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Design of Medical Cold Chain Information Acquisition System Based on Linear Prediction

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Abstract

There are many problems in existing medical cold chain information acquisition system, such as poor mobility of monitoring points, short wireless transmission distance, and frequent data errors. In order to deal with the above problems, a medical cold chain information acquisition system is designed based on 5G technology and LoRa self networking technology. At the same time, a self-adaptive secondary Kalman filter method is proposed to predict the collected information linearly. The system is divided into two parts, which are data acquisition module and coordinator. The collected information of the data acquisition module is reported to the coordinator through LoRa network. After the coordinator performs linear prediction processing on the information, the dada is uploaded to the cloud platform through 5G network, which can realize the monitoring of medical cold chain information at any time and anywhere. The system and the proposed method are simulated and tested. The test results show that the system can complete the data acquisition and processing accurately, the system error is small, and the occasional error can be eliminated in time. The proposed method has smaller prediction error than the traditional filtering method.

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Acknowledgements

This research was financially supported by Shandong Province key research and development programs of China (2019GGX105001) and the College Science and technology project of Shandong Province of China (J15LN59, J16LN75, and J16LN78).

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Authors and Affiliations

  1. Qingdao Huanghai University, Qingdao, Shandong, China

    Peixue Liu, Li Dong & Aixia Cao

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  1. Peixue Liu
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  2. Li Dong
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  3. Aixia Cao
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Correspondence to Peixue Liu.

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Liu, P., Dong, L. & Cao, A. Design of Medical Cold Chain Information Acquisition System Based on Linear Prediction. Wireless Pers Commun 115, 1197–1209 (2020). https://doi.org/10.1007/s11277-020-07618-2

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  • DOI: https://doi.org/10.1007/s11277-020-07618-2

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