Digital battlefield simulation teaching method and system based on 5G communication network

Abstract

The invention relates to a digital battlefield simulation teaching method and system based on a 5G communication network, and the method comprises the steps: M1, obtaining the data information of a simulation entity type of a battle unit, the data information of an initial deployment position, the data information of an initial parameter, and the data information of an external environment parameter, and acquiring confrontation data information of a fighting unit in a digital battlefield environment in real time, and predicting information of a confrontation process of the fighting unit by adopting a grey wolf optimized BP neural network regression prediction algorithm to obtain predicted data information of the confrontation process of the fighting unit. According to the invention, not only is the digital battlefield simulated, the teaching interactivity is greatly improved, but also students can personally participate and experience the complexity and uncertainty of wars by simulating the battlefield environment in real time, so that the understanding of strategic tactics is deepened.

Description

Technical Field

[0001] The present invention relates to the technical field of digital battlefield simulation teaching, and in particular to a method and system for digital battlefield simulation teaching based on a 5G communication network. Background Art

[0002] With the rapid development of the Internet of Things and digital technology, the application of simulated digital battlefields in teaching is becoming more and more extensive. The Internet of Things technology enables various devices on the battlefield to be interconnected, providing a more realistic and intuitive battlefield environment for teaching. At the same time, the development of digital technology also makes the acquisition, transmission and processing of battlefield information more efficient, providing richer and more diverse teaching resources for teaching.

[0003] Therefore, how to use digital technology to conduct battlefield simulation teaching has become an urgent problem that we need to solve. Summary of the invention

[0004] In view of the above problems, the present invention provides a method and system for digital battlefield simulation teaching based on 5G communication network, which not only simulates the digital battlefield to greatly improve the interactivity of teaching, but also through real-time simulation of the battlefield environment, students can personally participate in and experience the complexity and uncertainty of the war, thereby deepening their understanding of strategy and tactics.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0006] A method for digital battlefield simulation teaching based on 5G communication network, the method comprising:

[0007] M1. Obtain data information of the simulation entity type, initial deployment position, initial parameter and external environment parameter of the combat unit, and collect confrontation data information of the combat unit in the digital battlefield environment in real time;

[0008] M2. Based on the data information of the simulation entity type of the combat unit, the data information of the initial deployment position, the data information of the initial parameters and the data information of the external environment parameters, the information of the confrontation process of the combat unit is predicted using the BP neural network regression prediction algorithm optimized by the gray wolf to obtain the predicted data information of the confrontation process of the combat unit;

[0009] M3. Based on the predicted data information of the confrontation process of the combat unit and the confrontation data information of the combat unit in the digital battlefield environment, the confrontation data of the combat unit is optimized by using a crazy adaptive whale optimization algorithm to obtain the optimized combat data information of the confrontation unit;

[0010] M4. Based on the optimized battle data information of the confrontation unit, a model of the confrontation results of the digital battlefield based on the dynamic learning factor is constructed, the confrontation results of the confrontation unit are characterized, and the data information of the confrontation results of the confrontation unit is obtained.

[0011] Furthermore, in step M2, the prediction of the information of the confrontation process of the opposing unit using the gray wolf optimized BP neural network regression prediction algorithm includes:

[0012] M21. Based on the data information of the simulation entity type of the combat unit, the data information of the initial deployment position, the data information of the initial parameters and the data information of the external environment parameters, a simulation parameter matrix of the combat unit is constructed, and the simulation parameter matrix of the combat unit is input into the BP neural network for training and learning, and the weight and bias of the BP neural network are initialized to obtain the data information of the weight and bias of the initialized BP neural network;

[0013] M22. Based on the weight and bias data information of the initialized BP neural network, the gray wolf population is initialized, the population parameters are determined, and the data information of the initialized gray wolf population is obtained;

[0014] M23. Based on the data information of the initialized gray wolf population, establish the fitness function Q of the population individuals,

[0015]

[0016] Among them, x is the data information of the gray wolf population after initialization, α1, α2 and α3 are the fitness determining factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals;

[0017] M24. Based on the data information of the fitness values ​​of the individuals in the population, establish a target optimization function W,

[0018]

[0019] Among them, y is the data information of the fitness value of the population individual, β1, β2 and β3 are the target optimization constant factors, and the weights and biases of the BP neural network are optimized to obtain the optimized BP neural network;

[0020] M25. Based on the optimized BP neural network, input the simulation parameter matrix of the combat unit to determine the network regression prediction function R,

[0021]

[0022] Among them, y is the simulation parameter matrix of the combat unit, δ1, δ2 and δ3 are network regression prediction factors, which predict the information of the confrontation process of the combat unit to obtain the predicted data information of the confrontation process of the combat unit.

[0023] Furthermore, the network regression prediction factors δ1, δ2 and δ3 are,

[0024]

[0025] Among them, y is the simulation parameter matrix of the combat unit.

[0026] Furthermore, the constraints of the target optimization constant factors β1, β2 and β3 are:

[0027] Furthermore, the constraint function f of the fitness determining factors α1, α2 and α3 of the individuals in the population is,

[0028]

[0029] The value range of the constraint function f is (0,1).

[0030] Furthermore, in step M3, the optimization of the confrontation data of the combat unit by using the crazy adaptive whale optimization algorithm includes:

[0031] M31. Based on the predicted data information of the confrontation process of the combat unit and the confrontation data information of the combat unit in the digital battlefield environment, the whale population is initialized, the whale population parameters and the maximum number of iterations are determined, and the data information of the initialized whale population is obtained;

[0032] M32. Based on the data information of the initialized whale population, establish the fitness function P of the individual whale population,

[0033]

[0034] Among them, z is the data information of the whale population after initialization, γ1, γ2 and γ3 are the variation difference factors of the individuals in the whale population, and the fitness values ​​of the individuals in the whale population are calculated to obtain the data information of the fitness values ​​of the individuals in the whale population;

[0035] M33. Based on the data information of the fitness values ​​of the individual whale populations, an optimization function S based on a crazy adaptive learning factor is established.

[0036]

[0037] Among them, r is the data information of the fitness value of the individual whale population, η1, η2 and η3 are crazy adaptive learning factors, which optimize the confrontation data of the combat unit to obtain the optimized combat data information of the combat unit.

[0038] Furthermore, the constraint function g of the crazy adaptive learning factors η1, η2 and η3 is,

[0039]

[0040] Among them, the value range of the constraint function g is (2,3).

[0041] Furthermore, in step M4, the model of the confrontation result of the digital battlefield based on the dynamic learning factor is constructed to characterize the confrontation result of the combat unit, including:

[0042] M41. Based on the optimized battle data information of the confrontation unit, construct a feature extraction function G of the battle data of the confrontation unit,

[0043]

[0044] Among them, h is the battle data information of the confrontation unit after optimization, λ1, λ2 and λ3 are the feature extraction factors of the confrontation unit, and the confrontation data of the confrontation unit is subjected to feature extraction to obtain the confrontation feature data information of the confrontation unit;

[0045] M42. Based on the confrontation feature data information of the confrontation unit, establish a confrontation result prediction function H based on a dynamic learning factor,

[0046]

[0047] Among them, a is the adversarial feature data information of the adversarial unit, μ1, μ2 and μ3 are dynamic learning factors;

[0048] Based on the confrontation result prediction function H based on the dynamic learning factor, the confrontation result of the combat unit is characterized to obtain data information of the confrontation result of the combat unit.

[0049] In order to achieve the above-mentioned objectives and other related objectives, the present invention also provides a system for digital battlefield simulation teaching based on 5G communication network, including a computer device, which is programmed or configured to execute any one of the steps of the method for digital battlefield simulation teaching based on 5G communication network.

[0050] In order to achieve the above-mentioned objectives and other related objectives, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the methods for digital battlefield simulation teaching based on a 5G communication network.

[0051] The present invention has the following positive effects:

[0052] 1. The present invention adopts the Gray Wolf optimized BP neural network regression prediction algorithm to predict the information of the confrontation process of the opposing units, and combines the crazy adaptive whale optimization algorithm to optimize the confrontation data of the opposing units. It not only simulates the digital battlefield and greatly improves the interactivity of teaching, but also simulates the battlefield environment in real time. Students can personally participate in and experience the complexity and uncertainty of the war, thereby deepening their understanding of strategy and tactics.

[0053] 2. The present invention characterizes the confrontation results of the opposing units by constructing a model of the confrontation results of the digital battlefield based on dynamic learning factors. Not only does the simulation of the digital battlefield improve the practicality of teaching, students can conduct actual combat exercises in a virtual environment and exercise their ability to cope with various battlefield situations. The simulation of the digital battlefield also enhances students' interest in learning. Through the immersive experience, it stimulates their desire to explore military knowledge and improves learning effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0055] Figure 2 A schematic diagram of the process flow of the gray wolf optimized BP neural network regression prediction algorithm of the present invention;

[0056] Figure 3 It is a flow chart of the crazy adaptive whale optimization algorithm of the present invention;

[0057] Figure 4 A schematic diagram of a flow chart of constructing a model of confrontation results of a digital battlefield based on dynamic learning factors according to the present invention;

[0058] Figure 5 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0059] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0060] Example 1: Figure 1 As shown, a method for digital battlefield simulation teaching based on 5G communication network, the method comprising:

[0061] M1. Obtain data information of the simulation entity type, initial deployment position, initial parameter and external environment parameter of the combat unit, and collect confrontation data information of the combat unit in the digital battlefield environment in real time;

[0062] M2. Based on the data information of the simulation entity type of the combat unit, the data information of the initial deployment position, the data information of the initial parameters and the data information of the external environment parameters, the information of the confrontation process of the combat unit is predicted using the BP neural network regression prediction algorithm optimized by the gray wolf to obtain the predicted data information of the confrontation process of the combat unit;

[0063] M3. Based on the predicted data information of the confrontation process of the combat unit and the confrontation data information of the combat unit in the digital battlefield environment, the confrontation data of the combat unit is optimized by using a crazy adaptive whale optimization algorithm to obtain the optimized combat data information of the confrontation unit;

[0064] M4. Based on the optimized battle data information of the confrontation unit, a model of the confrontation results of the digital battlefield based on the dynamic learning factor is constructed, the confrontation results of the confrontation unit are characterized, and the data information of the confrontation results of the confrontation unit is obtained.

[0065] In this embodiment, if Figure 2 As shown, in step M2, the use of the gray wolf optimized BP neural network regression prediction algorithm to predict the information of the confrontation process of the combat unit includes:

[0066] M21. Based on the data information of the simulation entity type of the combat unit, the data information of the initial deployment position, the data information of the initial parameters and the data information of the external environment parameters, a simulation parameter matrix of the combat unit is constructed, and the simulation parameter matrix of the combat unit is input into the BP neural network for training and learning, and the weight and bias of the BP neural network are initialized to obtain the data information of the weight and bias of the initialized BP neural network;

[0067] M22. Based on the weight and bias data information of the initialized BP neural network, the gray wolf population is initialized, the population parameters are determined, and the data information of the initialized gray wolf population is obtained;

[0068] M23. Based on the data information of the initialized gray wolf population, establish the fitness function Q of the population individuals,

[0069]

[0070] Among them, x is the data information of the gray wolf population after initialization, α1, α2 and α3 are the fitness determining factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals;

[0071] M24. Based on the data information of the fitness values ​​of the individuals in the population, establish a target optimization function W,

[0072]

[0073] Among them, y is the data information of the fitness value of the population individual, β1, β2 and β3 are the target optimization constant factors, and the weights and biases of the BP neural network are optimized to obtain the optimized BP neural network;

[0074] M25. Based on the optimized BP neural network, input the simulation parameter matrix of the combat unit to determine the network regression prediction function R,

[0075]

[0076] Among them, y is the simulation parameter matrix of the combat unit, δ1, δ2 and δ3 are network regression prediction factors, which predict the information of the confrontation process of the combat unit to obtain the predicted data information of the confrontation process of the combat unit.

[0077] In this embodiment, the network regression prediction factors δ1, δ2 and δ3 are,

[0078]

[0079] Among them, y is the simulation parameter matrix of the combat unit.

[0080] In this embodiment, the constraints of the target optimization constant factors β1, β2 and β3 are:

[0081]

[0082] In this embodiment, the constraint function f of the fitness determining factors α1, α2 and α3 of the individuals in the population is,

[0083]

[0084] The value range of the constraint function f is (0,1).

[0085] Example 2: Based on the method of digital battlefield simulation teaching based on 5G communication network in Example 1, the present invention is further illustrated and described below.

[0086] like Figure 1 As shown, a method for digital battlefield simulation teaching based on 5G communication network, the method comprising:

[0087] M1. Obtain data information of the simulation entity type, initial deployment position, initial parameter and external environment parameter of the combat unit, and collect confrontation data information of the combat unit in the digital battlefield environment in real time;

[0088] M2. Based on the data information of the simulation entity type of the combat unit, the data information of the initial deployment position, the data information of the initial parameters and the data information of the external environment parameters, the information of the confrontation process of the combat unit is predicted using the BP neural network regression prediction algorithm optimized by the gray wolf to obtain the predicted data information of the confrontation process of the combat unit;

[0089] M3. Based on the predicted data information of the confrontation process of the combat unit and the confrontation data information of the combat unit in the digital battlefield environment, the confrontation data of the combat unit is optimized by using a crazy adaptive whale optimization algorithm to obtain the optimized combat data information of the confrontation unit;

[0090] M4. Based on the optimized battle data information of the confrontation unit, a model of the confrontation results of the digital battlefield based on the dynamic learning factor is constructed, the confrontation results of the confrontation unit are characterized, and the data information of the confrontation results of the confrontation unit is obtained.

[0091] In this embodiment, if Figure 3 As shown, in step M3, the optimization of the confrontation data of the combat unit by using the crazy adaptive whale optimization algorithm includes:

[0092] M31. Based on the predicted data information of the confrontation process of the combat unit and the confrontation data information of the combat unit in the digital battlefield environment, the whale population is initialized, the whale population parameters and the maximum number of iterations are determined, and the data information of the initialized whale population is obtained;

[0093] M32. Based on the data information of the initialized whale population, establish the fitness function P of the individual whale population,

[0094]

[0095] Among them, z is the data information of the whale population after initialization, γ1, γ2 and γ3 are the variation difference factors of the individuals in the whale population, and the fitness values ​​of the individuals in the whale population are calculated to obtain the data information of the fitness values ​​of the individuals in the whale population;

[0096] M33. Based on the data information of the fitness values ​​of the individual whale populations, an optimization function S based on a crazy adaptive learning factor is established.

[0097]

[0098] Among them, r is the data information of the fitness value of the individual whale population, η1, η2 and η3 are crazy adaptive learning factors, which optimize the confrontation data of the combat unit to obtain the optimized combat data information of the combat unit.

[0099] In this embodiment, the constraint function g of the crazy adaptive learning factors η1, η2 and η3 is,

[0100]

[0101] Among them, the value range of the constraint function g is (2,3).

[0102] In this embodiment, if Figure 4 As shown, in step M4, the model of the confrontation result of the digital battlefield based on the dynamic learning factor is constructed to characterize the confrontation result of the combat unit, including:

[0103] M41. Based on the optimized battle data information of the confrontation unit, construct a feature extraction function G of the battle data of the confrontation unit,

[0104]

[0105] Among them, h is the battle data information of the confrontation unit after optimization, λ1, λ2 and λ3 are the feature extraction factors of the confrontation unit, and the confrontation data of the confrontation unit is subjected to feature extraction to obtain the confrontation feature data information of the confrontation unit;

[0106] M42. Based on the confrontation feature data information of the confrontation unit, establish a confrontation result prediction function H based on a dynamic learning factor,

[0107]

[0108] Among them, a is the adversarial feature data information of the adversarial unit, μ1, μ2 and μ3 are dynamic learning factors;

[0109] Based on the confrontation result prediction function H based on the dynamic learning factor, the confrontation result of the combat unit is characterized to obtain data information of the confrontation result of the combat unit.

[0110] In this embodiment, the present invention provides a system for digital battlefield simulation teaching based on 5G communication network, including a computer device, which is programmed or configured to execute any step of the method for digital battlefield simulation teaching based on 5G communication network.

[0111] like Figure 5 As shown, this system mainly consists of six modules, namely simulation model development and management, simulation scenario editing and management, operation status monitoring and guidance, data collection and management, distributed simulation engine, and comprehensive situation display.

[0112] Simulation model development and management assists model developers to complete the design of model interfaces and the description of related parameter information, and automatically generates model interface code and model development framework code based on model description information. Equipment models are called atomic models, which mainly include input interfaces, output interfaces, attribute interfaces and internal simulation logic. Atomic models are built based on the idea of ​​Agent, which can perceive battlefield environment and situation information, make inference decisions and schedule execution.

[0113] Simulation scene editing and management

[0114] Define the types, composition, initial deployment positions, initial parameters, external environment parameters and other information of the Red and Blue parties in the scenario, and complete the manual or automatic planning and editing of various combat entities in the air, on the surface and on the ground. Involve the setting of simulation environment (geographic environment, meteorological environment, electromagnetic interference environment, etc.).

[0115] Operation status monitoring and guidance.

[0116] Control the progress of simulation operation, support start / pause / stop, acceleration, deceleration and other operations. Monitor the operation status of each simulation node (equipment digital prototype, target simulation system), timely discover errors in system operation, interrupt or modify the simulation process, improve simulation efficiency, ensure the integrity of simulation data, and enhance the credibility of simulation data.

[0117] Data collection and management.

[0118] Data acquisition is used to collect current online simulation data, store it in a formatted manner in the database, manage the stored simulation data, and support test data playback.

[0119] Distributed simulation engine.

[0120] The simulation driver engine is the core component of the entire simulation system. It is used to drive the operation of the simulation model. Its main functions include simulation resource scheduling, simulation model calculation and data bus management. The simulation engine implements the operation control process of distributed collaborative simulation.

[0121] In this embodiment, the present invention provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the methods for digital battlefield simulation teaching based on a 5G communication network.

[0122] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0123] In summary, the present invention not only greatly improves the interactivity of teaching by simulating a digital battlefield, but also enables students to personally participate in and experience the complexity and uncertainty of war through real-time simulation of the battlefield environment, thereby deepening their understanding of strategies and tactics.

[0124] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for digital battlefield simulation teaching based on 5G communication network, characterized in that: The method comprises: M1. Obtain data information of the simulation entity type, initial deployment position, initial parameter and external environment parameter of the combat unit, and collect confrontation data information of the combat unit in the digital battlefield environment in real time; M2. Based on the data information of the simulation entity type of the combat unit, the data information of the initial deployment position, the data information of the initial parameters and the data information of the external environment parameters, the information of the confrontation process of the combat unit is predicted using the BP neural network regression prediction algorithm optimized by the gray wolf to obtain the predicted data information of the confrontation process of the combat unit; M3. Based on the predicted data information of the confrontation process of the combat unit and the confrontation data information of the combat unit in the digital battlefield environment, the confrontation data of the combat unit is optimized by using a crazy adaptive whale optimization algorithm to obtain the optimized combat data information of the confrontation unit; M4. Based on the optimized battle data information of the confrontation unit, a model of the confrontation results of the digital battlefield based on dynamic learning factors is constructed, the confrontation results of the confrontation unit are characterized, and data information of the confrontation results of the confrontation unit is obtained.

2. The method for digital battlefield simulation teaching based on 5G communication network according to claim 1 is characterized in that: In step M2, the prediction of the information of the confrontation process of the combat unit by using the BP neural network regression prediction algorithm optimized by the gray wolf includes: M21. Based on the data information of the simulation entity type of the combat unit, the data information of the initial deployment position, the data information of the initial parameters and the data information of the external environment parameters, a simulation parameter matrix of the combat unit is constructed, and the simulation parameter matrix of the combat unit is input into the BP neural network for training and learning, and the weight and bias of the BP neural network are initialized to obtain the data information of the weight and bias of the initialized BP neural network; M22. Based on the weight and bias data information of the initialized BP neural network, the gray wolf population is initialized, the population parameters are determined, and the data information of the initialized gray wolf population is obtained; M23. Based on the data information of the initialized gray wolf population, establish the fitness function Q of the population individuals, Among them, x is the data information of the gray wolf population after initialization, α1, α2 and α3 are the fitness determining factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals; M24. Based on the data information of the fitness values ​​of the individuals in the population, establish a target optimization function W, Among them, y is the data information of the fitness value of the population individual, β1, β2 and β3 are the target optimization constant factors, and the weights and biases of the BP neural network are optimized to obtain the optimized BP neural network; M25. Based on the optimized BP neural network, input the simulation parameter matrix of the combat unit to determine the network regression prediction function R, Among them, y is the simulation parameter matrix of the combat unit, δ1, δ2 and δ3 are network regression prediction factors, which predict the information of the confrontation process of the combat unit to obtain the predicted data information of the confrontation process of the combat unit.

3. The method for digital battlefield simulation teaching based on 5G communication network according to claim 2 is characterized in that: The network regression prediction factors δ1, δ2 and δ3 are, Among them, y is the simulation parameter matrix of the combat unit.

4. The method for digital battlefield simulation teaching based on 5G communication network according to claim 2 is characterized in that: The constraints of the target optimization constant factors β1, β2 and β3 are:

5. The method for digital battlefield simulation teaching based on 5G communication network according to claim 2 is characterized in that: The constraint function f of the fitness determining factors α1, α2 and α3 of the individuals in the population is, The value range of the constraint function f is (0,1).

6. The method for digital battlefield simulation teaching based on 5G communication network according to claim 1 is characterized in that: In step M3, the optimization of the confrontation data of the combat unit by using the crazy adaptive whale optimization algorithm includes: M31. Based on the predicted data information of the confrontation process of the combat unit and the confrontation data information of the combat unit in the digital battlefield environment, the whale population is initialized, the whale population parameters and the maximum number of iterations are determined, and the data information of the initialized whale population is obtained; M32. Based on the data information of the initialized whale population, establish the fitness function P of the individual whale population, Among them, z is the data information of the whale population after initialization, γ1, γ2 and γ3 are the variation difference factors of the individuals in the whale population, and the fitness values ​​of the individuals in the whale population are calculated to obtain the data information of the fitness values ​​of the individuals in the whale population; M33. Based on the data information of the fitness values ​​of the whale population individuals, an optimization function S based on the crazy adaptive learning factor is established. Among them, r is the data information of the fitness value of the individual whale population, η1, η2 and η3 are crazy adaptive learning factors, which optimize the confrontation data of the combat unit to obtain the optimized combat data information of the combat unit.

7. The method for digital battlefield simulation teaching based on 5G communication network according to claim 6 is characterized in that: The constraint function g of the crazy adaptive learning factors η1, η2 and η3 is, Among them, the value range of the constraint function g is (2,3).

8. The method for digital battlefield simulation teaching based on 5G communication network according to claim 1 is characterized in that: In step M4, the model of the confrontation result of the digital battlefield based on the dynamic learning factor is constructed to characterize the confrontation result of the combat unit, including: M41. Based on the optimized battle data information of the confrontation unit, construct a feature extraction function G of the battle data of the confrontation unit, Among them, h is the battle data information of the confrontation unit after optimization, λ1, λ2 and λ3 are the feature extraction factors of the confrontation unit, and the confrontation data of the confrontation unit is subjected to feature extraction to obtain the confrontation feature data information of the confrontation unit; M42. Based on the confrontation feature data information of the confrontation unit, establish a confrontation result prediction function H based on a dynamic learning factor, Among them, a is the adversarial feature data information of the adversarial unit, μ1, μ2 and μ3 are dynamic learning factors; Based on the confrontation result prediction function H based on the dynamic learning factor, the confrontation result of the combat unit is characterized to obtain data information of the confrontation result of the combat unit.

9. A digital battlefield simulation teaching system based on 5G communication network, including computer equipment, characterized in that: The computer device is programmed or configured to execute the steps of the method for digital battlefield simulation teaching based on 5G communication network as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that is programmed or configured to execute the method for digital battlefield simulation teaching based on a 5G communication network as described in any one of claims 1 to 8.

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