2711results about "Text database browsing/visualisation" patented technology

The invention discloses a large model prompt project optimization system and method fusing a domain knowledge graph. The system comprises an analysis module, a template generation engine module, a large model interaction interface module, a feedback analysis module and an optimization strategy module. And the analysis module forms a constraint coding signal containing an entity attribute incidence matrix. The template generation engine module forms an enhanced prompt text stream with a reservoir physical property parameter slot; the large model interaction interface module receives the enhanced prompt text stream and generates a question and answer response data stream containing geological terminologies; the feedback analysis module forms a feedback signal containing semantic deviation measurement through a semantic error vector calculation algorithm; and the optimization strategy module forms a parameter optimization instruction signal and transmits the parameter optimization instruction signal to the analysis module to complete iterative updating of the constraint condition. According to the large model prompt project optimization system fusing the domain knowledge graph, the problem of low answer accuracy of a large model in the oil-gas exploration field due to lack of professional constraints can be solved.

A knowledge management system may generate a plurality of prompts based on divisions of documents of unstructured text, each prompt relevant to a division of unstructured text. At least one prompt is generated such that a corresponding division of unstructured text is a response to said at least one prompt. The system may generate prompt embeddings for the plurality of prompts corresponding to the plurality of documents of unstructured text. The system may generate prompt-embedding clusters to group similar prompts from one or more documents of unstructured text. The system may receive a query. The system may convert the query to one or more query embeddings. The system may identify one or more prompts that are relevant to the query based on comparing the one or more query embeddings to the prompt embeddings. The system may identify one or more documents in one or more prompt-embedding clusters.

A method of performing data analysis according to a natural language query from a user includes: receiving, from a user terminal, a user input including the natural language query requesting the data analysis; determining, based on the user input, at least one database among a plurality of databases as a target database; generating a prompt based on the user input and the target database; inputting the prompt into a code generation model to obtain a structured query (SQL) statement; executing the SQL statement to generate a result of the data analysis on the target database; and transmitting the result of the data analysis to the user terminal, where the result of the data analysis is displayed on a screen of the user terminal.

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 belongs to the technical field of knowledge visualization, and discloses a traditional Chinese medicine acupuncture knowledge visual display system based on a knowledge graph. Comprising the steps that a traditional Chinese medicine ancient book data acquisition unit, a clinical case data acquisition unit and an ultrasonic image data acquisition unit acquire data and integrate the data into traditional Chinese medicine acupuncture data, and the traditional Chinese medicine acupuncture data is preprocessed to obtain perfect traditional Chinese medicine acupuncture data; constructing a knowledge graph based on the perfect traditional Chinese medicine acupuncture data; constructing an optimization network model to optimize the knowledge graph to obtain a high-quality knowledge graph; constructing an incremental learning framework, updating the high-quality knowledge graph based on the incremental learning framework, and outputting a real-time updated knowledge graph; constructing an interactive three-dimensional human body model based on the real-time updated knowledge graph; based on the real-time updated knowledge graph and the interactive three-dimensional human body model, outputting an executable medical strategy for the application case, and sending the executable medical strategy to the student terminal; and the teaching quality is improved while knowledge visualization is realized.

This disclosure describes utilizing a causal query system to determine causal outcomes for domain-specific causal queries using a framework that includes causal graphs for targeted domains, a large generative model (LGM), and other models or systems. In various implementations, the causal query system provides a framework that includes generating domain-specific causal graphs, encoding or mapping the causal graphs with local data values, and using the encoded causal graphs to determine causal outcomes to causal queries. In some implementations, the causal query system uses the LGM and data resources (e.g., external sources) to populate missing values of an embedded causal graph before using the causal graph to determine causal outcomes.

The invention discloses a law shield AI intelligent case handling all-in-one machine system based on a large language model, and relates to the technical field of law artificial intelligence and judicial informatization, the system comprises an integrated terminal device, a case semantic modeling module, a class case knowledge engine, a risk prediction module, a large language model service interface and an intelligent document generation module; the system constructs a case semantic graph through multi-modal information fusion and a graph neural network, performs legal rule path matching and similarity reasoning based on a class case database, outputs structured legal suggestions and standard legal instruments in combination with user context recognition and large language model multi-round generation capability, and realizes dynamic updating of the semantic graph. The method improves the automation, structuring and interpretability of case processing, and is suitable for intelligent case handling scenes such as legal assistance, litigation assistance and judicial mediation.

A device may receive, at a server system, a free-form query. The device may select two-way communication history records from a database associated with the free-form query to identify selected history records. The device may generate individual summaries of individual history records of the selected history records by processing a set of corresponding individual history records using a chunking algorithm, constructing language responses from outputs of the chunking algorithm using a large language model, and aggregating the language responses. The device may process the language responses using the large language model to generate an individual summary for corresponding individual history records.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, cultural research and the like, and discloses a multi-modal abstract generation and output method, which comprises the following steps of: acquiring target domain data and cleaning to obtain structured data; extracting entities and association relationships among the entities from the structured data, and constructing a target domain knowledge graph; generating a text abstract containing entity associated information based on the target domain knowledge graph; matching the image data and the audio data corresponding to the text abstract, and integrating the text abstract, the image data and the audio data to generate a multi-modal abstract; and constructing a user portrait according to the user behavior data, and outputting the multi-modal abstract based on the user portrait. According to the method, extraction and analysis of entities and association relationships thereof are optimized through knowledge graph construction; through multi-modal fusion, associated display of texts, images and voices is realized; and through user portrait optimization output, the efficiency and adaptability of knowledge summarization are improved.

A computer-implemented method executed using one or more processors of an incident management system, the computer-implemented method comprising accessing one or more data sets of information associated with an incident event corresponding to an incident associated with a computer system; generating a prompt based on the one or more data sets of information, wherein generating the prompt comprises generating a plurality of sub-prompts to be provided to a machine-learning model for generating a report of the incident event in accordance with a predetermined criteria; inputting the prompt into a machine-learning model that has been trained to generate a report of the incident event based on the prompt; outputting, by the machine-learning model, the report of the incident event, wherein the report comprises an analysis of the incident event; transmitting the report to one or more computing devices associated with the computer system.

The invention discloses a BIM model structure design review method and system based on a large model, and relates to the technical field of building information modeling, and the method comprises the steps: carrying out BIM model data preprocessing based on an AI large model; adopting an AI large model built-in rule engine to perform structural design specification review on the BIM model, and automatically generating a review report based on a natural language processing technology; bIM model collision detection is executed based on a deep learning algorithm, potential structure conflicts are identified, error points are displayed in combination with three-dimensional visualization, and adjustment suggestions are provided according to historical data and an optimization algorithm. According to the method, the data quality and consistency are improved, key information in the BIM model is clearer and more concise, the accuracy of the review process is improved, potential problems in the structural design process can be found and processed in time, the overall intelligent level of the BIM model is improved, and the building design process is more accurate, intelligent and efficient.

The invention discloses a financial branch mechanism-oriented modular knowledge graph and retrieval enhanced large model fusion interaction method and system. The method comprises the steps that a query vector of a target object is obtained, and the query vector comprises a problem vector used for expressing the query requirement of the target object and a post vector used for reflecting post information of the target object in a financial branch; determining a system module which can be accessed by the target object according to the post vector; determining a sub-graph region corresponding to the problem vector in the knowledge graph; and retrieving a document paragraph corresponding to the sub-graph region in a sub-index database corresponding to the system module, and inputting the document paragraph into the large model to generate a target answer corresponding to the query vector. According to the method and the device, the technical problem that a question-answering system adopted in the related technology is lack of identification of user post identities and cannot provide matched answers according to different post responsibilities and permissions of financial branches is solved.

A method and related system for generating document embeddings within an embedding space based on a set of structured documents by determining (i) a first vector based on a first segment of a first document and (ii) a second vector based on a second segment of the first document and updating association vectors indicating the second segment based on a distance between the first and second vectors. The method also includes generating a document embedding based on the association vectors, generating a candidate vector based on a candidate document, and determining a result indicating that a second distance between the candidate vector and a first document embedding satisfies a document embedding distance threshold. The method may also include generating a new document by providing, to a text generation model, a portion of the candidate document and a portion of the second segment of the first document.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses an intelligent correction method and device based on semantic analysis, equipment and a medium. The method comprises the following steps: extracting a structured document content from an explanation data source, extracting an auxiliary explanation text corresponding to each unit in the structured document content from the explanation data source, carrying out semantic understanding processing on the structured document content and the auxiliary explanation texts by utilizing a semantic analysis module to generate a semantic analysis result, and executing a correction operation through an intelligent correction module based on the semantic analysis result. And after the correction operation is completed, the structured document content and the auxiliary explanation text are input into a format reconstruction module, and a specification-combined document is generated and output. According to the method, semantic understanding is carried out by combining structured document content and the auxiliary explanation text, and an automatic and standardized document compliance processing flow is realized based on intelligent correction and format reconstruction driven by a compliance semantic analysis result.

A system includes a plurality of nodes. Each of the plurality of nodes includes memory; a network interface; and a hardware processor coupled with the memory and the network interface. The system further includes a computing device. The computing device includes memory storing a master key, security keys encrypted using the master key, and associations between the security keys and the nodes. The computing device further includes a network interface and a hardware processor coupled with the memory and the network interface. The processor is configured to receive, from a node of the one or more nodes, a request for a security key of the one or more security keys, identify an association between the node and the security key within the associations, decrypt the security key using the master key to generate a decrypted security key, and communicate the decrypted security key to the node.

The invention discloses an accurate international communication digital human real-time dialogue method fused with a multi-modal technology. The method comprises the following steps: S1, constructing a digital human image and tone; s2, propagation content generation and problem guidance; s3, semantic analysis and intention clarification based on the real-time voice dialogue; s4, geographic preference modeling and path planning; s5, cross-context propagation content generated based on retrieval enhancement is generated; s6, visually displaying the propagation content; and S7, carrying out digital human-driven multi-language propagation content real-time output and feedback closed-loop optimization. Through accurate utterance expression analysis, accurate international propagation problem recommendation is provided, cross-context propagation content generation based on semantic understanding is realized, digital people with voice features and visual images are constructed, real-time dialogue interaction of users is realized, and user experience is improved. The system can carry out geographic modeling according to the region where the accurate problem is located, language preference and propagation object culture characteristics, and differential propagation path planning is achieved.

A system receives a plurality of scientific documents for inclusion in an extended knowledge graph. For each respective scientific document of the plurality of scientific documents, the system classifies the respective scientific document with a theoretical framework of a plurality of theoretical frameworks each comprising terms and principles associated with a particular scientific topic, extracts metainformation of the respective scientific document, structures the metainformation in a document-specific ontology model that further comprises an indication of the theoretical framework, generates a plurality of text chunks from the respective scientific document of a given size, and generates, using a first ML model, one or more concepts from each of the plurality of text chunks. The system generates, using a second ML model, the extended knowledge graph using each of the plurality of text chunks, each concept, and the metainformation, and stores the extended knowledge graph in a graph document database.

The invention relates to the technical field of multi-modal large language models, in particular to a government affair service content navigation method and system based on a large language model, and the method comprises the following steps: receiving multi-modal information input by a user through texts, voices or pictures; the voice is converted into a text, and character information in the picture is analyzed by using an OCR (Optical Character Recognition) technology; fusing multi-modal data, and inputting the fused multi-modal data into a large language model for semantic understanding and context association analysis; matching items are retrieved in combination with a local government affair knowledge base, and an initial recommendation list is generated; the recommendation result is displayed through the intelligent assistant, and interaction optimization options are provided; the method has the beneficial effects that the current government affair service content navigation mode is optimized through the multi-modal capability, the retrieval enhancement capability and the content generation capability of the large language model, so that the navigation is more modal and more intelligent, and meanwhile, the privacy and authority of data reply are ensured.

A query answering system may access a collection of data sources to populate an index. A query answering system derives content from a collection of data sources to create synthetic chunks that are each representative of a portion of content from one or more of the data sources. A query answering system populates the index with the synthetic chunks. A query answering system identifies a subset of the synthetic chunks as relevant to a user query, generates a large language model (LLM) prompt that includes the subset of the synthetic chunks from the index and the user query, provides the LLM prompt to an LLM., and generates a response to the user query based on output of the LLM.

The invention discloses a declarative knowledge construction method and device for semantic and situational knowledge collaborative modeling, computer equipment and a readable storage medium, and relates to the field of data processing.The method comprises the steps that firstly, a multi-modal document is analyzed, a chapter abstract is extracted, and structured content is obtained; entities, events and multi-modal knowledge points are extracted from the structured content, and cross-modal fusion is carried out on the entities, the events and the multi-modal knowledge points; carrying out anaphora resolution based on the fused knowledge points, and constructing a double atlas containing a knowledge atlas, a affair atlas and a four-dimensional relation triple; clustering the double maps to obtain a theme community, and performing association mapping on the community, the triple and the entity event, the chapter abstract and the multi-modal knowledge point to form association knowledge; vectorizing the associated knowledge and establishing a vector knowledge index; and carrying out compression ratio and accuracy evaluation on the knowledge through an evaluation system, and feeding back and optimizing the whole knowledge construction process. According to the method, multi-modal knowledge deep fusion and semantic scene collaborative modeling are realized, and the knowledge structuring degree and the application reliability are improved.

The invention discloses a medical health question and answer method and device based on an intelligent agent and a medium, and relates to the technical field of artificial intelligence medical question and answer, and the method comprises the steps: receiving an intention analysis report through a master control intelligent agent, accessing a dynamic dialogue context pool, and generating an intelligent agent cooperation instruction according to the state of the intention analysis report and the state of the dynamic dialogue context pool; performing credibility evaluation on the preliminary medical answer report by using a credibility calibration agent, generating a confidence score and an evidence conflict level, and performing multi-dimensional weighted fusion and risk mode recognition by using a dynamic risk evaluation strategy to generate a diversified disposal instruction; and when the diversified treatment instruction is issuing permission, performing safety compliance check on the preliminary medical answer report to generate compliance medical answers. According to the invention, multi-dimensional control of credibility and security of medical answers is realized, and finally the beneficial effects of providing personalized medical questions and answers and ensuring that information is real, reliable, compliant and safe are achieved.

The embodiment of the invention provides a credible traceability presentation file generation method and system based on document content. The method comprises the steps of obtaining a to-be-processed document input by a user; performing semantic recognition on the content information of the to-be-processed document through the hierarchical semantic segmentation model, and performing information slicing on the to-be-processed document according to a semantic recognition result to obtain a content structure tree; the content structure tree comprises document content slices matched with the key content information, a logical relationship between the document content slices and traceable information corresponding to the document content slices; and mapping the document content slices and the traceable information in the content structure tree into corresponding presentation file contents according to a logical relationship by adopting a structure tree-based visual mapping engine model to obtain a target presentation file, and displaying the target presentation file to a user. The method can improve the generation efficiency of the presentation file, optimizes the working process, and assists in improving the working efficiency of related post personnel.

Implementations relate to generating an external entity-summarization database to store summarized descriptions for feature(s) of entities (e.g., brand A laptop, brand B laptop, brand C SUV, etc.) that belong to one or more entity classes (e.g., laptop, SUV) using a first generative model. Implementations relate to utilizing the external entity-summarization database in generating a response responsive to a user query seeking information (e.g., recommendation of one or more entities) of a particular entity class. For instance, a summarized description for features of one or more entities that belong to the particular entity class can be identified based on querying the external entity-summarization database using a query-based embedding generated for the user query. The summarized description can be used to generate the response responsive to the user query.

The invention discloses a cross-domain heterogeneous data query system and method based on a large model and a knowledge graph, belongs to the technical field of information retrieval, and aims to solve the technical problem of complex relation reasoning in cross-domain heterogeneous data query. Comprising a data input and preprocessing module used for collecting multi-modal data to obtain feature vectors; the knowledge graph construction and management module is used for constructing a knowledge graph and providing query service based on the knowledge graph; the bidirectional enhancement module is used for writing the reasoning result of the large language model into a knowledge graph and carrying out version management; the domain adaptation layer is used for carrying out model training on the large language model based on a lightweight adapter and a domain adaptation mechanism; the real-time query and reasoning module verifies and supplements the candidate answers based on a knowledge graph to generate an initial answer, and explains a reasoning path based on a causal reasoning network to generate a final answer; and the interpretability and transparency module is used for displaying knowledge in the knowledge graph through a visual interface and providing auditing service based on the operation day.

The invention discloses a task planning method and device for a large language model, a storage medium and equipment. The method comprises the steps that firstly, a target field to which a target task text belongs is determined; then, M text blocks similar to the target task text are screened out from the knowledge document of the target domain; inputting the key information of the M text blocks into a preset large language model in combination with the first prompt on the basis of a multi-dimensional prompt text abstract generation mode to obtain a text abstract of the M text blocks, and inputting the text abstract of the M text blocks into the large language model in combination with the second prompt to obtain N subtasks corresponding to the target text. According to a pre-constructed tool description mapping relation, tools for executing the N sub-tasks are called through a large language model, and processing results of the N sub-tasks are obtained by executing the tools so as to form a final processing result of the target task. Therefore, the overall efficiency and quality of executing the target task by the large language model are improved.

A system and method for vector embedding of service incident data is described. In one aspect, a computer-implemented method comprising receiving service incident data includes free-form text data, structured metadata, and human-generated comments, constructing a graph representation of a service incident, the graph includes nodes representing the free-form text data, structured metadata, and human-generated comments of the service incident, and edges connecting related nodes, generating vector embeddings for the nodes and edges of the graph representation, applying dimensionality reduction to the vector embeddings to generate reduced embeddings, and storing the reduced embeddings and the vector embeddings in a database.

The invention provides an AI auxiliary reading method and system based on dynamic partitioning knowledge classification self-verification, and relates to the technical field of artificial intelligence, the method comprises the following steps: carrying out dynamic semantic partitioning on a super-long text to obtain a plurality of initial text units with consistent internal semantics; performing topic model construction according to the initial text unit and a layered Dirichlet process to obtain a topic distribution tag; receiving a user query request, and extracting a retrieval text set according to the hierarchical retrieval strategy and a retrieval text corresponding to the user query request; inputting the constraint cue word extracted by the topic distribution tag corresponding to the retrieval text set, the retrieval text set and the retrieval text into a text generation model to output a to-be-calibrated text; if the error scoring function of the to-be-calibrated text and any initial text unit in the retrieval text set is smaller than a preset error threshold value, the to-be-calibrated text serves as a credible text to be output to a user. According to the invention, the credibility and accuracy of the answer can be enhanced.

The invention discloses an RAG-based multi-round dialogue generation method and device and a medium, and the method comprises the steps: determining a current dialogue question as a to-be-queried dialogue question when the current dialogue question is the first round of question and answer; when the question answering is not the first round of question answering, judging whether semantic rewriting is carried out on the current dialogue question or not; when semantic rewriting is needed, performing semantic rewriting on the current dialogue problem according to historical dialogue problems before the current dialogue problem, and determining the semantic rewritten dialogue problem as a to-be-queried dialogue problem; when semantic rewriting is not needed, determining the current dialogue question as a to-be-queried dialogue question; when semantic rewriting is not needed, representing that the current dialogue question and the historical dialogue question belong to topics in different topic directions; and in the vector database, performing keyword retrieval and vector retrieval on the to-be-queried dialogue question to obtain a reference so as to generate answer information of the current dialogue question according to a preset large language model. And the multi-round dialogue generation accuracy is improved.

The invention relates to a nurse handover management system and method, and relates to the technical field of data processing, and the system comprises a real-time monitoring module which is used for constructing an individualized baseline database of a patient; the data integration module is used for outputting a multi-dimensional structured data set; the intelligent analysis engine is used for identifying clinical state change information of the patient and generating a corresponding risk early warning score; the decision support module is used for generating targeted nursing intervention suggestions; the interactive shift exchange platform is used for generating a structured shift exchange abstract based on the risk early warning score, the clinical change information and the nursing intervention suggestion, and dynamically displaying the structured shift exchange abstract through a mixed reality technology; and the closed-loop optimization module is used for receiving shift-change feedback information and subsequent execution data of the shift-change party, evaluating the completeness of shift-change information, updating a nursing plan, and optimizing and adjusting analysis and recommendation parameters of the system. The system can improve the accuracy and efficiency of nurse handover management.