94 results about "Natural language analysis" patented technology

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention provides a recruitment patient determination method and device for a clinical test, a computer program product and electronic equipment, and relates to the technical field of medical big data, and the method comprises the steps: obtaining medical data of a plurality of patients, and converting the medical data into structured data; performing natural language analysis on the group entering standard information of the clinical test according to a large language model to generate a structured screening condition; performing data filtering on the structured data based on the structured screening condition through intelligent agents corresponding to a plurality of accommodating and arranging conditions of a clinical test to determine a recall result, matching the recall result based on the plurality of accommodating and arranging conditions, and determining an accommodating and arranging score of the medical data of each patient according to a plurality of matching results; and determining a target recruitment patient corresponding to the clinical test based on the recruitment score. According to the invention, the patient recruitment efficiency and accuracy can be improved.

The invention discloses an agricultural environment intelligent control system based on artificial intelligence and a control method thereof, and belongs to the technical field of agricultural intelligent control and artificial intelligence application. According to the method, agricultural sensor data and image data are jointly packaged into a standardized AI input structure by adopting prompt engineering and semantic modeling means; a general large language model or an image recognition model is accessed, and comprehensive evaluation of complex crop states is realized through customizing a Prompt semantic structure; constructing a natural language analysis engine, and converting the control suggestion returned by the AI into a structured command; the Agent module is used for completing control opportunity judgment, control path planning and effect feedback adjustment, and strategic intervention on the whole agricultural control process is achieved.

The invention discloses a page generation method, system and device and a medium, and the method comprises the steps: obtaining page demand content and target page configuration through responding to a page generation instruction of a target field; obtaining a component knowledge base of the target field and a layout rule base configured by the target page; extracting a target component demand set, a target layout demand and a target priority demand from the page demand content; matching in a component knowledge base according to the target component demand set to obtain a corresponding candidate component set; matching in a layout rule base according to the target layout requirement to obtain a corresponding layout template; generating a target layout scheme according to the candidate component set and the layout template based on the target priority demand; the target page is generated according to the target layout scheme, automatic configuration and generation of the page can be achieved through natural language analysis and intelligent matching logic, and then page configuration and deployment are rapidly completed.

The invention discloses an unmanned aerial vehicle global cruise pan-scene intelligent analysis platform. The platform comprises the steps of S100, task generation; s200, executing acquisition; s300, returning the data; s400, processing and structuring are carried out; s500, performing preliminary examination and prediction; s600, feeding back a closed loop; and S700: reporting and filing. The method has the advantages that a complete closed loop of task arrangement, unmanned aerial vehicle cruising, data acquisition, structured processing, event identification, risk prediction, re-check labeling and result filing is constructed through intelligent body driving and side cloud cooperation; natural language analysis, change detection, target detection and tracking, video anomaly analysis and trend modeling are fused, high-precision recognition and early warning of multiple scenes and multiple objects are achieved, and cross-department collaborative governance and scientific decision are effectively supported.

A system includes a processor that is configured to detect occurrence of a disaster, activate a plurality of unmanned aerial vehicles and collect image and video data of a disaster-affected area, analyze the collected image and video data and generate a damage assessment as a three-dimensional map, visualize the generated three-dimensional map and distribute countermeasure instructions, receive information from residents of shelters and rescue workers, and extract important information by natural language analysis, and determine rescue activity instructions and support goods transport routes based on the extracted information.

The invention provides an intelligent question and answer and traceability analysis system and method based on structured enterprise data, and the system comprises a data interface and standardization module which is used for accessing a multi-source structured database, and carrying out the standardization processing of the multi-source structured database meeting a preset requirement, and obtaining the standardized multi-source structured database; the natural language analysis and field mapping module is used for performing intention recognition and target entity extraction on a natural language input by a user, screening fields in the standardized multi-source structured data based on the extracted target entity and the recognized intention, and obtaining data corresponding to the fields obtained after screening; the rule engine and logical reasoning module is used for matching a judgment rule based on the recognition intention and performing logical judgment according to the judgment rule based on the acquired corresponding data to obtain a judgment result; and the intelligent reply generation module is used for generating a structured reply based on the judgment result.

The invention provides a service logic vulnerability detection method and device, equipment, a medium and a program product, and relates to the technical field of network security. The method comprises the following steps: constructing a business process state machine through a key entity obtained by performing natural language analysis on a business document; the business process state machine is used for describing state changes of the business process in different stages; obtaining an operation sequence generated based on the interaction operation through a flow agent deployed in a network environment; carrying out modeling and feature extraction on the operation sequence based on a hierarchical attention circulation network to obtain operation behavior features; comparing the operation behavior characteristics with a business process state machine to obtain a vulnerability detection result; the vulnerability detection result comprises permission violation and / or flow abnormality.

The invention discloses a switching operation safety checking system based on artificial intelligence, and the system comprises a model building module which obtains an enhanced domain language model through a self-supervised pre-training technology and a power grid dispatching professional feature dictionary, and then obtains a natural language analysis model through the combination with a bidirectional long-short-term memory network; the text analysis module utilizes a natural language analysis model to convert the preprocessed scheduling service natural language text into structured objectified data which identifies operation equipment, an operation equipment state, an operation event, operation rule information, an operation event intention and operation logic information; the safety checking module is used for carrying out topology analysis, image-text comparison, intelligent ticket forming, maintenance linkage, sand table rehearsal and checking before order issuing before switching operation is executed based on objectified data; key link checking, operation equipment state checking and operation event time sequence checking are carried out in the switching operation execution process. According to the invention, the safety and the working efficiency of switching operation 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 discloses an electronic design multi-agent system with graph structure control and feedback iteration capabilities and an operation method thereof. The electronic design multi-agent system comprises a natural language analysis module, a decision planning agent, a functional module agent set, a schematic diagram construction agent, an assembly agent, a simulation verification agent and a feedback iteration module. The system receives a circuit design requirement described by a natural language, automatically constructs a sub-task dependency graph, distributes parallel tasks, retrieves matched components and generates a local schematic diagram through a multi-agent cooperation mechanism, and assembles the local schematic diagram into a complete schematic diagram. The system automatically triggers task re-planning, device re-matching and graph structure optimization through simulation verification and error feedback to form closed-loop iterative control. According to the method, automatic conversion from the natural language to the schematic diagram conforming to the electrical specification is achieved, the problems that a traditional EDA tool is complex in operation and existing AI aided design lacks systematicness and self-optimization capacity are solved, the design threshold is remarkably lowered, and the design efficiency and quality are improved.

The invention discloses a DIKWP natural language analysis enhancement method combined with a large language model, and belongs to the technical field of artificial intelligence and natural language processing. The method is realized through the following steps of: firstly, performing semantic pre-analysis on natural language input of a user by utilizing a large language model (LLM), and extracting five semantic fragments of data, information, knowledge, wisdom and intention; then the content is mapped to a DIKWP semantic map node in a structured mode through a DIKWP semantic mapping template system; then semantic consistency verification and logical reasoning are carried out through an LLM and DIKWP dual-channel cooperation mechanism; and finally, iteratively optimizing ambiguous, missing or conflicting contents by means of a language-semantic interaction closed loop until a complete and consistent DIKWP semantic map is generated. According to the method, the semantic comprehension ability of a large language model and the structural reasoning advantage of the DIKWP map are effectively fused, the accuracy, consistency and interpretability of complex natural language analysis are remarkably improved, and the method can be widely applied to intelligent question answering, knowledge modeling and decision support systems.

The invention discloses a vertical domain intelligence analysis method based on a large language model. The method comprises the following steps: receiving a natural language analysis demand of a user; based on pre-constructed vertical domain specific task planning knowledge, intelligently decomposing the demand into a subtask list corresponding to a preset dimension by using a large language model, and semantically matching a proper information acquisition tool for each subtask to generate a structured task plan; the system schedules and executes tool sets of network search, webpage capture, internal query and the like according to a plan, and performs cross verification and cleaning on collected fragmented information; and inputting the verified data into a large language model for integration analysis, and optionally carrying out multi-round iteration self-evaluation optimization to automatically generate a high-quality vertical domain intelligence analysis report. According to the method, full-process and high-intelligence automatic processing of information analysis in the vertical field from demand understanding to report generation is realized, and the method has excellent planning capability, process interpretability, result reliability and excellent modular expansibility.

The invention discloses an unmanned aerial vehicle inspection maintenance planning method and device based on natural language man-machine interaction, and relates to the technical field of unmanned aerial vehicle multi-task cooperative scheduling. According to the scheme, natural language tasks are understood through semantic analysis, and maintenance procedures and resource requirements are automatically reasoned by using a knowledge graph; then, a multi-objective optimization model fusing inspection time efficiency, cost, preventive benefits and maintenance technician task load balance is constructed; and adopting an adaptive algorithm to solve and generate a candidate maintenance plan set, determining a target maintenance plan based on multi-dimensional evaluation, and finally synchronously outputting a maintenance scheduling scheme and an updated inspection task allocation scheme. According to the scheme, dependence on data entry of artificial experience and scheme planning is reduced through natural language analysis and knowledge graph reasoning, automation of maintenance decision is achieved, meanwhile, maintenance and inspection are uniformly optimized under a multi-target framework, a preventive maintenance mechanism is introduced, conversion from passive response to active prevention is achieved, and the maintenance efficiency is improved. And the overall utilization efficiency of resources is improved.

This application relates to the field of computer technology and provides a regression data analysis method, computer device, and medium based on unsupervised learning. The method includes: analyzing regression logs using a natural language processing model to determine the natural language analysis results associated with the regression logs; determining the error type associated with the regression logs based on the natural language analysis results associated with the regression logs using an unsupervised learning model; and determining whether the regression logs contain anomalous regression test cases based on the natural language analysis results associated with the regression logs using an anomaly detection model, and, when the regression logs contain the anomalous regression test cases, determining the error type associated with the anomalous regression test cases. This provides efficient error localization and saves on localization components, thus helping to improve verification efficiency.

The invention provides a collaborative development process data synchronization and notification method. According to the method, an automatic process is triggered by monitoring a test-submitted event message queue, development data is positioned based on module identification, key operation features (such as database change and task start and stop) are analyzed and extracted by using natural language, and feature extraction precision is improved by combining a dynamic keyword library and cross-modal fusion; the timeliness and reliability of risk reminding are ensured through template message generation and multi-channel delivery; a risk propagation range is further predicted by means of a dependency graph model, and a closed-loop early warning mechanism is formed. According to the method, the problem of delay caused by information lagging and insufficient collaboration in the development process is solved, the risk of manual operation is reduced, and the efficiency and controllability of project management are improved.

The invention discloses a multi-modal clinical scientific research data processing method and system based on neural symbol hybrid reasoning, and belongs to the field of medical informatization and artificial intelligence. The method comprises the steps of receiving a natural language analysis instruction; analyzing the instruction into a structured formal problem description data object through a language understanding unit by utilizing a nerve-symbol hybrid inference engine, and selecting a data analysis algorithm according to an expert rule base through a symbol inference unit; then, generating a directed acyclic graph analysis flow based on the selected algorithm; and finally, according to the directed acyclic graph analysis flow, processing the multi-modal clinical scientific research data stored in the database, and generating an analysis report containing quantitative indexes. The technical problems that in the prior art, a clinical scientific research data analysis process is split, efficiency is low, and a semantic gap exists in man-machine interaction are solved, automation and intelligence of scientific research analysis are achieved, and the efficiency, depth and scientificity of data processing are remarkably improved.

The invention provides a hydrological measurement and control method and device based on a DSL rule engine and electronic equipment, and relates to the technical field of hydrological measurement and control, and the method comprises the steps: obtaining a user input rule which comprises a natural language and a DSL input rule; based on the large model analysis function, performing natural language analysis on the user input rule to obtain a DSL syntax tree; obtaining an intermediate rule tree according to the DSL syntax tree, the intermediate rule tree being used for representing a directed tree or a directed acyclic graph having a hierarchical structure; and performing node-by-node matching on the real-time monitoring hydrological data according to the intermediate rule tree to obtain a hydrological measurement and control strategy. According to the invention, the accuracy of hydrological measurement and control is improved.

The invention discloses a multi-service scene-oriented data query analysis method and system, belongs to the technical field of big data analysis and intelligent decision making, and aims to solve the technical problem of how to realize automatic query, intelligent analysis and visual presentation of data under multi-service scenes. Comprising the steps of performing intention recognition and service classification on a user request based on an agent-driven natural language analysis and classification model, and generating a query instruction, a data analysis strategy and a visual suggestion; executing the query instruction, and analyzing a query result in real time; and rendering an interactive data chart based on the returned analysis result and the visual suggestion, and supporting the user to carry out drilling, screening and prediction simulation operations based on the data chart.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for table detection using text streams. One of the methods includes detecting, in a text stream and using column identification data, text for one or more cells in a table; creating, using the text for at least some of the one or more cells in the table, a data structure for the cell a) that associates two or more values from the table and b) for use by a downstream system as part of a natural language analysis process of data from the text stream; and storing, in memory, the data structure.

The invention aims to carry out natural language understanding under the assistance of a large model in a large and small model fusion mode, generate a query SQL (Structured Query Language) statement and carry out query and analysis on business data. In order to achieve the purpose, the technical scheme of the invention discloses an intelligent data retrieval method based on big and small model fusion, which is innovative application of an artificial intelligence technology and a business scene, is optimized and invented aiming at different modules, and realizes the intelligent data retrieval through data processing, natural language analysis and a big model technology. The problems of inaccurate intention recognition, SQL generation deviation and the like of a traditional mode are solved, and the accuracy of data query is improved.

The invention discloses a financial analysis report generation method, and relates to the technical field of natural language processing and information extraction, and the method comprises the steps: enabling natural language analysis to be a core position before generation, carrying out the differential analysis of a criterion text, and extracting the constraints such as a disclosure item, a metering caliber, a period, a unit and currency; then, structured and normalized alignment with multi-source heterogeneous financial data is completed, so that the response of the system to external criterion change does not depend on a large amount of instant annotation or frequent whole-model fine tuning any more; on the basis of a consistency verification score and an adaptive threshold value calculated by a constraint graph, rigid violation and tolerable deviation are distinguished and can be quantitatively positioned, and all-link backtracking of evidence anchor points is matched to ensure that each paragraph and each conclusion have audible sources, so that compliance risks caused by outdated calibers and disclosure missing are remarkably reduced.

The invention relates to the technical field of financial data analysis, in particular to an interactive financial data retrieval and analysis system based on big data. Comprising a data access module, a data unification module, a semantic understanding and intention recognition module, a retrieval execution module, a financial analysis module and an interactive visualization module. The system can access financial information from multi-source data and perform cleaning, standardization, main data alignment and management to generate data for query and analysis. Query intentions and entities are recognized through natural language analysis, structured semantic representation is constructed, and multi-dimensional aggregation, backtracking and predictive analysis are achieved. And the retrieval execution module and the financial analysis module cooperate to ensure that the result is reproducible and credible. And the interactive visualization module dynamically presents the analysis result. According to the method, accurate understanding and structured representation of natural language query of the user are realized, so that financial data retrieval can quickly respond to the intention of the user, the query accuracy and efficiency are improved, and the reliability and precision of financial decisions are improved.

A battery electrochemical intelligent analysis system and method based on charge-discharge curve data includes: a user interaction layer that receives charge-discharge data files and parameter configurations, and obtains natural language analysis requirements; an intelligent analysis layer, based on the LangGraph framework, adopting a two-layer architecture of coordination and execution layers, managing and coordinating supervisory agents, data processing agents, electrochemical analysis agents, data plotting agents, quality review agents, analysis report agents, and question-answering agents through state graphs, completing a complete intelligent analysis task from data processing, electrochemical parameter analysis, chart plotting, quality review, report generation to question-answering interaction; and an output layer that receives charts, reports, and question-answering results generated by the intelligent analysis layer, converts the format, and transmits them to the user interaction layer for display or download. This invention achieves automated and intelligent battery electrochemical data analysis without programming, lowers the professional threshold, and improves analysis efficiency and standardization.

Provide a system. 【Solution means】 Means for extracting business requirements from users by natural language analysis and systematizing problems, Means for automatically generating the specifications of an intelligent agent based on the problems, Means for selecting and adjusting an intelligent model suitable for the requirements, Means for arranging the selected intelligent model on a digital infrastructure, Means for monitoring the activities of the agent in real time and collecting performance data, Means for verifying the collected performance data and self-improving the model, Means for managing an agent for improving the business process in logistics on a mobile terminal and generating it by inputting problems in natural language, A system including the above.

The application provides a self-evolving intelligent fund analysis method, relates to the field of data processing, and comprises the following steps: receiving a natural language analysis instruction, combining a large language model decomposition task, extracting account fund flow characteristics and matching with historical cases, and generating a weighted execution instruction; identifying a transfer path based on a fund correlation graph, extracting a priority path subgraph structure according to a user identifier, and forming a directional analysis model; performing node risk scoring on the fund correlation graph, outputting a risk account identifier and an analysis report, and realizing intelligent and accurate fund risk identification.

Provided herein are systems, methods, and non-transient computer-readable storage media for providing contextualized responses using a virtual coworker within an email account. The disclosed techniques access an email thread, set a conversation context window to encompass the entire email thread, identify contextually relevant keywords within the subject line and email body, and search the email account for related email messages. A context library is created, comprising the email message and related email messages found in the email account. A response is generated based on user input derived from the context library, using a combination of prompts derived from natural language analysis and pre-programmed prompts. Additional features include analyzing the context library to define user input, engineering the initial prompt for the response, incorporating a knowledge base, reading attachments, and implementing digital security measures.

This invention relates to the field of natural language processing technology, specifically to a large-model-driven intelligent voice robot interaction method and system, comprising: acquiring several words from the current word sequence and historical word sequences; acquiring the contextual semantic matching degree of words in the current word sequence based on the distance between words in the current word sequence and historical word sequences and the semantic similarity between words; acquiring the structural fit degree of each word in the current word sequence based on the adjacent words of each word in the current word sequence and historical word sequences; and acquiring the reliability of words in the current word sequence by combining the user's current interaction round with the contextual semantic matching degree of words in the current word sequence, thereby acquiring the user's actual expressive intent. This invention improves the accuracy of recognizing the user's true expressive intent by analyzing the user's current and historical speech signals to acquire the reliability of words in the current speech signal.

This invention discloses an intelligent recommendation method and system for group meeting locations based on a large language model. The method includes: outputting individual and location profiles from the large language model and inputting them into a spatial optimization model to obtain a group center; setting a search radius from the group center to generate a candidate location set, along with the individual movement cost and average movement cost corresponding to each candidate location; classifying and semantically annotating the candidate locations with tags to obtain a structured ranking result, and further generating a structured recommendation result. Thus, individual and location profiles are generated based on natural language analysis, and the movement cost of individual users to various locations is introduced for reverse weighted iteration to filter and obtain a candidate set with high accessibility. Then, multi-round chain reasoning is performed using full-process data to select highly feasible recommendation options from the candidate set that align with the interests of the user group, ensuring that group meetings can be successfully arranged without strong resistance from individual users.

The invention provides a building energy consumption analysis method and system based on an MCP service model and an intelligent agent, and relates to the technical field of building energy saving and artificial intelligence, and the method comprises the steps: constructing and packaging an MCP service model, and abstracting the MCP service model into a standardized function interface after packaging; acquiring and storing operation equipment and equipment operation parameters in the building; an input request of a user is obtained, a task intention in the input request is recognized, the task intention is converted into specific API calling according to the task intention, and the API obtains structured energy consumption data by calling an MCP function interface; combining the structured energy consumption data returned by the MCP with the cue word template, transmitting the combined Agents to the large language model, and outputting to obtain a report; and visualizing or exporting the report. According to the invention, full-automatic generation from complex building energy consumption data to a structured report containing natural language analysis and visual charts is realized.