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Development of Basic Data Manual for Intelligent Analysis of Grain Condition

Authors :
LU Yan-hui
LI Xin-ze
WU Wen-fu
CUI Hong-wei
XU Yan
HAN Feng
LI Zhi-min
FENG Bo
SHI Jing-feng
ZHANG Ji
Source :
Liang you shipin ke-ji, Vol 31, Iss 2, Pp 47-55 (2023)
Publication Year :
2023
Publisher :
Academy of National Food and Strategic Reserves Administration, 2023.

Abstract

Based on the implementing grain security projects and instituting other relevant grain security policies, China has promoted the process of digitalization and intelligence of grain storage and built the world's largest grain Internet of Things, since the 18th National Congress of the Communist Party of China. But, the resulting large amount of grain data, mainly based on temperature, can not effectively support decision-making and management on grain storage. Based on the study of the multi-field coupling theory of grain pile ecosystem, our research team further extracted the continuity principle, periodicity principle, and coordination principle of digital supervision of grain reserves, and proposed a series of intelligent strategies such as AID, ABC, 6R, SIN, CAE, O, and U. However, the application of these strategies directly faces the original data, which has some problems such as low efficiency and large error. To apply the intelligent strategies efficiently and correctly, the basic data manual of intelligent analysis of grain condition has been developed, according to the original grain condition data of typical granaries and main grain varieties in different grain storage ecological regions in China. The development of this manual is expected to consolidate the foundation of intelligent analysis of grain situation, which could promote the digital monitoring of grain storage quantity and quality, and realize the transformation from "manual defense" to “technical defense”.

Details

Language :
English, Chinese
ISSN :
10077561
Volume :
31
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Liang you shipin ke-ji
Publication Type :
Academic Journal
Accession number :
edsdoj.197c31293d06489586ea34f8676252e7
Document Type :
article
Full Text :
https://doi.org/10.16210/j.cnki.1007-7561.2023.02.007