An adaptive learning translation system and method for oil and gas geological survey professional vocabulary

Abstract

The present invention discloses an adaptive learning oil and gas geological survey professional vocabulary translation system and method, which constructs a professional vocabulary translation model for popular interpretation of professional vocabulary, and filters out query output results matching the interpretation and translation content of the translation model from a knowledge base; collects all users' query requests, corresponding query output results, and each user's feedback on the query to form a data set; classifies the data set according to the category to which the professional vocabulary in the query request belongs, integrates each type of query request and corresponding feedback into a feedback data training set, and triggers the professional vocabulary translation model to perform adaptive learning and updating when the number of samples in the feedback data training set reaches a threshold; the present invention can gradually improve in long-term operation, better serve users' query needs, and improve query accuracy and response speed.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geological survey, and in particular to an adaptive learning oil and gas geological survey professional vocabulary translation system and method. Background Art

[0002] Oil and gas geological surveys are a crucial component of oil and gas exploration, encompassing multiple disciplines including geology, geophysics, and geochemistry. With the advancement of information technology, the use of artificial intelligence and natural language processing (NLP) for oil and gas geological survey document retrieval has become a research hotspot. These technologies aim to improve the efficiency and accuracy of information retrieval, helping geologists quickly obtain the geological data and technical information they need.

[0003] The relevant key technologies mainly include natural language processing (NLP) technology, deep learning models, knowledge base management technology, and user feedback mechanism.

[0004] In the field of oil and gas geological surveys, existing document query systems generally adopt the following implementation solutions:

[0005] 1. Direct dialogue with professional vocabulary translation model + knowledge base query: After the user enters a query request, the system directly communicates through a common language model (such as BERT, GPT, etc.), extracts the keywords entered by the user, and then searches for relevant documents from the knowledge base through keyword matching.

[0006] 2. Rule matching + keyword search: Extract keywords from user-entered questions through predefined rules and keyword lists, and then search for relevant documents from the knowledge base through keyword matching.

[0007] The technical flaws of the aforementioned document query system include poor model adaptability: Existing systems are typically trained once and then not updated, failing to optimize as new data accumulates and changes. Over time, the system's performance gradually deteriorates, making it impossible to maintain a long-term competitive advantage. Summary of the Invention

[0008] The purpose of the present invention is to provide an adaptive learning oil and gas geological survey professional vocabulary translation system and method to solve the technical problem that the existing technology is usually not updated after one-time training and cannot self-optimize with the accumulation and change of new data.

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

[0010] An adaptive learning oil and gas geological survey professional vocabulary translation system, characterized by comprising:

[0011] Professional vocabulary translation model, used to translate professional vocabulary in query content into easily understandable content;

[0012] A user query feedback collection module is used to record each user's feedback information on their oil and gas geological survey content, wherein each user's oil and gas geological survey content includes query content and translation output content, and the oil and gas geological survey content is classified according to the keywords in the query content;

[0013] The reverse training module forms a feedback data training set based on the classification of the query content and the feedback information corresponding to each query content, and uses the feedback data training set to adjust the professional vocabulary translation model to form an adaptive learning mechanism.

[0014] As a preferred embodiment of the present invention, a vocabulary translation database is further included, wherein the vocabulary translation database includes a vocabulary table and a relationship table, wherein the vocabulary table stores professional vocabulary and its corresponding popular explanation, and the relationship table is used to store the association relationship between different professional vocabulary, wherein the association relationship includes synonyms, antonyms, and hyponymy;

[0015] The professional vocabulary translation model is trained based on the vocabulary translation database until the professional vocabulary translation model can interpret the professional vocabulary translation into easily understandable content.

[0016] As a preferred solution of the present invention, a synonym library is also included, and the implementation method of creating the synonym library is as follows:

[0017] Automatically identify and extract high-frequency keywords from the user's query content, annotate the collected keywords, and store each keyword and its synonymous professional vocabulary in the synonym library;

[0018] The professional vocabulary translation model calls the synonym library to convert the keywords into matching professional vocabulary based on the keywords in the identified user query content.

[0019] As a preferred solution of the present invention, the dataset of the synonym library is divided into a training set, a validation set, and a test set to train the professional vocabulary translation model, and the fine-tuning accuracy of the professional vocabulary translation model for the query content is evaluated by cross-validation, and the fine-tuning optimization is continuously performed until the fine-tuning accuracy of the professional vocabulary translation model for the query content meets the expectations;

[0020] When the professional vocabulary translation model is trained using the training set, the relationship table corresponding to each professional vocabulary in the synonym library is called, and the synonym group of each professional vocabulary in each synonym library is added, and the professional vocabulary translation model is trained using the synonym group of each professional vocabulary and the relationship table corresponding to each professional vocabulary to increase the data diversity of the training set;

[0021] The training is stopped when the fine-tuning accuracy of the professional vocabulary translation model on the validation set no longer improves to avoid overfitting.

[0022] As a preferred solution of the present invention, the basic model of the professional vocabulary translation model is a pre-trained general language model;

[0023] The general language model is fine-tuned using the vocabulary translation database to enhance the recognition and translation accuracy of the professional vocabulary translation model for professional terms in the field of oil and gas geological survey.

[0024] As a preferred solution of the present invention, the user query feedback collection module includes a user feedback log unit, which is used to store user query content, query output results, and user feedback, and organize them into a structured format to ensure that each type of query content has corresponding feedback. Based on the classification of the query content and the feedback information corresponding to each query content, a feedback data training set is formed, and the feedback information of each query content is used as a label for the data;

[0025] Feedback classification unit, which uses classification algorithms to automatically classify user query content based on the professional vocabulary in the query content;

[0026] The data cleaning unit is used to clean the query content in the user feedback log unit, remove invalid or duplicate data, and ensure the integrity and consistency of the feedback data training set.

[0027] As a preferred solution of the present invention, the reverse training module uses the feedback data training set to adjust the professional vocabulary translation model in the following manner:

[0028] Collect user query content and feedback information from each query from the literature database, expert review, and internal project data collection data, and trigger the update of the professional vocabulary translation model in real time when the data set reaches a threshold;

[0029] Training the professional vocabulary translation model using a fine-tuning technique, and after the training is completed, performing a performance evaluation on the professional vocabulary translation model using the vocabulary table of the vocabulary translation database;

[0030] The parameters of the professional vocabulary translation model are adjusted based on the performance evaluation result until the performance evaluation result of the professional vocabulary translation model is better than that of the previous professional vocabulary translation model.

[0031] In addition, the present invention also provides an adaptive learning method for translating professional vocabulary for oil and gas geological surveys, comprising the following steps:

[0032] Step 100: Construct a professional vocabulary translation model that provides popular explanations for professional vocabulary. The professional vocabulary translation model translates the professional vocabulary explanations into easily understood content. Based on the explanations and translations of the translation model, query output results that match the explanations and translations are screened from the knowledge base.

[0033] Step 200: Collect all user query requests, corresponding query output results, and each user's feedback on the query to form a data set;

[0034] Step 300: Classify the data set according to the category to which the professional vocabulary in the query request belongs, integrate each type of query request and the corresponding feedback into the feedback data training set, and when the number of samples in the feedback data training set reaches a threshold, trigger the professional vocabulary translation model to perform adaptive learning and update.

[0035] As a preferred solution of the present invention, in step 200, the query request includes query content, timestamp, and user ID information;

[0036] Each user's feedback on the query includes satisfaction, errors and improvement suggestions. The query request is secondary classified according to positive feedback and negative feedback, and the query request with negative feedback is used to perform adaptive learning on the professional vocabulary translation model.

[0037] As a preferred solution of the present invention, after training the professional vocabulary translation model using the feedback data training set, the performance of the professional vocabulary translation model is evaluated using the vocabulary table of the vocabulary translation database, wherein the performance indicators include precision, recall rate, and F1 score;

[0038] Determine whether to update the model based on the evaluation results. When the performance of the trained professional vocabulary translation model is better than the old professional vocabulary translation model, the old professional vocabulary translation model is replaced; otherwise, the old professional vocabulary translation model is retained.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] (1) Adaptive learning capability: The system has adaptive learning capabilities. By collecting user query requests and their feedback, it can continuously update and fine-tune the professional vocabulary interpretation model to improve its performance. A closed-loop mechanism for data processing and feedback is established to ensure that each user interaction provides new learning opportunities for the model. The system can gradually improve over the long term, better serving user query needs and improving query accuracy and response speed.

[0041] (2) Dynamic update to improve work efficiency: Through the self-training mechanism and continuous learning framework model, ensure that the system can adapt to the latest developments in the field of oil and gas geological surveys, provide the latest translation services, and maintain the system's advancement and practicality.

[0042] (3) Improve user experience: This system supports a user feedback mechanism, which continuously improves the system’s query accuracy and response speed through user feedback to better meet user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0044] Figure 1 A flow chart of a method for translating professional vocabulary for oil and gas geological surveys according to an embodiment of the present invention; DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0046] like Figure 1 As shown, the present invention provides an adaptive learning oil and gas geological survey professional vocabulary translation system, comprising:

[0047] Professional vocabulary translation model, used to translate professional vocabulary in query content into easily understandable content;

[0048] A user query feedback collection module is used to record each user's feedback information on their oil and gas geological survey content, wherein each user's oil and gas geological survey content includes query content and translation output content, and the oil and gas geological survey content is classified according to the keywords in the query content;

[0049] The reverse training module forms a feedback data training set based on the classification of query content and the feedback information corresponding to each query content, and uses the feedback data training set to adjust the professional vocabulary translation model to form an adaptive learning mechanism.

[0050] Among them, the basic model of the professional vocabulary translation model is a pre-trained general language model. When constructing the professional vocabulary translation model, the vocabulary translation database is used to train the basic general language model, and the basic model is fine-tuned using literature in the field of oil and gas geological surveys to enhance the model's recognition and translation accuracy of professional terms.

[0051] The vocabulary translation database includes a vocabulary table and a relationship table. The vocabulary table stores professional vocabulary and its corresponding popular explanations. The relationship table is used to store the associations between different professional vocabulary, including synonyms, antonyms, and hyponyms.

[0052] The professional vocabulary translation model is trained based on the vocabulary translation database until the professional vocabulary translation model can interpret the professional vocabulary translation into easily understandable content.

[0053] When constructing the vocabulary translation database, we collected specialized vocabulary by using academic search engines (e.g., Google Scholar, Web of Science) and specialized databases (e.g., IEEE Xplore, SpringerLink) to search for relevant research reports, academic papers, and technical manuals. This included a large amount of literature on oil and gas geological surveys, including but not limited to oil and gas industry research reports and white papers, and other specialized training data. This ensured that the data covered a wide range of topics, including but not limited to geological structure, sedimentology, geochemistry, exploration technology, and reservoir engineering.

[0054] The data standardization of the above-mentioned large amount of literature in the field of oil and gas geological survey was carried out, including professional vocabulary annotation such as "sandstone oil reservoir", "carbonate gas reservoir", "fault", etc., keywords, keyword diversity and context relevance, where each word or subword is marked as keyword (1) or non-keyword (0).

[0055] Perform data cleaning on professional vocabulary and use regular expressions to remove punctuation marks, special characters, etc. in the text. Combine hash algorithms and set operations to detect and remove duplicate data and perform formatting to ensure text quality and avoid duplicate data affecting model training results.

[0056] Use the NLTK and spaCy libraries for word segmentation, dividing the text into words or phrases. Add custom word segmentation rules based on the professional vocabulary in the field of oil and gas geology, for example, treat "sandstone reservoir" as a whole word.

[0057] Furthermore, when extracting professional vocabulary from the user's query content and translating the professional vocabulary into easy-to-understand content, it is necessary to use the professional vocabulary translation model to perform keyword fine-tuning training, that is, to adjust the keywords in the user's query content that are synonymous with professional terms so that the extracted keywords can match the vocabulary list of the vocabulary translation database.

[0058] In order to enable the professional vocabulary translation model to fine-tune the keywords in the query content, a synonym library is used to train the professional vocabulary translation model. The implementation method of creating a synonym library is as follows:

[0059] Automatically identify and extract high-frequency keywords from user queries, annotate the collected keywords, and store each keyword and its synonymous professional vocabulary in a synonym database;

[0060] The professional vocabulary translation model uses the synonym library to convert keywords into matching professional vocabulary based on the keywords in the identified user query content.

[0061] Leveraging natural language processing techniques (such as named entity recognition (NER) to identify proper nouns such as names of people, places, and organizations, algorithms such as TF-IDF and TextRank to extract keywords from documents, and topic models such as LDA (Latent Dirichlet Allocation) to identify document topics and keywords), we can automatically identify and extract frequently occurring professional terms and keywords from user query records. Integrating expert knowledge, we can annotate manually collected questions, identify the keywords and professional terms within them, and manually construct a vocabulary containing common terms and their synonyms, expanding a single term into multiple related terms. For example, we can use word embedding models (such as Word2Vec and GloVe) to automatically expand the synonym library.

[0062] We analyze the usage of keywords in user queries in different contexts and categorize and replace terms with similar meanings but different expressions. For example, we can fine-tune "geological structure" to "structural characteristics," "fault analysis," or "fold research"; "reservoir type" to "sandstone reservoir," "carbonate gas reservoir," or "unconventional oil and gas reservoir"; and "resource assessment" to "reserve calculation," "economic evaluation," or "development potential analysis." Keyword fine-tuning is accomplished by building a synonym library or using word embedding models (such as Word2Vec and GloVe) to achieve semantic similarity matching between terms.

[0063] When training the professional vocabulary translation model, the dataset of the synonym library is divided into a training set, a validation set, and a test set to train the professional vocabulary translation model. The fine-tuning accuracy of the professional vocabulary translation model for the query content is evaluated through cross-validation, and adjustments and optimizations are continuously made until the fine-tuning accuracy of the professional vocabulary translation model for the query content meets expectations.

[0064] Among them, when using the training set to train the professional vocabulary translation model, the relationship table corresponding to each professional vocabulary in the synonym library is called, and the synonym group of each professional vocabulary in each synonym library is added. The synonym group of each professional vocabulary and the relationship table corresponding to each professional vocabulary are used to train the professional vocabulary translation model to increase the data diversity of the training set.

[0065] Training is stopped when the fine-tuning accuracy of the professional vocabulary translation model on the validation set no longer improves to avoid overfitting.

[0066] When fine-tuning the professional vocabulary translation model in this embodiment to test keywords in query content, we used a dataset of 1,000 oil and gas geology reports. The training set (700 samples) was randomly divided into K subsets (typically K = 5 or 10). Each subset was used as a validation set, and the remaining K-1 subsets were used as training sets. K training and validation cycles were performed. The test results showed an average accuracy of 0.854, an average precision of 0.872, an average recall of 0.834, and an average F1 value of 0.854.

[0067] Furthermore, the user query feedback collection module includes a user feedback log unit, which is used to save the user's query content, query output results and user feedback, and organize them into a structured format to ensure that each type of query content has corresponding feedback. Based on the classification of the query content and the feedback information corresponding to each query content, a feedback data training set is formed, and the feedback information of each query content is used as the label of the data.

[0068] The feedback classification unit automatically classifies the query content using a classification algorithm based on the professional vocabulary in the user's query content.

[0069] The data cleaning unit is used to clean the query content in the user feedback log unit, remove invalid or duplicate data, and ensure the integrity and consistency of the feedback data training set.

[0070] Collect user query requests, query output, and user feedback from the logs. Organize the collected data into a structured format to ensure that each query request has corresponding feedback. Remove invalid or duplicate data to ensure data quality.

[0071] Automatically categorize query requests using classification algorithms (such as support vector machines, random forests, and deep learning models) to better manage and utilize this data. Manual review of the automatic classification results ensures accuracy. For example, query requests can be categorized into categories such as "geological feature analysis," "reservoir evaluation," "drilling engineering," and "production management."

[0072] User feedback corresponding to each query request is integrated into the training dataset to form labeled training samples. This integrated data is cleaned to remove invalid or duplicate data to ensure the integrity and consistency of the dataset. The number of samples in each category is regularly counted to ensure data balance.

[0073] Set a trigger condition to trigger the model's adaptive learning update when the number of samples in the training dataset reaches a threshold. For example, when the number of samples in a certain category reaches 1000, the model's adaptive learning update is triggered.

[0074] The reverse training module uses the feedback data training set to adjust the professional vocabulary translation model in the following way:

[0075] Collect user query content and feedback information for each query from the literature database, expert review, and internal project materials. When the data set reaches the threshold, the professional vocabulary translation model is updated in real time.

[0076] The professional vocabulary translation model is trained using fine-tuning technology. After the training is completed, the performance of the professional vocabulary translation model is evaluated using the vocabulary table of the vocabulary translation database.

[0077] Parameters of the professional vocabulary translation model are adjusted based on the performance evaluation result until the performance evaluation result of the professional vocabulary translation model is better than that of the previous professional vocabulary translation model.

[0078] Use fine-tuning techniques to train oil and gas geological survey models, such as pre-trained models like BERT and Bi-LSTM. After training, use a validation set to evaluate model performance, including metrics like accuracy, recall, and F1 score. Finally, based on the evaluation results, decide whether to update the model. If the new model outperforms the old one, replace it; otherwise, retain it.

[0079] In summary, this embodiment provides an adaptive learning model for translating professional vocabulary in the field of oil and gas geological surveys, including a general language model that can interpret professional vocabulary in the field of oil and gas geological surveys; a user query feedback collection module that is used to record each user's feedback information on the query translation content, wherein each user's query translation content includes the query content and the translation output content, and the query translation content is classified according to the keywords in the query content; a training data set is formed based on the classification of the query content and the feedback information corresponding to each query content, and the general language model is adjusted using the training data set to form an adaptive learning mechanism.

[0080] In addition, the present invention also provides an adaptive learning method for an oil and gas geological survey professional vocabulary translation system, comprising the following steps:

[0081] Step 100: Construct a professional vocabulary translation model that provides popular explanations of professional vocabulary. The professional vocabulary translation model translates the professional vocabulary explanations into easily understood content. Based on the explanations and translations of the translation model, query output results that match the explanations and translations are selected from the knowledge base.

[0082] Step 200: Collect all user query requests, corresponding query output results, and each user's feedback on the query to form a data set;

[0083] In step 200, the query request includes query content, timestamp, and user ID information;

[0084] Each user's feedback on the query includes satisfaction, errors and improvement suggestions. The query requests are secondary classified into positive feedback and negative feedback, and the query requests with negative feedback are used to adaptively learn the professional vocabulary translation model.

[0085] Step 300: Classify the data set according to the category of the professional vocabulary in the query request, integrate each type of query request and the corresponding feedback into the feedback data training set, and when the number of samples in the feedback data training set reaches a threshold, trigger the professional vocabulary translation model to perform adaptive learning and update.

[0086] After training the professional vocabulary translation model using the feedback data training set, the performance of the professional vocabulary translation model is evaluated using the vocabulary table of the vocabulary translation database. The performance indicators include precision, recall rate, and F1 score.

[0087] Determine whether to update the model based on the evaluation results. When the performance of the trained professional vocabulary translation model is better than the old professional vocabulary translation model, the old professional vocabulary translation model is replaced; otherwise, the old professional vocabulary translation model is retained.

[0088] After one month of adaptive learning, the system's query accuracy for the oil and gas geological survey vocabulary translation model increased from 90% to 96%, a 6 percentage point improvement. Response time decreased from 2.4 seconds to 1.7 seconds, a 29% improvement.

[0089] Therefore, the oil and gas geological survey professional vocabulary translation system provided in this embodiment possesses adaptive learning capabilities. By collecting user query requests and their feedback, it continuously updates and fine-tunes the professional vocabulary interpretation model, improving its performance. A closed-loop mechanism for data processing and feedback is established, ensuring that each user interaction provides new learning opportunities for the model. This system can gradually improve over time, better serving user query needs and increasing query accuracy and response speed.

[0090] Through the self-training mechanism and continuous learning framework model, the system can adapt to the latest developments in the field of oil and gas geological surveys, provide the latest translation services, and maintain the system's advancement and practicality.

[0091] It also supports a user feedback mechanism, which continuously improves the system's query accuracy and response speed through user feedback to better meet user needs.

[0092] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection 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 scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.

Claims

1. An adaptive learning oil and gas geological survey professional vocabulary translation system, characterized by: include: Professional vocabulary translation model, used to translate professional vocabulary in query content into easily understandable content; A user query feedback collection module is used to record each user's feedback information on their oil and gas geological survey content, wherein each user's oil and gas geological survey content includes query content and translation output content, and the oil and gas geological survey content is classified according to the keywords in the query content; A reverse training module forms a feedback data training set based on the classification of the query content and the feedback information corresponding to each query content, and uses the feedback data training set to adjust the professional vocabulary translation model to form an adaptive learning mechanism; The database further includes a vocabulary translation database, the vocabulary translation database including a vocabulary table and a relationship table, the vocabulary table storing professional vocabulary and its corresponding popular explanation, the relationship table storing the association relationship between different professional vocabulary, the association relationship including synonyms, antonyms and hyponyms; Training the professional vocabulary translation model based on the vocabulary translation database until the professional vocabulary translation model can interpret the professional vocabulary translation into easily understandable content; The reverse training module uses the feedback data training set to adjust the professional vocabulary translation model in the following manner: Collect user query content and feedback information from each query from the literature database, expert review, and internal project data collection data, and trigger the update of the professional vocabulary translation model in real time when the data set reaches a threshold; Using fine-tuning technology to train the professional vocabulary translation model, and after the training is completed, using the vocabulary table of the vocabulary translation database to evaluate the performance of the professional vocabulary translation model; The parameters of the professional vocabulary translation model are adjusted based on the performance evaluation result until the performance evaluation result of the professional vocabulary translation model is better than that of the previous professional vocabulary translation model.

2. The adaptive learning oil and gas geological survey professional vocabulary translation system according to claim 1 is characterized in that: It also includes a synonym library, and the implementation method of creating the synonym library is as follows: Automatically identify and extract high-frequency keywords from the user's query content, annotate the collected keywords, and store each keyword and its synonymous professional vocabulary in the synonym library; The professional vocabulary translation model calls the synonym library to convert the keywords into matching professional vocabulary based on the keywords in the identified user query content.

3. The adaptive learning oil and gas geological survey professional vocabulary translation system according to claim 2 is characterized in that: The data set of the synonym library is divided into a training set, a validation set, and a test set to train the professional vocabulary translation model, and the fine-tuning accuracy of the professional vocabulary translation model to the query content is evaluated by cross-validation, and the fine-tuning optimization is continuously performed until the fine-tuning accuracy of the professional vocabulary translation model to the query content meets the expectations; When the professional vocabulary translation model is trained using the training set, the relationship table corresponding to each professional vocabulary in the synonym library is called, and the synonym group of each professional vocabulary in each synonym library is added, and the professional vocabulary translation model is trained using the synonym group of each professional vocabulary and the relationship table corresponding to each professional vocabulary to increase the data diversity of the training set; The training is stopped when the fine-tuning accuracy of the professional vocabulary translation model on the validation set no longer improves to avoid overfitting.

4. The adaptive learning oil and gas geological survey professional vocabulary translation system according to claim 1 is characterized in that: The basic model of the professional vocabulary translation model is a pre-trained general language model; The general language model is fine-tuned using the vocabulary translation database to enhance the recognition and translation accuracy of the professional vocabulary translation model for professional terms in the field of oil and gas geological survey.

5. The adaptive learning oil and gas geological survey professional vocabulary translation system according to claim 1 is characterized in that: The user query feedback collection module includes a user feedback log unit, which is used to store user query content, query output results, and user feedback, and organize them into a structured format to ensure that each type of query content has corresponding feedback. Based on the classification of the query content and the feedback information corresponding to each query content, a feedback data training set is formed, and the feedback information of each query content is used as a label for the data; Feedback classification unit, which uses classification algorithms to automatically classify user query content based on the professional vocabulary in the query content; The data cleaning unit is used to clean the query content in the user feedback log unit, remove invalid or duplicate data, and ensure the integrity and consistency of the feedback data training set.

6. An adaptive learning method for a professional vocabulary translation system for oil and gas geological survey based on the adaptive learning according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 100: Construct a professional vocabulary translation model that provides popular explanations for professional vocabulary. The professional vocabulary translation model translates the professional vocabulary explanations into easily understood content. Based on the explanations and translations of the translation model, query output results that match the explanations and translations are screened from the knowledge base. Step 200: Collect all user query requests, corresponding query output results, and each user's feedback on the query to form a data set; Step 300: Classify the data set according to the category of the professional vocabulary in the query request, integrate each type of query request and the corresponding feedback into the feedback data training set, and when the number of samples in the feedback data training set reaches a threshold, trigger the professional vocabulary translation model to perform adaptive learning and update.

7. The adaptive learning method of the oil and gas geological survey professional vocabulary translation system according to claim 6, characterized in that: In step 200, the query request includes query content, timestamp, and user ID information; Each user's feedback on the query includes satisfaction, errors and improvement suggestions. The query request is secondary classified according to positive feedback and negative feedback, and the query request with negative feedback is used to perform adaptive learning on the professional vocabulary translation model.

8. The adaptive learning method of the oil and gas geological survey professional vocabulary translation system according to claim 6, characterized in that: After training the professional vocabulary translation model using the feedback data training set, the performance of the professional vocabulary translation model is evaluated using the vocabulary table of the vocabulary translation database, wherein the performance indicators include precision, recall rate, and F1 score; Determine whether to update the model based on the evaluation results. When the performance of the trained professional vocabulary translation model is better than the old professional vocabulary translation model, the old professional vocabulary translation model is replaced; otherwise, the old professional vocabulary translation model is retained.

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