Research on intelligent maintenance and energy consumption optimization of green highway driven by intelligent perception and big data

With the continuous promotion of the national “dual-carbon” strategy, the construction and operation and maintenance of green transportation infrastructure are gradually entering into a new stage of intelligence and low-carbonization. As an important transportation carrier, the energy consumption and maintenance efficiency of highway in its whole life cycle has become a key issue in the construction of green transportation system. This paper focuses on the goal of intelligent maintenance and energy consumption optimization of green highway, and systematically researches the solution path based on intelligent perception and big data technology. A multi-source heterogeneous sensing system is constructed to realize real-time sensing of road structural status, environmental conditions and operational loads; big data analysis and artificial intelligence algorithms are integrated to propose intelligent maintenance and fault prediction methods for the whole life cycle of the road; and energy management and carbon emission control in the process of highway operation and maintenance are realized by constructing energy consumption analysis models and optimization strategies. The study shows that the in-depth integration of intelligent perception and big data technology can not only significantly improve the operational safety and maintenance efficiency of highway facilities, but also effectively reduce energy consumption and carbon emissions, providing new ideas and technical support for the sustainable development of green highway.