AI 中文总结
提出通用天文台图框架，将太阳系L2点天文台建模为加权图，用于分布式天空覆盖评估和基于Q学习的行星际路由优化。
AI 中文摘要
本研究提出了通用天文台图（UOG），这是一个用于太阳系内分布式天文观测的AI驱动框架。所提出的架构将位于太阳-行星L2拉格朗日点的自主天文台建模为加权图中的节点，而通信链路则表示为图边，其特征由多目标物理和运行指标定义，包括行星际距离、通信延迟、传输功率和链路可靠性。由此产生的图提供了合作行星际天文台网络的统一数学表示。本研究考察了一个包含地球、火星、木星、土星、天王星和海王星的六天文台太阳系配置。瞬时天空覆盖分别使用200,000方向的斐波那契球、2,000,000方向的固定种子蒙特卡洛计算和确定性球面积分进行评估。三种方法均得出完整的网络联合覆盖，在所采用的指向模型下，六天文台交集约为0.43%完整，平均成对Jaccard相似度约为24.96%。通信路由随后被表述为有限时域马尔可夫决策过程，并使用表格Q学习求解。奖励函数在节点参与度和距离相关可靠性代理与距离、光时延迟和距离平方传输功率代理之间取得平衡。学习到的地球-土星-天王星-海王星路线也是四跳约束下所有41条可行简单路径中折扣回报最高的路线。该框架为顺序覆盖评估和多目标路由提供了可复现的基线；时间相关的星历、任务特定的可见性、校准的链路预算和可扩展的图策略仍是未来工作。
英文摘要
This research proposes the Universal Observatory Graph (UOG), an AI-driven framework for distributed astronomical observation across the Solar System. The proposed architecture models autonomous observatories located at the Sun planet L2 Lagrange points as nodes in a weighted graph, while communication links are represented as graph edges characterized by multi-objective physical and operational metrics, including interplanetary distance, communication latency, transmission power, and link reliability. The resulting graph provides a unified mathematical representation of a cooperative interplanetary observatory network. This proposal examines a six-observatory Solar System configuration comprising Earth, Mars, Jupiter, Saturn, Uranus and Neptune. Instantaneous sky coverage is evaluated independently using a 200,000 direction Fibonacci sphere, a 2,000,000 direction fixed seed Monte Carlo calculation and deterministic spherical integration. All three methods yield complete network union coverage, approximately 0.43% complete six observatory intersection and approximately 24.96% mean pairwise Jaccard similarity under the adopted pointing model. Communication routing is subsequently formulated as a finite horizon Markov decision process and solved using tabular Q-learning. The reward balances node participation and a distance dependent reliability proxy against distance, light time latency and a distance squared transmission power proxy. The learned Earth-Saturn-Uranus-Neptune route is also the highest discounted return route among all 41 feasible simple paths under the four hop constraint. The framework provides a reproducible baseline for sequential coverage assessment and multi objective routing; time dependent ephemerides, mission specific visibility, calibrated link budgets and scalable graph policies remain future work.
