Spatiotemporal vector retrieval has emerged as a critical paradigm in modern information retrieval, enabling efficient access to massive, heterogeneous data that evolve over both time and space. However, existing spatiotemporal retrieval methods are often extensions of conventional vector search systems that rely on external filters or specialized indices to incorporate temporal and spatial constraints, leading to inefficiency, architectural complexity, and limited flexibility in handling heterogeneous modalities. To overcome these challenges, we present a unified spatiotemporal vector retrieval framework that integrates temporal, spatial, and semantic cues within a coherent similarity space while maintaining scalability and adaptability to continuous data streams. Specifically, we propose (1) a Rotary-based Unified Encoding Method that embeds time and location into rotational position vectors for consistent spatiotemporal representation; (2) a Circular Incremental Update Mechanism that supports efficient sliding-window updates without global re-encoding or index reconstruction; and (3) a Weighted Interest-based Retrieval Algorithm that adaptively balances modality weights for context-aware and personalized retrieval. Extensive experiments across multiple real-world datasets demonstrate that our framework substantially outperforms state-of-the-art baselines in both retrieval accuracy and efficiency, while maintaining robustness under dynamic data evolution. These results highlight the effectiveness and practicality of the proposed approach for scalable spatiotemporal information retrieval in intelligent systems.

翻译：时空向量检索已成为现代信息检索的关键范式，能够高效访问随时间与空间演化的海量异构数据。然而，现有的时空检索方法通常是传统向量搜索系统的扩展，依赖外部过滤器或专用索引来纳入时空约束，导致效率低下、架构复杂，且在处理异构模态时灵活性有限。为克服这些挑战，我们提出了一种统一的时空向量检索框架，该框架将时间、空间和语义线索整合到一个连贯的相似性空间中，同时保持对连续数据流的可扩展性和适应性。具体而言，我们提出了（1）一种基于旋转的统一编码方法，将时间和位置嵌入旋转位置向量以实现一致的时空表示；（2）一种循环增量更新机制，支持高效的滑动窗口更新，无需全局重新编码或索引重建；（3）一种基于加权兴趣的检索算法，可自适应平衡模态权重，实现上下文感知和个性化检索。在多个真实世界数据集上的大量实验表明，我们的框架在检索准确性和效率方面均显著优于现有先进基线方法，同时在动态数据演化下保持鲁棒性。这些结果凸显了所提方法在智能系统中进行可扩展时空信息检索的有效性和实用性。