Knowledge Graph- and Bayesian Network-Based Intelligent Diagnosis of Highway Diseases: A Case Study on Maintenance in Xinjiang

The management of highway diseases has entered an era of big data, necessitating advanced methodologies to handle complex, heterogeneous data from diverse sources. This study introduces a novel approach that integrates knowledge graphs and Bayesian networks to enhance the intelligent diagnosis of highway diseases, addressing the unique challenges of road maintenance in diverse geographical contexts. A Bayesian network model was developed by combining expert knowledge with local data features, mathematically representing diagnostic knowledge through a probability matrix. Training data were utilized to compute and identify the optimal Bayesian network, forming the basis of an intelligent diagnostic framework. Empirical analysis of maintenance records from highways in Xinjiang demonstrated the efficiency of this framework, accurately diagnosing both common and specialized diseases while outperforming traditional methods. This approach not only supports intelligent knowledge management and application in highway maintenance but also provides a scalable solution adaptable to varied geographical conditions. The findings offer a pathway for advancing highway disease management, promoting more efficient, precise, and sustainable road maintenance practices.