127 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 data security classification and grading method and system based on a large language model, and relates to the technical field of language analysis, and the method comprises the steps: converting bank internal business data of each layer into text data through text conversion, unifying the data form of multi-layer or multi-modal data into text data, and storing the text data in a database; and a reliable basis is provided for subsequent language semantic recognition. According to the method, the text data is partitioned and sorted, subsequent context language understanding is facilitated, the context window length under each service scene is set, the context semantic understanding requirement of each service scene is met, and the reliability and accuracy of semantic association are improved. The internal business data of the bank is classified and graded based on the risk value of the business scene and the sensitive value of the composite field, the accuracy and adaptability of data security classification and grading are improved, security protection strategies of different degrees are carried out for different levels of different types of data, and the requirements of bank data storage and prevention are met.

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 data query tool method and system based on an AI language model. The method comprises the steps that a data query request input by a target user is obtained; inputting the data query request into a basic language layer of the language analysis model to obtain basic semantic request data; inputting the basic semantic data into a general knowledge layer of a language analysis model to obtain fused semantic request data; inputting the fused semantic request data into a professional knowledge layer of a language analysis model to obtain professional semantic request data; mapping the professional semantic request data into a database to obtain an SQL (Structured Query Language) query statement; based on the SQL query statement, accessing a database to obtain a preliminary query result; and auditing and verifying the preliminary query result to obtain a target query result. According to the method, the accuracy of the query result is improved, and comprehensive consideration and design are carried out in the aspect of specialty, so that the complex and changeable data query scene requirements are met.

The invention discloses a multi-view data category identification method based on comparative learning and private information. The method comprises the following steps: obtaining mixed features from a plurality of input views by using an auto-encoder; obtaining a private feature set and a public semantic feature set according to the mixed features, performing same-view instance neighbor private feature learning by taking the private feature set as an object to obtain useful private features, and performing cross-view semantic information consistency feature learning by taking the public semantic feature set to obtain aligned public semantic features; and a K-Means clustering prediction model is introduced to generate useful pseudo labels of private features, corresponding constraint is carried out according to the pseudo labels of the private features and the aligned prediction labels of the public semantic features, and a final label with the maximum prediction probability average value in all views is used as a classification label of multi-view data. According to the method, the accuracy of multi-view data classification is integrally improved, and the method can be applied to various fields including multi-view data, such as computer vision and natural language analysis.

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 relates to the technical field of text language analysis, in particular to a method for evaluating emotion in client evaluation text content, which comprises the following steps of: S1, establishing a basic emotion database through the client evaluation text content, performing similarity screening, invalid deletion and coding on data, and removing texts without evaluation value to obtain a basic emotion database; carrying out natural language preprocessing on the residual evaluation texts; s2, performing sentiment classification on the evaluation of the customer based on a deep learning model, tracking the change of the evaluation of the customer through time sequence analysis, judging the change of the customer on products and services at different time points by using clustering analysis, and constructing a personalized portrait of the customer, in the method, the change of the evaluation of the customer is analyzed and tracked through A I, and a theme label is distributed for an evaluation text; and meanwhile, when the BERT algorithm model is trained, the training effect of the model is enhanced through MLM mask, NSP prediction and customer personalized portrait, and the purpose of truly extracting semantics and emotions in the text is achieved.

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.

A system for managing access to services in a computing system. The system includes processes to allow users to request access to services, and the automated review of such requests. The system is configured to consider a wide range of factors, including the content of the request and the service requested, information relating to the user, and metadata relating to the service, as well as user behaviours. Natural language analysis is utilised to analyse a user's request for access and the service to which access is requested.

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 a virtual human action driving method and device based on language analysis and a storage medium, and belongs to the technical field of virtual human. The method comprises the following steps: acquiring motion posture data of a virtual human and voice data at the current moment, wherein the motion posture data comprises motion information of each joint of the virtual human; according to the voice data and the action posture data, determining evaluation indexes of action fluency degrees of all first action data corresponding to the voice data; virtual human actions are generated according to the first action data and second action data, the second action data are transition action data determined according to the first action data and the evaluation indexes, and the technical effect of improving the fluency degree of the virtual human actions is achieved.

The invention discloses a language analysis method and system based on an index semantic layer and a large language model. The invention relates to the technical field of data visualization analysis. Reading metadata constructed by a predefined data index model; metadata is used to construct a Prompt full word of an index, and a basis is provided for subsequent natural language query; generating vector representation for the same metadata in the index semantic layer; the vectors are used for being subsequently matched with natural language requests input by a user; in the technical scheme of the invention, a user can use a natural language to carry out data query and visual analysis, and does not need to learn a complex query language or an operation interface. According to the method, the hidden intention and context in the user query can be understood, the user query is matched with the metadata vector in the index semantic layer, the query intention of the user can be recognized more accurately, and analysis errors are avoided. And the reasoning ability of the large language model further enhances the query accuracy.

The invention discloses a knowledge graph link prediction method, device and equipment, a medium and a program product, and the method comprises the steps: generating a candidate tail entity set based on a head entity element and a relation element of a to-be-queried triple in a knowledge graph, the candidate tail entity set comprising a plurality of candidate tail entity elements, performing semantic analysis on the to-be-queried triad through the language analysis model to obtain semantic information of the to-be-queried triad, screening the candidate tail entity set based on the semantic information, generating a to-be-scored tail entity set according to a screening result, and performing comprehensive scoring on each to-be-scored tail entity element in the to-be-scored tail entity set to obtain a scoring result. According to a comprehensive scoring result, target tail entity elements are screened out from a to-be-scored tail entity set, link prediction is carried out on the knowledge graph based on the target tail entity elements and the to-be-queried triad, potential features in the triad are effectively mined, accurate completion of missing triad entities is achieved, and the accuracy of the whole knowledge graph is improved. And the accuracy of knowledge graph link prediction is greatly improved.

To provide a schedule adjustment system for improving convenience of users.SOLUTION: A communication data acquisition unit (201) of a schedule adjustment system (1) acquires communication data representing content of communication performed with a communication tool. An adjustment schedule data acquisition unit (203) acquires adjustment schedule data representing an adjustment schedule to be adjusted through communication on the basis of communication data and a machine learning model capable of executing language analysis. A schedule transmission unit (206) transmits the adjustment schedule data to a schedule management tool cooperating with the communication tool.SELECTED DRAWING: Figure 5

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 closure three-address code conversion method based on static analysis, belongs to the technical field of computer software, and aims to solve the problem of static analysis of dynamic type language closure processing. A three-address code intermediate representation is generated through function-level modeling, a closure variable dependency relationship is searched and judged through a recursive action domain, a closure structural body is explicitly constructed, and a parameter transmission instruction is injected, so that the problems that a fixed memory layout of a traditional compiler is incompatible with a dynamic language characteristic, and an intermediate representation of a static analysis tool is missing are solved; accurate capture of closure variables, efficient memory utilization and cross-language analysis compatibility are realized, and code quality detection and performance optimization capability during operation are remarkably improved.

The invention discloses a live broadcast data processing method and system, and relates to the technical field of data processing. The live broadcast data processing method comprises the following steps: acquiring user behavior data of a current time period in real time, respectively performing data analysis to obtain an interaction index and a preference index of the current time period, acquiring text information of the current time period, and performing language analysis processing to obtain a comment emotion index of the current time period in a live broadcast process; according to the method and the device, the corresponding live broadcast measures are taken for the next time period based on the live broadcast effect feedback index of the current time period in the live broadcast process, so that the live broadcast content and the interaction strategy are optimized in real time; and therefore, the live broadcast content and the strategy can be adjusted more flexibly and accurately, and the viscosity and the activeness of audiences can be improved.

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 present invention relates to the technical field of language analysis, and discloses a method and system for interpreting medical literature based on a large language model, including: constructing a relationship network between words to obtain a first relationship network; determining a first association relationship between each text word and other words in the text, and assigning weights to the first association relationship according to the number of associations; constructing a relationship network for the words in the text according to the text content to obtain a second relationship network; determining a second association relationship between each text word and other words in the text, and assigning weights to the second association relationship; optimizing the association path according to the first relationship network and the weights assigned to the first relationship network, and obtaining an interpretation result through the content interpretation on the path. It overcomes the deficiencies of traditional calculation methods in terms of processing efficiency and semantic understanding. Greatly improves the accuracy and efficiency of literature analysis, and greatly improves the convenience of knowledge acquisition.

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 provides an old people comprehensive evaluation system and method based on a big language model, and the system comprises a data collection module which is used for collecting the data of old people; the big language model processing engine is used for analyzing the collected data of the old people by utilizing a pre-trained big language model to obtain a big language analysis result; and the assessment report generation module is used for generating a health assessment report and personalized suggestions based on the big language analysis result, and completing the comprehensive assessment of the elderly. According to the technical scheme, through an automatic evaluation process, manual intervention is reduced, and the evaluation efficiency is improved; and the accuracy and objectivity of evaluation are improved by using the deep learning ability of the large language model.

The invention provides a patient assistance intelligent auditing system based on deep learning, which comprises an image preprocessing module, an intelligent detection module, an intelligent identification module and a natural language analysis processing module, and is characterized in that the image preprocessing module is used for preprocessing an original image material uploaded by a patient; the intelligent detection module is used for performing intelligent detection on the corrected image through an intelligent detection model based on a YOLO3 algorithm to obtain a to-be-recognized area; the intelligent identification module is used for acquiring original text information of all rows in a to-be-identified area; and the natural language analysis processing module is used for verifying and correcting the original text information through a pattern matching algorithm. The auditing system performs intelligent auditing and key information detection and identification on the patient materials based on a deep learning method, so that the auditing efficiency of the patient assistance materials can be greatly improved, the dispensing process of patient assistance is accelerated, the workload of auditing personnel is reduced, and the human resource cost is reduced.

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.