Treffer: 面向飞行模拟训练的云边端协同智能仿真平台设计.

In response to the demands of large-scale cross-regional networked tactical confrontation flight simulation training, an intelligent simulation platform architecture based on cloud-edge-end collaboration has been designed. The architecture supported by big data and centered around intelligent large models, intelligent agents and real-time simulation models, enables multi-modal operation integrating peacetime and training running modes through cross-regional cloud-edge-end resource integration, dynamic reuse, collaborative simulation and virtual-real fusion. Based on an analysis of the functional requirements of this intelligent simulation platform, the study focuses on designing its hierarchical architecture, network structure, and synchronized simulation strategies. It investigates critical aspects including cloud-edge-end simulation task allocation, operational modes, and application scenarios. Three key technical challenges are explored including integration of cloud-based XR and AI technologies, cloud-based convergence of big data, large models and real-time simulation, and real-time interactive cloud-edge-end collaborative simulation. This work provides a reference model for constructing a new-generation intelligent simulation platform that supports all-scenario, full-system, multi-element training, thereby advancing intelligent transformation in flight simulation training domains. [ABSTRACT FROM AUTHOR]

针对大规模跨地域组网战术对抗飞行模拟训练的需求, 设计了一种基于云边端协同的智能仿真平台架构, 该架构以大数据为支撑, 以智能大模型+智能代理+实时仿真模型为核心, 通过云边端跨地域资源整合、动态复用、协 同仿真和虚实融合, 可实现平时和训练时相结合的多模态运行。 在分析该平台功能需求的基础上, 重点设计了平台 的层级架构、网络结构和同步仿真策略, 研究了平台的云边端仿真任务分配、运行模式和应用场景等问题, 探讨了平 台的云 XR 和 AI 融合、大数据和大模型与实时仿真的云化融合、云边端协同仿真实时交互等 3 个方面的关键技术问 题, 为构建新一代面向全场景、全体系、多要素的智能仿真平台提供了参考模型, 可推动飞行模拟训练领域的智能化 转型。 [ABSTRACT FROM AUTHOR]