A method and system for target recognition based on multi-source spatio-temporal data assembly

Abstract

The invention belongs to the field of data processing, and in particular relates to a target recognition method and system based on multi-source spatio-temporal data assembly. The present invention includes a space normalization module, a time normalization module, a data standardization module for standardizing raw data, a heterogeneous data connection module, an assembly initialization module, a group combination counting and matching result judging module, and a result The verification module and historical data connection and generation verification module use the above modules to normalize the spatial characteristics of information sources, standardize the space and time dimensions of data from different sources, and set its data identification code driven by a selected data source As a keyword, the data identification codes from other data sources that appear at the same time and at the same place will be integrated and counted. The present invention adopts the method of splitting calculation first and then merging results to calculate step by step, processes massive data, has high adaptability to equipment performance, flexible calculation, high real-time performance and accurate results.

Description

technical field [0001] The invention belongs to the field of data processing, and specifically relates to a method and system for target identification based on multi-source spatiotemporal data assembly. Paint is a technical field for target identification based on multi-source data in electric power marketing, electric power production, and e-government informationization . Background technique [0002] In the fields of e-government, power marketing, and production, it is often necessary to combine the information collected by different types of equipment to finally achieve target identification. The person is associated. Compared with traditional data matching, these fields face problems such as different locations of acquisition equipment, different acquisition cycles, inaccurate collected data, more noisy data, different collected data formats, and huge amount of collected data. Contents of the invention [0003] Aiming at the problems in the prior art, the present i...

AI Technical Summary

Problems solved by technology

Compared with traditional data matching, these fields are faced with problems such as different locations of acquisition equipment, different acquisition cycles, inaccurate collected data, more noisy data, different collected data formats, and huge amount of collected data.

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