1343 results about "Annotation" patented technology

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an enhanced decision method, device, equipment and medium based on thinking chain annotation, which comprises the following steps: extracting core information features to generate a structured data set, loading a basic language model and executing supervision fine tuning to generate a fine-tuned model, fusing multi-modal input to generate fusion features, constructing a state observation space to receive the fusion features as input, generating reward signals based on a double reward mechanism and optimizing model parameters to generate an optimized model, deploying a monitoring module to dynamically adjust parameter configuration to generate an adaptive decision model, and outputting a decision response result. According to the method, multi-source data extraction, structured expression, multi-modal fusion, reinforcement learning optimization and dynamic adaptive mechanism fusion are carried out, so that the understanding ability of the model to complex data, reasoning transparency and the adaptive ability of the model to coping with environmental changes are remarkably improved.

The invention discloses a CAD drawing engineering quantity automatic identification and calculation method based on a large language model and image segmentation, and the method comprises the steps: extracting project annotation information in a CAD drawing, understanding and reasoning a project introduction through a large language model, and carrying out the structural expression of the project annotation information; the method comprises the following steps: preprocessing a graph in a CAD drawing by utilizing computer vision, analyzing a pipeline drawing of the CAD drawing by utilizing machine learning, identifying the type and position of a component in the pipeline drawing, and realizing semantic segmentation and instance segmentation; and fusing the structured project annotation information, the semantic segmentation result and the instance segmentation result, counting and calculating the project amount of each pipeline component in the CAD drawing image, and outputting a report according to classification. According to the method, OCR recognition, natural language processing, graph semantic recognition, engineering logic calculation and other technologies are integrated, the CAD drawing processing efficiency and the engineering quantity calculation accuracy are greatly improved, and intelligent support is provided for design, construction, drawing examination, budget and other links.

The invention discloses a defect diagnosis method and system based on multi-modal data cooperative training, and belongs to the technical field of defect diagnosis, and the method specifically comprises the steps: constructing a multi-modal cooperative diagnosis model comprising a feature extraction sub-network and a cross-modal attention module; after multi-modal data is collected and preprocessed, initial features are obtained through the feature extraction sub-network, attention weights are generated through the cross-modal attention module, weighted multi-modal features are obtained, multi-scale fusion features are obtained through the multi-scale feature extraction sub-network, and a preliminary diagnosis result is given through cascade processing. Meanwhile, the data integrity is detected, and a modal missing scene is coped with through cascade collaborative diagnosis; and finally, comparing the two types of diagnosis results with a defect labeling sample to obtain a multi-modal collaborative diagnosis model after training optimization, inputting to-be-diagnosed sample data, outputting a final diagnosis result and updating the defect labeling sample, and realizing efficient and accurate defect diagnosis.

The invention discloses a multi-modal harmful model factor detection method based on a thinking chain, and belongs to the field of harmful model factor detection, and the method comprises the steps: firstly obtaining an image-text model factor, and automatically generating the thinking chain containing a problem summary, an image subtitle, and harmful reasoning and conclusion; constructing an annotation data set according to the annotation data set; then, the multi-modal model is finely adjusted in two stages, in the first stage, the visual encoder and the language model are fully finely adjusted to improve image-text understanding, and in the second stage, the visual encoder is frozen, and the language model is finely adjusted through LoRA to strengthen reasoning; introducing reinforcement learning rewards to optimize implicit harmful discrimination; then extracting an entity relationship to construct a knowledge graph and a causal graph, and embedding into the model; and finally, dynamically updating the atlas through an RAG mechanism, so that a detection result adapts to an emerging model cause in real time.

The invention relates to document verification, in particular to a heterogeneous document set-oriented cross-modal semantic alignment and logic consistency verification system, which is used for heterogeneous document input, supports multi-format document input and comprises multi-modal elements including texts, pictures, tables and charts. The document analysis module is used for carrying out structured extraction on document contents; extracting multi-modal elements, identifying and classifying various elements in the document, and establishing position and type labels of a foundation; the knowledge graph construction module is used for uniformly modeling heterogeneous elements into a multi-modal knowledge graph; the graph neural network semantic alignment module is used for realizing accurate cross-modal semantic alignment by using a specially designed graph neural network based on the multi-modal knowledge graph; the hybrid consistency verification engine is used for performing logic consistency verification in combination with a symbol logic verification mechanism and a semantic consistency verification mechanism; according to the method, the defect that accurate cross-modal semantic alignment and logic consistency verification are difficult to carry out on professional documents with multi-modal elements can be effectively overcome.

The invention discloses an unsupervised semi-pairing cross-modal retrieval method and system based on deep learning, relates to the field of artificial intelligence, and is used for solving the problems of annotation data dependence, asymmetric semantic association and high-dimensional storage efficiency. According to the method, a double-branch visual encoder and a dynamic prompt text encoder are combined, dynamic weighting of visual-text features is achieved through gating cross attention, and modal redundancy interference is restrained. An enhancement strategy is generated through low-frequency semantic guidance, and the long-tail word coverage rate is increased; a dual-stage quantitative hierarchical index is constructed, coarse-grained clustering and fine-grained product quantitative compression feature storage is adopted, and million-level data real-time retrieval is supported. A degradation aware increment maintenance mechanism monitors data distribution offset through a KL divergence threshold, and triggers index reconstruction to maintain long-term update precision. According to the method, limitation of a traditional strong pairing model is broken through, cross-modal sensitive content second-level positioning is achieved, asymmetric semantic alignment is effectively solved, and retrieval efficiency is improved.

The invention provides a knowledge question and answer library agent construction method and system. Efficient knowledge management and question and answer are achieved through cooperation of multiple agents. According to the system, firstly, a multi-modal information extraction agent is constructed, and heterogeneous data such as texts, images and tables are converted into structured vectors and stored; meanwhile, the knowledge graph is dynamically constructed and continuously optimized by the self-adaptive knowledge graph construction agent, and a new relationship is derived through combination of symbolic logic and a graph neural network, so that an evolvable knowledge network is formed. In the question and answer stage, a query analysis agent deeply analyzes the intention of a user and generates sub-queries; retrieving the vector library and the knowledge graph in parallel by the retrieval enhancement generation agent; and the reasoning and synthesizing agent integrates multi-source information and generates an accurate answer with a complete source label through a large language model. Dynamic knowledge management, precise semantic analysis and system self-evolution are achieved, and the method is particularly suitable for professional field scenes needing high-reliability questions and answers.

The invention discloses a method based on a large language model and ATTamp; the invention discloses a CK framework penetration test automation method and device, and the method comprises the steps: firstly carrying out the structural analysis of multi-source input information and tool output, and guaranteeing that key fields are not discarded; then combining a retrieval enhancement generation technology and a network security knowledge base to provide domain knowledge support for the large language model, so as to generate a model with ATTamp; a penetration test task tree marked by CK tactics, technologies and sub-technologies; on the basis, an optimal tool is automatically selected through a tool resource library and a multi-dimensional screening mechanism, an execution instruction is generated, and finally an execution result is returned to the input analysis module to form a self-adaptive optimization test closed loop. According to the method, semantic fidelity compression and standardization processing of long information can be realized aiming at the problems of large output format difference, more information redundancy and the like of different penetration testing tools, and efficient, explainable and auditory technical support can be provided for automatic penetration testing in a complex network environment.

The invention discloses an AI fault prediction and diagnosis system and method based on a numerical control machine tool, and belongs to the technical field of fault diagnosis. The technical problem that efficient fault early warning and health management cannot be implemented in the full life cycle of a numerical control machine tool in an existing scheme is solved. Monitoring data covering the full life cycle and multiple working conditions of the numerical control machine tool, and providing high-quality annotation data for subsequent model training through association of a high-dimensional feature matrix and a fault tag; an improved variational mode decomposition algorithm is utilized, fault information of each mode is quantified in combination with wavelet packet energy entropy, time migration of multi-sensor data is eliminated through space-time alignment, weights of different features are adaptively distributed by utilizing an attention mechanism, and discrimination of fusion feature vectors is effectively enhanced; a long-term dependency relationship of a feature sequence is captured based on a bidirectional gating circulation unit, a convolutional neural network is improved to reinforce local detail features, and the fitting capability of a model to a complex fault mode can be effectively improved through cooperation of the two.

The invention discloses a real model consistency review system and method based on a large language model, according to the scheme, through integration of multi-source heterogeneous data, standardized preprocessing of real scene cloud or images is completed, and through combination of deep learning recognition and multi-scale feature analysis, accurate correspondence and intelligent comparison of a real model and a BIM component are achieved; performing semantic reasoning and logic verification by using a large language model, automatically detecting differences such as dimensional deviation, positioning deviation, missing and redundant components and the like, and generating structured difference data; furthermore, difference information is visually presented through graphical annotation and natural language description, and batch screening and multi-scale interaction are supported. Based on a difference analysis result, a standardized review report is automatically compiled, and statistical analysis, change trend tracking and multi-format export functions are provided. According to the scheme, the consistency review efficiency and reliability in a large-scale data environment can be guaranteed, and the quality management level of building construction and operation and maintenance stages can be effectively improved.

There are provided systems and methods for a fine-tuning system for large language models trained for open-ended domain-specific tasks. An online transaction processor or other service provider may provide computing services and platforms to entities, which may include chatbots, information retrieval systems, question-and-answer systems, and the like. To provide better LLM training and fine-tuning, which may improve LLM performance in answering users' questions in an automated manner, the service provider may implement a fine-tuning system that may utilize automated annotations of training data, such as query and response pairs. An LLM may be prompted to determine an annotation to such pairs, and the annotations may be used to label the training data. A fine-tuning system and operations may then be implemented to fine-tune the LLMs using different processes including question-answering, retrieval augmented generation, or a continuous fine-tuning based on a size of the training data.

The invention discloses a generation method of a generative adversarial network for planning a resource design stylized layout, and belongs to the technical field of artificial intelligence and machine learning, and the method comprises the following steps: S1, multi-source data collection: widely collecting a large number of existing planning resource samples; s2, data classification labeling; s3, constructing a generative adversarial network model; s4, performing model training; s5, style migration training; s6, optimizing the model; s7, generating a planning resource scheme; s8, optimizing the scheme; and S9, performing feedback iteration. According to the method, through integrating a technical path of multi-source design data acquisition, deep style feature extraction and generative adversarial network collaborative optimization, full-process automation from user demand input to high-quality planning resource layout generation is realized, and user personalized demands are met through a multi-scheme candidate and interactive adjustment function; and finally, constructing an incremental learning closed loop based on user feedback data, and continuously updating model parameters to adapt to an emerging design trend and an industry specification.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-modal mixed interview question generation system and method based on artificial intelligence. According to the method, customized interview questions are provided for candidates with different backgrounds and different skill levels by dynamically selecting questions based on candidate resumes and knowledge maps, the question difficulty and knowledge emphasis are automatically adjusted through multiple rounds of answering effects and interviewer labeling data, the effectiveness and flexibility of interview evaluation are improved, and the interview evaluation efficiency is improved. Answering results and behavior anomaly detection are fused, knowledge mastering is evaluated, potential non-behavior factors can be found, manual question screening time is shortened through an automatic process, consistency of interview experiences of different candidates is guaranteed, subjective prejudice is reduced, new knowledge documents and post description can be accessed at any time, a multi-modal graph is continuously iterated, and the method is high in practicability and high in practicability. And changes of new technical fields and new post requirements are adapted.

The invention provides a code reuse method and device based on AI drive, equipment and a medium. When a code submission event is detected, a code scanning process is triggered, a code analysis module driven by AI carries out multi-dimensional analysis on submitted codes, quantitative evaluation is carried out on the basis of a preset reusability evaluation model, and code snippets meeting a reuse standard are stored in a code knowledge base with a semantic index structure; analyzing annotation semantics based on a natural language processing technology through an AI retrieval engine, calculating semantic similarity between demand description and code snippets in a knowledge base in combination with a deep learning model, and positioning the code snippets which are optimally matched; after code multiplexing execution, performing incremental modification on the multiplexing code snippets and automatically generating change records; and carrying out value layering on the code snippets based on the Pareto analysis principle by using frequency trend prediction. Through combination of the AI technology and code reuse, the development efficiency can be remarkably improved.

The invention particularly relates to a method and a system for automatically generating legal instruments through a large model. The method comprises the following steps: preparing and marking data; training a large model; information processing; generating a document; and outputting and feeding back. According to the method, large model training and a multi-dimensional data processing mode are combined, so that automatic generation of the legal document is realized; in the data preparation stage, comprehensiveness and accuracy of legal knowledge are ensured through hierarchical labeling and knowledge base construction; a plurality of evaluation indexes, optimization algorithms and regularization means are applied in large model training, so that the model can accurately learn a legal language mode and a logic relationship; in the information processing link, the case information input by the user can be deeply understood by means of the natural language processing technology and knowledge graph construction, the legal document writing time is greatly shortened through the series of operations, errors and omissions caused by manual writing are reduced, and the legal document generation efficiency and quality are remarkably improved.

The invention relates to a digital archive intelligent processing method, a storage medium and a system, which are suitable for multi-source heterogeneous archive management scenes such as colleges and universities. The method comprises the following steps of: classifying structured and unstructured data such as paper archive scanning pieces and database views, and extracting metadata and entity information by adopting a scanning and OCR (Optical Character Recognition) technology; a complex table and document content are analyzed through a model, semantic analysis (entity recognition, relation extraction and event abstract) is achieved in combination with a language model of a Transform architecture, and a structured report containing a data abstract, an entity relation graph and abnormal annotations is generated. The system is internally provided with a parameter template automatic generation module, supports cross-page content continuous restoration and sensitive data encryption desensitization, and realizes safe sharing through an API interface. The method solves the problems of low efficiency, difficulty in multi-source data fusion and the like of traditional archive processing, improves the automation level and data value mining capability of archive management, and is suitable for intelligent upgrading of complex archive scenes.

The invention discloses an electric power system equipment image anomaly detection and quality diagnosis method based on a multi-modal visual language model. The method comprises the following steps: constructing a large-scale multi-modal data set comprising an electrical equipment image, an object detection annotation, a pairing question and answer knowledge base and an official supervision document, constructing a basic diagnosis model based on a visual language model, and carrying out instruction tuning; carrying out post-training on the model by adopting group relative strategy optimized reinforcement learning, and generating an interpretable step-by-step diagnostic reasoning chain; in the reasoning process, related knowledge is dynamically retrieved based on a retrieval enhancement generation technology of a graph structure, and the accuracy and compliance of a diagnosis decision are enhanced; and finally, generating a diagnosis report containing the exception type, the root cause and the decision suggestion. Compared with a traditional method, the method solves the three problems of data scarcity, opaque reasoning and knowledge isolation in the field of electric power detection, and has the remarkable advantages that the diagnosis process can be explained, complex multi-step reasoning is supported, and domain knowledge can be dynamically integrated.

The invention provides a cross-domain equipment fault diagnosis method and system based on large and small model cooperation, and relates to the technical field of equipment fault diagnosis. According to the method, the causal field generalization structure is introduced into the small model, explicit decomposition is carried out on the stable causal law and the field specific difference, and meanwhile, the causal field generalization structure is corrected by using the large model, so that the small model can automatically identify and retain the causal relationship which is universally applicable to each device and each field; therefore, the influence of inter-domain distribution difference is effectively eliminated. Theoretical analysis shows that the generalization error of the model mainly depends on the accuracy of the stable causal item, and the structure can minimize error drift caused by distribution drift. Therefore, the robustness of health state evaluation and fault prediction can be remarkably improved in a cross-domain scene, and the fault diagnosis model can still keep the prediction capability close to the training domain level under the condition of no target domain annotation data.

The invention discloses a lightweight three-dimensional data format generation method suitable for ship model labeling, which comprises the following steps of: exporting a three-dimensional model file from ship design software, extracting a model attribute information file of the three-dimensional model file, analyzing the three-dimensional model file and the model attribute information file, and calculating to obtain attribute value information of a geometric body model in a model; the attribute value information of the geometry model is integrated and then converted into a lightweight data exchange format, a lightweight data exchange format file is converted into a binary format, and a lightweight three-dimensional data format file is obtained through transcoding and compression; and the lightweight three-dimensional data format file is stored in a database after being subjected to name matching based on the model attribute information. The volume of the lightweight three-dimensional data format file is small, only key data of the reduced model are reserved according to the characteristics of the ship model, and binary coding conversion and compression are performed on the file, so that the volume of the lightweight three-dimensional data format file is much smaller than that of a model file in a general format, and storage and network transmission are greatly facilitated.

The embodiment of the invention discloses a code conversion method and device based on a domain-specific language and a medium, and relates to the technical field of code conversion, the method comprises the steps that a DSL file and a target conversion language input by a user are received, the DSL file comprises at least one class definition, and the class definition comprises a class name, a field declaration and a method signature declaration; the DSL file is analyzed to extract a plurality of class annotations marked in the class definition, and the class annotations comprise target language identification information, unique class identification information, construction mode identification information and array access mode identification information; and according to the target language identification information in the target conversion language and the class annotations, a matched specified class annotation set is extracted from the class annotations, a code file conforming to the grammar of the target conversion language is generated based on the specified class annotation set, and the specified class annotation set comprises the class annotations, field annotations and method annotations.

The invention provides an extensible remote sensing deep learning sample library construction method based on target region planning, which is applied to the technical field of remote sensing image processing, and comprises the following steps: determining target region ranges of a plurality of target regions based on image resolution and a plurality of target region center points; performing cutting and coding naming on the target region remote sensing image based on the vector boundary of the target region range and a preset coding rule to obtain coarse samples corresponding to the plurality of target regions respectively; inputting the coarse sample subjected to feature enhancement processing into a large language model to obtain corpus data of the coarse sample output by the large language model; performing feature alignment with the coarse sample based on the semantic relationship to obtain a feature semantic mapping result of the coarse sample; constructing a coarse sample knowledge graph based on the semantic relationship information and the feature semantic mapping result; and inputting the coarse sample knowledge graph into the open vocabulary target detection model to obtain potential samples output by the open vocabulary target detection model. According to the invention, dynamic extensible labeling of large-scale remote sensing samples can be realized.

The invention discloses a key marker gene-based intelligent cell type annotation method, which comprises the following steps of: constructing a static knowledge base by using known marker genes in a reference database, and endowing the marker genes with cell specific weights by using a TF-IDF method, so that the annotation accuracy and interpretability are improved. Meanwhile, under the condition that static matching is insufficient, the literature is understood through a large language model, mark information is extracted, dynamic completion of the knowledge base is achieved, the defect that updating of a traditional knowledge base is lagged is overcome, and good adaptability and expansibility are achieved. Besides, static and dynamic matching scores are fused in the annotation process, so that more robust cell type identification is realized, annotation requirements of multi-tissue, multi-species and novel cell states are adapted, high-precision and extensible cell type annotation can be realized in a scene with insufficient reference knowledge or a fuzzy sample, and the annotation efficiency is improved. And the method has good universality and practicability.

The embodiment of the invention provides an interface verification method and device based on a dynamic rule, a medium and a program product, and relates to the technical field of parameter verification. The method comprises the following steps: before calling interface processing, acquiring a user-defined annotation associated with an interface; determining a to-be-verified field and corresponding rule index information based on the user-defined annotation; acquiring a corresponding target verification rule from a rule cache list based on the rule index information; wherein the rule cache list is loaded and cached on the basis of a dynamic rule base under the condition of compiling initialization or rule change; and verifying the corresponding field to be verified based on the target verification rule. According to the embodiment of the invention, a cache rule-oriented verification mode is adopted to dynamically adapt to the business change of the rule base, so that the interface verification efficiency in a dynamic business scene can be effectively improved, and the maintenance cost can be reduced.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating modifications to an LLM based artificial intelligence assistant based on classifying the severity of errors and focusing the modifications on resolving high-severity errors. In particular, the disclosed systems receive prompts via an artificial intelligence assistant graphical user interface and generate responses to the prompts using the LLM based artificial intelligence assistant. Further, the disclosed systems determine errors in the responses using an annotation tool to generate annotated errors and an error analysis mechanism to generate indications of the errors based on the annotated errors. Additionally, the disclosed systems classify the errors as one of high-severity, mid-severity, or low-severity. Moreover, the disclosed systems generate modifications to components of the LLM based artificial intelligence assistant based on the high-severity errors.

The invention discloses an intelligent identification and management system and method for key commodities of a vegetable basket, and relates to the technical field of computer processing, and the system comprises a multi-modal data collection and preprocessing module which is used for collecting commodity images, temperature and humidity curves, vibration frequencies and electronic quality inspection reports through Internet of Things equipment, the block chain technology is adopted to encrypt and store production batches and detection values; and the intelligent labeling and label generation module is used for performing instance segmentation on the commodity image, analyzing semantic information of the quality inspection report and generating a multi-level quality label. According to the invention, through a multi-modal data fusion and dynamic association analysis technology, image recognition, sensor data and text analysis are integrated, an intelligent supervision system covering a production-retail full link is constructed, and on the basis of an adversarial training mechanism and an edge cloud collaborative architecture, the production-retail full link is completely supervised. The real-time identification capability of complicated illegal behaviors such as water-injected meat, pesticide residue exceeding and label tampering is obviously improved, and the whole-process safety guarantee of livelihood commodities is enhanced.

The invention provides a software monitoring method, system and device based on byte code replacement and risk awareness and a medium, and belongs to the technical field of software security. The method comprises the steps that a special annotation mark in a source code is scanned, a test code entry point is recognized, and metadata information is extracted; performing encryption processing on the metadata and generating a registry file with a digital signature; environment safety data are collected through four monitoring dimensions, and a comprehensive risk score is calculated; generating a grading fusing instruction according to the score; and finally executing the fusing operation of byte code replacement or thread sandbox isolation. According to the invention, accurate identification and real-time protection of the leakage risk of the test code are realized; through a multi-dimensional monitoring and hierarchical fusing mechanism, the system security is ensured, and the influence on the service performance is minimized; and byte code replacement and thread isolation technologies are adopted, so that security protection can be realized without restarting the application, and the availability and the operation and maintenance efficiency of the system are improved.

The invention discloses a power equipment image-text fusion labeling method and system based on a single-double mixing tower, and the method comprises the steps: carrying out the visual feature extraction to obtain image features, carrying out the text feature extraction to obtain text features, mining the deep features of an image and a text, maintaining the modal specificity, and carrying out the recognition of the image and the text. Processing the acquired image features and text features by adopting a cross attention mechanism to generate a bidirectional attention matrix, calculating a dynamic weight value based on the acquired bidirectional attention matrix, and generating weighted image features and weighted text features based on the dynamic weight value; and performing dynamic feature fusion on the weighted image feature and the weighted text feature to obtain a fusion result feature, and performing end-to-end multi-modal labeling based on the fusion result feature, so that the image and the text can be accurately associated, the labeling efficiency and accuracy are improved, adaptive feature fusion can be realized, the real-time problem of heterogeneous feature fusion is solved, and the real-time performance of the heterogeneous feature fusion is improved. The time consumed by multi-modal alignment is reduced from the minute level of manual intervention to the millisecond level.

The invention relates to the technical field of artificial intelligence and legal information processing, in particular to a security legal system intelligent question-answering system based on an RAG architecture, and is used for solving the problems of insufficient real-time performance, poor speciality of generated content, computing power dependence, data quality deviation, weak knowledge association and the like of a general large model in a legal scene in traditional legal consultation. According to the technical scheme, the method comprises the following steps: dynamically partitioning a law knowledge base, and constructing a high-dimensional vector index based on a BERT model to support semantic retrieval; calculating by fusing TF-IDF content similarity and cosine semantic similarity, and optimizing a retrieval result in combination with a sorting learning model; a rigorous Prompt template constraint generation logic is built in, legal provisions are cited forcibly, an efficacy level is marked, and an optimal result is screened through a multi-candidate answer evaluation mechanism. The system realizes efficient matching and specialized output of legal clauses, judicial cases and law knowledge, provides intelligent and highly credible legal consultation services for national security, personal data protection and common law scenes, and promotes deep fusion of artificial intelligence and legal information processing technologies.

The invention relates to the technical field of text data processing, and discloses a text data extraction method, system, equipment and medium based on multi-modal fusion and self-evolution learning, which comprises the following steps of: performing feature extraction and spatial alignment on a printed text, a handwritten annotation and a dynamic table of a mixed format document to obtain a semantic feature of an image-text table, and inputting the semantic feature into a dynamic analysis layer; analyzing metaphor expressions and synonymous heterogeneous fields through field extraction and a context semantic reasoning mechanism, and outputting structured data; performing grammar compliance verification by adopting a regularization engine, and performing comparison verification through a federal learning mechanism; and inputting the verified data into the reinforcement learning model, updating the analysis rule and the model parameters through strategy iteration, and feeding back the updated analysis rule and model parameters to the dynamic analysis layer to complete closed-loop optimization. According to the method, the processing precision and efficiency of the complex document are greatly improved, the manual intervention requirement is remarkably reduced, and meanwhile, the privacy protection and compliance requirements are met.