Rapid solving method and system for attack and defense game of multiple unmanned aerial vehicles

Abstract

The invention provides a rapid solving method and system for an attack and defense game of multiple unmanned aerial vehicles. According to the method, a strict game matrix is calculated through a zero-sum game matrix model, and a Nash equilibrium solution is obtained. Selecting a first initial strategy group of the first unmanned aerial vehicle formation and a second initial strategy group of the second unmanned aerial vehicle formation according to a Nash equilibrium solution, and solving a first optimal strategy group of the first unmanned aerial vehicle formation based on the attack and defense strategy probability and the second initial strategy group, and solving a second optimal strategy group of the second unmanned aerial vehicle formation based on the attack and defense strategy probability and the first initial strategy group. And if the calculated first optimal strategy group and / or the second optimal strategy group are / is not in the strict game matrix, updating the strict game matrix, and obtaining the Nash equilibrium solution again. And if the optimal strategy group is in the strict game matrix, setting a strategy group of the multi-unmanned aerial vehicle formation according to a Nash equilibrium solution. According to the invention, by introducing the attack and defense strategy, the solving efficiency of the multi-unmanned aerial vehicle target allocation scheme is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a method and system for quickly solving a multi-unmanned aerial vehicle attack and defense game. Background Art

[0002] Drones can autonomously complete given tasks in a variety of environments, effectively reducing labor costs. Compared with manned aircraft, drones have the characteristics of no casualties, strong maneuverability, light weight, and low cost. Due to the limited number and performance of weapons and sensors carried by a single drone, its ability to perform air combat missions is correspondingly limited, and multiple drones can better complete air combat missions through effective coordination. Therefore, the coordinated execution of tasks by multiple drones has received more and more attention. How to set the target allocation for drone formations for coordinated operations is a major problem at present.

[0003] In the prior art, through the multi-UAV target allocation model constructed based on the game theory method, experts and scholars mostly use the game theory method to solve it, including the exact solution algorithm and the heuristic solution algorithm.

[0004] Due to the high complexity of such problems, the scale of the problem will grow exponentially with the increase in the number of drones. Most of the existing solution algorithms are only applicable to the solution of small and medium-sized problems, or cannot achieve both solution efficiency and solution time. The exact algorithm is only applicable to the solution of small-scale weapon target allocation problems, and it is difficult to obtain the optimal solution for medium and large-scale problems within an effective time; the heuristic algorithm can make full use of domain knowledge to improve the efficiency of problem solving, but the quality of the solution depends on the setting of heuristic rules, the algorithm performance has a certain volatility, and it is easy to fall into the local optimum, resulting in low efficiency in solving the allocation solution. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides a method and system for quickly solving the attack and defense game of multiple UAVs, which solves the technical problem of low solution efficiency when obtaining target allocation solutions for multiple UAVs in the prior art.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] The present invention solves the technical problem by providing a fast solution method for a multi-UAV attack and defense game, which is executed by a computer and includes the following steps:

[0010] Constructing a zero-sum game matrix model; the zero-sum game matrix model includes multi-UAV formation information of a first UAV formation and a second UAV formation, multi-UAV strategy information and a payoff matrix; the multi-UAV strategy information includes a strategy group set of the UAV formation, the strategy group includes strategy pairs of multiple UAVs in the UAV formation, and the strategy pairs are used to characterize attack and defense strategies and strategy targets; the attack and defense strategies include attack strategies, avoidance strategies and interference strategies; the multi-UAV formation information includes UAV information of the UAV formation, and the UAV information includes attack and defense strategy probabilities;

[0011] Calculating a strict game matrix based on the zero-sum game matrix model, and solving a Nash equilibrium solution based on the strict game matrix;

[0012] Selecting a first initial strategy group of the first drone formation and a second initial strategy group of the second drone formation according to the Nash equilibrium solution; the initial strategy group is the strategy group with the highest probability in the strict game matrix;

[0013] Solving a first optimal strategy group for the first UAV formation based on the attack and defense strategy probability and the second initial strategy group, and solving a second optimal strategy group for the second UAV formation based on the attack and defense strategy probability and the first initial strategy group;

[0014] If the first optimal strategy group and / or the second optimal strategy group are not in the strict game matrix, updating the strict game matrix, and re-performing the step of solving the Nash equilibrium solution based on the strict game matrix based on the updated strict game matrix;

[0015] If the first optimal strategy group and the second optimal strategy group are both in the strict game matrix, the strategy group of the multi-UAV formation is set according to the Nash equilibrium solution.

[0016] Preferably, the profit matrix includes:

[0017]

[0018] Among them, S R represents the first UAV formation strategy group set, s R ∈S R represents a strategy group of the first UAV formation, S B represents the second UAV formation strategy set, s B ∈S B represents a strategy group of the second UAV formation;

[0019] (s R ,s B ) indicates that the first UAV formation adopts strategy group s R , the second drone formation adopts the strategy group sB Strategy group combination; u(s R ,s B ) is the first UAV formation in the strategy group combination (s R ,s B )

[0020] u(s R ,s B )=u R (s R ,s B )-u B (s R ,s B )

[0021]

[0022] Among them, u R (s R ,s B ) and u B (s R ,s B ) represent the expected survival values ​​of the first UAV formation and the second UAV formation respectively;

[0023] v i represents the value of drone i in the first drone formation, v j represents the value of drone j in the second drone formation; drone j is the strategic target of drone i; m represents the number of drones in the first drone formation, and n represents the number of drones in the second drone formation;

[0024] q i represents the avoidance probability of UAV i to the second UAV formation, h i represents the avoidance parameter of UAV i to the second UAV formation; q j represents the avoidance probability of UAV j to the first UAV formation, h j represents the avoidance parameter of UAV j to the first UAV formation;

[0025] t ij represents the interference probability of UAV i to UAV j, l ij represents the interference parameter of UAV i to UAV j; t ji represents the interference probability of UAV j to UAV i, l ji represents the interference parameter of UAV j to UAV i;

[0026] p ji represents the probability of drone j killing drone i, b ji represents the attack parameter of drone j on drone i; p ijrepresents the probability of drone i killing drone j, a ij Represents the attack parameter of drone i on drone j.

[0027] Preferably, calculating the strict game matrix based on the zero-sum game matrix model includes:

[0028] Select p strategy groups from the first drone formation strategy group set, and select q strategy groups from the second drone formation strategy group set; wherein p≥2, q≥2;

[0029] A strict game matrix is ​​generated based on the p strategy groups and the q strategy groups.

[0030] Preferably, solving the Nash equilibrium solution based on the strict game matrix includes:

[0031] The strict game matrix is ​​processed based on a preset probability distribution algorithm to solve a Nash equilibrium solution; the Nash equilibrium solution includes a first probability distribution of the p strategy groups and a second probability distribution of the q strategy groups;

[0032] Selecting a first initial strategy group of the first UAV formation and a second initial strategy group of the second UAV formation according to the Nash equilibrium solution includes:

[0033] The Nash equilibrium solution is traversed, and a strategy group with the largest probability is obtained according to the first probability distribution, and is determined as a first initial strategy group; and a strategy group with the largest probability is obtained according to the second probability distribution, and is determined as a second initial strategy group.

[0034] Preferably, solving the first optimal strategy group of the first drone formation based on the attack and defense strategy probability and the second initial strategy group includes:

[0035] Initializing a first drone vector of a first drone formation; the first drone vector is used to characterize the policy pair state of each drone in the first drone formation, the policy pair state including a determined state indicating that the drone has determined the policy pair and an undetermined state indicating that the drone has not determined the policy pair; in the initialized first drone vector, the policy pair state of all drones is an undetermined state;

[0036] Calculate the initial survival probability of the drone; obtain the first target drone in an undetermined state in the first drone formation based on the first drone vector; obtain multiple target drone strategy pairs of the target drone in the multi-drone strategy information;

[0037] Solving the marginal return value of the target drone strategy pair according to the initial survival probability of the drone, and setting the target drone strategy pair with the largest marginal return value as the target strategy pair of the target drone;

[0038] updating the policy pair state of the target drone in the first drone vector to a determined state, and detecting the updated first drone vector;

[0039] If there is a drone in an undetermined state in the updated first drone vector, re-performing the step of acquiring the first target drone in an undetermined state in the first drone formation based on the first drone vector based on the updated first drone vector;

[0040] If all drones in the updated first drone vector are in a determined state, a first optimal strategy group is determined according to the target strategy pairs of all drones.

[0041] Preferably, the calculating of the initial survival probability of the drone includes:

[0042] Set a first initial avoidance probability of the first drone formation and a second initial avoidance probability of the second drone formation; calculate a first initial non-destroyed probability of the drones in the first drone formation and a second initial non-destroyed probability of the drones in the second drone formation:

[0043]

[0044] in,

[0045] B_uninterference[j] represents the probability that UAV j is not interfered by the first UAV formation;

[0046] R_undes[i] represents the first initial probability of drone i not being destroyed;

[0047] R_uninterference[i] represents the probability that UAV i is not interfered by the second UAV formation;

[0048] B_undes[j] represents the second initial probability of drone j not being destroyed;

[0049] Calculating the initial survival probability of a first drone in the first drone formation according to the first initial avoidance probability and the first initial non-destruction probability; calculating the initial survival probability of a second drone in the second drone formation according to the second initial avoidance probability and the second initial non-destruction probability; comprising:

[0050] R_survival[i]=1-(1-R_evasive[i])·(1-R_undes[i])

[0051] B_survival[j]=1-(1-B_evasive[j])·(1-B_undes[j])

[0052] Among them, R_survival[i] represents the first initial survival probability of drone i, R_evasive[i] represents the first initial evasion probability of drone i, and R_undes[i] represents the first initial non-destroyed probability of drone i;

[0053] B_survival[j] represents the second initial survival probability of drone j, and B_evasive[j] represents the second initial evasion probability of drone j.

[0054] Preferably, solving the marginal return value of the target drone strategy pair according to the initial survival probability of the drone includes:

[0055] Solving the attack and defense strategy according to the target drone strategy;

[0056] Solve the updated survival probability of the drone according to the attack and defense strategy;

[0057] The marginal return value of the target drone strategy pair is calculated according to the updated survival probability of the drone and the initial survival probability of the drone.

[0058] Preferably, solving the updated survival probability of the drone according to the attack and defense strategy includes:

[0059] Determine a first drone and a second drone that match the target drone strategy;

[0060] If the attack and defense strategy is an attack strategy, the first drone updated survival probability of the first drone is set equal to the first drone initial survival probability; and the second drone updated survival probability of the second drone is calculated as:

[0061] j_survival=1-(1-q j )·(1-Δ B_undes [ipj])

[0062] Δ B_undes [i,p,j]=B_undes[j]·(1-R_uninterference[i]·p ij )

[0063] Among them, j_survival represents the updated survival probability of the second UAV;

[0064] [i,p,j] represents the drone triple formed by the first drone i and the target drone strategy pair [p,j]; q jrepresents the avoidance probability of the second UAV j to the first UAV formation;

[0065] If the attack and defense strategy is an avoidance strategy, the updated survival probability of the second drone is set equal to the initial survival probability of the second drone; and, in the second initial strategy group, it is detected whether there is a drone that uses an attack strategy to attack the first drone; if not, the updated survival probability of the first drone is set equal to the initial survival probability of the first drone, and if so, the updated survival probability of the first drone is calculated as:

[0066] i_survival=1-(1-q i )·(1-R_undes[i])

[0067] Among them, i_survival represents the updated survival probability of the first drone; q i represents the avoidance probability of the first UAV i to the second UAV formation;

[0068] If the attack and defense strategy is a jamming strategy, the updated survival probability of the first UAV is set to be equal to the initial survival probability of the first UAV, and the updated survival probability of the second UAV is set to be equal to the initial survival probability of the second UAV;

[0069] Solving the attack and defense strategy of the second UAV according to the second initial strategy group; and when the attack and defense strategy is an attack strategy, obtaining the attack target of the second UAV; calculating the association probability group of the UAV triple according to the attack target; the association probability group includes:

[0070] [i,p,j]=[j_target,j_target_undes,j_target_survival]

[0071]

[0072] j_target_survival=1-(1-R_evasive[j_target])·(1-j_target_undes)

[0073] in,

[0074] j_target represents the attack target, j_target_undes represents the probability that the first UAV of the attack target is not destroyed, and j_target_survival represents the updated survival probability of the first UAV of the attack target;

[0075] The calculating the marginal return value of the target drone strategy pair according to the updated survival probability of the drone and the initial survival probability of the drone includes:

[0076] δipj =u' R -u R

[0077]

[0078] Among them, δ ipj represents the marginal reward value of the target drone strategy pair;

[0079] Indicates the value of the first drone, Indicates the value of the second drone.

[0080] Preferably, before detecting the updated first drone vector, the method further includes:

[0081] Obtain a target attack and defense strategy, a target first UAV, and a target second UAV of the target strategy pair;

[0082] Updating the initial survival probability of the drone according to the target attack and defense strategy includes:

[0083] If the target attack and defense strategy is an attack strategy, the second initial probability of the target second UAV not being destroyed and the initial survival probability of the second UAV are updated as follows:

[0084] B_undes[j * ]=Δ B_undes [i * ,p * ,j * ]

[0085] B_survival[j * ] = j * _survival

[0086] Among them, i * Indicates the target first drone, p * represents the target attack and defense strategy, j * Indicates the target second drone;

[0087] If the target attack and defense strategy is an avoidance strategy, the first initial avoidance probability of the target first UAV and the initial survival probability of the first UAV are updated as follows:

[0088] R_evasive[i * ] = q i*

[0089] R_survival[i * ]=i * _survival

[0090] If the target attack and defense strategy is an interference strategy, obtain the target association probability group of the drone triple formed by the target first drone and the target strategy pair; obtain the attack target in the target association probability group, and update the first initial non-destruction probability of the attack target and the initial survival probability of the first drone according to the target association probability group.

[0091] The present invention solves the technical problem by providing a fast solution system for multi-UAV attack and defense games, the system comprising:

[0092] The model generation module is configured to construct a zero-sum game matrix model; the zero-sum game matrix model includes multi-UAV formation information of the first UAV formation and the second UAV formation, multi-UAV strategy information and a payoff matrix; the multi-UAV strategy information includes a strategy group set of the UAV formation, the strategy group includes strategy pairs of multiple UAVs in the UAV formation, and the strategy pairs are used to characterize attack and defense strategies and strategy targets; the attack and defense strategies include attack strategies, avoidance strategies and interference strategies; the multi-UAV formation information includes UAV information of the UAV formation, and the UAV information includes attack and defense strategy probabilities;

[0093] A strict game matrix acquisition module is configured to calculate a strict game matrix based on a zero-sum game matrix model, and solve a Nash equilibrium solution based on the strict game matrix;

[0094] The initial strategy group module is configured to select a first initial strategy group of the first drone formation and a second initial strategy group of the second drone formation according to a Nash equilibrium solution; the initial strategy group is a strategy group with the largest probability in a strict game matrix;

[0095] An optimal strategy group module is configured to solve a first optimal strategy group for the first UAV formation based on the attack and defense strategy probability and the second initial strategy group, and solve a second optimal strategy group for the second UAV formation based on the attack and defense strategy probability and the first initial strategy group;

[0096] a scheme allocation module, configured to update the strict game matrix if the first optimal strategy group and / or the second optimal strategy group are not in the strict game matrix, and re-execute the step of solving the Nash equilibrium solution based on the strict game matrix based on the updated strict game matrix;

[0097] If the first optimal strategy group and the second optimal strategy group are both in the strict game matrix, the strategy group of the multi-UAV formation is set according to the Nash equilibrium solution.

[0098] (III) Beneficial effects

[0099] The present invention provides a method and system for quickly solving the multi-UAV attack and defense game. Compared with the prior art, it has the following beneficial effects:

[0100] The present invention calculates a strict game matrix through a zero-sum game matrix model and solves a Nash equilibrium solution. According to the Nash equilibrium solution, a first initial strategy group of the first drone formation and a second initial strategy group of the second drone formation are selected, and the first optimal strategy group of the first drone formation is solved based on the attack and defense strategy probability and the second initial strategy group, and the second optimal strategy group of the second drone formation is solved based on the attack and defense strategy probability and the first initial strategy group. If the first optimal strategy group and / or the second optimal strategy group are not in the strict game matrix, the strict game matrix is ​​updated and the Nash equilibrium solution is solved again. If they are all in the strict game matrix, the strategy group of the multi-drone formation is set according to the Nash equilibrium solution. The present invention takes into account the attack and defense strategies and improves the efficiency of solving the target allocation scheme for multiple drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0102] Figure 1 A schematic diagram of a scenario of a method for quickly solving a multi-UAV attack and defense game provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0103] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0104] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0105] An embodiment of the present invention provides a method for quickly solving a multi-UAV attack and defense game, which is executed by a computer and includes the following steps:

[0106] S1. Construct a zero-sum game matrix model; the zero-sum game matrix model includes multi-UAV formation information of the first UAV formation and the second UAV formation, multi-UAV strategy information and a payoff matrix; the multi-UAV strategy information includes a strategy group set of the UAV formation, the strategy group includes strategy pairs of multiple UAVs in the UAV formation, and the strategy pairs are used to characterize attack and defense strategies and strategy targets; the attack and defense strategies include attack strategies, avoidance strategies and interference strategies; the multi-UAV formation information includes UAV information of the UAV formation, and the UAV information includes attack and defense strategy probabilities;

[0107] S2. Calculate the strict game matrix based on the zero-sum game matrix model, and solve the Nash equilibrium solution based on the strict game matrix;

[0108] S3, selecting a first initial strategy group of the first drone formation and a second initial strategy group of the second drone formation according to the Nash equilibrium solution; the initial strategy group is the strategy group with the largest probability in the strict game matrix;

[0109] S4, solving a first optimal strategy group for the first UAV formation based on the attack and defense strategy probability and the second initial strategy group, and solving a second optimal strategy group for the second UAV formation based on the attack and defense strategy probability and the first initial strategy group;

[0110] S5. If the first optimal strategy group and / or the second optimal strategy group are not in the strict game matrix, the strict game matrix is ​​updated, and the step of solving the Nash equilibrium solution based on the strict game matrix is ​​re-executed based on the updated strict game matrix;

[0111] If the first optimal strategy group and the second optimal strategy group are both in the strict game matrix, the strategy group of the multi-UAV formation is set according to the Nash equilibrium solution.

[0112] Figure 1 This is a schematic diagram of a scenario of a method for quickly solving a multi-UAV attack and defense game provided by an embodiment of the present invention. Each step is analyzed in detail below.

[0113] In step S1, a zero-sum game matrix model is constructed. Considering that the strategies adopted by the two drone formations will affect the other party's benefits, a zero-sum game matrix model G = (P, S, U) is modeled. The game model includes a drone part, a strategy part, and a benefit part.

[0114] Among them, P = {R, B} is the multi-drone formation information, which represents the participants of the game, and the multi-drone formation information includes drone information. The two parties of the game include two multi-drone formations, which are respectively referred to as the red party and the blue party in the embodiment of the present application. R represents the red party, which is called the first drone formation in the embodiment of the present application. B represents the blue party, which is called the second drone formation in the embodiment of the present application.

[0115] It should be noted that each drone in the first drone formation is collectively referred to as the first drone; each drone in the second drone formation is collectively referred to as the second drone. Each drone formation can include a number of drones. The red side has m drones and the blue side has n drones.

[0116] The drone information also includes the value of each drone. The value of the R-squared drone is expressed as The value of Party B’s drone is expressed as

[0117] The strategy part S includes the multi-drone strategy information. Strategy part S = S R ×S B , represents the strategy set of the two drone formations in the game. R represents the strategy group set of the first UAV formation, that is, the strategy group set of the red team, s R ∈S R Represents a strategy group of the red team. B represents the strategy group set of the second UAV formation, that is, the strategy group set of the blue team, s B ∈S B Represents a strategy group of the blue team. The strategy group includes the strategy pair of each drone in the drone formation.

[0118] The strategy pair includes the current UAV, the attack and defense strategy and the strategy target; the attack and defense strategy includes the attack strategy, the avoidance strategy and the interference strategy; the strategy group includes the strategy pairs of multiple UAVs in the multi-UAV formation.

[0119] When executing a multi-drone game, each drone can adopt three strategies: attack strategy, avoidance strategy and interference strategy. At the same time, drones can use strategies to target any opponent's drones. The targets are called strategic targets. Drones can be equipped with fire attack weapons and electronic jamming weapons. Drones can attack a certain enemy drone, avoid attacks from a certain enemy drone, or interfere with the attacks of a certain enemy drone, but can only choose to execute one strategy at a time. Drones can use strategy pairs to play games. Strategy pairs refer to the attack and defense strategies and strategic targets adopted by a certain drone.

[0120] Taking the i-th drone of the red side as an example, its strategy pair is (p, j), which means that it adopts the attack and defense strategy p, and the strategy target is the j-th drone of the blue side. Since the red drone can adopt 3 strategies, and the strategy target can be set to any of the n drones of the blue side, each red drone can have (3×n) strategy pairs. Similarly, the blue drone can adopt 3 strategies, and the strategy target can be set to any of the m drones of the red side, so each blue drone can have (3×m) strategy pairs.

[0121] It should be noted that for a drone formation, when performing a mission, each drone will adopt a strategy pair, and the strategy pairs adopted by all drones together constitute the current strategy group of the drone formation. As the strategy pairs adopted by each drone may be different, the overall strategy group of the drone formation will also be different.

[0122] In the present application embodiment, using represents a strategy group of R party,

[0123] It represents the strategy pair adopted by drone i of party R, including attack and defense strategies and strategy goals. represents the strategy adopted by drone i, They represent the three offensive and defensive strategies of attack, avoidance, and interference respectively; Indicates the strategy target of attack, avoidance, and interference of drone i of party R. For example, the strategy group formed by the attack and defense strategy of party R is [(1,2),(2,3),(3,3)], which means that drone 1 of party R attacks drone 2 of party B, drone 2 of party R avoids the attack of drone 3 of party B, and drone 3 of party R interferes with drone 3 of party B.

[0124] use It can represent a strategy group of Party B. Represents the strategy pair adopted by drone j of party B. represents the attack and defense strategy adopted by UAV j, They represent three offensive and defensive strategies: attack, evade, and interfere; Indicates the target for the drone to attack, avoid, or interfere with.

[0125] For drone formations, each drone selects a strategy pair, and the strategy pairs of all drones are counted to form a strategy group for the drone formation. Different strategy groups can be obtained by different strategy pairs selected by drones. All possible strategy group situations are counted to generate a strategy group set as a game plan that can be adopted by the drone formation.

[0126] For the red team, since the m red drones can each have (3×n) strategy pairs, the number of strategy pairs of the red team formation is τ R is (3×n) m Similarly, the number of strategies for the blue team is τ B =(3×m) n The strategy space of the two adversaries is equal to the product of the number of possible strategy pairs that the two adversaries may adopt. The size of the strategy space of the game is

[0127] S|=τ R ·τ B =(3×n)m (3×m) n .

[0128] In some embodiments, each attack and defense strategy can be set with a strategy probability, including a kill probability corresponding to the attack strategy, an avoidance probability corresponding to the avoidance strategy, and an interference probability corresponding to the interference strategy. The three strategy probabilities are expressed as follows:

[0129] The kill probability represents the probability that one drone successfully destroys another drone. The kill probability of R party i against B party j can be expressed as The killing probability matrix of party R against party B can be expressed as where p ij represents the probability of R drone i killing B drone j. Similarly, the probability of B j killing R drone i can be expressed as The killing probability matrix of party B against party R can be expressed as

[0130] The avoidance probability represents the probability that a drone successfully avoids an attack by another drone. The avoidance probability of party R i against party B’s attack can be expressed as The avoidance probability matrix of party R to party B is expressed as Similarly, the probability of B party j avoiding R party's attack can be expressed as The avoidance probability matrix of party B to party R can be expressed as

[0131] The interference probability represents the probability that one drone successfully interferes with the attack of another drone. The interference probability of R party i to B party j can be expressed as The interference probability matrix of party R to party B can be expressed as Similarly, the interference probability of party B j to party R i can be expressed as The interference probability matrix of party B to party R can be expressed as

[0132] Each probability can be pre-generated, and each strategy probability of the drone can be randomly generated.

[0133] The profit part U is the profit matrix. The profit matrix is ​​expressed as:

[0134]

[0135] Among them, S R represents the first UAV formation strategy group set, s R ∈S R represents a strategy group of the first UAV formation, S B represents the second UAV formation strategy set, s B ∈S BRepresents a strategy group of the second drone formation.

[0136] (s R ,s B ) indicates that the first UAV formation adopts strategy group s R , the second drone formation adopts the strategy group s B Strategy group combination; u(s R ,s B ) is the first UAV formation in the strategy group combination (s R ,s B )

[0137] u(s R ,s B )=u R (s R ,s B )-u B (s R ,s B )

[0138]

[0139] Among them, u R (s R ,s B ) and u B (s R ,s B ) represent the expected survival values ​​of the first UAV formation and the second UAV formation respectively;

[0140] v i represents the value of drone i in the first drone formation, v j represents the value of drone j in the second drone formation; drone j is the strategic target of drone i; m represents the number of drones in the first drone formation, and n represents the number of drones in the second drone formation;

[0141] q i represents the avoidance probability of UAV i to the second UAV formation, h i represents the avoidance parameter of UAV i to the second UAV formation; q j represents the avoidance probability of UAV j to the first UAV formation, h j represents the avoidance parameter of UAV j to the first UAV formation;

[0142] t ij represents the interference probability of UAV i to UAV j, l ij represents the interference parameter of UAV i to UAV j; t ji represents the interference probability of UAV j to UAV i, l jirepresents the interference parameter of UAV j to UAV i;

[0143] p ji represents the probability of drone j killing drone i, b ji represents the attack parameter of drone j on drone i; p ij represents the probability of drone i killing drone j, a ij Represents the attack parameter of drone i on drone j.

[0144] Represents the total expected survival value of all drones on party R.

[0145] represents the survival probability of drone i on party R; represents the probability that drone i fails to evade successfully and is destroyed, represents the probability that drone i fails to evade the attack;

[0146] represents the probability of drone i being destroyed;

[0147] represents the probability that drone i is not destroyed;

[0148] represents the probability that drone i is not destroyed by drone j of party B, represents the probability that drone i is destroyed by drone j, It represents the probability that drone j is not successfully interfered by drone R.

[0149] The three parameter settings are as follows:

[0150]

[0151] Among them, a ij represents the attack parameter of drone i in the first drone formation against drone j in the second drone formation; m represents the number of drones in the first drone formation, and n represents the number of drones in the second drone formation;

[0152] represents the attack and defense strategy adopted by drone i; They represent attack strategy, avoidance strategy and interference strategy respectively; It indicates the strategic target that drone i targets using the attack and defense strategy.

[0153] The first avoidance strategy parameter matrix is:

[0154]

[0155] Among them, h iIt represents the avoidance parameter of UAV i to the second UAV formation.

[0156]

[0157] Among them, l ij represents the interference parameter of UAV i to UAV j.

[0158] The second strategy parameter matrix includes a second attack strategy parameter matrix, a second avoidance strategy parameter matrix, and a second interference strategy parameter matrix.

[0159]

[0160] Among them, b ji represents the attack parameter of drone j on drone i;

[0161] represents the attack and defense strategy adopted by UAV j, They represent attack strategy, avoidance strategy and interference strategy respectively; represents the policy goal of UAV j.

[0162]

[0163] Among them, h j represents the avoidance parameter of UAV j to the first UAV formation.

[0164]

[0165] Among them, l ji represents the interference parameter of UAV j to UAV i.

[0166] For the payoff matrix of the game, u(s R ,s B ) is the R square in the strategy group combination (s R ,s B ), the benefit value of Party B is -u(s R ,s B ).

[0167] In step S2, a strict game matrix is ​​calculated based on the zero-sum game matrix model, and a Nash equilibrium solution is solved based on the strict game matrix.

[0168] Select p strategy groups from the first drone formation strategy group set, and select q strategy groups from the second drone formation strategy group set, where p≥2 and q≥2.

[0169] A strict game matrix is ​​generated based on the p strategy groups and the q strategy groups. A first strict game set of the first drone formation is generated based on the p strategy groups, and a second strict game set of the second drone formation is generated based on the q strategy groups.

[0170] The strict game matrix is ​​processed based on a preset probability distribution algorithm to solve a Nash equilibrium solution, wherein the Nash equilibrium solution includes the first probability distribution of the p strategy groups and the second probability distribution of the q strategy groups.

[0171] In step S3, a first initial strategy group of the first drone formation and a second initial strategy group of the second drone formation are selected according to the Nash equilibrium solution, wherein the initial strategy group is the strategy group with the highest probability in the strict game matrix.

[0172] Specifically, the Nash equilibrium solution can be traversed, and the strategy group with the highest probability can be obtained according to the first probability distribution and determined as the first initial strategy group; the strategy group with the highest probability can be obtained according to the second probability distribution and determined as the second initial strategy group.

[0173] In step S4, a first optimal strategy group of the first drone formation is solved based on the attack and defense strategy probability and the second initial strategy group, and a second optimal strategy group of the second drone formation is solved based on the attack and defense strategy probability and the first initial strategy group.

[0174] The first optimal strategy group of the first UAV formation can be solved first, including the following steps:

[0175] S401, initialize a first drone vector of a first drone formation. The first drone vector is used to characterize the policy pair state of each drone in the first drone formation, and the policy pair state includes a determined state indicating that the drone has determined the policy pair and an undetermined state indicating that the drone has not determined the policy pair. In the initialized first drone vector, the policy pair state of all drones is an undetermined state.

[0176] In the embodiment of the present application, the first drone vector bool_R=[b i ] 1×m To record whether the drones in the first drone formation have determined the strategy pair. If the strategy pair has been determined, the strategy pair state is set to the determined state, which can be represented by 1; if the strategy pair has not been determined, the strategy pair state is set to the undetermined state, which can be represented by 0. The first drone vector contains m elements, each element corresponds to a first drone, and the strategy pair state of the drone is represented by setting the element to 1 or 0. Initially, all elements are 0, that is, all drones have not determined the strategy pair.

[0177] S402, calculating the initial survival probability of the UAV, including the following steps:

[0178] A first initial avoidance probability of the first UAV formation and a second initial avoidance probability of the second UAV formation are set, wherein the initial avoidance probability can directly adopt the aforementioned avoidance probability.

[0179] Calculating a first initial probability of drones in the first drone formation not being shot down and a second initial probability of drones in the second drone formation not being shot down comprises:

[0180]

[0181] Wherein, B_uninterference[j] represents the probability that UAV j is not interfered by the first UAV formation; R_undes[i] represents the first initial probability that UAV i is not destroyed;

[0182] R_uninterference[i] represents the probability that UAV i is not interfered by the second UAV formation;

[0183] B_undes[j] represents the second initial probability of drone j not being destroyed.

[0184] Calculating a first drone initial survival probability of drones in the first drone formation according to the first initial avoidance probability and the first initial non-destruction probability; calculating a second drone initial survival probability of drones in the second drone formation according to the second initial avoidance probability and the second initial non-destruction probability; comprising:

[0185] R_survival[i]=1-(1-R_evasive[i])·(1-R_undes[i])

[0186] B_survival[j]=1-(1-B_evasive[j])·(1-B_undes[j])

[0187] Among them, R_survival[i] represents the first initial survival probability of drone i, R_evasive[i] represents the first initial evasion probability of drone i, and R_undes[i] represents the first initial non-destroyed probability of drone i;

[0188] B_survival[j] represents the second initial survival probability of drone j, and B_evasive[j] represents the second initial evasion probability of drone j.

[0189] S403: Acquire a first target drone in an undetermined state in the first drone formation based on the first drone vector.

[0190] S404: Acquire multiple policy pairs of the target drone from the multi-drone policy information, which are referred to as target drone policy pairs in the embodiment of the present application.

[0191] S405, solving the marginal reward value of the target drone strategy pair according to the initial survival probability of the drone, and setting the target drone strategy pair with the largest marginal reward value as the target strategy pair of the target drone. The marginal reward value acquisition steps are as follows:

[0192] S4051. Obtain an attack and defense strategy according to the target UAV strategy pair, that is, determine the attack and defense strategy in the target UAV strategy pair.

[0193] For each target UAV strategy pair, a triple can be generated based on the first UAV (i.e., the target UAV) that adopts the strategy pair and the strategy pair, represented as current UAV-attack and defense strategy-strategy target, denoted as [i, p, j], which characterizes the combination of UAV and adopted strategy pair.

[0194] S4052, obtaining the updated survival probability of the drone according to the attack and defense strategy. This includes the following steps:

[0195] Determine the first drone and the second drone that match the target drone strategy pair, that is, determine the current target drone (the first drone in the first drone formation) and the strategy target in the target drone strategy pair (the second drone in the second drone formation).

[0196] If the attack and defense strategy is an attack strategy, the first drone updated survival probability of the first drone is set equal to the first drone initial survival probability; and the second drone updated survival probability of the second drone is calculated as:

[0197] j_survival=1-(1-q j )·(1-Δ B_undes [ipj])

[0198] Δ B_undes [i,p,j]=B_undes[j]·(1-R_uninterference[i]*p ij )

[0199] Among them, j_survival represents the updated survival probability of the second UAV;

[0200] [i,p,j] represents the drone triple formed by the first drone i and the currently adopted target drone strategy [p,j]. j represents the avoidance probability of the second UAV j to the first UAV formation.

[0201] If the attack and defense strategy is an avoidance strategy, the updated survival probability of the second drone is set equal to the initial survival probability of the second drone; and, in the second initial strategy group, it is detected whether there is a drone that uses an attack strategy to attack the first drone; if not, the updated survival probability of the first drone is set equal to the initial survival probability of the first drone, and if so, the updated survival probability of the first drone is calculated as:

[0202] i_survival=1-(1-q i )·(1-R_undes[i])

[0203] Among them, i_survival represents the updated survival probability of the first drone; q i represents the avoidance probability of the first UAV i to the second UAV formation;

[0204] If the attack and defense strategy is an interference strategy, the updated survival probability of the first UAV is set equal to the initial survival probability of the first UAV, and the updated survival probability of the second UAV is set equal to the initial survival probability of the second UAV.

[0205] When the attack and defense strategy is an interference strategy, some data can also be calculated.

[0206] The attack and defense strategy of the second drone is obtained according to the second initial strategy group. In the current target drone strategy pair, the strategy target of the target drone (i.e., the second drone) can be determined. In the second initial strategy group, the strategy pair of each second drone is determined, so the attack and defense strategy of the second drone can be obtained.

[0207] When the attack and defense strategy of the second drone is an attack strategy, the attack target of the second drone is obtained (the strategy target is determined according to the strategy pair). The association probability group of the drone triplet is obtained according to the attack target. The association probability group includes:

[0208] [i,p,j]=[j_target,j_target_undes,j_target_survival]

[0209]

[0210] j_target_survival=1-(1-R_evasive[j_target])·(1-j_target_undes)

[0211] in,

[0212] j_target represents the attack target, j_target_undes represents the probability that the first UAV of the attack target is not destroyed, and j_target_survival represents the updated survival probability of the first UAV of the attack target.

[0213] S4053, calculating the marginal return value of the target drone strategy pair according to the updated survival probability of the drone and the initial survival probability of the drone. Including:

[0214] δ ipj =u' R -u R

[0215]

[0216] Among them, δ ipj represents the marginal reward value of the target drone strategy pair;

[0217] Indicates the value of the first drone, Indicates the value of the second drone.

[0218] S406: Update the policy pair state of the target drone in the first drone vector to a determined state, and detect the updated first drone vector.

[0219] In some embodiments, before detecting the updated first drone vector, some probability data may be updated, including:

[0220] After setting the target drone strategy pair with the largest marginal return value as the target strategy pair of the target drone, the target attack and defense strategy, the target first drone, and the target second drone of the target strategy pair can be obtained. Among them, the attack and defense strategy in the target strategy pair is the target attack and defense strategy, the strategy target in the target strategy pair is the target second drone, and the first drone currently using the target strategy pair (that is, the target drone in the previous step) is the target first drone. The drone triple formed by the first drone and the adopted target strategy pair is expressed as [i * ,p * ,j * ].

[0221] The initial survival probability of the drone can be updated according to the target attack and defense strategy, including the following steps:

[0222] If the target attack and defense strategy is an attack strategy, the second initial probability of the target second UAV not being destroyed and the initial survival probability of the second UAV are updated as follows:

[0223] B_undes[j * ]=Δ B_undes [i* ,p * ,j * ]

[0224] B_survival[j * ] = j * _survival

[0225] Among them, i * Indicates the target first drone, p * represents the target attack and defense strategy, j * Indicates the target second drone.

[0226] If the target attack and defense strategy is an avoidance strategy, the first initial avoidance probability of the target first UAV and the initial survival probability of the first UAV are updated as follows:

[0227] R_evasive[i * ] = q i*

[0228] R_survival[i * ]=i * _survival

[0229] If the target attack and defense strategy is a jamming strategy, then the target association probability group of the drone triple formed by the target first drone and the target strategy pair is obtained. In the above steps, the association probability groups of multiple drone triples can be obtained. Therefore, through the current drone triple [i * ,p * ,j * ] can query the corresponding target association probability group.

[0230] The target association probability group can be expressed as [j * _target,j * _target_undes,j * _target_survival]. * _target is the target of attack, * _target_undes is the probability that the first drone of the attack target is not destroyed, j * _target_survival updates the survival probability of the first drone attacking the target.

[0231] The attack target in the target association probability group can be obtained, and the first initial non-destruction probability of the attack target and the initial survival probability of the first UAV can be updated according to the target association probability group. The first initial non-destruction probability of the attack target is updated to the non-destruction probability of the first UAV, including: R_undes[j* _target]=j * _target_undes.

[0232] Update the initial survival probability of the first UAV of the attack target to the updated survival probability of the first UAV, including: R_survival[j * _target]=j * _target_survival.

[0233] S407: If there is a drone in an undetermined state in the updated first drone vector, re-execute the step of acquiring the first target drone in an undetermined state in the first drone formation based on the first drone vector based on the updated first drone vector.

[0234] If all drones in the updated first drone vector are in a determined state, a first optimal strategy group is determined according to the target strategy pairs of all drones. The target strategy pairs of all drones constitute the first optimal strategy group.

[0235] The above steps disclose the steps of solving the first optimal strategy group of the first drone formation according to the attack and defense strategy probability and the second initial strategy group. Referring to the above steps, the second optimal strategy group of the second drone formation can be solved according to the attack and defense strategy probability and the first initial strategy, and the first drone information and the second drone information can be replaced, which will not be repeated here.

[0236] In step S5, if the first optimal strategy group and / or the second optimal strategy group are not in the strict game matrix, the strict game matrix is ​​updated, and the step of solving the Nash equilibrium solution based on the strict game matrix is ​​re-executed based on the updated strict game matrix. Including the following cases:

[0237] The first optimal strategy group is in the strict game matrix, but the second optimal strategy group is not in the strict game matrix. The first optimal strategy group is not in the strict game matrix, but the second optimal strategy group is in the strict game matrix. Neither the first optimal strategy group nor the second optimal strategy group is in the strict game matrix. In these cases, the first optimal strategy group and / or the second optimal strategy group that are not in the strict game set can be added to the strict game set, and the steps of solving the Nash equilibrium solution in S2 are re-executed.

[0238] If both the first optimal strategy group and the second optimal strategy group are in the strict game matrix, the strategy group of the multi-UAV formation is set according to the Nash equilibrium solution. For example, the strategy group with the highest probability in the Nash equilibrium solution can be determined as the final strategy group.

[0239] The embodiment of the present invention further provides a fast solution system for multi-UAV attack and defense game, characterized in that the system comprises:

[0240] The model generation module is configured to construct a zero-sum game matrix model; the zero-sum game matrix model includes multi-UAV formation information of the first UAV formation and the second UAV formation, multi-UAV strategy information and a payoff matrix; the multi-UAV strategy information includes a strategy group set of the UAV formation, the strategy group includes strategy pairs of multiple UAVs in the UAV formation, and the strategy pairs are used to characterize attack and defense strategies and strategy targets; the attack and defense strategies include attack strategies, avoidance strategies and interference strategies; the multi-UAV formation information includes UAV information of the UAV formation, and the UAV information includes attack and defense strategy probabilities;

[0241] A strict game matrix acquisition module is configured to calculate a strict game matrix based on a zero-sum game matrix model, and solve a Nash equilibrium solution based on the strict game matrix;

[0242] The initial strategy group module is configured to select a first initial strategy group of the first drone formation and a second initial strategy group of the second drone formation according to a Nash equilibrium solution; the initial strategy group is a strategy group with the largest probability in a strict game matrix;

[0243] An optimal strategy group module is configured to solve a first optimal strategy group for the first UAV formation based on the attack and defense strategy probability and the second initial strategy group, and solve a second optimal strategy group for the second UAV formation based on the attack and defense strategy probability and the first initial strategy group;

[0244] a scheme allocation module, configured to update the strict game matrix if the first optimal strategy group and / or the second optimal strategy group are not in the strict game matrix, and re-execute the step of solving the Nash equilibrium solution based on the strict game matrix based on the updated strict game matrix;

[0245] If the first optimal strategy group and the second optimal strategy group are both in the strict game matrix, the strategy group of the multi-UAV formation is set according to the Nash equilibrium solution.

[0246] It is understandable that the target allocation system provided in the embodiment of the present invention corresponds to the fast solution method. The explanation, examples, beneficial effects and other aspects of the relevant contents can refer to the corresponding contents in the fast solution method of multi-UAV attack and defense game, which will not be repeated here.

[0247] It should be noted that, through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform. The above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform the method of each embodiment or some parts of the embodiment. In the specification provided here, a large number of specific details are explained. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some examples, known methods, structures and techniques are not shown in detail so as not to blur the understanding of this specification.

[0248] In this article, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0249] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fast solution method for a multi-UAV attack and defense game, the fast solution method is executed by a computer, characterized in that: The following steps are involved: Constructing a zero-sum game matrix model; the zero-sum game matrix model includes multi-UAV formation information of a first UAV formation and a second UAV formation, multi-UAV strategy information and a payoff matrix; the multi-UAV strategy information includes a strategy group set of the UAV formation, the strategy group includes strategy pairs of multiple UAVs in the UAV formation, and the strategy pairs are used to characterize attack and defense strategies and strategy targets; the attack and defense strategies include attack strategies, avoidance strategies and interference strategies; the multi-UAV formation information includes UAV information of the UAV formation, and the UAV information includes attack and defense strategy probabilities; Calculating a strict game matrix based on the zero-sum game matrix model, and solving a Nash equilibrium solution based on the strict game matrix; Selecting a first initial strategy group of the first drone formation and a second initial strategy group of the second drone formation according to the Nash equilibrium solution; the initial strategy group is the strategy group with the highest probability in the strict game matrix; Solving a first optimal strategy group for the first UAV formation based on the attack and defense strategy probability and the second initial strategy group, and solving a second optimal strategy group for the second UAV formation based on the attack and defense strategy probability and the first initial strategy group; If the first optimal strategy group and / or the second optimal strategy group are not in the strict game matrix, updating the strict game matrix, and re-performing the step of solving the Nash equilibrium solution based on the strict game matrix based on the updated strict game matrix; If the first optimal strategy group and the second optimal strategy group are both in the strict game matrix, the strategy group of the multi-UAV formation is set according to the Nash equilibrium solution.

2. The rapid solution method according to claim 1, characterized in that: The profit matrix includes: Among them, S R represents the first UAV formation strategy group set, s R ∈S R represents a strategy group of the first UAV formation, S B represents the second UAV formation strategy set, s B ∈S B represents a strategy group of the second UAV formation; (s R ,s B ) indicates that the first UAV formation adopts strategy group s R , the second drone formation adopts the strategy group s B Strategy group combination; u(s R ,s B ) is the first UAV formation in the strategy group combination (s R ,s B ) u(s R ,s B )=u R (s R ,s B )-u B (s R ,s B ) in, u R (s R ,s B ) and u B (s R ,s B ) represent the expected survival values ​​of the first UAV formation and the second UAV formation respectively; v i represents the value of drone i in the first drone formation, v j represents the value of drone j in the second drone formation; drone j is the strategic target of drone i; m represents the number of drones in the first drone formation, and n represents the number of drones in the second drone formation; q i represents the avoidance probability of UAV i to the second UAV formation, h i represents the avoidance parameter of UAV i to the second UAV formation; q j represents the avoidance probability of UAV j to the first UAV formation, h j represents the avoidance parameter of UAV j to the first UAV formation; t ij represents the interference probability of UAV i to UAV j, l ij represents the interference parameter of UAV i to UAV j; t ji represents the interference probability of UAV j to UAV i, l ji represents the interference parameter of UAV j to UAV i; p ji represents the probability of drone j killing drone i, b ji represents the attack parameter of drone j on drone i; p ij represents the probability of drone i killing drone j, a ij Represents the attack parameter of drone i on drone j.

3. The rapid solution method according to claim 1, characterized in that: The calculating of a strict game matrix based on the zero-sum game matrix model comprises: Select p strategy groups from the first drone formation strategy group set, and select q strategy groups from the second drone formation strategy group set; wherein p≥2, q≥2; A strict game matrix is ​​generated based on the p strategy groups and the q strategy groups.

4. The rapid solution method according to claim 3, characterized in that: Solving the Nash equilibrium solution based on the strict game matrix includes: Processing the strict game matrix based on a preset probability distribution algorithm to calculate a Nash equilibrium solution; the Nash equilibrium solution includes a first probability distribution of the p strategy groups and a second probability distribution of the q strategy groups; Selecting a first initial strategy group of the first UAV formation and a second initial strategy group of the second UAV formation according to the Nash equilibrium solution includes: The Nash equilibrium solution is traversed, and a strategy group with the largest probability is obtained according to the first probability distribution, and is determined as a first initial strategy group; and a strategy group with the largest probability is obtained according to the second probability distribution, and is determined as a second initial strategy group.

5. The rapid solution method according to claim 2, characterized in that: Solving a first optimal strategy group for the first drone formation based on the attack and defense strategy probability and the second initial strategy group includes: Initializing a first drone vector of a first drone formation; the first drone vector is used to characterize the policy pair state of each drone in the first drone formation, the policy pair state including a determined state indicating that the drone has determined the policy pair and an undetermined state indicating that the drone has not determined the policy pair; in the initialized first drone vector, the policy pair state of all drones is an undetermined state; Calculate the initial survival probability of the drone; Acquire a first target drone in an undetermined state in the first drone formation based on the first drone vector; Acquire multiple target drone strategy pairs of the target drone in the multi-drone strategy information; Solving the marginal return value of the target drone strategy pair according to the initial survival probability of the drone, and setting the target drone strategy pair with the largest marginal return value as the target strategy pair of the target drone; updating the policy pair state of the target drone in the first drone vector to a determined state, and detecting the updated first drone vector; If there is a drone in an undetermined state in the updated first drone vector, re-performing the step of acquiring the first target drone in an undetermined state in the first drone formation based on the first drone vector based on the updated first drone vector; If all drones in the updated first drone vector are in a determined state, a first optimal strategy group is determined according to the target strategy pairs of all drones.

6. The rapid solution method according to claim 5, characterized in that: The calculating of the initial survival probability of the drone includes: Setting a first initial avoidance probability of the first UAV formation and a second initial avoidance probability of the second UAV formation; Calculating a first initial probability of drones in the first drone formation not being shot down and a second initial probability of drones in the second drone formation not being shot down comprises: in, B_uninterference[j] represents the probability that UAV j is not interfered by the first UAV formation; R_undes[i] represents the first initial probability of drone i not being destroyed; R_uninterference[i] represents the probability that UAV i is not interfered by the second UAV formation; B_undes[j] represents the second initial probability of drone j not being destroyed; Calculating the initial survival probability of a first drone in the first drone formation according to the first initial avoidance probability and the first initial non-destruction probability; calculating the initial survival probability of a second drone in the second drone formation according to the second initial avoidance probability and the second initial non-destruction probability; comprising: R_survival[i]=1-(1-R_evasive[i])·(1-R_undes[i]) B_survival[j]=1-(1-B_evasive[j])·(1-B_undes[j]) in, R_survival[i] represents the first initial survival probability of drone i, R_evasive[i] represents the first initial evasion probability of drone i, and R_undes[i] represents the first initial non-destroyed probability of drone i; B_survival[j] represents the second initial survival probability of drone j, and B_evasive[j] represents the second initial evasion probability of drone j.

7. The rapid solution method according to claim 6, characterized in that: Solving the marginal return value of the target drone strategy pair according to the initial survival probability of the drone includes: Solving the attack and defense strategy according to the target drone strategy; Solve the updated survival probability of the drone according to the attack and defense strategy; The marginal return value of the target drone strategy pair is calculated based on the updated survival probability of the drone and the initial survival probability of the drone.

8. The rapid solution method according to claim 7, characterized in that: According to the attack and defense strategy, the updated survival probability of the drone is solved, including: Determine a first drone and a second drone that match the target drone strategy; If the attack and defense strategy is an attack strategy, the first drone updated survival probability of the first drone is set equal to the first drone initial survival probability; and the second drone updated survival probability of the second drone is calculated as: j_survival=1-(1-q j )·(1-Δ B_undes [ipj]) Δ B_undes [i,p,j]=B_undes[j]·(1-R_uninterference[i]·p ij ) in, j_survival indicates the updated survival probability of the second drone; [i,p,j] represents the drone triple formed by the first drone i and the target drone strategy pair [p,j]; q j represents the avoidance probability of the second UAV j to the first UAV formation; If the attack and defense strategy is an avoidance strategy, the updated survival probability of the second drone is set equal to the initial survival probability of the second drone; and, in the second initial strategy group, it is detected whether there is a drone that uses an attack strategy to attack the first drone; if not, the updated survival probability of the first drone is set equal to the initial survival probability of the first drone, and if so, the updated survival probability of the first drone is calculated as: i_survival=1-(1-q i )·(1-R_undes[i]) in, i_survival indicates the updated survival probability of the first drone; q i represents the avoidance probability of the first UAV i to the second UAV formation; If the attack and defense strategy is a jamming strategy, the updated survival probability of the first UAV is set to be equal to the initial survival probability of the first UAV, and the updated survival probability of the second UAV is set to be equal to the initial survival probability of the second UAV; Solving the attack and defense strategy of the second UAV according to the second initial strategy group; and when the attack and defense strategy is an attack strategy, obtaining the attack target of the second UAV; calculating the association probability group of the UAV triple according to the attack target; the association probability group includes: [i,p,j]=[j_target,j_target_undes,j_target_survival] j_target_survival=1-(1-R_evasive[j_target])·(1-j_target_undes) in, j_target represents the attack target, j_target_undes represents the probability that the first UAV of the attack target is not destroyed, and j_target_survival represents the updated survival probability of the first UAV of the attack target; The calculating the marginal return value of the target drone strategy pair according to the updated survival probability of the drone and the initial survival probability of the drone includes: δ ipj =in' R -in R in, δ ipj represents the marginal reward value of the target drone strategy pair; Indicates the value of the first drone, Indicates the value of the second drone.

9. The rapid solution method according to claim 6, characterized in that: Before detecting the updated first drone vector, it also includes: Obtain a target attack and defense strategy, a target first UAV, and a target second UAV of the target strategy pair; Updating the initial survival probability of the drone according to the target attack and defense strategy includes: If the target attack and defense strategy is an attack strategy, the second initial probability of the target second UAV not being destroyed and the initial survival probability of the second UAV are updated as follows: B_undes[j * ]=D B_undes [i * ,p * ,j * ] B_survival[j * ]=j * _survival in, i * Indicates the target first drone, p * represents the target attack and defense strategy, j * Indicates the target second drone; If the target attack and defense strategy is an avoidance strategy, the first initial avoidance probability of the target first UAV and the initial survival probability of the first UAV are updated as follows: R_survival[i * ]=i * _survival If the target attack and defense strategy is an interference strategy, obtain the target association probability group of the drone triple formed by the target first drone and the target strategy pair; obtain the attack target in the target association probability group, and update the first initial non-destruction probability of the attack target and the initial survival probability of the first drone according to the target association probability group.

10. A fast solution system for multi-UAV attack and defense game, characterized in that: The system comprises: The model generation module is configured to construct a zero-sum game matrix model; the zero-sum game matrix model includes multi-UAV formation information of a first UAV formation and a second UAV formation, multi-UAV strategy information and a payoff matrix; the multi-UAV strategy information includes a strategy group set of the UAV formation, the strategy group includes strategy pairs of multiple UAVs in the UAV formation, and the strategy pairs are used to characterize attack and defense strategies and strategy targets; the attack and defense strategies include attack strategies, avoidance strategies and interference strategies; the multi-UAV formation information includes UAV information of the UAV formation, and the UAV information includes attack and defense strategy probabilities; A strict game matrix acquisition module is configured to calculate a strict game matrix based on the zero-sum game matrix model, and solve a Nash equilibrium solution based on the strict game matrix; An initial strategy group module is configured to select a first initial strategy group of the first drone formation and a second initial strategy group of the second drone formation according to the Nash equilibrium solution; the initial strategy group is the strategy group with the largest probability in the strict game matrix; an optimal strategy group module, configured to solve a first optimal strategy group for a first UAV formation based on the attack and defense strategy probability and the second initial strategy group, and to solve a second optimal strategy group for a second UAV formation based on the attack and defense strategy probability and the first initial strategy group; a scheme allocation module, configured to update the strict game matrix if the first optimal strategy group and / or the second optimal strategy group are not in the strict game matrix, and re-execute the step of solving the Nash equilibrium solution based on the strict game matrix based on the updated strict game matrix; If the first optimal strategy group and the second optimal strategy group are both in the strict game matrix, the strategy group of the multi-UAV formation is set according to the Nash equilibrium solution.

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