92 results about "Language analysis" patented technology

The invention provides a call center dialogue sentiment analysis method and system fusing voice and text, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a voice signal of a call center and a corresponding transliteration text, extracting intonation, speed, sound intensity and pause sentiment features from the voice signal, meanwhile, deep language analysis is carried out on the transliterated text, and semantic emotion features related to context are extracted; and performing cross-modal correlation analysis on the voice emotion features and the semantic emotion features, and generating a time sequence correction coefficient for feature alignment by constructing a corresponding relation analysis framework between feature sequences. According to the method, language emotion information in voice and text is integrated, more accurate and comprehensive recognition of conversation emotion of the call center is realized, customer satisfaction is improved, and reliable emotion analysis basis is provided for efficiently processing customer appeals.

The invention discloses a multi-language data analysis-oriented extensible system, method, equipment and medium, and relates to the technical field of multi-language analysis, the multi-language data analysis-oriented extensible system comprises a grammar analysis module used for analyzing a query language and generating a language-specific syntax tree structure after analysis; the grammar adaptation module is used for converting a language-specific grammar tree structure into an abstract grammar tree AST in a uniform format; the abstract syntax tree processing module injects a permission control strategy, a field desensitization rule and an alarm exception expression into the AST; the business processing module is used for executing business-related rule check and label injection; the query plan generation module is used for constructing a logic query path and a corresponding distributed execution scheme; and the task scheduling module issues the distributed execution scheme to the corresponding execution node. According to the method, by constructing a unified multi-language grammar analysis framework and a standardized abstract syntax tree AST representation mechanism, compatibility and fusion of multiple query languages such as SQL, SPL and natural language are achieved.

The invention discloses a conversation-based multi-modal feature analysis method and electronic equipment, and relates to the technical field of language analysis, and the method comprises the following steps: collecting multi-modal evaluation data of a conversation process of a to-be-evaluated object, and carrying out the feature marking of the multi-modal evaluation data to obtain state feature points; performing statistics on occurrence frequency and feature intensity of similar features in the state feature points to obtain feature correlation parameters; extracting a historical language state feature distribution rule in different evaluation scenes from the historical evaluation data set, and calculating a language state influence factor between a language state tendency value and a feature correlation parameter in the historical evaluation data according to the historical language state feature distribution rule; the target influence factor is screened out from the language state influence factors according to the real-time language state feature distribution feature of the current conversation scene, and the effect is that clear guidance is provided for formulation of a subsequent intervention strategy.

The invention relates to the technical field of text generation, in particular to a text generation method and system based on natural language processing. Comprising the following steps: S1, collecting corpus data, preprocessing the collected corpus data, and then performing meaning label analysis on the corpus data; s2, acquiring real-time cue words and historical cue words output by a user, and performing demand language analysis on the real-time cue words and the historical cue words by using a natural language processing technology; the method comprises the following steps: setting a professional degree benchmark, determining a professional degree evaluation dimension according to domain knowledge, carrying out professional degree scoring on candidate texts, analyzing real-time and historical demand languages of a user, determining the professional level of the user, and predicting the professional degree required by the current demand by referring to related historical output texts. The dynamic adjustment mechanism can ensure that the generated text is not too hard and obscure and not too light and brief, and the applicability and practicability of the text are greatly improved.

The invention belongs to the technical field of single-cell multi-omics, and discloses a single-cell multi-omics data analysis system, method and device and a storage medium, a data import module is used for reading sequencing data from different sequencing platforms and storing the sequencing data as S4 objects; the cross-language analysis module is used for calling a single-cell multi-omics analysis tool based on an R language and a Python language to analyze an S4 object according to an analysis process in a unified framework; the deep learning optimization module is used for training a deep learning model based on the cross-language interaction interface and integrating an analysis result of the deep learning model to an object S4; and the interaction and extension module is used for obtaining an analysis process and visualizing an analysis result, constructing a user-defined single-cell multi-omics analysis tool in a plug-in form and loading the tool to the S4 object for analysis. Cross-sequencing platform data compatibility, multi-language tool seamless integration and deep learning framework efficient access are achieved, and the problems of data isomerism, language ecological splitting and algorithm expansibility in the prior art are solved.

The invention discloses a method and a system for searching by combining user input information, and particularly relates to the technical field of language paragraption.The method comprises the following steps: firstly, converting a plurality of user inputs into an input semantic structure containing keywords, semantic relationships and context tags; comparing the structures, extracting interactive focuses and generating a semantic consensus score; constructing a fusion path diagram according to the consensus score, the keyword weight and the semantic adaptation degree; semantic fusion and conflict mediation are executed according to the path diagram, and a unified expression structure is generated; and finally, sorting the keywords according to semantic levels and task orientation, and constructing a comprehensive search expression to finish query output. According to the method, through semantic analysis and cross comparison input by multiple users, the consensus content is extracted, the fusion path diagram is constructed, and it is ensured that the fusion sequence is reasonable and semantic consistency is achieved; and finally, a comprehensive semantic expression structure is generated and a search expression is constructed, so that accurate alignment of query contents and user intentions is realized, and representativeness and accuracy of search results are improved.

The invention discloses a system and a method for automatically assembling a data analysis process by a large language model based on Faiss, and belongs to the technical field of data analysis and artificial intelligence. The system comprises a client, a server and a data analysis platform. The method comprises: receiving a natural language analysis demand of a user; identifying a user intention as process assembly or suggestion through a large language model; encoding the demand into a query vector, and performing similarity retrieval by using a Faiiss vector library pre-constructed based on the analysis tool description information to obtain a first candidate tool set; inputting the set and original requirements into a large language model for fine arrangement and logic assembly to generate a second candidate tool set; and finally converting into a structured process file which can be directly identified and executed by a data analysis platform. According to the method, the problem that a structured data analysis process cannot be automatically constructed in the prior art is solved, end-to-end intelligent generation from natural language description to an executable analysis scheme is realized, the use threshold is remarkably reduced, and the analysis efficiency is improved.

The invention provides a complex scene visual recognition rapid construction method based on natural language description, and relates to the technical field of computer vision and natural language processing, and the method comprises the steps: obtaining natural language description information inputted by a user; performing language analysis and element extraction on the natural language description information to obtain a scene semantic table; decoupling the scene semantic table into M atomic conditions, and obtaining an atomic instruction set; performing visual feature extraction on the to-be-recognized image by using a visual encoder to generate an image feature vector; converting the atomic instruction set into an atomic instruction vector set, and outputting an atomic instruction verification set; generating a relation verification matrix; and executing global logic expression verification operation based on the atomic instruction verification set and the relationship verification matrix, and outputting comprehensive confidence. The technical problems that in the prior art, semantic understanding depth is insufficient, complex scene understanding is limited, and self-adaptive capacity is insufficient are solved.

The invention discloses an expression language analysis method and device and a storage medium, and relates to the technical field of electronic digital data processing.The expression language analysis method comprises the steps that in response to an expression text input by a user in an editor, an analyzer integrated to a current program is called; analyzing an abstract syntax tree corresponding to the expression text based on the analyzer; and in response to function operations for the abstract syntax tree, outputting a function execution result at the editor, the function operations including syntax highlighting, code checking, automatic completion and expression calculation. According to the method and the device, the technical problem that expression languages and analysis tools in related technologies are closed sources and cannot be compatible with own systems is solved, and the technical effect of automatically generating the analyzer to analyze the expressions of the currently defined grammar rules is achieved.

The embodiment of the invention provides a driving behavior adjustment method and device based on driving simulation equipment, a storage medium and a program product. In the embodiment of the invention, an emotion analysis model and a language analysis model based on artificial intelligence are provided, and at least one image corresponding to a driver is collected in a process that the driver uses a driving simulation device to practice a target driving project. Calling an emotion analysis model to analyze user emotion information of the driver according to the image including the face; if the emotion information of the user is negative emotion, the reason and contact suggestion information for generating the negative emotion based on the language analysis model is called according to the exercise data of the driver on the target driving project and in combination with the signal data of the vehicle part of the driving simulation equipment, and the user can conveniently perform the negative emotion according to the personal conditions of different drivers. Individual differences are considered, intelligent feedback and personalized solutions are provided, and more efficient and high-quality driving training services are provided for users.

The invention relates to a method for functional analysis of a source system (1), wherein a syntax tree model (3) is generated from the program code data of the source system (1). In order to propose a method for functional analysis of a source system (1), in particular with extensive program code data, which method makes it possible to better identify interrelated functional units and, with high result quality and at the same time low resource requirements, to improve further processing by means of a large language model, the invention provides that a syntax analysis unit (4) generates a syntax embedding model by decomposing the syntax tree model (3) into individual syntax element embeddings (5), each representing a syntax element, and stores said syntax embedding model in a syntax embedding model memory (6), and that a language analysis unit (8) generates a language embedding model by decomposing the program code data into individual language element embeddings (9), each representing a language element, and stores said language embedding model in a language embedding model memory (10), whereupon, by means of a query unit (12), at least one query embedding (13) is generated from a query (11) representing a functional element of the source system (1) and those syntax element embeddings (5) and language element embeddings (9) which lie within a predefined range of similarity to the query embedding (13) are retrieved from the syntax embedding model memory (6) and the language embedding model memory (10), whereupon the syntax elements represented by the retrieved syntax element embeddings (5) and the language elements represented by the retrieved language element embeddings (9) are transferred as result elements (14) to an output unit (15), which generates therefrom a functional description dataset (16) and outputs the latter.

The application discloses a talk-based multi-modal feature analysis method and electronic equipment, and relates to the technical field of language analysis, and the technical solution points thereof comprise the following steps: collecting multi-modal evaluation data of a talk process of an object to be evaluated, performing feature marking on the multi-modal evaluation data to obtain state feature points; counting the occurrence frequency and feature intensity of the same features in the state feature points to obtain feature correlation parameters; extracting historical language state feature distribution rules in different evaluation scenes from a historical evaluation data set, calculating a language state influence factor between a language state tendency value in the historical evaluation data and the feature correlation parameters according to the historical language state feature distribution rules; and screening a target influence factor from the language state influence factor according to the real-time language state feature distribution characteristics of the current talk scene, so that clear guidance is provided for the subsequent development of intervention strategies.

The invention discloses a platform product optimization and research and development process remodeling method based on AI drive, comprising the following steps: constructing a general MCP service: dynamically generating an OpenAPI standard document through API scanning registration; analyzing the user language and generating a visual chart or a text abstract; analyzing the natural language, and converting the structured data returned by the API into the visual chart or the text abstract for structural integration and feedback; and establishing a non-invasive AI enabling mechanism: intercepting and processing an API calling request, and providing an interaction component through front-end SDK embedding. Through cooperation of the MCP service and the WebAI assistant, low-invasion AI enabling of a traditional platform product is achieved, and the problems that API integration cost is high, task execution is fragmented and the like are solved. And in combination with a plug-in architecture, the platform can quickly expand the AI capability, and the research and development efficiency and the user experience are remarkably improved.

The invention provides a lightweight anaphora ambiguity resolution multi-task learning method, and relates to the technical field of intelligent language processing of a power system, and the method comprises the steps: S1, collecting power production command related documents to construct a lightweight power semantic knowledge base; s2, on the basis of the lightweight electric power semantic knowledge base, constructing an ultra-lightweight semantic analysis model through double-layer knowledge distillation; s3, performing lightweight deployment on the ultra-lightweight semantic analysis model based on an INT8 quantification technology to obtain a quantification model capable of running on edge equipment; s4, inputting the electric power semantic analysis task into a quantitative model obtained by lightweight deployment by adopting an end-side collaborative reasoning strategy, performing complexity evaluation and intelligent shunting processing, and outputting a semantic analysis result with high confidence; according to the method, high-precision semantic understanding under the condition of extremely low computing resources can be realized, and a practical and reliable intelligent language analysis capability is provided for a power production command system.

The invention discloses a fault processing method, a communication device and a system. The method comprises the following steps: receiving a first request; the first request is related to the fault information; performing language analysis on the first request to obtain a language analysis result; first information is sent, the first information is determined based on the language analysis result, and the first information is used for indicating a request result of the first request. The method can process the request through the first node, does not depend on manpower, and can improve the time consumption of fault processing.

The invention provides a credit report generation system and method based on a language analysis large model, and relates to the field of generative artificial intelligence. The system comprises an interaction layer, an intelligent agent layer, a basic platform layer and a data layer. The data layer is configured to access and store multi-source heterogeneous data of bank users; the interaction layer is configured to receive and output a report template and user information input by a user to the basic platform layer; the basic platform layer is configured to call a workflow corresponding to the report template, call at least one kind of heterogeneous data required by report generation from the data layer, and perform structured processing on user information and the called heterogeneous data to obtain a structured data set; and the agent layer is configured to construct prompt information interacting with a preset language analysis large model based on the workflow and the structured data set, call the language analysis large model based on the prompt information to generate a credit report, and feed back the credit report to the interaction layer to be displayed to a user.

The invention discloses a chat record crime element automatic labeling method based on natural language processing, and relates to the technical field of electronic data intelligent analysis, and the method comprises the following specific steps: sequentially carrying out data preprocessing to analyze multi-platform data and uniformly encode, carrying out semantic analysis to identify crime intentions and entities, and carrying out entity association reasoning on crime elements; and marking elements by using a structured label, and finally carrying out risk scoring and generating a judicial analysis report so as to form a complete crime element analysis process. According to the method, multi-language processing, dynamic weight and hybrid reasoning technologies are integrated, multi-platform data are compatible, the multi-language analysis problem is solved, the criminal entity recognition precision is improved, and the labeling efficiency and accuracy are greatly improved; meanwhile, a full-link judicial system is constructed, crime elements are standardized, electronic evidence export is supported, case mastering is assisted through risk scoring and multi-dimensional reports, a rule base is dynamically updated to adapt to new crimes, the working intensity of judicial personnel is reduced, and reliable support is provided for network crime attack.

The invention discloses a legal document fall positioning and quality inspection method and device based on semantic understanding, and relates to the technical field of legal review, and the method comprises the steps: analyzing electronic document data, extracting text content, associating the text content with corresponding coordinate metadata, and generating multi-modal document data; performing deep semantic and structural analysis on the multi-modal document data, extracting a text sequence, executing natural language analysis and semantic analysis, obtaining a phrase role labeling result, judging a termination position, and obtaining a semantic boundary; based on the semantic boundary and the coordinate metadata, performing retrieval positioning on the multi-modal document data, extracting coordinate text data and page evidence image segments, obtaining a structured entity and segmented signature blocks through entity recognition and signature detection, and constructing a signature element map; according to the method, automatic verification and accurate fund positioning of the document are realized, the processing efficiency is improved, manual intervention is reduced, and the method is widely applicable to automatic examination and compliance inspection of the electronic document.

This application belongs to the field of financial technology and is applied to the training of intelligent customer service question-answering models in the insurance industry. It relates to a GraphQL-based Chinese question-answering model training method and related equipment. The method includes generating GraphQL query statements corresponding to target Chinese question data according to GraphQL specifications; obtaining answer data corresponding to the target Chinese question data through a language analysis model; constructing a first text pair set and a second text pair set, inputting them into a T5 question-answering model pre-trained in an end-to-end mode for T5 question-answering model training, and obtaining a trained T5 question-answering model; obtaining new Chinese question data and inputting it into the trained T5 question-answering model to obtain the model output as answer data. By introducing a dual-tower model structure into the end-to-end T5 question-answering model and constructing query statements according to GraphQL specifications, an end-to-end Chinese graph question-answering model with effective intermediate representations is trained, and the output of the answer data is subject to good quality control.

This application relates to the field of requirements analysis technology, specifically providing an AI-based natural language requirements analysis method and system. The method includes the following steps: acquiring natural language requirements text, then using AI-based feature extraction technology to extract features from the natural language requirements text to obtain text domain features, user style features, and task features; determining the combination of language analysis modules and the parameters corresponding to each language analysis module in the combination based on the text domain features, user style features, and task features; determining the execution order of each language analysis module in the combination based on the text domain features and task features, then constructing a requirements analysis process based on the execution order and the combination of language analysis modules; and using the requirements analysis process to perform requirements analysis on the natural language requirements text to obtain requirements information. This method enables the analysis process to adapt to users' personalized expressions and accurately analyze different analysis tasks.

The invention discloses an unmanned aerial vehicle visual angle three-dimensional visual positioning method, which comprises the following steps: extracting candidate objects and landmarks from a three-dimensional scene, extracting object features by using an object encoder, and extracting landmark features by using a landmark encoder; constructing a language analysis strategy based on prompt learning, and analyzing the overall natural language description into a plurality of partial language descriptions; constructing an object feature enhancement module based on landmark reference, describing and mining neighbor features between an object and a landmark by using a part of natural languages to enhance feature representation of objects near the landmark, and guiding a positioning network to pay attention to the objects near the landmark; an object fine positioning module based on relation perception is designed to make full use of relation information between objects, and unmanned aerial vehicle visual angle three-dimensional visual positioning is achieved.

The present invention discloses a data security classification and grading method and system based on a large language model, which relates to the field of language analysis technology, including converting each layer of bank internal business data into text data through text conversion, unifying the data form of multi-layer or multimodal data into text data, and providing a reliable basis for subsequent language semantic recognition. The text data is divided into blocks and sorted to facilitate subsequent contextual language understanding, and the context window length under each business scenario is set to adapt to the contextual semantic understanding requirements of each business scenario, thereby improving the reliability and accuracy of semantic association. The classification and grading of the bank's internal business data based on the risk value of the business scenario and the sensitive value of the composite field improves the accuracy and adaptability of data security classification and grading, and implements different degrees of security protection strategies for different types of data at different levels, thereby ensuring the bank's data storage and prevention needs.

The invention relates to the technical field of natural language processing, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a text intention analysis method, device and equipment and a medium. Dividing the original data according to the obtained target division strategy to obtain a plurality of target sub-data sets, screening a plurality of obtained federal parameter efficient fine-tuning algorithms according to the obtained client resource condition and fine-tuning task requirements to obtain a target fine-tuning algorithm, and adjusting a preset large language model to obtain a fine-tuning result. And carrying out resource consumption analysis on the adjustment process, carrying out performance optimization on the preliminary adjustment model to obtain a target large language model, and carrying out text language analysis on the obtained target text data by utilizing the target large language model to determine a target text intention. According to the method, the stability of the analysis performance of the large language model and the interpretability of the text intention are effectively improved.

A language processing computer is provided that can improve the accuracy of language analysis and the accuracy of semantic analysis, which are the prerequisites for learning by artificial intelligence. [Solution] The language processing computer 1 comprises a syntactic analysis unit 110 that performs syntactic analysis of an input document and divides the document into word-based elements; a mapping unit 120 that maps each of the divided words to a semantic structure; an evaluation unit 130 that evaluates the semantic match or similarity between the words divided into word-based elements; a memory unit 300 that associates each word, the evaluated score, and the document and stores them as a semantic unit dictionary; an extraction unit 210 that extracts character strings from a specified document in units of phrases, propositions, or logic based on the semantic unit dictionary; and a processing unit 220 that processes the analysis or translation of the specified document based on the extracted character strings.

The invention discloses an enterprise data processing method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: sending a received user query utterance to a large model, and determining a target service manual corresponding to the user query utterance according to a preset service description map; in response to a received service manual query request fed back by the large model, sending the content of a target service manual to the large model, so that the large model generates an SQL query statement according to the user query utterance and the content of the target service manual; receiving an SQL (Structured Query Language) query statement sent by the large model, and executing the SQL query statement in the offline database to obtain a structured query result; sending the structured query result to the large model, and indicating the large model to generate a query result based on natural language description according to the structured query result; and feeding back a query result based on the natural language description to the user. According to the invention, the effect of analyzing the business data of the enterprise by using the natural language is realized.

The invention relates to the technical field of multi-language processing, and discloses an intelligence mining platform for multi-language data processing, which comprises a multi-source heterogeneous data access module for converting multi-field original data into intermediate format data; the multi-language adaptive analysis module performs language detection, semantic alignment and language analysis on text data, audio and video data and structured data in the intermediate format data to generate analysis results containing semantic vectors; a multi-format data processing module extracts key information with a cross-language semantic identifier from an analysis result; the heterogeneous data fusion module associates and fuses the key information, and constructs a knowledge graph and an event chain to form unified knowledge representation; the knowledge extraction and generation module extracts intelligence elements from the knowledge spectrogram or the event chain. Through multi-module cooperative processing, unified access, analysis, key information extraction, fusion and intelligence generation of multi-language and multi-format data are realized, and structured and high-value intelligence support is provided for related fields.

The application belongs to the technical field of intelligent sports auxiliary learning. A body movement learning clue labeling method and system based on cross-modal mapping are proposed. Language analysis is performed on the voice explanation in the action teaching video to obtain sentence-by-sentence text and corresponding time markers. The sentence-by-sentence text is analyzed to extract entities in the sentence-by-sentence text and the corresponding roles of each entity. The motion characteristics of the action demonstration subject are extracted from the action teaching video. The recognized entities are mapped to graphics to generate visual clues. According to the visual clues, language analysis results and motion characteristics, the visual clues are laid out to the corresponding time and space positions to obtain the final body movement learning clue labeling result. By constructing an optimized graphic clue, the action points can be more intuitively and vividly presented, helping learners clearly understand the action essentials through intuitive visual prompts, and improving the learning effect.

To provide a system for supporting the intellectual training and growth of a child while reducing the labor of a parent.SOLUTION: The voice recognition system includes a means for acquiring voice uttered by a user, a voice recognition means for converting the acquired voice into text data, and a communication means for transmitting the converted text data. A system comprising: language analysis means for extracting a user's intention and emotional state; response generation means for generating an appropriate response based on the extracted intention and emotional state; communication means for receiving the generated response as text data; speech synthesis means for converting the received response text into voice data; and voice changer means for applying character voices to the converted voice data.SELECTED DRAWING: Figure 1

An object of the system according to the embodiment is to correct a user's text in real time and prevent misunderstanding and trouble.SOLUTION: A system includes a language analysis unit, a correction unit, an application providing unit, and an SNS function addition unit. The language analysis unit analyzes the text of the user. The proofreading unit proofreads the text analyzed by the language analysis unit in real time. The application providing unit provides the text proofread by the proofreading unit to the user. The SNS function adding unit provides the text proofread by the proofreading unit to the SNS.SELECTED DRAWING: Figure 1