, forming an executable combination of the available window-resource combination of each candidate task, and calculating the selection probability of each executable combination; S34: Following the same operation as step S33, obtain a 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 using the roulette wheel method based on the selection probability to add to the feasible solution. Update the taboo task list, candidate task list, resource remaining amount list, and ant position. S35, for one ant, repeat steps S32 to S34 until the set of executable combinations of available window-resource combinations of all candidate tasks is empty, that is, a feasible solution is constructed; S36, calculate the objective function value of the feasible solution constructed by each ant, update the global optimal solution, and update the pheromone matrix; S37, add 1 to the number of iterations, and determine whether the current number of iterations reaches the set maximum number of iterations. If so, output the global optimal solution obtained when the objective function value in the historical iteration is the largest, and formulate the launch task plan and resource scheduling plan based on the global optimal solution; otherwise, return to step S32 until the iteration ends.

7. The method for planning and scheduling multi-launch site space launch missions based on resource surplus according to claim 6, characterized in that: In S32, the process of constructing the available window-resource combination set of the candidate task includes the following operations: Constructing a set of available window-resource combinations for the non-consumable resource requirements of the candidate tasks; Screening available window-resource combinations of consumable resource requirements for candidate tasks; The available window-resource combination set of non-consumable resource requirements and the available window-resource combination set of consumable resource requirements constitute the available window-resource combination set of the candidate task.

8. The method for planning and scheduling multi-launch site space launch missions based on resource surplus according to claim 6, characterized in that: In said S33, the candidate task m is satisfied. i The probability P of selecting the executable combination of the lth available window-resource combination i,l for: Among them, M CAN is the candidate task set, m i ∈M CAN ; is the candidate task set M CAN The number of tasks in L i It's M CAN Candidate tasks m i The total number of executable combinations of available window-resource combinations, τ i,l is the candidate task m i The pheromone when selecting the executable combination of the lth available window-resource combination, is the previous task that has been inserted into the feasible solution and the current candidate task m i The pheromones between It is task m i The pheromone between the time windows in the executable combination of its lth available window-resource combination, It is task m i Pheromones between all resources in the executable combination of its lth available window-resource combination The average value of J i,l Candidate task m i Select the total number of resources of the executable combination of the lth available window-resource combination; n i,l is the candidate task m i The heuristic function for selecting an executable combination of the lth available window-resource combination, is the candidate task m i Select the total number of all resource types of the executable combination of the lth available window-resource combination; ω i,w It is task m i The weight of the w-th time window.

9. A computer program product comprising computer-readable instructions, characterized in that: When executed by a processor, the computer-readable instructions implement the steps of the multi-launch site space launch mission planning and scheduling method based on resource surplus as described in any one of claims 1 to 8.

10. An electronic device, characterized in that: include: Memory, which stores program instructions; A processor is connected to the memory and executes program instructions in the memory to implement the steps of the multi-launch site space launch mission planning and scheduling method based on resource surplus as described in any one of claims 1 to 8.

Images