Abstract
Traditional supply chain quality data integration methods costed a lot in integrating product quality, but the integration accuracy was very low and the effect is poor. In order to solved this problem, a supply chain of agricultural products was set up based on the artificial intelligence integration method of block chain using quality data. The framework of agricultural product supply chain was designed. The supply chain included four steps of production, processing, trade and consumption. Based on the frame, the workflow of the supply chain of agricultural products was expounded. The feasibility of the construction of agricultural product supply chain was verified by the experiment. The experimental results showed that the design of intelligent integration method can effectively reduce cost and improve the accuracy of integration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ruimin, Z., Zhang, W.: Risk and control of financial science and Technology Innovation – based on large data, artificial intelligence, block chain research. China Manag. Inf. 13(19), 33–36 (2017)
Fang, H., Tong, S., Du, J., et al.: Design and research of intelligent learning robot based on block chain technology - intelligent learning robot. Distance Educ. Mag. Large-Scale Learn. Serv. Syst. 35(4), 42–48 (2017)
Zhang, F., Wang, L., Chai, Y.: An improved unbalanced data integration classification algorithm for oversampling. Minicomput. Syst. 39(10), 36–42 (2018)
Qin, L., Tian, Y., Jiao, H.: The influence of production fragmentation on supply chain integration and unsalable agricultural products-an adjusted intermediary model. Jiangsu Agric. Sci. 46(09), 335–340 (2018)
Chaoyang, Xu, P., Lou, J., et al.: Chemical recommendation model. J. Northeast. Norm. Univ. (Natural Science), 49(2), 84–88(2017)
Xie, W.: GMIC global financial innovation summit: when big data, artificial intelligence, block chain technologies encounter finance. China Econ. Wkly. 12(18), 50–51 (2016)
Zhao, B., Xu, S., Huang, Z.: Simulation of robot path intelligent search method under intensive obstacle environment simulation. Comput. Simul. 34(2), 393–396 (2017)
Fresh, J.: The important direction of the future transformation and development of China’s financial industry: “block chain +”. Southern finance 12(12), 87–91 (2016)
Xing, G.: Artificial intelligence, financial digital new side. China Financ. Comput. 12(5), 15–18 (2017)
Yuxiang, H.: FiMAX “endorsement” block chain new standard. Chin. Financ. 21(2), 75–76 (2018)
Li, F.: Electrocardiogram point diagram - new vision for large data analysis. Pract. Electrocardiol. J. 27(1), 7–8 (2018)
Xia, D., Wang, Y., Zhao, X., et al.: Incremental interactive data integration method for intelligent people’s livelihood. Comput. Res. Dev. 54(3), 586–596 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, K. (2019). Artificial Intelligence Integration Method for Agricultural Product Supply Chain Quality Data Based on Block Chain. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-36402-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36401-4
Online ISBN: 978-3-030-36402-1
eBook Packages: Computer ScienceComputer Science (R0)