基于光明大模型的变电站设备智能监控方法及系统
By using the Guangming Big Data Model for text cleaning and feature fusion, the problem of redundant information interference in substation equipment monitoring was solved. This enabled deep semantic representation of signal names and multi-dimensional feature fusion, improving the accuracy of signal matching and the rationality of classification decisions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
- Filing Date
- 2026-05-09
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies cannot effectively remove redundant information from unstructured alarm signal texts in substation equipment monitoring, cannot convert signal names into deep semantic feature vectors, and cannot perform multi-feature fusion and comprehensive similarity calculation, resulting in limited accuracy of signal matching and classification.
The Guangming Big Data Model is used for text cleaning and standardized segmentation. Punctuation characters and common prefixes are removed, core signal name fragments are extracted and converted into deep semantic feature vectors. Feature fusion and similarity calculation are performed in combination with a standardized knowledge base to achieve automatic classification decision.
By fusing deep semantic feature vectors with multi-dimensional features, the accuracy of signal matching and the rationality of classification decisions are improved, redundant information interference is reduced, and deep semantic representation of signal names and comprehensive presentation of multi-dimensional features are achieved.
Smart Images
Figure CN122159482B_ABST