A multi-agent strategic scenario generation system based on a large language model

Abstract

The present invention provides a multi-agent strategic scenario generation system based on a large language model, comprising the following steps: modeling scenarios and systems, requiring systematic modeling of attempt filing, deduction situations, events and scenarios, and strategic-campaign-tactical-level red and blue subsystems and white subsystems; initializing the red and blue attempt filings. When a user uses the system, they are prompted to enter relevant information about the military deduction. After entering the information as required, the system uses the global attempt filing to generate an agent, receives the user input information, and generates the attempt filing in JSON format. The present invention provides a multi-agent strategic scenario generation system based on a large language model, designing a game subsystem based on a multi-level agent vertical collaboration relationship of strategy-campaign-tactics. Through the multi-level game subsystem, the task granularity of the scenario designed by the large language model is refined, effectively improving the quality of the scenario design.

Description

Technical Field

[0001] This invention belongs to the field of simulation and game deduction, and particularly relates to a multi-agent deduction scenario generation system based on a large language model. Background Art

[0002] A military scenario is a detailed description of a scenario. By creating a hypothetical situation, it simulates possible military actions and events. The content of a military scenario includes: attempt case, basic scenario and supplementary scenario. The attempt case is based on in-depth analysis and comprehensive assessment of multi-dimensional information on the international strategic landscape, national security environment, and the balance of power between the enemy and us. It aims to clarify operational intentions, set campaign or combat objectives, and preliminarily plan the strategic path and key steps required to achieve the objectives. The basic scenario is a product that is further refined and concretized on the basis of the attempt case. It is a detailed operational plan formed through rigorous battlefield environment simulation, force deployment design and action sequence arrangement. The supplementary scenario is an important supplement and improvement to the basic scenario. It predicts and provides plans for various variables that may arise in actual combat. This document is organized according to the chronological order of battlefield events, covering combat time, combat area and key elements of specific events.

[0003] Strategy, campaign and tactics are three different levels of concepts in military theory, corresponding to different time and space scopes, command levels and action purposes. The strategic level usually refers to the overall planning and guidance of a war or conflict by a country or group over a longer period of time (such as several years to decades). It focuses on the overall, directional and long-term goal setting and resource allocation, involving the comprehensive application of multiple fields such as politics, economy, diplomacy and culture, as well as how to serve the realization of national strategic interests through military means. The campaign level is the organization and implementation of a series of combat activities in a shorter time span (such as several months to several weeks) and in a specific area. The operational plan is the concretization and decomposition of strategic objectives, aiming to achieve a series of intermediate and local military objectives to support the gradual realization of strategic intentions. The tactical level refers to the actual combat operations of troops in specific battlefield spaces (such as battlefields, urban blocks, and sea areas) and in a short period of time (usually a few hours to a few days) in actual combat. The tactical level focuses on specific combat skills and operating methods, including the detailed planning and execution of troop organization, firepower allocation, maneuver routes, and attack methods. In summary, the three levels of strategy, campaign, and tactics constitute a complete planning and execution system for military operations from top to bottom, and jointly promote the development of war or conflict towards the established goals.

[0004] A large language model is a complex system built on artificial intelligence technology that is designed to understand, generate, and process natural language. This model is trained with large-scale data sets, enabling it to learn and simulate complex patterns and structures in human language. Large language models have a wide range of applications, including but not limited to text generation, language translation, sentiment analysis, dialogue systems, question-answering systems, and text summarization. A notable feature of these models is their adaptability and flexibility, which allows them to handle a variety of different types and styles of language tasks. With the improvement of computing power and the increase in data availability, large language models have demonstrated significant effectiveness in processing complex language tasks.

[0005] The excellent content generation and natural language understanding capabilities of large language models make them of great application value in military scenario design. However, existing large language model technology still has the following problems when designing military scenarios:

[0006] Large language model alignment issues. When generating long texts, large language models often have logical inconsistencies. This problem is particularly fatal in military scenario design because military operations require strict logic and consistent strategies. The model may describe the enemy and one's own action plans in the same scenario, but these plans may conflict with each other in time and space.

[0007] Insufficient mission details. When generating military scenarios, large language models often lack specific mission details, including troop deployment, specific parameters of weapons and equipment, and detailed steps for mission execution. These details are crucial for military simulations and actual combat plans.

[0008] The text length is controlled. When generating long texts, existing large language models are prone to the problem of out-of-control text length. The generated content may be too short and not meet actual needs. In military scenario design, it is necessary to ensure the amount of text generated in order to provide more sufficient battlefield information.

[0009] Therefore, it is necessary to provide a multi-agent strategic scenario generation system based on a large language model to solve the above technical problems. Summary of the Invention

[0010] The present invention provides a multi-agent strategic scenario generation system based on a large language model, which solves the problems of agent decision-making limitations and imperfect feedback mechanism in traditional military simulation.

[0011] To solve the above technical problems, the present invention provides a multi-agent strategic scenario generation system based on a large language model, comprising the following steps:

[0012] S1. Modeling scenarios and systems requires systematic modeling of attempt case formation, situation simulation, events and scenarios, and strategic-operational-tactical-level red and blue subsystems and white subsystems.

[0013] S2. Initialize the red and blue team's intention filing. When the user uses the system, he is prompted to enter relevant information about the military deduction. After entering the information as required, the system uses the global intention filing to generate an agent, receives the user input information, and generates an intention filing in JSON format. The Markov subchain corresponding to this step is:

[0014] Input→IP→MX→AX

[0015] Among them, Input represents user input, IF represents attempt filing, MX represents basic military scenario, and AX represents supplementary scenario. This step completes the transformation from user input to attempt filing in the system. After the attempt filing is generated, the Python JSON toolkit is used to segment the attempt filing to obtain the attempt filing from different perspectives of the red and blue forces, which is used to generate the basic scenario in the next stage.

[0016] S3. Based on the basic assumptions of the attempt filing, the present invention uses a cyclical workflow of event-situation generation. First, each red and blue subsystem converts its respective attempt filing into a turn-based situation. Then, the automated confrontation generation workflow is cyclically executed. In the cycle, one side's forces convert the received event-situation into a dynamic situation. The turn-based situation, which is a combination of the static and dynamic situations, is converted into a turn-based strategy. The turn-based strategy is further converted into turn-based tactics. The turn-based tactics are converted into event-situation and transmitted to the opposing side. This cycle continues. After n cycles set by the user, the confrontation workflow ends.

[0017] S4. Supplementary scenarios are constructed based on basic scenarios. Supplementary scenarios are an improvement to the basic scenarios. Predictions are made for different scenarios in actual combat and pre-selected options are given. Supplementary scenarios are constructed through three forms: re-simulation, result tree, and man-in-the-loop. Finally, the attempt to file a case, basic scenario, and supplementary scenario are combined to output a complete military scenario.

[0018] Preferably, said S1 comprises the following steps:

[0019] S11. Modeling the attempt case. The attempt case is a pre-set setting before military scenario simulation. It includes the strategic layout with the country as the main body and the detailed description of the initial battle scene of the local war. The attempt case is modeled as a tuple including the strategic background and the battle description. Its mathematical model is:

[0020] IF=(SC,CD)

[0021] Among them, IF stands for attempt to establish a case, which includes an introduction to the strategic background SC and a focused campaign description CD;

[0022] S12. Modeling the deductive situation. The deductive situation refers to the overall situation derived from the analysis and integration of the battle situation. It is an abstract concept extracted from specific integration. To meet the needs of multi-faceted and complex situation analysis, the situation is divided into static situation, dynamic situation, and turn-based situation, and modeled separately.

[0023] S13. Modeling of simulation events and basic scenarios. The designed military scenarios are event-driven, characterized by driving simulation progress based on key events in the battlefield or strategic environment. The simulation events and basic scenarios are modeled in turn.

[0024] S14. Construct a Red-Blue subsystem based on a multi-level agent vertical collaboration relationship between strategy, campaign, and tactics. This subsystem is responsible for playing the roles of the Red and Blue teams, conducting battle deduction and game from the perspective of one side. It includes: a verification agent, a situation summary agent, a strategy generation agent, a campaign generation agent, and a tactical generation agent.

[0025] S15. Construct the white side subsystem, which is responsible for receiving global information and implementing the corresponding functional modules from a global perspective. The subsystem includes an attempt to file a case agent and a global event generation and verification agent.

[0026] Preferably, the S11 includes the following steps:

[0027] S111. Modeling the strategic context. In military scenarios, the strategic context aims to describe the macro-environmental conditions and strategic situational basis for a campaign or conflict. This includes a description of the regional security environment, an analysis of the causes of war, and speculation on adversary intentions. The strategic context is modeled as follows:

[0028] SC=(RX,CC,AS)

[0029] Among them, SC is the strategic background, RS is the description of the regional security environment, CC is the analysis of the causes of war, and AS is the opponent's strategy;

[0030] S112. Modeling the battle description. The battle description is a summary of battle-level information. It requires a detailed description of the environmental information of the battle to be held in the hypothetical scenario, the military armaments of both sides, and the military plans. It is modeled as follows:

[0031] CD-(MA, BI, MP)

[0032] Among them, CD represents campaign description, MA represents military armament, EI represents environmental information, and MP represents military plan. Military armament describes the basic military strength of a party during the military scenario process. The formula is:

[0033]

[0034] in, Indicates the type of troops. Indicates the armed forces in the region,

[0035] Indicates the equipment resource information of the party;

[0036] Environmental information (EI) describes the objective environment in which the military scenario is located. Its formula is:

[0037]

[0038] in, Indicates the time of assumption, Indicates the desired area, Indicates weather composition;

[0039] The military plan describes the overall strategic measures of the entire force and its formula is:

[0040]

[0041] in, Indicates combat objectives. Indicates basic tactics.

[0042] Indicates the principle of warfare;

[0043] After modeling the mission scenario, it is necessary to model the simulated situations and events in the military scenario.

[0044] Preferably, the S12 includes the following steps:

[0045] S121. Model the static situation. The static situation is a summary of the information of both sides before the battle is simulated. It does not change as the battle progresses. The mathematical definition of the static situation is:

[0046]

[0047] in, Represents the static situation, which is an abstract summary extracted from the military armament MA, environmental information EI and military plan MP in the attempt to file an IF;

[0048] S122. Model the dynamic situation. The dynamic situation is defined as a comprehensive evaluation of the current scenario based on the changing battle situation during the simulation, including time, dynamic changes in military power, and basic conditions. The dynamic situation is time-sensitive and variable. In

[122] , the definition of the dynamic situation is:

[0049]

[0050] in, Indicates dynamic situation, Indicates the current time, Indicates the current round's military deployment. Indicates the basic situation of the current battle;

[0051] S123. Model the round situation, which is used to describe the overall battle situation within a round. The formula is:

[0052]

[0053] in Indicates the round situation. Indicates a static state. represents the dynamic situation, n represents the current round number, and in the initialization stage (n = 0), the round situation is the static situation. As the scenario generation begins, the round situation becomes a combination of the static situation and the dynamic situation.

[0054] S124. Model the ending situation. The ending situation is an assessment of the overall battle situation at the end of the military scenario, the degree of completion of both sides' strategic objectives, and the loss of opposing forces. The ending situation is defined as:

[0055]

[0056] in, Indicates the end situation, SO indicates the overall battle situation, OC indicates the completion of strategic goals, and FL indicates the loss of opposing forces.

[0057] Preferably, the S14 includes the following steps:

[0058] S131. Modeling of deduction events. Deduction events are the basic units of basic scenarios, describing a certain action of a certain party and the changes in the subject and environment caused by the action. In summary, the basic scenario is defined as:

[0059]

[0060] Among them, ME is the deduction event, which is composed of the entity action Task and interaction composition;

[0061] S132. Model the basic scenario. A basic scenario is an imagined or pre-defined war or conflict scenario. It is generated by the set of all simulated events and the situation at the end of the simulation. Its mathematical definition is:

[0062]

[0063] MX represents the basic scenario, which includes the attempt to file a case IF, a set of events in n rounds and end the situation

[0064] Preferably, the S14 includes the following steps:

[0065] S141. Construct a verification agent model. The present invention uses a generation-verification adversarial collaboration model, in which the generation agent is responsible for generating an initial solution. The verification agent provides feedback on the initial solution and passes it to the generation agent for regeneration. When the number of modifications exceeds a certain number, the verification solution is output. The verification agent is defined as receiving input from the large model and outputting corresponding modification suggestions. The mathematical model is:

[0066]

[0067] in, It is the model of the verification agent. The model input {Input} changes according to the usage scenario of the verification agent, and the corresponding prompt words will also change. Advice represents the modification suggestions for the verification agent output, which includes: whether the model output maintains temporal and spatial consistency; whether the model output conforms to the overall logic; and whether the grammar, sentence structure and wording of the model output are appropriate.

[0068] S142. Construct a strategy generation agent. The function of the strategy generation agent is to make an evaluative summary based on the intention case received by the troops and generate the army's macro-strategy. The mathematical model is:

[0069]

[0070] in, represents the strategy generation agent, which takes the attempt to file a case IF as input and outputs a static situation;

[0071] S143. Construct a situation summary agent. The function of the situation summary agent is to follow the continuous progress of the deduction events and dynamically summarize the battlefield. It takes the deduction events as input and outputs the dynamic situation. The mathematical model is:

[0072]

[0073] in, is the situation summary agent, ME is the deduction event, It is a dynamic situation;

[0074] S144. Construct a campaign generation agent. The campaign generation agent is responsible for continuously adjusting the troops' campaign strategy based on the dynamic changes in the battlefield situation. Its implementation is as follows: to receive the round situation and the modification suggestions of the verification agent, it generates the overall campaign strategy for the current round. The campaign agent is modeled as:

[0075]

[0076] in, It is the working model of the campaign agent, which is based on the turn-based situation And verify the agent's suggestion Advlce as input and output round battle Turn-based campaigns include: Military Objectives: This dimension focuses on clear, quantifiable military goals, such as destroying enemy forces, capturing key areas, or controlling important resources in strategic operations;

[0077] Resourcing and force allocation: This involves the efficient allocation of available resources and personnel, including decisions regarding force size, organizational structure, fire support, and logistics, to achieve strategic objectives;

[0078] Time factor: Considering time constraints and urgency, some goals need to be achieved immediately, while others allow for longer-term pursuit;

[0079] Comparative analysis of enemy and friendly forces: Analyze the relative strengths, weaknesses, capabilities, and potential vulnerabilities of friendly and enemy forces to assess the feasibility of strategic objectives and the associated risks;

[0080] S145. Construct a tactical generation agent. The function of the tactical generation agent is to receive the round battle situation and the modification suggestions of the verification agent, and generate the tactical strategy for the current round. The tactical agent is modeled as:

[0081]

[0082] in, represents a tactical agent, which uses turn-based strategies Modify the advice to input and output round tactics and strategies

[0083] Turn-based tactical strategies include: combat maneuvers, which refer to specific actions and movements on the battlefield designed to gain tactical advantage;

[0084] Fires Employment: Effectively manage and employ fire resources to support the conduct of tactical operations;

[0085] Terrain Utilization: Maximizing the advantages of the battlefield's natural topography to achieve tactical objectives;

[0086] Force coordination: Ensure seamless cooperation and coordination between various troop units and elements during tactical engagements.

[0087] Preferably, the S15 includes the following steps:

[0088] S151. Construct an agent for generating an attempt to file a case. The function of the agent is to receive and verify user input and output an attempt to file a case. The model of the agent is:

[0089]

[0090] in, It is the agent for generating the attempted case. It receives user input and outputs the JSON format of the attempted case. At the same time, if the user input information is incomplete, the model will feedback the missing information details, try to fill in the gaps, and prompt the user to verify and modify it.

[0091] S152. Construct a global event generation agent. The function of the global event generation agent is to receive the turn strategies and tactics output by the red and blue subsystems, and consider and integrate previous events to generate the latest event. The formula is:

[0092]

[0093] in, It is a global event generating agent, which receives And a list of all previous events Output Events

[0094] S153. Construct a global situation summary agent. The function of the global situation summary agent is to generate a summary evaluation of the battle situation after the game is completed. It receives a list of all game events and the intentions of both parties, and generates an ending situation. Its formula is:

[0095]

[0096] in, It is a digital model of the global situation summary agent, which attempts to file an IF, all the deduction events Input, output end state

[0097] Preferably, said S3 comprises the following steps:

[0098] S31. The Red and Blue subsystems generate a turn situation by attempting to file a case and deducing events. The Red and Blue subsystems use a strategic generation agent to generate a static situation based on the attempted case. If the current round is the initial round, the turn situation is the static situation. If it is not the initial round, there is an event input from the opposing force. At this time, the situation summarizer is called to generate a dynamic situation based on the event. The static situation and dynamic situation are combined into the turn situation. The process of this step is as follows:

[0099]

[0100] Among them, the strategy generation agent Generate static situation based on attempt to file case IF Situation Generation Agent Generate dynamic situation based on simulated event ME The round situation is It is a dynamic situation and static situation Under different conditions and in different combinations,

[0101] Through this step, the transition from attempting to file a case to a round situation is achieved;

[0102] S32. The Red and Blue subsystem generates a round-based campaign strategy based on the round-based situation. The Red and Blue subsystem generates the round-based campaign strategy using a campaign generation-verification agent cyclic workflow. First, the campaign generation agent takes the round-based situation as input and outputs the initial campaign strategy. The formula for this step is:

[0103]

[0104] in, The strategy generation agent starts with The round situation is input and the output is the initial round strategy C1;

[0105] In the next loop, the verification agent takes the initial campaign strategy as input and outputs modification suggestions. The initial campaign agent takes the modification suggestions and the previous campaign strategy as input and outputs the modified campaign. The verification agent again takes the modified campaign as input and outputs modification suggestions. After looping for the number of rounds set by the user, it outputs the final campaign. The formula for this stage is:

[0106]

[0107] in, The recursive formula indicates that the verification agent uses the strategy C of the previous round in the kth round of loop k-1 Input, output current modification suggestion Advice k , It means that the verification agent takes the previous round of campaign strategy and modification suggestions as input and outputs the modification strategy C for the Kth round of cycles k , when k=n, output the final round strategy

[0108] S33. The Red and Blue subsystems generate round-by-round tactical strategies based on the round-by-round campaign strategies. The Red and Blue subsystems generate round-by-round tactical strategies using a tactical generation-verification agent loop workflow. This workflow is basically the same as the previous step and is expressed as:

[0109]

[0110] V(T k-1 )→Advice k

[0111]

[0112] in, It is a tactic generating agent, which takes the round strategy as input in the initial round and outputs the initial tactic T1. In the next k cycles, it verifies the agent V(T k-1 ) Accept the k-1th modified strategy T k-1 , output modification suggestions Tactical Generation Agent T(T k-1 , Advice k ) Lm The modified strategy T in round k-1 k-1 , the kth modification suggestion Advice k , output T k , when k=m, output round strategy

[0113] S34. The White subsystem generates deduction events through turn-based tactical strategies. The White subsystem generates deduction events in the form of a global event generation-verification agent loop workflow. This workflow is basically the same as the previous step and is expressed as follows:

[0114]

[0115] V(E k-1 )→Advice k

[0116]

[0117] in, Represents the process of the global event generation agent generating the initial event. After the next k cycles, the generation-verification flow loop generates modification suggestions and modification strategies, and outputs the deduction event of this round when km

[0118] S35. After n cycles, the ending situation is generated. The events generated by one unit will enter the other unit, and the cycle of steps S31-S34 will be restarted. Each cycle will output the deduction events of that round. After n cycles set manually, all deduction events are input into the global situation summary agent, and the ending situation is output. The process formula is:

[0119]

[0120] in, Indicates the overall situation summary agent, IF is the attempt to file a case, It is an event library composed of all deduction events. To end the situation, the attempt to file a case, the event library, and the ending situation are combined to form a basic scenario.

[0121] Preferably, said S4 comprises the following steps:

[0122] S41. Supplementary scenarios are constructed through replay. The principle of the replay module is to control a local variable in the attempt case and replay it to observe the impact of the variable on the overall battle situation. There are two forms of replay: user-defined replay and large-scale customized replay.

[0123] S42. Constructing Supplementary Scenarios Based on the Result Tree. When the user chooses to construct supplementary scenarios using the result tree model in this invention, the system will initiate a case based on the initially set strategic intent and re-simulate and analyze the entire operational sequence using advanced backtracking algorithms and probabilistic deduction techniques. During this process, for each key deduction event node, the system not only thoroughly records at least three different potential development outcomes, but also recursively and logically infers and expands on each possible outcome, forming a multi-level, comprehensive result deduction tree structure.

[0124] S43. Supplementary assumptions are constructed based on the form of people in the loop. The present invention designs and implements a dynamic supplementary assumption mode constructed based on the human-in-the-loop mode. The core of this system is that users deeply participate in the simulated battlefield environment according to their own strategic needs and preset camp identities, and receive multi-dimensional and all-round battlefield situation information accurately pushed by the system in real time. This information covers but is not limited to the enemy and friendly position distribution, resource reserves, and key data on environmental changes. After receiving this situation information, users can make flexible arrangements at the tactical level according to actual conditions, formulate marching routes, adjust defense layouts, and optimize troop allocation; at the same time, they can also efficiently deploy various resources at the strategic level, including but not limited to weapons and equipment, logistics supplies, and core human resources. Every decision made by the user is real-time and interactive. Once the decision instruction is issued, it will be seamlessly connected to the system and used as input parameters in the next round of simulation calculation process.

[0125] Preferably, the S41 includes the following steps:

[0126] S411. Users can initiate a deep reconstruction of the intention case through customized strategies. During the enhanced scenario construction phase, users can flexibly specify one or more sets of variable parameters in the intention plan. Based on this, the system will re-run the updated intention plan to generate more detailed and rich supplementary hypothetical scenarios. In military simulation applications, users can freely set different weather conditions and troop deployment configurations to trigger the system to recalculate and analyze the results of the established strategy.

[0127] S412. By using a large language model to deeply customize and recalculate basic scenarios, the model can provide diversified variable adjustment suggestions for established intention plans and present these strategic options to users. Users have decision-making power in this process and can choose to adopt any change plan recommended by the model and make detailed manual adjustments based on these suggestions. Once the intention plan is confirmed and revised by the user, the system will re-implement the deduction process based on the updated plan, thereby generating a more comprehensive set of supplementary scenarios that meet actual needs.

[0128] Compared with related technologies, the multi-agent strategic scenario generation system based on a large language model provided by the present invention has the following beneficial effects:

[0129] The present invention provides a multi-agent strategic scenario generation system based on a large language model. It designs a game subsystem based on the vertical collaborative relationship of multi-level agents in strategy, campaign and tactics. Through the multi-level game subsystem, the task granularity of the scenario designed by the large language model is refined, effectively improving the quality of the scenario design.

[0130] Using a generative-verification adversarial collaborative agent framework, compared to the traditional single large model, this agent framework can promptly correct errors and inconsistencies in the generated agent's content creation, enhance logical consistency, effectively improve the output quality of the large language model, and enhance the overall deduction assumption level.

[0131] It demonstrates more stable robustness and uses a multi-agent red-blue confrontation framework to simulate the system and create military scenarios. Compared with the direct game and generation of a single intelligent model, it considers multiple variables and uncertain factors, making the simulation closer to the actual battlefield situation.

[0132] By using a more comprehensive feedback mechanism, employing replays, outcome trees, and humans in the loop, we can deeply explore the potential outcomes of battlefield simulations, the possible effectiveness of each decision, and the impact of different factors on simulation results, thereby achieving closed-loop feedback that effectively guides users to improve future action plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0133] Figure 1 A schematic diagram of a preferred embodiment of a multi-agent strategic scenario generation system based on a large language model provided by the present invention;

[0134] Figure 2 It is a structural diagram of the system framework;

[0135] Figure 3 A schematic diagram of the workflow for event generation;

[0136] Figure 4 Generate a schematic diagram of the workflow for the basic scenario;

[0137] Figure 5 Generate schematic diagrams showing scenario details for both the proposed technology and the baseline technology.

[0138] Figure 6 Schematic diagram of responding to reviews for the model. DETAILED DESCRIPTION

[0139] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0140] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 and Figure 6 ,in, Figure 1 A schematic diagram of a preferred embodiment of a multi-agent strategic scenario generation system based on a large language model provided by the present invention; Figure 2 It is a structural diagram of the system framework; Figure 3 A schematic diagram of the workflow for event generation; Figure 4 Generate a schematic diagram of the workflow for the basic scenario; Figure 5 Generate schematic diagrams showing scenario details for both the proposed technology and the baseline technology. Figure 6 A multi-agent strategy scenario generation system based on a large language model includes the following steps:

[0141] S1. Modeling scenarios and systems requires systematic modeling of attempt case formation, situation simulation, events and scenarios, and strategic-operational-tactical-level red and blue subsystems and white subsystems.

[0142] S2. Initialize the red and blue team's intention filing. When the user uses the system, he is prompted to enter relevant information about the military deduction. After entering the information as required, the system uses the global intention filing to generate an agent, receives the user input information, and generates an intention filing in JSON format. The Markov subchain corresponding to this step is:

[0143] Input→IF→MX→AX

[0144] Among them, Input represents user input, IF represents attempt filing, MX represents basic military scenario, and AX represents supplementary scenario. This step completes the transformation from user input to attempt filing in the system. After the attempt filing is generated, the Python JSON toolkit is used to segment the attempt filing to obtain the attempt filing from different perspectives of the red and blue forces, which is used to generate the basic scenario in the next stage.

[0145] S3. Based on the basic assumptions of the attempt filing, the present invention uses a cyclical workflow of event-situation generation. First, each red and blue subsystem converts its respective attempt filing into a turn-based situation. Then, the automated confrontation generation workflow is cyclically executed. In the cycle, one side's forces convert the received event-situation into a dynamic situation. The turn-based situation, which is a combination of the static and dynamic situations, is converted into a turn-based strategy. The turn-based strategy is further converted into turn-based tactics. The turn-based tactics are converted into event-situation and transmitted to the opposing side. This cycle continues. After n cycles set by the user, the confrontation workflow ends.

[0146] S4. Supplementary scenarios are constructed based on basic scenarios. Supplementary scenarios are an improvement to the basic scenarios. Predictions are made for different scenarios in actual combat and pre-selected options are given. Supplementary scenarios are constructed through three forms: re-simulation, result tree, and man-in-the-loop. Finally, the attempt to file a case, basic scenario, and supplementary scenario are combined to output a complete military scenario.

[0147] Said S1 comprises the following steps:

[0148] S11. Modeling the attempt case. The attempt case is a pre-set setting before military scenario simulation. It includes the strategic layout with the country as the main body and the detailed description of the initial battle scene of the local war. The attempt case is modeled as a tuple including the strategic background and the battle description. Its mathematical model is:

[0149] IF-(SC, CD)

[0150] Among them, IF stands for attempt to establish a case, which includes an introduction to the strategic background SC and a focused campaign description CD;

[0151] S12. Modeling the deductive situation. The deductive situation refers to the overall situation derived from the analysis and integration of the battle situation. It is an abstract concept extracted from specific integration. To meet the needs of multi-faceted and complex situation analysis, the situation is divided into static situation, dynamic situation, and turn-based situation, and modeled separately.

[0152] S13. Modeling of simulation events and basic scenarios. The designed military scenarios are event-driven, characterized by driving simulation progress based on key events in the battlefield or strategic environment. The simulation events and basic scenarios are modeled in turn.

[0153] S14. Construct a Red-Blue subsystem based on a multi-level agent vertical collaboration relationship between strategy, campaign, and tactics. This subsystem is responsible for playing the roles of the Red and Blue teams, conducting battle deduction and game from the perspective of one side. It includes: a verification agent, a situation summary agent, a strategy generation agent, a campaign generation agent, and a tactical generation agent.

[0154] S15. Construct the white side subsystem, which is responsible for receiving global information and implementing the corresponding functional modules from a global perspective. The subsystem includes an attempt to file a case agent and a global event generation and verification agent.

[0155] The S11 includes the following steps:

[0156] S111. Modeling the strategic context. In military scenarios, the strategic context aims to describe the macro-environmental conditions and strategic situational basis for a campaign or conflict. This includes a description of the regional security environment, an analysis of the causes of war, and speculation on adversary intentions. The strategic context is modeled as follows:

[0157] SC=(RS,CC,AS)

[0158] Among them, SC is the strategic background, RS is the description of the regional security environment, CC is the analysis of the causes of war, and AS is the opponent's strategy;

[0159] S112. Modeling the battle description. The battle description is a summary of battle-level information. It requires a detailed description of the environmental information of the battle to be held in the hypothetical scenario, the military armaments of both sides, and the military plans. It is modeled as follows:

[0160] CD=(MA,EI,MP)

[0161] Among them, CD represents campaign description, MA represents military armament, EI represents environmental information, and MP represents military plan. Military armament describes the basic military strength of a party during the military scenario process. The formula is:

[0162]

[0163] in, Indicates the type of troops. Indicates the armed forces in the region,

[0164] Indicates the equipment resource information of the party;

[0165] Environmental information (EI) describes the objective environment in which the military scenario is located. Its formula is:

[0166]

[0167] in, Indicates the time of assumption, Indicates the desired area, Indicates weather composition;

[0168] The military plan describes the overall strategic measures of the entire force and its formula is:

[0169]

[0170] in, Indicates combat objectives. Indicates basic tactics.

[0171] Indicates the principle of warfare;

[0172] After modeling the mission scenario, it is necessary to model the simulated situations and events in the military scenario.

[0173] The S12 includes the following steps:

[0174] S121. Model the static situation. The static situation is a summary of the information of both sides before the battle is simulated. It does not change as the battle progresses. The mathematical definition of the static situation is:

[0175]

[0176] in, Represents the static situation, which is an abstract summary extracted from the military armament MA, environmental information EI and military plan MP in the attempt to file an IF;

[0177] S122. Model the dynamic situation. The dynamic situation is defined as a comprehensive evaluation of the current scenario based on the changing battle situation during the simulation, including time, dynamic changes in military power, and basic conditions. The dynamic situation is time-sensitive and variable. In

[122] , the definition of the dynamic situation is:

[0178]

[0179] in, Indicates dynamic situation, Indicates the current time, Indicates the current round's military deployment. Indicates the basic situation of the current battle;

[0180] S123. Model the round situation, which is used to describe the overall battle situation within a round. The formula is:

[0181]

[0182] in Indicates the round situation. Indicates a static state. represents the dynamic situation, n represents the current round number, and in the initialization stage (n = 0), the round situation is the static situation. As the scenario generation begins, the round situation becomes a combination of the static situation and the dynamic situation.

[0183] S124. Model the ending situation. The ending situation is an assessment of the overall battle situation at the end of the military scenario, the degree of completion of both sides' strategic objectives, and the loss of opposing forces. The ending situation is defined as:

[0184]

[0185] in, Indicates the end situation, SO indicates the overall battle situation, OC indicates the completion of strategic goals, and FL indicates the loss of opposing forces.

[0186] The S14 includes the following steps:

[0187] S131. Modeling of deduction events. Deduction events are the basic units of basic scenarios, describing a certain action of a certain party and the changes in the subject and environment caused by the action. In summary, the basic scenario is defined as:

[0188]

[0189] Among them, ME is the deduction event, which is composed of the entity action Task and interaction composition;

[0190] S132. Model the basic scenario. A basic scenario is an imagined or pre-defined war or conflict scenario. It is generated by the set of all simulated events and the situation at the end of the simulation. Its mathematical definition is:

[0191]

[0192] MX represents the basic scenario, which includes the attempt to file a case IF, a set of events in n rounds and end the situation

[0193] The S14 includes the following steps:

[0194] S141. Construct a verification agent model. The present invention uses a generation-verification adversarial collaboration model, in which the generation agent is responsible for generating an initial solution. The verification agent provides feedback on the initial solution and passes it to the generation agent for regeneration. When the number of modifications exceeds a certain number, the verification solution is output. The verification agent is defined as receiving input from the large model and outputting corresponding modification suggestions. The mathematical model is:

[0195]

[0196] in, It is the model of the verification agent. The model input {Input} changes according to the usage scenario of the verification agent, and the corresponding prompt words will also change. Advice represents the modification suggestions for the verification agent output, which includes: whether the model output maintains temporal and spatial consistency; whether the model output conforms to the overall logic; and whether the grammar, sentence structure and wording of the model output are appropriate.

[0197] S142. Construct a strategy generation agent. The function of the strategy generation agent is to make an evaluative summary based on the intention case received by the troops and generate the army's macro-strategy. The mathematical model is:

[0198]

[0199] in, represents the strategy generation agent, which takes the attempt to file a case IF as input and outputs a static situation;

[0200] S143. Construct a situation summary agent. The function of the situation summary agent is to follow the continuous progress of the deduction events and dynamically summarize the battlefield. It takes the deduction events as input and outputs the dynamic situation. The mathematical model is:

[0201]

[0202] in, is the situation summary agent, ME is the deduction event, It is a dynamic situation;

[0203] S144. Construct a campaign generation agent. The campaign generation agent is responsible for continuously adjusting the troops' campaign strategy based on the dynamic changes in the battlefield situation. Its implementation is as follows: to receive the round situation and the modification suggestions of the verification agent, it generates the overall campaign strategy for the current round. The campaign agent is modeled as:

[0204]

[0205] in, It is the working model of the campaign agent, which is based on the turn-based situation and verify the agent's advice as input and output turn-based battles Turn-based campaigns include: Military Objectives: This dimension focuses on clear, quantifiable military goals, such as destroying enemy forces, capturing key areas, or controlling important resources in strategic operations;

[0206] Resourcing and force allocation: This involves the efficient allocation of available resources and personnel, including decisions regarding force size, organizational structure, fire support, and logistics, to achieve strategic objectives;

[0207] Time factor: Considering time constraints and urgency, some goals need to be achieved immediately, while others allow for longer-term pursuit;

[0208] Comparative analysis of enemy and friendly forces: Analyze the relative strengths, weaknesses, capabilities, and potential vulnerabilities of friendly and enemy forces to assess the feasibility of strategic objectives and the associated risks;

[0209] S145. Construct a tactical generation agent. The function of the tactical generation agent is to receive the round battle situation and the modification suggestions of the verification agent, and generate the tactical strategy for the current round. The tactical agent is modeled as:

[0210]

[0211] in, represents a tactical agent, which uses turn-based strategies Modify the advice to input and output round tactics and strategies

[0212] Turn-based tactical strategies include: combat maneuvers, which refer to specific actions and movements on the battlefield designed to gain tactical advantage;

[0213] Fires Employment: Effectively manage and employ fire resources to support the conduct of tactical operations;

[0214] Terrain Utilization: Maximizing the advantages of the battlefield's natural topography to achieve tactical objectives;

[0215] Force coordination: Ensure seamless cooperation and coordination between various troop units and elements during tactical engagements.

[0216] The S15 comprises the following steps:

[0217] S151, construct an attempt to file a case generating intelligent agent, the function of the attempt to file a case generating intelligent agent is to receive and

[0218] Verify user input and output the attempt to file a case. The model of this agent is:

[0219]

[0220] in, It is the agent for generating the attempted case. It receives user input and outputs the JSON format of the attempted case. At the same time, if the user input information is incomplete, the model will feedback the missing information details, try to fill in the gaps, and prompt the user to verify and modify it.

[0221] S152. Construct a global event generation agent. The function of the global event generation agent is to receive the turn strategies and tactics output by the red and blue subsystems, and consider and integrate previous events to generate the latest event. The formula is:

[0222]

[0223] in, It is a global event generating agent, which receives And a list of all previous events Output Events

[0224] S153. Construct a global situation summary agent. The function of the global situation summary agent is to generate a summary evaluation of the battle situation after the game is completed. It receives a list of all game events and the intentions of both parties, and generates an ending situation. Its formula is:

[0225]

[0226] in, It is a digital model of the global situation summary agent, which attempts to file an IF, all the deduction events Input, output end state

[0227] The S3 includes the following steps:

[0228] S31. The Red and Blue subsystems generate a turn situation by attempting to file a case and deducing events. The Red and Blue subsystems use a strategic generation agent to generate a static situation based on the attempted case. If the current round is the initial round, the turn situation is the static situation. If it is not the initial round, there is an event input from the opposing force. At this time, the situation summarizer is called to generate a dynamic situation based on the event. The static situation and dynamic situation are combined into the turn situation. The process of this step is as follows:

[0229]

[0230] Among them, the strategy generation agent Generate static situation based on attempt to file case IF Situation Generation Agent Generate dynamic situation based on simulated event ME The round situation is It is a dynamic situation and static situation Under different conditions and in different combinations,

[0231] Through this step, the transition from attempting to file a case to a round situation is achieved;

[0232] S32. The Red and Blue subsystem generates a round-based campaign strategy based on the round-based situation. The Red and Blue subsystem generates the round-based campaign strategy using a campaign generation-verification agent cyclic workflow. First, the campaign generation agent takes the round-based situation as input and outputs the initial campaign strategy. The formula for this step is:

[0233]

[0234] in, The strategy generation agent starts with The round situation is input and the output is the initial round strategy C1;

[0235] In the next loop, the verification agent takes the initial campaign strategy as input and outputs modification suggestions. The initial campaign agent takes the modification suggestions and the previous campaign strategy as input and outputs the modified campaign. The verification agent again takes the modified campaign as input and outputs modification suggestions. After looping for the number of rounds set by the user, it outputs the final campaign. The formula for this stage is:

[0236]

[0237] in, The recursive formula indicates that the verification agent uses the strategy C of the previous round in the kth round of loop k-1 Input, output current modification suggestion Advice k , It means that the verification agent takes the previous round of campaign strategy and modification suggestions as input and outputs the modification strategy C for the Kth round of cycles k , when k=n, output the final round strategy

[0238] S33. The Red and Blue subsystems generate round-by-round tactical strategies based on the round-by-round campaign strategies. The Red and Blue subsystems generate round-by-round tactical strategies using a tactical generation-verification agent loop workflow. This workflow is basically the same as the previous step and is expressed as:

[0239]

[0240] V(T k-1 )→Advice k

[0241]

[0242] in, It is a tactic generating agent, which takes the round strategy as input in the initial round and outputs the initial tactic T1. In the next k cycles, it verifies the agent V(T k-1 ) Accept the k-1th modified strategy T k-1 , output modification suggestions Tactical Generation Agent T(T k-1 , Advice k ) Lm The modified strategy T in round k-1 k-1 , the kth modification suggestion Advice k , output T k , when k=m, output round strategy

[0243] S34. The White subsystem generates deduction events through turn-based tactical strategies. The White subsystem generates deduction events in the form of a global event generation-verification agent loop workflow. This workflow is basically the same as the previous step and is expressed as follows:

[0244]

[0245] V(E k-1 )→Advice k

[0246]

[0247] in, Represents the process of the global event generation agent generating the initial event. After the next k cycles, the generation-verification flow loop generates modification suggestions and modification strategies, and outputs the deduction event of this round when km

[0248] S35. After n cycles, the ending situation is generated. The events generated by one unit will enter the other unit, and the cycle of steps S31-S34 will be restarted. Each cycle will output the deduction events of that round. After n cycles set manually, all deduction events are input into the global situation summary agent, and the ending situation is output. The process formula is:

[0249]

[0250] in, Indicates the overall situation summary agent, IF is the attempt to file a case, It is an event library composed of all deduction events. To end the situation, the attempt to file a case, the event library, and the ending situation are combined to form a basic scenario.

[0251] The S4 comprises the following steps:

[0252] S41. Supplementary scenarios are constructed through replay. The principle of the replay module is to control a local variable in the attempt case and replay it to observe the impact of the variable on the overall battle situation. There are two forms of replay: user-defined replay and large-scale customized replay.

[0253] S42. Constructing Supplementary Scenarios Based on the Result Tree. When the user chooses to construct supplementary scenarios using the result tree model in this invention, the system will initiate a case based on the initially set strategic intent and re-simulate and analyze the entire operational sequence using advanced backtracking algorithms and probabilistic deduction techniques. During this process, for each key deduction event node, the system not only thoroughly records at least three different potential development outcomes, but also recursively and logically infers and expands on each possible outcome, forming a multi-level, comprehensive result deduction tree structure.

[0254] S43. Supplementary assumptions are constructed based on the form of people in the loop. The present invention designs and implements a dynamic supplementary assumption mode constructed based on the human-in-the-loop mode. The core of this system is that users deeply participate in the simulated battlefield environment according to their own strategic needs and preset camp identities, and receive multi-dimensional and all-round battlefield situation information accurately pushed by the system in real time. This information covers but is not limited to the enemy and friendly position distribution, resource reserves, and key data on environmental changes. After receiving this situation information, users can make flexible arrangements at the tactical level according to actual conditions, formulate marching routes, adjust defense layouts, and optimize troop allocation; at the same time, they can also efficiently deploy various resources at the strategic level, including but not limited to weapons and equipment, logistics supplies, and core human resources. Every decision made by the user is real-time and interactive. Once the decision instruction is issued, it will be seamlessly connected to the system and used as input parameters in the next round of simulation calculation process.

[0255] The S41 includes the following steps:

[0256] S411. Users can initiate a deep reconstruction of the intention case through customized strategies. During the enhanced scenario construction phase, users can flexibly specify one or more sets of variable parameters in the intention plan. Based on this, the system will re-run the updated intention plan to generate more detailed and rich supplementary hypothetical scenarios. In military simulation applications, users can freely set different weather conditions and troop deployment configurations to trigger the system to recalculate and analyze the results of the established strategy.

[0257] S412. By using a large language model to deeply customize and recalculate basic scenarios, the model can provide diversified variable adjustment suggestions for established intention plans and present these strategic options to users. Users have decision-making power in this process and can choose to adopt any change plan recommended by the model and make detailed manual adjustments based on these suggestions. Once the intention plan is confirmed and revised by the user, the system will re-implement the deduction process based on the updated plan, thereby generating a more comprehensive set of supplementary scenarios that meet actual needs.

[0258] See also Figure 4 The events generated by the loop are entered into the event library. After the loop ends, the global situation summary agent outputs the basic assumptions.

[0259] The Markov subchain corresponding to this step is:

[0260] IF→MX

[0261] Among them, IF represents the attempt to file a case, and MX represents the basic scenario. This step completes the process of the system generating the basic scenario based on the attempt to file a case.

[0262] Appendix 1: Complete Scenarios for This Technique and Baseline Model Generation

[0263] This technique generates complete scenarios:

[0264] Attempt to file a case

[0265] In this military scenario, titled "The Battle of Kleita Pass," a fierce clash unfolds at dawn at the strategic mountain pass in the Kleita region. Weather conditions are extremely challenging, with intermittent snowfall significantly reducing visibility and making the terrain extremely treacherous.

[0266] The Reds deployed a conventional force equipped with heavy armor and mechanized infantry. Their arsenal included T-90 main battle tanks, BMP-3 infantry fighting vehicles, Mi-28 attack helicopters for air support, AK-12 assault rifles, and 2S19 Msta-S self-propelled artillery. Their strategic goal was to control the mountain pass, severing the Blue Alliance's supply lines and fragmenting their forces. To achieve this, they planned to breach the pass using armored columns while simultaneously utilizing air superiority to suppress any resistance. They adhered to the principle of overwhelming force to rapidly breach enemy defenses.

[0267] The Blue Force responds with highly advanced and mobile infantry units equipped with exoskeleton technology and supported by cyber warfare units. Their equipment includes unmanned aerial vehicles (UAVs) for reconnaissance and precision strikes, directed energy weapons, electromagnetic pulse grenades, and stealthy vertical take-off and landing (VTOL) aircraft for rapid deployment. Their operational objective is to defend mountain passes and maintain control of vital supply routes. In executing their defensive strategy, they plan to use their familiarity with the terrain to set up ambushes, employ guerrilla tactics, including strike-and-retreat attacks, and maximize the use of intelligence gathered by drones to effectively strike enemy assets. In addition, they will use cyber warfare capabilities to disrupt the Red Force's command and control systems. The Blue Force's guiding principles are flexibility, adaptability, and the use of asymmetric tactics to offset the enemy's advantages in numbers and firepower.

[0268] Round 1

[0269] Red Army Campaign Decision

[0270] The “military objective”: to quickly control the mountain pass within the first hours of dawn, isolate the enemy forces and establish an untouchable stronghold, while ensuring that logistical routes remain open and secure for continued support.

[0271] "Resource and Force Allocation": "Deploy T-90 main battle tanks as the vanguard to break through enemy defenses in the mountain pass, followed by BMP-3 infantry fighting vehicles carrying mechanized infantry. Keep Mi-28 attack helicopters on standby to conduct reconnaissance and air fire support missions at any time. Strategically deploy 2S19 Msta-S artillery units in the rear to concentrate fire on strong enemy defenses or incoming reinforcements. Develop detailed logistical contingency plans, including equipment maintenance and supply line management in adverse weather conditions."

[0272] "Time Factor": "The operation was conducted at dawn when visibility was lowest to maximize surprise, with the goal of achieving the operational objective before the enemy could launch a significant counterattack or receive reinforcements."

[0273] Red team tactical decision

[0274] "Combat Operation": T-90 main battle tanks were deployed in a spearhead formation to break through enemy defenses at the narrowest point of the mountain pass. Mechanized infantry, equipped with BMP-3s, quickly seized and expanded the area of ​​control. Mi-28 attack helicopters were coordinated to conduct real-time reconnaissance and immediately strike enemy targets. After the breakthrough, the troops were split to block escape routes and simultaneously surround enemy positions on both sides of the mountain pass.

[0275] Firepower Deployment: Prioritize using T-90 main battle tanks for suppressive fire against fortified enemy positions. 2S19Msta-S artillery units will provide concentrated fire support based on aerial reconnaissance. Mi-28 attack helicopters will be kept on standby to rapidly respond to anti-tank threats and provide close air support to advancing armored forces.

[0276] Terrain Exploitation: Exploiting the terrain's natural bottlenecks to draw enemy forces into annihilation zones where heavy armor can decisively engage. Deploy infantry units on high ground and in bottlenecks to counter guerrilla tactics and prevent flanking maneuvers, ensuring clear logistical routes through less challenging terrain.

[0277] Force Coordination: Implement strict communications protocols to synchronize the movements and firepower of all units, integrating air-ground operations for surprise and overwhelming advantage. Enhance intelligence gathering to anticipate and neutralize the Blue Alliance's guerrilla tactics and attempts to exploit terrain for ambushes. Maintain redundancy in communications systems and ensure close coordination between combat units and logistics teams to promptly address maintenance and supply needs.

[0278] Blue side battle decision

[0279] Military objectives: Rapidly capture and control the mountain pass within the next two hours, eliminate enemy artillery positions, disrupt enemy command and control systems through immediate cyber warfare, and maintain uninterrupted control of supply routes.

[0280] Resource and Force Allocation: Continuously deploy drones for real-time reconnaissance and precision strikes against enemy strongholds, communication nodes, and critical assets. Deploy exoskeleton-enhanced infantry and cyber warfare specialists to infiltrate enemy defenses, disrupt communication networks, and establish fortified ambush points. Utilize T-90 tanks and BMP-3 infantry fighting vehicles as vanguards to aggressively penetrate enemy defenses, while Mi-28 attack helicopters provide continuous air cover and suppressive fire. Deploy artillery positions to conduct immediate counter-fire strikes and close support for ground forces, striking well-fortified enemy positions.

[0281] Time Factor: Exploiting the limited window of early dawn and deteriorating weather to execute the operation quickly, prioritizing the critical tasks of capturing mountain passes and disrupting enemy command structures before the enemy can take advantage of improved visibility or receive reinforcements.

[0282] Force Comparison: Although Red Force forces are technologically advanced and possess enhanced mobility, firepower, and intelligence capabilities, they may face an enemy with superior numbers and strong positions. Rapid, targeted strikes and asymmetric warfare can exploit the enemy's mobility, real-time intelligence, and vulnerability to cyberattacks to minimize exposure to enemy firepower and maximize disruption to its coordination.

[0283] Blue team tactical decision

[0284] Combat Maneuver: Deploy exoskeleton-enhanced infantry and cyber warfare units to infiltrate enemy lines, disrupt command networks, and pave the way for a synchronized advance led by T-90 tanks and BMP-3 infantry fighting vehicles, protected by Mi-28 attack helicopters.

[0285] Firepower Employment: Strategically employing directed energy weapons and electromagnetic pulse (EMP) grenades against fortified enemy positions, particularly artillery emplacements. Coordinate with Mi-28 attack helicopters to conduct precision suppressive fire on priority targets identified by real-time drone reconnaissance, while ensuring that artillery positions are prepared for immediate counter-fire actions and provide close support to advancing ground forces.

[0286] Terrain Exploitation: Utilize the rugged terrain to establish strong ambush points at key bottlenecks and reinforcement routes in mountain passes. Exploit intermittent snowfall and reduced visibility to enhance concealment and conduct raids from advantageous high ground and natural cover.

[0287] Force Coordination: Maintain continuous communications between all combat units, facilitate real-time intelligence sharing, and make rapid tactical adjustments based on drone feedback, emphasizing seamless coordination within the initial two-hour window when surprise is most important.

[0288] Round 2

[0289] Red Army Campaign Decision

[0290] Military Objective: Rapidly control a strategic mountain pass to sever the Blue Alliance's supply lines and isolate its forces, while defending against enemy infiltrators and neutralizing cyber warfare threats.

[0291] Resource and Force Allocation: Consider deploying armored columns led by T-90 tanks and BMP-3 infantry fighting vehicles along the axis of advance, supported by dedicated Mi-28 helicopter units for aerial surveillance, counter-infiltration measures, and close air support. Augment infantry squads with integrated drone surveillance equipment and dedicated cybersecurity teams to detect, deter, and counter Blue's asymmetric tactics and cyberattacks. Assign artillery units to standby for precision counterfire strikes against identified energy weapon sites and potential electromagnetic pulse (EMP) threats.

[0292] Time Factor: Execute the decisive phase within the first few hours of dawn, exploiting the advantage of surprise to complete control of the mountain pass before the Blue team can effectively consolidate or reinforce its defensive positions.

[0293] While the Red Force possesses significant advantages in conventional firepower and armor, the Blue Force's exoskeleton-equipped infantry and stealth capabilities present unique challenges in current weather conditions and terrain. Their use of directed energy weapons and cyber warfare requires a comprehensive strategy that combines traditional force projection with advanced surveillance, robust electronic warfare capabilities, and rapid response mechanisms to mitigate the enemy's advantages.

[0294] Red team tactical decision

[0295] Combat Maneuver: Rapidly advance armored columns led by T-90 tanks, followed by infantry units in BMP-3 infantry fighting vehicles, to immediately secure positions. Coordinate with Mi-28 helicopters to conduct aerial reconnaissance and provide precision close air support to eliminate identified enemy positions and stealth assets. Deploy infantry squads equipped with drones to conduct active surveillance and rapidly respond to infiltrators, seamlessly integrating with cybersecurity teams to detect, analyze, and mitigate cyber threats in real time.

[0296] Firepower Employment: Leverage concentrated firepower from T-90 tanks and 2S19 Msta-S self-propelled howitzers to suppress and destroy Blue's fortified positions and energy weapon emplacements. Coordinate with Mi-28 attack helicopters to conduct precision strikes against stealthy enemy assets, ensuring continuous air cover and suppression during the advance through the mountain pass. Maintain dedicated artillery reserves ready to conduct counter-battery strikes in response to detected directed energy weapons or EMP threats, and establish backup communications protocols in the event of network disruption.

[0297] Terrain Exploitation: Utilize the natural cover provided by mountainous terrain and intermittent snowfall to obscure operations and reduce enemy visibility. Strategically position artillery units on high ground within mountain passes to optimize indirect fire support, and establish hardened observation posts as part of an early warning system. Equip infantry units with cold-weather equipment and specialized mountain warfare training to effectively navigate complex environments.

[0298] Force Coordination: Implement a resilient, unified command structure that integrates all arms—armor, infantry, artillery, aviation, and cybersecurity—into a cohesive force. Enhance secure communications channels with redundancy to facilitate real-time intelligence sharing across units and specialized departments. Develop contingency plans for cyberattacks, including alternate communication methods and pre-planned responses to asymmetric tactics and cyber threats. Continuously optimize situational awareness and adjust tactics to changing battlefield conditions to maintain tactical advantage.

[0299] Blue side battle decision

[0300] Military objective: Quickly capture and maintain control of the mountain pass, eliminate enemy resistance, and destroy the Blue side's command structure to gain the operational initiative.

[0301] Resource and Force Allocation: Leverage T-90 main battle tanks for rapid breakthroughs, while BMP-3 infantry fighting vehicles immediately consolidate positions. Deploy cyber warfare forces to counter Blue's electronic warfare efforts and protect lines of communication. Coordinate artillery fire to suppress enemy strongholds, paving the way for advancing forces. Stealth vertical takeoff and landing aircraft insert exoskeleton-enhanced infantry into strategic high points to defend against ambushes. Utilize drones for persistent reconnaissance, identify priority targets, and conduct precision strikes against enemy command centers.

[0302] Time Factor: Take advantage of dawn and adverse weather conditions to quickly seize key points in the mountain pass before the Blue Force can mount an effective counterattack. Prioritize securing control of the mountain pass while establishing fortified positions and persistent surveillance to achieve long-term control.

[0303] Comparison of Enemy and Our Strength: Anticipate Blue's guerrilla tactics and ambushes, leveraging our numerical superiority in heavy armor and advanced equipment (exoskeleton infantry, directed energy weapons, stealth vertical take-off and landing aircraft) to mitigate their advantage. Cyber ​​forces will balance potential disruptions to our command networks. By exploiting environmental factors and technological advantages, we will minimize casualties and maximize combat effectiveness.

[0304] Blue team tactical decision

[0305] Combat Maneuver: At dawn, T-90 main battle tanks rapidly coordinated an advance through mountain passes, exploiting surprise and reduced visibility. Simultaneously, stealthy vertical takeoff and landing aircraft inserted exoskeleton-enhanced infantry into strategic high points, reinforced key positions, and anticipated potential ambush sites to secure supply routes and bottlenecks.

[0306] Firepower deployment: Guided by real-time drone intelligence, artillery fire is precisely deployed to suppress enemy positions and eliminate threats before the enemy can launch a counterattack. Directed energy weapons are reserved for countering Blue Force armored resistance and destroying its hardened assets, ensuring continued disruption of its combat capability.

[0307] Terrain Exploitation: Use rough terrain as a natural barrier to guide enemy forces into pre-determined kill zones, where they can concentrate the firepower of BMP-3 infantry fighting vehicles and other assets. Ensure all units are proficient in setting and evading ambushes at key points within the mountain passes.

[0308] Force Coordination: Maintaining an impenetrable communications network protected by proactive cyber warfare measures ensures seamless coordination between all unit elements. Constantly updating battlefield intelligence gathered by drones guides combat maneuvers, accurately and timely deploys firepower, and adjusts tactics to maintain control of the mountain pass. To ensure long-term control, a troop rotation and supply system is implemented, allowing for rest and resupply between engagements, thereby maintaining the endurance and effectiveness of our forces during prolonged operations.

[0309] Round 3

[0310] Red Army Campaign Decision

[0311] Resource and Force Allocation: Deploy a dedicated reconnaissance team, equipped with drone coverage, to locate and monitor stealthy enemy VTOL aircraft and infiltrators. Assign a mechanized infantry unit, closely supported by Mi-28 attack helicopters, to eliminate infiltrators and protect vital communications nodes. Organize a concentrated armored assault led by T-90 tanks and BMP-3 infantry fighting vehicles to penetrate mountain passes, with 2S19 Msta-S self-propelled howitzers providing precision suppressive fire to preemptively eliminate known and suspected ambush sites. Maintain a reserve platoon to respond to emerging directed energy weapons and electromagnetic pulse (EMP) threats.

[0312] Time Factor: Ensure full security of communications networks and eliminate all infiltrators within the first hour (07:45, Day 1). The main assault on the pass should begin no later than 08:00, Day 1, taking advantage of improved visibility at dawn to overwhelm enemy defenses.

[0313] The Blue Force relies on stealth, asymmetric tactics, and cyber warfare, while the US holds a decisive advantage in firepower, armor, and air superiority. By leveraging real-time intelligence and adapting to enemy tactics, the Blue Force's weaknesses will be identified and exploited. Given the vulnerability of communications systems and their reliance on technology, enhanced cybersecurity measures and backup plans are crucial to maintaining command and control throughout the operation.

[0314] Red team tactical decision

[0315] Firepower deployment: Conduct preemptive strikes on identified enemy positions, using 2S19 Msta-S self-propelled howitzers for precise suppressive fire, adjusting firepower based on real-time updates from reconnaissance teams. Coordinate with Mi-28 attack helicopters to provide continuous close air support and rapidly respond to any hostile activity detected on the ground.

[0316] Terrain Utilization: Take advantage of intermittent snowfall to shield advancing forces, especially when moving through areas not under direct enemy observation. Strategically locate artillery positions and observation posts on high ground within mountain passes to optimize battlefield surveillance and fire control. Prioritize passage through less difficult terrain to minimize delays caused by equipment failure or adverse conditions.

[0317] Force Coordination: Strengthen command structures and establish multiple, redundant communication channels to ensure all units maintain situational awareness and can rapidly adapt to changes. Implement protocols for real-time intelligence sharing and regular situation reporting. Develop contingency plans for responding to GPS jamming or communications outages, assigning reserve platoons to specific tasks, and immediately responding to directed energy weapons and electromagnetic pulse attacks, including manually overriding critical systems when necessary.

[0318] Blue side battle decision

[0319] Military objective: Quickly capture and firmly control the mountain pass, decisively eliminate the Blue team's exoskeleton infantry and cyber warfare forces, and ensure continuity of command and control by maintaining control of communication nodes.

[0320] Resource and Force Allocation: Command a synchronized advance of T-90 tanks and BMP-3 infantry fighting vehicles, capitalizing on improved visibility and providing precision artillery support with the 2S19 Msta-S system. Deploy mechanized infantry equipped with exoskeletons to reinforce captured positions and conduct aggressive sweeps to eliminate infiltrator threats. Utilize stealthy vertical takeoff and landing aircraft to deploy rapid reaction teams deep into enemy territory, providing protection from directed energy weapons (DEW) and electromagnetic pulse (EMP), and systematically disrupting the Blue command structure. Coordinate Mi-28 helicopters to provide sustained close air support, while synchronizing drone reconnaissance to obtain real-time intelligence and conduct precision strikes against high-priority enemy assets.

[0321] Time Factor: Quickly accomplish initial objectives (capture mountain passes, protect communications nodes) within the first day while weakening enemy resistance; gradually advance from day 1 to day 2, pursuing comprehensive area control.

[0322] Power Balance: Given the Red's advantages in firepower, mobility, and advanced technology, including drones and DEW / EMP countermeasures, these advantages should be used to offset the Blue's familiarity with the terrain and potential reliance on guerrilla tactics. Preemptive capture of control points, intelligence-driven maneuver, and disruption of the Blue's command network will mitigate its potential numerical superiority and asymmetric threat.

[0323] Blue team tactical decision

[0324] Firepower Deployment: Utilizing the 2S19 Msta-S self-propelled howitzer system, in coordination with advancing armored forces, precision strikes will be conducted against identified Blue Force positions and exoskeleton infantry concentrations. Directed energy weapons carried by rapid reaction teams will be actively employed to disable critical enemy assets, while electromagnetic pulse (EMP) grenades will be tactically deployed to disrupt Blue Force electronic systems and communications nodes.

[0325] Terrain Exploitation: Strategically deploying forces on high ground to provide indirect fire support and guide Blue forces into predetermined kill zones where Red combined forces can be most effective. Optimizing drone flight paths to proactively identify Blue ambush sites and guerrilla tactics along high ground and valleys, and feeding real-time intelligence back to coordinating forces.

[0326] Force Coordination: Integrate cyber warfare tactics into synchronized maneuvers to proactively jam or hijack Blue's communications networks, further disrupting command and control. Continuously update artillery fire missions and close air support from Mi-28 helicopters based on real-time drone intelligence to ensure a rapid and decisive response to evolving battlefield conditions. Facilitate seamless communications between all Red units, implementing a unified and flexible tactical approach that leverages technological advantages and offsets Blue's familiarity with the terrain.

[0327] Event List

[0328] Round 1

[0329] "Day 1, 05:30, strategically important mountain passes in the Kleita region - intermittent snowfall and reduced visibility,

[0330] Red Force: T-90 main battle tanks, under the cover of dawn and low visibility, began to advance through the narrowest passages. BMP-3 infantry fighting vehicles followed closely behind, occupying positions behind the tanks. Mi-28 attack helicopters were in the air, providing real-time reconnaissance and suppressive fire on enemy positions. Artillery units remained on standby at designated strategic locations, ready to provide concentrated fire support if needed. Red Force forces coordinated their operations across complex terrain, established defensive positions to prevent potential guerrilla counterattacks, and strictly adhered to communications protocols to ensure synchronized operations and rapid response to dynamic combat scenarios.

[0331] "Day 1, 06:45, strategically important mountain passes in the Kleita region - continued intermittent snowfall, visibility remains significantly reduced;

[0332] Blue Force: Exoskeleton-equipped infantry units rapidly infiltrated Red Force defenses, aiming to disrupt communications networks and sow chaos. Simultaneously, cyber warfare operators launched complex attacks targeting Red Force command infrastructure. Stealthy vertical take-off and landing aircraft (VTOL) deployed troops to tactically advantageous positions, setting up ambush points along possible reinforcement routes. Real-time intelligence from unmanned aerial vehicles (UAVs) enabled the precise targeting of Red Force positions with directed energy weapons and electromagnetic pulse (EMP) grenades. Blue Force artillery units remained poised to conduct counterattacks and support advancing friendly forces. Over the next two hours, Blue Force meticulously coordinated its asymmetric attack, exploiting challenging environmental conditions to neutralize Red Force's numerical superiority and heavy armor, while maintaining secure and efficient communications to exploit the evolving battle dynamics.

[0333] Round 2

[0334] "Day 1, 08:00, strategically important mountain passes in the Kleita region - continued intermittent snowfall, visibility remains low.

[0335] Red Force: As visibility improved, T-90 main battle tanks and BMP-3 infantry fighting vehicles advanced through the mountain pass, launching an armored push. This push was closely coordinated with precision suppressive fire from 2S19 Msta-S artillery units, striking identified and suspected enemy positions. After completely eliminating infiltrators, mechanized infantry units secured and consolidated the area around the communications node, forming a protective barrier. Mi-28 attack helicopters maintained aerial vigilance, ready to provide air support for any enemy activity. Reconnaissance teams, working in conjunction with unmanned aerial vehicles (UAVs), continuously fed real-time intelligence to the command center, ensuring rapid tactical adaptation. Rapid reaction platoons remained alert, ready to respond to any emerging directed energy weapon (DEW) or electromagnetic pulse (EMP) threats, thereby strengthening the resilience of the Red Force's command and control system throughout the operation.

[0336] "Day 1, 10:00, strategically important mountain pass in the Kleita region - intermittent snowfall

[0337] Blue Force: Responding to the Red Force's armored advance and artillery assault, exoskeleton-equipped infantry conducted precision strikes against Red Force communications nodes. Simultaneously, cyber warfare units escalated their electronic jamming operations. Stealth vertical take-off and landing (VTOL) aircraft rapidly deployed small strike teams to strategic locations, ambushing advancing Red Force mechanized infantry and T-90 tanks. Real-time intelligence from unmanned aerial vehicles (UAVs) guided the precise use of directed energy weapons and electromagnetic pulse (EMP) grenades against high-value targets, aiming to degrade Red Force air support and command capabilities. Blue Force artillery batteries maintained a defensive posture, ready to launch counterattacks against Red Force offensives. All actions were coordinated through secure and reliable communications channels.

[0338] Round 3

[0339] "Day 1, 12:00, intermittent snowfall continues at the strategically important mountain pass in the Kleta region.

[0340] Red Force: After rapidly regrouping and redeploying its forces, mechanized infantry and armored columns, supported by air support from Mi-28 attack helicopters, advanced to seize strategic high ground and control key bottlenecks. Simultaneously, special forces penetrated deep behind Blue Force lines to locate and disrupt stealth vertical takeoff and landing (VTOL) deployment sites. Electronic warfare (EW) teams immediately launched precision operations against Blue Force cyber assets within the first hour of regrouping. Simultaneously, Red Force artillery units, such as the 2S19 Msta-S, began conducting suppressive fire missions against targeted directed energy weapon (DEW) assets and enemy artillery positions. In a coordinated advance, Red Force infantry and armored units, supported by precise close air support, systematically eliminated Blue Force exoskeleton infantry. Taking advantage of adverse weather conditions and treacherous terrain, Red Force consolidated its defenses at key mountain passes, drawing enemy forces into pre-defined kill zones. Additionally, reserve infantry reinforcements were deployed to key areas as part of a comprehensive defense plan and impending counterattack, while secure communications networks were maintained to ensure seamless coordination between artillery, air, and ground forces.”

[0341] "Day 1, 14:30, strategically important mountain pass in the Kleta region - intermittent snowfall

[0342] Blue Force: In rapid action, exoskeleton-equipped infantry and special forces quickly seized and fortified positions abandoned by the Red Force due to the shifting battle lines. They effectively targeted Red Force communications infrastructure and VTOL deployment points through surgical strikes. Simultaneously, cyber warfare units continued to degrade Red Force's C4ISR systems. Following a three-hour strategic timeline, Blue Force coordinated precision artillery and air strikes targeting targeted directed energy weapon (DEW) assets and enemy artillery positions, successfully neutralizing long-range threats. Taking advantage of the rugged terrain and intermittent snowfall, which reduced visibility, they set ambushes along anticipated reinforcement routes, targeting Red Force armored columns and mechanized infantry. As part of a multi-layered defensive strategy, reserve infantry reinforcements reinforced critical bottlenecks while maintaining secure and jam-resistant communications networks to facilitate real-time intelligence sharing and rapid response to the evolving battle situation.

[0343] Complete scenario generated by ChatGPT3.5

[0344] Scene Setting

[0345] At dawn, the clash between the Iron Vanguard and the Azure Alliance in the Cleta region unfolded, showcasing two distinct military strategies and tactics. The Iron Vanguard, with its heavily armored columns and superior firepower, attempted to rapidly breach the mountain pass, severing the Azure Alliance's supply lines and fragmenting its defenses. However, the complex terrain of the Cleta region and the Azure Alliance's precise reflexes presented a formidable challenge to the Iron Vanguard.

[0346] The Kleta region is nestled in a rugged mountain range, with narrow valleys and dense forests. This terrain offers excellent defensive advantages, but also limits the mobility of large armored vehicles. The weather conditions are harsh, with intermittent snowfall reducing visibility and adding uncertainty and danger to the battlefield.

[0347] Iron Vanguard Tactics and Challenges

[0348] The Iron Vanguard planned to rapidly breach the mountain pass with a main armored column, utilizing air support to strike at the Azure Alliance's defenses. They relied on the heavy armor and mobility of T-90 main battle tanks and BMP-3 infantry fighting vehicles, while Mi-28 attack helicopters provided aerial fire support. Their tactical goal was to rapidly destroy the enemy's defenses with overwhelming firepower and numerical superiority, fragmenting the Azure Alliance forces and securing strategic locations.

[0349] However, faced with complex terrain and the highly mobile response of the Azure Alliance, the Iron Vanguard faced multiple challenges:

[0350] Terrain Restrictions: Narrow valleys and dense forests limit the mobility and firepower deployment of armored units, making them easy targets for the Azure Alliance's guerrilla tactics.

[0351] Weather impact: Snowfall reduces visibility, increasing the risks and difficulties for armored forces in mountain operations, especially for helicopter flight and fire support.

[0352] The Azure Alliance used its drones for real-time reconnaissance and exoskeleton-equipped infantry squads to conduct guerrilla warfare. They used directed energy weapons and electromagnetic pulse grenades to disrupt the Iron Vanguard's communications and electronic equipment, effectively weakening its command and combat effectiveness.

[0353] Azure Alliance Counterattack and Strategy

[0354] The tactics of the Azure Alliance focus on flexibility and technological advancement:

[0355] Defensive Strategy: Utilizing terrain advantages to establish ambush points and defensive positions, relying on highly mobile infantry squads to conduct guerrilla warfare. They gather intelligence through drones and utilize cyber warfare capabilities to disrupt enemy command systems and communication links.

[0356] Precision strikes: Through directed energy weapons and precision strikes, focus on destroying the Iron Vanguard Army’s key assets, such as main battle tanks and command centers, effectively weakening its combat capabilities.

[0357] Tactical Retreat: Utilizing the mobility of high-tech infantry, the Azure Alliance launches short, effective attacks and quickly withdraws, avoiding prolonged direct confrontations with armored forces.

[0358] in conclusion

[0359] The battles in the Clerta region reflected the integration of technology and tactics in modern warfare, as well as the profound impact of terrain and weather conditions on battlefield dynamics. The confrontation between the Iron Vanguard and the Azure Alliance showcased the contrast between traditional armored forces and high-tech mobile forces, with each side seeking strategic advantage by exploiting its own strengths and the opponent's weaknesses.

[0360] Complete assumptions of iFlytek Spark generation

[0361] Military Scenario: Battle of the Kleita Pass

[0362] 1. Background

[0363] In the fictional region of Kleita, two opposing armies—the Iron Vanguard and the Azure Alliance—are locked in a fierce battle for control of a strategic terrain. The Iron Vanguard, led by General Orlov, boasts powerful armored units and highly mechanized infantry, while the Azure Alliance is known for its agile and technologically advanced combat methods, including highly mobile infantry, drone reconnaissance and precision strikes, and exoskeleton-equipped infantry squads. Each side hopes to secure victory in this battle, control the mountain pass, and sever the other's supply lines.

[0364] 2. War Objectives and Plans

[0365] Iron Vanguard: Control the mountain pass, severing the Azure Alliance's supply lines and dividing their forces. Plan to use armored columns to breach the pass, while utilizing air support to suppress any resistance from above. The principle of warfare is to rapidly break through enemy lines with overwhelming force.

[0366] Azure Alliance: Defend mountain passes and maintain control of supply routes. Knowledge of the terrain will be used to establish ambush points and defensive positions. Rely on drones to gather intelligence and conduct precision strikes against key enemy assets, such as command centers and tank battalions. Tactics will include guerrilla warfare, utilizing the high-tech mobility of infantry to conduct strike-and-retreat attacks, and utilizing cyber warfare capabilities to disable and confuse the enemy's command system. Principles of warfare emphasize flexibility, adaptability, and asymmetric warfare to offset the Iron Vanguard's numerical superiority and superior firepower.

[0367] Battle process

[0368] At dawn, both sides began deploying their forces. The Iron Vanguard, leveraging its armor and air support, attempted to quickly break through the Azure Alliance's defenses. The Azure Alliance, relying on the terrain and drones, set up ambush points and defensive positions, luring the Iron Vanguard into a trap.

[0369] During the battle, both sides suffered losses, but the Iron Vanguard maintained their advantage. They successfully broke through part of the Azure Alliance's defenses, but found that the Azure Alliance had set up a powerful encirclement.

[0370] Faced with the Azure Alliance's encirclement, the Iron Vanguard Army was in a difficult situation. They attempted to break out, but were met with fierce resistance from the Azure Alliance. Ultimately, the Iron Vanguard Army was forced to retreat, and the two sides engaged in a fierce pursuit battle near the mountain pass.

[0371] After a day of fighting, both sides suffered heavy casualties. Although the Iron Vanguard Army failed to completely occupy the mountain pass, it successfully stopped the Azure Alliance's attack; the Azure Alliance suffered heavy losses during the retreat, but successfully saved the mountain pass and supply line.

[0372] Compared with related technologies, the multi-agent strategic scenario generation system based on a large language model provided by the present invention has the following beneficial effects:

[0373] The present invention provides a multi-agent strategic scenario generation system based on a large language model. It designs a game subsystem based on the vertical collaborative relationship of multi-level agents in strategy, campaign and tactics. Through the multi-level game subsystem, the task granularity of the scenario designed by the large language model is refined, effectively improving the quality of the scenario design.

[0374] Using a generative-verification adversarial collaborative agent framework, compared to the traditional single large model, this agent framework can promptly correct errors and inconsistencies in the generated agent's content creation, enhance logical consistency, effectively improve the output quality of the large language model, and enhance the overall deduction assumption level.

[0375] It demonstrates more stable robustness and uses a multi-agent red-blue confrontation framework to simulate the system and create military scenarios. Compared with the direct game and generation of a single intelligent model, it considers multiple variables and uncertain factors, making the simulation closer to the actual battlefield situation.

[0376] By using a more comprehensive feedback mechanism, employing replays, outcome trees, and humans in the loop, we can deeply explore the potential outcomes of battlefield simulations, the possible effectiveness of each decision, and the impact of different factors on simulation results, thereby achieving closed-loop feedback that effectively guides users to improve future action plans.

[0377] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any effective structural or effective process transformation made using the contents of the present invention's description and drawings, or any direct or indirect application in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-agent strategic scenario generation system based on a large language model, characterized by: The following steps are involved: S1. Modeling scenarios and systems requires systematic modeling of attempt case formation, situation simulation, events and scenarios, and strategic-operational-tactical-level red and blue subsystems and white subsystems. S2. Initialize the red and blue team's intention filing. When the user uses the system, he is prompted to enter relevant information about the military deduction. After entering the information as required, the system uses the global intention filing to generate an agent, receives the user input information, and generates an intention filing in JSON format. The Markov subchain corresponding to this step is: Input→IF→MX→AX Among them, Input represents user input, IF represents attempt filing, MX represents basic military scenario, and AX represents supplementary scenario. This step completes the transformation from user input to attempt filing in the system. After the attempt filing is generated, the Python JSON toolkit is used to segment the attempt filing to obtain the attempt filing from different perspectives of the red and blue forces, which is used to generate the basic scenario in the next stage. S3. Build a basic scenario based on the attempt case, using a cyclical workflow of event-situation simulation. First, each red and blue subsystem converts its own attempt case into a turn-based situation. Then, the automated confrontation generation workflow is looped. In this loop, one side's forces convert the received simulation events into a dynamic situation. The turn-based situation, which is a combination of the static and dynamic situations, is converted into a turn-based strategy. The turn-based strategy is further converted into turn-based tactics. The turn-based tactics are converted into simulation events and transmitted to the opposing side. This loop continues. After n cycles set by the user, the confrontation workflow ends. S4. Supplementary scenarios are constructed based on basic scenarios. Supplementary scenarios are improvements to basic scenarios. Predictions are made for different scenarios in actual combat and pre-selected options are given. Supplementary scenarios are constructed through three forms: re-simulation, result tree, and man-in-the-loop. Finally, the attempt to file a case, basic scenario, and supplementary scenario are combined to output a complete simulation scenario.

2. The multi-agent strategic scenario generation system based on a large language model according to claim 1, characterized in that: Said S1 comprises the following steps: S11. Modeling the attempt case. The attempt case is a pre-set setting before military scenario simulation. It includes the strategic layout with the country as the main body and the detailed description of the initial battle scene of the local war. The attempt case is modeled as a tuple including the strategic background and the battle description. Its mathematical model is: IF=(SC,CD) Among them, IF stands for attempt to establish a case, which includes an introduction to the strategic background SC and a focused campaign description CD; S12. Modeling the deductive situation. The deductive situation refers to the overall situation derived from the analysis and integration of the battle situation. It is an abstract concept extracted from specific integration. To meet the needs of multi-faceted and complex situation analysis, the situation is divided into static situation, dynamic situation, and turn-based situation, and modeled separately. S13. Modeling of simulation events and basic scenarios. Designing military scenarios with an event-driven structure, characterized by driving simulation progress based on key events in the battlefield or strategic environment. S14. Construct a Red-Blue subsystem based on a multi-level agent vertical collaboration relationship between strategy, campaign, and tactics. This subsystem is responsible for playing the roles of the Red and Blue teams, conducting battle deduction and game from the perspective of one side. It includes: a verification agent, a situation summary agent, a strategy generation agent, a campaign generation agent, and a tactical generation agent. S15. Construct the white side subsystem, which is responsible for receiving global information and implementing the corresponding functional modules from a global perspective. The subsystem includes an attempt to file a case agent and a global event generation and verification agent.

3. The multi-agent strategic scenario generation system based on a large language model according to claim 2, characterized in that: The S11 includes the following steps: S111. Modeling the strategic context. In war game scenarios, the strategic context aims to describe the macro-environmental conditions and strategic situational basis for a campaign or conflict. This includes a description of the regional security environment, an analysis of the causes of war, and speculation about adversary intentions. The strategic context is modeled as follows: SC=(RS,CC,AS) Among them, SC is the strategic background, RS is the description of the regional security environment, CC is the analysis of the causes of war, and AS is the opponent's strategy; S112. Modeling the battle description. The battle description is a summary of battle-level information. It requires a detailed description of the environmental information of the battle to be held in the hypothetical scenario, the military armaments of both sides, and the military plans. It is modeled as follows: CD=(MA,EI,MP) Among them, CD represents campaign description, MA represents military armament, EI represents environmental information, and MP represents military plan. Military armament describes the basic military strength of a party during the military scenario process. The formula is: MA=(MA branch , MA force , MA equipment ) Among them, MA branch Indicates military type, MA force Indicates the armed forces in the region, MA equipment Indicates the equipment resource information of the party; Environmental information (EI) describes the objective environment in which the military scenario is located. Its formula is: NO=(NO time ,NO zone ,NO weather ) Among them, EI time Indicates the assumed time, EI zone Indicates the assumed area, EI weather Indicates weather composition; After modeling the mission scenario, it is necessary to model the simulated situations and events in the military scenario.

4. The multi-agent strategic scenario generation system based on a large language model according to claim 2, characterized in that: The S12 includes the following steps: S121. Model the static situation. The static situation is a summary of the information of both sides before the battle is simulated. It does not change as the battle progresses. The mathematical definition of the static situation is: MS static =(MA,EI,MP) Among them, MS static Represents the static situation, which is an abstract summary extracted from the military armament MA, environmental information EI and military plan MP in the attempt to file an IF; S122. Model the dynamic situation. The dynamic situation is defined as a comprehensive evaluation of the current scenario based on the changing battle situation during the simulation, including time, dynamic changes in military power, and basic conditions. The dynamic situation is time-sensitive and variable. The definition of the dynamic situation is: MS dynamic =(E Time ,M equipment-d ,S dynamic ) Among them, MS dynamic Indicates dynamic situation, E Time Indicates the current time, M equipment-d Indicates the current round of military deployment, S dynamic Indicates the basic situation of the current battle; S123. Model the round situation. The round situation is used to describe the overall battle situation in a round. The formula is: Among them, MS Round Indicates the turn situation, MS static Indicates static situation, MS dynamic represents the dynamic situation, n represents the current round number, and in the initialization stage, n = 0, the round situation is the static situation. As the scenario generation begins, the round situation becomes a combination of the static situation and the dynamic situation; S124. Model the ending situation. The ending situation is an assessment of the overall battle situation at the end of the military scenario, the degree of completion of both sides' strategic objectives, and the loss of opposing forces. The ending situation is defined as: MS end =(SO,OC,FL) Among them, MS end Indicates the end situation, SO indicates the overall battle situation, OC indicates the completion of strategic goals, and FL indicates the loss of opposing forces.

5. The multi-agent strategic scenario generation system based on a large language model according to claim 2, characterized in that: The S14 includes the following steps: S131. Modeling of deduction events. Deduction events are the basic units of basic assumptions, describing a certain action of a certain party and the changes in the subject and environment caused by the action. The basic assumption is defined as: ME=(ME entity ,ME action ,ME task ,ME interaction ) Among them, ME is the deduction event, which is composed of entity ME entity , Action ME action , Mission ME task and interactive ME interaction composition; S132. Model the basic scenario. A basic scenario is an imagined or pre-defined war or conflict scenario. It is generated by the set of all simulated events and the situation at the end of the simulation. Its mathematical definition is: MX represents the basic scenario, which includes the attempt to file a case IF, a set of events in n rounds And end situation MS end .

6. The multi-agent strategic scenario generation system based on a large language model according to claim 2, characterized in that: The S14 includes the following steps: S141. Construct a verification agent model, using a generation-verification adversarial collaboration model. The generation agent is responsible for generating an initial solution, and the verification agent provides feedback on the initial solution and passes it to the generation agent for regeneration. When the number of modifications exceeds a certain number, the verification solution is output. The verification agent is defined as receiving input from the large model and outputting corresponding modification suggestions. The mathematical model is: V({Input}) llm →Advice Among them, V({Input}) llm It is the model of the verification agent. The model input {Input} changes according to the usage scenario of the verification agent, and the corresponding prompt words will also change. Advice represents the modification suggestions for the verification agent output, including: whether the model output maintains temporal and spatial consistency; whether the model output conforms to the overall logic; and whether the grammar, sentence structure and wording of the model output are appropriate. S142. Construct a strategy generation agent. The function of the strategy generation agent is to make an evaluative summary based on the intention case received by the troops and generate the army's macro-strategy. The mathematical model is: Z(IF) llm →MS static Among them, Z(IF) llm represents the strategy generation agent, which takes the attempt to file a case IF as input and outputs a static situation; S143. Construct a situation summary agent. The function of the situation summary agent is to follow the continuous progress of the deduction events and dynamically summarize the battlefield. It takes the deduction events as input and outputs the dynamic situation. The mathematical model is: T(ME) llm →MS dynamic Among them, T(ME) llm is the situation summary agent, ME is the deduction event, MS dynamic It is a dynamic situation; S144. Construct a campaign generation agent. The campaign generation agent is responsible for continuously adjusting the troops' campaign strategy based on the dynamic changes in the battlefield situation. Its implementation is as follows: to receive the round situation and the modification suggestions of the verification agent, it generates the overall campaign strategy for the current round. The campaign agent is modeled as: C(MS round ,Advice) llm →C round Among them, C(MS round ,Ans) llm It is the working model of the campaign agent, which uses the turn-based MS round and verification agent suggestion Advice as input, output round battle C round , turn-based campaigns include: Military objectives: This dimension focuses on clear, quantifiable military objectives, such as destroying enemy forces, occupying key areas, or controlling important resources in strategic operations; Resourcing and force allocation: This involves the efficient allocation of available resources and personnel, including decisions regarding force size, organizational structure, fire support, and logistics, to achieve strategic objectives; Time factor: Considering time constraints and urgency, some goals need to be achieved immediately, while others allow for longer-term pursuit; Comparative analysis of enemy and friendly forces: Analyze the relative strengths, weaknesses, capabilities, and potential vulnerabilities of friendly and enemy forces to assess the feasibility of strategic objectives and the associated risks; S145. Construct a tactical generation agent. The function of the tactical generation agent is to receive the round battle situation and the modification suggestions of the verification agent, and generate the tactical strategy for the current round. The tactical agent is modeled as: T(C round ,Advice) llm →T round Among them, T(C round ,Advice) llm represents a tactical agent, which uses a turn-based strategy C round , modify the advice to input and output round tactical strategy T round ; Turn-based tactical strategies include: combat maneuvers, which refer to actions and movements performed on the battlefield to achieve tactical advantage; Fires Employment: Effectively manage and employ fire resources to support the conduct of tactical operations; Terrain Utilization: Maximizing the advantages of the battlefield's natural topography to achieve tactical objectives; Force coordination: Ensure seamless cooperation and coordination between various troop units and elements during tactical engagements.

7. The multi-agent strategic scenario generation system based on a large language model according to claim 2, characterized in that: The S15 comprises the following steps: S151. Construct an agent for generating an attempt to file a case. The function of the agent is to receive and verify user input and output an attempt to file a case. The model of the agent is: I({Input}) llm →IF Among them, I({Input}) llm It is the agent for generating the attempted case. It receives user input and outputs the JSON format of the attempted case. At the same time, if the user input information is incomplete, the model will feedback the missing information details, try to fill in the gaps, and prompt the user to verify and modify it. S152. Construct a global event generation agent. The function of the global event generation agent is to receive the turn strategies and tactics output by the red and blue subsystems, and consider and integrate previous events to generate the latest event. The formula is: E(C round ,T round ,{ME1,ME2…ME n-1 }) llm →ME n Among them, E(C round ,T round ,{ME1,ME2…ME n-1 }) llm It is a global event generating agent, which receives C in the nth round round ,T round And the list of all previous events {ME1, ME2…ME n-1 }, output event ME n ; S153. Construct a global situation summary agent. The function of the global situation summary agent is to generate a summary evaluation of the battle situation after the game is completed. It receives a list of all game events and the intentions of both parties, and generates an ending situation. Its formula is: in, It is a digital model of the global situation summary agent, which attempts to file an IF, all the deduction events Input, output end state MS end .

8. The multi-agent strategic scenario generation system based on a large language model according to claim 1, characterized in that: The S3 includes the following steps: S31. The Red and Blue subsystems generate a turn situation by attempting to file a case and deducing events. The Red and Blue subsystems use a strategic generation agent to generate a static situation based on the attempted case. If the current round is the initial round, the turn situation is the static situation. If it is not the initial round, there is an event input from the opposing force. At this time, the situation summarizer is called to generate a dynamic situation based on the event. The static situation and dynamic situation are combined into the turn situation. The process of this step is as follows: Z(IF) llm →MS static T(ME) llm →MS dynamic Among them, the strategy generation agent Z(IF) llm Generate static situation MS based on attempt to file case IF static , situation generation agent T(ME) llm Generate dynamic situation MS based on deduction event ME dynamic , the turn situation is MS round , it is a dynamic situation MS dynamic and static situation MS static Under different conditions and different combinations, Through this step, the transition from attempting to file a case to a round situation is achieved; S32. The Red and Blue subsystem generates a round-based campaign strategy based on the round-based situation. The Red and Blue subsystem generates the round-based campaign strategy using a campaign generation-verification agent cyclic workflow. First, the campaign generation agent takes the round-based situation as input and outputs the initial campaign strategy. The formula for this step is: C(MS round ) llm →C1 Among them, C(MS round ) llm The strategy generating agent starts with MS round The round situation is input and the output is the initial round strategy C1; In the next loop, the verification agent takes the initial campaign strategy as input and outputs modification suggestions. The initial campaign agent takes the modification suggestions and the previous campaign strategy as input and outputs the modified campaign. The verification agent again takes the modified campaign as input and outputs modification suggestions. After looping for the number of rounds set by the user, it outputs the final campaign. The formula for this stage is: V(C k-1 ) llm →Advice k C(C k-1 ,Advice m ) llm →C k →C round (k=m) Among them, V(C k-1 ) llm →Advice k The recursive formula indicates that the verification agent uses the strategy C of the previous round in the kth round of loop k-1 Input, output current modification suggestion Advice k ,C(C k-1 ,Advice k ) llm It means that the verification agent takes the previous round of campaign strategy and modification suggestions as input and outputs the modification strategy C for the Kth round of cycles k , when k=m, output the final round strategy C round ; S33. The Red and Blue subsystems generate round-by-round tactical strategies based on the round-by-round campaign strategies. The Red and Blue subsystems generate round-by-round tactical strategies using a tactical generation-verification agent loop workflow. This workflow is consistent with the previous step and is expressed as: T(C round ) llm →T1 V(T k-1 )→Advice k T(T k-1 ,Advice k ) llm →T k →T round (k=m) Among them, T(C round ) llm It is a tactic generating agent, which takes the round strategy as input in the initial round and outputs the initial tactic T1. In the next k cycles, it verifies the agent V(T k-1 ) Accept the k-1th modified strategy T k-1 , output modification suggestions Advice k , tactical generation agent T(T k-1 ,Advice k ) llm The modified strategy T in round k-1 k-1 , the kth modification suggestion Advice k , output T k , when k=m, output round strategy T round ; S34. The White subsystem generates deduction events through turn-based tactical strategies. The White subsystem generates deduction events in the form of a global event generation-verification agent loop workflow. This workflow is consistent with the previous step and is expressed as follows: E(T round ) llm →ME1 V(E k-1 )→Advice k E(E k-1 ,Advice k ) llm →ME k →ME round (k=m) Among them, E(T round ) llm →ME1 represents the process of the global event generation agent generating the initial event. After the next k cycles, the generation-verification flow loop generates modification suggestions and modification strategies, and outputs the round deduction event ME when k=m round ; S35. After n cycles, the ending situation is generated. The events generated by one unit will enter the other unit, and the cycle of steps S31-S34 will be restarted. Each cycle will output the deduction events of that round. After n cycles set manually, all deduction events are input into the global situation summary agent, and the ending situation is output. The process formula is: in, Indicates the overall situation summary agent, IF is the attempt to file a case, It is the event library composed of all deduction events, MS end To end the situation, the attempt to file a case, the event library, and the ending situation are combined to form a basic scenario.

9. The multi-agent strategic scenario generation system based on a large language model according to claim 1, characterized in that: The S4 comprises the following steps: S41. Supplementary scenarios are constructed through replay. The principle of the replay module is to control a local variable in the attempt case and replay it to observe the impact of the variable on the overall battle situation. There are two forms of replay: user-defined replay and large-scale customized replay. S42. Build supplementary scenarios based on the outcome tree. After the user chooses to use the outcome tree model to build supplementary scenarios, the system will initiate a case based on the initially set strategic intent and re-simulate and analyze the entire operational sequence using advanced backtracking algorithms and probabilistic deduction techniques. During this process, for each key deduction event node, the system not only comprehensively records at least three different potential development outcomes, but also recursively and logically infers and derives each possible outcome, forming a multi-level, comprehensive outcome deduction tree structure. S43. Supplementary scenarios are constructed based on the form of human-in-the-loop. A dynamic supplementary scenario model based on the human-in-the-loop model is designed and implemented. The core of this system is that users deeply participate in the simulated battlefield environment according to their own strategic needs and preset camp identities, and receive multi-dimensional and all-round battlefield situation information accurately pushed by the system in real time. This information covers the key data of enemy and friendly position distribution, resource reserves, and environmental changes. After receiving this situation information, users can make flexible arrangements at the tactical level according to actual conditions, formulate marching routes, adjust defense layouts, and optimize troop allocation; at the same time, they can also efficiently deploy various resources at the strategic level, including weapons and equipment, logistics supplies, and core human resources. Every decision made by the user is real-time and interactive. Once the decision instruction is issued, it will be seamlessly connected to the system and used as input parameters in the next round of simulation calculations.

10. The multi-agent strategic scenario generation system based on a large language model according to claim 9, characterized in that: The S41 includes the following steps: S411. Users can initiate a deep reconstruction of the intention case through customized strategies. During the enhanced scenario construction phase, users can flexibly specify one or more sets of variable parameters in the intention plan. Based on this, the system will re-run the updated intention plan to generate more detailed and rich supplementary hypothetical scenarios. In military simulation applications, users can freely set different weather conditions and troop deployment configurations to trigger the system to recalculate and analyze the results of the established strategy. S412. By using a large language model to deeply customize and recalculate basic scenarios, the model can provide diversified variable adjustment suggestions for established intention plans and present these strategic options to users. Users have decision-making power in this process and can choose to adopt any change plan recommended by the model and make detailed manual adjustments based on these suggestions. Once the intention plan is confirmed and revised by the user, the system will re-implement the deduction process based on the updated plan, thereby generating a more comprehensive set of supplementary scenarios that meet actual needs.

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