Agricultural pest intelligent diagnosis system based on big data

Abstract

The invention provides an agricultural pest intelligent diagnosis system based on big data. The system comprises a data storage system, a data analysis system, a data mining system and a data identification system. According to the system provided by the invention, an agricultural pest big data diagnosis platform and a portal are realized by adopting a Portal technology, by virtue of a multi-dimensional data service provided by the system, different early warning and analysis models are customized and are shown in a simple and lucid graphical mode, by combining a multi-dimensional analysis tool, a user can carry out multi-dimensional and multi-angle analysis on analysis indexes from a data cube, current status and trend of each index are scientifically and accurately known, and a decision analysis report, containing text, figures, charts and reports, in a PDF or DOC form can be automatically generated.

Description

technical field [0001] The invention relates to the field of big data, in particular to an intelligent diagnosis system for agricultural diseases and insect pests based on big data. Background technique [0002] In the control system of crop diseases and insect pests, the diagnosis of diseases and insect pests is still a weak link. Whether various types of diseases and various pests can be diagnosed correctly will directly affect the success or failure of crop disease and insect pest control work. [0003] However, the correct diagnosis of pests and diseases puts forward higher requirements for pest control personnel. In recent years, the abnormally changing ecological environment and changeable pest disasters have brought great difficulties to the control of pests and diseases, because as a professional technology The diagnosis of diseases and insect pests requires a certain process of study and practice. In the process of fighting against pests and diseases for a long tim...

AI Technical Summary

Problems solved by technology

[0011] 1. The existing database is relatively simple, and there is a risk of misjudgment or even misjudgment for diseases with similar symptoms or rare diseases, and the richness of the database needs to be greatly improved;

[0012] Second, the more important thing is that its implementation method is run on a single node, which has congenital deficiencies in data read and write concurrency, data processing efficiency, and data storage capacity

When the amount of data is large and concurrent, using the traditional single-node method may cause transmission failure and loss of data resources due to the long-term failure of the server to respond, especially the transmission, storage, and analysis of video files. There is a great risk of downtime;

[0013] 3. Unstructured data in the system cannot be processed automatically, and massive data analysis still needs to rely on manual operations by a large number of technicians

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