A method for allocating tasks of coordinated countermeasures for integrated sea and air unmanned intelligent equipment

Abstract

The present invention discloses a method for allocating tasks for coordinated confrontation of unmanned intelligent devices in sea and air, comprising the following steps: first, establishing a model for coordinated confrontation of unmanned intelligent devices in sea and air; second, initializing quantum fish quantum positions and setting parameters; third, calculating the fitness function value of the quantum fish position; fourth, updating the quantum fish quantum position using a free search strategy, determining whether the fitness value of the i-th quantum fish is greater than its empirical position fitness value; if so, executing step five; otherwise, executing step six; fifth, updating the quantum fish quantum position using a whale adsorption strategy; sixth, updating the quantum fish quantum position using a host-detachment strategy; and seventh, iteratively updating to a maximum number of iterations, mapping the optimal quantum fish position into a sea and air integrated unmanned coordinated confrontation task allocation matrix, and outputting the matrix. The present invention reduces the difficulty of problem solving, overcomes the drawback of easily falling into local convergence, and improves the optimization rate.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned intelligent equipment confrontation, and relates to a method for allocating tasks of sea-air integrated unmanned intelligent equipment collaborative confrontation, in particular a method based on quantum A method for allocating coordinated countermeasure tasks for integrated sea-air unmanned intelligent equipment based on fish mechanism. Background Art

[0002] Integrated sea-air unmanned intelligent equipment refers to a relatively complete sea-air unmanned confrontation system composed of ship-borne drones on large ships and unmanned boats around the ships. How to achieve confrontation task allocation under the coordinated confrontation between ship-borne drones and unmanned boats is the research focus.

[0003] According to existing literature, Liang Guoqiang et al. published a paper titled "UAV collaborative multi-task allocation based on discrete particle swarm optimization" in Computer Simulation (2018, 35(2): 22-28). Based on the UAV collaborative multi-task allocation model, they comprehensively considered complex constraints such as task time constraints and ammunition constraints and proposed a multi-UAV collaborative multi-task allocation method based on discrete particle swarm optimization. Experimental simulations verified that this method can solve the multi-UAV collaborative combat task allocation problem under complex multi-constraint conditions. The designed discrete particle swarm task allocation method solves the multi-UAV confrontation task allocation problem to a certain extent. However, this method is prone to local convergence and the convergence speed is too slow. It requires a certain number of iterations to obtain the optimal solution. Summary of the Invention

[0004] In view of the above existing technologies, the technical problem to be solved by the present invention is to provide a quantum-based The method of allocating tasks of sea-air integrated unmanned intelligent equipment to coordinate confrontation and design quantum coding The evolution mechanism of fish quantum position, a new quantum The fish mechanism method increases the optimization rate and overcomes the disadvantage that the previous methods are prone to falling into local convergence.

[0005] To solve the above technical problems, the present invention provides a method for allocating tasks for coordinated countermeasures of integrated sea-air unmanned intelligent equipment, comprising the following steps:

[0006] Step 1: Establish a task allocation model for coordinated countermeasures of integrated sea and air unmanned intelligent equipment;

[0007] Step 2: Initial quantum The quantum position of the fish and set the parameters;

[0008] Step 3: Calculate quantum The fitness function value of the fish position;

[0009] Step 4: Update the quantum using the free search strategy The quantum position of the fish, determine the quantum of the i Is the fitness value of the fish greater than the fitness value of its experience position, i=1,2,3,…,K1, when the greater than condition is met, the i-th quantum The fish performs local search through step 5; otherwise, the i-th quantum The fish conducts a local search through step six;

[0010] Step 5: Update quantum using the whale adsorption strategy The quantum position of the fish, perform step seven;

[0011] Step 6: Update quantum using the off-host strategy The quantum position of the fish, perform step seven;

[0012] Step 7: Determine whether quantum The maximum number of iterations of the fish is K2, and the iteration is terminated, and the optimal quantum The position of the fish is mapped to the sea-air integrated unmanned collaborative confrontation task allocation matrix and output; otherwise, let the number of iterations k = k+1, and find the quantum position corresponding to the maximum fitness value of the k+1th iteration as the optimal quantum The quantum position of fish Continue to step 4.

[0013] Furthermore, the establishment of a sea-air integrated unmanned intelligent equipment collaborative confrontation task allocation model in step one includes:

[0014] Assume that there are N shipborne drones and The surface unmanned boat can perform confrontation missions, and the collection of sea-air integrated unmanned intelligent equipment is defined as Among them, the ship-borne UAV U n The attribute set is U n ={v n ,l n ,w n ,r n}, v n For shipborne drones U n The sailing speed, l n For shipborne drones U n The initial position of the large ship, w n For shipborne drones U n The amount of ammunition carried, r n For shipborne drones U n Range; Unmanned Surface Vehicle The attribute set is Unmanned surface vehicle The sailing speed is Unmanned surface vehicle The initial position of Unmanned surface vehicle The amount of ammunition carried, Unmanned surface vehicle Assuming that the enemy has M sea surface targets, the set of sea surface targets is defined as T = {T1, T2, ..., T M}, where T M is the Mth sea surface target;

[0015] The task allocation matrix of sea-air integrated unmanned collaborative confrontation is: in, When x n,m =1 means shipborne UAV U n Attack surface targets T m , m=1,2,…,M,x n,m =0 means shipborne UAV U n Do not attack surface targets T m ; When x n,m =1 means unmanned surface boat Attack surface targets T m , x n,m =0 means unmanned surface boat Do not attack surface targets T m ;

[0016] Establishing the maximization objective function for the coordinated countermeasure task allocation of integrated sea and air unmanned intelligent equipment Where m=1,2,…,M; E(·) is the judgment function. , the function returns a value of 1, and When , the function returns the value 0; C1 is the mission constraint penalty item, C2 is the ammunition constraint penalty item, C3 is the range constraint penalty item, α1, α2 and α3 are the weight factors of the constraint penalty items C1, C2 and C3 respectively;

[0017] The established model needs to meet three constraints, namely mission constraints, ammunition constraints and range constraints; the mission constraints are m=1,2,…,M, indicating that at most one shipborne UAV or one surface unmanned boat can attack the surface target T m ; The ammunition constraints of shipborne UAVs are Indicates shipborne drone U nThe number of sea targets attacked cannot exceed the amount of ammunition carried; the ammunition constraint of the surface unmanned boat is Unmanned surface vehicle The number of sea targets attacked cannot exceed the amount of ammunition carried; the range constraint of the shipborne UAV is D n ≤r n , Among them, D n For shipborne drones U n The total flight distance; assuming that the shipborne UAV U n Attack sea targets T1, T2 and T3 in sequence. At this time, the shipborne drone U n The total flight distance is D n =d 0,1 +d 1,2 +d 2,3 +d 0,3 , d 0,1 is the distance between the large ship and the sea surface target T1, d 1,2 is the distance between the sea surface target T1 and the sea surface target T2, d 2,3 is the distance between the sea surface target T2 and the sea surface target T3, d 0,3 is the distance between the large ship and the sea target T3, the shipborne drone U n The total flight distance includes the return distance; the range constraint of the surface unmanned vehicle is Unmanned surface vehicle The total sailing distance; assuming that the surface unmanned boat Attack the sea targets T1, T2 and T3 in sequence. The total sailing distance of the surface unmanned boat is Unmanned surface vehicle The distance between the initial position and the sea surface target T1, d 1,2 is the distance between the sea surface target T1 and the sea surface target T2, d 2,3 is the distance between the sea surface target T2 and the sea surface target T3;

[0018] Convert task constraints into task constraint penalties Where |·| is the absolute value function, which converts the ammunition constraints of shipborne UAVs and surface unmanned boats into ammunition constraint penalty terms. Convert the range constraints of shipborne UAVs and surface unmanned boats into range constraint penalty items is the judgment function, for E(D n ,r n ), if D n ≥r n If the function is set to true, it returns 1; otherwise, it returns 0.

[0019] Furthermore, the initial quantum in step 2 The quantum position of the fish and the parameters to be set include:

[0020] Set the population size to K1, the maximum number of iterations to K2, and the random initial quantum in the initial population. The quantum position of the fish, quantum of the i-th fish The initial quantum position of the first generation of fish is h=1,2,…,S,i=1,2,3,…,K1,where S is the maximum dimension of the quantum position vector, and any dimension of all quantum positions is a random number between [0,1]. The position of the fish is obtained by measuring the quantum position; if the i-th quantum in the k-th iteration The quantum position of the fish is i=1,2,3,…,K1,k∈{1,2,…,K2}, then the i-th quantum in the k-th iteration is measured The fish's position is i=1,2,…,K1,k∈{1,2,…,K2},the measurement rule is represents the i-th quantum The h-th dimension variable of the fish position, is a random number between [0,1], h=1,2,…,S, k∈{1,2,…,K2}.

[0021] Furthermore, in step 3, the quantum The fitness function values ​​for the fish position include:

[0022] The i-th quantum of the k-th generation Fish Position A sea-air integrated unmanned collaborative confrontation task allocation matrix is ​​mapped, and the mapping rule is: of Corresponding to the first row of the sea-air integrated unmanned collaborative confrontation task allocation matrix 1,1 ,x 1,2 ,…,x 1,M ; Corresponding to the second row of the sea-air integrated unmanned collaborative confrontation task allocation matrix 2,1 ,x 2,2 ,…,x 2,M ; and so on, Corresponding to the last row of the sea-air integrated unmanned collaborative confrontation task allocation matrix The task allocation matrix is ​​denoted as The maximum dimension S satisfies

[0023] The kth iteration of the i-th quantum Fish Position Mapping into the sea-air integrated unmanned collaborative confrontation task allocation matrix Get the i-th quantum of the k-th iteration The fitness function value of the fish i=1,2,…,K1; by comparing all quantum The fish fitness function value finds the quantum position corresponding to the maximum fitness value of the kth iteration as the optimal quantum The quantum position of fish

[0024] Furthermore, in step 4, the free search strategy is used to update the quantum The quantum position of the fish, determine the quantum of the i Is the fitness value of the fish greater than the fitness value of its experience position, i=1,2,3,…,K1, when the greater than condition is met, the i-th quantum The fish performs local search through step 5; otherwise, the i-th quantum The fish conducts a local search through step six including:

[0025] In the free search strategy, the i-th quantum The h-dimensional quantum rotation angle of the fish is i=1,2,…,K1,h=1,2,…,S,ε is a random integer between [1,K1],ζ i,h 、 is a random number between [0,1], is the εth quantum The h-th dimension variable of the fish position, is the optimal quantum for the kth iteration h-th dimension variable of fish position;

[0026] Using quantum revolving gate to update the i-th quantum in the free search strategy The h-dimensional quantum position of the fish: h=1,2,…,S,i=2,3,…,K1;according to the measurement rules, the quantum position Each dimension of the measurement gets the position Then calculate The fitness function value of And Perform assignment, and the assignment rules are as follows:

[0027] quantum When the fish attaches to the swordfish moving at high speed, it adjusts its position on the swordfish. The h-dimensional quantum rotation angle of the fish's empirical quantum position is Where h = 1, 2, ..., S, i = 2, 3, ..., K1, is the i-th quantum The h-th dimension variable of the fish's position in the previous generation, ξ i,h is a Gaussian random number with a mean of 0 and a variance of 1. The quantum revolving gate is used to update the i-th quantum The h-dimensional empirical quantum position of the fish I quantum The empirical quantum position of fish Measured as empirical position and calculate The fitness function value of Compare and The size of When it is larger, the i-th quantum Fish conduct local search through step 5; when Greater than or equal to When the i-th quantum Fish search locally through step six.

[0028] Furthermore, in step 5, we use the whale adsorption strategy to update the quantum The quantum positions of fish include:

[0029] when quantum When the fish's host changes from swordfish to whale, it feeds on the food residue on the whale. Fish adopt the strategy of attracting whales to update quantum The quantum position of the fish; the quantum position of the i-th fish in the whale adsorption strategy The h-dimensional quantum rotation angle of the fish is Where h = 1, 2, ..., S, i = 2, 3, ..., K1, for and The Euclidean distance of is a random number between [0,1];

[0030] Use the quantum revolving door to update the i-th quantum in the whale adsorption strategy The h-dimensional quantum position of the fish: Then calculate The fitness function value of And for the k+1th iteration, the i-th quantum The quantum position of fish Assign values. The assignment rules are as follows

[0031] Furthermore, in step 6, we use the off-host strategy to update the quantum The quantum positions of fish include:

[0032] when quantum When the fish's host is still a swordfish, and the swordfish has found a sea area with abundant food, the quantum The fish will leave the host to take food, at this time the quantum Fish use host-detachment strategy to update quantum The quantum position of the fish, the quantum of the i-th fish in the strategy of leaving the host The h-dimensional quantum rotation angle of the fish is h=1,2,…,S,i=2,3,…,K1,λ is the decision value, is a random number between [0,1];

[0033] Using quantum revolving door to update the i-th quantum in the host separation strategy The h-dimensional quantum position of the fish: Then calculate The fitness function value of And for the k+1th iteration, the i-th quantum The quantum position of fish Assign values. The assignment rules are as follows

[0034] The beneficial effects of the present invention: The present invention designs a quantum-based A method for allocating tasks for coordinated countermeasures of integrated sea-air unmanned intelligent devices based on the Fish Mechanism was developed. In a sea-air unmanned countermeasure scenario, a coordinated countermeasure strategy for shipborne drones and unmanned boats on large ships was designed. Taking into account multiple complex constraints and using mission efficiency as the objective function, countermeasure tasks were allocated to shipborne drones and unmanned boats, ultimately achieving a coordinated countermeasure mission against multiple targets.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) Few literatures have studied the problem of sea-air integrated unmanned intelligent equipment confrontation in the context of large-scale ship confrontation. This paper designs a sea-air integrated unmanned intelligent equipment collaborative confrontation task allocation model. The shipborne drones on large ships and the unmanned boats around the ships achieve sea-air collaborative unmanned confrontation, breaking the limitation of previous literature that only a few drones in the air can coordinate confrontation.

[0037] (2) The present invention adopts the penalty function method, which converts several constraints into penalty terms and substitutes them into the objective function, so that the constrained problem with multiple complex constraints is converted into an unconstrained problem, simplifying the model and reducing the difficulty of solving the problem.

[0038] (3) The present invention designs quantum coding The evolution mechanism of fish quantum position, a new quantum Fish mechanism method, quantum The free search strategy of fish is used for global search, the whale adsorption strategy and the host separation strategy are used for local search. The three strategies synergistically optimize the fitness function, overcoming the shortcomings of previous methods that are prone to local convergence, and also improving the optimization rate of the evolutionary mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The quantum-based Schematic diagram of the fish mechanism's sea-air integrated unmanned intelligent equipment collaborative confrontation task allocation method;

[0040] Figure 2 for The location distribution of unmanned intelligent equipment and sea surface targets;

[0041] Figure 3 for The location distribution of unmanned intelligent equipment and sea surface targets;

[0042] Figure 4 for The location distribution of unmanned intelligent equipment and sea surface targets;

[0043] Figure 5 for The objective function convergence curve. DETAILED DESCRIPTION

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] Combine Figure 1 , the present invention comprises the following steps:

[0046] Step 1: Establish a collaborative confrontation task allocation model for integrated sea and air unmanned intelligent equipment.

[0047] Assume that there are N shipborne drones and The surface unmanned boat can perform confrontation missions, and the collection of sea-air integrated unmanned intelligent equipment is defined as Among them, the ship-borne UAV U n The attribute set is U n ={v n ,l n ,w n ,r n}, v n For shipborne drones U n The sailing speed, l n For shipborne drones U nThe initial position of the large ship, w n For shipborne drones U n The amount of ammunition carried, r n For shipborne drones U n Unmanned surface boat The attribute set is Unmanned surface vehicle The sailing speed is Unmanned surface vehicle The initial position of Unmanned surface vehicle The amount of ammunition carried, Unmanned surface vehicle Assuming that the enemy has M sea surface targets, the set of sea surface targets is defined as T = {T1, T2, ..., T M}, where T M is the Mth sea surface target.

[0048] The task allocation matrix of sea-air integrated unmanned collaborative confrontation is: in, When x n,m =1 means shipborne UAV U n Attack surface targets T m , m=1,2,…,M,x n,m =0 means shipborne UAV U n Do not attack surface targets T m ; When x n,m =1 means unmanned surface boat Attack surface targets T m , x n,m =0 means unmanned surface boat Do not attack surface targets T m .

[0049] Establishing the maximization objective function for the coordinated countermeasure task allocation of integrated sea and air unmanned intelligent equipment Where m=1,2,…,M. E(·) is the judgment function. , the function returns a value of 1, and When , the function returns the value 0. C1 is the mission constraint penalty term, C2 is the ammunition constraint penalty term, C3 is the range constraint penalty term, and α1, α2, and α3 are the weight factors of the constraint penalty terms C1, C2, and C3 respectively.

[0050] The established model needs to meet three constraints, namely mission constraints, ammunition constraints and range constraints. Mission constraints are m=1,2,…,M, indicating that at most one shipborne UAV or one surface unmanned boat can attack the surface target T m The ammunition constraints for shipborne drones are: Indicates shipborne drone U n The number of sea targets attacked cannot exceed the amount of ammunition carried; the ammunition constraint of the surface unmanned boat is Unmanned surface vehicle The number of sea targets attacked cannot exceed the amount of ammunition carried. The range constraint of the shipborne UAV is D n ≤r n , Among them, D n For shipborne drones U n Assume that the shipborne UAV U n Attack sea targets T1, T2 and T3 in sequence. At this time, the shipborne drone U n The total flight distance is D n =d 0,1 +d 1,2 +d 2,3 +d 0,3 , d 0,1 is the distance between the large ship and the sea surface target T1, d 1,2 is the distance between the sea surface target T1 and the sea surface target T2, d 2,3 is the distance between the sea surface target T2 and the sea surface target T3, d 0,3 is the distance between the large ship position and the sea surface target T3, due to the shipborne drone U n After completing all its missions, it has to return home, so the ship-borne UAV U n The total flight distance includes the return distance. The range constraint of the surface unmanned vehicle is Unmanned surface vehicle Assuming that the surface unmanned boat Attack the sea targets T1, T2 and T3 in sequence. The total sailing distance of the surface unmanned boat is Unmanned surface vehicle The distance between the initial position and the sea surface target T1, d 1,2 is the distance between the sea surface target T1 and the sea surface target T2, d 2,3 is the distance between sea surface target T2 and sea surface target T3.

[0051] Convert task constraints into task constraint penalties Where |·| is the absolute value function. The ammunition constraints of shipborne UAVs and surface unmanned boats are converted into ammunition constraint penalty terms Convert the range constraints of shipborne UAVs and surface unmanned boats into range constraint penalty items E(·) is the judgment function. n ,r n ) as an example, if D n ≥r n If the function is set to true, it returns 1; otherwise, it returns 0.

[0052] Step 2: Initial quantum The quantum position of the fish and set the parameters.

[0053] Set the population size to K1 and the maximum number of iterations to K2. In the initial population, the random initial quantum The quantum position of the fish, quantum of the i-th fish The initial quantum position of the first generation of fish is h=1,2,…,S,i=1,2,3,…,K1,where S is the maximum dimension of the quantum position vector, and any dimension of all quantum positions is a random number between [0,1]. The position of the fish can be obtained by measuring the quantum position. If the i-th quantum in the k-th iteration The quantum position of the fish is i=1,2,3,…,K1,k∈{1,2,…,K2}, then the i-th quantum in the k-th iteration can be measured The fish's position is i=1,2,…,K1,k∈{1,2,…,K2},the measurement rule is represents the i-th quantum The h-th dimension variable of the fish position, is a random number between [0,1], h=1,2,…,S, k∈{1,2,…,K2}.

[0054] Step 3: Calculate quantum Fitness function value of the fish position.

[0055] The i-th quantum of the k-th generation Fish Position A sea-air integrated unmanned collaborative confrontation task allocation matrix is ​​mapped. The specific mapping rules are: of Corresponding to the first row of the sea-air integrated unmanned collaborative confrontation task allocation matrix Corresponding to the second row of the sea-air integrated unmanned collaborative confrontation task allocation matrix 2,1 ,x 2,2 ,…,x2,M ; and so on, Corresponding to the last row of the sea-air integrated unmanned collaborative confrontation task allocation matrix The task allocation matrix is ​​denoted as Therefore, the maximum dimension S must satisfy

[0056] The kth iteration of the i-th quantum Fish Position Mapping into the sea-air integrated unmanned collaborative confrontation task allocation matrix Get the i-th quantum of the k-th iteration The fitness function value of the fish i=1,2,…,K1. By comparing all quantum The fish fitness function value finds the quantum position corresponding to the maximum fitness value of the kth iteration as the optimal quantum The quantum position of fish

[0057] Step 4: Update the quantum using the free search strategy The quantum position of fish.

[0058] Update the quantum according to the free search strategy The quantum position of the fish, the quantum of the i-th fish in the free search strategy The h-dimensional quantum rotation angle of the fish is i=1,2,…,K1,h=1,2,…,S,ε is a random integer between [1,K1],ζ i,h 、 is a random number between [0,1], is the εth quantum The h-th dimension variable of the fish position, is the optimal quantum for the kth iteration h-th dimension variable of fish position.

[0059] Using quantum revolving gate to update the i-th quantum in the free search strategy The h-dimensional quantum position of the fish: h=1,2,…,S,i=2,3,…,K1。 According to the measurement rules, the quantum position Each dimension of the measurement gets the position Then calculate The fitness function value of And Assign values. The assignment rules are as follows quantum When the fish attaches to the swordfish moving at high speed, it will also adjust its position on the swordfish. The h-dimensional quantum rotation angle of the fish's empirical quantum position is Where h = 1, 2, ..., S, i = 2, 3, ..., K1, is the i-th quantum The h-th dimension variable of the fish's position in the previous generation, ξ i,h is a Gaussian random number with a mean of 0 and a variance of 1. Use the quantum revolving gate to update the i-th quantum The h-dimensional empirical quantum position of the fish I quantum The empirical quantum position of fish Measured as empirical position and calculate The fitness function value of Compare and The size of When it is larger, the i-th quantum Fish conduct local search through step 5; when Greater than or equal to When the i-th quantum Fish search locally through step six.

[0060] Step 5: Update quantum using the whale adsorption strategy The quantum position of fish.

[0061] when quantum When the fish's host changes from swordfish to whale, it will feed on the food residue on the whale. Fish adopt the strategy of attracting whales to update quantum The quantum position of the fish. In the whale adsorption strategy, the i-th quantum The h-dimensional quantum rotation angle of the fish is Where h = 1, 2, ..., S, i = 2, 3, ..., K1, for and The Euclidean distance of is a random number between [0,1].

[0062] Use the quantum revolving door to update the i-th quantum in the whale adsorption strategy The h-dimensional quantum position of the fish: Then calculate The fitness function value of And for the k+1th iteration, the i-th quantum The quantum position of fish Assign values. The assignment rules are as follows

[0063] Step 6: Update quantum using the off-host strategy The quantum position of fish.

[0064] when quantum When the fish's host is still a swordfish, and the swordfish has found a sea area with abundant food, the quantum The fish will leave the host to eat food. Fish use host-detachment strategy to update quantum The quantum position of the fish. In the strategy of leaving the host, the quantum of the i-th fish is The h-dimensional quantum rotation angle of the fish is h=1,2,…,S,i=2,3,…,K1,λ is the decision value, is a random number between [0,1].

[0065] Using quantum revolving door to update the i-th quantum in the host separation strategy The h-dimensional quantum position of the fish: Then calculate The fitness function value of And for the k+1th iteration, the i-th quantum The quantum position of fish Assign values. The assignment rules are as follows

[0066] Step 7: Determine whether quantum The maximum number of iterations of the fish is K2, and the iteration is terminated, and the optimal quantum The position of the fish is mapped to the sea-air integrated unmanned collaborative confrontation task allocation matrix and output; otherwise, let k = k + 1, and find the quantum position corresponding to the maximum fitness value of the k + 1th iteration as the optimal quantum The quantum position of fish Continue to step 4.

[0067] The present invention will be further described below with reference to specific parameters.

[0068] The quantum The fish optimization method is QRO, and the discrete pigeon swarm optimization method is denoted as DPIO. To investigate the performance of the multi-UAV SEAD mission planning method using the Fish Mechanism, three simulation tests and two convergence curve simulation plots were conducted. The population size was set to K1 = 100, λ = 0.2, α = 0.01, α1 = -1, α2 = -1, and α3 = -1. Targets were randomly distributed within a 5 km x 5 km area. All shipborne UAVs and surface unmanned vehicles were assumed to travel at a constant speed in meters per second. The position of the large vessel was (2500, 0) in meters. The mission execution time for each surface target was 10 seconds. Table 1 lists the attribute parameters of the shipborne UAVs.

[0069] Table 1 Attribute parameters of shipborne UAV

[0070]

[0071] Table 2 gives the attribute parameters of the surface unmanned vehicle.

[0072] Table 2 Attribute parameters of surface unmanned vehicle

[0073]

[0074] In the first set of experiments, N = 2 shipborne drones and The surface unmanned boats perform the task of confronting M=9 sea surface targets. The shipborne drone information is numbered 1 and 2 in Table 1, and the surface unmanned boat information is numbered 1 and 2 in Table 2. The target positions are as follows: Figure 2 As shown in Figure 3, the population size is set to K1 = 100 and the number of iterations is set to K2 = 500. Table 3 shows the task planning solutions obtained by the optimal solutions of the QRO and GA methods. It can be seen that the optimal task allocation solution generated by the QRO method is more reasonable than that of the DPIO method. This task allocation solution groups several adjacent tasks, such as surface targets T2 and T6, and T1 and T8, to minimize the navigation distance of the unmanned intelligent device.

[0075] Table 3 Task allocation solutions measured by the optimal solutions of the QRO method and the DPIO method

[0076]

[0077]

[0078] In the second set of experiments, N = 4 shipborne drones and The surface unmanned boats perform the task of confronting M=18 sea surface targets. The shipborne drone information is numbered 1, 2, 3 and 4 in Table 1, and the surface unmanned boat information is numbered 1 and 2 in Table 2. The target positions are as follows: Figure 3 As shown in Figure 4, the population size is set to K1 = 100 and the number of iterations is set to K2 = 500. Table 4 shows the task planning solutions measured by the optimal solutions of the QRO method and the DPIO method. It can be seen that the optimal task allocation matrix generated by the DPIO method does not meet the constraints, and therefore the optimal solution is much smaller than the optimal solution generated by the QRO method.

[0079] Table 4. Task planning schemes measured by the optimal solutions of the QRO method and the DPIO method

[0080]

[0081] In the third set of experiments, N = 8 shipborne drones and The surface unmanned boats perform the task of confronting M=30 sea surface targets. The information of shipborne drones is shown in Table 1, the information of surface unmanned boats is shown in Table 2, and the target positions are shown in Table 3. Figure 4 As shown in the figure. The population size is set to K1 = 100 and the number of iterations is set to K2 = 500. Table 5 shows the task planning solutions measured by the optimal solutions of the QRO method and the DPIO method. As the task scale gradually increases, the gap between the objective function values ​​of the two methods also becomes increasingly larger. The task planning solution measured by the optimal solution of the DPIO method even shows that a task is repeatedly executed by multiple unmanned intelligent devices. However, the task planning solution measured by the optimal solution of the QRO method can still meet the three task constraint penalty terms, and the optimal solution can basically reach the critical optimal value.

[0082] Table 5 Task planning solutions measured by the optimal solutions of the QRO and DPIO methods

[0083]

[0084] In order to further verify the convergence of the QRO method, the DPIO method is selected for comparative simulation. The convergence analysis is as follows: Figure 5 Figure 5 When N=4, On a large scale, we set the number of iterations to K2 = 500, the population size to K1 = 100, and conducted 100 independent repeated experiments. The simulation results were averaged. It can be seen that the convergence performance of the QRO method is significantly better than that of the DPIO method.

Claims

1. A method for allocating tasks for coordinated countermeasures of integrated sea and air unmanned intelligent equipment, characterized in that: The following steps are involved: Step 1: Establish a task allocation model for coordinated countermeasures of integrated sea and air unmanned intelligent equipment; There are N ship-borne drones and Surface unmanned boats can perform confrontation tasks. If M enemy surface targets are detected, the sea-air integrated unmanned collaborative confrontation task allocation matrix is: in, When x n,m =1 means shipborne UAV U n Attack surface targets T m , m=1,2,…,M,x n,m =0 means shipborne UAV U n Do not attack surface targets T m ; When x n,m =1 means unmanned surface boat Attack surface targets T m , x n,m =0 means unmanned surface boat Do not attack surface targets T m ; Step 2: Initial quantum The quantum position of the fish and set the parameters; Step 3: Calculate quantum The fitness function value of the fish position; Step 4: Update the quantum using the free search strategy The quantum position of the fish, determine the quantum of the i Is the fitness value of the fish greater than the fitness value of its experience position, i=1,2,3,…,K1, when the greater than condition is met, the i-th quantum The fish performs local search through step 5; otherwise, the i-th quantum The fish conducts a local search through step six; Step 5: Update quantum using the whale adsorption strategy The quantum position of the fish, perform step seven; when quantum When the fish's host changes from swordfish to whale, it feeds on the food residue on the whale. Fish adopt the strategy of attracting whales to update quantum The quantum position of the fish; the quantum position of the i-th fish in the whale adsorption strategy The h-dimensional quantum rotation angle of the fish is Where h = 1, 2, ..., S, i = 2, 3, ..., K1, for and The Euclidean distance of is a random number between [0,1]; To measure the i-th quantum in the k-th iteration The location of the fish, K1 is the population size, Use the quantum revolving door to update the i-th quantum in the whale adsorption strategy The h-dimensional quantum position of the fish: Using quantum revolving gate to update the i-th quantum in the free search strategy The h-dimensional quantum position of the fish, and then calculate The fitness function value of And for the k+1th iteration, the i-th quantum The quantum position of fish Assign values. The assignment rules are as follows Step 6: Update quantum using the off-host strategy The quantum position of the fish, perform step seven; when quantum When the fish's host is still a swordfish, and the swordfish has found a sea area with abundant food, the quantum The fish will leave the host to take food, at this time the quantum Fish use host-detachment strategy to update quantum The quantum position of the fish, the quantum of the i-th fish in the strategy of leaving the host The h-dimensional quantum rotation angle of the fish is λ is the decision value, is a random number between [0,1], is the optimal quantum for the kth iteration h-th dimension variable of fish position; Using quantum revolving door to update the i-th quantum in the host separation strategy The h-dimensional quantum position of the fish: Then calculate The fitness function value of And for the k+1th iteration, the i-th quantum The quantum position of fish Assign values. The assignment rules are as follows Step 7: Determine whether quantum The maximum number of iterations of the fish is K2, and the iteration is terminated, and the optimal quantum The position of the fish is mapped to the sea-air integrated unmanned collaborative confrontation task allocation matrix and output; otherwise, let the number of iterations k = k+1, and find the quantum position corresponding to the maximum fitness value of the k+1th iteration as the optimal quantum The quantum position of fish Continue to step 4.

2. The method for allocating tasks for coordinated countermeasures of integrated sea-air unmanned intelligent equipment according to claim 1, characterized in that: Step 1 of establishing a sea-air integrated unmanned intelligent equipment collaborative confrontation task allocation model includes: Assume that there are N shipborne drones and The surface unmanned boat can perform confrontation missions, and the collection of sea-air integrated unmanned intelligent equipment is defined as Among them, the ship-borne UAV U n The attribute set is v n For shipborne drones U n The sailing speed, l n For shipborne drones U n The initial position of the large ship, w n For shipborne drones U n The amount of ammunition carried, r n For shipborne drones U n Range; Unmanned Surface Vehicle The attribute set is Unmanned surface vehicle The sailing speed is Unmanned surface vehicle The initial position of Unmanned surface vehicle The amount of ammunition carried, Unmanned surface vehicle Assuming that the enemy has M sea surface targets, the set of sea surface targets is defined as T = {T1, T2, ..., T M }, where T M is the Mth sea surface target; Establishing the maximization objective function for the coordinated countermeasure task allocation of integrated sea and air unmanned intelligent equipment Where m=1,2,…,M; E(·) is the judgment function. , the function returns a value of 1, and When , the function returns the value 0; C1 is the mission constraint penalty item, C2 is the ammunition constraint penalty item, C3 is the range constraint penalty item, α1, α2 and α3 are the weight factors of the constraint penalty items C1, C2 and C3 respectively; The established model needs to meet three constraints, namely mission constraints, ammunition constraints and range constraints; the mission constraints are Indicates that at most one shipborne drone or one surface unmanned boat attacks the sea surface target T m ; The ammunition constraints of shipborne UAVs are Indicates shipborne drone U n The number of sea targets attacked cannot exceed the amount of ammunition carried; the ammunition constraint of the surface unmanned boat is Unmanned surface vehicle The number of sea targets attacked cannot exceed the amount of ammunition carried; the range constraint of the shipborne UAV is Among them, D n For shipborne drones U n The total flight distance; assuming that the shipborne UAV U n Attack sea targets T1, T2 and T3 in sequence. At this time, the shipborne drone U n The total flight distance is D n =d 0,1 +d 1,2 +d 2,3 +d 0,3 , d 0,1 is the distance between the large ship and the sea surface target T1, d 1,2 is the distance between the sea surface target T1 and the sea surface target T2, d 2,3 is the distance between the sea surface target T2 and the sea surface target T3, d 0,3 is the distance between the large ship and the sea target T3, the shipborne drone U n The total flight distance includes the return distance; the range constraint of the surface unmanned vehicle is Unmanned surface vehicle The total sailing distance; assuming that the surface unmanned boat Attack the sea targets T1, T2 and T3 in sequence. The total sailing distance of the surface unmanned boat is Unmanned surface vehicle The distance between the initial position and the sea surface target T1, d 1,2 is the distance between the sea surface target T1 and the sea surface target T2, d 2,3 is the distance between the sea surface target T2 and the sea surface target T3; Convert task constraints into task constraint penalties Where |·| is the absolute value function, which converts the ammunition constraints of shipborne UAVs and surface unmanned boats into ammunition constraint penalty terms. Convert the range constraints of shipborne UAVs and surface unmanned boats into range constraint penalty items E(·) is the judgment function, for E(D n ,r n ), if D n ≥r n If the function is set to true, it returns 1; otherwise, it returns 0.

3. The method for allocating tasks for coordinated countermeasures of integrated sea-air unmanned intelligent equipment according to claim 1, characterized in that: The initial quantum The quantum position of the fish and the parameters to be set include: Set the population size to K1, the maximum number of iterations to K2, and the random initial quantum in the initial population. The quantum position of the fish, quantum of the i-th fish The initial quantum position of the first generation of fish is Among them, S is the maximum dimension of the quantum position vector, and any dimension of all quantum positions is a random number between [0,1]. The position of the fish is obtained by measuring the quantum position; if the i-th quantum in the k-th iteration The quantum position of the fish is Then the measurement results are the i-th quantum in the k-th iteration The fish's position is The measurement rule is represents the i-th quantum The h-th dimension variable of the fish position, is a random number between [0,1], h=1,2,…,S, k∈{1,2,…,K2}.

4. The method for allocating tasks for coordinated countermeasures of integrated sea-air unmanned intelligent equipment according to claim 1, characterized in that: Step 3: Calculate the quantum The fitness function values ​​for the fish position include: The i-th quantum of the k-th generation Fish Position A sea-air integrated unmanned collaborative confrontation task allocation matrix is ​​mapped, and the mapping rule is: of Corresponding to the first row of the sea-air integrated unmanned collaborative confrontation task allocation matrix 1,1 ,x 1,2 ,…,x 1,M ; Corresponding to the second row of the sea-air integrated unmanned collaborative confrontation task allocation matrix 2,1 ,x 2,2 ,…,x 2,M ; and so on, Corresponding to the last row of the sea-air integrated unmanned collaborative confrontation task allocation matrix The task allocation matrix is ​​denoted as The maximum dimension S satisfies The kth iteration of the i-th quantum Fish Position Mapping into the sea-air integrated unmanned collaborative confrontation task allocation matrix Get the i-th quantum of the k-th iteration The fitness function value of the fish By comparing all quanta The fish fitness function value finds the quantum position corresponding to the maximum fitness value of the kth iteration as the optimal quantum The quantum position of fish 5. The method for allocating tasks for coordinated countermeasures of integrated sea-air unmanned intelligent equipment according to claim 1 is characterized by: Step 4 describes the update of quantum using the free search strategy The quantum position of the fish, determine the quantum of the i Is the fitness value of the fish greater than the fitness value of its experience position, i=1,2,3,…,K1, when the greater than condition is met, the i-th quantum The fish performs local search through step 5; otherwise, the i-th quantum The fish conducts a local search through step six including: In the free search strategy, the i-th quantum The h-dimensional quantum rotation angle of the fish is ε is a random integer between [1,K1], ζ i,h 、 is a random number between [0,1], is the εth quantum The h-th dimension variable of the fish position, is the optimal quantum for the kth iteration h-th dimension variable of fish position; Using quantum revolving gate to update the i-th quantum in the free search strategy The h-dimensional quantum position of the fish: According to the measurement rules, the quantum position Each dimension of the measurement gets the position Then calculate The fitness function value of And Perform assignment, and the assignment rules are as follows: quantum When the fish attaches to the swordfish moving at high speed, it adjusts its position on the swordfish. The h-dimensional quantum rotation angle of the fish's empirical quantum position is Where h = 1, 2, ..., S, i = 2, 3, ..., K1, is the i-th quantum The h-th dimension variable of the fish's position in the previous generation, ξ i,h is a Gaussian random number with a mean of 0 and a variance of 1. The quantum revolving gate is used to update the i-th quantum The h-dimensional empirical quantum position of the fish I quantum The empirical quantum position of fish Measured as empirical position and calculate The fitness function value of Compare and The size of When it is larger, the i-th quantum Fish conduct local search through step 5; when Greater than or equal to When the i-th quantum Fish search locally through step six.

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