44 results about "Semantic link" patented technology

The invention discloses a knowledge conversion and fusion processing method and system for massive power grid operation data, and the method specifically comprises the steps: obtaining the attributes, including the alarm type, timestamp and severity, of equipment granularity based on alarm event data collected by a power distribution automation terminal, and carrying out the data cleaning and standardization processing, converting the multi-source heterogeneous alarm data into uniform event granularity representation; fusing the alarm event data and the operation event data, carrying out space-time alignment and causal association on the two types of events based on timestamps and equipment ID attributes, constructing a cross-data-source event semantic link network, and carrying out association analysis on a semantic relationship between the events; and according to a fault diagnosis result, historical records related to fault processing are extracted from the operation event knowledge base, similar fault processing modes are matched, and a specific fault processing scheme is generated in combination with factors of severity and influence range of the current fault.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications—such as capabilities, objects, mappings, policies, and workflows—as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

The invention relates to the technical field of large language models, in particular to a reservoir dam safety evaluation report multi-dimensional examination method based on a large language model. A reservoir dam safety evaluation report is processed by constructing a multi-dimensional insight brain-assisting model; using the risk preview to output a risk early warning text; performing scene verification, extracting parameters to drive external simulation, and outputting a comparison analysis report; a natural language processing technology is adopted, a traceable data link is constructed, and a semantic link traceability set is generated; and the multi-dimensional insight auxiliary brain model performs comprehensive review on the semantic link traceability set, records review difficult points, and generates a multi-dimensional review report through a man-machine common intelligence report engine. At present, a review method of a reservoir dam safety evaluation report has the problem of single review dimension. Therefore, the invention provides a reservoir dam safety evaluation report multi-dimensional examination method based on a large language model.

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 provides a context compression method based on multi-round dialogue intention graph construction, which comprises the following steps: S1, dialogue data acquisition and preprocessing: carrying out natural language processing on each dialogue unit; s2, intention atlas construction is achieved through node design and edge design, and each node comprises original text content and structured semantic information; s3, carrying out context compression and graph structure cutting, and only retaining sub-graphs forming a core semantic link; s4, dynamic context management and topic jump processing: in a multi-topic dialogue, when a user jumps or switches to a new topic, a system records a sub-graph of a current active topic, and contextual nodes of an inactive topic are frozen; s5, context sequence generation and model input: linearizing node contents in the cut sub-graph according to a dependent link sequence to generate a compressed context sequence, and transmitting the context sequence and current user input to a large language model for reasoning; and S6, continuous updating and feedback optimization are carried out.

The invention discloses an intelligent bidding document auditing method based on a block chain, and belongs to the technical field of data processing, and the method specifically comprises the following steps: analyzing a technical clause dependency relationship by means of a natural language processing technology, and generating a semantic link; establishing a mathematical constraint network and a dynamic verification condition for the parameter data; extracting spatio-temporal information from the qualification file, and dynamically binding the spatio-temporal information with project geographic coordinates; then, scanning a semantic link breakpoint matching missing pattern library, positioning a parameter value domain conflict range, verifying qualification file validity and a space-time matching degree, and obtaining a file defect marked with a risk level; secondly, extracting peripheral semantic features of defect nodes, screening candidate terms from a knowledge base, carrying out progressive transformation, retaining a framework, injecting quantitative indexes, and adding territory restriction clauses; and finally, simulating different correction combinations in a virtual environment, comprehensively evaluating technical feasibility, cost fluctuation and risk probability, and screening out an optimal correction scheme.

The invention relates to the technical field of trademark image retrieval, and discloses a trademark image retrieval method and system based on deep learning, and the method comprises the steps: obtaining a trademark image; scoring the trademark image to obtain image complexity; according to the trademark image and the image complexity, wavelet transformation is carried out to obtain a feature vector, dynamic adjustment is carried out, and feature representation is obtained; according to the feature representation, inputting the feature representation into a preset semantic association model to obtain candidate association information; according to the candidate association information, calculating the similarity between the image semantic vector and the brand node by using a knowledge graph reasoning technology, and obtaining an association result based on a similarity threshold value; dynamically generating a text label of the trademark image according to an association result, and mapping the text label and an entity in the brand knowledge base by adopting an entity linking technology to obtain a mapping result; and inputting the mapping result into a preset semantic link network to obtain a trademark retrieval result. According to the method, the accuracy of trademark image retrieval can be improved.

The invention provides a clinical data intelligent labeling and quality control method and system, and relates to the technical field of data processing, and the method comprises the steps: constructing a medical knowledge graph, carrying out the vectorization expression of nodes, extracting the context correlation features of a clinical text through a multi-head attention mechanism, and calculating the semantic similarity to form a hierarchical semantic link network; knowledge reasoning is carried out based on reinforcement learning to obtain an implicit association path, and the implicit association path is converted into a labeling rule and optimized. According to the method, efficient and intelligent labeling of the clinical data is realized, the labeling accuracy and consistency are improved, and the manual labeling cost is reduced.

Systems and methods are configured for implementing a learning memory system organized as a network of multi-level and heterogeneous cues and dynamic association of cues with data units. In various embodiments, one or more hives are constructed within the memory system. Each hive is responsible for storing data of a particular modality. In addition, one or more localities are constructed for each hive. Each of the localities for a particular hive includes one or more data units that are semantically related and interconnected based on a relation to each other. Each of these data units contains a data element, features of the data element, and parameters relevant to the data element. Further, a cue bank is constructed for each hive to store cues configured to semantically link one or more data units across the various localities for a particular hive.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications — such as capabilities, objects, mappings, policies, and workflows — as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

The invention discloses a flexible operation and maintenance early warning device based on artificial intelligence. The flexible operation and maintenance early warning device comprises a multi-source data acquisition module, a data preprocessing and fusion module, an AI state recognition module, a risk reasoning and knowledge graph module, a flexible strategy engine module and an early warning output module. The device realizes cleaning, noise reduction, time alignment and feature fusion of multi-source data by collecting equipment operation indexes, log behaviors, security events and environmental parameters. The AI state recognition module is used for recognizing an operation state, a behavior mode and potential abnormity, and the risk reasoning and knowledge graph module completes risk source positioning and reason verification based on an entity relationship and a semantic link. And the flexible strategy engine dynamically generates an early warning strategy according to a reasoning result, and pushes early warning information in a multi-channel manner through an early warning output module. The device can realize high-accuracy, interpretable and self-adaptive operation and maintenance early warning capability, and is suitable for intelligent operation and maintenance management of hospital machine rooms and key business systems.

The invention relates to the technical field of semantic retrieval, in particular to an intelligent law retrieval system based on a semantic reasoning atlas, which comprises a semantic element extraction module for extracting law elements to generate a semantic unit set, a law semantic atlas construction module for establishing concept nodes and reasoning atlases, a semantic association reasoning module for screening paths to generate a reasoning sequence, and a semantic association reasoning module for establishing a semantic association reasoning sequence. The retrieval intention mapping module constructs a mapping set in combination with user query, and the legal result aggregation module matches provisions and cases and generates legal semantic retrieval results through screening, sorting and integration. According to the method, law element dependency is recognized through semantic decomposition, a causal semantic link is formed through structured aggregation expression, logic paths are dynamically integrated to achieve law association accurate modeling, query intention strong mapping is constructed through semantic similarity and logic constraints, the result interpretation depth and aggregation capability are improved, matching is made to conform to the law context, and the method has the advantages of being high in practicability and easy to popularize. And the identification and inference precision of the system on logic association is effectively enhanced.

The invention discloses a journal paper intelligent typesetting and management method and system, and belongs to the technical field of management, and the method comprises the steps: constructing a journal typesetting parameter knowledge base, and carrying out the structural representation and semantic description of the typesetting requirements of various periodicals through the ontology and semantic web technology, and obtaining a standardized journal typesetting parameter ontology library; introducing a graph-based reference link analysis method for the recognized structured paper content, and converting a reference relationship in the paper into a semantic link graph; the content is uniquely identified through a Hash algorithm, and rapid retrieval and updating are achieved.

The application discloses a document semantic DOM network construction method and system, belongs to the technical field of document processing and natural language processing, and the PDF document semantic DOM network construction method executes the following steps through a computer: a large language model is used to perform semantic role and function type analysis on information units extracted from a PDF document, and semantic nodes are generated; a preliminary hierarchical tree structure is constructed in combination with layout features and semantic content, and an optimization algorithm is used to adjust the tree; relationship confidence is calculated based on the judgment of the large language model and a preset weighting formula, so that a semantic link across levels is established, and finally a semantic DOM network is formed; and the output semantic network can reveal deep argumentation logic of a document, supports advanced semantic queries, and provides a high-quality structured data basis for downstream knowledge graph construction and an intelligent question answering system.

In an illustrative embodiment, systems and methods for extracting, and organizing, and visualizing details of options provided by multiple organizations responsive to a risk fulfillment request include analyzing unstructured electronic documents to recognize various quote aspects in their contents, label the quote aspects according to a classification, and store semantically linked quote aspects. The systems and methods, for example, may enhance semantically-linked groups of quote aspects with attributes according to a corresponding ontology, and confirm the labeling, grouping, and enhancing through feedback interactions performed with a user via a graphical display. The confirmed information may be used to generate a visualization of options for fulfilling the request, each option qualified and / or color-coded through automated learned analysis for review by the user.

The application discloses an improved knowledge graph construction method and system for textile weaving, relates to the technical field of textile manufacturing, and solves the problems of insufficient feature description ability of the knowledge graph of the prior art for the textile weaving industry, difficulty in expressing the time sequence logic evolution characteristics and the causal relationship under the multi-source disturbance, lack of entity boundary definition and semantic recognition mechanism for weaving professional terms, and serious semantic mismatch of relationship extraction, the method comprising: constructing a knowledge base data set of the improved knowledge graph, modeling five concept domain ontologies, defining a semantic link paradigm, enhancing co-occurrence of graph entity boundaries, and enhancing attention graph extraction with relationship type perception, carrying out weaving knowledge entity recognition and relationship extraction result reasoning output, introducing a structured energy consumption semantic network to realize the transition of the model from data driving to knowledge driving, and realizing the transition from "numerical correlation" to "causal interpretability" through an embedded semantic layer fusion mechanism.

The application provides a semantic analysis and keyword-driven document consistency comparison method. First, the directory structure and weight model are constructed by analyzing the directory of the input document, and the key chapters are located and the content is extracted. Then, the pre-trained language model is used to generate semantic vectors, and combined with multi-layer semantic analysis technology, the keyword capture and similar semantic search of the document are carried out. Through multi-level semantic comparison at the word, sentence and paragraph levels, combined with semantic link tracking and intelligent adjustment mechanism, the differences between documents are accurately identified to ensure content consistency. Cosine similarity, Euclidean distance and other methods are used to match and verify the content, identify logical contradictions and information omissions, and finally output structured text. The application can effectively improve the accuracy and efficiency of document comparison and ensure the accuracy and consistency of document content.

The invention relates to the technical field of enterprises, and aims to solve the problems that in the existing expert semantic network construction process, an expert still stays in an unstructured carrier of text archives, manual records and experience summarization, so that the ability boundary of an expert is fuzzy, professional attributes lack clear semantic connection, and systematic semantic link expression is difficult to form; the enterprise field expert semantic network construction system comprises a pre-division module, an updating module, a reconstruction module and a network construction module. Through semantic hierarchy construction, relation extraction and network expansion mechanisms, the internal logic of expert knowledge can be subjected to deeper semantic modeling, and the expert knowledge originally dispersed in documents, experience records and unstructured texts can be extracted, analyzed and organized in a unified manner, so that the expert knowledge has expandability, relevance and reasonability, and the expert knowledge can be extracted, analyzed and organized in a unified manner. Therefore, the integrity and accuracy of the expert knowledge in the enterprise field in the semantic description level are effectively improved.

The present invention proposes a spatio-temporal semantic query optimization method that integrates an attribute graph database and a relational database. It belongs to database querying, and uses a hybrid storage method of GDBMS and RDBMS to manage spatio-temporal data, and optimizes the indexing of spatio-temporal entities through specific indexing strategies. In the time dimension, MTT indexing and time table partitioning are used; in the space dimension, Geohash encoding is adopted for partitioning. A spatio-temporal entity semantic link between databases is established through an entity association mechanism based on object mapping. The hybrid query is converted into executable sub-queries for GDBMS and RDBMS, and data synchronization is performed between the two databases. The method of the present invention can bridge the attribute graph and the spatial engine to achieve efficient spatio-temporal semantic querying. The cross-database spatio-temporal encoding index accelerates graph traversal and pattern matching. It supports customizable geo-spatio-temporal analysis and enhances semantic representation capabilities. The lightweight and low-code architecture is conducive to integration with existing GIS platforms.

The invention discloses a differentiated building efficient interaction sensing general calculation modular intelligent connection regulation and control device and application, and belongs to the technical field of building electric power regulation and control. The device comprises a sensing unit, a communication unit, a computing unit and a cloud platform which are connected through a unified bus. The sensing unit collects multi-dimensional information and guarantees data quality, the communication unit integrates multiple protocols to achieve cross-protocol semantic intercommunication, the computing unit architecture adapts to different requirements of differentiated buildings, and the cloud platform provides management scheduling services. The invention further provides a hierarchical and domain-divided building power grid interaction semantic modeling method, three layers of sub-models and an end-to-end semantic link are constructed, and efficient interaction between the building and the power grid is achieved. The method solves the problem of pain points such as heterogeneous intercommunication, adapts to various types of buildings, and provides support for transformation of the buildings to active energy units and construction of novel electric power systems.

The invention provides a bidding file abstract generation method and system based on a large language model, and relates to the technical field of natural language process.The method comprises the steps that firstly, domain terms are extracted from an input to-be-processed bidding file set to construct a term library, and file units are split to form a domain semantic chain; inputting the term library into a pre-trained large language model to adjust the semantic analysis weight so as to obtain a semantic analysis model adaptive to the field; inputting semantic nodes in the semantic chain into the semantic analysis model to extract core expressions so as to form a dynamic abstract fragment set; determining an abstract fragment association sequence according to a semantic node connection relationship, and joining to obtain a preliminary fusion abstract text; and finally, semantic feedback of the reader is received, abstract fragment expressions and sequences are adjusted, a semantic chain is updated, and a final abstract is generated and output, so that the high-quality bidding document abstract can be efficiently generated.

The invention discloses a quota term content standardization conversion method based on semantic link and retrieval enhancement, and relates to the technical field of standardization conversion, the method comprises the following steps: determining a structured field of a quota term based on multi-source data of power grid quota business; performing semantic understanding on a to-be-processed quota clause text through the trained large language model, mapping a corresponding field in the quota clause text into a structured field, and outputting the structured field; performing grammar and business verification on the structured field output by the large language model, and obtaining an object candidate field from the verified structured field; performing standard entity alignment in a pre-constructed power grid entity dictionary and a pre-constructed knowledge graph through a semantic link module according to the object candidate fields; inputting the structured field aligned by the standard entity into a retrieval enhancement generation module used for realizing structured quota record joint generation; the whole process is well documented and is convenient to audit and maintain.

The invention discloses a personalized recommendation method for taxi passenger carrying. The method comprises the following steps: S1, generating a multi-target taxi passenger carrying hot spot area based on big data; s2, constructing a taxi travel knowledge graph; and S3, recommending a personalized cruise path of the taxi based on the knowledge graph and an interest capture recommendation algorithm. The invention provides a taxi passenger carrying personalized recommendation method which comprises the steps of multi-target taxi passenger carrying hotspot generation based on big data, taxi travel knowledge graph construction and personalized recommendation based on a knowledge graph and an interest capture recommendation algorithm. The knowledge graph is used for providing richer context information and semantic association information for the recommendation system, semantic linking is carried out on various information, so that the recommendation accuracy and interpretability are improved, an interest module is introduced, the recommendation system can better understand the requirements and interests of drivers, and the recommendation effect is improved. Fine-grained interests of taxi drivers on different attributes are considered, and more accurate personalized cruise paths are recommended for the taxi drivers according to demand preferences of the taxi drivers.

The invention provides a traffic logistics-oriented large model knowledge base construction method and system, and the method comprises the steps: carrying out the semantic relation mining based on a standard term set in the traffic logistics field, generating a structured term graph, constructing a template knowledge probe set, inputting a pre-training large language model, and carrying out the directional activation detection operation, thereby obtaining an implicit knowledge neuron cluster, the method comprises the following steps of: carrying out space-time correlation analysis on an activation response mode of the neural network, extracting distribution characteristics of neuron activation intensity and a dependency relationship among the characteristics, mapping an implicit knowledge neuron cluster into a semantic traffic logistics field knowledge unit, carrying out hierarchical organization and semantic linking according to a logic correlation degree and a functional attribute classification result, and carrying out hierarchical classification on the neural network. Generating a traffic logistics field structured knowledge network; and calling a standard traffic logistics business problem set to perform knowledge utility verification, and dynamically optimizing and adjusting knowledge units and association relationships to obtain a large model knowledge base, thereby providing high-quality knowledge support for intelligent application in the field of traffic logistics.

The application provides a semantic task processing method and device, electronic equipment and storage medium. The semantic task processing method comprises: acquiring language interaction information input by a user in a multi-round dialogue, analyzing the language interaction information, and generating a plurality of basic semantic tasks; generating a semantic task link based on a dependency relationship between the basic semantic tasks, and assigning a corresponding task intelligent agent to each basic semantic task based on the semantic task link; executing the corresponding basic semantic task through each task intelligent agent, generating a task signature of each basic semantic task, and generating a semantic link fingerprint based on the task signature. The application analyzes the language interaction information into basic semantic tasks, structures the basic semantic tasks into executable and traceable semantic task links, and finally generates a semantic link fingerprint, which guarantees the flexibility of the generated question and answer, and improves the controllability, reliability and traceability of complex multi-step queries.

In an illustrative embodiment, systems and methods for extracting, and organizing, and visualizing details of options provided by multiple organizations responsive to a risk fulfillment request include analyzing unstructured electronic documents to recognize various quote aspects in their contents, label the quote aspects according to a classification, and store semantically linked quote aspects. The systems and methods, for example, may enhance semantically-linked groups of quote aspects with attributes according to a corresponding ontology, and confirm the labeling, grouping, and enhancing through feedback interactions performed with a user via a graphical display. The confirmed information may be used to generate a visualization of options for fulfilling the request, each option qualified and / or color-coded through automated learned analysis for review by the user.

Techniques are disclosed for evaluating immutable ledger requests using a semantic taxonomy. An example system includes a memory comprising instructions, and a processor configured to execute the instructions. Example instructions include providing a semantic taxonomy of semantically linked terms and phrases; using the semantic taxonomy to validate input fields of requests for immutable ledger transactions according to one or more predefined templates; and submitting the validated requests to an immutable ledger.

The application provides a bidding document abstract generation method and system based on a large language model, and relates to the technical field of natural language processing. First, the inputted to-be-processed bidding document set is extracted to construct a term library, and the document unit is split to form a domain semantic chain. Then, the term library is inputted into a pre-trained large language model to adjust the semantic parsing weight, so as to obtain a domain-adapted semantic parsing model. Then, the semantic nodes in the semantic chain are inputted into the semantic parsing model to extract core expressions and form a dynamic abstract fragment set. Then, the abstract fragment association order is determined according to the connection relationship of the semantic nodes, and a preliminary fusion abstract text is obtained. Finally, the reader semantic feedback is received to adjust the abstract fragment expression and order, the semantic chain is updated to generate a final abstract, and the final abstract is outputted, so that a high-quality bidding document abstract can be efficiently generated.

The invention discloses a document semantic DOM (Document Object Model) network construction method and system, and belongs to the technical field of document processing and natural language process.The PDF document semantic DOM network construction method executes the following steps through a computer: carrying out semantic role and function type analysis on an information unit extracted from a PDF document by utilizing a large language model to generate semantic nodes; constructing a preliminary hierarchical tree structure in combination with layout features and semantic contents, and adjusting the tree by adopting an optimization algorithm; the relationship confidence is calculated based on judgment of a large language model and a preset weighting formula, so that a hierarchical semantic link is established, a semantic DOM network is finally formed, the output semantic network can reveal deep argumentation logic of a document, advanced semantic query is supported, and the semantic DOM network can be widely applied to the field of document teaching. And a high-quality structured data basis is provided for downstream knowledge graph construction and an intelligent question and answer system.