A method and device for constructing a flight equipment simulation environment

Abstract

The application discloses a kind of flight equipment simulation environment construction method and device, the device includes: scenario editing subsystem, force simulation subsystem, operation control subsystem, environment simulation subsystem and simulation resource subsystem;The scenario editing subsystem is used to obtain the scenario information input by user, provides outfield scenario editing platform for user, generates the scenario file of specified format according to the scenario information;The force simulation subsystem is used to analyze the scenario file, obtains force simulation information;The environment simulation subsystem is used to analyze the scenario file, obtains environment simulation information.

Description

Technical Field

[0001] This invention relates to the fields of industrial data processing, virtual reality simulation, and strategy optimization, and specifically to a method and apparatus for constructing a flight equipment simulation environment. Background Technology

[0002] Digital modeling of real flight environments provides pilots and operators with highly realistic training scenarios, significantly improving their operational skills and proficiency. It also offers an efficient verification platform for equipment demonstration, ensuring the effectiveness and applicability of new equipment in real-world environments. Furthermore, simulation environments provide safe and controllable experimental conditions for testing and innovation. By simulating complex environmental situations and multi-scenario confrontations, it is possible to continuously optimize action design and enhance overall mission performance capabilities.

[0003] The maritime flight environment is dynamic and ever-changing, involving many factors and variables. How to conduct accurate and comprehensive simulation modeling of the maritime flight environment and equipment is a problem that needs to be solved. Summary of the Invention

[0004] This invention primarily addresses the problem of how to accurately and comprehensively simulate and model the maritime flight environment and equipment. This invention discloses a method and apparatus for constructing a flight equipment simulation environment.

[0005] In a first aspect, the present invention discloses a flight equipment simulation environment construction device, comprising: a scenario editing subsystem, a force simulation subsystem, an operation control subsystem, an environment simulation subsystem, and a simulation resource subsystem;

[0006] The scenario editing subsystem is used to acquire scenario information input by the user, provide the user with a field scenario editing platform, support the user in constructing adversarial scenarios on a two-dimensional map, generate scenario files in a specified format based on the scenario information, and send the scenario files to the force simulation subsystem and the environment simulation subsystem. The scenario information includes the scenario area, virtual target configuration information and their action plan information. The scenario file includes force simulation information and environment simulation information. The environment simulation information records the priority, computational bandwidth consumption, simulation type and simulation task processing function of all environmental simulation elements in each simulation scenario.

[0007] The force simulation subsystem is connected to the scenario editing subsystem and the operation control subsystem. It is used to parse the scenario file to obtain force simulation information; based on the force simulation information, it drives the simulation model library to perform digital modeling and simulation of each force simulation unit, generates force simulation files, and sends the force simulation files to the operation control subsystem.

[0008] The environment simulation subsystem is connected to the scenario editing subsystem and the operation control subsystem. It is used to parse the scenario file to obtain environment simulation information; based on the environment simulation information, it drives each simulation environment element to perform simulation calculations to generate corresponding simulation environment element files; using all simulation environment element files, it constructs an environment simulation file and sends the environment simulation file to the operation control subsystem.

[0009] The operation control subsystem is used to realize dynamic management and coordinated scheduling of the entire simulation process, achieve precise control and situation management of the simulation process, and load the force simulation files and environment simulation files into the simulation engine to build and display the simulation environment.

[0010] The simulation resource subsystem, connected to the operation control subsystem, includes a simulation model library, a simulation database, a simulation data library, and a hardware operation platform. It has management functions for various simulation resources, provides simulation resource support for various simulation applications, and provides a hardware operation platform for other subsystems.

[0011] The environmental simulation subsystem includes an environmental simulation allocation module, an environmental simulation operation module, a typical atmospheric environment simulation module, a typical electromagnetic environment simulation module, and a typical marine environment simulation module.

[0012] The environment simulation allocation module is used to parse the scenario file to obtain environment simulation information; allocate resources to each simulation module according to the environment simulation information, generate environment simulation resource allocation information, and send the environment simulation resource allocation information to the environment simulation running module.

[0013] The environmental simulation operation module is connected to the environmental simulation allocation module, the typical atmospheric environment simulation module, the typical electromagnetic environment simulation module, and the typical marine environment simulation module, respectively. It is used to drive each environmental simulation module to perform simulation calculations based on the environmental simulation resource allocation information, obtain the corresponding simulation environment element files, construct an environmental simulation file using all simulation environment element files, and send the environmental simulation file to the operation control subsystem.

[0014] The typical atmospheric environment simulation module is used to simulate the atmospheric environment, complete the functions of maintaining, editing, archiving and opening newly created or existing atmospheric environment data, and provide users with an atmospheric environment setting platform, including a static atmospheric model and a typical wind field model.

[0015] The typical electromagnetic environment simulation module is used to calculate electromagnetic clutter in a defined area and simulate complex electromagnetic environments.

[0016] The typical marine environment simulation module is used to perform environmental simulation calculations for typical meteorological simulation, sea state simulation, free atmospheric turbulence simulation at the sea surface, and wake flow simulation, according to simulation requirements.

[0017] The environment simulation allocation module is used to allocate resources to each simulation module based on the received environment simulation information, and generate environment simulation resource allocation information, including:

[0018] The environment simulation allocation module parses the scenario file to obtain environment simulation information; it then performs quantitative calculation on the simulation task processing function of each environment simulation element in each simulation scenario in the environment simulation information to obtain the total number of task processing for each environment simulation element.

[0019] Based on each simulation scenario, the priority, computational bandwidth consumption, simulation type, and total number of tasks of all environmental simulation elements in the environmental simulation information are quantized and encoded to obtain the requirement encoding vector corresponding to each environmental simulation element.

[0020] Based on each simulation scenario, the demand coding vectors corresponding to all environmental simulation elements are quantitatively evaluated and calculated to obtain quantitative evaluation values.

[0021] Based on all simulation scenarios, an environmental simulation resource allocation model is constructed.

[0022] The environmental simulation resource allocation model is solved to obtain environmental simulation resource allocation information.

[0023] The expression for the quantization calculation is:

[0024]

[0025] Where R is the total number of tasks to be processed for environmental simulation elements, f(t) is the simulation task processing function at time t, h(t) is the preset simulation processing response function, t0 is the reference time for the start of the simulation, and τ represents the simulation duration.

[0026] The process involves quantifying and evaluating the requirement encoding vectors corresponding to all environmental simulation elements for each simulation scenario to obtain quantified evaluation values, including:

[0027] For each simulation scenario, a requirement encoding matrix is ​​constructed by using the requirement encoding vector corresponding to an environmental simulation element as a row vector.

[0028] The singular value decomposition is performed on the demand encoding matrix to obtain a singular value vector; the singular value vector is a vector constructed from all singular values.

[0029] Linear fitting is performed on the elements and element index values ​​of the singular value vector to obtain a singular approximation polynomial;

[0030] The index of the largest element in each column vector of the demand encoding matrix is ​​used as input, and the singular approximation polynomial is used to calculate the output value corresponding to each column vector.

[0031] By using the output values ​​of all column vectors, a singular output vector is constructed.

[0032] For each row vector of the demand encoding matrix, perform difference fusion calculation with the singular output vector to obtain the corresponding difference value;

[0033] The difference value is determined as the quantitative evaluation value of the environmental simulation element corresponding to the row vector of the demand coding matrix.

[0034] The expression for the difference fusion calculation is:

[0035]

[0036] Where, x i and y i D represents the i-th element of the row vector of the demand encoding matrix and the i-th element of the singular output vector, respectively. i (x i -y i ) represents the i-th order Weiber function, and q represents the difference value.

[0037] A second aspect of this invention discloses a method for constructing a flight equipment simulation environment, implemented using the aforementioned flight equipment simulation environment construction device, comprising:

[0038] Using the scenario editing subsystem, the scenario information input by the user is obtained, a scenario file in a specified format is generated, and the scenario file is sent to the force simulation subsystem and the environment simulation subsystem.

[0039] The force simulation subsystem is used to parse the scenario file to obtain force simulation information; based on the force simulation information, the simulation model library is driven to perform digital modeling and simulation of each force simulation unit, generate force simulation files, and send the force simulation files to the operation control subsystem.

[0040] The environment simulation subsystem is used to parse the scenario file to obtain environment simulation information; based on the environment simulation information, each simulation environment element is driven to perform simulation calculations to generate corresponding simulation environment element files; using all simulation environment element files, an environment simulation file is constructed, and the environment simulation file is sent to the operation control subsystem.

[0041] The operation control subsystem is used to load the force simulation file and environment simulation file into the simulation engine to build and display the simulation environment.

[0042] The environmental simulation subsystem is used to parse the scenario file to obtain environmental simulation information; based on the environmental simulation information, various simulation environment elements are driven to perform simulation calculations to generate corresponding simulation environment element files; and using all simulation environment element files, an environmental simulation file is constructed, including:

[0043] The environment simulation allocation module is used to parse the scenario file to obtain environment simulation information; based on the environment simulation information, resources are allocated to each simulation module to generate environment simulation resource allocation information, and the environment simulation resource allocation information is sent to the environment simulation running module.

[0044] Using the aforementioned environment simulation operation module, based on the environment simulation resource allocation information, each environment simulation module is driven to perform simulation calculations to obtain the corresponding simulation environment element files. Using all the simulation environment element files, an environment simulation file is constructed.

[0045] The beneficial effects of this invention are as follows:

[0046] This invention solves the problem of accurate and comprehensive simulation modeling of maritime flight environments and equipment. The device comprises a scenario editing subsystem, a force simulation subsystem, an operation control subsystem, an environment simulation subsystem, and a simulation resource subsystem, implemented through a five-layer structure. This decoupling of the various business modules enables rapid deployment and iteration of simulation modeling.

[0047] In this invention, the typical atmospheric environment simulation module, the typical electromagnetic environment simulation module, and the typical marine environment simulation module are the core components of the environmental simulation subsystem. They form an organic whole through a unified resource scheduling framework and a multi-physics coupling mechanism. Their technical advantages are not only reflected in the specialized capabilities of each module, but also in the "1+1+2" multiplier effect achieved through system-level collaboration.

[0048] The simulation resource allocation process of this invention transforms the physical characteristics of environmental simulation into computable mathematical features. Through the cross-disciplinary integration of matrix theory, integral transformation, and optimization theory, it constructs a complete closed loop of "physical meaning - mathematical expression - engineering implementation," providing a reusable methodology for resource scheduling in complex systems. Its innovation lies not only in the technical aspects but also in establishing a "quantitative evaluation paradigm" for environmental simulation resource allocation, driving the field's paradigm shift from experience-driven to data-driven approaches. Attached Figure Description

[0049] Figure 1 This is an overall composition diagram of the device of the present invention;

[0050] Figure 2 Flowchart of the implementation of the method of the present invention;

[0051] Figure 3 Functional composition diagram of the environmental simulation subsystem. Detailed Implementation

[0052] To better understand the content of this invention, an embodiment is provided here.

[0053] Figure 1 This is an overall composition diagram of the device of the present invention; Figure 2 Flowchart of the implementation of the method of the present invention; Figure 3 Functional composition diagram of the environmental simulation subsystem.

[0054] In a first aspect, the present invention discloses a flight equipment simulation environment construction device, comprising: a scenario editing subsystem, a force simulation subsystem, an operation control subsystem, an environment simulation subsystem, and a simulation resource subsystem;

[0055] The scenario editing subsystem is used to acquire scenario information input by the user, provide the user with a field scenario editing platform, support the user in constructing adversarial scenarios on a two-dimensional map, setting scenario information, generating scenario files in a specified format, and sending the scenario files to the force simulation subsystem and the environment simulation subsystem; the scenario information includes the scenario area, virtual target configuration information and their action plan information; the scenario file includes force simulation information and environment simulation information;

[0056] The force simulation information records the activity trajectory and behavior rules of each force unit in the simulation; the environment simulation information records the priority, computational bandwidth consumption, simulation type, and simulation task processing function of all environmental simulation elements in each simulation scenario; one element of the simulation task processing function is the number of simulation tasks that need to be processed for an environmental simulation element in a simulation scenario at a given moment.

[0057] The scenario editing subsystem includes three modules: scenario and topic management, entity and resource management, and extended function management.

[0058] The scenario editing subsystem primarily provides users with a field scenario editing platform. It allows for the construction of adversarial scenarios on a 2D map, the delineation of game areas, and the configuration of virtual targets and their game plans. It transforms the game action plans and intentions during the simulation process into computer-readable electronic and digital documents, generating field scenario files in a specified format. This provides support for later scenario deduction, analysis, research, optimization, and statistical analysis. In application, scenario files are created using the field scenario editing function according to simulation requirements, including empty-space, empty-area, and formation scenarios. The field scenario editing software interface features a visual design, grouping similar functions according to industry practices, providing a simple and easy-to-use interface, prompts for important commands, hints for parameter input ranges, and reasonable default values.

[0059] The scenario editing subsystem mainly consists of three parts: scenario and task management, entity and resource management, and extended function management. The scenario and task management section serves as the interface between users and the scenario library, and also implements scenario loading. Through scenario management, users can maintain, edit, archive, open, and load new or existing scenarios; through task management, users can maintain, edit, archive, open, load, and add / delete scenarios within new or existing tasks; scenarios support multi-party confrontation, allowing configuration of multiple identifiers required for the current scenario and the relationships between them; it can statistically analyze the deployed and saved forces in the scenario for quick user viewing; it provides deployment layer management functionality, enabling multi-user collaborative scenario editing by editing scenarios on deployment layers at different nodes; and it provides collaborative management functionality, allowing multiple users to edit the same scenario online simultaneously, enabling multi-user collaboration in large-scale scenario scenarios.

[0060] For the entity and resource management section, entity management includes entity deployment and maintenance. With the support of simulation models and the external environment, scenario developers can deploy entities, modify entity configurations, determine game task information under different game environments, and generate the scenarios required for game simulation, based on the game plan. Scenario resources are simulation elements designed to assist scenario creation and facilitate scenario developers in completing various game tasks. These include regional resources, route resources, network resources, and formation resources. Resource management provides functions for adding, editing, and saving scenario and project-related resources.

[0061] The extended functionality management section includes features such as scenario preview, plugin management, display configuration, map plotting, and attribute grouping. Scenario preview allows users to modify scenarios at any time during runtime and continue simulation; plugin management provides functions such as disabling, enabling, and logging various functional plugins; display configuration management includes configuring display elements such as icons, sensors, kill ranges, and command and control relationships, including visibility control, color, and line type configurations; map plotting involves marking military situations with symbols, pictographs, and text on professional maps to create situation maps, decision maps, planning maps, and transit maps; map control includes map zooming, panning, and resetting functions.

[0062] The force simulation subsystem digitally models and simulates various forces in a real field environment, providing a "realistic" game-like confrontation environment for simulation applications. The force simulation software consists of several modules, including model assembly settings, simulation engine operation, and heterogeneous model integration.

[0063] The model assembly and setup module focuses on parametric modeling and component-based assembly of mission entities. Through modular component configuration, it supports flexible customization of functional units such as sensors and weapon systems. This module also provides target characteristic modules, creating entity templates that meet the needs of various game simulations. The module supports graphical model assembly dynamic adjustment and categorized management, ensuring that the model accurately maps the modular characteristics of real equipment while adapting to the rapid iteration needs of operational innovation.

[0064] The simulation engine module leverages parallel discrete event simulation technology to drive dynamic simulations of integrated air and sea operations. Through time management, event scheduling, and gridded entity management, it achieves temporal coordination and situational synchronization for actions such as formation reconnaissance and electronic jamming. The engine supports multi-threaded concurrent computation and dynamic module loading, allowing for flexible adjustment of simulation speed and real-time state saving to meet the efficiency requirements of various resolution-based adversarial simulations. Simultaneously, through data distribution and a human-in-the-loop control interface, it enables real-time interaction with external command and control positions and weapon systems, providing a closed-loop verification environment for relevant operational training on the platform.

[0065] The heterogeneous model access module provides a standardized integration framework for heterogeneous resources such as external simulation models, training systems, and simulators. For compliant algorithm models (such as target recognition algorithms), a white-box integration approach is used to directly embed them into the simulation process. For independently developed entity-level models, functional calls are achieved through component encapsulation and interface adaptation. For distributed systems, a three-layer communication architecture is used for data interconnection, supporting multiple access modes such as compatible proxies and embedded dynamic libraries, ensuring that models of different resolutions can operate collaboratively under a unified situation. This module significantly improves the system compatibility of equipment joint game simulation.

[0066] Depending on the technical characteristics of the various external models themselves, they can be integrated into the simulation environment using methods such as standard integration, component integration, and distributed interconnection integration.

[0067] Standard integration refers to an integration method where the model runs directly within the system, is managed by the system, and interacts with the system and other models. It is suitable for models developed using the model design requirements specified in the model development specifications of a simulation platform. Standard integration is a white-box integration method; the model to be integrated is modeled according to a standard model architecture, the model source code is added to the model development code framework, and after compilation, it provides applications to the system, supporting multi-threaded calls.

[0068] Component integration refers to encapsulating the model to be integrated as an independent dynamic library, which is then loaded and called by the simulation system through a pre-defined interface. This method is suitable for integrating external models into the system. Component integration can take three different forms:

[0069] 1) Algorithm-level component integration

[0070] External models are relatively simple algorithm components that provide algorithmic support to the system or other model products through function calls;

[0071] 2) Solid-level component integration

[0072] The external model is a functional component that encapsulates entities and equipment components. The entity-level components need to be modified to add interfaces for entity-level component initialization, attribute setting and acquisition, function call, simulation intervention, etc.

[0073] 3) Integration of multi-entity interactive components

[0074] The external model is a functional component that encapsulates multiple internally interactive simulation entities. The multi-entity interactive component needs to be modified to add interfaces for initialization of the multi-entity interactive component, setting and obtaining the attributes of the internal entities, function calls, simulation intervention, etc.

[0075] The simulation environment provides general interface support and modeling method implementation support for all the above methods. For specific business models, it is necessary to customize the model interaction and interface data structure according to the business interface, simulation granularity and resolution, and then integrate the model.

[0076] Distributed interconnection and integration is generally suitable for situations where the models to be integrated are relatively independent systems. Through distributed interconnection and integration, external models (systems) interact with the simulation platform and are controlled by the simulation platform.

[0077] The force simulation subsystem is connected to the scenario editing subsystem and the operation control subsystem. It is used to parse the scenario file to obtain force simulation information; based on the force simulation information, it drives the simulation model library to perform digital modeling and simulation of each force simulation unit, generates force simulation files, and sends the force simulation files to the operation control subsystem.

[0078] The force simulation subsystem includes a model configuration setting module, a simulation engine, and a heterogeneous model access module.

[0079] The environment simulation subsystem is connected to the scenario editing subsystem and the operation control subsystem. It is used to parse the scenario file to obtain environment simulation information; based on the environment simulation information, it drives each simulation environment element to perform simulation calculations, generates corresponding simulation environment element files, uses all simulation environment element files to construct an environment simulation file, and sends the environment simulation file to the operation control subsystem.

[0080] The operation control subsystem is used to realize dynamic management and coordinated scheduling of the entire simulation process, achieve precise control and situation management of the simulation process, load the force simulation files and environment simulation files into the simulation engine, and realize the construction and display of the simulation environment, including the situation display module, the guidance and control module and the simulation interconnection module.

[0081] The simulation resource subsystem, connected to the operation control subsystem, includes a simulation model library, a simulation database, a simulation data library, and a hardware operation platform. It has management functions for various simulation resources, provides simulation resource support for various simulation applications, and provides a hardware operation platform for other subsystems.

[0082] The operation control subsystem, as the core command center of the system, undertakes the dynamic control and coordinated scheduling functions of the entire simulation process. This system consists of three parts: a situation display module, a guidance and control module, and a simulation interconnection module. Through multi-dimensional functional collaboration, it achieves precise control and situation management of the simulation process.

[0083] The situation display module, a core component of the flight equipment simulation environment, is primarily responsible for presenting multi-dimensional situational information in real time, providing personnel with intuitive and accurate situational awareness. This module utilizes electronic chart and air situation information fusion display technology to visualize the maritime and airspace environment, including troop deployment, movement trajectories, and game dynamics. The system can analyze sensor detection data, information shared with neighboring units, and virtual situational data generated by the simulation system in real time. After data fusion processing, it presents a comprehensive view of the situational environment from multiple perspectives and layers. In electromagnetic environment simulation, the module can dynamically display key information such as electromagnetic spectrum distribution, radar detection range, and electronic interference areas, supporting trainees in accurately assessing the electromagnetic situation. The module also features an event triggering mechanism, automatically popping up an alert window when important action events occur and supporting action debriefing analysis through trajectory playback. To meet the needs of different command levels, the system provides customized situation display modes, including a macro-level global situational overview as well as detailed situational observation for specific task units. All displayed content can be synchronized in real time with the simulation process, ensuring the timeliness and accuracy of situational information.

[0084] The command and control module, serving as the command center of the flight equipment simulation environment, undertakes the crucial functions of training process management and situation control. This module achieves unified scheduling of the entire simulation system through an integrated control interface, allowing command and control personnel to dynamically adjust the exercise process and mission environment parameters according to training needs. The system features scenario loading and real-time editing capabilities, enabling modification of key elements such as troop deployment and field environment conditions during training to ensure flexibility and relevance. The module's built-in rule engine monitors action events and equipment status in real time during the simulation, automatically adjusting the field situation or issuing command and control instructions when preset conditions are triggered, effectively maintaining the continuity and realism of the training. In collaborative training scenarios, the module provides a multi-party communication and coordination mechanism to ensure real-time information exchange between commanders, trainees, and system operators. Through comprehensive data recording and playback functions, command and control personnel can conduct post-training analysis and review of the entire training process, facilitating post-training evaluation and action research.

[0085] The simulation interconnection module serves as the system integration hub of the flight equipment simulation environment, primarily enabling collaborative operation and data interaction among distributed simulation nodes. This module constructs a unified data exchange framework, supporting the interconnection of various simulation resources, including real-time data fusion between physical equipment, virtual simulation systems, and constructed simulation models. Through a standardized protocol conversion mechanism, the module effectively resolves interface adaptation issues between heterogeneous systems, ensuring that various simulation equipment and subsystems operate collaboratively under a unified spatiotemporal reference.

[0086] The simulation interconnection module employs a publish-subscribe model to dynamically distribute situational information, intelligently pushing differentiated field situational data based on the permission level and functional requirements of each node. For real-time performance, the module ensures low-latency transmission of critical task instructions and situational updates through priority queue management and time synchronization services. To meet the needs of complex training scenarios, the system supports dynamic addition and removal of simulation nodes, possessing online expansion capabilities and automatic isolation of faulty nodes, ensuring the organizational flexibility of large-scale distributed training. Simultaneously, the module's built-in data verification mechanism verifies the integrity of information transmitted across systems, providing reliable data interaction guarantees for the entire simulation environment.

[0087] The simulation resource subsystem enables the overall management of various resources used in the simulation system, including simulation model libraries, simulation databases, and simulation data libraries, and has management functions for various simulation resources.

[0088] The simulation model library stores game models for relevant simulation applications. These models primarily simulate the behavior and capabilities of simulated entities during the game between the red and blue sides, generating various simulation entity data during the game and interaction processes. The force models in the library include basic game units such as air, ground, and water forces, the opponent's own organizational strength, and all possible support forces obtained during the game, providing simulated combat forces for the simulation environment.

[0089] The simulation database is built upon the needs of the game and the characteristics of the equipment, comprehensively covering multi-dimensional data resources to support highly realistic simulation scenarios. The database should contain detailed performance parameters of the flight equipment, such as flight characteristics, payload capacity, and weapon system configuration, ensuring that the simulation environment can accurately simulate the actual game effectiveness of various equipment. The database also integrates complex external environmental data, including marine meteorological conditions, electromagnetic spectrum distribution, and topographic information, providing fundamental support for simulating realistic game environments. Furthermore, the data also stores relevant state data, detection data, event data, target attribute changes, mission status, communication status, damage assessment, and other result data from the simulation process.

[0090] The construction of a simulation database is a crucial component of flight equipment simulation environments, providing comprehensive, accurate, and dynamic data support for the simulation system. This database should encompass flight equipment performance parameters, field environment, force and equipment information, formation coordination game data, historical game data, and case studies. This data will ensure that the simulation environment can accurately simulate the actual performance of the equipment, simulate a realistic game environment, provide reference for action verification and optimization, help analyze cases, summarize experiences, and improve the relevance and effectiveness of simulation applications.

[0091] The environmental simulation subsystem includes an environmental simulation allocation module, an environmental simulation operation module, a typical atmospheric environment simulation module, a typical electromagnetic environment simulation module, and a typical marine environment simulation module.

[0092] The environment simulation allocation module is used to parse the scenario file to obtain environment simulation information; allocate resources to each simulation module according to the environment simulation information, generate environment simulation resource allocation information, and send the environment simulation resource allocation information to the environment simulation running module.

[0093] The environmental simulation operation module is connected to the environmental simulation allocation module, the typical atmospheric environment simulation module, the typical electromagnetic environment simulation module, and the typical marine environment simulation module, respectively. It is used to drive each environmental simulation module to perform simulation calculations based on the environmental simulation resource allocation information, obtain the corresponding simulation environment element files, construct an environmental simulation file using all simulation environment element files, and send the environmental simulation file to the operation control subsystem.

[0094] The typical atmospheric environment simulation module is used to simulate the atmospheric environment, and to complete the functions of maintaining, editing, archiving, opening and loading new or existing atmospheric environment data. It provides users with an atmospheric environment setting platform, including a static atmospheric model and a typical wind field model.

[0095] The typical electromagnetic environment simulation module is used to calculate electromagnetic clutter in a defined area and simulate complex electromagnetic environments.

[0096] The typical marine environment simulation module is used to realize environmental simulation calculation functions such as typical meteorological (wind, fog, etc.) simulation, sea state simulation, sea surface free atmospheric turbulence simulation, and ship wake simulation according to simulation requirements.

[0097] The environment simulation allocation module is used to allocate resources to each simulation module based on the received environment simulation information, and generate environment simulation resource allocation information, including:

[0098] The environment simulation allocation module performs quantitative calculation on the simulation task processing function of each environment simulation element in each simulation scenario in the environment simulation information to obtain the total number of task processing for each environment simulation element.

[0099] Based on each simulation scenario, the priority, computational bandwidth consumption, simulation type, and total number of tasks of all environmental simulation elements in the environmental simulation information are quantized and encoded to obtain the requirement encoding vector corresponding to each environmental simulation element.

[0100] Based on each simulation scenario, the demand coding vectors corresponding to all environmental simulation elements are quantitatively evaluated and calculated to obtain the quantitative evaluation value of each environmental simulation element.

[0101] Based on all simulation scenarios, an environmental simulation resource allocation model is constructed.

[0102] The environmental simulation resource allocation model is solved to obtain environmental simulation resource allocation information.

[0103] The environmental simulation elements include atmospheric simulation, marine simulation, and electromagnetic simulation, which correspond to typical atmospheric environment simulation modules, typical marine environment simulation modules, and typical electromagnetic environment simulation modules, respectively.

[0104] The simulation task processing function is the number of simulation tasks that need to be processed for an environmental simulation element in a simulation scenario at each time step. Its input is the time value and its output is the number of simulation tasks.

[0105] The expression for the quantization calculation is:

[0106]

[0107] Where R is the total number of tasks to be processed for environmental simulation elements, f(t) is the simulation task processing function at time t, h(t) is the preset simulation processing response function, t0 is the reference time for the start of the simulation, and τ represents the simulation duration.

[0108] The preset simulation processing response function can be a Gaussian function or a sinc function;

[0109] The quantization encoding process includes:

[0110] The priority value of each environmental simulation element is normalized to obtain a normalized priority value; for priority P i Its normalized priority is denoted as Where P max and P min These represent the upper and lower limits of the priority value range, respectively;

[0111] The computational bandwidth consumption of environmental simulation elements is divided into M1 intervals from high to low. Each interval is set with a corresponding computational bandwidth consumption coefficient. The corresponding computational bandwidth consumption coefficient is determined according to the interval in which the computational bandwidth consumption of the environmental simulation element is located.

[0112] The simulation types of all environmental simulation elements are divided into several categories. Each simulation type has a corresponding simulation type coefficient. The corresponding simulation type coefficient is determined based on the simulation type value of the environmental simulation element.

[0113] By utilizing the normalized priority value, simulation type coefficient, computational bandwidth consumption coefficient, and total number of tasks for each environmental simulation element, a corresponding requirement encoding vector is constructed.

[0114] The process involves quantifying and evaluating the requirement encoding vectors corresponding to all environmental simulation elements for each simulation scenario to obtain quantified evaluation values, including:

[0115] For each simulation scenario, a requirement encoding matrix is ​​constructed by using the requirement encoding vector corresponding to an environmental simulation element as a row vector.

[0116] The singular value decomposition is performed on the demand encoding matrix to obtain a singular value vector; the singular value vector is a vector constructed from all singular values.

[0117] Linear fitting is performed on the elements and element index values ​​of the singular value vector to obtain a singular approximation polynomial;

[0118] The index of the largest element in each column vector of the demand encoding matrix is ​​used as input, and the singular approximation polynomial is used to calculate the corresponding output value.

[0119] By using the output values ​​of all column vectors, a singular output vector is constructed.

[0120] For each row vector of each demand encoding matrix, perform difference fusion calculation with the singular output vector to obtain the corresponding difference value;

[0121] The expression for the difference fusion calculation is:

[0122]

[0123] Where, x i and y i D represents the i-th element of the row vector of the demand encoding matrix and the i-th element of the singular output vector, respectively. i (x i -y i ) represents the i-th order Weiber function, and q represents the difference value;

[0124] The difference value is determined as the quantitative evaluation value of the environmental simulation element corresponding to the row vector of the demand coding matrix;

[0125] Employing the Weber function as a variance metric, this approach cleverly leverages the characteristic of Weber's Law that "perceived variance is proportional to the baseline value": when a high-priority element deviates from the singular output vector, its contribution variance q increases significantly, driving resource allocation towards higher-value elements. The fourth-order summation (i = 1-4) corresponds to the cumulative variances of priority, type, bandwidth, and workload, respectively, achieving a comprehensive measurement of multi-dimensional errors and avoiding decision-making errors caused by biases in a single indicator.

[0126] The singular approximation polynomial uses the element indices of the singular value vector as independent variables, rather than directly using the magnitude of the singular values. This design captures the "positional importance" of each element in the scene (e.g., the third column vector corresponds to the electromagnetic module, and its index has a higher weight in the landing scenario), breaking through the limitations of traditional evaluations that rely solely on numerical magnitude. The selection of the largest element index in the column vector as input ensures that the polynomial output value accurately corresponds to the dominant element in the scene. For example, in an ocean scenario, the largest element index of the ocean module's column vector will drive the polynomial output to a high value, ensuring its priority in resource allocation.

[0127] The linear fitting process uses the element index value as the independent variable and the elements of the singular value vector as the dependent variable.

[0128] The expression for the environmental simulation resource allocation model is:

[0129] Objective function: max C = F(AB),

[0130] Constraint: line(A)≤T,

[0131] Where A is the resource allocation matrix to be solved, the element in the i-th row and j-th column of the resource allocation matrix is ​​1, which means that the j-th environmental simulation element is used in the i-th simulation scenario, and the element in the i-th row and j-th column of the resource allocation matrix is ​​0, which means that the j-th environmental simulation element is not used in the i-th simulation scenario, F(AB) represents the summation of all elements of matrix AB, B is the evaluation matrix, the element in the j-th row and i-th column of the evaluation matrix represents the quantitative evaluation value of the j-th environmental simulation element in the i-th simulation scenario, line(A) represents the summation value of each column vector of matrix A, and T is the preset participation threshold value, which can be 2;

[0132] The simulation types include synchronous simulation, asynchronous simulation, external digital-driven simulation, and distributed simulation.

[0133] The solution to the environmental simulation resource allocation model can be obtained using a genetic algorithm or an ant colony algorithm.

[0134] The process of allocating resources to each simulation module based on the received environmental simulation information and generating environmental simulation resource allocation information transforms the physical characteristics of environmental simulation into computable mathematical features. Through the cross-disciplinary integration of matrix theory, integral transformation, and optimization theory, a complete closed loop of "physical meaning - mathematical expression - engineering implementation" is constructed, providing a reusable methodology for resource scheduling of complex systems. Its innovation lies not only in the technical level but also in establishing a "quantitative evaluation paradigm" for environmental simulation resource allocation, promoting the paradigm shift in this field from experience-driven to data-driven.

[0135] Traditional resource allocation often uses the number of tasks as the sole indicator. This solution, however, achieves a precise characterization of the actual computing power requirements of environmental simulation elements by calculating the total number of tasks processed. It introduces a simulation processing response function g(t) (such as a Gaussian function) to perform a weighted integral on the instantaneous task function f(t), ensuring that the total task calculation considers not only the number of tasks but also the response priority in the time dimension (e.g., instantaneous tasks experiencing sudden electromagnetic interference have higher weight). For example, in a typhoon scenario, the wind speed change task in the atmospheric module (f(t) increases pulse-like) is weighted by the peak value of the Gaussian function g(t), resulting in a dynamic higher total task processing count than the ocean module task under stable sea conditions, ensuring priority computing power for critical scenarios. Innovatively, discrete resource demand parameters are transformed into computable demand encoding vectors, achieving a unified measurement of cross-dimensional resource demands. Priority normalization eliminates the differences in priority value ranges between different modules (e.g., atmospheric module priority 0-10 points vs. electromagnetic module 0-100 points), making multi-module evaluations comparable. Interval coefficients that consume computational bandwidth (e.g., dividing into 5 intervals corresponding to coefficients of 0.2-1.0) and classification coding of simulation types (e.g., atmospheric turbulence corresponding to 0.3, electromagnetic clutter corresponding to 0.7) transform qualitative descriptions into quantitative features, laying the foundation for subsequent matrix operations.

[0136] By employing Singular Value Decomposition (SVD) of the demand encoding matrix and constructing singular approximation polynomials, a leap from local task evaluation to optimal global resource allocation is achieved. Singular value decomposition reveals the inherent correlation between the demands of various environmental elements (e.g., the strong coupling between atmospheric wind speed and ocean waves forms a significant peak in the singular value vector), avoiding the resource allocation imbalance caused by traditional independent evaluation. Polynomial fitting, using the index of the largest element in the column vector as input, accurately captures the dominant environmental elements in each simulation scenario (e.g., the dominant role of the electromagnetic module in the landing scenario), ensuring that resource allocation is tilted towards key elements while maintaining global balance.

[0137] A 0-1 integer programming model for resource allocation matrix A is constructed to achieve dynamic resource binding across multiple scenarios and elements. The objective function max C = F(AB) transforms the quantitative evaluation values ​​of each element in different scenarios into a global performance index through the summation of matrix products, ensuring consistency between resource allocation and scenario value. The constraint line(A) ≤ T (e.g., T = 2) limits the number of times a single environmental element can be reused across scenarios, preventing excessive resource consumption by high-frequency elements (such as electromagnetic modules) from causing insufficient computing power for other modules.

[0138] The environmental simulation subsystem primarily provides users with field environment simulations. It can be invoked by other system modules and, given specified environmental information input, outputs information such as the various impacts on the task entity. It generates simulation information in a specified format, supporting the deduction, analysis, research, optimization, and statistics of simulation applications. The environmental simulation subsystem is implemented using environmental simulation software.

[0139] The environmental simulation software mainly consists of three parts: a typical atmospheric environment simulation module, a typical electromagnetic environment simulation module, and a typical marine environment simulation module. It includes modules for static atmosphere models, typical wind field models, radar and target simulation, region-based electromagnetic noise, specular and diffuse clutter, echo simulation calculations, typical meteorological conditions, sea states, free atmospheric turbulence at the sea surface, and wake turbulence, among other software functions. The environmental simulation software is composed of... Figure 3 As shown.

[0140] Environmental simulation can construct adversarial simulation environments. Given atmospheric environment information, typical magnetic field information, and typical marine environment information, these multiple environmental fields are not completely independent; they have certain data interoperability interfaces and coupling mechanisms. For example, in the electromagnetic environment simulation and marine environment simulation modules, sea state information is related to environmental input quantities; free atmospheric turbulence at the sea surface and atmospheric turbulence in typical wind fields share certain commonalities in their formation mechanisms and calculation methods. Therefore, environmental simulation will fully consider the coupling effects between multiple physics fields, providing a more realistic environmental background for simulation applications.

[0141] The atmospheric environment simulation module handles the maintenance, editing, archiving, and loading of newly created or existing atmospheric environment data. It provides users with an atmospheric environment setting platform, including a static atmospheric model and typical wind field models. The static atmospheric model provides static atmospheric parameters at the aircraft's current altitude, including density, pressure, static temperature, and speed of sound, for calculating aerodynamic physical quantities. The typical wind field models include constant wind, atmospheric turbulence, and wind shear, allowing users to set wind field conditions for the simulation area and simulate takeoffs and landings under crosswind, cloud penetration, and wind shear conditions. This module can construct an adversarial environment background, providing atmospheric environment information. It converts the aircraft's attitude information and surrounding environmental parameters during the simulation into computer-readable electronic and digital documents, generating a formatted environmental parameter file to provide environmental background support for later simulations.

[0142] In actual wind fields, wind direction and speed constantly change with time and location, making it difficult to accurately describe using time and space. Therefore, there is no truly constant wind. A constant wind only indicates that the average wind speed within the studied spatial area remains constant over a relatively long time period, but the instantaneous values ​​still exhibit fluctuations around the average. In meteorology, it is often represented by the average wind speed measured over two minutes at a fixed altitude.

[0143] (1) Low-level winds: (0-600m)

[0144] Low-level winds are often described using empirical formulas, the most widely used being logarithmic formulas, as follows:

[0145]

[0146] Among them, v h —Wind speed at a height h above the ground, in m / s;

[0147] v0—Wind speed at ground level or test point height h0, m / s;

[0148] z0—Ground condition coefficient, which can take the following values ​​depending on the terrain:

[0149] (2) Wind in the air (600m to ceiling):

[0150] The constant wind in the air is usually described by a linear model, that is:

[0151] v h =k×(h-600)+v h600

[0152] Where k is the proportionality coefficient of wind speed to height change, which users can set according to training needs, v h600This module displays the wind speed at a height of 600m above the ground, automatically calculated and transmitted based on low-altitude winds. It's worth noting that while the wind speed scalar can be solved using relevant formulas, the wind direction needs to be manually input by the user according to the training scenario requirements. This module can also transmit relevant information to other application modules.

[0153] For atmospheric turbulence models, studying the impact of changing wind fields on aircraft motion requires establishing mathematical models to describe the phenomenon of changing wind fields. Changing wind fields encompass motions at various temporal and spatial scales, and their generation mechanisms and development processes differ.

[0154] Currently, simplified wind field models, also known as engineering models, are widely used in research on the impact of changing wind fields on aircraft motion. These models primarily describe the relationships between basic physical parameters of the wind field while neglecting some secondary influencing factors. Under certain conditions, these simplified wind field models can basically reflect the most essential mechanisms and physical processes of the atmospheric disturbance phenomena under study. Their advantages include convenient parameter adjustment and flexible use, thus enabling them to represent the real changing wind field to the greatest extent possible.

[0155] Atmospheric turbulence refers to continuous random fluctuations superimposed on mean wind. Although turbulence is deterministic according to the Navier-Stokes equations, the continuity equation, and other conditions, it is practically impossible to directly integrate these equations to calculate turbulence, mainly due to the small structures within the turbulence and their short durations relative to the observation time. This situation is entirely due to the nonlinear nature of fluid dynamic systems. Turbulent phenomena must be described using stochastic process theory and methods. Currently, the most widely used turbulence model on flight simulators is the Dryden model.

[0156] The Dryden model has the advantage of a simple and easily processed spectral function form, allowing for conjugate decomposition, which is crucial throughout the simulation process. Model energy spectral function:

[0157]

[0158] Similarly, using formula The spectrum converts the spatial spectrum into the temporal spectrum:

[0159]

[0160] Taking an inverse Fourier transform of the time spectrum function in the above equation yields the corresponding time-related function:

[0161]

[0162] in:

[0163]

[0164] l3=3l / π

[0165] l4=4l / π

[0166]

[0167] The correlation function in the above formula is used to check whether the simulated turbulence pattern matches the actual situation, that is, to check the feasibility of the simulation. At the same time, setting the independent variable to zero, we can obtain the theoretical values ​​of turbulence velocity and gradient variance.

[0168]

[0169] Wind shear refers to the change in wind speed vector or its components along the vertical or horizontal direction. Wind shear is a vector value that reflects the changes in wind direction and speed between two points under study. In meteorology, the horizontal movement of air is conventionally called wind, while the vertical movement of air is called vertical movement. Wind shear refers to the sudden change in the wind speed vector (including wind speed and direction) at any two points in space over a period of time. Because wind shear is difficult to predict and can be highly destructive, in atmospheric environments where low-level wind shear occurs, both the magnitude and direction of wind speed undergo unpredictable changes, which in turn trigger a series of chain reactions: the changing airspeed causes a change in the lift experienced by the aircraft, and according to the laws of mechanics, the change in lift further alters the aircraft's altitude.

[0170] Wind shear mainly includes three types: vertical shear of horizontal winds, horizontal shear of horizontal winds, and vertical wind shear. The theoretical calculation formulas for each type of wind shear are as follows:

[0171] ① Vertical shear of horizontal wind:

[0172] Vertical wind shear refers to the change in horizontal wind speed or direction between two points within a certain vertical distance. The calculation formula is:

[0173]

[0174] Where WS is the wind shear intensity between two points, WD is the wind speed vector difference between two points, Dis is the distance between two points, u1 and u2 are the wind speed magnitudes of the upper and lower layers respectively, and θ is the wind direction difference between the upper and lower layers.

[0175] ② Horizontal shear of horizontal winds:

[0176] Horizontal wind shear refers to the change in wind speed between two points in the horizontal direction. The formulas for calculating wind shear intensity are shown in the two equations above.

[0177] ③ Vertical wind shear:

[0178] Vertical wind shear refers to the change in the horizontal direction between two points in an updraft or downdraft (vertical wind). This type of wind shear often occurs within the influence range of thunderstorm clouds. The intensity of vertical wind shear, over the same spatial distance, is mainly determined by the variation in the size of the vertical wind itself. Strong downdrafts pose the greatest threat to flight safety. Based on the suggestions of renowned meteorologists Fujita and Coles, a standard called the downdraft numerical value was proposed, which is determined from the downdraft velocity and the divergence value of the area it reaches. Later, downdrafts with a diameter of less than 4 kilometers, which pose the greatest threat, were referred to as micro-downdrafts. The formula for calculating the divergence value within an 800-meter diameter is as follows:

[0179]

[0180] Δu=u1-u2

[0181] Δv=v1-v2

[0182] Δx=Δy=800

[0183] in Here, u1 is the horizontal x-velocity at the location where the downdraft arrives, u2 is the horizontal x-velocity at a location 800m away from the location where the downdraft arrives, v1 is the horizontal y-velocity at the location where the downdraft arrives, and v is the horizontal y-velocity at a location 800m away from the location where the downdraft arrives.

[0184] The electromagnetic environment simulation module serves as the interface between specific simulation applications and the electromagnetic environment library, and also acts as the implementer of electromagnetic environment settings. Through this module, users can calculate electromagnetic clutter in the task area based on the defined task scenario, simulating complex electromagnetic environments. This primarily includes modules for region-based electromagnetic noise settings, specular and diffuse reflection clutter settings, echo simulation calculations, post-processing, and waveform visualization. Specifically, the electromagnetic noise setting module allows for setting the range, start and end frequencies, start and end times, and noise intensity (light, moderate, heavy) for different types of electromagnetic interference within a specific area of ​​the field. The reflection clutter module primarily handles specular and diffuse reflection clutter generated by sea surfaces, land, clouds, chaff, and rainfall, and can simultaneously calculate and analyze the coupling effects of various environmental factors, including: 1) sea waves, water temperature, and salinity; 2) earth conductivity, dielectric constant, surface cover, and surface undulation; 3) cloud particle size, cloud thickness, cloud area, and water content; 4) rainfall rate; and 5) chaff length and chaff distribution. The echo simulation calculation and post-processing module integrates the calculation results of other modules to achieve functions such as waveform superposition analysis to obtain radar echoes and visualized waveform images.

[0185] The marine environment simulation module performs functions such as simulation of typical meteorological conditions (wind, fog, etc.), sea state, free atmospheric turbulence at sea surface, and wake flow, based on the simulation content. The simulation results of sea state, free atmospheric turbulence at sea surface, and wake flow are all generated based on mathematical models, experimental data, and empirical models, and marine environmental data can be transferred to other simulation modules through built-in interfaces.

[0186] Time series of random ocean wave rise can be modeled using the autoregressive moving average (ARMA) process. The spectral density of an ARMA process consists of the quotient of a polynomial, which can approximate a given wave spectrum well. Furthermore, since ship motion is typically simulated using differential equations, a time-continuous ARMA process is also necessary. The basic idea of ​​ARMA is to treat the object of prediction as a series of time-varying and interconnected random sequences, and to use a corresponding mathematical model for description and analysis, thereby achieving optimal prediction at the level of minimum variance. ARMA models have three forms: autoregressive (AR), moving-average (MA), and autoregressive moving-average (ARMA).

[0187] Autoregressive moving average models are generally time-discrete difference equations. However, considering the case of continuous time, these difference equations can be rewritten as differential equations. Introducing the time-continuous autoregressive moving average model, also known as the CARMA (continuous-time ARMA) model, the corresponding linear differential equation is expressed as:

[0188] x (p) (t)+a1x (p-1) (t)+a2x (p-2) (t)+…+a p x(t)

[0189] =ε(t)+b1ε (1) (t)+b2ε (2) (t)+…+b q ε (q) (t)

[0190] In the formula, the superscript represents the order of the derivative with respect to t.

[0191] Consider a CARMA(p, q) process y(t), expressed in state-space form:

[0192] y = c′u(t)

[0193] In the formula, the state vector u(t)∈R pAnd satisfies the linear Ito differential equation:

[0194] du(t) = Au(t) + bdW(t)

[0195] In the formula: W is the Wiener process; dW(t)=W(t+dt)-W(t) is an increment of the unit Wiener process; dW(t) / dt is a white noise sequence, and its power spectral density S(ω)≡1 is taken.

[0196]

[0197] In the formula, 0 ≤ q < p. The spectral density corresponding to the above CARMA(p, q) process is: where S = iω. It is worth noting that the state-space representation of the CARMA model given by this definition is not unique.

[0198] Generally, waves affecting the sea are treated as a stationary random process. White noise is input as an external load disturbance, and an appropriate filter is introduced to filter it, obtaining colored noise that conforms to certain statistical characteristics, so as to simulate the wave spectrum of real sea conditions.

[0199] This paper adopts the ITTC two-parameter spectrum as the standard wave spectrum. It uses significant wave height and characteristic period as parameters, has a simple structure, and is suitable for fully developed waves. Its expression is:

[0200]

[0201] In the formula: H 1 / 3 It is the meaningful wave height; T1 is the characteristic period.

[0202] Using the ITTC two-parameter spectrum as the standard wave spectrum, a filter model is constructed to approximate the wave spectrum. The filter model is constructed using several different orders of CARMA(p,q) processes.

[0203] To make the filtered colored noise more consistent with the statistical characteristics of real ocean waves, a higher-order filter model is needed. The fourth-order filter is still constructed based on the autoregressive moving average model. Simulation comparisons show that the fourth-order filter CARMA(4,3) provides the best fit to the ITTC two-parameter spectrum. However, increasing the filter order significantly increases the computational cost in solving practical problems. Therefore, when selecting a filter mathematical model, both fitting accuracy and computational complexity should be considered. The CARMA(2,1) model is chosen for low-order filters, while the CARMA(4,3) model is chosen for high-order filters.

[0204] In reality, ocean waves are primarily generated by wind, a highly complex stochastic process. Due to the variability and randomness of wind direction, waves can propagate in various directions, resulting in undulating wave surfaces resembling hills of varying shapes. To simplify the problem, we assume that the waves propagate in only one fixed direction, with their crests and troughs parallel to each other and perpendicular to the wave's direction of propagation. This type of wave is called a "binary irregular wave," or a long-crest wave. In the study of stochastic waves, the Longuet-Higgins model is the most widely used in engineering. The Longuet-Higgins model considers long-crest waves to be composed of countless cosine waves with different amplitudes, frequencies, and initial phases superimposed. The wave surface displacement at a fixed point can be expressed as:

[0205]

[0206] In the formula ζ Ai ω i ε i Let ε be the amplitude, frequency, and initial phase of the i-th cosine wave, respectively. i The phase is a random phase uniformly distributed from 0 to 2π. Amplitude ζ Ai Relationship with spectrum:

[0207]

[0208] In the simulation, the relevant sense wave height H can be determined. 1 / 3 The characteristic period T1 is set to simulate the ITTC dual-parameter spectrum corresponding to different sea states, as well as the colored noise power spectrum corresponding to the CARMA(2,1) and CARMA(4,3) filters. Then, the wave history is generated according to the Longuet-Higgins model to simulate the instantaneous value of the wave surface.

[0209] Generally, flight simulations establish environments based on calm atmospheres, without considering the impact of atmospheric disturbances on aircraft flight. However, the real atmosphere contains various atmospheric disturbances that significantly affect aircraft performance. Therefore, to increase the realism of marine environment simulations, it is necessary to establish a free atmospheric turbulence model of the sea surface that resembles the real atmospheric environment and apply it to marine environment simulations. Atmospheric disturbances include many forms: low-altitude steady-state winds, atmospheric turbulence, and gusts. Among these airflow disturbances, atmospheric turbulence is the most complex and the most difficult to model. This module will design a shaping filter for the mathematical model of atmospheric turbulence based on the Dryden model, using white noise signals as input, and obtaining an accurate atmospheric turbulence sequence through appropriate corrections, thereby performing accurate simulations of free atmospheric turbulence at the sea surface.

[0210] Actual atmospheric turbulence is a highly complex physical phenomenon. To prevent the study of aircraft response from becoming overly complicated, appropriate assumptions can be made while ensuring model accuracy. Generally, atmospheric turbulence is a stochastic function closely related to time and location; this functional relationship is constructed based on a large amount of measurement and statistical data. In aerospace engineering applications, it can be assumed that the statistical characteristics of atmospheric turbulence (i.e., mean and root mean square error, as well as correlation and spectral functions) are neither variable with time (assuming turbulence is stationary) nor with location (assuming turbulence is uniform). Under these assumptions, the needs of analyzing aircraft performance characteristics can be met.

[0211] The commonly used atmospheric turbulence model is the one proposed by Dryden based on a large amount of measurement and statistical data. Its corresponding exponential longitudinal correlation function is: f(ξ)=e -ξ / L After obtaining f(ξ) and g(ξ) based on measurement and statistical data, the longitudinal and transverse spectral functions of the Dryden model can be obtained by performing a Fourier transform on the results, as follows:

[0212]

[0213] in:

[0214]

[0215] The obtained spectral function is based on the assumptions of "stationarity" and "uniformity," which means that the asymptotic properties of the model at infinity do not conform to actual turbulence theory. However, this does not affect practical engineering applications. The advantage of the Dryden model is that its spectral form is relatively simple and can be processed using conventional mathematical methods, which is essential for numerical simulation of turbulence.

[0216] This invention employs a layered architecture design, constructing a complete technical system covering data support, simulation calculation, application services, and capability output. Through the collaborative operation of multiple modules, the system achieves vertical integration from basic data management to advanced simulation applications, providing a full-process digital support platform for equipment verification and related research. Each layer adheres to standardized interface specifications, ensuring the system possesses excellent scalability and compatibility, capable of adapting to the complex needs of flight equipment simulation.

[0217] The device of this invention consists of five parts from bottom to top: a basic resource layer, an operational support layer, an application component layer, and a simulation application layer, specifically including:

[0218] (I) Basic Resource Layer

[0219] The basic resource layer constitutes the underlying support system of the entire simulation environment. This layer integrates three major resource systems: simulation model library, database, and resource repository. It stores simulation models and various types of data in the simulation, and realizes unified management and dynamic access to resources through standardized application interfaces, providing highly reliable resource support for upper-layer simulation applications.

[0220] (II) Operational Support Layer

[0221] The runtime support layer constructs the system's basic operational framework, providing key services such as timing synchronization and resource scheduling. This layer achieves parallel computation of multi-resolution models through a high-performance simulation engine, maintains clock synchronization of distributed nodes through a unified time service, and completes protocol conversion for heterogeneous systems using an access proxy service. A data acquisition service aggregates the operational status of each module in real time, forming a closed-loop monitoring system, while a dynamic resource allocation mechanism automatically optimizes system performance based on computational load, ensuring the stable operation of large-scale adversarial simulations.

[0222] (III) Application Component Layer

[0223] The application component layer encapsulates fundamental capabilities into reusable functional modules, forming application support tailored to specific business needs. This layer includes a model assembly toolchain, supporting component-based modeling of airborne sensors; an integrated scenario editing platform, enabling visual construction and multi-person collaborative arrangement of mission scenarios; and a deployed control console system, providing full-process intervention capabilities for operations. The environmental simulation component simulates complex environmental effects such as atmospheric turbulence and electromagnetic interference, while the situation display module achieves the fusion and presentation of multi-dimensional external field information, collectively forming a complete simulation application development ecosystem.

[0224] (iv) Simulation Application Layer

[0225] The simulation application layer directly addresses training and equipment verification needs, constructing various targeted solutions. This layer rapidly generates typical scenarios based on lower-level components, simulating air defense system behavior through a virtual force system. A 2D / 3D joint situational awareness system provides a three-dimensional representation of the external space, while the environmental simulation system offers dynamic environmental backgrounds such as meteorology and oceanography, collectively forming a multi-layered simulation application.

[0226] A second aspect of this invention discloses a method for constructing a flight equipment simulation environment, implemented using the aforementioned flight equipment simulation environment construction device, comprising:

[0227] Using the scenario editing subsystem, the scenario information input by the user is obtained, a scenario file in a specified format is generated, and the scenario file is sent to the force simulation subsystem and the environment simulation subsystem.

[0228] The force simulation subsystem is used to parse the scenario file to obtain force simulation information; based on the force simulation information, the simulation model library is driven to perform digital modeling and simulation of each force simulation unit, generate force simulation files, and send the force simulation files to the operation control subsystem.

[0229] The environment simulation subsystem is used to parse the scenario file to obtain environment simulation information; based on the environment simulation information, each simulation environment element is driven to perform simulation calculations to generate corresponding simulation environment element files; using all simulation environment element files, an environment simulation file is constructed, and the environment simulation file is sent to the operation control subsystem.

[0230] The operation control subsystem is used to load the force simulation file and environment simulation file into the simulation engine to build and display the simulation environment.

[0231] The environmental simulation subsystem is used to parse the scenario file to obtain environmental simulation information; based on the environmental simulation information, various simulation environment elements are driven to perform simulation calculations to generate corresponding simulation environment element files; and using all simulation environment element files, an environmental simulation file is constructed, including:

[0232] The environment simulation allocation module is used to parse the scenario file to obtain environment simulation information; based on the environment simulation information, resources are allocated to each simulation module to generate environment simulation resource allocation information, and the environment simulation resource allocation information is sent to the environment simulation running module.

[0233] Using the aforementioned environment simulation operation module, based on the environment simulation resource allocation information, each environment simulation module is driven to perform simulation calculations to obtain the corresponding simulation environment element files. Using all the simulation environment element files, an environment simulation file is constructed.

[0234] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A device for constructing a flight equipment simulation environment, characterized in that, include: The system comprises a scenario editing subsystem, a force simulation subsystem, an operation control subsystem, an environment simulation subsystem, and a simulation resource subsystem. The scenario editing subsystem is used to acquire scenario information input by the user, provide the user with an outdoor scenario editing platform, support the user to construct adversarial scenarios on a two-dimensional map, generate scenario files in a specified format based on the scenario information, and send the scenario files to the force simulation subsystem and the environment simulation subsystem; the scenario information includes the scenario area, virtual target configuration information and their action plan information; The scenario file includes force simulation information and environmental simulation information; The environmental simulation information records the priority, computational bandwidth consumption, simulation type, and simulation task processing function of all environmental simulation elements in each simulation scenario. The force simulation subsystem is connected to the scenario editing subsystem and the operation control subsystem, and is used to parse the scenario file to obtain force simulation information; Based on the troop simulation information, the simulation model library is driven to perform digital modeling and simulation on each troop simulation unit, generate troop simulation files, and send the troop simulation files to the operation control subsystem. The environment simulation subsystem is connected to the scenario editing subsystem and the operation control subsystem, and is used to parse the scenario file to obtain environment simulation information; Based on the environmental simulation information, each simulation environment element is driven to perform simulation calculations, generating corresponding simulation environment element files. Using all simulation environment element files, an environmental simulation file is constructed, and the environmental simulation file is sent to the operation control subsystem. The environmental simulation subsystem includes an environmental simulation allocation module, an environmental simulation operation module, a typical atmospheric environment simulation module, a typical electromagnetic environment simulation module, and a typical marine environment simulation module. The environment simulation allocation module is used to allocate resources to each simulation module based on the received environment simulation information, and generate environment simulation resource allocation information, including: The environment simulation allocation module parses the scenario file to obtain environment simulation information; it then performs quantitative calculation on the simulation task processing function of each environment simulation element in each simulation scenario in the environment simulation information to obtain the total number of task processing for each environment simulation element. Based on each simulation scenario, the priority, computational bandwidth consumption, simulation type, and total number of tasks of all environmental simulation elements in the environmental simulation information are quantized and encoded to obtain the requirement encoding vector corresponding to each environmental simulation element. Based on each simulation scenario, the demand coding vectors corresponding to all environmental simulation elements are quantitatively evaluated and calculated to obtain the quantitative evaluation value of each environmental simulation element. Based on all simulation scenarios, an environmental simulation resource allocation model is constructed. The environmental simulation resource allocation model is solved to obtain environmental simulation resource allocation information; The expression for the quantization calculation is: Where R is the total number of tasks to be processed for environmental simulation elements, f(t) is the simulation task processing function at time t, h(t) is the preset simulation processing response function, t0 is the reference time for the start of the simulation, and τ represents the simulation duration.

2. The flight equipment simulation environment construction device as described in claim 1, characterized in that, The operation control subsystem is used to realize dynamic management and coordinated scheduling of the entire simulation process, achieve precise control and situation management of the simulation process, and load the force simulation files and environment simulation files into the simulation engine to build and display the simulation environment. The simulation resource subsystem, connected to the operation control subsystem, includes a simulation model library, a simulation database, a simulation data library, and a hardware operation platform. It has management functions for various simulation resources, provides simulation resource support for various simulation applications, and provides a hardware operation platform for other subsystems.

3. The flight equipment simulation environment construction device as described in claim 1, characterized in that, The environment simulation allocation module is used to parse the scenario file to obtain environment simulation information; allocate resources to each simulation module according to the environment simulation information, generate environment simulation resource allocation information, and send the environment simulation resource allocation information to the environment simulation running module. The environmental simulation operation module is connected to the environmental simulation allocation module, the typical atmospheric environment simulation module, the typical electromagnetic environment simulation module, and the typical marine environment simulation module, respectively. It is used to drive each environmental simulation module to perform simulation calculations based on the environmental simulation resource allocation information, obtain the corresponding simulation environment element files, construct an environmental simulation file using all simulation environment element files, and send the environmental simulation file to the operation control subsystem.

4. The flight equipment simulation environment construction device as described in claim 3, characterized in that, The typical atmospheric environment simulation module is used to simulate the atmospheric environment, complete the functions of maintaining, editing, archiving and opening newly created or existing atmospheric environment data, and provide users with an atmospheric environment setting platform, including a static atmospheric model and a typical wind field model. The typical electromagnetic environment simulation module is used to calculate electromagnetic clutter in a defined area and simulate complex electromagnetic environments. The typical marine environment simulation module is used to perform environmental simulation calculations for typical meteorological simulation, sea state simulation, free atmospheric turbulence simulation at the sea surface, and wake flow simulation, according to simulation requirements.

5. The flight equipment simulation environment construction device as described in claim 1, characterized in that, The process involves quantifying and evaluating the demand encoding vectors corresponding to all environmental simulation elements for each simulation scenario, resulting in a quantified evaluation value for each environmental simulation element. This includes: For each simulation scenario, a requirement encoding matrix is ​​constructed by using the requirement encoding vector corresponding to an environmental simulation element as a row vector. The singular value decomposition is performed on the demand encoding matrix to obtain a singular value vector; the singular value vector is a vector constructed from all singular values. Linear fitting is performed on the elements and element index values ​​of the singular value vector to obtain a singular approximation polynomial; The index of the largest element in each column vector of the demand encoding matrix is ​​used as input, and the singular approximation polynomial is used to calculate the output value corresponding to each column vector. By using the output values ​​of all column vectors, a singular output vector is constructed. For each row vector of the demand encoding matrix, perform difference fusion calculation with the singular output vector to obtain the corresponding difference value; The difference value is determined as the quantitative evaluation value of the environmental simulation element corresponding to the row vector of the demand coding matrix.

6. The flight equipment simulation environment construction device as described in claim 5, characterized in that, The expression for the difference fusion calculation is: Where, x i and y i D represents the i-th element of the row vector of the demand encoding matrix and the i-th element of the singular output vector, respectively. i (x i -y i ) represents the i-th order Weiber function, and q represents the difference value.

7. A method for constructing a flight equipment simulation environment, characterized in that, This is achieved using the flight equipment simulation environment construction device as described in any one of claims 1 to 6, comprising: Using the scenario editing subsystem, the scenario information input by the user is obtained, a scenario file in a specified format is generated, and the scenario file is sent to the force simulation subsystem and the environment simulation subsystem. The force simulation subsystem is used to parse the scenario file to obtain force simulation information; based on the force simulation information, the simulation model library is driven to perform digital modeling and simulation of each force simulation unit, generate force simulation files, and send the force simulation files to the operation control subsystem. The environment simulation subsystem is used to parse the scenario file to obtain environment simulation information; based on the environment simulation information, each simulation environment element is driven to perform simulation calculations to generate corresponding simulation environment element files; using all simulation environment element files, an environment simulation file is constructed, and the environment simulation file is sent to the operation control subsystem. The operation control subsystem is used to load the force simulation file and environment simulation file into the simulation engine to build and display the simulation environment.

8. The method for constructing a flight equipment simulation environment as described in claim 7, characterized in that, The environmental simulation subsystem is used to parse the scenario file to obtain environmental simulation information; based on the environmental simulation information, various simulation environment elements are driven to perform simulation calculations to generate corresponding simulation environment element files; and using all simulation environment element files, an environmental simulation file is constructed, including: The environment simulation allocation module is used to parse the scenario file to obtain environment simulation information; based on the environment simulation information, resources are allocated to each simulation module to generate environment simulation resource allocation information, and the environment simulation resource allocation information is sent to the environment simulation running module. Using the aforementioned environment simulation operation module, based on the environment simulation resource allocation information, each environment simulation module is driven to perform simulation calculations to obtain the corresponding simulation environment element files. Using all the simulation environment element files, an environment simulation file is constructed.

Images