SPAC系统中农作物水循环知识融合模型研究

项目名称： SPAC系统中农作物水循环知识融合模型研究

项目编号： No.61273329

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 李淼

作者单位： 中国科学院合肥物质科学研究院

项目金额： 82万元

中文摘要： 农业水循环-水平衡对流域尺度的农业用水计算与决策极为重要。90年代以来，有关学者利用SPAC模型开展了农作物水循环研究，但受土壤、作物、肥水等不确定因素影响，系统参数难以确定。近年来开展的在农业水循环模型中融入知识模型的方法研究，模型之间数据与知识如何融合，是其中的难点。 依此，将以黑河流域为例，重点开展以下工作： 1、SPAC定量解析模型各阶段的参数简约、定标及尺度转化方法，建立灌区尺度下SPAC泛化模型；2、农作物水循环系统中离散数据与作物生长机理之间的关联关系，解决复杂环境下不确定因素的知识表示问题；3、实验数据与农作物各生长阶段知识间的信息传递机制、协同工作机制，建立农作物水循环知识融合模型。 项目实施后将解决模型融合过程中知识抽取、表达、映射，参数简约、转化等关键问题，并据此构造黑河流域农业生态水循环模型，为农业用水分析提供新的技术手段。

中文关键词： SPAC系统；农作物水循环；知识融合；黑河流域；蒸散发估算模型

英文摘要： Agricultural water cycle-water balance is very important to the scale of river basin agricultural water calculation and decision making. Since the 1990s, the water cycle analysis of crops has been studied by SPAC model. However, the system parameters are difficult to be determined by some uncertain factor, such as soil, crop, fertilizer, water and so on. In the recent years, the knowledge model method has been introduced for the analysis. But it is one of the difficult point how fuse data and knowledge between models. In view of the above, with the Heihe River basin as an example, the project will focus on the following researches: 1, To study the parameters contraction, parameters calibration and scale conversion method of SPAC quantitative analytical model in each stage, and to establish SPAC generalization model in irrigation district scale; 2, To study the relationship between discrete data and crop growth mechanism in the crops water cycle system during the crops growth, and to solve the problem of representation knowledge for uncertainty factors of water cycle under the complex environment; 3, To study the methods and mechanism of information transmission, structure transformation and collaborative work between experimental data and all levels of knowledge, and to establish a knowledge reasoning model o

英文关键词： SPAC system；The crop water cycle；Knowledge fusion；Heihe river basin；Eto estimation model