Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

Frontiers in Public Health

article

Wenjing Yan¹  Zesheng Zhang¹  Qingchuan Zhang¹  Ganggang Zhang²  Qiaozhi Hua³  Qiao Li⁴ 

[1] National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University;Digital Campus Construction Center, Capital Normal University;Computer School, Hubei University of Arts and Science;Chongqing Key Laboratory of Intelligent Perception and Blockchain Technology, Chongqing Technology and Business University

关键词: graph neural network;    agricultural products;    public healthcare;    deep data analysis;    smart management;   

DOI  :  10.3389/fpubh.2022.847252

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Agricultural is an indispensably public healthcare industry for human beings at any time and smart management of it is of great significance. Since substantial technical advance relies on long-term efforts and continuous progress, reasonably scheduling the distribution of agricultural products acts as a key aspect of smart public healthcare. The most intuitive factor affecting the distribution of agricultural products is its dynamic price. Forecasting price fluctuations in advance can optimize the distribution of agricultural products and pave the way to smart public healthcare. Most researchers study the prices of various agricultural products separately, without considering the interaction of different agricultural products in the time dimension. This study introduces a typical deep learning model named graph neural network (GNN) for this purpose and proposes deep data analysis-based agricultural products management for smart public healthcare (named GNN-APM for short). The highlight of GNN-APM is to take latent correlations among multiple types of agricultural products into consideration when modeling evolving rules of price sequences. A case study is set up with the use of real-world data of the agricultural products market. Simulative results reveal that the designed GNN-APM functions well.

【 授权许可】

CC BY