Design and Simulation Research on Digital Twin Task Driven Dynamic Control Model for Manufacturing Enterprises
Abstract
This article proposes a task driven dynamic operation management model based on digital twin technology from the perspective of manufacturing enterprises adapting to complex and diverse environments to improve management performance. It comprehensively utilizes various methods of information collection and fusion, real time status response feedback control, and task priority sorting to achieve a dynamic optimal structure that supports collaboration of multiple task operations. By using Anylogic multi agent simulation tool to model and simulate the model, multiple typical scenarios for task execution were set, and the model was evaluated for resource utilization, response time delay, and task scheduling quality. The experimental results showed that the model in this paper can effectively improve task execution efficiency (by 12.4%), task resource allocation and matching degree (by 17.8%), and task response timeliness (by an average time reduction of 23.5%), which can meet the requirements of intelligent production and business management.
References
In the process of intelligent transformation, manufacturing enterprises often face problems such as complex scheduling, delayed feedback, and unstable operation of information systems. This study proposes a dynamic operation and control model for information systems based on digital twins, which relies on task mapping mechanisms to reflect manufacturing status in real time and optimizes multi node path decision-making through dynamic programming strategies. The model structure covers key aspects such as virtual real synchronization modeling, structured task generation, dynamic path evolution, and system integration operation, enhancing the system's perception breadth and control capabilities. The experiment is based on a typical manufacturing dataset for simulation, covering task scheduling and path ablation testing, to verify the superiority of the model in resource utilization, scheduling latency, and stability. Research can provide support for manufacturing enterprises to build a future oriented intelligent information system control framework.
DOI: https://doi.org/10.31449/inf.v49i10.9667
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