176 results about "Natural language analysis" patented technology

The invention discloses a multi-agent interactive efficient data analysis system, which comprises a natural language analysis unit, which is used for carrying out preliminary analysis on a natural language query request input by a user based on a natural language analysis model, and predicting a user intention to obtain the user intention; the task intelligent routing unit is used for exchanging information through a communication mechanism between the intelligent agents according to the intention of the user and distributing the data analysis tasks to the corresponding intelligent agents; the agent execution unit is used for executing various operations required by a data analysis task by an agent, including task disassembly and strategy evaluation, information storage and memory calling, external tool use and action execution, and obtaining an analysis result; and the agent report generation unit is used for generating a data analysis report based on the analysis result according to a preset report template and a formatting rule. And automation of a data analysis task is realized through efficient cooperation and a task allocation mechanism among multiple agents.

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 relates to the technical field of artificial intelligence, and particularly provides an intelligent question asking method and device, based on a large model and semantic modeling, the method comprises the following steps: S1, a user interaction module receives a natural language question input by a user; s2, the semantic modeling module collects and integrates related data sources; s3, an analysis rule is built in the rule analysis module; s4, the large model processing module understands and answers the natural language question input by the user; s5, the SQL conversion module generates a universal logic SQL for the natural language problem input by the user through rules and natural language analysis; s6, the data processing and indexing module establishes a data indexing system; s7, establishing a dynamic feedback and learning mechanism; and S8, establishing a security and privacy protection mechanism. Compared with the prior art, the accuracy and timeliness of intelligent question answering of the user questions can be improved, and accurate answering of specific big data scene tasks is achieved.

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.

A system for managing access to objects in a computing system. The system includes processes to allow users to request access to objects, 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 object requested, information relating to the user, and metadata relating to the document, as well as user behaviours. Natural language analysis is utilised to analyse a user's request for access and the object to which access is requested.

The invention relates to a new media AI marketing content creation method and device, equipment and a medium. The method comprises the steps of obtaining user demand configuration, and obtaining a creation intention vector through natural language analysis; based on the platform characteristic knowledge base, extracting a corresponding structure specification and a propagation mechanism according to the target platform, and encoding to obtain a platform characteristic vector; obtaining historical content interaction data corresponding to the audience group, and generating a user-content interaction vector by adopting collaborative filtering and a label similarity algorithm in combination with the content keyword; and calling an artificial intelligence large model, and performing content generation according to the creation intention vector, the platform feature vector and the user-content interaction vector to obtain content creation data. By adopting the method, the goal of automatic, high-quality and personalized new media marketing content creation in a multi-platform environment can be realized by means of natural language analysis, knowledge structure extraction, large model generation constraint and the like.

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 free employee management platform based on an online technology, which can convert unstructured text data into structured semantic vectors by performing natural language analysis and semantic embedding coding on project requirements and free employee resumes. The semantic vectors can be used to calculate a matching degree and a comprehensive score between a project demand and a resume of a freestyle, thereby utilizing the comprehensive score to decide whether to recommend the freestyle to an employer. According to the free employee management platform based on the online technology, a more efficient, accurate and flexible recruitment solution is provided through an automatic and intelligent mode, and the defects of a traditional recruitment mode are effectively overcome. The platform not only improves the recruitment efficiency of enterprises, but also provides more opportunities for freestyle employees, and promotes further development of the freestyle employee market.

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 auxiliary diagnosis and treatment system based on a large-scale language model, and belongs to the technical field of medical image analysis, and the auxiliary diagnosis and treatment system comprises a medical image agent based on a large-scale pre-training language model, a tool assembly and a storage pool module, the tool assembly comprises a report generation module, an image segmentation module, a focus detection module, a report evaluation module, a diagnosis and treatment assistant module and a storage pool module. According to the auxiliary diagnosis and treatment system based on the large-scale language model, natural languages input by a user are analyzed and converted into specific task prompts through the medical image intelligent body, complex medical image problems are automatically decomposed, tool assemblies are matched and called, detailed step-by-step solutions are generated, and compared with an existing device, the auxiliary diagnosis and treatment system based on the large-scale language model has the advantages that the efficiency is high; the problem that the operation is complex and not visual enough is avoided, the operation is simplified, and the use threshold is lowered.

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.

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 present application provides an automatic control system and method for a coating machine, which relates to the field of intelligent control. The system first obtains the slurry coating state image and the slurry characteristic text description of the target detection area, and then uses machine learning and natural language analysis technology to perform image encoding on the slurry coating state image, and semantic encoding on the slurry characteristic text description, and generates the detection surface density corresponding to the slurry in the target detection area based on the cross-modal significant interaction of the core clues of the two, so as to adjust the coating machine in combination with the target surface density corresponding to the slurry. In this way, by fusing multimodal data, it is possible to reduce the dependence on environmental factors of a single sensor or parameter estimation method, better understand and process complex coating processes, and make adaptive adjustments according to the characteristics of different slurries, with high flexibility.

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 discloses a network advertisement pushing intelligent management system based on natural language analysis processing, and relates to the technical field of advertisement pushing management. Comprising a storage module which creates an advertisement database and is used for storing advertisement data to obtain a storage information set and extracting keyword information of the storage information set to obtain storage data items; and the acquisition module is used for acquiring natural language information input by a target user to obtain a natural information item, and obtaining a target advertisement item based on a matching relationship between the natural information item and the storage data item. According to the method, at least two pushing time nodes are set and correspond to browsing level items of different levels respectively, matching of the browsing level items is carried out according to the combination result of the real-time time items and the real-time chapter items, the target time threshold value is obtained through matching, and advertisement time is pushed according to the specific browsing time of the user. Advertisement pushing time can be changed according to specific demand conditions and interesting degrees of different users, and the intelligent effect of advertisement pushing is improved.

The present application provides an artificial intelligence-based digital emergency plan intelligent management system, which relates to the field of data intelligent analysis. It uses artificial intelligence-based natural language analysis and understanding technology to semantically understand the natural language description of the emergency input by the user and the first digital emergency plan extracted from the digital emergency plan database, so as to intelligently judge whether to return the first digital emergency plan as a recommended emergency plan based on the two-way attention feature interaction representation between the emergency semantic understanding coding feature and the first digital emergency plan semantic coding feature. In this way, by automatically analyzing and understanding the deep semantic information of the emergency and the digital emergency plan related information, it is conducive to quickly finding the most appropriate response plan, and ensuring that the recommended emergency plan is more in line with the actual situation, thereby significantly improving the overall efficiency and reliability of emergency management.

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.

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 relates to the technical field of electronic warrants, and particularly discloses an electronic warrant full-process management and risk management and control system based on a block chain, and the system comprises the steps: calling a user credit record and a user insurance record according to the identity information of a user; and data splitting and semantic coding are carried out by using a natural language analysis and processing algorithm based on deep learning, causal association semantic dynamic migration is carried out on coded record features, and credit risk multi-mode joint representation is generated, so that automatic judgment on whether an insurance request is within a credit risk security level is realized. In this way, complex semantic information in the user credit record and the user insurance record can be effectively analyzed and utilized, and the credit risk judgment basis is more comprehensive and accurate. Meanwhile, the model can be continuously updated and adjusted according to new data and is not limited by a fixed formula and a preset coefficient factor any more, and the accuracy and effectiveness of risk judgment in different scenes 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.