84 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 discloses a financial knowledge graph construction method and system based on artificial intelligence, and relates to the field of artificial intelligence data processing. The method comprises the following steps: performing multi-dimensional semantic analysis on a heterogeneous financial data source, and extracting a structured semantic fragment; constructing a financial entity perception unit, identifying a multi-granularity entity and generating a unique code; generating a preliminary relation graph based on the event cascade relation and the attachment structure, and injecting a semantic translation label; normalizing the atlas relationship through semantic separation and a label reconstruction mechanism to form a financial relationship network with consistent semantics; executing evolution increment iteration in combination with the newly added corpus, and dynamically updating nodes and edge sets; and performing semantic consistency and structural integrity evaluation on an iteration result, and outputting a stable financial knowledge graph structural body. By introducing a multi-factor semantic analysis model, a causal relationship modeling mechanism and a graph evolution iteration strategy, systematic improvement of the financial knowledge graph in the aspects of structural expression precision, semantic reasoning ability and dynamic adaptability is achieved.

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 discloses a form content autonomous efficient filling method and device based on a multi-modal large model. The method comprises the following steps: receiving input data in a text, image, document or voice form; performing cross-modal information deep fusion and feature extraction on the input data through the multi-modal large model; a knowledge base and historical data are called, and form filling content is generated through intelligent reasoning and semantic conversion; and performing compliance decision judgment on the generated content, if the generated content is normal, finishing filling and recording a log, and if the generated content is illegal, finishing filling and recording the log after exception processing. Wherein cosine similarity calculation is adopted for cross-modal feature correlation analysis, weighted summation is adopted for feature fusion, corresponding formulas are adopted for semantic understanding result optimization and weight updating, and a scoring formula is adopted for compliance quantification. According to the method, multi-modal input can be efficiently processed, and the accuracy and compliance of form filling are 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 relates to the technical field of visual reasoning, and provides a safety monitoring multi-modal model reasoning method and device. According to a user problem and a user image in a security monitoring scene, an object position in a visual scene is converted into text information, and the text information, the user problem and the corresponding user image serve as input information; obtaining visual features according to the input information through a cross-modal semantic converter; constructing a visual scene of the user image into hierarchical description comprising scene description and object description; modeling the context of the user image according to the user question, and generating a text prompt of a visual scene; reasoning is carried out through a large language model according to the visual features, the hierarchical description and the text prompt, reasoning output is obtained, and the problems that in a multi-modal scene, the complex scene perception ability is insufficient, and the large language model reasoning ability is insufficient in utilization in the prior art are solved.

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.

The invention discloses a construction element detection method and a construction management method, and the method comprises the steps: obtaining construction data, building standard data and building log data of a target building, the construction data comprising at least one of text data, voice data, image data and video data; modal feature extraction is carried out on the construction data based on the building standard data, structural features of the target building are obtained, and the structural features comprise quality element features, safety element features, progress element features and cost element features; semantic conversion is conducted on the structural features according to the building log data, construction detection logs of the target building are obtained, and the construction detection logs comprise a quality log, a safety log, a progress log and a cost log; by automatically generating the classified construction detection log from the structured element features, the automatic and standardized output of the detection result is realized, and the efficiency and intelligent level of construction detection are greatly improved.

The invention provides a multi-word expression-oriented machine translation differential test method aiming at the problem of inaccurate multi-word expression semantic translation in a mainstream machine translation system. The method comprises the following steps that a word segmentation tool based on deep learning is adopted to divide words into vocabulary units, syntactic labels are distributed in combination with a pre-training sequence marking model, and a dependency analysis tool spaCy is utilized to mark the syntactic relation between the words; converting the tagged corpus into a standard CoNLL format, extracting a multi-word expression of a sentence through an automatic tool, and establishing a test data set of a sentence-level and phrase-level corresponding relationship; inputting the test set into a multi-translation system to generate a translation, and using an alignment tool AWESOME to accurately locate a corresponding relationship between a source language and a target language MWEs; the translation similarity is calculated based on BERTScore, mistranslation, translation omission and non-translation are recognized through an intra-group and inter-group dual check mechanism in combination with a dynamic threshold value, and evaluation of the translation accuracy of machine translation on multi-word expression is completed. According to the method provided by the invention, multi-word expression translation errors can be accurately recognized, and the accuracy of phrase-level semantic translation of a machine translation system is finely evaluated through a differential test method.

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 application discloses a teaching aid robot multi-modal semantic cooperative scheduling method and system, and relates to the technical field of machine semantic translation processing. The teaching aid robot multi-modal semantic cooperative scheduling method and system comprises the following steps: S1, collecting and preprocessing the board area image and the teacher hand key point trajectory, and constructing a standardized teaching scene data set; S2, analyzing the semantic propagation convergence efficiency of the board area, and completing the sorting selection of the target board area; S3, evaluating the semantic binding stability degree, and adjusting the voice expression rhythm space pointing trajectory; S4, evaluating the space positioning execution effect and performing intensity evaluation, and correcting the voice emphasis time length and action dwell time. In the multi-role cooperative teaching scene, the existing robot cannot jointly analyze the teacher's ambiguous reference semantic and the real-time visual focus, gesture trajectory and historical semantic context, resulting in the drift between the semantic pointing and the actual board space position, and it is difficult to realize accurate semantic positioning.

The invention discloses a data conversion and integration method and system, and the method comprises the following steps: 1, extracting product research and development data from an IPD system through a standard interface, wherein the product research and development data comprises a product structure, technological process design and technological parameters; 2, mapping the extracted product research and development data to a unified data model, wherein the unified data model is subjected to hierarchical structured storage according to the product, the working procedure, the working step and the technological parameters; 3, based on a preset mapping rule and a semantic conversion function, converting the unified data model data into standard process flow data which can be identified by an MES system; and 4, synchronously pushing the converted data to an MES system through an adaptive interface, and recording a data version and an operation log. Through the implementation of the invention, the data model is unified, IPD and MES data semantics are unified, the problems of system isolation and data obstruction are solved, and the automatic conversion rate of research and development data is remarkably improved.

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.

This application relates to a multi-agent retrieval enhancement generation method and system, which includes the following steps: parsing the task instruction through a first agent to generate a retrieval feature set; matching several corresponding second agents and performing directional semantic transformation on the original semantic vector to generate a query statement set; filtering several third agents and executing query operations through the third agents to obtain a preliminary retrieval set; inputting the preliminary retrieval set and the retrieval feature set into a fourth agent for verification to generate optimized retrieval results. In summary, the multi-agent retrieval enhancement generation method and system provided in this application realizes intelligent parsing of task instructions, cross-modal semantic transformation, dynamic database partitioning scheduling, and optimized verification of retrieval results through a multi-agent collaborative mechanism, which has the effects of improving retrieval accuracy and efficiency, enhancing adaptability to complex query intentions, and optimizing the collaborative retrieval capabilities of heterogeneous databases.

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 an MCP-based log-to-context structured data conversion and retrieval analysis system, and relates to the technical field of data processing. The system comprises a semantic conversion module, a context index module, a semantic retrieval engine and a rule analysis engine, all the modules complete operation such as capability declaration, index modeling, expression analysis and condition judgment through a unified interface, and a complete data processing and safety analysis process is formed. Structured semantic modeling and rule linkage analysis of log data are realized through a module capability registration and context state driving mechanism; the log is subjected to field analysis, protocol identification and semantic annotation to generate structured semantic data, a context model containing a behavior path, a state sequence and an index dimension is constructed, and cooperative operation of expression retrieval and rule matching is supported. The system has the characteristics of semantic driving, uniform structure, rule self-adaption and closed-loop feedback, and is suitable for security detection and semantic analysis tasks in a multi-source heterogeneous environment.

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.