Author(s)

Xiao Zheng

Corresponding Author

Xiao Zheng

Abstract

As an important part of urban infrastructure, the construction efficiency and quality of municipal road projects directly affect urban development and residents' lives. However, there are some problems in the traditional project management mode, such as data fragmentation, insufficient response and resource mismatch, which lead to time overruns and increased costs. The purpose of this study is to explore the intelligent control method of municipal road engineering progress and quality based on multi-source data drive. Firstly, an "air-sky-ground" integrated data collection network is constructed, which integrates heterogeneous data such as Internet of Things (IoT), unmanned aerial vehicle (UAV), video surveillance and manual reporting, and preprocesses them to lay the foundation for subsequent analysis. Secondly, a multi-modal fusion model based on deep learning is proposed to realize the deep fusion of visual data and sensing data, and a virtual-real linkage system is constructed by combining digital twinning technology. Finally, an intelligent identification and prediction algorithm is designed to realize dynamic prediction of progress, early warning and accurate traceability of quality defects, and a resource optimization model is constructed to realize dynamic allocation of resources. Through case verification, the intelligent management and control system can effectively improve the efficiency and quality of municipal road engineering construction and reduce the comprehensive cost. This study provides theoretical and technical support for intelligent management and control of municipal road engineering, and has broad application prospects.

Keywords

Intelligent management and control, municipal road, project progress and quality, multi-source data