A multi-objective assignment and path planning method based on simulated annealing algorithm

Abstract

The application discloses a multi-target distribution and path planning method based on a simulated annealing algorithm, and first, a multi-target distribution model is established, relevant variables are defined, and a target function is constructed to minimize total flight cost while considering task execution time and fuel limit; secondly, a double-loop model is constructed, the problem is abstracted into a Hamilton loop problem, including an outer Hamilton loop and an inner Hamilton loop, which are responsible for path planning between regional nodes and path planning of targets in a region respectively; finally, a simulated annealing algorithm is used to solve the TSP model of the double Hamilton loop, and through parameter setting, initial solution generation, solution transformation, Metropolis criterion application and cooling strategy, an optimal multi-target distribution strategy and corresponding path planning are found. The method can effectively handle large-scale, multi-target and multi-constraint complex problems, improve calculation efficiency, and find a solution close to the global optimum, and has important practical application value and market prospect.

Description

Technical Field

[0001] This invention belongs to the field of aircraft technology, specifically relating to a multi-target allocation and trajectory planning method based on simulated annealing algorithm. Background Technology

[0002] In modern military and civilian fields, the problems of multi-target allocation and trajectory planning are increasingly demonstrating their importance. These problems involve how to effectively allocate and plan multiple targets within limited resources and time to achieve optimal mission execution efficiency and effectiveness. For example, in military operations, precise trajectory planning is required for strike and reconnaissance missions to ensure that attacks are completed during the period when enemy defenses are weakest, while avoiding unnecessary waste of resources and risks.

[0003] Limitations of Traditional Methods: Traditional multi-objective assignment and trajectory planning methods, such as linear programming, integer programming, and dynamic programming, while providing effective solutions in certain situations, often exhibit low computational efficiency and insufficient optimization when facing large-scale, multi-objective, and multi-constraint complex problems. These problems typically possess high nonlinearity and dynamic variability, making traditional optimization algorithms difficult to adapt. Furthermore, these methods often require significant computational resources and time when dealing with practical problems, and it is difficult to guarantee finding the global optimum.

[0004] Application Background of Simulated Annealing Algorithm: Simulated Annealing (SA), as a heuristic search algorithm, was initially proposed by Metropolis et al. based on the physical annealing process. This algorithm provides an effective search strategy for solving complex optimization problems by simulating the thermodynamic phenomena during the annealing process of matter. The core idea of ​​the SA algorithm is to allow for poor solutions during the search process to avoid getting trapped in local optima and gradually approach the global optimum. This algorithm has shown significant advantages in solving the Traveling Salesman Problem (TSP) and other combinatorial optimization problems.

[0005] In the fields of multi-objective assignment and trajectory planning, the SA algorithm is gaining increasing attention. This algorithm can handle the dynamic and uncertain nature of problems, effectively exploring the solution space and finding optimal or near-optimal solutions through iterative search and adaptive adjustment. However, existing SA algorithms still face some challenges when applied to multi-objective assignment and trajectory planning problems, such as the complexity of parameter settings, algorithm convergence speed, and solution quality.

[0006] The Potential of Simulated Annealing (SA): Despite its challenges, the SA algorithm holds significant potential for application in multi-objective assignment and trajectory planning. Through reasonable parameter tuning and algorithm improvements, SA can effectively handle large-scale problems, enhancing solution quality and computational efficiency. Furthermore, the flexibility and adaptability of SA allow it to be combined with other optimization techniques to form hybrid algorithms, further improving the optimization level and robustness of the algorithm. Summary of the Invention

[0007] To overcome the shortcomings of existing technologies, this invention provides a multi-objective allocation and trajectory planning method based on simulated annealing. First, a multi-objective allocation model is established, defining relevant variables and constructing an objective function to minimize the total flight cost while considering mission execution time and fuel constraints. Second, a dual-loop model is constructed, abstracting the problem into a Hamiltonian loop problem, including an outer Hamiltonian loop and an inner Hamiltonian loop, responsible for trajectory planning between regional nodes and for trajectory planning of targets within a region, respectively. Finally, the simulated annealing algorithm is used to solve the dual Hamiltonian loop TSP model. By setting control parameters, generating initial solutions, transforming solutions, applying the Metropolis criterion, and employing a cooling strategy, the optimal multi-objective allocation strategy and corresponding trajectory planning are found. This invention can effectively handle large-scale, multi-objective, and multi-constraint complex problems, improve computational efficiency, and find solutions close to the global optimum, possessing significant practical application value and market prospects.

[0008] The technical solution adopted by this invention to solve its technical problem is as follows:

[0009] Step 1: Establish a multi-objective allocation model;

[0010] Key variables in the multi-objective allocation process are defined, including flight cost, mission execution time, and loiter time.

[0011] Construct an objective function that comprehensively considers the cost of the regional loop, the cost of the target loop, and the total cost of completing all objectives, with the goal of minimizing the total flight cost.

[0012] Step 2: Establish a double-loop model;

[0013] The multi-objective allocation and trajectory planning problem is abstracted into a Hamiltonian loop problem, including an outer Hamiltonian loop and an inner Hamiltonian loop. The outer Hamiltonian loop is responsible for trajectory planning between regional nodes, while the inner Hamiltonian loop is responsible for trajectory planning of targets within a region. By classifying target nodes in the inner loop and allocating regional nodes in the outer loop, the multi-objective allocation problem is transformed into a TSP problem.

[0014] Step 3: Solving based on the simulated annealing algorithm;

[0015] The simulated annealing algorithm is used to solve the TSP model of the constructed double Hamiltonian loop;

[0016] The implementation of the algorithm includes setting control parameters, generating initial solutions, transforming solutions to generate new solutions, applying the Metropolis criterion, and implementing cooling strategies.

[0017] We obtain a near-globally optimal multi-objective allocation strategy and corresponding trajectory planning.

[0018] Preferably, step 1 specifically comprises:

[0019] Step 1-1: Define the variables for multiple objectives as follows:

[0020]

[0021] set up This represents the cost of the m-th trajectory in the n-th mission, from region node j to target j'. The analysis is divided into two parts: when flying between different geographical nodes, it is necessary to start from the geographical node. When the mission proceeds to another location node, it is then executed; however, when executing a mission within the same location, only the straight-line distance between the two targets needs to be calculated. Flight time is calculated from flight speed; S represents the current running time of this mission. This represents the time when goal j is achieved. This indicates the moment when the aircraft is positioned between two targets. This indicates the time the trajectory remained at target j.

[0022] Step 1-2: Select the function that minimizes the total flight cost as the objective function. This objective function is composed of the cost of the regional loop, the cost of the target loop, and the total cost of completing the task across all targets.

[0023]

[0024] in, These represent the target cost of passing through the i-th regional node during the m-th trajectory planning of the n-th mission, the target cost of passing through the j-th regional node during the m-th trajectory planning of the n-th mission, the cost of performing missions by flying between different regional nodes, and the dwell time of the trajectory at the j-th target.

[0025] Preferably, step 2 specifically comprises:

[0026] Assume that there are multiple targets J within each geographic node i.i =(j i1 ,j i2 ,j i3 ,j i4 ,…,j in ), thus obtaining j i ∈i; When planning and designing flight routes, aircraft performing missions consider logistical support and replenishment with airports as both starting and ending points, thereby continuously constructing Hamiltonian closed loops, ultimately forming multiple Hamiltonian closed loops that traverse region nodes i, i.e., outer Hamiltonian loops; within the region, for the target J to be attacked or reconnoitered... i Perform trajectory planning, each time from J i Choose a limited number of J ik Perform trajectory planning, and finally traverse all targets J = (j1,j2,j3,…,j…) n Therefore, it is abstracted as an open Hamiltonian loop, i.e., an inner Hamiltonian loop;

[0027] Step 2-1: Classification of target nodes on the inner ring road;

[0028] For all target nodes J = (j1,j2,j3,…,j…) n They are classified into three categories:

[0029] a. Adjustment nodes: Used to adjust unreasonable aspects in the model;

[0030] b. Remote nodes: Remote nodes are constructed based on the longest flight distance of the aircraft. If the flight route cannot be directly reached between two regional nodes, a remote node is added in the middle for adjustment.

[0031] c. Ordinary nodes: In target allocation and route planning, ordinary nodes are nodes that are allocated as target problems;

[0032] Step 2-2: Assignment of regional nodes along the outer ring road;

[0033] When modeling and solving the outer Hamiltonian loop, it is treated as a multiple traveling salesman problem (MTSP). Hamiltonian loops are continuously constructed, and each loop must eventually return to the starting point. By constructing "replicated nodes", the MTSP is transformed into a TSP problem for simplified calculation and solution.

[0034] Preferably, step 3 specifically comprises:

[0035] Step 3-1: Setting control parameters: The control parameters that need to be set are cooling rate q, initial temperature T0, ending temperature Tend, and chain length L;

[0036] Step 3-2: Initial solution: An initial solution S is generated using a random permutation method. During the algorithm's operation, the planned target nodes are continuously deleted until all target nodes are traversed. At this point, the stored target nodes are empty.

[0037] Step 3-3: Solution Transformation to Generate New Solution: By transforming the current solution S1, a new multi-objective allocation strategy and the planned trajectory are generated; the transformation method used is to generate two target nodes to be swapped by generating random numbers, and to generate a new path, i.e., a new feasible solution S2, by using the two-neighborhood transformation method.

[0038] Steps 3-4: Metropolis Criterion: If the cost under the current solution S1 is f(S1), and the cost under the new solution S2 is f(S2), and the cost difference is df = f(S2) - f(S1), then the Metropolis criterion is:

[0039]

[0040] If df < 0, the new target allocation strategy is accepted with probability 1; otherwise, the new target allocation strategy is accepted with probability exp(-df / T).

[0041] Steps 3-5: Cooling: Cool down using a cooling rate q, i.e., T′ = qT. If T′ is less than the end temperature, stop iterating and output the current target allocation strategy and the planned path; otherwise, continue iterating.

[0042] Step 4: Obtain the optimal multi-objective allocation strategy and the corresponding planned trajectory.

[0043] A computer program that causes a computer to perform the above-described multi-objective allocation and trajectory planning methods.

[0044] An electronic device includes: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to enable the electronic device to perform the above-described multi-target allocation and trajectory planning method.

[0045] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the above-described multi-target allocation and trajectory planning method.

[0046] A chip includes a processor for retrieving and running a computer program from a memory, causing a device equipped with the chip to perform the aforementioned multi-target allocation and trajectory planning method.

[0047] A computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the above-described multi-target allocation and trajectory planning method.

[0048] The beneficial effects of this invention are as follows:

[0049] 1. Comprehensive consideration of multi-objective allocation and trajectory planning: This invention not only considers the allocation of objectives, but also comprehensively considers trajectory planning, thereby achieving optimal resource allocation and efficient task execution.

[0050] 2. Construction of the dual-loop model: By abstracting the problem into a Hamiltonian loop problem, this invention provides a new perspective for handling multi-objective allocation and trajectory planning problems, making the problem easier to solve.

[0051] 3. Optimized application of simulated annealing algorithm: This invention improves the search efficiency and solution quality of the simulated annealing algorithm by optimizing the parameter settings and search strategy, enabling better solutions to be found in complex multi-objective allocation and trajectory planning problems.

[0052] 4. In summary, this invention provides an innovative method for multi-objective allocation and trajectory planning, which can effectively solve complex problems in practical applications and has significant practical application value and broad market prospects. Attached Figure Description

[0053] Figure 1 This is a general flight path map for each aircraft.

[0054] Figure 2 Enlarged map of key regional routes;

[0055] Figure 3 This is a schematic diagram of a double-loop model;

[0056] Figure 4 A diagram illustrating the simplification of the MTSP problem into the TSP problem;

[0057] Figure 5 To simulate the annealing algorithm flowchart, see: (a) route diagram for region A, (b) route diagram for region B, (c) route diagram for region C, (d) route diagram for region D, and (e) route diagram for region E. Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0059] This invention proposes a multi-objective allocation and trajectory planning method based on the simulated annealing algorithm, aiming to overcome the limitations of traditional methods and leverage the advantages of the SA algorithm to provide a new solution to the multi-objective allocation and trajectory planning problem. This method transforms the complex multi-objective allocation and trajectory planning problem into a solvable optimization problem by constructing a multi-objective allocation model and a dual-loop model, and then uses the simulated annealing algorithm to solve it, aiming to achieve optimal resource allocation and trajectory planning results.

[0060] This invention achieves effective allocation and trajectory planning for multiple targets by comprehensively considering the complexity and variability of tasks. The technical solution of this invention includes the following key steps:

[0061] Multi-objective allocation model establishment: This invention first defines key variables in the multi-objective allocation process, including flight cost, mission execution time, and dwell time. An objective function is constructed that comprehensively considers the cost of the regional loop, the cost of the target loop, and the total cost of all targets completing their tasks, with the goal of minimizing the total flight cost.

[0062] Dual-loop model establishment: This invention abstracts the multi-objective allocation and trajectory planning problem into a Hamiltonian loop problem, including an outer Hamiltonian loop and an inner Hamiltonian loop. The outer loop is responsible for trajectory planning between regional nodes, while the inner loop is responsible for trajectory planning of targets within a region. Through target node classification in the inner loop and regional node allocation in the outer loop, this invention effectively transforms the complex multi-objective allocation problem into a more manageable TSP problem.

[0063] Solution based on simulated annealing algorithm: This invention employs the simulated annealing algorithm to solve the constructed TSP model with two Hamiltonian loops. The algorithm implementation includes setting control parameters, generating initial solutions, transforming solutions to generate new solutions, applying the Metropolis criterion, and implementing a cooling strategy. Through these steps, this invention can find a near-globally optimal multi-objective allocation strategy and corresponding trajectory planning while ensuring computational efficiency.

[0064] Example:

[0065] The multi-objective allocation and trajectory planning method based on simulated annealing algorithm mainly includes three steps: establishing a multi-objective allocation model, establishing a dual-loop model, and solving the multi-objective allocation and trajectory planning problem based on simulated annealing algorithm.

[0066] Step 1: Establish the target allocation model.

[0067] First, the variables are defined for multiple objectives as follows:

[0068]

[0069] set up Let m represent the cost of the m-th trajectory in the n-th mission, from region node j to target j'. To simplify the model, we will... The analysis is divided into two parts: when flying between different geographical nodes, it is necessary to start from the geographical node. Upon reaching another regional node, the mission is executed; however, when executing a mission within the same region, if the distance is short, only the straight-line distance between the two targets needs to be calculated. Flight time is calculated using flight speed. S represents the current running time of this mission (e.g., flight speed). This represents the time when goal j is achieved, while This indicates a specific point in time between two targets (this is to limit flight time to within mission and onboard fuel requirements). This indicates the dwell time of the trajectory at target j.

[0070] The objective function is chosen as the function that minimizes the total flight cost. This objective function is composed of the cost of the regional loop, the cost of the target loop, and the total cost of completing the task across all targets.

[0071]

[0072] Assume there are a total of 31 regional nodes and 201 multi-target objectives:

[0073]

[0074] When conducting multi-target planning, the execution time for strike missions cannot exceed 5 hours, while the execution time for reconnaissance missions cannot exceed 8 hours, and both must be carried out within the time window of 7:00 to 19:00.

[0075]

[0076] The arrival time at the target must be within the time window when the enemy is unprepared or negligent in their defenses:

[0077]

[0078] Taking the above considerations into account, the target allocation model is established as follows:

[0079]

[0080] Step 2: Establish the dual-loop model.

[0081] A Hamiltonian graph is an undirected graph that takes a given starting point and travels to a given ending point, passing through all other nodes exactly once. In graph theory, it refers to a graph containing Hamiltonian cycles. A closed Hamiltonian path is called a Hamiltonian cycle, and a path containing all vertices in the graph is called a Hamiltonian path. The model problem in step one can be abstracted as a Hamiltonian cycle starting from the airport starting point and returning to the airport starting point within a specified time.

[0082] When building the model, it is assumed that for each regional node i, there are multiple targets J within this region. i =(j i1 ,j i2 ,j i3 ,j i4 ,…,j in ), easily obtained j i From an overall perspective, when planning and designing flight routes, aircraft performing missions need to consider logistical support, starting and ending at airports, thereby continuously constructing Hamiltonian closed loops. This ultimately forms multiple Hamiltonian closed loops traversing regional nodes i, i.e., the outer Hamiltonian loop. Within the region, it is necessary to consider the targets J to be attacked or reconnoitered. i Perform trajectory planning, each time from J i Choose a limited number of J ik Perform trajectory planning, and finally traverse all targets J = (j1,j2,j3,…,j…) n Therefore, it can be abstracted as an open Hamiltonian loop, i.e., an inner Hamiltonian loop. The inner and outer loop models are as follows: Figure 1 As shown.

[0083] Step 1: Classification of target nodes in the inner ring road.

[0084] For all target nodes J = (j1,j2,j3,…,j…) n If we categorize them, they can be roughly divided into three categories:

[0085] a. Adjustment Nodes: As the name suggests, adjustment nodes are used to correct inconsistencies in the model. Because these nodes are reachable by multiple task nodes within a short time, assigning an adjustment node as the previous (or next) target node in a given region will affect the overall target allocation and route planning. Therefore, after the program runs, these adjustment nodes can be manually modified to make the program's results more reasonable.

[0086] b. Long-distance nodes: Long-distance nodes are constructed based on the longest flight distance of the aircraft. Due to the long distances between regions, the flight path sometimes cannot reach directly between two regional nodes. Therefore, it is necessary to manually add other nodes for adjustment. Thus, long-distance nodes are also a very important aspect that must be considered in the overall target allocation plan.

[0087] c. Ordinary nodes: Ordinary nodes are neither adjustment nodes nor remote nodes. In target allocation and route planning, these nodes are considered as simple nodes that can be allocated in general target problems.

[0088] Step 2: Regional Node Allocation for Outer Ring Road

[0089] When modeling and solving external Hamiltonian loops, it can be treated as a Multiple Traveling Salesman Problem (MTSP). The traveling salesman needs to continuously construct Hamiltonian loops, each of which must eventually return to the starting point. The TSP is an NP-hard problem, meaning it cannot be represented and computed using polynomials, and the MTSP cannot be solved using ordinary methods. However, for this type of closed-loop Hamiltonian loop MTSP with a single starting point, the MTSP can be simplified by constructing "replicated nodes," transforming it into a TSP problem for easier computation and solution.

[0090] like Figure 2 As shown, solving this MTSP problem is very difficult. By using the "replicating nodes" method, the starting point is replicated. Based on the number of Hamiltonian loops N, it is replicated N-1 times. This transforms it into the ordinary TSP problem shown in the right figure, greatly simplifying the model solution and thus improving computational efficiency.

[0091] Finally, by integrating the inner and outer loops for multi-objective allocation and trajectory planning, the problem can be transformed into a TSP problem for modeling and solving.

[0092] Step 3: Solve the multi-objective allocation and trajectory planning based on the simulated annealing algorithm.

[0093] The TSP model described above is an NP-hard problem, so it cannot be expressed using polynomials and thus cannot be solved using mathematical functions. Here, the heuristic algorithm of simulated annealing is used to calculate the above model.

[0094] The idea behind the simulated annealing (SA) algorithm was first proposed by Metropolis et al. Its starting point is the similarity between the annealing process of solid materials in physics and general combinatorial optimization problems. SA provides an effective approach and general framework for solving the TSP problem, which is difficult to handle using traditional methods, and has gradually developed into an iterative adaptive heuristic probabilistic search algorithm. The flowchart of the algorithm for solving the constructed TSP model with two Hamiltonian loops based on simulated annealing is shown below. Figure 3 As shown.

[0095] For this model, the implementation of the SA algorithm mainly includes the following aspects:

[0096] (1) Setting of control parameters: The main control parameters that need to be set are cooling rate q, initial temperature T0, ending temperature Tend, and chain length L.

[0097] (2) Initial solution: For the TSP problem with 201 target nodes, an initial solution S is generated by random permutation. During the algorithm operation, the planned target nodes are continuously deleted until all target nodes are traversed. At this time, the stored target nodes are empty.

[0098] (3) Solution transformation to generate new solution: By transforming the current solution S1, a new multi-objective allocation strategy and the planned trajectory are generated. The transformation method used is to generate random numbers to generate the two target nodes to be exchanged. The two-neighborhood transformation method is used to generate a new path, i.e. a new feasible solution S2.

[0099] (4) Metropolis Criterion: If the cost under the current solution S1 is f(S1), and the cost under the new solution S2 is f(S2), and the cost difference is df = f(S2) - f(S1), then the Metropolis criterion is:

[0100]

[0101] If df < 0, the new target allocation strategy is accepted with probability 1; otherwise, the new target allocation strategy is accepted with probability exp(-df / T).

[0102] (5) Cooling: Cooling is performed using a cooling rate q, i.e., T′=qT. If T is less than the end temperature, the iteration stops and the current target allocation strategy and the planned path are output; otherwise, the iteration continues.

[0103] By combining steps one, two, and three, the optimal multi-objective allocation strategy and the corresponding planned trajectory are obtained.

[0104] Based on the established multi-objective allocation and trajectory planning model, and through optimization using the simulated annealing algorithm, the overall flight path diagram for each aircraft can be obtained as follows: Figure 4 As shown, the aircraft can plan a reasonable flight path based on the target node, thus achieving the goal of completing the mission quickly.

[0105] To more clearly illustrate the aircraft's mission activities within the mission area, the flight path diagram of the aircraft operating near the target has been enlarged and shown in the image below. Figure 5 It can be seen that the aircraft can effectively allocate targets in each flight path using the constructed method, and the resulting flight path can ensure the optimality of the flight path while guaranteeing the arrival rate of each target.

Claims

1. A multi-objective assignment and trajectory planning method based on simulated annealing algorithm, characterized in that, The method comprises the following steps: Step 1: establishing a multi-target allocation model; Key variables in the multi-target allocation process are defined, including flight cost, task execution time and residence time; A target function is constructed, which comprehensively considers the cost of the regional loop, the cost of the target loop and the total cost of completing all tasks, and aims to minimize the total flight cost; Step 2: establishing a double-loop model; The multi-target allocation and track planning problem is abstracted as a Hamilton loop problem, including an outer Hamilton loop and an inner Hamilton loop; the outer Hamilton loop is responsible for track planning between regional nodes, and the inner Hamilton loop is responsible for track planning of targets in the region; through target node classification in the inner loop and regional node allocation in the outer loop, the multi-target allocation problem is converted into a TSP problem; Step 3: solution based on simulated annealing algorithm; The simulated annealing algorithm is used to solve the TSP model of the double Hamilton loop constructed; The algorithm implementation includes setting of control parameters, generation of initial solution, generation of new solution through solution transformation, application of Metropolis criterion and cooling strategy; A multi-target allocation strategy close to the global optimum and corresponding track planning are obtained.

2. The multi-objective assignment and trajectory planning method based on simulated annealing algorithm according to claim 1, characterized in that, The step 1 is specifically: Step 1-1: define variables for multi-targets as follows: Let Cijj' (n) denote the cost of the mth track of the nth task from the geographical node j to the target j' ; Let Cijj' (n) = Cij (n) + Cj' (n), where Cij (n) is the cost of the flight from the geographical node i to the geographical node j, and Cj' (n) is the cost of the task at the target j'. Cij (n) = Sij (n) / Vf, where Sij (n) is the distance between the geographical node i and the geographical node j, and Vf is the flight speed. Cj' (n) = Tj' (n) - Tj (n), where Tj (n) is the time when the task at the target j is started, and Tj' (n) is the time when the task at the target j is completed. Cij (n) = Sij (n) / Vf, where Sij (n) is the distance between the geographical node i and the geographical node j, and Vf is the flight speed. Cj' (n) = Tj' (n) - Tj (n), where Tj (n) is the time when the task at the target j is started, and Tj' (n) is the time when the task at the target j is completed. Step 1-2: select a function with minimum total flight cost as the target function, and the target function is composed of the cost of the regional loop, the cost of the target loop and the total cost of completing all tasks, namely: wherein, respectively represent the target cost of the i regional node in the mth track planning of the nth task, the flight execution cost between different regional nodes, the target cost of the j regional node in the mth track planning of the nth task, and the dwell time of the j target of the track.

3. The multi-objective assignment and trajectory planning method based on simulated annealing algorithm according to claim 2, characterized in that, The step 2 is specifically: Assume that there are multiple targets J within each geographic node i. i =(j i1 ,j i2 ,j i3 ,j i4 ,…,j in ), thus obtaining j i ∈i; When planning and designing flight routes, aircraft performing missions consider logistical support and replenishment with airports as both starting and ending points, thereby continuously constructing Hamiltonian closed loops, ultimately forming multiple Hamiltonian closed loops that traverse region nodes i, i.e., outer Hamiltonian loops; within the region, for the target J to be attacked or reconnoitered... i Perform trajectory planning, each time from J i Choose a limited number of J ik Perform trajectory planning, and finally traverse all targets J = (j1,j2,j3,…,j…) n Therefore, it is abstracted as an open Hamiltonian loop, i.e., an inner Hamiltonian loop; Step 2-1: inner loop target node classification; Classify all target nodes J = (j1, j2, j3,..., j n ) into three categories: a. Adjusting node: used to adjust unreasonable places in the model; b. Long-distance node: the long-distance node is constructed based on the longest aircraft range, and if the aircraft route cannot directly reach between two regional nodes, a long-distance node is added in the middle for adjustment; c. Ordinary node: in target allocation and track planning, the ordinary node is regarded as a node for allocation of the target problem; Step 2-2: outer loop regional node allocation; When modeling and solving the outer Hamilton loop, it is regarded as a multi-traveler problem MTSP, and Hamilton loops are constantly constructed, each loop must finally return to the starting point, and the MTSP is converted into a TSP problem by constructing a "copy node" to simplify calculation and solution.

4. The multi-objective assignment and trajectory planning method based on simulated annealing algorithm according to claim 3, characterized in that, The step 3 is specifically: Step 3-1: setting of control parameters: the control parameters to be set include cooling rate q, initial problem T0, end temperature Tend and chain length L; Step 3-2: initial solution: an initial solution S is generated by using a random arrangement method, and in the algorithm running process, the planned target nodes are constantly deleted until all target nodes are traversed, at which time the stored target nodes are empty; Step 3-3: New solution is generated by transforming the current solution S1 to produce a new multi-objective allocation strategy and the planned path; the transformation is to generate two target nodes to be exchanged by using a random number generating method, and to generate a new path by using a two-neighbor transformation method, i.e. a new feasible solution S2; Step 3-4: Metropolis criterion: if the cost under the current solution S1 is f(S1), the cost under the new solution S2 is f(S2), and the cost difference is df = f(S2) - f(S1), then the Metropolis criterion is: If df < 0, the new multi-objective allocation strategy is accepted with a probability of 1; otherwise, the new multi-objective allocation strategy is accepted with a probability of exp(-df / T); Step 3-5: Cooling: cooling is performed by using a cooling rate q, i.e. T' = qT, if T' is less than an ending temperature, then the iteration is stopped and the current multi-objective allocation strategy and the planned path are output; otherwise, the iteration is continued; Step 4: An optimal multi-objective allocation strategy and the corresponding planned path are obtained.

5. A computer program, characterised in that, The computer program causes a computer to execute the method of any one of claims 1 to 4.

6. An electronic device, comprising: Comprise: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method of any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1 to 4.

8. A chip, characterized by Comprise: a processor, which is used to call and run a computer program from a memory, so that the device installed with the chip executes the method of any one of claims 1 to 4.

9. A computer program product, characterised in that, The computer program product comprises a computer storage medium, the computer storage medium stores a computer program, and the computer program comprises instructions executable by at least one processor, and when the instructions are executed by the at least one processor, the method of any one of claims 1 to 4 is implemented.

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