Numéro |
Sci. Tech. Energ. Transition
Volume 79, 2024
Emerging Advances in Hybrid Renewable Energy Systems and Integration
|
|
---|---|---|
Numéro d'article | 56 | |
Nombre de pages | 13 | |
DOI | https://doi.org/10.2516/stet/2024055 | |
Publié en ligne | 26 août 2024 |
Regular Article
Multi-dimensional early warning of the entire supply chain of power materials based on RFID technology
Supply Chain Service Center, Shenzhen Power Supply Co., Ltd., Shenzhen 223003, China
* Corresponding author: siping_h@126.com
Received:
24
May
2024
Accepted:
10
July
2024
Early warning system needs to process a large amount of real-time data, and carry out in-depth analysis and mining of these data to identify potential risks and hidden dangers. However, existing data processing and analysis capabilities may not be able to meet this demand. To this end, a multi-dimensional early warning of the entire supply chain of power materials based on radio frequency identification (RFID) technology is designed. According to the real-time failure probability of power materials, the probability of early warning accidents is calculated and classified, and the risk is graded based on these probabilities. Through the methods of bonus points, deduction points, and grade evaluation, the risk early warning indicators of different stages in the whole supply chain of power materials were quantified. The objective and rationality of risk assessment can be ensured by means of comprehensive weight. A multi-dimensional early warning system based on RFID technology is established, combining multiple linear regression models and particle swarm optimization algorithms to determine the time window of multi-dimensional early warning, and carry out dynamic monitoring and early warning of the supply chain. The experimental results show that the early warning effect of the design method can reach 95% and the highest early warning effect can reach 98% at 10 s. The average warning error is only 2.91%, and the average warning time is only 1.34 s, which is more accurate in identifying the number of first-level risks, second-level risks, and third-level risks.
Key words: RFID technology / Electric power materials / Full supply chain / Multi-dimensional early warning / Time window
© The Author(s), published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.
Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.
Le chargement des statistiques peut être long.