1088results about "Text database browsing/visualisation" patented technology

The invention discloses a text abstract generation method and system based on sparse attention acceleration, and the method comprises the steps: reading long text data, carrying out word segmentation and embedded coding processing, extracting a sequence feature vector, and mapping the sequence feature vector into a query matrix Q, a key matrix K and a value matrix V; constructing an abstract generation network, wherein the abstract generation network comprises a sparse attention calculation module, a feedforward calculation module, a prediction head module and a key value cache module; inputting the query matrix Q, the key matrix K and the value matrix V into an abstract generation network, passing through a backbone network formed by stacking a sparse attention calculation module and a feedforward calculation module for multiple times, processing by a prediction header module, and caching a historical decoding state in real time through a key value caching module to obtain an initial text feature vector; and based on the initial text feature vector, executing an autoregressive decoding process through an abstract generation network, and outputting a final abstract result. According to the method, the decoding process can be accelerated, and the semantic integrity and coherence of the generated abstract are ensured.

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

A method, a system, and a computer program product for generating a benchmarking dataset. One or more queries for generation of one or more summaries of one or more electronic documents are received. The queries are modified using one or more parameters associated with the electronic documents to generate modified queries. The electronic documents are sent to a generative artificial intelligence (AI) model. The generative AI model generates summaries of the electronic documents based on at least one of: the initial queries and the modified queries. One or more labels for the electronic documents are generated using the summaries.

Embodiments of the described technologies determine input signals, where the input signals are specific to a user of the user network. The input signals are input to a set of artificial intelligence (AI) models. In response to the input signals, the first set of AI models output a first set of AI-derived signals relating to the input signals. At least one prompt template is applied to the first set of AI-derived signals to create at least one prompt. The at least one prompt is input to at least one generative AI model. In response to the at least one prompt, the at least one generative AI model outputs at least one thought starter machine-generated by the at least one generative AI model. The at least one thought starter includes digital content configured to be distributed via the user network.

The invention discloses a knowledge document-oriented method for structurally and stably presenting large language model output. The method comprises the following steps of: preprocessing a knowledge document and converting the knowledge document into a text by a system; the back-end service module adopts a prompt word engineering technology based on a preset JSON mode, constructs a structured prompt containing clear instructions and output format constraints, calls a large language model to carry out analysis and information extraction on text contents, and forces the model to generate structured JSON data following the preset mode; the back-end service provides access to the structured JSON data for the front-end application; and the front end executes templated mapping rendering according to a preset user interface component template isomorphic to the JSON mode, and fills the content of each field of the JSON into a corresponding visual component. According to the method, through the cooperation of the back-end constraint prompt word engineering and the front-end modularized template rendering, the defects that the output content format of a large language model is unstable and is difficult to be directly applied to standardized interface presentation are relieved.

The invention discloses a business-adaptive RAG knowledge base rapid question and answer implementation method, relates to the technical field of natural language processing and knowledge base retrieval, and realizes deep fusion and accurate context extraction of fragmented and structured business knowledge by constructing a business enhanced knowledge base and executing multi-stage dynamic retrieval. When an original business document is subjected to intelligent structured analysis and segmentation, a link business entity is identified, rich business attribute metadata is marked, in the process, an unstructured text is converted into an enhanced knowledge fragment carrying clear business semantics, a semantic vector is generated through an embedded model, and on the basis, the semantic vector is subjected to semantic segmentation; according to the method, rapid preliminary screening based on keywords and metadata, fine arrangement based on semantic vectors and association expansion based on a business knowledge graph are sequentially executed, the progressive retrieval strategy ensures that retrieval results are highly related to business contexts, and the accuracy of generated answers is improved.

The invention relates to the field of clinical terms and the technical field of digital coding, and discloses a data processing method for ICD coding recommendation of outpatient medical records, and the method comprises the following steps: carrying out the preprocessing of outpatient medical record texts, including cleaning, segmentation, coding standardization and punctuation standardization; carrying out medical entity identification and assertion identification on the preprocessed medical record text by adopting a medical entity identification model and an assertion identification model, generating structured information containing evidence fragment offset, and analyzing unstructured and spoken contents by carrying out preprocessing and medical entity identification and assertion identification on the medical record text, so as to obtain a pre-processed medical record text; key information of negative expression and time sequence dependence is captured, structured and standardized processing of medical entity information is guaranteed, the code error and missing risks in code recommendation are reduced, and the accuracy of medical quality control and medical insurance settlement is improved.

The invention provides a photovoltaic knowledge question-answering method, system and device based on a ReAct architecture, and relates to the technical field of photovoltaic information. The method comprises the steps that a photovoltaic industry knowledge base is obtained, original photovoltaic industry knowledge documents are processed through analysis, abstract generation and abstract vectorization, and a three-layer storage structure containing an abstract vector index table, a document metadata table and a complete document storage library is formed; obtaining a user question, initializing a ReAct cycle and creating a memory storage structure according to the user question and a retrieval tool set constructed based on the three-layer storage structure; the ReAct circulation is executed until the end, and each round of circulation comprises the steps that questions, system cue words and memory data are integrated to generate a context, a round tool and parameters are obtained through reasoning of a large language model, and accordingly the tool is called to retrieve a knowledge base, update memory and remove duplication; after the loop is terminated, an answer is generated based on the memory data. According to the method, when the photovoltaic problem is solved, the key diagnosis link and the solution can be deeply given out without omitting the key diagnosis link and the solution.

Data from manufacturing is highly uncontextualized and siloed, requiring expert knowledge of context and substantial data pre-processing to support meaningful queries and visualizations. To address this problem, data for a number of sources in a manufacturing context can be retrieved and converted into an intermediate representation in a natural language or near-natural language form, which can in turn be ingested by a generative AI engine, along with suitable prompts by the user to summarize, analyze, and make recommendations based on the data.

Techniques for transcribing and / or summarizing multimedia collaboration sessions are discussed herein. For example, users can communicate within a teleconferencing meeting associated with a channel. In some examples, a first machine learning model may be configured to receive audio-visual data and user interaction data (e.g., selected emojis, detected gestures, messages or text input by a user, a thread of messages, etc.) and output a teleconferencing meeting summary. In some examples, the teleconferencing meeting summary can include one or more highlights, action items, and / or artificial intelligence (AI) notes generated using a machine learning model(s).

Various embodiments of the present disclosure provide prompt engineering and text quality assessment techniques for improving generative text outputs. The techniques include identifying a training cluster for an input document, generating a candidate prompt for a generative machine learning model based on the training cluster and a prompt template, providing the candidate prompt to the generative machine learning model to receive at least a portion of a candidate document, generating a plurality of quality metrics for the candidate prompt based on the candidate document, and selecting the candidate prompt from a plurality of candidate prompts based on the plurality of quality metrics.

The embodiment of the invention provides a context information management method and device based on hierarchical memory, equipment and a medium, and the method comprises the steps: analyzing a task request to generate a sub-task node sequence containing a target description and acceptance function, and arranging a plurality of agents to cooperatively execute a task, and after the audit is passed, a complete execution context is stored in a long-term memory, and a structured abstract is generated and stored in a short-term memory, so that the problem that the context management mechanism of the existing multi-agent system is single is effectively solved, stable maintenance of cross-session cognitive continuity is realized, and the efficiency of the multi-agent system is improved. The method improves the execution reliability of a complex long-process task, optimizes the information storage and retrieval efficiency through hierarchical memory, reduces the risk of semantic deviation accumulation and amplification, clarifies the task target and acceptance standard of each stage, enhances the multi-agent cooperation consistency, and improves the efficiency of task execution. And the execution efficiency and the task completion quality of the intelligent agent system in a complex application scene are comprehensively improved.

The invention discloses a knowledge question-answering method and system based on a large language model and a semantic abstract. The system comprises a tree structure abstract generation subsystem which comprises a knowledge base configuration module, a file management module, a document analysis module and a knowledge block management module and is used for automatically generating tree structure hierarchical knowledge blocks for large manual documents or multi-chapter manual documents; the dual-channel retrieval engine subsystem comprises a question rewriting module, a semantic abstract retrieval module, a detail knowledge block retrieval module, a father-son retrieval module and a prompt word construction module, and all the modules work cooperatively to ensure that semantic abstract knowledge blocks and detail knowledge blocks related to the question of the user are retrieved. According to the method, by introducing the semantic abstract based on the tree structure and the two-channel retrieval, perception of global knowledge and accurate extraction of local fine-grained knowledge can be achieved at the same time, and then the ability of a knowledge base question-answering system in processing the general and inductive problems is improved.

The invention relates to the technical field of literature processing, and discloses a literature review generation method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a research theme input by a user, and forming a reference literature set; according to the number of literatures in the reference literature set, a clustering strategy is selected, a structured review is generated based on a clustering result, and generation of the structured review comprises generation of alternative titles, construction of a multi-level outline, generation of research status paragraphs according to the outline and generation of brief reviews; receiving editing operation of a user on the outline, the research status paragraph and the brief review in the process of generating the structured review, and performing quality evaluation and optimization processing on the research status paragraph and the brief review; and when the current paragraph is researched to be generated, the reference literature is matched and the reference mark is inserted, a standard reference literature list is generated, and a traceability link from the in-text reference to the original literature is established. According to the method, literature review with a rigorous structure and coherent logic can be generated, and disordered stacking of information is avoided.

The invention discloses a marketing copywriting generation method and device and a storage medium, and relates to the technical field of natural language generation, and the method comprises the steps: obtaining a to-be-optimized original marketing copywriting, a structured tag and environment variable information; inputting the original marketing copywriting, the structured tag and the environment variable information into a causal reasoning generation model; in response to a virtual intervention instruction of a user on at least one target intervention label in the structured labels, executing anti-factual reasoning through a causal reasoning generation model, generating an optimized marketing copywriting corresponding to the virtual intervention instruction, and calculating a corresponding predicted interaction amount; and outputting the optimized marketing copywriting and the predicted interaction amount. According to the method, the anti-factual causal reasoning and the large language model generation capability are fused, the user is supported to preview and optimize the copywriting and the interaction effect in real time through the virtual intervention label, multi-strategy low-cost and efficient simulation evaluation is realized on the premise that real delivery is not needed, and the intelligent level, the decision-making efficiency and the result interpretability of copywriting optimization are remarkably improved.

The invention discloses an electronic file content anti-counterfeiting and tampering detection method adopting a locked signature, and belongs to the field of information security. According to the method, file content is automatically segmented according to semantics through a context coding self-adaptive segmentation algorithm, and each segment of content fingerprint is generated by applying a content embedding disturbance weighted digest algorithm. And constructing each segmented abstract into a directed relation chain by adopting an embedded relation chain transformation signature mechanism, and carrying out digital signature overall locking. During detection, the segmented abstract and the relation chain structure are compared again, and precise positioning and tracing of tiny tampering, segmented change and structure adjustment of the text content are achieved. And through a signature duration chain entropy analysis algorithm, performing comparison and evolution path analysis on previous version states, and outputting a detailed traceability report. According to the method, authenticity verification and tampering detection of the electronic file content are greatly improved, and the method is suitable for the fields of data anti-counterfeiting, compliance supervision, electronic evidence management and the like.

The invention provides a data retrieval system and method based on a large language model and a readable storage medium. The system comprises a data analysis module, a multi-mode integration module, a semantic retrieval module and a post-processing module which are arranged in sequence. The data analysis module analyzes the original engineering data to generate structured data; the multi-modal integration module maps the structured data to the same semantic vector space to construct a vector database, and establishes a cross-modal association index to construct a multi-modal engineering knowledge graph; the semantic retrieval module is based on a large language model and a retrieval enhancement technology, responds to a natural language query of a user, retrieves related information from a vector database and a multi-modal engineering knowledge graph and generates answers; the post-processing module performs abstract generation on the natural language answers based on a large language model and generates associated recommendation content. According to the method, the query accuracy, the retrieval efficiency and the user query interaction experience can be remarkably improved; and the scheme reuse rate can be improved based on the recommended content.

The invention discloses a chip data batch structured processing method and device, equipment and a storage medium, and relates to the technical field of chip data management, and the method comprises the steps: analyzing a chip data manual into structured text content through a document processor, and generating a target abstract document corresponding to the chip data manual; constructing an initial chip data table based on chip basic information, extracting and classifying the target abstract document to obtain a target parameter, and constructing a target chip data table based on the target parameter and the initial chip data table; abstract document blocks are extracted based on the target abstract document through a document partitioning device, vectorization processing is conducted on the abstract document blocks, and a target semantic vector is generated and stored in a vector knowledge base. By constructing the chip data table and the vector database, the processing automation degree, the query efficiency and the retrieval flexibility and accuracy of the chip data are improved.

The invention relates to the field of DevOps intelligent execution, in particular to an MCP-based DevOps task intelligent arrangement and execution method and system, and the method comprises the steps: obtaining a tool function of each MCP server to form a global tool library; receiving a natural language task instruction of a user, and inputting the natural language task instruction into the large language model; performing task decomposition on the natural language task instruction to generate a structured tool calling plan; the client side executes the structured tool calling plan and calls target tool functions on the corresponding MCP server in sequence or in parallel through the MCP according to the execution logic and the dependency relationship; monitoring execution of each tool function, determining a subsequent process or triggering a preset rollback operation according to an execution state, and feeding back a final execution result to the large language model; and generating a natural language abstract containing the overall task execution state based on the execution result, and outputting the natural language abstract to a user. And cross-environment seamless operation automation is realized.

The invention relates to the technical field of document intelligent analysis, and discloses a document content self-adaptive analysis method and system based on a large model. The method comprises the following steps: acquiring original data flow of a to-be-analyzed document, wherein the original data flow comprises a text coding sequence, a layout structure mark and a multimedia embedding feature; the data stream is input into a pre-trained multi-modal large model, and a document semantic graph structure, a concept-containing node set, a relation edge weight matrix and a cross-modal alignment index are generated through context sensing analysis; performing dynamic hierarchical clustering on the semantic graph structure to obtain a hierarchical topic tree containing core topic branches, secondary topic branches and leaf node association strength; extracting a document logic framework containing chapter division suggestions, key information positioning coordinates and a cross reference mapping table according to the topic tree; a result is generated based on an adaptive analysis strategy optimization framework, and the strategy adjusts clustering granularity and relation mining depth according to document type features.

The invention belongs to the technical field of artificial intelligence and data analysis, and discloses a data analysis method and system based on multi-layer intention recognition and multiple agents, which remarkably improves the accuracy and robustness of natural language understanding through an innovative multi-layer intention recognition technology and improves the accuracy and robustness of natural language understanding. A common user can perform complex data analysis without mastering professional skills; through a multi-agent cooperative working mechanism, specialized division of labor of different agents is realized, the problem of capability dispersion of a single agent is avoided, and the analysis efficiency and the result quality are remarkably improved; through a real-time streaming processing technology and intelligent visual matching, complete transparency of a code execution process and intelligent recommendation of chart types are realized, and the user experience and the professionality of an analysis result are greatly improved; through an end-to-end integration solution, full-link intelligent processing from data input to visual large screen generation is realized, and a complete data analysis platform is provided for enterprise decision support.

The invention relates to the technical field of knowledge graph question answering, and discloses a knowledge graph fused question answering system which comprises a knowledge graph construction module, a natural language understanding module, a knowledge graph query module and an answer output module which are connected in sequence. The knowledge graph construction module performs knowledge extraction on the unstructured text to obtain triple data to construct a knowledge graph; the natural language understanding module converts a question input by a user in a natural language form into a structured query statement; the knowledge spectrogram query module performs retrieval in a knowledge graph according to the structured query statement and returns knowledge sub-graphs related to the query statement; and the answer output module inputs the knowledge sub-graph into an end-to-end generative model based on a copy network mechanism, generates a natural and smooth text answer and presents the text answer to the user. Through cooperative work of all the modules, efficient knowledge extraction, accurate intention understanding, efficient knowledge retrieval and high-quality answer generation are achieved, and the question and answer requirements of users can be met more efficiently and accurately.

The invention discloses a power grid dispatching report generation method based on natural language processing, relates to the technical field of power grid dispatching report processing, and aims at solving the problems that a traditional report generation mode is low in efficiency and large in subjective deviation. The method comprises the steps that multi-source data are integrated, the data are preprocessed, and multi-dimensional time sequence data are output; generating an event atlas based on the high-dimensional data abstract and the state recognition result; converting the structured data into basic natural language expression, and filling dynamic content; generating a report text; generating a dynamic visual chart and an image-text report based on the generated report text, the event graph and trend data generated by the time sequence analysis model; an image-text report is adopted, the report is divided into key suggestions and detail descriptions, and highlighted high-risk event prompts are generated. According to the method, the report generation efficiency can be improved, subjective deviation in manual analysis is eliminated, and objectivity and specialty of report content are ensured.

Proposed is a method and system for displaying data sets on a user interface. The system may retrieve first, second and third datasets from a plurality of databases. The system may then analyze the datasets to determine anomalies and fraud indicators in the datasets. The system may then generate and display an anomaly alert and a fraud alert respectively on first and second areas of a user interface. The system may display a first set of review data on a third area of the user interface. The system may receive a second set of review data and generate a final audit report on a fourth area of the user interface. The system may display an indication of the final audit report on a fourth area of the user interface such that information contained in the first to fourth areas is simultaneously visible via a same display screen.

The invention provides a large model context processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining a new action step request, updating a context view tree and a source interaction data tree based on the new action step request, and determining a pressure coefficient according to the updated context view tree; according to the pressure coefficient, determining whether a to-be-compressed node exists in the updated context view tree or not; if the to-be-compressed node exists, combining the updated source interaction data tree and the updated context view tree to perform back-to-source compression on the to-be-compressed node so as to update the updated context view tree and the updated source interaction data tree, and returning to the step of determining the pressure coefficient according to the updated context view tree until the to-be-compressed node does not exist; and based on the updated context view tree, expanding the context view tree into context content and inputting the context content into the large language model. By adopting the method, the large language model can accurately understand and respond to the new action step request.

The invention relates to the technical field of software, and discloses a nursing shift change system based on homologous heterogeneous data fusion and a large language model, and the system comprises a shift change generation module which comprises a first generation unit and a second generation unit, calling a large language model to generate global summary information of nursing shift change; and the second generation unit is used for calling a large language model to generate corresponding detailed handover information only based on the original data of the key event. And the report synthesis module is used for integrating the global summary information and the detailed handover information according to a preset structure to generate a nursing handover report. According to the method, the panoramic data view of the patient is constructed based on the unified time axis, so that structured data and unstructured data which are originally dispersed in different systems can be organized and called in the same semantic space, and a consistent data basis is provided for subsequent data analysis and processing.

The invention belongs to the technical field of natural language processing and power system intelligent information processing. Comprising the following steps: S1, preparing data; s2, performing named entity recognition and relation extraction on the text block by utilizing a pre-trained large language model; s3, constructing a knowledge graph in the urban power distribution field by using the triple summary; s4, generating a semantic abstract with a hierarchical structure for each theme community; s5, querying and retrieving: retrieving related information from the vector index and the knowledge graph in parallel by receiving a natural language query of a user, and fusing results into a context knowledge set; and S6, an answer generation step: inputting the query and the context knowledge set thereof into a large language model, and generating an answer which is coherent in semantics, accurate in facts and covers query requirements through a preset prompt strategy guide model. According to the method, the accuracy and practicability of the question-answering system are remarkably improved, and core technical support is provided for intelligent operation and maintenance of the power distribution network.

A system generates a first agent for the first persona based on both a first plurality of changes and first source data. The system determines a first plurality of vector embeddings for the first agent based on the first source data and determines a second plurality of vector embeddings based on second source data. The system determines a similarity value based on the first plurality of vector embeddings and the second plurality of vector embeddings. Based on the similarity value, the system determines that the first source data is associated with a second persona different from the first persona. The system generates a second agent for the second persona based on the first source data.

The invention relates to the technical field of railway four-power operation and maintenance, provides a railway four-power operation and maintenance knowledge intelligent question-answering method and system based on a large language model, and solves the problems of low logic accuracy and poor field applicability of an operation and maintenance question-answering result. The method comprises the following steps: acquiring a question and answer request containing a device identifier, position data and a question text; performing syntactic analysis on the problem text to obtain a target equipment entity and an operation intention, positioning graph nodes in the railway four-electric interlocking relation knowledge graph according to the equipment identifier, performing graph traversal, obtaining associated equipment entities, an electrical connection relation and interlocking logic information, and combining the associated equipment entities, the electrical connection relation and the interlocking logic information into a regulation constraint condition set; performing vector retrieval based on the equipment identifier and the position data to obtain an operation and maintenance knowledge fragment; and inputting the regulation constraint condition set and the operation and maintenance knowledge fragment into the large language model, and carrying out logic constraint on the generation process according to the regulation constraint condition set to generate an intelligent question and answer result text. The logic accuracy and field applicability of the operation and maintenance question and answer result are improved.

Text generation prompts may be determined based on an input document and a text generation prompt template. The text generation prompts may include text from the input document and questions related to the text. The text generation prompts may be sent to a remote text generation modeling system, which may respond with text generation prompt response messages including novel text portions generated by a text generation model. The text generation prompt response messages may be parsed to generate answers corresponding with the questions.