Multi-source heterogeneous non-point source pollution big data association and retrieval method based on spatial and temporal characteristics and supervision platform

A multi-source heterogeneous and non-point source pollution technology, applied in the field of big data processing, can solve the problems of not considering the temporal and spatial characteristics of multi-source heterogeneous data of non-point source pollution, and the inability to track contribution, so as to facilitate real-time monitoring of data and efficient retrieval and management, to achieve the effect of data association

Pending Publication Date: 2019-10-15
ANHUI UNIVERSITY
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Problems solved by technology

These two methods do not consider the spatio-temporal characteristics of multi-source heterogeneous data of non-point source

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  • Multi-source heterogeneous non-point source pollution big data association and retrieval method based on spatial and temporal characteristics and supervision platform
  • Multi-source heterogeneous non-point source pollution big data association and retrieval method based on spatial and temporal characteristics and supervision platform
  • Multi-source heterogeneous non-point source pollution big data association and retrieval method based on spatial and temporal characteristics and supervision platform

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[0038] The implementation of the present invention will be described in detail below in conjunction with the drawings and examples.

[0039] Agricultural non-point source pollution has the characteristics of wide area and large volume, many polluting subjects, scattered and concealed pollution sources, random and uncertain time and space of pollution occurrence, etc., which is related to soil health, cultivated land quality, river and lake water quality, drinking water safety, and agricultural products. Quality and food safety is a huge project that is very difficult to manage, and it needs to be comprehensively handled and controlled by zoning, grading, and time-by-time. To this end, departments at all levels across the country have adopted a variety of technical means and sensing devices, such as global positioning system GPS, remote sensing RS, ground wireless sensor network WSN, etc., to obtain pollution monitoring point data, remote sensing grid data, point / line / Multi-so...

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Abstract

The invention discloses a multi-source heterogeneous non-point source pollution big data association and retrieval method based on spatial and temporal characteristics. The method comprises: obtainingtime features and space features of the multi-source heterogeneous non-point source pollution data; dividing the geographic space of the target area into a plurality of subspaces, forming initial grids, dividing the initial grid step by step to form sub-grids of all levels; coding each sub-grid; determining a spatial code of the multi-source heterogeneous non-point source pollution data; introducing a time feature code into each sub-grid code; increasing temporal dimensions, using a multi-stage gridding organization and index model; using time and space position matching, to realize data association and retrieval. The invention also provides a multi-source heterogeneous non-point source pollution big data supervision platform. Compared with the prior art, the multi-source heterogeneous non-point source pollution big data supervision platform comprehensively considers the spatial and temporal characteristics of the multi-source heterogeneous big data, facilitates the realization of data association, greatly optimizes retrieval, facilitates the real-time monitoring of data by using a crawling module, and achieves efficient retrieval and management.

Description

technical field [0001] The invention belongs to the technical field of big data processing, and in particular relates to a method for associating and retrieving big data of multi-source heterogeneous area source pollution based on spatio-temporal characteristics and a big data supervision platform adopting the method. Background technique [0002] Multi-source heterogeneous area source pollution big data has the characteristics of diverse sources, inconsistent data formats, and large differences in data volume. Existing association and retrieval methods mostly use data ontology to construct association features, such as: using latitude and longitude as the association primary key, Or find out the correlation between different data based on the data content. These two methods do not consider the spatio-temporal characteristics of multi-source heterogeneous data of non-point source pollution, and cannot track and dynamically analyze the contribution of different data sources t...

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Application Information

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IPC IPC(8): G06F16/22G06F16/29G06F16/951
CPCG06F16/2228G06F16/29G06F16/951
Inventor 胡根生赵晋陵梁栋段运生阮莉敏黄林生张东彦翁士状
Owner ANHUI UNIVERSITY
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