Building Protection Data Release Planning Based on Multifeature Deep Learning

Abstract

With the rapid development of China’s economy, the protection of buildings has attracted the attention of many researchers. Although there is no such massive demolition in the past, natural damage still exists. Identify the collected historical building protection data through multifeature deep learning, and provide protection plans through the information in the database. In order to solve the problem of restoration of natural damage more professionally and efficiently, this paper collects the architectural features and restoration methods of each building in different processes through multifeature deep learning based on the current state of building information in China. Based on the collected information, this paper establishes the building information model, and stores and manages the building information. According to the Newton deep learning optimization algorithm, this paper enhances the algorithm to accurately collect building information and uses the collaborative filtering algorithm to provide users with a repair plan. This paper uses the GRU-based recommendation model to pass the threshold cycle unit algorithm for the probability of each building being selected in the list of similar buildings at a time point. Under the two conditions of 10 and 20 recommended numbers, the user coverage rate of the recommended case of deatomized building photos can reach 100%. And this paper recommends high-probability solutions for users to achieve automation, diversification, and intelligence.

City Architectural Color Recognition Based on Deep Learning and Pattern Recognition

“…Pattern recognition is primarily the process of describing, classifying, judging, and recognizing various things in the real world using a computer imitation of the human brain. Guo Chunhua et al used Newton's deep learning optimization algorithm to enhance the accurate collection of building information and utilized a collaborative filter algorithm to provide users with maintenance meth-ods [10]. Huang Yong et al utilized deep learning to assess and rate the damage state of buildings, providing valuable assistance for post-disaster reconstruction [11].…”