| 摘 要: 在乡村振兴战略背景下，针对农业生产者、消费者及政府政策制定者之间，存在信息不对称和信息传递滞后性的问题，以及农业数据的分散化问题，以农业数据特征和大数据技术特点为研究对象，提出了一套农业决策信息挖掘系统。首先，该系统通过数据库设计对多源异构数据进行整合，打通了不同数据之间的壁垒，并基于前后端分离的系统开发方案，为农业生产者和消费者提供决策支持；其次，该系统利用长短期记忆网络(Long Short-Term Memory,LSTM)等大数据处理技术，对序列农业数据进行深度学习趋势预测，挖掘农业数据的潜在信息，并设计了在Web平台中融合数据处理技术的具体应用方案。分析结果表明，该系统能够帮助农业生产者和消费者充分发掘农业数据中的潜在价值。 |
| 关键词: 决策信息挖掘；大数据处理技术；系统开发 |
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中图分类号: TP311
文献标识码: A
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| 基金项目: 上海高校智库内涵建设计划(战略研究)项目“上海加强新动能培育和关键核心技术突破研究”(1022303001)；上海市级新农科研究与改革实践项目“面向新农科建设的知农爱农新型人才需求研究”(5220303001). |
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| Design of Agricultural Decision Information Mining System based on Big Data Technology |
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ZHU Xiaodong, FU Junyu
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(Business School, University of Shanghai for Science and Technology, Shanghai 200093, China )
zhuxd@usst.edu.cn; fujunyu0319@163.com
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| Abstract: In the context of rural revitalization strategy, this paper proposes to design an agricultural decision-making information mining system for the problems of information asymmetry and information transmission lag among agricultural producers, consumers and government policy makers as well as the decentralization of agricultural data. This research takes agricultural data characteristics and the features of big data technology as the research objects. Firstly, the system integrates multi-source heterogeneous data through database design, breaks the barriers between different data, and provides decision support for agricultural producers and consumers based on the system development scheme of front and back-end separation. Secondly, big data processing technologies such as Long Short-Term Memory (LSTM) is used to predict the deep learning trend of sequential agricultural data, mine the potential information of agricultural data, and design a specific application scheme integrating data processing technology in the Web platform. The analysis results show that the proposed system can help agricultural producers and consumers fully explore the potential value of agricultural data. |
| Keywords: decision information mining; big data processing technology; system development |
