中国农业科学 ›› 2020, Vol. 53 ›› Issue (14): 2859-2871.doi: 10.3864/j.issn.0578-1752.2020.14.010

• 耕作栽培·生理生化·农业信息技术 • 上一篇    下一篇

农产品监测预警模型集群构建理论方法与应用

许世卫(),邸佳颖,李干琼,庄家煜   

  1. 中国农业科学院农业信息研究所/农业农村部农业信息服务技术重点实验室/北京市农业监测预警工程技术研究中心,北京 100081
  • 收稿日期:2020-06-03 接受日期:2020-07-14 出版日期:2020-07-16 发布日期:2020-08-10
  • 联系方式: 许世卫,E-mail:xushiwei@caas.cn
  • 基金资助:
    农业部农业信息预警专项“中国农产品信息监测与预警模型系统”;中国农业科学院科技创新工程项目

The Methodology and Application of Agricultural Monitoring and Early Warning Model Cluster

XU ShiWei(),DI JiaYing,LI GanQiong,ZHUANG JiaYu   

  1. Agricultural Information Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-Informatics, Ministry of Agriculture and Rural Affairs/Beijing Engineering Research Center of Agricultural Monitoring and Early Warning, Beijing 100081
  • Received:2020-06-03 Accepted:2020-07-14 Published:2020-07-16 Online:2020-08-10

摘要:

【目的】农产品供给与需求的准确分析测定,是农业监测预警能力提升的重要表现。构建产品多品种多环节模型集群理论方法,可高效解决单一环节或单一模型难以解决的分析技术难题。【方法】在农产品供需的重要要素即生产量、消费量、贸易量、价格等分析预测过程中,针对农产品品种间关联性强,自然、社会、经济诸多影响因素纠缠,模型多变量强耦合、非线性、参数时变的特点,提出多品种农产品“因素分类解耦、参数转用适配”方法,以构建多时空维度的监测预警模型集群。【结果】利用“因素分类解耦、参数转用适配”技术方法,研究构建了不同农产品的生产类、消费类、贸易类、价格类的模型集群。这些模型集群可用于对不同时空维度的水稻、玉米、小麦、肉类等主要农产品供需的长中短期的分析预测,支撑形成了农业展望中的主要农产品平衡表,其中主要农产品全国年度生产量6年平均预测精度高于97%。【结论】研究提出的农产品监测预警模型集群构建理论及其方法,有效提升了农产品多品种模型集群的求解效率和准确率,增强了农产品供需分析预测的系统性与智能性,为系统揭示农产品复杂的时空供需变化特征、促进农产品市场调控科学性和可预见性,提供了新技术方法。

关键词: 农产品, 多品种, 供需预测, 因素分类解耦, 参数转用适配, 模型集群

Abstract:

【Objective】The accurate prediction and evaluation of agricultural product supply and demand is an important manifestation for the improvement of agricultural monitoring and early warning capabilities. A multi-variety multi-link model cluster to construct that can efficiently solve analytical technical problems which are difficult to solve with single links or single models.【Method】 The methodology characterized by "factor classification decoupling, parameter conversion adaptation" for multi-variety agricultural products was proposed to build a multi-temporal dimension monitoring and early warning model cluster, which took into account the important factors of agricultural products supply and demand, namely production, consumption, trade volume, price, etc., the strong linkage among commodities, the entangled complex natural, social and economic factors, and the multivariate strong coupling, non-linear, time-varying characteristics of parameters in the model development.【Result】 The model clusters were developed covering production, consumption, trade and price for different agricultural products, based on the "factor classification decoupling, parameter conversion adaptation" methodology. These model clusters could be used to analyze and project the supply and demand situation of major agricultural products including rice, corn, wheat and meat in different spatial and temporal dimensions, and to support the generation of major agricultural products balance sheets in the China Agricultural Outlook Report. The 6-year average forecast accuracy was higher than 97%.【Conclusion】The methodology of agricultural monitoring and early warning model cluster proposed in the paper has effectively improved the solution efficiency and accuracy of agricultural product multi-variety model clusters, enhanced the systematic and intelligent analysis and projection of agricultural supply and demand. The research provided a new technical method for systematically revealing the complex characteristics of supply and demand of agricultural products in time and space, and promoting the scientific and predictable regulation of agricultural products market.

Key words: agricultural products, multi varieties, supply and demand forecast, factor classification decoupling, parameter conversion and adaptation, model cluster