The Intersection of Blockchain Technology and Knowledge Engineering for Secure Data Sharing in AI-Enabled Intelligent Engineering Systems: A Review

Abstract

Blockchain and knowledge engineering work together, which can help to share data safely and meaningfully in intelligent AI system, especially in intelligent manufacturing, energy infrastructure and self-operated network physical system (CPS). Using the principles of system engineering, CPS theory and ideas about integrity, delay and semantic fidelity, this paper focuses on how decentralized ledgers provide unchangeable sources and untrusted verification, and how ontologies and knowledge maps provide the meaning needed to understand different data between organizations. Artificial intelligence technologies-such as reinforcement learning for adaptive sharing strategies, computer vision for real-time verification of the real world, and edge computing for local fast computing-provide more help by allowing dynamic optimization and verifiable decision logs. This paper uses recent books to analyze the theoretical basis, integration methods, examples in manufacturing and important infrastructure, and the tension between safety benefits and performance, growth capacity and rules. People have always kept a critical eye, comparing better traceability and collective intelligence with risks, such as consensus delay in real-time circulation, meaning problems and new attack methods unique to AI. The new idea includes a larger CPS feedback model with verifiable sensory points and a proposed ontology consensus governance mechanism. Finally, the review shows the standardization of mixed architecture paths and priorities, which is very important for building a strong, clear and intelligent engineering ecosystem.