Intelligent simulation experiment method and device

Abstract

The application discloses an intelligent simulation experiment method and device, and the method comprises the following steps: obtaining task demand information; constructing an intelligent simulation experiment model; and processing the task demand information by using the intelligent simulation experiment model to obtain optimized experiment scheme data. The intelligent simulation experiment model is constructed by a data input intelligent agent, a simulation resource access intelligent agent, a basic scenario generation intelligent agent, an experiment scenario generation intelligent agent, an experiment operation control intelligent agent, an experiment data analysis intelligent agent and an experiment scheme recommendation intelligent agent. The software application in the form of the intelligent agent is used to replace the traditional combat simulation experiment software, and the combat simulation experiment task AI intelligent agent is used to replace manual operation, so that the intelligent combat simulation experiment is realized, and the problems of high personnel requirement, great energy consumption and few innovative conclusions in the traditional combat simulation experiment are solved.

Description

Technical Field

[0001] This invention relates to the field of simulation, and in particular to an intelligent simulation experiment method and apparatus. Background Technology

[0002] Combat simulation experiments refer to exploratory practices that use modeling and simulation techniques to explore new concepts, tactics, and weaponry by simulating combat processes. Traditional combat simulation experiments primarily involve generating multiple related experimental scenarios based on a single basic simulation scenario. The optimal solution is selected by comparing and analyzing the simulation results across these scenarios. Currently, the general steps for combat simulation experiments are: ① Modeling elements within a specific combat scenario; ② Constructing simulation scenarios; ③ Selecting relevant factors as experimental factors; ④ Setting the value space for these experimental factors; ⑤ Generating multiple experimental scenarios; ⑥ Simulating and running each of the multiple scenarios; ⑦ Analyzing the simulation results for each scenario; ⑧ Optimizing tactics or weaponry performance based on the experimental analysis results.

[0003] Current combat simulation experiments require manual operation of the relevant software following the above steps to complete the simulation analysis. The current methods for conducting combat simulation experiments have several problems: First, they place high demands on personnel. Combat simulation experiments, which utilize modeling and simulation technology, are pre-practice exercises for military activities. Therefore, they require personnel who possess both military expertise and proficiency in modeling and simulation technology. In reality, military personnel often lack sufficient mastery of modeling and simulation techniques, and modeling and simulation technicians often lack military expertise, hindering the effective conduct of combat simulation experiments. Second, combat simulation experiments are time-consuming and energy-intensive. Organizing an effective combat simulation experiment requires clarifying the experimental objectives, methods, plans, and analysis content, setting appropriate factors and conditions, and operating multiple software modules. The process involves numerous steps, a long cycle, and significant personnel effort. Third, it is difficult to draw innovative conclusions. The purpose of combat simulation experiments is to discover unknown but useful information. However, relying solely on manual analysis, experimentation, and exploration is limited by both the large scope of exploration and low efficiency. Furthermore, it is constrained by personnel's existing knowledge and rigid experimental combinations, making it difficult for conclusions to exceed the scope of existing knowledge. These conclusions often become quantitative verifications of qualitative understandings, lacking significant innovation. Summary of the Invention

[0004] This invention addresses the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments by proposing an intelligent combat simulation experiment method and device. This invention comprehensively utilizes generative AI large-scale model technology, scenario generation technology, parallel simulation computing technology, simulation experiment technology, data analysis technology, and AI agent construction technology. It constructs data input agents, simulation resource access agents, basic scenario generation agents, experimental scenario generation agents, experimental operation control agents, experimental data analysis agents, and experimental scheme recommendation agents. In each step of the traditional combat simulation experiment, intelligent agent-based software applications replace traditional combat simulation experiment software, and combat simulation experiment task AI agents replace manual operations, thus achieving intelligent combat simulation experiments.

[0005] To address the aforementioned technical problems, a first aspect of the present invention discloses an intelligent simulation experiment method, the method comprising:

[0006] S1, Obtain task requirement information;

[0007] S2, construct an intelligent simulation experiment model;

[0008] S3. The intelligent simulation experiment model is used to process the task requirement information to obtain the optimal experimental scheme data.

[0009] As an optional implementation, in the first aspect of the present invention, the intelligent simulation experiment model includes:

[0010] Data input agent, simulation resource access agent, basic scenario generation agent, experimental scenario generation agent, experimental operation control agent, experimental data analysis agent, and experimental scheme recommendation agent;

[0011] The data is input into the intelligent agent to obtain task requirement information;

[0012] The simulation resource access agent is used to retrieve the scenario background information based on the task requirement information and extract the simulation resource data.

[0013] The basic scenario generating agent is used to receive and process the task requirement information and generate basic scenario data.

[0014] The experimental scenario generation agent is used to process the basic scenario data to obtain the experimental scenario data.

[0015] The experimental operation control intelligent agent is used to receive and process experimental scenario instructions, allocate experimental scenario operation tasks, monitor the status and process of experimental scenario operation, control and monitor the operation status of simulation experiment, and acquire simulation experiment data.

[0016] The experimental data analysis agent is used to perform performance analysis and evaluation, result comparison and cause analysis on the simulation experimental data to obtain evaluation result data.

[0017] The experimental scheme recommends an intelligent agent, which is used to process the evaluation result data to obtain the optimal experimental scheme data.

[0018] The data input agent, simulation resource access agent, basic scenario generation agent, experimental scenario generation agent, experimental operation control agent, experimental data analysis agent, and experimental scheme recommendation agent are sequentially connected.

[0019] As an optional implementation, in the first aspect of the present invention, the step of processing the task requirement information using the intelligent simulation experiment model to obtain preferred experimental scheme data includes:

[0020] S31, The intelligent simulation experiment model is used to process the task requirement information to obtain simulation result data;

[0021] S32, Based on the task requirement information, the simulation result data is analyzed and processed using the intelligent simulation experiment model to obtain the optimal experimental scheme data.

[0022] As an optional implementation, in the first aspect of the present invention, the step of processing the task requirement information using the intelligent simulation experiment model to obtain simulation result data includes:

[0023] S311, using the simulated resource access agent, process the task requirement information to obtain basic task data;

[0024] S312, using the basic scenario to generate an intelligent agent, processing the task requirement information to obtain basic scenario data;

[0025] S313, The intelligent simulation experiment model is used to process the basic task data and the basic scenario data to obtain simulation result data.

[0026] As an optional implementation, in the first aspect of the present invention, the process of using the intelligent simulation experiment model to process the task basic data and the basic scenario data to obtain simulation result data includes:

[0027] S3131, Initialize the number of experiments N = 1;

[0028] S3132, determine whether the number of experiments is greater than the first number threshold, and obtain the first number of experiments judgment result;

[0029] If the result of the first number of experiments is negative, execute S3133;

[0030] If the result of the first number of experiments is yes, execute S3134;

[0031] S3133, The intelligent agent generated by the experimental scenario processes the basic scenario data to obtain key experimental factor information.

[0032] S3134, determine whether the number of experiments is greater than the second number threshold, and obtain the second number of experiments judgment result;

[0033] If the result of the second experiment count is yes, execute S3135;

[0034] If the result of the second experiment count is negative, execute S3138;

[0035] S3135, The experimental scenario generating agent processes the key experimental factor information to obtain experimental scheme data and experimental scenario data.

[0036] S3136, Based on the experimental scheme data, the experimental operation control agent is used to process the experimental scenario data to obtain the experimental scenario result data.

[0037] S3137, Based on the experimental objective information, the intelligent agent using the experimental data analysis is used to perform discrimination processing on the experimental scenario result data to obtain the experimental scenario judgment result;

[0038] When the experimental scenario is determined to be true, execute S3138;

[0039] When the experimental scenario judgment result is negative, the number of experiments is increased by 1; the experimental data analysis agent analyzes and processes the experimental scenario result data to obtain updated key experimental factor information; the key experimental factor information is updated with the updated key experimental factor information, and S3134 is executed.

[0040] S3138, Process the experimental scenario result data to obtain simulation result data.

[0041] As an optional implementation, in the first aspect of the present invention, the step of using the experimental scenario generating agent to process the key experimental factor information to obtain experimental scheme data and experimental scenario data includes:

[0042] S31351, Based on the basic scenario data, the experimental scenario generating agent is used to match and process the key experimental factor information to obtain experimental scheme data.

[0043] S31352, The experimental scenario is generated by an intelligent agent to verify the experimental scheme data and obtain the experimental scenario data.

[0044] As an optional implementation, in the first aspect of the present invention, the step of analyzing and processing the simulation result data using the intelligent simulation experiment model based on the task requirement information to obtain preferred experimental scheme data includes:

[0045] S321, Process the task requirement information to obtain the experimental objective information;

[0046] S322, Analyze and process the simulation result data to obtain experimental scenario instruction information;

[0047] S323, using the intelligent simulation experimental model, the experimental scheme recommendation agent processes the experimental objective information and the experimental scenario instruction information to obtain the optimal experimental scheme data.

[0048] The second aspect of this invention discloses an intelligent simulation experiment device, the device comprising: a data acquisition unit, a model building unit, and a data processing unit;

[0049] The data acquisition unit is used to acquire task requirement information;

[0050] The model building unit is used to build intelligent simulation experiment models;

[0051] The data processing unit is used to process the task requirement information using the intelligent simulation experiment model to obtain the optimal experimental scheme data.

[0052] A third aspect of this invention discloses an intelligent simulation experiment device, the device comprising:

[0053] Memory containing executable program code;

[0054] A processor coupled to the memory;

[0055] The processor calls the executable program code stored in the memory to execute some or all of the steps in the intelligent simulation experiment method disclosed in the first aspect of the present invention.

[0056] The fourth aspect of the present invention discloses a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, which, when invoked, execute some or all of the steps in the intelligent simulation experiment method disclosed in the first aspect of the present invention.

[0057] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0058] This invention proposes an intelligent combat simulation experiment method and device. By constructing an intelligent simulation experiment model composed of a data input intelligent agent, a simulation resource access intelligent agent, a basic scenario generation intelligent agent, an experimental scenario generation intelligent agent, an experimental operation control intelligent agent, an experimental data analysis intelligent agent, and an experimental scheme recommendation intelligent agent, the intelligent simulation experiment model replaces traditional combat simulation experiment software with intelligent agent-based software applications, and replaces manual operation with combat simulation experiment task AI intelligent agents, thus realizing intelligent combat simulation experiments and solving the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic diagram illustrating an application scenario of the intelligent simulation experimental device provided in this embodiment of the invention;

[0061] Figure 2 This is a flowchart illustrating an intelligent simulation experiment method disclosed in an embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram of the structure of an intelligent simulation experimental device disclosed in an embodiment of the present invention;

[0063] Figure 4 This is a schematic diagram of another intelligent simulation experimental device disclosed in an embodiment of the present invention. Detailed Implementation

[0064] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0066] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0067] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0068] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0069] It should be noted that the artificial intelligence-related technologies that may be involved in this application will be 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 that knowledge to obtain optimal 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 kind of intelligent machine that can react in a way similar to human intelligence. Artificial intelligence is the study of the design principles and implementation methods of various intelligent machines, enabling machines to have the functions of perception, reasoning, and decision-making.

[0070] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0071] Computer vision (CV) is a science that studies how to enable machines to "see." More specifically, it refers to machine vision, which uses cameras and computers to replace human eyes in recognizing and measuring targets, and then performs image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping (SLAM), and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0072] Monomodal information refers to data of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information refers to data that includes at least two types of monomodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved in these tasks.

[0073] Large models refer to artificial neural network models with a very large number of parameters. In the field of artificial intelligence, large models typically refer to models with hundreds of millions to trillions of parameters. These models usually need to be trained on large-scale datasets and require a significant amount of computing resources for optimization and tuning. Large models are commonly used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is a type of AI that can create new content and ideas, including dialogues, stories, images, videos, and music. In this embodiment, the large model can be a language model of the scale of ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Qianyitongwen model, MiniMax model, Xinghuo model, Llama model, 360GPT model, Qwen model, Baichuan model, Yunque model, vivoLM model, and Wenxin Yiyan, etc., and this embodiment does not limit the scope of the large model.

[0074] This application provides an intelligent simulation experiment method, apparatus, computer equipment, and computer-readable storage medium, which will be described in detail below.

[0075] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the application scenario of the intelligent simulation experimental device provided in this application within a simulation system. The simulation system may include a computer device 100, which integrates the intelligent simulation experimental device, such as... Figure 1 Computer equipment in the country.

[0076] In this embodiment, the computer device 100 can be a standalone server, a server network, or a server cluster. For example, the computer device 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0077] It is understood that the computer device 100 used in the embodiments of this application can be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 may be a desktop terminal or a mobile terminal, and may also be one of a mobile phone, tablet computer, laptop computer, etc.

[0078] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of computer devices shown is more or less, for example Figure 1 Only one computer device is shown in the diagram. It is understood that the system may also include one or more other services, which are not limited here.

[0079] In addition, such as Figure 1 As shown, the simulation system may also include a memory 200 for storing historical simulation data, simulation result data, key factor parameter data, and optimization simulation system information.

[0080] It should be noted that, Figure 1 The schematic diagram of the application scenario of the intelligent simulation experimental device shown is merely an example. The simulation system and scenario described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of simulation control and management systems and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0081] This invention discloses an intelligent simulation experiment method and apparatus. Addressing the problems of high personnel requirements, high effort consumption, and limited innovative conclusions in traditional combat simulation experiments, it employs an intelligent simulation experiment model composed of a data input agent, a simulation resource access agent, a basic scenario generation agent, an experimental scenario generation agent, an experimental operation control agent, an experimental data analysis agent, and an experimental scheme recommendation agent. This replaces traditional combat simulation experiment software with an agent-based software application, and replaces manual operation with an AI agent for combat simulation experiment tasks, thus achieving intelligent combat simulation experiments. Detailed descriptions follow.

[0082] Example 1

[0083] Please see Figure 2 , Figure 2 This is a flowchart of an intelligent simulation experiment method disclosed in an embodiment of the present invention. Figure 2 The described intelligent simulation experiment method is applied to simulation systems, such as local servers or cloud servers used in simulation systems; however, this embodiment of the invention is not limited to these applications. Figure 2 As shown, the intelligent simulation experiment method includes:

[0084] The method includes:

[0085] S1, Obtain task requirement information;

[0086] S2, construct an intelligent simulation experiment model;

[0087] S3. The intelligent simulation experiment model is used to process the task requirement information to obtain the optimal experimental scheme data.

[0088] As can be seen, the intelligent simulation experiment method described in this embodiment, through an intelligent simulation experiment model composed of a data input intelligent agent, a simulation resource access intelligent agent, a basic scenario generation intelligent agent, an experimental scenario generation intelligent agent, an experimental operation control intelligent agent, an experimental data analysis intelligent agent, and an experimental scheme recommendation intelligent agent, replaces traditional combat simulation experiment software with software applications in the form of intelligent agents, and replaces manual operation with combat simulation experiment task AI intelligent agents, thereby realizing intelligent combat simulation experiments and solving the problems of high personnel requirements, large energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0089] Optionally, the intelligent simulation experiment model includes:

[0090] Data input agent, simulation resource access agent, basic scenario generation agent, experimental scenario generation agent, experimental operation control agent, experimental data analysis agent, and experimental scheme recommendation agent;

[0091] The data is input into the intelligent agent to obtain task requirement information;

[0092] It should be noted that the data input intelligent agent includes software that provides an interactive interface for user input, or directly reads user parameter files; this embodiment does not impose any restrictions.

[0093] The simulation resource access agent is used to retrieve the scenario background information based on the task requirement information and extract the simulation resource data.

[0094] The basic scenario generating agent is used to receive and process the task requirement information and generate basic scenario data.

[0095] The experimental scenario generation agent is used to process the basic scenario data to obtain the experimental scenario data.

[0096] The experimental operation control intelligent agent is used to receive and process experimental scenario instructions, allocate experimental scenario operation tasks, monitor the status and process of experimental scenario operation, control and monitor the operation status of simulation experiment, and acquire simulation experiment data.

[0097] It should be noted that the experimental operation control agent includes a large instruction conversion model and a system control instruction conversion tool;

[0098] It should be noted that the instruction conversion model and the system control instruction conversion tool are interconnected in terms of data.

[0099] It should be noted that the instruction conversion model is used to receive and process the experimental scenario instructions output by the experimental scenario generation agent, and output the obtained instruction conversion parameters to the system control instruction conversion tool. The system control instruction conversion tool performs instruction conversion and outputs the conversion result to the instruction conversion model. The instruction conversion model processes the conversion result, obtains and sends control instruction information to the simulation engine. The simulation engine sends the simulation system's operating status information to the instruction conversion model.

[0100] The experimental data analysis agent is used to perform performance analysis and evaluation, result comparison and cause analysis on the simulation experimental data to obtain evaluation result data.

[0101] The experimental scheme recommends an intelligent agent, which is used to process the evaluation result data to obtain the optimal experimental scheme data.

[0102] The data input agent, simulation resource access agent, basic scenario generation agent, experimental scenario generation agent, experimental operation control agent, experimental data analysis agent, and experimental scheme recommendation agent are sequentially connected.

[0103] As can be seen, the intelligent simulation experiment method described in this embodiment, by constructing an intelligent simulation experiment model composed of a data input intelligent agent, a simulation resource access intelligent agent, a basic scenario generation intelligent agent, an experimental scenario generation intelligent agent, an experimental operation control intelligent agent, an experimental data analysis intelligent agent, and an experimental scheme recommendation intelligent agent, provides a technical foundation for subsequent replacement of traditional combat simulation experiment software with intelligent agent-based software applications, replacement of manual operation with combat simulation experiment task AI intelligent agents, and realization of intelligent combat simulation experiments. It solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0104] Optionally, the simulation resource access agent is used to retrieve scenario background information based on the task requirement information and extract simulation resource data, including:

[0105] It should be noted that the simulated resource access agent includes a vectorization processing module, a vector database, a large data query model, and a structured resource database;

[0106] S201, using the vectorization processing module, the domain data is encoded and vectorized to obtain retrieval vector information;

[0107] S202, based on the retrieval vector information, the vector database is searched to obtain the first retrieval result data;

[0108] It should be noted that, in this embodiment, the vector database is Baidu Vector Database;

[0109] S203, the data query big model uses the set conditions to search the structured resource database and obtain the second search result data;

[0110] S204, the simulation resource data is obtained by combining the first search result data and the second search result data.

[0111] As can be seen, the intelligent simulation experiment method described in this embodiment, based on the task requirement information, utilizes the simulation resource access intelligent agent to retrieve the scenario background information and obtain simulation resource data. This provides a technical foundation for subsequent software applications in the form of intelligent agents to replace traditional combat simulation experiment software, and for using combat simulation experiment task AI intelligent agents to replace manual operations, thereby realizing intelligent combat simulation experiments. It solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0112] Optionally, the basic scenario generating agent is used to receive and process the task requirement information and generate basic scenario data, including:

[0113] S211, Obtain the task requirement information;

[0114] It should be noted that, in this embodiment, the task requirement information is a combat mission document;

[0115] S212, based on Baidu Vector Database, the basic information of the intelligent agent is extracted and a large model is generated using the basic scenario, and the task requirement information is processed to obtain task type information and task information;

[0116] S213, determine whether the task type information is equal to the first preset value, and obtain the task type determination result;

[0117] It should be noted that, in this embodiment, the first preset value represents the task of generating predetermined basic information;

[0118] If the task type determination result is yes, execute S214;

[0119] If the task type determination result is negative, execute S215;

[0120] S214, Perform first data processing on the task information to obtain first basic scenario data;

[0121] S215, Perform second data processing on the task information to obtain second basic scenario data;

[0122] S216, The first basic scenario data and the second basic scenario data are processed together to obtain basic scenario data.

[0123] As can be seen, the intelligent simulation experiment method described in this embodiment utilizes the basic scenario to generate an intelligent agent, receives and processes the task requirement information, and generates basic scenario data. This provides a technical foundation for subsequent software applications in the form of intelligent agents to replace traditional combat simulation experiment software, and for using combat simulation experiment task AI intelligent agents to replace manual operations, thereby realizing intelligent combat simulation experiments. It solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0124] Optionally, the first data processing of the task information to obtain the first basic scenario data includes:

[0125] S2141, Generate a large model using the scenario-based information, process the task information, and obtain simulation area information;

[0126] It should be noted that processing the task information to obtain simulation area information means delineating the simulation area based on the task information to obtain simulation area information;

[0127] S2142, Generate a large model using regional resources, process the simulation region information, and obtain simulation region resource information;

[0128] It should be noted that the process of generating a large model using regional resources to process the simulation region information and obtain simulation region resource information means that the large model generates simulation region resource information by searching the network according to the defined regional range.

[0129] It should be noted that the simulated area resource information includes, but is not limited to, buildings, obstacles, and routes within the area;

[0130] S2143, Utilize route resources to generate a large model, process the resource information of the simulation area, and obtain the first basic scenario data;

[0131] It should be noted that the process of generating a large model using route resources to process the resource information of the simulation area and obtain route resource information of the simulation area means that the large model generating the route resources extracts the first basic scenario data based on the resource information of the simulation area.

[0132] It should be noted that the first basic scenario data is the route resource information of the simulation area.

[0133] As can be seen, the intelligent simulation experiment method described in this embodiment performs first data processing on the task information to obtain first basic scenario data, which provides a technical foundation for subsequent software applications in the form of intelligent agents to replace traditional combat simulation experiment software, and for using AI intelligent agents in combat simulation experiment tasks to replace manual operation, thereby realizing intelligent combat simulation experiments. It solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0134] Optionally, the second data processing of the task information to obtain the second basic scenario data includes:

[0135] S2151, The task information is parsed and processed to obtain a set of subtask information;

[0136] The subtask information set includes information on several subtasks;

[0137] S2152, by parsing and processing any of the subtask information, subtask type information and subtask to be processed information are obtained;

[0138] S2153, Match the subtask type information, and the subtask object generation large model processes the subtask information to be processed to obtain the first target subtask information;

[0139] It should be noted that the first target subtask information refers to the first target subtask information obtained after parsing and processing the subtask information to be processed by the large model generated from the subtask object according to the subtask type.

[0140] S2154, Match the subtask type information, and the subtask object carrying relationship generation large model processes the first target subtask information to obtain the second target subtask information;

[0141] It should be noted that the second target subtask information refers to the second target subtask information obtained after parsing and processing the first target subtask information by generating a large model based on the subtask type and the subtask object carrying relationship.

[0142] S2155, Match the subtask type information, and the subtask object deployment model processes the second target subtask information to obtain the third target subtask information;

[0143] It should be noted that the third target subtask information refers to the third target subtask information obtained after parsing and processing the second target subtask information according to the subtask type and the deployment model of the subtask object.

[0144] S2156, Match the subtask type information, and generate a large model by assembling subtask objects to process the third target subtask information to obtain the fourth target subtask information;

[0145] It should be noted that the fourth target subtask information refers to the fourth target subtask information obtained after parsing and processing the third target subtask information by generating a large model based on the subtask type through the subtask object grouping.

[0146] S2157, All the information of the fourth objective sub-tasks is combined in sequence to obtain the second basic scenario data;

[0147] It should be noted that the term "sequential combination" means combining according to a specific order.

[0148] As can be seen, the intelligent simulation experiment method described in this embodiment performs a second data processing on the task information to obtain a second basic scenario, which provides a technical foundation for subsequent software applications in the form of intelligent agents to replace traditional combat simulation experiment software, and for using combat simulation experiment task AI intelligent agents to replace manual operation, thereby realizing intelligent combat simulation experiments. It solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0149] Optionally, the step of comprehensively processing the first basic scenario data and the second basic scenario data to obtain basic scenario data includes:

[0150] S2161, Generate a large model using the charge-and-response relationship, process the first basic scenario data and the second basic scenario data to obtain charge-and-response relationship information;

[0151] S2162, using the sub-task object action plan big model, process the second basic scenario data to obtain task action plan information;

[0152] S2163, using a large model of content merging and rule verification, the command and control relationship information and the task action plan information are processed to obtain target basic scenario data;

[0153] S2164, Using a format conversion tool, the target basic scenario data is processed to obtain basic scenario data;

[0154] It should be noted that, in this embodiment, the format conversion tool is ToolCalling;

[0155] It should be noted that ToolCalling is a technical pattern in AI applications that allows models to enhance their functionality by calling external APIs or tools, primarily used for information retrieval and automated operations.

[0156] As can be seen, the intelligent simulation experiment method described in this embodiment comprehensively processes the first basic scenario data and the second basic scenario data to obtain basic scenario data. This provides a technical foundation for subsequent software applications in the form of intelligent agents to replace traditional combat simulation experiment software, and for using AI intelligent agents in combat simulation experiment tasks to replace manual operations, thereby realizing intelligent combat simulation experiments. It solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0157] Optionally, the experimental scenario generating agent is used to process the basic scenario data to obtain experimental scenario data, including:

[0158] S221, Obtain basic scenario data;

[0159] Initialize experimental factor information and factor level values;

[0160] S222, using the experimental factor matching large model, the basic scenario data, the experimental factor information and factor level value information are processed to obtain key experimental factor information;

[0161] It should be noted that the experimental factors refer to the variables that affect the experimental results in the experiment, and the factors of interest are usually selected as experimental factors.

[0162] It should be noted that the factor level value represents a reasonable value for the influencing factor represented by the experimental factor;

[0163] For example, temperature is selected as the influencing factor in the experiment, and the factor level is set to (-30, 40).

[0164] It should be noted that the range of the factor level values ​​is based on the range of the business attributes of the influencing factors. In this invention, the experimental data analysis model is intelligently adjusted according to the experimental results and business domain knowledge.

[0165] S223, Generate a large model using the experimental scheme, process the experimental factor information, and obtain the first experimental scheme data;

[0166] It should be noted that the experimental scheme generates a large model that can automatically process the experimental factor information to obtain the first experimental scheme data.

[0167] S224, Using the experimental scheme to verify the large model, the data of the first experimental scheme is processed to obtain the data of the second experimental scheme;

[0168] S225, Using the experimental scenario generation tool, the data of the second experimental scheme is processed to obtain the data of the first experimental scenario;

[0169] S226, Using the experimental scenario simulation service model, process the data of the first experimental scenario to obtain experimental data;

[0170] S227, Utilize the experimental data analysis model to process the experimental data, obtain experimental factor information and factor level value information, and return to S222.

[0171] As can be seen, the intelligent simulation experiment method described in this embodiment uses an intelligent agent generated from the experimental scenario to process the basic scenario data and obtain the experimental scenario data. This provides a technical foundation for subsequent software applications in the form of intelligent agents to replace traditional combat simulation experiment software, and for using AI intelligent agents for combat simulation experiment tasks to replace manual operations, thereby realizing intelligent combat simulation experiments. It solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0172] Optionally, the experimental data analysis agent is used to perform performance analysis and evaluation, result comparison, and cause analysis on the simulation experimental data to obtain evaluation result data, including:

[0173] S231, Based on the task requirement information, obtain the experimental objective information and experimental requirement information;

[0174] S232, using the large evaluation calculation model, call the analysis and evaluation tool to evaluate and process the experimental objective information and the experimental requirement information to obtain experimental evaluation result information;

[0175] Using a large data analysis model, the experimental objective information and experimental requirement information are analyzed and processed to obtain experimental analysis results.

[0176] S233, using a large comparative analysis model, the experimental evaluation results are compared and analyzed to obtain experimental comparison results.

[0177] S234, Using the large-scale abductive analysis model, the experimental comparison results and experimental analysis results are processed to obtain key experimental factor information;

[0178] The results are used to present a large model, and the experimental evaluation results and the experimental comparison results are processed to obtain parameter adjustment information.

[0179] S235, the key experimental factor information and the parameter adjustment information are combined by category to obtain evaluation result data.

[0180] As can be seen, the intelligent simulation experiment method described in this embodiment utilizes an intelligent agent to perform performance analysis and evaluation, result comparison, and cause analysis on the simulation experiment data to obtain evaluation result data. This provides a technical foundation for subsequent software applications in the form of intelligent agents to replace traditional combat simulation experiment software, and for using AI intelligent agents for combat simulation experiment tasks to replace manual operations, thereby realizing intelligent combat simulation experiments. It also solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0181] Optionally, the experimental scheme recommends an intelligent agent for processing the evaluation result data to obtain preferred experimental scheme data, including:

[0182] S241, Based on the task requirement information, obtain the experimental objective information and experimental requirement information;

[0183] S242, using the preferred scheme to recommend a large model, the experimental objective information, the experimental requirement information, and the experimental scenario instruction information are processed to obtain experimental scenario data;

[0184] It should be noted that, in this embodiment, the experimental scenario data is an experimental scenario file;

[0185] S243, The experimental scenario data is processed using an experimental scenario text conversion tool to obtain experimental scheme data;

[0186] It should be noted that, in this embodiment, the experimental scheme data is the experimental scheme text;

[0187] S244, the preferred scheme recommendation large model processes the experimental scheme data to obtain the preferred experimental scheme data;

[0188] It should be noted that, in this embodiment, the preferred experimental scheme data is in file format, and the content includes the preferred experimental scheme and its description;

[0189] As can be seen, the intelligent simulation experiment method described in this embodiment uses an intelligent agent to process the evaluation result data to obtain the optimal experimental scheme data. This realizes the replacement of traditional combat simulation experiment software with software applications in the form of intelligent agents, the replacement of manual operation with AI intelligent agents for combat simulation experiment tasks, and the realization of intelligent combat simulation experiments. It solves the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0190] Optionally, the step of processing the task requirement information using the intelligent simulation experiment model to obtain optimized experimental scheme data includes:

[0191] S31, The intelligent simulation experiment model is used to process the task requirement information to obtain simulation result data;

[0192] S32, Based on the task requirement information, the simulation result data is analyzed and processed using the intelligent simulation experiment model to obtain the optimal experimental scheme data.

[0193] As can be seen, the intelligent simulation experiment method described in this embodiment processes the task requirement information using the intelligent simulation experiment model to obtain optimized experimental scheme data. By utilizing the intelligent simulation experiment model, it realizes the replacement of traditional combat simulation experiment software with intelligent agent-type software applications and the replacement of manual operation with combat simulation experiment task AI intelligent agents, thereby achieving intelligent combat simulation experiments and solving the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0194] Optionally, the step of processing the task requirement information using the intelligent simulation experiment model to obtain simulation result data includes:

[0195] S311, using the simulated resource access agent, process the task requirement information to obtain basic task data;

[0196] S312, using the basic scenario to generate an intelligent agent, processing the task requirement information to obtain basic scenario data;

[0197] S313, The intelligent simulation experiment model is used to process the basic task data and the basic scenario data to obtain simulation result data.

[0198] As can be seen, the intelligent simulation experiment method described in this embodiment processes the task requirement information using the intelligent simulation experiment model to obtain simulation result data. By utilizing the intelligent simulation experiment model, it realizes the replacement of traditional combat simulation experiment software with intelligent agent-type software applications and the replacement of manual operation with combat simulation experiment task AI intelligent agents, thereby achieving intelligent combat simulation experiments and solving the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0199] Optionally, the process of using the intelligent simulation experiment model to process the task base data and the base scenario data to obtain simulation result data includes:

[0200] S3131, Initialize the number of experiments N = 1;

[0201] S3132, determine whether the number of experiments is greater than the first number threshold, and obtain the first number of experiments judgment result;

[0202] If the result of the first number of experiments is negative, execute S3133;

[0203] If the result of the first number of experiments is yes, execute S3134;

[0204] S3133, The intelligent agent generated by the experimental scenario processes the basic scenario data to obtain key experimental factor information.

[0205] S3134, determine whether the number of experiments is greater than the second number threshold, and obtain the second number of experiments judgment result;

[0206] If the result of the second experiment count is yes, execute S3135;

[0207] If the result of the second experiment count is negative, execute S3138;

[0208] S3135, The experimental scenario generating agent processes the key experimental factor information to obtain experimental scheme data and experimental scenario data.

[0209] S3136, Based on the experimental scheme data, the experimental operation control agent is used to process the experimental scenario data to obtain the experimental scenario result data.

[0210] S3137, Based on the experimental objective information, the intelligent agent using the experimental data analysis is used to perform discrimination processing on the experimental scenario result data to obtain the experimental scenario judgment result;

[0211] When the experimental scenario is determined to be true, execute S3138;

[0212] When the experimental scenario judgment result is negative, the number of experiments is increased by 1; the experimental data analysis agent analyzes and processes the experimental scenario result data to obtain updated key experimental factor information; the key experimental factor information is updated with the updated key experimental factor information, and S3134 is executed.

[0213] S3138, Process the experimental scenario result data to obtain simulation result data.

[0214] As can be seen, the intelligent simulation experiment method described in this embodiment uses the intelligent simulation experiment model to process the basic task data and the basic scenario data to obtain simulation result data. By using the intelligent simulation experiment model, it realizes the replacement of traditional combat simulation experiment software with intelligent agent-type software applications and the replacement of manual operation with combat simulation experiment task AI intelligent agents, thus realizing intelligent combat simulation experiments and solving the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0215] Optionally, the step of using the experimental scenario-generating agent to process the key experimental factor information to obtain experimental scheme data and experimental scenario data includes:

[0216] S31351, Based on the basic scenario data, the experimental scenario generating agent is used to match and process the key experimental factor information to obtain experimental scheme data.

[0217] S31352, The experimental scenario is generated by an intelligent agent to verify the experimental scheme data and obtain the experimental scenario data.

[0218] As can be seen, the intelligent simulation experiment method described in this embodiment uses the experimental scenario generation agent to process the key experimental factor information to obtain experimental scheme data and experimental scenario data. By using the intelligent simulation experiment model, it realizes the replacement of traditional combat simulation experiment software with software applications in the form of intelligent agents and the replacement of manual operation with combat simulation experiment task AI intelligent agents, thereby realizing intelligent combat simulation experiments and solving the problems of high personnel requirements, large energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0219] Optionally, the step of analyzing and processing the simulation result data using the intelligent simulation experiment model based on the task requirement information to obtain the optimal experimental scheme data includes:

[0220] S321, Process the task requirement information to obtain the experimental objective information;

[0221] S322, Analyze and process the simulation result data to obtain experimental scenario instruction information;

[0222] S323, using the intelligent simulation experimental model, the experimental scheme recommendation agent processes the experimental objective information and the experimental scenario instruction information to obtain the optimal experimental scheme data.

[0223] As can be seen, the intelligent simulation experiment method described in this embodiment, based on the task requirement information, uses the intelligent simulation experiment model to analyze and process the simulation result data to obtain the optimal experimental scheme data. By using the intelligent simulation experiment model, it realizes the replacement of traditional combat simulation experiment software with intelligent agent-type software applications and the replacement of manual operation with combat simulation experiment task AI intelligent agents, thereby realizing intelligent combat simulation experiments and solving the problems of high personnel requirements, high energy consumption, and few innovative conclusions in traditional combat simulation experiments.

[0224] Example 2

[0225] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an intelligent simulation experimental device disclosed in an embodiment of the present invention. Figure 3 The described apparatus can be applied to simulation systems, such as local servers or cloud servers used in simulation systems, and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the device includes: a data acquisition unit 101, a model building unit 102, and a data processing unit 103;

[0226] The data acquisition unit 101 is used to acquire task requirement information;

[0227] The model building unit 102 is used to build an intelligent simulation experiment model;

[0228] The data processing unit 103 is used to process the task requirement information using the intelligent simulation experiment model to obtain the optimal experimental scheme data.

[0229] The device provided in this embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the intelligent simulation experiment method described in the aforementioned embodiment one.

[0230] Example 3

[0231] Please see Figure 4 , Figure 4 This is a schematic diagram of another intelligent simulation experimental device disclosed in an embodiment of the present invention. Figure 4 The described apparatus can be applied to simulation systems, such as local servers or cloud servers used in simulation systems, and the embodiments of the present invention are not limited thereto. Figure 4 As shown, the device may include:

[0232] Memory 201 storing executable program code;

[0233] Processor 202 coupled to memory 201;

[0234] The processor 202 calls the executable program code stored in the memory 201 to execute the steps in the intelligent simulation experiment method described in Embodiment 1.

[0235] Example 4

[0236] This invention discloses a computer-readable storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps in the intelligent simulation experiment method described in Embodiment 1.

[0237] Example 5

[0238] This 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 cause a computer to perform the steps in the intelligent simulation experiment method described in Embodiment 1.

[0239] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0240] Through the detailed 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 necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0241] Finally, it should be noted that the intelligent simulation experiment method and apparatus disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent simulation experiment method, characterized in that, The method includes: S1, Obtain task requirement information; S2, construct an intelligent simulation experiment model; S3, The intelligent simulation experiment model is used to process the task requirement information to obtain experimental scheme data; The intelligent simulation experiment model includes: Data input agent, simulation resource access agent, basic scenario generation agent, experimental scenario generation agent, experimental operation control agent, experimental data analysis agent, and experimental scheme recommendation agent; The data is input into the intelligent agent to obtain task requirement information; The simulation resource access agent is used to retrieve the scenario background information based on the task requirement information and extract the simulation resource data. The basic scenario generating agent is used to receive and process the task requirement information and generate basic scenario data. The experimental scenario generation agent is used to process the basic scenario data to obtain the experimental scenario data. The experimental operation control intelligent agent is used to receive and process experimental scenario instructions, allocate experimental scenario operation tasks, monitor the status and process of experimental scenario operation, control and monitor the operation status of simulation experiment, and acquire simulation experiment data. The experimental data analysis agent is used to perform performance analysis and evaluation, result comparison and cause analysis on the simulation experimental data to obtain evaluation result data. The experimental scheme recommends an intelligent agent, which is used to process the evaluation result data to obtain the experimental scheme data. The data input agent, simulation resource access agent, basic scenario generation agent, experimental scenario generation agent, experimental operation control agent, experimental data analysis agent, and experimental scheme recommendation agent are sequentially connected via data connections. The step of processing the task requirement information using the intelligent simulation experiment model to obtain experimental scheme data includes: S31, The intelligent simulation experiment model is used to process the task requirement information to obtain simulation result data; S32, Based on the task requirement information, the simulation result data is analyzed and processed using the intelligent simulation experiment model to obtain the experimental scheme data.

2. The intelligent simulation experiment method according to claim 1, characterized in that, The process of using the intelligent simulation experiment model to process the task requirement information and obtain simulation result data includes: S311, using the simulated resource access agent, process the task requirement information to obtain basic task data; S312, using the basic scenario to generate an intelligent agent, processing the task requirement information to obtain basic scenario data; S313, The intelligent simulation experiment model is used to process the basic task data and the basic scenario data to obtain simulation result data.

3. The intelligent simulation experiment method according to claim 2, characterized in that, The process of using the intelligent simulation experiment model to process the basic task data and the basic scenario data to obtain simulation result data includes: S3131, Initialize the number of experiments N = 1; S3132, determine whether the number of experiments is greater than the first number threshold, and obtain the first number of experiments judgment result; If the result of the first number of experiments is negative, execute S3133; If the result of the first number of experiments is yes, execute S3134; S3133, The intelligent agent generated by the experimental scenario processes the basic scenario data to obtain key experimental factor information. S3134, determine whether the number of experiments is greater than the second number threshold, and obtain the second number of experiments judgment result; If the result of the second experiment count is yes, execute S3135; If the result of the second experiment count is negative, execute S3138; S3135, The experimental scenario generating agent processes the key experimental factor information to obtain experimental scheme data and experimental scenario data. S3136, Based on the experimental scheme data, the experimental operation control agent is used to process the experimental scenario data to obtain the experimental scenario result data. S3137, Based on the experimental objective information, the intelligent agent using the experimental data analysis is used to perform discrimination processing on the experimental scenario result data to obtain the experimental scenario judgment result; When the experimental scenario is determined to be true, execute S3138; When the experimental scenario judgment result is negative, the number of experiments is increased by 1; the experimental data analysis agent analyzes and processes the experimental scenario result data to obtain updated key experimental factor information; the key experimental factor information is updated with the updated key experimental factor information, and S3134 is executed. S3138, Process the experimental scenario result data to obtain simulation result data.

4. The intelligent simulation experiment method according to claim 3, characterized in that, The process of using the experimental scenario-generating agent to process the key experimental factor information to obtain experimental scheme data and experimental scenario data includes: S31351, Based on the basic scenario data, the experimental scenario generating agent is used to match and process the key experimental factor information to obtain experimental scheme data. S31352, The experimental scenario is generated by an intelligent agent to verify the experimental scheme data and obtain the experimental scenario data.

5. The intelligent simulation experiment method according to claim 1, characterized in that, Based on the task requirement information, the simulation result data is analyzed and processed using the intelligent simulation experiment model to obtain experimental scheme data, including: S321, Process the task requirement information to obtain the experimental objective information; S322, Analyze and process the simulation result data to obtain experimental scenario instruction information; S323, using the intelligent simulation experiment model's experimental scheme recommendation agent, the experimental objective information and the experimental scenario instruction information are processed to obtain experimental scheme data.

6. An intelligent simulation experimental device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent simulation experiment method as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the intelligent simulation experiment method as described in any one of claims 1-5.

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