Research on the Integration Technology of Multi Source Heterogeneous Spatiotemporal Data for Tile Pyramid Model Nested Spatiotemporal Cube Optimization

在特定业务场景中，时空数据统一具有核心战略价值。它是联合行动体系的神经脉络，是精确决策的关键基础，也是环境态势感知的前提条件。时空数据不统一可能导致数据融合难度增大、环境态势感知受限、决策效率降低、资源调度和协同配合困难等问题，这不仅影响行动任务的准确性和高效性，还可能对行动安全和国家安全带来重大隐患。本研究基于业内瓦片金字塔模型与Gail Langran时空立方体模型的技术原理理解，本研究提出一种基于瓦片金字塔模型的嵌套优化方法。通过数学建模与算法设计，并引入GRU网络实现时空特征融合，探索利用瓦片金字塔模型结构弥补时空立方体模型中处理不同时间和空间分辨率的数据，优化多源异构时空数据统一的管理和分析，提高数据融合的可靠性和效率，提升业务效能提供新思考。In specific business scenarios, the unification of spatiotemporal data has core strategic value. It is the neural network of the joint action system, the key foundation for precise decision-making, and the prerequisite for environmental situational awareness. Inconsistent spatiotemporal data may lead to increased difficulty in data fusion, limited environmental situational awareness, reduced decision-making efficiency, and difficulties in resource scheduling and collaborative cooperation. This not only affects the accuracy and efficiency of operational tasks, but also may pose significant risks to operational and national security. This study proposes a nested optimization method based on the understanding of the technical principles of the tile pyramid model and the Gail Langran spatiotemporal cube model in the industry. By using mathematical modeling and algorithm design, and introducing GRU network to achieve spatiotemporal feature fusion, we explore the use of tile pyramid model structure to compensate for the processing of data with different temporal and spatial resolutions in spatiotemporal cube models, optimize the unified management and analysis of multi-source heterogeneous spatiotemporal data, improve the reliability and efficiency of data fusion, and provide new ideas for enhancing business efficiency.