Research on Indoor Navigation and Emergency Response Scheme Design of Rail Transit Station Using Virtual Reality Fusion Technology

With the continuous development of urban rail transit systems, indoor navigation and emergency response have become vital issues. Based on the virtual reality fusion technology, and with deep learning and principal component analysis, this paper proposes the indoor navigation and emergency response scheme for rail transit stations. Deep learning neural network is used for scene recognition, and principal component analysis extracts indoor scene features. The results of numerical examples show that the scene feature values, scene complexity, deep learning network parameters, and positioning errors of different sample libraries impact the results of navigation and emergency response. This paper reveals the mechanism of indoor navigation and emergency response of rail transit stations through a theoretical analysis framework. At the same time, we emphasize quality control, interaction mechanism construction, and evaluation systems. In the context of high-quality development, this paper provides theoretical support to meet the needs of urban rail transit systems in the new era. It improves the possibility of achieving high-quality development goals.