668 results about "Parsing" patented technology

The invention discloses a law compliance auditing response method driven by an enterprise knowledge graph, and relates to the technical field of artificial intelligence, and the method comprises the following steps: carrying out semantic analysis on a law and regulation text, constructing a context hierarchical expression model, dividing each law and regulation into a title semantic block, a subject semantic block, a limit semantic block and a constraint semantic block, and generating a hierarchical semantic graph. According to the method, the structured analysis of the regulation provisions is realized by constructing the hierarchical semantic graph, and the regulation provisions are accurately aligned with the business entities in the enterprise knowledge graph, so that the reasonable regulation-enterprise semantic mapping relationship is constructed. And boundary reasoning, confidence scoring, context backtracking and semantic correction are combined to realize intelligent discrimination and correction of the applicability of the regulatory obligations. Models and rules are continuously updated through a feedback optimization learning mechanism, the dynamic adaptive capacity of the system and the accuracy of compliance suggestions are improved, the misjudgment risk is remarkably reduced, and the intelligence and the practical application value of the system are enhanced.

The invention relates to the technical field of metadata generation, and discloses a data platform metadata automatic generation method based on a large model, which comprises the following steps of: firstly, performing unified standardization and preliminary grammatical analysis on an original SQL (Structured Query Language) code to obtain a structured intermediate representation; on the basis, preliminary blood relationship analysis based on rules is carried out, and simple column references are quickly identified and processed. For complex expressions which are difficult to accurately analyze by a traditional method, code snippets and context information of the complex expressions are accurately extracted and submitted to a large language model for deep semantic understanding and complex blood relationship analysis. And finally, integrating the complex consanguinity analyzed by the large model with the initial consanguinity list to form a comprehensive and accurate field-level consanguinity, and further generating complete data platform metadata. In this way, the defect that a traditional analysis tool understands complex semantics is effectively overcome, and the accuracy and integrity of metadata generation are remarkably improved.

An intelligent task-type dialogue method based on a large language model, comprising: acquiring a dialogue with a user; identifying and parsing input information of the user to obtain an identification and parsing result; on the basis of the identification and parsing result, synchronously updating the state of a dialogue state tracker; on the basis of the intelligent guidance of the tracker, entering a proper dialogue scene node; executing a preset Action to complete a specific task; and on the basis of the current dialogue node context and dialogue historical data, generating a feedback message of a robot. In the method, by combining the large language model, rapid adaptation and efficient configuration of a scene can be achieved, and by dynamically loading the configuration information of a specific scene, a round of dialogue process can be efficiently pushed on the basis of preset process logic. Also disclosed are an intelligent task-type dialogue system based on a large language model, an electronic device, and a computer program product.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a cue word compiling and generating method and system for a large language model.The method comprises the steps that in response to a compiling calling request, a PDL protocol is received to serve as a compiling input text; performing lexical and grammatical analysis on the compiled input text, and constructing a corresponding abstract syntax tree AST as an intermediate representation according to a context-independent method; semantic verification and structure correction are carried out on the AST, and when semantics are effective, structure optimization is carried out to generate an optimized AST; and traversing the optimized AST, matching a cue word template in a cue word generation rule base, mapping semantic nodes into structured text fragments, splicing the structured text fragments to form a final cue word text, and outputting a compilation result consistent with PDL protocol semantics. By introducing a PDL-oriented compiling mechanism, automatic analysis, structure optimization and high-quality cue word generation of an input protocol are realized before cue words of a large language model are generated, and the development efficiency and the output quality of the cue words are improved.

The invention discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

The invention relates to a military training-oriented dynamic multi-modal knowledge graph retrieval method, which comprises the following steps of: acquiring a multi-source data set to generate a unified original event stream; based on the original event flow, preliminarily constructing a knowledge graph by adopting a Bayesian prior-based tetrad model, locally embedding and updating through a military training event incremental graph neural network, and dynamically re-estimating the confidence of nodes and edges through a Bayesian evidence propagation algorithm; a pre-training language model in the military field is adopted for analysis and semantic coding, a query semantic vector is generated, a retrieval module is constructed, and a retrieval result is output based on the knowledge graph; and constructing a military retrieval strategy optimization network based on a reinforcement learning framework, dynamically adjusting a retrieval strategy, writing back user feedback data, and optimizing node embedding and retrieval strategy parameters. According to the method, multi-source heterogeneous data can be integrated, a dynamic credible graph is constructed, retrieval accuracy, timeliness and self-adaption are improved, and military training decisions are supported.

The invention discloses a research and development platform language intelligent analysis method based on multi-system fusion, and relates to the technical field of language intelligent analysis. According to the method, various language data in a multi-system fusion research and development platform are efficiently processed, cleaned and coded to generate a standardized data set by customizing a data adaptation interface, deep lexical and syntactic analysis and semantic role labeling are performed on the standardized data set, related features are extracted, a semantic mapping network is constructed, and semantic feature vectors are generated; cross-modal feature fusion is realized, deep meanings of language data are comprehensively captured, rich feature input is provided for intelligent analysis, a constructed intelligent analysis model can perform accurate intelligent analysis and intention recognition on natural language instructions and technical documents, and the intelligent analysis model can be used for performing intelligent analysis and intention recognition on the technical documents by means of compiling environment simulation, debugging information semantic optimization and the like. The model training effect is improved, and the analysis accuracy and the hardware compatibility are improved through hardware behavior simulation, real-time data flow analysis, user feedback and other modes.

The invention discloses a construction method and system of a power distribution network visualization platform based on SG-CIM, and relates to the technical field of construction of power distribution network visualization platforms. According to the construction method of the power distribution network visualization platform based on the SG-CIM, power text information of a power distribution network is obtained and input into a pre-trained semantic recognition model for analysis, an equipment attribute triple and an equipment relation triple of each piece of power equipment are obtained, topology reconstruction processing is carried out on the equipment relation triple, and the power distribution network visualization platform based on the SG-CIM is constructed. According to the method, a power equipment topological structure diagram is generated, graph modeling processing is carried out, a semantic knowledge graph is generated, then quality verification analysis is carried out, and a semantic structure coordination index is obtained. Therefore, the uniformity of semantic expression and topological connection logic of each power device in the power distribution network is guaranteed from the source, and the intelligent analysis capability, the operation visualization capability and the automatic verification capability of the power distribution network structure are improved.

The invention discloses an Abaqus simulation input file generation and result analysis method based on an MCP-Agent, and the method comprises the steps: deeply coupling a large language model, retrieval enhancement generation and a model calling protocol, and achieving a'demand-modeling-solving-post-processing 'closed loop driven by a natural language in a whole Abaqus simulation process for the first time. The modeling period is obviously shortened, and the professional threshold is reduced; by means of a security execution sandbox and a parameter traceability log, it is ensured that each step of function call and parameter modification can be audited and reproduced, and the traceability and interpretability of a simulation result are greatly improved; enterprise private knowledge hot plugging and incremental indexing are supported, so that industry experience can be continuously precipitated and instantly reused, and the result accuracy and the engineering efficiency are further improved; in addition, role-based authority management ensures the security of a core algorithm and sensitive data while opening cooperation, and is suitable for CAE simulation analysis scenes in the field of high-end equipment manufacturing such as automobiles and aerospace.

The invention provides an AI semantic model and robot interconnection method and system based on an MCP protocol and a medium. A natural language instruction is received, and a structured operation sequence is generated through a semantic analysis model; extracting a semantic tag dynamic matching service engine in the sequence, and generating an operation request containing a tool identifier and a parameter template; if the operation authority is verified based on the verification security token, triggering secondary authorization of the user, and if the authority is passed, packaging the operation request into a security instruction according to an encryption rule; a transmission protocol channel is dynamically selected based on the real-time flag bit, an executable instruction stream is generated, and therefore the target tool is driven to execute operation and feed back execution state data; according to the method, the problem of AI and robot instruction heterogeneity is solved through dynamic matching of semantics and robot tools, the unauthorized operation is intercepted based on authority verification, data security is guaranteed through field-level encryption, and a safe and efficient man-machine interaction framework is provided for intelligent manufacturing.

The invention relates to the technical field of information extraction, in particular to an intelligent table filling method and system based on deep semantic analysis. The intelligent table filling method based on deep semantic analysis comprises the following steps: performing content extraction and format standardization on an input document to form a standard document text; a large language model is adopted, deep semantic analysis is executed on a standard document text, an entity of the document is recognized, and corresponding semantics are analyzed; analyzing semantics, position coordinates and logic partitions of fields in the target Excel template; establishing a corresponding semantic mapping relationship based on the semantic similarity between the entity of the document and the field of the target Excel template in the vector space; and based on the semantic mapping relationship, executing a hierarchical filling operation according to a semantic similarity threshold. According to the intelligent table filling and reporting method and system based on deep semantic analysis, the accuracy of automatic filling and reporting is improved through format verification, logic consistency check and integrity verification.

The invention relates to an AI-driven code automatic completion method, device and equipment and a storage medium thereof, and the method comprises the steps: constructing a grammar structure and variable definition of a code through semantic analysis, analyzing inter-line logic association and a variable transfer chain based on context-dependent modeling, the problem that in the prior art, a completion result deviates from an intention due to semantic fault is solved. A variable function and a logic target are identified in combination with deep semantic reasoning, a multi-path candidate scheme is generated, semantic complete paths are screened, and the defect of logic consistency deficiency in a multi-path decision scene is overcome; finally, path selection is dynamically optimized by fusing historical behavior data of a developer, a basic code is generated through template instantiation, a personalized completion result is output through logic verification and style adaptation, the intention understanding accuracy and the style adaptation capacity under a complex code scene are improved, and intelligent completion which is logically coherent and conforms to developer habits is achieved.

The invention provides a family-school interaction classroom note analysis system based on OCR and a large language model. The family-school interaction classroom note analysis system comprises a note scanning module, an OCR recognition module, a note analysis module and a family-school pushing module. Through linkage of all the modules, automatic analysis of classroom notes, efficient cooperation of families and schools and personalized learning closed loop are achieved, and transformation of education from unified teaching to personalized cultivation is promoted.

The invention discloses an artificial intelligence large model development-oriented low-code automatic generation and deployment system, which comprises the following steps of: performing semantic analysis on a natural language modeling demand input by a user, generating a structured modeling target, and constructing and optimizing a task flow chart. The system generates a corresponding low-code modeling instruction according to the optimized task flow chart, completes automatic matching of tasks and resources by adopting an improved shortest completion time scheduling algorithm in combination with the real-time state of heterogeneous computing resources, and automatically deploys the generated modeling tasks to a target computing environment in a containerization manner, so that the modeling efficiency is improved. And unified operation control and process monitoring are carried out, so that automatic closed loop from semantic input to model deployment is realized.

The invention provides a page component configuration method and device, equipment, a medium and a program product, and can be applied to the technical field of artificial intelligence and the field of financial science and technology. The method comprises the following steps: in response to a received component configuration request sent by an object, determining a target rule expression matched with a demand description text on the basis of a plurality of preset rule expressions, historical operation information of the object and the demand description text included in the component configuration request; based on a preset analysis engine, the target rule expression is analyzed into a target configuration instruction adopting a target programming language, and the preset rule expression and the target programming language have similar grammatical structures; and performing front-end page rendering based on the target configuration instruction to obtain a component configuration result corresponding to the component configuration request.

The invention discloses an industrial measurement data intelligent analysis report generation method and system based on a large language model, and relates to the technical field of industrial measurement, and the method comprises a multi-source data and abstraction module which carries out the structural processing of original data DAT output by measurement analysis software; and the large language model service and interface module is used for submitting the PCT to a selected large language model LLM through a multi-model adaptation interface to execute semantic reasoning and output a structured text TXT. Structured analysis and semantic association modeling are carried out on original measurement data through multi-source data and an abstract module, when overall alignment deviation or local feature anomaly exists in the original data, a hierarchical dependency relationship between the data can be established through a semantic graph structure, a deviation transmission path is revealed, and the accuracy of the measurement data is improved. The large language model is analyzed in a unified data context, and the utilization depth of measurement data can be effectively improved, so that the accuracy and integrity of report analysis are improved.

The invention discloses a user intention recognition method based on context semantic analysis, which comprises the following steps of: collecting multiple rounds of session texts, generating a semantic analysis graph, and constructing a symbol logic space consisting of an intention logic predicate, a slot constraint rule and a knowledge template and a continuous semantic space consisting of context embedded vectors; defining a dual mapping mechanism of a folding operator and an unfolding operator, and constraining reversible consistency through folding reconstruction loss; in the inference stage, intention candidates are generated based on a knowledge base, folding-unfolding circulation is executed for semantic alignment and logic verification, when an alignment error meets a threshold value, a semantic state vector subjected to folding alignment is output, and a final recognition result of a deliberate map, sub-intentions and slot position parameters is generated through an intention decoder. Through the bidirectional folding mapping and cyclic correction mechanism of the symbol logic space and the continuous semantic space, the accuracy, stability and interpretability of user intention recognition are improved.

The invention provides a file positioning management method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. According to the method, files are collected from multiple sources and subjected to standardization processing, an element set is generated in combination with multi-modal analysis of texts, images, audios, videos and tables, cross-modal alignment is achieved through a semantic representation model, hierarchical indexes of semantics, keywords and relations are constructed, and a unique traceability identifier is generated; in the query stage, intention recognition and joint retrieval are carried out, a result subjected to permission verification and traceability information labeling is output, online optimization and incremental reconstruction are executed based on user feedback, and comprehensiveness, accuracy, traceability and self-adaptive optimization of file positioning are achieved.

The invention provides a user-defined script dynamic editing and executing method and system of a Unity engine. The method comprises the following steps that codes are input and preprocessed; executing text cleaning, word segmentation and annotation preprocessing according to the line feed as a code line set; grammatical analysis and semantic understanding; constructing an abstract syntax tree, identifying a syntax structure, and combining context analysis semantics to generate a symbol table; processing errors in real time; positioning an error line through a multi-stage matching strategy, highlighting an error position through a visual interface, and generating a repair suggestion in combination with a line number mapping table, error propagation analysis and a multi-evidence fusion mechanism; executing dynamic codes; explaining and executing the corrected script, and feeding back an execution result; through interaction with a Unity engine, operation control of a script on a game object and a scene is realized; through three core technologies of semantic weight dynamic matching, an anti-interference navigation state machine and a hybrid sorting algorithm, the long-standing problems of low matching precision, poor interaction experience and the like in the field of code completion are solved.

The invention provides a natural language-driven scientific research naming and ranking patient matching method and system, belongs to the technical field of artificial intelligence and medical information processing, and solves the problems of high manual workload, difficulty in semantic analysis, insufficient logical reasoning ability and the like in an existing cancer patient screening mode. The method comprises the following steps: analyzing a containing and arranging condition in a natural language text form in the tumor field to obtain a structured containing and arranging condition logic rule set; performing entity recognition, relation extraction and portrait construction on the multi-modal medical record of the cancer patient to obtain a comprehensive cancer portrait of the patient; and performing logic matching on the comprehensive cancer portrait of the patient and the structured accommodating and discharging condition rule set, determining whether the patient meets accommodating and discharging conditions, and generating a traceable matching interpretation path. The method can effectively solve the problems of large manual workload, difficulty in semantic analysis, insufficient logical reasoning ability and the like in the existing cancer patient screening mode.

The invention discloses a large language model security testing method based on evolutionary dynamic adversarial attacks, and relates to the technical field of security testing. Comprising the following steps: 1, creating a dynamic attack generation engine, constructing an adversarial evolution architecture by utilizing the dynamic attack generation engine, and generating a test sample based on the adversarial evolution architecture for a large language model security test; the method comprises the following steps: 1, establishing a large-scale language model, 2, calculating investigation parameters of harmlessness, honesty and helpfulness based on a Constancy AI principle, calculating a security alignment gap index SAGI by using the investigation parameters, and intelligently judging a value drift condition of the large-scale language model according to the security alignment gap index SAGI, 3, establishing a multi-modal joint defense engine, and carrying out intelligent judgment on the value drift condition of the large-scale language model according to the value drift condition of the large-scale language model. Steganalysis, syntax tree analysis and audio anomaly detection functions are integrated, and all-around threat detection coverage of texts, codes, images and voices is carried out on a detected large language model.

The invention belongs to the technical field of data analysis, and discloses a structured data analysis method and device.The method comprises the steps that a to-be-processed natural language is obtained, a first cue word is obtained based on the natural language, and the first cue word is used for guiding generation of structured data; inputting the first cue word into a preset large language model to return a processing result for generating the structured data; performing regular extraction on the processing result to remove an unstructured part in the natural language, and returning a removal result; and re-inputting the rejecting result into the preset large language model to obtain a re-processing result for generating the structured data. According to the method, the accuracy of outputting the structured data by the large language model can be remarkably improved.

The invention discloses a knowledge intelligent analysis and question-answering method and system based on time sequence knowledge graph retrieval enhancement, relates to the technical field of natural language processing, and aims to solve the problem that a wrong answer is generated due to the fact that a model possibly retrieves semantic-related but time-inconsistent information due to a time-insensitive retrieval mechanism in an existing method. According to the method, a dynamic time sequence knowledge graph modeling and hierarchical interactive retrieval mechanism is introduced. According to the framework, entities and relationships are bound with specific timestamps through time annotation in a knowledge construction stage, and a dynamic graph capable of evolving along with time is formed; in a retrieval stage, through time query decomposition (decomposing a multi-time constraint complex query into single-time sub-queries, and gradually screening related knowledge by a three-layer interactive retriever (time sequence sub-graph retrieval, node-level retrieval and knowledge-level retrieval), time conflicts and redundant interference are reduced, and finally answers with consistent contexts and accurate time are generated.

The invention provides a code stream real-time rendering method and device, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as large language models, hypertext markup languages, streaming processing and webpage generation. The method comprises the steps of obtaining a code stream of a hypertext markup language continuously output by a large model and used for generating a target webpage; performing streaming processing on the continuously acquired code stream according to the following preset streaming analysis mechanism: in response to the detected complete script tag, extracting the script to be processed corresponding to the complete script tag to a preset script management system; in response to the detected frame tag, processing and displaying the content of the frame tag in a sub-page created for the frame tag; the real-time streaming result is presented as a real-time web page rendering result corresponding to the portion of code received from the code stream. By applying the method, the rendering efficiency of obtaining the target webpage based on the code can be remarkably improved, and the waiting time of a user is shortened.

The invention relates to the technical field of computer programming languages, and particularly discloses a computer language programming method based on business logic. Aiming at the defects of deep coupling of business logic and technology implementation, high cross-domain adaptation cost, hardware operation code redundancy, high asynchronous programming complexity and the like in the prior art, the invention provides the following core schemes: a modularized development framework, a cross-platform adaptive mechanism, an event hierarchical model, a hardware intention analysis engine and a code, namely a document system. According to the method, the development complexity of a multi-field system is remarkably reduced, the business logic expression efficiency and maintainability are improved, the method is suitable for cloud micro-services, embedded equipment and mixed language scenes, and a technical basis is provided for intelligent programming.

The invention relates to the technical field of computers, in particular to a code sample optimization method and system based on a language large model, and the method comprises the steps: firstly, obtaining a source code sample, determining a code sample boundary, and generating a mark sequence; then, based on a compiling result, a static analysis result and a unit test result, a quality label is generated, and a training sample set is formed; then, taking a Transform neural network model of bidirectional context coding as an encoder, and carrying out training under the supervision of quality annotation to obtain a quality evaluation model; and finally, screening according to a preset quality score threshold to obtain a high-quality code sample set. According to the technical scheme, by combining the deep grammar analysis and context understanding of the code samples, the accuracy and efficiency of automatic screening are improved, manual intervention is reduced, and the method can adapt to automatic evaluation of a large-scale code library.

The invention provides a software architecture governance system based on automatic modeling and holographic penetration analysis. The software architecture governance system comprises multiple modules: a project management module used for managing all analysis projects; the source code management module is used for pulling, updating and deleting project source codes; the analysis drive management module is used for uniformly managing and calling analysis drive programs; the multi-language analysis driving cluster is integrated with analysis driving programs corresponding to multiple programming languages and is responsible for analyzing source codes, generating a standardized software chart model and storing the standardized software chart model in a database; the rule engine module is used for supporting a complex code analysis service; the cross-project integrated analysis module generates a system architecture diagram according to the plurality of software models and stores the system architecture diagram into a diagram model library; the multi-dimensional data fusion interface module provides penetrating analysis interfaces of a code layer, a design layer and a framework layer; and the architecture model visualization module is responsible for displaying the system architecture in a multi-dimensional visualization manner. The method can be used for automatic modeling, tracing analysis and interactive visual presentation of a complex system architecture.

The invention provides a code recommendation method and device based on a double-flow hybrid network, relates to the field of code semantic analysis, and solves the technical problem that long-distance dependency capture and calculation efficiency improvement cannot be synchronously realized in the prior art. The method comprises the following steps: analyzing a source code to obtain a feature sequence; performing double-flow feature extraction on the feature sequence to generate a time sequence flow feature and a context flow feature; the time sequence flow features are used for capturing local grammar features; the context flow features are used for capturing long-distance dependence; and dynamically fusing the time sequence flow features and the context flow features, and outputting recommended code nodes based on the fused features. The method and the device are used in a user operation visual arrangement process.