Abstract
With the continuous development of the logistics industry, it is difficult to ensure the safety of the goods by traditional means of transportation. “Blockchain + ” has gradually become a wave of financial technology sweeping the world. However, the application of any technology is not automatically realized. It needs to provide portrait guidance technology in related fields. Its application research in supply chain finance is not only in line with national policies. With the support of the government, the practical problems in the development of the industry can be solved more effectively. The thoughts and suggestions made from the perspective of industry organizers have direct guiding significance for this article, especially the industry. The application of Internet of things technology in logistics can better meet the customer’s supervision of logistics process. The purpose of this study is to build a supply chain financial logistics supervision system based on Internet of things technology, and analyze its role. In this study, intelligent sensor is used as an important means in logistics supervision. Firstly, goods are classified effectively in the dynamic resource allocation algorithm under limited bandwidth, and then the goods are scanned by RFID technology, and then tracked and supervised in real time. This research is simulated on the MATLAB software of stado distributed database server. The results show that the static algorithm with 5.2 Hz is far less than the 14 Hz of the dynamic allocation algorithm in this study. The total es value of the operational risk based on the Internet of things technology is 303.3446 million yuan, which is 6962.65 less than that of the traditional mode operation This shows that the logistics supervision system based on the Internet of things technology effectively reduces the operational risk of the supply chain fund, and the distribution detection accuracy of the supervision system is high. The conclusion is that the supply chain logistics monitoring system based on Internet of things technology is more effective than traditional logistics mode, the goods are safer and the risk is smaller. This research contributes to the intelligent development of logistics supervision system.





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Adil M, Jan MA, Mastorakis S, Song H, Jadoon MM, Abbas S, Farouk A (2021a) Hash-MAC-DSDV: Mutual authentication for intelligent IoT-based cyber-physical systems. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2021.3083731
Adil M, Song H, Ali J, Jan MA, Attique M, Abbas S, Farouk A (2021b) EnhancedAODV: A robust three phase priority-based traffic load balancing scheme for internet of things. IEEE Internet Things J 9(16):14426–14437
Chang P, Li W, Shi G et al (2018) The VraSR regulatory system contributes to virulence in Streptococcus suis via resistance to innate immune defenses. Virulence 9(1):771–782
Chen C, Zhang KZK, Gong X et al (2019) Dual mechanisms of reinforcement reward and habit in driving smartphone addiction. Internet Res 29(6):1551–1570
Chen W, Wang W (2016) (2016) Positive periodic solutions for a model of gene regulatory system with time-varying coefficients and delays. Adv Difference Equ 1:1–9
Don LX, Nin T, Me KY et al (2017) Mean first passage time and stochastic resonance in a transcriptional regulatory system with non-gaussian noise. Fluctuation Noise Lett 16(01):201–207
Feldheim YS, Zusman T, Speiser Y et al (2016) The Legionella pneumophila CpxRA two-component regulatory system: new insights into CpxR’s function as a dual regulator and its connection to the effectors regulatory network. Mol Microbiol 99(6):1059–1079
Han W, Wang P, Dong H (2020) Influence of egoistic and altruistic bequest motives on the willingness to participate in reverse mortgages in China. Asian Economic J 34(4):430–463
Hasegawa T, Matsumoto M, Hata N et al (2019) Homologous role of CovRS two omponent regulatory system in NAD+ lycohydrolase activity in Streptococcus dysgalactiae subsp equisimilis as in Streptococcus pyogenes. APMIS 127(2):87–92
Huang GC, Liano K, Pan MS (2018) Do open-market stock repurchases convey firm-specific or industry-wide information? Evidence from REITs. J Economics Finance 43(2):382–397
Ishfaq AH, Jamil AM, Muzammal AM et al (2018) Positive selection drives the evolution of endocrine regulatory bone morphogenetic protein system in mammals. Oncotarget 9(26):18435–18445
Jihan L, Hyungjin K, Younwon P (2018) Review of the regulatory periodic inspection system from the viewpoint of defense-in-depth in nuclear safety. Nucl Eng Technol 50(7):997–1005
Khalaf OI, Abdulsahib GM, Kasmaei HD, Ogudo KA (2020) A new algorithm on application of blockchain technology in live stream video transmissions and telecommunications. Intern J e-Collaborat (IJeC) 16(1):16–32
Khalaf OI, Abdulsahib GM (2021) Optimized dynamic storage of data (ODSD) in IoT based on blockchain for wireless sensor networks. Appl, Peer-to-Peer Netw. https://doi.org/10.1007/s12083-021-01115-4
Kitouni I, Benmerzoug D, Lezzar F (2018) Smart agricultural enterprise system based on integration of internet of things and agent technology. J Org End User Comput 30(4):64–82
Lambrecht B, Pawlina G, Teixeira JCA (2016) Making, buying and concurrent sourcing: implications for operating leverage and stock beta. Rev Finance 20(3):1013–1043
Mima T, Gotoh K, Yamamoto Y et al (2018) Expression of collagenase is regulated by the VarS/VarA two-component regulatory system in Vibrio alginolyticus. J Membr Biol 251(1):51–63
Mnn A, Mm B, Sovn C, Vko D, Acr E, Wavj E et al (2019) Activation of the immune-inflammatory response system and the compensatory immune-regulatory system in antipsychotic naive first episode psychosis. Eur Neuropsychopharmacol 29(3):416–431
Nakamoto K (2017) A new pain regulatory system via the brain long chain fatty acid receptor GPR40/FFA1 signal. Yakugaku Zasshi J Pharm Soc Japan 137(2):199–204
Pérez-Morales D, Bustamante V, H. (2016) The global regulatory system Csr senses glucose through the phosphoenolpyruvate: carbohydrate phosphotransferase system. Mol Microbiol 99(4):623–626
Quayes S, Jamal AMM (2016) Impact of demographic change on stock prices. Quart Rev Economics Finance 60(May):172–179
Roy T, Barman S (2016) Design and development of cancer regulatory system by modeling electrical network of gene. Microsyst Technol 22(11):2641–2653
Salem J, Champliaud H, Feng Z et al (2016) Experimental analysis of an asymmetrical three-roll bending process. Int J Adv Manuf Technol 83(9–12):1823–1833
Shi JC, Luo M, Dong T et al (2017) External noise and external signal induced transition of gene switch and coherence resonance in the genetic regulatory system. Acta Biotheor 65(2):135–150
Soane A (2019) Building a safer future: UK government proposals for reform of the building safety regulatory system. Struc Eng 97(7):31–33
Wu W, Liu Y, Wu CH, Tsai SB (2020) An empirical study on government direct environmental regulation and heterogeneous innovation investment. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.120079
Xie E, Reddy KS, Liang J (2017) Country-specific determinants of cross-border mergers and acquisitions: a comprehensive review and future research directions. J World Bus 52(2):127–183
Yang J, Tang G, Tang S (2017) Modelling the regulatory system of a chemostat model with a threshold window. Mathemat Comp Simul. 132:220–235
Yeh JY, Chen CH (2020) A machine learning approach to predict the success of crowdfunding fintech project. J Enterpri Inform Manag. https://doi.org/10.1108/JEIM-01-2019-0017
Zhang C, Qiao M, Yun W (2017) Trinity comprehensive regulatory system about quantity, quality and ecology of cultivated land. Transac Chin Soc Agri Machinery 48(1):1–6
Zhou Z, Gong L, Wang X et al (2016) The role of regulatory B cells in digestive system diseases. Inflamm Res 66(4):1–7
Zorina A, Sinetova MA, Kupriyanova EV (2016) Synechocystis mutants defective in manganese uptake regulatory system, ManSR, are hypersensitive to strong light. Photosyn Res. 130(1–3):11–17
Acknowledgements
This work is supported by: Zhejiang Natural science foundation [LY20G030004].
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Zhejiang Natural science foundation, LY20G030004, Xiaojun Liu.
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Liu, X., Wang, Y., Wang, J. et al. Supply chain financial logistics supervision system based on blockchain technology. J Ambient Intell Human Comput 14, 11059–11069 (2023). https://doi.org/10.1007/s12652-022-04452-1
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DOI: https://doi.org/10.1007/s12652-022-04452-1