Formation cooperative task allocation method based on hierarchical mixed auction algorithm

Abstract

The invention provides a formation cooperative task allocation method based on a hierarchical mixed auction algorithm, and the method comprises the steps: carrying out the tactical decision of a manned aerial vehicle through quantifying the attribute values of tasks in a battlefield, determining the priorities of different types of tasks, and carrying out the task auction allocation solution through the corresponding types of unmanned aerial vehicles according to the task types, the solution rate of the algorithm and the rationality of the allocation scheme are improved, a target function is improved by introducing a coverage factor and a penalty term, the rationality of task allocation is optimized, and a task reallocation mechanism is formulated, that is, the manned aerial vehicle performs real-time dynamic reallocation on tasks according to the remaining resources of the unmanned aerial vehicle and the execution cost. According to the method, the advantage of human intelligence of manned aircraft pilots is exerted, the unmanned aerial vehicle most suitable for the selected task can be quickly found, dynamic task allocation is realized in a complex and changeable environment, the task load of the unmanned aerial vehicles is balanced, and the unmanned aerial vehicles in an unmanned aerial vehicle cluster are used as reasonably as possible.

Description

Technical Field

[0001] The present invention relates to the field of manned / unmanned coordinated combat and combat decision-making, and belongs to a manned / unmanned aerial vehicle task allocation method based on a hierarchical decision-making mechanism and a contract network auction algorithm in a multi-aircraft coordinated air combat process. Background Art

[0002] As far as the current technical level of UAVs is concerned, when they perform tasks in a dynamic and complex environment, they still have problems such as poor autonomous cognitive ability, susceptible to interference in communication, and insufficient reliability. Therefore, a manned / unmanned aerial vehicle mixed formation collaborative combat mode is generated. The efficient on-the-spot judgment and decision-making ability of manned aircraft can complement the technical and tactical advantages of UAVs. The manned aircraft command and control system and the autonomous control system of UAVs work together to complete the tactical command and control of the formation, giving full play to their respective advantages. Manned / unmanned aerial vehicle task assignment problems (AP) are essentially combinatorial optimization problems and belong to NP-complete problems (Non-Polynomial Complete, NPC). For a given combat mission, combat aircraft group and its weapon resources, based on battlefield situation information and combat effectiveness evaluation function, it is necessary to find an appropriate fighter to perform the task for each combat subtask, and finally meet the maximum overall combat effectiveness value of all combat aircraft performing the task. The result of task assignment is affected by multiple conditions such as battlefield situation changes, problem solving scale, combat effectiveness objective function, and weapon resource constraints. According to the different task assignment planning structures, multi-machine collaborative task assignment can be divided into two types: centralized assignment and distributed assignment.

[0003] Centralized task allocation transmits the situational awareness information of the entire battlefield to the information processing center, generates the combat task allocation plan of the entire combat aircraft group through the allocation strategy of the information processing center, and then sends it to each fighter, and each fighter performs the corresponding combat task. This type of method was first proposed and applied to solve many types of problems in classical mathematical models such as linear programming, covariance control, dynamic programming, game theory, etc. When solving simple task allocation problems, the optimal solution method is usually used, such as exhaustive method and mixed integer linear programming (MILP). The time complexity of the optimal solution method is generally very high, and it is only suitable for solving small-scale allocation tasks. When the battlefield scale is large, the search space of the solution is too large, and the computational cost increases exponentially, and this type of method will no longer be applicable. In centralized allocation, since each fighter needs to always maintain communication with the information processing center, the limitation of the communication scale will greatly limit the size of the task that the combat aircraft group can handle, and it is easy to cause the single point failure problem of the entire combat mission, that is, once the central information processing center has a problem, it means that the task allocation has failed, which can no longer meet the current battlefield environment that is constantly expanding and complex and changing.

[0004] Distributed task allocation planning disperses the role of the information processing center to different fighters, and the fighters calculate separately, thereby reducing the demand for communication bandwidth and processing center computing power, overcoming the shortcomings of centralized task allocation, and at the same time better meeting the current dynamic and complex battlefield environment. The traditional algorithms for distributed task allocation are mainly auction algorithms and contract network protocols based on market mechanisms. The former is based on the consistency of situational awareness of different allocation units, and the latter is based on the consistency of task space. Compared with centralized methods, it can significantly shorten the task planning time. In manned / unmanned aerial vehicle collaborative task allocation, manned aircraft plays the role of contract party A, and unmanned aerial vehicles play the role of competing merchants. There are generally two types of auction algorithms. The first is the traditional auction algorithm, that is, there is a central system as the auction manager responsible for the auction. It is responsible for calculating the bid of each bidder and selecting the bidder with the highest bid; the second is to select the auction manager from the various agents participating in the auction and play the role of the central system manager. Although the auction algorithm can solve the consistency problem, its solution process is very time-consuming. There are two new consensus algorithms that can achieve fast calculations and solve the problem of robust distributed algorithms for consistency to solve the task allocation problem. They are: Consensus-Based Auction Algorithms (CBAA) and Consensus-Based Bundle Algorithm (CBBA). Among them, the CBBA algorithm is a derivative of the CBAA algorithm for solving the multi-task allocation problem. Both algorithms use market-based decision-making strategies as distributed task selection mechanisms and use local communication consistency strategies as conflict avoidance mechanisms. It is proved that under reasonable assumptions and network connectivity conditions, the proposed algorithms have fast convergence characteristics.

[0005] There are many methods for manned / unmanned aerial vehicle collaborative task allocation, each with its own advantages and disadvantages. Among them, the market-based competition method adopts a competitive mechanism of bidding and tendering, which can coordinate tasks between multiple drones and has the advantages of low communication volume and good robustness. However, this type of method also has the following problems, such as not considering the priority of tasks before the task auction, limited ability to handle emergencies during task execution, such as the appearance of new targets or the damage of drones, and often limited to the problem of single-task allocation for a single machine, and insufficient ability to allocate multiple tasks to multiple machines. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the present invention provides a formation collaborative task allocation method based on a hierarchical hybrid auction algorithm. Based on the hierarchical decision-making mechanism and the contract network auction algorithm, the present invention designs a task allocation algorithm for manned / unmanned aerial vehicle collaborative combat, namely a hierarchical decision-making algorithm. In the task bundle construction stage, the manned aircraft first makes tactical decisions by quantifying the four attribute values ​​of the value, threat, interference and unknown of the task in the battlefield, determines the priority of different types of tasks, and then assigns the task auction allocation solution to the corresponding type of unmanned aerial vehicle according to the task type, thereby improving the algorithm's solution rate and the rationality of the allocation scheme. In addition, the objective function is improved by introducing a coverage factor and a penalty term, further optimizing the rationality of task allocation. Moreover, for emergencies such as new tasks and damage to unmanned aerial vehicles, a task redistribution mechanism is formulated, that is, the manned aircraft dynamically redistributes tasks in real time according to the remaining resources and execution costs of the unmanned aerial vehicles.

[0007] The technical solution adopted by the present invention to solve the technical problem is: before the UAV performs the task, the manned aircraft first autonomously obtains the initial information of the task area. The manned aircraft makes a decision based on the four attributes of the task, namely, the value, threat value, interference value and unknown value, and selects a task type that is urgently needed to be executed or has the highest priority for the entire operation, and generates a reasonable task auction order; then, the relevant UAVs, based on the manned aircraft task execution decision, use the contract network auction algorithm to bid for the task based on the improved objective function. Repeat the above steps until all tasks are assigned or the UAVs are assigned tasks.

[0008] The technical solution adopted by the present invention to solve the technical problem is:

[0009] 1) Phase 1: Generate task auction order

[0010] Step 1: Initialize the mission attribute set, drone type set, and mission execution constraint set;

[0011] Set the mission type to Attack Mission T atc , Reconnaissance mission T rec , interference task T jam and lure task T dec There are four types of drones, and the drone types are divided into attack drones and atc , reconnaissance drone U rec , jamming drone U jam and decoy drone U dec There are four types: attack drones perform attack missions and also have reconnaissance capabilities; reconnaissance drones perform reconnaissance missions; jamming drones perform jamming missions; decoy drones mainly perform deception missions and also have reconnaissance capabilities; specific task execution constraint sets are as follows: Figure 1 As shown;

[0012] Step 2: Initialize the number of drones n and the number of tasks m, and the attribute value of each task;

[0013] Suppose there are n drones in the drone set U = {U1,U2,U3,…,U n}, the task set T contains m tasks = {T1, T2, T3, …, T m}; Task T j There are five attributes: position (X j ,Y j ), value V(T j ), threat value Th(T j ), interference value J(T j ) and the unknown value X(T j ), j = 1, 2, 3, ..., m, set the attribute values ​​corresponding to each task;

[0014] Step 3: Set the resource space for the task;

[0015] Build the resource vector Tas of the task:

[0016]

[0017] Where j = 1, 2, 3, ..., m, m is the number of tasks, Tas j Represents task T j The resources required to be completed, with four parameters; Represents task T j The amount of ammunition required to complete the task, Represents task T j The required amount of reconnaissance is completed. Represents task T j The amount of interference required to be completed, Represents task T j The amount of bait required to be completed;

[0018] Step 4: Set the resource space of the drone;

[0019] Since the resources for the tasks that can be performed by each UAV are limited, based on the principle of resource constraints, the resource vector Re s of each UAV is constructed as:

[0020]

[0021] Where i = 1, 2, 3, ..., n, n is the number of drones, Res i Indicates drone U i A resource vector with five parameters: Indicates drone U i The amount of ammunition, Indicates drone Ui The amount of reconnaissance, Indicates drone U i The amount of bait, Indicates drone U i The amount of interference, Indicates drone U i The amount of oil;

[0022] Construct the resource vector Req required for the drone to perform the mission:

[0023]

[0024] Where i represents the drone number, j = 1, 2, 3, ..., m, m is the number of missions, Indicates drone U i Execute Task T j The resources required and available, the resource vector has five parameters; Indicates drone U i Execute Task T j The amount of ammunition available, Indicates drone U i Execute Task T j The amount of reconnaissance that can be provided, Indicates drone U i Execute Task T j The amount of interference that can be provided, Indicates drone U i Execute Task T j The amount of bait that can be provided, Indicates drone U i Complete the task T j Required oil quantity;

[0025] Step 5: Initialize the weight coefficient, normalization coefficient and number of iterations of each attribute value;

[0026] Step 6: Calculate the total attribute values ​​of the tasks in the combat area;

[0027] Step 7: Determine the priority of the task according to the decision function;

[0028] Manned aircraft according to the task T j The value V(T j ), threat value Th(T j ), interference value J(T j ) and the unknown value X(T j ) and other four attributes to make decisions and determine the priority of the task; the decision function Class {T} for:

[0029] Class {T} =max[Kv ·V score ,K th ·Th score ,K j ·J score ,K x ·X score ] (5)

[0030] Determine the current environmental attributes based on the output of the decision function. v ·V score When the value is the largest among the four values, the environment of the mission area is determined to be a high-value environment, the UAV will first perform the attack mission, and the attack UAV will first perform the mission auction; when K th ·Th score When the value is the largest among the four, the environment of the mission area is defined as a high-threat environment, the UAV will first perform the deception mission, and the decoy UAV will first perform the task assignment; when K j ·J score When the value is the largest, the mission area environment is defined as a high interference environment, the UAV first performs the interference task, and the interference type UAV first performs the auction task; when K x ·X score When the value is the largest, the mission area environment is defined as a high unknown environment, the UAV first performs the reconnaissance mission, and the reconnaissance UAV first performs the auction mission;

[0031] Step 8: Arrange the drones and tasks in descending order according to the resources required for the tasks;

[0032] After determining the type of task to be executed first, Figure 1 According to the constraints of the UAV tasks, the UAVs capable of executing this type of tasks are selected and sorted in descending order according to the UAV resources, and the tasks of this type are sorted in descending order according to the task resources; other tasks of different types are auctioned in order from high to low according to the task type attribute values;

[0033] 2) Phase 2: Drone Mission Auction

[0034] After the manned aircraft determines the auction order of the tasks, it feeds back the decision results to the drone, which then executes the auction algorithm. Before each bidding, each task will check its own resource vector Tas j , if a parameter in its resource vector If s=1,2,3,4, it means that the requirement of the task has been met and there is no need to bid for a new drone for the requirement;

[0035] Assume that UAV U i The task sequence is Indicates drone U iThe tasks performed are T1, T2 and T3;

[0036] Step 9: Calculate the benefits of the drone completing the mission;

[0037] Benefits(U i (T j )) indicates drone U i Complete the task T j The benefits come from three aspects: first, the value attribute V(T j ), such as the benefits of destroying high-value targets such as base stations and airports; the second is the threat value attribute Th (T j ), such as the benefits of destroying high-threat targets such as enemy air defense radars or anti-aircraft guns; the third is the interference value attribute J(T j ), such as the benefits of interfering with enemy radar or communications systems;

[0038] Computing UAV i Complete the task T j Benefits(U i (T j ));

[0039] Step 10: Calculate the cost of the drone completing the mission;

[0040] Cost includes fuel consumption cost, time cost and destruction cost. When the UAV is in the uniform cruising stage, the flight distance and fuel consumption are nearly linearly related. The distance cost is used to replace the fuel consumption cost and time cost. i Complete the task T j Cost(U i (T j )) is calculated as:

[0041] Cost(U i (T j ))=K d D(U i ,T j )+K p P i j (8)

[0042] Among them, D(U i ,T j ) indicates drone U i Complete the task T j The distance cost, P i j Indicates drone U i Complete the task T j The probability of being destroyed;

[0043] Step 11: Calculate the net benefit of the drone completing the mission;

[0044] Rwd(U i (T j )) indicates drone U i Complete the task T j The net income is calculated as follows:

[0045] Rwd(U i (T j ))=Benefits(U i (T j ))-Cost(U i (T j )) (9)

[0046] Step 12: Each UAV constructs a local task sequence according to the task objectives to be assigned;

[0047] Based on the market mechanism, UAVs are self-interested and always choose the most beneficial tasks for themselves. On the basis of the auction order of tasks determined by manned aircraft, related heterogeneous UAVs U i According to the completion of T j Net income after Rwd(U i (T j )) bid for tasks in the task type it performs; then establish their respective local task sequences;

[0048] Step 13: Calculate the size of the drone's mission area;

[0049] use The value of U i The size of the task area, The value is related to the UAV i proportional to the actual extent of the mission area; The value of U i The average distance between all adjacent tasks in the task sequence;

[0050] Step 14: Calculate the target coverage factor;

[0051] During the task assignment process, some targets may not be on the UAV i Local Task Sequence However, the coverage factor is within the range that the UAV mission area can cover. Therefore, in the subsequent dynamic task allocation, the cost of the UAV to execute these goals is low. The coverage factor can make the individual UAVs perform the tasks in the mission area more evenly, avoiding concentration in high-value or low-cost target areas, which is conducive to dynamic task allocation and further optimization of the objective function.

[0052] Step 15: Calculate the penalty clause;

[0053] In order to balance the use of individuals in the drone cluster and make full use of the resources carried by each drone, a penalty term P for the objective function is proposed:

[0054]

[0055] Among them, μ is a negative constant, L i For UAV i The number of tasks executed, For allocation plan X n×m The average number of missions performed by all drones in Indicates drone U i Task set The number of tasks contained in When the penalty term is 0, no penalty is incurred; during the auction process, L i and The larger the difference, the greater the penalty for the profit obtained from the auction task, which can limit the number of tasks performed by the drone, thereby balancing the benefits of each drone system and optimizing the overall effect of the algorithm;

[0056] Step 16: Calculate the objective function;

[0057] The UAV objective function G is used to quantify the benefits of the UAV executing the task sequence, which is calculated as follows:

[0058]

[0059] Among them, G(X n×m ) represents the task allocation scheme X n×m The objective function includes four items: task benefit, task cost, coverage factor and penalty clause. The coverage factor mainly considers the associated cost between tasks. Specifically, the new task discovered by the drone is associated with its own local task sequence. If the new task is within the combat path of the drone, the subsequent dynamic task allocation has a lower cost for the drone.

[0060] Step 17: Determine whether a new task appears in the task area. If so, return to step 8; if no new task appears, proceed to step 18;

[0061] Step 18: Determine whether any drone has disappeared. If so, return to step 8; if not, proceed to step 19;

[0062] Step 19: Optimize the allocation algorithm. After the optimization is completed, the number of iterations increases by 1.

[0063] The ultimate goal of the allocation algorithm is to optimize the objective function and find the optimal or suboptimal task allocation solution X for the objective function. n×m , the mathematical expression of the optimized objective function is as follows:

[0064] max[G(X n×m )](18)

[0065] Step 20: Determine whether the number of iterations M has been reached. If the number of iterations M has been reached, output the optimal task allocation solution X obtained in step 19. n×m ; If the number of iterations M is not reached, go to step 5.

[0066] In step 5, weight coefficients a1, a2, a3, and a4 are set; a1 is the value V(T j ) corresponding to the weight coefficient, a2 is the threat value Th(T j ) corresponding to the weight coefficient, a3 is the interference value J(T j ) corresponding to the weight coefficient, a4 is the unknown value X(T j ) corresponding weight coefficient, and a1, a2, a3, a4>0;

[0067] Set the normalization coefficient K v ,K th ,K j ,K x ; where K v is the normalization coefficient corresponding to the value, K th is the normalization coefficient corresponding to the threat value, K j is the normalization coefficient corresponding to the interference value, K x is the normalization coefficient corresponding to the unknown value;

[0068] Set the normalization coefficient K of the task attribute to the profit function a ,K b ,K c ; where K a represents the weight of the profit function on the value, K b represents the weight of the benefit function on the threat, K c Represents the weight of the benefit function on interference; sets the normalization coefficient K d ,K p ; where K d Represents the normalized coefficient of the distance cost, K p Represents the normalized coefficient of the cost of being destroyed;

[0069] Set the contract network auction algorithm iteration termination number M. When the iteration number is reached, stop the auction and output the final task allocation result.

[0070] In step 6, the total attribute values ​​of the tasks in the combat area are:

[0071]

[0072] Where m is the sum of all tasks in the combat zone, V score Represents the total value of all tasks in the combat zone; Th score Indicates the total threat value of all missions in the combat zone; J score Represents the total interference value of all tasks in the combat zone; X score Represents the total unknown value for all missions in the combat zone.

[0073] In step 9, the calculation of the UAV U i Complete the task T j Benefits(U i (T j The formula for ) is:

[0075] Benefits(U i (T j ))=K a V(T j )+K b Th(T j )+K c J(T j ) (6)

[0076]

[0077] Among them, V(T j ), Th(T j ), J(T j ) The three values ​​reflect the properties of the current task. When the environment changes, these properties will also change. a is the weight coefficient of the objective function to the benefit function.

[0078] In step 13, it is assumed that the drone U i The task sequence is but The calculation of is as follows:

[0079]

[0080] Among them, s represents the number of tasks, (x i ,y i ) and (x i+1 ,y i+1 ) represents the coordinate position of the corresponding task, D(T i ,T i+1 ) represents task T i and Task T i+1 The distance between.

[0081] In step 14, the coverage factor Cover is defined as:

[0082]

[0083] in, It's a drone i The set of tasks that can be covered, sum(T) is the allocation scheme X n×m The total number of tasks in T cov represents the covering task set, Represents task T j and UAV i The shortest distance of tasks in the local task sequence, (x i ,y i ) and (x j ,y j ) is the coordinate position of the corresponding task; when When task T j In UAV i is within the coverage range and belongs to the coverage task set T cov .

[0084] The beneficial effect of the present invention is that since the auction sequence of the traditional contract network auction algorithm is randomly generated, it indicates that the auction of the task is disordered, and the type of task and the priority of different types of tasks are not considered, which will not only affect the algorithm's solution rate but also lead to unreasonable task allocation. The manned / unmanned aerial vehicle collaborative contract network auction algorithm proposed by the present invention can give play to the advantages of human intelligence of manned aircraft pilots, not only can quickly find the most suitable unmanned aerial vehicle for the selected task, but also can realize dynamic task allocation in a complex and changeable environment. By considering the coverage factor and the penalty clause, the set objective function is more conducive to maximizing the overall benefit, and then the overall task execution efficiency can be optimized. In addition, the introduction of the penalty clause can balance the task load of the unmanned aerial vehicle and use the unmanned aerial vehicle in the unmanned aerial vehicle cluster as reasonably as possible. In an environment with the advantages of unmanned aerial vehicle clusters, in the mode of coordinated combat between manned and unmanned aerial vehicle clusters, the present invention can efficiently and quickly give a classified and orderly allocation method to the heterogeneous unmanned aerial vehicle clusters of manned and unmanned aerial vehicle collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 Constraint relationships of four heterogeneous UAVs.

[0086] Figure 2 Manned / UAV collaborative task allocation algorithm process.

[0087] Figure 3 Initial situation diagram of the combat scenario.

[0088] Figure 4Schematic diagram of task allocation results in a high-value environment.

[0089] Figure 5 A graph showing how the total revenue of task allocation changes with the number of iterations. DETAILED DESCRIPTION

[0090] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0091] The manned / unmanned aerial vehicle task allocation method proposed in this invention based on the hierarchical decision-making mechanism and the contract network auction algorithm has the following overall implementation process: Figure 2 The following is a further clear and complete description of the technical solution in conjunction with the accompanying drawings and specific implementation cases:

[0092] 1) Phase 1: Generate task auction order

[0093] Step 1: Initialize the mission attribute set, drone type set, and mission execution constraint set;

[0094] In the present invention, the task type is set to attack task T atc , Reconnaissance mission T rec , interference task T jam and lure task T dec There are four types of drones. atc , reconnaissance drone U rec , jamming drone U jam and decoy drone U dec There are four types. Attack drones mainly perform attack missions and also have reconnaissance capabilities; reconnaissance drones perform reconnaissance missions; jamming drones perform jamming missions; and decoy drones mainly perform deception missions and also have reconnaissance capabilities. The specific task execution constraint set is as follows: Figure 1 shown.

[0095] Step 2: Initialize the number of drones n and the number of tasks m, and the attribute value of each task;

[0096] Suppose there are n drones in the drone set U = {U1,U2,U3,...,U n}, the task set T contains m tasks = {T1, T2, T3, ..., T m}. n = 10, m = 20. Among all the 10 drones, U1-U4 are set as attack drones, U5-U7 are set as reconnaissance drones, U8-U9 are set as decoy drones, and U 10 Set as a jamming drone. According to the actual combat scenario, all drones in the present invention take off from the same initial position (1000, 1000) to perform tasks. Task T j(j=1,2,3,...,m) has five attributes: position (X j ,Y j ), value V(T j ), threat value Th(T j ), interference value J(T j ) and the unknown value X(T j ), set the attribute values ​​corresponding to each task. In the first combat scenario, different types of tasks are distinguished by different colors and shapes. Among them, the red triangle represents an attack task; the blue rectangle represents a reconnaissance task; the green circle represents a deception task; and the yellow five-pointed star represents an interference task. The specific combat scenario initial situation diagram is as follows Figure 3 shown.

[0097] Step 3: Set the resource space for the task;

[0098] Build the resource vector Tas of the task:

[0099]

[0100] Where j = 1, 2, 3, ..., m, m is the number of tasks, Tas j Represents task T j The resources required to be completed, which has four parameters; Represents task T j The amount of ammunition required to complete the task, Represents task T j The required amount of reconnaissance is completed. Represents task T j The amount of interference required to be completed, Represents task T j The amount of bait required to be completed. In the case of the present invention, the location of the task and the first four values ​​in the resource vector of the task are randomly generated. In this case, the resource vectors of 20 tasks are randomly generated, and the specific task information is shown in Table 1:

[0101] Table 1 Initial information of each type of task

[0102]

[0103]

[0104] Step 4: Set the resource space of the drone;

[0105] In actual battlefield situations, since each drone has limited resources for executing tasks, the resource vector Res of each drone is constructed based on the principle of resource constraints:

[0106]

[0107] Where i = 1, 2, 3, ..., n, n is the number of drones. i Indicates drone U i A resource vector with five parameters: Indicates drone U i The amount of oil, Indicates drone U i The amount of ammunition, Indicates drone U i The amount of reconnaissance, Indicates drone U i The amount of bait, Indicates drone U i The initial information of the UAVs is shown in Table 2. In the table, the initial positions of the UAVs are the same, which conforms to the actual battlefield environment. The key resource vectors of the same type of UAVs are different, which facilitates their distinction.

[0108] Construct the resource vector Req required for the drone to perform the mission:

[0109]

[0110] Where i represents the drone number, j = 1, 2, 3, ..., m, m is the number of missions, Indicates drone U i Execute Task T j The resources required and available, the resource vector has five parameters; Indicates drone U i Execute Task T j The amount of ammunition available, Indicates drone U i Execute Task T j The amount of reconnaissance that can be provided, Indicates drone U i Execute Task T j The amount of interference that can be provided, Indicates drone U i Execute Task T j The amount of bait that can be provided, Indicates drone U i Complete the task T j Required oil quantity;

[0111] Table 2 Initial information of each drone

[0112]

[0113] Step 5: Initialize the weight coefficient, normalization coefficient and number of iterations of each attribute value;

[0114] Set weight coefficients a1, a2, a3, a4, corresponding to the value V(T j ), threat value Th(T j ), interference value J(T j ) and the unknown value X(T j ), and a1, a2, a3, a4> 0. In the experiment, a1=a2=a3=a4=1 is set.

[0115] Set the normalization coefficient K v ,K th ,K j ,K x ; where K v is the normalization coefficient corresponding to the value, K th is the normalization coefficient corresponding to the threat value, K j is the normalization coefficient corresponding to the interference value, K x is the normalization coefficient corresponding to the unknown value. In the experiment, K is set v =K th =K j =K x =0.6.

[0116] Set the normalization coefficient K of the task attribute to the profit function a ,K b ,K c ; where K a represents the weight of the profit function on the value, K b represents the weight of the benefit function on the threat, K c Represents the weight of the benefit function on interference; sets the normalization coefficient K d ,K p ; where K d Represents the normalized coefficient of the distance cost, K p Indicates the normalized coefficient of the cost of being destroyed. In the experiment, K is set a =0.5, K b =K c =K d =K p =0.8.

[0117] Set the contract network auction algorithm to terminate the iteration number M. When the iteration number is reached, stop the auction and output the final task allocation result. In the experiment, set the iteration number M = 30.

[0118] Step 6: Calculate the total attribute values ​​of the tasks in the combat area;

[0119]

[0120] Where m is the sum of all tasks in the combat zone. scoreRepresents the total value of all tasks in the combat zone; Th score Indicates the total threat value of all missions in the combat zone; J score Represents the total interference value of all tasks in the combat zone; X score Represents the total unknown value for all missions in the combat zone.

[0121] Step 7: Determine the priority of the task according to the decision function;

[0122] Manned aircraft according to the task T j The value V(T j ), threat value Th(T j ), interference value J(T j ) and the unknown value X(T j ) and other four attributes to make decisions and determine the priority of the task; the decision function Class {T} for:

[0123] Class {T} =max[K v ·V score ,K th ·Th score ,K j ·J score ,K x ·X score ] (5)

[0124] Determine the current environmental attributes based on the output of the decision function. v ·V score When the value is the largest among the four values, the environment of the mission area is determined to be a high-value environment, the UAV will first perform the attack mission, and the attack UAV will first perform the mission auction; when K th ·Th score When the value is the largest among the four, the environment of the mission area is defined as a high-threat environment, the UAV will first perform the deception mission, and the decoy UAV will first perform the task assignment; when K j ·J score When the value is the largest, the mission area environment is defined as a high interference environment, the UAV first performs the interference task, and the interference type UAV first performs the auction task; when K x ·X score When the value is the largest, the mission area environment is defined as a high unknown environment, the UAV first performs the reconnaissance mission, and the reconnaissance UAV first performs the auction mission. In the present invention, the battlefield attribute value K can be obtained by calculation. v ·V score =96.49, K th ·Th score =81.47, K j ·J score =63.24, Kx ·X score =90.58. At this point, it can be determined that the current environment is a high-value environment, so the drone will prioritize the attack mission.

[0125] Step 8: Arrange the drones and tasks in descending order according to the resources required for the tasks;

[0126] After determining the type of task to be executed first, Figure 1 According to the constraints of the UAV tasks, the unmanned aircraft suitable for executing this type of task are selected and sorted in descending order according to the UAV resources, and the tasks of this type are sorted in descending order according to the task resources. Other tasks of different types are auctioned in order from high to low according to the task type attribute values.

[0127] 2) Phase 2: Drone Mission Auction

[0128] After the manned aircraft determines the auction order of the tasks, it feeds back the decision results to the drone, which then executes the auction algorithm. Before each bidding, each task will check its own resource vector Tas j , if a parameter in its resource vector If s=1,2,3,4, it means that the requirement of the task has been met and there is no need to bid for a new drone for this requirement.

[0129] Assume that UAV U i The task sequence is Indicates drone U i The tasks executed are T1, T2 and T3.

[0130] Step 9: Calculate the benefits of the drone completing the mission;

[0131] Benefits(U i (T j )) indicates drone U i Complete the task T j The benefits come from three aspects: first, the value attribute V(T j ), such as the benefits of destroying high-value targets such as base stations and airports; the second is the threat value attribute Th (T j ), such as the benefits of destroying high-threat targets such as enemy air defense radars or anti-aircraft guns; the third is the interference value attribute J(T j ), such as the benefits of interfering with enemy radar or communications systems;

[0132] UAV i Complete the task T j Benefits(U i (T j )) is calculated as follows:

[0133] Benefits(U i (T j ))=K a V(T j )+K b Th(T j )+K c J(T j ) (6)

[0134]

[0135] Among them, V(T j ), Th(T j ), J(T j ) The three values ​​reflect the properties of the current task. When the environment changes, these properties will also change. a is the weight coefficient of the objective function to the benefit function.

[0136] Step 10: Calculate the cost of the drone completing the mission;

[0137] Cost includes fuel consumption cost, time cost, and destruction cost. When the UAV is in the constant speed cruising stage, the flight distance and fuel consumption are nearly linearly related. In the present invention, the distance cost is used to replace the fuel consumption cost and time cost. i Complete the task T j Cost(U i (T j )) is calculated as:

[0138] Cost(U i (T j ))=K d D(U i ,T j )+K p P i j (8)

[0139] Among them, D(U i ,T j ) indicates drone U i Complete the task T j The distance cost, P i j Indicates drone U i Complete the task T j The probability of being destroyed.

[0140] Step 11: Calculate the net benefit of the drone completing the mission;

[0141] Rwd(U i (T j)) indicates drone U i Complete the task T j The net income is calculated as follows:

[0142] Rwd(U i (T j ))=Benefits(U i (T j ))-Cost(U i (T j )) (9)

[0143] Step 12: Each UAV constructs a local task sequence according to the task objectives to be assigned;

[0144] Since the attack mission is auctioned first, the attack drone flies out of the base first; and since the attack drone has the ability to perform reconnaissance missions, the attack drone will also compete for reconnaissance missions near its mission; considering the situation of cross-execution of tasks, U1 first bids for tasks T3, T2, and T13 with a higher price, and U2 puts tasks T5, T10, and T11 into its local task queue, resulting in the two reconnaissance drones having no tasks to perform. The algorithm of the present invention adds an adaptive restriction penalty clause to the objective function. Except for drones U6 and U7, all drones can perform about three tasks during the auction process.

[0145] Step 13: Calculate the size of the drone's mission area;

[0146] use The value of U i The size of the task area, The value is related to the UAV i proportional to the actual extent of the mission area; The value of U i The average distance between all adjacent tasks in the task sequence is assumed to be i The task sequence is but The calculation of is as follows:

[0147]

[0148] Among them, s represents the number of tasks, (x i ,y i ) and (x i+1 ,y i+1 ) represents the coordinate position of the corresponding task, D(T i ,T i+1 ) represents task T i and Task T i+1 The distance between.

[0149] Step 14: Calculate the target coverage factor;

[0150] During the task assignment process, some targets may not be on the UAV i Local Task Sequence However, the number of targets in the mission area is not large, but it is within the coverage of the UAV mission area. Therefore, in the subsequent dynamic task allocation, the cost of the UAV to execute these targets is low; the coverage factor can make individual UAVs perform tasks in the mission area more evenly, avoiding concentration in high-value or low-cost target areas, which is conducive to dynamic task allocation and further optimization of the objective function.

[0151] The coverage factor Cover is defined as:

[0152]

[0153] in, It's a drone i The set of tasks that can be covered, sum(T) is the allocation scheme X n×m The total number of tasks in T cov represents the covering task set, Represents task T j and UAV i The shortest distance of tasks in the local task sequence, (x i ,y i ) and (x j ,y j ) is the coordinate position of the corresponding task; when When task T j In UAV i is within the coverage range and belongs to the coverage task set T cov .

[0154] Step 15: Calculate the penalty clause;

[0155] In order to balance the use of individuals in the drone cluster and make full use of the resources carried by each drone, a penalty term P for the objective function is proposed:

[0156]

[0157] Among them, μ is a negative constant, L i For UAV i The number of tasks executed, For allocation plan X n×m The average number of missions performed by all drones in Indicates drone U i Task set The number of tasks contained in When the penalty term is 0, no penalty is incurred; during the auction process, L i and The larger the difference, the greater the penalty for the profit obtained from the auction task. This can limit the number of tasks performed by the drone, thereby balancing the benefits of each drone system and optimizing the overall effect of the algorithm.

[0158] Step 16: Calculate the objective function;

[0159] The UAV objective function G is used to quantify the benefits of the UAV executing the task sequence, which is calculated as follows:

[0160]

[0161] Among them, G(X n×m ) represents the task allocation scheme X n×m The objective function includes four items: task benefit, task cost, coverage factor and penalty clause; the coverage factor mainly considers the association cost between tasks. Specifically, the new task discovered by the UAV is associated with its own local task sequence. If the new task is within the combat path of the UAV, the subsequent dynamic task allocation has a lower cost for the UAV.

[0162] Step 17: Determine whether there is a new task in the task area. If yes, return to step 8; if no, proceed to the next step;

[0163] Due to the complex and changeable environment of the combat mission area, some targets may not be detected during the initial detection process of the manned aircraft. When the UAV is performing the task, it can detect these targets based on its own radar. When a new task is detected in the combat mission area, the UAV that discovers the new task transmits the information of the task to the manned aircraft by broadcasting. The manned aircraft immediately determines the type of task and the resources required to complete the task, and then searches for UAVs that can perform this type of task (generally near the newly discovered task, the principle of proximity). The auction algorithm is used to allocate tasks among these UAVs. The specific process is that these UAVs immediately check their local queues and resource surplus. If conditions permit, they will bid for the task based on the received information, otherwise they will not bid. Then the winner will insert these tasks into the appropriate position of its local task sequence according to the attribute value, or rearrange the local task queue according to the remaining resources.

[0164] Step 18: Determine whether any drone has disappeared. If so, return to step 8; if not, proceed to the next step;

[0165] In actual combat environments, drones are at risk of being destroyed while performing missions. Once a drone is destroyed, the mission it carries will not be completed. Therefore, manned aircraft will monitor the drones regularly. If a drone fails to send status information to the manned aircraft within the specified time, the manned aircraft will determine that the drone is destroyed, and at the same time, take back its unexecuted mission and reallocate it. This allocation mechanism is similar to the task allocation mechanism when a new target is discovered. The manned aircraft first determines the type of unexecuted mission and the resources required to complete the mission, and then searches for drones that can perform this type of mission (generally near the newly discovered mission, the principle of proximity). The found drones are then assigned tasks using an auction algorithm. The specific process is that these drones immediately check their local queues and resource surplus. If conditions permit, they will bid for the mission based on the received information, otherwise they will not bid. Then the winner inserts these tasks into the appropriate position of their local task sequence based on the attribute value, or rearranges the local task queue based on the remaining resources.

[0166] Step 19: Optimize the allocation algorithm. After the optimization is completed, the number of iterations increases by 1.

[0167] The ultimate goal of the allocation algorithm is to optimize the objective function and find the optimal or suboptimal task allocation solution X for the objective function. n×m , the mathematical expression of the optimized objective function is as follows:

[0168] max[G(X n×m )](18)

[0169] Step 20: Determine whether the number of iterations M has been reached. If the number of iterations M has been reached, output the optimal task allocation solution X obtained in step 19. n×m ; If the number of iterations M is not reached, go to step 5.

[0170] The present invention selects typical combat cases to analyze the effectiveness of the hierarchical decision auction algorithm, and proves the feasibility and effectiveness of the manned / unmanned aerial vehicle hierarchical decision auction algorithm. The specific allocation results are shown in Table 3. At the same time, in order to show the superiority of the algorithm, the control variable method is used to limit the simulation conditions. Under different combat scale dimensions, the algorithm is compared with the Consensus-Based Bundle Algorithm (CBBA) in terms of total task allocation revenue, number of iterations, and task allocation completion time.

[0171] Table 3 Task allocation results in high-value battlefield environment

[0172]

[0173]

[0174] Table 3 shows the task allocation results in a high-value environment. Attack UAVs are assigned tasks first. Since they have the ability to perform reconnaissance tasks, they will seize the tasks of reconnaissance UAVs, which may result in some reconnaissance UAVs not being assigned tasks.

[0175] The combat scale in the multi-task allocation phase is determined by the number of fighters N and the number of tasks M, keeping other information unchanged. The number of iterations and task allocation operation time of 6 groups of simulation results under different combat scales are collected by changing the number of fighters N and the number of tasks M. In order to avoid the influence of random factors on the simulation results, the battlefield information parameters of the two algorithms under the same combat scale are completely consistent, and the simulation result data collection adopts the method of taking the average value of multiple simulations. The simulation results are shown in Table 4:

[0176] Table 4 Results of time efficiency of task allocation at different combat scales

[0177]

[0178] (1) Analysis of the number of iterations

[0179] In general, the number of iterations of the algorithm of the present invention is generally lower than that of the CBBA algorithm. The algorithm of the present invention will first prioritize the task types according to the battlefield attributes, and will sort the capability attributes of the UAVs and tasks before the task auction, thus avoiding unnecessary competitive auctions and reducing the number of iterations of the algorithm.

[0180] (2) Analysis of allocated operation time

[0181] The allocation operation time of the algorithm of the present invention is lower than that of the CBBA algorithm, and the average operation time is reduced by about 67.38%. The hierarchical decision auction algorithm is based on the determination of the order of task types. As the scale of the allocation increases, the allocation operation time generally increases accordingly; in addition, for the same reason, the allocation operation time of the CBBA algorithm is not very sensitive to the scale of the number of tasks, but is greatly affected by the number of fighters. Finally, compared with the allocation of a small number of tasks, the number of iterations and operation time of the multi-task allocation process are greatly increased, which is mainly determined by the complexity of the problem model.

[0182] The total revenue of task allocation of the two algorithms changes with the number of iterations. Figure 5As shown. Here we set a scenario where ten drones are assigned to twenty tasks. The solid red line indicates how the total revenue of the task changes with the number of iterations when the hierarchical decision auction algorithm is used; the dotted blue line indicates how the total revenue of the task changes with the number of iterations when the CBBA algorithm is used. It can be seen that the hierarchical decision algorithm converges faster than the CBBA algorithm with the increase in the number of iterations, and the revenue almost reaches the highest when the number of iterations is 25, while the number of iterations of the CBBA algorithm is close to 35. This is because the task sequence of the CBBA algorithm is randomly generated, and there are two stages of auction and consistency in the task auction process, so the iteration will be relatively slow. The task allocation algorithm of the present invention has the opportunity to generate a reasonable task sequence first, and then hand it over to the drone for auction. The tasks are orderly and reasonably allocated, thereby avoiding task conflicts, that is, there is no consistency stage, so the iteration will be fast. At the same time, because the objective function of the algorithm of the present invention adopts a coverage factor and a penalty term, the task allocation is more reasonable and effective, so it will have a higher task allocation benefit than the CBBA algorithm. Based on the above experimental results, the advantages of the hierarchical decision auction algorithm are verified, indicating that the algorithm converges faster than the CBBA algorithm and obtains higher profit values. At the same time, it performs better in dynamic and complex environments, making task allocation more reasonable and effective.

Claims

1. A method for allocating team collaborative tasks based on a hierarchical hybrid auction algorithm, characterized in that The following steps are included: 1) Phase 1: Generate task auction sequence Step 1: Initialize the mission attribute set, drone type set, and mission execution constraint set; Set the mission type to attack mission T atc , Reconnaissance mission T rec , interference task T jam and lure task T dec There are four types of drones, and the drone types are divided into attack drones and atc , reconnaissance drone U rec , jamming drone U jam and decoy drone U dec There are four types: attack drones perform attack missions and also have reconnaissance capabilities; reconnaissance drones perform reconnaissance missions; jamming drones perform jamming missions; decoy drones mainly perform deception missions and also have reconnaissance capabilities; Step 2: Initialize the number of drones n and the number of tasks m, and the attribute value of each task; Suppose there are n drones in the drone set U = {U1, U2, U3, ..., U n }, a task set T containing m tasks = {T1, T2, T3, ..., T m }; Task T j There are five attributes: position (X j ,Y j ), value V(T j ), threat value Th(T j ), interference value J(T j ) and the unknown value X(T j ), j = 1, 2, 3, ..., m, set the attribute values ​​corresponding to each task; Step 3: Set the resource space for the task; Build the resource vector Tas of the task: Where j = 1, 2, 3, ..., m, m is the number of tasks, Tas j Represents task T j The resources required to be completed, with four parameters; Represents task T j The amount of ammunition required to complete the task, Represents task T j The required amount of reconnaissance is completed. Represents task T j The amount of interference required to be completed, Represents task T j The amount of bait required to be completed; Step 4: Set the resource space of the drone; Since the resources for the tasks that can be performed by each UAV are limited, based on the principle of resource constraints, the resource vector Res of each UAV is constructed as: Where i = 1, 2, 3, ..., n, n is the number of drones, Res i Indicates drone U i A resource vector with five parameters: Indicates drone U i The amount of ammunition, Indicates drone U i The amount of reconnaissance, Indicates drone U i The amount of bait, Indicates drone U i The amount of interference, Indicates drone U i The amount of oil; Construct the resource vector Req required for the drone to perform the mission: Where i represents the drone number, j = 1, 2, 3, ..., m, m is the number of missions, Indicates drone U i Execute Task T j The resources required and available, the resource vector has five parameters; Indicates drone U i Execute Task T j The amount of ammunition available, Indicates drone U i Execute Task T j The amount of reconnaissance that can be provided, Indicates drone U i Execute Task T j The amount of interference that can be provided, Indicates drone U i Execute Task T j The amount of bait that can be provided, Indicates drone U i Complete the task T j Required oil quantity; Step 5: Initialize the weight coefficient, normalization coefficient and number of iterations of each attribute value; Step 6: Calculate the total attribute values ​​of the tasks in the combat area; Step 7: Determine the priority of the task according to the decision function; Manned aircraft according to the task T j The value V(T j ), threat value Th(T j ), interference value J(T j ) and the unknown value X(T j ) four attributes to make decisions and determine the priority of the task; the decision function Class {T} for: Class {T} =max[K v ·V score ,K th ·Th score ,K j ·J score ,K x ·X score ] (5) Determine the current environmental attributes based on the output of the decision function. v ·V score When the value is the largest among the four values, the environment of the mission area is determined to be a high-value environment, the UAV will first perform the attack mission, and the attack UAV will first perform the mission auction; when K th ·Th score When the value is the largest among the four, the environment of the mission area is defined as a high-threat environment, the UAV will first perform the deception mission, and the decoy UAV will first perform the task assignment; when K j ·J score When the value is the largest, the mission area environment is defined as a high interference environment, the UAV first performs the interference task, and the interference type UAV first performs the auction task; when K x ·X score When the value is the largest, the mission area environment is defined as a high unknown environment, the UAV first performs the reconnaissance mission, and the reconnaissance UAV first performs the auction mission; Step 8: Arrange the drones and tasks in descending order according to the resources required for the tasks; After determining the task type to be performed first, according to the UAV task constraint relationship in Figure 1, select UAVs capable of performing this type of task and sort them in descending order based on UAV resources, and at the same time sort this type of task in descending order based on task resources; other different types of tasks are auctioned in order from high to low based on the task type attribute value; 2) Phase 2: Drone Mission Auction After the manned aircraft determines the auction order of the tasks, it feeds back the decision results to the drone, which then executes the auction algorithm. Before each bidding, each task will check its own resource vector Tas j , if a parameter in its resource vector If s=1,2,3,4, it means that the requirement of the task has been met and there is no need to bid for a new drone for the requirement; Assume that UAV U i The task sequence is Indicates drone U i The tasks performed are T1, T2 and T3; Step 9: Calculate the benefits of the drone completing the mission; Benefits(U i (T j )) indicates drone U i Complete the task T j The benefits come from three aspects: first, the value attribute V(T j ), such as the benefits of destroying high-value targets such as base stations and airports; the second is the threat value attribute Th (T j ), such as the benefits of destroying high-threat targets such as enemy air defense radars or anti-aircraft guns; the third is the interference value attribute J(T j ), such as the benefits of interfering with enemy radar or communications systems; Computing UAV i Complete the task T j Benefits(U i (T j )); Step 10: Calculate the cost of the drone completing the mission; Cost includes fuel consumption cost, time cost and destruction cost. When the UAV is in the uniform cruising stage, the flight distance and fuel consumption are nearly linearly related. The distance cost is used to replace the fuel consumption cost and time cost. i Complete the task T j Cost(U i (T j )) is calculated as: Among them, D(U i ,T j ) indicates drone U i Complete the task T j The distance cost, Indicates drone U i Complete the task T j The probability of being destroyed; Step 11: Calculate the net benefit of the drone completing the mission; Rwd(U i (T j )) indicates drone U i Complete the task T j The net income is calculated as follows: Rwd(U i (T j ))=Benefits(U i (T j ))-Cost(U i (T j )) (9) Step 12: Each UAV constructs a local task sequence according to the task objectives to be assigned; Based on the market mechanism, UAVs are self-interested and always choose the most beneficial tasks for themselves. On the basis of the auction order of tasks determined by manned aircraft, related heterogeneous UAVs U i According to the completion of T j Net income after Rwd(U i (T j )) bid for tasks in the task type it performs; then establish their respective local task sequences; Step 13: Calculate the size of the drone's mission area; use The value of U i The size of the task area, The value is related to the U i is proportional to the actual extent of the mission area; The value of U i The average distance between all adjacent tasks in the task sequence; Step 14: Calculate the target coverage factor; During the task assignment process, some targets may not be on the UAV i Local Task Sequence However, the coverage factor is within the range that the UAV mission area can cover. Therefore, in the subsequent dynamic task allocation, the cost of the UAV to execute these goals is low. The coverage factor can make the individual UAVs perform the tasks in the mission area more evenly, avoiding concentration in high-value or low-cost target areas, which is conducive to dynamic task allocation and further optimization of the objective function. Step 15: Calculate the penalty clause; In order to balance the use of individuals in the drone cluster and make full use of the resources carried by each drone, a penalty term P for the objective function is proposed: Among them, μ is a negative constant, L i For UAV i The number of tasks executed, For allocation plan X n×m The average number of missions performed by all drones in Indicates drone U i Task set The number of tasks contained in When the penalty term is 0, no penalty is incurred; during the auction process, L i and The larger the difference, the greater the penalty for the profit obtained from the auction task, which can limit the number of tasks performed by the drone, thereby balancing the benefits of each drone system and optimizing the overall effect of the algorithm; Step 16: Calculate the objective function; The UAV objective function G is used to quantify the benefits of the UAV executing the task sequence, which is calculated as follows: Among them, G(X n×m ) represents the task allocation scheme X n×m The objective function includes four items: task benefit, task cost, coverage factor and penalty clause. The coverage factor mainly considers the associated cost between tasks. Specifically, the new task discovered by the drone is associated with its own local task sequence. If the new task is within the combat path of the drone, the subsequent dynamic task allocation has a lower cost for the drone. Step 17: Determine whether a new task appears in the task area. If so, return to step 8; if no new task appears, proceed to step 18; Step 18: Determine whether any drone has disappeared. If so, return to step 8; if not, proceed to step 19; Step 19: Optimize the allocation algorithm. After the optimization is completed, the number of iterations increases by 1. The ultimate goal of the allocation algorithm is to optimize the objective function and find the optimal or suboptimal task allocation solution X for the objective function. n×m , the mathematical expression of the optimized objective function is as follows: max[G(X n×m )](18) Step 20: Determine whether the number of iterations M has been reached. If the number of iterations M has been reached, output the optimal task allocation solution X obtained in step 19 n×m ; If the number of iterations M is not reached, go to step 5.

2. The formation collaborative task allocation method based on the hierarchical hybrid auction algorithm according to claim 1 is characterized by: In step 5, weight coefficients a1, a2, a3, and a4 are set; a1 is the value V(T j ) corresponding to the weight coefficient, a2 is the threat value Th(T j ) corresponding to the weight coefficient, a3 is the interference value J(T j ) corresponding to the weight coefficient, a4 is the unknown value X(T j ) corresponding weight coefficient, and a1, a2, a3, a4>0; Set the normalization coefficient K v ,K th ,K j ,K x ; where K v is the normalization coefficient corresponding to the value, K th is the normalization coefficient corresponding to the threat value, K j is the normalization coefficient corresponding to the interference value, K x is the normalization coefficient corresponding to the unknown value; Set the normalization coefficient K of the task attribute to the profit function a ,K b ,K c ; where K a represents the weight of the profit function on the value, K b represents the weight of the benefit function on the threat, K c Represents the weight of the benefit function on interference; sets the normalization coefficient K d ,K p ; where K d Represents the normalized coefficient of the distance cost, K p Represents the normalized coefficient of the cost of being destroyed; Set the contract network auction algorithm iteration termination number M. When the iteration number is reached, stop the auction and output the final task allocation result.

3. The formation collaborative task allocation method based on the hierarchical hybrid auction algorithm according to claim 1 is characterized by: In step 6, the total attribute values ​​of the tasks in the combat area are: Where m is the sum of all tasks in the combat zone, V score Represents the total value of all tasks in the combat zone; Th score Indicates the total threat value of all missions in the combat zone; J score Represents the total interference value of all tasks in the combat zone; X score Represents the total unknown value for all missions in the combat zone.

4. The formation collaborative task allocation method based on the hierarchical hybrid auction algorithm according to claim 1 is characterized by: In step 9, the UAV U is calculated. i Complete the task T j Benefits(U i (T j The formula for ) is: Benefits(U i (T j ))=K a V(T j )+K b Th(T j )+K c J(T j ) (6) Among them, V(T j ), Th(T j ), J(T j ) The three values ​​reflect the properties of the current task. When the environment changes, these properties will also change. a is the weight coefficient of the objective function to the benefit function.

5. The formation collaborative task allocation method based on hierarchical hybrid auction algorithm according to claim 1 is characterized by: In step 13, it is assumed that the drone U i The task sequence is but The calculation of is as follows: Among them, s represents the number of tasks, (x i ,y i ) and (x i+1 ,y i+1 ) represents the coordinate position of the corresponding task, D(T i ,T i+1 ) represents task T i and Task T i+1 The distance between.

6. The formation collaborative task allocation method based on hierarchical hybrid auction algorithm according to claim 1 is characterized by: In step 14, the coverage factor Cover is defined as: in, It's a drone i The set of tasks that can be covered, sum(T) is the allocation scheme X n×m The total number of tasks in T cov represents the set of covered tasks, Represents task T j and UAV i The shortest distance of tasks in the local task sequence, (x i ,y i ) and (x j ,y j ) is the coordinate position of the corresponding task; when When task T j In UAV i is within the coverage range and belongs to the coverage task set T cov .

7. An electronic device, characterized in that: include: one or more processors; Memory; One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, and the program codes can be called by a processor to execute the method according to any one of claims 1 to 6.

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