Method for unified assignment of maneuvering routes based on coordination mechanism

Abstract

The application discloses a method for unified distribution of motor routes based on a cooperative mechanism, which comprises the following steps: collecting information of standby nodes, transfer nodes, emission nodes and road nodes in motor routes of vehicles; determining hypothesis conditions, constraint conditions and a target function; and using a particle swarm algorithm to obtain an optimal solution of a solution space. The application realizes overall optimization and meets different requirements of different nodes by setting different node balance conditions for different nodes; and the target function and the constraint conditions are highly linearized, so that the model is convenient to understand and solve.

Description

Technical Field

[0001] This invention belongs to the field of transportation technology, and in particular relates to a method for unified allocation of mobile routes based on a collaborative mechanism. Background Technology

[0002] Missile launches require transportation by vehicles, and the optimal allocation of missile vehicle transportation tasks is a combinatorial optimization problem with numerous constraints. These constraints include: 1) Minimizing transportation time and cost: Different launch locations and road and weather conditions place different demands on parameters such as convoy speed and distance. Therefore, large-scale route selection is needed based on factors such as convoy size, vehicle speed, and traffic conditions to find the optimal route and minimize transportation time and cost. 2) Minimizing risk and ensuring safety: Safety factors must be considered during transportation. For example, road conditions, weather, and the operational environment can all affect convoy movement. To ensure transportation safety, risk factors must be considered during route planning. 3) Distance and route along the way: During missile transportation, the convoy needs to stop and refuel en route. The impact of these interruptions on the journey must be considered, and appropriate distances and routes must be selected to ensure the missiles reach their destination in the shortest possible time. 4) Traffic flow: Road traffic flow must be considered to avoid wasting time due to excessive congestion. 5) Road conditions: Different roads have different conditions, and the carrying capacity of the convoy and the road conditions need to be considered to ensure that the missiles can reach their destination safely.

[0003] The difficulty of solving this problem increases dramatically with the total number of missiles and the complexity of the mission, making missile formation strike mission allocation an NP-hard problem. Solving for the allocation and maneuver schemes of missile vehicles under multi-wave attacks with the shortest exposure time will not yield an optimal solution; a model needs to be established to seek a solution under acceptable conditions. Summary of the Invention

[0004] In view of this, the present invention proposes a unified allocation method for maneuver routes based on a cooperative mechanism, comprising the following steps:

[0005] Collect information on standby nodes, transfer nodes, transmission nodes, and road nodes along the vehicle's movement route;

[0006] Determine the assumptions, constraints, and objective function;

[0007] The optimal solution in the solution space is found using the particle swarm optimization algorithm.

[0008] Furthermore, the following assumptions are made:

[0009] Assumption 1: The symmetric matrix representing the connection relationships between standby nodes, retransfer nodes, transmission nodes, and road nodes is as follows:

[0010]

[0011] Where p represents the number of standby areas, q represents the number of retransmission areas, l represents the number of launch points, and s represents the number of road nodes.

[0012] This indicates that there is a road from node i to j, allowing only one-way travel.

[0013] This indicates that there is a road from node i to j, allowing bidirectional travel;

[0014] This indicates that there is no connection between node i and node j;

[0015] There are c launch vehicles C1, C2, C3, C4, C5, C6, C7, C8, C9 ... k ... C c Ck represents the k-th launch vehicle, k = 1, 2, ..., c, in the standby area. There is a first Type of launch vehicle , ,but

[0016] ;

[0017] Assumption 2: (1) Each launcher can be used for any one of n types of missiles, and only one missile can be launched at any time;

[0018] (2) Each wave must fire simultaneously;

[0019] (3) The same launch point cannot be used continuously;

[0020] (4) The trajectories must not intersect;

[0021] (5) Each retransfer point can only retransfer one launch vehicle at a time, and the number of launch vehicles that each retransfer point can accommodate at the same time shall not exceed its retransfer capacity;

[0022] (6) Matrix 1, 2, ..., They are respectively:

[0023] i = 1, 2, ..., p represents standby nodes 1 to p;

[0024] i = p+1, p+2,..., p+q represents the retransmission of nodes 1 to q;

[0025] i = p + q + 1, p + q + 2, ..., p + q + l represent transmitting nodes 1 to l;

[0026] i = p + q + l + 1, p + q + l + 2, ..., p + q + l + s represents road nodes 1 to s;

[0027] (7) Since the same launch point cannot be used for two consecutive waves, the number of launch vehicles c does not exceed half of the number of launch points l, i.e. c <= l / 2;

[0028] Assumption 3: (1)

[0029]

[0030] (2) This indicates the time when the k-th launching vehicle enters node j from node i. These represent the first entry and the second entry, respectively.

[0031] (3) This indicates the time when the k-th launch vehicle leaves node j. These represent the first departure and the second departure, respectively.

[0032] (4) N + (i) represents the set of adjacent nodes starting from i, N - (i) represents the set of neighboring nodes to i;

[0033] Objective function:

[0034] Exposure time includes the total time the launch vehicle spends maneuvering on the road, the waiting time when encountering oncoming traffic at road junctions, and the waiting time at the launch site.

[0035] Road maneuver time T1:

[0036]

[0037] in ;

[0038] Waiting time T2 for vehicles meeting at road junctions:

[0039]

[0040]

[0041] The waiting time at the launch point is T3:

[0042] This indicates the k-th launch vehicle. The waiting time at the launch point is then

[0043]

[0044] The objective function is T = T1 + T2 + T3

[0045] = +

[0046] + .

[0047] Furthermore, the constraints are as follows:

[0048] Constraint 1: The number of missiles allocated cannot exceed the total number of missiles, i.e.

[0049] ;

[0050] M i Let i represent the i-th type of missile, i = 1, 2, ..., n, T. j Let j represent the j-th target, j=1,2,...,m. The above formula indicates whether the i-th type of missile constitutes a fire strike relationship with the j-th target.

[0051] Constraint 2: The number of missiles stored in each re-transfer area shall not exceed its missile capacity. However, the number of missiles stored in the transfer area plus the number of missiles initially carried by the launch vehicle should not be less than the number of missiles that need to be allocated, i.e.

[0052]

[0053] Constraint 3: The number of launch vehicles that each retransfer zone can accommodate at the same time shall not exceed its retransfer capacity. Right now

[0054] ;

[0055] Z β This represents the β-th standby region;

[0056] Constraint 4: Ballistics do not intersect, meaning that the line segments formed by each pair of lines connecting the launch point to the target point have no intersection points within the corresponding value range. This means that any two pairs of launch points and target points are considered first.

[0057] and

[0058] The equations of the two lines are then expressed as follows:

[0059]

[0060] The x-coordinate of their intersection point is

[0061]

[0062] ;

[0063] Constraint 5: For standby nodes, the following must be satisfied:

[0064]

[0065] Constraint 6: For the transmitting nodes, the two waves share 2c nodes and need to satisfy the following conditions.

[0066]

[0067] Constraint 7: Since the launch vehicle leaves the launch site to reload after the first wave is completed, there should be c launch nodes that satisfy this constraint.

[0068]

[0069] Constraint 8: For retransmission nodes and road nodes, there are...

[0070]

[0071] i=p+1,p+2,...,p+q,p+q+1,...,p+q+l+1,p+q+l+2,...,p+q+l+s;

[0072] Constraint 9: Time Relationship Constraint, for node j, let

[0073]

[0074]

[0075] i,j=p+1,p+2,...,p+q+l+s

[0076] Constraint 10: Only one launch vehicle can be loaded at a loading node at a time, and the average time t for the reloading operation is [not specified]. z ,but

[0077] ;

[0078] Constraint 11: The waiting time between two launch vehicles traveling towards each other must meet the following requirements.

[0079] or

[0080]

[0081] in

[0082]

[0083] Constraint Twelve:

[0084] This indicates the k-th launch vehicle. The waiting time at the launch point should meet the following requirements:

[0085]

[0086] ;

[0087]

[0088] .

[0089] Furthermore, the specific steps of the solution method are as follows:

[0090] (1) Parameter initialization: Initialize missile type, missile-target matching, number of missiles, and coordinates of each standby area, reload area, launch point and target to be hit; set learning factor and inertial weight and maximum iteration generation, set the current evolution generation to 1, randomly generate particle swarm within the domain, including the initial position and velocity of the particles, and take the initial position as the historical best position of the individual, i.e. the individual extreme value;

[0091] (2) Particle encoding design: Each particle in the population represents a potential feasible solution to the problem, and the encoding length of the particle represents the number of decision variables contained in the solution; in order to reduce the randomness of the penalty coefficient setting, the constraints in the fire strike task allocation model are taken into account when the initial population is generated; when generating particle encoding, the temporal constraints of task allocation, the irreversible constraints of task allocation, and the resource constraints of task allocation are considered.

[0092] (3) Calculate the fitness value;

[0093] (4) Velocity and position update: The velocity and position of all particles are updated according to the basic formula of particle swarm optimization to generate a new population;

[0094] (5) If the maximum number of iterations is reached, the algorithm terminates and outputs the missile fire strike mission allocation result; otherwise, return to step 4 and repeat.

[0095] Furthermore, the fitness value is calculated as follows:

[0096] The fitness value of a particle is calculated using the missile strike fire mission allocation objective function as the fitness function, and the position of the particle with the highest fitness is taken as the historical optimal position of the particle swarm, i.e., the swarm extreme value.

[0097] In the early stages of the algorithm search, the penalty coefficients corresponding to each constraint function are small, which encourages the particles to search in a wider solution space, generating as many permissible solutions as possible, while reducing the probability of the particles getting trapped in local optima.

[0098] As the number of iterations increases, the penalty coefficient will gradually increase, and the particles will gradually converge to the feasible solution region that satisfies the constraints.

[0099] Furthermore, the speed update formula:

[0100]

[0101] Where r1 and r2 are random variables in the range (0,1);

[0102] Position update formula:

[0103]

[0104] Recalculate the fitness value of each particle's current position and compare it with its individual extreme value. If a better value is found, update the particle's historical best position to its current position. Then, compare each particle's individual extreme value with the population extreme value. If a better value is found, update the particle's individual extreme value to the current population extreme value, that is, use the particle's current position as the population's historical best position.

[0105] Furthermore, c1 is calculated as follows:

[0106]

[0107] This is the previous dynamic particle's own empirical learning factor. This is the self-learning factor of the dynamic particle from the previous time.

[0108] The calculation method for c2 is as follows:

[0109]

[0110] This is the empirical learning factor from the previous dynamic particle swarm. For the previous dynamic particle swarm empirical learning factor

[0111] The beneficial effects of this invention are as follows:

[0112] By setting different node balancing conditions for different nodes, overall optimization was achieved, and different requirements for different nodes were met.

[0113] The objective function and constraints are highly linearized, making the model easy to understand and solve. Attached Figure Description

[0114] Figure 1 The processing flowchart of this invention. Detailed Implementation

[0115] The present invention will be further described below with reference to the accompanying drawings, but this is not intended to limit the present invention in any way. Any modifications or substitutions made based on the teachings of the present invention shall fall within the protection scope of the present invention.

[0116] This invention constructs a general task allocation model to achieve the goal of minimizing overall exposure time. For a two-wave fire strike mission, it finds the optimal path from the starting point D1 / D2 to the 12 launch points of the first wave, optimizes the loading order of the reloading points, and the launch points for re-entering the second wave of strike operations, thereby minimizing the overall exposure time. The Floyd algorithm is used to find the shortest path using a general algorithm, combined with a genetic algorithm employing a brute-force search optimization technique. MATLAB tools are used to define the specific coordinates of the standby area, reloading area, launch points, and targets to be struck in a coordinate system.

[0117] Assumptions:

[0118] Assumption 1: Given known input parameters and unknown variables, assume a symmetric matrix representing the connection relationships between all nodes (including standby nodes, retransmission nodes, transmission nodes, and road nodes).

[0119]

[0120] Where p represents the number of standby areas, q represents the number of retransmission areas, l represents the number of launch points, and s represents the number of road nodes.

[0121] This indicates that there is a road from node i to j, allowing only one-way travel.

[0122] This indicates that there is a road from node i to j, allowing bidirectional travel;

[0123] This indicates that there is no connection between node i and node j.

[0124] There are c launch vehicles C1, C2, C3, C4, C5, C6, C7, C8, C9 ... k ... C c Ck represents the k-th launch vehicle, k = 1, 2, ..., c, in the standby area. There is a first Type of launch vehicle , , ( ) , then

[0125]

[0126] Assumption 2: (1) Each launcher can be used for any one of n types of missiles, and only one missile can be launched at a time; (2) Each wave must be fired simultaneously.

[0127] (3) The same launch point cannot be used continuously;

[0128] (4) The trajectories must not intersect;

[0129] (5) Each retransfer point can only retransfer one launch vehicle at a time, and the number of launch vehicles that each retransfer point can accommodate at the same time shall not exceed its retransfer capacity;

[0130] (6) Matrix 1, 2, ..., They are divided into

[0131] i = 1, 2, ..., p represents standby nodes 1 to p;

[0132] i = p+1, p+2,..., p+q represents the retransmission of nodes 1 to q;

[0133] i = p + q + 1, p + q + 2, ..., p + q + l represent transmitting nodes 1 to l;

[0134] i = p + q + l + 1, p + q + l + 2, ..., p + q + l + s represents road nodes 1 to s.

[0135] (7) Since the same launch point cannot be used for two consecutive waves, the number of launch vehicles c does not exceed half of the number of launch points l, i.e. c<=l / 2.

[0136] Assumption 3: (1)

[0137]

[0138] (2) , This indicates the time when the k-th launch vehicle enters node j from node i. These represent the first entry and the second entry, respectively.

[0139] (3) , This indicates the time when the k-th launch vehicle leaves node j. These represent the first departure and the second departure, respectively.

[0140] (4)N + (i) represents the set of adjacent nodes starting from i, N - (i) represents the set of neighboring nodes to i.

[0141] Objective function:

[0142] Exposure time includes the total time the launch vehicle spends maneuvering on the road, the waiting time when encountering oncoming traffic at road junctions, and the waiting time at the launch site.

[0143] Road maneuver time T1:

[0144]

[0145] in

[0146] Waiting time T2 for vehicles meeting at road junctions:

[0147]

[0148]

[0149] Waiting time T3 at the launch point

[0150] make Indicates the k-th launch vehicle The waiting time at the launch point is then

[0151]

[0152] The objective function can be set as T = T1 + T2 + T3

[0153] = +

[0154] +

[0155] Constraints:

[0156] Constraint 1: The number of missiles allocated cannot exceed the total number of missiles, i.e.

[0157] ;

[0158] M i Let i represent the i-th type of missile, i = 1, 2, ..., n, T. j Let j represent the j-th target, j=1,2,...,m. The above formula indicates whether the i-th type of missile constitutes a fire strike relationship with the j-th target.

[0159] Constraint 2: The number of missiles stored in each re-transfer area shall not exceed its missile capacity. However, the number of missiles stored in the transfer area plus the number of missiles initially carried by the launch vehicle should not be less than the number of missiles that need to be allocated, i.e.

[0160]

[0161] Constraint 3: The number of launch vehicles that each retransfer zone can accommodate at the same time shall not exceed its retransfer capacity. Right now

[0162] ;

[0163] Z β This represents the β-th standby region;

[0164] Constraint 4: Ballistics do not intersect. This means that the line segments formed by each pair of lines connecting the launch point to the target point have no intersection points within the corresponding range of values. Specifically, this means first considering any two pairs of launch points and target points.

[0165] and

[0166] The equations of the two lines can then be expressed as follows:

[0167]

[0168] The x-coordinate of their intersection point is

[0169]

[0170] ;

[0171] Constraint 5: For standby nodes, the following must be satisfied:

[0172]

[0173] Constraint 6: For the transmitting nodes, the two waves share 2c nodes and need to satisfy the following conditions.

[0174]

[0175] Constraint 7: Since the launch vehicle leaves the launch site to reload after the first wave is completed, there should be c launch nodes that satisfy this constraint.

[0176]

[0177] Constraint 8: For retransmission nodes and road nodes, there are...

[0178]

[0179] i=p+1,p+2,...,p+q,p+q+1,...,p+q+l+1,p+q+l+2,...,p+q+l+s

[0180] Constraint 9: Time Relationship Constraint, for node j, let

[0181]

[0182]

[0183] i,j=p+1,p+2,...,p+q+l+s

[0184] Constraint 10: Only one launch vehicle can be loaded at a loading node at a time, and the average time t for the reloading operation is [not specified]. z ,but

[0185]

[0186] Constraint 11: The waiting time between two launch vehicles traveling towards each other must meet the following requirements.

[0187] or

[0188]

[0189] in

[0190]

[0191] Constraint Twelve:

[0192] This indicates the k-th launch vehicle. The waiting time at the launch point should meet the following requirements:

[0193]

[0194] ;

[0195]

[0196] ;

[0197] Solution method:

[0198] Missile fire strike mission allocation is a large-scale nonlinear programming problem. Traditional iterative or analytical linear optimization techniques often struggle to find the optimal or near-optimal solution. Therefore, this paper employs a smart optimization method—Particle Swarm Optimization (PSO)—that does not require prior knowledge of the mathematical characteristics of the optimal solution. PSO is an effective global optimization algorithm. Particles in the PSO algorithm are the basic building blocks of the algorithm, and their position vectors correspond to candidate solutions in the solution space of the optimization problem.

[0199] The way a particle's position is updated (size and direction) is influenced by the velocity vector acting on it and its variation, which determines the particle's spatial search capability.

[0200] The specific steps are as follows:

[0201] (1) Parameter initialization. Initialize the missile type, missile-target matching, number of missiles, and coordinates of each standby area, reload area, launch point, and target to be attacked. Set the learning factor, inertial weight, and maximum iteration generation, set the current evolution generation to 1, randomly generate a particle swarm within the domain, including the initial position and velocity of the particles, and use the initial position as the historical best position of the individual, i.e., the individual extreme value.

[0202] (2) Particle Encoding Design. Based on the basic idea of ​​the PSO algorithm, each particle in the population represents a potential feasible solution to the problem, and the encoding length (dimensionality) of the particle represents the number of decision variables included in the solution. This invention adopts matrix encoding. To reduce the randomness of the penalty coefficient setting, some simple constraints in the fire strike task allocation model can be considered during the initial population generation. This reduces the difficulty of setting the penalty coefficient without excessively reducing the search space. Based on the characteristics of fire strike task allocation, the temporal constraints, irreversible constraints, and resource constraints of task allocation can be considered during particle encoding generation.

[0203] (3) Fitness calculation. The fitness value of the particles is calculated using the missile strike fire mission allocation objective function as the fitness function, and the position of the particle with the highest fitness is taken as the historical optimal position of the particle swarm, i.e., the population extreme value. The introduction of dynamic penalty coefficient is actually based on the idea of ​​wide entry and strict exit. In the early stage of the algorithm search, the penalty coefficients corresponding to each constraint function are small, which can encourage the particles to search in a wider solution space, generate as many permissible solutions as possible (including feasible solutions and some infeasible solutions), and at the same time reduce the probability of the particles getting trapped in local optima; as the number of iterations increases, the penalty coefficient will gradually increase, and the particles will gradually converge to the feasible solution region that satisfies the constraint conditions.

[0204] (4) Velocity and position update. The velocity and position of all particles are updated according to the following basic particle swarm formula to generate a new population.

[0205] Speed ​​update formula

[0206]

[0207] Where r1 and r2 are random variables in (0,1).

[0208] Position update formula:

[0209]

[0210] Recalculate the fitness value of each particle's current position and compare it with its individual extreme value. If a better value is found, update the particle's historical best position to its current position. Then, compare each particle's individual extreme value with the population extreme value. If a better value is found, update the particle's individual extreme value to the current population extreme value, that is, use the particle's current position as the population's historical best position.

[0211] The calculation method for c1 is as follows:

[0212]

[0213] This is the previous dynamic particle's own empirical learning factor. This refers to the dynamic particle's own empirical learning factor from the previous iteration; initially (during the first and second updates). The value is 2. The value is 1.

[0214] The calculation method for c2 is as follows:

[0215]

[0216] This is the empirical learning factor from the previous dynamic particle swarm. The empirical learning factor for the dynamic particle swarm from the previous iteration, initially... The value is 2. The value is 1. The particle's own empirical learning factor and the dynamic particle's own empirical learning factor are no longer simple monotonically decreasing or monotonically increasing trends, which is conducive to better updating.

[0217] (5) Stop condition judgment. If the maximum number of iterations is reached, the algorithm terminates and outputs the missile fire strike mission allocation result; otherwise, return to step 4 and repeat.

[0218] The beneficial effects of this invention are as follows:

[0219] By setting different node balancing conditions for different nodes, overall optimization was achieved, and different requirements for different nodes were met.

[0220] The objective function and constraints are highly linearized, making the model easy to understand and solve.

[0221] As used herein, the term "preferred" is meant as an example, illustration, or illustration. Any aspect or design described herein as "preferred" need not be construed as being more advantageous than other aspects or designs. Rather, the use of the term "preferred" is intended to present the concept in a specific manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusionary "or." That is, unless otherwise specified or clear from the context, "X uses A or B" naturally includes either of the permutations. That is, if X uses A; X uses B; or X uses both A and B, then "X uses A or B" is satisfied in any of the foregoing examples.

[0222] Furthermore, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art based on a reading and understanding of this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the aforementioned components (e.g., elements, etc.), the terminology used to describe such components is intended to correspond to any component (unless otherwise indicated) that performs the specified function of said component (e.g., is functionally equivalent to it), even if structurally not equivalent to the disclosed structure performing the functions in the exemplary implementations of this disclosure shown herein. Moreover, although specific features of this disclosure have been disclosed with respect to only one of several implementations, such features may be combined with one or more features of other implementations that may be desirable and advantageous for a given or particular application. Furthermore, with regard to the use of the terms “comprising,” “having,” “containing,” or variations thereof in the Detailed Description or claims, such terms are intended to be included in a manner similar to the term “including.”

[0223] The functional units in this invention embodiment can be integrated into a processing module, or each unit can exist physically separately, or multiple units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. The aforementioned devices or systems can execute the storage methods in the corresponding method embodiments.

[0224] In summary, the above embodiments are one implementation of the present invention, but the implementation of the present invention is not limited to the embodiments described above. Any changes, modifications, substitutions, combinations, or simplifications made that deviate from the spirit and principle of the present invention should be considered equivalent substitutions and are included within the protection scope of the present invention.

Claims

1. A method for unified allocation of maneuver routes based on a collaborative mechanism, characterized in that, Includes the following steps: Collect information on standby nodes, transfer nodes, transmission nodes, and road nodes along the vehicle's movement route; Determine the assumptions, constraints, and objective function; The optimal solution in the solution space is found using the particle swarm optimization algorithm. The following assumptions are made: Assumption 1: The symmetric matrix representing the connection relationships between standby nodes, retransfer nodes, transmission nodes, and road nodes is as follows: Where p represents the number of standby areas, q represents the number of retransmission areas, l represents the number of launch points, and s represents the number of road nodes. This indicates that there is a road from node i to j, allowing only one-way travel. This indicates that there is a road from node i to j, allowing bidirectional travel; This indicates that there is no connection between node i and node j; There are c launch vehicles C1, C2, C3, C4, C5, C6, C7, C8, C9 ... k ... C c C k This indicates that the k-th launch vehicle, k = 1, 2, ..., c, is in the standby area. There is a first Type of launch vehicle , ,but Assumption 2: (1) Each launcher can be used for any one of n types of missiles, and only one missile can be launched at any time; (2) Each wave must fire simultaneously; (3) The same launch point cannot be used continuously; (4) The trajectories must not intersect; (5) Each retransfer point can only retransfer one launch vehicle at a time, and the number of launch vehicles that each retransfer point can accommodate at the same time shall not exceed its retransfer capacity; (6) Matrix 1, 2, ..., They are respectively: i = 1, 2, ..., p represents standby nodes 1 to p; i = p+1, p+2,..., p+q represents the retransmission of nodes 1 to q; i = p + q + 1, p + q + 2, ..., p + q + l represent transmitting nodes 1 to l; i = p + q + l + 1, p + q + l + 2, ..., p + q + l + s represents road nodes 1 to s; (7) Since the same launch point cannot be used for two consecutive waves, the number of launch vehicles c does not exceed half of the number of launch points l, i.e. c <= l / 2; Assumption 3: (1) (2) This indicates the time when the k-th launching vehicle enters node j from node i. These represent the first entry and the second entry, respectively. (3) This indicates the time when the k-th launch vehicle leaves node j. These represent the first departure and the second departure, respectively. (4) N + (i) represents the set of adjacent nodes starting from i. (i) represents the set of neighboring nodes to i; Objective function: Exposure time includes the total time the launch vehicle spends maneuvering on the road, the waiting time when encountering oncoming traffic at road junctions, and the waiting time at the launch site. Maneuvering time on the road T1: in ; Waiting time T2 for vehicles meeting at road junctions: The waiting time at the launch point is T3: This indicates the k-th launch vehicle. The waiting time at the launch point is then The objective function is T = T1 + T2 + T3 = + + 。 2. The method for unified allocation of maneuver routes based on a collaborative mechanism according to claim 1, characterized in that, The constraints are as follows: Constraint 1: The number of missiles allocated cannot exceed the total number of missiles, i.e. ； M i Let i represent the i-th type of missile, i = 1, 2, ..., n, T. j Let j represent the j-th target, j = 1, 2, ..., m. This indicates whether the i-th type of missile constitutes a fire strike relationship with the j-th target; Constraint 2: The number of missiles stored in each re-transfer area shall not exceed its missile capacity. However, the number of missiles stored in the transfer area plus the number of missiles initially carried by the launch vehicle should not be less than the number of missiles that need to be allocated, i.e. Constraint 3: The number of launch vehicles that each retransfer zone can accommodate at the same time shall not exceed its retransfer capacity. Right now Z β This represents the β-th standby region; Constraint 4: Ballistics do not intersect, meaning that the line segments formed by each pair of lines connecting the launch point to the target point have no intersection points within the corresponding value range. This means that any two pairs of launch points and target points are considered first. and The equations of the two lines are then expressed as follows: The x-coordinate of their intersection point is ； Constraint 5: For standby nodes, the following must be satisfied: Constraint 6: For the transmitting nodes, the two waves share 2c nodes and need to satisfy the following conditions. Constraint 7: Since the launch vehicle leaves the launch site to reload after the first wave is completed, there should be c launch nodes that satisfy this constraint. Constraint 8: For retransmission nodes and road nodes, there are... i=p+1,p+2,...,p+q,p+q+1,...,p+q+l+1,p+q+l+2,...,p+q+l+s; Constraint 9: Time Relationship Constraint, for node j, let i,j=p+1,p+2,...,p+q+l+s Constraint 10: Only one launch vehicle can be loaded at a loading node at a time, and the average time t for the reloading operation is [not specified]. z ,but ; Constraint 11: The waiting time between two launch vehicles traveling towards each other must meet the following requirements. or in Constraint Twelve: This indicates the k-th launch vehicle. The waiting time at the launch point should meet the following requirements: ； 。 3. The method for unified allocation of maneuver routes based on a collaborative mechanism according to claim 1, characterized in that, The specific steps of the solution method are as follows: (1) Parameter initialization: Initialize missile type, missile-target matching, number of missiles, and coordinates of each standby area, reload area, launch point and target to be hit; set learning factor and inertial weight and maximum iteration generation, set the current evolution generation to 1, randomly generate particle swarm within the domain, including the initial position and velocity of the particles, and take the initial position as the historical best position of the individual, i.e. the individual extreme value; (2) Particle encoding design: Each particle in the population represents a potential feasible solution to the problem, and the encoding length of the particle represents the number of decision variables contained in the solution; in order to reduce the randomness of the penalty coefficient setting, the constraints in the fire strike task allocation model are taken into account when the initial population is generated; when generating particle encoding, the temporal constraints of task allocation, the irreversible constraints of task allocation, and the resource constraints of task allocation are considered. (3) Calculate the fitness value; (4) Velocity and position update: The velocity and position of all particles are updated according to the basic formula of particle swarm optimization to generate a new population; (5) If the maximum number of iterations is reached, the algorithm terminates and outputs the missile fire strike mission allocation result; otherwise, return to step (4) and repeat.

4. The method for unified allocation of maneuver routes based on a collaborative mechanism according to claim 3, characterized in that, Fitness is calculated as follows: The fitness value of a particle is calculated using the missile strike fire mission allocation objective function as the fitness function, and the position of the particle with the highest fitness is taken as the historical optimal position of the particle swarm, i.e., the swarm extreme value. In the early stages of the algorithm search, the penalty coefficients corresponding to each constraint function are small, which encourages the particles to search in a wider solution space, generate as many permissible solutions as possible, and at the same time reduce the probability of the particles getting stuck in local optima. As the number of iterations increases, the penalty coefficient will gradually increase, and the particles will gradually converge to the feasible solution region that satisfies the constraints.

5. The method for unified allocation of maneuver routes based on a collaborative mechanism according to claim 4, characterized in that, Speed ​​update formula: Where r1 and r2 are random variables in (0,1), c1 is the empirical learning factor of the dynamic particle itself, and c2 is the empirical learning factor of the dynamic particle swarm. It is the optimal position that particle i has experienced in the d-th dimension. and These are the d-th dimension flight velocities of particle i in the k-th and (k+1)-th generations, respectively. It is the optimal position found by the entire group in the d-th dimension, where w is the velocity inertia weight; Position update formula: and These are the d-th dimension position vectors of particle i in the k-th and (k+1)-th generations, respectively; Recalculate the fitness value of each particle's current position and compare it with its individual extreme value. If a better value is found, update the particle's historical best position to its current position. Then, compare each particle's individual extreme value with the population extreme value. If a better value is found, update the particle's individual extreme value to the current population extreme value, that is, use the particle's current position as the population's historical best position.

6. The method for unified allocation of maneuver routes based on a collaborative mechanism according to claim 5, characterized in that, The calculation method for c1 is as follows: This is the previous dynamic particle's own empirical learning factor. This is the self-learning factor of the dynamic particle from the previous time. The calculation method for c2 is as follows: This is the empirical learning factor from the previous dynamic particle swarm. This is the empirical learning factor for the dynamic particle swarm from the previous time.

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