908 results about "Semantic consistency" patented technology

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 SysML model automatic generation system based on mixed AI and domain knowledge, and the system comprises a preprocessing module which is used for carrying out the text preprocessing and structural enhancement of an engineering document of a PDF or Word version; the NLP extraction module is used for identifying six types of core entities by adopting aviation corpus fine tuning BERT, constructing a document-level relational graph by utilizing GNN, modeling a cross-paragraph dependency relationship, calling LLM for semantic fuzzy sentences to generate a thinking chain, extracting a reasoning path and solving ambiguity; the rule conversion engine module is used for mapping the entity relation graph into a SysML memory object tree; and the controllable generation module is used for carrying out limited decoding on the LLM by utilizing a Guidance framework. The invention further discloses an automatic SysML model generation method based on the mixed AI and domain knowledge. According to the method, the problems of low manual modeling efficiency and poor semantic consistency in traditional MBSE implementation are solved.

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

The invention discloses a cross-modal interactive image restoration method fusing text semantic guidance and visual structure priori, which comprises the following steps of: firstly, acquiring natural language description input by a user and an image to be restored, and generating a semantic segmentation map of the image through a semantic segmentation model; encoding the text and image semantics by using a pre-trained cross-modal encoding model to obtain text and semantic features; guiding a semantic alignment attention module through Prompt to realize deep fusion of multi-modal semantic features and image space features; structural enhancement and regulation of image features are realized by constructing a text guide weight graph, performing element-level modulation on the text guide weight graph and the optimized semantic segmentation graph, constructing a cross-modal structure semantic feature graph and generating a structural modulation factor; a four-stage image restoration network is adopted, and a high-quality restoration image conforming to semantic guidance and structure prior is generated step by step. According to the method, the semantic consistency, the structural integrity and the visual reality sense of an image restoration result are improved.

A system and method for persistent cognitive computation with temporally synchronized multimodal processing implements a geometric approach to artificial intelligence through typed latent entities within a dynamic manifold substrate. The system maintains a latent manifold incorporating heterogeneous data modalities where local curvature reflects semantic density and typed entities are stratified according to structural properties. Temporal synchronization coordinates asynchronous multimodal data streams through generation of temporal alignment fields within the manifold that preserve semantic coherence across modal boundaries. Type-aware geometric operations enforce operation legality based on entity type and local manifold geometry, enabling structured recombination, compression, and traversal while preventing semantic distortion. The system executes synchronized manifold reorganization during idle periods through coordinated optimization operations including perturbation analysis and topological surgery. This architecture enables persistent memory through geometric encoding where frequently accessed concepts develop high-curvature regions and cognitive patterns emerge from usage-based manifold evolution.

The invention relates to the technical field of computer aided design, in particular to a text-driven CAD modeling method and system based on a diffusion and visual language model.The method comprises the steps that natural language text description is obtained, and CAD semantic features of the natural language text description are extracted; carrying out geometric standardization on the CAD semantic features by adopting a fine-tuning diffusion model, and generating a CAD view image conforming to engineering specifications; carrying out fusion by adopting a fine-tuned visual language model to generate a parameterized CAD construction sequence; a three-mode alignment mechanism is adopted, and the semantic consistency of the CAD semantic features, the CAD view images and the CAD construction sequences is checked; performing verification and post-processing on the CAD construction sequence, and outputting an executable Python code or STEP file; the CAD modeling method disclosed by the invention performs explicit modeling based on flexible modal description, and has the characteristics of high geometric constraint and high usability.

The invention discloses a natural language-to-SQL (Structured Query Language) method and system based on RAG (Random Access Gateway) enhancement, and the method comprises the steps: constructing a query intention graph through dependency syntax analysis, recognizing and complementing semantic missing components, and forming complete semantic representation; vector representation is carried out on the business term segments by adopting vectorization coding, accurate definitions of business terms are obtained from a factory structured knowledge base, and an enhanced context set is formed through expansion retrieval in a low-confidence region; an SQL template mapping network is established based on historical query records, a mapping relation matrix is generated through field candidate matching, mapping conflict positions are identified, and multiple SQL candidate sequences are generated; grammar verification is carried out on the candidate sequence to identify grammar errors, semantic consistency verification is carried out to calculate the intention alignment degree, and an optimal SQL statement is selected through a deviation correction factor; and performing formatting processing and statistical abstract on an execution result, and providing data query capability for scenes such as factory quality management and equipment maintenance.

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 electronic medical record LLM generation method based on animal injury, which realizes dialogue structuring and timestamp synchronization through multistage speech recognition and role affiliation. Using standardized medical term mapping and coding to align the free text to a standardized medical entity, and constructing a high-confidence medical entity network based on a semantic anchor point pool; according to the method, context-sensitive entity relationship extraction is realized by combining a large language model and a semantic enhancement template, a high-accuracy structured relationship chain is generated through clinical logic rule set verification, and finally, an electronic medical record template under diagnosis and treatment specifications is automatically filled and privacy desensitization processing is completed. The semantic consistency, the structural accuracy and the data security of automatic generation of the electronic medical record are improved, and standardization and intelligent circulation of medical information are effectively promoted.

The invention discloses a large-model-driven automatic knowledge graph construction method based on a confidence feedback mechanism, and aims to improve the structural accuracy and semantic consistency in a structured triple generation process, and perform structural constraint guidance by using a few-sample prompt mechanism and a cross validation mechanism of a heterogeneous large model. And the control capability of the large language model on the triple format is enhanced, so that format offset and semantic redundancy in the generation process are reduced. And meanwhile, a multi-dimensional confidence evaluation system is constructed, model consensus judgment, semantic rationality analysis and knowledge consistency verification are fused, and refined quantification and screening of triple quality are realized. According to the method, a confidence backtracking feedback strategy is introduced, a generation-verification-optimization closed-loop process is constructed, the expression and correction capability of the system on a complex knowledge structure is enhanced, the dependence on an external API is effectively reduced, the consumption of computing resources is reduced, and the operation efficiency of the system and the feasibility of engineering deployment are remarkably improved.

The invention discloses an archive data management labeling method and device based on computer application, and relates to the technical field of archive information management and intelligent labeling, and the device comprises a semantic analysis module, an allogenic character normalization module, a context modeling module, a historical semantic alignment module, a context feedback module, a processing module and a model optimization module. The device can automatically monitor file input, clean and segment texts, quickly and normatively replace foreign characters and intelligently convert simple and traditional Chinese characters; a semantic vector is generated based on a Transform model, semantic consistency is detected, and if semantic drift is found, rollback adjustment is carried out; the processing module is combined with a rule engine and a depth model to complete entity labeling and multi-label reasoning; and the optimization module dynamically records and feeds back annotation data, and supports self-learning and continuous improvement of the model. By continuously optimizing the judgment logic, the system has self-learning and evolutionary capabilities, so that the archive labeling process is more and more intelligent, and the labeling result is more and more accurate.

The invention discloses a live-action three-dimensional dynamic change modeling method based on digital twinning. The method comprises the following steps: collecting multi-source data; carrying out space registration and time synchronization processing, and outputting a fusion data set; carrying out three-dimensional modeling, optimizing model parameters, and generating a live-action three-dimensional model; virtual-real mapping is carried out, and generated virtual space data and state parameters thereof are stored; performing differential analysis to generate a change detection result data set; performing dynamic change modeling to generate an updated virtual model; executing geometric accuracy optimization and semantic consistency check, and outputting an optimized virtual model; bidirectional data flow association and dynamic updating are executed, and the live-action three-dimensional dynamic change modeling process is completed. The real scene dynamic updating is realized by adopting the improved Gaussian sputtering modeling, and the method has the advantages of high precision, strong real-time performance and virtual-real synchronization.

The invention discloses a text and image bidirectional alignment method and system based on multi-hop parallel reasoning, and the method comprises the steps: analyzing and marking a text, and obtaining a multi-granularity text feature group; image generation is processed, grids and target output are combined into a multi-scale visual feature group, an alignment index table is established, a sub-target chain is constructed based on visual features, the position and the dependency relationship are recorded, and explicit constraints are injected into five elements; recalling the candidate area, generating an anchor point through verification and fusion, writing the anchor point into a cross-modal index, locally completing three-layer decoupling scoring based on anchor point geometry and the cross-modal index, and outputting a three-state evidence in combination with a threshold value; the method comprises the steps of generating an evidence set through geometric and semantic consistency calibration, generating cross-hop evidence according to a rule, weighting and pooling the evidence set, generating a shared vector and traceable meta-information, inputting a task head output result and generating an evidence list and an auditing path, and according to the method, local alignment, constraint verification and evidence accumulation are completed hop by hop. And high-precision, traceable and interpretable cross-modal correspondence is realized.

The invention provides a scene reconstruction and semantic understanding method and system based on a visual large model, and relates to the field of computer vision and three-dimensional reconstruction, and the method comprises the steps: obtaining multi-view image data of a target scene, inputting a pre-training visual large model, and outputting a joint feature representation and attention weight matrix; generating three-dimensional coordinate values and semantic probability distribution of spatial sampling points in a three-dimensional space according to the joint feature representation, and constructing a spatial semantic field; clustering the spatial sampling points by using the attention weight matrix, performing semantic consistency enhancement, and converting the spatial sampling points into deterministic semantic tags; constructing a geometric optimization objective function, and adjusting three-dimensional coordinate values; and extracting continuous space sampling points with the same semantic tag to form an object boundary, constructing a scene topological graph, deducing a scene functional structure, and generating a navigation path. According to the method, the unification of accurate geometric reconstruction and deep semantic understanding of the scene is realized, the three-dimensional reconstruction precision and semantic analysis accuracy are improved, and reliable support is provided for intelligent navigation.

The invention relates to the technical field of artificial intelligence and information processing, and particularly discloses a multi-modal file intelligent classification and label generation method and system. The method comprises the steps of obtaining text paragraphs, image segments and video key frame data in a file, performing format recognition and region separation, and generating multi-modal content structure data; semantic features are extracted based on the data, and fused semantic representation is constructed; generating a main label, a sub-label and a keyword set by using the fused semantic vector, and constructing a multi-level label structure; and further performing label redundancy elimination and structure optimization to generate a label atlas, and performing consistency analysis and feedback optimization through the label atlas and the semantic representation structure. Compared with the prior art, the method has the advantages that various modal information can be effectively fused, the accuracy, hierarchical structure and semantic consistency of archive label generation are improved, and the method has high intelligence, self-adaption and sustainable optimization capabilities and is suitable for various scenes such as archive management, content auditing and semantic archiving.

The invention relates to the technical field of text feature fusion, in particular to an adaptive text guide fiber bundle feature fusion method and system based on a large language model. The method comprises the steps of performing multi-scale potential attention fusion based on features after fiber bundle feature fusion, including potential multi-scale feature embedding, inter-scale attention modeling, multi-scale fusion and residual enhancement, and attention regularization and scale consistency constraint; performing pseudo token sequence generation and aggregation based on features after multi-scale potential attention fusion, including pseudo token embedding representation, token serialization and slice mapping, semantic consistency and context modeling, and sequence aggregation and decodable interface generation; and processing the multi-modal fusion features subjected to pseudo token sequence generation and aggregation by using a large language model to generate natural language output. The invention provides an adaptive text-guided fiber bundle feature fusion method based on a large language model, which realizes efficient collaboration and semantic interpretable fusion of multi-modal information.

The invention relates to the technical field of voice processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a multi-speaker dialogue voice analysis method, device and equipment and a medium, and the method comprises the steps: obtaining a to-be-analyzed multi-speaker dialogue voice, determining a naturalness score based on an acoustic feature, and obtaining a multi-speaker dialogue voice analysis result; determining a semantic consistency score based on voice embedding and semantic embedding corresponding to a preset text, determining a speaker consistency score based on embedding of a plurality of speakers of the same speaker, determining an interaction rationality score based on voice alternate overlapping duration, determining a diversity score based on a variance of voice features, and fusing the scores, the comprehensive mass fraction is obtained. According to the invention, through quantitative evaluation of five dimensions of naturalness, semantic consistency, speaker consistency, interaction rationality and diversity, a comprehensive quality scoring system is established, so that the evaluation result simultaneously reflects voice fluency, content matching degree, identity stability, interaction rhythm rationality and feature richness.

The invention provides a field NL2SQL data automatic synthesis method and system based on a large language model, and the method comprises the steps: building an SQL template library through collecting a public data set and real business query data, carrying out the grading according to the SQL complexity, and forming an SQL template set covering different difficulties; specific SQL queries are generated based on an SQL template library and a large language model, verification is carried out through multiple mechanisms such as grammar check, execution verification and result dimension consistency check, and correctness and performability of the SQL queries are ensured. Generating a plurality of candidate natural language questions according to the verified SQL query and the large language model, calculating a semantic consistency score of each candidate question and the SQL query through a cross consistency evaluation mechanism, screening out the question with the most consistent semantics as a final question-answer pair sample, and meanwhile, eliminating low-quality samples by setting a consistency threshold value, so as to obtain a final question-answer pair sample; and the data accuracy and consistency are further improved.

The invention provides a construction state monitoring and risk assessment method and device based on BIM multi-mode conversion, and relates to the technical field of building information models. According to the method, a standardized image mode is generated by analyzing and extracting component information of a BIM model, and a BIM text mode is generated by using natural language description; constructing a graph structure mode based on space and construction logic, and realizing unified alignment and deep fusion of multi-modal data through multi-level modal alignment and a cross-modal attention mechanism to obtain a cross-modal fusion representation which is used for inputting a state recognition model and automatically detecting an execution deviation so as to monitor a construction state; and then introducing a deviation conduction mechanism to quantitatively calculate a comprehensive risk index of the component so as to carry out risk assessment. According to the method, the fusion representation which not only keeps semantic consistency but also conforms to construction logic can be obtained, the abstract cross-modal semantic features are converted into quantifiable and interpretable construction states and risk indexes, and powerful support is provided for intelligent analysis and application in a construction scene.

The invention discloses a Token level cache matching method and system of a large language model and a storage medium. The method comprises the following steps: constructing a local context fragment; generating a context embedding vector of the current Token, and calculating a context entropy value, a semantic consistency index and a semantic change gradient; for the target Token, determining a semantic category of the target Token; inputting the semantic category to which the target Token belongs into a Hash decision maker to obtain a Hash granularity level; if the Hash is the first-level Hash, executing fixed-length Hash and mapping the Hash to a semantic cache bucket based on a semantic theme or a context range of the first-level Hash; if the first-level hash is the second-level hash, dynamically adjusting the hash length according to the semantic similarity with the adjacent Token; if the Hash granularity level of the target Token is a third-level Hash, constructing a high-dimension context representation and executing a fine Hash operation; and matching the hash result with the KV in the pre-stored cache, and executing corresponding operation according to the matching result.

The invention provides a semantic aerial view visual relocation method and device in a non-exposed scene, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a multi-view image sequence under a non-exposed scene (such as a tunnel, an underground pipe gallery or an underground parking lot); semantic recognition is carried out based on a pre-trained semantic target detection model, and spatial consistency semantic features are extracted through a semantic-geometric dual-channel fusion mechanism combining a semantic mask and geometric constraints; the method comprises the following steps of: realizing three-dimensional reconstruction by using a voxel micro-renderable modeling method (VGGT), and generating a dense three-dimensional semantic point cloud fusing semantics and a geometric structure; two-dimensional semantics are mapped to a three-dimensional space through a projection and back projection relation, and point cloud semantics are endowed; main structure planes such as the ground, the left wall surface and the right wall surface are extracted, and a two-dimensional semantic aerial view with semantic annotation is generated; and pose estimation is carried out based on a reciprocal matching strategy guided by a semantic mask, so that visual repositioning with high precision, high robustness and semantic interpretability is realized. The method breaks through the problems of low precision, sparse features and poor semantic consistency of traditional visual repositioning in a non-exposed environment, and can be widely applied to the fields of intelligent transportation, underground inspection and unmanned system positioning.

The invention provides a mathematical proposition automatic formalization method and device based on reinforcement learning, and relates to the technical field of mathematics, and the method comprises the steps: obtaining a to-be-formalized mathematical proposition expressed by a natural language; inputting a mathematical proposition to be formalized into the target automatic formalized model to generate formalized language expression; according to the target automatic formalized model, after a sample data set of a mathematical proposition is input into an initial automatic formalized model to obtain formalized language expression corresponding to the sample data set, a reward signal is generated based on the formalized language expression corresponding to the sample data set through a grammar checker and a semantic consistency check model; and a group relative strategy optimization algorithm is adopted to carry out multi-round iteration updating on the model parameters of the initial automatic formalization model by using the reward signal. According to the method, the requirement for training data is greatly reduced, the training efficiency is improved, the training effect of an automatic form model is improved, and then the accuracy of automatic formalization of the mathematical proposition is improved.

The invention belongs to the technical field of image processing, tracking and recognition, and relates to a multi-mode re-recognition method based on semantic-style decoupling distillation. The method depends on a multi-modal re-identification model which comprises a multi-modal feature extractor comprising a teacher branch module and a student branch module, a decoupling distillation module and a hierarchical self-supervised learning module, and comprises the following steps: constructing a mixed multi-modal feature extractor sharing a shallow layer and an independent deep layer to extract mixed features; performing dual supervision of semantic distillation and style distillation, modeling modal-invariant semantic information and modal-specific style information, and realizing effective decoupling of a feature space; a hierarchical self-supervised learning space is constructed, and in combination with intra-modal and cross-modal comparative learning, images under local damage and style disturbance conditions are scrambled; according to the method, recognition performance and reasoning efficiency are both considered, semantic features and modal specificity styles are effectively separated, semantic consistency, feature robustness and network learning efficiency are cooperatively improved, and modal specificity is also reserved.

The invention discloses a document analysis evaluation method and system based on multi-modal semantic consistency. The method comprises the following steps: acquiring multi-modal contract document data, and preprocessing the multi-modal contract document data to obtain preprocessed data; extracting contract element features from the preprocessed data to generate a fusion feature vector; based on the fusion feature vector, evaluating the semantic consistency of the document elements by using a double-constraint loss function and a three-level judgment mechanism to obtain a cross-modal semantic consistency quantification result; based on the cross-modal semantic consistency quantification result, calculating a basis and semantic enhancement index, and integrating the basis and semantic enhancement index into a comprehensive evaluation index; and outputting the comprehensive evaluation index and the corresponding related suggestions, and guiding contract intelligent analysis and algorithm optimization. By implementing the method provided by the invention, intelligent and accurate document analysis can be realized, and the problems of dependence on geometric positioning, missing of cross-modal association and insufficient logic verification in the existing document analysis technology are solved.

The invention belongs to the technical field of artificial intelligence, and discloses a knowledge extraction method and system based on semantic consistency evaluation and hybrid verifiable reward, and the method comprises the steps: constructing a training data set with evidence labeling; based on the training data set, reinforcement learning training is carried out on a pre-trained large language model, a group strategy optimization GRPO algorithm is adopted, and model output is evaluated by using a mixed reward function; and on the basis of an evaluation result of the mixed reward function, updating parameters of a large language model so as to generate structured knowledge which is correct in format, accurate in content and provided with verifiable evidence. According to the method, in reinforcement learning training, effective decoupling format, content and credibility evaluation is realized, and refined feedback is provided for the model; when the model outputs knowledge, traceable original text evidence is provided for the model, so that the credibility and the interpretability of the model are enhanced, the accuracy of outputting the JSON format by the model is improved, and the accuracy and the integrity of the model in the aspect of content extraction are enhanced.

The invention discloses a knowledge graph question-answer interaction method and system based on artificial intelligence, and the method comprises the steps: receiving a natural language question input by a user, carrying out the hierarchical analysis of entity words and intention words in the question through a dynamic weight distribution mechanism, and generating an enhanced semantic vector containing entity association strength and intention probability distribution; traversing the knowledge graph based on the enhanced semantic vector, and constructing a hierarchical query sub-graph comprising a core entity, an associated entity and an implicit relationship; performing reasoning calculation on the hierarchical query subgraph by adopting a multi-path collaborative reasoning algorithm to generate a candidate answer set; and performing semantic consistency verification and redundancy elimination on the candidate answer set, generating an adaptive answer and an association explanation path in combination with historical interaction preference of the user, and synchronously feeding back a newly discovered entity association relationship to the knowledge graph for incremental updating to form a question and answer interaction closed loop. By utilizing the embodiment of the invention, the accuracy and interpretability of questions and answers can be improved, and the user interaction experience and the information acquisition efficiency are improved.

The invention belongs to the technical field of cross-modal information retrieval, and provides a quick cross-modal retrieval method and system fused with fine-grained semanteme, multi-modal data to be retrieved are obtained, a trained cross-modal retrieval model is used for learning to obtain continuous Hash features, discrete Hash codes are generated according to the continuous Hash features, and the multi-modal data to be retrieved are retrieved according to the discrete Hash codes. Performing cross-modal matching and retrieval based on Hamming distance matching, and feeding back a retrieval result; in the training process of the cross-modal retrieval model, the fusion loss function, the cross-modal alignment loss function and the shared subspace loss function are subjected to weighted fusion, and corresponding parameters are optimized with the purpose of minimizing the total loss function after weighted fusion until training requirements are met. According to the method, the cross-modal semantic consistency among different modal features is optimized, so that the generated Hash code can reflect semantic association among multi-modal data more accurately, and the precision and efficiency of cross-modal retrieval are improved.

The invention relates to the technical field of large language models, and discloses a zero-illusion large language model output method and system, and the method comprises the steps: sequentially carrying out the deterministic lexical analysis and standardized mapping of a received original query, and generating a standard term set; locking to-be-reasoned items through hierarchical matching to form a deterministic retrieval result set; executing five rounds of progressive verification processing on the result set to obtain an integrated result set with an LLM verification mark; and outputting a layered zero illusion result through multiple verification and auditing of structure compliance, semantic consistency and knowledge base double recheck. According to the method, probabilistic reasoning is replaced by full-link deterministic operation, so that each code can be traced back to a credible knowledge source, illusion is thoroughly eliminated, absolute reliability and complete traceability of an output result in a professional field are realized by a verifiable deterministic path, and a foundation is laid for zero-illusion application.

The invention discloses an AI-based archive digitization full-process management and control method, which comprises the following steps: acquiring archive image data and text data, constructing an original archive data set, and generating an initial directory set; constructing an archive directory template set, executing template matching and structure adaptation, and generating an initial structure recognition result; setting a classification rule of directory deviation and structural contradiction, constructing a deviation rule base, executing rule scanning and node comparison, and generating a deviation contradiction label sequence; constructing an evolution state vector, inputting the evolution state vector into an improved GMN model, and generating a structure repair state vector through a structure guiding unit, a deviation sensing unit, a path fusion unit and a node evolution unit in sequence; and executing result updating and conflict rearrangement, completing standard template alignment and semantic coverage verification, and generating a final structure recognition result. According to the method, the structural integrity, the semantic consistency and the automatic repair capability of directory recognition are improved, and the method has relatively high adaptability and intelligent level.

The invention discloses a hatred model factor identification method and system based on dual granularity symmetry and context perception alignment. The method comprises the following steps: firstly, respectively extracting a text mode and a visual mode from input image data containing text information, and carrying out feature coding and embedding so as to obtain text mode features and visual mode features; according to the method, a mode of constructing same-granularity and cross-granularity dual symmetric factors in visual and text modal representation is adopted to control modal internal feature fusion, so that a function of generating high-semantic-consistency modal internal feature representation is realized; in the inter-modal representation fusion process, a context perception cross-modal alignment strategy is introduced, and dual symmetric modal information is used as a prompt, so that the understanding of memetic context information is enhanced; whether aggressive or discrimination information aiming at individuals or groups is transmitted or not can be accurately identified by excavating potential emotional tendency and value orientation, and the identification accuracy and efficiency of the hatred model factor are improved.