System and method for realizing risk heterogeneous data standardization fusion based on graph data

CN117688213BActive Publication Date: 2026-06-19ZHENGZHOU UNIV

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Patents(China)
Current Assignee / Owner: ZHENGZHOU UNIV
Filing Date: 2023-11-16
Publication Date: 2026-06-19

AI Technical Summary

Technical Problem

Existing heterogeneous data management systems for risk relief scenarios are inadequate in terms of data semantic fusion and query analysis. They fail to effectively integrate data from different sources and fail to meet the diverse downstream application needs and dynamic requirements.

Method used

The system and methodology based on graph data achieve deep integration of data format and semantics through a data format unification module, a semantic unification module, and a data mining module. It supports multiple data format conversions and entity deduplication, and provides multi-granularity query and exploratory analysis functions.

Benefits of technology

It achieves deep fusion of heterogeneous data and semantic integration of multi-source data, supports diverse data application needs, improves the granularity of data query and the ability to analyze relationships, and meets the diverse application requirements of risk relief data.

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Abstract

This invention relates to the field of data fusion technology, specifically to a system and method for standardized fusion of heterogeneous risk data based on graph data. The system comprises a data format unification module, a semantic unification module, and a data mining module connected sequentially. The data format unification module includes a data import component and a data export component. The data import component receives data from different sources and in different formats, converting this data into an original attribute graph. The data export component converts the attribute graph into other data formats. The semantic unification module includes a schema unification component and an entity unification component. The schema unification component uses a schema alignment algorithm based on a metadata center for schema alignment. The entity unification component removes duplicate entities with the same semantics from the attribute graph. The data mining module provides metadata-level, entity-level, and relation-level data query and exploratory analysis functions. This invention can effectively integrate heterogeneous risk data.

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