In-Depth Discussion on the Construction of Security Collaborative Management and Control System Based on Digital Twins and Intelligent Sensing in the United Nations Clean Coal Project
Main Article Content
Abstract
With the rapid development of Electromagnetic Waves, Antennas and Propagation technologies, communication-enabled sensing infrastructures and wireless monitoring networks have become fundamental components for intelligent industrial safety management. To address the challenges of dynamic risk perception, fragmented information exchange, and delayed emergency response in large-scale clean coal projects, this study proposes a security collaborative management and control framework integrating digital twins and intelligent sensing under a unified “one platform, two mappings, three linkages” architecture. The framework combines digital twin synchronization, IoT sensing, UWB– Bluetooth positioning, AI-based behavior recognition, edge computing, and multi-source data fusion to establish real-time interactions between physical entities and virtual models for dynamic risk prediction and collaborative decision-making. A quantitative risk assessment model and collaborative response index are further introduced to support adaptive warning generation, intelligent emergency dispatching, and closed-loop safety management. Experimental implementation demonstrates an early intervention rate of 93.9%, hazard identification accuracy above 95%, emergency response time below 5 minutes, and resource scheduling accuracy exceeding 96%, confirming the effectiveness of the proposed architecture. The integrated framework provides a scalable methodology for communication-oriented sensing, distributed information propagation, intelligent monitoring, and cyber–physical system coordination, offering valuable engineering references for smart industrial infrastructures and Electromagnetic Waves, Antennas and Propagation applications.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
K. Asgari, Q. Huang, H. Khoshdast, et al., “A review on bioflotation of coal and minerals: classification, mechanisms, challenges, and future perspectives,” Mineral Processing and Extractive Metallurgy Review, vol. 45, no. 1, pp. 46-76, 2024, doi: 10.1080/08827508.2022.2121919.
I. Rashid M, A. Isah U, M. Athar, et al., “Energy and Chemicals Production from Coal-based Technologies: A Review,” ChemBioEng Reviews, vol. 10, no. 5, pp. 841-851, 2023, doi: 10.1002/cben.202200023.
R. Srivastava R, K. Rajak D, S. Ilyas, et al., “Challenges, regulations, and case studies on sustainable management of industrial waste,” Minerals, vol. 13, no. 1, pp. 51-57, 2022, doi: 10.3390/min13010051.
H. Zhan, G. Hwang B, and P. Krishnankutty, “Embracing digital transformation for sustainable development: Barriers to adopting digital twin in asset management within Singapore’s energy and chemicals industry,” Sustainable Development, vol. 33, no. 2, pp. 2864-2887, 2025, doi: 10.1002/sd.3270.
N. Zhang, “Research on the Optimization and Application of New Energy Technologies in Clean Coal Conversion,” Journal of Environmental and Building Engineering, vol. 1, no. 1, pp. 1-6, 2024, doi: 10.70767/jebe.v1i1.98.
M. Nikitenko S, V. Goosen E, and O. Kavkaeva, “Modeling of flexible value chains based on clean coal processing technologies,” Russian Mining Industry S, vol. 2, no. 1, pp. 126-134, 2023, doi: 10.30686/1609-9192-2023-S2-126-134.
S. Misra, C. Roy, T. Sauter, et al., “Industrial Internet of Things for safety management applications: A survey,” IEEE Access, vol. 10, no. 1, pp. 83415-83439, 2022, doi: 10.1109/ACCESS.2022.3194166.
H. Liu C and Y. Wu Z, “Advanced authentication of IoT sensor network for industrial safety,” Internet of Things, vol. 27, no. 1, pp. 101297-101310, 2024, doi: 10.1016/j.iot.2024.101297.
A. Zahid, A. Ferraro, A. Petrillo, et al., “Exploring the role of digital twin and industrial metaverse technologies in enhancing occupational health and safety in manufacturing,” Applied Sciences, vol. 15, no. 15, pp. 8268-8276, 2025, doi: 10.3390/app15158268.
H. Wang, L. Lv, X. Li, et al., “A safety management approach for Industry 5.0′ s human-centered manufacturing based on digital twin,” Journal of Manufacturing Systems, vol. 66, no. 1, pp. 1-12, 2023, doi: 10.1016/j.jmsy.2022.11.013.
J. Park and D. Kang, “Artificial intelligence and smart technologies in safety management: a comprehensive analysis across multiple industries,” Applied Sciences, vol. 14, no. 24, pp. 11934-11942, 2024, doi: 10.3390/app142411934.
Y. Wang and H. Chung S, “Artificial intelligence in safety-critical systems: a systematic review,” Industrial Management & Data Systems, vol. 122, no. 2, pp. 442-470, 2022, doi: 10.1108/IMDS-07-2021-0419.
A. Abdelatif, D. Nettour, R. Chaib, et al., “Improvement of enterprise risk visualization: risk mapping,” Technology audit and production reserves, vol. 6, no. 2/74, pp. 28-36, 2023, doi: 10.15587/2706-5448.2023.291959.
L. Gualtieri, A. Revolti, and P. Dallasega, “A human-centered conceptual model for integrating Augmented Reality and Dynamic Digital Models to reduce occupational risks in industrial contexts,” Procedia Computer Science, vol. 217, no. 1, pp. 765-773, 2023, doi: 10.1016/j.procs.2022.12.273.
Z. Nazir and J. Bollen M H, “Graphical Ways to Visualize Operational Risk Results for Transmission System Contingencies,” Electricity, vol. 3, no. 3, pp. 442-462, 2022, doi: 10.3390/electricity3030023.
A. Barlybayev, A. Sharipbay, G. Shakhmetova, et al., “Development of a Flexible Information Security Risk Model Using Machine Learning Methods and Ontologies,” Applied Sciences, vol. 14, no. 21, pp. 9858-9861, 2024, doi: 10.3390/app14219858.
A. Wahid, G. Breslin J, and A. Intizar M, “Prediction of machine failure in industry 4.0: a hybrid CNN-LSTM framework,” Applied Sciences, vol. 12, no. 9, pp. 4221-4231, 2022, doi: 10.3390/app12094221.
J. Vanga, P. Ranimekhala D, S. Jonnala, et al., “Fault classification of three phase induction motors using Bi-LSTM networks,” Journal of Electrical Systems and Information Technology, vol. 10, no. 1, pp. 28-36, 2023, doi: 10.1186/s43067-023-00098-x.
L. Coutinho R W and A. Boukerche, “Design of edge computing for 5g-enabled tactile internet-based industrial applications,” IEEE Communications Magazine, vol. 60, no. 1, pp. 60-66, 2022, doi: 10.1109/MCOM.001.21261.
M. Sharma, A. Tomar, and A. Hazra, “Edge computing for industry 5.0: Fundamental, applications, and research challenges,” IEEE Internet of Things Journal, vol. 11, no. 11, pp. 19070-19093, 2024, doi: 10.1109/JIOT.2024.3359297.
B. Srinivasan, U. Iqbal M, A. Shahab M, et al., “Review of virtual reality (VR) applications to enhance chemical safety: from students to plant operators,” ACS Chemical Health & Safety, vol. 29, no. 3, pp. 246-262, 2022, doi: 10.1021/acs.chas.2c00006.
V. Holusa, M. Vanek, F. Benes, et al., “Virtual reality as a tool for sustainable training and education of employees in industrial enterprises,” Sustainability, vol. 15, no. 17, pp. 12886-12893, 2023, doi: 10.3390/su151712886.
P. Fraga-Lamas, M. Fernández-Caramés T, R. da Cruz A M, et al., “An overview of blockchain for Industry 5.0: towards human-centric, sustainable and resilient applications,” IEEE Access, vol. 12, no. 1, pp. 116162-116201, 2024, doi: 10.1109/ACCESS.2024.3435374.
S. Ahmadisheykhsarmast, S. Aminbakhsh, R. Sonmez, et al., “A transformative solution for construction safety: Blockchain-based system for accident information management,” Journal of industrial information integration, vol. 35, no. 1, pp. 100491-100501, 2023, doi: 10.1016/j.jii.2023.100491.
N. Gupta, “From Data Silos to Unified Intelligence: Building a Scalable Data Management Strategy,” International Journal of Scientific Research in Science, Engineering and Technology, pp. 395-397, 2023, doi: 10.32628/ijsrset2358718.
R. Kusumawati, “Integrating big data analytics into supply chain management: Overcoming data silos to improve real-time decision-making,” International Journal of Advanced Computational Methodologies and Emerging Technologies, vol. 15, no. 2, pp. 17-26, 2025.
O. Fei T A, N. Xuemin S U, U. Yonghuai Z H, et al., “makeTwin: A reference architecture for digital twin software platform,” Chinese Journal of Aeronautics, vol. 37, no. 1, pp. 1-18, 2024, doi: 10.1016/j.cja.2023.05.002.
P. Kovalyov S, “Distributed energy resources management: from digital twin to digital platform,” IFAC-PapersOnLine, vol. 55, no. 9, pp. 460-465, 2022, doi: 10.1016/j.ifacol.2022.07.080.
P. Jia, X. Wang, and X. Shen, “Accurate and efficient digital twin construction using concurrent end-to-end synchronization and multi-attribute data resampling,” IEEE Internet of Things Journal, vol. 10, no. 6, pp. 4857-4870, 2022, doi: 10.1109/JIOT.2022.3221012.
Y. Jiang, M. Li, M. Li, et al., “Digital twin-enabled real-time synchronization for planning, scheduling, and execution in precast on-site assembly,” Automation in Construction, vol. 141, no. 1, pp. 104397-104403, 2022, doi: 10.1016/j.autcon.2022.104397.
R. Rashidi M and S. Chinara, “A Comprehensive Study and Performance Evaluation of Indoor Positioning Methods in BLE and UWB Technologies,” IEEE Internet of Things Journal, vol. 12, no. 22, pp. 46475-46499, 2025, doi: 10.1109/JIOT.2025.3611794.
M. Rosiak, M. Kawulok, and M. Ma´ckowski, “The effectiveness of UWB-based indoor positioning systems for the navigation of visually impaired individuals,” Applied Sciences, vol. 14, no. 13, pp. 5646-5649, 2024, doi: 10.3390/app14135646.
K. Dhalmahapatra, S. Das, and J. Maiti, “On accident causation models, safety training and virtual reality,” International journal of occupational safety and ergonomics, vol. 28, no. 1, pp. 28-44, 2022, doi: 10.1080/10803548.2020.1766290.
H. Stefan, M. Mortimer, and B. Horan, “Evaluating the effectiveness of virtual reality for safety-relevant training: a systematic review,” Virtual Reality, vol. 27, no. 4, pp. 2839-2869, 2023, doi: 10.1007/s10055-023-00843-7.
J. Sanchez-Cubillo, J. Del Ser, and L. Martin J, “Toward fully automated inspection of critical assets supported by autonomous mobile robots, vision sensors, and artificial intelligence,” Sensors, vol. 24, no. 12, pp. 3721-3735, 2024, doi: 10.3390/s24123721.
M. Daud M, M. Saad M H, H. Kamarudin N, et al., “APPLICATION OF AUTOMATED INTELLIGENT VIDEO SURVEILLANCE SYSTEM WITH DEEP LEARNING CAPABILITY FOR SAFETY MONITORING IN MANUFACTURING SECTOR,” Technology Management, vol. 8, no. 33, pp. 42-56, 2023, doi: 10.35631/JISTM.833004.