A Large-Language-Model-Oriented Dual-Channel Agent Framework for Intelligent Generation of Simulation Scenarios

Abstract

Constructing simulation scenarios manually is time-consuming and often depends on platform-specific modeling experience. Existing large-language-model (LLM) methods are promising for interpreting operational documents, but they still struggle with long-document parsing, incomplete platform interfaces, auditable task execution, and cross-lingual equipment-name normalization. This paper proposes a dual-channel LLM-agent framework for intelligent generation of simulation scenarios. The method standardizes Word-based scenario inputs, extracts scenario elements into a schema-constrained JSON intermediate representation, and decomposes the generation process into planning and execution. A DLL/Lua dual-channel Plan Agent assigns basic object and scenario-property operations to a C# object model, while allocating fine-grained unit, mission, and environment operations to Lua-based scripting interfaces. An Exec Agent further integrates two-stage hot-pluggable tool loading, side-effect-aware read/write-separated scheduling, and a Smart-Matcher module that combines BM25 retrieval, multilingual vector retrieval, reciprocal rank fusion, and low-confidence LLM reranking. Experiments on representative red-blue simulation scenarios show an average end-to-end generation time of 168 s, a first-round planning success rate of 92.0%, a post-Replan success rate of 100.0%, and Top-1/Top-5 equipment matching performance of 95.6% and 98.4%, respectively.