In the era of big data, a variety of challenges are emerging in hospital information construction, such as inadequate top-level design, information islands, data specification and data sharing, internal and external business integration, as well as data security. Hospital information system shares many similarities with the human nervous system. We aimed to discuss these challenges based on the concept and principles of bionics and traditional Chinese medicine theory and tried to provide a new strategy for hospital information construction by learning through the characteristics of the human nervous system.

Accurate visual re-localization is very critical to many artificial intelligence applications, such as augmented reality, virtual reality, robotics and autonomous driving. To accomplish this task, we propose an integrated visual re-localization method called RLOCS by combining image retrieval, semantic consistency and geometry verification to achieve accurate estimations. The localization pipeline is designed as a coarse-to-fine paradigm. In the retrieval part, we cascade the architecture of ResNet101-GeM-ArcFace and employ DBSCAN followed by spatial verification to obtain a better initial coarse pose. We design a module called observation constraints, which combines geometry information and semantic consistency for filtering outliers. Comprehensive experiments are conducted on open datasets, including retrieval on R-Oxford5k and R-Paris6k, semantic segmentation on Cityscapes, localization on Aachen Day-Night and InLoc. By creatively modifying separate modules in the total pipeline, our method achieves many performance improvements on the challenging localization benchmarks.