| Hu Hai (胡 海),Dai Chaofan,Chen Tao,Liu Jiaqi.[J].高技术通讯(英文),2026,32(3):260~269 |
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| Decision-tree and LLM assessment of training and readiness for command and control system |
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| DOI：10. 3772 / j. issn. 1006-6748. 2026. 03. 005 |
| 中文关键词: |
| 英文关键词: command and control, data-driven analysis, operational situation assessment |
| 基金项目: |
| Author Name | Affiliation | | Hu Hai (胡 海) | (*National Key Laboratory of Information Systems Engineering,National University of Defense Technology, Changsha 410073, P. R. China ) | | Dai Chaofan | | | Chen Tao | | | Liu Jiaqi | |
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| 中文摘要: |
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| 英文摘要: |
| Military training and combat readiness are core supports for operational situation assessment in
command and control (C2) systems, the existing studies mostly focus on qualitative descriptions,
lacking data-driven quantitative analysis to explore factors influencing their assessment results. In
this paper, we propose an analytical framework integrating data simulation, decision tree (DT) and
large language model (LLM) to fill this gap. We focus on two tasks: military training assessment
prediction and combat readiness characterization. The process is divided into two steps: first, simu-
late a distinct squadron data, generating 512 quarterly training samples and 1 536 monthly readiness
samples for DT-based three-level label prediction; second, combine expert experience and domain
norms to build report templates, using LLM guided by structured prompts to identify key influencing
factors and generate actionable suggestions. Experimental verification uses metrics like F1-score and
expert review to demonstrate the framework’s effectiveness in C2 scenarios. |
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