A data link time slot allocation method based on improved differential algorithm

Abstract

The application discloses a data link time slot allocation method based on an improved differential algorithm, and comprises the following steps: dividing a time frame of a data link into a plurality of time slots, one population in one time frame, initializing the population by using a chaos algorithm, and introducing a classical DE algorithm; setting initialization parameters, executing a loop to obtain a data link time slot allocation strategy; performing mutation and crossover operations on the obtained data link time slot allocation strategy by using an adaptive algorithm; comparing vectors in a selected population with parent vectors one by one by using a greedy algorithm to determine whether the vectors are legal solutions, and retaining the legal solutions; executing a loop until a loop termination condition is met, and outputting an optimal solution as an optimal time slot allocation strategy in the data link under the current condition. The embodiment of the application proposes a dynamic time slot allocation strategy, reduces jitter in a transmission process, and makes time slot distribution of each node as balanced as possible.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a data link time slot allocation method based on an improved differential algorithm. Background Art

[0002] As practical application demands for communication performance continue to improve, adopting a reasonable time slot allocation strategy is the key to ensuring communication efficiency and quality during the communication process. However, the current time slot allocation strategy has problems such as a single algorithm, easy to fall into local optimality, and weak generalization ability.

[0003] With the increasing demand for time slots, a reasonable time slot allocation strategy plays a crucial role in the performance of communication networks. Liu Xing et al. transformed the time slot allocation problem into a shortest path loop problem and, based on this, proposed a new time slot allocation scheme that uses a traversal algorithm to dynamically plan paths. However, as the number of nodes increases, the algorithm becomes time-consuming and its global optimization capability is poor. Fu Yanfang et al. proposed a hybrid time slot allocation protocol based on grey correlation analysis. This method uses grey correlation analysis to determine the overall score of each competing node, which then determines its priority. Fixed time slots are then allocated based on this priority. However, this algorithm is not suitable for environments with a large number of nodes and suffers from low channel utilization. Wang Rui et al. proposed a time slot allocation algorithm based on a genetic tabu search. This algorithm combines a genetic algorithm with a tabu search algorithm, increasing the probability of obtaining a global optimal solution. However, the algorithm relies too heavily on fixed parameter settings, resulting in blind mutations and a potential for not always obtaining the global optimal solution.

[0004] The current time slot allocation strategy has problems such as single algorithm, easy to fall into local optimality, weak generalization ability, and large time jitter. Summary of the Invention

[0005] The embodiment of the present application provides a data link time slot allocation method based on an improved differential algorithm, which is used to propose a dynamic time slot allocation strategy to reduce jitter during transmission and make the time slot distribution of each node as balanced as possible.

[0006] The present invention provides a method for allocating data link time slots based on an improved differential algorithm, comprising the following steps:

[0007] The data link time frame is divided into multiple time slots, one time frame is one population, the chaos algorithm is used to initialize the population, and the classic DE algorithm is introduced;

[0008] Set initialization parameters and execute loop to obtain data link time slot allocation strategy;

[0009] The obtained data link time slot allocation strategy is subjected to mutation and crossover operations using an adaptive algorithm;

[0010] The vectors in the selected population are compared with the parent vectors one by one through the greedy algorithm to determine whether they are legal solutions and retain the legal solutions;

[0011] The loop is executed until the loop termination condition is met, and the optimal solution is output as the optimal time slot allocation strategy in the data link under the current situation.

[0012] Optionally, use a chaos algorithm to initialize the population and introduce the classic DE algorithm, including:

[0013] The standard Logistic chaos mapping method is defined as follows:

[0014] X i+1 =μ*X i (1-X i ),i∈{1,2,…},μ∈(0,4],X i ∈[0,1]

[0015] Among them, μ is the control parameter, i represents the i-th chaotic variable, X i+1 Represents the chaotic variable generated from i to j;

[0016] The improved standard Logistic chaos mapping method satisfies:

[0017]

[0018]

[0019] in, represents the i-th parent chaotic variable, represents the i+1th parent chaotic variable, Represents the chaotic variable of the i+1th generation.

[0020] Optionally, setting initialization parameters includes using population initialization operations based on an improved differential algorithm:

[0021]

[0022] Among them, X j_min is the minimum boundary value of the j-th dimension gene, X j_max is the maximum boundary value of the j-th dimension gene.

[0023] Optionally, adaptive algorithms for mutation and crossover operations include:

[0024] The time frames in the data chain represent individuals, and the genes in the individuals represent the occupancy of time slots in the actual scenario;

[0025] According to the maximum number of iterations N set max and the current number of iterations N currentDetermine the selection probability P, satisfying:

[0026]

[0027] According to the selection probability P, the appropriate strategy is selected to reflect the degree of evolution of the data link time slot allocation population, satisfying:

[0028]

[0029] Among them, DE represents the differential algorithm, current-to-rand represents a random search around the current individual, rand represents that the basis vector is a random individual, rand-to-best represents a random search around the best individual, and current-to-best represents a search around the best individual and the current individual.

[0030] The search range of the DE algorithm is expanded using the following adaptive mutation scaling factor and adaptive crossover factor:

[0031]

[0032]

[0033] Where F is the variation scaling factor, F min represents the minimum variation scaling factor, F max represents the maximum variation scaling factor, CR is the cross scaling factor, and CR min Indicates the minimum cross scaling factor, CR max Indicates the maximum cross scaling factor.

[0034] Optionally, a greedy algorithm is used to compare the vectors in the selected population with the parent vectors one by one to determine whether they are legal solutions, including:

[0035] The vectors in the selected data link time slot allocation population are compared with the parent vectors one by one through the greedy algorithm. If the selected vector is better than the parent vector, the parent vector is replaced by the selected vector. Otherwise, the parent vector is retained and the comparison is continued, satisfying:

[0036]

[0037] Among them, f() is the optimization objective function for finding the minimum value.

[0038] Optionally, for the disordered individual solutions judged as illegal solutions, use one gene to represent a time slot in a time frame, and arrange the genes from small to large;

[0039] For duplicate solutions that are determined to be illegal, according to the allocation requirements of Link16 time slots, each time slot uniquely corresponds to one command message.

[0040] Optionally, after any crossover and mutation operation, the following is also included:

[0041] Perform genetic testing on individuals, and if there are duplicate genes, replace the duplicate genes with genes that do not appear in the individual to transform them into feasible solutions.

[0042] An embodiment of the present application also proposes a data link time slot allocation device based on an improved differential algorithm, including a processor and a memory, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the data link time slot allocation method based on the improved differential algorithm as described above are implemented.

[0043] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the aforementioned data link time slot allocation method based on the improved differential algorithm are implemented.

[0044] The embodiment of the present application proposes a dynamic time slot allocation strategy. The method of the present application can reduce jitter during the transmission process and make the time slot distribution of each node as balanced as possible.

[0045] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0047] Figure 1 Schematic diagram of the flow of the data link time slot allocation method according to an embodiment of the present application;

[0048] Figure 2 A schematic diagram of an infeasible solution generated by cross-calculation of the data link time slot allocation method according to an embodiment of the present application;

[0049] Figure 3 A schematic diagram of a repeated solution generated by cross-calculation of a data link time slot allocation method according to an embodiment of the present application;

[0050] Figure 4 This is a schematic diagram of the Link16 data link of the data link time slot allocation method according to an embodiment of the present application;

[0051] Figure 5This is a comparison diagram of the time slot allocation balance of the three algorithms in the embodiment of the present application;

[0052] Figure 6 This is a diagram illustrating the relationship between the differential strategy and the degree of balance in an embodiment of the present application;

[0053] Figure 7 This is an illustration of the relationship between the number of time slots that need to be allocated and time slot jitter in an embodiment of the present application;

[0054] Figure 8 This is an illustration of the relationship between the number of idle time slots and time slot jitter in an embodiment of the present application;

[0055] Figures 9(a)-9(d) The following is a comparison of the generalization capabilities of the three algorithms in the embodiments of this application. DETAILED DESCRIPTION

[0056] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0057] In order to ensure the periodic transmission of node messages, reduce jitter during transmission, and make the time slot distribution of each node as balanced as possible, the dynamic time slot allocation strategy is improved based on the characteristics of the differential evolution algorithm in the intelligent optimization algorithm. The embodiment of the present application provides a data link time slot allocation method based on the improved differential algorithm. Figure 1 As shown, the method of the embodiment of the present application first allocates a population for each time frame in the data link time slot, then uses the introduction of a chaos algorithm to obtain an initialized population, and then obtains a possible solution through the adaptive mutation crossover algorithm and problem solution processing mechanism. Then, through greedy selection, the vectors in the selected population are compared one by one with the parent vector until the loop ends, at which point the optimal solution is obtained, thereby calculating the optimal time slot allocation strategy in the data link under the current situation. Specifically, it includes the following steps:

[0058] In step S101, the data link time frame is divided into multiple time slots, with one population per time frame. A chaotic algorithm is used to initialize the population, and a classic DE algorithm is introduced. In some specific examples, for example, the entire data link time frame can be divided into 1536 time slots. Based on the number of currently occupied time slots, pending time slots, and idle time slots, a chaotic algorithm is used to initialize the population, and a classic DE algorithm is introduced to set initialization parameters.

[0059] In step S102, a loop is executed to obtain a data link time slot allocation strategy.

[0060] In step S103, the obtained data link time slot allocation strategy is subjected to mutation and crossover operations using an adaptive algorithm.

[0061] In step S104, the vectors in the selected population are compared with the parent vectors one by one through a greedy algorithm to determine whether they are legal solutions, and the legal solutions are retained.

[0062] In step S105, a loop is executed until a loop termination condition is satisfied, and the optimal solution is output as the optimal time slot allocation strategy in the data link under the current situation.

[0063] The embodiment of the present application proposes a dynamic time slot allocation strategy. The method of the present application can reduce jitter during the transmission process and make the time slot distribution of each node as balanced as possible.

[0064] In some specific examples, the chaos algorithm is used to initialize the population and the classic DE algorithm is introduced, including:

[0065] The specific embodiment of this application is defined based on the standard Logistic chaos mapping method:

[0066] X i+1 =μ*X i (1-X i ),i∈{1,2,…},μ∈(0,4],X i ∈[0,1]

[0067] Among them, μ is the control parameter, i represents the i-th chaotic variable, X i+1 represents the chaotic variable generated from i to j; when 0<μ≤1, the sequence has a characteristic that no matter what value the system starts from or what value μ takes, the system gradually approaches 0 as the number of iterations increases. When , the Logistic Chaotic Map can generate chaotic variables between the interval [0,1], and divide [0,1] into 10 intervals, namely (0,0.1], (0.1,0.2], (0.2,0.3], ..., (0.9,1], which mainly leads to uneven distribution in the intervals (0,0.1] and (0.9,1]. The improved standard Logistic Chaotic Map method in this application example satisfies:

[0068]

[0069]

[0070] in, represents the i-th parent chaotic variable, represents the i+1th parent chaotic variable, Represents the chaotic variable of the i+1th generation. By optimizing the chaos algorithm, the chaotic variables in areas 1 and 10 are evenly distributed to other areas, making the algorithm more ergodic. Then, the population initialization operation based on the improved difference algorithm is performed. In some specific examples, setting the initialization parameters includes using the population initialization operation based on the improved difference algorithm:

[0071]

[0072] Among them, X j_min is the minimum boundary value of the j-th dimension gene, X j_max is the maximum boundary value of the j-th dimension gene.

[0073] The embodiment of the present application takes advantage of the randomness, ergodicity and regularity of chaotic sequences and introduces the classic DE algorithm for improvement, thus ensuring the randomness and diversity of the population.

[0074] In the embodiment of the present application, the time frames in the data link represent individuals, and the genes in the individuals represent the time slot occupancy in the actual scenario, that is, the individuals represent a possible solution. The embodiment of the present application uses the selection probability parameter to make the individual gene crossover and mutation process in the data link time slot allocation more flexible, thereby increasing the search range in the early stage of the algorithm and accelerating the convergence of the algorithm in the later stage to avoid falling into the local optimum. In some specific examples, the mutation and crossover operations using the adaptive algorithm include:

[0075] According to the maximum number of iterations N set max and the current number of iterations N current Determine the selection probability P, satisfying:

[0076]

[0077] According to the selection probability P, the appropriate strategy is selected to reflect the degree of evolution of the data link time slot allocation population, satisfying:

[0078]

[0079] Among them, DE represents the differential algorithm, current-to-rand represents a random search around the current individual, rand represents that the basis vector is a random individual, rand-to-best represents a random search around the best individual, and current-to-best represents a search around the best individual and the current individual.

[0080] To address the issue of the flexibility of the mutation scaling factor, the global search capability is improved to meet the diversity of the data link time slot allocation population in the early stage of the search, and the local search around the dominant individuals and accelerated convergence capability is improved as the inferior individuals are eliminated and the superior individuals become apparent in the later stage of the search. The embodiment of the present application uses the following adaptive mutation scaling factor and adaptive crossover factor to expand the search range of the DE algorithm:

[0081]

[0082]

[0083] Where F is the variation scaling factor, F min represents the minimum variation scaling factor, F max represents the maximum variation scaling factor, CR is the cross scaling factor, and CR min Indicates the minimum cross scaling factor, CR max represents the maximum cross scaling factor, in some examples, F min , CR min Take 0.1, F max , CR max Take 0.9.

[0084] The adaptive mutation and crossover scheme designed in this application's embodiments allows the DE algorithm to expand its search range while reducing algorithmic time consumption. At the start of the data link slot allocation algorithm, a smaller mutation scaling factor F and a larger crossover factor CR can be selected to ensure a sufficient search range and good global search capabilities. As the search progresses, the mutation scaling factor F increases and the crossover factor CR decreases, accelerating the convergence of the data link slot allocation algorithm and preventing it from falling into local optima.

[0085] In some specific examples, a greedy algorithm is used to compare the vectors in the selected population with the parent vectors one by one to determine whether they are legal solutions, including:

[0086] The vectors in the selected data link time slot allocation population are compared with the parent vectors one by one through the greedy algorithm. If the selected vector is better than the parent vector, the parent vector is replaced by the selected vector. Otherwise, the parent vector is retained and the comparison is continued, satisfying:

[0087]

[0088] Among them, f() is the optimization objective function for solving the minimum value. It shows that the target value of the former is better than that of the latter, and vice versa.

[0089] According to the problems encountered by the differential algorithm in the time slot allocation process, there are mainly two illegal solutions: disordered individual solutions and repeated individual solutions. In some specific examples, for disordered individual solutions judged as illegal solutions, a gene is used to represent the time slot in a time frame, and the genes are arranged from small to large, so that the algorithm can more realistically use genes to simulate the time slot allocation process, which is convenient for calculating the jitter between time slots. Crossover operation is an indispensable operation in the differential algorithm, and the crossover operation generates disordered solutions that affect the algorithm. The embodiment of the present application prevents disordered solutions from having a negative impact on the algorithm optimization results by sorting such solutions generated. As Figure 2 As shown, individual α and individual β undergo a crossover operation, and the genes cross at the crossover point 2, thereby obtaining individual γ and disordered individual δ, so after ordered processing it becomes 2-7-8-9.

[0090] For duplicate solutions that are judged as illegal solutions, according to the allocation requirements of Link16 time slots, each time slot uniquely corresponds to a command message, and there cannot be duplicate genes on the same individual, such as Figure 3 As shown, individuals A and B produce individuals C and D due to crossover operation, and individual C has a repeated gene 6.

[0091] In some specific examples, after each crossover and mutation operation, the following step further includes: performing a genetic test on the individual; if there are duplicate genes, the duplicate genes are replaced with genes that do not appear in the individual to convert it into a feasible solution. To eliminate duplicate genes, in the embodiments of the present application, after each crossover and mutation operation, a genetic test is further performed on the individuals to determine whether there are duplicate genes. If there are duplicate genes, they are replaced with genes that do not appear in the individual, thereby converting them into feasible solutions.

[0092] The proposed algorithm is used to simulate the Link16 data link time slot allocation strategy through simulation software. Figure 4 This example uses the time slot interval variance G defined by the standard logistic chaotic map to represent the time slot jitter Var. Time slot balance is measured by the magnitude of the time slot jitter Var. Simulation experiments are conducted on three aspects: time slot balance, reliability, and generalization capability. These experiments are compared with differential algorithms and genetic algorithms. The simulation experiments demonstrate the stability, generalization, and time slot equalization capabilities of this proposed algorithm.

[0093] The embodiment of the present application also proposes a simulation example of a data link time slot allocation method based on an improved differential algorithm. In order to simplify the traversal process and thus reduce the amount of calculation, the total number of time slots is set to 100, 50 idle time slots are randomly generated, and 20 time slots need to be allocated. The simulation results are as follows: Figure 5 、 Figure 6The experimental results show that, under the allocation of the three algorithms, the time slot jitter value decreases with the increase of the number of iterations and finally tends to be stable. However, the jitter value of the algorithm proposed in this embodiment is lower than that of the other two algorithms, and the time slot balancing ability is better. Figure 5 It can be seen that the algorithm proposed in the embodiment of the present application converges quickly in the early stage and has good local exploration capabilities in the later stage, so that the best global optimal solution is finally obtained.

[0094] In order to further verify the performance of the algorithm of the embodiment of the present application, this example studies the relationship between the time slot jitter size and the time slot jitter size under different time slot allocation and idle time slot environments. The experimental results show that the number of time slots that need to be allocated is proportional to the time slot jitter size value. As the number of time slots required for allocation increases, the time slot balance becomes worse. The experimental results show that due to the large intervals between the randomly generated idle time slot sequences, the number of idle time slots is inversely proportional to the jitter size value. As the number of idle time slots increases, the time slot balancing ability becomes stronger. Figure 7 、 8 As shown, it can be seen from the simulation results that the jitter values ​​of the algorithms in the embodiments of the present application are smaller than those of the differential algorithm and the genetic algorithm, and the time slot allocation problem can be effectively handled.

[0095] In order to verify the generalization ability of the algorithm of the embodiment of the present application, this example designs 4 sets of available idle time slots N and time slots to be allocated NW parameter combinations to conduct simulation experiments to measure the convergence of the algorithm. The 4 sets of combinations are N = 500, NW = 300; N = 300, NW = 100; N = 100, NW = 30; N = 50, NW = 12. Figures 9(a)-9(d) The experimental results show that the differential algorithm and the genetic algorithm have a fast convergence speed in the early stage, which makes the algorithm easily fall into the local optimum. The algorithm of the embodiment of the present application has good local exploration ability and good generalization ability. As a result, the algorithm proposed in the embodiment of the present application not only has better jitter values ​​than other algorithms in several different situations, but also has better convergence ability.

[0096] The embodiment of the present application first uses a chaos algorithm to initialize the population, increase the population diversity and avoid premature convergence of the algorithm, and then uses the selection probability parameter to make the crossover and mutation process more flexible, so that the search range is increased in the early stage of the algorithm and the local exploration capability is enhanced in the later stage of the algorithm to avoid falling into the local optimum, thereby improving the algorithm's optimization ability, more balanced time slot allocation, better stability, and stronger generalization ability.

[0097] An embodiment of the present application also proposes a data link time slot allocation device based on an improved differential algorithm, including a processor and a memory, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the data link time slot allocation method based on the improved differential algorithm as described above are implemented.

[0098] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the aforementioned data link time slot allocation method based on the improved differential algorithm are implemented.

[0099] It should be noted that, in the various embodiments of the present application, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0100] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0101] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0102] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are protected by this application.

Claims

1. A data link time slot allocation method based on an improved differential algorithm, characterized in that: The steps include: The data link time frame is divided into multiple time slots, one time frame is one population, the chaos algorithm is used to initialize the population, and the improved differential algorithm is introduced; Set initialization parameters and execute loop to obtain data link time slot allocation strategy; The obtained data link time slot allocation strategy is subjected to mutation and crossover operations using an adaptive algorithm; The vectors in the selected population are compared with the parent vectors one by one through the greedy algorithm to determine whether they are legal solutions and retain the legal solutions; Execute the loop until the loop termination condition is met, and output the optimal solution as the optimal time slot allocation strategy in the data link under the current situation; The mutation and crossover operations using adaptive algorithms include: The time frames in the data chain represent individuals, and the genes in the individuals represent the occupancy of time slots in the actual scenario; According to the maximum number of iterations set and the current iteration number Determine the selection probability P, satisfying: According to the selection probability P, the appropriate strategy is selected to reflect the degree of evolution of the data link time slot allocation population, satisfying: in, represents the improved differential algorithm, Indicates a random search around the current individual. represents the basis vector as a random individual, represents a random search around the optimal individual, Indicates searching around the optimal individual and the current individual; The search range of the improved difference algorithm is expanded using the following adaptive mutation scaling factor and adaptive crossover factor: Where F is the variation scaling factor, represents the minimum variation scaling factor, represents the maximum variation scaling factor, is the cross scaling factor, represents the minimum cross scaling factor, Indicates the maximum cross scaling factor.

2. The data link time slot allocation method based on the improved differential algorithm according to claim 1, characterized in that: Using the chaos algorithm to initialize the population, the improved differential algorithm is introduced, including: The standard Logistic chaos mapping method is defined as follows: in, For the control parameters, Indicates the A chaotic variable, express arrive j The chaotic variables generated; The improved standard Logistic chaos mapping method satisfies: in, Indicates the parent chaotic variables, Indicates the +1 parent chaos variable, Indicates the +1 child chaos variable.

3. The data link time slot allocation method based on the improved differential algorithm according to claim 2, characterized in that: Setting the initialization parameters includes using the population initialization operation based on the improved differential algorithm: in, is the minimum boundary value of the j-th dimension gene, is the maximum boundary value of the j-th dimension gene.

4. The data link time slot allocation method based on the improved differential algorithm according to claim 1, wherein: The vectors in the selected population are compared with the parent vectors one by one through a greedy algorithm to determine whether they are legal solutions. The vectors in the selected data link time slot allocation population are compared with the parent vectors one by one through the greedy algorithm. If the selected vector is better than the parent vector, the parent vector is replaced by the selected vector. Otherwise, the parent vector is retained and the comparison is continued, satisfying: in, To find the minimum value of the optimization objective function.

5. The data link time slot allocation method based on the improved differential algorithm according to claim 4, characterized in that: For the disordered individual solutions that are judged as illegal solutions, use one gene to represent the time slot in a time frame, and arrange the genes from small to large; For duplicate solutions that are determined to be illegal, according to the allocation requirements of Link16 time slots, each time slot uniquely corresponds to one command message.

6. The data link time slot allocation method based on the improved differential algorithm according to claim 5, characterized in that: After any crossover and mutation operation, it also includes: Perform genetic testing on individuals, and if there are duplicate genes, replace the duplicate genes with genes that do not appear in the individual to transform them into feasible solutions.

7. A data link time slot allocation device based on an improved differential algorithm, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the data link time slot allocation method based on the improved differential algorithm as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the data link time slot allocation method based on the improved differential algorithm according to any one of claims 1 to 6.

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