Agricultural non-point source pollution multi-source heterogeneous big data association method and big data supervision platform adopting same

Abstract

The invention relates to an agricultural non-point source pollution multi-source heterogeneous big data association method based on attribute classification. Compared with the prior art, the defect that efficient association is difficult to perform according to the data attributes is overcome. The method comprises the following steps of judging whether multi-source heterogeneous big data belongs to the quantitative data or the qualitative data; classifying the quantitative data by adopting a support vector machine, metric learning and other methods; obtaining the quantitative characteristics of the qualitative data by adopting a text semantic mining method, and classifying the qualitative data by adopting a support vector machine, a metric learning method and the like; encoding the classified result to realize the association of multi-source heterogeneous big data. The invention further provides an agricultural non-point source pollution big data supervision platform. According to thepresent invention, the attributes of the agricultural non-point source pollution multi-source heterogeneous big data are used as the classification bases, different processing methods are adopted forquantitative and qualitative data, the classification of the agricultural non-point source pollution multi-source heterogeneous big data is achieved, and the correlation is conducted by means of the generated tree structure soil pollution attribute codes.

Description

technical field [0001] The invention belongs to the technical field of big data processing, and in particular relates to a method for associating multi-source heterogeneous big data of agricultural non-point source pollution and a big data supervision platform adopting the method. Background technique [0002] At present, the problem of agricultural non-point source pollution is prominent. In the comprehensive treatment, it is necessary to select small watersheds with prominent agricultural environmental problems and strong representativeness, increase source control, and implement comprehensive agricultural non-point source pollution control projects. In order to ensure the monitoring effect and work efficiency in engineering applications, it is urgent to build a big data monitoring platform for agricultural non-point source pollution, to realize the standardization and rapid correlation of multi-source heterogeneous data, and to serve functions such as agricultural non-poin...

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Problems solved by technology

[0008] In order to overcome the shortcomings of the above-mentioned prior art, the object of the present invention is to provide a multi-source heterogeneous big data association method for agricultural non-point source pollution and a big data supervision platform using the method. The problem of high cost; the second aspect solves the problem of slow big data association; the third aspect solves the association problem of quantitative and qualitative data of different types, different contents, and loose structures; the fourth aspect solves the problem of the current big data supervision platform The data is not easy to retrieve and it is not easy to monitor in real time

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