A multi-modal anomaly confidence evaluation and hierarchical disposal method in a thermal power plant inspection scene

By using multimodal data fusion technology, the problems of false alarms and missed alarms in the anomaly assessment during thermal power plant inspections have been solved, enabling accurate risk assessment and differentiated handling of equipment anomalies, and ensuring the safe and stable operation of thermal power plant equipment.

CN122264436APending Publication Date: 2026-06-23HEBEI HUADIAN SHIJIAZHUANG THERMOELECTRICITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HEBEI HUADIAN SHIJIAZHUANG THERMOELECTRICITY
Filing Date
2026-03-27
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

In existing thermal power plant inspection technologies, there are problems of false alarms and omissions in anomaly assessment and handling, and the risk level classification is not accurate enough, making it difficult to achieve differentiated and graded handling.

Method used

Multimodal data fusion technology is adopted to collect multi-source heterogeneous modal data, extract defect geometric features and deterioration trend features, combine them with the fault correlation mechanism of thermal power equipment to perform feature fusion and weight optimization, and construct a multi-dimensional judgment matrix for abnormal risk assessment and graded handling.

Benefits of technology

It has improved the anti-interference capability and accuracy of anomaly assessment, realized the refined classification and handling of equipment anomaly risks, and ensured the safe and stable operation of key equipment in thermal power plants.

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Abstract

The application discloses a kind of multi-modal anomaly confidence evaluation and hierarchical disposal methods under the scene of power plant inspection, it is related to the technical field of power plant, the method includes: establishing standardization multi-modal dataset;The correlation weight correction and feature fusion are carried out to defect geometric feature and deterioration trend feature;From the corresponding modal branch is separated in multi-modal fusion feature, and the abnormal confidence is obtained in combination with time attenuation factor and preset equipment grade coefficient;Combining preset equipment safety hazard level and fault diffusion spreading risk, a multi-dimensional decision matrix is constructed, and the equipment anomaly is divided into different abnormal risk levels based on the multi-dimensional decision matrix;Match preset hierarchical disposal plan, and execute corresponding disposal measures.The application improves the anti-interference ability and determination accuracy of anomaly confidence evaluation, completes differentiated operation and maintenance disposal, and guarantees the safe, stable and efficient operation of key equipment in power plant.
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