工业中的数字孪生:全面综述_41页_2mb
报告摘要
Digital Twin in Industries: A Comprehensive Survey Summary
Abstract Summary
Digital Twin (DT) technology integrates digital replicas with physical systems to enhance operational efficiency and enable real-time monitoring across diverse industrial sectors. This survey comprehensively examines DT fundamentals, its key enablers (including edge computing, machine learning, and wireless communications), and its applications in manufacturing, healthcare, transportation, energy, agriculture, space, and robotics.
Key Points
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Digital Twin Fundamentals: DT creates dynamic virtual representations of physical systems, enabling real-time monitoring, simulation, and analysis for improved decision-making.
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Enabling Technologies: The integration of edge computing (reducing latency), machine learning (predictive analytics), and advanced wireless communication systems (supporting real-time data) powers DT capabilities.
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Industrial Applications:
- Manufacturing: Enhances production efficiency through predictive maintenance and supply chain optimization.
- Healthcare: Facilitates personalized treatment plans, improves patient monitoring.
- Transportation: Optimizes fleet management, traffic control, and autonomous vehicle operations.
- Energy: Supports grid management, renewable energy integration, and energy storage optimization.
- Agriculture: Enables precision farming and optimized resource management.
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Security and Privacy: DT-based systems face challenges related to operational software attacks, privilege escalation, rogue devices, and data extraction. Countermeasures include enhanced encryption, access control, and runtime verification frameworks.
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Future Directions: Research should focus on DT integration with 6G networks, big data analytics, standardized frameworks, and quantum computing to further enhance industrial capabilities and security.
This survey underscores the transformative potential of DT technology across industries while identifying critical research challenges and future opportunities.
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