Early Warning Method for Road Construction Based on Natural Language Processing

In advancing smart city construction, the real-time and accurate extraction of road construction information has become crucial for urban traffic management and power grid maintenance. Traditional methods, which rely on manual reporting and government announcements for information gathering, are time-consuming and inefficient. This paper proposes integrating data mining and natural language processing (NLP) technologies to merge multi-source heterogeneous road construction information to predict the risks associated with power grid lines. Using advanced NLP techniques and pre-trained models, key information such as construction sites, timings, and reasons are effectively extracted from the collected data, providing an efficient solution for proactive construction risk management.