101 results about "Natural language parsing" 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 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 invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a page generation method and device based on a natural language, equipment and a medium, and the method comprises the steps: obtaining a demand description in a natural language form, analyzing the demand description based on a preset intention recognition material, and generating a user intention; retrieving in the page feature vector library according to user intention to obtain a page template, performing graphical user interface rendering on the page template to generate a visual component set, injecting associated data into a target component in the visual component set to generate a target page, publishing the target page or publishing and collecting use effect data after modification, and displaying the use effect data. And updating the page feature vector library by using the use effect data. By combining natural language analysis, intention recognition, vector retrieval, graphical rendering and effect data updating, automation from demand input to page generation is realized, manual intervention and development cycle are greatly reduced, and page generation efficiency and quality are improved.

The invention discloses a software project development method based on a low-code platform, which belongs to the technical field of software development, and specifically comprises the following steps: receiving original demand description through an embedded natural language parser, and establishing a semantic incidence matrix to extract demand elements; encoding the demand elements into three-dimensional structured demand vectors; mapping the demand vector to a three-dimensional orthogonal feature space, calculating the coordinates of a functional module, and generating an initial topological structure based on a Delou inner triangulation algorithm; rendering an interactive three-dimensional network map on a visual interface; monitoring user operation, and recalculating the adjacency relation of the affected module when the displacement of the module exceeds a threshold value or the connection edge is reconstructed; compared with a historical version, when a key service path is interrupted or a data flow ring is broken, a compensatory connection bridge is activated; a deployable architecture with a version traceability identifier is output, and a traceability code is built in the module to reversely map an original demand vector.

The invention discloses an online resource text and image layout system based on natural language processing, and belongs to the technical field of natural language processing. An image semantic understanding module; a layout generation and optimization module; an interactive adjustment module; a multi-modal content fusion module; the aesthetic evaluation and compliance verification module is used for automatically evaluating the aesthetic degree and compliance of the generated layout; and the dynamic response type adaptation module is used for adjusting the layout in real time according to the sizes of terminal equipment and a screen, ensuring the cross-platform consistency, generating a multi-resolution layout scheme based on a Flexbox response type rule, and dynamically adjusting the element stacking sequence, the personalized recommendation module and the real-time cooperation and version management module through reinforcement learning. And the edge calculation module improves the efficiency of large-scale layout generation, supports an offline or low-delay scene, compresses a CNN model by using a knowledge distillation or quantification technology, and adapts to edge equipment. The invention also discloses a method.

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 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 provides a geographic knowledge complex question-answering method based on a space-time agent architecture. The geographic knowledge complex question-answering method comprises the following contents: natural language analysis: accurately understanding intentions of users and information contained in questions; multi-modal geographic entity recognition: processing non-text data contained in user input; generating a WKT format and injecting a prompt; knowledge graph retrieval enhancement; in the spatial computing tool chain arrangement, an LLM generates a problem solving scheme after combining multi-modal information, WKT geometry and related data resources associated with a knowledge graph, tools or functions needing to be called and an execution sequence of the tools or the functions are listed, and the series of tools adopt a model context protocol; in answer generation and response, the method can effectively solve the problems that in the prior art, when complex geographic questions and answers are processed, question input recognition is incomplete, reasoning steps are not accurate, and the answer basis is not real, and more intelligent and reliable question and answer support is provided for the field of natural resource planning and the like.

The invention relates to the technical field of intelligent process management, and discloses a multi-node dynamic warning and compliance supervision authentication process management system, which comprises a dynamic rule generation module, a real-time data integration module, a predictive intervention module, a compliance verification module and a closed-loop feedback module. The efficiency and compliance weight are dynamically optimized through reinforcement learning and Monte Carlo tree search, a global task dependency relationship is constructed in combination with a distributed graph database, the time sequence convolutional network is used for predicting the overdue probability, the priority is adjusted based on the game theory, and dynamic resource allocation is achieved. Meanwhile, compliance verification logic is generated based on natural language analysis, cross-department knowledge migration and credible evidence storage are completed through federated learning and a block chain technology, and rule strategies and model parameters are optimized in a closed-loop mode through an incremental updating mechanism. According to the method, the problems of rule stiffness, data island and supervision lag of a traditional system are solved, the process efficiency can be improved, the compliance risk is reduced, and self-adaptive continuous optimization is realized.

The invention discloses a database installation and deployment method based on an MCP protocol, and relates to the technical field of database deployment. In order to solve the problems of high requirement on professional knowledge of a user and tedious privacy configuration in traditional database automatic deployment, the scheme adopted by the invention comprises the following steps: realizing standardized configuration of privacy information based on an MCP protocol, and completing related settings by the user through an operation interface; the method comprises the following steps of: receiving a natural language deployment instruction input by a user and triggering a deployment process by virtue of natural language analysis capability of an MCP protocol: calling MCP Tools by an MCP Server to acquire full-amount environment information of a server, and matching to obtain an adaptive installation package version; detecting dependence conditions of a target environment, automatically supplementing missing software packages, and automatically executing database installation, initialization configuration and monitoring component deployment; and the MCP Server summarizes the state information of the whole process and returns a deployment result. According to the invention, automatic and rapid installation and deployment of the database can be realized.

The invention relates to a standard information automatic acquisition interface calling system, which comprises a natural language analysis module used for receiving a text, image or voice query request input by a user, extracting standard related semantic information through a semantic structuring mode, and sending the standard related semantic information to a server; the semantic information comprises standard subject terms, applicable objects and time dimension labels; the standard matching engine is used for retrieving matched standard entities in a constructed standard knowledge graph based on semantic information, and the standard knowledge graph is constructed by aggregating a plurality of standard databases and supports ontology reasoning and fuzzy semantic matching; wherein the natural language analysis module and the standard matching engine cooperate to complete an automatic processing flow from natural language input to standard entity return.

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 provides a distribution robot interaction method and system, and relates to the technical field of automation, and the method comprises the steps: receiving a natural language which is sent by a target user and is related to a distribution instruction, carrying out the recognition of the natural language, carrying out the analysis of the natural language, and determining a distribution intention; creating or modifying a robot delivery task for the delivery instruction according to the delivery intention; controlling the delivery robot to deliver articles to the target user according to the delivery intention; under the condition that the delivery robot arrives near the second delivery address, sending a pickup message to the target user; in response to a pickup operation executed by the target user based on the pickup message, opening a cabin door of a robot storage cabin where an article associated with the robot delivery task is located, or in response to feedback question information of the target user based on the pickup message, generating voice reply information, and sending the voice reply information to the target user; and controlling the delivery robot to interact with the target user based on the voice reply information. According to the invention, intelligent distribution can be realized.

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 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.

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 relates to the technical field of software development and operation and maintenance automation, and provides a ChatOps drive-based workflow construction method and system. The ChatOps drive-based workflow construction method comprises the following steps: S1, setting a robot background in a chat platform, receiving an instruction input by a user through a predefined command or natural language analysis, and analyzing and identifying a task type and a parameter through the robot background; s2, the robot background sends the task type and parameters to a workflow engine through an API of the assembly line platform, and the workflow engine is matched with workflow according to a preset workflow template; s3, the workflow engine sequentially executes the assembly line tasks corresponding to the workflow sub-nodes through the API, and the robot background feeds back the task states to the chat platform in real time; and S4, the robot background feeds back assembly line task execution progress information to chat in real time. According to the method, the situation that a traditional process work black box is not transparent is relieved, and the personnel communication cost and the frequent switching time of all tool platforms are reduced.

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.

This technology generally relates to techniques for validating freeform entries in web forms, such that providers of a service can receive an optimal amount of information from potential users of that service in the initial correspondence. The techniques may include receiving a query from a consumer and analyzing the query using natural language parsing of a machine learning model, such as a large language model. The machine learning model may generate a series of prompts related to the content of the query. The user may provide additional information based on the series of prompts. The techniques may further include analyzing the additional information to determine if the prompts were addressed. If the prompts were addressed, the techniques may include outputting a complete message to the user.

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.

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 an AI-driven intelligent performance assessment report generation and analysis system and method. According to the system, data quality is ensured through a data import and verification module; calling a large language model by using a natural language analysis module, and automatically analyzing the assessment method described by the natural language into a structured rule; automatically calculating, by a calculation engine module, scores based on rules and data; and finally, the report generation module outputs a visual report. The system is further integrated with a knowledge base supporting user-defined calculation, an intelligent to-do management module and a virtual assistant question answering module. According to the method, the full-process automation and intelligence of performance assessment from data entry, rule analysis to report generation are realized, the problems of low efficiency, high subjectivity, insufficient flexibility and high error rate in the prior art are effectively solved, and the assessment efficiency and fairness are remarkably improved.

The application provides a smart home layered intention analysis and safe execution method and system and a storage medium, and relates to the technical field of smart home control. The method analyzes natural language into structured intention by constructing control context and home capability graph; adopts a layered mechanism to constrain retrieval candidate entities and calculate relevance score; performs risk scoring on control plan based on five dimensions of device risk level, control range, remote access state, time period and instruction conflict state, and adopts a hierarchical confirmation strategy; generates an execution graph and performs state checking, automatically reverts when a strong consistency scene fails; stores the correction result as personalized preference and sets a confidence decay rule. The application can significantly reduce the range of candidate devices, support contextual dialogue, control high-risk devices in stages, reduce the risk of miscontrol, ensure the consistency of complex scene execution, continuously improve the accuracy of alias recognition, and thus improve the efficiency, safety and user experience of smart home control.