Transformer fault diagnosis method and system based on fusion knowledge graph and large language model

Abstract

The invention discloses a transformer fault diagnosis method and system based on a fusion knowledge graph and a large language model. The method comprises the steps of obtaining an unstructured text and a semi-structured text; constructing a joint entity relationship extraction model and a question and answer module, wherein the joint entity relationship extraction model comprises a coding layer, a head entity recognition layer and a tail entity and relationship joint recognition layer; the question and answer module comprises a LangChain model and a large language model, and the output of the LangChain is used as the input of the large language model; obtaining a fault problem text, inputting the fault problem text into the LangChain model, outputting professional knowledge, inputting the professional knowledge and the fault problem text into the large language model, and outputting a professional fault diagnosis answer; and interactively combining the professional fault diagnosis answer with the high-quality knowledge triple text, and outputting a final fault diagnosis answer. According to the method, the problems of weak information relevance, low decision generation efficiency and the like in the operation and maintenance process of the transformer can be solved in a targeted manner, and the health management level of the power transformer is greatly improved.

Claims

1. A transformer fault diagnosis method based on the fusion of knowledge graph and large language model, characterized in that: include: Obtain unstructured text of transformer operation and maintenance and semi-structured text of transformer failure, and pre-process the obtained unstructured text and semi-structured text; Constructing a joint entity relationship extraction model and a question-answering module, wherein the joint entity relationship extraction model is used to extract knowledge from preprocessed unstructured text and semi-structured text, output original knowledge triple text, and perform knowledge fusion on the original knowledge triple text to obtain high-quality knowledge triple text, wherein the joint entity relationship extraction model includes an encoding layer, a head entity recognition layer, and a tail entity and relationship joint recognition layer; the question-answering module includes a LangChain model and a large language model, and the output of the LangChain is used as the input of the large language model; Obtain the fault problem text, input the fault problem text into the LangChain model and output professional knowledge, input the professional knowledge and fault problem text into the large language model, and output professional fault diagnosis answers; Interactively combine professional fault diagnosis answers with high-quality knowledge triple text to output the final fault diagnosis answer.

2. The transformer fault diagnosis method based on fusion knowledge graph and large language model according to claim 1 is characterized in that: The preprocessing includes filtering stop words and outlier processing; wherein, the method for filtering stop words is: constructing a stop word dictionary based on the unstructured text and semi-structured text, and then using the stop word dictionary to filter the stop words of the unstructured text and semi-structured text; the outlier processing includes denoising, text normalization and missing value processing, wherein the denoising is to remove errors and irrelevant texts in the unstructured text and semi-structured text, the text normalization is to convert the unstructured text and semi-structured text into a consistent format, and the missing value processing is to use interpolation method for processing.

3. The transformer fault diagnosis method based on the fusion of knowledge graph and large language model as claimed in claim 1 is characterized in that: The encoding layer uses the BERT model, which is a language representation model based on a multi-layer bidirectional Transformer, used to extract feature information and learn deep representation by jointly adjusting the context of each word. Specifically: h0=SW S +W P h α =Trans(h α-1 ),α∈[1,N] Among them, S represents the one-hot encoding vector matrix of the words in the input sentence, W S represents the word embedding matrix, W P represents the position embedding matrix, h α represents the hidden state vector, that is, the context representation of the input sentence at the αth layer, and N is the number of Transformer modules.

4. The transformer fault diagnosis method based on fusion knowledge graph and large language model as claimed in claim 1 is characterized in that: The head entity recognition layer uses two identical binary classifiers to detect the start and end positions of the head entity respectively, and assigns a binary tag to each token to mark whether the current tag corresponds to the start and end positions of the head entity. The specific operations for each token are as follows: in, and They represent the probability of marking the i-th token of the input text as the start position and the end position of the head entity respectively; x i is the encoded representation of the i-th token in the input sequence, i.e. x i =h N [i], where W(·) represents the weight matrix, b(·) represents the bias term, and σ represents the sigmoid activation function.

5. The transformer fault diagnosis method based on fusion knowledge graph and large language model according to claim 1 is characterized in that: The specific operations for each token in the tail entity and relationship joint identification layer are as follows: in, and They represent the probability of identifying the i-th token in the input sequence as the start and end position of the tail entity, respectively. Represents the encoded representation vector of the k-th entity detected by the previous layer.

6. The transformer fault diagnosis method based on fusion knowledge graph and large language model according to claim 1 is characterized in that: The specific method for knowledge fusion of the original knowledge triple text is: fusion digestion and entity disambiguation; the fusion digestion adopts the semantic similarity method, that is, improving the similarity of word vectors between different synonyms to perform fusion digestion; the entity disambiguation adopts the deep learning method to perform entity linking on the identified entities.

7. The transformer fault diagnosis method based on fusion knowledge graph and large language model according to claim 1 is characterized in that: The professional fault diagnosis answer and the high-quality knowledge triple text are interactively combined through extraction and transformation, specifically including: information extraction and structural text readability; the information extraction is to embed an attention key and value into the prefix at each layer of the large language model, that is: Among them, K∈R l×d , represents the given original key vector; V∈R l×d , represents the given original value vector; P k and P v Represent a trainable vector respectively; (i) represents the part of the vector corresponding to the i-th attention head; The structured text is made readable by converting the nodes in the high-quality knowledge triple text into prompt texts and inputting them into a large language model to output the final fault diagnosis answer.

8. Transformer fault diagnosis system based on fusion knowledge graph and large language model, characterized by: include: The text acquisition module is configured to: acquire unstructured text of transformer operation and maintenance and semi-structured text of transformer failure, and pre-process the acquired unstructured text and semi-structured text; The knowledge graph and question-answering model construction module is configured to: construct a joint entity relationship extraction model and a question-answering model, wherein the joint entity relationship extraction model is used to extract knowledge from preprocessed unstructured text and semi-structured text, output original knowledge triple text, and perform knowledge fusion on the original knowledge triple text to obtain high-quality knowledge triple text, wherein the joint entity relationship extraction model includes an encoding layer, a head entity recognition layer, and a tail entity and relationship joint recognition layer; the question-answering model includes a LangChain model and a large language model, and the output of the LangChain is used as the input of the large language model; The fault diagnosis answer output module is configured to: obtain the fault problem text, input the fault problem text into the LangChain model and output professional knowledge, input the professional knowledge and the fault problem text into the large language model, and output a professional fault diagnosis answer; Interactively combine professional fault diagnosis answers with high-quality knowledge triple text to output the final fault diagnosis answer.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the transformer fault diagnosis method based on the fusion of knowledge graph and large language model as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the transformer fault diagnosis method based on the fusion of knowledge graph and large language model as described in any one of claims 1 to 7 are implemented.

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