18 results about "Knowledge services" patented technology

The application discloses a syntax robustness reinforcement training method for large language model sequence knowledge reasoning, comprising the following steps: firstly, sequence knowledge question and answer samples are acquired, and question samples with different syntax structures but semantic equivalence are constructed for the same sequence knowledge unit to form training data containing original questions, synonymous rewritten questions and standard answers; secondly, the large language model is trained in the first stage based on the training data, the direct answering ability of the original question to the standard answer is learned through multi-task supervision fine tuning, and a small amount of synonymous rewritten auxiliary samples are used to make the model preliminarily adapt to the compound output structure of "synonymous rewriting-answer"; thirdly, a group relative strategy optimization algorithm is used in the second stage, and a composite reward function composed of answer correctness reward, semantic maintenance reward and format reward is combined to optimize the "synonymous rewriting-answer" track generated by the model through reinforcement learning; finally, a lightweight direct answer interface alignment is performed to make the model converge to the final answering interface of "input original question-direct output answer". The application is suitable for sequence knowledge question and answer, time sequence question and answer, process reasoning, hierarchical relationship judgment, education question and answer and enterprise knowledge service scenes, and can improve the answering accuracy and consistency of the large language model under the condition of multiple syntax variants.

This invention proposes a method and system for semantic enhancement of data assets based on knowledge graphs and large-scale models. The method includes: performing lexical segmentation and functional classification on the natural language query input by the user; generating context-aware word-level semantic representations using a context-aware semantic encoder and aggregating them to obtain query-level semantic representations; combining the functional classification results with the query semantics, constructing a structural path generation request in the knowledge graph; performing path search and scoring to filter target paths; when a path is missing or the score is insufficient, locating semantically weak links and generating structural completion suggestions; finally, generating a response based on valid paths or completion suggestions, and writing the suggestions into the candidate enhancement area of ​​the knowledge graph when conditions are met. This invention improves the semantic understanding accuracy and graph interaction capabilities of natural language queries in data asset management scenarios, supporting interpretable and evolvable knowledge services.

The application discloses a policy knowledge graph construction method for infrastructure field, and relates to the technical field of knowledge graph, which comprises the following steps: S1, knowledge modeling is performed on the policy knowledge graph of the infrastructure field, and entity types, entity attributes and entity relationships are determined; S2, provincial and ministerial infrastructure policy provisions are collected and preprocessed to obtain an initial data set; S3, an initial keyword library of the infrastructure field is constructed, the initial keyword set and historical infrastructure policy texts are input into a large model, the initial keyword library is expanded, and then the infrastructure policy texts are classified; S4, the classified infrastructure policy texts are jointly extracted by using a pretraining language model to obtain structured knowledge; and S5, a Neo4j graph database is used to construct an infrastructure policy knowledge storage system, and the structured knowledge is stored in association. The method realizes automatic collection, structured analysis, associated mining and dynamic updating of multi-source policies, and provides accurate and timely policy knowledge services for enterprises.

This invention discloses a method, system, and computer-readable storage medium for real-time personalized content generation, relating to the field of artificial intelligence content generation technology. It constructs and continuously updates a real-time state base for interactive objects, forming a controlled context snapshot of the current generation round based on this base. It jointly determines the target content's path location, single-generation granularity, whether an intervention action is triggered, the verification dimension after the current round's output, and the verification triggering conditions. The target content for the current round is then generated. After output, process feedback and task progress results are collected, and the verification results are combined to determine whether to continue generation, correct generation, downgrade output, switch paths, or switch interventions. The process feedback, task progress results, verification results, and corresponding adjustment results are written back to the real-time state base for use in subsequent rounds. This invention is applicable to learning, training, knowledge services, interest and skills guidance, task prompts, and other scenarios where content can be generated in real-time by artificial intelligence.

The application discloses a kind of publishing field knowledge service construction methods based on knowledge graph, it is related to intelligent digital publishing technical field, including, according to publishing content structure node set, constructs publishing content structure diagram, and in combination with publishing original resource set executes publishing field semantic information extraction and semantic correlation analysis, obtains publishing semantic diagram;From the structural distribution characteristics and content anchoring characteristics of semantic node in publishing resource in publishing semantic diagram are extracted, and the anchor point edge between publishing semantic diagram and publishing content structure diagram is established, and the multi-granularity anchor point double diagram is formed;Based on graph service execution plan, execute double diagram collaborative retrieval and service path generation, obtain knowledge service candidate set, and carry out service ordering and arrangement, generate publishing field knowledge service content.The application realizes the collaborative mapping of semantic relationship and content bearing position, enhances knowledge organization precision and service direction.

The application relates to the technical field of artificial intelligence, in particular to a question and answer method and system based on LLM fusion knowledge graph and retrieval enhancement generation, which takes a document set as input, extracts corresponding local knowledge graphs from the document set by means of a large language model, marks entities connected with known entities and not appearing in the local knowledge graphs as related entities through graph structure connection relationships, forms new local knowledge graphs, and sorts the connected components in descending order of edges and weights, and when the weights are the same, sorts the entities in dictionary order as a second sorting key, and after sorting, the final answer is generated by inputting the obtained documents and questions into a large language model. The application can effectively improve the accuracy and explainability of the question and answer, solve the illusion problem of the large language model, and provide reliable knowledge service support for complex decision-making scenarios.

The application relates to the technical field of electric digital data processing, in particular to a knowledge service platform for inheritance and popularization of folk bone-setting prescriptions, corresponding steps comprising: constructing core nodes and basic topological structures of a semantic field based on a standard knowledge system; constructing a mapping relationship between text and nodes and an extended node to obtain a semantic association path in the basic topological structure according to the similarity between included semantic fragments and the core nodes; constructing a relationship edge in the semantic association path based on the co-occurrence relationship between the included semantic fragments, determining the weight value of the relationship edge based on the source of the relationship edge, and obtaining a target semantic field; screening candidate nodes to obtain an archived knowledge subgraph based on the candidate node set of the unrecorded semantic fragments in the target semantic field, the relationship edges between the candidate nodes and the weight values of the relationship edges, so as to archive folk bone-setting prescription knowledge; and the technical scheme provides reliable technical support for systematic management, accurate inheritance and popularization and value reservation of the folk bone-setting prescriptions.

This invention discloses a method for constructing a knowledge base in the field of early childhood education that integrates knowledge graphs and large language models, belonging to the field of early childhood education method optimization technology. It involves collecting and cleaning preprocessing early childhood education data from multiple channels; extracting entities and relationships using a customized model to construct a structured knowledge graph; fine-tuning the large language model for the early childhood education domain; integrating knowledge from the knowledge graph and model through a gating mechanism with double verification; establishing a monthly update mechanism to achieve incremental learning; furthermore, it achieves personalized knowledge recommendations based on user profiles, optimizes interactive question answering through a combination of retrieval and models, and ensures data security through anonymization and access control. This invention, through the integration of multi-source data and intelligent technologies, constructs a precise and dynamic early childhood education knowledge base, more accurately understands children's needs, generates content that aligns with their cognitive development, achieves personalized learning recommendations, improves the interactive experience and knowledge service quality, while ensuring data security and promoting the digital upgrade of the early childhood education field.

This invention relates to an intelligent research and study question-and-answer method and system based on a three-layer knowledge base and multi-layer retrieval enhancement, belonging to the fields of artificial intelligence, natural language processing, and scientific research knowledge service technology. The method includes: acquiring multi-source data from a research team; performing text extraction, OCR recognition, content cleaning, chapter recognition, and text segmentation on the multi-source data to obtain standardized text data; constructing a three-layer knowledge base based on the standardized text data, consisting of a summary index layer, a structured information layer, and a text block layer; querying and rewriting user research and study questions and recalling multi-layer candidate evidence, and obtaining a final context set through deduplication and re-sorting; generating research and study question-and-answer results based on user questions, the final context set, and evidence source information, and outputting source traceability information. This invention can improve the efficiency of scientific research data retrieval, question-and-answer generation, and knowledge reuse, and enhance the accuracy and traceability of question-and-answer results.

The application belongs to the technical field of knowledge product construction, and particularly relates to a knowledge service platform monograph construction method, which comprises the following steps: step one, document demand change sensing: analyzing user search logs of the knowledge service platform to sense changes in document demand of the user; step two, document demand content analysis: after judging that the user's document demand has changed, analyzing documents that have interacted with the knowledge service platform and the user within a demand change interval, mining core content of the user's document demand within the demand change interval, and providing topics and initial query words of the topics for monograph construction; step three, document query word expansion: performing semantic expansion on the initial query words of the topics to obtain an expanded query word sequence; and step four, monograph construction: performing semantic search on the knowledge service platform by using the multiple groups of expanded query word sequences of the topics to construct a monograph from obtained documents.

The application discloses a power plant safety knowledge question and answer method and system based on a large language model, which comprises the following steps: receiving a power plant safety-related question request input by a user, calling a corresponding knowledge source according to the type of the question, wherein a vector library knowledge fragment is searched for a rule query type question, a vector library and a knowledge graph subgraph are jointly searched for an accident disposal type question, and a vector library and real-time operation and maintenance data are fused for an equipment operation and maintenance type question; inputting the question and the searched knowledge into a fine-tuned power plant field large language model to generate a reply, and outputting the reply after verifying the accuracy of the reply through a field rule library, so that the accuracy and timeliness of safety knowledge service can be improved.

This invention relates to the field of agricultural information technology, specifically to a method for multi-source heterogeneous breeding data fusion and knowledge graph construction. The method includes: collecting heterogeneous raw data containing genotypes, phenotypes, environments, pedigrees, and management logs; performing structured transformation and semantic annotation to generate standardized data objects; based on a pre-defined breeding domain ontology model, performing entity recognition and relation extraction on the standardized data to form an initial set of knowledge triples containing entities, relations, and attributes; calling a knowledge fusion engine to perform conflict detection and resolution on this set, achieving entity disambiguation, relation normalization, and assigning confidence weights to generate a unified fused breeding knowledge graph; and querying, reasoning, and visualizing the graph according to business needs to generate knowledge service interfaces for specific breeding objectives. This invention improves the automation level, semantic accuracy, and consistency of fusion results in breeding knowledge construction.

This invention discloses an intelligent product cognitive modeling and multilingual document generation and verification system and method, belonging to the fields of knowledge engineering and natural language processing. The system adopts a three-layer architecture of "knowledge planning layer + knowledge production layer + knowledge service layer," and also includes two core components: "workflow engine + testing framework." The knowledge planning layer is used to construct a meta-knowledge system and quantitatively evaluate its completeness through coverage metrics; the knowledge production layer is used to perform document generation, terminology standardization, structure optimization, fact verification, and automated testing; the knowledge service layer is used to perform multilingual release, version archiving, and feedback collection; the workflow engine performs intelligent scheduling, using task priority sorting to form a closed-loop processing flow; the testing framework is used to generate execution evidence and calculate hash digests for audit trails. This invention improves document accuracy from unverifiable to 100% observable, achieves knowledge coverage of user intent of 1, and increases development efficiency by 18 times.

This invention relates to a reliable question-answering method for archives based on graph context retrieval and knowledge graph enhancement, belonging to the fields of computer software and artificial intelligence. This invention utilizes a multimodal large-scale language model (MLLM) to extract key entities and relationships between entities from historical archives, and automatically identifies low-confidence fields to generate tags requiring manual review. This reduces the workload of manual review while ensuring the accuracy of information extraction. Then, based on a given knowledge base, it retrieves previous related questions and answers according to the query question to enhance the context. Finally, it combines the information extracted from historical archives with the large-scale language model (MLLM) to generate a reliable answer. This invention significantly improves processing efficiency and knowledge service capabilities while maintaining the value of archival documents.

This invention discloses an intelligent optimization management method for feature data mining and extraction in digital archives, belonging to the field of digital information processing technology. It effectively overcomes the inherent defects of traditional digital archive processing, such as fixed and rigid processes, low processing accuracy, missing associations, and inability to evolve, by constructing an optimization management framework integrating intelligent perception, multi-engine parallel processing verification, multi-modal feature fusion, and knowledge graph mining. This significantly improves the accuracy and depth of archive information extraction, achieves intelligent and adaptive processing, automatically constructs semantic associations and knowledge networks between archives, and provides intelligent retrieval services based on semantic understanding and graph association. It achieves a leap from "single keyword matching" to "intent understanding and related recommendation," thereby completing a leapfrog upgrade of digital archive management from "digital storage" to "intelligent knowledge service."

The present disclosure belongs to the technical field of nuclear power, and particularly relates to an intelligent knowledge service system and method. The system provided by the present disclosure is composed of three core components, namely, knowledge service, a scientific research industry large model technology engine, a knowledge labeling service platform and a large model landing solution module for a specific field, aiming to create a knowledge-enhanced large model that can be integrated into an industry production system, and to provide private deployment services, explore, research and realize the commercialization of AI in the field of knowledge service and scientific research, promote the intelligent upgrading of knowledge service, and promote the innovation and development of scientific research.