42 results about "Knowledge generation" patented technology

The invention discloses an ancient poetry multi-mode teaching system based on HSK classification, and belongs to the field of intelligent teaching. Comprising a learning evaluation portrait module, a learning path generation module, a physiological emotion monitoring module, a cognitive load analysis module, a vocabulary grammar enhancement module, a modal semantic analysis module, an artistic conception analogy generation module, a holographic scene generation module, a feedback interaction experience module, a content dynamic adjustment module, a recitation pronunciation correction module and an expansion knowledge generation module. A performance analysis feedback module and a review memory enhancement module; according to the method, learning tasks can be updated in real time along with the change of the ability of students, a one-step teaching mode is avoided, the learning efficiency and adaptability are improved, the learning experience and continuity are greatly improved, cultural faults can be effectively reduced, non-native Chinese language persons can understand the deep significance of ancient poetry more easily, and the learning efficiency is improved. The memory depth of the learner to the poetry artistic conception and vocabularies is greatly improved, and the learning motivation is also improved.

The invention relates to the technical field of computers, and discloses a fragmentation block chain federal learning method based on a large language model multi-agent, and the method comprises the steps: S1, initializing a client; s2, dynamic fragmentation scheduling and distribution; s3, generating and uploading local knowledge; s4, intelligent agent collaborative routing and knowledge acquisition; s5, knowledge fusion and model updating; and S6, repeatedly executing the steps S3 to S5 until the model converges or reaches a preset number of iterations. According to the invention, under a decentralized and fragmented federated learning architecture, the complex reasoning ability of a large language model and the autonomous cooperation mechanism of three multi-agent systems, namely a fragmented scheduling agent, a fragmented knowledge state agent and a global knowledge routing agent, are deeply fused; according to the mechanism, dynamic optimization of a bottom layer fragment structure and intelligent routing of high-value knowledge are achieved in an intelligent mode, and therefore the overall efficiency and model performance of a system under the condition of heterogeneous data and heterogeneous equipment are remarkably improved.

The invention discloses a building industry multi-subject collaborative full life cycle carbon emission management system and method, and relates to the technical field of building industry carbon emission management. The system comprises a carbon perception and data collaboration module, a carbon simulation and deduction module, a carbon optimization and collaborative regulation and control module, a carbon accounting and auditing traceability module and a carbon decision and knowledge generation module. Multi-source heterogeneous data is converted into a carbon flow unit with time, space and responsibility subject attributes, data checking, locking and compliance sharing are achieved by means of a multi-subject data collaboration pool, carbon data barriers among all responsibility subjects are broken through, the problems of fragmentization of carbon data collection and lack of collaboration are solved, and the data collection efficiency is improved. And meanwhile, the carbon accounting and audit traceability module completes multi-dimensional accounting based on the carbon flow unit, and establishes a digital carbon chain pointing to an original data source and a responsibility subject, so that full-link traceability of carbon emission data is realized.

The invention discloses a retrieval enhancement generation method and system based on long-term memory and multi-source knowledge iterative fusion. The method comprises the following steps: firstly, after user inquiry is received, triggering long-term memory database retrieval, external knowledge base retrieval and large language model internal knowledge generation in parallel to construct a multi-source candidate knowledge base, and improving result correlation by adopting a self-adaptive retrieval method based on a dynamic top-k value; secondly, inputting user query and multi-source knowledge into the large language model for iterative fusion, and generating a final answer through conflict detection, fusion processing and confidence evaluation; and finally updating the long-term memory database. The method can improve the accuracy and stability of the generated answers in a multi-round interaction and complex task scene, reduces the risk of answer one-sidedness, inference incompleteness or fact deviation, enhances the multi-source knowledge utilization ability and long-term learning ability of the system, and improves the user experience. Therefore, the comprehensive performance and reliability of the system in applications such as intelligent question answering, information retrieval assistance and text generation are obviously expanded.

The invention discloses an electronic archive intelligent data management method and system based on a large model. The method comprises the following steps: firstly, acquiring an electronic archive file, performing unified access, identifying a text through an OCR (Optical Character Recognition) technology, and reserving original layout coordinate information; analyzing the document page by page to generate a page-level structured object containing text and position mapping; then, on the basis of a preset domain prompt template, calling a large language model to extract file metadata from the page-level object, recognizing entities and relationships in combination with file knowledge ontology, and generating a semantic triple containing an evidence source page number; and finally, fusing the metadata and the triple to construct an electronic archive knowledge graph with a page-level traceability function. According to the invention, through a whole-process semantic governance mechanism from access, analysis to knowledge generation, the problems of difficult analysis of unstructured data, weak semantic understanding and lack of traceability in traditional archive management are effectively solved, and the intelligent level and data credibility of archive governance are significantly improved.

The application discloses a brain map drawing electronic pen and a handwriting knowledge generation and database construction device and method, and relates to the technical field of knowledge generation and database construction, and specifically relates to a brain map drawing electronic pen and a handwriting knowledge generation and database construction device and method.

This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate data associated with a plurality of entities, the aggregated data reflecting one or more relationships between two or more of the plurality of entities. Attribute data identifying loan amounts, property values, and appraisal sources may be extracted from the aggregated data. A knowledge graph data structure may be generated having a plurality of subject notes corresponding to the extracted attribute information. Statistical distributions of attributes associated with one or more appraisal sources may be generated and an anomaly in a first statistical distribution may be detected based on a comparison of the first statistical distribution with a second statistical distribution.

This application provides a method and system for dynamic knowledge updating of a large-scale model for operation and maintenance (O&M) technical services. It relates to the field of O&M technology, and models the knowledge generation triggering factors, iteration impact range, and knowledge association strength in multi-source O&M knowledge update sources to obtain a knowledge full-link evolution set including knowledge generation trigger records, iteration impact assessment results, and cross-knowledge association networks. Then, dynamic coupling rules are generated based on the real-time scenario characteristics of O&M tasks and this knowledge full-link evolution set. These rules are used to filter and integrate the knowledge full-link evolution set to obtain a scenario-coupled knowledge set. This set is embedded into the knowledge architecture of the large-scale O&M technical service model, and storage node parameters and retrieval logic are adjusted to obtain the basic updated model. Finally, the basic updated model is dynamically adapted in multiple dimensions, and the knowledge call priority sequence and storage node parameters are adjusted in conjunction with changes in scenario characteristics to generate an adapted updated model, thereby improving the adaptability of the large-scale O&M technical service model.

The application relates to a medical field knowledge fusion method based on label and content hierarchical fusion, and belongs to the technical field of knowledge fusion and artificial intelligence. Medical knowledge of different knowledge graphs is fused to construct a medical field knowledge base and provide large-scale medical industry knowledge services. The method comprises three steps of defining a meta label of an entity in a medical knowledge graph, data label alignment, and label content fusion. The method has the following characteristics: firstly, in the data label alignment, a data label alignment method based on integration of string measurement and semantic measurement is designed, and data label alignment is performed by using string features and semantic features. Secondly, in the label content fusion, a joint mechanism of content aggregation verification and unstructured knowledge generation is proposed, which aims to extract common knowledge of different granularities in different knowledge graphs, verify contradictory knowledge, and convert common triple knowledge into unstructured sentences to construct a medical knowledge base.

The embodiment of the invention discloses a medical knowledge base knowledge generation method and electronic equipment. The method comprises the steps of determining a knowledge template of a medical knowledge base under the condition of obtaining input data; and obtaining a knowledge generation model, and according to the input data and the knowledge template, through the knowledge generation model, generating target knowledge to be stored in the medical knowledge base so as to store the target knowledge in the medical knowledge base. According to the technical scheme, the problem that the knowledge generation efficiency of the medical knowledge base is low can be solved.

The embodiment of the invention provides a cross-data-source knowledge generation method and device and electronic equipment, and relates to the technical field of information, and the method comprises the steps: obtaining a configuration template, declaring a knowledge template in the configuration template, and enabling the knowledge template to comprise at least one piece of first to-be-filled data, the configuration template also declarates a first service type, a first interface identifier and a first query statement corresponding to each piece of first to-be-filled data, calling a first interface through a first service to access a first data source for each piece of first to-be-filled data, and querying in the first data source according to the corresponding first query statement, and obtaining a query result of the first to-be-filled data, and filling the query result of each first to-be-filled data into a knowledge template to obtain knowledge. According to the embodiment of the invention, the cost of multi-source access in large model application is reduced.

The invention relates to a code knowledge generation method and device, a computer readable storage medium and a computer program product, and the method comprises the steps: extracting an internal code of each layer of method called by each service method and a quoted data query statement from a project code, and obtaining a first code extraction file; determining a first text length of the first code extraction file, and taking a service method of which the first text length exceeds a first preset threshold value as an entry method; for each data access method in the project code, reversely tracing a call chain corresponding to each entry method, and extracting codes necessary for the operation of the data access method from codes of each layer of method included in the call chain to obtain a second code extraction file; and performing code analysis on the second code extraction file and the first code extraction file of which the first text length does not exceed a first preset threshold value through the large model to generate code knowledge. According to the method, the accuracy and reliability of code knowledge extraction can be remarkably improved.

The invention provides a moving behavior prediction method and device based on a natural language large model agent, and relates to the technical field of artificial intelligence, the method is applied to an LLM agent, and the method comprises the following steps: converting moving behavior track data of a target user into a user moving behavior prediction task described by a natural language; calling a space-time memory module, and extracting long-term memory, short-term memory and user portrait information of the target user based on the moving behavior track data; calling a geographic knowledge generation module, aligning the multi-scale urban space structure based on the movement behavior trajectory data to generate a first candidate place, and calling a sharing mode mining module to generate a second candidate place according to the trajectory data of similar users; and a comprehensive reasoning module is called, and a final prediction result of the user movement behavior is generated through a natural language large model based on the long-term memory, the short-term memory, the user portrait information, the first candidate place and the second candidate place of the target user, so that the prediction accuracy is improved.

The invention relates to the technical field of knowledge bases, can be applied to the field of science and technology finance / digital medical treatment, and discloses a knowledge base knowledge generation method and device, electronic equipment and a storage medium. The method comprises the steps that after knowledge generation process parameters configured by a user are obtained, the knowledge generation process is organized and managed in a unified mode, and the process parameters comprise knowledge data source types, knowledge generation structure requirements, knowledge generation rules and knowledge storage strategies. Collecting to-be-processed data based on the configured knowledge data source, processing the to-be-processed data under the constraint of the generation rule, and outputting structured knowledge content according to a preset structure; then manual auditing, intelligent auditing or combined auditing is executed according to the auditing configuration; after the audit is passed, the knowledge content is written into the target knowledge base according to a storage strategy, and vectorization processing is completed. According to the method, integrated automatic execution of knowledge generation regularization control, quality auditing and warehousing processing is realized.

The invention discloses a context dynamic tracking and course learning method oriented to multi-round dialogue perception. The method comprises the following steps: S1, performing utterance feature extraction in an utterance feature extraction module: extracting a semantic feature vector of each utterance in a dialogue through a first pre-training language model; the first pre-training language model is a RoBERTa model; s2, performing common sense feature extraction in a common sense feature extraction module: extracting common sense feature vectors related to emotion dynamic from the semantic feature vectors through a second pre-training knowledge generation model; the second pre-training knowledge generation model is a COMET model; s3, performing dynamic tracking modeling through a context dynamic tracker, and performing speaker information modeling through a speaker information module; and S4, course learning training: introducing a dynamic weight distribution mechanism and a weight loss function to carry out adaptive training on the dialogue sample. According to the method, dynamic contexts, multi-dimensional speaker features and common knowledge can be deeply fused, and the understanding of complex dialogue emotions is improved.

The invention provides a large-model knowledge dynamic updating method and system for operation and maintenance technology service, relates to the technical field of operation and maintenance, and aims to generate a trigger factor, an iterative influence range and knowledge association strength modeling for knowledge in a multi-source operation and maintenance knowledge updating source. Obtaining a knowledge full-link evolution set comprising a knowledge generation trigger record, an iteration influence evaluation result and a cross-knowledge association network; a dynamic coupling rule is generated based on operation and maintenance task real-time scene features and the knowledge full-link evolution set; screening and integrating the knowledge full-link evolution set by utilizing a dynamic coupling rule to obtain a scene coupling knowledge set; embedding the model into a knowledge architecture of an operation and maintenance technology service large model, and adjusting storage node parameters and retrieval logic to obtain a basic updated model; and finally, carrying out multi-dimensional dynamic adaptation on the model after basic updating, adjusting a knowledge calling priority sequence and storage node parameters in combination with scene feature changes, generating an adaptive updated model, and improving the adaptability of the operation and maintenance technology service large model.

The application belongs to the technical field of industrial digitization and artificial intelligence, and particularly relates to a kind of automobile parts enterprise knowledge generation and intelligent question answering system based on large model technology, through integrating multiple function platforms, the knowledge retrieval and generation path of "plug-in-knowledge base-large model" is constructed, and the large model technology is combined with the actual knowledge data of enterprise deeply, in the form of multi-modal of voice / text input / output + digital person audio picture synchronous dynamic interaction, the full-link intelligent service of "intention recognition-knowledge retrieval-business execution" is realized, the goals of efficiency improvement and digital operation agile iteration and continuous upgrading of manufacturing, research and development, operation and other businesses are achieved, the technical problems of poor adaptability of general model industry knowledge, split of multi-modal interaction and business system, low utilization rate of enterprise massive heterogeneous data and low efficiency of internal knowledge acquisition are solved.

The invention discloses an air and space big data intelligent data management system and method based on four-database collaboration and knowledge generation. A core layer adopts a temporary storage database, a controlled database, a product database and a knowledge database to form a four-database architecture; and the service layer adopts a data governance engine, a productization engine, a knowledge generation engine and an intelligent enabling engine. The system combines a data governance engine and a productization engine to carry out collaborative linkage to carry out data governance. Depositing knowledge assets into a knowledge base by relying on a knowledge generation engine; performing intelligent enabling application of each link of the data governance process by means of an intelligent enabling engine; and an intelligent enabling application effect is fed back, and knowledge assets in the knowledge base are optimized. According to the intelligent closed-loop data governance system, data governance implicit knowledge is converted into dominant knowledge and reused, the self-adaptability and the intelligent level of the data governance process are improved through knowledge reverse enabling, and then an intelligent closed-loop data governance system capable of self-learning, self-optimizing and continuously adapting to service requirements is formed.

According to the multi-modal personalized content generation and interpretation method and device for interaction between the user and the expert agent, the medium, the program product and the terminal provided by the invention, a traditional single text output mode is broken through, personalized generation of multi-modal contents such as texts, voices and videos is realized in combination with user knowledge preferences and user cognitive features, and the user experience is improved. The multi-modal personalized content adapts to receiving habits of different users. According to the method, the domain knowledge graph and the interaction context are fused, hierarchical disassembly, cross-domain association and personalized application suggestion of knowledge are realized, a user is helped to quickly master core knowledge and application scenes through deep interpretation, and the problem that interpretation of a traditional scheme is shallow is solved. According to the method, a closed-loop feedback optimization mechanism of user feedback, model optimization and strategy adjustment is established, it is ensured that knowledge generation and interpretation ability is dynamically upgraded along with user requirements, the method adapts to continuously-changing user requirements and domain knowledge updating, and therefore the service quality is continuously improved.

The invention provides a large-model intelligent document generation and optimization method and system fused with external retrieval, and relates to the technical field of artificial intelligence and document generation, and the method comprises the steps: obtaining theme information, constructing retrieval query, forming a knowledge fragment set, removing redundancy, and constructing a structured knowledge unit; according to the method, a dynamic routing mechanism is used for calculating the adaptation degree of knowledge units and paragraphs to generate an alignment index, a language model is controlled to selectively reference knowledge generation content, consistency verification and correction are carried out, and the accuracy, correlation and logic consistency of document generation are improved.

The application discloses a kind of food additive knowledge generation method and system based on multi-modal, it is related to food additive knowledge generation technical field, by regulation rule slot disassembly module and product scene slot construction module, regulation side and scene side are respectively transcribed into slot chain, so that regulation constraint object, food category, use condition, label requirement and other information and ingredient table, label identification, process specification, detection specification and question and answer target form the structure basis that can be aligned item by item, avoid only stay in the level of article original display or keyword hit.Multiple modal semantic module sorts out regulation text, ingredient table text, label image identification text, detection item specification, process specification text and question and answer request, so that subsequent processing is carried out under the same semantic fragment system, reduce the alignment fracture caused by the difference of expression from different sources, so that scene input has sustainable acceptance ability.

The invention belongs to the technical field of flood control and prevention, and particularly relates to a geographic scene and social data collaboratively driven flood event construction method, which comprises the following steps of S1, social media data acquisition and preprocessing; s2, carrying out disaster-oriented geographic scene ontology structured modeling; s3, constructing a flood disaster situation knowledge graph driven by a large language model; s4, a flood disaster multi-mode spatio-temporal narrative expression mechanism; and S5, performing semantic knowledge reasoning driven disaster spatio-temporal evolution visual expression. Through a technical closed loop of semantic analysis-knowledge generation-spatio-temporal deduction-visual narration, a full-chain path of disaster information from fragmented perception to systematic cognition is reconstructed, and the disaster information expression spans from static display to dynamic narration and from macroscopic description to causal reasoning. The real-time cognition and autonomous response ability of the public to disaster risks is enhanced, and the scientificity and social collaboration of disaster risk management are remarkably enhanced.

This application discloses a method for constructing a vehicle diagnosis and repair knowledge base, an electronic device, and a storage medium, relating to the field of vehicle diagnosis and repair. The method includes: Step S1, knowledge base creation involves uploading knowledge documents to the knowledge base, then sequentially performing standardized processing including document reading, cleaning, segmentation, knowledge generation, and knowledge storage, before saving the knowledge to a database. The process of saving knowledge to the database uses a template method to define a unified document parsing and processing flow, and a bridging pattern to decouple each processing stage. The specific processing at each stage uses a strategy pattern to load different implementation methods. The cleaning stage uses a chain of responsibility pattern to flexibly configure the rule execution order. Step S6, diagnosis and repair knowledge query involves standardizing the text of a user's question, retrieving it from the knowledge base, and generating a corresponding question based on a large language model. The method utilizes plug-in technology to enable configurability of knowledge document parsing rules.

The invention discloses a decision scheduling method, system and device for a virtual power plant and a medium, and the method comprises the steps: obtaining multi-modal data and a current knowledge graph of the virtual power plant; the current knowledge graph comprises a plurality of pieces of first knowledge and second knowledge; the first knowledge is a new knowledge triple added to the previous knowledge graph in the updating process of the previous knowledge graph, and the second knowledge is an existing knowledge triple reserved by the previous knowledge graph in the updating process of the previous knowledge graph; the knowledge generation time of the second knowledge is smaller than a knowledge elimination threshold; performing feature extraction and fusion on the multi-modal data to obtain multi-modal features; and inputting the multi-modal features and the current knowledge graph into the inference model subjected to fine tuning training for decision inference to obtain a scheduling strategy output by the inference model subjected to fine tuning training. The method can effectively improve the adaptive capacity of the virtual power plant scheduling decision. The invention relates to the technical field of power dispatching.

The invention discloses an intelligent question and answer method and device for oil and gas exploration and development, and the method comprises the steps: building a question and answer library and an oil and gas knowledge graph based on an obtained oil and gas exploration and development knowledge document; inputting the user query into a preset oil and gas word extraction model, and extracting to obtain a keyword; the keyword comprises an entity; retrieving the oil and gas knowledge graph based on the entity to obtain a query sub-graph; retrieving a question and answer library based on the keywords to obtain query characters; obtaining context input according to the query sub-graph and the query text; and inputting the context input into a preset oil and gas knowledge generation model to generate a retrieval answer. According to the method, question and answer library retrieval and knowledge graph retrieval are combined, information from the two sources is integrated together to form more complete context input with richer semantic information, the accuracy, continuity and depth of retrieval answers are remarkably improved, more comprehensive answers with higher interpretability are provided, and the retrieval efficiency is improved. And the user experience and the complex task processing capability are further improved.

This invention provides a federated learning method and system supporting heterogeneous models and data, relating to the field of artificial intelligence, and aims to address the problem of decreased discriminative power of global knowledge in heterogeneous federated learning. The method includes: a server generating and distributing a global prototype using a trainable generator; a client training its local model based on the global prototype, performing intra-class clustering for each data feature after training to generate multiple local clustering prototypes and uploading them; and the server collecting the local clustering prototypes and training its generator using a composite loss such as focus contrastive loss and orthogonal regularization to proactively generate more discriminative global knowledge. This invention effectively improves classification accuracy and robustness, enhances the discriminative power of global knowledge, and ensures communication efficiency and privacy security through a two-way co-evolutionary mechanism of refined knowledge extraction by the client and proactive knowledge generation by the server.

The invention discloses a grammar analysis method and a related device, and relates to the technical field of language learning, a preset grammar knowledge base at least comprises a textbook grammar knowledge base, and the textbook grammar knowledge base comprises a first pre-stored object and grammar knowledge of the first pre-stored object; the first pre-stored object is a high-frequency object in a fixed teaching material range, the grammar analysis model is a large language model with a grammar knowledge generation function, after a target object input by a user is obtained, the target object is inquired from a preset grammar knowledge base, and when the target object is not inquired from the preset grammar knowledge base, the grammar analysis model is a large language model with a grammar knowledge generation function. The grammar analysis model is utilized to perform grammar analysis on the target object, the generated result of the grammar analysis model is obtained and serves as the grammar analysis result, and in the scheme, for the target object which cannot provide grammar analysis based on the preset grammar knowledge base, the grammar analysis of the target object by utilizing the large language model is supported; therefore, according to the scheme, the reliability of grammar analysis can be improved, and then the grammar learning quality of the user is guaranteed.

The invention discloses an urban space analysis method and system for multi-agent collaborative retrieval enhanced generation. The method comprises the steps that firstly, multi-source heterogeneous urban space ground feature data is subjected to normalization processing, semantic-space double indexes are constructed, and after a user puts forward task requirements through a natural language, task analysis is conducted through a multi-mode large language model, and data autonomous exploration is conducted through the semantic-space double indexes; after task analysis and data autonomous exploration are completed, multi-agent collaborative spatial semantic reasoning and analysis are carried out based on the reflection enhanced task graph; by constructing an intelligent compiling mechanism of geographic semantics and visual expression, visual conversion is automatically carried out on a structured conclusion obtained by multi-agent collaborative reasoning and spatial data. Through a five-layer collaborative architecture of task analysis, data retrieval, spatial reasoning, knowledge generation and visual closed loop, efficient joint query and complete analysis report output of city-level heterogeneous data under the drive of a natural language are realized.

The invention discloses a defect detection method based on deep learning interpretable knowledge guidance, and relates to the field of computer vision. According to the invention, the problem of poor defect classification accuracy caused by unstable detection performance of a traditional artificial intelligence algorithm based on deep learning is solved. According to the method, in a knowledge generation stage, semantic features (namely, attribution significance heat maps) are extracted from the knowledge generation stage by utilizing an interpretable technology and are used as structured prior information for guiding learning in a subsequent guide learning stage. And in the guided learning stage, through a specially designed knowledge fusion mechanism, priori knowledge obtained previously is integrated into the guided learning classification network, so that the network can fuse early-stage knowledge and cooperatively enhance the characterization capability with current features, thereby improving the defect classification accuracy. The method is mainly used for defect detection.

The application provides a robot process skill generation system and method based on a double helix coupling architecture, applied to the technical field of intelligent manufacturing, robot autonomous decision and adaptive control, by designing "knowledge generation" and "performance evaluation" as two spiral chains that can evolve independently but are tightly coupled, and realizing real-time bidirectional feedback through a bidirectional coupling and evolution control center, forming a real closed-loop evolution system. The knowledge spiral layer is responsible for storing and dynamically mutating the process knowledge graph to generate candidate skills, and the performance spiral layer executes and quantitatively evaluates in a simulation or real environment, and the evaluation results directly drive the knowledge spiral layer to update the graph in a targeted manner, fundamentally breaking the barrier between knowledge and execution, forming a closed-loop co-evolution mechanism of "knowledge generation-performance verification-feedback update", so that the robot process skill generation system has the ability of continuous self-evolution, high sample efficiency, explainability and safety controllability.