Deep belief network-based power communication safety production multi-source heterogeneous data adaptive fusion method

By combining principal component analysis, TF-IDF, CNN, and DBN, the problems of feature redundancy and insufficient cross-modal correlation of multi-source heterogeneous data in power communication systems are solved, enabling more comprehensive data analysis and fault detection, and improving the security and reliability of power communication systems.

CN121117934BActive Publication Date: 2026-03-24CHANGCHUN UNIV OF SCI & TECH
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Patent Information

Application Number
CN202511235034.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-09-01
Publication Date
2026-03-24
Estimated Expiration
2045-09-01

AI Technical Summary

Technical Problem

Existing power communication systems suffer from data silos, feature redundancy, and insufficient cross-modal correlation mining when processing multi-source heterogeneous data, making it difficult to adapt to the ever-changing power communication environment and limiting the accuracy and reliability of data analysis results.

Method used

Principal component analysis is used to reduce the dimensionality of structured data. Unstructured data features are extracted using TF-IDF and a pre-trained CNN network. Feature selection is performed using recursive feature elimination. Cross-modal feature interaction and fusion are achieved using a multi-layer stacked deep belief network (DBN). The training process is optimized using the Nadam algorithm to generate a comprehensive feature vector.

Benefits of technology

It has improved the data processing capabilities and security management level of the power communication system, enhanced the accuracy of fault detection and real-time monitoring capabilities, reduced the false alarm rate, optimized resource allocation, improved operational efficiency and targeted equipment maintenance, and promoted the intelligent and automated development of power communication.

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Abstract

The application relates to the field of power communication safety production management and solves the problems that existing data processing methods adopt single-dimensional analysis and are mostly static, data islands exist, feature redundancy exists, cross-modal correlation mining is insufficient, the methods are difficult to adapt to the changing power communication environment, and the accuracy and reliability of data analysis results are limited. The application obtains structured and unstructured data related to power communication system safety generation, performs preprocessing, then applies PCA dimension reduction to the structured data; for the unstructured data, jointly uses TF-IDF and a pre-trained CNN to extract a feature vector; through RFE, key features are adaptively selected until the best feature set is found; and the DBN network and the Nadam optimization algorithm are used to fuse the multi-source features, improve the comprehensiveness and accuracy of analysis, and optimize power communication safety management.
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