Intelligent analysis method for multi-source unstructured data

By deploying intelligent secure acquisition networks and real-time data pipelines in the power system, combined with graph neural networks and data virtualization technology, the analysis problems of complexity and diversity of multi-source unstructured data in the power system are solved, efficient data processing and intelligent analysis are achieved, and decision-making support capabilities are improved.

CN120011604AActive Publication Date: 2025-05-16FUJIAN YIRONG INFORMATION TECH
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Patent Information

Application Number
CN202510488175.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-18
Publication Date
2025-05-16
Estimated Expiration
2045-04-18

AI Technical Summary

Technical Problem

The complexity and diversity of multi-source unstructured data in power systems is difficult to meet the real-time and accuracy requirements through traditional data processing and analysis methods, traditional databases are difficult to store and manage large-scale multimodal data, and static models are difficult to express dynamic relationships in the data.

Method used

Using intelligent analysis methods for multi-source unstructured data, we use edge terminals to build an intelligent secure acquisition network, use Apache Kafka to build real-time data pipelines, combine graph neural network (GCN) to model complex network relationships, and use data virtualization technology to achieve unified access and query of different data sources.

Benefits of technology

It significantly improves the analysis intelligence level and decision-making support capabilities of multi-source unstructured data in the power system, realizes efficient storage, management and query of data, can effectively capture complex patterns and exceptions in the data, and supports data-driven decision-making.

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Abstract

The invention relates to an intelligent analysis method for multi-source unstructured data, and the method comprises the steps: S1, constructing an intelligent safety collection network, obtaining power multi-source unstructured data, and carrying out the preliminary data filtering and cleaning processing; s2, constructing a real-time data pipeline through Apache Kafka, continuously receiving data streams from an intelligent security acquisition network, and integrating data extracted from an external data source to ensure data integrity and timeliness; s3, storing the multi-source unstructured data obtained by the real-time data pipeline, and realizing seamless integration and unified access of different data sources by using a data virtualization technology; s4, performing complex network relation modeling on the integrated multi-source unstructured data by using a graph neural network GCN to obtain dynamic relation characteristics among the data; and S5, performing intelligent analysis in combination with the integrated multi-source unstructured data and the dynamic relationship characteristics among the data. According to the method, the intelligent level and decision support capability of multi-source unstructured data analysis of the power system are remarkably improved.
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Citation Information

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