Multi-source element modeling and risk quantitative analysis under spatio-temporal grid

This work suggests a multi-source element modeling and risk quantification approach based on spatio-temporal grid subdivision to tackle the problem of spatio-temporal coupling modeling for multi-source elements in complex battlefield scenarios. Firstly, in the spatial dimension: A novel grid subdivision architecture compatible with the BeiDou grid location code (BDGC) is designed to significantly improve spatial data storage and computational efficiency in order to address the storage redundancy resulting from the triple virtual extension in current global subdivision models such as geographic coordinate subdividing grid with one dimension integral cording on 2ⁿ-tree(GeoSOT) and the efficiency gap issue associated with the “letter + decimal digit” encoding scheme of the BDGC. In the temporal dimension: To address the limitations caused by the quintuple temporal base extensions in the BeiDou subdivision time code (BDTC), an innovative dual-baseline temporal encoding system with “absolute-relative” layers is proposed. This successfully overcomes the spatio-temporal fusion bottleneck for heterogeneous elements by achieving a four-dimensional joint representation that integrates geographical position and time. Secondly, a two-level nested spatio-temporal reference framework comprising a “global reference grid - local combat grid” is constructed. This architecture balances local adaptability with global consistency by supporting the dynamic change of grid granularity based on equipment systems or the battlefield environment across several operational domains. Finally, to resolve the contour distortion problem inherent in traditional minimum bounding rectangle (MBR) methods when characterizing the geometric features of complex terrain and airspace elements, a three-dimensional realistic scene topological feature modeling method based on the ray-intersection judgment algorithm is proposed. Leveraging the spatio-temporal grid subdivision architecture, a unified quantitative model for enemy operational units is constructed. Combined with risk levels, the battlefield space is partitioned into free space, contested space, and denied space. Simulation experiments demonstrate that: The proposed method enables rapid multi-scale reconstruction of battlefield multi-source elements; At an approximate grid resolution of 1000 meters, the average relative error for spatial representation is only 1.414%; Compared to traditional methods, the average relative error is reduced by 49.37% and time efficiency is improved by 99.65%; It can meet the real-time response requirements for UAV swarm trajectory planning. This work provides a validated new paradigm for intelligent environmental modeling in UAV swarm operations.