385 results about "Semantic network" patented technology

The present invention provides a method and system for extracting and analyzing long text information and association based on a large model, which relates to the technical field of natural language processing, including determining sliding window parameters based on semantic density values ​​to divide long text into blocks, extracting feature vectors of text blocks and calculating semantic relevance, and constructing a hierarchical clustering feature matrix to merge text blocks to obtain semantic fragments. Afterwards, the semantic fragments are syntactically analyzed to construct a semantic dependency tree, and the semantic network is extracted and the entity relationship strength value is calculated to construct a semantic association graph. Finally, the node features are extracted using a pre-trained large model, the semantic representation is fused through a hierarchical attention mechanism, the features are enhanced by propagating an adaptive graph network, and candidate paths are generated through a hierarchical decision network, the optimal path is selected, and the associated path and probability distribution are output. The present invention can effectively extract key information from long texts and analyze the association relationship between entities, thereby improving the accuracy and efficiency of information extraction.

The invention discloses an intelligent file classification and retrieval method and system, and the method comprises the steps: carrying out the unified format conversion and feature extraction of text, image and audio data according to the original data format of a file through a multi-modal data fusion algorithm, and obtaining a standardized file feature data set; based on a deep learning adaptive classification model, performing classification processing on the standardized archive feature data set to generate multi-level archive classification labels; analyzing the semantic relationship between the archive classification labels through an enhanced semantic network construction technology, generating a semantic association map, and optimizing the semantic association map to obtain an optimized semantic association map; and according to a retrieval request input by a user, performing semantic matching and correlation sorting by utilizing the optimized semantic correlation graph, and outputting archive information most related to the retrieval request. By utilizing the embodiment of the invention, the relevance and accuracy of retrieval can be improved, and modernization and intellectualization of archive management are promoted.

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention provides an agent context understanding method and system driven by a multi-modal semantic network, and relates to the technical field of data processing, and the method comprises the steps: carrying out semantic label extension and anaphora resolution processing, and marking a time anchor point and an anaphora target of each semantic segment; each context semantic fragment is converted into semantic nodes, and directed connection is generated according to the time relation and semantic association between the semantic nodes; based on a user instruction, matching related semantic nodes, and calculating a comprehensive matching score; comparing the comprehensive matching score with a preset comprehensive matching score threshold value, and screening candidate semantic nodes; dynamically updating the current memory weight of the semantic node according to the comprehensive matching score of the semantic node; extracting a semantic node sequence with the highest current memory weight as a context semantic path, and outputting semantic entities in the path in a structured manner; according to the invention, the autonomy and accuracy of the context understanding of the intelligent agent are improved.

The invention relates to the technical field of building information modeling, in particular to a building drawing geometric feature extraction and visual modeling method and system, and the method comprises the steps: carrying out the self-adaptive noise reduction, contrast enhancement and line refinement processing of an original building drawing image, and carrying out the automatic layer separation based on colors and line types; linear geometric features and specific symbol geometric features in the drawing image are extracted, and a geometric feature set is constructed; component instantiation, attribute assignment and topological relation reasoning are carried out by using a predefined building component semantic rule base, and a building component semantic network is generated; and mapping the semantic network to a parameterized three-dimensional modeling engine, calling an IFC standard three-dimensional template, performing parameter driving and automatic assembly, generating a three-dimensional building model with semantic information and a spatial structure, and performing visual output. According to the method, efficient and standardized conversion from a two-dimensional building drawing to a three-dimensional building model can be realized, and the method has the remarkable advantages of processing complex drawings and high-precision modeling.

The invention relates to a shipping risk assessment method, which comprises the following steps of: S1, establishing a semantic network of entities related to shipping and relationships among the entities, and determining a shipping basic information knowledge graph; s2, acquiring multi-source data related to shipping risks, and extracting risk entities and risk information from the multi-source data; s3, expanding a shipping basic information knowledge graph according to the risk entity and the risk information, constructing a risk association knowledge graph, and obtaining relationships between different risk factors and ship risks; s4, the risk factors are clustered, a risk identification model is constructed according to a clustering result to carry out quantitative evaluation on risks, a shipping risk level is obtained according to a shipping risk inference rule, if the shipping risk level is a low risk or a high risk, direct output is carried out, and otherwise, S5 is carried out; and S5, based on a quantitative evaluation result, carrying out weighted average calculation on the multi-factor risk to obtain a risk index, and determining a shipping risk level of the ship according to the risk index. Compared with the prior art, the method has the advantages of being high in precision and reliability.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

A system for controlling robot task decision-making on the basis of a semantic network and a knowledge base, the system comprising: a semantic network module, which is used for providing a semantic network for constructing a task decision-making behavior tree, wherein the semantic network is supplemented and expanded by means of question-answering results of a large language model and a knowledge base; a large language model module, which is used for using the large language model to perform knowledge question-answering and output the question-answering results; a knowledge base module, which is used for providing knowledge for the construction of a task decision-making behavior tree; a task decision-making module, which is used for constructing the task decision-making behavior tree on the basis of the semantic network module, the large language model module and the knowledge base module, and specifically, constructing nodes of the decision-making tree by means of querying the semantic network, performing node parameter filling by means of the question-answering results of the large language model, and performing node parameter filling by means of the knowledge base; and a variable storage module, which is used for storing temporary variables of when the task decision-making behavior tree is constructed. On the basis of the system, a decision-making behavior tree is constructed more accurately.

The invention provides a rapid construction and reuse method and system for an application framework, and relates to the technical field of application framework construction, and the method comprises the following steps: mapping code snippets to a high-dimensional tensor space, extracting multi-level code features, and constructing a code association graph and a deep semantic network; constructing a hierarchical Bayesian network based on the spatial-temporal feature map, executing variational reasoning and adaptive particle filtering, and generating a reconstruction code; driving a multi-level generation network by using a dual-dimension discriminator, optimizing a code structure and a dependency relationship, and generating a final optimization code through program slicing and incremental synthesis; according to the method, rapid construction and reuse of the application framework are realized, and the code quality and the execution efficiency are improved.

The invention discloses a surface defect detection method and system based on feature focusing refinement, and relates to the technical field of defect detection. The method comprises the steps that a target feature focusing refining defect detection model comprises a target edge focusing feature extraction network, a target multiplexing fusion semantic network, a target semantic-channel-space weighted refining network and a target detection layer; outputting a plurality of defect edge multi-scale focusing feature vectors based on the to-be-detected image through a target edge focusing feature extraction network; inputting each defect edge multi-scale focusing feature vector into a target multiplexing fusion semantic network to correspondingly generate a plurality of multiplexing fusion semantic features; correspondingly determining a plurality of semantic-channel-space weighted refinement features based on each multiplexing fusion semantic feature by adopting a target semantic-channel-space weighted refinement network; and outputting a defect detection result of the to-be-detected image based on each semantic-channel-space weighted refinement feature through a target detection layer. And the small target defect detection precision is improved.

A system and method for accessing drilling records. A semantic web for drilling records is built, wherein building includes converting drilling records retrieved from a database into a Resource Description Framework (RDF) format and storing the RDF formatted drilling records in the semantic web. Transfer learning is applied to drilling domain knowledge to obtain a large language model (LLM) with drilling domain knowledge. A schema is applied to unstructured records to extract information from unstructured records and the extracted information is then stored in RDF format to the semantic web. The LLM is configured to query the semantic web and to manipulate RDF formatted data within the semantic web.

The invention relates to the technical field of knowledge maps, in particular to a digital human knowledge map construction method for famous and old traditional Chinese medicine experience live inheritance, which comprises the following steps of: acquiring traditional Chinese medicine literature data, disassembling literatures sentence by sentence, cutting continuous statements into independent vocabulary units, and marking the grammatical effect of each vocabulary in the sentence; by analyzing a combination rule between adjacent vocabularies, multiple entities in a text are identified, and a literature entity set is generated. According to the method, through grammar labeling and context mode analysis, the accuracy and coverage of entity extraction are improved, and through combination of co-occurrence frequency and modification relation derivation, entity association interpretability and semantic network dimension optimization, dynamic weight calculation and attribute fusion, the self-adaptive optimization capability of the knowledge graph is achieved; according to the method, a matching degree calculation and closed-loop verification mechanism is adopted, the accuracy of clinical taboo recognition is improved, a term system and a dynamic evolution mechanism are structured, and a traditional Chinese medicine knowledge system considering deep semantic expression and persistent evolution is constructed.

The invention discloses an intelligent archive opening and identifying method based on a large model, particularly relates to the technical field of archive data auditing, and is used for solving the problems of insufficient cross-modal data analysis capability, lagging rule updating and low man-machine cooperation efficiency in the prior art. Fusing cross-modal features of texts, images and metadata through a hybrid expert model to generate multi-modal feature vectors, and dynamically allocating the multi-modal feature vectors to a rule network, a semantic network and a domain network for cooperative processing based on attention weights; the rule network parameters are optimized through gradient projection constraint, and regulation-driven real-time adaptation is achieved; matching sensitive data in combination with a multi-dimensional feature matrix of auditing personnel, and optimizing task allocation accuracy; removing redundant links by utilizing value flow analysis to generate a lightweight process, and recording as a tamper-proof evidence chain through a block chain evidence storage solidification operation; the auditing efficiency and accuracy are improved, and the compliance traceability is guaranteed.

A method and system controlled by a human machine interface (HMI) connected to an industrial process control and automation system controlling an industrial process. The HMI collects information and data from a semantic network in response to a natural language request input into the HMI by an operator. The HMI generates a visual layout for the received information and data that is displayed to the operator using the visual layout.

The invention discloses an information extraction task-oriented cue word design and optimization method and system, and the method comprises the steps: carrying out the preprocessing of related texts in a field, obtaining the preprocessing text data, and building an environment system among a front-end interface, a large language model and an ontology database; cue words are designed on the basis of the cognitive linguistics principle, when a query request is input in a front-end interface, the preprocessed text data are sent to a large language model so that domain ontology-based information extraction can be carried out according to the cue words, and matching with an ontology database is carried out to obtain a preliminary information matching result; feedback information is obtained through a multi-round dialogue result of the large language model and a manual proofreading result based on a preliminary information matching result, the design structure of cue words is optimized, and a semantic network constructed based on a domain ontology in an environment system is optimized. According to the method, different levels of linguistics are combined, and the cue words are ensured not only to be correct in language structure, so that a large model is guided to generate more accurate and useful results.

The invention provides a semantic input-based artistic font generation method, which comprises the following steps of: acquiring text data required by artistic font generation, and performing word segmentation processing on a text by adopting a word segmentation method to obtain a keyword list; performing semantic extension on each keyword in the keyword list according to a pre-constructed HowNet knowledge base to obtain synonyms, hypernyms and hyponyms of the keywords; performing word frequency statistics and TF-IDF weight calculation on the expanded keyword list, and screening out a core semantic concept required by the keyword art font generation with the highest weight; calculating semantic similarity among the core semantic concepts, and constructing a semantic similarity matrix; taking the screened core semantic concepts as nodes and the semantic similarity as weights of edges, and constructing a semantic network atlas; k-means clustering analysis is carried out on the semantic concept vectors, concepts with high semantic similarity are clustered according to a clustering result, and a semantic vector representation model required by artistic font generation is constructed.

The invention discloses a resume analysis method and system based on a dynamic semantic network, and relates to the technical field of data information processing. The method comprises inputting a resume; according to the file type of the resume, multi-mode resume analysis engine processing is carried out, and information extraction is carried out; constructing a dynamic semantic network according to the extracted information; and outputting structured data based on the stored knowledge graph in combination with an industry self-adaptive mechanism to complete resume analysis. The resume analysis system is used for realizing the resume analysis method. According to the resume analysis method and system, the effects of high resume analysis accuracy, high processing efficiency and high industry adaptability can be achieved.

The invention discloses an instruction-driven personalized fashion image editing method. The method comprises the following steps: 1, defining categories of editing tasks, and constructing an original image-reference image-target image-text editing instruction quaternary data set for different editing tasks; 2, constructing a target semantic network, generating target image semantic information following an editing instruction and an original image, and taking the target image semantic information as human body semantic information of an editing model; 3, constructing a unified editing network, including constructing a semantic control network, adding a visual joint module and applying a low-rank fine tuning module, so that a plurality of editing tasks use the same framework to obtain corresponding editing capabilities; and 4, constructing a multi-task low-rank adjustment module, and enabling the framework to have the capability of aligning different editing instructions to different editing tasks through joint training. Finally, an independent and unified framework among different tasks is realized. According to the method, experiments are carried out on a constructed specific data set, and good results are obtained in both quantitative and qualitative aspects.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention discloses an online medical inquiry method based on language reasoning analysis of a large language model. The method comprises the following steps: acquiring doctor seeing information of a patient; constructing a medical knowledge base and a drug database; carrying out keyword extraction on the doctor seeing information of the patient based on the medical concept to construct an inference tuple database containing symptom elements, diagnosis elements, complication elements and treatment elements; and constructing a corresponding semantic network by taking each element in the reasoning tuple database as a node and taking a medical reasoning direction as a relation edge, and forming a reasoning model by the semantic network and a pre-trained large language model. The invention further provides an online medical inquiry system. According to the method provided by the invention, the medical concept can be fused into the man-machine interaction process to optimize the logicality of the inquiry result, so that more reliable medical suggestions are provided for the user.

The invention belongs to the field of examination systems, and particularly relates to an online examination automatic test paper composition method based on a knowledge graph, and the method comprises the following steps: S1, data collection; s2, constructing an atlas; s3, collecting and analyzing examinee information; s4, analyzing examination requirements; s5, formulating a test paper generation strategy; and S6, test paper generation and verification. According to the online examination automatic test paper composition method based on the knowledge graph, knowledge materials are widely collected through multiple channels, data mining and semantic network technologies are used for carefully analyzing knowledge point relations, and the constructed knowledge graph is distinct in hierarchy and rich in semantics. And formulating a strategy by using an intelligent algorithm according to knowledge level evaluation and examination requirements of examinees during paper composition. For weak or important examination knowledge points of examinees, the number of questions, score weights and difficulty levels can be accurately controlled, blindness of traditional test paper composition is avoided, test paper is made to closely fit the reality of the examinees, and the knowledge mastering and application ability of the examinees is accurately evaluated.

The invention belongs to the technical field of information management, and discloses an intelligent education activity management system and method based on a large model. The system comprises four layers of data acquisition and access, processing and storage, intelligent analysis and decision, and user interaction and application. Education data are analyzed through fusion of transfer learning and a large model, an activity process is optimized based on a semantic network and the large model, and personalized learning services are provided by utilizing emotion interaction and the large model. The method is implemented through the steps of data acquisition, processing, model training, application and the like. Experiments show that the method can significantly improve student scores, improve educational activity organization efficiency and student satisfaction, provide powerful support for intelligent management of educational activities, and have good application prospects.

The invention provides a scientific research document information extraction method and device, and the method comprises the steps: constructing a term semantic network from a scientific research document; performing confidence degree sorting on the term semantic network to obtain a candidate term extraction result in the scientific research document; according to a candidate term extraction result, extracting concept attributes from the term semantic network; and based on the candidate term extraction result and the concept attribute, clustering is carried out, and a domain knowledge graph is constructed. The method comprises the following steps: constructing a term semantic network, carrying out confidence ranking, obtaining candidate term extraction results of scientific research documents, obtaining concept attributes of the term semantic network, and clustering the candidate term extraction results and the concept attributes to obtain a domain knowledge graph. Automatic acquisition of key information such as domain terms, concepts and attributes in different types of scientific research documents is realized, a description system of corresponding domain knowledge is constructed, a logic structure implied in the scientific research documents is accurately acquired, and powerful support is provided for management, analysis and utilization of scientific research information.

The invention relates to the technical field of information pushing, in particular to a platform user interest recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring user action data and environment perception data, analyzing the user action data and the environment perception data, and constructing a unified space-time semantic network; according to the unified space-time semantic network, analyzing scene features of the user, and determining a multi-dimensional dynamic scene feature set; and analyzing the multi-dimensional dynamic scene feature set, determining composite scene demand information of the current user, and retrieving and pushing real-time demand information of the user according to the composite scene demand information. According to the method and the device, the perception capability of the pushed information to the user demand scene is improved, the multi-dimensional dynamic adaptation of the pushed information to the platform user is realized, the information pushing accuracy for the platform user is improved, and the composite information demand of the user in a complex scene is met.

The invention discloses a multi-task semantic communication system resource allocation method based on deep reinforcement learning, and belongs to the technical field of wireless communication, and the method comprises the steps: constructing a semantic relay assisted multi-task semantic communication network model; based on a multi-task semantic communication network model, establishing a multi-task resource allocation optimization model by taking maximization of multi-task user experience quality as a target; wherein the multi-task resource allocation optimization model is used for adjusting power allocation, sub-channel allocation and transmission semantic symbolic number allocation of each task so as to maximize the multi-task user experience quality; and constructing and training a hybrid deep reinforcement learning model to obtain a strategy network capable of realizing optimal resource allocation, and performing wireless resource allocation optimization on the basis of the multi-task resource allocation optimization model. According to the invention, while the limitation of the user equipment is met, the semantic network deployment overhead is reduced, and the efficient utilization of spectrum bandwidth resources is realized.

The invention discloses a graphene industry application discovery method based on a large model and knowledge graph analysis, which comprises the following steps: S1, collecting patent literatures, scientific research papers, market analysis reports and industry news related to graphene, and constructing a multi-source heterogeneous data set; s2, preprocessing the multi-source heterogeneous data set; s3, performing information extraction on the processed multi-source heterogeneous data set by utilizing a BERT model, wherein the extracted information comprises technical key points, application fields, market demands and technical trends; s4, constructing a graphene patent and technology trend knowledge graph, and integrating the extracted key information into a unified semantic network; and S5, performing application discovery analysis based on the constructed graphene patent and technology trend knowledge graph, identifying a potential application scene of the graphene material, and providing application optimization suggestions and new market opportunities. According to the method, the analysis efficiency of a research institution is remarkably improved, and a scientific basis is provided for strategic decision making of the research institution in an emerging market.

The invention discloses a cue word logic configuration method based on context semantic enhancement, and belongs to the technical field of artificial intelligence and natural language processing. The method comprises the steps of data collection, data preprocessing, dynamic semantic anchor point generation, semantic field strong quantitative modeling and cue word logic configuration. Dynamically generating a semantic anchor point set capable of representing core semantics based on a semantic network structure contribution degree calculation and aging attenuation mechanism; further, a three-dimensional semantic field model is constructed, the timeliness intensity, the emotional intensity and the credibility intensity of each semantic anchor point are quantified, and comprehensive semantic field intensity data are obtained in combination with a multi-dimensional attenuation weighting and credibility correction method; according to the method, the context adaptability, the logic consistency and the dynamic adjustability of prompt word configuration can be remarkably improved, and the method has good application value and popularization prospects.

The invention provides a control method and system of a double-arm clothes folding robot, and relates to the technical field of robot control, and the control method constructs a perceived control closed loop by fusing visual, force and tactile multi-modal data. The touch sense and the force sense make up the blind area of the vision in the self-shielding scene, and grabbing, sliding and pleating perception is improved. Based on clothing joint characterization of a visual semantic network, geometric morphology, semantic components and confidence coefficient are structured and coded, a robust state fusing prior and real-time observation is provided for planning, and decision errors caused by deformation and noise are reduced. Long-time-history folding is decomposed into sub-target sequences through a hierarchical strategy, action closed-loop correction is conducted through multi-modal data, and cooperative control over grabbing, folding and pleating is achieved. And finally, a torque instruction is generated based on a control algorithm, interaction compliance and safety are ensured, and the folding success rate and operation stability of the double-arm clothes folding robot under different materials, sizes and initial forms are integrally improved.

The invention discloses a method for constructing a cultural relic knowledge organization, expression and characterization model for a Song charm theme. The method comprises the following steps: S1, constructing a knowledge classification system according to Song charm Shaoxing cultural relic types; s2, modeling knowledge entities and association thereof by using a semantic network; and S3, performing semantic representation of entities and relationships by using the multi-modal data. According to the method, systematic classification and semantic association of cultural relic knowledge are realized, and the structured level of cultural relic data is improved; the accuracy of knowledge reasoning and recommendation is improved, and intelligent service capability is provided for related fields of cultural relics; cross-medium and multi-dimensional knowledge integration and expression are supported, and technical support is provided for research and propagation of cultural heritage; meanwhile, the method is high in universality and can be popularized to other cultural heritage protection scenes, and wider value application is achieved.

The invention discloses a park digital twinning modeling method based on a generative AI technology, and relates to the technical field of digital twinning modeling, and the method comprises the steps: carrying out the alignment of point cloud, image and state data, completing the preprocessing through topological adaptive filtering and multi-resolution voxelization, frequency domain harmonic fusion and hypersurface texture excitation and time delay coupling fuzzy clustering, and obtaining a digital twinning modeling result; constructing a cross-modal spine network driven by a holographic entropy film, generating a hierarchical token through graph attention, and inputting a surge tuned diffusion converter model for iterative denoising and focus decoding to obtain a three-dimensional fragment; and fusing the fragments in a voxel space by using a cross attention kernel, and adaptively updating parameters through an entropy pulse closed loop until errors converge, so as to generate a high-precision digital twinborn model. A cross-modal semantic network is constructed by constructing spectral mapping and a holographic entropy film, a three-dimensional fragment is efficiently generated in a self-adaptive surge tuned diffusion converter framework through focus fusion, and the cross-modal semantic coupling efficiency, the generative reasoning convergence speed and the multi-fragment voxel fusion continuity are improved.