农业大数据学报 ›› 2021, Vol. 3 ›› Issue (2): 67-74.doi: 10.19788/j.issn.2096-6369.210207

• 研究综述 • 上一篇    

油料(油菜、花生)全产业链大数据的建设

蒋锐1(), 黄凤洪1(), 吴渝1, 霍梦佳2, 刘华威3   

  1. 1.中国农业科学院油料作物研究所,武汉 430062
    2.中国农业科学院农业信息研究所,北京 100081
    3.神州数码信息服务股份有限公司,北京 100094
  • 收稿日期:2021-02-15 出版日期:2021-06-26 发布日期:2021-08-31
  • 通讯作者: 黄凤洪 E-mail:jiangrui@oilcrops.cn;huangfenghong@caas.cn
  • 作者简介:蒋锐,男,工程师,研究方向:智慧院所与数字化农业技术的研究和应用; E-mail:jiangrui@oilcrops.cn
  • 基金资助:
    中国农业科学院油料(油菜、花生)全产业链大数据建设试点项目

Big Data Construction of Oil Crops (Rapeseed, Peanut) Whole Industrial Chain

Rui Jiang1(), Fenghong Huang1(), Yu Wu1, Mengjia Huo2, Huawei Liu3   

  1. 1.Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China
    2.Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 10081, China
    3.Digital China Information Service Company LTD, Beijing 10081, China
  • Received:2021-02-15 Online:2021-06-26 Published:2021-08-31
  • Contact: Fenghong Huang E-mail:jiangrui@oilcrops.cn;huangfenghong@caas.cn

摘要:

农产品全产业链大数据建设对于推动农业发展方式加快转变,促进农业转型升级、提质增效和可持续发展,加快农业现代化进程有着重要意义。我国重要农产品全产业链大数据建设刚刚起步,发展基础薄弱,,在乡村信息基础设施不健全,农产品加工链不完善、产业链数据资源分散,天空地一体化数据获取能力较弱、覆盖率低等多个问题。本文以油料(油菜、花生)为例,分析全产业链大数据建设现状,明确油料全产业链大数据平台建设思路与原则,提出平台建设的基本架构与主要功能,面向油料作物产量预测、价格预测、政策分析等主题开发集成气象产量预测模型、遥感产量预测模型、价格监测模型以及政策主题演化模型以及语义对比分析模型等,支持多场景数据挖掘与应用服务。本文探索形成需求为导向的,大数据、自然语言处理以及人工智能技术为依托的油料全产业链大数据平台建设方案,支持基础环境、资源投入、生产加工和流通消费等重要领域和关键环节数据资源汇聚与分析挖掘,可有效增强数字技术研发应用能力,打造全产业链单品种大数据治理与应用示范样板,为全面推进农产品全产业链大数据建设提供可复制、可借鉴、可推广的机制、模式和经验,示范引领农业农村大数据建设,从而促进提升农业生产经营和管理服务数字化水平,助推农业农村现代化。

关键词: 油料, 全产业链, 大数据, 数据平台建设, 农业大数据

Abstract:

A data platform serving an agricultural industry value chain can accelerate the transformation of agricultural techniques, promote agricultural upgrades, improve the quality and efficiency and sustainable development, and accelerate the process of agricultural modernization. In China, the construction of such a platform for important agricultural products is still in the early stages and the data foundations are weak, facing challenges such as limited rural information infrastructure, incomplete information about agricultural product processing, data resources fragmented across the industry, and limited data acquisition effectiveness and scope. Taking oil crops (rape, peanut) as an example, this paper analyzes current efforts to construct a data platform for an industry value chain. It applies the concepts and principles of big data platforms in the context of the oil crop industry, and proposes a framework and key functions required of the platform. In addition, this paper also develops a set of models relevant to an agriculture-focused data platform, which support data mining and application services. These include an integrated meteorological yield prediction model, a remote sensing yield prediction model, a price monitoring model, a policy topic evolution model, and a semantic comparative analysis model. This paper then explores the construction scheme of a data platform for the oil crops industry using big data, natural language processing and artificial intelligence technology. This platform supports aggregating, analyzing, and mining important data about the crop growing environment, input resources, production and processing, distribution, and consumption. By collating relevant data resources, the platform can enhance digital technology R&D and application capabilities, simplify data governance, and demonstrate applications for the industry value chain. The paper highlights replicable, accountable and well-established approaches for constructing a comprehensive data platform for an agricultural industry value chain, with the goal of promoting the digitalization of agricultural production, operations and management, and modernization of agricultural and rural areas.

Key words: oil crops, whole industry chain, big data, data platform construction, agricultural big data

中图分类号: 

  • G203