286 results about "Keyword extraction" patented technology

The invention belongs to the field of question and answer generation, and provides an interactive retrieval enhancement question and answer generation method and system based on a knowledge graph, and the method comprises the steps: carrying out the document partitioning based on an original document set, generating a global block set, carrying out the entity extraction of each text block in the global block set, and obtaining an entity set; performing relation extraction on entity subsets in each text block in the entity set to obtain a global relation set; generating a plurality of sub-knowledge maps based on the global block set, the entity set and the global relationship set, and performing entity fusion and relationship fusion on the sub-knowledge maps to obtain a knowledge map; performing keyword extraction and semantic embedding on the original problem to obtain a dense vector, performing semantic embedding based on the knowledge graph to obtain an embedded vector, and generating a candidate entity set according to the dense vector and the embedded vector; and based on the candidate entity set, utilizing a large language model calling tool to carry out extended search to generate a candidate information set, and utilizing a large language model to obtain an answer to the original question based on the candidate information set.

The invention relates to the technical field of red tide anomaly detection, in particular to a red tide anomaly detection method and system based on an improved multi-mode Transform. The method comprises the following steps: acquiring a remote sensing image and text data; respectively carrying out data preprocessing according to the obtained remote sensing image and text data; performing visual positioning and text selection based on the preprocessed data; performing cross-modal feature learning on the basis of a hierarchical Transform of a multi-modal capsule mechanism; guiding an attention mechanism based on a semantic path to carry out image-semantic feature alignment optimization; and carrying out multi-modal knowledge distillation on the optimized features. According to the method, an image and text preprocessing module, a visual positioning module, a keyword extraction module and other modules are combined, multi-angle accurate perception of a complex red tide scene is achieved, and the bottleneck that a red tide area is difficult to accurately recognize under the condition that data are single and information dimensions are limited in a traditional method is broken through.

The invention discloses a document retrieval method and device, equipment, a medium and a program product, and relates to the technical field of document processing. The method comprises the steps of obtaining a query statement of a document retrieval party, at least two candidate documents and candidate abstract vectors and candidate full-text vectors of the candidate documents; performing keyword extraction on the query statement to obtain a query keyword, and performing vectorization on the query statement to obtain a query statement vector; according to the query keyword, the query statement vector, each candidate document and a candidate abstract vector of each candidate document, performing coarse screening on each candidate document to obtain at least two coarse screening documents; and according to the query statement vector and the candidate full-text vector of each coarse screening document, performing fine arrangement on each coarse screening document to obtain a target retrieval document. According to the technical scheme provided by the embodiment of the invention, the accuracy of document retrieval is improved.

The invention provides an electric power document keyword extraction method based on Prompt and a knowledge graph, relates to the technical field of electric power document processing, and constructs a lightweight multi-level index knowledge graph in the electric power field by combining entity type and relation type division based on an electric power industry standard document and an electric power field corpus. The method comprises the following steps: performing vector modeling on a power document, constructing a multi-level index from an entity to a vector, realizing standardized semantic modeling and efficient hybrid retrieval of a power document field background, and obtaining a topic vector and a core paragraph of the power document in combination with power key information; according to the method, entity types are indexed in a knowledge graph by using subject vectors, similar entities are obtained to form knowledge sub-graphs, so that multilayer Prompt is obtained to guide a large language model to extract keywords, then knowledge graph similarity constraints are introduced to decode the output of the large language model, the keyword recognition capability in the power field is improved, and the keyword recognition efficiency is improved. And the accuracy of keyword type identification and the normalization of term naming are both considered.

The invention discloses an AI large model-based arrival person screening method and device, a medium and equipment, and belongs to the field of screening, and the method comprises the steps of firstly obtaining screening conditions input by a user, including text content, image data and qualification information requirements, and to-be-screened arrival person data; next, performing word segmentation, keyword extraction and semantic matching on the text content of the person arrival data by utilizing a preset semantic analysis model in combination with BiLSTM, CRF and LDA technologies, and outputting a semantic score; meanwhile, note styles are recognized through the text classification model, logic judgment is conducted, and styles and logic verification scores are obtained. In addition, element detection and style verification are carried out on the image data through the multi-modal recognition model, and image matching scores are output; the qualification scoring module calculates qualification scores according to a preset weight formula. And finally, fusing the multi-dimensional scores to generate a comprehensive score, and carrying out accurate screening on the arriving persons according to the comprehensive score.

The invention discloses a multi-agent multi-modal table question and answer method and system based on a cognitive collaboration mechanism. The method and system are particularly suitable for extracting accurate information from a complex, unstructured or multi-modal data source and answering questions of a user. The system is composed of four core modules, namely a task ontology analysis agent, a multi-modal clue perception agent, a logic verification agent and an arbitration fusion agent, and the task ontology analysis agent, the multi-modal clue perception agent, the logic verification agent and the arbitration fusion agent are integrated by receiving user questions and multi-modal table input. The intelligent agents cooperate to complete the processes of keyword extraction, intention recognition, vision-text cross-modal analysis, clue generation, logic verification, result fusion and the like, and an efficient cognitive closed loop is formed. The system not only supports operation on standard table data, but also can process visual clues such as complex header structures, cell merging modes, font styles and the like, so that the ability of understanding fuzzy or incomplete input is improved. Successful tests in a plurality of practical application scenes prove that the method has strong fault-tolerant capability and accuracy.

The invention provides an image video retrieval method based on a domain fine-tuning large language model, which comprises the following steps: performing fine-tuning on a pre-training model to obtain a fine-tuning pre-training model for intention classification and keyword extraction; performing dynamic iteration screening on an optimal prompt template through Monte Carlo tree search in combination with a hidden Markov model (HMM); performing noise filtering on the keyword list, and predicting category labels of the filtered keywords through a conditional random field model to obtain a keyword enhancement set; combining with the user intention to generate a query condition, and obtaining a candidate resource set; and according to the similarity between the user query text and the candidate resource set, and in combination with the optimal prompt template, obtaining the resource path with the highest matching score between the user query and the candidate resource, and obtaining the retrieved image or video, so that the identification deviation possibly occurring when a general model processes proper nouns and terminologies can be effectively solved, and the user experience is improved. And the retrieval accuracy and response speed are improved, so that the retrieval accuracy and professional adaptability are improved.

The invention discloses an intelligent information processing method and system for a shipping service scene. The method comprises the following steps: screening out an information text related to the shipping service scene through multi-dimensional processing; carrying out basic field identification, geographic element standardization, special keyword extraction and multi-dimensional collaborative structured information extraction of industry label code mapping on each information text; determining a first-level classification label and a second-level classification label of the information text by adopting a two-level classification strategy combining LLM and a rule engine; the title is translated and rewritten through LLM, a multi-level structured abstract is generated, structured event element information and industry value scores are extracted, and an effective information text is obtained; and performing information cluster division on each effective information text through LLM and a density clustering algorithm, reserving an optimal information text for each information cluster, extracting a core semantic structure, performing aggregation processing on the information texts with the same core semantic structure and a secondary classification label, and generating a unified event narration text.

The invention belongs to the field of computer vision and industrial design assistance, particularly relates to an industrial drawing similarity detection method based on deep learning, and aims to realize efficient and accurate similarity analysis of industrial drawings. Comprising the following steps: collecting an industrial drawing data set, and carrying out target detection labeling and feature extraction on each drawing; a YOLO model is adopted to detect a target, and a non-maximum suppression method is adopted to remove duplication so as to obtain each visual angle of the part in the drawing and an overall marking frame; recognizing characters in the drawing, and obtaining part names and material information through keyword extraction and domain dictionary filtering; local and global feature vectors are extracted, multi-modal feature alignment is realized through comparative learning, and part semantic representation based on image-text association is constructed. And storing the extracted image and OCR text feature vectors into a Faiss index database, establishing a multi-modal mapping relation of local and global feature vectors and text semantics, extracting the image and text features of a to-be-detected drawing, and outputting a similarity result through similarity retrieval of a vector database.

The invention relates to the technical field of literature retrieval, in particular to a two-step literature retrieval method and system based on keyword extension. Comprising the following steps: calibrating an initial word, inputting a user keyword, querying a preset high-frequency subject word bank, and executing a calibration rule; preliminary retrieval: performing retrieval in an AuthorKeywords field of a target database by using the calibrated seed keyword to obtain a preliminary retrieval result set; and extracting and purifying extended keywords. According to the two-step method provided by the invention, through automatic expansion and parallel field retrieval, wider coverage can be realized in a single process, it is expected that operation rounds required by a user for repeatedly trying different keyword combinations can be obviously reduced, and the automatic expansion keyword extraction, filtering and multi-field parallel retrieval mechanism can improve the efficiency of the user. According to the method provided by the invention, a relatively wide literature range can be covered by single execution, so that a relatively comprehensive retrieval result can be expected to be obtained more efficiently through a structured automatic process, and the time cost of repeated trial and error and screening of researchers is saved.

The invention relates to the technical field of data processing, in particular to a multi-modal medical data intelligent association analysis system based on deep learning. The system comprises a data acquisition module used for acquiring a plurality of first data and a plurality of second data of a target patient, the first data being structured medical data, and the second data being unstructured medical data; the keyword extraction module is used for extracting a plurality of keywords of each piece of second data; the clustering module is used for clustering the multiple pieces of second data based on the multiple keywords of each piece of second data to obtain multiple class clusters; the time sequence analysis module is used for performing time sequence analysis on the treatment time period based on the plurality of class clusters so as to determine a plurality of diagnosis and treatment time slices; and the correlation analysis module is used for performing correlation analysis on the second data and the first data in each diagnosis and treatment time slice according to the plurality of class clusters so as to obtain a correlation analysis result. According to the invention, the association analysis effect of the multi-modal medical data can be improved.

The invention provides a hospital health education intelligent question and answer method based on a large language model and a knowledge base, and belongs to the technical field of intelligent question and answer, and the method comprises the steps: collecting hospital health education related document data for preprocessing, and converting a text block into a vector form through a vector embedding model, storing in an AI native database in combination with metadata, and constructing an index to form a hospital health education knowledge base; receiving health consultation questions input by a user, performing semantic analysis and keyword extraction, and retrieving related knowledge fragments from a hospital health education knowledge base; carrying out fusion reordering on the retrieved knowledge fragments, calculating correlation scores, and selecting the first K knowledge fragments with the highest scores; and substituting the selected knowledge fragments and the user question into a preset cue word engineering template, generating a cue word input large language model, generating an answer for the user question, and feeding back the answer to the user. The acceptability and practicability of the patient on health knowledge are enhanced, and the efficiency of hospital health education and the satisfaction degree of the patient are improved.

The invention relates to the technical field of legal consultation services, in particular to an intelligent question and answer matching method based on machine learning, which comprises the following steps of: acquiring legal consultation text recognition subject behavior objects and articles, analyzing and carding statement logic in sections, generating a semantic hierarchical table, extracting key phrases and integrating semantic units, and constructing a semantic network to generate a mapping table. Comparing question answer semantic logic to generate a matching corresponding set, reviewing offset revised text to generate a revised set, and rechecking consistency arrangement to generate an intelligent matching result set. According to the method, legal text logic relations are analyzed and processed through semantic layering, question and answer matching precision is optimized, keyword extraction and semantic network construction are combined, semantic consistency recognition and matching flexibility is enhanced, semantic offset and logic inconsistency are corrected, result connection coherence is ensured, mismatching is reduced compared with the prior art, question and answer accuracy is improved, and the method is suitable for popularization and application. Legal consultation is more intelligent and accurate, and a reliable matching result is provided.

The invention provides a vehicle-mounted log uploading method and system, vehicle-mounted equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that log data of a target vehicle is obtained and subjected to structured processing, and structured log data comprising multiple structured log texts are obtained; based on a preset keyword library, performing keyword extraction on each structured log text through a keyword extraction algorithm to obtain a plurality of keywords; based on a natural language processing algorithm, determining the semantic similarity of any two keywords; based on the semantic similarities, performing clustering analysis on the plurality of structured log texts to obtain a plurality of log clusters; obtaining an abnormal score of each log cluster according to a preset scoring factor corresponding to each log cluster; according to each abnormal score, screening out at least one target log cluster from the plurality of log clusters, the target log cluster comprising at least one target log; and uploading each target log to a cloud server. According to the invention, the uploading efficiency of the vehicle-mounted log is improved.

The invention discloses an online diagnosis and treatment data supervision method and system based on artificial intelligence, which is used for carrying out real-time intelligent supervision on free text diagnosis and treatment records generated by an online medical platform, and specifically comprises the following steps: carrying out semantic coding on text fields such as basic information, chief complaint, present medical history, past history and diagnosis of a patient; executing necessary filling and logic consistency verification based on a predefined multi-level rule base; clustering analysis is carried out on the semantic vectors by adopting a density clustering algorithm and the like, and semantic abnormity or duplicate records are identified; filtering junk texts and test data by using keyword extraction and named entity recognition technologies; and finally, outputting a structured supervision report and adding a quality label to each record. Through the method, various quality and compliance problems in the diagnosis and treatment data can be found and labeled in real time, and powerful support is provided for compliance supervision of an internet medical platform, improvement of service quality and guarantee of data credibility; meanwhile, a rule-driven and AI model-driven'double-track 'framework is adopted in the method, and an active learning mechanism is combined, so that the system can continuously perform self-learning and iterative evolution and continuously adapt to a new data mode and a new supervision requirement.

The invention discloses an intelligent archive keyword indexing method based on multi-modal semantic association, and relates to the technical field of archive keyword indexing. The method comprises the following steps: initially performing text segmentation of different target detection on text modal information; semantic expansion information block segmentation of corresponding target detection is carried out on image, audio and video modal information in an associated manner in sequence, and keyword extraction weight value statistics of positive samples with similar semantics and negative samples with opposite semantics is carried out on segmented sub-information blocks, and the segmented sub-information blocks are combined; the method comprises the following steps of: segmenting information blocks of image, audio and video modal information which are respectively used as initial processing objects in sequence, combining segmented text sub-information I, image sub-information II, audio sub-information III and video sub-information IV, and carrying out keyword extraction weight value statistics; and summarizing the test sample set obtained by statistics to realize keyword indexing through an established file keyword extraction model test. The keyword indexing accuracy can be improved.

The invention provides a work order clustering and theme extraction method, system and device and a medium, and belongs to the technical field of natural language processing and data mining. The method comprises the steps of obtaining original work order data, performing data cleaning, extracting work order abstracts from the original work order data by using a text abstract generation technology according to preset abstract constraints, and generating an abstract set; inputting the work order abstract into a language model, generating a semantic vector of a preset dimension, constructing a vector index through an FAISS library, and converting the abstract set into a set of numerical vectors; clustering the numerical vectors by using an improved K-means algorithm, and outputting K groups of work order digests with balanced quantity; for each group of work order abstracts, screening representative abstracts based on text similarity; extracting keywords from the representative abstracts by adopting a keyword extraction technology, and generating a keyword list of each group of work order abstracts; and for the keyword list of each group of work order abstracts, generating a theme tag by combining keywords.

The invention relates to a mixed RAG retrieval method and device based on constraint keyword extraction. The method comprises the following steps: extracting a binding keyword from a retrieval problem of a user; the constraint keyword is a structured information unit playing a role in limiting or filtering a retrieval result corresponding to the retrieval problem; constructing a query statement corresponding to the retrieval problem based on the extracted constraint keyword; performing data filtering on data in a vector database by utilizing the constructed query statement, and screening out first target data in the vector database; the first target data comprises a target document fragment and a target document vector; calculating the similarity between every two first target data and the retrieval problem, and determining a preset first quantity of first target data with the highest similarity with the retrieval problem as a retrieval result. By the adoption of the method, the problems that an existing RAG retrieval method is too large in calculation amount and low in retrieval result precision can be solved.

The present application relates to the technical field of data processing, and more particularly to a method and system for intelligent analysis of unstructured text data based on a large model. The method comprises: obtaining unstructured text data to be analyzed, and performing cleaning and word segmentation processing to obtain a plurality of candidate words; calculating the global regular interference degree of each candidate word; based on the global regular interference degree, calculating the fault semantic specificity factor of each candidate word; determining the initial node weight of the candidate word in the TextRank algorithm; running the TextRank algorithm based on the initial node weight to extract an implicit fault chain summary; inputting the implicit fault chain summary into a large model, instructing the large model to perform fault evolution analysis and output maintenance suggestions. The present application optimizes keyword extraction through interference degree suppression, semantic factor enhancement and dynamic entropy analysis, thereby more accurately identifying fault words and improving the quality of large model input and the reliability of fault analysis.

The application relates to the technical field of psychological counseling, in particular to a psychological counseling method and system based on AI technology. The method comprises the following steps: obtaining target voice of a psychological counseling object and performing semantic keyword extraction; analyzing the semantic keywords, determining a dialogue theme and performing preliminary dialogue; tracking the degree of closure of the eyes of the psychological counseling object in real time to obtain a real-time eye closure degree value; comparing the real-time eye closure degree value with a preset eye closure degree value to obtain a comparison result, combining the comparison result and an analysis result to determine a real-time emotional state; according to the real-time emotional state and the dialogue theme, extracting a corresponding counseling scheme to perform psychological counseling. When performing psychological counseling on the psychological counseling object, the degree of closure of the eyes and the analysis result are combined to determine the emotional state of the psychological counseling object, a psychological counseling scheme corresponding to the emotion is extracted, and the psychological counseling object is subjected to psychological counseling, so that the accuracy of the psychological counseling is ensured.

The specification provides a feedback data determination method and device for user intent and electronic equipment. The input data of the user is received; the input data is subjected to intent recognition processing to obtain a target intent corresponding to the input data, and the input data is subjected to keyword extraction processing to obtain a first keyword corresponding to the input data; a search condition corresponding to the target intent is obtained, and it is judged whether the first keyword satisfies the search condition; in the case where the first keyword does not satisfy the search condition, corresponding second keywords are obtained according to the user data of the user; based on the second keywords and the target intent, first feedback data corresponding to the input data is determined; and the target feedback data corresponding to the input data is determined according to the prompt word corresponding to the target intent and the first feedback data by using a preset large language model. Thus, the accuracy and matching degree of the user input data and the search feedback result are improved.

This invention discloses a long text matching method combining noise filtering and a divide-and-conquer strategy. The method includes: constructing a long text matching model, which comprises a keyword extraction layer, an association extraction layer, and a filtering layer. The keyword extraction layer extracts keywords from the text; the filtering layer filters noise from the text based on sentence similarity to obtain a denoised text sequence; and the association extraction layer further removes keywords from the denoised text sequence to obtain the remaining associated text. The long text matching model is trained with the optimization objective of minimizing a set overall loss function, which reflects the global matching distribution and combines the keyword and association matching distributions. For the target text, real-time text matching is performed using the trained long text matching model. This invention improves the generalization ability and accuracy of text matching.

A display device comprises a voice recognition unit performing voice recognition, a conversation-derived term extraction unit extracting conversation-derived terms from conversation information derived from the voice recognition, a search keyword storage storing the conversation-derived terms and search keyword in an associated manner, a search keyword extraction unit extracting search keywords from the search keyword storage using the conversation-derived terms, a material storage unit storing each page of a plurality of presentation materials, a search term of each page, and a score of the search term in an associated manner, a relevant page information extraction unit extracting a page of presentation material associated with the search keyword, a selection term extraction unit extracting the search keywords, and a selection term display unit causing the selection terms to be displayed on a display unit.

The invention discloses an AI-based schizophrenia clinical thinking training simulation system, which relates to the technical field of artificial intelligence, and is characterized by comprising the steps of constructing a dynamic knowledge base, receiving and preprocessing multi-modal input, performing standardized mapping by extracting keywords, and then performing judgment of a rule layer and a semantic layer. A student question method and a standard answer method in the dynamic knowledge base are sent into a model for response generation, then four-dimensional quantitative scoring and clinical operation multi-dimensional scoring are carried out, error codes and positioning sentences thereof are output, and finally personalized training courses are recommended to students; and hot-updating the dynamic knowledge base continuous optimization model. According to the method, four dimensions of the ICD-10 diagnostic standard are converted into a quantitative scoring algorithm capable of being automatically executed, quantitative assessment of clinical operation specifications is integrated, a diagnosis and operation double-track evaluation system is constructed, the problems that a traditional method is lack of evaluation standards and high in subjectivity are solved, and the evaluation efficiency is improved. Therefore, the evaluation of the clinical thinking ability of the medical students becomes objective, comprehensive and evidence-based.

A kind of detection method and device of fake APP, terminal, the method includes: determining training data;Keyword extraction is carried out to the brief introduction of each pair of target APP and the brief introduction of suspected APP, and the data vector of each keyword is determined;Determine the weight value of each keyword, and the data vector of each keyword in the target APP is weighted based on the weight value Operation is carried out to obtain target APP data vector, and obtain suspected APP data vector;The vector comparison result of target APP data vector and suspected APP data vector is calculated, the absolute value of the vector comparison result is determined as first data vector, and the square of the vector comparison result is determined as second data vector;Obtain the training text vector of the pair of target APP and suspected APP;Training fake detection model.The present application can enhance the association between keywords in model training phase, reduce the misjudgment rate.

The application discloses a kind of sequence model and knowledge graph combined question and answer method, comprising: knowledge acquisition processing method, knowledge graph construction method, model construction method, question determination and answer generation method.Corresponding question and answer strategy is designed for different types of data sets, "field professional knowledge semantic data" is constructed from field professional knowledge data set, and professional answer in knowledge graph is obtained by keyword extraction and template matching."Field knowledge basic question and answer semantic data" is constructed from field knowledge basic question and answer data set, and the best answer of field knowledge chat data set is obtained using sentence vector similarity algorithm, to train Seq2seq model, as the supplement of knowledge graph.Combining the above question and answer strategy, this method can not only answer professional field knowledge, but also answer daily chat system.The method can accurately and efficiently answer questions in different fields, to some extent, so that the question and answer system can understand the intention of user, and is more intelligent.

The invention discloses a semantic structure feature-based ancient book content integrity detection method and system, and relates to the technical field of information processing, the system comprises a data acquisition unit, a chapter integrity analysis unit, a paragraph integrity analysis unit, an integrity comprehensive analysis unit, a result feedback unit and a display terminal; structural features of chapters and paragraphs are accurately extracted by utilizing a semantic structure recognition technology, and the content integrity of the ancient books is comprehensively detected from the aspects of chapters and paragraphs in combination with an integrity analysis model, so that the defect that a traditional means depends on manual proofreading or simple page number comparison and keyword extraction, and the efficiency is high is effectively overcome. According to the method, the problems of typesetting confusion, chapter page missing, quotation omission and fuzzy paraphrasing which are common in the ancient books are difficult to deal with, automatic and multi-level analysis of the structural content and semantic level content integrity of the ancient books is realized, and the detection and recognition efficiency of the content integrity of the ancient books is improved.

The application discloses a keyword extraction method, system, device and storage medium. The method comprises the following steps: obtaining a sensitive word group list, removing stop words and splicing words in each sensitive word group, and obtaining preprocessed text; based on the preprocessed text, a word vector is constructed, and a mapping relationship between a word group in the preprocessed text and the splicing words is established; an initial cluster center vector is selected in the word vector, a Hamming distance between a remaining word vector and the initial cluster center vector is calculated, the word vector is divided into clusters according to the Hamming distance, and a preliminarily divided keyword cluster is obtained; based on the word vector and the preliminarily divided keyword cluster, an adaptive clustering algorithm is adopted to obtain a similar word vector cluster; and the similar word vector cluster is spliced with the splicing words according to the mapping relationship, so that a keyword list is generated; and it is ensured that the obtained keywords can accurately reflect the characteristics of the text content.

The application provides a micro-class teaching method and system, wherein the micro-class teaching method comprises the following steps: constructing a knowledge classification database; obtaining the demand of a target object and determining the course content according to the demand of the target object; carrying out knowledge point keyword extraction and scoring on the course content, forming an index item with scores and adding the index item to the knowledge classification database, and establishing the association between the knowledge classification database and the teaching courseware; obtaining the target explanation knowledge point from the course content and obtaining the first teaching information, the second teaching information, the third teaching information and the fourth teaching information according to the target explanation knowledge point; and sending the final teaching information to a specified position after obtaining the final teaching information according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information. The application can meet the demand of students in different situations and improve the learning efficiency of students.

To enable the valuation of patents. [Solution] An information processing system comprising: a keyword extraction unit that extracts keywords from the claims of a patent to be verified; a search unit that searches for information about a business or product that matches the extracted keywords; an infringement determination unit that estimates the degree to which the searched information about the business or product satisfies the constituent elements of the claim; and a valuation unit that evaluates the value of the patent according to the degree.