12 results about "Semantic framework" patented technology

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.

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.

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 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.

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.

This invention relates to the field of legal annotation technology, and discloses a rapid annotation system for intelligent legal document research. The system includes an intent interpretation module, a semantic framework module, an element injection module, an iterative correction module, a node encapsulation module, and an index generation module. It interprets the user's annotation actions in legal documents to obtain the annotation intent; based on the annotation intent, it performs deep semantic analysis of the legal document context to construct an annotation structure framework; it injects the semantic units and logical elements of the legal document into the annotation structure framework in a structured manner to obtain a preliminary draft of the annotation content; according to correction instructions, iteratively reduces the preliminary draft of the annotation content to obtain a standardized annotation document; it encapsulates the standardized annotation document into nodes to obtain annotation knowledge nodes; and it integrates the annotation knowledge nodes into a personal annotation resource library to obtain an annotation index. This invention can improve the efficiency of rapid annotation in intelligent legal document research.