With the increasing adoption of low-voltage measurement devices in distribution networks to accommodate customers’ diversified electricity consumption characteristics, the surge in low-voltage equipment has rendered traditional topology management methods inefficient in meeting real-time query and analysis demands. To address this issue, this paper proposes a hybrid structure combining graph and relational models to reconstruct conventional power information models. The graph model characterizes the topological relationships among low-voltage devices, while the relational model describes their electrical measurement parameters. By leveraging subgraph aggregation features of device topology, the energized state of switches is treated as connectivity credentials between graph nodes, and their closing duration serves as a coefficient measuring connection tightness. The edge attributes in the graph model store time-series data of the energized states at both ends, enabling online topology reconstruction during backtracking by referencing these sequences. Furthermore, the topology graph is partitioned using minimum spanning tree (MST) and spectral clustering algorithms to decompose large-scale low-voltage device nodes into multiple subgraphs, thereby reducing the time complexity of topology queries. Non-switching devices’ measurement parameters are described via the relational model, where node identifiers from topology traversal results are joined with the relational model to retrieve voltage and other data. The fusion of these models facilitates low-voltage topology backtracking and analytical applications. Compared to standalone relational or graph models, the proposed method demonstrates superior performance and storage efficiency, providing methodological support for complex computations in large-scale low-voltage topology management.