Efficient Line-Tile-Component Multi-Granularity Spatiotemporal Indexing Method for Digital Twin Railroad Tunnel Data
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摘要:
长大铁路隧道工程采用多工点并行同向或反向掘进等复杂施工组织策略，导致数字孪生三维模型时空分布稀疏、时空重叠度大，已有的时空索引针对连续均匀时空分布特征，难以满足时间、里程、语义多维度高效检索需要，制约了数字孪生应用的实时虚实互馈效率。为此，提出一种适用于铁路隧道数字孪生模型的多维度高效时空索引方法，设计了时间、里程、语义多维度关联的铁路隧道三维瓦片数据结构，建立了全局最优解约束的有向包围空间纠偏机制，实现了铁路隧道线路-瓦片-构件多粒度时空索引。利用典型长大铁路隧道孪生模型数据验证了所提方法的有效性，结果表明，所提方法在时间、里程、语义多维度混合检索时间平均值为135.44 ms，与现有典型时空索引对比，提升了三维空间单一维度与时间-里程混合维度的检索效率，可有效支撑铁路隧道数字孪生三维模型高效时空检索及高性能可视化分析等应用。
Abstract:ObjectivesComplex construction organization strategies such as parallel same direction and reverse boring for long railroad tunnel projects lead to sparse spatiotemporal distribution and large spatiotemporal overlap in the digital twin three-dimensional model. The existing spatiotemporal indexes for the continuous uniform spatiotemporal distribution characteristics can not satisfy the needs of efficient retrieval of time, mileage, and semantics in multi-dimensions, and they also restrict the efficiency of the digital twin application in real-time virtual and real mutual feeds.
MethodsThis paper proposes a multi-dimensional efficient spatiotemporal indexing method applicable to the digital twin model of railroad tunnels. First, a 3D tile data structure of railroad tunnels associated with time, mileage, and semantic multi-dimensions is designed. Second, an oriented bounding box corrective mechanism constrained by the global optimal solution is established, and then a multi-granular spatiotemporal index of railroad tunnel lines-tiles-components is realized. Finally, the effectiveness of the method is verified using typical long railroad tunnel twin model data.
ResultsThe results show that the average value of the proposed method in the mixed retrieval time of time, mileage and semantic multi-dimensions is 135.44 ms, which improves the retrieval efficiency of the 3D spatial single dimension and the time-mileage mixed dimension comparing with the existing typical spatial and temporal index.
ConclusionsThe proposed method can effectively support the efficient spatial and temporal retrieval of the digital twin 3D model of railroad tunnels, and the application of high-performance visualization and analysis.
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Keywords:
- digital twin railroad /
- railroad tunnel /
- 3D tile /
- spatiotemporal indexing /
- multi-granularity
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图 1 长带状铁路隧道孪生模型时空不规则分布特点
Figure 1. Character of Spatial and Temporal Irregular Distribution for the Twin Model of the Railroad Tunnel
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图 2 铁路隧道多维度三维瓦片逻辑结构
Figure 2. Logic Structure of Railroad Tunnels Multi-Dimensional Tile
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图 3 铁路隧道多维度三维瓦片物理结构
Figure 3. Physical Structure of Railroad Tunnels Multi-Dimensional Tile
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图 4 全局最优约束的有向包围空间纠偏流程
Figure 4. Process of Oriented Bounding Box Correction with Global Optimality Constraints
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图 5 线路-瓦片-构件多粒度时空索引原理
Figure 5. Principle of Line-Tile-Component Multi-Granularity Spatiotemporal Indexing
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图 6 线路-瓦片-构件多粒度时空索引流程
Figure 6. Process of Line-Tile-Component Multi-Granularity Spatiotemporal Indexing
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图 7 线路-瓦片-构件多粒度时空索引更新流程
Figure 7. Update of Line-Tile-Component Multi-Granularity Spatiotemporal Indexing
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图 10 不同时空分布场景适用性测试
Figure 10. Applicability Test for Different Spatial and Temporal Distribution Scenarios
表 1 试验数据集描述
Table 1 Description of Experimental Datasets
数据集 时间跨度 里程跨度 语义类型 模型数 离散空间数 时空关联数 规则分布数据集 2021-06-16—2023-07-20 DK296+350—DK342+935 初支、衬砌、附属设施等 16 032 0 1 不规则分布数据集 15 468 3 $ \ge $3 
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