721 results about "Semantic relation" patented technology

The invention discloses a computer equipment fault monitoring system and method based on artificial intelligence, and relates to the technical field of computer equipment fault monitoring. The system comprises a data access module, a semantic analysis module, a knowledge graph construction module, a dynamic semantic association module, a data fusion processing module, a decision output module and an adaptive optimization module. The data access module collects and standardizes hardware, software and network data; the semantic analysis module extracts and enhances semantic tags; the knowledge graph construction module forms a data semantic relation network; the dynamic semantic association module screens potential semantic relationships; the data fusion processing module generates a multi-dimensional feature vector; the decision output module triggers fault early warning; and constructing a feedback knowledge graph of the self-adaptive optimization module. According to the method, through event-driven interpolation, dynamic weight fusion, closed-loop feedback optimization and the like, the problems of multi-source data alignment, semantic fusion and dynamic adaptation are solved, the fault monitoring accuracy and the system adaptability are improved, and the method is suitable for fault monitoring and early warning of computer equipment.

The invention discloses a cockpit active recommendation system and method based on a knowledge graph and semantic reasoning, and relates to the technical field of intelligent cockpits. The system receives natural language voice input of a user, executes voice recognition and semantic analysis, extracts user intention, keywords and slot entities, generates structured semantic information, constructs or calls a knowledge graph structure with semantic relation edges in combination with environment context information, and obtains the knowledge graph structure with the semantic relation edges. Semantic path reasoning is carried out based on the path dependence weight and the semantic similarity, a semantic edge label guided graph attention mechanism is introduced to calculate a path consistency score, a candidate recommendation set is generated, the semantic fitting degree and the path score are fused to sort and output recommendation content, and the graph edge weight and the user portrait are updated based on user feedback. According to the method, semantic understanding precision, recommendation path interpretability and system adaptive capacity are improved, and the method is suitable for personalized voice recommendation, man-machine interaction and scene linkage control tasks in an intelligent cockpit.

The invention relates to the technical field of artificial intelligence and big data analysis, in particular to a data operation system and method based on a knowledge graph, and the method comprises the steps: extracting an entity and semantic relationship from multi-source business data, and constructing a dynamic evolvable initial knowledge graph; node features are aggregated, and multi-dimensional situation state vectors are generated in combination with gating loop unit modeling behavior path dependence; through a structure-semantic coupling attribution scoring mechanism, a statistical information gain and semantic similarity are fused to identify a core driving factor, and a causal regression model of the factor and an operation target is established; dynamically adjusting the edge weight and the structure of the atlas in real time, and triggering a new path discovery mechanism to continuously optimize the atlas; and according to a quantitative business target, reversely extracting a high-confidence influence path from the atlas, and generating a personalized strategy combination through intervention simulation and multi-target Pareto optimization, thereby realizing intelligent recommendation and decision closed loop driven by an operation target. According to the invention, higher-precision operation situation awareness and strategy generation are realized.

The invention relates to a government affair file information extraction and question answering method and device and a medium, and the method comprises the steps: carrying out the entity extraction of a government affair file through employing a BERT-CRF joint model, and obtaining a structured entity set; performing relation extraction on the structured entity set to generate a semantic relation set between the entities; constructing a knowledge graph according to the structured entity set and the semantic relationship set, storing entity nodes into a graph database, and storing an embedded vector of an entity text into a vector database; when a query request of a user is received, relation query of the graph database and semantic retrieval of the vector database are carried out, sub-graph structures and semantic matching vectors related to query are extracted, and a mixed retrieval result is obtained; and inputting the mixed retrieval result into a large language model, and generating a question and answer response text conforming to a preset format by applying a dynamic prompt template. According to the method, the document processing efficiency and accuracy are effectively improved, and a solid technical support is provided for intelligent management of government affair documents.

The invention discloses a unified management method and device for multi-protocol Internet of Things equipment, and relates to the technical field of Internet of Things communication and protocol management, and the method comprises the steps: constructing a cross-protocol semantic knowledge graph through a graph neural network based on a structured gene-semantic mapping table, and generating a dynamic semantic relation matrix through a node embedding method; according to the dynamic semantic relationship matrix, deploying a genetic algorithm engine at an edge node to dynamically recombine and optimize protocol genes, screening protocol variants adapted to a network environment through an evolution sandbox pressure test, and generating an optimized protocol gene pool; and based on the optimized protocol gene pool, performing real-time conversion on the heterogeneous protocol message in a protocol conversion gateway in combination with a multi-thread parallel analysis architecture to generate a standardized semantic data stream. Unified expression and management of complex heterogeneous protocols are achieved, the protocol conversion process is simplified, cross-protocol semantic understanding and compatibility are improved, and device flexibility and analysis efficiency are improved.

A system and method for latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses spatiotemporal media into navigable latent representations while preserving geometric and semantic relationships through tensor structure maintenance. A latent hyperspace manager organizes compressed representations as geodesic trajectories within a geometric manifold structure based on differential geometry principles. A geodesic trajectory mapper computes optimal navigation paths through the high-dimensional space, while symbolic anchors positioned at semantically significant locations serve as persistent reference points. Spatiotemporal routing protocols manage navigation decisions across multiple temporal scales. A strategy caching system preserves successful navigation patterns for reuse, enabling continuous learning. The system generates synthetic content during navigation to support infinite zoom capability, allowing exploration beyond original media boundaries. Cross-modal fusion combines diverse input modalities into unified representations, applicable to immersive media exploration, scientific visualization, and surveillance analysis.

The invention discloses a semantic segmentation method for a low-resolution road scene, and aims to solve the problems of difficulty in small target recognition, fuzzy details, texture information loss and the like existing in a low-resolution image in the conventional semantic segmentation technology. The method comprises the following steps: (1) collecting a low-resolution road scene image and a corresponding semantic tag; (2) constructing a semantic segmentation model consisting of an edge guidance module (BGM), a double-domain feature decomposer (DDFD), a domain alignment attention fusion module (DAAFM) and a double-layer attention context aggregation module (HACAM); (3) designing a joint loss function to carry out multi-scale supervision on semantic regions, edges and middle features; (4) carrying out model training by utilizing the road scene image; and (5) outputting a semantic segmentation result map and an edge prediction map. The boundary perception capability is enhanced by introducing learnable pixel difference convolution, the extraction precision of a small target and a global structure is improved by combining frequency domain and spatial domain feature alignment, and context semantic relationship expression is optimized by fusing a channel and a spatial attention mechanism. The method effectively improves the semantic segmentation precision and boundary restoration capability of the model in a low-resolution complex road environment, and is suitable for intelligent analysis tasks of road images in scenes of automatic driving, intelligent traffic, severe weather and the like.

The invention relates to the field of agricultural information processing and artificial intelligence, in particular to a farmland yield prediction method and system based on a heterogeneous graph neural network, and the method comprises the steps: obtaining multi-source farmland data, and extracting an initial feature vector of a farmland plot node; on the basis of the initial feature vector, constructing a heterogeneous graph structure containing multiple semantic relationships; performing node feature updating on the heterogeneous graph structure by using a heterogeneous graph neural network, and dynamically aggregating information of multiple types of neighbor nodes through relation-aware message passing and an edge propagation gating mechanism; performing enhancement processing on the node features by using a space-time dependency enhancement mechanism and a knowledge-guided reasoning mechanism; and outputting a regression prediction result of the farmland yield through a prediction module based on the enhanced node features. The invention aims to realize modeling and intelligent yield prediction based on multi-source heterogeneous data in an agricultural system, and improve the environmental adaptability, prediction generalization ability and interpretability of farmland yield prediction.

The invention discloses a document link construction and evolution relation tracking method based on fragment-level semantic alignment, which relates to the technical field of document management, and comprises the following steps: carrying out structured analysis on a plurality of input documents, and dividing the document content into a plurality of semantic fragments; performing vectorization processing on each semantic fragment, and constructing a document-fragment-vector semantic mapping relationship; different document fragment-level semantic alignment is realized through cross-document semantic vector similarity calculation, a position offset tolerance algorithm and context association analysis, and a semantic link network is established; fusing the link relationship, the timestamp, the version information and the author information, and constructing a document evolution diagram taking the documents or fragments as nodes and evolution links as edges to record semantic flow paths between the documents; and outputting the semantic link network and the document evolution diagram in a structured form. The method can solve the problems that semantic relations between documents are missing, evolution paths are invisible, content reuse cannot be traced, and the structure induction capability is insufficient.

The invention discloses a multi-modal metadata alignment fusion method and device, equipment and a storage medium, and the method comprises the steps: carrying out the metadata extraction of structured data, unstructured text data and image data, and generating multi-modal metadata with semantic annotations; establishing a shared semantic embedding space, and mapping the multi-modal metadata to the shared semantic embedding space for coding to obtain a unified spatial vector; determining an alignment candidate pair from the unified spatial vector through similarity calculation, and identifying a semantic relationship of the alignment candidate pair; and performing conflict detection on the aligned candidate pairs and the corresponding semantic relationships, and resolving conflicts based on weight weighting fusion to obtain a unified metadata system. According to the method, improvement and optimization are carried out from multiple aspects of multi-modal data processing, semantic understanding, alignment accuracy, conflict resolution and the like, the defects in the prior art are overcome, and a more accurate and comprehensive multi-modal metadata alignment fusion result can be provided.

The invention provides a structured processing method and device for a contract text, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of the contract text, generating a standardized word sequence, and enabling the standardized word sequence to comprise a plurality of word sequences and part-of-speech tagging information of each word sequence; the standardized word sequence is input into a named entity recognition model to recognize key entities in the contract text, key terms in the contract text are extracted based on a machine learning algorithm, the key entities include both parties of the contract, the amount of the contract and the name of a product, and the key terms include payment terms, delivery terms, default responsibility terms and force majeure terms; through dependency syntactic analysis and a semantic role labeling model, analyzing to obtain a semantic relationship between the key entities and the key terms; and storing the extracted key entities, key terms and semantic relationships in a target database in a structured form, and establishing a target index to support quick query. According to the invention, the error rate of manual processing is reduced.

The invention provides a method and a system for intelligently generating and dynamically adjusting a public emergency plan, which are oriented to the field of public safety emergency. The method comprises the following steps: fusing multi-source heterogeneous data to construct an extensible domain knowledge graph, establishing a standardized emergency instruction library, and carrying out multi-dimensional information labeling; automatic extraction of disaster elements is realized based on an entity recognition model of deep learning; analyzing association rules among the emergency entities through a semantic relationship mining technology; key information such as a disaster chain and resource distribution is rapidly obtained by using a map reasoning mechanism; carrying out cross-department resource collaborative allocation by adopting a multi-objective optimization algorithm to generate an optimal disposal scheme; a high-dynamic adjustment mechanism is constructed, and an emergency plan is dynamically optimized based on situation evolution prediction and real-time monitoring data; and finally, automatic generation and versioning management of the plan are realized. Through multi-mechanism cooperation of knowledge modeling, intelligent element analysis, semantic reasoning, dynamic optimization and predictive adjustment, the emergency plan generation efficiency, situation adaptability and resource allocation rationality are remarkably improved, and support is provided for quick response and scientific decision under emergencies.

The invention discloses a method for converting a natural language into a low code based on multi-modal reinforcement learning, which comprises the following steps of: performing word segmentation, embedding and multi-layer feature extraction on a natural language instruction input by a user, combining a self-attention mechanism and a graph attention mechanism, extracting and optimizing an original semantic feature vector, and automatically identifying a business field; semantic relationship triples in the domain knowledge graph are fused, and a multi-modal semantic alignment and context enhancement strategy is adopted, so that the accuracy and stability of semantic representation are remarkably improved; when semantic drift or ambiguity is detected, a multi-candidate correction mechanism is triggered to obtain a better analysis result, and the robustness of semantic understanding is enhanced; and finally, mapping an analysis result into an instruction which can be identified by a low-code platform, automatically generating a code structure, continuously optimizing a semantic model and a knowledge graph based on user feedback, realizing adaptive learning, and improving the conversion efficiency and quality from a natural language to codes.

The invention discloses a policy recommendation method and system based on a heterogeneous knowledge graph, and relates to the field of policy recommendation, and the method comprises the steps: obtaining an original policy text from a plurality of heterogeneous policy data sources, and extracting policy-related information; mapping the policy-related information into policy entity nodes; mapping the enterprise portrait data into enterprise entity nodes; defining and generating multiple types of edges between the policy entity nodes and the enterprise entity nodes based on a preset semantic relationship so as to construct a heterogeneous knowledge graph, constructing a graph neural network model, and executing embedding calculation on various nodes in the graph so as to obtain node representation vectors; and obtaining a matching degree, and outputting a recommended policy entry set according to the matching degree as policy recommendation information of the target enterprise. The method not only breaks through the problems of format dependence, rule stiffness and semantic shallow hierarchy of a traditional policy recommendation system, but also has relatively high universality, interpretability and actual deployment value.

The invention discloses a pavement throwing detection method and device based on deep learning, electronic equipment and a program product. The method is realized through a trained detection model, the model adopts an LMSADet detection head, a multi-scale feature extraction and space attention mechanism is introduced into a task branch, and multi-scale modeling is decoupled from a backbone network and a neck network and integrated to the detection head so as to fit a detection task to directly optimize local details and scale differences of a throwing target. In order to suppress background interference and improve the recognition effect of fuzzy boundaries, the neck network is added into an MSHA module so as to efficiently capture the semantic relation between the thrown object and the background and enhance the regional understanding ability. A C3ESP module is introduced into the backbone network, deep features are extracted through stacking depth separable convolution, and information loss is avoided in combination with residual optimization fusion; meanwhile, a PEMA attention mechanism is introduced, the importance of different receptive field features is dynamically adjusted, the model focuses on key features, data information is captured more comprehensively, and therefore the detection performance is remarkably improved.

The invention relates to an automatic assembly system and method for multi-scale digital twin components, and the system comprises a semantic relationship construction module which is used for carrying out explicit definition on the structural features, functional attributes, spatial layout requirements and logic dependency relationships of the multi-scale components, and constructing semantic relationships among the three types of components; the semantic reasoning and constraint engine module is used for carrying out logical reasoning through the semantic relationship constructed by the semantic relationship construction module and judging whether the component combination meets the assembly constraint or not; the assembly generation and configuration module is used for generating an assembly topological structure and a connection sequence of a system shelf according to the component candidate set output by the semantic reasoning and constraint engine module; and the man-machine interaction and visualization module is used for supporting a feedback closed loop between the engineer and the system and providing a visual display interface. The problems that an assembly method depends on artificial experience and is difficult to support high-frequency and multi-scene production line reconstruction are solved, and the method has higher semantic interpretation capacity, automatic combination capacity and context adaptive capacity.

The invention discloses a three-dimensional dynamic scene graph construction method based on 3D Gauss, and belongs to the field of computer body intelligence. The implementation method comprises the following steps of: realizing object perception and semantic feature extraction of open vocabularies by utilizing a visual basic model; a 3D Gaussian scene with high fidelity and continuous object semantics is constructed through multi-view multi-dimension optimization of 3D Gaussian representation; constructing a multi-level three-dimensional scene graph, extracting spatial levels and semantic relationships among objects by using 3D spatial positions and semantic tags of instance objects existing in a semantic Gaussian graph, and constructing a multi-level spatial semantic topology to accurately represent an environment layout; according to the method for realizing local updating for the Gaussian scene graph based on the environmental structural similarity, environmental change detection is carried out through real-time RGB-D observation and the structural similarity between high-quality rendering views of the Gaussian scene graph, and corresponding local updating is carried out by using rapid training and differentiable rendering of 3D Gaussian representation. And the capability of adapting to a complex dynamic environment of the 3D Gaussian scene graph is improved.

The invention provides a classical music cross-modal high-reliability experimental data set construction method for full-scene teaching application, and the method comprises the steps: constructing an original data set through collecting audio, video, music score and other multi-modal data, carrying out the standardization of each modal data through a preprocessing technology, and obtaining a multi-modal data set in a unified format; according to the semantic feature vectors, a knowledge graph is constructed, nodes represent semantic relations between music elements, edges represent semantic relations between the elements, a graph convolutional network is adopted to encode the knowledge graph, and semantic representation of the teaching content is obtained; a virtual reality rendering technology is adopted, multi-modal teaching resources are integrated into an immersive teaching scene, a content sequence is presented in real time, and dynamic teaching experience is obtained; and continuously collecting interaction data of the learner, updating the knowledge graph and the semantic feature vector, and optimizing the cross-modal fusion model by adopting an incremental learning algorithm to obtain a self-adaptive teaching data set.

The present application provides a large language model-based query statement generation method, apparatus, and device, and a medium. The large language model comprises a plurality of encoders. The method comprises: acquiring a query text; performing vectorization processing on the query text to obtain a target vector representation corresponding to the query text; inputting the target vector representation into the plurality of encoders, to perform encoding processing on the target vector representation by means of weight matrices of the encoders, so as to obtain context vectors corresponding to the encoders, wherein different encoders have different weight matrices; and generating a target query statement on the basis of the context vectors corresponding to the encoders. A model is allowed to focus on different information features in different attention heads, independently capture different aspects of a query text, and process information at multiple abstraction levels, thereby better capturing complex and abstract semantic relationships, effectively distinguishing the importance of information when processing complex and fuzzy questions, improving the accuracy of query statement generation.

The invention relates to a cross-modal document information extraction method based on space-semantic alignment, and belongs to the field of artificial intelligence, computer vision and natural language processing. According to the method, the spatial feature and semantic information bidirectional alignment model is designed, by constructing the spatial feature and semantic feature bidirectional alignment model, the document layout information can dynamically adjust attention distribution of text semantic features, meanwhile, semantic information reversely optimizes the spatial features, collaborative modeling of spatial layout and semantic information is achieved, and the document layout efficiency is improved. Therefore, the accuracy and robustness of complex document information extraction are improved. According to the method, a hierarchical cross-modal information extraction model is designed, through the hierarchical cross-modal information extraction model, the overall structure of a document is recognized on the global level, local key content is focused on the regional level, fine modeling is conducted on fine-grained texts and visual elements on the entity level, and accurate recognition of a cross-modal entity and the semantic relation of the cross-modal entity is achieved; and the generalization ability and applicability of information extraction are enhanced.

The invention relates to a man-machine interaction dialogue method, system and device based on a natural language and a medium. The method comprises the steps that firstly, multi-modal interaction data is acquired and preprocessed, and segmented words and syntax are analyzed through a natural language processing technology to construct intention feature vectors; combining the intention feature vector with a historical dialogue record, and using a pre-trained language model to generate context semantic elements containing a semantic relationship; if the context semantic elements are matched with the preset scene feature library information, predicting a user intention change trend by adopting a reinforcement learning model to obtain an intention prediction result; and finally, evaluating user intention change based on an intention prediction result, extracting associated domain knowledge by utilizing a knowledge graph if significant change occurs, and inputting the associated domain knowledge into a dialogue generation model to obtain a natural language reply sequence. According to the method, the understanding precision of the user intention is improved, the dynamic prediction of the intention change is realized, the relativity and coherence of reply are guaranteed, and a more efficient processing path is provided for natural language man-machine interaction.

The invention belongs to the technical field of urban low-carbon planning, and relates to a low-carbon community evaluation index library dynamic construction method and system. The method comprises the steps of knowledge graph mode layer construction, knowledge extraction, semantic relation enhancement and fusion and graph database storage. Defining the types of the entities and the semantic relationship between the entities to obtain a mode layer with a hierarchical topological structure; knowledge extraction: mapping the entity relationship in the mode layer into a knowledge extraction template; after an extraction result is subjected to subgraph structured organization, vector space mapping is carried out, semantic association strength among indexes is calculated, and entity alignment is carried out by adopting a knowledge fusion mechanism driven by a large language model; and importing the knowledge graph subjected to semantic relationship enhancement into a graph database through a query language. According to the method, efficient construction and expansion of the cross-domain index library can be realized; the adaptability of the index system is enhanced; and in combination with a knowledge fusion feedback mechanism, the mode layer is dynamically adjusted, so that the data processing efficiency and accuracy are improved.

The invention discloses a network threat knowledge automatic extraction method, electronic equipment and a storage medium, and the method comprises the following steps executed by a computer hardware system: collecting threat intelligence data related to an APT organization from a multi-source network security text, and processing the threat intelligence data to generate a standardized corpus; using the pre-training sentence vector model to generate semantic embedding for a corpus input text and a manual annotation example library text, and retrieving similar examples to construct an ICL prompt template; inputting a large language model subjected to LoRA fine tuning, and extracting structured triples of multiple types of entities and semantic relationships; generating standardized entity nodes and updated relation information by adopting semantic aggregation; and constructing an APT organization network threat intelligence knowledge graph and outputting a structured file. The method provides key technical support for APT attack tracing, threat situation awareness and automatic security policy generation.

The invention belongs to the technical field of data processing, and discloses a construction method and system based on a medical examination medical knowledge base and a medium. Comprising the steps that a basic medical data set is collected, data cleaning is conducted, and a structured medical data set is output; constructing a multistage medical dictionary and establishing semantic mapping with the structured medical data set to generate a standard medical data set; carrying out medical related entity extraction on the standard medical data set and establishing a semantic relationship of each medical entity to obtain a medical entity set; performing semantic alignment on the medical entity set to obtain a semantic alignment entity set; constructing a medical examination knowledge rule base and constructing an initial medical examination knowledge graph in combination with the semantic alignment entity set; performing entity association extension on the initial medical examination knowledge graph, and outputting an enhanced medical examination knowledge graph; real-time clinical data are collected, and the medical examination knowledge graph is updated and enhanced based on the real-time clinical data to obtain the medical examination knowledge base, so that convenience is provided for medical decision making and intelligent reasoning diagnosis.

The invention discloses an entity digitization and link framework algorithm based on a heterogeneous graph attention network, and the algorithm comprises the following steps: S1, heterogeneous information network construction: carrying out the unified modeling of all multi-source heterogeneous data into a heterogeneous information network containing various types of nodes and various types of edges, s2, meta-path definition and guidance: defining "meta-paths" connecting different types of nodes to capture a complex deep semantic relationship, S3, heterogeneous graph attention network embedding: adopting an attention mechanism to enable a model to automatically learn importance of different neighbor nodes and different meta-paths, generating a final embedding vector of each entity, and establishing a heterogeneous graph attention network model; according to the method, the information fidelity is higher, modeling is directly conducted on different types of nodes and relations on a heterogeneous graph, more abundant and heterogeneous semantic information in data can be reserved compared with a multi-view method, and the end-to-end learning ability is higher; and the complexity of manually designing a fusion strategy is reduced.

The invention discloses a retrieval enhanced warehouse-level code completion method based on semantic topology and disambiguation redundancy pruning. Firstly, a key value code library for accurate mapping of source code fragments and induction sub-graphs is constructed through a specific slicing algorithm; in the retrieval stage, four-level layered optimization is adopted, structural similarity is evaluated by extracting a deep semantic relationship, trimming accurate duplicate items and using a new graph-based measurement (tradeoff is performed on editing according to topological importance), results are reordered to maximize correlation and diversity, candidate items are systematically refined, and the retrieval efficiency is improved. The problems of retrieval redundancy solidification and surface similarity misleading are solved, meanwhile, cross-module dependence is analyzed through an external perception identifier disambiguator, and the problem of cross-file symbol ambiguity is solved; and finally, fusing an optimization result to generate a prompt to drive the LLM to generate higher-quality output. According to the invention, through coordination of semantic and structural signals, strong performance can be obtained even in a large-scale and resource-limited code library. Meanwhile, the design allows it to be orthogonally complementary to other cross-file methods, providing collaborative improvements when used in combination.

The invention provides a dynamic spectrum prediction method based on a time sequence knowledge graph, a medium and equipment. The method comprises the following steps: constructing a communication spectrum coordination knowledge graph framework structure fusing static knowledge and dynamic knowledge; constructing a knowledge embedding model based on a cyclic evolution network, and realizing semantic representation of entities and relationships thereof in an electromagnetic spectrum space; through a knowledge fusion method, static knowledge embedding and dynamic knowledge embedding are fused with historical communication node frequency data so as to realize feature interaction of a static knowledge graph and a dynamic knowledge graph, and features of communication nodes in a frequency domain are extracted at the same time; constructing a space-time diagram convolutional neural network model, extracting space correlation characteristics between nodes, capturing a time evolution rule of node attributes, and dynamically predicting communication frequency; according to the method, the characteristics of the communication node frequency are effectively extracted from the communication semantic relationship, and the prediction precision of the communication frequency is remarkably improved.

The invention discloses a special reasoning model-oriented data set construction method, and particularly relates to the technical field of data set construction. According to the method, domain task text information, rule constraint information and causal dependency information are analyzed, and a dependency structure chart of a target reasoning task is constructed; on the basis of the dependency structure diagram, topology path expansion and anti-fact topology path backtracking analysis are carried out according to the semantic relation direction and the constraint reverse relation, forward path topology feature data and reverse topology verification data are formed, and therefore a forward and reverse dependency consistency matrix is established for topology conflict recognition, and a consistency correction result is output; and finally, according to a consistency correction result, carrying out topology label labeling, topology equivalence judgment and multi-path admission screening to obtain a topology fidelity sample set, and carrying out dependent signature labeling and topology label coding to generate a special reasoning model training data set of the target reasoning task. And the reasoning path reliability and the data set structuring level of the special reasoning model are improved.

The invention provides a ship design drawing structured vector analysis method and an intelligent question and answer method. The method comprises the following steps: constructing a drawing primitive initial data set; generating a semantic annotation entity set; mapping the semantic annotation entity set to a two-dimensional coordinate system to generate a relation annotation entity pair set; repeated entities with the same semantic content and the coordinate position error lower than a set threshold value are removed, and an effective entity relation set is constructed; converting the effective entity relationship set into a structured semantic relationship set; inputting the drawing primitive initial data set into a neural network target detection model to generate an image recognition entity set; and fusing the structured semantic relationship set and the image recognition entity set to construct a drawing semantic map, finely adjusting the large language model, and outputting a text question and answer result. According to the method, ship drawing automatic analysis and semantic question and answer efficient fusion are realized, and the structure understanding precision and the interaction response capability are improved.

The invention discloses a traditional Chinese medicine ancient book mapping knowledge domain construction method and device, and a medium. The method comprises the steps of generating structured text data; constructing an entity recognition model based on a pre-training language model in the field of traditional Chinese medicine, and performing entity recognition; constructing a single-book graph structure by taking entities as nodes and taking context semantics of the entities as initial edge weights; performing representation learning on nodes in combination with a graph neural network model, fusing context features, position information and graph structure features of entities, extracting semantic relationships among the entities, and constructing a single-book knowledge graph; the method comprises the following steps: fusing entities from a plurality of single-book knowledge maps based on a cross-map attention mechanism, constructing a cross-map attention matrix, dynamically adjusting a matching threshold value of the entities according to the interaction strength of the entities and the number of common neighbors, carrying out entity alignment and edge weight fusion, and generating a global traditional Chinese medicine ancient book knowledge map. According to the invention, automatic identification of entities and relationships in traditional Chinese medicine ancient books, knowledge graph construction and multi-book knowledge ablation are realized.