中国饲料营养大数据分析平台研制
收稿日期: 2022-05-09
网络出版日期: 2022-07-13
基金资助
科技部数据平台项目(NASDC2022XM02)
“十四五”国家重大科技攻关项目(2021YFD2000800)
重庆市技术创新与应用发展专项重点项目(cstc2019jscx-gksbX0091)
Development of China Feed Nutrition Big Data Analysis Platform
Received date: 2022-05-09
Online published: 2022-07-13
饲料粮缺口的逐渐加大,导致中国饲料粮安全问题逐步转化为粮食安全问题。因此,全面整合饲料营养基础数据资源,提高一切可利用饲料资源的营养价值,成为中国今后长期保障国家粮食安全的技术措施之一。本研究依据16类中国饲料原料描述规范和属性数据标准,全面用数字化模式收集整理了自“六五”至“十三五”期间累积的50万条以上已有饲料资源的种类、空间分布、饲料成分含量及营养价值特性数据,利用MySQL网络数据库及PHP程序语言,开发了新一代饲料营养大数据分析平台(http://www.chinafeeddata.org.cn/)并提供Web数据共享功能。首先,平台提供所有入库数据的可视化分析,可实现单个或多个饲料多种养分和多种图形模式的直观比对。通过二维码技术提供所有饲料营养属性数据及饲料实体样本溯源数据的移动端实时分享与下载服务。其次,平台构建了通过已知饲料概略养分在线预测其他有效养分的回归模型,为饲料原料养分变异提供动态分析。最后,平台基于地理信息系统技术,将饲料概略养分和主要矿物元素含量数据与其所处的地理位置分布相结合,实现了饲料营养数据地理信息图谱的分布查询及对比分析,同时提供各种数据的下载方式,为已有饲料数据的全面应用带来便利。研究表明,拓展饲料资源数据并提供饲料养分的预测分析模型,可最大化利用已有饲料养分数据的价值,进一步嵌入各类饲料配方的网络计算模块,可以达到饲料营养数据的一站式服务及数据的最大化升值服务。
熊本海 , 赵一广 , 罗清尧 , 郑姗姗 , 高华杰 . 中国饲料营养大数据分析平台研制[J]. 智慧农业, 2022 , 4(2) : 110 -120 . DOI: 10.12133/j.smartag.SA202205003
The shortage of feed grain is continually worsening in China, which leads to the transformation of feed grain security into national food security. Therefore, comprehensively integrating the basic data resources of feed nutrition and improving the nutritional value of all available feed resources will be one of the key technical strategies to ensure national food security in China. In this study, based on the description specification and attribute data standard of 16 categories of Chinese feed raw materials, more than 500,000 pieces of data on the types, spatial distribution, chemical composition and nutritional value characteristics of existing feed resources, which were accumulated through previous projects from the sixth Five-Year Plan to the thirteenth Five-Year Plan period, were digitally collected, recorded, categorized and comprehensively analyzed. By using MySQL relational database technology and PHP program, a new generation of feed nutrition big data online platform (http://www.chinafeeddata.org.cn/) was developed and web data sharing service was provided as well. First of all, the online platform provided visual analysis of all warehousing data, which could realize the visual comparison of a single or multiple feed nutrients in various graphic forms such as scatter diagram, histogram, curve line and column chart. By using two-dimensional code technology, all feed nutrition attribute data and feed entity sample traceability data could be shared and downloaded remotely in real-time on mobile phones. Secondly, the online platform also incorporated various regression models for prediction of feed effective nutrient values using readily available feed chemical composition in the datasets, providing dynamic analysis for feed raw material nutrient variation. Finally, based on Geographic Information System technology, the online platform integrated the data of feed chemical composition and major mineral element concentrations with their geographical location information, which was able to provide the distribution query and comparative analysis of the geographic information map of the feed raw material nutrition data at both provincial and national level. Meanwhile, the online platform can also provide a download service of the various datasets, which brought convenience to the comprehensive application of existing feed nutrition data. This research also showed that expanding feed resource data and providing prediction and analysis models of feed effective nutrients could maximize the utilization of the existing feed nutrition data. After embedding online calculation modules of various feed formulation software, this platform would be able to provide a one-stop service and optimize the utilization of the feed nutrition data.
1 |
黑龙江省大豆协会. 我国饲料粮缺口有多大[EB/OL]. [2021-02-09].
|
2 |
闫文义. 2021年我国进口主要农产品数量、价格和近5年进口数量变化分析[J]. 黑龙江粮食, 2022, 2: 22-23.
|
3 |
中粮科技. 2021年玉米行业发展分析报告[EB/OL]. [2021-07-16].
|
4 |
熊本海, 罗清尧, 赵峰, 等. 中国饲料成分及营养价值表(2021年第32 版)制订说明[J]. 中国饲料, 2021(23): 97.
|
5 |
熊本海, 罗清尧, 郑姗姗, 等. 中国饲料成分及营养价值表(2020年第31 版)制订说明[J]. 中国饲料,2020(21): 87-97.
|
6 |
中国农业科学院北京畜牧兽医研究所, 中国饲料数据库情报网中心, 动物营养学国家重点实验室. 中国饲料成分及营养价值表(2019年第30 版)制订说明[J]. 中国饲料, 2019(21): 97.
Institute of Animal Sciences of Chinese Academy of Agricultural Sciences, China Feed Database Information Network Center, State Key Laboratory of Animal Nutrition. Introduction of tables of feed composition and nutritive values in China (2019, 30th edition)[J]. China Feed, 2019(21): 97.
|
7 |
中国饲料行业信息网.我国畜禽饲料资源数据库建设启动[EB/OL]. [2014-07-08].
|
8 |
百度专网地图[DB/OL]. [2022-05-30].
|
9 |
徐辉. QRCode二维条码编解码系统的应用研究[D]. 南京: 南京邮电大学, 2011.
|
10 |
美国国家研究委员会. 猪营养需要(第十一次修订版, 2012)[M]. 印遇龙, 阳成波, 敖志刚主译. 北京: 科学出版社, 2014.
|
11 |
法国国家农业科学研究院. 饲料成分与营养价值表[M]. 谯仕彦, 王旭, 王德辉主译. 北京: 中国农业大学出版社, 2008.
|
12 |
潘晓花, 杨亮, 庞之洪, 等. 猪饲料有效能值预测模型的构建[J]. 动物营养学报, 2015, 27(5): 1450-1460.
|
13 |
廖秀冬, 张丽阳, 吕林, 等. 我国畜禽饲料资源中矿物元素含量分布的调查[J]. 中国农业科学 2019, 52(11): 1970-1972.
|
14 |
陈志勇, 张丽阳, 马雪莲, 等. 我国畜禽饲料资源中常量元素钙含量分布的调查[J]. 中国农业科学, 2019, 52(11): 1973-1981.
|
15 |
张铁鹰, 张丽阳, 刘俊丽, 等. 我国畜禽饲料资源中微量元素砷含量分布的调查研究[J]. 中国农业科学 2020, 53(21): 4507-4515.
|
16 |
美国国家研究委员会. 奶牛营养需要量(第七次修订版, 2001)[M]. 孟庆翔主译. 北京: 中国农业大学出版社, 2002.
|
17 |
熊本海, 杨亮. 种猪精细养殖综合技术平台[M]. 北京: 中国农业科技出版社, 2015.
|
18 |
熊本海, 罗清尧, 庞之洪. 奶牛营养参数与典型日粮配方[M]. 北京: 中国农业科技出版社, 2003.
|
19 |
熊本海, 蒋林树. CNCPS体系演变、模型及饲料成分表[M]. 北京: 中国农业科技出版社, 2015.
|
/
〈 |
|
〉 |