58 results about "Semantic translation" patented technology

The invention discloses a touch feedback enhanced multi-mode robot grabbing control system and method. The touch feedback enhanced multi-mode robot grabbing control system comprises a touch feedback enhancement module, a visual touch language large model semantic generation module and a touch enhancement diffusion control optimization module. The tactile feedback enhancement module is based on a multi-agent cooperation system, performs semantic level and signal level processing on tactile signals, and realizes upper and lower layer coordination through a feedback routing mechanism. And the visual touch language large model module adopts a touch encoder based on CLIP to realize multi-mode semantic conversion and material attribute identification. And the tactile enhancement diffusion control module adopts a BRIDGeR style interpolation diffusion technology, and combines visual embedding and tactile constraint to realize high-frequency optimization of an action track. According to the method, the material identification accuracy, the precision operation success rate and the complex scene self-adaptive capability are remarkably improved.

The invention provides a Text-to-SQL (Structured Query Language) conversion method based on semantic modeling, which relates to the technical field of natural language processing and data analysis, and comprises the following steps: designing a structured semantic model comprising a fact table and a dimension table by adopting a dimension modeling method, and defining business field division, data model definition and model dictionary construction; performing word segmentation processing on the natural language question input by the user, matching the natural language question with dimensions, indexes and dimension values in the structured semantic model based on the model dictionary library, and generating Schema information containing database types, table names, field definitions and filtering conditions; calling the large model, and converting the natural language problem into a preliminary semantic SQL in combination with Schema information and a preset rule; calling the large model again, performing logic verification and grammar optimization on the preliminary semantic SQL, and generating a corrected semantic SQL; based on Schema information and data model definition, the corrected semantic SQL is converted into an executable database SQL statement.

The invention discloses an unmanned aerial vehicle rescue scheduling optimization method and device based on semantic translation modeling, and relates to the technical field of unmanned aerial vehicle emergency rescue intelligent scheduling, and the method comprises the steps: obtaining emergency rescue task description information; a natural language rule in the task description information is converted into a standardized constraint expression, and a response time target, a coverage range target, resource parameters and network structure parameters are recognized; constructing an initial scheduling model, and solving the initial scheduling model through an improved multi-objective optimization algorithm to obtain an initial scheduling scheme of the unmanned aerial vehicle; and when an interference event in the execution process of the initial scheduling scheme is detected, constructing a dynamic re-optimization model by taking the initial scheduling scheme as a disturbance reference, solving the dynamic re-optimization model by taking the scheme disturbance cost and the arrival late time as optimization targets, and outputting a repair scheduling scheme and an interpretable decision result. And the accuracy, the real-time performance and the decision credibility of emergency rescue scheduling are obviously improved.

The invention relates to the technical field of network deployment, in particular to a method and system for interactively configuring parameters through an LLM and a router gateway. The method comprises the following steps: arranging corresponding data of a natural language, a router command and a command execution result, and forming Quest / Answer training data required by a large model for providing a semantic mapping basis of a router configuration field for the large model; and training the general model through a large model fine tuning technology, so that the general model translates a router configuration related natural language into a specific router command and translates a command execution result into a natural language. According to the method, corresponding data pairs of a natural language, a router command and an execution result are constructed, and targeted fine adjustment is performed on a large model, so that the model has a bidirectional semantic translation capability in the field of router configuration, and the technical bottleneck that a traditional interaction scheme depends on independent interface development is broken through.

The invention discloses an inspection agent collaborative perception system based on semantic driving, and relates to the technical field of intelligent inspection, and the system comprises a prototype mapping module which collects inspection target category information and inspection target monitoring indexes, and generates an inspection task semantic prototype set and an inspection task semantic mapping table through semantic conversion; the semantic map module is used for collecting inspection image data and inspection video data through an inspection agent, performing pixel-level visual feature matching based on an inspection task semantic prototype set, and generating a semantic confidence map and a semantic request map; the coupling mutual sending module is used for executing sparse selection and directional mutual sending of semantic supply and demand coupling under the common constraint of the semantic confidence graph and the semantic request graph, and generating a multi-source sparse semantic feature queue; and the fusion remarking module is used for executing position-level semantic attention fusion and measurable sensitivity recalibration on the multi-source sparse semantic feature queue to generate a fusion probability graph and a fusion instance table.

The invention provides an integrated edge box with multi-protocol bidirectional conversion and intelligent reasoning, in the edge box, a multi-protocol access and bidirectional semantic conversion module analyzes protocol messages from different protocol devices through a plurality of pre-configured protocol adapters, and an analysis result is written into a unified data cache and message bus module; converting the content of the protocol message into a semantic object; according to a mapping rule defined by the semantic object, the semantic object is converted into a message format required by a target protocol, and semantic-driven cross-protocol bidirectional conversion is realized; the unified data caching and message bus module is responsible for data caching, format unification, inter-module communication and task scheduling; the edge AI reasoning and dynamic decision module obtains equipment data from the unified data cache and message bus module for AI reasoning; according to the scheme of the invention, heterogeneous equipment is enabled to cooperatively operate in a unified intelligent edge system, and cross-protocol semantic intercommunication and intelligent self-adaptive operation in a real sense are achieved.

This disclosure relates to the field of data governance technology, and provides a method, system, apparatus, medium, and device for constructing data indicator services. The method includes: acquiring data source information and extracting indicator metadata and underlying basic data from it; inputting the indicator metadata into an indicator governance framework and the underlying basic data into an indicator calculation framework; performing semantic transformation on the indicator metadata in the indicator governance framework to obtain an indicator semantic knowledge base and generate indicator definition information; establishing a bidirectional communication channel between indicator governance and indicator calculation, and sending the indicator definition information to the indicator calculation framework through this channel; receiving indicator query requests; performing indicator calculations in the indicator calculation framework based on the request and the indicator definition information, combined with the underlying basic data, to generate indicator calculation results; outputting the calculation results to a user terminal and feeding back the calculation results to the indicator governance framework through the bidirectional channel. This embodiment improves the consistency and response efficiency of indicator services.

The invention provides an academic knowledge base construction method and system based on distributed crawlers and GraphRAG, and belongs to the technical field of computer information processing and knowledge management. The method comprises the following steps: constructing a distributed crawler architecture based on Selenium + Scrapy, completing character recognition and document logic structure reconstruction, storing a document as a Markdown format file, uploading the Markdown format file to a Dify knowledge base, segmenting the Markdown format file into knowledge blocks, and generating vector representation; extracting a semantic relationship between the entities, and constructing a knowledge graph according to the determined reasoning relationship; synchronously integrating the constructed knowledge graph and the segmented knowledge blocks into an intelligent agent, and constructing an intelligent agent process; by inputting a natural language question, an intelligent agent is triggered to execute semantic analysis, vector representation recall and knowledge graph reasoning, and an interpretable answer is returned. The problems that the data collection coverage rate is insufficient, text semantic conversion is distorted, deep knowledge association is missing, and knowledge base deployment is complex are solved.

The invention discloses a document classification method, computer equipment and a storage medium. The document classification method comprises the following steps: receiving a target classification original document; specific information used for referring to a specific instance in the target classification original document is filtered out through a first generative model, common features of the category to which the target classification original document belongs are reserved, and standardized description of the target classification original document is generated; performing similarity retrieval in a rule database based on the standardized description, and recalling a plurality of candidate classification rules; and inputting the standardized description and the candidate classification rule into a second generative model to obtain a final classification result. According to the method, document expressions are aligned with classification rule expressions through semantic conversion, and the document classification accuracy is improved to a certain extent by adopting a mode of combining retrieval and reasoning.

The invention provides a man-machine interaction method and system based on cross-language bidirectional mapping. The method comprises the steps of obtaining first language original information data; analyzing the structured first language intention information to obtain a structured first language intention information set containing the first identification information; performing semantic conversion processing to generate second language expression data, establishing a cross-language bidirectional association relationship between the first identification information and a second language interface element identifier in the process, and generating an interactive second language information interface; receiving user interaction operation to generate behavior data; and according to the behavior data and the association relationship, directly obtaining the first identification information through query, and generating and outputting first language result information data. According to the invention, by establishing and querying the bidirectional association relationship, secondary translation of user operation is completely skipped, lossless and efficient closed-loop interaction from cross-language information identification to business instruction execution is realized, and the problems of insufficient interactivity and semantic distortion in the prior art are solved.

The application provides a silver government-enterprise multi-level permission control method and system, solves the problem of dynamic permission adaptation and control of heterogeneous systems by constructing an emergency permission object and a permission semantic adaptation gateway for semantic conversion and local execution feedback, can realize dynamic permission semantic adaptation and control of cross-institution heterogeneous systems in an emergency scene, and has the advantages of improving the flexibility and adaptability of permission changes.

The invention relates to the technical field of brain-computer interfaces, in particular to an imaginary voice classification method and system based on transfer learning, and the method comprises the steps: training a full-module model through health feature electroencephalogram signals, extracting spatial attention features through a perceptual coding module, capturing cross-brain region correlation features in combination with a sequence correlation module, and classifying the cross-brain region correlation features; semantic conversion from motion to language is realized; multi-modal noise is introduced through the injury simulation module, electroencephalogram signal characteristics in an injury scene are simulated, and the model in the first stage has higher robustness; by freezing the perceptual coding layer parameters of the first-stage model and only updating the subsequent module parameters, the obtained second-stage model improves the classification accuracy and generalization ability in the damage feature space; according to the method, the accuracy of the language intention recognition task is improved, and the method has higher individual adaptability and anti-noise capability.

The application discloses a CC attack intelligent defense method, device and equipment based on a large language model, comprising: performing traffic semantic translation on captured real-time network traffic to generate a semantic description object including a time sequence behavior sequence and context features; inputting the semantic description object into a domain fine-tuned large language model to perform attack intention reasoning to obtain a threat judgment result, generating a corresponding dynamic defense strategy description based on the threat judgment result; compiling the dynamic defense strategy description into an executable instruction sequence, and issuing the executable instruction sequence to a downstream security device to drive execution to obtain an execution result; based on the execution result, monitoring network traffic situation and business indicators after the downstream security device executes the strategy to generate strategy effect evaluation data; and optimizing the large language model based on whole-link data including the semantic description object, the threat judgment result, the dynamic defense strategy description and the strategy effect evaluation data.

The application relates to an artificial intelligence technology and discloses a picture data enhancement method based on semantic conversion, which comprises the following steps: text coding is performed on the picture categories in a financial training picture set to obtain a category semantic feature vector of each financial picture; picture features of each financial picture are extracted, and a predicted financial category semantic vector corresponding to the picture features is calculated; a semantic conversion module is parameter-adjusted according to the predicted semantic feature vector and the category semantic feature vector to obtain an optimized semantic conversion module; target financial enhancement pictures are data-coded to obtain picture coding features; the picture coding features are subjected to semantic conversion by using the optimized semantic conversion module to obtain a semantic conversion vector, and the semantic conversion vector is decoded to obtain financial enhancement data. The application further discloses a picture data enhancement device based on semantic conversion, equipment and a medium. The application can improve the data enhancement effect of financial related pictures.

This invention discloses a method and apparatus for optimizing UAV rescue scheduling based on semantic translation modeling, relating to the field of intelligent scheduling technology for UAV emergency rescue. The method includes: acquiring emergency rescue task description information; converting natural language rules in the task description information into standardized constraint expressions, and identifying response time targets, coverage targets, resource parameters, and network structure parameters; constructing an initial scheduling model, solving the initial scheduling model using an improved multi-objective optimization algorithm to obtain an initial UAV scheduling scheme; when interference events are detected during the execution of the initial scheduling scheme, constructing a dynamic re-optimization model based on the initial scheduling scheme as the disturbance benchmark, solving the dynamic re-optimization model with the scheme disturbance cost and arrival delay time as optimization objectives, and outputting a repair scheduling scheme and interpretable decision results, significantly improving the accuracy, real-time performance, and decision credibility of emergency rescue scheduling.

The application discloses a low-voltage network anomaly diagnosis method based on multi-source heterogeneous data fusion, relates to the technical field of low-voltage network anomaly diagnosis and operation and maintenance, and comprises the following steps: collecting multi-source data, pre-processing the data by edge computing, adjusting the collection interval according to changes, processing the data by deep learning and other technologies, converting according to semantics, improving formula standardization, fusing the data by federated learning, dynamically weighting and intelligently interpolating, constructing a rule base, optimizing by reinforcement learning, setting a dynamic threshold, training a model based on Transform and GNN, improving the effect by contrast learning, monitoring data in real time, warning in multiple ways when an anomaly occurs, assisting in troubleshooting by AR, and repairing the anomaly by a robot. The application can fuse multi-source heterogeneous data and improve data quality, the diagnosis model and the rule base are accurate and efficient, anomalies can be found in time, the operation and maintenance cost is reduced through intelligent early warning, AR-assisted troubleshooting and robot repair, the stable operation of the low-voltage network is ensured, and the economic and social benefits are remarkable.

This invention discloses a method, system, and storable medium for intelligent adaptation of theatrical scripts based on cultural translation. It relates to the field of cultural translation technology and achieves full-process standardization and intelligence in the cross-cultural adaptation of theatrical scripts through script data collection and standardized text preprocessing, three-layer cultural semantic translation based on a fine-tuned Transformer model of the script using a cross-cultural semantic library, directional reconstruction of narrative structure combining the target country's theatrical narrative paradigm and audience preference evolution characteristics, audience-specific adaptation verification and iterative optimization, generation of standardized adaptation guidance instructions throughout the entire process, and a systematic design of the entire chain based on audience behavior feedback after online dissemination, including paragraph association weighting and reverse optimization of core parameters. While fully preserving the core cultural connotations and artistic value of the original script, it significantly improves adaptation efficiency and cross-cultural acceptance, constructing a self-optimizing and continuously iterative intelligent adaptation closed-loop system for theatrical scripts.

The application discloses an image processing method and system for a dynamic monitoring instrument, and relates to the technical field of image processing, and comprises the following steps: exporting image frames in a monitoring window through a target dynamic monitoring instrument to obtain a monitoring image frame sequence; traversing the monitoring image frame sequence to perform image quality scoring and identification, obtaining an identified monitoring image frame sequence, performing semantic conversion analysis and multi-scale feature recognition to obtain a structured description text sequence and an identified multi-scale feature group sequence; performing intra-group feature interaction on the identified multi-scale feature group sequence to determine an identified interactive multi-scale feature sequence; performing double memory iteration on the identified interactive multi-scale feature sequence and the structured description text sequence to determine target iterative memory, and performing identification to determine an image processing result of the monitoring window. The application solves the technical problems of low dynamic image monitoring recognition accuracy and poor reliability in the prior art, and achieves the technical effect of improving abnormal recognition accuracy and stability.

The invention discloses a digital semantic understanding and conversion system and method based on multi-modal feature fusion, the contribution degrees of three types of features including semantics, mathematical and context in different digital type recognition are displayed through a multi-modal feature importance thermodynamic diagram, a macroscopic-microscopic double-layer classification structure is adopted, error propagation is effectively blocked, and the recognition efficiency is improved. Through three-way cooperation of a rule engine, a statistical model and a semantic engine, high-robustness decision making is realized, and an intelligent conversion routing and grammar adaptation mechanism is developed for a digital semantic conversion link. The method can effectively solve the technical bottlenecks of insufficient accuracy, poor expandability, strong training data dependence and the like of a traditional method in complex number type recognition, and is suitable for multiple application scenes such as TTS voice generation, natural language generation, financial document processing, education science and technology and the like; accurate conversion from digital character strings to semantic expressions conforming to human language habits can be achieved.

The invention discloses a medical statistics large model agent, a construction method and equipment thereof and a storage medium. The method comprises the following steps: constructing a structured medical statistics knowledge base by performing semantic analysis and knowledge extraction on medical statistics related guidelines, regulatory literatures and academic papers; contextual information of a medical statistics target task is obtained, and the contextual information is provided for a large language model after being subjected to structured semantic conversion through a unified cognitive interface; generating and executing a structured task instruction based on the medical statistics knowledge base and the context information through a large language model; and verifying and optimizing an output result of executing the structured task instruction through a closed-loop reflection iteration mechanism, and improving a statistical analysis process in the large language model based on an optimization result, so as to obtain a medical statistical large model agent. According to the invention, statistics of medical data is realized through the medical statistics large model agent, and the efficiency and quality of medical statistics are improved.

The invention discloses a machine fault prediction method and system based on a large language model, and the method comprises the steps: collecting grinding machine data and reference data of a preset automobile bearing supply chain, and carrying out the preprocessing of the grinding machine data and the reference data; performing feature semantic conversion on the grinding machine data by adopting a large language model to obtain industrial language data, performing node perception according to the industrial language data to obtain grinding machine processing nodes, and performing turning labeling according to the reference data and the grinding machine processing nodes to obtain turning abnormal nodes; building a grinding machine dynamic state map according to the grinding machine processing nodes and the turning abnormal nodes, and performing fault causal analysis according to the grinding machine dynamic state map to obtain a fault development principle; and constructing a grinding machine fault prediction model according to the fault development principle, inputting to-be-predicted data into the grinding machine fault prediction model, and outputting a fault prediction result.

The application discloses a kind of multimodal fusion knowledge graph's traffic dynamic occlusion tracking method and system, the method includes respectively to the feature extraction and target tracking of two-dimensional image and three-dimensional point cloud data of driving vehicle, obtain two-dimensional trajectory and three-dimensional trajectory;Based on the target tracking algorithm of adaptive weight, two-dimensional trajectory and three-dimensional trajectory are fused;Based on urban road traffic scene, ontology model is constructed;Vehicle trajectory is mapped into ontology model by semantic description, and urban scene knowledge graph is obtained;The semantic conversion of urban scene knowledge graph is carried out, and the occlusion target tracking is carried out by inference machine to establish rule, and target tracking result is obtained.The system includes first trajectory extraction module, second trajectory extraction module, trajectory fusion module, model construction module, atlas generation module and tracking inference module.Using the application can realize high-precision trajectory tracking under complex conditions.The application can be widely applied in target detection technical field.

The invention relates to the technical field of databases, and particularly discloses a kernel-level multi-protocol supporting database system and a data communication method. According to the system, the support capabilities of multiple database protocols such as MySQL and PostgreSQL are originally integrated in a database kernel to form a unified database instance. The basic principle is that client connections are directly distributed through an instantaneous routing mechanism of a network port; syntax and deep semantics (such as transactions, types and error codes) of different protocols are converted into a unified relational algebraic expression in a coordinated mode through a built-in unified semantic core layer; and optimizing and executing the unified semantic expression through a protocol-independent core processing layer, and realizing cross-protocol execution plan and result reuse by using a global DAG cache based on semantic hash. According to the method, high-performance and high-compatibility multi-protocol data service is realized, and the problems of performance overhead and incomplete semantic conversion caused by an external proxy architecture are thoroughly eliminated.

The invention provides a collaborative innovation platform based on a DIKWP model and a TRIZ theory, and belongs to the technical field of artificial intelligence and innovative design. The platform takes an artificial consciousness operating system (AI-OS) as a core architecture, and deeply fuses data-information-knowledge-intelligence-intention (DIKWP) five-layer semantic storage and a TRIZ algorithm library reasoning solving function: when a user submits an innovation demand or a technical problem, the platform firstly uses a DIKWP model to perform hierarchical analysis on problem semantics, and identifies potential contradictions and key targets; and then a built-in TRIZ contradiction solving engine and a causal chain analysis engine are matched with corresponding conflict models and inventive principles, and an innovative solution is automatically generated. When the scheme is generated, a general problem solving thought is converted into an executable scheme in combination with domain knowledge, and an inference chain of semantic conversion, a TRIZ principle and a result is output for explanation and verification. The platform also supports plug-in extension, and can be in butt joint with an industry-specific knowledge graph, simulation software and external AI service to enhance the solving capability of problems in specific fields. Compared with the prior art, the method has the advantages that the target-driven semantic closed-loop analysis and the TRIZ algorithm are combined, the value alignment of the solution and the user intention is realized, the process can be explained, the robust iterative optimization capability is realized, the innovation threshold is remarkably reduced, and the problem solving efficiency is improved.

The invention discloses entity mapping-oriented multi-modal data resource management equipment, which is characterized in that fine multi-modal analysis and feature extraction are carried out on an original data unit uploaded by a user to generate an encoded data packet containing a data fragment ID (Identity), an entity ID, an image vector, an audio vector, a text vector and a keyword; therefore, a foundation is laid for unified management of different modal data; furthermore, semantic transformation is carried out on the multi-modal vector in the coded data packet by utilizing the image coding book and the audio coding book, an index document is formed, and an index system is updated to support subsequent efficient retrieval, when a user submits a complex query, the system can carry out two-stage mixed query execution based on the updated index system, and the user experience is improved. And the sorted result set can be quickly and accurately obtained. In this way, semantic management, entity-level association and efficient retrieval of the multi-modal data are achieved, and the capitalization value and the utilization efficiency of data resources are remarkably improved.

The application relates to a multi-tenant database updating method and system. The method realizes standardized representation of heterogeneous database attributes by constructing a table structure model containing a label dictionary, and solves the logical conflict problem of multi-tenant same-name table definition by using type compatible logic to perform conflict merging. In addition, the accuracy of heterogeneous syntax analysis and SQL synthesis is ensured by cooperating with the semantic conversion technology of expression tree technology. Furthermore, the technical controllability of the database change process and the system running stability are improved while reducing the complexity of the heterogeneous environment adaptation by combining the risk score quantization judgment based on the operation action and the data scale.

Provided are a communication method and system based on a socket interface. The communication method comprises: when a local node and a remote node support a shared memory communication (SMC) protocol and a target link is reachable, registering into a kernel a local memory address applied for in a user mode, so as to obtain local address registration information, wherein the target link corresponds to an underlying hardware device type of an SMC; sending the local address registration information to the remote node, and acquiring remote address registration information from the remote node; and after target data has been written in the user mode into a space of the local memory address, reading the target data from the user mode by means of direct memory access, and transmitting the target data to a space of a remote memory address corresponding to the remote address registration information. The technical problem in the related art of the communication performance being reduced due to it being necessary to involve a CPU in memory copying and semantic translation between a kernel mode and a user mode during an SMC-protocol-based communication is solved.

The application discloses a process-driven hybrid ontology modeling method for mineral exploration knowledge graph, establishes a multi-level semantic conversion chain from a field mineralization process to a process geological product and then to an exploration mark as a mapping rule from theory to ontology, sets a non-directly observable mineralization process class as a non-instantiable mineralization process concept class, sets a process geological product class and an exploration mark class as instantiable entity classes, realizes epistemological separation of the concept class and the entity class, constructs an ontology level from top to bottom with the conversion chain as an ontology skeleton, extracts knowledge instances from multi-source literature from bottom to top and classifies the knowledge instances into the ontology, enriches the result, reversely corrects the ontology skeleton and forms an iterative closed loop. The application takes a dynamic process instead of a static product as an organization center, enables the knowledge graph to have a cause-and-effect explanation ability and a scale constraint ability, realizes dynamic association of a mineralization mechanism and an exploration mark, and can be widely applied to knowledge graph construction in the field of mineral exploration.

The invention relates to a visual model based on a joint expert structure and a picture processing method. The visual model comprises a feature extraction module and a decoding module. The feature extraction module comprises N semantic conversion modules based on a joint expert structure; the feature extraction module is used for receiving an input target image, performing multi-scale feature extraction on the target image through the N semantic conversion modules, and outputting N-scale first feature maps corresponding to the N semantic conversion modules; and the decoding module is used for receiving the first feature maps of the N scales, calling a target decoder corresponding to the target visual task to process the first feature maps of the N scales, and outputting an image processing result of the target image. According to the embodiment of the invention, through the multi-scale feature extraction capability, the processing precision and efficiency of image target detection and segmentation tasks are remarkably improved, and the generalization capability of the model and the adaptability to different visual tasks are enhanced.

The invention relates to a rapid method for developing static items and adjusting and optimizing details based on a large model code platform. The method comprises the steps that an actual style parameter set after target page elements are rendered is automatically obtained through an agent module; mapping a user natural language instruction into a structured professional style adjustment instruction through a semantic translation module; combining the parameter set with the professional instruction to construct a structured composite cue word, and inputting the structured composite cue word into a large model code platform to generate a target code; and automatically applying the target code to the target element and updating the display effect in real time to form an iterative optimization closed loop. According to the method, through a collaborative mechanism of element parameter capture, semantic translation and parameter injection, the problem of low tuning efficiency caused by lack of professional term ability and debugging skills of non-professional users is solved, and semantic deviation and trial and error times in the interaction process of the users and a large model are remarkably reduced; the detail tuning efficiency of the static project is remarkably improved.