Edge computing architecture design of asphalt construction quality data collection transmission analysis integration

In recent years, with the rapid improvement of computer computing performance, the application of artificial intelligence (AI) technology in various fields has become increasingly widespread. In asphalt pavement construction, real-time collection, efficient transmission, and accurate analysis of construction quality data are the core links to ensure project quality and safety. However, traditional monitoring methods are inefficient and difficult to meet the needs of modern engineering construction. Edge computing technology greatly shortens the data transmission link by leading the data processing and analysis functions from the cloud to the edge equipment on the construction site, so as to better meet the strict real-time requirements of security monitoring. Based on this background, this paper proposes an intelligent monitoring system combining deep learning (DL) and edge computing for real-time monitoring of asphalt road construction quality. By integrating the efficiency of edge computing and the powerful analytical ability of DL, the system not only improves the accuracy of security monitoring, but also significantly improves the efficiency of supervision. The results show that the system can accurately monitor the construction quality of asphalt pavement, effectively reduce the incidence of safety accidents, and minimize economic losses.