AI-Driven Cross-Cloud Operations Language Standardisation and Knowledge Sharing System

Abstract

With the widespread adoption of multi-cloud architectures, intelligent management across multiple clouds has also increased. However, due to the significant differences in interface design, syntax standards, and command rules among different cloud platforms, multi-cloud operation language structures are numerous, unstandardized, and dispersed, greatly affecting the reuse of knowledge and team collaboration efficiency. Therefore, this paper proposes a knowledge collaboration framework for the standardization of intelligent operational languages based on AI-driven cloud interoperability. This framework creates a universal standardized operational language and an intelligent command knowledge base in multi-cloud environments through unified language structure construction, AI semantic parsing, and command knowledge integration technologies. First, starting from the reasons behind cross-cloud language differences, the study explores that the root causes of these differences lie in semantic ambiguity and fragmented knowledge architecture. Then, by leveraging AI-based semantic interpretation models and semantic similarity evaluation methods, common operational language specification elements across different operating systems are constructed to form a single semantic architecture, resulting in a knowledge system that can be learned, transferred, and shared. Finally, relying on a knowledge-graph-based task recommendation strategy, intelligent sharing and reasoning at the semantic level are achieved, promoting multi-level association and automatic reuse of work knowledge.