135 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 embodiment of the invention discloses a multi-agent collaborative data visualization analysis method and device and a medium, and relates to the technical field of visualization analys.The method comprises the steps that a natural language analysis request input by a user is received, task splitting is conducted on the natural language analysis request through a preset task planning agent, and the task splitting result is obtained; generating corresponding task plan information, obtaining real-time operation data of a preset functional agent cluster, carrying out agent distribution on the plurality of sub-tasks based on the real-time operation data and the task plan information, determining a target functional agent corresponding to each sub-task, and sending the target functional agent to a server; the target function agent comprises any one of a data analysis agent and a visual display agent; and executing the corresponding data analysis subtask through the data analysis agent to obtain a data analysis result, performing agent cooperative verification on the data analysis result, and after the verification is passed, generating visual analysis data corresponding to the data analysis result through the visual display agent.

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 provides a beach big data geographic space intelligent application service method based on a large model Agent, and relates to the technical field of geographic space data artificial intelligence processing and analysis. According to the method, a distributed platform comprising a parallel computing cluster, a spatial database and a large language model is constructed, and standardized storage and metadata management of multi-source remote sensing, vector and attribute data are supported. The system generates a spatio-temporal query instruction through natural language analysis, completes cross-source matching and spatial calculation, outputs an analysis result in a multi-modal form such as a map, a chart and a text, and realizes intelligent and interactive service of geographic data processing.

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 relates to the technical field of relation extraction, in particular to an invoice relation extraction method based on natural language analysis, and the method comprises the following steps: analyzing multiple features such as an image seal, a two-dimensional code and a handwritten annotation, matching text and image space information, checking the consistency of the two-dimensional code and a tax-control code, and recognizing the field combination and the abnormality of the amount and a payer. According to the method, detail features such as seals, two-dimensional codes and handwritten marks in invoice images are analyzed through multi-level fusion, space joint judgment of image-text content is achieved, the credibility of information verification is improved through character safety comparison and coding consistency, deep relation mining between payment behaviors and entities is enhanced by means of a context semantic dependency structure, and the payment efficiency is improved. According to the method, elements such as the amount and the payer are dynamically classified and aggregated, automatic identification of a complex transaction relationship is realized, intelligent judgment and real-time label generation of abnormal risk behaviors are further realized through linkage clustering and difference tracking, and the active risk control capability of financial management is improved.

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 discloses an intelligent question answering method and device based on a computational thinking model, equipment and a medium. The method comprises the following steps: training a computational thinking model by adopting a supervised fine tuning and reinforcement learning method to obtain a trained computational thinking model; the question data are input into the computational thinking model, the computational thinking model comprises a natural language reasoning module and a code reasoning module which are alternately performed, language instructions and code blocks are alternately generated in the dialogue context, and task reasoning is iteratively performed until answers corresponding to the question data are generated; and outputting an answer corresponding to the question data. According to the method, a computational thinking model is provided, the computational thinking model is an interactive reasoning framework, task reasoning is performed through interlaced natural language analysis and code execution, and iterative language-code circulation not only enhances the robustness and interpretability of the model, but also improves the accuracy of problem reasoning.

The invention relates to the technical field of blood transfusion pipeline detection, and discloses a blood transfusion pipeline jitter detection method based on an intelligent sensor. According to the method, an initial control instruction generation layer receives an operation instruction, and a natural language analysis unit identifies a pipeline number and a preset jitter threshold parameter; the multi-mode signal detection layer collects pressure waveform signals and three-dimensional motion track signals of a blood transfusion pipeline in real time, and establishes a feature matching database to store reference signal feature combinations corresponding to different pipeline models. The dynamic mapping layer combines the real-time detection signal features with the reference signal features to perform similarity matching; the double-domain interpolation calculation layer generates an anti-jitter control parameter according to a matching result; the anti-phase jitter elimination execution layer outputs a phase compensation instruction according to the anti-jitter control parameters; the parameter optimization layer updates a feature matching database according to the historical jitter residual volume data; and the abnormal decision-making layer judges an unrestrained jitter state and triggers an alarm protocol, and also implements a full-period updating mechanism to refresh a reference signal feature combination.

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 provides an enterprise manufacturing resource configuration optimization method based on intelligent interaction and template mapping, and belongs to the technical field of intelligent manufacturing. Comprising the following steps: receiving a resource configuration request input by a user, and performing natural language analysis on the resource configuration request to generate structured resource configuration demand information; according to the structured resource configuration demand information, matching and calling a preset resource configuration template; based on the called resource configuration template, in combination with real-time resource state data in the ERP system, performing analog computation of resource configuration, and generating at least one resource configuration scheme; and feeding back the at least one resource configuration scheme to the user, and receiving feedback information of the user on the resource configuration scheme. User requirements are analyzed through intelligent interaction, an optimization strategy is matched through template mapping, real-time data simulation calculation is combined, feedback is received, and intelligentization, high efficiency and continuous optimization of enterprise manufacturing resource configuration are achieved.

A method uses natural language processing for visual analysis of a dataset, in a messaging application. The method includes receiving a first natural language (NL) input directed to a data source, in a messaging application. The first NL input includes at least one underspecified or ambiguous utterance. The method also includes parsing the first NL input into tokens based on a grammar and the data source. The method also includes generating and displaying an intermediate NL response, based on the tokens. In response to receiving a user input to provide missing information in the at least one underspecified or ambiguous utterance, the method includes generating an input query based on the user input and querying the data source using the input query, to obtain a result set. The method also includes generating and displaying a first NL output and a snapshot of a data visualization based on the result set.

The invention relates to the technical field of electronic design automation and artificial intelligence, in particular to an AI-assisted multi-target PCB automatic design system and method, and the system comprises a user interface layer which is used for a user to input a request through a Web-based interface; the natural language analysis module is used for processing the request and generating a JSON (JavaScript Object Notation) containing function requirements; the device selection module queries an internal device database and suggests an ESP32-C3 MCU (Microprogrammed Control Unit), a compatible relay and a support circuit; the circuit generation module is used for creating a netlist and a schematic diagram file and comprises an MCU (Microprogrammed Control Unit), a relay driver and related passive elements; the layout optimization module runs a multi-target algorithm and is integrated with PCB design software; experimental verification shows that compared with a traditional one-way process, by means of the method, the requirement interpretation accuracy can be improved to 94.2% from 76.3%, the design intention preservation rate is improved to 89.5% from 42.7%, the requirement change adaptation time is shortened to 2.1 h from 8.4 h on average, and the requirement interpretation efficiency and accuracy are improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a mobile phone RPA automatic system based on natural language analysis. The system comprises a voice input interface module which is used for providing an interactive interface for a user and converting voice input by the user into a text; the natural language processing module is used for performing preprocessing, intention classification, entity extraction and task disassembly on the text converted by the voice input interface module and generating structured task description; the RPA module is used for generating a corresponding RPA instruction list by utilizing a mapping rule according to the structured task description, calling an RPA execution engine to sequentially analyze RPA instructions, and simulating user operation on a mobile phone through a UI automatic tool to complete a task; and the feedback display interface module is used for providing feedback information according to the task completion condition and allowing the user to select to continue the operation or re-initiate the task.

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.

To personify the home as a part of the family and make it a partner. [Solution] The information processing device 1 receives a message from a member (S51), requests an external generation AI to perform natural language analysis processing on the message (S54), generates a search formula for searching a family DB that stores family member data based on the results of the natural language analysis processing (S55), requests the family DB to search for data related to the message using the search formula (S56), provides the data related to the searched message and persona information previously set for the house, and requests the external generation AI to create a response to the message (S58), and outputs the response created by the generation AI as text, audio, or a photo or video via a user terminal 4, 5, a speaker 12 installed in the house, or a display 15 installed in the house (S60).

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.