Aircraft-ship collaborative task allocation method based on quantum physics optimization mechanism

Abstract

The present invention provides a method for allocating collaborative tasks between aircraft and ships based on a quantum slingshot optimization mechanism. The method uses the geographic locations of all targets within a certain sea area and the corresponding airspace as prior knowledge and employs a fuzzy C-means clustering method to intelligently obtain the platform's designated navigation marks. The platform, equipped with multiple drones and unmanned boats, sails toward a designated navigation mark and temporarily suspends navigation at the designated navigation mark. Taking into account various constraints and based on the quantum slingshot optimization mechanism, multiple drones are launched into the air to collaboratively perform designated tasks on several airspace targets and then return home. Multiple unmanned boats are launched into the sea to collaboratively perform designated tasks on several sea area targets and then return home. The platform then sails toward the next designated navigation mark until it reaches its destination. Simulation experiments have demonstrated the effectiveness of the method for allocating collaborative tasks between aircraft and ships based on the quantum slingshot optimization mechanism, and it can be applied in actual engineering.

Description

Technical Field

[0001] The present invention relates to a method for allocating drone-ship collaborative tasks based on a quantum physics optimization mechanism, and belongs to the field of collaborative control of drones and unmanned ships. Background Art

[0002] Vehicle-vessel collaboration utilizes a mobile control platform as a launch and logistics platform for drones and unmanned vessels, and uses the drones and unmanned vessels as auxiliary reconnaissance, strike, tracking, or assessment tools for the mobile control platform. While ensuring the operational and survivability of the drones and unmanned vessels, the three platforms collaborate to maximize the effectiveness of the collaborative operation. Within a specific sea area and corresponding airspace, a platform equipped with multiple drones and unmanned vessels navigates from a starting position. When it reaches a designated beacon, it pauses. Multiple drones launch into the air to coordinate and execute a mission against the airspace target. Multiple unmanned vessels descend to the sea to coordinate and execute a mission against the sea target. Once all drones and unmanned vessels have returned, the platform sails to the next designated beacon until it reaches its destination. Vehicle-vessel collaboration encompasses both multi-machine and multi-vessel collaboration, and still requires consideration of five fundamental constraints: mission time, mission sequencing, vehicle-vessel performance, route feasibility, and multi-machine / multi-vessel coordination.

[0003] In recent years, some scholars have conducted some research on the method of aircraft-ship collaborative task allocation. Ma Huawei et al. (Ma Huawei, Zhu Yimin, Hu Xiaoxuan. Unmanned aerial vehicle (UAV)-ship-aircraft collaborative task planning based on particle swarm algorithm [J]. Systems Engineering and Electronics, 2016, 38(07): 1583-1588.) took multi-aircraft collaboration as the research background, constructed a multi-aircraft collaborative task allocation model and the corresponding cost function, and finally used the adaptive particle swarm algorithm to solve the multi-aircraft collaborative task allocation scheme at each established navigation mark. It is worth noting that all established navigation marks in the model need to be pre-set rather than intelligently obtained. Lu Fengling (Lu Fengling. Unmanned aerial vehicle (UAV)-ship-aircraft collaborative task planning based on ant colony algorithm [J]. Ship Science and Technology, 2019, 41(18): 67-69.) also took multi-aircraft collaboration as the research background, mainly solving the path optimization problem during the return process of the UAV. Wang Shupeng et al. (Wang Shupeng, Xu Wang, Liu Xiangde, Deng Xiaolong. Multi-UAV collaborative task allocation based on adaptive genetic algorithm [J]. Electronic Information Countermeasures Technology, 2021, 36(01): 59-64.) used an adaptive genetic algorithm to solve the multi-UAV collaborative task allocation scheme. Zhou Jing (Zhou Jing. Research on target allocation algorithm in multi-unmanned ship collaborative naval warfare [J]. Modern Navigation, 2021, 12(03): 210-212.) used the classic Hungarian algorithm to solve the target allocation scheme for multi-unmanned ship collaborative strikes. The search results of existing literature show that there are relatively few research results related to aircraft-ship collaborative task allocation, while there are relatively more research results related to multi-UAV or multi-unmanned ship collaborative task allocation. In addition, the swarm intelligence optimization algorithms used in the existing multi-machine collaboration and multi-ship collaboration methods still have problems such as slow convergence speed, poor convergence accuracy, and easy to fall into local extreme values. Summary of the Invention

[0004] To address the shortcomings and deficiencies of existing methods, the present invention designs a method for aircraft-vessel collaborative task allocation based on a quantum swarm optimization mechanism. Using the geographic location of all targets within a specific sea area and the corresponding airspace as prior knowledge, a fuzzy C-means clustering method is used to intelligently retrieve the platform's designated navigation marks. The platform, equipped with multiple drones and unmanned boats, navigates toward a designated navigation mark, pausing at that mark. Taking into account various constraints and based on the quantum swarm optimization mechanism, multiple drones take off to collaboratively perform designated tasks on several airspace targets before returning to their destination. Multiple unmanned boats then descend to collaboratively perform designated tasks on several sea area targets before returning to their destination. The platform then sails toward the next designated navigation mark until it reaches its destination.

[0005] The purpose of the present invention is achieved as follows: Step 1: Establish a machine-ship collaborative task allocation model.

[0006] Set the platform's property set in, is the starting position of the platform, The platform destination location meets That is, the platform always keeps sailing on the sea level, N uav and N uuv are the number of drones and unmanned ships respectively, M uav and M uuv are the number of targets in airspace and sea respectively, K uav and K uuv are the number of air and sea missions respectively. Set the attribute set of the drone n′ in, is the navigation speed of the UAV n′, is the mission payload of UAV n′, n′=1,2,...,N uav ; Set up unmanned boat The attribute collection in, To carry unmanned ships The sailing speed, To carry unmanned ships mission load, Set the attribute set of the spatial target m′ in, is the geographical location of the airspace target m′, and satisfies m′=1,2,...,M uav ; Set sea area targets The attribute collection in, For maritime targets geographical location and meet Set the attribute set of the spatial task k′ in, is the fixed time required for the airspace mission k′ to be executed by the carried UAV, which is rigidly determined by the performance of the carried UAV. is the time interval constraint for airspace tasks, k′=1,2,...,K uav ; Set sea area tasks The attribute collection in, For sea missions The fixed time required for the carried unmanned ship to execute is determined by the performance of the carried unmanned ship. To limit the time interval of sea missions,

[0007] Step 2: Intelligently obtain all established sea level navigation marks based on the fuzzy C-means clustering method.

[0008] (1) Determine the number of navigation aids based on the number of airspace and sea targets Among them, λ beais the density factor of the navigation mark, round(·) is the numerical rounding function, M con =M uav +M uuv is the total number of targets.

[0009] (2) Initialize the fuzzy C-means clustering method and set the parameters. Set the maximum number of iterations to P, the iteration number label to an integer p, and the iteration convergence factor to λ fcm At the pth iteration, the location of the cluster center φ Target Membership degree to cluster center φ and satisfy the membership constraints φ=1,2,...,M bea , p=1,2,...,P. In the first iteration, p=1, the initial position of each cluster center is randomly generated in the operation area, and the initial membership degree is randomly generated under the membership constraint.

[0010] (3) Define and calculate the objective function of the fuzzy C-means clustering method. The objective function can be expressed as υ∈[1,+∞), where υ is the fuzzy factor, The target at the pth iteration The Euclidean distance from the cluster center φ. Note that when calculating the Euclidean distance between a target in an airspace or sea area and the cluster center, the position of the target projected onto the sea level is used.

[0011] (4) Update membership. Define the target The membership update formula for the cluster center φ is:

[0012] (5) Update the cluster center. The position update formula of the cluster center φ is defined as

[0013] (6) Iteration termination judgment. Judge whether the maximum number of iterations P is reached or whether If it is not reached or satisfied, set p = p + 1 and return to step (4); otherwise, output the location of each cluster center.

[0014] (7) Obtain all the established sea level beacons. First, fill the third dimension of each cluster center position with the value 0 to form the sea level beacon position; second, arrange the beacon numbers in two ways: in ascending order of the first dimension of the position and in ascending order of the second dimension, and calculate the total distance of the platform from the starting position to the destination position under the two arrangement methods; finally, determine the beacon arrangement method according to the principle of minimum total distance, and define the position of the beacon τ

[0015] (8) All airspace targets and sea area targets are classified as airspace targets and sea area targets at each navigation mark according to the closest distance, and the airspace target m′ at the navigation mark τ is defined. τ The location is Maritime targets The location is τ=1,2,...,M bea , M′ τ and are the number of targets in the airspace and sea area at the beacon τ respectively.

[0016] Step 3: The platform carries multiple drones and unmanned boats and sails from the starting position to the designated navigation mark.

[0017] Initialize the navigation mark number τ=1, the platform carries N uav drones and N uuv The unmanned ship starts from the starting position Towards this established navigation mark position Sailing.

[0018] Step 4: Construct the cost function for allocating the multi-aircraft and multi-ship collaborative tasks at this given beacon.

[0019] Define the cost function for multi-machine collaborative task allocation at a given beacon τ Define the cost function for multi-ship collaborative task allocation at a given beacon τ in, is the multi-machine collaborative task allocation matrix at the beacon τ, is the multi-ship collaborative task allocation matrix at the beacon τ, is the domain of integers, and are the k′th and k′th matrices of the multi-machine and multi-ship collaborative task allocation matrices, respectively. column matrix, and The drone n′ and the unmanned boat are respectively carried at the beacon τ Working time, and are the airspace task k′ and the sea task at the beacon τ respectively. In the airspace target m′ τ and maritime targets The execution time at λ t and λ c are the task time and task load constraint penalty factors respectively. is a numerical correction function. When the value is non-negative, no correction is made. When the value is negative, the correction is set to 0. Γ n′ (·)and is the matrix element number extraction function, and the element values ​​are n′ and Based on the multi-machine or multi-vessel collaborative task allocation matrix and the attributes of each drone or unmanned vessel, the operating time of each drone or unmanned vessel can be determined. The specific steps are as follows. For simplicity, only the multi-machine collaboration case is presented, but the multi-vessel collaboration case can be derived similarly.

[0020] (1) Airspace missions are numbered from 1 to K. uav The order is carried out by the UAVs in sequence, initially k′=1; the initial position mark of the UAV n′ at the initialization beacon τ End position mark Sailing time Detention time Airspace mission execution time Working time Initialize the airspace target m′ at the beacon τ τ The restricted time Initialize the airspace task k′ at the beacon τ and the airspace target m′ τ Execution time at

[0021] (2) The number of drones carried is from 1 to N. uav The current airspace task k′ is executed in the order of n′, and initially n′=1.

[0022] (3) Determine whether n′≤N uav If satisfied, continue execution; otherwise, go to step (7).

[0023] (4) Determine whether the UAV n′ needs to perform the current airspace mission k′, that is, determine whether If satisfied, initialize the number of executions of the drone n′ for the current airspace mission k′ Continue execution; otherwise, update the number of the carried UAV, set n′=n′+1, and return to step (3).

[0024] (5) Let the final position mark of the drone n′ be Let the navigation update time of the drone n′ be Let the retention time of the drone n′ be updated Let the airspace mission of the drone n′ be updated Let the operation update time of the drone n′ be Let the initial position mark of the drone n′ be The corresponding airspace target carrying the UAV n′ is restricted for a certain time Let the execution time of the current airspace task k′ at the corresponding airspace target be in, It is a function to extract the row value of matrix elements, and the element value is n′, and the elements are sorted by row. times; d(·) is the Euclidean distance between vectors.

[0025] (6) Determine whether the UAV n′ has completed all current airspace missions, that is, whether it satisfies If satisfied, update the number of the drone on board, set n'=n'+1, Return to step (3); otherwise, update the number of executions of the current airspace mission k′ by the UAV n′, and let Return to step (5).

[0026] (7) All UAVs on board return to the base, i.e. whether k′=K uav If satisfied, all the drones on board will return home, and the navigation time of the drone n′ will be updated Let the operation update time of UAV n′ be n′=1,2,...,N uav ; Otherwise, update the spatial task number, set k′=k′+1, and return to step (2).

[0027] Step 5: Initialize the quantum silk fish school and set parameters.

[0028] Set the size of quantum silk fish school to h, the silk fish school can be divided into a single male and multiple females, the maximum number of iterations Iteration number label At the g-th iteration, the quantum position of the i-th quantum fish in the u-dimensional search space is The j-th qubit of its quantum position j=1,2,...,u,g=1,2,...,G。 Apply the quantum silk fish optimization mechanism to solve the multi-machine collaborative task allocation matrix at the beacon τ, and let u=M′τK uav , solve the multi-ship collaborative task allocation matrix time In the initial generation, g=1, and each dimensional quantum bit of the quantum position of the initial generation quantum fish is initialized to a uniform random number in the interval [0,1].

[0029] Step 6: Define and calculate the estrogen level in the quantum silk fish.

[0030] At the gth iteration, each dimension of the quantum fish quantum position is mapped into the discrete solution space to obtain the mapping state of the quantum fish quantum position The mapping equation is defined as Among them, ceil(·) is the function of rounding up the value. The quantum fish optimization mechanism is used to solve the multi-machine collaborative task allocation matrix at the beacon τ. Then, the mapping state of the quantum position of the i-th quantum fish is transformed into the cost function of the multi-machine collaborative task allocation at the beacon τ to obtain the cost value Solving the multi-ship collaborative task allocation matrix The mapping state of the quantum position of the i-th quantum fish is transformed into the cost function of the multi-ship collaborative task allocation at the beacon τ to obtain the cost value Among them, the superscript Specifically refers to the matrix according to M′ τ ×K uav dimensional reconstruction, the superscript ° refers to the matrix according to The lower the cost value corresponding to the quantum position mapping state of the quantum silk fish, the lower the estrogen level in the quantum silk fish at that quantum position.

[0031] Step 7: Sort all the quantum silk fish according to the estrogen levels in their bodies and identify the male quantum silk fish.

[0032] Arrange all the quantum silk fish in order of estrogen levels from low to high, and define the quantum silk fish with the lowest estrogen level in the quantum silk fish group as male quantum silk fish, and the rest as female quantum silk fish.

[0033] Step 8: The quantum silk fish swims deterministically and randomly in turn, and uses a simulated quantum revolving gate to evolve the quantum state of the quantum silk fish during the swimming process.

[0034] (1) Deterministic movement: The update equation of the j-dimensional quantum position of the i-th quantum fish under deterministic movement is defined as follows: in, is the j-th dimension simulated quantum rotation angle of the i-th quantum silk fish under deterministic movement, is a uniform random number in the interval [0,1]. is a standard normal random number, i=1,2,...,h,j=1,2,...,u. The new quantum position generated by the i-th quantum silk fish after deterministic swimming

[0035] (2) Random movement: The update equation of the j-dimensional quantum position of the i-th quantum fish under random movement is defined as in, is the j-th simulated quantum rotation angle of the i-th quantum silk fish under random movement, is a uniform random number in the interval [0,1], μ1 and μ2 are random quantum fish labels, i=1,2,...,h, j=1,2,...,u. The new quantum position generated by the random swimming of the i-th quantum fish

[0036] Step 9: Apply the greedy selection strategy to determine the quantum position of the next generation of quantum fish.

[0037] Calculate the estrogen levels in all quantum fish at the original quantum position and the newly generated quantum position. The greedy selection of quantum positions with lower estrogen levels as the quantum positions of the next generation of quantum silk fish

[0038] Step 10: Evolution termination judgment, output multi-machine or multi-ship collaborative task allocation matrix.

[0039] Determine whether the maximum number of iterations G has been reached. If not, set g = g + 1 and return to step seven. Otherwise, terminate the mechanism evolution and transform the quantum position mapping state of the male quantum silk fish of the optimal generation of quantum silk fish into the corresponding dimension and output it as the multi-machine or multi-ship collaborative task allocation matrix.

[0040] Step 11: At this designated navigation mark, multiple unmanned aerial vehicles will be launched in coordination, and multiple unmanned boats will be launched in coordination.

[0041] The platform suspends navigation at the beacon τ, based on the multi-machine collaborative task allocation matrix, at most N uav The drone is launched into the air and aimed at the navigation mark τ M′ τ Airspace targets collaboratively execute K uav Return after airspace mission; Based on the multi-ship collaborative task allocation matrix, at most N uuv The unmanned boat went to sea to the navigation mark τ Coordinated execution of K sea area targets uuv Returned after the sea mission.

[0042] Step 12: Determine the platform's heading destination.

[0043] Determine whether τ=M bea If not, update the established navigation mark number, set τ = τ + 1, the platform carries multiple drones and unmanned boats to sail to the next established navigation mark, and return to step 4; otherwise, the platform will sail from the established navigation mark M bea Towards the destination Sailing.

[0044] Compared with existing technologies, the present invention offers the following advantages: Based on aircraft-vessel collaboration, the present invention uses a priori knowledge of the geographic locations of all targets within a specific sea area and the corresponding airspace, and employs a fuzzy C-means clustering method to intelligently determine the platform's designated navigational marks. The platform, equipped with multiple drones and unmanned boats, navigates toward a designated navigational mark, pausing navigation at the designated mark. Taking into account various constraints and based on a quantum fish optimization mechanism, multiple drones launch collaboratively to perform a designated airspace mission against several airspace targets before returning. Multiple unmanned boats launch collaboratively to perform a designated sea area mission against several sea area targets before returning. The platform then navigates to the next designated navigational mark until it reaches its destination. The quantum fish optimization mechanism designed in this invention is inspired by the transmutable physique and polygamous population of fish, and uses a simulated quantum revolving door to evolve the quantum fish's quantum state, resulting in rapid convergence and high accuracy. Simulation experiments demonstrate the effectiveness of the aircraft-vessel collaborative task allocation method based on the quantum fish optimization mechanism, which can be applied in practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the aircraft-ship collaborative task allocation method designed by the present invention based on the quantum fish optimization mechanism.

[0046] Figure 2 is a distribution map of airspace targets, sea area targets and established navigation marks.

[0047] Figure 3 It is the relationship curve between the cost function value of multi-machine collaborative task allocation and the number of evolutions of the quantum fish optimization mechanism.

[0048] Figure 4 It is the relationship curve between the cost function value of multi-ship collaborative task allocation and the number of evolutions of the quantum silk fish optimization mechanism.

[0049] Figure 5 is a curve showing the relationship between the cost function value of multi-machine collaborative task allocation and the number of evolutions of different optimization mechanisms.

[0050] Figure 6 is the relationship curve between the cost function value of multi-ship collaborative task allocation and the number of evolutions of different optimization mechanisms. DETAILED DESCRIPTION

[0051] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0052] like Figure 1 As shown, the present invention designs a method for allocating aircraft-ship collaborative tasks based on a quantum physics optimization mechanism, and the method comprises the following steps:

[0053] Step 1: Establish a collaborative task allocation model between aircraft and ships.

[0054] Set the platform's property set in, is the starting position of the platform, The platform destination location meets That is, the platform always keeps sailing on the sea level, N uav and N uuv are the number of drones and unmanned ships respectively, M uav and M uuv are the number of targets in airspace and sea respectively, K uav and K uuv are the number of air and sea missions respectively. Set the attribute set of the drone n′ in, is the navigation speed of the UAV n′, is the mission payload of UAV n′, n′=1,2,...,N uav ; Set up unmanned boat The attribute collection in, To carry unmanned ships The sailing speed, To carry unmanned ships mission load, Set the attribute set of the spatial target m′ in, is the geographical location of the airspace target m′, and satisfies Setting maritime targets The attribute collection in, For maritime targets geographical location and meet Set the attribute set of the spatial task k′ in, is the fixed time required for the airspace mission k′ to be executed by the carried UAV, which is rigidly determined by the performance of the carried UAV. is the time interval constraint for airspace tasks, k′=1,2,...,K uav ; Set sea area tasks The attribute collection in, For sea missions The fixed time required for the carried unmanned ship to execute is determined by the performance of the carried unmanned ship. To limit the time interval of sea missions,

[0055] Step 2: Intelligently obtain all established sea level navigation marks based on the fuzzy C-means clustering method.

[0056] (1) Determine the number of navigation aids based on the number of airspace and sea targets Among them, λ beais the density factor of the navigation mark, round(·) is the numerical rounding function, M con =M uav +M uuv is the total number of targets.

[0057] (2) Initialize the fuzzy C-means clustering method and set the parameters. Set the maximum number of iterations to P, the iteration number label to an integer p, and the iteration convergence factor to λ fcm At the pth iteration, the location of the cluster center φ Target Membership degree to cluster center φ and satisfy the membership constraints In the first iteration, p=1, the initial position of each cluster center is randomly generated in the operation area, and the initial membership degree is randomly generated under the membership constraint.

[0058] (3) Define and calculate the objective function of the fuzzy C-means clustering method. The objective function can be expressed as Among them, υ is the fuzzy factor, The target at the pth iteration The Euclidean distance from the cluster center φ. Note that when calculating the Euclidean distance between a target in an airspace or sea area and the cluster center, the position of the target projected onto the sea level is used.

[0059] (4) Update membership. Define the target The membership update formula for the cluster center φ is:

[0060] (5) Update the cluster center. The position update formula of the cluster center φ is defined as

[0061] (6) Iteration termination judgment. Judge whether the maximum number of iterations P is reached or whether If it is not reached or satisfied, set p = p + 1 and return to step (4); otherwise, output the location of each cluster center.

[0062] (7) Obtain all the established sea level beacons. First, fill the third dimension of each cluster center position with the value 0 to form the sea level beacon position; second, arrange the beacon numbers in two ways: in ascending order of the first dimension of the position and in ascending order of the second dimension, and calculate the total distance of the platform from the starting position to the destination position under the two arrangement methods; finally, determine the beacon arrangement method according to the principle of minimum total distance, and define the position of the beacon τ

[0063] (8) All airspace targets and sea area targets are classified as airspace targets and sea area targets at each navigation mark according to the closest distance, and the airspace target m′ at the navigation mark τ is defined. τ The location is Maritime targets The location is M′ τ and are the number of targets in the airspace and sea area at the beacon τ respectively.

[0064] Step 3: The platform carries multiple drones and unmanned boats and sails from the starting position to the designated navigation mark.

[0065] Initialize the navigation mark number τ=1, the platform carries N uav drones and N uuv The unmanned ship starts from the starting position Towards this established navigation mark position Sailing.

[0066] Step 4: Construct the cost function for allocating the multi-aircraft and multi-ship collaborative tasks at this given beacon.

[0067] Define the cost function for multi-machine collaborative task allocation at a given beacon τ Define the cost function for multi-ship collaborative task allocation at a given beacon τ in, is the multi-machine collaborative task allocation matrix at the beacon τ, is the multi-ship collaborative task allocation matrix at the beacon τ, is the domain of integers, and are the k′th and k′th matrices of the multi-machine and multi-ship collaborative task allocation matrices, respectively. column matrix, and The drone n′ and the unmanned boat are respectively carried at the beacon τ Working time, and are the airspace task k′ and the sea task at the beacon τ respectively. In the airspace target m′ τ and maritime targets The execution time at λ t and τ c are the task time and task load constraint penalty factors respectively. is a numerical correction function. When the value is non-negative, no correction is made. When the value is negative, the correction is set to 0. Γ n′ (·)and is the matrix element number extraction function, and the element values ​​are n′ and Based on the multi-machine or multi-vessel collaborative task allocation matrix and the attributes of each drone or unmanned vessel, the operating time of each drone or unmanned vessel can be determined. The specific steps are as follows. For simplicity, only the multi-machine collaboration case is presented, but the multi-vessel collaboration case can be derived similarly.

[0068] (1) Airspace missions are numbered from 1 to K. uav The order is carried out by the UAVs in sequence, initially k′=1; the initial position mark of the UAV n′ at the initialization beacon τ End position mark Sailing time Detention time Airspace mission execution time Working time Initialize the airspace target m′ at the beacon τ τ The restricted time Initialize the airspace task k′ at the beacon τ and the airspace target m′ τ Execution time at

[0069] (2) The number of drones carried is from 1 to N. uav The current airspace task k′ is executed in the order of n′, and initially n′=1.

[0070] (3) Determine whether n′≤N uav If satisfied, continue execution; otherwise, go to step (7).

[0071] (4) Determine whether the UAV n′ needs to perform the current airspace mission k′, that is, determine whether If satisfied, initialize the number of executions of the drone n′ for the current airspace mission k′ Continue execution; otherwise, update the number of the carried UAV to nHn′+1 and return to step (3).

[0072] (5) Let the final position mark of the drone n′ be Let the navigation update time of the drone n′ be Let the retention time of the drone n′ be updated Let the airspace mission of the drone n′ be updated Let the operation update time of the drone n′ be Let the initial position mark of the drone n′ be The corresponding airspace target carrying the UAV n′ is restricted for a certain time Let the execution time of the current airspace task k′ at the corresponding airspace target be in, It is a function to extract the row value of matrix elements, and the element value is n′, and the elements are sorted by row. times; d(·) is the Euclidean distance between vectors.

[0073] (6) Determine whether the UAV n′ has completed all current airspace missions, that is, whether it satisfies If satisfied, update the number of the drone on board, set n'=n'+1, Return to step (3); otherwise, update the number of executions of the current airspace mission k′ by the UAV n′, and let Return to step (5).

[0074] (7) All UAVs on board return to the base, i.e. whether k′=K uav If satisfied, all the drones on board will return home, and the navigation time of the drone n′ will be updated Let the operation update time of UAV n′ be n′=1,2,...,N uav ; Otherwise, update the spatial task number, set k′=k′+1, and return to step (2).

[0075] Step 5: Initialize the quantum silk fish school and set parameters.

[0076] Set the size of quantum silk fish school to h, the silk fish school can be divided into a single male and multiple females, the maximum number of iterations Iteration number label At the g-th iteration, the quantum position of the i-th quantum fish in the u-dimensional search space is The j-th qubit of its quantum position j=1,2,...,u,g=1,2,...,G。 Apply the quantum fish optimization mechanism to solve the multi-machine collaborative task allocation matrix at the beacon τ when u=M′ τ K uav , solve the multi-ship collaborative task allocation matrix time In the initial generation, g=1, and each dimensional quantum bit of the quantum position of the initial generation quantum fish is initialized to a uniform random number in the interval [0,1].

[0077] Step 6: Define and calculate the estrogen level in the quantum silk fish.

[0078] At the gth iteration, each dimension of the quantum fish quantum position is mapped into the discrete solution space to obtain the mapping state of the quantum fish quantum position The mapping equation is defined as Among them, ceil(·) is the function of rounding up the value. The quantum fish optimization mechanism is used to solve the multi-machine collaborative task allocation matrix at the beacon τ. Then, the mapping state of the quantum position of the i-th quantum fish is transformed into the cost function of the multi-machine collaborative task allocation at the beacon τ to obtain the cost value Solving the multi-ship collaborative task allocation matrix The mapping state of the quantum position of the i-th quantum fish is transformed into the cost function of the multi-ship collaborative task allocation at the beacon τ to obtain the cost value Among them, the superscript Specifically refers to the matrix according to M′ τ ×K uav dimensional reconstruction, the superscript ° refers to the matrix according to The lower the cost value corresponding to the quantum position mapping state of the quantum silk fish, the lower the estrogen level in the quantum silk fish at that quantum position.

[0079] Step 7: Sort all the quantum silk fish according to the estrogen levels in their bodies and identify the male quantum silk fish.

[0080] Arrange all the quantum silk fish in order of estrogen levels from low to high, and define the quantum silk fish with the lowest estrogen level in the quantum silk fish group as male quantum silk fish, and the rest as female quantum silk fish.

[0081] Step 8: The quantum silk fish swims deterministically and randomly in turn, and uses a simulated quantum revolving gate to evolve the quantum state of the quantum silk fish during the swimming process.

[0082] (1) Deterministic movement: The update equation of the j-dimensional quantum position of the i-th quantum fish under deterministic movement is defined as follows: in, is the j-th dimension simulated quantum rotation angle of the i-th quantum silk fish under deterministic movement, is a uniform random number in the interval [0,1]. is a standard normal random number, i=1,2,...,h,j=1,2,...,u. The new quantum position generated by the i-th quantum silk fish after deterministic swimming

[0083] (2) Random movement: The update equation of the j-dimensional quantum position of the i-th quantum fish under random movement is defined as in, is the j-th simulated quantum rotation angle of the i-th quantum silk fish under random movement, is a uniform random number in the interval [0,1], μ1 and μ2 are random quantum fish labels, i=1,2,...,h, j=1,2,...,u. The new quantum position generated by the random swimming of the i-th quantum fish

[0084] Step 9: Apply the greedy selection strategy to determine the quantum position of the next generation of quantum fish.

[0085] Calculate the estrogen levels in all quantum fish at the original quantum position and the newly generated quantum position. The greedy selection of quantum positions with lower estrogen levels as the quantum positions of the next generation of quantum silk fish

[0086] Step 10: Evolution termination judgment, output multi-machine or multi-ship collaborative task allocation matrix.

[0087] Determine whether the maximum number of iterations G has been reached. If not, set g = g + 1 and return to step seven. Otherwise, terminate the mechanism evolution and transform the quantum position mapping state of the male quantum silk fish of the optimal generation of quantum silk fish into the corresponding dimension and output it as the multi-machine or multi-ship collaborative task allocation matrix.

[0088] Step 11: At this designated navigation mark, multiple unmanned aerial vehicles will be launched in coordination, and multiple unmanned boats will be launched in coordination.

[0089] The platform suspends navigation at the beacon τ, based on the multi-machine collaborative task allocation matrix, at most N uav The drone is launched into the air and aimed at the navigation mark τ M′ τ Airspace targets collaboratively execute K uav Return after airspace mission; Based on the multi-ship collaborative task allocation matrix, at most N uuv The unmanned boat went to sea to the navigation mark τ Coordinated execution of K sea area targets uuv Returned after the sea mission.

[0090] Step 12: Determine the platform's heading destination.

[0091] Determine whether τ=M bea If not, update the established navigation mark number, set τ = τ + 1, the platform carries multiple drones and unmanned boats to sail to the next established navigation mark, and return to step 4; otherwise, the platform will sail from the established navigation mark M bea Towards the destination Sailing.

[0092] As shown in Figure 2, the platform carries 5 drones and 5 unmanned boats from the starting position. Start sailing at the destination and head towards the destination after passing through several established navigation marks During this period, the four airspace missions of reconnaissance, strike, tracking, and assessment were carried out against 50 airspace targets, and the three seaspace missions of reconnaissance, strike, and assessment were carried out against 50 seaspace targets. uav =5, N uuv =5, M uav =50, M uuv =50,K uav =4, K uuv =3,λbea =4,λ t =1,λ c =5, υ = 2. The attributes of the carried UAV are shown in Table 1, the attributes of the carried UAV are shown in Table 2, and the attributes of the airspace and sea area missions are shown in Table 3.

[0093] Table 1

[0094]

[0095] Table 2

[0096]

[0097] Table 3

[0098]

[0099] In Figures 5 and 6, the aircraft-ship collaborative task allocation method based on the quantum silk fish optimization mechanism is denoted as QSF; the aircraft-ship collaborative task allocation method based on the quantum gray wolf optimization mechanism is denoted as QGWA. Figures 5(a) and 5(b) correspond to the relationship curves between the cost function value of multi-aircraft collaborative task allocation at beacons 1 and 3, respectively, and the number of evolution times of different optimization mechanisms. Figures 6(a) and 6(b) correspond to the relationship curves between the cost function value of multi-ship collaborative task allocation at beacons 1 and 3, respectively, and the number of evolution times of different optimization mechanisms. In QSF, h = 20 and G = 100 are set; in QGWA, the quantum gray wolf group size is set to 20, and the maximum number of iterations is 100. The number of experiments is 50, and the experimental results are presented as statistical averages. As can be seen from Figures 2-6, the aircraft-ship collaborative task allocation method designed by this invention based on the quantum silk fish optimization mechanism is effective and can be applied in actual engineering.

Claims

1. The aircraft-ship collaborative task allocation method based on quantum physics optimization mechanism is characterized by: Here are the steps: Step 1: Establish a collaborative task allocation model between aircraft and ships; Step 2: Intelligently obtain all the established navigation marks on the sea level based on the fuzzy C-means clustering method; Step 3: The platform carries multiple drones and unmanned boats to sail from the starting position to the predetermined navigation mark; initialize the navigation mark number τ = 1, and the platform carries N uav drones and N uuv The unmanned ship starts from the starting position Towards this established navigation mark position sailing; Step 4: Construct the cost function for allocating the multi-aircraft and multi-ship collaborative tasks at the given beacon; Step 5: Initialize the quantum silk fish swarm and set parameters; Step 6: Define and calculate the estrogen level in the quantum silk fish; At the g-th iteration, each dimension of the quantum position of all quantum silk fish is mapped into the range of discrete solution space, and the mapping state of the quantum silk fish quantum position is obtained: The mapping equation is defined as: Where i = 1, 2, ..., h, j = 1, 2, ..., u, ceil(·) is the function for rounding up the value. is the mapping degree; Applying quantum silk fish optimization mechanism to solve the multi-machine collaborative task allocation matrix at the beacon τ Then, the mapping state of the quantum position of the i-th quantum fish is transformed into the cost function of the multi-machine collaborative task allocation at the beacon τ to obtain the cost value Solving the multi-ship collaborative task allocation matrix The mapping state of the quantum position of the i-th quantum fish is transformed into the cost function of the multi-ship collaborative task allocation at the beacon τ to obtain the cost value Among them, the superscript Specifically refers to the matrix according to M' τ ×K uav dimensional reconstruction, the superscript ° refers to the matrix according to Dimensional reconstruction, the reconstruction method is row first and then column; N uav and N uuv The numbers of drones and unmanned ships respectively; Step 7: Sort all the quantum silk fish according to their estrogen levels and determine the male quantum silk fish; arrange all the quantum silk fish in order from low to high according to their estrogen levels, and define the quantum silk fish with the lowest estrogen level in the quantum silk fish group as male quantum silk fish, and the rest as female quantum silk fish; Step 8: The quantum silk fish swims deterministically and stochastically in turn, and uses a simulated quantum revolving gate to evolve the quantum state of the quantum silk fish during the swimming process; (1) The update equation for the j-dimensional quantum position of the i-th quantum fish under deterministic movement is: in, is the j-th dimension simulated quantum rotation angle of the i-th quantum silk fish under deterministic movement, is a uniform random number in the interval [0,1]. is a standard normal random number; the new quantum position generated by the i-th quantum silk fish after deterministic swimming is: (2) The update equation of the j-dimensional quantum position of the i-th quantum fish under random movement is: in, is the j-th simulated quantum rotation angle of the i-th quantum silk fish under random movement, The uniform random number in the interval, μ1 and μ2 are the labels of random quantum silk fish, and the new quantum position generated by the random swimming of the i-th quantum silk fish is: Step 9: Apply a greedy selection strategy to determine the quantum position of the next generation of quantum silk fish; calculate the estrogen levels of all quantum silk fish at the original quantum position and the newly generated quantum position, and greedily select the quantum position with the lower estrogen level in the quantum position set as the quantum position of the next generation of quantum silk fish; Step 10: Evolution termination judgment, output of multi-machine or multi-ship collaborative task allocation matrix; Determine whether the maximum number of iterations G has been reached. If not, set g = g + 1 and return to step 7. Otherwise, terminate the mechanism evolution and transform the quantum position mapping state of the male quantum silk fish of the optimal generation of quantum silk fish into the corresponding dimension and output it as the multi-machine or multi-ship collaborative task allocation matrix. Step 11: At this designated navigation mark, multiple unmanned aerial vehicles are coordinated to take off, and multiple unmanned boats are coordinated to go to sea; The platform suspends navigation at the beacon τ, based on the multi-machine collaborative task allocation matrix, at most N uav A drone was launched to the navigation mark τ M τ ′ airspace targets collaboratively execute K uav Return after airspace mission; Based on the multi-ship collaborative task allocation matrix, at most N uuv The unmanned boat went to sea to the navigation mark τ Coordinated execution of K sea area targets uuv Return after the sea mission; Step 12: Determine the platform's heading destination position; Determine whether τ=M bea :If not satisfied, then update the established navigation mark number, set τ = τ + 1, the platform carries multiple drones and unmanned boats to sail to the next established navigation mark, and return to step 4; otherwise, the platform sails from the established navigation mark M bea Towards the destination Sailing.

2. The method for allocating aircraft-ship collaborative tasks based on quantum physics optimization mechanism according to claim 1 is characterized in that: Step 1 specifically includes: platform attribute set in, is the starting position of the platform, The platform destination location meets N uav and N uuv are the number of drones and unmanned ships respectively, M uav and M uuv are the number of targets in airspace and sea respectively, K uav and K uuv are the number of airspace and sea missions respectively; the attribute set of the drone n′ in, is the navigation speed of the UAV n′, is the mission payload of UAV n′, n′=1,2,...,N uav ; Equipped with unmanned boat The attribute collection in, To carry unmanned ships The sailing speed, To carry unmanned ships mission load, Attribute set of spatial target m′ in, is the geographical location of the airspace target m′, and satisfies m′=1,2,...,M uav ; Maritime target The attribute collection in, For maritime targets geographical location and meet Attribute set of spatial task k′ in, is the fixed time required for the airspace mission k′ to be executed by the carried UAV, which is rigidly determined by the performance of the carried UAV. is the time interval constraint for airspace tasks, k′=1,2,...,K uav ; Set sea area tasks The attribute collection in, For sea missions The fixed time required for the carried unmanned ship to execute is determined by the performance of the carried unmanned ship. To limit the time interval of sea missions, 3. The method for allocating aircraft-ship collaborative tasks based on quantum physics optimization mechanism according to claim 1 is characterized in that: Step 2 specifically includes: (1) Determine the number of navigation aids based on the number of airspace and sea targets Among them, λ bea is the density factor of the navigation mark, round(·) is the numerical rounding function, M con =M uav +M uuv is the total number of targets; (2) Initialize the fuzzy C-means clustering method and set the parameters; set the maximum number of iterations to P, the iteration number label to an integer p, and the iteration convergence factor to λ fcm ; At the pth iteration, the location of the cluster center φ Target Membership degree to cluster center φ and satisfy the membership constraints (3) Define and calculate the objective function of the fuzzy C-means clustering method as Among them, υ is the fuzzy factor, The target at the pth iteration Euclidean distance from the cluster center φ; (4) Update membership, target The membership update formula for the cluster center φ is: (5) Update the cluster center. The position update formula of the cluster center φ is: (6) Iteration termination judgment: judge whether the maximum number of iterations P is reached or satisfied If it is not reached or satisfied, set p = p + 1 and return to step (4); otherwise, output the location of each cluster center; (7) Obtain the positions of all established navigation marks and navigation marks τ on the sea level (8) Airspace target m at beacon τ τ The position of ′ is Maritime targets The location is M τ 'and are the number of targets in the airspace and sea area at the beacon τ respectively.

4. The method for allocating aircraft-ship collaborative tasks based on quantum physics optimization mechanism according to claim 1 is characterized in that: Step 4 specifically includes: multi-machine collaborative task allocation cost function at a given beacon τ: Cost function for multi-ship collaborative task allocation at a given beacon τ: in, is the multi-machine collaborative task allocation matrix at the beacon τ, is the multi-ship collaborative task allocation matrix at the beacon τ, is the domain of integers, and are the k′th and k′th matrices of the multi-machine and multi-ship collaborative task allocation matrices, respectively. column matrix, and The drone n′ and the unmanned boat are respectively carried at the beacon τ Working time, and are the airspace task k′ and the sea task at the beacon τ respectively. In the airspace target m τ ′ and sea area targets The execution time at λ t and λ c They are task time and task load constraint penalty factors respectively; is a numerical correction function. When the value is non-negative, no correction is made. When the value is negative, the correction is set to 0. Γ n′ (·)and is the matrix element number extraction function, and the element values ​​are n′ and The operation time of the carried UAVs or unmanned ships can be determined based on the multi-machine or multi-ship collaborative task allocation matrix and the attributes of each carried UAV or unmanned ship.

5. The method for allocating aircraft-ship collaborative tasks based on quantum physics optimization mechanism according to claim 1 is characterized in that: Step 5 specifically includes: setting the size of the quantum silk fish school to h, the silk fish school can be divided into a single male and multiple females, and the maximum number of iterations Iteration number label At the g-th iteration, the quantum position of the i-th quantum fish in the u-dimensional search space is The j-th qubit of its quantum position Applying the quantum fish optimization mechanism to solve the multi-machine collaborative task allocation matrix at the beacon τ, let u = M τ 'K uav , solve the multi-ship collaborative task allocation matrix time In the initial generation, g=1, and each dimensional quantum bit of the quantum position of the initial generation quantum fish is initialized to a uniform random number in the interval [0,1].

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