1243 results about "Parsing" patented technology

The invention discloses an intelligent programming auxiliary method and system based on a multi-modal AI language model, and belongs to the technical field of programming auxiliary tools. The method comprises the following steps: a multi-modal input processing stage; a dynamic context modeling stage; a hierarchical semantic analysis stage; in the code generation stage, codes are generated in two stages by adopting a Codex-Plus large model; the reinforcement learning driven code optimization stage is used for carrying out multi-objective optimization and reward function design on the codes generated in the code generation stage; a multi-dimensional feedback stage; an interaction and visualization stage; code semantic deep analysis, dynamic context sensing, multi-target optimization generation and real-time interactive feedback are realized by fusing code texts, natural language description, developer behavior data and a domain knowledge graph, and programming efficiency and code quality can be remarkably improved.

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

The invention provides an analyzable anti-attack network security method and system based on an AI unified model. The method comprises the following steps: S1, carrying out attack source tracing and attack mode identification on an input data stream; s2, performing protocol structure analysis and grammar element extraction on the input data stream in a grammar verification layer, and starting a grammar rule matching process to obtain a grammar exception perception set; s3, fusing attack vector information on the basis of a grammar anomaly perception set in a semantic analysis layer, constructing a semantic relation graph, and outputting a semantic risk vector; s4, taking the semantic risk vector as input, combining a business scene, resource constraint and strategy preference, modeling a defense target, and outputting an optimal response path; and S5, forming a model evolution path based on local feedback and global collaboration. Through a three-layer full-information analysis mechanism and behavior feedback driving, interpretable recognition of attack intentions and collaborative optimization of defense paths are realized, attack recognition is comprehensive, response decision is accurate, and strategy evolution is controllable.

The invention belongs to the technical field of artificial intelligence and big data, and discloses a customer data processing and insight system based on a big language model. The system is composed of a multi-source data access module, a data preprocessing and label fusion module, a large language model semantic understanding module, a knowledge enhancement and semantic linkage module, an insight generation and visualization module, an intelligent strategy output module and a feedback learning and self-optimization module. According to the method, multi-source heterogeneous data such as texts, voices and structured behaviors are integrated, and the deep semantic analysis capability of a large language model is combined, so that global modeling of customer behaviors and intentions is realized; a multi-modal synchronous acquisition and standardization mechanism eliminates data format barriers, and a dynamic label mechanism adapts to context changes, so that the system can capture deep semantic association in customer expression, and compared with a traditional keyword matching method, the semantic understanding accuracy is improved by more than 40%, and a more complete data base is provided for insight generation.

The invention relates to the technical field of intelligent operation and maintenance and artificial intelligence crossing of a power system, in particular to an intelligent power work order processing method of an RPA fused multi-modal large model, which analyzes multi-modal work order data such as texts, voices, images and the like through a domain adaptation large language model, and realizes fault key information extraction and conflict resolution in combination with a dynamic knowledge graph; performing work order priority scoring and resource allocation by using space-time constraint reinforcement learning; an analysis result is converted into an automatic execution script through an RPA engine, and a whole-process closed loop of order sending, processing and feedback is achieved; meanwhile, a feedback optimization and conflict resolution cooperation mechanism is constructed, and the knowledge graph and the model precision are continuously iterated. The method improves work order processing efficiency and analysis precision, enhances decision scientificity, and is suitable for an intelligent operation and maintenance scene of a power system.

The invention discloses an adaptive question-answering system based on knowledge distillation and multi-modal dynamic fusion, and the system comprises a knowledge distillation module which is used for migrating knowledge of a teacher model pre-trained on corpora in the communication field to a lightweight student model, achieving model compression through optimizing a distillation loss function, and obtaining a multi-modal dynamic fusion model; the loss function comprises a soft label output by the teacher model and a KL divergence constraint output by the student model; the multi-modal knowledge fusion module comprises a feature extraction unit, a self-adaptive weighting unit and an attention fusion unit; the self-adaptive inference engine comprises a semantic analysis unit; according to the cross-modal reasoning method and system, semantic alignment of equipment parameters, protocol texts and topological graphs is achieved through the multi-modal dynamic fusion technology, and the cross-modal reasoning accuracy is improved; compared with an original model, the lightweight student model has the advantage that the reasoning speed is increased in a protocol analysis task.

The invention provides a large model cue word design method, system and device in an industrial scene and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a private cue word template library, and forming a domain special knowledge unit set through multi-source industrial data collection, intelligent semantic analysis and classified storage; customizing a four-layer structured template, sequentially establishing a task overview layer, a step disassembly layer, an instruction refining layer and an output specification layer, and constructing a layered mapping model from task definition to output execution; template library management and iterative optimization are carried out, and dynamic updating and performance improvement of a cue word system are realized through centralized system management, multi-source feedback acquisition and data-driven optimization. According to the method, a cue word design and optimization system based on a hierarchical structure is constructed for complex task requirements in an industrial scene, and the task processing performance of a large model in scenes of industrial production, quality detection, equipment operation and maintenance and the like is improved.

The invention provides a semantic understanding method based on natural language processing and a science and technology operation platform system.The method comprises the steps that firstly, a target interaction data set containing a text instruction sequence, collaboration document content and resource scheduling request records in a science and technology management scene is obtained, and then cross-modal semantic alignment processing is conducted on the target interaction data set; and generating a structured semantic analysis result containing an intention decision tree and the like. Then, based on a dynamic process arrangement rule, the result is mapped to a predefined process node topological space, an initial process execution path set containing a node execution sequence chain and the like is obtained, conflict resolution is conducted on the set according to real-time system resource state data, and an optimized executable process path containing resource allocation priority and the like is generated; and finally, the optimized executable flow path is input into a science and technology management central system, automatic deployment and execution state tracking of a cross-department cooperation flow are realized, and the semantic understanding and flow processing capability of a science and technology management scene is effectively improved.

The invention discloses a speech recognition and natural language processing integration method and system, and the method comprises the steps: carrying out the multi-modal data fusion according to a speech signal of a user, context text information and environment sensor data, and obtaining a fused multi-modal feature vector; inputting the multi-modal feature vector into a speech recognition model based on an adaptive deep neural network, and performing speech-to-text processing to obtain text output; inputting the text output into a semantic analysis model based on a graph neural network, and performing context semantic analysis and user intention recognition to obtain semantic representation of the user intention and a confidence score of the semantic representation; and according to the semantic representation and the confidence score thereof, dynamically adjusting parameters of the speech recognition model and the semantic analysis model by using a feedback optimization technology based on reinforcement learning, and generating a model optimization strategy. According to the embodiment of the invention, the accuracy of speech recognition and the semantic comprehension capability of natural language processing can be improved.

The invention provides a low-code rule engine method and platform supporting dynamic service adjustment, and relates to the technical field of data processing.The method comprises the steps that original rule data are obtained, and the original rule data are subjected to structured conversion and unified format specification; generating a visual rule component, presenting rule logic through a graphical interface, supporting business personnel to edit and modify without coding, and converting a modification result into component rule data; performing real-time analysis and verification on the component rule data to obtain operation rule data; performing version identification on the operation rule data, and storing the operation rule data in a rule warehouse; based on the current service context, matching an applicable version, automatically selecting the operation rule data, and loading the operation rule data to a rule execution engine for execution; the autonomy and accuracy of the low-code rule engine are improved.

The invention provides a front-end automatic testing method of a large language model based on LLM, and belongs to the technical field of front-end automatic testing. The method comprises five core steps of test intention recognition and semantic analysis, page structure analysis and target element positioning, automatic generation of a test operation sequence and assertion logic, test script execution and dynamic regression verification, and test feedback analysis and continuous training. The strong semantic understanding and context reasoning capabilities of the LLM are utilized to perform semantic analysis and simulation on a front-end page structure, a user behavior process and business logic, so that automatic generation and maintenance of a test script are realized. According to the method, the test coverage rate and the generation efficiency are greatly improved, the maintenance cost is remarkably reduced, the readability and the intelligence of the test case are enhanced, and the method is suitable for the modern Web front-end application test of rapid iteration.

The embodiment of the invention provides a multi-source heterogeneous data integration method and device fusing a large model conversion operator. The method comprises the steps that heterogeneous data are collected from a multi-source heterogeneous data source, preprocessing operation of data cleaning is carried out, and preprocessed data features are obtained; inputting the preprocessed data features and the target format into a large language model, and generating a conversion operator including data structure analysis, field mapping and type adaptation; and based on the conversion operator, performing distributed parallel data conversion and integration in a distributed computing framework, and integrating and verifying the integrated data. According to the scheme, by introducing the intelligent reasoning ability of a large language model, the efficiency optimization of distributed calculation and the quality verification mechanism of the whole process, the core problems of the traditional data integration technology in the aspects of rule stiffness, high manual dependency and quality control deficiency are systematically solved.

The invention relates to the technical field of data optimization, and discloses a financial data warehouse intelligent SQL (Structured Query Language) optimization method based on a large model, which comprises the following steps: S1, SQL statement analysis and preprocessing: carrying out word segmentation, grammatical analysis and logic structure analysis on an SQL statement submitted by a user, and identifying keywords, table structures and query conditions in query; s2, large model semantic understanding and optimization suggestion: performing semantic understanding and logical reasoning on the analyzed SQL statements by using a large model, and automatically generating optimization suggestions, the optimization suggestions including index recommendation, query rewriting and execution plan optimization; and S3, generating and selecting a dynamic execution plan. According to the method, the advantages of a large model in the aspects of natural language processing and intelligent decision making are fully utilized and combined with the specific requirements of the financial data warehouse, and the execution efficiency of SQL query and the overall performance of the system are remarkably improved; the query response time of the financial data warehouse can be effectively shortened, and the resource utilization rate is improved.

The invention discloses a multi-mode instruction analysis system for a humanoid robot. Comprising a voice input module, a visual input module, a voiceprint feature extraction module, an object recognition and pose estimation module, a multi-modal alignment network based on a space-time attention mechanism, a scene semantic tree construction module, an instruction node mapping module, a confidence evaluation module and a decision module. According to the system, accurate alignment of voice and visual information is realized through a space-time attention mechanism, environment information is represented in combination with a scene semantic tree structure, and the instruction analysis accuracy is improved. And dynamically evaluating the confidence coefficient by adopting a fuzzy instruction backtracking algorithm, and if the confidence coefficient is lower than a threshold value, starting multi-round dialogue clarification to reduce misoperation. According to the method, multi-modal data are fused, the historical interaction learning ability is optimized, the understanding efficiency and interaction robustness of complex instructions are remarkably improved, the method is suitable for scenes such as family service and logistics storage, and the intelligent level of man-machine cooperation is enhanced.

The invention relates to the technical field of artificial intelligence and machine learning, in particular to a government affair industry intelligent information retrieval and pushing system and method based on a large model, and the system comprises an intelligent semantic understanding and query analysis module, a semantic-driven efficient retrieval module, a generation-enhanced intelligent content generation module, and a personalized pushing and feedback optimization module. The method has the beneficial effects that a natural language query request input by a user is received through the intelligent semantic understanding and query analysis module, semantic analysis and intention recognition are performed by utilizing a pre-trained large language model, key information is extracted, and query logic is optimized through a context sensing mechanism. Then, a semantic-driven efficient retrieval module quickly retrieves document fragments most relevant to user query from mass data of government affair cloud, precise matching is achieved through semantic vectorization and an efficient vector retrieval technology, and a retrieval strategy is dynamically optimized in combination with user feedback.

The invention discloses an interface parameter extraction method and device based on slot filling and a medium, and relates to large model application. The method comprises the steps that semantic analysis is conducted on natural language query through a pre-trained large language model, an intermediate parameter set containing intention types and slot filling parameters is generated, and accurate conversion from natural language parameters to structured interface specifications is achieved through multi-stage standardization processing; based on a dynamic matching mechanism of parameters and interface metadata, an interface calling chain including parallel calling, transaction compensation and data conversion nodes is constructed, and a multi-source aggregation data set is generated through cross-interface data verification and space-time alignment processing; and finally, in combination with dynamic template selection and semantic generation rules, outputting a multi-modal interaction report fusing the structured visual component and natural language interpretation.

The invention discloses an intelligent document analysis method and system, the method is executed by the intelligent document analysis system, and the method comprises the following steps: carrying out layout analysis on a PDF page by adopting a deep learning model; merging the block list from bottom to top by adopting a recursive algorithm; carrying out balance optimization on the binary tree structure; and outputting a result of the processed binary tree structure by adopting a preorder traversal mode. Layout analysis is carried out by adopting a deep learning model, various complex typesetting formats such as multi-column layout, image-text mixed typesetting, tables, lists and the like can be effectively identified and processed, and semantically continuous text blocks are ensured to keep continuity in a tree structure through a tree structure optimization module; a global semantic error correction module is added to carry out global document semantic representation learning and carry out adaptive adjustment and error correction on a preliminary structure, so that deep ambiguity is eliminated, logic errors are repaired, and the consistency of a final analysis result and human reading logic on the semantic level is maximized.

Provided in the embodiments of the present disclosure are a question-answering processing method, and a device, a product and a storage medium. In the question-answering processing method, after a query instruction is acquired, target knowledge information that matches the query instruction can be acquired from among a plurality of pieces of knowledge information in a knowledge base, and the query instruction and content-parsed text that corresponds to the target knowledge information are input into a large language model for question-answering processing, wherein the content-parsed text that corresponds to the target knowledge information is obtained by means of performing content parsing on a target document element that corresponds to the target knowledge information, and when the target document element comprises a document element of a non-text modality, content parsing is performed on the target document element before the target document element is input into the large language model, such that the document element of the non-text modality in the target document element can be understood by the large language model, so as to provide question-answering reference knowledge with a relatively high reliability for the large language model. Therefore, the accuracy of answering of the large language model for the query instruction can be improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, culture and art and the like, and discloses a feature fusion-based audio analysis method, which comprises the following steps of: obtaining a target audio in a target field and a target text associated with the target audio, extracting a music feature vector of the target audio, extracting a text semantic feature vector of the target text, fusing the music feature vector and the text semantic feature vector to generate fusion features, constructing a knowledge graph containing knowledge nodes of the target domain, inputting the fusion features into the knowledge graph for semantic analysis, and generating an analysis result. According to the method, deep semantic analysis is performed by fusing audio and text features and combining a knowledge graph, so that multi-modal understanding of audio data is realized, the accuracy and interpretability of an analysis result are improved, and the relevance between the analysis result and industry knowledge is enhanced; therefore, the applicability of the audio analysis technology in the fields of culture and art, medical health, finance and the like is improved.

The invention provides an intelligent marketing copywriting generation and effect evaluation method driven by a large language model, relates to the technical field of language models, and comprises the steps of constructing a hierarchical cross-modal knowledge graph and establishing a knowledge retrieval index. Semantic analysis is performed based on a bidirectional attention mechanism, related knowledge is retrieved by using query vectors, and an initial marketing copywriting is generated. Performing knowledge consistency verification to generate a knowledge-enhanced marketing copywriting, and generating a candidate copywriting set by adopting a diversity sampling strategy; and performing knowledge coverage, creativity and expected effect scoring on the candidate copywriting by using a multi-target evaluation network, screening an optimal copywriting through a Pareto optimization algorithm, and taking a generated path of the optimal copywriting as a positive sample to update a knowledge graph and decoder network parameters. Through knowledge graph enhancement and multi-target evaluation optimization, high-quality and high-matching-degree marketing copywriting can be generated, and the marketing effect is improved.

The invention relates to the field of standard comparison analysis, in particular to a method for automatically identifying differences of domestic and overseas standard files. A method for automatically identifying differences of standard files at home and abroad comprises the following four steps of S1, standard file analysis and information extraction, S2, semantic understanding and information integration, S3, standard difference comparison, S4, compliance analysis and suggestion, and in combination with a rule engine and a natural language generation technology, evaluating difference influences and outputting adjustment suggestions. According to the method, comprehensive analysis of the standard file is realized through multi-modal integration and structured processing, and a knowledge base with cross-language and format is constructed; clause association reasoning is realized through deep semantic analysis and knowledge graph construction; according to the method, accurate difference recognition is achieved through hybrid embedding and a dynamic threshold strategy, the accuracy and efficiency of standard comparison are remarkably improved, the manual intervention cost is reduced, and an efficient solution is provided for standard system analysis in international trade and technical cooperation.

The invention discloses a supply chain contract intelligent review system and method based on a large language model, and relates to the technical field of contract review. Aiming at the defect that the existing contract review generally depends on fixed template and keyword matching, the adopted scheme comprises the following steps: receiving a contract text through a text acquisition module; preprocessing the text through a text preprocessing module; the large language model analysis module adopts a pre-trained large language model to carry out deep semantic understanding on a text and extract key information; a supply chain management domain knowledge graph is constructed through a graph construction module, and compliance verification is assisted; the intelligent analysis module performs multi-dimensional risk identification and compliance evaluation on contract content in combination with a big language model analysis result and a knowledge graph; and the visualization module provides an interactive user interface for a user to upload a contract text, and displays an examination result, a risk prompt and a compliance suggestion of the contract text. According to the invention, the supply chain contract text can be automatically examined and risk early warning can be carried out.

The invention provides a Cypher-stack type alignment generation method based on a large language model, and belongs to the field of knowledge graph questions and answers. The method comprises the steps that user query and knowledge graph mode information are input into the large language model to generate an initial Cypher query statement; based on a thinking chain prompt technology, retrieving from the knowledge graph to obtain a retrieval result related to user query, and correcting the initial Cypher query statement by the large language model based on the retrieval result to obtain a corrected Cypher query statement; and executing the corrected Cypher query statement. The invention further provides a Cypher-stack type alignment generation device based on the large language model. Through the semantic analysis capability of a large language model and a thinking chain guiding technology, a Cypher query statement is automatically generated and corrected in two stages, and the problem that the query intention of a user and knowledge graph data are difficult to accurately align when the Cypher query is generated due to the diversity of query requirements of the user and a semantic gap between the knowledge graphs is solved.

The invention relates to the technical field of agricultural intellectualization, in particular to an artificial intelligence decision-making system for unmanned agricultural operation, which comprises an acquisition module, a construction module, a monitoring module, an AI decision-making module, a data fusion decision-making module and an execution control module which are in mutual signal connection, the semantic analysis module is used for collecting peasant household interview records and farm work log text data and carrying out semantic analysis on unstructured texts by adopting a natural language processing technology; the construction module is used for receiving the empirical feature vector and constructing a multi-dimensional associated knowledge graph by adopting a graph neural network; and the AI decision module is used for receiving the environment feature matrix, training by adopting a deep reinforcement learning model in combination with a general agricultural data set, and generating a decision scheme with a confidence score. According to the method, collaborative decision-making of regional implicit knowledge and multi-source environment data is realized by fusing a dynamic weighting mechanism of a peasant experience knowledge graph and deep reinforcement learning.

The invention discloses a law compliance auditing response method driven by an enterprise knowledge graph, and relates to the technical field of artificial intelligence, and the method comprises the following steps: carrying out semantic analysis on a law and regulation text, constructing a context hierarchical expression model, dividing each law and regulation into a title semantic block, a subject semantic block, a limit semantic block and a constraint semantic block, and generating a hierarchical semantic graph. According to the method, the structured analysis of the regulation provisions is realized by constructing the hierarchical semantic graph, and the regulation provisions are accurately aligned with the business entities in the enterprise knowledge graph, so that the reasonable regulation-enterprise semantic mapping relationship is constructed. And boundary reasoning, confidence scoring, context backtracking and semantic correction are combined to realize intelligent discrimination and correction of the applicability of the regulatory obligations. Models and rules are continuously updated through a feedback optimization learning mechanism, the dynamic adaptive capacity of the system and the accuracy of compliance suggestions are improved, the misjudgment risk is remarkably reduced, and the intelligence and the practical application value of the system are enhanced.

The invention belongs to the field of multi-mode sensing fusion, and relates to an intelligent multi-mode patrol sensing method, system and equipment and a storage medium, which are used for carrying out automatic patrol, real-time sensing and abnormity warning in a complex environment. Comprising the steps of natural language instruction analysis, task decomposition and distribution, intelligent agent scheduling and execution, multi-modal information collection and fusion, anomaly recognition and event response, result feedback and task closed loop, and through the integration of an OWL framework and a DeepSeek-R1 large model, a multi-modal data fusion technology and an NL-SLAM autonomous navigation algorithm, the multi-modal data fusion algorithm and the multi-modal data fusion technology are integrated. The problems that in the prior art, dynamic task understanding based on natural languages cannot be achieved, a sensing system lacks efficient multi-modal data fusion, so that the anomaly recognition precision is low, and complex tasks or emergencies are difficult to deal with are solved, various abnormal conditions in a complex environment are effectively dealt with, and the method is suitable for popularization and application. The stability and expansibility of task execution are improved, and the navigation success rate and the task completion rate of the system in an unstructured environment are enhanced.

The invention discloses a multi-agent cooperation molecular optimization method based on large language model driving. The method comprises the following steps: S1, task initialization and input analysis; s2, constructing an intelligent agent cluster; s3, task decomposition and scheduling execution; s4, performing expert optimization and tool calling; s5, performing evaluation and feedback screening; and S6, multi-round optimization and result output: the scheduling agent adjusts an optimization strategy and redistributes tasks according to a feedback result of the step S5, drives the expert agent to execute a next round of optimization operation, circulates the steps until the evaluation agent judges that an optimization target is met, and outputs a final optimization molecule and related attribute information thereof. And outputting an optimized path and an intermediate result for tracing analysis. According to the method, multi-round optimization and evaluation feedback iteration of a molecular structure are realized by fusing the knowledge reasoning ability of a large language model and an efficient interaction mechanism between intelligent agents.

The invention relates to the technical field of multi-modal data processing and semantic retrieval, in particular to a multi-modal heterogeneous knowledge fusion construction and semantic enhancement retrieval system based on a large model, which comprises a data acquisition module, a semantic analysis module, a knowledge fusion module and a retrieval optimization module. Multi-modal data such as texts, images and audios are uniformly expressed and deeply analyzed by introducing a large model technology, a knowledge graph is dynamically constructed, a structure is optimized in combination with a user query intention, and meanwhile accurate sorting and screening are achieved through a semantic enhancement algorithm. According to the method, the semantic comprehension capability and the intelligent level of the system can be improved, the real-time and diversified scene requirements are met, and the accuracy and the adaptability of a retrieval result are remarkably enhanced.

The invention discloses an unmanned aerial vehicle highway intelligent patrol method and system based on patrol demands, and belongs to the field of unmanned aerial vehicle technologies, intelligent traffic systems and artificial intelligence, and the method comprises the steps: obtaining a natural language instruction, and carrying out the semantic analysis to generate a structured task vector; constructing and generating a self-adaptive inspection strategy based on the task vector and fusing real-time data; distributing the strategy to the unmanned aerial vehicle for autonomous inspection, and identifying and generating a classification event alarm; and finally responding to an alarm and triggering a processing mode to generate a closed-loop inspection task report. According to the method, a technical path of combining multi-level reasoning based on a large language model and dynamic behavior strategy synthesis is adopted, and a complete cognition and action framework from semantic intention understanding, behavior decision making to closed-loop response is constructed, so that autonomous generation and intelligent execution of a complex and fuzzy inspection task can be realized; and the automation level, the response speed and the decision-making intelligence of highway inspection are obviously improved.

The invention relates to the technical field of metadata generation, and discloses a data platform metadata automatic generation method based on a large model, which comprises the following steps of: firstly, performing unified standardization and preliminary grammatical analysis on an original SQL (Structured Query Language) code to obtain a structured intermediate representation; on the basis, preliminary blood relationship analysis based on rules is carried out, and simple column references are quickly identified and processed. For complex expressions which are difficult to accurately analyze by a traditional method, code snippets and context information of the complex expressions are accurately extracted and submitted to a large language model for deep semantic understanding and complex blood relationship analysis. And finally, integrating the complex consanguinity analyzed by the large model with the initial consanguinity list to form a comprehensive and accurate field-level consanguinity, and further generating complete data platform metadata. In this way, the defect that a traditional analysis tool understands complex semantics is effectively overcome, and the accuracy and integrity of metadata generation are remarkably improved.