Multi-level spatiotemporal indexing method for railway geo-geological digital twin data
Railway engineering projects invole the management of extensive and diverse geographical and geological data, characterized by numerous sources and irregular spatial distribution. Efficient retrieval of such massive and heterogeneous datasets, especially those exhibiting twin characteristics, is critical for enabling dynamic updates, real-time calculations, and the provision of intelligent services. However, existing spatial-temporal indexing methods face challenges in meeting the demands of cross-scale, multimodal, and efficient real-time retrieval applications across various tasks.In response to these challenges, this paper introduces a storage structure model specifically designed to accommodate the distinctive features of railway geographical and geological spatiotemporal data. The key innovation lies in the integration of a multi-level spatiotemporal index, combining the strengths of the GeoHash-Quad spatial index and the data version time index. This novel approach aims to address the intricate complexities associated with railway data by incorporating a dynamic caching mechanism driven by a data frequency partition model, facilitating a collaborative memory-disk storage strategy. The multi-level spatiotemporal index is a comprehensive solution that captures both spatial and temporal dimensions, acknowledging the dynamic nature of railway data. This design enables effective management of the intricate relationships between geographical and geological elements over time. The dynamic caching mechanism, guided by the data frequency partition model, ensures adaptive storage of frequently accessed data, contributing to enhanced real-time retrieval performance in diverse railway scenarios. To evaluate the effectiveness of the proposed approach, the study selects typical large-scale geological and geographical data from railway tunnels. Comparative testing is conducted against GeoMesa, a widely used distributed geographical big data storage and indexing framework. Results indicate that the method presented in this paper outperforms GeoMesa, particularly in real-time retrieval scenarios for railway geographical and geological spatiotemporal data. In summary, this paper contributes to the advancement of storage and indexing techniques for railway geographical and geological data by presenting a tailored solution that enhances real-time retrieval performance. The proposed model's effectiveness is demonstrated through rigorous testing and comparison, showcasing its potential to support efficient management and retrieval of railway geographical and geological digital twin spatiotemporal data in distributed environments. With its dynamic caching mechanism and multi-level spatiotemporal index, the approach addresses the evolving project requirements and real-time application scenarios, contributing to the optimization of railway data handling in complex and dynamic settings.
digital twinrailway engineeringspatiotemporal indexingmemory-disk cooperativegeographic geological storage mode
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