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

Abstract

The Model of Industrial Process Modelling is being revised in light of the deep integration of Industrial Internet of Things and Big Data. The previous mechanical model is too simple and not very general; therefore, data-driven methods cannot provide causal explanations. Therefore, the framework of Big Data-driven Agent-based Modelling and Dynamic Collaborative Optimisation in Industrial Processes is put forward here. Graph Neural Networks and Knowledge Graphs are combined at the level of a single agent to organise the data and integrate information from all places and times in order to learn deep features. Constraints on the physical information have been added for the construction of the mechanism-data hybrid model, and reinforcement learning is used to develop an autonomous decision-making agent. Change the topology and lightweight protocol of collaboration according to the degree of process coupling. Hierarchical Reinforcement Learning and Game Theory are used to divide the problem and resolve conflicts; Federated Learning and Topology Reconstruction are employed to improve fault tolerance and self-healing. The Development of the Stage for the Edge-Cloud Collaborative Inference Framework has begun. Online learning and concept drift detection are used to change the parameters, and then multi-objective evolutionary algorithms with emergence monitoring control the behaviour of the group. Provide the theoretical basis and technical path for the construction of intelligent industrial systems with autonomous perception, cooperative decision-making and continuous development.