2807 results about "Semantic vector" patented technology

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention belongs to the technical field of knowledge retrieval, and discloses a multi-modal large model-based dynamic vector knowledge base construction and retrieval method, which comprises the following steps of: obtaining a multi-source heterogeneous modal data set, and carrying out preprocessing and modal standardization processing on the multi-source heterogeneous modal data set to obtain a standardized multi-modal data set; performing feature extraction and semantic vector representation generation by using the pre-trained multi-modal large model, and constructing a multi-modal knowledge vector set; semantic association analysis and hierarchical clustering are carried out on the multi-modal knowledge vector set, and a structured vector knowledge base is constructed; performing semantic similarity calculation and relation modeling on the vector knowledge base to form a vector relation network; intention analysis and vector representation are performed based on mixed modal query information input by a user, and efficient similarity retrieval is realized in combination with a vector relation network; dynamic optimization is carried out through user feedback, personalized retrieval result adjustment is achieved, and the problem of limitation of a traditional retrieval system during multi-modal data processing is effectively solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a content generation and optimization method based on a knowledge graph, which comprises the following steps: constructing a multi-source knowledge database, extracting core concepts and knowledge contents, and constructing the knowledge graph. Performing semantic analysis to generate semantic vector representation and a keyword list; retrieving the associated text fragment based on the semantic vector and the keyword list, and inputting the associated text fragment into a generation model to generate initial answer content; and utilizing the knowledge graph to match the domain entity and the knowledge graph node, generating a logical reasoning path, optimizing the initial answer content, and generating the final answer content. According to the method, content generation of accurate retrieval, deep knowledge association and logical reasoning enhancement is realized by fusing a multi-source knowledge database, knowledge graph reasoning and generation optimization; semantic vector matching and keyword retrieval are combined, so that the accuracy of knowledge acquisition is improved; and through knowledge graph reasoning path construction, the answer logic is coherent.

The invention discloses a power plant operation and maintenance knowledge intelligent query method based on a large language model and an RAG technology. The method comprises the following steps: constructing a power plant operation and maintenance knowledge vector library covering structured, semi-structured and unstructured data; receiving a natural language question of a user, inputting an improved instruction to align a preprocessor, and generating a question semantic vector and an intention tag; relevant knowledge fragments are retrieved and sorted through a semantic matching retriever in combination with the intention labels; constructing a large language model cue word structure based on the retrieval result and the original question, generating candidate answers and recording a reference path; and finally, performing term specification and consistency verification according to the expert rule base, and outputting a structured and traceable final answer. According to the invention, the improved RAG technology is fused to realize intelligent query of the operation and maintenance knowledge of the power plant.

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

The invention discloses an equipment intelligent safeguard system based on an offline large model, and relates to the field of equipment management and safeguard, and the system comprises a collection module which collects an equipment surface defect image and a time sequence signal through an unmanned plane, collects a text log, processes the defect image, the time sequence signal and the text log, and generates a multi-modal data set; the feature fusion module is used for performing feature extraction on the multi-modal data set and fusing the three features to generate a multi-modal feature; the updating module is used for extracting a fault triple from the semantic vector, constructing a Bayesian causal graph according to the fault triple, dynamically updating node confidence and generating a knowledge graph; the maintenance strategy module is used for matching the multi-modal features with a knowledge graph and positioning a fault root cause; and the lightweight model deployment module is used for pre-training a lightweight model in an edge server, accelerating parameter aggregation by using a quantum annealing algorithm, adjusting and optimizing a global large model according to the aggregated parameters, and generating a strategy in combination with reinforcement learning.

The invention relates to the technical field of computer systems for natural language processing or semantic processing, and discloses an enterprise-level schedule planning and knowledge base-oriented intelligent collaborative question-answering system and an enterprise-level schedule planning and knowledge base-oriented intelligent collaborative question-answering method. The system comprises a vectorization processing module, an RAG knowledge base module and the like. The system converts natural language input of a user and enterprise knowledge data into semantic vectors, and retrieves related enterprise knowledge fragments from a vector database based on semantic similarity. The large language model core module analyzes the user intention, generates a preliminary answer and identifies whether a schedule type operation request is included or not; and if the schedule operation request exists, the system splits the request through the multi-agent cooperation module and distributes the request to the corresponding agent to obtain a task processing result. And finally, semantic consistency fusion is carried out on the preliminary answer and a task processing result through a context fusion module, a comprehensive answer is generated by a large language model and is returned to a user side, and unified intelligent response of enterprise knowledge and schedule service is realized.

The invention relates to the technical field of artificial intelligence and intelligent education, and provides a mathematical teaching knowledge graph generation method and system based on artificial intelligence, which are used for optimizing and improving a teaching knowledge graph generation technology to realize more intelligent and accurate teaching knowledge graph generation, and the method comprises the following steps: obtaining a multi-source teaching data set; extracting a mathematical knowledge point entity set from the textbook text data, and generating an association relationship set among mathematical knowledge point entities in the mathematical knowledge point entity set according to the test question structure data; performing hierarchical classification processing on the mathematical knowledge point entity set based on a preset semantic analysis model to obtain knowledge point hierarchical structure data, and calculating weight distribution data of the mathematical knowledge point entities based on the association relationship set; and generating a dynamic knowledge graph topological structure according to the knowledge point hierarchical structure data and the weight distribution data, wherein nodes in the dynamic knowledge graph topological structure comprise semantic vectors and association strength parameters of mathematical knowledge point entities.

The invention discloses an electronic medical record intelligent evaluation method based on complex quality control indexes, and relates to the field of medical information processing and artificial intelligence. The method comprises the steps that an original electronic medical record text is collected and preprocessed, and structured diagnosis and treatment information and an event sequence diagram are extracted; through prompt word chain construction and a semantic reasoning mechanism, a large language model is guided to intelligently evaluate complex quality control indexes in medical records. The complex quality control indexes comprise diagnosis basis sufficiency, treatment scheme rationality, key result and change record integrity, treatment measure integrity and causal relationship rationality. According to the method, technologies such as a medical knowledge graph, a graph neural network and semantic vector retrieval are utilized to realize external knowledge recall and causal reasoning support; a self-consistency reasoning mechanism, a self-reflection mechanism and a multi-model cross validation mechanism are introduced to improve the accuracy and credibility of an evaluation result; and finally, outputting a structured quality control report and a visual reasoning chain. According to the method, the intelligence and refinement level in a complex medical quality control task can be remarkably improved, high interpretability and practical value are achieved, and the method is suitable for application scenes such as hospital quality management, scientific research evaluation and medical document standardization.

The invention discloses an intention classification method and device based on vector retrieval and context awareness and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of extracting business metadata and associating the business metadata with typical problem examples to generate a standardized service description document; encoding the standardized service description document into a high-dimensional semantic vector through a pre-training language model, and constructing a neighbor search index to store the high-dimensional semantic vector; splicing the user identity information and the current question text into an enhanced query statement, and encoding the enhanced query statement into a context-aware dynamic query vector through a semantic model; performing similarity retrieval based on the dynamic query vector to obtain candidate intelligent services, performing business domain filtering, context weighted sorting and dynamic priority rearrangement, and outputting target recommendation services; by collecting interactive behavior data of a target recommendation service, quality scoring is performed on service descriptions and problem examples based on a preset evaluation rule, and the service descriptions and the problem examples of which the quality scores are lower than a quality threshold value are updated.

The invention relates to the technical field of medical artificial intelligence and knowledge graph construction, in particular to a rare disease knowledge graph construction method based on modal injection and multi-modal fusion. Comprising the following steps: S1, collecting multi-modal medical information including texts, images and genes; s2, standardization processing is carried out, and a three-layer metadata structure is constructed; s3, complementing missing modal data, and performing feature extraction and unified dimension conversion on the modal data to realize representation alignment in a shared semantic space; s4, performing multi-level semantic fusion to obtain a unified fusion semantic vector; and S5, constructing a double-layer structure system rare disease knowledge graph comprising an ontology layer and an instance layer. According to the method, multi-modal medical information of texts, images and genes is selected to construct the knowledge graph of the rare disease, the application range, coverage and accuracy of the knowledge graph are improved, correspondence adaptation of rare cases during clinical diagnosis and treatment of the rare disease can be achieved, and the method has high recognition capacity.

The invention relates to the technical field of intelligent recommendation, and discloses a hybrid retrieval RAG-based resource recommendation method and system, and the method comprises the steps: collecting resource text data, and constructing a vector library and a tag library; expanding the user question based on the language model to obtain a plurality of semantic extension questions; performing intention recognition, judging whether the user question is a resource recommendation question, and if yes, determining a target classification type; screening the data according to the field definition in the tag library to obtain a candidate knowledge fragment set; obtaining candidate vectors, mapping the user question and the semantic extension question into query vectors, calculating the similarity between the query vectors and each candidate vector, and selecting knowledge supplement content; and performing resource splicing on all the knowledge supplement contents to generate resource recommendation answers. According to the method, a structured label screening mechanism and a semantic vector fine arrangement mechanism are fused, and the problems of recall redundancy, matching deviation and the like caused by the fact that an existing RAG system only depends on semantic similarity retrieval are solved.

The invention discloses a privacy enhanced intelligent search method and system based on multi-round iteration. The method comprises the following steps: performing hierarchical semantic analysis on a query input by a user; splitting the complex query into sub-queries based on a task dependency graph algorithm; according to the sub-query, retrieving an evidence fragment from the multi-source data, constructing a semantic element coverage matrix to detect a knowledge gap, and if an uncovered element exists, generating a supplementary sub-query for iterative completion until a preset termination condition is met; integrating cross-modal data through a federated learning technology, and generating a structured knowledge graph fragment in combination with semantic vector alignment and an evidence fusion algorithm; performing dynamic desensitization processing on the retrieval result; and a closed-loop iterative updating mechanism is formed based on a user explicit and implicit feedback optimization retrieval strategy. The problems of traditional intelligent search in the aspects of semantic understanding depth, complex problem reasoning, search result accuracy and integrity, user privacy security and the like are effectively solved.

Disclosed in the present application are a knowledge graph construction method and apparatus, and a storage medium, an electronic device and a computer program product. The method comprises: acquiring first natural language text; using an extraction model to perform entity extraction on the first natural language text, so as to obtain a first entity and a first entity relationship, and determining a corresponding first entity type and first relationship type; using a semantic encoder to determine a first semantic vector and a second semantic vector respectively corresponding to the first entity type and the first relationship type; on the basis of calculated first distances between the first semantic vector and cluster centers of a plurality of entity types and calculated second distances between the second semantic vector and cluster centers of a plurality of relationship types, determining a target entity type for the first entity type and a target relationship type for the first relationship type; and on the basis of the first entity, the first entity relationship, the target entity type and the target relationship type, constructing a target knowledge graph.

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention belongs to the field of natural language processing, and provides a task complexity driven graph semantic multi-agent collaborative decision-making method and system.The task complexity driven graph semantic multi-agent collaborative decision-making method comprises the steps that a task text is obtained and subjected to semantic coding to obtain a task semantic vector, evaluation is conducted based on the task semantic vector to obtain a complexity vector, and a task complexity score of the complexity vector is calculated; the task semantic vector and the complexity vector are fused to obtain a task representation vector, an agent capability relation graph is constructed, the participation probability of each agent node is obtained according to the task representation vector and the agent capability relation graph, and a dynamic agent combination scheme is formed; and performing task decomposition according to the agent combination scheme, constructing a sub-task dependency graph, scheduling the execution sequence of the sub-tasks through topological sorting, realizing cooperative execution of the agents, and generating a task result. According to the method, precise matching and efficient cooperation of the agent combination are realized, and the capability of processing complex tasks and the resource utilization efficiency of the multi-agent system are remarkably improved.

The invention provides an industrial chain atlas modeling method and system based on deep learning, and relates to the technical field of industrial chain analysis and modeling, and the method comprises the steps: collecting multi-source heterogeneous data in an industrial chain, including demand information, resource states, process features and operation and maintenance data, and preprocessing the multi-source heterogeneous data to form a standardized data set; and performing semantic vectorization processing on the text type data in the standardized data set, and performing node embedding on the multi-relation graph to generate entity low-dimensional representation fusing text and structural features. According to the method, by integrating multi-source heterogeneous data, fusing text and structural features, constructing an industrial chain knowledge graph and performing dynamic optimization, accurate adjustment of production line configuration and task allocation strategies is realized, meanwhile, the industrial trend is effectively predicted, risk paths are analyzed and resource bottlenecks are identified in combination with a risk propagation model, a scientific basis is provided for industrial chain decision-making, and the risk propagation model has a wide application prospect. And the operation efficiency and stability of an industrial chain are improved.

The invention relates to the technical field of natural language processing, in particular to a semantic synthesis and text vectorization-based data lake metadata governance method, which comprises the following steps of: performing vector coding of field content keywords and adjacent context words on the basis of field names, data types and description information of data sources in a data lake; and extracting semantic co-occurrence groups of the fields in the differentiated contexts, identifying distances between semantic vectors, and judging whether the distances are in a similar field grouping range or not to obtain a field semantic collection quantity. According to the method, a semantic co-occurrence relation is extracted through field keywords and context vector coding, a semantic hierarchical structure is constructed, the field division precision and abstract ability are improved, the consistency is judged in combination with a context semantic structure, a high-frequency alternative path is adjusted to optimize a semantic structure, and word vector similarity and a structure retention rate are fused to realize field merging; the continuity and the accuracy of a treatment structure are improved, and the intelligence and the consistency of metadata treatment in the data lake are enhanced.

The invention discloses an Agent-based tool combination and task processing method and device, equipment and a medium, and the method comprises the steps: carrying out the natural language processing of a task instruction inputted by a user, and generating a structured semantic vector containing task semantics; performing similarity matching on the structured semantic vector and a predefined tool node semantic tag in a knowledge base, and outputting a candidate tool node set related to the current task; performing dynamic priority ranking on the candidate tool node set based on context parameters to generate an initial thinking chain; converting the thinking chain into a tree flow chart structure, and performing dynamic addition and deletion or parameter adjustment on tool nodes in the flow chart according to a feedback or preset optimization strategy; and updating tool combinational logic according to the adjusted thinking chain, processing the task instruction, and outputting a task processing result. The tool nodes are matched through the Agent agents, and the thinking chain is generated through dynamic sorting, so that the task processing efficiency and accuracy are improved.

The invention discloses a high-accuracy threat intelligence assisted network threat tracing method, which comprises the following steps: S1, collecting and preprocessing multi-source network security data, and constructing a time-marked event sequence set; s2, constructing an optimized Transform network model, and processing an attack event sequence by using position coding and time embedding; s3, a black swan optimization algorithm is initialized, and a Transform structure hyper-parameter is dynamically optimized; s4, outputting an attack event semantic vector, and constructing an attack path semantic map; s5, the intelligence information vector is embedded into a Transform hidden space; s6, calculating semantic similarity and dependency intensity, and generating an attack source candidate set and a traceability path; s7, outputting an attack traceability path, a starting point node and an information label, and generating a structured traceability report; and S8, according to the traceability result feedback, updating the black swan algorithm and the Transform model. The method is used for realizing intelligent modeling of multi-source network attack events and high-accuracy traceability analysis of attack source nodes.

The invention discloses a multi-modal semantic and physical law driven remote sensing image generation method, belongs to the technical field of computer vision and remote sensing image generation, and aims to solve the problems of insufficient cross-modal semantic alignment, low reliability of a generation result and insufficient physical mechanism fusion. The four-stage method comprises the following steps: firstly, rejecting low-quality samples from original data and unifying a spatial scale; then, extracting a multi-modal semantic vector by adopting a BLIP model and a CLIP model, and introducing a remote sensing physical rule to carry out vector optimization; then position coding and physical constraint conditions are embedded in the submerged space, and multi-source information joint modeling is achieved through a cross-modal encoder; and finally, by taking text description, physical priori knowledge and diffusion time steps as joint conditions, performing de-noising reasoning based on a Transform architecture, and completing back diffusion reconstruction by means of a trans-attention mechanism. According to the method, physical rationality and semantic consistency are improved, and a more reliable technical normal form is provided for remote sensing image generation in the fields of disaster monitoring, military simulation and the like.

The invention discloses a data query method and system based on a natural language text. The method comprises the following steps: dynamically shielding the natural language text to generate a semantic vector sequence; key entities and association relationships thereof in the semantic vector sequence are extracted, and a structured entity relationship set is obtained; matching the structured entity relationship set with knowledge graph nodes to generate a heterogeneous semantic graph; performing heterogeneous fusion on the heterogeneous semantic graph and text syntactic features to generate intermediate query representation; processing the intermediate query representation to generate an executable statement carrying a semantic association identifier; and verifying the semantic consistency of the response content of the executable statement and the original input, and iterating to regenerate the executable statement when the semantic consistency does not pass the verification. And efficient and accurate natural language query conversion and response generation are realized through dynamic shielding enhanced semantic robustness, knowledge graph path extension and weight iterative optimization, heterogeneous feature fusion and a multi-modal verification mechanism.

The invention relates to the technical field of natural language processing, and provides an LLM-based complex language question SQL query generation method and system, and the method comprises the steps: responding to a target language question request; splitting a requested target language question sentence into one or more core clauses through a language model LLM, converting the core clauses into semantic vectors, calculating a matching score of each core clause and a question normal vector of each table in the schema knowledge document, performing sorting and screening according to the score size and a score threshold, and returning a recall result of the core clauses; combining and sorting the recall results of all the core clauses, and screening to generate a recall result of the target language question; and generating a Prompt through a Prompt script, and calling an LLM (Language Model) to automatically generate an SQL (Structured Query Language) query statement. The method does not depend on field fine tuning, does not need to reconstruct a wide table, and effectively solves the problem that a target table is difficult to position when a large language model processes coded value mapping, multiple tables and complex business logic by constructing a virtual table and splitting a question sentence into clauses containing complete semantics and matching the clauses.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention relates to a low-altitude intelligent question and answer construction method and system based on dynamic parameters. The method comprises the following steps: collecting low-altitude domain data, cleaning the low-altitude domain data, generating a semantic vector index, and constructing a low-altitude domain knowledge base based on the semantic vector index; receiving a natural language query of a user, analyzing a query intention, extracting keywords in the natural language query, and matching a corresponding candidate word quantity based on query types of the natural language query of the user, the query types at least comprising high-frequency phrase query and low-frequency long-tail query; and respectively carrying out fusion semantic retrieval and keyword retrieval, carrying out secondary sorting on the candidate results based on a preset resorter, preferentially sorting the candidate results related to the query intention, and outputting the corresponding candidate results. By adopting the method, a combined domain retrieval enhancement generation mechanism is provided, so that the professionality and accuracy of answers are improved; and the retrieved knowledge base content is re-screened to increase the hit probability of the knowledge base.

The invention discloses an intelligent contract risk recognition method and system, and relates to the technical field of text recognition, and the method comprises the steps: extracting a semantic vector of a to-be-recognized file; obtaining a first risk identification result based on the semantic vector and the review list; performing semantic matching on the semantic vector and the review knowledge graph to obtain a potential risk; obtaining a first risk category based on the potential risk and the review knowledge spectrogram, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule base, and obtaining a compliance result based on the semantic vector and the clause rule base; obtaining a complete result based on the semantic vector and the standardized contract template library; and obtaining a total risk identification result based on the above identification result, thereby solving the problems of low risk identification efficiency and low accuracy caused by the fact that an existing contract term risk identification method depends on the determination of the license experience of professionals.

The invention discloses a dynamic interaction method based on cooperation of a multi-modal dynamic fusion large model and an intelligent agent. The method comprises the following steps: performing feature extraction on user voice information to obtain a voice coding vector, a text semantic vector and an emotion feature vector; performing dynamic weight feature fusion on the voice coding vector, the text semantic vector and the emotion feature vector through a multi-modal dynamic fusion large model to obtain a fusion feature vector; inputting the fusion feature vector into an intention-scene coupling network, and identifying to obtain a user intention label; and identifying according to the user behavior log to obtain a user portrait tag, inputting the user intention tag and the user portrait tag into an autonomous decision-making agent, generating a target decision-making action through a lightweight policy network, and then interacting with the user according to the target decision-making action. The intelligent interaction efficiency and accuracy of the customer service system are improved, the interaction experience of the user is also improved, and the method can be widely applied to the technical field of artificial intelligence.

The invention provides a large model training data synthesis method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of original military corpus data, generating a semantic vector through a pre-training language model, dividing the semantic vector into a plurality of topic clusters through clustering analysis, extracting the keyword and representative sample of each topic cluster, forming a theme ontology library; selecting associated topic pairs based on the topic similarity matrix, and generating a synthetic sample through a predefined template; controlling the language style of the synthetic sample through the cue word instruction, and generating a stylized sample; dividing difficulty grades of the stylized samples to obtain graded samples; performing multi-dimensional quality evaluation and screening on the graded samples to obtain screened samples; the screened samples are used for training a large language model, and according to the performance of the large language model in each theme task, theme weights are adjusted, a synthesis strategy is updated, and a sample structure is optimized. According to the method, high-quality and diversified training data can be provided for large model training.

The invention discloses a financial data intelligent entry and verification method, which comprises the following steps: acquiring an original document image, and processing the original document image by adopting an image enhancement algorithm to obtain an optimized document image; extracting a structured field from the optimized document image, determining field coordinates, and generating a structured data table; performing semantic analysis on the non-standardized text in the structured data table to generate semantic vector representation; identifying missing fields according to the semantic vector representation, querying a historical database, and generating preliminary filling data; comparing the preliminary filling data with historical data through a cross validation mechanism to obtain a validated field value; processing the verified field value by adopting a classification model, and determining a document type; arranging the verified field value according to the document type, and generating a standardized data record; key indexes are extracted from the standardized data records, the index logic relation is verified, and corrected data are obtained.

The invention belongs to the technical field of natural language processing and information retrieval. The invention provides a bidding document multi-modal duplicate checking method and system based on a large model, and the method comprises the steps: carrying out the structural analysis and multi-modal feature extraction of an input bidding document, and generating the feature representation of semantic blocks and non-text elements; performing deep semantic coding on text blocks by using a dynamic context-aware large language model, and retaining logic relevance of a long text in combination with hierarchical position coding; efficient matching of massive semantic vectors is achieved through a mixed retrieval framework, and calculation efficiency is optimized in combination with a distributed calculation framework and a hardware acceleration instruction; and finally, performing structured feature reconstruction on non-text contents such as tables, charts and the like to realize cross-modal semantic association analysis.