With the advancement of the "dual-carbon" strategy and the rapid construction of new power systems, traditional power grids are facing challenges such as large-scale renewable energy integration, increasingly complex operating structures, and insufficient intelligent operation and maintenance capabilities. Metaverse technology integrates digital twins, the Internet of Things, artificial intelligence, 5G communication, big data, and virtual reality technologies, providing a new development direction for digital power grid construction. Based on the Fujian digital power grid pilot project, this paper systematically studies the application of metaverse technology in digital power grids. The study analyzes the development background of new power systems and the limitations of traditional grids, proposes the overall architecture of metaverse-based digital power grids, discusses key technologies such as digital twins, AI, and blockchain, and further explores practical scenarios including intelligent inspection, renewable energy forecasting, carbon emission monitoring, and disaster warning. The results show that metaverse technology can effectively improve the visualization, intelligence, and collaborative operation capabilities of power grids, providing important support for future smart energy systems.

With the expansion of global trade, the explosive growth of e-commerce, and the continuous improvement of logistics infrastructure, logistics systems have become increasingly large-scale and complex. Research on logistics systems based on complex network theory has provided effective approaches for addressing the complexity-related challenges faced by modern logistics operations. Drawing on core journal publications on logistics system complex networks indexed in the China National Knowledge Infrastructure (CNKI) and the Web of Science (WoS) from 2015 to 2024, this study employs the bibliometric visualization software CiteSpace to conduct a systematic visual analysis. The research examines publication trends, research institutions, countries, keyword co-occurrence, and keyword clustering to identify and compare major research hotspots in China and abroad. Based on these findings, the study summarizes the current state of research and discusses future development directions.