35 results about "Semantic framework" patented technology

The invention belongs to the crossing field of artificial intelligence and material informatics, discloses a multi-layer material knowledge graph construction method and system based on a large model, and solves the problems of low utilization rate of multi-modal information, flat knowledge representation, uncontrollable quality and insufficient entity alignment precision in the prior art. The method comprises the following steps: analyzing material science and technology literatures, extracting multi-modal information of texts, tables and images, and preprocessing the multi-modal information into structured data; constructing a three-layer semantic framework of a concept term layer, a material entity layer and a material data layer; extracting knowledge by a large model layer by layer to generate a triple, and performing multi-dimensional quality evaluation and dynamic threshold filtering; entity fusion is realized by adopting hybrid alignment of semantic embedding and literal similarity and large model discrimination, and an atlas is maintained by combining incremental updating and version management. According to the method, knowledge integrity and accuracy are improved, entity heterogeneous scenes in the material field are adapted, whole-process automation is achieved, and high-quality knowledge support is provided for material research and development and data sharing.

The invention belongs to the technical field of knowledge graph construction, and particularly relates to a multi-source heterogeneous data fusion and knowledge graph automatic construction system, which comprises a semantic extraction module for extracting minimum semantic fragments from multi-source data and performing cross-source alignment to generate a symbolized semantic framework; the data purification module converts multi-source data into symbolic predicates according to the framework, filters low-evidence data and outputs a purification set; the ternary generation module inputs the initial triad candidate set into a minimum rule grammar, and extracts entities, relationships and attributes to generate an initial triad candidate set; the disambiguation calibration module generates entity two-hop topological fingerprints based on the candidate set, completes disambiguation alignment and corrects conflicts, and outputs unambiguous structured knowledge; the mapping fusion module fuses the mapping information with the minimum connected ontology, and constructs and verifies an initial mapping knowledge domain; and the incremental updating module processes newly added data, performs incremental merging and maintains consistency, and forms a complete knowledge graph. According to the method, through full-link automatic construction, efficient fusion and high-quality atlas are realized.

The invention discloses an intelligent switch fault diagnosis method based on multilayer data reasoning, which relates to the field of switches, collects and fuses multi-source data of a switch, converts entities, attributes and relationships in a multi-source fusion data set into an RDF triple based on a predefined network operation and maintenance ontology model, and performs fault diagnosis on the RDF triple. The method comprises the steps of constructing a switch fault diagnosis knowledge graph, dynamically generating an SPARQL query statement based on real-time monitoring data, performing graph traversal and logical reasoning on the switch fault diagnosis knowledge graph, and outputting a switch fault diagnosis conclusion. According to the method, comprehensive upgrading of switch fault diagnosis from passive response to active prevention is achieved, the network operation and maintenance efficiency and decision quality are remarkably improved, equipment monitoring, topological relations and service data are integrated through a unified semantic framework, a structured knowledge graph is constructed, and fault diagnosis has semantic interpretability.

The invention provides a multi-mode interactive data real-time fusion planning execution system, and belongs to the technical field of computer data processing. According to the method, a dynamic scene calculation graph is constructed and updated in real time, split multi-modal sensing data is integrated into a structured semantic framework, conversion from passive data processing to active scene understanding is achieved, relevance and potential importance of all elements in a scene are calculated in advance before a user instruction is sent, and therefore the user experience is improved. According to the method, intention anchoring can be achieved when an instruction is issued, cognition and response delay of a system is greatly shortened, interaction instantaneity is improved, a traditional linear task planning normal form is subverted by providing a task generation and elastic execution mechanism based on graph reasoning, and a task sequence containing alternative nodes is generated, so that the task planning efficiency is improved. And the execution unit directly subscribes to the state change of the scene graph, so that rapid link switching of a bottom layer is realized without depending on complete re-planning of an upper layer.

The invention provides a multi-personality decision simulation and game method for military artificial intelligence. Simulation of an AI opponent model on complex human behaviors is enhanced through a DIKWP semantic framework. The system firstly configures personality parameters based on a psychological model (such as MBTI, big five personalities), and maps the personality parameters to an AI cognitive model to construct a behavior preference model; then, a noise injection algorithm is adopted to simulate emotion and cognitive deviation, and unique deviation of different personalities in decision making is reproduced; the multi-agent self-gaming engine enables AIs with different personality characteristics to repeatedly game in a red-blue confrontation environment, optimizes a strategy and evaluates winning rate distribution. And the decision fusion analysis module summarizes the data, generates personality performance comparison and behavior pedigree diagrams under each tactical situation, and predicts opponent behaviors. The DIKWP semantic integration interface ensures that a personality simulation result can be seamlessly connected with a knowledge graph and a strategic intention module of an existing AI system. According to the method, AI opponent modeling is enriched on the semantic level, the decision simulation degree of a real commander is improved, and as a supplement of an existing AI simulation system, the ability of military AI in the aspects of strategic pre-judgment and cognitive game is enhanced.

The invention relates to the technical field of tunnel water inrush accident safety evaluation, in particular to a knowledge extraction method and device for tunnel construction water inrush accident hidden dangers and a storage medium, and the method comprises the following steps: S1, collecting all data related to the tunnel construction water inrush accident hidden dangers, and analyzing the features of the water inrush accident hidden dangers, constructing a knowledge semantic framework, and extracting entities and entity relationship categories; s2, acquiring a corpus set to be processed, preprocessing data in the corpus set, constructing a data set, and grading the data in the data set according to sources; compared with the prior art, the method has the advantages that compared with a traditional knowledge extraction method, resistance sampling and type constraint optimization shielding processing are added, so that the problem that false negative samples can only be generated depending on high-quality input is avoided, the samples are further screened, the main beam of the input samples is improved, and the model training effect is guaranteed.

The embodiment of the invention relates to the technical field of natural language understanding, and discloses a semantic understanding method, device and equipment and a storage medium, the method comprises the following steps: when user input is received, transmitting the user input to a preset artificial intelligence model through a LangChain framework to perform semantic understanding and generate a semantic understanding result; if the semantic understanding result contains a function call instruction, selecting a target function call from a plurality of registered function calls through a LangChain framework according to the function call instruction so as to execute corresponding instruction operation; transmitting an execution result called by the target function to a preset artificial intelligence model through a LangChain framework so as to generate answer content input for the user; and the generated answer content is returned to the user through a LangChain framework. By applying the technical scheme provided by the invention, the problems of high development difficulty, obvious technical threshold and difficulty in non-professional personnel to participate in development due to the fact that a structured semantic framework is defined by a domain expert in the prior art can be solved.

The invention relates to the field of coal bed gas exploration, and particularly discloses a coal bed gas multi-source heterogeneous data fusion and management method based on a knowledge graph, which establishes a unified semantic framework by constructing a coal bed gas field ontology model and definitely defining entity types of geology, wells, engineering, data and the like as well as attributes and relationships thereof. Structured knowledge is extracted from multi-source heterogeneous data based on the framework, knowledge fusion is achieved through entity alignment and conflict resolution, and finally the processed knowledge is stored in a graph database to construct a coal bed gas knowledge graph. Based on the knowledge graph, intelligent query, visual correlation analysis, decision support and other services can be provided. According to the method, semantic-level deep fusion of coal bed gas multi-source heterogeneous data is realized through a knowledge graph technology, a data island is broken through, and a unified knowledge network is constructed; the analysis and mining capability of the complex incidence relation is enhanced through a graph structure expression mode, and multi-hop query and causal inference are supported.

This invention relates to the field of dynamic monitoring technology for foundation pits, specifically a dynamic monitoring method for deep foundation pit support construction methods that integrates digital twins. The method includes the following steps: obtaining construction stage numbers and binding them to support units; collecting excavation depth and component status data and writing them into a digital twin model; matching design elevations and verifying status consistency to determine stage completion; updating stage attributes and synchronously displaying the construction monitoring status. In this invention, support units and spatial ranges are associated with construction stage numbers as the main line. Component identification, excavation depth, installation status, and acceptance status are integrated to form a unified status expression. Stage completion is automatically determined through stage condition matching and consistency verification. The displayed content is updated synchronously with stage switching, enabling the support status, excavation status, and construction progress to be linked within the same semantic framework. This reduces human judgment bias, lowers the risk of stage misjudgment, and improves the consistency between monitoring results and on-site construction, as well as the efficiency of dynamic control.

The application discloses a traffic safety scene text generation method, comprising: obtaining an initial parameter set input by a user; generating prompt information based on the initial parameter set and a preset semantic constraint template; the semantic constraint template comprises a plurality of non-numeric mandatory items and their arrangement order; receiving scene description information generated by a generative artificial intelligence model in response to an inference request based on the input prompt information; and generating a traffic safety scene file based on the scene description information. The application generates highly structured input prompt information by receiving an initial parameter set input by a user and combining a preset semantic constraint template. Since the semantic constraint template clearly specifies a plurality of mandatory non-numeric items and their arrangement order, and rigidly stipulates that no numerical parameters are allowed in the prompt information, the generative artificial intelligence model can be strictly limited within the preset semantic framework when receiving instructions to perform inference and creation, thereby improving the semantic consistency of the generated text.

The invention discloses an insurance customer demand matching and business recommendation method and device based on a knowledge graph, and the method comprises the steps: carrying out the targeted preprocessing of structured data and unstructured data in an insurance business scene, and extracting standardized entity, relation and attribute data; s2, converting the standardized data extracted in the step S1 into an RDF / RDFS format of a unified semantic framework to obtain a multi-source independent initial knowledge graph; through an entity alignment method combining semantic standardization and semantic matching, redundancy and noise of a multi-source initial knowledge graph are eliminated, and a global customer knowledge network is formed through fusion; based on the fused global customer knowledge network, customer portraits are perfected through rule reasoning, and potential association relationships among entities are identified; and realizing accurate matching and personalized recommendation of customer demands and insurance businesses through risk level evaluation, hidden demand mining and claim settlement risk check in combination with the improved customer portrait and the inference result.

The invention discloses an oral cavity multi-modal data fusion method based on a knowledge graph, and belongs to the technical field of oral medicine artificial intelligence and medical big data fusion. The method comprises the following steps: firstly, constructing a stomatology knowledge graph with a'top-layer multiplexing-specialized extension 'double-layer architecture, and forming a standardized semantic framework; then, multi-modal data such as structured medical records, unstructured texts and CBCT images of the patient are collected and subjected to quality control; then, analyzing and mapping each modal data into a standardized semantic representation associated with the knowledge graph by applying a natural language processing and computer vision technology; and finally, under a unified semantic framework, deep integration of multi-source information is realized through cross-modal semantic alignment, confidence weighted fusion and conflict resolution, and a patient dynamic comprehensive data view containing a three-dimensional panorama, a time axis and a semantic network is generated. According to the method, deep semantic understanding and collaborative utilization of oral multi-source heterogeneous data are realized, and a comprehensive and credible data basis is provided for precise diagnosis and treatment.

The invention provides a three-level environmental risk assessment linkage method and system for an industrial park, relates to the technical field of environmental risk management, and aims to realize a unified semantic framework of enterprise-level, special-level and park-level data by constructing an environmental risk assessment ontology. According to the method, an event-driven dynamic updating mechanism is adopted, data recalculation and risk map adjustment are automatically triggered, and the timeliness and accuracy of environmental risk response are improved. The whole system effectively solves the problems of data islands, updating lagging, verification missing and the like, and provides real-time and accurate decision support for environment management of industrial parks.

The invention discloses an evaluation double-layer directory construction method and system based on an electric power AI application scene. The method comprises the steps of obtaining a business specification document; the business specification document is analyzed to obtain core elements, and the core elements comprise business scenes, observation objects and attributes of the observation objects; constructing a multi-level nested structure based on the core elements to obtain a preliminary double-layer directory; reviewing and adjusting the initial double-layer catalogue to obtain an adjusted double-layer catalogue; verifying and perfecting the adjusted double-layer catalogue by using historical business data to obtain a corrected double-layer catalogue; and finally auditing the corrected double-layer catalogue to obtain an evaluated double-layer catalogue. By implementing the method provided by the invention, three major problems of fuzzy structure hierarchical relationship, lack of business and technical team collaborative semantic framework and high construction cost of the traditional knowledge graph in vertical business scene application can be solved, so that efficient and low-cost knowledge graph construction which is closely combined with business logic is realized.

The present disclosure provides a system for semantic analysis and automatic solution of a mathematical application problem. The system includes: a semantic framework mode matching module, configured to: perform mode matching on a clause vocabulary sequence with a semantic framework, to form local semantic information of the mathematical application problem; a scenario semantic analysis module, configured to: form, based on a local feature vocabulary string of a scenario, a global semantic feature vocabulary string of the scenario; a data meta-variable naming module, configured to: generate a global semantic name of a data meta-variable based on a local name of a variable; a module for explicitly expressing a computation relationship, configured to: explicitly express an explicit computation rule and an implicit computation rule between data meta-variables, to construct a dynamic semantic circle; and a module for implementing a machine thinking mechanism, configured to solve the questionable data meta-variable.

The application relates to the technical field of intelligent text generation, and discloses a travel note automatic generation method and system based on AI, which comprises the following steps: obtaining an original data set during a travel; clustering the original data to obtain a clustered data group; calculating a time sequence dependency relationship based on the clustered data group, and constructing a time sequence correlation graph; generating a time sequence paragraph sequence according to the correlation graph; calculating the logical fluency among semantic elements and sorting to form a semantic arrangement sequence; performing connection optimization and framework construction on the semantic sequence to obtain a coherent semantic framework; combining the clustered data group to arrange a draft structure and generate a complete note draft; and finally adjusting emotional elements according to the draft to generate travel note content. The application can enhance the semantic coherence and emotional presentation effect of note content.

Embodiments of the present disclosure relate to a semantic framework for variable haptic output. The invention provides methods and apparatuses that organize multiple haptic output variants into a tightly integrated semantic framework that uses various information about alert conditions and triggers, application context and other conditions to provide a haptic output system that shares characteristics between related events. In some embodiments, an event class or application class provides a basis for corresponding haptic output. In some embodiments, whether an alert prominence setting is on or off is provided as a basis for adding an increased prominence haptic output to standard haptic output for an alert. In some embodiments, consistent haptics provide branding of associated application classes, applications and / or contexts.

The invention discloses a text-to-SQL (Structured Query Language) conversion method and system based on context awareness. The method comprises the steps of performing word segmentation processing on text input to obtain a vocabulary unit sequence; a semantic role labeling model is applied to extract a subject-called structure and a modification relation, and semantic hierarchical representation is obtained; performing rule matching and ambiguity analysis through a grammar rule base to obtain a refined matching combination; mapping the matching combination to an SQL component to generate a preliminary SQL structure draft; analyzing the semantic framework, and dividing a query module sequence; identifying components of clauses such as WHERE and GROUP BY, and optimizing a division boundary; role overlapping conflicts are processed, and contradictions are resolved; and finally, updating the division strategy to obtain structured semantic analysis output. The system correspondingly comprises a text word segmentation module, a semantic role labeling module, a rule matching and ambiguity analysis module, an SQL structure generation and optimization module and the like. According to the method, ambiguity, nesting and complex semantics in a natural language are effectively processed through context perception and iterative optimization, and the accuracy and robustness of text-to-SQL (Structured Query Language) conversion are improved.

The invention discloses a semantic modeling and rule reasoning-based machine learning project permission compliance analysis method, which is characterized in that unified modeling is carried out on machine learning workflow by adopting a method of constructing a unified semantic framework, and a permission compliance analysis result is obtained by combining a license semantic rule. According to the method, automatic analysis of rule reasoning is carried out on the right relation between multiple types of assets such as codes, data sets and models and operation behaviors of the assets, so that systematic compliance detection of complex machine learning workflows is realized, and the rule reasoning comprises a construction stage, a reasoning stage and an analysis stage. Compared with the prior art, the method has the advantages that clear and operable technical support is provided for model asset management and compliance decision making, unified expression of multiple types of assets such as models, data sets and software components is achieved, the limitation that traditional software license analysis is only suitable for code dependence is broken through, and the method has good application prospects.

This invention discloses a method and system for monitoring the entire process of a dye delivery robot, relating to the field of industrial robot operation monitoring technology. The method includes the following steps: S1, constructing a full-process operation monitoring benchmark for the dye delivery robot; S2, aligning the robot's operation process using the full-process operation monitoring benchmark; S3, using the robot's operation alignment results to perform stage coupling on the dye delivery process; S4, using the operation-delivery coupling results to determine the continuity of the entire process; and S5, using the continuity determination results to construct the full-process operation monitoring results. This invention, by setting stage coupling based on the robot's operation alignment results, can provide clear and reliable stage-level input for subsequent full-process continuity determination within a unified semantic framework for all stages, using the benchmark operation stage identifier in the operation alignment results as the sole criterion. This effectively solves the problem of the separation between the operation state and the delivery state.

The invention discloses an oil and gas geological data integrated analysis method and system, and relates to the technical field of geological information, and the method comprises the steps: carrying out the standardization processing of multi-source heterogeneous original geological data, and obtaining a standardized data file set; based on the standardized data file set, constructing a geological cognition sub-graph corresponding to the geological analysis task through a dynamic semantic alignment technology; based on the geological cognition subgraph, an analysis calculation unit matched with the nodes and the knowledge edges is coordinated and driven, interaction and iteration are carried out under a unified semantic framework, and an instantiated geological cognition model is generated; key uncertainty nodes are identified from the instantiated geological cognitive model, and a structured anti-fact geological scene hypothesis set is constructed based on the key uncertainty nodes; and based on the structured anti-factual geological scene hypothesis set, executing multiple rounds of analysis processes to perform automatic deduction, and generating an uncertainty quantitative result body. According to the method, the whole-process intelligence from data integration to risk quantitative decision is realized.

Embodiments of the present disclosure relate to a semantic framework for variable haptic output. The invention provides methods and apparatuses that organize multiple haptic output variants into a tightly integrated semantic framework that uses various information about alert conditions and triggers, application context and other conditions to provide a haptic output system that shares characteristics between related events. In some embodiments, an event class or application class provides a basis for corresponding haptic output. In some embodiments, whether an alert prominence setting is on or off is provided as a basis for adding an increased prominence haptic output to standard haptic output for an alert. In some embodiments, consistent haptics provide branding of associated application classes, applications and / or contexts.

Embodiments of the present application provide a kind of multi-domain multi-intention spoken language semantic understanding model training method.The method comprises: inputting the ontology item data with annotation to spoken language semantic understanding model;Based on the ontology coding module in spoken language semantic understanding model, the semantic coding of dialogue text is carried out, and initial ontology feature and initial vector are obtained;Based on the graph encoder of bidirectional relationship graph attention network, graph neural network coding is carried out to initial ontology feature and initial vector, and semantic coding and structure coding are obtained;The probability distribution of spoken language semantic understanding result is obtained by inputting semantic coding and structure coding to the decoder in spoken language semantic understanding model.The embodiments of the present application start from multi-intention spoken language semantic understanding task, and expand spoken language semantic understanding task to multi-domain setting and hierarchical semantic framework output.A multi-intention data set originated from real scene in industry is constructed, and the multi-domain multi-intention spoken language semantic understanding task is effectively solved.

This invention discloses an AI-based intelligent hydrological management method, system, and storage medium, aiming to improve the intelligence and decision-making accuracy of hydrological management. The method receives and preprocesses multi-source heterogeneous hydrological operational data; performs semantic parsing and data fusion verification based on a hydrological semantic normalization framework; loads data and constructs a semantic association network through three-dimensional dynamic permission configuration; parses user natural language intent and matches the optimal hydrological operational scenario model; schedules AI agents to collaboratively process data; merges results and iteratively optimizes them before outputting decision support data; and feeds back relevant parameters to dynamically optimize the semantic framework and scenario model. The system includes corresponding functional modules, encrypted data transmission, and tamper-proof operation logs, with decision data accompanied by a traceability link. This invention achieves efficient hydrological data processing and intelligent decision-making, ensuring management security and reliability.

The invention relates to the technical field of artificial intelligence, and provides a task subgroup grouping method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the semantic analysis of a task instruction, obtaining a task target, and determining an intention atlas composed of task nodes and a dependency relationship based on a preset task ontology atlas and the task target; constructing an agent capability map by structurally organizing capability information of a plurality of available agents; and calculating the capability matching degree and intention consistency between the task node and the intelligent agent in the intention map, and forming a task sub-group adaptive to the task node based on the capability matching degree and the intention consistency. According to the method, a unified cognitive structure for a task target is provided for all agents by constructing an intention map, and the problem of collaboration obstacle caused by lack of a shared semantic framework is solved; based on the capability matching degree and the intention consistency, the task subgroup used for executing the task nodes in the intention graph is obtained, and the overall collaborative intelligence and task execution robustness of the multi-agent system are remarkably improved.

The application belongs to the field of intelligent classification, and specifically relates to a garbage classification method and system based on intelligent analysis, which comprises a visual feature extraction module, a structured semantic knowledge base construction module, a semantic prior generation module, a classifier training module and a garbage classification module; the application introduces double-flow coding of local texture and global shape into a semantic enhancement framework, and realizes adaptive fusion through a learnable gate to dynamically adjust the degree of dependence on the two kinds of features; the application proposes a four-dimensional semantic ontology as a semantic framework for garbage classification, and designs a two-level routing network to realize dimension-aware expert decomposition, so that hierarchical design makes semantic modeling of garbage classification more fine; through retrieval and optimization of the dimension gate, the application ensures that the retrieved garbage classification text and the sample are highly matched in important dimensions, and further enhances the consistency and class discriminability of cross-view representation through comparison of semantic consistency loss, so as to finally improve the garbage classification efficiency.

The application provides an industrial design review method and system based on artificial intelligence, which comprises the following steps: performing design intention semantic analysis on industrial design drawings to generate design intention semantic model data, and forming a unified review semantic framework by constructing a multi-dimensional constraint knowledge graph; generating design elements and constraint correlation mapping data based on the unified review semantic framework, and generating design intention differentiation result data by identifying intentional design features and negligent design defects through design intention differentiation processing; generating optimization scheme evaluation report data based on the design intention differentiation result data and the multi-dimensional constraint knowledge graph, and performing final review decision to obtain final design review result data; and generating industrial design comprehensive review data based on the final design review result data and risk early warning and improvement suggestion generation processing. By constructing a unified review semantic framework, the design intention, manufacturing constraints and user requirements are integrated, and the comprehensiveness and accuracy of the industrial design review are improved.

Embodiments of the present disclosure relate to a semantic framework for variable haptic output. Methods and devices organize multiple haptic output variants into a tightly integrated semantic framework that uses various information about alert conditions and triggers, application contexts and other conditions to provide a haptic output system that shares characteristics between related events. In some embodiments, an event class or application class provides a basis for corresponding haptic output. In some embodiments, whether an alert-prominence setting is on provides a basis for adding increased prominence haptic output to standard haptic output for alerts. In some embodiments, consistent haptics are provided for branding of associated application classes, applications and / or contexts.

The invention relates to a multi-subject language model and inference model decision weight collaboration method, which breaks the island between models by automatically identifying the subject and the question type of a task, matching with a multi-subject collaboration knowledge base, and determining an initial decision weight and whether to carry out the indication of intermediate result interaction based on a matching result and a collaboration rule; when the language model interacts with the reasoning model, the language model transmits a semantic framework understood by the language model to the reasoning model to assist the language model in understanding question meaning and constraint; the reasoning model feeds the logic constraint back to the language model, assists the language model in expression verification, realizes information exchange and guidance of the language model and the reasoning model, effectively avoids erroneous judgment caused by capability limitation of a single model for complex tasks needing text understanding and logic derivation, and improves the reliability of the system. Therefore, the accuracy of processing the complex cross-capability task is improved.

The application belongs to the technical field of knowledge graph construction, and specifically relates to a multi-source heterogeneous data fusion and knowledge graph automatic construction system, which comprises a semantic extraction module, a data purification module, a triple generation module, a disambiguation calibration module, a graph fusion module and an incremental update module.