AI Agent-Driven Intelligent Catalog Framework: A Governance-Centered Approach for Cleaning and Normalization of Heterogeneous Industrial Sensor Data.

The rapid development of the Industrial Internet of Things (IIoT) generates massive heterogeneous sensor data, complicating data cleaning and normalization. Existing algorithmcentric methods often treat quality issues in isolation and lack unified governance. This paper proposes a governance-centered framework for multi-source industrial sensor data. We introduce an Intelligent Catalog as the semantic governance layer to standardize metadata and achieve semantic alignment before numerical processing. Building upon this, an AI Agent-driven mechanism dynamically orchestrates cleaning and normalization strategies based on real-time data status and heterogeneous features. This framework modularly integrates classical algorithms (e.g., PCA, KPCA, LSTM) without model dependency. Experimental results on public IIoT datasets demonstrate that our framework significantly outperforms baseline methods in normalization consistency, noise robustness, and stability across heterogeneous data. By shifting from an algorithm-centered to a governance-centered paradigm, this approach provides a scalable and adaptive solution for complex industrial sensor data management.