Methods, systems, and medium for establishing physical model of power grid knowledge graph

Abstract

The invention provides a method, a system and a medium for establishing a physical model for a power grid knowledge graph. The method comprises the following steps: determining a table mode for defining a table object and a field thereof; generating table information of all table objects according to a table mode based on the first data source to generate a physical table set of a physical model;determining a relationship mode for defining a relationship between the source table object and the target table object; for each pair of source table objects and target table objects subjected to deduplication processing in the second data source, generating corresponding table relationship information according to a relationship mode based on the second data source so as to generate a relationship set of the physical model; building a physical model including table objects, fields, and relationships based on the physical table set and the relationship set. By utilizing the scheme of the invention, knowledge extraction can be carried out on different data sources, and leakage detection and vacancy filling are carried out on the existing model to make up the design weakness of the existingmodel, so that a more reasonable management and control model is provided for a user, and information matching of a unified data model is supported.

Description

technical field [0001] The present invention relates to knowledge graph technology, and more specifically, to a method for establishing a physical model for power grid knowledge graph, a corresponding system, and a computer-readable storage medium. Background technique [0002] With the further development of knowledge graph technology, knowledge graph has laid a foundation for large-scale knowledge base organization and intelligent application with its powerful semantic processing ability and knowledge organization ability. A knowledge graph consists of a large number of entities and entity associations. Through the knowledge graph, entities such as landmarks, people's names, cities, sports teams, buildings, geographical features, movies, celestial bodies, and works of art can be retrieved and information related to these entities can be obtained. This is the key to building intelligent applications that incorporate the collective intelligence of the network and understand...

AI Technical Summary

Problems solved by technology

This kind of traditional information extraction methods cannot work efficiently in the face of massive relationship categories in Internet texts, that is, it is unrealistic to train an extractor for each target relationship. Network Text We cannot specify the type of relationship in advance

[0004] In addition, the current enterprise-level data model-based knowledge resource classification, intelligent search, and cross-domain knowledge fusion and representation are still in their infancy, lacking an intuitive and popular model interface for relevant managers and business personnel, and the logic of the data model Link search capabilities and static semantic analysis and evaluation capabilities are also severely limited

Data models such as the State Grid Corporation’s Enterprise Common Data Model (SG-CIM) are comprehensive abstractions of corporate-level grid, assets, and financial data. The following problems still exist in the three supporting aspects: (1) The quality of model design still needs to be improved, that is, in the current model design results, there are still some data objects with inconsistent abstraction levels, inaccurate entity relationships, incomplete data objects and (2) The model mapping rate is not high, that is, each unit conducts mapping comparisons based on different versions of physical models, resulting in a relatively low average mapping rate. (3) Lack of tool support, that is, the current data model management and control mostly adopts offline methods, the process is complicated, the communication efficiency is low, and the model design results are relatively abstract, which makes it difficult for personnel at all levels to understand the model, and the application ability is insufficient. The quality of iterative improvement cannot be guaranteed

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