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This paper explores the utilization of digital twin technology for the management of urban road infrastructure challenges, specifically addressing issues related to snow and water accumulation. The study emphasizes the constraints associated with traditional road maintenance methods and proposes the implementation of a digital twin city framework to augment real-time monitoring and enhance emergency response efficiency. Through the integration of real-time data from sensors, traffic, and weather sources, the digital twin model replicates road conditions and forecasts potential issues, thereby enabling early warning systems. Furthermore, the research delves into the application of conductive asphalt concrete and superhydrophobic materials to mitigate road-related challenges. The results indicate that digital twin technology significantly enhances economic and operational efficiency in urban road infrastructure management when compared to conventional approaches.

Aiming at the problems of low manual assembly process efficiency and low yield of micro-miniature parts. A vision system based on ORB feature matching for fast coaxial alignment of micro-miniature parts automated assembly is proposed. The coaxial alignment module is the main hardware, the assembly part and the base part can be imaged in the industrial camera; the ORB features are extracted from the known part template image and the part image obtained by the vision imaging system, and the RANSAC algorithm is adopted to match the ORB features of the template image and the actual part image, the pose of parts is calculated by feature matching results. At the same time, the image coordinate system and the motion axis coordinate system are calibrated, and the motion control system is driven by the transformation matrix of the two coordinate systems to complete the assembly between the assembly part and the base part. The assembly experiment shows the system can complete the automated assembly of micro-miniature parts.