330 results about "Entity type" patented technology

The invention provides a supply chain sales anomaly detection and root cause analysis system and method fused with a knowledge graph, and the system comprises a demand collection and preprocessing module which is used for connecting an order system, a supply chain system, a customer relationship management system and an external data source, and completing the data cleaning, entity analysis and feature extraction; the supply chain knowledge graph construction module is used for defining an entity type and a relationship type; the real-time anomaly detection module is used for accessing a sales index data stream, performing anomaly detection in combination with lightweight filtering and a graph neural network model, and calculating node and global anomaly scores; and the visual report generation module is used for automatically generating a visual report. According to the method, the dynamic supply chain knowledge graph is constructed, the graph neural network is applied, multi-source heterogeneous data is deeply fused, the complex dependency relationship between entities is effectively captured, the accuracy and timeliness of sales anomaly detection are remarkably improved, automatic positioning of abnormal root causes and evidence chain tracing are achieved, and the analysis efficiency is greatly improved.

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

The invention belongs to the technical field of natural language processing and multi-modal information extraction, and particularly relates to a multi-modal named entity recognition method based on semantic alignment and cross-modal graph fusion, which comprises the following steps: S1, acquiring a data sample containing a text sequence and image content; s2, encoding the text and the image into vectors respectively; s3, similarity is calculated through a trainable bilinear function, and optimization is carried out through loss comparison; s4, cross-modal attention is used to enhance association information between modals; s5, determining the proportion of reserved image information through a modal matching module; s6, introducing a gating mechanism to dynamically fuse visual and text features; s7, realizing local and global information complementation by a cross-modal graph fusion model; and S8, inputting the fused representation into the CRF layer to predict the entity type. According to the method, fine semantic alignment can be realized in a weak image-text correlation context, and balance between local entity recognition and global semantic understanding can be achieved.

The invention relates to an unmanned aerial vehicle fault traceability analysis method and device, equipment and a storage medium. The method comprises the steps of defining entity types and relationship types among entities based on a predefined fault ontology model to construct a mode layer of an unmanned aerial vehicle fault knowledge graph; based on the mode layer, extracting a fault triple from the multi-source operation data of the unmanned aerial vehicle by using a mixed extraction model, and constructing a fault knowledge graph containing instance data; endowing a dynamic weight probability representing confidence to a relation edge in the fault knowledge graph, and generating a probabilistic fault knowledge graph; and mapping to-be-analyzed fault information to the probabilistic fault knowledge graph, performing traceability analysis by using a hybrid inference engine, and outputting a fault reason and a transmission path. According to the method, structured deep fusion of domain knowledge and data value is realized, and the traceability conclusion is improved from qualitative judgment to quantitative decision support with confidence measurement.

The invention discloses a multi-level construction and intelligent recall strategy implementation method, system and device for an energy policy knowledge graph and a medium, and belongs to the technical field of energy policy monitoring, and the method comprises the steps: obtaining energy policy text data, carrying out energy policy correlation analysis, and extracting entity information, relation clues and text vector representation from an energy policy text; carrying out knowledge graph construction by utilizing the extracted entity information, relation clues and vector representation, and generating a knowledge graph structure comprising an entity type, an entity attribute, a relation type and confidence; updating the knowledge graph according to the operations of newly adding, revising and revoking the policy document; and executing a multi-path recall strategy including longitudinal traceability recall and transverse comparison recall by using the knowledge graph structure, and generating a relevance recall result related to the target policy. According to the invention, efficient traceability, accurate comparison and full-link intelligent management of energy policies are realized, and the policy retrieval efficiency and accuracy are remarkably improved.

The present invention relates to the technical field of wireless network optimization operations and maintenance, and provides a two-stage optimized wireless network optimization field long entity recognition method and system. The method comprises: using a pretrained long entity recognition model to process acquired text content to be recognized to obtain a long entity recognition result; by means of a first-stage predecessor task, acquiring a pretrained model TelBert having domain knowledge; and in a second stage, introducing semantic information related to an entity to obtain a machine reading comprehension framework-based long entity recognition model, and decoding the entity by means of a dual-pointer network. According to the present invention, knowledge in a specific field is learned by adding an entity type prediction task, the text representation learning capability of a base model is enhanced, and the difficulty of model tuning in a few-shot scenario is alleviated; the entity recognition model is improved to obtain an MRC-LER model suitable for document-level long entity recognition; and a semantic similarity-based evaluation index is proposed, and the effective extraction rate of entity key information is reasonably evaluated.

The invention provides a large model named entity recognition method based on representative sample selection and context enhancement, which comprises a representative sample selection module, an entity knowledge construction module, a dynamic context selection module, a large model calling module and an iterative feedback optimization module, according to representative sample selection, samples with representativeness and information diversity are automatically selected from unlabeled data for labeling through a sample screening strategy based on clustering, entity description integration aims at each entity type, a plurality of high-quality instances are extracted from labeled samples, and standardized entity definition or description prompts are constructed. According to the dynamic context selection, for to-be-recognized text content, a context example most relevant to a target text is dynamically selected from a historical annotation sample or a description set through a semantic similarity retrieval mechanism to serve as auxiliary prompt input, and the adaptability and generalization ability of LLM in a complex or variable scene are improved.

The invention discloses a knowledge graph-based search engine relation chain recommendation method, which comprises the following steps of: receiving a relation chain recommendation request submitted by a user through a search engine to obtain initial query information; performing semantic analysis and intention recognition on the initial query information, and determining a corresponding entity type and a relationship type; calling a dynamic knowledge selection and evaluation mechanism according to the entity type and the relationship type, and dynamically screening the original knowledge graph to obtain effective knowledge sub-graphs; performing diffusion type reasoning based on a knowledge graph diffusion model to generate a candidate relation chain path set; evaluating the quality score of each candidate path in real time; and performing dynamic screening according to the path quality score, and outputting a relation chain path recommendation result. According to the method, accurate mining and dynamic optimization of the knowledge graph relation chain path are realized, and the accuracy of a recommendation result and the flexibility of user interaction are improved.

The embodiment of the invention provides an abnormal working condition data analysis and early warning method and system for a drying machine, and the method comprises the steps: firstly constructing a diagnosis knowledge graph of the abnormal working condition of the drying machine, which comprises entity types of drying machine parts, operation parameters, abnormal working condition types, environmental factors and the like; then, working condition parameter data collected by a drying machine in real time is subjected to correlation mapping with the working condition parameter data, knowledge graph instantiation data are generated, multi-hop correlation reasoning is carried out according to a reasoning rule set in the diagnosis knowledge graph, and the diagnosis knowledge graph instantiation data is obtained; and constructing an abnormal association path set containing entity nodes, relation edges and attribute values, screening out a target abnormal association path of which the comprehensive confidence exceeds a preset threshold value from the abnormal association path set, generating dryer abnormal working condition early warning information according to information contained in the target abnormal association path, and sending the dryer abnormal working condition early warning information to a dryer monitoring terminal. Therefore, accurate analysis and timely early warning of the abnormal working condition of the drying machine are realized.

The invention relates to the technical field of agriculture, in particular to a large-model-driven agricultural knowledge graph analysis method and system, and aims to realize standardized processing of multi-source heterogeneous data through a three-stage preprocessing process and combine with a field adaptive large-model training technology so as to realize the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis system. A special system containing 2000 + entity types of crops / diseases / farming operation and the like is constructed. In the entity extraction link, the large model zero sample learning ability is utilized, novel agricultural entities can be automatically recognized, the entity recognition accuracy is improved by 35% compared with a traditional method, and particularly in cross-modal alignment of pest and disease damage images and text description, feature vector Euclidean distance minimization is achieved through a ResNet50-BERT fusion model, and the alignment precision reaches 92% or above. The dynamic updating mechanism captures three core periodicals and policy documents in real time on the basis of web crawlers, the monthly updating frequency of the knowledge graph is improved to four times in combination with an incremental updating algorithm, the timeliness and integrity of agricultural knowledge are ensured, and technical guarantee is provided for precise agricultural data management.

The invention provides an electric power document keyword extraction method based on Prompt and a knowledge graph, relates to the technical field of electric power document processing, and constructs a lightweight multi-level index knowledge graph in the electric power field by combining entity type and relation type division based on an electric power industry standard document and an electric power field corpus. The method comprises the following steps: performing vector modeling on a power document, constructing a multi-level index from an entity to a vector, realizing standardized semantic modeling and efficient hybrid retrieval of a power document field background, and obtaining a topic vector and a core paragraph of the power document in combination with power key information; according to the method, entity types are indexed in a knowledge graph by using subject vectors, similar entities are obtained to form knowledge sub-graphs, so that multilayer Prompt is obtained to guide a large language model to extract keywords, then knowledge graph similarity constraints are introduced to decode the output of the large language model, the keyword recognition capability in the power field is improved, and the keyword recognition efficiency is improved. And the accuracy of keyword type identification and the normalization of term naming are both considered.

The invention discloses a structured data storage method based on natural language transformation, which comprises the following steps of: a system initialization configuration stage: deploying a protocol adapter in a local memory of a PC (Personal Computer) client, and loading natural language processing pipeline configuration parameters; a heterogeneous data acquisition stage: capturing a multi-source text data stream through the protocol adapter, uniformly converting the multi-source text data stream into a standardized data packet, and sending the standardized data packet to a message queue theme; a text cleaning stage: a named entity recognition stage: inputting the pure text data into an NER module deployed with a language model loader; in the conditional feature extraction stage, feature vectors are generated for texts meeting preset conditions on the basis of entity type tags in the entity recognition result; and a consistent storage stage: inserting the entity identification results into a relational database in batches, and updating the entity mapping relationship in the cache. According to the method, the intelligent level of cache management is remarkably improved, and the access fluency of the key data of the user is guaranteed.

Systems, devices, and techniques are disclosed for efficient knowledge graph indexing and retrieval. Document chunks may be generated from documents. Summarizations may be generated from document chunks. Entity types, entity properties, relations, and relation properties may be generated from a subset of the summarizations. A schema including entity types, entity properties, relations, and relation properties may be generated. Entity property triplets and entity relation triplets may be generated from the summarizations based on the schema and linked to the document chunks. A knowledge graph including nodes representing entities from the entity property triplets and entity relation triplets and edges representing the entity property triplets and the entity relation triplets may be generated. A search query may be received. Nodes and edges of the knowledge graph that include the entities, the entity property triplets and the entity relation triplets most similar to keywords of the search query may be determined.

The invention discloses a combined multi-modal entity relationship extraction and generation method based on multi-view comparative learning, and particularly relates to the technical field of entity relationship extraction. The method comprises the following steps: converting triples of entity relationships in all extracted texts into a sequence consisting of position indexes of a head entity and an entity type thereof, a tail entity and an entity type thereof and a relationship between two entities, and generating a target index sequence from end to end in multi-modal input through a BART-based coding-decoding model; three positive samples are constructed for each training sample based on entity, image and context enhancement, a multi-view comparative learning algorithm is introduced, the algorithm adopts a cross entropy target of in-batch negative samples to minimize the distance between the positive samples, and intervals of a head entity and a tail entity in a sentence are specified through a target index sequence to obtain a multi-view comparative learning algorithm; and a category of the relationship so that the multi-modal representation can capture semantic similarities between samples with similar entities and relationship mentions.

The invention discloses a supply chain fraud behavior early warning method and device based on a large language model, and relates to the field of data analysis, and the method comprises the steps: obtaining supply chain multi-source data, and carrying out the processing of the supply chain multi-source data according to the data type; in the fine tuning process, a LoRA module is injected into a linear layer of a pre-trained large language model base, and the rank value of the LoRA module is dynamically adjusted according to the gradient; according to the text word segmentation data, the entity type, the aligned historical order data, the aligned historical logistics data and the dynamic space-time diagram, constructing a multi-modal prefix guide vector; the multi-modal prefix guide vector and an original input sequence of a linear layer of a pre-trained large language model base are spliced and then input into the linear layer of the pre-trained large language model base, a supply chain fraud behavior early warning model subjected to fine adjustment is obtained through fine adjustment, and early warning is conducted on supply chain fraud behaviors of related suppliers. According to the method, the problems of low supply chain fraud behavior identification accuracy, large training parameters and the like in the prior art are solved.

The invention provides a geological knowledge graph completion method based on a graph convolutional network, and the method comprises the steps: obtaining to-be-completed geological knowledge graph information, and enabling each entity in the geological knowledge graph information to have corresponding entity type information; the entity type information and the entity name are spliced to generate the entity representation containing the entity type information, so that the semantic ambiguity of the same-name entities is eliminated, and the uniqueness of the entities is improved; inputting the entity representation into a pre-training model to generate an initial entity vector; on the basis of the initial entity vector, an updated entity vector is calculated through a graph convolutional network model, and cross-sentence and cross-paragraph long-path entity association is captured; processing the updated entity vector through a scoring function based on tensor decomposition to obtain a triple score; and according to the triple score, high-confidence triads are accurately screened, and the problem of incomplete relation caused by cross-text association missing in a traditional geological knowledge map is effectively solved.

The invention discloses a coal mine hidden danger event intelligent reasoning method based on a knowledge graph, and belongs to the technical field of coal mine safety, and the method specifically comprises the steps: obtaining a to-be-deduced entity with a real-time data flow recognition state parameter exceeding a threshold value range; calling a state transition rule set according to the entity type and constructing an independent deduction process; executing state transition calculation in each deduction process to generate a state evolution sequence; state combination matching is carried out based on the relation mode of the static knowledge graph, and entity state pairs meeting relation triggering conditions are recognized; establishing a cross-process data channel between the related deduction processes and performing parameter conversion; state deduction is executed again based on the input parameters, and deduction network topology is constructed; and finally, a complete hidden danger evolution path is obtained through reverse tracking parameter transfer relation integration. According to the method, dynamic deduction of the coal mine hidden danger forming process and accurate identification of the linkage risk conduction path are realized, and the accuracy of potential safety hazard early warning is effectively improved.

The embodiment of the invention provides a large-model-based natural language query analysis and database field matching method, which comprises the following steps of: performing intention analysis and entity recognition on natural language query of a user based on a large language model (LLM), and extracting a potential keyword set; for each extracted keyword, binding an entity type through an STAM mechanism, and constructing an entity-type mapping relationship; a column description vector library is constructed, efficient retrieval is achieved in combination with an approximate nearest neighbor ANN algorithm, and results of semantic vector matching, editing distance matching and traceability enhancement matching are combined through a multi-strategy fusion matching mechanism to form a final candidate column set; a database metadata interface is connected, information is analyzed, a basic structure is constructed, semantic enhancement field description is generated, and output is organized in a tetrad structure; and reversely deducing the affiliated table through column matching, judging the table structure value by combining the information density in the table and the connectivity with other tables, eliminating redundant tables, and optimizing database representation.

The invention discloses a business database natural language question and answer processing method fused with a large model technology. The method comprises the following steps: S1, receiving an emergency query request input by a user in a natural language form; and S2, carrying out analysis and intention recognition on the emergency query request based on a large language model, and generating a group of candidate query intentions arranged in a descending order according to matching degree scores and corresponding key entity information. According to the method, a core link can be optimized by introducing a large language model technology: in an intention recognition stage, intention recognition accuracy is improved through a formula of fusing semantic similarity and entity type matching degree; a priority-based asynchronous execution strategy is adopted to improve the response efficiency; the response reliability is guaranteed through real-time and conflict verification; meanwhile, the system performance is continuously improved in combination with an offline model optimization link, the defects of an existing processing method are finally overcome, and an accurate, efficient and reliable solution is provided for business database natural language question answering in an enterprise emergency scene.

The invention discloses an oil and gas pipeline fault prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining and preprocessing pipeline associated data corresponding to an oil and gas pipeline based on sensing equipment, and obtaining pipeline target data; defining an entity type and a relationship type, and constructing a dynamic knowledge graph based on the pipeline target data, the entity type and the relationship type; when the leakage point is detected, determining a target sub-graph corresponding to the leakage point based on the dynamic knowledge graph, generating a migration sequence of the target sub-graph, and converting the migration sequence into a vector to be matched; and matching the to-be-matched vector with a sub-graph in a historical graph library to obtain a similar sub-graph, and determining a target fault probability of the oil and gas pipeline based on the similar sub-graph. The problem that an existing method is difficult to meet the requirements for dynamism, accuracy and the like of corrosion prediction in a complex pipeline system is solved, a dynamic knowledge graph is constructed as a core drive, Internet of Things perception, multi-modal data analysis and artificial intelligence are fused, and real-time prediction of the pipeline state is achieved.

The invention discloses an insurance intelligent question and answer method based on a large model RAG technology. The method comprises the following steps: constructing a vectorization knowledge base; receiving a question of a user, extracting an insurance proprietary entity from the question by combining the big language model with the cue word project, and labeling an entity type; the entity is linked with the standard product full name in the knowledge base, and question sentences are automatically clarified or rewritten based on a confirmation result; performing intention recognition by using a prompt word project in combination with a preset intention type, executing semantic routing according to a recognition result, and guiding a question sentence to a vectorized knowledge retrieval, structured data query or FAQ processing flow; in vectorization knowledge base retrieval, a multi-path recall and reordering method is adopted, and top-K reference knowledge blocks are output; and calling a large language model to generate a natural language answer. According to the method, high-precision, low-cost and extensible intelligent question answering can be realized in an insurance service scene, and the agent query efficiency and question answering experience are remarkably improved.

The invention provides a method and system for constructing a knowledge representation learning model based on multi-dimensional information interaction and dynamic frequency perception. The method comprises the steps of constructing a multi-dimensional information encoder, constructing a historical query encoder and constructing a multi-dimensional perception decoder. According to the method, firstly, multi-dimension, entity type and time structure information in time knowledge graph reasoning is effectively modeled through a multi-dimensional information encoder; next, a historical query encoder aggregates their neighbouring semantics through constructed query-related historical facts, and additionally obtains duplicated global candidate facts through dynamic historical frequency coding, thereby providing global constraints for scoring in the decoder. Finally, the entity representation is decoded using a multi-dimensional perceptual decoder.

The invention provides a diversified text sensitive data synthesis method for desensitization effect evaluation, which comprises the following steps: constructing a sensitive entity system meeting desensitization evaluation requirements, the sensitive entity system comprises a general field, a medical field and a financial field, and each field comprises a plurality of entity types; obtaining an original data set, counting entity distribution on the original data set, constructing a target distribution model, and designing a diversified strategy based on entity types and sentence patterns; guiding the large language model to generate candidate corpora according to the target distribution model and the diversification strategy, performing character-level alignment labeling and consistency verification on the candidate corpora, and generating a synthetic data set based on the candidate corpora; and performing multi-dimensional quality verification on the synthetic data set from the data layer, the entity layer and the semantic layer to obtain an evaluation result, and feeding back the evaluation result to a closed-loop controller to adjust a quota and a generation parameter so as to generate a final synthetic data set. The method can be applied to validity evaluation of a data desensitization tool, and the problems of single evaluation dimension, data sparsity and the like in text desensitization evaluation are solved.

The invention relates to the technical field of data processing, and provides an encrypted anonymous network flow analysis and identification method based on flow reconstruction, which comprises the following steps of: obtaining message sequences of a plurality of unknown protocols of a plurality of sessions; obtaining a plurality of initial clusters of the session; marking an entropy value mutation point of each message sequence; obtaining a plurality of message class clusters of each initial cluster; obtaining a static feature vector of each message sequence; obtaining a mapping symbol of each message sequence, and obtaining a receiving probability between different message sequences; performing semantic analysis on the message sequence, and constructing a semantic annotation sequence of the message sequence; constructing a protocol semantic map based on the entity type and the affiliation relationship between the message sequence and the unknown protocol and session; and analyzing an unknown protocol through the protocol semantic map. The invention aims to solve the problem that the recognition efficiency and the recognition precision are reduced due to the fact that the network flow depends on matching with a known feature library.

The invention provides an operation and maintenance system based on a large language model, and relates to the technical field of operation and maintenance, in the operation and maintenance system, a dialogue module determines an associated entity type name according to an intermediate question text; establishing an association relationship between each preset operation and maintenance tool corresponding to the association entity type name and the large language model; sending the intermediate problem text to a large language model to enable the large language model to work with an entity operation and maintenance agent identity corresponding to the associated entity type name, and obtaining target data according to the intermediate problem text and a preset operation and maintenance tool having an association relationship with the large language model; obtaining an initial feedback text corresponding to the intermediate problem text according to the target data; a corresponding entity operation and maintenance agent identity is given to the large language model, so that the large language model can dynamically associate and call a preset operation and maintenance tool corresponding to the entity, target data are automatically collected, and an initial feedback text is generated; and the response efficiency and the accuracy of the obtained initial feedback text are improved.

The invention provides an intelligent work order submission method and system based on a large model and a storage medium, and the method comprises the steps: obtaining work order event information, inputting the work order event information into a pre-trained intelligent work order submission model for semantic recognition, and obtaining event semantics; determining a work order template table according to the event semantics, and performing entity identification on the work order event information to obtain a work order entity; performing information filling in the work order template table according to the entity type of the work order entity to obtain a target work order table, and determining a work order approval flow according to the form identifier of the work order template table and the event semantics; and submitting the work order to the target work order table according to the work order approval flow. According to the embodiment of the invention, on the basis of the entity type of the work order entity, work order information filling can be automatically carried out on the work order template table according to the work order event information, a user does not need to manually fill the work order information, and the work order submission efficiency is improved.

The invention relates to the technical field of surveying and mapping geographic information science, in particular to a method for quickly constructing a knowledge graph based on structured spatial data, which comprises the following specific steps of: classifying according to expressed entity objects to obtain structured spatial data corresponding to each type of entities, and uniquely coding the structured spatial data of each type of geographic entities to obtain a unique coding result; connecting attribute information corresponding to each type of entity objects according to the codes; extracting different types of entity types, attributes and relation constraints, and analyzing a topological relation between the entities by utilizing mapping of keywords and codes of an attribute table; discovering an implicit relationship between entities through path reasoning or correlation analysis, and storing the mined topological relationship and implicit relationship in CSV; the method comprises the following steps: importing CSV data into a graph database in batches through a Cypher statement to form a structured spatial data RDF triple; and on the basis of the characteristics of strong interactivity, clear entities and relationships and the like of the knowledge graph, rapid retrieval and query of massive entities can be well supported.

A computer-implemented system and method relate to factual knowledge retrieval with entity type clarification. A set of candidates is generated for a first prompt. The set of candidates provide a solution to the first prompt. A set of second prompts is generated based on the set of candidates. A set of entity types is generated using the set of second prompts. The set of entity types categorize the set of candidates. The set of entity types is output via a user interface. A selected entity type is received via the user interface. The selected entity type is chosen from among the set of entity types. A selected candidate is output. The selected candidate corresponds to the selected entity type.

Provided in the embodiments of the present disclosure are a knowledge graph construction method, a storage medium, an electronic device and a computer program product. The method comprises: acquiring text to be processed and a plurality of schema extraction tasks of different entity types; on the basis of the plurality of schema extraction tasks, extracting a plurality of pieces of entity schema information from said text; converting the plurality of pieces of entity schema information into a plurality of triples; and constructing a knowledge graph on the basis of the plurality of triples.

The present disclosure proposes a method, an apparatus and a computer program product for entity extraction based on edge computing. A web document may be obtained. A text feature of the web document may be identified. A visual feature corresponding to the text feature may be identified. An entity type sequence corresponding to the web document may be extracted based on the text feature and the visual feature.