Big Data-Driven Agent Modeling and Dynamic Collaborative Optimization for Industrial Processes

Abstract

The deep integration of the Industrial Internet of Things and big data is driving a paradigm shift in industrial process modeling. Traditional mechanistic models struggle to capture nonlinearity and time-varying characteristics, while data-driven methods lack causal logic. To address this, this paper proposes a big data-driven framework for agent-based modeling and dynamic collaborative optimization in industrial processes. At the individual agent level, the framework constructs a structured representation integrating graph neural networks and knowledge graphs through spatiotemporal alignment of multi-source data and deep feature extraction. It incorporates physical information constraints to achieve mechanism-data hybrid modeling, and combines neural architecture search with reinforcement learning to generate autonomous decision-making agents. At the collaborative level, a dynamic topology and lightweight protocol based on process coupling degree is designed. Hierarchical reinforcement learning and game theory are utilized for task decomposition and conflict resolution, while federated learning and topology reconstruction enhance fault tolerance and self-healing capabilities. At the evolutionary level, an edge-cloud collaborative inference framework is established. Online learning and concept drift detection enable parameter adaptation, and multi-objective evolutionary algorithms combined with emergence monitoring regulate collective behavior. This research provides a theoretical framework and technical pathway for building industrial intelligent systems characterized by autonomous perception, collaborative decision-making, and continuous evolution.