AI 中文总结
针对地月通信的延迟、轨道可见性等问题，提出以知识为中心的通信框架，定义知识熵与知识新鲜度，通过模拟和龙江-2数据验证分布式知识融合可提升运行知识，保障任务成功。
AI 中文摘要
未来月球基础设施需要将通信作为一种持续服务，而非特定任务能力。通信、数字孪生和人工智能（AI）研究已分别推进了自主性，但大多假设系统状态最终可与地面真值达成一致。地月通信违背了这一假设，因为传播延迟根本上受限于光速，可见性由轨道几何决定，且自主决策常先于观测确认。本文提出一种以知识为中心的通信框架，其中数字孪生持续将延迟观测、通信物理、学习模型、不确定性量化及任务目标整合为不断演化的运行知识状态。为量化已知内容、剩余不确定性及时效性，本文针对每条通信链路维护的融合贝叶斯信念定义了两个量：知识熵，即后验信念的微分熵；知识新鲜度，将信息年龄（AoI）从单一信息流推广至融合运行知识状态的时间有效性。本文证明分布式知识融合可通过减少不确定性系统性提升运行知识，并通过与龙江-2月球微卫星飞行数据的模拟及对比，展示其对任务成功的重要性。
英文摘要
Future lunar infrastructure requires communication as a persistent service rather than a mission specific capability. Communication, digital twin, and artificial intelligence (AI) research have advanced autonomy independently while largely assuming that system state can eventually be reconciled with ground truth. Cislunar communication violates this assumption because propagation delay is fundamentally limited by the speed of light, visibility is governed by orbital geometry, and autonomous decisions often precede confirming observations. Here we introduce a knowledge-centric communication framework in which a digital twin continuously integrates delayed observations, communication physics, learned models, uncertainty quantification, and mission objectives into an evolving Operational Knowledge State. To quantify what is known, how uncertain it remains, and how current it is, we define two quantities over the fused Bayesian belief maintained for each communication link: \emph{Knowledge Entropy}, the differential entropy of the posterior belief, and \emph{Knowledge Freshness}, which generalizes Age of Information (AoI) from a single information stream to the temporal validity of the fused Operational Knowledge State. We prove that distributed knowledge fusion systematically improves operational knowledge by reducing uncertainty and demonstrate its importance for mission success through simulations and comparisons with flight data from the Longjiang-2 lunar micro-satellite.
Comments37 pages, 8 Figures, 2 Tables
