Cyber-Physical System Improve by AI Models in Causal Approach for Energy Supply (on Transport) Systems

Abstract

With the development of control materials and information technologies, cyber-physical systems have become a pivotal area for the description and improvement of large-scale interdisciplinary objects. These systems represent the integration of computing, networking, and physical processes, where embedded computers and networks monitor and control the physical processes, often with feedback loops where physical processes aff ect computations and vice versa. In today’s society, as technology continues to advance, the challenges we face also become increasingly complex. Understanding and improving complex systems, such as energy supply and traffi c management, require not only technological support but also a deep understanding of the “semantics” of these systems-namely, the meanings and relationships between components of the system. This paper aims to explore how semantic models built on cause-and-effect relationships can help us better understand and optimize complex systems, and how these models can enhance our comprehension and improvement of such systems.