A cluster distributed dynamic target allocation method based on preference coalition game

Abstract

The application discloses a kind of cluster distributed dynamic target allocation methods based on preference coalition game, define decision variable matrix to represent the matching relationship between target and aircraft, clear cluster aircraft target conflict constraint and target matching constraint condition, establish cluster communication topology;Based on the probability of aircraft to target attack success, construct attack efficiency function, and then design aircraft benefit function, establish aircraft optimization performance index;Based on the benefit function and optimization performance index of aircraft, design the target selection rule based on preference, establish preference coalition game model;Based on preference coalition game model and maximum information gain algorithm, design distributed dynamic target allocation algorithm, so that cluster aircraft reaches Nash stable partition through local information interaction under communication topology.The application can solve the problem of large-scale cluster dynamic target allocation, with small communication load, strong adaptability and other advantages.

Description

Technical Field

[0001] This invention belongs to the field of distributed task allocation, specifically relating to a cluster-based distributed dynamic target allocation method based on preference alliance game theory. Background Technology

[0002] Swarm aircraft are systems composed of a large number of different types of aircraft with simple structures. Through functional complementarity and cooperation among the aircraft, swarm aircraft can adapt to complex and diverse mission scenarios such as emergency rescue and reconnaissance operations, improving mission efficiency and success rate, and have broad application prospects.

[0003] Target allocation is a core module of the decision-making layer for swarm aircraft. Serving as a guide for mission execution, it aims to match aircraft with targets by analyzing information about the aircraft, targets, and the environment, providing crucial information for subsequent trajectory planning and individual aircraft control. However, due to the large scale of swarm aircraft and the frequent failures of individual aircraft, it is difficult for operators to directly allocate and adjust targets for each aircraft. Therefore, research on distributed dynamic target allocation methods for swarms is of great significance for expanding swarm size and improving swarm reliability.

[0004] The cluster-distributed dynamic target allocation method needs to meet the following requirements: (1) Local information availability, meaning that individual aircraft can only obtain information about some relevant aircraft and cannot grasp the information of the entire cluster. (2) Independent decision-making by individuals, meaning that individual aircraft need to select the optimal target based on the obtained local information and update their matched target. (3) Convergence of the algorithm, meaning that through the target selection and target update of individual aircraft, the target allocation result of the cluster can tend to remain unchanged. (4) Fault tolerance of the algorithm, meaning that when some individual aircraft fail, can the cluster autonomously optimize and adjust the target allocation result of the cluster through the independent decision-making of individual aircraft? Existing target allocation methods are difficult to meet the above requirements. Therefore, this invention addresses the above requirements and designs a cluster-distributed dynamic target allocation method based on the theory of preference alliance game. Summary of the Invention

[0005] Purpose of the invention: This invention provides a cluster-based distributed dynamic target allocation method based on preference alliance game theory, which can solve the problem of real-time distributed target allocation in large-scale clusters, reduce cluster communication load, and improve cluster reliability.

[0006] Technical solution: The present invention provides a cluster-distributed dynamic target allocation method based on preference alliance game theory, comprising the following steps:

[0007] (1) Define the decision variable matrix to characterize the matching relationship between the target and the aircraft, clarify the target conflict constraints and target matching constraints of the cluster aircraft, and establish the cluster communication topology;

[0008] (2) Construct a strike effectiveness function based on the probability of the aircraft successfully striking the target, and then design the aircraft benefit function and establish the aircraft optimization performance index.

[0009] (3) Based on the aircraft's payoff function and optimization performance index, design a preference-based objective selection rule and establish a preference alliance game model;

[0010] (4) Based on the preference alliance game model and the maximum information gain algorithm, a distributed dynamic target allocation algorithm is designed so that the cluster of aircraft can reach the Nash stable partition through local information interaction under the communication topology.

[0011] Further, the decision variable matrix defined in step (1) to characterize the matching relationship between the target and the aircraft is as follows:

[0012] Given p (number of targets and m aircraft, p > m); define the decision variable matrix A = [α ij ] m×(p+1) Where target 0 corresponds to standby, α ij ∈{0,1} represents the aircraft i∈M and the target. The matching relationship, α ij =1 indicates a successful match; otherwise, the match fails.

[0013] Furthermore, the target conflict constraint and target matching constraint conditions of the cluster aircraft in step (1) are as follows:

[0014] Considering that each aircraft can only engage one target, establish target conflict constraints:

[0015]

[0016] Considering the limited strike range and maneuverability of the aircraft, based on the coordinate information of the aircraft i∈M Coordinate information of target j∈P The center coordinates of the no-fly zone l∈Q Coverage radius R l Establish target matching constraints:

[0017]

[0018]

[0019] Q ij ={l∈Q|d ijl <R l}

[0020] Among them, D ij Considering the combined long-range strike capability and maneuverability of the aircraft, λ is a positive constant, ρ i It is a parameter characterizing the long-range strike capability of an aircraft. It is a parameter characterizing the maneuverability of an aircraft; d ij It is the straight-line distance from aircraft i to target j; The distance d is the overlap between the line connecting aircraft i and target j and the no-fly zone. ijl Q is the perpendicular distance from the center coordinates of the no-fly zone l to the line connecting aircraft i and target j. ij It is the set of no-fly zones that aircraft i needs to avoid when traveling from target j.

[0021] Furthermore, the cluster communication topology described in step (1) is as follows:

[0022] Based on the target matching constraint, the set of real targets that aircraft i∈M can select is defined as follows: An undirected graph G is constructed with the set of aircraft as nodes to represent the communication topology of the aircraft cluster; the adjacency matrix of the undirected graph G is B = [β]. ik ] m×m , where if P i ∩P k If ≠φ and i≠k, then β ik =β ki =1; otherwise β ik =β ki =0; Definition Let i be the set of neighbors of aircraft i.

[0023] Furthermore, the implementation process of step (2) is as follows:

[0024] The closer the distance between aircraft i and target j, the shorter the overlap between the line connecting them and the no-fly zone, and the stronger the strike effectiveness of the aircraft; based on this, a single aircraft i against target j∈P is established. i The strike effectiveness function, i.e., the strike effectiveness of aircraft i against target j∈P i The probability function for a successful strike is as follows:

[0025]

[0026] Define the currently selected target The collection of aircraft is Λ j ,Target The benefits of a successful strike are determined by the set Λ j Internal aircraft allocation, aircraft i selects target The payoff function is:

[0027]

[0028] The local optimization objective for aircraft i is established as follows:

[0029]

[0030] in, For the optimized performance indicators of aircraft i, s i The target selected for aircraft i The set of targets selected for the i-th neighbor of the aircraft.

[0031] Furthermore, the probability of a successful strike by an aircraft in step (2) also includes the probability of multiple aircraft successfully striking target j:

[0032]

[0033] Among them, Λ j Select target for the current task A collection of aircraft, u ij Let i be the probability that a single aircraft i successfully strikes target j.

[0034] Furthermore, the probability of successfully striking target 0 is set to 1.

[0035] Furthermore, the implementation process of step (3) is as follows:

[0036] Define symbol > i Characterize the preference relationship between aircraft i∈M and the coalition, if for target j, μ∈P i ∪{0}, satisfying:

[0037]

[0038] So Λ j > i Λ μ This indicates that the i-type aircraft is more advanced than the one that joined the Coalition Λ. μ Prefer to join the alliance Λ j ;

[0039] Based on this preference relationship, the target selection rule for aircraft is established as follows: Given a coalition partition Ω = {Λ0,Λ1,…Λ p For any aircraft i∈M, if and only if there exists a target and Make The aircraft will then match the target s. i Adjust to target That is, from the alliance Adjusted to the league

[0040] Based on the set of aircraft M and the set of targets Based on the aforementioned preference-based goal selection rules, a preference alliance game model is established.

[0041] Further, the distributed dynamic target allocation algorithm in step (4) includes target selection and information transmission as well as information reception and target updating, wherein:

[0042] Target selection and message sending are as follows:

[0043] 1) Introducing variables Characterize the target selected by aircraft i, and set its initial value s. i Introducing sets To prevent aircraft i from repeatedly selecting the same target, its initial value is set to P. i ∪{0}\{s i};

[0044] 2) Determine the set Check if it is an empty set. If it is, skip to step 3; if it is not an empty set, then check the set... Select any target If satisfied This indicates that aircraft i is more suitable for joining the alliance. Prefer to join an alliance Therefore Assign to And From the set Remove from the middle, skip to 2);

[0045] 3) Calculate the flight vehicle i by the Alliance Adjusted to the league Post-optimization performance index J i The increase ΔJ i , will ΔJ i Send to all neighbors of aircraft i; when At that time, the increase ΔJ i =0;

[0046] Information reception and target update are as follows:

[0047] Based on the received information on changes in aircraft performance indicators within the neighbor set, determine whether the requirements are met. If this condition is met, it means that the current aircraft i has changed its target selection to... Afterwards, if the increase in its performance index is greater than that of its neighbors, then, based on the idea of ​​the maximum information gain algorithm, this iteration ends, and aircraft i changes its target selection to... If this condition is not met, it means there exists a neighbor whose performance index increases by an amount greater than or equal to that of aircraft i. In this case, the current iteration ends, and aircraft i still selects target s.i .

[0048] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are: the present invention can not only effectively solve the dynamic target allocation problem of large-scale swarm aircraft, but also has significant advantages such as low communication load and strong adaptability; the present invention significantly improves the collaborative combat capability and mission execution efficiency of swarm aircraft through intelligent game mechanism and efficient algorithm design, and has high application value and promotion prospects. Attached Figure Description

[0049] Figure 1 This is a flowchart of the present invention;

[0050] Figure 2 This is a schematic diagram of the target matching constraints for the aircraft according to an embodiment of the present invention;

[0051] Figure 3 This is a flowchart of the cluster-distributed dynamic target allocation algorithm according to an embodiment of the present invention;

[0052] Figure 4 To simulate the target allocation results of the cluster under fault-free conditions on the experimental platform;

[0053] Figure 5 To illustrate the change curves of cluster revenue under the simulated fault-free condition of the experimental platform;

[0054] Figure 6 This is the result of target allocation for the cluster under simulated aircraft failure conditions on the experimental platform;

[0055] Figure 7 This is a graph showing the change in cluster revenue under simulated aircraft failure conditions on the experimental platform. Detailed Implementation

[0056] The present invention will now be described in further detail with reference to the accompanying drawings.

[0057] like Figure 1 As shown, this invention provides a cluster-distributed dynamic target allocation method based on preference alliance game theory, specifically including the following steps:

[0058] Step 1: Define the decision variable matrix to characterize the matching relationship between the target and the aircraft, clarify the target conflict constraints and target matching constraints of the swarm aircraft, and establish the swarm communication topology.

[0059] The target allocation scenario considered in this embodiment is as follows: Figure 2 As shown, this includes the aircraft, the target, and the no-fly zone; among which:

[0060] Aircraft: m aircraft, the set of aircraft is defined as The coordinate information of the aircraft i∈M is denoted as

[0061] Objectives: There are p (p > m) objectives, and the set of objectives is defined as follows: Here, the set P = {1, 2, ..., p} corresponds to the actual targets, and target 0 corresponds to the pending targets, i.e., not matching any target. The coordinate information of target j ∈ P is denoted as... Value denoted as V j This refers to the reward gained after the target is successfully hit; specifically, the value of being on standby is 0.

[0062] No-fly zones: There are q no-fly zones, and the set of no-fly zones is defined as follows: The center coordinates of the no-fly zone l∈Q are: The coverage radius is denoted as R. l Neither the aircraft nor the target were within the no-fly zone.

[0063] Consider assigning p (p > m) targets to m aircraft. To characterize the matching relationship between targets and aircraft, a decision variable matrix is ​​defined. Where, α ij ∈{0,1} represents the aircraft i∈M and the target. The matching relationship, where α ij =1 indicates that aircraft i and target j are successfully matched; otherwise, aircraft i and target j fail to match.

[0064] Considering that each aircraft can only engage one target, the following target conflict constraints are established:

[0065]

[0066] Due to the limited strike range and maneuverability of aircraft, when selecting targets, it is necessary to consider not only whether the straight-line distance from the aircraft to the target meets the strike range requirements, but also whether the aircraft can avoid no-fly zones. To reflect this constraint, for aircraft i∈M and target j∈P, the following target matching constraint is established:

[0067]

[0068] Among them, D ij The combined long-range strike capability and maneuverability of an aircraft are represented by a weighted sum; λ is a positive constant, and ρ... i It is a parameter characterizing the long-range strike capability of an aircraft, d ij It is the straight-line distance from aircraft i to target j. It is a parameter characterizing the maneuverability of an aircraft. It is the distance where the line connecting aircraft i and target j overlaps with the no-fly zone, such as... Figure 2 As shown; d ijlQ is the perpendicular distance from the center coordinates of the no-fly zone l to the line connecting aircraft i and target j. ij It is the set of no-fly zones that aircraft i needs to avoid when traveling from target j.

[0069] Based on the target matching constraint, the set of real targets that aircraft i∈M can select is defined as follows: To conserve communication resources and reduce communication interference, for any two aircraft i,k∈M, a condition is met if and only if they can select the same real target, i.e., P. i ∩P k Communication only occurs when the distance between them is ≠ φ. Therefore, an undirected graph G is constructed with the set of aircraft as nodes to represent the communication topology of the aircraft cluster. The adjacency matrix of this undirected graph G is defined as B = [β]. ik ] m×m , where if P i ∩P k If ≠φ and i≠k, then β ik =β ki =1, otherwise β ik =β ki =0. Definition Let i be the set of neighbors of aircraft i.

[0070] Step 2: Construct a strike effectiveness function based on the probability of the aircraft successfully striking the target, then design the aircraft's profit function, and establish the aircraft's optimized performance index.

[0071] For aircraft i and target j∈P i The effectiveness of the attack and the distance between the two d ij The overlap distance between the line connecting the two and the no-fly zone Related to, d ij The smaller the size, the greater the striking power; The smaller the value, the greater the strike effectiveness. Based on this relationship, we can establish the relationship between aircraft i and target j∈P. i The strike effectiveness function, i.e., the strike effectiveness of aircraft i against target j∈P i The probability function for a successful strike is as follows:

[0072]

[0073] Since the number of targets p is greater than the number of aircraft m, there are often situations where multiple aircraft coordinate to attack the same target. Therefore, the currently selected target is defined as... The collection of aircraft is Λ j According to the target j∈P of aircraft i i The strike effectiveness function can be used to obtain the target j∈P. i The probability of being successfully attacked is:

[0074]

[0075] Specifically, the probability of successfully hitting target 0 is set to 1.

[0076] If the aircraft collection Λ j If target j is successfully hit, then the target value V j It was obtained through the collaborative efforts of multiple aircraft and needs to be shared among Λ. j For each aircraft within the range, if aircraft i chooses target j, its payoff function can be expressed as:

[0077]

[0078] Among them, |Λ j | indicates the alliance Λ j The number of aircraft inside the spacecraft.

[0079] As can be seen from the cluster communication topology, for any aircraft i ∈ M, it can only obtain information from its neighboring aircraft. Therefore, the local optimization objective for aircraft i is established as follows:

[0080]

[0081] in, For the optimized performance indicators of aircraft i, s i The target selected for aircraft i The target set selected for the neighbors of aircraft i. It can be seen that the local optimization objective of aircraft i is to maximize the total gain of itself and the aircraft in its neighbor set.

[0082] Step 3: Based on the aircraft's payoff function and optimization performance index, design preference-based target selection rules and establish a preference alliance game model.

[0083] Select target The collection of aircraft Λ j Defined as Alliance Λ j Define the confederate partitions of the cluster as Ω = {Λ0, Λ1, ... Λ p}. Definition symbol > i The preference relation of aircraft i∈M to their respective coalitions is represented by the following relationship for any two coalitions Λ. j Λ μ If Λ j > i Λ μ , j,μ∈P i ∪{0} indicates that aircraft i is better off than joining the alliance Λ μ Prefer to join the alliance Λ j From the payoff function and optimization objective of aircraft i, we can further derive the aircraft's preference relationship with the coalition: if for objective j, μ∈Pi ∪{0}, satisfying:

[0084]

[0085] So Λ j > i Λ μ Based on this preference relationship, the target selection rule for aircraft can be established as follows: Given a coalition partition Ω = {Λ0, Λ1, ... Λ...} p For any aircraft i∈M, if and only if there exists a target and Make The aircraft will then match the target s. i Adjust to target That is, adjustments from the alliance To the Alliance The alliance partition consists of Ω = {Λ0,Λ1,…Λ} p Updated to

[0086] Based on the set of aircraft M and the set of targets Based on the aforementioned preference-based target selection rules, a preference-based coalition game model can be established. Correspondingly, the target allocation problem can be transformed into a coalition partitioning problem, i.e., finding a set of targets that match the swarm of aircraft. and its corresponding alliance partition Such that for any aircraft i∈M and target All meet Because each aircraft in this partition prefers its current chosen target, none of them will improve their gains by changing their chosen target. This type of alliance partitioning is called a Nash stable partition. Under a Nash stable partition, it is easy to obtain... It is easy to see that this is consistent with the local optimization goal of aircraft i.

[0087] Step 4: Based on the preference alliance game model and the maximum information gain algorithm, a distributed dynamic target allocation algorithm is designed so that the cluster of aircraft can reach the Nash stable partition through local information interaction under the communication topology.

[0088] To enable swarm aircraft to reach the Nash stable partition through local information exchange within the communication topology, such as Figure 3 As shown, the distributed dynamic target allocation algorithm corresponds to the decision-making process of aircraft i in one iteration, and consists of two sub-steps.

[0089] Target selection and message delivery:

[0090] 1) Introducing variables Characterize the target selected by aircraft i, and set its initial value s. i Introducing sets To prevent aircraft i from repeatedly selecting the same target, its initial value is set to P. i ∪{0}\{s i}

[0091] 2) Determine the set Check if it is an empty set. If it is, skip to step 3; if it is not an empty set, then check the set... Select any target If satisfied This indicates that aircraft i is more suitable for joining the alliance. Prefer to join an alliance Therefore Assign to And From the set Remove from the middle, skip to 2).

[0092] 3) Calculate the flight vehicle i by the Alliance Adjusted to the league * Post-optimization performance index J i The increase ΔJ i (when When the increase is 0), ΔJ i Send to all neighbors of aircraft i.

[0093] Information reception and target update. Based on the received information on changes in aircraft performance indicators within the neighbor set, determine whether the target is satisfied. If this condition is met, it means that the current aircraft i has changed its target selection to... Afterwards, if the increase in its performance index is greater than that of its neighbors, then, based on the idea of ​​the maximum information gain algorithm, this iteration ends, and aircraft i changes its target selection to... If this condition is not met, it means there exists a neighbor whose performance index increases by an amount greater than or equal to that of aircraft i. In this case, the current iteration ends, and aircraft i still selects target s. i .

[0094] To verify the effectiveness of the present invention, the present invention performs simulations under both fault-free and fault conditions.

[0095] The fault-free scenario simulation is based on the Matlab simulation platform, considering a task allocation scenario consisting of 10 aircraft and 4 targets. The coordinate information of each aircraft, the coordinates and importance information of each target, and the center coordinates and coverage radius information of each no-fly zone are shown in Tables 1, 2 and 3.

[0096] Table 1. Coordinate information of each aircraft simulated on the experimental platform.

[0097] aircraft number x-axis ordinate aircraft number x-axis ordinate 1 15 12 6 9 56 2 5 20 7 8 63 3 2 30 8 13 70 4 6 45 9 14 82 5 8 50 10 16 86

[0098] Table 2 shows the coordinates and importance information of each target simulated on the experimental platform.

[0099]

[0100]

[0101] Table 3 shows the center coordinates and coverage radius information of each no-fly zone simulated on the experimental platform.

[0102] No-fly zone number Central x-coordinate Central ordinate Coverage radius 1 40 33 6

[0103] For a flight vehicle i∈M, let ρ i =ρ=0.016, λ = 1. At the initial moment, all aircraft are set to standby, and the task allocation results are as follows: Figure 4 As shown. By Figure 4 It can be seen that the task allocation result for the drone is as follows:

[0104] Target 1: {1,2,3}; Target 2: {4,5}; Target 3: {7,8,9}; Target 4: {6,10}.

[0105] Figure 5 The curve showing the change of the total cluster revenue with the number of iterations is presented. It can be seen that under the distributed task algorithm designed in this invention, the total cluster revenue continuously increases and finally converges at the 11th iteration.

[0106] In fault scenario simulations, the distributed task allocation algorithm proposed in this invention can adapt to failure scenarios. Figure 3 It can be seen that for any aircraft i∈M, after removing the failed aircraft, the currently selected target is not to improve the performance index. If the goal is to maximize the optimal objective, then in this iteration, at least one of spacecraft i and its neighboring spacecraft will change its objective. The cluster will then continue iterating until each spacecraft chooses an optimal objective that maximizes its own performance metrics.

[0107] To compare with the fault-free scenario, the coordinate information of each aircraft, the coordinates and importance information of each target, and the center coordinates and coverage radius information of each no-fly zone are the same as in Tables 1, 2, and 3. The cluster adjusts its task allocation based on the fault-free scenario. Considering the set of aircraft experiencing a complete failure as {6, 10}, the cluster's task allocation results after the failure are as follows: Figure 6 As shown. By Figure 6 It can be seen that the task allocation result for the drone is:

[0108] Target 1: {1,2,3}; Target 2: {4,5}; Target 3: {7,8}; Target 4: {9}

[0109] Figure 7 The curves showing the change of the total cluster revenue with the number of iterations are presented. It can be seen that the total cluster revenue decreases in the 12th iteration due to the occurrence of a complete failure. Subsequently, under the designed distributed task algorithm, the total cluster revenue continues to increase and finally converges in the 13th iteration.

[0110] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A cluster-distributed dynamic target allocation method based on preference alliance game theory, characterized in that, Includes the following steps: (1) Define the decision variable matrix to characterize the matching relationship between the target and the aircraft, clarify the target conflict constraints and target matching constraints of the cluster aircraft, and establish the cluster communication topology; (2) Construct a strike effectiveness function based on the probability of the aircraft successfully striking the target, and then design the aircraft benefit function and establish the aircraft optimization performance index; (3) Design a preference-based objective selection rule based on the aircraft's payoff function and optimization performance index, and establish a preference alliance game model; (4) Based on the preference alliance game model and the maximum information gain algorithm, a distributed dynamic target allocation algorithm is designed so that the cluster of aircraft can reach the Nash stable partition through local information interaction under the communication topology; The decision variable matrix defined in step (1) to characterize the matching relationship between the target and the aircraft is as follows: Given One goal and A flying machine, < Define the decision variable matrix. ,in, Characterizing the aircraft and target The matching relationship, This indicates a successful match; otherwise, the match failed, where M is the set of aircraft. For the target set, , Corresponding to the real target, the target Corresponds to being on standby; The target conflict constraints and target matching constraints of the cluster aircraft in step (1) are as follows: Considering that each aircraft can only engage one target, establish target conflict constraints: ； Considering the limited strike range and maneuverability of aircraft, based on the aircraft coordinate information ,Target coordinate information No-fly zone center coordinates Coverage radius Establish target matching constraints: ； ； ； ； ； ； in, Combining the long-range strike capability and maneuverability of integrated aircraft, A positive constant. It is a parameter characterizing the long-range strike capability of an aircraft. It is a parameter characterizing the maneuverability of an aircraft; It is an aircraft To the target The straight-line distance; It is an aircraft With the goal The distance between the line connecting the two and the no-fly zone. It is a no-fly zone Center coordinates to the aircraft With the goal The perpendicular distance of the line connecting them. It is an aircraft To the target A list of no-fly zones that need to be avoided; The cluster communication topology described in step (1) is as follows: Define the aircraft based on the target matching constraints. The set of real targets that can be selected is Construct an undirected graph with the set of aircraft as nodes. To characterize the communication topology of an aircraft swarm; undirected graph The adjacency matrix is , among which, if ,but ;otherwise ;definition For aircraft The set of neighbors; The implementation process of step (2) is as follows: aircraft and target The closer the distance between them, the shorter the overlap between the line connecting them and the no-fly zone, and the stronger the strike effectiveness of the aircraft; based on this, a single aircraft... For the target The strike effectiveness function, i.e., the aircraft For the target The probability function for a successful strike is as follows: ； Define the currently selected target The collection of aircraft is ,Target The benefits of a successful strike are determined by the collective. Internal aircraft sharing, aircraft Select target The payoff function is: ； Establish an aircraft The local optimization objective is as follows: ； in, Value refers to the reward obtained after the target is successfully attacked. For aircraft Optimized performance metrics For aircraft The chosen target For aircraft The target set chosen by the neighbors; The probability of a successful strike by an aircraft in step (2) also includes multiple aircraft striking the target. Probability of a successful strike: ； in, Select target for the current task A collection of aircraft. For a single aircraft For the target The probability of a successful strike; The implementation process of step (3) is as follows: Define symbols Characterizing the aircraft The preference relationship with the alliance, if for the target ,satisfy: ； So , indicating aircraft Compared to joining the league Prefer to join an alliance ; Based on this preference relationship, the target selection rule for aircraft is established as follows: Given a coalition partition For any aircraft If and only if a target exists and , making Only then will the aircraft match the current target. Adjust to target That is, from the alliance Adjusted to the league ; Based on aircraft set Target set Based on the aforementioned preference-based goal selection rules, a preference alliance game model is established.

2. The cluster-distributed dynamic target allocation method based on preference alliance game theory according to claim 1, characterized in that, Step (4) of the distributed dynamic target allocation algorithm includes target selection and information transmission as well as information reception and target update, wherein: Target selection and message sending are as follows: 1) Introducing variables Characterizing the aircraft Select the target and set its initial value. Introducing sets To avoid aircraft Repeatedly select the same target and set its initial value to... ; 2) Determine the set Is it an empty set? If it is, skip to step 3; if it is not an empty set, then in the set... Select any target If satisfied This indicates that the aircraft Compared to joining the league Prefer to join an alliance Therefore, Assign to and will From the set Remove from the middle, skip to 2); 3) Calculate the aircraft By Alliance Adjusted to the league Post-optimization performance metrics The increase ,Will Send to the aircraft All neighbors; Information reception and target update are as follows: Based on the received information on changes in aircraft performance indicators within the neighbor set, determine whether the requirements are met. If the condition is met, it indicates that the current aircraft... Change target selection to Afterwards, if the increase in its performance metrics is greater than that of its neighbors, then, based on the idea of ​​the maximum information gain algorithm, this iteration ends, and the spacecraft... Change target selection to If this condition is not met, it means there exists a neighbor whose performance index increases by an amount greater than or equal to that of the aircraft. So, this iteration is now complete, and the spacecraft... Still choose target ;when At that time, the increase It is 0.

3. The cluster-distributed dynamic target allocation method based on preference alliance game theory according to claim 1, characterized in that, For the target The probability of a successful strike is set to .

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