32 results about "Natural language semantics" patented technology

The invention relates to the field of natural language semantic analysis, in particular to a natural language-driven CIM scene interaction method and system, and the method comprises the steps: receiving a natural language interaction instruction inputted by a user side in real time; analyzing the natural language interaction instruction, and extracting scene data in the natural language interaction instruction; calling a semantic mapping library based on the extracted scene data, and mapping the scene data into corresponding CIM platform functions and space elements through the semantic mapping library; according to the CIM platform function and the space element, generating an interface calling instruction of the CIM platform, sending the interface calling instruction to the CIM platform, and executing a corresponding space element operation through the CIM platform; an operation result returned by the CIM platform is received, multi-modal feedback information is generated based on the operation result, and the multi-modal feedback information comprises visual feedback carried out in the CIM scene; visually feeding back synchronous natural language explanation feedback; and outputting the multi-mode feedback information to the user side.

The invention discloses a psychological state text classification method based on large model generative data enhancement, and relates to the technical field of natural language processing. According to the method, patient psychological semantic clusters are automatically found by clustering a limited number of original samples, and then a natural language semantic template of each patient psychological semantic cluster is extracted; then, under the dual control of emotional polarity and psychological themes by utilizing a large language model, according to the natural language semantic templates, psychological state texts with consistent themes and diversified expressions are generated as enhanced samples; according to the method, high-quality sample generation is carried out by utilizing a natural language semantic template obtained based on clustering through a large language model so as to expand a model training sample, and the authenticity and diversity of the generated enhanced sample can be ensured through the large model generation type data enhancement method; therefore, the classification accuracy and generalization ability of the psychological state text classification model obtained through training are improved.

This application discloses a user behavior attribution method and related products. The method constructs a behavior sequence for each user based on multimodal behavior data of multiple users within a preset time window. Based on each user's behavior sequence, corresponding natural language semantic tags are determined, and these tags are converted into corresponding semantic vectors. The semantic vectors of multiple users in the current time window and in historical time windows are clustered to obtain clustering results for the current and historical time windows. The clustering results of the two time windows are compared to determine the semantic drift trajectory of each user group. Based on the semantic drift trajectories of multiple user groups, the contribution of each user group to the fluctuation of preset business indicators is quantified, and the quantification results are generated, along with an attribution report. Compared to the low accuracy of existing user behavior attribution technologies, this application has significant advantages.

Technologies for machine learning-based entity understanding and resolution are disclosed. Data for training an entity resolution model is collected to learn semantic relationships associated with entity names. The entity names are provided in a domain of documents that follow the semantic conventions differently from natural language semantic conventions. The data includes entries each specifying an entity name and a label. The entity resolution model is trained using the data to learn and generalizes the semantic relationships and is deployed to serve requests for resolving an entity name from text extract from a document image.

The invention relates to the field of language control systems, and discloses a virtual reality natural language interaction control system based on big data, and the system comprises a data acquisition unit which collects the interaction original data of a virtual reality scene according to a sensor array; performing natural language semantic analysis on the interaction original data to obtain user intention semantic feature data; performing virtual scene correlation analysis according to the user intention semantic feature data to obtain scene correlation data; a data prediction unit; through natural language semantic analysis in the data acquisition unit, intention feature data of the user can be extracted, so that the system can more accurately understand the requirements of the user in a virtual reality environment, the naturalness and fluency of interaction are improved, and in addition, by combining scene correlation analysis and user behavior pattern mining, the interaction efficiency is improved. The interactive intention of the user can be dynamically predicted, so that the user demand is responded in advance, and the immersive experience is enhanced.

The application provides a multi-interface cooperative natural language data query method and system, relates to the technical field of natural language processing, and obtains text query data transmitted by a character input interface, voice query data transmitted by a voice input interface and touch instruction data transmitted by a touch selection interface; the voice query data and the touch instruction data are respectively optimized to obtain a multi-interface fusion text set; based on a preset natural language semantic analysis annotation data set, a semantic analysis result is obtained; a unified query instruction is generated through digital information transmission technology; data sources corresponding to each query field information in the semantic analysis result are determined; and an initial query result is obtained according to preset retrieval rules of each data source, so that accurate analysis and cross-interface unified execution of a natural language query intention are realized, and the accuracy, robustness and interactive experience of data query in a complex scene are improved.

The invention discloses a hybrid multi-modal document retrieval enhancement method, system and device and a storage medium. The method comprises the following steps: performing format analysis on an unstructured document; generating a text vector, a visual vector and a structured table vector based on an analysis result, and constructing three index libraries for storing the text vector, the visual vector and the structured table vector respectively; in response to the natural language queried by the user, recalling candidate text paragraphs, candidate pictures and candidate tables related to the semantics of the natural language queried by the user; merging at least two types of candidate elements in adjacent candidate text paragraphs, candidate pictures and candidate tables in the same page into a mixed fragment; assembling a plurality of mixed fragments to form a multi-modal context; and inputting the multi-modal context into the large language model to obtain an answer. According to the method, adjacent candidate elements of the same page are merged, spatial position association of elements in a document is considered, and cross-modal information is automatically integrated, so that the obtained answers pay more attention to context association.

This application discloses a graph-based natural language control method, apparatus, device, and medium for accelerators, relating to the field of particle accelerator control technology. Through deep integration of a natural language semantic scheduling module and an accelerator domain knowledge graph, it achieves precise conversion from unstructured natural language instructions to standardized device control commands. The method includes: receiving first natural language text input by a user; parsing the first natural language text into control intentions and parameter slots based on a pre-trained natural language semantic scheduling module; mapping the control intentions and parameter slots to accelerator devices based on a pre-constructed accelerator domain knowledge graph, utilizing the device topology and operational dependencies within the accelerator domain knowledge graph, and inferring and generating a control command sequence; and distributing the control command sequence to the target accelerator device to control the target accelerator device to perform corresponding operations.

The invention provides a code snapshot management method and device and related equipment. The method comprises the following steps: monitoring a change event of a code file; obtaining multiple pieces of evaluation dimension information corresponding to the change event, and fusing the multiple pieces of evaluation dimension information to obtain a semantic importance score corresponding to the change event; when it is determined that the score is larger than a preset score threshold value, semantic analysis is conducted on codes in the change file, corresponding code semantic information is obtained, and according to the code semantic information, a code snapshot is generated and stored. According to the method, an automatic triggering mechanism based on semantic importance evaluation is adopted for code change evaluation, whether snapshots are generated or not is intelligently decided on the basis of an automatic snapshot triggering mechanism, problem positioning is converted into semantic retrieval through natural language semantic matching, the analysis period is greatly shortened, the uncertainty of manual judgment is eliminated, and the analysis efficiency is improved. According to the generated code snapshots, the storage cost is reduced on the premise of ensuring that any historical snapshots can be completely reconstructed.

The application provides an agent identity verification method based on a trust mechanism, belongs to the technical field of artificial intelligence and network security, and is used for solving the problems that in related technologies, external credentials cannot distinguish real agents from fake requests and are vulnerable to prompt injection attacks. In the application, a natural language semantic task is issued by a verification direction request party as a verification task, the request party generates a high-cost natural language response meeting a preset quality constraint, the verification party performs lightweight verification with a lower computing resource than the generation cost, and if the verification is passed, the request party is determined to be an agent with a legal semantic generation capability. The application realizes identity verification based on the endogenous semantic capability of the agent as the trust basis, naturally resists fake and flooding attacks through a cost asymmetry mechanism, and is immune to prompt injection at the protocol level through an output locking mechanism, and can be widely applied to trusted communication scenarios of an agent network.

The application provides a kind of track traffic emergency dispatching method and intelligent agent system based on subgraph retrieval and scene perception, belongs to the field of track traffic emergency dispatching.The application first converts unstructured event description and knowledge text into semi-structured scene parameter set through large language model, and then automatically maps the semi-structured scene parameters into the binary activation state of the corresponding constraint in the mixed integer linear programming model through the scene perception constraint activation function, thereby realizing reliable bridging between natural language semantic understanding and strict mathematical constraint modeling, avoiding the illusion risk of directly processing mathematical constraints by large language model and the limitations of traditional optimization methods that cannot understand unstructured text, and solving to generate train working diagram adjustment scheme, through multi-dimensional credibility evaluation framework and intelligent agent iterative dispatching closed loop to guarantee the reliability of dispatching decision.

The automated generation of a natural language explanation of what code does. The code is structured to perform tasks because the code itself semantically specifies that those tasks are to be performed. A task-centric representation of the code is automatically generated that includes a task representation of each of some or all of the tasks to be performed as specified by the code. Natural language utterances are then automatically generated by generating a corresponding natural language utterance that semantically describes in natural language the corresponding task represented by the corresponding task representation. Controls are rendered for each natural language utterance that each permit a user to edit the corresponding natural language utterance. After editing, the code itself may be automatically modified or regenerated to reflect the changed natural language utterances.

Techniques for augmenting training data include accessing training data comprising a plurality of training examples comprising a first training example comprising a first natural language utterance and a first logical form for the first natural language utterance. A second natural language utterance is generated by adding or replacing one or more values in the first natural language utterance. A logical form for the second natural language utterance is generated. A second training example is generated, comprising the second natural language utterance and the logical form for the second natural language utterance. The training data is augmented by adding the second training example to the plurality of training examples to generate an augmented training data set. A machine learning model is trained to generate logical forms for utterances using the augmented training data set.

Implementations of semantic parsing using pre-trained language models are provided. One aspect includes a computing system for semantic parsing of natural language. The computing system comprises processing circuitry and memory containing instructions that, when executed, cause the processing circuitry to receive a request comprising a natural language utterance and generate a formal meaning representation using the natural language utterance and a language model comprising a semantic parser that has been prompted with training data generated by providing a dataset comprising a set of unlabeled programmatic scripts and a seed programmatic script, generating a set of parsed natural language descriptions by inputting the set of unlabeled programmatic scripts into an inverse semantic parser, generating a set of re-parsed programmatic scripts by inputting the set of parsed natural language descriptions into the semantic parser, and determining a set of labeled programmatic scripts by validating the set of re-parsed programmatic scripts.

The application provides a security communication method for agent interaction, belongs to the technical field of artificial intelligence and network communication, and is used for solving the problems that the agent identity authentication is unreliable, vulnerable to prompt word injection and flooding attack in the related art. The method comprises the following steps: a verification party generates a semantic challenge and issues it to a request party; the request party generates a natural language semantic response by using a large language model, and embeds API information in the response in a semantic steganography manner; the verification party receives the semantic response and performs legality verification, which comprises whether the semantic correlation and the generation cost are higher than a threshold value; after the verification is passed, the API information is extracted and an invocation is performed, and a structured result is output. The application takes semantic capability as a trust root, realizes integrated identity authentication and secure API invocation through asymmetric cost semantic challenge and semantic steganography, is immune to injection attack from the protocol level, and naturally resists flooding attack.

The invention discloses a sitting posture semantic understanding and intervention model and method based on pressure sensing, and the model employs a multi-mode sitting posture semantic model to achieve the high-quality mapping from pressure sensing data to natural language semantics. The multi-modal sitting posture semantic model is composed of three core modules including a sensor embedding module, a sensor query alignment module and a multi-context prompt module, and sensor semantic information is structurally injected into a large language model by introducing multi-dimensional semantic clues at an input end, so that the large language model is guided to generate a response conforming to posture semantics. According to the model and the method, on the premise of protecting user privacy, improving use convenience and ensuring non-intrusive experience, accurate perception and intelligent intervention of the sitting posture state of the user are achieved, the recognition precision, proposal specialty, adjustment intelligence and deployment flexibility of the system are remarkably improved, and the user experience is improved. The system breaks through many bottlenecks in the aspect of intelligent intervention of sitting posture health in the prior art, and has wide application value and popularization prospect.

This application discloses a method for classifying psychological state texts based on large-scale generative data augmentation, relating to the field of natural language processing technology. This method automatically discovers patient psychological semantic clusters by clustering a limited number of original samples, then extracts the natural language semantic template for each patient's psychological semantic cluster. Subsequently, under the dual control of emotional polarity and psychological theme, a large-scale language model is used to generate psychological state texts with consistent themes and diverse expressions according to these natural language semantic templates as augmented samples. This method utilizes the large-scale language model to generate high-quality samples based on the natural language semantic templates obtained from clustering to expand the model's training samples. This large-scale generative data augmentation method can ensure the authenticity and diversity of the generated augmented samples, thereby improving the classification accuracy and generalization ability of the trained psychological state text classification model.

The application relates to the technical field of database query, and discloses a method and device for converting natural language into a database query language, a vehicle and a medium, the method comprising the following steps: based on a Skill system carrying a large language model, a retrieval enhancement model and a natural language query engine, gradually disclosing skill information to the large language model in sequence by means of a progressive disclosure mechanism, and by the large language model, natural language information is normalized into standard text based on the skill information and initial business terms determined by the retrieval enhancement model, after the standard text is confirmed to be correct, the retrieval enhancement model is used for matching library table mapping information, and a database query language is generated by a natural language rule engine. The application can realize semantic layer-by-layer checking and accurate information disclosure throughout the whole process, avoid semantic deviation and generation errors from the process level, significantly improve the natural language semantic understanding precision and the accuracy and reliability of the database query language generation, and can stably adapt to complex and changeable business query scenarios of enterprises.

The invention discloses a content-assisted synthetic audio detection method, which comprises the following steps of: extracting acoustic and natural language semantic features from each section of voice in parallel, determining the level of a collaborative alarm according to the two types of features, and on the basis, adjusting the level of the collaborative alarm according to a text intention and an acoustic style while detecting a synthetic voice. A natural language processing method is deeply introduced into a synthetic audio detection process, a semantic acoustic double-path conjoint analysis architecture is constructed, and the limitation that a traditional detection scheme only depends on acoustic fingerprints, spectrum features or model generation traces is broken through.

The invention relates to a video retrieval system and method based on natural language semantic recognition, and belongs to the technical field of computer vision and information retrieval. The system comprises a text input module, a semantic recognition algorithm module, a database retrieval module and a result presentation module. The method comprises the steps of receiving a natural language text query input by a user; performing preprocessing, feature vectorization and semantic reasoning on a text through a semantic recognition algorithm model, decoding a target subject and an attribute tag, and forming a structured retrieval condition; performing matching retrieval in a mass video / image database according to the condition; and returning and presenting a retrieval result. According to the method, the video content retrieval is directly performed by using the natural language, the user operation is simplified, the retrieval accuracy and efficiency are improved, and the method can be widely applied to security monitoring, intelligent transportation and content management platforms.

The invention provides a semantic map creating and using method and system, a robot and a medium, belongs to the technical field of artificial intelligence and robots, and aims to solve the problem that an existing semantic map creating method depends on predefined categories and cannot find unknown semantic concepts. The method comprises the following steps: collecting multi-modal data in an environment and adding spatial position information; multi-modal features are extracted; clustering the multi-modal features by adopting an unsupervised clustering algorithm to form a data cluster; generating a natural language semantic tag for the data cluster by using a pre-trained multi-modal large model; constructing a multi-dimensional semantic map; and responding to the natural language query. According to the method, through the core process of first unsupervised clustering and then large model marking, automatic discovery of unknown semantics is realized, and the environment understanding ability of the robot and the naturalness of man-machine interaction are improved.

The application belongs to the technical field of industrial control protocol reverse engineering, and discloses an unknown industrial control protocol semantic inference method based on model learning. Natural language semantic descriptions of common industrial control protocol fields are vectorized and clustered, the semantic categories of the industrial control protocol fields are summarized, and different field value categories are defined. Unknown industrial control protocol messages are preprocessed, the change rules of different field values are extracted, and normalized sequence data is obtained. The unknown industrial control protocol semantic inference problem is converted into a field multi-classification task based on time series. The normalized sequence data is input into a field sequence classification model based on BiLSTM-FCN for classification. The method improves the accuracy and robustness of unknown industrial control protocol field semantic identification.

The invention discloses a natural language processing method and device, which is applied to the technical field of natural language processing and intelligent information systems, and comprises the following steps: acquiring initial natural language data; if the initial natural language data is target language data, processing the target language data to obtain candidate results; processing each candidate result based on an RRF algorithm to obtain a first identification result; processing each candidate result based on a pre-trained large language model to obtain a second recognition result; analyzing first semantic association information between the first recognition result and each candidate result, analyzing second semantic association information between the second recognition result and each candidate result, and determining a third recognition result according to the first semantic association information and the second semantic association information; and determining a recognition result of the initial natural language data from the third recognition result. According to the natural language processing method provided by the invention, accurate and reliable natural language semantic recognition can be realized.

This application provides a method and system for intelligent MCP code generation based on a unified service model, as well as a computing device and computer-readable storage medium. The method first employs a dual-path schema parsing mechanism of "saving before conversion + enhancing after conversion" to ensure 100% integrity of API metadata. This high-fidelity data foundation provides deep context-aware capabilities for subsequent natural language semantic retrieval, significantly improving the accuracy of fuzzy matching and the success rate of API calls. Simultaneously, a unified intermediate model layer decouples specification parsing from code generation, utilizing template technology to automatically construct multi-language MCP projects from a single API specification. This application not only guarantees the integrity and interpretability of MCP service definitions but also achieves high-performance interaction and high-quality code output, effectively improving the accuracy and development efficiency of large-scale model calls to MCP tools.

This disclosure relates to a content recommendation method, device, and medium, applicable to the field of computer application technology. It includes: training a semantic alignment model by constructing an aligned sample set consisting of initial semantic identifiers and corresponding target object text descriptions; establishing a bidirectional mapping relationship between the two; aligning the semantic identifiers, originally limited to business contexts, with the natural language semantic space; and obtaining enhanced semantic identifiers that retain the feature representations of business data while deeply integrating the pre-trained knowledge of a general-purpose model, thereby significantly improving the performance of the generative recommendation model in terms of semantic understanding depth. This semantic alignment mechanism effectively solves the semantic gap problem in existing technologies, enabling the generative recommendation model trained using enhanced semantic identifiers and corresponding target object text descriptions to fully utilize the knowledge transfer capabilities of the general-purpose model, significantly enhancing accuracy in handling cold-start scenarios and target object recommendations.

The application discloses a code snippet generation method and system based on a project private component library semantic index, belongs to the technical field of software development tools, and comprises the following steps: scanning a current project, identifying a private component library relied on by the project, performing syntax analysis on the private component to extract metadata, and constructing a semantic feature index based on the metadata; in response to a coding input event in an integrated development environment, analyzing a semantic intention to generate an intention vector; matching the intention vector with the semantic feature index, combining project context sorting to determine a target private component; and automatically generating a calling code snippet according to the metadata of the target private component and outputting the calling code snippet to the integrated development environment. The application realizes intelligent recommendation of a private component and automatic generation of code based on natural language semantics, can reduce the cognitive burden of a developer, improve development efficiency, guarantee code specification, promote component reuse, and completes a processing process locally, thereby guaranteeing code safety.

This invention relates to the fields of big data and artificial intelligence, and more particularly to a DataAgent implementation method for NL-OSI-SQL. This invention discloses an NLOSISQL DataAgent implementation method, belonging to the field of big data and artificial intelligence. This invention introduces the OSI Open Semantic Exchange standard as an intermediate layer, adopting a layered architecture of natural language → OSI semantics → SQL. It first converts natural language into standardized OSI semantic files, and then translates them into SQL executable by the target computing engine. This solves the problems of low accuracy, high development cost, poor cross-platform compatibility, weak interpretability, and untraceable lineage inherent in traditional NLSQL, NLDSLSQL, and NLMDLSQL. This invention has advantages such as high accuracy, low development cost, strong cross-platform compatibility, good scalability, and interpretability and traceability, and is suitable for natural language intelligent querying and indicator calculation scenarios in enterprise-level multi-platform data warehouses, lake warehouses, and multi-cloud environments.

The invention relates to the technical field of artificial intelligence and game development, in particular to a game engine-oriented AI Agent tool calling method and virtual equipment, and the method comprises the following steps: receiving a natural language tool calling request sent by an AI Agent through a standard protocol; and tool matching and parameter analysis are carried out based on a pre-established tool definition system adopting natural language semantic description and multi-dimensional tags. The general request parameters are automatically converted into strong data types specific to the game engine, and all converted instructions are ensured to be safely executed in a main thread of the game engine through a task queue scheduling mechanism of the main thread. In addition, the method also supports the quick retrieval of the tool through the reverse index and the hot update of the tool based on file monitoring. According to the method, the problem that a large language model is difficult to directly understand and call a complex game engine API is effectively solved, and the technical threshold and the operation risk of participation of AI in game development are remarkably reduced.