AI Driven Path for Activating Intangible Cultural Heritage in Design Protection Practice of Intelligent Reconstruction of Traditional Craftsmanship Elements and Modern Design Transformation

Main Article Content

W. Zhang

Abstract

Against the backdrop of cultural digitalization and the rapid development of intelligent information technologies, the activation and sustainable inheritance of design-oriented intangible cultural heritage (ICH) face challenges including inheritance discontinuity, insufficient innovation, and limited market adaptability. Traditional craftsmanship elements, particularly those embedded in textile-related cultural heritage, contain rich aesthetic and technical knowledge that requires systematic digital reconstruction for contemporary applications. Leveraging the capabilities of artificial intelligence in pattern recognition, data mining, intelligent generation, and information processing, this study constructs a four-dimensional analytical framework integrating “AI technology–element reconstruction–design transformation –dynamic inheritance”. The framework systematically investigates the internal mechanism of AI-driven activation of traditional craftsmanship elements and proposes a four-stage activation model involving element deconstruction, intelligent reconstruction, design transformation, and market validation. Through the digital extraction and intelligent reorganization of traditional weaving, dyeing, embroidery, and related craft elements, the proposed approach enhances design efficiency, cultural fidelity, and market adaptability. The study provides a practical pathway for the intelligent preservation and innovative transformation of traditional craftsmanship and offers methodological references for digital cultural communication and intelligent design systems.

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How to Cite
Zhang, W. (2026). AI Driven Path for Activating Intangible Cultural Heritage in Design Protection Practice of Intelligent Reconstruction of Traditional Craftsmanship Elements and Modern Design Transformation. Advanced Electromagnetics, 15(3), 6921–6930. https://doi.org/10.7716/aem.v15i3.3771
Section
Research Articles

References

Z. Yang, Y. Li, L. Sun, et al., “Intelligent Logistics Sorting Technology Based on PaddleOCR and SMITE Parameter Tuning,” Applied Sciences, vol. 16, no. 2, pp. 767-767, 2026, doi: 10.3390/app16020767.

View Article

Z. Wang, Y. Luo, Y. Du, et al., “Merge-YOLO: An accurate detection model for book packaging defects in intelligent logistics scenarios,” PLOS One, vol. 21, no. 1, Art. no. e0340205, 2026, doi: 10.1371/journal.pone.0340205.

View Article

L. Nina, “Research on Intelligent Logistics Path Optimization Algorithm Based on Deep Learning,” Journal of Electrical Engineering & Technology, pp. 1-11, 2025, doi: 10.1007/s42835-025-02527-5.

View Article

W. Zhang, Z. Qian, S. Ma, et al., “Optimization of intelligent logistics strategies for platform-based supply chain management networks,” Computers & Industrial Engineering, vol. 212, Art. no. 111689, 2026, doi: 10.1016/j.cie.2025.111689.

View Article

O. D. Y. Ozen, C. Akcicek, and Y. Ozturkoglu, “Machine learning applications in smart logistics: analysing barriers for future practices,” Journal of Engineering, Design and Technology, vol. 23, no. 6, pp. 2105-2123, 2025, doi: 10.1108/JEDT-03-2024-0137.

View Article

E. Arsenio, T. J. Aparicio, R. Henriques, et al., “Assessing the co-evolution of intermodal freight transport research and patenting technology trends for advancing green and intelligent logistics,” Research in Transportation Business & Management, vol. 64, Art. no. 101553, 2026, doi: 10.1016/j.rtbm.2025.101553.

View Article

J. Ryu, S. Jang, B. Park, et al., “Design and deployment of an integrated network architecture leveraging RabbitMQ for optimizing automation systems in smart logistics,” Computers and Electrical Engineering, vol. 128, no. PA, Art. no. 110721, 2025, doi: 10.1016/j.compeleceng.2025.110721.

View Article

H. J. Tee, I. M. Solihin, S. K. Chong, et al., “Advancing Intelligent Logistics: YOLO-Based Object Detection with Modified Loss Functions for X-Ray Cargo Screening,” Future Transportation, vol. 5, no. 3, Art. no. 120, 2025, doi: 10.3390/futuretransp5030120.

View Article

Z. Zararsiz, “Development of a mathematical model using bipolar fuzzy credibility number and application of intelligent logistics management and warehousing to a multi-attribute decision making method,” International Journal of System Assurance Engineering and Management, vol. 16, no. 9, pp. 1-17, 2025, doi: 10.1007/s13198-025-02849-7.

View Article

N. Wang, “Application of Improved Genetic Algorithm Based on Multi-objective Optimization in the Layout of Intelligent Logistics Park,” Modern Economics & Management Forum, vol. 6, no. 4, 2025, doi: 10.32629/memf.v6i4.4240.

View Article

A. Arishi and P. Ahuja, “Multi-Agent Reinforcement Learning for truck-drone routing in smart logistics: A comprehensive review,” Computers and Electrical Engineering, vol. 127, no. PA, Art. no. 110529, 2025, doi: 10.1016/j.compeleceng.2025.110529.

View Article

A. Hlali, Emerging Trends in Smart Logistics Technologies. IGI Global Scientific Publishing, 2025, doi: 10.4018/979-8-3373-2434-0.

View Article

H. Tan and J. Zhang, “An Examination of How Intelligent Logistics Systems Affect Cross-border E-commerce’s Operational Effectiveness,” Frontiers in Economics and Management, vol. 6, no. 3, pp. 40-49, 2025, doi: 10.6981/FEM.202503_6(3).0006.

View Article

S. Zhang, Q. Han, H. Zhu, et al., “Real time task planning for order picking in intelligent logistics warehousing,” Scientific Reports, vol. 15, no. 1, Art. no. 7331, 2025, doi: 10.1038/s41598-025-88305-9.

View Article

C. Ruan and W. Su, “Research on Robot+ Campus Intelligent Logistics Simulation Based on PQFactory Platform,” Frontiers in Computing and Intelligent Systems, vol. 11, no. 2, pp. 11-15, 2025, doi: 10.54097/hkst6m46.

View Article

L. Liu, Y. Chen, A. Li, et al., “Research on Intelligent Logistics Warehouse Scheduling Optimization Based on Integrated Reinforcement Learning,” International Journal of High Speed Electronics and Systems, 2025, doi: 10.1142/S0129156425407156.

View Article

W. A. Abbas, K. N. S. Marwat, A. A. Fuqaha, et al., “Mathematical Modelling of ANP for Trust Based IoT Device Categorization in Secured Smart Logistics,” Journal of Network and Systems Management, vol. 33, no. 3, Art. no. 69, 2025, doi: 10.1007/s10922-025-09941-0.

View Article

Z. Feng, “Analysis of Technology Brokerage-Driven Supply Chain Collaboration Mechanisms in Smart Logistics,” Exploration of Educational Management, vol. 3, no. 6, 2025, doi: 10.12417/3029-2328.25.06.001.

View Article

W. Dilmi, E. S. Ferik, F. Ouerdane, et al., “Technical Aspects of Deploying UAV and Ground Robots for Intelligent Logistics Using YOLO on Embedded Systems,” Sensors (Basel, Switzerland), vol. 25, no. 8, Art. no. 2572, 2025, doi: 10.3390/s25082572.

View Article

L. Shao, Y. Liu, and J. Shao, “Research on the Application of Artificial Intelligence Technology in Intelligent Logistics Scheduling,” Frontiers in Computing and Intelligent Systems, vol. 11, no. 3, pp. 95-97, 2025, doi: 10.54097/rkmksg13.

View Article

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