24511results about "Natural language data processing" patented technology

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a content generation and optimization method based on a knowledge graph, which comprises the following steps: constructing a multi-source knowledge database, extracting core concepts and knowledge contents, and constructing the knowledge graph. Performing semantic analysis to generate semantic vector representation and a keyword list; retrieving the associated text fragment based on the semantic vector and the keyword list, and inputting the associated text fragment into a generation model to generate initial answer content; and utilizing the knowledge graph to match the domain entity and the knowledge graph node, generating a logical reasoning path, optimizing the initial answer content, and generating the final answer content. According to the method, content generation of accurate retrieval, deep knowledge association and logical reasoning enhancement is realized by fusing a multi-source knowledge database, knowledge graph reasoning and generation optimization; semantic vector matching and keyword retrieval are combined, so that the accuracy of knowledge acquisition is improved; and through knowledge graph reasoning path construction, the answer logic is coherent.

The invention provides a standard document automatic generation and multi-dimensional auditing method and system based on a large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: 1, building a distributed database of a multi-source document, and analyzing a heterogeneous text through natural language processing to obtain a standardized knowledge network; step 2, extracting index elements based on the standardized knowledge network, and forming a structured parameter library through verification and verification; and step 3, based on the structured parameter library, constructing a template library, analyzing user demands in combination with semantic matching, and automatically generating a standard document outline. The document generation efficiency and quality are improved, the manual auditing cost is reduced, and the auditing comprehensiveness and accuracy are enhanced.

The invention discloses a hybrid enhanced enterprise smart law platform system based on a generative language large model. Four modules including a hybrid enhanced legal knowledge engine, a multi-modal legal document analysis module, a risk quantitative evaluation module and a compliance verification workflow work cooperatively. The hybrid enhanced legal knowledge engine integrates multi-source data, realizes real-time updating and semantic reasoning, and comprises map construction, a rule base and an incremental learning mechanism; the multi-modal legal document analysis module performs structured analysis on the heterogeneous document to generate a feature vector; the risk quantitative evaluation module is combined with Monte Carlo simulation and an analytic hierarchy process, quantifies the risk according to a compliance reference and analysis characteristics, and outputs a thermodynamic diagram and a report; a compliance verification workflow is driven by a finite-state machine, a verification module and a conflict detection module are integrated, a generative language large model is called to generate an improved scheme, and audit records are solidified and fed back for optimization. And the system runs according to the processes of analysis, supply rule, bias calculation and verification correction, so that the intelligence and accuracy of legal affair processing are improved.

The invention relates to the technical field of intelligent operation and maintenance and artificial intelligence crossing of a power system, in particular to an intelligent power work order processing method of an RPA fused multi-modal large model, which analyzes multi-modal work order data such as texts, voices, images and the like through a domain adaptation large language model, and realizes fault key information extraction and conflict resolution in combination with a dynamic knowledge graph; performing work order priority scoring and resource allocation by using space-time constraint reinforcement learning; an analysis result is converted into an automatic execution script through an RPA engine, and a whole-process closed loop of order sending, processing and feedback is achieved; meanwhile, a feedback optimization and conflict resolution cooperation mechanism is constructed, and the knowledge graph and the model precision are continuously iterated. The method improves work order processing efficiency and analysis precision, enhances decision scientificity, and is suitable for an intelligent operation and maintenance scene of a power system.

The invention discloses a urinary surgery diagnosis and treatment data intelligent processing system based on artificial intelligence, and the system comprises the following modules: an original diagnosis and treatment data collection and preprocessing module which is used for collecting the data of a urinary surgery patient; the stage annotation text construction module is used for constructing a stage annotation text set; the image description structuring module is used for constructing a structured image description sequence; the cross-modal semantic fusion module is used for extracting a cross-modal joint semantic representation vector sequence; the named entity recognition module is used for outputting a structured medical entity set; the entity semantic graph construction module is used for constructing an entity semantic graph structure; the diagnosis and treatment relationship extraction module is used for outputting a diagnosis and treatment relationship set of a triple structure; the structured packaging module is used for outputting a structured diagnosis and treatment data unit; and the intelligent diagnosis and treatment decision module is used for performing illness state recognition, treatment path generation and reasoning suggestion output. According to the invention, multi-modal semantic modeling and graph neural reasoning are fused, and an intelligent diagnosis and treatment relation recognition system is constructed.

The invention discloses an RAG enhanced Text-to-SQL (Structured Query Language) query method and system for a large-scale database environment. The method comprises the following steps: constructing a vector database; based on the original query of the user, generating query enhancement description aiming at Schema recall and query enhancement description aiming at SQL (Structured Query Language) by utilizing LLM (Logistics Language Model); carrying out Schema recall and historical question and answer pair recall operations in a double-way parallel manner by utilizing an RAG technology and relying on the constructed vector database; based on query enhancement description, recalled Schema and historical question and answer pairs, generating an SQL query statement by using LLM in combination with RAG, and realizing Text-to-SQL conversion; the generated SQL query statement is subjected to post-processing optimization through a database interface, the post-processing optimization comprises grammar verification, performance optimization and error correction, the SQL query statement is combined for execution and feedback, efficient, accurate and reliable natural language query is achieved, and continuous optimization can be conducted through user feedback.

The invention relates to the technical field of automatic report generation, and provides a test analysis and report generation method and system based on large model retrieval enhancement, and the method comprises the steps: firstly carrying out the semantic task decomposition of user query, and meanwhile, achieving the multi-dimensional information extraction through the butt joint of a knowledge vector library and a structured image-text knowledge system established by a private knowledge base module. And three strategies of RAG, Self-RAG and Graph-RAG are combined to enhance the generation capability of the large model so as to accurately obtain background knowledge. And constructing a standard SQL statement according to a structured query requirement, and performing data filling according to template prompt by an output result fusion module in combination with a query background and an SQL execution result to form a test report. By optimizing a retrieval enhancement generation method, professional knowledge supplement related to query is realized, and the reasoning ability of a large model in a professional scene is effectively enhanced, so that the accuracy and the intelligent level of data analysis are improved.

A system and method for securing inter-process communications (IPCs) between generative AI and external tool servers, including intercepting IPCs having a requested operation, performing a security analysis on the IPCs for security threats, performing a permission validation for permissions for the requested operation of each IPC, and either approving or blocking the requested operation of each IPC based on the security analysis and the permission validation.

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention discloses a method and system for dynamically calling a Java statistical analysis interface based on LLM and MCP protocols, belongs to the technical field of data processing and analysis technology, artificial intelligence technology and application program interface (API) integration, and aims to solve the technical problem of how to improve the response speed and flexibility of statistical analysis requirements. In order to reduce the development and maintenance cost of a statistical analysis function, the adopted technical scheme is as follows: a Java annotation mechanism is utilized to mark and describe an existing Java statistical analysis system, a Java method of which the annotation is defined is obtained, and the Java method of which the annotation is defined can be automatically identified by an MCP server and registered as an MCP tool; the MCP server follows an MCP protocol and communicates with the LLM serving as an MCP client, and the LLM dynamically discovers available MCP tools through the MCP protocol; after natural language query of a user is understood through LLM, one or more appropriate MCP tools are intelligently selected, calling parameters are generated, and an MCP server is requested to execute through an MCP protocol.

Implementations relate to processing an image of a machine-read-able identifier (e.g., a QR code or other 2D barcode) to extract information that is encoded in the machine-readable identifier, and using the extracted information in automatically populating input fields of a fillable form that is being rendered at an application (e.g., a web browser application) of a computing device. Some implementations convert certain information, extracted from a machine-readable identifier, to a converted format and cause the converted format information to be populated in a corresponding input field in lieu of the certain information in its raw format that is extracted from the machine-readable identifier.

The invention belongs to the technical field of air traffic control and conduction, and particularly relates to an intelligent question answering system method for air traffic control and conduction business knowledge. According to the method and the system, the refined and context-aware information retrieval capability of the air traffic control communication service knowledge and the expert-level air traffic control communication service knowledge question and answer capability are provided, the knowledge management and application efficiency is effectively improved, and powerful support is provided for digital transformation of related services. Comprising the following steps: S1, a document content extraction and storage process; s2, a content retrieval and generation process; s3, content slice optimization is carried out through extraction and similarity calculation of adjacent paragraphs or contents, complete contents are in the same slice, and the content retrieval efficiency is improved; according to the method, the label library and the business rule library are continuously iteratively optimized, so that the provided service is always in the optimal state.

The present disclosure relates to the technical field of document recognition, and provides an intelligent document recognition method and apparatus, an electronic device and a storage medium. The method comprises: extracting text information and layout information from a document to be recognized; acquiring a template containing a specified field; on the basis of an industry knowledge base corresponding to the document, adding a label to the text information; and, on the basis of the layout information and the label, using a large language model to recognize text information matched with the specified field from the text information to which the label has been added, the large language model being a large language model that has been trained by using the industry knowledge base. In the embodiments of the present disclosure, document recognition results more conform to industry attributes, improving document recognition accuracy.

The invention discloses an agricultural pest question-answering method based on knowledge graph adaptive hybrid retrieval enhancement. The method comprises the following steps: 1) data acquisition and arrangement; 2) construction of a knowledge graph and a vector library; 3) constructing a question and answer pre-classification system; 4) building and training a question type automatic classification model: automatically identifying and classifying the questions input by the user by using a small classification model to form a self-adaptive retrieval classifier; 5) self-adaptive knowledge retrieval based on question types: according to the queried question types, self-adaptively and dynamically selecting different retrieval methods of the knowledge graph and the vector library; 6) selecting a corresponding thinking chain reasoning method according to retrieval results of the knowledge graph and the vector library, and constructing a Prompt cue word with high pertinence; according to the method, the knowledge graph and vector library retrieval are fused, so that the answering precision and accuracy are improved, and by integrating a thinking chain (CoT) reasoning framework, the decision-making process of disease and insect pest problem analysis and answering has a clear logic chain.

The invention discloses a double-engine government affair question and answer method based on large model fine tuning and RAG retrieval, belongs to the field of government affair digitization and natural language processing, and combines large model language understanding generation ability, retrieval enhancement generation technology and a structured reasoning mode. The defects of a traditional government affair question and answer method in the aspects of dynamic policy response, complex semantic understanding and compliance control are overcome. Government affair field knowledge is adapted through large-model fine adjustment, and high-precision and timeliness answering of government affair consultation is realized in combination with vector retrieval and a dynamic updating mechanism. The core innovation of the method lies in deep fusion of a double-engine architecture and dynamic knowledge management, the accuracy and response efficiency of government affair questions and answers are improved on the premise of ensuring policy compliance, and the method is suitable for intelligent upgrading of scenes such as government affair service halls and online consultation platforms.

The invention relates to the technical field of image data processing, and discloses a power grid drawing intelligent review method and system based on a knowledge graph, and the method comprises the steps: driving a multi-mode large language model based on a domain specific prompt project, extracting a power grid domain knowledge triple from structured text data, and then carrying out the quality evaluation and conflict detection, performing automatic resolution on the conflict knowledge to obtain a candidate knowledge set; checking and confirming the candidate knowledge set through a man-machine cooperation verification mechanism, and constructing a power grid design specification knowledge graph; and identifying to-be-reviewed elements in the to-be-reviewed power grid drawing through the multi-modal large language model, querying the power grid design specification knowledge graph according to the generated structured query statement, performing compliance judgment on the to-be-reviewed power grid drawing based on the obtained query data packet, and generating a review report. According to the method and the device, the efficiency and the reliability of automatic drawing review can be improved, and meanwhile, the interpretability and the traceability of review results are ensured.

The invention belongs to the field of natural language processing, particularly relates to a multi-modal document generation method and device based on multi-agent collaboration, and aims to solve the problems that an existing method is low in intention recognition accuracy, limited in retrieval range and not professional enough in content generation. The method comprises the following steps: generating a structured template; analyzing the text input by the user to identify a writing intention, and determining a target template; vectorizing each candidate resource feature to obtain a corresponding sparse vector, a dense vector and a knowledge vector, and performing semantic alignment; extracting context features of the input text, respectively performing multi-path retrieval recall, evaluating and sorting recall results, and screening out target features; and constructing a thinking chain in combination with the knowledge graph, and generating a multi-modal document according to the target template. According to the method, the outline structure can be extracted, the picture / table style can be recognized, the templates adaptive to different document types can be dynamically generated, and full-process automation from user input to document output is achieved.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

The invention discloses a method for constructing a cross-modal knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the collection, structural analysis, modal recognition and classification, cleaning and standardization processing, so as to form structured multi-modal data; extracting entities and relationships of the identified modals from the structured multi-modal data, and summarizing the entities and relationships to form a multi-modal knowledge element set; mapping different modal entities in the multi-modal knowledge element set to a unified semantic space, and generating a unified entity relationship set through semantic matching, alignment and fusion; and normalizing the data into knowledge triples, and storing and organizing the knowledge triples through a graph database to form a cross-modal knowledge graph. According to the method, the problems of difficulty in multi-modal heterogeneous information alignment and difficulty in entity relationship extraction can be relieved, semantic association is enhanced, and knowledge graph integrity and multi-scene adaptability are improved.

The invention discloses a multi-path recall mixed retrieval method and system based on intelligent dynamic weight distribution, and aims to solve the problems that semantic comprehension and keyword matching are difficult to balance and the adaptability is poor due to the adoption of a fixed weight in the existing retrieval technology. The invention provides a multi-path recall mechanism fusing vector semantic retrieval, BM25 keyword retrieval and entity retrieval. A query feature vector containing 13-dimensional features such as semantic complexity, keyword density and entity coverage rate is constructed, a query type is recognized in combination with an SVM and a random forest integration model, a dynamic weight distribution algorithm is designed, and the final weight of each retrieval path is calculated in real time. And an adaptive multi-source enhanced reciprocal ranking fusion (AMSE-RRF) algorithm is further adopted to carry out optimization fusion on multiple paths of results, and a depth reordering model can be selected to improve the precision. According to the method, the accuracy and robustness of retrieval can be remarkably improved in multiple scenes of medical treatment, finance, government affairs and the like according to a millisecond-level self-adaptive adjustment strategy of query features.

The embodiment of the invention provides a government affair digital human dynamic interaction method and system based on a multi-mode large model. The method is applied to the technical field of government affair intelligent services, and comprises the following steps: acquiring a policy announcement text, and performing cleaning and structuring processing to obtain a structured policy data set; and extracting old and new policy data, performing difference comparison, marking key change fields, and generating policy change data. Abstracting and element extraction are carried out on the change data to form structured semantic fragments, and the structured semantic fragments are incrementally embedded into the policy knowledge graph. And generating a question and answer pair sample based on the updated knowledge graph, and carrying out self-supervised fine tuning training on the multi-modal large model. A user inputs multi-modal data, and the model generates a government affair response and feeds back the government affair response. According to the scheme, the multi-modal large model can continuously keep the latest policy knowledge; the model is enabled to generate accurate government affair response with consistent context while understanding multi-modal input such as text, voice and image, and timeliness, accuracy and interactive experience of policy interpretation are improved.

The invention discloses an airport intelligent service question answering method and system based on AI, relates to the technical field of artificial intelligence, and comprises the steps of performing combined modeling by using a time sequence and a multi-head attention mechanism, extracting key behavior characteristics, fusing intention recognition and behavior prediction, and improving the answer reasoning accuracy. The method comprises the following steps: selecting a semantic knowledge base, constructing deep semantic representation and accurately matching the deep semantic representation with the semantic knowledge base, extracting high-weight phrases in combination with a sliding window mechanism, generating unique semantic fingerprints by utilizing a Karp-Rabin hash function, improving the speed and accuracy of answer matching, and performing fuzzy control and redundant information elimination on candidate answers by adopting a rough set tolerance model. Semantic similarity judgment is carried out according to the intersection and union set proportion between the upper and lower approximate sets, the problems of semantic drift and repeated candidates in traditional matching are effectively relieved, and through reinforcement learning and A / B testing, the question and answer strategy is continuously optimized, and the response effect of the system and the user satisfaction degree are enhanced.

The invention relates to a medical information processing and intelligent data management technology, and discloses a hospital multi-source heterogeneous data integration management system and method based on an EMPI, and the system carries out the real-time subscription and batch extraction of the data of an HIS, an EMR, an LIS, a PACS, a DRG and a pre-hospital first-aid system through an interface adapter, and generates a unified main index identifier through the matching of the EMPI, and carries out the real-time subscription and batch extraction of the data of the HIS, the EMR, the LIS, the PACS, the DRG and the pre-hospital first-aid system. Field-level cleaning, standardization and semantic mapping are carried out on the free text and the structured data, an intermediate standardized record set is formed, an event graph is constructed, event cluster recognition is carried out in combination with time, clinical entities, doctor seeing scenes and department similarity, consistency judgment and EM iterative fusion are carried out on the basis of data source reliability, and event cluster recognition is carried out. And outputting a unified patient file set and a downstream service interface. According to the invention, high-precision integration, standardized management and traceable application of multi-source data are realized, and a reliable data basis is provided for clinical decision support, medical insurance management and scientific research.

Embodiments of the present invention provide a target multi-modal model system and construction method, a video processing model training method, and a video processing method. The video processing model training method comprises: inputting a video sample and each initial text sample into a video processing model, wherein the initial text sample is a text for performing category description on video content of the video sample; using the video processing model to perform feature extraction on the video sample to obtain a temporal motion feature and a fused image feature; using the video processing model to perform feature extraction on the initial text sample to obtain a dynamic text feature and a fused text feature; and training the video processing model on the basis of the temporal motion feature and the dynamic text feature, and the fused image feature and the fused text feature.

The invention provides a charger fault diagnosis method based on a knowledge graph, which realizes efficient and self-adaptive fault diagnosis and maintenance through multi-technology fusion, constructs a structured knowledge base, integrates charger parts, fault modes and detection methods, stores entities and causal relationships and detection association by adopting a graph database, extracts information through a BERT model, and realizes fault diagnosis and maintenance based on the knowledge graph. Marking causal strength and dynamically adjusting the causal strength, converting spoken description of a user into structured data, matching a fault path in a knowledge graph, dynamically adjusting a weight, analyzing time sequence data, matching a time sequence mode in the knowledge graph in combination with dynamic time warping, generating a candidate path, optimizing a diagnosis path weight, and designing a multi-target reward function; a visual report is generated, the fault probability in the future seven days is predicted, maintenance suggestions are generated in combination with risk levels, knowledge maps and models are continuously optimized through user feedback, diagnosis accuracy and maintenance efficiency are improved, and manual intervention requirements are reduced.

The embodiment of the invention provides an intelligent project text generation method and device, and the method achieves the precise screening of positive and negative training corpora through the construction of a multi-dimensional scoring mechanism, and the comprehensive evaluation of the text coverage degree, the technical correlation degree, the fluency and the professional term specification degree. And constructing a complete knowledge graph based on the relation extraction model and the external knowledge base, and fusing the enterprise technical feature matrix and the basic information to generate an enterprise portrait vector. By transforming a decoding layer structure of a pre-training large model, enterprise portrait vectors are used as condition vectors to be embedded, and personalized text generation is achieved. According to the method, the defects of the traditional technology in the aspects of training sample quality evaluation, knowledge graph integrity and enterprise feature fusion are effectively overcome, and the professionality and pertinence of project text generation are remarkably improved.

The invention discloses a user query complexity adaptive knowledge graph retrieval enhancement generation method, which comprises the following steps of: firstly, quantifying user query complexity according to a complexity measurement index contained in user query, and analyzing and complementing hidden logic of the user query; then, establishing a corresponding relationship between the user query complexity and the knowledge graph retrieval range and between the user query complexity and the knowledge graph retrieval strategy, and adaptively adjusting the knowledge graph retrieval range in combination with the user query complexity; then, adaptively screening out an optimal group of knowledge reasoning paths in the determined knowledge graph retrieval range based on reinforcement learning; and finally, answering the user query based on the selected knowledge reasoning path by utilizing a large language model. According to the method, the static limitation of the existing knowledge retrieval strategy is broken, the knowledge retrieval range and the retrieval strategy can be flexibly determined according to the query complexity of the user, and the retrieval precision and the retrieval efficiency are both considered.

Systems and methods for generating augmented search results are disclosed. An example method is performed by one or more processors of a search results ranking system and includes receiving a transmission over a communications network from a computing device associated with a user of the search results ranking system, the transmission including a search query, submitting, to a vector database, a token query matching a tokenized version of the search query against a plurality of data assets, submitting, to the vector database, one or more vector queries matching a vectorized version of the search query against the plurality of data assets, identifying, based on results of the token query and the one or more vector queries, contextually relevant results among the plurality of data assets, and generating augmented search results for the search query based on the contextually relevant results.

The invention discloses a multi-agent collaborative reasoning system for intelligent teaching intervention, and relates to the technical field of artificial intelligence and education, and the system comprises a data collection and convergence module which collects data of learning behaviors, emotional states, academic scores and knowledge point mastering conditions of students by means of a classroom behavior analysis system, a camera, a microphone and a learning management system; according to the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the precision and individuation of teaching intervention are realized by constructing the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the system collects multi-dimensional data of students through the data acquisition and convergence module, and through the processing of the data conversion and cleaning module, the accuracy and availability of the data are ensured; the teaching strategy planning module generates a dynamic decision model by means of a large language model, a customized intervention strategy can be generated according to the learning condition and problem root of students, and the multi-agent collaborative reasoning module further improves the intelligent level of the system.