摘要
Abstract
Combat-oriented exercise training serves as a core enabler for enhancing system-of-systems operational capabilities,validating equipment effectiveness,and optimizing command decision-making.As a fundamental data element,exercise training data underpins after-action review,doctrine validation,capability certification,and command decision-making.To address prevailing challenges in current exercise training activities,including fragmented data collection,inconsistent standards,difficulties in fusing multi-source heterogeneous data,the absence of a full-lifecycle governance mechanism,an incomplete quality evaluation system,and inadequate levels of data security and reuse,a full-lifecycle theoretical framework for high-quality exercise training datasets was established.It systematically investigated key construction technologies,such as requirement modeling,standard system formulation,data preprocessing and quality control,and precise annotation.A two-layer evaluation model integrating"static foundational quality"and"dynamic operational effectiveness"was proposed,along with a corresponding evaluation indicator system for exercise training datasets.Validation was conducted using data from joint exercise training.The results demonstrated that the proposed method achieved significant improvements across five critical dimensions:completeness,accuracy,spatiotemporal consistency,annotation consistency,and reproducibility,while exhibiting stronger adaptability and robustness.It was proven effective and feasible for assessing the quality of joint exercise training data,thereby offering theoretical support and technical pathways for the digital transformation of combat-oriented exercise training and the empowerment of system-of-systems operations through data elements.关键词
实战化演训/高质量数据集/构建技术/评估模型/数字化转型Key words
combat-oriented exercise training/high-quality dataset/construction technology/evaluation model/digital transformation分类
信息技术与安全科学
