A Dynamic Resource Scheduling Model for Advertising Production in Virtual-Physical Fusion Scenarios
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Abstract.Virtual-physical fusion advertising production integrates physical filming with virtual elements, presenting new challenges for resource management. This paper proposes a dynamic resource scheduling model for virtual-physical fusion advertising production, employing a three-tier resource pool architecture design to establish multi-dimensional resource profiles and real-time status monitoring mechanisms. An adaptive scheduling strategy engine was developed to achieve data-driven decision optimisation and feedback control, effectively addressing issues such as fluctuating computing power, multi-source heterogeneous resources, and cross-regional collaboration. Application validation demonstrates that this model significantly improves resource utilisation and reduces task response time, providing an efficient resource scheduling solution for virtual-reality integrated advertising production.
Keywords.
Virtual-reality integration
Advertising production
Resource scheduling
Dynamic scheduling
Multi-dimensional resource profiling
Adaptive algorithms
APA Citation
Meng Xia (2025). A Dynamic Resource Scheduling Model for Advertising Production in Virtual-Physical Fusion Scenarios. Journal of Computer Science and Artificial Intelligence, 4(2), 5-9. https://doi.org/10.54097/4qpkhb33
References
- Kamdjou H M, Baudry D, Havard V, et al. Resource-constrained extended reality operated with digital twin in industrial internet of things [J]. IEEE Open Journal of the Communications Society, 2024, 5: 928-950.
- Wang Y, Wang X, Liu A, et al. Ontology of 3D virtual modeling in digital twin: A review, analysis and thinking [J]. Journal of Intelligent Manufacturing, 2025, 36(1): 95-145.
- Guo X, Zhang F. A Review of the Identification Methods of Digital Twin Coal Rock Driven by Multimodal Model [C]//International Conference on Intelligent Computing. Singapore: Springer Nature Singapore, 2025: 27-37.
- Liu J, Zainudin E S, As’ arry A B, et al. Modeling of the blockchain-empowered cloud 4D printing services collaboration digital twin platform oriented on supply–demand: J. Liu et al [J]. Soft Computing, 2025, 29(2): 977-1004.
- Sun X, Zhang F, Wang J, et al. Digital twin for smart manufacturing equipment: modeling and applications [J]. The International Journal of Advanced Manufacturing Technology, 2025: 1-18.
- Fei X, Jiang H, Zhou J, et al. Digital twin modeling method for wind turbine generator system based on operational mechanism and scenario fusion [J]. The International Journal of Advanced Manufacturing Technology, 2025: 1-23.
- Balasubramaniam S, Sumina S, Kumar K S, et al. Machine learning based models for implementing digital twins in healthcare industry [M]//Metaverse Technologies in Healthcare. Academic Press, 2024: 135-162.
- Su Z, Wang C, Kuang Z. Unfolding cognitive themes and emotion dynamics in the ‘metaverse and tourism’: an LDA-DTM in the Chinese context [J]. Current Issues in Tourism, 2025: 1-23.
- Fang X, Wang H, Liu G, et al. Industry application of digital twin: from concept to implementation [J]. The International Journal of Advanced Manufacturing Technology, 2022, 121(7): 4289-4312.
- Wang C, Liu X, Feng F, et al. A modeling method of digital twin connection model based on virtual sensor [J]. Journal of Intelligent Manufacturing, 2025: 1-19.
- Patel Y S, Townend P, Singh A, et al. Modeling the Green Cloud Continuum: integrating energy considerations into Cloud–Edge models [J]. Cluster Computing, 2024, 27(4): 4095-4125.
- Mohamed A G, Marzouk M. A BIM-based semantic approach for fund-allocation of building components [J]. Architectural Engineering and Design Management, 2024, 20(3): 471-493.
- Zhao Z. A Comprehensive Review of Reinforcement Learning in Intelligent Allocation and Optimization of Educational Resources [J]. Journal of Economic Theory and Business Management, 2024, 1(6): 15-24.
- Yang C, Zhao B, Zhou Q, et al. MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing [J]. arXiv preprint arXiv:2412.13684, 2024.
- Zhang S. Hyper-parameter tuning for text guided image editing [J]. arXiv preprint arXiv:2407.21703, 2024.