Research on database architecture optimization of virtual teaching and research office based on multi-source heterogeneous data fusion

At present, the virtual teaching and research section is faced with the problems of wide data sources, heterogeneous formats and scattered storage, which makes it difficult to integrate and deeply utilize the data. In view of this situation, this study puts forward an optimization scheme of multi-source heterogeneous data fusion database architecture for virtual teaching and research office. By analyzing the characteristics of virtual teaching and research room data, a mathematical model of multimodal data fusion is established, and a four-layer architecture of "access-fusion-storage-service" is designed. The architecture implements the core class VirtualResearchDB based on PySpark, including data adaptation, fusion engine, mixed storage and query optimization module. Some key technologies, such as dynamic construction of knowledge map, federated learning fusion mechanism, hybrid storage optimization and intelligent caching strategy, are also realized in the study. The experimental results show that this scheme is significantly superior to the traditional methods in terms of data fusion quality and query performance. At the same time, in the cross-school teaching and research cooperation scenario, zero knowledge verification is realized through federated learning, which completely eliminates the risk of data leakage and greatly reduces resource consumption. This study provides theoretical support and practical guidance for the transformation of virtual teaching and research room from "data accumulation" to "data empowerment", which has important cross-research value of educational big data and significance for the construction of virtual teaching and research room.