Design and Implementation of a Multidimensional Visualization Reconstruction System for Old Urban Spaces Based on Neural Networks

Abstract

This article presents an in-depth study and analysis of the construction of a convolutional neural network model and multidimensional visualization system of old urban space and proposes the design of a multifaceted visualization reconstruction system of old urban space based on a neural network. It also quantitatively analyzes the essential spatial attribute characteristics of urban shadow areas as nodes of the overall urban dynamic network in three dimensions—spatial connection strength, spatial connection distance, and spatial connection direction—summarizes the characteristics of urban old spatial structure from the perspective of a dynamic network, and then proposes the model of urban old spatial design from the perspective of an active network. The shallow depth of the network structure is used to reduce the parameters in the learning process of reconfigurable convolutional neural networks using data sets so that the model learns more general features. For the situation where the number of data sets is small, data augmentation is used to expand the size of the data sets and improve the recognition accuracy of the reconfigurable convolutional neural network. A real-time update method of multifaceted data visualization for big data scenarios is proposed and implemented to reduce the network load and network latency caused by charts of multidimensional data changes, reduce the data error rate, and maintain the system stability in the old urban space concurrency scenario.