Professional vocabulary translation and analysis method and system for oil and gas geological survey

Abstract

The invention discloses an oil and gas geological survey professional vocabulary translation and analysis method and a system thereof, a dynamically updated database is established, the database stores a corresponding relationship between professional vocabularies and corresponding translation explanation contents, and a translation model is trained based on the database; explaining complex professional vocabularies into popular and easy-to-understand contents by utilizing the translation model; screening out keywords from the interpretation translation content; and constructing a knowledge base, matching analysis contents for the professional vocabularies from the knowledge base according to keywords in the interpretation and translation contents, and outputting the matched analysis contents, so that the professional vocabularies in the oil and gas geology field can be accurately identified and translated, complex professional terms are converted into popular and easy-to-understand languages, and the interpretation and translation of the professional vocabularies in the oil and gas geology field can be realized. By performing secondary analysis on the explained characters, related documents can be more accurately matched from the knowledge base, concise and clear answers are formed, the conditions of mismatching and missed matching are reduced, and secondary explanation which is easier to understand is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geological survey, and in particular to a method and system for translating and analyzing professional vocabulary for oil and gas geological survey. Background Art

[0002] In the oil and gas geology industry, professional terminology is a highly specialized and technical field. It involves a large number of professional words and terms that are difficult for non-professionals to understand, affecting the efficiency of interdisciplinary communication. With the increasing frequency of interdisciplinary and international communication, the accurate translation and interpretation of these professional terms is particularly important.

[0003] The existing professional vocabulary translation and parsing methods are divided into two methods: general language model direct dialogue + knowledge base query and rule matching + keyword search. Among them, the general language model direct dialogue + knowledge base query method is: extract the keywords entered by the user, and then search for relevant documents from the knowledge base through keyword matching. However, this query method is mainly suitable for document query tasks in various fields, especially in non-professional fields. However, when dealing with highly professional problems, due to the lack of in-depth understanding of professional vocabulary, the knowledge base matching rate is low and the query results are inaccurate.

[0004] The rule matching + keyword search method extracts keywords from the questions entered by the user through predefined rules and keyword lists, and then searches for relevant documents from the knowledge base through keyword matching. It is suitable for query tasks with clear rules and fixed keywords. It is simple to implement and does not require complex model training. It has poor flexibility and cannot handle complex and diverse query requests. It is particularly easy to miss important professional terms in oil and gas geology.

[0005] Therefore, although the above scheme meets the needs of gas geological survey professional vocabulary translation and analysis technology to a certain extent, it still has some shortcomings, which are mainly reflected in the following aspects:

[0006] (1) Low knowledge base matching rate: The field of oil and gas geological survey involves a large number of professional terms, which often have specific meanings and usages. Conventional language models have difficulty accurately identifying these professional terms. If professional terms are not accurately recognized, subsequent translation and analysis will produce errors, affecting the overall performance of the system.

[0007] (2) Poor translation quality of professional vocabulary: Due to the high degree of professionalism in the field of oil and gas geological survey, many key words need further explanation before they can be converted into a form that is easy for ordinary users to understand. Even if professional vocabulary is identified, most of the existing professional vocabulary translation methods use multiple search methods to output the first-level explanation of the professional technical vocabulary. The content of the first-level explanation may also contain a relatively large number of professional vocabulary. Non-professional users may find it difficult to understand the first-level explanation of professional technical vocabulary and the core content of the information, which affects the effect of information acquisition and leads to poor user experience. Summary of the invention

[0008] The purpose of the present invention is to provide a method and system for translating and parsing professional vocabulary in oil and gas geological surveys, so as to solve the technical problem that most professional vocabulary translation methods in the prior art use multiple retrieval methods to output the primary interpretation of the professional technical vocabulary, and non-professional users may find it difficult to understand the interpretation of the professional technical vocabulary.

[0009] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0010] A method for translating and parsing professional vocabulary for oil and gas geological surveys, comprising the following steps:

[0011] Step 100: Establish a dynamically updated database, wherein the database stores the correspondence between professional vocabulary and its corresponding translation interpretation content, train a translation model based on the database, and use the translation model to interpret complex professional vocabulary into easy-to-understand content;

[0012] Step 200: Filter out keywords from the interpreted translation content;

[0013] Step 300: construct a knowledge base, match the parsed content for the professional vocabulary from the knowledge base according to the keywords in the interpreted translation content, and output the matched parsed content.

[0014] As a preferred solution of the present invention, in step 100, the implementation method of establishing a dynamic update database is:

[0015] Establishing a vocabulary, the vocabulary storing professional vocabulary and the corresponding interpretation and translation content of each professional vocabulary, each entry of the vocabulary including professional vocabulary, the interpretation and translation content of the professional vocabulary, and the vocabulary category and source of the professional vocabulary, the vocabulary category including geological terms, equipment names and technical methods;

[0016] Establishing a relationship table, the relationship table is used to store the association relationship between different professional vocabulary, the association relationship includes synonyms, antonyms and hyponyms;

[0017] The vocabulary table and the relationship table are stored in a relational database.

[0018] As a preferred solution of the present invention, user feedback and data from a literature database are collected regularly, the relationship table and vocabulary table in the database are updated and annotated in real time, and vocabulary is supplemented, and the translation model is trained based on the updated content to improve the instant accuracy of the professional vocabulary;

[0019] The vocabulary table and the relationship table record the creation time, update time and version number of each vocabulary in real time.

[0020] As a preferred solution of the present invention, the translation model adopts an encoder-decoder structure, wherein the encoder encodes the input professional vocabulary into an intermediate representation, and the decoder decodes the intermediate representation into easy-to-understand text. The implementation method of using the database to train the translation model is:

[0021] Selecting a model of encoder-decoder structure as a translation model, and dividing the vocabulary in the database into a training set and a validation set;

[0022] Convert each professional word and its popular explanation in the vocabulary of the database into an input format acceptable to the translation model, and calculate the difference between the output sequence of the translation model and the real sequence using a loss function;

[0023]

[0024] Where yi is the true popular explanation, pi is the accurate probability of the popular explanation generated by the translation model, and N represents the number of samples;

[0025] Based on the calculation result of the loss function, parameters of the encoder and decoder of the translation model are fine-tuned respectively until the loss value of the output sequence of the translation model meets expectations.

[0026] As a preferred solution of the present invention, a specific implementation method of fine-tuning the encoder of the translation model based on the calculation result of the loss function is:

[0027] Inputting the professional vocabulary in the vocabulary into the encoder of the translation model;

[0028] The encoder encodes the professional vocabulary into an intermediate representation Z;

[0029] Compare the intermediate representation Z generated by the encoder with the desired intermediate representation Z true Compare and calculate the loss value Loss dencoder =||ZZ true || 2 ;

[0030] Based on the calculated loss value, updating the parameters of the encoder,

[0031] As a preferred solution of the present invention, a specific implementation method of fine-tuning the decoder of the translation model based on the calculation result of the loss function is:

[0032] Inputting the intermediate representation generated by the encoder into the decoder;

[0033] The decoder gradually generates a sequence of words in the target language until a complete popular interpretation is generated;

[0034] Compare the generated popular interpretation y with the expected popular interpretation y t Compare and calculate the loss value Where yt is the true popular explanation, pt is the accurate probability of the popular explanation generated by the translation model, and T represents the number of samples;

[0035] Based on the calculated loss value, updating the parameters of the decoder,

[0036] As a preferred solution of the present invention, in step 200, the implementation method of filtering out keywords from the interpreted translation content is:

[0037] Segment and clean the interpretation and translation content of the professional vocabulary;

[0038] Calculate the frequency of each word in the paragraph, the inverse document frequency of each word in the knowledge base, and the TF-IDF value of each word; select several words with high TF-IDF values ​​as keywords;

[0039] Create a labeled dataset that contains paragraphs and corresponding keywords, classify each word as a keyword or non-keyword, use the labeled dataset to train a classification model, and use the trained classification model to extract keywords from new paragraphs;

[0040] Define the part of speech in the interpretation translation content of each professional vocabulary in the vocabulary table in the database, and annotate the part of speech of each keyword in the interpretation translation content output by the translation model. Use natural language processing tools to extract the part of speech tags in the paragraph, and select words that meet the rules as keywords.

[0041] As a preferred solution of the present invention, in step 300, the knowledge base is constructed as follows:

[0042] Data entry: Enter the collected data into the knowledge base in a unified format. Each collected data includes document title, author, abstract, keywords, and full text link;

[0043] Data processing: Use natural language processing technology and machine learning algorithms to segment documents, extract key information and summarize, classify the collected data, and add keyword tags corresponding to each data;

[0044] Data storage: using word embedding technology to convert text data obtained from data processing into vector form and store it in the knowledge base;

[0045] Matching query: Use vector similarity matching algorithm to retrieve the most relevant documents corresponding to the keywords in the professional vocabulary translation content from the knowledge base.

[0046] As a preferred solution of the present invention, the method for retrieving the most relevant documents corresponding to the keywords in the professional vocabulary interpretation translation content from the knowledge base using the vector similarity matching algorithm is as follows:

[0047] Knowledge base text preprocessing: including word segmentation, stop word removal, and denoising operations, breaking down documents into valid words, and identifying and extracting key parts of documents;

[0048] Keyword vectorization: vectorize the keywords in the translated interpretation content;

[0049] Text vectorization: Use the bag-of-words model to ignore the grammar and order in the text, represent the text as a vector of word frequency, and use the TF-IDF method to convert the document into a vector representation, where TF represents word frequency and IDF represents inverse document frequency. The TF-IDF formula is:

[0050]

[0051] Where N is the total number of documents, df(t) is the number of documents containing word t, and the TF-IDF matrix is ​​reduced using singular value decomposition;

[0052] Similarity calculation: using the similarity between the vectorized keywords and the text to select the most relevant documents from the knowledge base, wherein the similarity between the keywords and the text is calculated using the cosine similarity and / or the Euclidean distance method;

[0053] Matching and retrieving the most relevant documents: sorting the documents in the knowledge base according to the calculated similarity, and selecting several documents with high similarity as matching results.

[0054] In addition, the present invention also provides a translation and analysis system for oil and gas geological survey professional vocabulary translation and analysis method, including:

[0055] A translation unit, which trains a translation model based on a data set storing professional vocabulary and explanations of the professional vocabulary, so that the translation model can decode the professional vocabulary into easily understandable explanations;

[0056] A screening unit, used to screen out keywords from the explanation content of professional vocabulary;

[0057] A knowledge base unit, used to store parsed content corresponding to keywords in the interpretation content of the professional vocabulary;

[0058] A query matching unit, which performs vectorization processing on the keyword and the documents in the knowledge base, and retrieves the document most relevant to the user query from the knowledge base based on the similarity between the keyword and the document after the vectorization processing;

[0059] Key Information Extraction and Generation Unit: Extracts key information from the most relevant matching documents and generates coherent answers.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] (1) Improve the ability to identify and translate professional terms: By designing a professional vocabulary recognition algorithm and translation module (fine-tuned oil and gas geological survey model), the system can accurately identify and translate professional terms in the field of oil and gas geology, converting complex professional terms into easy-to-understand language so that non-professionals can also understand the core content of the information, thereby improving the comprehensibility and accuracy of information and promoting effective communication and collaboration between different disciplines. In the actual standard test data set, the translation accuracy of the present invention reached 88%, while the translation accuracy of the prior art was only 72%.

[0062] (2) Optimize the knowledge base matching process: By performing a secondary analysis on the interpreted text, the system can more accurately match relevant documents from the knowledge base, form concise and clear answers, reduce false matches and missed matches, and form a more understandable secondary explanation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0064] Figure 1 A flowchart of a method for translating and parsing professional vocabulary according to an embodiment of the present invention; DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] like Figure 1 As shown, the present invention provides a method for translating and parsing professional vocabulary for oil and gas geological survey, comprising the following steps:

[0067] Step 100: Establish a dynamically updated database, which stores the correspondence between professional vocabulary and its corresponding translation interpretation content, use the data in the database to train the translation model, and use the translation model to interpret complex professional vocabulary into easy-to-understand content;

[0068] In step 100, the implementation method of establishing a dynamic update database is as follows:

[0069] (1) A translation model with an encoder-decoder structure is used to establish a vocabulary, which stores professional vocabulary and the corresponding interpretation translation content of each professional vocabulary, wherein the encoder encodes the input professional vocabulary into an intermediate representation, and the decoder decodes the intermediate representation into easy-to-understand text.

[0070] Each entry in the vocabulary list includes a professional vocabulary, an explanation and translation content of the professional vocabulary, and a vocabulary category and source of the professional vocabulary; wherein the vocabulary category includes geological terms, equipment names, and technical methods.

[0071] (2) establishing a relationship table, which is used to store the associations between different professional vocabulary words, wherein the associations include synonyms, antonyms, and hyponyms;

[0072] (3) The vocabulary table and the relationship table are stored in a relational database. In this embodiment,

[0073] The translation model used when building the vocabulary table adopts an encoder-decoder structure, and a relational database (such as MySQL, PostgreSQL) is used to store the vocabulary table and relational table, supporting structured queries and transaction management. NoSQL databases (such as MongoDB, Cassandra) are used to store unstructured data, supporting flexible data models and high concurrent access.

[0074] The database is an updateable mechanism. By regularly collecting user feedback and data from the literature database, the relationship tables and vocabulary tables in the database are updated and annotated and vocabulary supplemented in real time. The creation time, update time and version number of each word are recorded in real time in the vocabulary tables and relationship tables.

[0075] Regularly collect user feedback and data from the literature database, update and annotate the relationship tables and vocabulary tables in the database in real time, and supplement vocabulary. Train the translation model based on the updated content to improve the instant accuracy of the professional vocabulary. The specific implementation method is as follows:

[0076] The updated explanation content of the professional vocabulary is input into the translation model to optimize the model parameters of the translation model, wherein the explanation content of the professional vocabulary is specifically collected from user feedback, expert knowledge, academic papers, textbooks, technical reports, journal reports, etc., and these data all contain a professional vocabulary and its corresponding popular explanation, for example, the popular explanation corresponding to "reservoir" is "the stratum that stores oil and gas"; the corresponding explanation of "geological structure" is "the structure and morphology inside the earth's crust."

[0077] After translating professional vocabulary into easy-to-understand words, the matching key content of documents can be retrieved from various documents based on the translated words, which can improve the accuracy of professional vocabulary translation. The experiment used a test set containing 1,000 professional words for oil and gas geological surveys, and evaluated the system without using the professional vocabulary interpretation model and the system with the professional vocabulary interpretation model. Experimental results: By using the professional vocabulary interpretation model, the performance of the system in professional vocabulary interpretation is significantly improved, the accuracy is increased by 25%, and the BLEU score and ROUGE score are increased by 20% and 20% respectively.

[0078] Without the professional vocabulary explanation model: the accuracy of the professional vocabulary explanation is 60%, the BLEU score is 0.65, and the ROUGE score is 0.60. With the professional vocabulary explanation model: the accuracy of the professional vocabulary explanation is 85%, the BLEU score is 0.85, and the ROUGE score is 0.80.

[0079] Use the updated database to train the translation model to improve the accuracy of professional vocabulary translation. The specific implementation method is as follows:

[0080] Model selection: Use an encoder-decoder structure such as Transformer, LSTM, or GRU.

[0081] Pre-trained model: You can choose a pre-trained model, such as BERT, T5, etc., and then fine-tune it.

[0082] Training process: Convert professional vocabulary and popular explanations into input formats acceptable to the model (usually word embeddings or index sequences), then divide the dataset into training and validation sets, and define the loss function and optimizer. The following is an example:

[0083] Input vocab = {“reservoir”: [1,2,3], “lithology”: [4,5,6], “geological structure”: [7,8,9]}

[0084] Target vocab = {"strata that store oil and gas": [10,11,12,13,14], "properties and types of rocks": [15,16,17,18,19], "structure and morphology of the earth's crust": [20,21,22,23,24]}

[0085] Model loss function selection: This selection is often used for the cross entropy loss function of sequence generation tasks, such as translation models. It is mainly used to measure the difference between the sequence generated by the model and the real sequence.

[0086] formula:

[0087] where y i is the true label, p i is the probability predicted by the model, and N is the number of samples. The goal of the loss function is to minimize the difference between the predicted probability and the true label.

[0088] The encoder is optimized and fine-tuned through professional data to better capture the semantic features of professional vocabulary. The specific steps include: input professional vocabulary to the encoder; the encoder encodes the professional vocabulary into an intermediate representation (intermediate state); compare the generated intermediate representation with the expected intermediate representation, calculate the loss value, and use the mean square error (MSE) or other suitable loss functions; use optimization algorithms such as gradient descent to update the encoder parameters to reduce the difference.

[0089] For example: Enter the professional term: "reservoir"

[0090] The encoder generates an intermediate representation: Z

[0091] Expected intermediate representation: Z true

[0092] Calculate loss: Loss dencoder =||ZZ true || 2

[0093] Update parameters:

[0094] The decoder is fine-tuned with professional data so that it can accurately decode the intermediate representation into easy-to-understand language. The specific steps include: inputting the intermediate representation generated by the encoder into the decoder; the decoder gradually generates a sequence of words in the target language until a complete sentence is generated; comparing the generated popular explanation with the expected popular explanation, calculating the loss value, and using the cross entropy loss function; using optimization algorithms such as gradient descent to update the decoder parameters and reduce the loss value.

[0095] For example: Enter the professional term: "reservoir"

[0096] Enter intermediate representation: Z

[0097] The decoder generates a simple explanation: "The formation where oil and gas are stored"

[0098] Expected popular explanation: "The formation that stores oil and gas"

[0099] Calculate the loss: where y t is the true label, p t is the probability predicted by the model, and T represents the number of samples.

[0100] Update parameters:

[0101] Intermediate representation intermediate process: Input vocabulary: "reservoir"

[0102] Intermediate representation: Z = [0.1, 0.5, -0.2, 0.8, ...] (assuming it is a 128-dimensional vector)

[0103] The decoder generates a layman’s explanation: “A formation in which oil and gas are stored.

[0104] The translation model provided by this embodiment can also realize a self-learning process, that is, input professional vocabulary, generate an intermediate representation through the encoder, and then generate a popular explanation through the decoder; use the loss function to calculate the difference between the generated popular explanation and the real popular explanation; calculate the gradient through the back propagation algorithm, and use the optimization algorithm (such as Adam, SGD) to update the model parameters; repeat the above steps until the model converges or reaches the predetermined training rounds.

[0105] Through the above-mentioned fine-tuning operation of the translation model, the performance of the translation model has been significantly improved, especially the accuracy and robustness when dealing with professional problems have been significantly improved. In the experiment, we used a test set containing 1,000 professional problems in oil and gas geological surveys to evaluate the unfine-tuned model and the fine-tuned model respectively. The main evaluation indicators include accuracy, F1 score, recall rate, etc. The experimental results show that: Unfine-tuned model: accuracy is 60%, F1 score is 0.58, and recall rate is 0.62. Fine-tuned model: accuracy is 85%, F1 score is 0.83, and recall rate is 0.87.

[0106] Step 200: Filter out keywords from the interpreted translation content.

[0107] In step 200, after the professional vocabulary is interpreted into easy-to-understand text, a paragraph-style text is formed. In order to achieve matching retrieval of the corresponding parsed document from the knowledge base, it is necessary to screen out the keywords for matching retrieval from the interpreted easy-to-understand text, thereby ensuring the relevance, representativeness and uniqueness of the keywords and improving the accuracy and coverage of the retrieval.

[0108] The screening principles include the following:

[0109] Relevance: Keywords should be closely related to the core meaning of professional vocabulary and accurately reflect the meaning of professional vocabulary. For example, the keywords extracted from "reservoir" include "reservoir", "oil and gas", "stratum", etc. These keywords directly reflect the definition of reservoir.

[0110] Representativeness: Keywords should be highly representative and able to summarize the main content of the paragraph. For example, the keywords extracted from "lithology" include "lithology", "rock", "property", "type", and "mineral", which cover the main characteristics of lithology.

[0111] Uniqueness: Keywords should have a certain degree of uniqueness and avoid overly common words in order to improve the accuracy of the search. The keywords extracted from "geological structure" include "geological structure", "crust", "structure", "morphology", and "surface". These keywords have high uniqueness and avoid common words such as "is" and "of".

[0112] Frequency: Keywords appear more frequently in a paragraph, which helps improve the coverage of the search. For example, the keywords extracted from "reservoir", "oil and gas", "stratum", "porosity", and "rock" appear multiple times in the paragraph, ensuring the coverage of the search.

[0113] Based on the above, the specific implementation steps and methods for selecting keywords from the explanation sentences of professional vocabulary are as follows:

[0114] (1) Preprocessing: Perform preprocessing operations such as word segmentation, stop word removal, and stem extraction on paragraphs to ensure data consistency and reduce inventory.

[0115] (2) Extract keywords;

[0116] Method 1: TF-IDF (Term Frequency-Inverse Document Frequency): A statistical method used to evaluate the importance of a word in a document or corpus.

[0117] Steps: Calculate the term frequency (TF) of each word in the paragraph; Calculate the inverse document frequency (IDF) of each word in the entire knowledge base; Calculate the TF-IDF value of each word; Select several words with the highest TF-IDF values ​​as keywords.

[0118] Method 2: TextRank: A graph-based ranking algorithm for extracting keywords from text.

[0119] Steps: Construct a word co-occurrence graph, where the nodes are words and the edge weights are the co-occurrence frequencies between words; use the PageRank algorithm to calculate the weight of each node; select several words with the highest weights as keywords.

[0120] Method 3: Rule-based method: Manually filter keywords according to pre-defined rules.

[0121] Steps: Define the part of speech of the keyword (such as noun, verb, adjective, etc.); use natural language processing tools (such as NLTK, spaCy) to extract the part of speech tags in the paragraph; select words that meet the rules as keywords.

[0122] Method 4: Model-based approach: Automatically extract keywords using machine learning or deep learning models.

[0123] Steps: Prepare a labeled dataset containing paragraphs and corresponding keywords; train a classification model (such as SVM, BERT, etc.) to classify each word as a keyword or non-keyword; use the trained model to extract keywords from new paragraphs.

[0124] Step 300: construct a knowledge base, match the parsed content for the professional vocabulary from the knowledge base according to the keywords in the interpreted translation content, and output the matched parsed content.

[0125] The implementation method of constructing the knowledge base is as follows:

[0126] (1) Data entry: The collected information (user feedback, expert knowledge, academic papers, textbooks, technical reports, journal reports, etc.) is entered into the knowledge base in a unified format, including document title, author, abstract, keywords, and full-text link.

[0127] (2) Data processing and storage: Use natural language processing technology and machine learning algorithms to segment documents, extract key information and summarize them. Classify the collected information and add corresponding keyword information. Use word embedding technology to convert the text data obtained from data processing into vector form and store it in the knowledge base.

[0128] (3) Matching query: Use vector similarity matching algorithm to retrieve the most relevant documents corresponding to the keywords in the professional vocabulary interpretation translation content from the knowledge base. Output the matching analysis content, including detailed definitions, application examples, related literature, etc., to help users better understand professional vocabulary.

[0129] The specific implementation principle and method of the knowledge base vector similarity matching algorithm are as follows:

[0130] (1) Text preprocessing: including word segmentation, stop word removal, denoising (removing irrelevant information such as headers, footers, copyright notices, etc.), etc., to decompose the text into valid words.

[0131] Identify and extract key parts of the document, such as title, abstract, keywords, body paragraphs, etc. If necessary, specific words or concepts in the document can be manually annotated to facilitate subsequent automatic processing.

[0132] (2) Feature selection: mainly uses the word frequency features in the statistical knowledge base as the dimension of the vector.

[0133] (3) Vectorization: Use the Bag of Words (BoW) model to ignore the syntax and order in the text and represent the text as a vector of word frequencies. Use the TF-IDF (Term Frequency-Inverse Document Frequency) method to convert the document into a vector representation, where TF represents term frequency and IDF represents inverse document frequency. The TF-IDF formula is:

[0134]

[0135] Where N is the total number of documents and df(t) is the number of documents containing word t. Truncated SVD (singular value decomposition) is used to reduce the dimension of the TF-IDF matrix to reduce the dimension of the vector and improve the calculation efficiency.

[0136] (4) Similarity calculation: In the system, documents and queries are converted into vectors, so different similarity measures can be used to compare them, including cosine similarity and Euclidean distance methods.

[0137] Cosine similarity: Calculates the cosine value of the angle between two vectors. The cosine similarity range is [-1,1]. The larger the value, the higher the similarity. Formula:

[0138]

[0139] Euclidean distance: Calculates the Euclidean distance between two vectors. The smaller the value, the higher the similarity. Formula:

[0140]

[0141] (5) Match and retrieve the most relevant documents: Sort the candidate documents according to the calculated similarity, and select the documents with the highest similarity as the results and return them to the user.

[0142] In order to effectively match the search content from these documents and organize the output, all data sources should be clearly marked during the data collection process, and then stored in a unified format to facilitate subsequent processing, such as PDF to text, HTML to text, etc.

[0143] In addition, for quick search, an index is usually built in advance so that potentially relevant documents can be quickly located during actual retrieval. That is, full-text search engines (such as Elasticsearch) or custom indexing mechanisms are used to index the words and their locations in the document. Algorithms such as LDA (Latent Dirichlet Allocation) are used to perform topic modeling on the document to help understand the overall content and classification of the document.

[0144] Content extraction and organization: For the selected documents, automatic summarization techniques (such as TextRank, BERT-based summarization, etc.) can be used to generate a brief summary that highlights the information relevant to the query. If the summary is not sufficient to meet the needs, paragraphs containing key information can be further extracted.

[0145] Formatted output: Organize the extracted content into an easy-to-read format, such as a list, table, or paragraph form, and provide links to the original text for users to study in depth.

[0146] By using the vocabulary translation database, the system's performance in vocabulary translation has been significantly improved, with accuracy increased by 20%, recall and precision increased by 20% and 15% respectively.

[0147] The present invention has successfully developed a professional intelligent question-answering system and method for oil and gas geological surveys, which realizes the conversion of complex oil and gas geological terms into easy-to-understand language. This model not only has powerful language understanding and generation capabilities, but also can accurately capture and interpret the meaning of professional vocabulary, significantly improving the credibility of the system and user experience, and providing great convenience for non-professional users. These technical means make the present invention have significant advantages in the practical application of the field of oil and gas geological surveys.

[0148] The translation and analysis system of the above-mentioned oil and gas geological survey professional vocabulary translation and analysis method specifically includes:

[0149] A database unit, used to store each professional vocabulary and the explanation content of the professional vocabulary, so as to decode the professional vocabulary into easy-to-understand explanation content;

[0150] A screening unit, used to screen out keywords from the explanation content of professional vocabulary;

[0151] The knowledge base unit is used to store the parsed content corresponding to the keywords in the interpretation content of the professional vocabulary.

[0152] Compared with the prior art, the present invention has the following beneficial effects:

[0153] (1) Improve the ability to identify and translate professional terms: By designing a professional vocabulary recognition algorithm and translation module (fine-tuned oil and gas geological survey model), the system can accurately identify and translate professional terms in the field of oil and gas geology, converting complex professional terms into easy-to-understand language so that non-professionals can also understand the core content of the information, thereby improving the comprehensibility and accuracy of information and promoting effective communication and collaboration between different disciplines. In the actual standard test data set, the translation accuracy of the present invention reached 88%, while the translation accuracy of the prior art was only 72%.

[0154] (2) Optimize the knowledge base matching process: By performing a secondary analysis on the interpreted text, the system can more accurately match relevant documents from the knowledge base, form concise and clear answers, reduce false matches and missed matches, and form a more understandable secondary explanation.

[0155] On the same test data set, the knowledge base matching accuracy of the present invention reaches 92%, while the matching accuracy of the prior art is only 75%. When processing documents of the same length, the average query response time of the present invention is reduced by 30%. Specifically, the prior art takes 5 seconds to process a query request, while the present invention only takes 3.5 seconds.

[0156] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A method for translating and parsing professional vocabulary for oil and gas geological survey, characterized in that: The following steps are involved: Step 100: Establish a dynamically updated database, wherein the database stores the correspondence between professional vocabulary and its corresponding translation interpretation content, train a translation model based on the database, and use the translation model to interpret complex professional vocabulary into easy-to-understand content; Step 200: Filter out keywords from the interpreted translation content; Step 300: construct a knowledge base, match the parsed content for the professional vocabulary from the knowledge base according to the keywords in the interpreted translation content, and output the matched parsed content.

2. The method for translating and analyzing professional vocabulary for oil and gas geological survey according to claim 1 is characterized in that: In step 100, the implementation method of establishing a dynamic update database is as follows: Establishing a vocabulary, the vocabulary storing professional vocabulary and the corresponding interpretation and translation content of each professional vocabulary, each entry of the vocabulary including professional vocabulary, the interpretation and translation content of the professional vocabulary, and the vocabulary category and source of the professional vocabulary, the vocabulary category including geological terms, equipment names and technical methods; Establishing a relationship table, the relationship table is used to store the association relationship between different professional vocabulary, the association relationship includes synonyms, antonyms and hyponyms; The vocabulary table and the relationship table are stored in a relational database.

3. The method for translating and analyzing professional vocabulary for oil and gas geological survey according to claim 2 is characterized in that: Regularly collect user feedback and data from the literature database, update and annotate the relationship tables and vocabulary tables in the database in real time, and supplement vocabulary, and train the translation model based on the updated content to improve the instant accuracy of the professional vocabulary; The vocabulary table and the relationship table record the creation time, update time and version number of each vocabulary in real time.

4. The method for translating and analyzing professional vocabulary for oil and gas geological survey according to claim 3 is characterized in that: The translation model adopts an encoder-decoder structure, wherein the encoder encodes the input professional vocabulary into an intermediate representation, and the decoder decodes the intermediate representation into easy-to-understand text. The implementation method of using the database to train the translation model is as follows: Selecting a model of encoder-decoder structure as a translation model, and dividing the vocabulary in the database into a training set and a validation set; Convert each professional word and its popular explanation in the vocabulary of the database into an input format acceptable to the translation model, and calculate the difference between the output sequence of the translation model and the real sequence using a loss function; Where yi is the true popular explanation, pi is the accurate probability of the popular explanation generated by the translation model, and N represents the number of samples; Based on the calculation result of the loss function, parameters of the encoder and decoder of the translation model are fine-tuned respectively until the loss value of the output sequence of the translation model meets expectations.

5. The method for translating and analyzing professional vocabulary for oil and gas geological survey according to claim 4 is characterized in that: The specific implementation method of fine-tuning the encoder of the translation model based on the calculation result of the loss function is: Inputting the professional vocabulary in the vocabulary into the encoder of the translation model; The encoder encodes the professional vocabulary into an intermediate representation Z; Compare the intermediate representation Z generated by the encoder with the desired intermediate representation Z true Compare and calculate the loss value Loss dencoder =||ZZ true || 2 ; Based on the calculated loss value, updating the parameters of the encoder, 6. The method for translating and analyzing professional vocabulary for oil and gas geological survey according to claim 4 is characterized in that: The specific implementation method of fine-tuning the decoder of the translation model based on the calculation result of the loss function is: Inputting the intermediate representation generated by the encoder into the decoder; The decoder gradually generates a sequence of words in the target language until a complete popular interpretation is generated; Compare the generated popular interpretation y with the expected popular interpretation y t Compare and calculate the loss value Where yt is the true popular explanation, pt is the accurate probability of the popular explanation generated by the translation model, and T represents the number of samples; Based on the calculated loss value, updating the parameters of the decoder, 7. The method for translating and analyzing professional vocabulary for oil and gas geological survey according to claim 1 is characterized in that: In step 200, the implementation method of filtering out keywords from the interpreted translation content is as follows: Segment and clean the interpretation and translation content of the professional vocabulary; Calculate the frequency of each word in the paragraph, the inverse document frequency of each word in the knowledge base, and calculate the TF-IDF value of each word; Select several words with high TF-IDF values ​​as keywords; Create a labeled dataset that contains paragraphs and corresponding keywords, classify each word as a keyword or non-keyword, use the labeled dataset to train a classification model, and use the trained classification model to extract keywords from new paragraphs; Define the part of speech in the interpretation translation content of each professional vocabulary in the vocabulary table in the database, and annotate the part of speech of each keyword in the interpretation translation content output by the translation model. Use natural language processing tools to extract the part of speech tags in the paragraph, and select words that meet the rules as keywords.

8. The method for translating and analyzing professional vocabulary for oil and gas geological survey according to claim 1 is characterized in that: In step 300, the knowledge base is constructed as follows: Data entry: Enter the collected data into the knowledge base in a unified format. Each collected data includes document title, author, abstract, keywords, and full text link; Data processing: Use natural language processing technology and machine learning algorithms to segment documents, extract key information and summarize, classify the collected data, and add keyword tags corresponding to each data; Data storage: using word embedding technology to convert text data obtained from data processing into vector form and store it in the knowledge base; Matching query: Use vector similarity matching algorithm to retrieve the most relevant documents corresponding to the keywords in the professional vocabulary translation content from the knowledge base.

9. The method for translating and analyzing professional vocabulary for oil and gas geological survey according to claim 8 is characterized in that: The implementation method of using the vector similarity matching algorithm to retrieve the most relevant documents corresponding to the keywords in the professional vocabulary interpretation translation content from the knowledge base is as follows: Knowledge base text preprocessing: including word segmentation, stop word removal, and denoising operations, breaking down documents into valid words, and identifying and extracting key parts of documents; Keyword vectorization: vectorize the keywords in the translated interpretation content; Text vectorization: Use the bag-of-words model to ignore the grammar and order in the text, represent the text as a vector of word frequency, and use the TF-IDF method to convert the document into a vector representation, where TF represents word frequency and IDF represents inverse document frequency. The TF-IDF formula is: Where N is the total number of documents, df(t) is the number of documents containing word t, and the TF-IDF matrix is ​​reduced using singular value decomposition; Similarity calculation: using the similarity of the vectorized keywords and text to select the most relevant documents from the knowledge base, wherein the similarity of the keywords and text is calculated using the cosine similarity and / or Euclidean distance method; Matching and retrieving the most relevant documents: sorting the documents in the knowledge base according to the calculated similarity, and selecting several documents with high similarity as matching results.

10. A translation and analysis system based on the oil and gas geological survey professional vocabulary translation and analysis method according to any one of claims 1 to 9, characterized in that: include: A translation unit, which trains a translation model based on a data set storing professional vocabulary and explanations of the professional vocabulary, so that the translation model can decode the professional vocabulary into easily understandable explanations; A screening unit, used to screen out keywords from the explanation content of professional vocabulary; A knowledge base unit, used to store parsed content corresponding to keywords in the interpretation content of the professional vocabulary; A query matching unit, which performs vectorization processing on the keyword and the documents in the knowledge base, and retrieves the document most relevant to the user query from the knowledge base based on the similarity between the keyword and the document after the vectorization processing; Key Information Extraction and Generation Unit: Extracts key information from the most relevant matching documents and generates coherent answers.

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