Data fusion method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides a data fusion method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: classifying to-be-fused data from different sources, and obtaining a plurality of subclass data; searching similar data of each sub-category data in a pre-established standard database, and determining the similarity between the similar data and the sub-category data; and fusing the sub-category data with a standard database according to the similarity. According to the method, the classification error rate can be reduced, invalid data in thefusion database is reduced, and the quality of database data is improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a data fusion method, device, electronic equipment and computer-readable storage medium. Background technique [0002] There are many data fusion scenarios in today's society. The traditional data fusion method is usually based on the specific description of a column of fields, find the specific fields in the data to be fused, align and merge them, and then fuse them into a new database. In the case of data misalignment In this case, more columns need to be added to the original database to achieve the effect of data fusion. In the existing technology, only by manually judging whether each column field is consistent, resulting in wrong classification information in the fusion database; in addition, because the data to be fused from different sources may contain a large amount of repeated information, while the existing fusion technology Data to be fused w...

AI Technical Summary

Problems solved by technology

Because the existing technology only manually judges whether each column field is consistent, resulting in wrong classification information in the fusion database; in addition, because the data to be fused from different sources may contain a large amount of repeated information, while the existing fusion technology The data to be fused will not be identified, thus causing data redundancy issues

In summary, there may be duplicate and wrong classification information in the fusion database obtained by the existing data fusion methods, resulting in a large amount of data and poor quality in the fusion database.

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