Agricultural big data management system and method based on county regions

Abstract

The invention discloses an agricultural big data management system and method based on county regions. The method comprises the steps that a data set and data unit module carries out the standard carding and normalization of agricultural product data according to an industrial chain, a data type, a variety, an industry and a sub-industry, and formulates a data collection standard table; the data acquisition module acquires agricultural product data in the area according to the data acquisition standard table; the data reviewing and cleaning module reviews and cleans the data according to a data cleaning and checking rule and then stores the data to the data storage module; the data query module performs data query on the data storage module according to the data screening rule, and performs data display under the condition that the condition is correct; and the data visualization module performs agricultural product data visualization operation on the data storage module through the application terminal. The county-level agricultural big data integrated management system provided by the invention sets data acquisition standard data report unification, acquires required data in various modes, and provides a data basis for a big data application terminal.

Description

technical field [0001] The invention relates to the technical field of agricultural big data, in particular to a county-based agricultural big data management system and method. Background technique [0002] Agriculture is the foundation of the national economy. According to the division of the agricultural industry chain, agricultural big data is mainly concentrated in the fields of agricultural environment and resources, agricultural production, agricultural market, and agricultural management. Agricultural environment and resources mainly include land resource data, water resource data, meteorological resource data, biological resource data and disaster data. Agricultural production data include planting production data and breeding production data. Agricultural market data include market supply and demand information, price quotes, market information on means of production, prices and profits, circulation market and international market information, etc. Agricultural m...

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Problems solved by technology

[0003] Agricultural big data has been widely used in developed countries. my country has just started, and there are the following deficiencies in practical application: agricultural information islands are serious, agricultural departments are managed in sections, departmental application systems are mostly vertical architectures, isolated systems, and the degree of information sharing Low; the comprehensive utilization rate of data is not high

Compared with other industries, agricultural big data has the characteristics of scattered sources, huge quantity, diverse formats, complex types, and disordered structure. The workload of data analysis and sorting is heavy, and the comprehensive utilization rate of data is not high; market supply and marketing information is asymmetric, and agricultural products are affected by the market. The impact fluctuates greatly, but farmers have limited access to market information, and it is difficult to grasp the latest market information in a timely manner, resulting in a disconnection of information between agricultural producers and consumers; extensive management, my country's agricultural production is relatively scattered, and it is difficult to collect and analyze agricultural related data , it is difficult to achieve accurate, efficient and timely processing, resulting in inaccurate agricultural decision-making

[0004] At the same time, a more prominent problem is: the application of agricultural big data management is mainly concentrated in developed areas and large urban areas, and cannot go deep into the grassroots, especially county areas. China's county area agriculture does not lack funds, technology, talents and policies. is data

The main manifestation of the lack of data is that the county government’s industrial planning is mismatched, it is difficult to attract investment, and local enterprises are not big or strong; for enterprises, strategic planning is unbalanced, production and marketing decisions are random, and risk management is backward; "Agricultural cycle", lack of new breeding technology, lack of brand awareness, etc.

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