A Method and System for Task Planning and Scheduling of Successive Sorties of Shipborne Helicopter Groups

Abstract

The present application discloses a method and system for mission planning and scheduling of wave departure and recovery of a shipborne helicopter group, which relates to the field of mission planning. The method includes: establishing a mission planning model for wave departure and recovery of a shipborne helicopter group; the mission planning model for wave departure and recovery of a shipborne helicopter group includes: logical constraints, resource constraints, and an objective function; the logical constraints are the process constraints of six stages of the helicopter and the timing constraints of each stage; obtaining the mission requirements for departure and recovery; based on the mission requirements for departure and recovery, taking the logical constraints and resource constraints as constraint conditions, and aiming at minimizing the objective function value, using a competitive particle swarm optimization algorithm with a hybrid elite mutation strategy to solve the start time and end time of six stages of the helicopter, so as to obtain an optimal mission planning and scheduling scheme. The present application can improve the utilization rate of deck resources and the mission response efficiency.

Description

Technical Field

[0001] The present application relates to the field of mission planning, and particularly to a method and system for mission planning and scheduling of wave-based launch and recovery of shipborne helicopter groups. Background Art

[0002] The rapid launch and efficient recovery capabilities of shipborne helicopter groups, as a key strategic support for the aviation mission system, are of self-evident importance. However, when current maritime platforms execute missions, they face a prominent contradiction between dynamic mission requirements and limited deck resources: in the longitudinal dimension, shipborne helicopters need to go through multiple operation stages such as transportation, support, launch, and recovery; in the horizontal dimension, due to the non-linear layout characteristics of the narrow and long deck space and the dynamic occupancy mode of multiple positions, traditional aviation operation scheduling models have significant limitations.

[0003] After a comprehensive analysis of the relevant technical systems, there are currently three major problems: First, the deck operation space presents highly unstructured characteristics, and traditional rule-based grid modeling methods are difficult to accurately depict the dynamic occupancy relationship of positions; second, there are different timing constraints and resource requirements in each operation stage, and there is a lack of a collaborative optimization framework throughout the entire process; finally, the mainstream rotation scheduling mechanism adopts a fixed-cycle operation mode, which is difficult to cope with dynamic requirements such as sudden mission insertion and priority adjustment in modern battlefields. Especially in a high-intensity confrontation environment, traditional solutions are prone to problems such as a decrease in deck resource utilization rate and poor timeliness of mission response delay. Summary of the Invention

[0004] The purpose of the present application is to provide a method, system, device, medium, and product for mission planning and scheduling of wave-based launch and recovery of shipborne helicopter groups, which can improve the deck resource utilization rate and mission response efficiency.

[0005] To achieve the above object, the present application provides the following solutions.

[0006] In the first aspect, the present application provides a method for mission planning and scheduling of wave-based launch and recovery of shipborne helicopter groups, including the following steps.

[0007] Establish a mission planning model for wave-based launch and recovery of shipborne helicopter groups; the mission planning model for wave-based launch and recovery of shipborne helicopter groups includes: logical constraints, resource constraints, and an objective function; the logical constraints are the process constraints of six stages of the helicopter and the timing constraints of each stage; the six stages of the helicopter include, in sequence: the pre-launch transportation stage, the maintenance and service support stage, the launch and departure stage, the mission flight stage, the recovery and arrival stage, and the post-recovery transportation stage; the resource constraints are the constraints of the transportation equipment, support personnel, and supply resources required by the helicopter; the objective function is composed of the mission window satisfaction rate of the helicopter launch mission and the deck operation time of the helicopter on the ship.

[0008] Obtain the requirements of the deployment and recovery mission; the requirements of the deployment and recovery mission include: a set of mission waves, a set of helicopters participating in the operation in each wave, the earliest start time and the latest start time of each wave mission.

[0009] Based on the requirements of the deployment and recovery mission, taking logical constraints and resource constraints as constraints, and aiming at minimizing the objective function value, use the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter, and obtain the optimal mission planning and scheduling plan; the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy is an algorithm improved based on the particle swarm optimization algorithm using a competitive hybrid elite mutation strategy.

[0010] In a second aspect, the present application provides a ship helicopter group wave deployment and recovery mission planning and scheduling system, including the following modules.

[0011] A model establishment module for establishing a ship helicopter group wave deployment and recovery mission planning model; the ship helicopter group wave deployment and recovery mission planning model includes: logical constraints, resource constraints, and an objective function; the logical constraints are the process constraints of the six stages of the helicopter and the timing constraints of each stage; the six stages of the helicopter include in sequence: the pre-deployment transportation stage, the maintenance support stage, the departure stage, the mission flight stage, the recovery entry stage, and the post-recovery transportation stage; the resource constraints are the constraints of the transportation equipment, support personnel, and supply resources required by the helicopter; the objective function is composed of the mission window satisfaction rate of the helicopter deployment mission and the deck operation time of the helicopter on the ship.

[0012] An acquisition module for obtaining the requirements of the deployment and recovery mission; the requirements of the deployment and recovery mission include: a set of mission waves, a set of helicopters participating in the operation in each wave, the earliest start time and the latest start time of each wave mission.

[0013] A mission planning and scheduling module for, based on the requirements of the deployment and recovery mission, taking logical constraints and resource constraints as constraints, and aiming at minimizing the objective function value, using the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter, and obtaining the optimal mission planning and scheduling plan; the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy is an algorithm improved based on the particle swarm optimization algorithm using a competitive hybrid elite mutation strategy.

[0014] According to the specific embodiments provided by the present application, the present application has the following technical effects.

[0015] The present application provides a method and system for planning and scheduling the wave departure and recovery tasks of a shipborne helicopter group. Under the premise of resource constraints, through the process constraints of the six stages of the helicopter and the timing constraints of each stage, the start and end times of each stage of the helicopter departure and recovery can be accurately constrained, so as to accurately calculate the start and end times of each stage of the helicopter. Then, by using the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy, the start and end times of the six stages of the helicopter can be quickly solved, so as to obtain the mission window time of the helicopter departure mission and the time of the helicopter operating on the deck. At the same time, the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy can accurately schedule the start and end times of the six stages of the helicopter, thereby improving the utilization rate of deck resources and the mission response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic flowchart of a method for planning and scheduling the wave departure and recovery tasks of a shipborne helicopter group in an embodiment of the present application.

[0018] Figure 2 It is a schematic structural diagram of a system for planning and scheduling the wave departure and recovery tasks of a shipborne helicopter group in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0020] In the related art, traditional heuristic algorithms or single-stage optimization methods are adopted. Among them, traditional heuristic algorithms (such as genetic algorithms and simulated annealing) have certain applicability in solving complex optimization problems, but there are the following limitations: (1) High computational cost: Traditional heuristic algorithms usually require a large number of iterations and computing resources. Especially when dealing with large-scale multi-wave problems, the computing time may increase significantly; (2) Difficult to handle complex constraints: For the multi-wave deployment and recovery tasks of shipborne helicopter groups, it involves complex deck resource constraints, task time window constraints, and multi-stage operation logics. Traditional algorithms may be difficult to handle effectively. (3) Limited global optimization ability: Although algorithms such as genetic algorithms and simulated annealing have certain global search capabilities, in complex task planning, they are prone to falling into local optimal solutions. Single-stage optimization methods (such as only optimizing the transportation or support stage) can improve efficiency in specific stages, but there are the following deficiencies: (1) Lack of global coordination: Single-stage optimization cannot consider the global constraints and mutual influences of the entire deployment and recovery process, which may lead to low overall efficiency. (2) Unable to meet the requirements of task time windows: The satisfaction rate of task time windows is an important indicator for measuring the task planning of shipborne helicopter deployment and recovery. Single-stage optimization is difficult to optimize task time windows globally. (3) Low resource utilization rate: Due to the lack of comprehensive optimization of the entire process, single-stage optimization may lead to unreasonable resource allocation, affecting the overall resource utilization rate.

[0021] The present application provides a method and system for task planning and scheduling of shipborne helicopter group wave deployment and recovery. Based on the requirements of deployment and recovery tasks, with logical constraints and resource constraints as constraint conditions, and with the goal of minimizing the objective function value, a competitive particle swarm algorithm with a hybrid elite mutation strategy is used to solve the start time and end time of six stages of the helicopter, and an optimal task planning and scheduling scheme is obtained, which improves the deck resource utilization rate and task response efficiency.

[0022] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] In an exemplary embodiment, as Figure 1 shown, a method for task planning and scheduling of shipborne helicopter group wave deployment and recovery is provided. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or can be jointly executed by a terminal and a server. In the embodiment of the present application, taking the application of this method to a server as an example for illustration, it includes the following steps 1 to step 3.

[0024] Step 1: Establish a planning model for the wave dispatch and recovery mission of a ship's helicopter fleet; the planning model for the wave dispatch and recovery mission of a ship's helicopter fleet includes: logical constraints, resource constraints and objective functions; the logical constraints are the process constraints of the six stages of the helicopter and the timing constraints of each stage; the six stages of the helicopter include: pre-dispatch transportation stage, maintenance service support stage, dispatch departure stage, mission flight stage, recovery on-site stage and post-recovery transportation stage (i.e., stage 1 to stage 6); the resource constraints are the constraints on the transportation equipment, support personnel and supply resources required for the helicopter; the objective function is composed of the mission window satisfaction rate of the helicopter dispatch mission and the helicopter's deck operation time on the ship.

[0025] Specifically, the logical constraints include: transportation phase constraints, maintenance and service phase constraints, dispatch and departure phase constraints, mission flight phase constraints, return and return phase constraints, and wave operation connection constraints.

[0026] Transportation phase constraints: This phase can be divided into the pre-dispatch transportation phase and the post-recovery transportation phase, but in essence, both are transfers from the initial parking position to the target parking position along a predetermined path. Since the path is composed of the posture at each moment, the transfer time is known when the path is determined. The constraints are expressed as follows.

[0027] .

[0028] in, For process The end time of For the Wave sequence Helicopter in Phase I A process; For process The start time of Indicates Class The transport equipment performs the process ,on the contrary , ; Time to untether the helicopter; For helicopter from the stand Transfer to the aircraft position The time function of corresponds to the selected path, where To execute the process The initial position of To execute the process target position.

[0029] The collision avoidance problem during the transportation process can be regarded as an intersection problem of time and space. Given a fixed path, it is only necessary to ensure that the distance between the transportation helicopters at each moment is greater than the safety distance. The constraint is expressed as follows.

[0030] 。

[0031] Among them, is the function of the spatial occupancy point set of the th helicopter at the moment; is the function of the spatial occupancy point set of the th helicopter at the moment; is the preset safety distance, that is, the minimum distance that needs to be maintained during helicopter transportation.

[0032] Constraints in the aircraft maintenance support stage: The support can only start when the helicopter is located at the takeoff and landing area or the parking position in the parking area, and there are timing requirements for the support process, which need to be executed in a fixed order. The constraint is expressed as follows.

[0033] 。

[0034] 。

[0035] Among them, is the end time of process , where is the th helicopter in the th process in the second stage; is the start time of process ; is the support time of process ; is the th helicopter in the th process in the second stage. For , among them, is the set of immediate predecessor processes of process .

[0036] Constraints in the takeoff and departure stage: After the helicopter completes all operation supports at the takeoff and landing parking position, it can take off and depart according to the plan arrangement. No additional resource allocation is required in this stage. The constraint is expressed as follows.

[0037] 。

[0038] Among them, is the end time of process , where is the th wave th helicopter in the th process of the 3rd stage; is the start time of process ; is the departure time of the helicopter.

[0039] Mission flight phase constraint: Only the established flight time needs to be considered in this phase, and the constraint is expressed as follows.

[0040] .

[0041] Among them, is the end time of process , where is the th wave th helicopter in the th process of the 4th stage; is the start time of process ; is the th wave helicopter flight time.

[0042] Recovery approach phase constraint: When the takeoff and landing apron is idle, the helicopter can recover and land on the ship. No additional resource allocation is required in this phase, and the constraint is expressed as follows.

[0043] .

[0044] Among them, is the end time of process , where is the th wave th helicopter in the th process of the 5th stage; is the start time of process ; is the helicopter recovery approach time.

[0045] Wave operation connection constraint: In one departure and recovery wave of the helicopter, it needs to go through six different stages, and the start and end times of each stage are strictly constrained by timing. If the helicopter is only guaranteed in the deck takeoff and landing area, it needs to execute each stage in sequence, and the constraint is expressed as follows.

[0046] .

[0047] Among them, is the process The end time; For process The end time, where Is the Wave The Helicopter at the Stage

[0048] In addition, since the helicopter's takeoff and recovery are both completed in the takeoff and landing area and cannot occupy the parking position at the same time, the time for the takeoff and recovery stages of adjacent wave helicopters needs to be reasonably planned. The constraints are expressed as follows.

[0049] .

[0050] Among them, Is any real number to ensure the inequality holds; Indicates that after performing the process of the 5th stage of the Wave helicopter, then perform the process of the 3rd stage of the Wave helicopter; Indicates the start time of process , Is In the Special case of Wave The Helicopter at the 3rd stage Is the Wave The Helicopter at the Stage

[0051] Specifically, the resource constraints include: transportation equipment constraints, support personnel constraints, support equipment constraints, supply resource constraints, and resource allocation constraints.

[0052] Transportation equipment constraints: During the transportation process of the helicopter, it needs to be completed jointly with the tractor or elevator. Limited by the single traction capacity, after the transportation equipment completes one operation, it must be transferred to the parking position of the next helicopter to be transported, so as to start a new round of operations. The constraints are expressed as follows.

[0053] .

[0054] Among them, Is the Type of transportation equipment from the aircraft position Transferred to the aircraft position Time, , among which, For the initial position of the execution process ; Indicates the th transfer equipment to complete the process and then execute the process , and vice versa . ; Is the start time of the process .

[0055] Personnel guarantee constraint: The execution of the guarantee process involves multiple professional fields and corresponding personnel configurations. The guarantee personnel need to complete a series of processes in a fixed order. Since this process may belong to different helicopters, after each process is completed, it needs to be transferred to the parking position of the next process. The constraint is expressed as follows.

[0056] .

[0057] Among them, Is the time for the th category of guarantee personnel to transfer from the position to the position . Among them, Is the target position for executing the process , Is the initial position for executing the process Is the nd helicopter in the th process in the second stage; Is the start time of the process ; Indicates that the process is guaranteed by the th category of the th guarantee personnel, otherwise

[0058] In addition, due to the limited space carrying capacity of the guarantee workstations, only a certain number of guarantee personnel can work at the same time. The constraint is expressed as follows.

[0059] .

[0060] Among them, Is Indicates that executing the process requires the th category of workstation space, otherwise ; Is for executing the process The required number of personnel in Category for the number of personnel that can be accommodated simultaneously in the workspace of the

[0061] Support equipment constraints: The support equipment is deployed at specific locations on the deck and provides support services for the helicopters at the parking positions within its coverage area. The constraints are as follows.

[0062] .

[0063] Among them, represents that after the th support equipment of Category completes process , and vice versa ; represents that the parking position can be covered by the th support equipment of Category .

[0064] In addition, according to the equipment characteristics, the support equipment can also be divided into two categories: shared and exclusive. For shared equipment, during the support period, it can provide services for all processes of the helicopter that require this equipment. The constraints are as follows.

[0065] .

[0066] Among them, is the number of support equipment of Category required for process

[0067] For exclusive equipment or shared equipment that provides support services for different helicopters, it can only provide services for a single process, i.e., plug-and-play, and requires a reset operation after each use to ensure subsequent processes. The constraints are as follows.

[0068] .

[0069] Among them, is the time when the support equipment interface of Category transfers from the parking position ; is; is the start time of process ; Indicates Class Ensure that the equipment completes the process Post-execution process ,on the contrary .

[0070] Supply resource constraints: The normal operation of the support equipment is inseparable from the corresponding supply resources. Although it is assumed that there are sufficient resources on board, the instantaneous supply capacity of resources is limited, that is, at the same time, the support equipment can be as follows.

[0071] .

[0072] in, Indicates Class Each protection device needs to consume Class supply resources, otherwise ; For the The maximum number of processes that a supply resource can guarantee simultaneously.

[0073] Resource allocation constraints: During deck operations, it is necessary to configure and use resources such as transportation equipment, support personnel, and support equipment to ensure that process requirements and resource supply are accurately matched. The constraints are expressed as follows.

[0074] .

[0075] .

[0076] .

[0077] in, For the Class Each transport equipment performs the process ,on the contrary , ; Indicates the process By Class A security personnel guarantee, otherwise ; Indicates the process By Class A guarantee device guarantee, otherwise .

[0078] Mission time window satisfaction rate and deck operation time: In helicopter sortie missions, the mission time window satisfaction rate is a key performance indicator that measures the ability of a helicopter sortie mission to be completed within a predefined time window. This objective reflects sortie efficiency and the reliability of the plan, denoted by as follows.

[0079] On the premise of ensuring the mission completion rate, the helicopter sortie rate and mission response speed of the ship should be increased as much as possible, and the deck operation time of the fleet should be reduced. Considering that in the operation process of each wave, only three stages, namely pre-sortie transportation, maintenance support, and post-recovery transportation, require decision-making. Therefore, the deck operation time can be expressed as the sum of the time required for these three stages in all waves, denoted by as follows.

[0080] To comprehensively consider the two objectives, a linear weighting method is adopted to combine them into a single objective function, enabling the optimization algorithm to find the best trade-off solution during the search process, denoted by as follows. Each stage of each helicopter includes multiple processes; the calculation formula of the objective function is as follows.

[0081] .

[0082] .

[0083] .

[0084] .

[0085] .

[0086] where is the objective function value; and are both weight values; is the maximum value of the mission window satisfaction rate of the helicopter sortie mission; is the minimum value of the mission window satisfaction rate of the helicopter sortie mission; is the mission window satisfaction rate of the helicopter sortie mission; is the deck operation time of the helicopter on the ship; is the maximum value of the deck operation time of the helicopter on the ship; is the minimum value of the deck operation time of the helicopter on the ship; is the mission window satisfaction condition for the th wave. If is within the mission window, it is defined as meeting the mission time window, the mission window satisfaction rate; is the set of mission waves; is the earliest start time of the wave mission; is the start time of the th helicopter in the th process in the 4th stage; is the latest start time of the wave mission; is the end time of the th helicopter in the th process in the 2nd stage; is the start time of the th helicopter in the th process in the 1st stage; is the end time of the th helicopter in the th process in the 6th stage; is the start time of the th helicopter in the

[0087] th process in the 6th stage.

[0087] Step 2: Obtain the mission requirements for takeoff and recovery; the mission requirements for takeoff and recovery include: a set of mission waves, a set of helicopters participating in the operation for each wave, the earliest start time and the latest start time of each wave mission. In addition, the mission requirements for takeoff and recovery also include: a set of processes for each helicopter in six stages; a set of processes that can be guaranteed by the deck parking area; a path library between different parking positions; the operation time for each process; a set of resource types and quantities required for each process; the safe distance for transportation; a set of transportation equipment types and the quantity of each type of equipment; a set of support personnel types and the quantity of each type of personnel; a set of support equipment types and the quantity of each type of equipment; a set of supply resource types and the maximum quantity for guaranteeing processes; a set of station space types; the earliest start time and the latest start time of each wave mission.

[0088] Step 3: Based on the mission requirements for takeoff and recovery, with logical constraints and resource constraints as the constraint conditions, and with the minimum objective function value as the goal, use a competitive particle swarm optimization algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter, and obtain the optimal mission planning and scheduling scheme; the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy is an algorithm improved based on the particle swarm optimization algorithm using a competitive hybrid elite mutation strategy. Each wave includes multiple helicopters. Step 3 specifically includes the following steps 31 to 37.

[0089] Step 31: Use random numbers to encode each process in the pre-deployment transportation stage, each process in the aircraft maintenance support stage, and each process in the post-recovery transportation stage of the deployment and recovery mission requirements, obtaining multiple encoded individuals, and the multiple encoded individuals form an initial population.

[0090] Step 32: Determine the priority of each encoded individual according to the size of the random number.

[0091] Step 33: Sort each encoded individual according to the priority of each encoded individual, obtaining multiple sorted encoded individuals.

[0092] Specifically, the method of using priority encoding is as follows: Each encoded individual is divided into multiple waves, each wave is three-segment encoding, and all three segments of encoding use priorities. The smaller the number, the higher the priority.

[0093] The first segment of encoding is the pre-deployment transportation priority encoding of the helicopter, expressed as a vector , indicating the th wave and the th helicopter's priority of the th process in the pre-deployment transportation stage.

[0094] The second segment of encoding is the aircraft maintenance support priority encoding of the helicopter, which is a vector , indicating the th wave and the th helicopter's priority of the th process in the aircraft maintenance support stage.

[0095] The third segment of encoding is the post-recovery transportation priority, which is a vector , indicating the th wave and the th helicopter's priority of the th process in the post-recovery transportation stage.

[0096] In the above formula, indicates the number of helicopters participating in the operation in the th wave. For example, indicates the number of helicopters participating in the operation in the 1st wave; indicates the th wave and the th aircraft's number of processes in the th stage (i.e., the six stages of the helicopter). For example, indicates the th aircraft's number of processes in the 2nd stage (i.e., the aircraft maintenance support stage) in the 1st wave. The vector composed of the three segments of encoding is an encoded individual.

[0097] Step 34: With logical constraints and resource constraints as constraints, a serial scheduling mechanism is used to decode each sorted coded individual to obtain multiple task planning and scheduling schemes; the task planning and scheduling schemes include: the start and end time of the pre-dispatch transportation phase, the start and end time of the maintenance and service support phase, and the start and end time of the post-recovery transportation phase; each task planning and scheduling scheme corresponds to a decoding individual, and multiple decoding individuals form a first population. Step 34 specifically includes the following steps 341 to 343.

[0098] Step 341: Based on the earliest start time of each wave, calculate the planned dispatch operation end time and planned recovery operation start time of each wave, and arrange them in ascending order by time to generate an operation set; the dispatch operation includes: pre-dispatch transportation stage, maintenance service support stage and dispatch departure stage; the recovery operation includes: recovery on-site stage and post-recovery transportation stage.

[0099] Specifically, based on the earliest execution time of the wave task To calculate the end time of the planned dispatch operation of the wave and the scheduled recycling job start time , and generate a job set by arranging the job time of each wave in ascending order .

[0100] In chronological order For each dispatch operation, the actual dispatch operation end time of the wave is determined by executing the dispatch operation decoding in step 342. and the actual recycling operation start time ,like If the scheduling order of the job is changed, update For the recovery operation, update the decision variables and When it is a recovery operation, it is necessary to first decide the transportation area of the helicopter after recovery according to the subsequent operation type, that is, the helicopter that needs to perform the mission again can park directly in the take-off and landing area, otherwise it must leave the take-off and landing area, and then execute the recovery operation decoding in step 342 to update the decision variables and Step 342 is repeated until all jobs are scheduled.

[0101] Step 342: In the order of priority of each coded individual, with logical constraints and resource constraints as constraints, decode each process of each stage in the dispatching operation and recycling operation of each coded individual, and determine the start time and end time of each stage in the dispatching operation and recycling operation of each decoded individual.

[0102] Specifically, for the dispatch operation decoding, it is divided into three steps in total. The first step: Define the time variable , the scheduled set , the process index flag ; The second step: Ascendingly sort the two-layer coding and in the dispatch operation stage to generate the set ; The third step: Select the process with the highest priority from , and calculate its wave number , helicopter number , process number and stage . If this process meets the scheduling conditions (that is, all the immediate predecessors have been scheduled and there is no blocked path in the pre-dispatch transportation stage), then determine the earliest feasible time that meets all the constraints of its stage, and update the decision variables of each stage of each helicopter in each wave and . Repeat this process until all processes have been scheduled.

[0103] Specifically, for the recovery operation decoding, it is divided into three steps in total. The first step, define the time variable , the scheduled set , the process index flag ; The second step, ascendingly sort the coding in the recovery operation stage to generate the set ; The third step, select the process with the highest priority from , calculate its wave number , helicopter number , process number and stage , if this process meets the scheduling conditions (that is, all the immediate predecessors have been scheduled and there is no blocked path in the post-recovery transportation stage), then determine the earliest feasible time that meets all the constraints of its stage, and then update the decision variables of each stage of each helicopter in each wave and . Repeat this process until all processes have been scheduled.

[0104] Step 343: According to the start time and end time of each stage in the dispatch operation and recovery operation of each decoded individual, determine the task planning and scheduling scheme corresponding to each decoded individual, and obtain multiple task planning and scheduling schemes.

[0105] Step 35: Based on the start and end time of the pre-dispatch transportation phase, the start and end time of the maintenance service support phase, and the start and end time of the post-recovery transportation phase, calculate the start and end time of the departure phase, the start and end time of the mission flight phase, and the start and end time of the recovery phase.

[0106] Step 36: Calculate the objective function value of each decoding individual according to the start and end time of the mission flight phase, the start and end time of the pre-dispatch transportation phase, the start and end time of the maintenance service support phase, and the start and end time of the post-recovery transportation phase.

[0107] Step 37: Determine whether the current iteration round reaches the preset maximum iteration round; if so, determine the decoding individual with the smallest objective function value as the optimal task planning and scheduling scheme; if not, introduce a particle swarm with a mixed elite mutation strategy to optimize the first population, obtain a child population, re-decode the child population, and calculate the objective function value of each encoding individual in the child population.

[0108] In an illustrative example, in step 37 , a particle swarm with a hybrid elite mutation strategy is introduced to optimize the first population to obtain a descendant population, which specifically includes the following steps 371 to 376 .

[0109] Step 371: randomly generate a certain number of particles and add them to the first population to obtain a second population; one particle is a coded individual.

[0110] Specifically, a certain number of particles are randomly generated. Each particle is an individual, representing a potential solution, with position (i.e., random number) and speed (i.e., the amount of change used to calculate the random number) attributes. The position represents the parameter value of the solution, and the speed determines the moving direction and step length of the particle in the solution space. Based on the particle's own experience and the experience of the group, the speed and position of each particle are updated to generate a new generation. The speed update formula is as follows.

[0111] .

[0112] in, For particles In the In the iteration The speed of the dimension; For particles In the In the iteration The speed of the dimension; is the inertia weight; and is the learning factor; and is the random weight; is the particle at the dimensional historical optimal position; is the particle at the th iteration, the dimensional position; is the global optimal position of the entire population at the dimensional. The position update formula is as follows.

[0113] .

[0114] Among them, is the particle at the th iteration, the dimensional position; is the particle at the th iteration, the dimensional position.

[0115] Step 372: Randomly match the encoded individuals in the second population to obtain particle pairs; a particle pair includes: two encoded individuals.

[0116] Step 373: Calculate the objective function values of the two encoded individuals in the particle pair respectively to obtain the objective function values of each encoded individual in the particle pair.

[0117] Step 374: Evolve the encoded individual with the lower objective function value in each particle pair using the reverse mutation strategy to obtain the mutated encoded individual.

[0118] Specifically, the encoded individuals generated in the second population are randomly assigned to particle pairs, and each particle pair contains two encoded individuals. In each particle pair, the individual with the lower objective function value undergoes reverse mutation strategy evolution, and the individual with the higher objective function value learns from the individual with the lower objective function value, thereby generating new offspring population individuals. Reverse mutation strategy: Generate another encoded individual through reverse learning. The specific operation is: According to the current encoded individual position, calculate its reverse position. The expression of the mutated encoded individual is as follows.

[0119] .

[0120] Among them, is the random number of the mutated encoded individual; is the random number of the encoded individual with the lower objective function value among the objective function values of the first encoded individual and the second encoded individual.

[0121] Step 375: Perform particle learning on the coding individual with a higher objective function value in each pair of particles to obtain the learned coding individual.

[0122] Specifically, in the particle learning process: calculate the difference vector between the two coding individuals, and update the position of the coding individual with a smaller objective function value to the weighted sum of the position of the individual with a larger objective function value and the difference vector. The expression of the learned coding individual is as follows.

[0123] 。

[0124] Among them, is the random number of the learned coding individual; is the random number of the coding individual with a higher objective function value among the objective function values of the first coding individual and the second coding individual; is the learning step coefficient; is the random number of the coding individual with a lower objective function value among the objective function values of the first coding individual and the second coding individual.

[0125] Step 376: Based on the mutated coding individual and the learned coding individual, form an offspring population.

[0126] The beneficial effects of a shipborne helicopter group wave departure and recovery mission planning and scheduling method proposed in this application are mainly manifested in the following aspects.

[0127] (1) The shipborne helicopter group wave departure and recovery mission planning model established in this application details the wave mission into six stages: pre-departure transportation, aircraft maintenance support, departure from the ship, mission flight, return to the ship, and post-recovery transportation. A nonlinear integer programming model is established, comprehensively considering logical constraints and resource constraints, so as to accurately calculate the start and end times of each stage of helicopter departure and recovery, accurately calculate the start and end times of the helicopter's mission execution, ensure the start and end times of personnel work, ensure the activation and deactivation times of equipment, and the start and end times of transportation equipment, and obtain the mission window time of the helicopter departure mission and the time of the helicopter's operation on the deck, providing accurate time for subsequent mission planning and scheduling.

[0128] (2) This application uses a competitive particle swarm algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter. Compared with traditional intelligent algorithms, the elite mutation and competition mechanisms in the algorithm can balance the exploration and development capabilities, and at the same time improve the convergence speed, and can obtain the solution results faster.

[0129] Based on the same inventive concept, an embodiment of the present application also provides a ship helicopter group sortie launch and recovery mission planning and scheduling system. The implementation solutions provided by this system for solving problems are similar to those described in the above method. Therefore, the specific limitations in one or more embodiments of the ship helicopter group sortie launch and recovery mission planning and scheduling system provided below can refer to the limitations on the ship helicopter group sortie launch and recovery mission planning and scheduling method in the foregoing, and will not be elaborated here.

[0130] In an exemplary embodiment, as Figure 2 shown, a ship helicopter group sortie launch and recovery mission planning and scheduling system is provided, which includes the following modules.

[0131] A model establishment module, configured to establish a ship helicopter group sortie launch and recovery mission planning model; the ship helicopter group sortie launch and recovery mission planning model includes: logical constraints, resource constraints, and an objective function; the logical constraints are the process constraints of the six stages of the helicopter and the timing constraints of each stage; the six stages of the helicopter sequentially include: the pre-launch transportation stage, the aircraft maintenance support stage, the launch departure stage, the mission flight stage, the recovery approach stage, and the post-recovery transportation stage; the resource constraints are the constraints on the transportation equipment, support personnel, and supply resources required by the helicopter; the objective function is composed of the mission window satisfaction rate of the helicopter launch mission and the deck operation time of the helicopter on the ship.

[0132] An acquisition module, configured to acquire the sortie launch and recovery mission requirements; the sortie launch and recovery mission requirements include: a mission wave set, a helicopter set participating in the operation for each wave, the earliest start time and the latest start time of each wave mission.

[0133] A mission planning and scheduling module, configured to, based on the sortie launch and recovery mission requirements, with the logical constraints and resource constraints as the constraint conditions, and with the minimum objective function value as the goal, use a competitive particle swarm optimization algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter, and obtain an optimal mission planning and scheduling plan; the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy is an algorithm improved based on the particle swarm optimization algorithm by adopting a competitive hybrid elite mutation strategy.

[0134] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0135] In this text, specific examples are used to illustrate the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for mission planning and scheduling of wave departure and recovery of a shipborne helicopter group, characterized in that The method for planning and scheduling the wave-based dispatch and recovery mission of a shipborne helicopter group includes: Establishing a wave-based dispatch and recovery mission planning model for the shipborne helicopter group; the wave-based dispatch and recovery mission planning model for the shipborne helicopter group includes: logical constraints, resource constraints, and an objective function; the logical constraints are the process constraints of the six stages of the helicopter and the timing constraints of each stage; the six stages of the helicopter sequentially include: the pre-dispatch transportation stage, the aircraft maintenance support stage, the departure stage, the mission flight stage, the recovery approach stage, and the post-recovery transportation stage; the resource constraints are the constraints of the transportation equipment, support personnel, and supply resources required by the helicopter; the objective function is composed of the mission window satisfaction rate of the helicopter dispatch mission and the deck operation time of the helicopter on the ship. Obtaining the dispatch and recovery mission requirements; the dispatch and recovery mission requirements include: a set of mission waves, a set of helicopters participating in the operation in each wave, the earliest start time and the latest start time of each wave mission. Based on the dispatch and recovery mission requirements, with logical constraints and resource constraints as the constraint conditions, and with the minimum objective function value as the goal, use a competitive particle swarm optimization algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter, and obtain the optimal mission planning and scheduling plan; the competitive particle swarm optimization algorithm with a hybrid elite mutation strategy is an algorithm improved based on the particle swarm optimization algorithm using a competitive hybrid elite mutation strategy. Each stage of each helicopter includes multiple processes; the calculation formula of the objective function is: ； ； ； ； ； Among them, is the objective function value; and are both weight values; is the maximum value of the mission window satisfaction rate for the helicopter dispatch mission; is the minimum value of the mission window satisfaction rate for the helicopter dispatch mission; is the mission window satisfaction rate for the helicopter dispatch mission; is the deck operation time of the helicopter on the ship; is the maximum value of the deck operation time of the helicopter on the ship; is the minimum value of the deck operation time of the helicopter on the ship; is the mission window satisfaction condition for the th wave; is the set of mission waves; is the earliest start time of the th wave mission; is the th wave, th helicopter's start time of the th process in the 4th stage; is the latest start time of the th wave mission; is the th wave, th helicopter's end time of the th process in the 2nd stage; is the th wave, th helicopter's start time of the th process in the 1st stage; is the th wave, th helicopter's end time of the th process in the 6th stage; is the th wave, th helicopter's start time of the th process in the 6th stage; Each wave includes multiple helicopters; each stage of each helicopter includes multiple processes. Based on the dispatch and recovery mission requirements, with logical constraints and resource constraints as the constraint conditions, and with the minimum objective function value as the goal, use a competitive particle swarm optimization algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter, and obtain the optimal mission planning and scheduling plan, specifically including: Using random numbers to encode each process in the pre-dispatch transportation stage, each process in the aircraft maintenance support stage, and each process in the post-recovery transportation stage in the dispatch and recovery mission requirements, to obtain multiple encoded individuals, and the multiple encoded individuals form an initial population. Determine the priority of each encoded individual according to the size of the random number. Sort each encoded individual according to the priority of each encoded individual to obtain multiple sorted encoded individuals. With logical constraints and resource constraints as the constraint conditions, use a serial scheduling mechanism to decode each sorted encoded individual to obtain multiple mission planning and scheduling plans; the mission planning and scheduling plan includes: the start time and end time of the pre-dispatch transportation stage, the start time and end time of the aircraft maintenance support stage, and the start time and end time of the post-recovery transportation stage; each mission planning and scheduling plan corresponds to a decoded individual, and the multiple decoded individuals form the first population. According to the start and end time of the pre-dispatch transportation phase, the start and end time of the maintenance service support phase, and the start and end time of the post-recovery transportation phase, calculate the start and end time of the departure phase, the start and end time of the mission flight phase, and the start and end time of the recovery phase; Calculate the objective function value of each decoding individual according to the start and end time of the mission flight phase, the start and end time of the pre-dispatch transportation phase, the start and end time of the maintenance support phase, and the start and end time of the post-recovery transportation phase; Determine whether the current iteration round has reached the preset maximum iteration round; If so, the decoding individual with the smallest objective function value is determined as the optimal task planning and scheduling solution; If not, a particle swarm with a hybrid elite mutation strategy is introduced to optimize the first population to obtain a child population, which is then re-decoded and the objective function value of each encoded individual in the child population is calculated.

2. The wave sortie recovery mission planning and scheduling method for shipborne helicopter groups according to claim 1, characterized in that The logical constraints include: transportation phase constraints, maintenance and service phase constraints, dispatch and departure phase constraints, mission flight phase constraints, return and return phase constraints and wave operation connection constraints.

3. The method for planning and scheduling the wave departure and recovery mission of a shipborne helicopter group according to claim 1, characterized in that The resource constraints include: transportation equipment constraints, support personnel constraints, support equipment constraints, supply resource constraints and resource allocation constraints.

4. The method for planning and scheduling the wave-based launch and recovery mission of a shipborne helicopter group according to claim 1, wherein With logical constraints and resource constraints as constraints, a serial scheduling mechanism is used to decode each sorted coded individual to obtain multiple task planning and scheduling schemes, including: Based on the earliest start time of each wave, the planned dispatching operation end time and the planned recovery operation start time of each wave are calculated, and the operation set is generated by arranging them in ascending order according to time; the dispatching operation includes: pre-dispatch transportation stage, maintenance service support stage and dispatch departure stage; the recovery operation includes: recovery on-site stage and post-recovery transportation stage; According to the priority order of each coded individual, with logic constraints and resource constraints as constraints, each process of each stage in the dispatching operation and recycling operation of each coded individual is decoded, and the start time and end time of each stage in the dispatching operation and recycling operation of each decoded individual are determined; According to the start time and end time of each stage in the dispatching operation and recovery operation of each decoding individual, the task planning and scheduling scheme corresponding to each decoding individual is determined, and multiple task planning and scheduling schemes are obtained.

5. The method for planning and scheduling the wave-based deployment and recovery mission of a shipborne helicopter group according to claim 1, wherein The particle swarm with hybrid elite mutation strategy is introduced to optimize the first population and obtain the offspring population, including: A certain number of particles are randomly generated and added to the first population to obtain a second population; one particle is a coding individual; Randomly match the coded individuals in the second population to obtain particle pairs; a particle pair includes: two coded individuals; Calculate the objective function values of the two coding individuals in the particle pair respectively, and obtain the objective function value of each coding individual in the particle pair; The coding individual with the lower objective function value in each coding individual of the particle pair is subjected to reverse mutation strategy evolution to obtain the mutated coding individual; Particle learning is performed on the coding individual with a higher objective function value among each pair of particles, and the learned coding individual is obtained; Based on the mutated coding individual and the learned coding individual, a subpopulation is formed.

6. The method for planning and scheduling the wave departure and recovery mission of a shipborne helicopter group according to claim 5, characterized in that The expression of the mutated coding individual is: ； Among them, is the random number of the mutated encoded individual; is the random number of the encoded individual with the lower objective function value among the first encoded individual and the second encoded individual.

7. The method for planning and scheduling the wave departure and recovery mission of a shipborne helicopter group according to claim 5, wherein The expression of the learned coding individual is: ； Among them, is the random number of the encoded individual after learning; is the random number of the encoded individual with the higher objective function value among the objective function values of the first encoded individual and the second encoded individual; is the learning step coefficient; is the random number of the encoded individual with the lower objective function value among the objective function values of the first encoded individual and the second encoded individual.

8. A mission planning and scheduling system for the wave-based launch and recovery of a shipborne helicopter group, characterized in that, The ship helicopter group sortie launch and recovery mission planning and scheduling system includes: A model establishment module for establishing a ship helicopter group sortie launch and recovery mission planning model; the ship helicopter group sortie launch and recovery mission planning model includes: logical constraints, resource constraints, and an objective function; the logical constraints are the process constraints of six stages of the helicopter and the timing constraints of each stage; the six stages of the helicopter sequentially include: pre-launch transportation stage, aircraft maintenance support stage, launch departure stage, mission flight stage, recovery approach stage, and post-recovery transportation stage; the resource constraints are the constraints of transportation equipment, support personnel, and supply resources required by the helicopter; the objective function is composed of the mission window satisfaction rate of the helicopter sortie mission and the deck operation time of the helicopter on the ship. An acquisition module for acquiring sortie launch and recovery mission requirements; the sortie launch and recovery mission requirements include: a mission sortie set, a helicopter set participating in operations in each sortie, the earliest start time and the latest start time of each sortie mission. A mission planning and scheduling module for, based on the sortie launch and recovery mission requirements, with logical constraints and resource constraints as constraint conditions, and with the minimum objective function value as the goal, using a competitive particle swarm algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter, and obtaining an optimal mission planning and scheduling plan; the competitive particle swarm algorithm with a hybrid elite mutation strategy is an algorithm improved based on the particle swarm algorithm using a competitive hybrid elite mutation strategy. Each stage of each helicopter includes multiple processes; the calculation formula of the objective function is: ； ； ； ； ； Among them, is the objective function value; and are both weight values; is the maximum value of the mission window satisfaction rate for helicopter sortie missions; is the minimum value of the mission window satisfaction rate for helicopter sortie missions; is the mission window satisfaction rate for helicopter sortie missions; is the helicopter's deck operation time on the ship; is the maximum value of the helicopter's deck operation time on the ship; is the minimum value of the helicopter's deck operation time on the ship; is the mission window satisfaction condition for the th wave; is the set of mission waves; is the earliest start time of the th wave mission; is the th wave, the start time of the th helicopter in the th process of the 4th stage; is the latest start time of the th wave mission; is the th wave, the end time of the th helicopter in the th process of the 2nd stage; is the th wave, the start time of the th helicopter in the th process of the 1st stage; is the th wave, the end time of the th helicopter in the th process of the 6th stage; is the th wave, the start time of the th helicopter in the th process of the 6th stage; Each sortie includes multiple helicopters; each stage of each helicopter includes multiple processes. Based on the sortie launch and recovery mission requirements, with logical constraints and resource constraints as constraint conditions, and with the minimum objective function value as the goal, using a competitive particle swarm algorithm with a hybrid elite mutation strategy to solve the start time and end time of the six stages of the helicopter, and obtaining an optimal mission planning and scheduling plan, specifically including: Using random numbers to encode each process in the pre-launch transportation stage, each process in the aircraft maintenance support stage, and each process in the post-recovery transportation stage in the sortie launch and recovery mission requirements, obtaining multiple coding individuals, and multiple coding individuals form an initial population; Determining the priority of each coding individual according to the size of the random number; Sorting each coding individual according to the priority of each coding individual, and obtaining multiple sorted coding individuals; Taking the logic constraint and the resource constraint as the constraint conditions, a serial scheduling mechanism is used to decode each sorted coding individual to obtain multiple task planning scheduling schemes; the task planning scheduling schemes include: the start time and end time of the pre-dispatch transportation phase, the start time and end time of the maintenance service support phase, and the start time and end time of the post-recovery transportation phase; each task planning scheduling scheme corresponds to a decoding individual, and multiple decoding individuals form a first population; According to the start and end time of the pre-dispatch transportation phase, the start and end time of the maintenance service support phase, and the start and end time of the post-recovery transportation phase, calculate the start and end time of the departure phase, the start and end time of the mission flight phase, and the start and end time of the recovery phase; Calculate the objective function value of each decoding individual according to the start and end time of the mission flight phase, the start and end time of the pre-dispatch transportation phase, the start and end time of the maintenance support phase, and the start and end time of the post-recovery transportation phase; Determine whether the current iteration round has reached the preset maximum iteration round; If so, the decoding individual with the smallest objective function value is determined as the optimal task planning and scheduling solution; If not, a particle swarm with a hybrid elite mutation strategy is introduced to optimize the first population to obtain a child population, which is then re-decoded and the objective function value of each encoded individual in the child population is calculated.

Images