2339results about "Text database indexing" patented technology

A data processing system implements receiving a call requesting a generative model to generate a semantic tree for a source content; constructing a first prompt including the source content and instructions to the model to analyze a semantic structure of the source content and to generate a semantic outline and content chunks of the source content, the semantic outline including one or more topics each connected with one or more of the content chunks, to compute one summary for each of the content chunks, to apply indices to reference each topic node of the semantic tree to one of the topics, and to apply indices to reference each leaf node of the semantic tree to one of the content chunks and the respective summary; providing the first prompt to the model and receiving the semantic tree of the source content; and storing the semantic tree in a database.

The invention provides an intelligent agent tool calling knowledge optimization method based on empirical path graph evolution, which comprises the following steps: when an intelligent agent successfully completes a task for the first time, recording an intelligent agent tool calling sequence, input and output parameters and an execution result, generating a structured calling log, the calling log is converted into a standardized calling path knowledge unit; performing structured representation and semantic representation on the calling path knowledge unit, storing the structured representation in a graph database, and storing the semantic representation in a vector database; task intentions, tool entities and calling paths are used as heterogeneous nodes, an experience path knowledge graph is constructed, the experience path knowledge graph is used for recording the multi-dimensional relation among tasks, paths and tools, execution performance attributes and feedback attributes are added to path nodes in the graph, and agent tool calling knowledge optimization is completed. And the purpose of improving the tool calling efficiency and robustness of the intelligent agent in the multi-task environment is achieved.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention relates to the technical field of artificial intelligence, can be applied to service scenes of pension service, financial science and technology, medical health and the like, and discloses an instruction understanding and task execution method, device, equipment and medium, and the method comprises the steps: receiving a voice instruction and a text instruction, and carrying out the cooperative processing through an instruction understanding model, and generating a structured task description; collecting environment data to construct a real-time environment model; generating a task execution strategy by utilizing a task execution model based on the structured task description and the real-time environment model; controlling the intelligent agent to execute the task according to the task execution strategy, and dynamically adjusting the action in combination with real-time sensor information; task execution data and user feedback information are collected, and the instruction understanding model and the task execution model are updated. According to the method, multi-modal information is fused through structural description, an execution strategy is generated in combination with real-time environment perception, actions are dynamically adjusted, model self-optimization is further achieved through execution data and feedback, and the understanding, decision-making and adaptive capacity of an intelligent agent is improved.

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 discloses a multi-modal fused intelligent question answering and knowledge retrieval method and system, and the method comprises the steps: building a multi-modal data index model oriented to a heterogeneous knowledge source, carrying out the feature mapping of text, image, table, chart, audio and video contents through a unified semantic embedding space, and generating a cross-modal index set; after a query request is received, performing semantic matching and structure matching on the cross-modal index set by using a multi-channel retriever to obtain candidate evidence fragments; and based on an evidence granularity decomposition strategy, performing minimum evidence unit division on text statements, table units, chart data points and multimedia frame contents in the candidate evidence fragments, and establishing a semantic consistency graph among the units. According to the method, high-credibility traceable generation of question and answer results is realized through multi-modal fusion and space-time consistency constraint, and the retrieval precision and interpretation transparency in a complex knowledge scene are remarkably improved.

The invention provides an intelligent medical question-answering system and method based on hybrid retrieval and lightweight reordering, and is applied to the technical field of medical data processing. According to the method, five types of core entities are extracted through the preset Chinese medical NER model, and the structured knowledge base is formed through relation extraction modeling association. Analyzing user Chinese query, extracting medical entities, identifying four types of appeals and converting the four types of appeals into semantic vectors; a PubMedBERT is adopted to encode a medical document to generate a vector for storage, BM25 and vector retrieval are executed in parallel when query is received, and candidate documents are generated through fusion of an RRF algorithm. And generating a score data set based on the prompt template, and training the lightweight model to sort and output an evidence set. In combination with query semantics, a simplified context is generated through retrieval, sorting and compression, and FlashAttention optimization calculation is integrated. An optimized U-Net segmentation image is utilized to generate a structured report, and multi-modal information is integrated to generate an accurate answer giving consideration to the image and medical knowledge through LLM reasoning.

The invention relates to the technical field of big language model reasoning, and discloses a big language model key value cache multiplexing method and system based on fuzzy semantic matching, and the method comprises the steps: generating a key value cache according to lexical elements in a historical reasoning request of a user, gathering a plurality of lexical elements into lexical element blocks, generating embedded vectors of the lexical element blocks, and building a vector database; calculating a cosine similarity between an embedded vector of a lexical block of a new reasoning request and a historical embedded vector in a vector database, and if a historical lexical block of which the cosine similarity exceeds a set threshold exists, obtaining a corresponding key value cache through a Hash index and multiplexing the key value cache; calculating an attention score, and dividing the plurality of lexical elements in the current lexical element block into keyword elements and non-keyword elements based on the attention score; key value caches of the keyword elements are recalculated; and the re-calculated key value cache of the keyword elements and the reuse key value cache of the non-key sub-elements form a mixed key value cache. According to the method, on the premise that the model precision is almost not reduced, the key value cache multiplexing technology is expanded to fuzzy semantic matching from accurate matching, unnecessary calculation overhead is effectively reduced, and then the reasoning efficiency is improved.

The invention relates to the technical field of risk analysis, in particular to a supply chain risk quantitative evaluation method and system based on a dynamic affair atlas, and the method comprises the steps: collecting multi-source heterogeneous data, constructing a four-dimensional space-time model comprising a time dimension, a geographic space dimension, a supply chain network space dimension and a risk influence space dimension, representing the supply chain event as four-dimensional spatio-temporal data; a supply chain entity is identified from the four-dimensional spatio-temporal data, risk events are extracted, a affair graph is constructed, and the affair graph takes the risk events as nodes and the evolution relation between the events as edges to calculate the relation weight between the events; calculating a probability quantized value of the risk conduction path based on the affair map, and obtaining a comprehensive risk score of the target entity; generating a risk mitigation strategy based on the comprehensive risk score; and monitoring the deviation between the actual risk occurrence condition and the prediction result, and updating the affair map and the risk mitigation strategy through adaptive parameter optimization and an incremental learning mechanism to form a self-evolutionary risk assessment system.

The invention relates to an enhanced LLM-RAG multi-hop question and answer method based on logic tree reasoning, and belongs to the technical field of new-generation information, and the method comprises the following steps: inputting a multi-hop question and answer question into a computer system; the computer system calls a pre-training large language model LLM, the multi-hop question-answer question is decomposed into a hierarchical logic tree in a recursive mode, and each node of the logic tree comprises a sub-question and a corresponding hypothesis answer; performing image retrieval from a structured knowledge source Wikidata and performing text retrieval from an unstructured knowledge source Wikipedia on the basis of each node sub-question and the hypothesis answer to obtain corresponding evidence; traversing the logic tree, verifying the consistency between the hypothetical answer of each node and the evidence through LLM, if the contradiction exists, reconstructing the corresponding sub-tree, and dynamically correcting the reasoning path; and integrating the verified logic tree node information, and outputting an accurate answer to the multi-hop question and answer question.

The invention relates to the technical field of artificial intelligence and large models, in particular to a multi-chain collaborative retrieval enhancement system, method and device based on a large model and a storage medium. The data extraction module is used for analysis; the mixed retrieval knowledge base construction module is used for cutting the document and constructing a mixed retrieval knowledge base by using the obtained document fragments; the query processing module is used for acquiring a query request of a user, performing mixed retrieval in the mixed retrieval knowledge base according to the query request, and outputting a document fragment corresponding to a retrieval result; the fusion sorting module is used for weighted fusion sorting; the answer generation module is used for inputting the document fragments and the query request into a large language model to generate answers; and the result output module is used for outputting an answer, wherein the answer comprises a traceability mark. According to the method, unified analysis and deep fusion can be carried out on the multi-modal heterogeneous data, multi-dimensional retrieval reasoning can be carried out, and the retrieval efficiency and the retrieval accuracy are improved.

The invention discloses a consultation method and system based on natural language processing and a legal knowledge graph, and relates to the field of data processing, and the method comprises the steps: receiving a multi-format legal consultation demand of a user, converting the multi-format legal consultation demand into a text, inputting the text into a BERT law NLP model, and analyzing key information through word segmentation, intention recognition and entity extraction; based on a pre-constructed multi-level legal knowledge graph, carrying out accurate and fuzzy retrieval and domain filtering in combination with an analysis result, and obtaining an associated law article, a case and a legal relationship; screening conflict law articles and similar cases, and inputting the conflict law articles and the similar cases into a graph neural network reasoning model to generate a preliminary conclusion; the conclusion is converted into a spoken consultation report through a natural language generation module, and output is customized according to a user scene; and if the user feedback satisfaction degree is less than the threshold value, iteratively optimizing the storage data to the historical library. The method has the advantages that accurate retrieval is realized based on the BERT model and the multi-level knowledge graph in the legal field, the oral personalized conclusion combined with the user scene is generated through GNN reasoning, and iterative optimization is performed through user feedback.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention relates to the technical field of data processing, and discloses a knowledge graph incremental updating and consistency checking method and system. The method and the device are used for solving the problem of low incremental updating and consistency checking efficiency of a large-scale knowledge graph. The method comprises the following steps: collecting a to-be-updated data source, sorting and separately storing data, and marking priority labels; performing change detection on the marked data source, identifying change items by comparing entities and relationships, and generating a log; incremental updating is executed based on the log, and nodes and edges are processed in a hierarchical fusion mode; preliminary consistency verification is carried out, and attribute uniqueness and relation directivity are checked; expanding a verification range, traversing an association path through cascade check, and recording problems; and optimizing storage according to the record, merging the update area, updating the index and cleaning the log. The method solves the problem of low efficiency of incremental updating and consistency verification of the large-scale knowledge graph, improves the response speed and the data accuracy of the system, and is suitable for a high-frequency dynamic data environment.

The invention relates to the technical field of electric power engineering information processing, and discloses a power grid power transformation engineering knowledge graph construction and retrieval method and system, and the method comprises the steps: carrying out the dynamic adaptive partitioning of a power grid power transformation engineering related document, and obtaining semantic coherent and independent text blocks; extracting entities and relationships based on the text blocks, and complementing implicit entities and relationships through a multi-round refining mode; performing fusion and disambiguation on the extracted and complemented entities and relationships to form a unified knowledge graph; performing hierarchical clustering on the formed knowledge graph to generate a multi-granularity community structure and a corresponding community report; intention resolution and pre-judgment guidance are carried out aiming at fuzzy questions of the user, and a retrieval strategy is optimized; and executing multi-hop semantic retrieval based on the optimized retrieval strategy, recalling related knowledge and generating answers. According to the method, automation, precision and intelligentization of power grid power transformation engineering knowledge graph construction and full-link retrieval can be realized.

The invention belongs to the technical field of text processing, and particularly relates to a method and system for retrieving DOCX document content based on keywords, which comprises the following steps: analyzing an Office Open XML structure of a DOCX document, combining with multi-dimensional features such as style names, and utilizing a title classification score model to accurately distinguish a title and a text, so that a semantic hierarchical structure of the document is effectively reserved; and secondly, a multi-level semantic extension mechanism is introduced, and a Sension-BERT, a HowNet knowledge base and a Word2Vec model are fused, so that intelligent extension of synonyms and synonyms of keywords is realized, and the recall rate and semantic understanding ability of retrieval are remarkably improved. And in addition, a BM25 model is combined with paragraph length normalization and structure position weight to calculate a correlation score, so that retrieval results are sorted more accurately and reasonably. The construction of the reverse index is combined with the position coding and compression optimization strategy, and the retrieval efficiency and the storage performance are both considered.

The embodiment of the invention discloses an academic research analysis method and device based on a large language model and a medium, and relates to the technical field of large language models.The method comprises the steps that under triggering of an academic research query request of a user, query text data is obtained, semantic analysis is conducted on the query text data, and a structured query task sequence is generated; querying a preset dynamic research knowledge graph based on the structured query task sequence, performing graph structure query and association expansion, and generating preliminary analysis result data with cross-thesis knowledge association; calling a field-specific scientific big language model subjected to pre-training and instruction fine tuning to process the preliminary analysis result data, and generating deep analysis result data; and quotation traceability processing and anti-illusion verification are carried out on the deep analysis result data to generate credible result data, the credible result data are returned to the user client for display, and the credible result data comprise original text fragment quotation labels and illusion evaluation indexes.

The invention discloses a progressive topic generation method based on semantic analysis and a knowledge graph. Performing preprocessing and semantic analysis on the question setting demand text, and extracting a necessary keyword set corresponding to the core knowledge points and an optional determiner set corresponding to the additional conditions; carrying out concept mapping in a college professional knowledge graph and associating with a course outline, constructing a hierarchical semantic constraint framework containing hard constraint and soft constraint, and carrying out consistency detection; adopting reverse index hard matching recall and knowledge graph soft extension recall to obtain candidate materials, and inputting the candidate materials into a field fine-tuning large language model to generate candidate questions; reordering is performed through multi-target learning ordering, teaching logic verification and quality evaluation are executed, and final questions are output; user feedback is received to form an incremental sample, and the generation model and the sorting model are updated, so that the accuracy, diversity and controllability of question generation are improved, and closed-loop optimization is supported.

The invention relates to the technical field of structured retrieval, and discloses a retrieval enhancement generation method and system based on LLM structured index and vector hybrid retrieval, and the retrieval enhancement generation method based on LLM structured index and vector hybrid retrieval comprises the steps of processing and analyzing a non-structural document, generating a hierarchical tree data extraction with document logic, and generating a hierarchical tree data extraction with document logic. The method comprises the following steps: obtaining a query demand, analyzing and identifying the query demand, self-matching any one or a combined retrieval strategy to quickly and accurately obtain a retrieval result in the complete logic positioning information, optimizing hierarchical tree data by adopting context perception, and establishing the complete logic positioning information in the hierarchical tree data through a reasoning path generation method. According to the method, through structured indexing, accurate positioning of specific chapters of the document, improvement of retrieval accuracy and support of multi-step logical reasoning, the long tail problem which cannot be processed by traditional RAG is solved, a clear and transparent retrieval path can be provided, and the system credibility is enhanced.

The invention provides a household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

The invention provides a private deployment-oriented multi-modal AI knowledge base construction system. The private deployment-oriented multi-modal AI knowledge base construction system comprises a knowledge storage module, an intelligent document loading module, a document partitioning engine module, a data enhancement engine module, a multi-language semantic vector alignment module and a private deployment module, the knowledge storage module comprises a knowledge authority management sub-module and a knowledge source management sub-module, the knowledge authority management sub-module is used for managing and storing knowledge from different sources, and the knowledge source management sub-module is used for managing electronic documents and multimedia documents; according to the method, intelligent identification, partitioning, vectorization and source file storage can be carried out on different types of electronic files, knowledge graph construction is carried out for specific fields, semantic relevance between texts and topics and key entities is fully considered, the method has wider applicability, higher robustness and controllability, the data leakage risk is effectively reduced, and the method is suitable for popularization and application. The method is suitable for enterprise sensitive data protection and personal user elastic computing power requirements.

The invention provides a multi-document industry knowledge assistant system based on a multi-modal knowledge graph. Comprising a knowledge slicing module used for receiving a multi-source industry document and performing layout and multi-mode analysis; the multi-modal knowledge graph module is used for establishing relation edges of the context relation, the affiliation relation and the reference relation to form a multi-modal knowledge graph; the context integration module is used for supplementing the slices with the context relationship or the affiliation relationship according to a preset hop count, and merging the textualized representations of the multi-modal slices to generate a complete knowledge slice package; and the answer generation module is used for generating a model to obtain an initial answer, performing self-evaluation and rewriting on the initial answer based on a preset reflection prompt, and outputting a final answer containing the slice identifier. According to the method, clause-level complete slicing and context retention, unified representation and alignment retrieval of multi-modal knowledge, hierarchical recall and accurate rearrangement based on the knowledge graph, and reflection-driven answer generation and evidence traceability can be realized.

The invention discloses an AI digital human interactive response method based on a large language model, and relates to the technical field of digital human interaction, and the method comprises the steps: analyzing collected user voice data and visual data through a natural language processing method, generating a cross-modal feature vector, carrying out the cross-modal association analysis of the cross-modal feature vector, and carrying out the cross-modal association analysis of the cross-modal feature vector. Generating a semantic association topological graph; calculating a vertex coordinate and a joint activity threshold value of the semantic association topological graph through high-digital human correlation, inputting the vertex coordinate and the joint activity threshold value into a constructed coordinate index database to execute attention weight calibration, and outputting a multi-dimensional association graph; and performing information density analysis based on the multi-dimensional association map, generating an information density gradient vector field, and dividing a high-density core region and a low-density edge region, the high-density core region generating a semantic core coding tensor, and the low-density edge region generating an edge feature package. According to the method, the cross-modal fusion vector is converted into the cross-modal feature vector, so that the modeling of the cross-modal association relationship is realized.

The embodiment of the invention provides a multi-modal fusion entity retrieval enhancement generation method and device, and the method comprises the steps: carrying out the blocking and adaptive text extraction of multi-modal data in an offline stage, obtaining the text block data corresponding to each modal data, extracting the entity and relation of the text block data according to a language model, constructing an entity triple, and carrying out the segmentation and adaptive text extraction of the entity triple. Fusing the entity triad with the text block data to obtain an offline knowledge graph, and constructing a data index; in the present stage, a query statement of a user is received, text block data most similar to the query statement are retrieved in a knowledge graph through a data index, after entity aggregation is carried out on the retrieved text block data, the text block data are reordered according to retrieval scores, an entity aggregation result is obtained, entity ordering is carried out according to the entity aggregation result, and the entity aggregation result is obtained. By means of the multi-modal retrieval method and device, the efficiency and accuracy of multi-modal retrieval can be improved.

The invention provides a retrieval method based on collaborative document tree reconstruction, which comprises the following steps: taking vector representation of each document block as a first-layer node of a multi-dimensional system document tree, executing GMM clustering on the nodes, and taking a structured abstract of a clustering center obtained by combining LLM as a next-layer node of the document tree; according to the vector representation of each document block and the hierarchical relationship and / or path distance among other nodes in the tree, based on a quinary training sample, enhancing an Embedding model and rearranging a Reranker model; representing a first cosine similarity between nodes except a root node in the tree based on a to-be-retrieved vector of the query text to obtain a global candidate text set; pruning and traversing nodes of the document tree layer by layer, and obtaining the similarity between the traversed nodes and the to-be-retrieved vector to obtain a fine candidate text set; and rearranging a union set of the global candidate text set and the fine candidate text set by adopting the enhanced Reranker model to obtain a retrieval result.

The invention relates to the technical field of information retrieval, and provides a multi-dimensional heterogeneous knowledge recall enhanced hybrid retrieval method and system.The method comprises the steps that texts recalled through keyword retrieval, sparse vector retrieval and dense vector retrieval are screened through a reciprocal sorting fusion algorithm, and a text type candidate knowledge list is obtained; based on user query, generating and checking a query statement through a large language model, and retrieving an entity-relationship-attribute triple from the knowledge graph library; based on user query, generating enhanced knowledge through a knowledge graph enhanced retrieval method fusing keyword retrieval, vector retrieval and community retrieval; and carrying out format alignment and duplicate removal on the text type candidate knowledge list, the triple result and the enhanced knowledge to form a multi-modal candidate pool, and carrying out reordering score calculation and ordering on each piece of recall knowledge in the multi-modal candidate pool through a reordering model and a business rule to obtain a final retrieval result. And the coverage blind area of single retrieval on heterogeneous knowledge is solved.

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

The invention provides an automatic data management method and system based on a multi-modal large model, and the method comprises the steps: collecting multi-source heterogeneous industrial data, and carrying out the standardization processing, and forming standardized multivariable time series data; constructing a process knowledge base, and performing semantic embedding coding on a process knowledge text and storing the process knowledge text; constructing and finely tuning a KTSF multi-modal large model, and fusing process knowledge semantics and multivariable time sequence data through a cross-modal attention mechanism to generate joint semantic representation; based on prediction of a KTSF multi-mode large model, outputting a residual error with actual data, and dynamically identifying abnormal data; performing attribution analysis; based on an attribution result, calling a KTSF multi-mode large model to generate a repair value, and performing intelligent correction on the abnormal data; the design quality evaluation and feedback learning module is used for calculating a data quality score and driving incremental updating of the model; and the design rule self-learning module is used for automatically extracting the governance rule through clustering analysis and updating the knowledge base.

The invention provides a method and system based on NLP file analysis, and relates to the technical field of natural language processing. According to the method, time and identifier unification and format and character set standardization are carried out on the multi-source file, layout segmentation, table structure extraction, reference analysis, term standardization and anaphora resolution are combined, semantic representation is constructed, a hierarchical index and a unique traceability identifier are generated, intention recognition, retrieval sorting, incremental updating and consistency verification are supported, and the method is suitable for large-scale popularization and application. Unification, semantization and traceability of the file analysis process are achieved, and the processing efficiency and accuracy are improved.