Method for determining number of attacks

Abstract

The invention discloses a method for determining the number of attacks. The method comprises the following steps: acquiring a plurality of target objects to be attacked by a first object; numbering the plurality of target objects to obtain a plurality of pieces of number information corresponding to the plurality of target objects; in response to the multiple pieces of number information, acquiring a first time period when the first object attacks any first target object in the multiple target objects starting from the target position; after the first target is attacked, acquiring a plurality of second time periods when the first target attacks a plurality of target objects except the first target object; acquiring a plurality of attack values of the first object attacking a plurality of other target objects except the first target object; the technical problem that the number of cluster aircrafts cannot be attacked in a short time due to the fact that the attack sequence of the cluster aircrafts cannot be determined when the airborne fixed directional load attacks the cluster aircrafts is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of determining an optimal attack number, and in particular to a method for determining an attack number. Background Art

[0002] When an airborne fixed pointing load attacks swarm aircraft, the attack sequence of the swarm aircraft must be determined so that more swarm aircraft can be attacked in a short time. However, there is currently no optimization algorithm for sorting swarm aircraft by airborne fixed pointing loads. Therefore, an attack sequence algorithm for airborne fixed pointing loads to attack swarm aircraft is proposed, so that the airborne fixed pointing load can attack the maximum number of swarm aircraft in a short time. Summary of the invention

[0003] An embodiment of the present invention provides a method for determining the number of attacks, so as to at least solve the technical problem that when an airborne fixed pointing payload attacks swarm aircraft, the attack order of the swarm aircraft cannot be determined, resulting in the inability to attack the maximum number of swarm aircraft in a short time.

[0004] According to one aspect of an embodiment of the present invention, a method for determining the number of attacks is provided. The method may include: obtaining multiple target objects that a first object is going to attack; numbering the multiple target objects to obtain multiple numbering information corresponding to the multiple target objects; in response to the multiple numbering information, obtaining a first time period for the first object to attack any first target object among the multiple target objects starting from the target position; after attacking the first target, obtaining multiple second time periods for the first object to attack multiple target objects other than the first target object; obtaining multiple attack values ​​for the first object to attack multiple target objects other than the first target object; determining a target time period based on the first time period, multiple second time periods, and multiple attack values, and the target time period is less than or equal to a preset time period threshold; and determining the maximum number of target objects that the first object can attack within the target time period using a hybrid genetic algorithm.

[0005] Optionally, the expression for obtaining the first time period in which the first object attacks any first target object among the multiple target objects from the target position is:

[0006] Timef=(a1a2a3…a n )

[0007] Among them, the elements in Timef (a1a2a3…a n ) represents the first time period in which the first object attacks any first target object among multiple target objects starting from the target position.

[0008] Optionally, the expression for obtaining multiple second time periods during which the first object attacks multiple target objects other than the first target object is:

[0009]

[0010] Among them, the element m in Time i,j Indicates that W is completed from multiple other target objects i Attack and knock down multiple other targets. j The required second time period, i, j represents the serial number information of other multiple target objects.

[0011] Optionally, the expression for obtaining multiple attack values ​​of the first object attacking multiple target objects other than the first target object is:

[0012] x i，j ∈{0,1},(i,j=1,2,…,n)

[0013] Among them, x i,j Indicates that W is completed from multiple other target objects i Attack and knock down multiple other targets. j , x i,j =1 means that W is completed from multiple other target objects i Attack and knock down multiple other targets. j , x i,j =0 means that W is completed from multiple other target objects i The attack started without targeting other multiple targets. j to attack.

[0014] Optionally, based on the first time period, multiple second time periods and multiple attack values, a target time period is determined, and an expression indicating that the target time period is less than or equal to a preset time period threshold is:

[0015]

[0016] Among them, t f is the first time period, t lim is the preset time threshold, where is the target time period, and the value range of i,j is 1 to n.

[0017] Optionally, the expression for determining the maximum number of target objects that the first object can attack within the target time period using the hybrid genetic algorithm is:

[0018]

[0019] Wherein, maxZ represents the maximum number of target objects that the first object can attack within the target time period.

[0020] Beneficial effects of the present invention:

[0021] The present invention proposes a method for determining the number of attacks. First, based on the typical application scenarios of airborne fixed pointing loads, the formation flight characteristics of the main attack targets (swarm aircraft) are studied; then a target sorting algorithm for swarm aircraft is designed; the route planning algorithm is based on a genetic algorithm, and a greedy algorithm and a simulated annealing algorithm are mixed in the population initialization stage and the optimization stage respectively. This method makes up for the shortcomings of a single algorithm, can give full play to the advantages of the three algorithms, and well improves the algorithm operation effect. The final designed algorithm is verified through experimental simulation, and it is iterated multiple times and gradually improved; the technical problem that when the airborne fixed pointing load attacks the swarm aircraft, the attack order of the swarm aircraft cannot be determined, resulting in the inability to attack the maximum number of swarm aircraft in a short time is solved, and the technical effect that when the airborne fixed pointing load attacks the swarm aircraft, the attack order of the swarm aircraft is determined according to the established mathematical model and the hybrid genetic algorithm, so that the airborne fixed pointing load attacks the maximum number of swarm aircraft in a short time is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 is a flow chart of a method for determining the number of attacks according to an embodiment of the present invention;

[0024] Figure 2 is a sequential crossover schematic diagram according to an embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of swapping, reversing, and inserting according to an embodiment of the present invention;

[0026] Figure 4 is a schematic diagram of a GA attacking a small-scale goose formation according to an embodiment of the present invention;

[0027] Figure 5 is a schematic diagram of a GASA attack on a small-scale goose formation according to an embodiment of the present invention;

[0028] Figure 6 is a schematic diagram of GASAGA attacking a small-scale goose formation according to an embodiment of the present invention;

[0029] Figure 7is a schematic diagram of attacking a small-scale goose formation using an exhaustive method according to an embodiment of the present invention;

[0030] FIG8 (1) is a schematic diagram of the attack sequence, attack time, and time required for the entire attack process of a GA attacking a goose formation according to an embodiment of the present invention;

[0031] FIG8(2) is a schematic diagram of the attack sequence of the GA attacking the goose formation according to an embodiment of the present invention;

[0032] FIG9 (1) is a schematic diagram of the attack sequence, attack time and the time required for the entire attack process of GASA attacking a goose formation according to an embodiment of the present invention;

[0033] FIG9(2) is a schematic diagram of the attack sequence of GASA attacking a goose formation according to an embodiment of the present invention;

[0034] FIG. 10 (1) is a schematic diagram of the attack sequence, attack time, and time required for the entire attack process of GASAGA attacking a goose formation according to an embodiment of the present invention;

[0035] FIG. 10( 2 ) is a schematic diagram of the attack sequence of GASAGA attacking a goose formation according to an embodiment of the present invention;

[0036] FIG11(1) is a schematic diagram of the attack sequence, attack time and the time required for the entire attack process of GA attacking parallel formations according to an embodiment of the present invention;

[0037] FIG11(2) is a schematic diagram of the attack sequence of GA attacking parallel formations according to an embodiment of the present invention;

[0038] FIG. 12( 1 ) is a schematic diagram of the attack sequence, attack time, and time required for the entire attack process of GASA attacking a parallel formation according to an embodiment of the present invention;

[0039] FIG12(2) is a schematic diagram of the attack sequence of GASA attacking a parallel formation according to an embodiment of the present invention;

[0040] FIG. 13( 1 ) is a schematic diagram of the attack sequence, attack time, and time required for the entire attack process of GASAGA attacking a parallel formation according to an embodiment of the present invention;

[0041] FIG13(2) is a schematic diagram of the attack sequence of GASAGA attacking a parallel formation according to an embodiment of the present invention;

[0042] FIG. 14 (1) is a schematic diagram of the attack sequence, attack time, and time required for the entire attack process of GA attacking an irregular formation according to an embodiment of the present invention;

[0043] FIG14(2) is a schematic diagram of the attack sequence of GA attacking irregular formations according to an embodiment of the present invention;

[0044] FIG. 15( 1 ) is a schematic diagram of the attack sequence, attack time, and time required for the entire attack process of GASA attacking an irregular formation according to an embodiment of the present invention;

[0045] FIG15(2) is a schematic diagram of the attack sequence of GASA attacking irregular formations according to an embodiment of the present invention;

[0046] FIG. 16 (1) is a schematic diagram of the attack sequence, attack time, and time required for the entire attack process of GASAGA attacking an irregular formation according to an embodiment of the present invention;

[0047] FIG16(2) is a schematic diagram of the attack sequence of GASAGA attacking irregular formations according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0049] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0050] Example 1

[0051] According to an embodiment of the present invention, a method for determining the number of attacks is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system comprising at least one set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0052] Figure 1 is a flow chart of a method for determining the number of attacks according to an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0053] Step S101: obtaining a plurality of target objects that a first object is going to attack.

[0054] In the technical solution provided in the above step S101 of the present invention, when multiple target objects attack a first object, multiple target objects that the first object is going to attack are obtained, wherein the first object and the multiple target objects may be aircraft.

[0055] Step S102: number the multiple target objects to obtain multiple numbering information corresponding to the multiple target objects.

[0056] In the technical solution provided in the above step S102 of the present invention, multiple target objects are numbered to obtain multiple numbering information corresponding to the multiple target objects, for example, W1, W2, W3, W4...Wn-1, Wn.

[0057] Step S103, in response to the plurality of numbering information, obtaining a first time period in which the first object attacks any first target object among the plurality of target objects from the target position.

[0058] In the technical solution provided in the above step S103 of the present invention, when the first object attacks any first target object among the target objects from the target position in the first time period, the first time period may be a specific time, and the first time period may be an initial consumption matrix.

[0059] Step S104, after attacking the first target, obtaining a plurality of second time periods in which the first object attacks a plurality of target objects other than the first target object.

[0060] In the technical solution provided in the above step S104 of the present invention, after attacking the first target, multiple second time periods in which the first object attacks multiple target objects other than the first target object are obtained, and the second time period may be a time consumption matrix.

[0061] Step S105: obtaining a plurality of attack values ​​of the first object attacking a plurality of target objects other than the first target object.

[0062] In the technical solution provided in the above step S105 of the present invention, when the first object attacks multiple target objects other than the first target object, multiple attack values ​​of the attack process are obtained. When the attack value is 1, it means that the multiple target objects other than the first target object are attacked. When the attack value is 0, it means that the multiple target objects other than the first target object are not attacked, and the number of attacks is equal to the number of targets attacked.

[0063] Step S106: determining a target time period based on the first time period, the plurality of second time periods and the plurality of attack values, and the target time period is less than or equal to a preset time period threshold.

[0064] In the technical solution provided in the above step S106 of the present invention, the first time period, multiple second time periods and multiple attack values ​​are calculated to determine a target time period, so that the target time period is less than or equal to a preset time period threshold.

[0065] Step S107: using a hybrid genetic algorithm to determine the maximum number of target objects that the first object can attack within the target time period.

[0066] In the technical solution provided in the above step S107 of the present invention, when the target time period is less than or equal to the preset time period threshold, a hybrid genetic algorithm is used to determine the maximum number of target objects that the first object can attack within the target time period, wherein the maximum number of target objects that can be attacked within the target time period includes the first object.

[0067] The above method of this embodiment is further introduced below.

[0068] As an optional embodiment, in step S103, the expression for obtaining the first time period of the first object attacking any first target object among the multiple target objects from the target position is:

[0069] Timef=(a1a2a3…a n ) (1)

[0070] Among them, the elements in Timef (a1 a2 a3 … a n ) represents the first time period during which the first object attacks any first target object among multiple target objects from the target position, (a1 a2 a3 … a n ) in which the subscripts 1, 2, 3...n respectively represent the numbering information of any first target object among multiple target objects.

[0071] As an optional implementation manner, in step S104, the expression for obtaining multiple second time periods in which the first object attacks multiple target objects other than the first target object is:

[0073] Among them, the element m in Time i,j Indicates that W is completed from multiple other target objects i Attack and knock down multiple other targets. j The required second time period, i, j represents the serial number information of other multiple target objects.

[0074] As an optional implementation manner, in step S105, the expression for obtaining multiple attack values ​​of the first object attacking multiple target objects other than the first target object is:

[0075] x i，j ∈{0,1},(i,j=1,2,…,n) (3)

[0076] Among them, x i,j Indicates that W is completed from multiple other target objects i Attack and knock down multiple other targets. j , x i,j =1 means that W is completed from multiple other target objects i Attack and knock down multiple other targets. j , x i,j =0 means that W is completed from multiple other target objects i The attack started without targeting other multiple targets. j to attack.

[0077] In this embodiment, when the first object attacks multiple target objects other than the first target object, the number of attacks needs to be equal to the number of attacks, and each target can be attacked at most once.

[0078] As an optional implementation manner, in step S106, based on the first time period, multiple second time periods and multiple attack values, a target time period is determined, and the expression that the target time period is less than or equal to the preset time period threshold is:

[0079]

[0080] Among them, t f is the first time period, t lim is the preset time threshold, where is the target time period, and the value range of i,j is 1 to n.

[0081] As an optional embodiment, in step S107, the expression for determining the maximum number of target objects that the first object can attack within the target time period using the hybrid genetic algorithm is:

[0082]

[0083] Wherein, maxZ represents the maximum number of target objects that the first object can attack within the target time period.

[0084] In this embodiment, the above formulas 1 to 5 are used and the number of attacks is equal to the number of attacks, and each target is attacked once, to establish a mathematical model:

[0085]

[0086] x i，j ∈{0,1},(i,j=1,2,…,n) (3)

[0087] Formula (6) and formula (7) respectively All of them mean that the first object attacks each of the other multiple target objects except the first target object once. Formula (8) It is used to indicate that the number of attacks is equal to the number of targets attacked, where S represents the number of attacks that the first target makes on multiple target objects other than the first target object. It indicates that S belongs to the set of V, and V indicates the maximum number of attacks that the first object performs on multiple target objects other than the first target object.

[0088] The mathematical model is solved using a hybrid genetic algorithm to obtain the optimal attack sequence and the maximum number of attacks.

[0089] 1. Input cluster data and parameters

[0090] (1) Individual coding

[0091] Chromosomes are generated using natural number coding according to the serial numbers of multiple target objects. The length of the chromosome is the number of multiple target objects. The coding order is used as the attack order for multiple target objects. At the same time, repeated gene coding is not allowed. For example, if the chromosome is 123456, it means that multiple target objects are attacked in sequence in the order of W1, W2, W3, W4, W5, and W6.

[0092] (2) Constructing “initial consumption matrix”, “time consumption matrix” and “preset time threshold”

[0093] The "initial consumption matrix" and "time consumption matrix" are calculated using the formula based on the input coordinate positions of multiple target objects and the first object and their speed information; the distances of multiple target objects from the first object are input to calculate the "preset time threshold", that is, the longest consumable time for the attack.

[0094] (3) Parameter setting of genetic algorithm and simulated annealing algorithm

[0095] Including population size, number of genetic algorithm iterations, generation gap, crossover probability, mutation probability, probability of exchange structure, probability of reversal structure, probability of insertion structure, etc., as shown in Table 1.

[0096] Table 1 Parameters of genetic algorithm and simulated annealing algorithm

[0097]

[0098] 2. Population initialization

[0099] (1) Generate an individual using the greedy algorithm

[0100] 1.1 Initialize the set of attacked targets visited to be empty, and the set of unattacked targets unvisited = {1, 2, 3, ..., n}.

[0101] 1.2 Assign all zeros on the main diagonal of the time consumption matrix time to infinity.

[0102] 1.3 Find the sequence number first corresponding to the minimum value from the initial consumption matrix distf, and select the target numbered Wfirst as the first attack target.

[0103] 1.4 Update the unattacked target set unvisited (unvisited == first) = [], the attacked target set visited = [visited, first], and assign the first attack point first to the immediately preceding point pre_point.

[0104] 1.5 Find the row pre_time corresponding to the immediate predecessor in the time consumption matrix time, exclude the targets that have been attacked, and find the column number corresponding to the minimum value in pre_time as the next immediate predecessor.

[0105] 1.6 Updated unvisited and visited.

[0106] 1.7 Repeat steps 1.5 and 1.6 until all targets are attacked.

[0107] 1.8 Output visited is the attack sequence result obtained by the greedy algorithm.

[0108] (2) Generate the remaining individuals of the population

[0109] The remaining NIND-1 individuals in the population are randomly generated according to the coding method to improve the diversity of the initial population.

[0110] 3. Select an action

[0111] (1) Fitness function

[0112] In order to better select excellent individuals, the fitness function of the genetic algorithm in the embodiment of the present invention is determined by two factors, wherein the two factors are: the number of target objects shot down and the total time consumed by the attack.

[0113] (2) Binary Tournament Selection

[0114] Through the fitness function, two individuals are randomly selected from the population to compete, and the individual with higher fitness is selected to be inherited to the next generation until the population is full.

[0115] 4. Crossover and mutation operations

[0116] (1) Cross

[0117] Set the crossover probability to perform crossover operation on chromosomes. This topic chooses to use Order Crossover (OX) as the chromosome crossover method.

[0118] The starting and ending positions are randomly selected in the two parent chromosomes, and a gene segment in the parent chromosome a is copied to the same position in the daughter chromosome a. According to the order of the genes that have not been copied in the parent chromosome b, these genes are filled into the missing positions in the daughter chromosome a. The daughter chromosome b is constructed in a similar way. According to an embodiment of the present invention, a sequential crossover method is provided. Figure 2 It is a schematic diagram of sequential crossover according to an embodiment of the present invention.

[0119] (2) Variation

[0120] There are three ways of mutation: exchange structure, reversal structure and insertion structure. The probability of occurrence of the three ways is set and one of them is selected by roulette wheel to operate. According to an embodiment of the present invention, a method of exchange, reversal and insertion is provided. Figure 3 It is a schematic diagram of exchange, reversal and insertion according to an embodiment of the present invention.

[0121] (2.1) Exchange structure: randomly select two genes in the target chromosome and swap their positions.

[0122] (2.2) Reversal structure: randomly select a segment in the target chromosome and perform a reverse operation on the genes in the segment.

[0123] (2.3) Insertion structure: randomly select a gene and an insertion point in the target chromosome and insert the gene after the insertion point.

[0124] 5. Simulated annealing operation

[0125] The fitness of the population after selection, crossover and mutation is calculated, and 30% of the individuals with the best fitness are selected and subjected to simulated annealing operation in turn.

[0126] (1) Generate new solutions

[0127] For individuals entering the simulated annealing operation, a roulette wheel method is used to select a new solution by exchanging structure, reversing structure or inserting structure. The specific implementation methods of the three methods are consistent with the mutation operation in the genetic algorithm.

[0128] (2) Judging and accepting the new interpretation

[0129] The Metropolis criterion is used to determine whether to accept the new solution. If the number of targets shot down by the new solution is greater than that of the current solution or the number of targets shot down by the new solution is equal to that of the current solution and the time consumed is less than that of the current solution, then the new solution is accepted. Otherwise, the new solution is accepted with a certain probability. The probability is expressed as follows:

[0130]

[0131] Among them, E(x new )、E(x old ) are the number of targets shot down by the new solution and the current solution respectively; F(x new )、F(x old ) are the total time required for the new solution and the current solution to complete the attack.

[0132] From this probability formula, we can see that the probability of accepting a new solution decreases as the temperature decreases. Let the initial temperature T = T0, and the temperature change rule be T = T*alpha.

[0133] (3) Algorithm termination

[0134] Repeat steps 1 and 2 until the maximum number of iterations of the outer loop is reached, then exit the loop. The individuals in the original population are replaced by the new individuals that have undergone simulated annealing.

[0135] 6. Record the global optimal individual and termination conditions

[0136] (1) Record the global optimal individual

[0137] After each iteration, the fitness values ​​of the best individual in the current population and the global best individual are determined, and the global best individual is retained or replaced. The maximum number of attacks after the iteration, the corresponding attack sequence and consumption time are output, and the attack route map of the global best individual after each iteration is drawn.

[0138] (2) Termination conditions

[0139] After reaching the maximum number of iterations, exit the iteration and draw a trend chart of the number of attacks of the global optimal individual after each iteration.

[0140] 7. Experimental part (matlab simulation)

[0141] In the experiment, GA, GASA, GASAGA are used to represent genetic algorithm, genetic algorithm hybrid simulated annealing algorithm, genetic algorithm hybrid simulated annealing algorithm and greedy algorithm respectively.

[0142] 7.1. Simulation results of attacking a small-scale flotilla

[0143] First, a small-scale goose formation of 11 targets is simulated, and the results are compared with the exhaustive method to verify the feasibility of the designed algorithm. Input the cluster coordinates of multiple target objects (as shown in Table 4.2), the distance of the multiple target objects detected from the important facilities protected by the first object is 39km, and the uniform flight speed of the multiple target objects is 43.61m / s. The "preset time period threshold" t is calculated by formula (13) lim = 321.03s, input the first object's current flight heading as 90°.

[0144]

[0145] Among them, t lim is the preset time period threshold, d is the horizontal distance between any first target object and the important facilities protected by the first object at the moment when the attack route of the first object is generated, b is the distance between the destination to be attacked by multiple target objects and the important facilities protected by the first object, and v represents the uniform flight speed of multiple target objects.

[0146] Table 2 Coordinates of small-scale geese formation clusters

[0147] coordinate 1 2 3 4 5 6 7 8 9 10 11 x 1500 1540 1580 1620 1660 1700 1460 1420 1380 1340 1300 y 1000 1020 1040 1060 1080 1100 1020 1040 1060 1080 1100

[0148] The initial consumption matrix Timef and the time consumption matrix Time are:

[0149] Time 小规模雁形 =(46.448.850.852.454.255.746.847.247.547.747.8)

[0150]

[0151] The GA, GASA, and GASAGA algorithms are used to obtain the optimal attack sequence and the corresponding number of attacks, and the optimal solution is obtained by exhaustive method. The results are shown in Table 3 below:

[0152] Table 3 Results of ten runs of three algorithms for goose formation

[0153]

[0154] From the simulation results, it can be seen that in the case of a small scale, GA, GASA, and GASAGA can all find an attack sequence with the largest number of attacks, and the optimal solution result is the same as that obtained by the exhaustive method, indicating that the three algorithms have similar capabilities in processing such small-scale problems and are feasible for processing such problems. The following specifically analyzes their attack sequences. According to an embodiment of the present invention, GA, GASA, GASAGA and the exhaustive method are provided for attacking a small-scale goose formation. Figure 4 is a schematic diagram of a GA attacking a small-scale goose formation according to an embodiment of the present invention; Figure 5 is a schematic diagram of a GASA attack on a small-scale goose formation according to an embodiment of the present invention; Figure 6 is a schematic diagram of GASAGA attacking a small-scale goose formation according to an embodiment of the present invention; Figure 7 It is a schematic diagram of attacking a small-scale goose formation using the exhaustive method according to an embodiment of the present invention.

[0155] Depend on Figures 4 to 7 It can be seen that compared with the optimal attack sequence obtained by the exhaustive method, the other three algorithms have the same number of attacks, but different attack sequences. The exhaustive method consumes the least time, which means that although the designed algorithm can obtain the maximum number of attacks, the attack sequence is still not optimal. Nevertheless, this result has met the expected design requirements.

[0156] Combining the initial consumption matrix and the time consumption matrix, the sum of the attack consumption time for each target is equal to the attack consumption time of the entire process. At the same time, observing the optimal attack sequence diagram obtained by the exhaustive method, the attack on the target is not continuous on the schematic diagram, that is, the next attack point is not the point closest to the current point, which means that the attack route consumption time of any two points is related to the position relationship between the two points and has no direct relationship with the distance. Therefore, the next attack point of each point is often far away from the current point, which is consistent with the design results in Chapter 3.

[0157] The feasibility of the algorithm has been demonstrated with the simulation verification in a small-scale case. This section also explains the attack sequence and attack diagram. Next, we verify whether the hybrid algorithm still meets the design expectations when facing three large-scale clusters.

[0158] 7.2. Simulation results of attacking a foe formation

[0159] According to the cluster coordinates of multiple target objects in Table 2, the distance between the multiple target objects detected and the important facilities protected by the first object is 73 km, and the uniform flight speed of the multiple target objects is 43.61 m / s. The "preset time threshold" t is calculated by formula (13): lim = 1100.66s, input the first object's current flight heading as 90°.

[0160] The initial consumption matrix Timef and the time consumption matrix Time are calculated as follows:

[0161] Time 雁形 =(46.39 48.75 50.75 52.54 54.19……53.99 54.07 54.16)

[0162]

[0163] The GA, GASA, and GASAGA algorithms are used to obtain the optimal attack sequence and the corresponding number of attacks. The results are shown in Table 4:

[0164] Table 4 Results of ten runs of three algorithms for goose formation

[0165]

[0166] Through the analysis of the experimental results, the experimental simulation was carried out using the goose formation. The maximum number of attacks in the ten running results of GA, GASA, and GASAGA were 35, 39, and 45 respectively, and the average number of attacks were 34.1, 38.1, and 44.4 respectively. The results with the largest number of attacks in the ten running results of GA, GASA, and GASAGA are shown respectively, as shown in Figures 8 to 10. According to an embodiment of the present invention, the results with the largest number of attacks in the ten running results of GA, GASA, and GASAGA are provided. Figure 8 is a schematic diagram of GA attacking a goose formation according to an embodiment of the present invention; Figure 9 is a schematic diagram of GASA attacking a goose formation according to an embodiment of the present invention; Figure 10 is a schematic diagram of GASAGA attacking a goose formation according to an embodiment of the present invention.

[0167] It should be noted that since the entire attack diagram is too large, the diagram is split into two parts for display. Diagram (1) specifically shows the attack sequence, attack time and the time required for the entire attack process on multiple target objects, and diagram (2) is the connection line of the attack sequence. Similarly, the discontinuity of the attack sequence in the diagram is also related to the time consumption matrix, which has been explained in the analysis of the results of the small-scale goose formation attack.

[0168] 7.3. Simulation results of parallel formation attack

[0169] According to the target cluster coordinates in Table 2, the distance between the multiple target objects detected and the important facilities protected by the first object is 68 km, and the uniform flight speed of the multiple target objects is 43.6 m / s. The "preset time threshold" t is calculated by formula (13): lim =986.24s, input the first object's current flight heading as 90.

[0170] The calculation of the initial consumption matrix Timef and the time consumption matrix Time are:

[0171] Time 平行 =(46.39 47.41 48.34 49.22 50.86……66.36 66.89 67.42)

[0172]

[0173] Three algorithms are used to obtain the number of attacks, and the results are shown in Table 5:

[0174] Table 5 Results of ten runs of three parallel formation algorithms

[0175]

[0176] Through the analysis of the experimental results, the parallel formation was used for experimental simulation. The maximum number of attacks in the ten running results of GA, GASA, and GASAGA were 31, 35, and 38 respectively, and the average number of attacks were 30.4, 33.9, and 37.8 respectively. The results with the largest number of attacks in the ten running results of GA, GASA, and GASAGA are also shown, as shown in Figures 11 to 13. According to an embodiment of the present invention, the results with the largest number of attacks in the ten running results of GA, GASA, and GASAGA are provided. Figure 11 is a schematic diagram of GA attacking a parallel formation according to an embodiment of the present invention; Figure 12 is a schematic diagram of GASA attacking a parallel formation according to an embodiment of the present invention; Figure 13 is a schematic diagram of GASAGA attacking a parallel formation according to an embodiment of the present invention.

[0177] 7.4. Simulation results of parallel formation attack

[0178] According to the target cluster coordinates in Table 2, the distance between the detected target cluster and our important facilities is 79 km, and the uniform flight speed of multiple target objects is 43.6 m / s. The "preset time threshold" t is calculated by formula (13): lim =1238.52s, input the first object's current flight heading as 90.

[0179] The calculation of the initial consumption matrix Timef and the time consumption matrix Time are:

[0180] Time 无规则 =(46.39 72.22 62.45 65.42 61.68……58.17 72.75 60.62)

[0181]

[0182] The same three algorithms are used to obtain the number of attacks, and the results are shown in Table 6:

[0183] Table 6 Results of ten runs of three algorithms for irregular formation

[0184]

[0185] Through the analysis of the experimental results, the experimental simulation was carried out using the irregular formation. The maximum number of attacks in the ten running results of GA, GASA, and GASAGA were 39, 47, and 50, respectively, and the average number of attacks were 38.1, 45, and 49.3, respectively. The results with the largest number of attacks in the ten running results of GA, GASA, and GASAGA are also shown, as shown in Figures 14 to 16. According to an embodiment of the present invention, the results with the largest number of attacks in the ten running results of GA, GASA, and GASAGA are provided. Figure 14 is a schematic diagram of GA attacking an irregular formation according to an embodiment of the present invention; Figure 15 is a schematic diagram of GASA attacking an irregular formation according to an embodiment of the present invention; Figure 16 is a schematic diagram of GASAGA attacking an irregular formation according to an embodiment of the present invention.

[0186] From the above different formations, it can be seen that by comparing the simulation results of GASAGA and the exhaustive method when the cluster size is small, it is concluded that GASAGA is feasible to solve this problem. In this case, the maximum number of attacks under the preset time threshold can be obtained.

[0187] By comparing and analyzing the attack simulation results of the three types of formations, it can be concluded that GASAGA is significantly better than the other two algorithms in solving the problem model. It meets the expected hybrid strategy in terms of avoiding falling into the local optimal solution and accelerating the convergence speed. The specific performance is as follows:

[0188] (1) The number of targets that can be attacked by GASAGA is much higher than that of GA and slightly higher than that of GASA. The maximum number of attacks by GASAGA is 1.31 times that of GA, and the minimum is 1.25 times. This shows that GASAGA and GASA after mixing simulated annealing algorithms can effectively improve the defect that GA is prone to fall into the optimal solution.

[0189] (2) The number of iterations required for GASAGA to reach the final result is much lower than that of GASA, which indicates that after mixing with the greedy algorithm, the convergence speed of GASAGA is significantly accelerated.

[0190] The hybrid algorithm designed in this paper can effectively solve the problem of attack order of cluster targets. At the same time, when used in conjunction with the single target attack curve designed by Comrade Guo Yang, it solves the problem of how to shoot down as many targets as possible before using high-cost air defense weapons when dealing with small drone cluster harassment.

[0191] In an embodiment of the present invention, a plurality of target objects to be attacked by a first object are obtained; the plurality of target objects are numbered to obtain a plurality of numbering information corresponding to the plurality of target objects; in response to the plurality of numbering information, a first time period for the first object to attack any first target object among the plurality of target objects starting from the target position is obtained; after attacking the first target, a plurality of second time periods for the first object to attack a plurality of target objects other than the first target object are obtained; a plurality of attack values ​​for the first object to attack a plurality of target objects other than the first target object are obtained; a target time period is determined based on the first time period, the plurality of second time periods and the plurality of attack values, and The target time period is less than or equal to a preset time period threshold; when the target time period is less than or equal to the preset time period threshold, a hybrid genetic algorithm is used to determine the maximum number of target objects that the first object can attack within the target time period, thereby solving the technical problem that when an airborne fixed pointing payload attacks cluster aircraft, the attack order of cluster aircraft cannot be determined, resulting in the inability to attack the maximum number of cluster aircraft in a short period of time. This achieves the technical effect that when an airborne fixed pointing payload attacks cluster aircraft, the attack order of cluster aircraft is determined according to the established mathematical model and the hybrid genetic algorithm, so that the airborne fixed pointing payload can attack the maximum number of cluster aircraft in a short period of time.

[0192] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0193] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0194] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0195] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0196] In addition, each functional unit in each embodiment of the present invention may be integrated into a first processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0197] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the number of attacks, characterized in that include: Acquire multiple target objects that the first object is going to attack; Numbering multiple target objects to obtain multiple numbering information corresponding to the multiple target objects; In response to the plurality of numbering information, obtaining a first time period in which the first object attacks any first target object among the plurality of target objects from the target position; After attacking the first target, obtaining multiple second time periods in which the first object attacks multiple target objects other than the first target object; Acquire multiple attack values ​​of the first object attacking multiple target objects other than the first target object; Determine a target time period based on the first time period, the plurality of second time periods and the plurality of attack values, wherein the target time period is less than or equal to a preset time period threshold; A hybrid genetic algorithm is used to determine the maximum number of target objects that the first object can attack within a target time period.

2. The method according to claim 1, characterized in that The expression for obtaining the first time period of the first object attacking any first target object among multiple target objects from the target position is: <h2 style=";text-align:left;direction:ltr">Timef=(a1 a2 a3…a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ) Among them, the elements in Timef (a1 a2 a3…a n ) represents the first time period in which the first object attacks any first target object among multiple target objects starting from the target position.

3. The method according to claim 1, characterized in that The expression for obtaining multiple second time periods during which the first object attacks multiple target objects other than the first target object is: Among them, the element m in Time i,j Indicates that W is completed from multiple other target objects i Attack and knock down multiple other targets. j The required second time period, i, j represents the serial number information of other multiple target objects.

4. The method according to claim 1, characterized in that The expression for obtaining multiple attack values ​​of the first object attacking multiple target objects other than the first target object is: x i，j ∈{0,1},(i,j=1,2,…,n) Among them, x i,j Indicates that W is completed from multiple other target objects i Attack and knock down multiple other targets. j , x i,j =1 means that W is completed from multiple other target objects i Attack and knock down multiple other targets. j , x i,j =0 means that W is completed from multiple other target objects i The attack started without targeting other multiple targets. j to attack.

5. The method according to claim 1, characterized in that Based on the first time period, multiple second time periods and multiple attack values, a target time period is determined, and the expression for the target time period being less than or equal to the preset time period threshold is: Among them, t f is the first time period, t lim is the preset time threshold, where is the target time period, and the value range of i,j is 1 to n.

6. The method according to claim 1, characterized in that The expression for determining the maximum number of target objects that the first object can attack within the target time period using the hybrid genetic algorithm is: Wherein, maxZ represents the maximum number of target objects that the first object can attack within the target time period.

7. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 6 when running.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

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