Entity relationship extraction method for wind tunnel fault text knowledge

Abstract

The invention discloses an entity relationship extraction method for wind tunnel fault text knowledge. The method comprises the following steps: 1, defining a knowledge structure; 2, dividing a training set and a test set; 3, performing entity labeling; 4, performing relation labeling; 5, performing data preprocessing; 6, inputting the training set into a model word embedding layer, and training a word embedding matrix; 7, inputting the word embedding matrix into a bidirectional GRU layer of the model, and extracting character-level features; 8, inputting a character-level feature set into a multi-head attention layer of the model, generating a weight vector, and multiplying the weight vector by the character-level features to obtain a sentence-level feature; 9, inputting the sentence-level feature into a model output layer to obtain a relation category; 10, performing iterative training; and 11, testing and evaluating the model; According to the wind tunnel fault entity relation extraction method based on a bidirectional GRU and a multi-head attention mechanism, knowledge is extracted from a wind tunnel fault text, conversion from unstructured fault data to structured data is achieved, and the utilization efficiency of the text knowledge in the wind tunnel health monitoring and fault diagnosis process is improved.

Description

Technical field: [0001] The present invention relates to the technical field of wind tunnel fault diagnosis and entity relationship extraction, in particular to an entity relationship extraction method for wind tunnel fault text knowledge, which is based on a bidirectional gate recurrent unit (Bidirectional Gate RecurrentUnit, abbreviated as BiGRU) and a multi-head attention mechanism (Multi-head Attention) Entity-Relationship Extraction Method for Wind Tunnel Fault Knowledge. Background technique: [0002] The wind tunnel is an important test equipment for studying the aerodynamic characteristics of aircraft, and its health status is crucial to the reliability of test results and the safety of test personnel. The design, manufacture, use, maintenance and other departments of the wind tunnel have accumulated a large amount of fault-related textual knowledge during the long-term production practice, which is of great value to the health status monitoring and fault diagnosis o...

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to propose a method for extracting entity relations of wind tunnel fault text knowledge. Aiming at the problem that the current wind tunnel fault text knowledge is usually stored in an unstructured form, which is not conducive to computer processing and understanding, and cannot be efficiently reused, the proposed An effective wind tunnel fault entity relationship extraction method is disclosed, specifically an entity relationship extraction method based on bidirectional GRU network (BiGRU) and multi-head attention mechanism (Multi-head Attention) for wind tunnel fault knowledge

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