Unmanned cluster route planning method based on quantum computing

Abstract

The invention relates to an unmanned cluster route planning method based on quantum computing. The method comprises the following steps: establishing a task environment; establishing an unmanned cluster route planning model, and converting the unmanned cluster route planning model into a secondary unconstrained binary optimization problem; solving the planning model by using a variable component sub-algorithm; judging whether the path corresponding to the solution is missing or redundant or not; judging whether a sub-loop exists in the obtained path or not, if yes, extracting all nodes contained in the sub-loop, and adding constraint conditions capable of eliminating the sub-loop in the planning model; and judging whether the obtained path has collision conflicts or not, if so, modeling all collision conflicts, and solving by using the variable component sub-algorithm again, otherwise, outputting a path planning result. The method provided by the invention not only improves the efficiency of path planning, but also reduces the demand for quantum computing physical resources, and has extremely high application value for collaborative operation of unmanned clusters.

Description

Technical Field

[0001] The present invention relates to the field of cluster route planning, and in particular to an unmanned cluster route planning method based on quantum computing. This technical field covers the cross-application of multiple disciplines such as quantum information science, computational optimization theory, and automatic control. Background Art

[0002] In the field of modern science and technology, unmanned swarm routing technology has attracted much attention due to its significant role in many important application scenarios, such as automated transportation systems, logistics distribution, and environmental monitoring. Individuals in the swarm all require efficient and intelligent routing to improve operational efficiency and safety. Therefore, swarm routing not only has practical application value, but also has research significance.

[0003] The unmanned swarm routing problem is essentially a complex optimization problem, the core of which is to find the optimal path for individuals in the swarm to meet specific mission requirements and constraints. Due to its inherent complexity, the swarm routing problem is usually classified as an NP-hard problem, which means that as the size of the swarm increases, the computational complexity increases exponentially, and traditional computational methods face huge challenges when dealing with large-scale swarms.

[0004] As an emerging computing paradigm, quantum computing, with its unique quantum superposition and quantum entanglement properties, has the potential to surpass traditional computing methods on certain problems. This feature of quantum computing gives it the potential to accelerate the solution of certain NP-hard problems, especially in scenarios that require large-scale parallel computing. Therefore, quantum computing is believed to be expected to provide a new solution to the cluster route planning problem.

[0005] Although quantum computing has great potential in theory for solving cluster routing problems, quantum algorithms for solving such problems are relatively rare. Existing research focuses on theoretical exploration of quantum algorithms and their application in specific fields, while the development of quantum algorithms for the specific problem of cluster routing is still in its infancy. Summary of the invention

[0006] The technical problem solved by the present invention is: the present invention proposes an unmanned cluster route planning method based on quantum computing, aiming to solve the technical difficulties faced by quantum computing in cluster route planning, especially how to efficiently use limited quantum resources to deal with cluster route planning problems containing collision avoidance constraints.

[0007] The solution to the technical problem of the present invention is: an unmanned swarm route planning method based on quantum computing, comprising the following steps:

[0008] Step 1: Establish the task environment. After the individuals in the unmanned cluster accept the mobile task, the number of individuals K participating in the task is determined, and the set V of n locations that the unmanned cluster needs to traverse and their location coordinate information are determined, starting from the starting position.

[0009] The set V of locations that the unmanned cluster needs to traverse is expressed as V = {1, 2, ..., n}; the starting position and the final destination of the individuals in the unmanned cluster are recorded as node 0, that is, the individuals in the unmanned cluster need to return to the starting position after completing the mobile task;

[0010] Step 2: Establish an unmanned swarm route planning model and transform it into a quadratic unconstrained binary optimization problem;

[0011] Step 3, using a variational quantum algorithm to solve the cluster routing model in step 2 to obtain a solution;

[0012] Step 4: Determine whether the path corresponding to the solution obtained in step 3 is missing or redundant. If so, return to step 3; otherwise, proceed to the next step;

[0013] Whether the path is missing means that the unmanned cluster individuals have not completely traversed all the location nodes that need to be reached; whether the path is redundant means that some of the location nodes that need to be reached are repeatedly reached by the individuals in the unmanned cluster for 2 or more times;

[0014] Step 5: Determine whether there is a sub-loop in the path obtained in step 4. If so, extract all nodes contained in the sub-loop, and add constraints that can eliminate the sub-loop in the unmanned swarm routing model in step 2, and execute step 3; otherwise, execute the next step;

[0015] The sub-loop refers to the situation where an individual in the unmanned cluster returns to the starting position without completing the traversal of all the position nodes that need to be reached;

[0016] Step 6: Determine whether the path obtained in step 5 has a collision conflict. If so, model all collision conflict situations, establish a cluster route planning model considering collision avoidance, and use the same variational quantum algorithm as in step 3 to solve it; otherwise, output the result of the path planning and the calculation ends;

[0017] Whether there is a collision conflict on the path refers to determining whether there is a collision between individuals in the unmanned cluster during the movement process.

[0018] Furthermore, the individuals in the unmanned cluster described in step 1 refer to unmanned platforms with carrying capabilities, including unmanned aerial vehicles, unmanned vehicles, and unmanned ships.

[0019] Furthermore, the unmanned cluster route planning model is: the cost between nodes i and j that the unmanned cluster needs to traverse is defined as w ij ;definition is the set of all starting positions that can reach position i after individuals in the unmanned cluster move, and is defined as is the set of all other nodes that individuals in the unmanned cluster can reach from node i; define x ij Is the individual in the unmanned cluster moved from position i to position j: If yes, then the variable x ij The value is 1, otherwise it is 0; the cost function of the unmanned swarm routing model constructed based on the above factors is:

[0020]

[0021] Among them, the constraints of the above cost function are:

[0022]

[0023] Furthermore, the transformation into a quadratic unconstrained binary optimization problem is:

[0024] H VRP =H c +H N +H0

[0025] Among them, H c , H N The expressions of and H0 are:

[0026]

[0027]

[0028]

[0029] In the above expression, λ1 and λ2 are two positive constants, H c represents the total cost of unmanned swarm routing planning, H N and H0 are the constraints of the unmanned swarm routing model. Define H VRP It is the cost function of the unmanned swarm routing model.

[0030] Furthermore, the variational quantum algorithm is as follows: define the vector θ as a free parameter in the quantum state evolution process, |ψ(θ)>=U(θ)ψ0> is the end state of the initial state ψ0> after the unit operator U(θ) of the parameter quantum circuit running on the quantum computer, and the cost function C(θ) is defined as follows:

[0031] C(θ)=<ψ(θ)|H|ψ(θ)>

[0032] The quantum circuit is composed of n quantum bits. 1) First, a quantum rotation Y gate needs to be applied to each quantum bit. 2) Then, a quantum CNOT gate is applied to all quantum bits satisfying i<j, requiring quantum bit i to control quantum bit j. 3) Finally, a quantum rotation Y gate is applied to each quantum bit again. The last two steps are repeated p times, where p represents the number of layers of the circuit. The parameter θ is continuously optimized and iterated until the algorithm converges.

[0033] Furthermore, the constraint conditions for eliminating the sub-loop are as follows:

[0034]

[0035] Among them, |Q| represents the number of nodes contained in the location node set Q that needs to be reached.

[0036] Furthermore, the modeling of all collision and conflict situations includes:

[0037] The difference in space and time between individuals (i, j) in an unmanned cluster must be less than their respective minimum safety standards Δ a With Δ t , that is: max{A,T}>0, where A i,t is the individual i in the unmanned cluster at t i The location at the moment, is the arrival point of individual i in the unmanned group The time delay, Δ a is the minimum safety standard of individuals in the unmanned cluster in space, Δ t It is the minimum safety standard in time for individuals in an unmanned swarm.

[0038] Furthermore, the solution is obtained by using the same variational quantum algorithm as in step 3, including: after the solution is obtained, it is necessary to determine whether the solution is infeasible during the actual movement of individuals in the unmanned cluster.

[0039] Furthermore, the determination of whether the solution is infeasible in the actual movement of individuals in the unmanned swarm includes: if not, executing the next step; if yes, adding an infeasible solution constraint condition to the solution process and performing calculation, wherein the infeasible solution constraint condition is:

[0040]

[0041] Where S infea =(s1,s2,…,s n ),s i ∈{0,1},i∈{1,2,…,n} represents an infeasible solution. represents the new solution obtained after adding the infeasible solution constraint, Ω0(S infea )={i|s i =0},Ω1(S infea )={i|s i =1}.

[0042] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the unmanned cluster path planning method based on quantum computing are implemented.

[0043] The beneficial effects of the present invention compared with the prior art are:

[0044] (1) Since the present invention adopts a variational quantum algorithm as a model solver, it realizes the processing of cluster route planning problems containing collision avoidance constraints under limited quantum resources. Compared with traditional algorithms in the prior art, it simplifies the problem solving process and improves the solving efficiency while meeting the collision avoidance requirements.

[0045] (2) The present invention optimizes the constraints of the model by introducing infeasible solution constraints and reduces the dependence on quantum bits in the sub-cycle elimination process. Even in the worst case, the number of quantum bits required is equivalent to the original constraints, ensuring the accuracy and efficiency of path planning.

[0046] (3) The quantum optimization algorithm proposed in this invention makes full use of limited quantum resources through step-by-step optimization, providing a feasible method for solving complex problems and having high resource utilization and computational efficiency when dealing with large-scale problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of an unmanned cluster path planning method based on quantum computing according to an embodiment of the present invention;

[0048] Figure 2 This is a task environment diagram of an unmanned cluster path planning simulation case based on quantum computing in an embodiment of the present invention;

[0049] Figure 3 It is the quantum computing algorithm framework used in the embodiments of the present invention;

[0050] Figure 4 This is the unmanned cluster path planning result in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] With the rapid development of quantum computing technology, its potential in solving complex optimization problems has been gradually explored, especially in scenarios that require processing large-scale data and searching in high-dimensional space. This invention aims to apply quantum computing technology to cluster route planning problems and provide a new path planning method for individuals in the cluster, in order to reduce energy consumption and improve task execution efficiency and safety. It is expected that the efficiency and accuracy of path planning will be improved through quantum algorithms, thereby promoting technological progress and application expansion in related fields.

[0052] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0053] The present invention proposes a quantum computing-based unmanned cluster path planning method, which uses a variational quantum algorithm to solve the path planning problem of unmanned clusters. The main process includes: building an algorithm framework, establishing a task environment, proposing a problem model, designing a quantum computing method and solving it.

[0054] 1. Establish an algorithm framework

[0055] like Figure 1 This paper introduces quantum computing methods to solve the path planning problem of unmanned swarms, taking into account the elimination of collisions between individuals in the unmanned swarm, so that the unmanned swarm can complete the task safely with the minimum energy consumption during the movement. The specific path planning task process includes:

[0056] Step 1: Establish the task environment. After the individuals in the unmanned cluster accept the mobile task, the number of individuals K participating in the task is determined, and the set V of n locations that the unmanned cluster needs to traverse and their location coordinate information are determined, starting from the starting position.

[0057] Step 2: Establish an unmanned swarm route planning model and transform it into a quadratic unconstrained binary optimization problem;

[0058] Step 3, using a variational quantum algorithm to solve the cluster routing model in step 2 to obtain a solution;

[0059] Step 4: Determine whether the path corresponding to the solution obtained in step 3 is missing or redundant. If so, return to step 3; otherwise, proceed to the next step;

[0060] Whether the path is missing refers to the fact that the unmanned cluster individuals have not completely traversed all the locations of the nodes that need to be reached;

[0061] Whether the path is redundant means that some of the location nodes to be reached are repeatedly reached by individuals in the unmanned cluster for 2 or more times;

[0062] Step 5: Determine whether the path obtained in step 4 has a sub-loop. If so, extract all nodes contained in the sub-loop, and add constraints that can eliminate the sub-loop in the unmanned swarm routing model in step 2 (and its corresponding quadratic unconstrained binary optimization problem), and execute step 3; otherwise, execute the next step;

[0063] The sub-loop refers to the situation where an individual in the unmanned cluster returns to the starting position without completing the traversal of all the position nodes that need to be reached;

[0064] Step 6: Determine whether the path obtained in step 5 has any collision conflict. If so, model all collision conflict situations, establish a cluster route planning model considering collision avoidance, and use the same variational quantum algorithm as in step 3 to solve it; if not, output the result of the path planning and the calculation ends;

[0065] Whether there is a collision conflict on the path refers to determining whether there is a collision between individuals in the unmanned cluster during the movement process.

[0066] 2. Establish the task environment

[0067] like Figure 2 As shown, an unmanned cluster path planning task execution environment is established. In this implementation case, two areas A and B are set, with a river between the two areas and a bridge between the rivers. The bridge supports two-way traffic, but can only travel in one direction at the same time. The individuals in the unmanned cluster in this implementation case are all unmanned vehicles, which require that unmanned vehicles cannot enter the river and can only travel between areas A and B through bridges. It is assumed that there is 1 unmanned vehicle in area A and 1 unmanned vehicle in area B. It is assumed that there are 5 location nodes that need to be reached by unmanned vehicles in area A and 5 location nodes that need to be reached by unmanned vehicles in area B.

[0068] In this implementation case, the two unmanned vehicles need to traverse all the location nodes that the unmanned vehicles need to reach, and collisions are not allowed during the movement. The total distance traveled by the two unmanned vehicles to achieve the above goals is required to be the minimum. The minimum safety standard distance of the unmanned vehicle Δ a Less than or equal to the length of the bridge in this implementation case.

[0069] 3. Propose a problem model

[0070] The set V of locations that the unmanned cluster needs to traverse is represented as V = {1, 2, ..., 10}; the starting point and the final destination of the individuals in the unmanned cluster are recorded as node 0. The cost between nodes i and j that the unmanned cluster needs to traverse is defined as w ij ;definition is the set of all starting positions that can reach position i after individuals in the unmanned cluster move, and is defined as is the set of all other nodes that individuals in the unmanned cluster can reach from node i; define x ij Is the individual in the unmanned cluster moved from position i to position j: If yes, then the variable x ij The value is 1, otherwise it is 0; the cost function of the unmanned swarm routing model constructed based on the above factors is

[0071]

[0072] Among them, the constraints of the above cost function are:

[0073]

[0074] In the above formula, the mathematical symbol It means "any one", and the mathematical symbol ∈ means "belongs to".

[0075] The transformation into a quadratic unconstrained binary optimization problem is:

[0076] H VRP =H c +H N +H0,

[0077] Among them, H c , H N The expressions of and H0 are:

[0078]

[0079]

[0080]

[0081] In the above expression, λ1 and λ2 are two positive constants, H c represents the total cost of unmanned swarm routing planning, H N and H0 are the constraints of the unmanned swarm routing model. Define H VRP It is the cost function of the unmanned swarm routing model.

[0082] 4. Design quantum computing methods and solve them

[0083] like Figure 3 As shown, a quantum computing algorithm is designed and solved. The hardware efficiency in the figure is Ansatz, which is a special term for quantum computing and refers to a quantum circuit with parameters.

[0084] Define the vector θ as a free parameter in the quantum state evolution process. |ψ(θ)>=U(θ)ψ0> is the final state of the initial state ψ0> after the unit operator U(θ) of the parameter quantum circuit running on the quantum computer. The cost function C(θ) is defined as follows:

[0085] C(θ)=<ψ(θ)|H|ψ(θ)>

[0086] The above quantum circuit consists of n qubits. 1) First, a quantum rotation Y gate needs to be applied to each qubit. 2) Then, a quantum CNOT gate is applied to all qubits that satisfy i<j (required to control qubit j for qubit i). 3) Finally, a quantum rotation Y gate is applied to each qubit again. The last two steps are repeated p times, where p represents the number of layers of the circuit (also called the circuit depth). The parameter θ is continuously optimized and iterated until the algorithm converges.

[0087] After obtaining the solution of the above calculation process, it is determined that the corresponding path is not missing or redundant, but there is a sub-loop. Extract all the nodes contained in the sub-loop and add the following constraints to eliminate the sub-loop:

[0088]

[0089] Among them, |Q| represents the number of nodes contained in the location node set Q that needs to be reached.

[0090] After adding the above sub-loop elimination constraint to the unmanned cluster path planning model, the solution is solved again. It can be found that the path corresponding to the solution has a collision conflict. The difference in space and time between individuals (i, j) in the unmanned cluster is less than their respective minimum safety standards Δ a With Δ t , that is, max{A,T}>0, here Among them A i,t For unmanned vehicles i The location at the moment, is the arrival point of individual i in the unmanned group After the solution is completed, it is determined whether the solution is infeasible in the actual movement of individuals in the unmanned cluster. If so, the infeasible solution constraint is added and calculated in the solution process. The addition of the infeasible solution constraint means Here S infea =(s1,s2,…,s n ),(s i ∈{0,1},i∈{1,2,…,n}) represents an infeasible solution. represents the new solution obtained after adding the infeasible solution constraint, Ω0(S infea)={i|s i =0},Ω1(S infea )={i|s i =1}. If not, execute the next step and get Figure 4 The unmanned cluster path planning results in this implementation case are shown.

[0091] In summary, the cluster route planning method based on quantum computing proposed in the present invention utilizes the advantages of quantum computing to provide an efficient and innovative solution to the cluster route planning problem.

[0092] The present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes Figure 1 The method described.

[0093] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0094] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0098] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. An unmanned swarm route planning method based on quantum computing, characterized in that: The following steps are involved: Step 1: Establish the task environment. After the individuals in the unmanned cluster accept the mobile task, the number of individuals K participating in the task is determined, and the set V of n locations that the unmanned cluster needs to traverse and their location coordinate information are determined, starting from the starting position. The set V of locations that the unmanned cluster needs to traverse is expressed as V = {1, 2, ..., n}; the starting position and the final destination of the individuals in the unmanned cluster are recorded as node 0, that is, the individuals in the unmanned cluster need to return to the starting position after completing the mobile task; Step 2: Establish an unmanned swarm route planning model and transform it into a quadratic unconstrained binary optimization problem; Step 3, using a variational quantum algorithm to solve the cluster routing model in step 2 to obtain a solution; Step 4: Determine whether the path corresponding to the solution obtained in step 3 is missing or redundant. If so, return to step 3; otherwise, proceed to the next step; Whether the path is missing means that the unmanned cluster individuals have not completely traversed all the location nodes that need to be reached; whether the path is redundant means that some of the location nodes that need to be reached are repeatedly reached by the individuals in the unmanned cluster for 2 or more times; Step 5: Determine whether there is a sub-loop in the path obtained in step 4. If so, extract all nodes contained in the sub-loop, and add constraints that can eliminate the sub-loop in the unmanned swarm routing model in step 2, and execute step 3; otherwise, execute the next step; The sub-loop refers to the situation where an individual in the unmanned cluster returns to the starting position without completing the traversal of all the position nodes that need to be reached; Step 6: Determine whether the path obtained in step 5 has a collision conflict. If so, model all collision conflict situations, establish a cluster route planning model considering collision avoidance, and use the same variational quantum algorithm as in step 3 to solve it; Otherwise, the path planning result is output and the calculation ends.

2. According to the unmanned swarm route planning method based on quantum computing according to claim 1, it is characterized in that: The individuals in the unmanned swarm described in step 1 refer to unmanned platforms with carrying capabilities, including unmanned aerial vehicles, unmanned vehicles, and unmanned ships.

3. According to the unmanned swarm route planning method based on quantum computing in claim 1, it is characterized in that: The unmanned cluster route planning model is: define the cost between nodes i and j that the unmanned cluster needs to traverse as w ij ;definition is the set of all starting positions that can reach position i after individuals in the unmanned cluster move, and is defined as is the set of all other nodes that individuals in the unmanned cluster can reach from node i; define x ij Is the individual in the unmanned cluster moved from position i to position j: If yes, then the variable x ij The value is 1, otherwise it is 0; the cost function of the unmanned swarm routing model constructed based on the above factors is: Among them, the constraints of the above cost function are:

4. The unmanned swarm route planning method based on quantum computing according to claim 3 is characterized in that: The transformation into a quadratic unconstrained binary optimization problem is: H VRP =H c +H N +H0 Among them, H c , H N The expressions of and H0 are: In the above expression, λ1 and λ2 are two positive constants, H c represents the total cost of unmanned swarm routing planning, H N and H0 are the constraints of the unmanned swarm routing model. Define H VRP It is the cost function of the unmanned swarm routing model.

5. The unmanned swarm route planning method based on quantum computing according to claim 1 is characterized in that: The variational quantum algorithm is as follows: define the vector θ as a free parameter in the quantum state evolution process, |ψ(θ)>=U(θ)ψ0> is the final state of the initial state ψ0> after the unit operator U(θ) of the parameter quantum circuit running on the quantum computer, and the cost function C(θ) is defined as follows: C(θ)=<ψ(θ)|H|ψ(θ)> The quantum circuit is composed of n quantum bits. 1) First, a quantum rotation Y gate needs to be applied to each quantum bit. 2) Then, a quantum CNOT gate is applied to all quantum bits satisfying i<j, requiring quantum bit i to control quantum bit j. 3) Finally, a quantum rotation Y gate is applied to each quantum bit again. The last two steps are repeated p times, where p represents the number of layers of the circuit. The parameter θ is continuously optimized and iterated until the algorithm converges.

6. The unmanned swarm route planning method based on quantum computing according to claim 3 is characterized in that: The constraints for sub-loop elimination are as follows: Among them, |Q| represents the number of nodes contained in the location node set Q that needs to be reached.

7. The unmanned swarm route planning method based on quantum computing according to claim 1 is characterized in that: The described method models all collision and conflict situations, including: The difference in space and time between individuals (i, j) in an unmanned cluster must be less than their respective minimum safety standards Δ a With Δ t , that is: max{A,T}>0, where A i,t is the individual i in the unmanned cluster at t i The location at the moment, is the arrival point of individual i in the unmanned group The time delay, Δ a is the minimum safety standard of individuals in the unmanned cluster in space, Δ t It is the minimum safety standard in time for individuals in an unmanned swarm.

8. The unmanned swarm route planning method based on quantum computing according to claim 1 is characterized in that: The solution is obtained by using the same variational quantum algorithm as in step 3, including: after the solution is obtained, it is necessary to determine whether the solution is infeasible during the actual movement of individuals in the unmanned cluster.

9. The unmanned swarm route planning method based on quantum computing according to claim 8, characterized in that: The determining whether the solution is infeasible in the actual movement process of individuals in the unmanned cluster includes: if not, executing the next step; if so, adding an infeasible solution constraint condition in the solution process and performing calculation, wherein the infeasible solution constraint condition is: Where S infea =(s1,s2,…,s n ),s i ∈{0,1},i∈{1,2,…,n} represents an infeasible solution. represents the new solution obtained after adding the infeasible solution constraint, Ω0(S infea )={i|s i =0},Ω1(S infea )={i|s i =1}.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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