A multi-sample simulation control method and system for air-ground game

Abstract

The present invention discloses a multi-sample simulation control method and system for air-ground game, the method comprising: obtaining user demand information; using a physical simulation model set to process the user demand information to obtain a multi-sample simulation data set; using a data dictionary to process the user demand information and the multi-sample simulation data set to obtain simulation result information. The present invention provides a multi-sample simulation control method and system for air-ground game, adopting a complete air-ground game element model, comprehensively considering the technical characteristics, command and control relationship and control process of various types of elements in the game, constructing a corresponding simulation model, and realizing fast and accurate simulation of complex scenes.

Description

Technical Field

[0001] The present invention relates to the field of modeling and simulation, and in particular to a multi-sample simulation control method and system for air-ground game. Background Art

[0002] The system game and performance evaluation simulation system uses system simulation, relies on scientific analysis and evaluation and simulation experiment design methods, and realizes scientific and accurate evaluation of performance. To this end, the present invention adopts a complete air-ground game element model, comprehensively considers the technical characteristics, command and control relationship and control process of various types of elements in the game, and constructs a corresponding simulation model to meet the urgent needs of performance evaluation in complex environments. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a multi-sample simulation control method and system for air-ground game, which adopts a complete air-ground game element model, can flexibly select the game force configuration in a specific task according to specific needs, and can also flexibly design and adjust the command and control relationship and control process according to the conclusions of the game strategy research, to meet the urgent needs of performance evaluation in complex environments.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a multi-sample simulation control method for air-ground game, the method comprising:

[0005] S1, obtain user demand information;

[0006] S2, using the entity simulation model set, processing the user demand information to obtain a multi-sample simulation data set;

[0007] S3, using a data dictionary, processing the user demand information and the multi-sample simulation data set to obtain simulation result information.

[0008] As an optional implementation, in the first aspect of the embodiment of the present invention, the user demand information is processed using the entity simulation model set to obtain a multi-sample simulation data set, including:

[0009] S21, processing the user demand information to obtain a simulation target model demand information set;

[0010] The simulation target model requirement information set includes a plurality of simulation target model requirement information; the simulation target model requirement information includes a simulation target model type and simulation target model parameters;

[0011] S22, using the entity simulation model set, processing the simulation target model requirement information set to obtain a multi-sample simulation data set;

[0012] The entity simulation model set includes: a first simulation model, a second simulation model, a third simulation model, a fourth simulation model, a fifth simulation model, a sixth simulation model and a seventh simulation model.

[0013] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the expression of the first simulation model is:

[0014]

[0015]

[0016]

[0017]

[0018] Where (x, y, z) represents the coordinates of the motion trajectory points of the first model during the simulation; v x represents the x-axis component of the motion velocity of the first model during the simulation; v y represents the y-axis component of the motion velocity of the first model during the simulation process; T represents the first input parameter, represents the thrust of the first model during the simulation process; α represents the second input parameter; α T represents the third input parameter; D represents the flight resistance of the first model during the simulation; L represents the flight lift of the first model during the simulation; W represents the gravity of the first model during the simulation; C L represents the lift coefficient of the first model during the simulation; C D is the drag coefficient of the first model during the simulation; δ represents the first density coefficient; represents the second density coefficient; γ represents the third density coefficient; h represents the altitude of the first model during the simulation process; A represents the wing area of ​​the first model during the simulation process; V represents the aircraft speed of the first model during the simulation process; represents the synthetic vector in the geocentric rectangular coordinate system; w represents the latitude value of the first model during the simulation; φ represents the longitude value of the first model during the simulation; θ represents the azimuth angle of the first model during the simulation; represents the inclination angle of the first model during the simulation process; T represents the transformation matrix from the body coordinate system to the geocentric rectangular coordinate system; F x F represents the original vector in the x direction in the axis coordinate system of the first model body; y F represents the original vector in the y direction in the axis coordinate system of the first model body; z Represents the original vector in the z direction in the first model body axis coordinate system.

[0019] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the second simulation model expression is:

[0020]

[0021]

[0022]

[0023] Where V represents the speed of the second model during the simulation; m represents the mass of the second model during the simulation; ψ v represents the first deflection angle of the second model during the simulation process; θ represents the first inclination angle of the second model during the simulation process; α represents the angle of attack of the second model during the simulation process; β represents the sideslip angle of the second model during the simulation process; X, Y, Z represent the projection of the aerodynamic force R of the second model during the simulation process on the coordinate axis of the coordinate system; t represents the time point during the simulation process; J y represents the moment of inertia of the second model around the y-axis during the simulation; J z Represents the moment of inertia of the second model around the z-axis during the simulation; ω y represents the projection of the rotational angular velocity of the second model relative to the ground coordinate system on the y-axis during the simulation; ω z Represents the projection of the rotational angular velocity of the second model relative to the ground coordinate system on the z-axis during the simulation; ∑M x Represents the algebraic sum of the moments of all external forces of the second model on the x-axis during the simulation; ∑M y Represents the algebraic sum of the moments of all external forces of the second model on the y-axis during the simulation; ∑M z represents the algebraic sum of the moments of all external forces of the second model on the z-axis during the simulation; represents the pitch angle of the second model during the simulation process; ψ represents the yaw angle of the second model during the simulation process; γ represents the roll angle of the second model during the simulation process; (x, y, z) represents the position coordinates of the second model during the simulation process.

[0024] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the third simulation model expression is:

[0025]

[0026]

[0027] Among them, D max Indicates the maximum detection distance of the third model; P t represents the transmission power of the third model; G represents the transmission / receiving power gain of the third model; λ represents the operating wavelength of the third model; σ represents the given reflection cross-sectional area of ​​the third model; N represents the minimum signal-to-noise ratio of the third model; k represents the Boltzmann constant; T s represents the noise temperature of the receiving system of the third model; B n represents the noise bandwidth before receiving detection of the third model; σt represents the actual effective reflection cross-sectional area of ​​the third model; σ s Represents the standard effective reflection cross-sectional area of ​​the third model; R max represents the direct line of sight distance of the third model; A represents the correction factor; h1 represents the altitude of the antenna center of the third model; h2 represents the target altitude of the third model.

[0028] As an optional implementation, in the first aspect of the embodiment of the present invention, the entity simulation model set is used to process the simulation target model requirement information set to obtain a multi-sample simulation data set, including:

[0029] S221, traversing the simulation target model requirement information set to obtain all simulation target model requirement information;

[0030] S222, processing any of the simulation target model requirement information to obtain a simulation target model type and simulation target model parameters;

[0031] S223, based on the simulation target model type, calling the corresponding simulation model to process the simulation target model parameters to obtain simulation target sample data;

[0032] S224, all the simulation target sample data are combined in order to obtain a multi-sample simulation data set.

[0033] As an optional implementation, in the first aspect of the embodiment of the present invention, the using of a data dictionary to process the user demand information and the multi-sample simulation data set to obtain simulation result information includes:

[0034] S31, processing the user demand information to obtain basic simulation information, simulation model entity deployment information and control instruction set;

[0035] S32, using a data dictionary, processing the simulation model entity deployment information to obtain a simulation entity requirement object set;

[0036] S33, based on the simulation entity requirement object set, performing matching processing on the multi-sample simulation data set to obtain a simulation object set;

[0037] S34, processing the simulation entity based on the control instruction set to obtain simulation result information.

[0038] A second aspect of an embodiment of the present invention discloses a multi-sample simulation control system for air-ground game, the system comprising:

[0039] A user information acquisition module, a first processing module and a second processing module;

[0040] The user information acquisition module is used to acquire user demand information;

[0041] The first processing module is used to process the user demand information using the entity simulation model set to obtain a multi-sample simulation data set;

[0042] The second processing module is used to process the user demand information and the multi-sample simulation data set using a data dictionary to obtain simulation result information.

[0043] The third aspect of the present invention discloses a multi-sample simulation control device for air-ground game, the device comprising:

[0044] A memory storing executable program code;

[0045] a processor coupled to the memory;

[0046] The processor calls the executable program code stored in the memory to execute part or all of the steps in the multi-sample simulation control method for air-ground game disclosed in the first aspect of the embodiment of the present invention.

[0047] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, some or all of the steps in the multi-sample simulation control method for air-ground game disclosed in the first aspect of an embodiment of the present invention are executed.

[0048] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0049] In the embodiment of the present invention, a complete air-ground game element model is adopted, the technical characteristics, command and control relationship and control process of various types of elements in the game are comprehensively considered, and a corresponding simulation model is constructed to achieve fast and accurate simulation of complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 It is a schematic diagram of a scenario of a multi-sample simulation control system for air-ground game provided by an embodiment of the present invention;

[0052] Figure 2 It is a flow chart of a multi-sample simulation control method for air-ground game disclosed in an embodiment of the present invention;

[0053] Figure 3 It is a structural schematic diagram of a multi-sample simulation control system for air-ground game disclosed in an embodiment of the present invention;

[0054] Figure 4 It is a structural schematic diagram of another multi-sample simulation control device for air-ground game disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] 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 part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0057] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0059] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0060] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0061] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0062] Computer Vision (CV) is a science that studies how to make machines "see". To put it more specifically, it refers to machine vision such as using cameras and computers to replace human eyes to identify and measure targets, and further processing graphics so that the computer processing becomes an image that is more suitable for human observation or transmission to instruments for detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and map construction, and other technologies, as well as common biometric recognition technologies such as face recognition and fingerprint recognition.

[0063] Unimodal information is data of only one type, such as text, image, audio, video, electromagnetic signal, etc. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved on the task.

[0064] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are often used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In an embodiment of the present application, the large model may be ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Qianyi Tongwen model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, and Wenxin Yiyan scale language models, which are not limited in the embodiments of the present application.

[0065] The embodiments of the present application provide a multi-sample simulation control method, apparatus, computer equipment, and computer-readable storage medium for air-ground game, which are described in detail below.

[0066] See also Figure 1 , Figure 1 Schematic diagram of a scenario of a multi-sample simulation control system for air-ground game provided in an embodiment of the present application. The multi-sample simulation control system for air-ground game may include a computer device 100, in which a multi-sample simulation control device for air-ground game is integrated, such as Figure 1 Computer equipment in.

[0067] In the embodiment of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0068] It is understandable that the computer device 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0069] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or less computer equipment as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the system can also include one or more other services, which are not limited here.

[0070] In addition, if Figure 1 As shown, the multi-sample simulation control system for air-ground game may also include a memory 200 for storing simulation data, such as simulation process data and simulation result data.

[0071] It should be noted that Figure 1 The scenario diagram of the multi-sample simulation control system for air-ground game shown is only an example. The multi-sample simulation control system and scenario for air-ground game described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the simulation control management system and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0072] The present invention discloses a multi-sample simulation control method and system for air-ground game, which adopts a complete air-ground game element model, can flexibly select the game force configuration in a specific task according to specific needs, and can also flexibly design and adjust the command relationship and control process according to the conclusion of the game strategy research, so as to meet the urgent needs of performance evaluation in complex environments. The following are detailed descriptions.

[0073] Embodiment 1

[0074] See also Figure 2 , Figure 2 : is a flow chart of a multi-sample simulation control method for air-ground game disclosed in an embodiment of the present invention. Figure 2The described multi-sample simulation control method for air-ground game is applied to a multi-sample simulation control management system, such as a local server or cloud server for a multi-sample simulation control system, and the embodiments of the present invention do not limit this. Figure 1 As shown, the multi-sample simulation control method for air-ground game may include the following operations:

[0075] S1, obtain user demand information;

[0076] It should be noted that the user demand information includes scenario information, equipment information, game force deployment information and simulation strategy information;

[0077] S2, using the entity simulation model set, processing the user demand information to obtain a multi-sample simulation data set;

[0078] S3, using a data dictionary, processing the user demand information and the multi-sample simulation data set to obtain simulation result information;

[0079] It should be noted that the data dictionary is used to define and describe the data items, data structures, data flows, data storage, and processing logic of the simulation data. It is a directory that records databases and application metadata that can be accessed by users. In this embodiment, the data dictionary is used to parse user demand information and multi-sample simulation data sets, and assemble and generate simulation model entity objects;

[0080] In this embodiment, the data dictionary includes model parameter data information and model assembly information;

[0081] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention can utilize a complete air-ground game element model, flexibly select the game force configuration in a specific task according to specific needs, flexibly design and adjust the command and control relationship and control process, and meet the urgent needs of performance evaluation in a complex environment.

[0082] In an optional embodiment, in the above step S2, the user demand information is processed using a physical simulation model set to obtain a multi-sample simulation data set, including:

[0083] S21, processing the user demand information to obtain a simulation target model demand information set;

[0084] It should be noted that the simulation target model requirement information set includes a plurality of simulation target model requirement information; the simulation target model requirement information includes a simulation target model type and simulation target model parameters;

[0085] S22, using the entity simulation model set, processing the simulation target model requirement information set to obtain a multi-sample simulation data set;

[0086] It should be noted that the entity simulation model set includes: a first simulation model, a second simulation model, a third simulation model, a fourth simulation model, a fifth simulation model, a sixth simulation model and a seventh simulation model;

[0087] It should be noted that the first simulation model is a platform simulation model; the second simulation model is an equipment simulation model; the third simulation model is a target detection simulation model; the fourth simulation model is a detection sensitivity analysis simulation model; the fifth simulation model is an interference simulation model; the sixth simulation model is a first motion simulation model; the seventh simulation model is a second motion simulation model;

[0088] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention is implemented, a complete air-ground game element model is designed and adopted, the game force configuration in a specific task is flexibly selected according to specific needs, and the command and control relationship and control process are flexibly designed and adjusted to meet the urgent needs of performance evaluation in a complex environment.

[0089] In another optional embodiment, in the above step S22, the expression of the first simulation model is:

[0090]

[0091]

[0092]

[0093]

[0094] Where (x, y, z) represents the coordinates of the motion trajectory points of the first model during the simulation; v x represents the x-axis component of the motion velocity of the first model during the simulation; v y represents the y-axis component of the motion velocity of the first model during the simulation process; T represents the first input parameter, represents the thrust of the first model during the simulation process; α represents the second input parameter; α T represents the third input parameter; D represents the flight resistance of the first model during the simulation; L represents the flight lift of the first model during the simulation; W represents the gravity of the first model during the simulation; C L represents the lift coefficient of the first model during the simulation; C D is the drag coefficient of the first model during the simulation; δ represents the first density coefficient; represents the second density coefficient; γ represents the third density coefficient; h represents the altitude of the first model during the simulation process; A represents the wing area of ​​the first model during the simulation process; V represents the aircraft speed of the first model during the simulation process; represents the synthetic vector in the geocentric rectangular coordinate system; w represents the latitude value of the first model during the simulation; φ represents the longitude value of the first model during the simulation; θ represents the azimuth angle of the first model during the simulation; represents the inclination angle of the first model during the simulation process; T represents the transformation matrix from the body coordinate system to the geocentric rectangular coordinate system; F x F represents the original vector in the x direction in the axis coordinate system of the first model body; y F represents the original vector in the y direction in the axis coordinate system of the first model body; z Represents the original vector in the z direction in the first model body axis coordinate system.

[0095] It should be noted that the lift coefficient C L The value range is 1.5 to 2;

[0096] It should be noted that, in this embodiment, the first density coefficient is set to 1.225, that is, δ=1.225;

[0097] It should be noted that, in this embodiment, the second density coefficient is set to 1000, that is, θ=1000;

[0098] It should be noted that, in this embodiment, the third density coefficient is 44.30, that is, γ=44.3;

[0099] It should be noted that, in this embodiment, the conversion matrix is ​​a 3X3 vector matrix;

[0100] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention adopts a complete air-ground game element model, flexibly selects the game force configuration in a specific task according to specific needs, flexibly designs and adjusts the command and control relationship and control process, and meets the urgent needs of performance evaluation in a complex environment.

[0101] In another optional embodiment, in the above step S22, the second simulation model expression is:

[0102]

[0103]

[0104]

[0105] Where V represents the speed of the second model during the simulation; m represents the mass of the second model during the simulation; ψ vrepresents the first deflection angle of the second model during the simulation process; θ represents the first inclination angle of the second model during the simulation process; α represents the angle of attack of the second model during the simulation process; β represents the sideslip angle of the second model during the simulation process; X, Y, Z represent the projection of the aerodynamic force R of the second model during the simulation process on the coordinate axis of the coordinate system; t represents the time point during the simulation process; J y represents the moment of inertia of the second model around the y-axis during the simulation; J z Represents the moment of inertia of the second model around the z-axis during the simulation; ω y represents the projection of the rotational angular velocity of the second model relative to the ground coordinate system on the y-axis during the simulation; ω z Represents the projection of the rotational angular velocity of the second model relative to the ground coordinate system on the z-axis during the simulation; ∑M x Represents the algebraic sum of the moments of all external forces of the second model on the x-axis during the simulation; ∑M y Represents the algebraic sum of the moments of all external forces of the second model on the y-axis during the simulation; ∑M z It represents the algebraic sum of the moments of all external forces of the second model on the z-axis during the simulation; represents the pitch angle of the second model during the simulation process; ψ represents the yaw angle of the second model during the simulation process; γ represents the roll angle of the second model during the simulation process; (x, y, z) represents the position coordinates of the second model during the simulation process.

[0106] It can be seen that by implementing the multi-sample simulation control method for air-ground game described in the embodiment of the present invention, designing a complete air-ground game element model, flexibly selecting the game force configuration in a specific task according to specific needs, and flexibly designing and adjusting the command and control relationship and control process, the urgent needs of performance evaluation in a complex environment can be met.

[0107] In another optional embodiment, in the above step S22, the third simulation model expression is:

[0108]

[0109]

[0110] Among them, D max Indicates the maximum detection distance of the third model; P t represents the transmission power of the third model; G represents the transmission / receiving power gain of the third model; λ represents the operating wavelength of the third model; σ represents the given reflection cross-sectional area of ​​the third model; N represents the minimum signal-to-noise ratio of the third model; k represents the Boltzmann constant; T s represents the noise temperature of the receiving system of the third model; B n represents the noise bandwidth before receiving detection of the third model; σ t represents the actual effective reflection cross-sectional area of ​​the third model; σs Represents the standard effective reflection cross-sectional area of ​​the third model; R max represents the direct line of sight distance of the third model; A represents the correction factor; h1 represents the altitude of the antenna center of the third model; h2 represents the target altitude of the third model.

[0111] It should be noted that the maximum detection distance in the third simulation model corresponds to the radar cross section (RCS) of the target. In general, the maximum detection distance D max It refers to the standard radar reflection cross-sectional area, so in actual modeling, it is necessary to convert it according to the actual radar reflection cross-sectional area of ​​the enemy target in the mission area to calculate the actual maximum detection distance of the third model;

[0112] It should be noted that, in this embodiment, the equivalent earth radius is used to replace the real radius of the earth, and a uniform atmosphere (with no refraction in this layer, and radio waves propagate in a straight line) is used to replace the real atmosphere; the correction coefficient is 4.12, that is, A=4.12;

[0113] It can be seen that by implementing the multi-sample simulation control method for air-ground game described in the embodiment of the present invention, designing a complete air-ground game element model, flexibly selecting the game force configuration in a specific task according to specific needs, and flexibly designing and adjusting the command and control relationship and control process, the urgent needs of performance evaluation in a complex environment can be met.

[0114] In another optional embodiment, in the above step S22, the fourth simulation model expression is:

[0115]

[0116] Where P represents the total probability of model detection; D represents the set of local decision vectors; R(D) represents the fusion decision rule; represents the detection probability of the i-th radar; S0 represents the set of radars that have not detected the target; S1 represents the set of radars that have detected the target; P0 represents the detection probability of the target electronic equipment;

[0117] It should be noted that, in this embodiment, the detection probability of the networked radar to the target depends on the fusion criterion adopted by the fusion center, that is, the "rank K criterion". For a network with 4 radars, when K=1, the detection probability of the networked radar to the target can be 4 times that of a single radar; but the value of K has a great correlation with the detection probability of the single-station radar to the target. When the detection probability of the single-station radar to the target is large, the larger K is, the greater the detection probability of the networked radar to the target; but when the detection probability of the single-station radar to the target is small, the increase of K is not conducive to the increase of the detection probability of the entire networked radar to the target;

[0118] It can be seen that by implementing the multi-sample simulation control method for air-ground game described in the embodiment of the present invention, designing a complete air-ground game element model, flexibly selecting the game force configuration in a specific task according to specific needs, and flexibly designing and adjusting the command and control relationship and control process, the urgent needs of performance evaluation in a complex environment can be met.

[0119] In another optional embodiment, in the above step S22, the fifth simulation model expression is:

[0120]

[0121] Among them, M h represents the first threshold, which is the threshold of the aircraft radar receiver; k represents the Boltzmann constant; B r represents the effective bandwidth of the radar receiver; T0 represents the standard value of the ambient temperature; σ n Represents the nth radar reflection cross-sectional area; N J represents the amount of noise increase; N represents the minimum signal-to-noise ratio;

[0122] It should be noted that the Boltzmann constant is 1.380649×10 -23 ;

[0123] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention is implemented, a complete air-ground game element model is designed and adopted, the game force configuration in a specific task is flexibly selected according to specific needs, and the command and control relationship and control process are flexibly designed and adjusted to meet the urgent needs of performance evaluation in a complex environment.

[0124] In another optional embodiment, in the above step S22, the sixth simulation model expression is:

[0125]

[0126] h sxy =v m (τ1+τ2+…+τ n-1 )=v m (n-1)t

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] Among them, D f Indicates the sixth model effective value, indicating the required value of the distance at which the radar detects the target; represents the average value of the maximum flight distance of the sixth model; represents the mean square error of the maximum flight distance of the sixth model; H represents the altitude of the site; v m Indicates the speed of the target; t1 indicates the first time of the sixth model, which indicates the time from the sixth model pressing the launch button to flying to the far boundary of the effective area; t2 indicates the second time of the sixth model, which indicates the time from the radar detecting the target to the system being ready for action; H2 indicates the junction height; P2 indicates the junction route shortcut; h sxy represents the depth of the effective area; τ represents the encounter interval; n represents the model number index; v m represents the target moving speed; t represents the launch interval; P max Indicates the maximum route shortcut; D sy Indicates the slant distance from the far boundary of the effective area; D sj Indicates the slant distance near the effective area; q max represents the maximum route shortcut; θ represents the sixth model altitude angle; φ represents the sixth model azimuth; (X m , H m ) represents the initial position coordinates of the target; V m represents the target speed; t represents the flight time of the sixth model; t s represents the system reaction time; λ represents the angle between the target track and the reference line; q represents the target sight angle; θ m represents the angle between the target and the reference line; R represents the relative distance between the sixth model and the target; v d represents the speed of the sixth model; k represents the ratio of the speed of the sixth model to the target speed; Indicates the derivative calculation of R; Indicates the derivative calculation of q; Indicates the derivative calculation of θ;

[0135] It can be seen that by implementing the multi-sample simulation control method for air-ground game described in the embodiment of the present invention, designing a complete air-ground game element model, flexibly selecting the game force configuration in a specific task according to specific needs, and flexibly designing and adjusting the command and control relationship and control process, the urgent needs of performance evaluation in a complex environment can be met.

[0136] In another optional embodiment, in the above step S22, the seventh simulation model expression is:

[0137]

[0138] Wherein, (x, y, z) represents the starting coordinate value of the seventh model; (xq, yq, zq) represents the encounter coordinate value of the seventh model and the target; t f represents the flight time of the seventh model; f() represents the uniform linear motion function.

[0139] It can be seen that by implementing the multi-sample simulation control method for air-ground game described in the embodiment of the present invention, designing a complete air-ground game element model, flexibly selecting the game force configuration in a specific task according to specific needs, and flexibly designing and adjusting the command and control relationship and control process, the urgent needs of performance evaluation in a complex environment can be met.

[0140] In another optional embodiment, in the above step S22, the use of the entity simulation model set to process the simulation target model requirement information set to obtain a multi-sample simulation data set includes:

[0141] S221, traversing the simulation target model requirement information set to obtain all simulation target model requirement information;

[0142] S222, processing any of the simulation target model requirement information to obtain a simulation target model type and simulation target model parameters;

[0143] It should be noted that the processing refers to extracting data according to character type identifiers;

[0144] S223, based on the simulation target model type, calling the corresponding target simulation model to process the simulation target model parameters to obtain simulation target sample data;

[0145] It should be noted that the target simulation model includes a first simulation model, a second simulation model, a third simulation model, a fourth simulation model, a fifth simulation model, a sixth simulation model and a seventh simulation model;

[0146] S224, all the simulation target sample data are combined in order to obtain a multi-sample simulation data set;

[0147] It should be noted that the sequential combination means combining in order of precedence.

[0148] It can be seen that by implementing the multi-sample simulation control method for air-ground game described in the embodiment of the present invention, designing a complete air-ground game element model, flexibly selecting the game force configuration in a specific task according to specific needs, and flexibly designing and adjusting the command and control relationship and control process, the urgent needs of performance evaluation in a complex environment can be met.

[0149] In another optional embodiment, in the above step S3, the use of a data dictionary to process the user demand information and the multi-sample simulation data set to obtain simulation result information includes:

[0150] S31, processing the user demand information to obtain basic simulation information, simulation model entity deployment information and control instruction set;

[0151] It should be noted that the processing of the user demand information means that the user demand information is structured according to the field type, and after combination, basic simulation information, simulation model entity deployment information and control instruction set are obtained;

[0152] The control instruction set includes a plurality of control instructions; the control instructions include a control method and a control flow;

[0153] S32, using a data dictionary, processing the simulation model entity deployment information to obtain a simulation entity requirement object set;

[0154] S33, based on the simulation entity requirement object set, performing matching processing on the multi-sample simulation data set to obtain a simulation object set;

[0155] S34, processing the simulation object set based on the control instruction set to obtain simulation result information.

[0156] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention is implemented, a complete air-ground game element model is designed and adopted, the game force configuration in a specific task is flexibly selected according to specific needs, and the command and control relationship and control process are flexibly designed and adjusted to meet the urgent needs of performance evaluation in a complex environment.

[0157] In another optional embodiment, in the above step S32, the use of a data dictionary to process the simulation model entity deployment information to obtain a simulation entity requirement object set includes:

[0158] S321, obtaining the simulation model entity deployment information;

[0159] It should be noted that the simulation model entity deployment information includes simulation entity type information, simulation entity quantity information, and simulation entity affiliation information;

[0160] S322, using a data dictionary, structuring the simulation model entity deployment information to obtain simulation model entity deployment structured information;

[0161] S323, performing a completion check on the simulation model entity deployment information to obtain a completion judgment result;

[0162] When the completion judgment result is yes, execute S324;

[0163] When the completion judgment result is no, executing S321;

[0164] S324, extracting parameters from the simulation model entity deployment structured information to obtain a model parameter set and a model data relationship information set;

[0165] S325, arranging and combining the model parameter set and the model data relationship information set to obtain a simulation entity requirement object set;

[0166] It should be noted that the permutation and combination refers to the combination according to model category information, model parameter information, affiliation information and hierarchical relationship information;

[0167] It should be noted that the simulation entity requirement object set includes a plurality of simulation entity requirement objects.

[0168] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention is implemented, a complete air-ground game element model is designed and adopted, the game force configuration in a specific task is flexibly selected according to specific needs, and the command and control relationship and control process are flexibly designed and adjusted to meet the urgent needs of performance evaluation in a complex environment.

[0169] In another optional embodiment, in the above step S33, matching processing is performed on the multi-sample simulation data set based on the simulation entity requirement object set to obtain a simulation object set, including:

[0170] S331, parsing the simulation entity requirement object set to obtain a model category information set and a simulation entity quantity information set;

[0171] It should be noted that the parsing process refers to extracting model category information and simulation entity quantity information according to the field type and name in the simulation entity requirement object;

[0172] S332, traversing the model category information set to obtain all target model category information and corresponding simulation entity quantity information;

[0173] S333, using any of the target model category information to match multiple sample simulation data sets to obtain target sample simulation data;

[0174] S334, obtaining user setting parameters;

[0175] S335, based on the target simulation model, processing the user setting parameters, the simulation entity quantity information and the user setting parameters to obtain a simulation object;

[0176] It should be noted that the target simulation model is a first simulation model, a second simulation model, a third simulation model, a fourth simulation model, a fifth simulation model, a sixth simulation model and a seventh simulation model;

[0177] S336, all the simulation objects are combined in order to obtain a simulation object set;

[0178] It should be noted that the sequential combination means combining in order of precedence.

[0179] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention is implemented, a complete air-ground game element model is designed and adopted, the game force configuration in a specific task is flexibly selected according to specific needs, and the command and control relationship and control process are flexibly designed and adjusted to meet the urgent needs of performance evaluation in a complex environment.

[0180] In another optional embodiment, in the above step S333, using any of the target model category information to match multiple sample simulation data sets to obtain target sample simulation data includes:

[0181] S3331, obtaining the target model category information;

[0182] S3332, traverse the multi-sample simulation data set to obtain all sample simulation data and sample quantities;

[0183] Initialize the number of matches to m = 1;

[0184] S3333, determining whether the matching quantity is greater than the sample quantity, and obtaining a processing quantity determination result;

[0185] When the result of the processing quantity judgment is no, execute S3334;

[0186] When the result of the processing quantity judgment is yes, execute S3331;

[0187] S3334, obtaining the mth record in the multi-sample simulation data set to obtain sample simulation data;

[0188] Determine whether the target model category information is consistent with the simulation model type of the sample simulation data, and obtain a matching determination result;

[0189] S3335, when the match judgment result is yes, obtaining target sample simulation data;

[0190] When the match determination result is no, the match number m is increased by 1, and S3333 is executed;

[0191] It should be noted that the matching means that the target model category is consistent with the simulation model type in the multi-sample simulation data set.

[0192] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention is implemented, a complete air-ground game element model is designed and adopted, the game force configuration in a specific task is flexibly selected according to specific needs, and the command and control relationship and control process are flexibly designed and adjusted to meet the urgent needs of performance evaluation in a complex environment.

[0193] In another optional embodiment, in the above step S34, the processing of the simulation object set based on the control instruction set to obtain simulation result information includes:

[0194] S341, traverse the control instruction set to obtain all control instruction information;

[0195] S342, processing the control instruction information to obtain target object type information and target instruction parameter information;

[0196] It should be noted that the processing means extracting information based on field names and types;

[0197] S343, using the target object type to match the simulation object set to obtain a target simulation object;

[0198] S344, processing the target simulation object using the target instruction parameter information to obtain target simulation result information;

[0199] It should be noted that the processing of the target simulation object means loading the target instruction parameter information and processing it using the entity simulation model to obtain the target simulation result;

[0200] S345, the collection of all target simulation result information is the simulation result information.

[0201] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention is implemented, a complete air-ground game element model is designed and adopted, the game force configuration in a specific task is flexibly selected according to specific needs, and the command and control relationship and control process are flexibly designed and adjusted to meet the urgent needs of performance evaluation in a complex environment.

[0202] In another optional embodiment, in the above step S343, using the target object type to match the simulation object set to obtain the target simulation object includes:

[0203] S3431, obtaining the target object type;

[0204] S3432, traversing the simulation object set to obtain all simulation objects and the total number of simulation objects;

[0205] Initialize the object processing quantity to 1;

[0206] S3433, determining whether the object processing quantity is greater than the total number of simulation objects, and obtaining a result of determining the object processing quantity;

[0207] S3434, when the result of judging the object processing quantity is no, executing S3435;

[0208] When the result of judging the object processing quantity is yes, executing S3431;

[0209] S3435, obtaining the mth record in the simulation object to obtain simulation object data;

[0210] Obtaining a first step of determining whether the target object type is consistent with the object type of the simulation object data, and obtaining an object matching result;

[0211] S3436, when the object matching result is consistent, obtaining a target simulation object;

[0212] When the object matching result is inconsistent, the object processing quantity is increased by 1, and S3433 is executed;

[0213] It should be noted that the matching means that the target object type is consistent with the object type of the simulation object data.

[0214] It can be seen that the multi-sample simulation control method for air-ground game described in the embodiment of the present invention is implemented, a complete air-ground game element model is designed and adopted, the game force configuration in a specific task is flexibly selected according to specific needs, and the command and control relationship and control process are flexibly designed and adjusted to meet the urgent needs of performance evaluation in a complex environment.

[0215] Embodiment 2

[0216] See also Figure 3 , Figure 3 : is a schematic diagram of a multi-sample simulation control system for air-ground game disclosed in an embodiment of the present invention. Figure 3 The described system can be applied to a multi-sample simulation control management system, such as a local server or a cloud server for a multi-sample simulation control system, and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the system may include:

[0217] User information acquisition module 101, first processing module 102 and second processing module 103;

[0218] The user information acquisition module 101 is used to acquire user demand information;

[0219] The first processing module 102 is used to process the user demand information using the entity simulation model set to obtain a multi-sample simulation data set;

[0220] The second processing module 103 is used to process the user demand information and the multi-sample simulation data set using a data dictionary to obtain simulation result information.

[0221] Embodiment 3

[0222] See also Figure 4 , Figure 4 : is a schematic diagram of the structure of a multi-sample simulation control device for air-ground game disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a multi-sample simulation control management system, such as a local server or a cloud server for a multi-sample simulation control system, and the embodiments of the present invention are not limited thereto. Figure 4 As shown, the device may include:

[0223] A memory 201 storing executable program codes;

[0224] a processor 202 coupled to the memory 201;

[0225] The processor 202 calls the executable program code stored in the memory 201 to execute the steps of the multi-sample simulation control method for air-ground game described in the first embodiment.

[0226] Embodiment 4

[0227] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps in the multi-sample simulation control for air-ground game described in the first embodiment.

[0228] Embodiment 5

[0229] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the multi-sample simulation control method for air-ground game described in the first embodiment.

[0230] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0231] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, a magnetic disk storage, a magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0232] Finally, it should be noted that the multi-sample simulation control method and device for air-ground game disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multi-sample simulation control method for air-ground game, characterized in that: The method comprises: S1, obtain user demand information; S2, using the entity simulation model set, processing the user demand information to obtain a multi-sample simulation data set; The method of processing the user demand information using the entity simulation model set to obtain a multi-sample simulation data set includes: S21, processing the user demand information to obtain a simulation target model demand information set; The simulation target model requirement information set includes a plurality of simulation target model requirement information; the simulation target model requirement information includes a simulation target model type and simulation target model parameters; S22, using the entity simulation model set, processing the simulation target model requirement information set to obtain a multi-sample simulation data set; The entity simulation model set includes: a first simulation model, a second simulation model, a third simulation model, a fourth simulation model, a fifth simulation model, a sixth simulation model and a seventh simulation model; Wherein, the expression of the first simulation model is: in, Indicates the coordinates of the motion trajectory points of the model during the simulation process; Indicates the speed of the model during simulation Axis component; Indicates the speed of the model during simulation Axis component; represents the first input parameter, which represents the thrust of the model during the simulation; Represents the second input parameter; Represents the third input parameter; Indicates the flight resistance of the model during the simulation; Indicates the model flight lift during the simulation; Represents the gravity of the model during simulation; represents the lift coefficient of the model during the simulation; is the drag coefficient of the model during simulation; represents the first density coefficient; represents the second density coefficient; represents the third density coefficient; Indicates the model altitude during simulation; Represents the wing area of ​​the model during simulation ； Indicates the movement speed of the model during the simulation; represents the resultant vector in the geocentric rectangular coordinate system; Indicates the latitude value of the model during simulation; Indicates the longitude value of the model during simulation; Indicates the azimuth of the model during simulation; Represents the transformation matrix from the body coordinate system to the geocentric rectangular coordinate system; Indicates the model body axis coordinate system The original vector of the direction; Indicates the model body axis coordinate system The original vector of the direction; Indicates the model body axis coordinate system The original vector of the direction; Wherein, the entity simulation model set is used to process the simulation target model requirement information set to obtain a multi-sample simulation data set, including: S221, traversing the simulation target model requirement information set to obtain all simulation target model requirement information; S222, processing any of the simulation target model requirement information to obtain a simulation target model type and simulation target model parameters; S223, based on the simulation target model type, calling the corresponding simulation model to process the simulation target model parameters to obtain simulation target sample data; S224, all the simulation target sample data are combined in order to obtain a multi-sample simulation data set; S3, using a data dictionary, processing the user demand information and the multi-sample simulation data set to obtain simulation result information.

2. The multi-sample simulation control method for air-ground game according to claim 1 is characterized in that: The second simulation model expression is: in, Indicates the quality of the model during simulation; Indicates the model deflection during simulation; Indicates the model inclination during simulation; represents the model angle of attack during the simulation; represents the sideslip angle of the model during the simulation; Represents the aerodynamic force of the model during simulation R Projection on the axes of the coordinate system; Indicates the time point during the simulation; Indicates that the model is around during simulation. The moment of inertia of the shaft; Indicates that the model is around during simulation. The moment of inertia of the shaft; Indicates the rotational angular velocity of the model relative to the ground coordinate system during the simulation. Projection on axis; Indicates the rotational angular velocity of the model relative to the ground coordinate system during the simulation. Projection on axis; Indicates that all external forces of the model during the simulation are Algebraic sum of moments about an axis; Indicates that all external forces of the model during the simulation are Algebraic sum of moments about an axis; Indicates that all external forces of the model during the simulation are The algebraic sum of the moments about the axes.

3. The multi-sample simulation control method for air-ground game according to claim 1 is characterized in that: The third simulation model expression is: in, Indicates the maximum detection distance of the model; Indicates the model transmission power; represents the model transmit / receive power gain; Indicates the model operating wavelength; Represents the given reflection cross-sectional area of ​​the model; Indicates the minimum signal-to-noise ratio of the model; represents the Boltzmann constant; represents the model receiving system noise temperature; It represents the noise bandwidth before receiving detection of the model; Indicates the actual effective reflection cross-sectional area of ​​the model; Represents the standard effective reflection cross-sectional area of ​​the model; Indicates the model's direct viewing distance; represents the correction factor; Indicates the altitude of the model antenna center; Indicates the target altitude of the third model.

4. The multi-sample simulation control method for air-ground game according to claim 1 is characterized in that: The method of using a data dictionary to process the user demand information and the multi-sample simulation data set to obtain simulation result information includes: S31, processing the user demand information to obtain basic simulation information, simulation model entity deployment information and control instruction set; S32, using a data dictionary, processing the simulation model entity deployment information to obtain a simulation entity requirement object set; S33, based on the simulation entity requirement object set, performing matching processing on the multi-sample simulation data set to obtain a simulation object set; S34, processing the simulation entity based on the control instruction set to obtain simulation result information.

5. A multi-sample simulation control device for air-ground game, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the multi-sample simulation control method for air-ground game as described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when called, are used to execute the multi-sample simulation control method for air-ground game as described in any one of claims 1-4.

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