73 results about "Knowledge resource" patented technology

The invention discloses a dynamic collaborative arrangement system and method based on an intelligent agent, and relates to the technical field of artificial intelligence. By integrating core modules of an agent communication protocol, knowledge base management, agent arrangement, security authentication, resource scheduling and the like, not only is a standardized agent cooperation framework and a distributed knowledge sharing mechanism provided, but also dynamic task arrangement and secure and controllable resource scheduling capability among agents are realized. The problem of how to effectively realize standardized communication and dynamic cooperation of agents among enterprises and unified management and shared utilization of knowledge resources in the prior art is solved, and particularly, the problem of how to construct an efficient, safe and extensible agent cooperation network in a multi-enterprise cooperation environment is solved.

The invention relates to a Han-Cross industry text classification method and system based on prompt learning and adaptive loss weighting, and belongs to the technical field of natural language processing. The method comprises the following steps: designing and constructing a universal prompt template; the method comprises the following steps: recombining a Han-Cross cross-border industry text classification data set, namely converting an original single sample into paired samples; in a few-sample and multi-language scene, related vocabularies are adopted as external knowledge resources, and vocabularies most related to the mapping labels are retrieved from the related vocabularies; expanding the vocabulary mapper by introducing synonyms and associated vocabularies; adopting a dynamic mixed loss function and applying the dynamic mixed loss function to a pre-training language model to optimize a few-sample classification task; and classifying Chinese and Vietnamese cross-border industry texts by using the optimized pre-training language model. The method shows a remarkable effect in Chinese and Vietnamese industry text classification tasks, and is particularly suitable for a few-sample learning scene with data scarcity and language imbalance.

The invention relates to the field of artificial intelligence, in particular to an intelligent standard knowledge retrieval system based on an AI large model, which comprises a standard knowledge management database, a multi-source acquisition preprocessing module, a semantic understanding and indexing module, an intelligent retrieval engine module, a knowledge enhancement module, a user interaction feedback module, a security control module and a deployment expansion module. The standard knowledge management database is used for storing standard knowledge design data, real-time retrieval data and feedback data and constructing a dynamically updated standard knowledge resource pool; through the multi-source standard knowledge acquisition and preprocessing module, multi-channel objective standard data can be integrated and dynamically updated, and the problem of knowledge fragmentation is solved; and through the semantic understanding and indexing module, industry professional semantic accurate matching is realized, the limitation of traditional keyword retrieval is broken through, and the accuracy and efficiency of standard knowledge retrieval are remarkably improved.

The invention discloses an industrial equipment after-sales intelligent auxiliary method and device, equipment and a storage medium, and the method comprises the steps: collecting equipment fault information inputted by a user, and converting the equipment fault information into a keyword set; obtaining multi-modal knowledge resources uploaded by a user, and labeling the multi-modal knowledge resources to generate a vector database containing semantic association; matching the keyword set with data in a vector database based on a retrieval enhancement generation mechanism to obtain knowledge fragments related to the equipment fault information; and calling a preset large language model to analyze the knowledge fragment to generate maintenance guidance content. According to the method, the multi-modal knowledge resources are converted into the vector database containing the semantic association, and the maintenance guidance content is generated for fault diagnosis of the equipment through the retrieval enhancement generation mechanism and the preset large language model, so that compared with the prior art, the equipment fault diagnosis efficiency and the response speed and accuracy of equipment maintenance are improved.

The invention provides a large model reasoning optimization method and system for operation and maintenance technical services, and the method comprises the steps: firstly obtaining a set comprising a plurality of to-be-processed operation and maintenance service requests, each to-be-processed operation and maintenance service request carrying operation and maintenance demand description information and an equipment operation scene identifier; performing priority dynamic calibration on operation and maintenance reasoning tasks based on the information, generating a priority list, deploying computing resources and operation and maintenance knowledge resources of large model reasoning according to the priority list and reasoning link requirements, forming a collaborative scheduling scheme, configuring a reasoning process according to the collaborative scheduling scheme, and executing large model reasoning; and finally, converting the result into an operation and maintenance response instruction in combination with the equipment operation scene identifier, and sending the operation and maintenance response instruction to the execution terminal, so that accurate processing of the operation and maintenance task, reasonable allocation of resources and efficient response of the operation and maintenance service are realized.

The invention provides a text factuality proofreading method and system, electronic equipment and a medium. The method comprises the steps that a to-be-corrected text input by a user is acquired; based on a retrieval enhancement generation technology, recalling reference information related to the text to be corrected from a pre-constructed multivariate knowledge base; filling the text to be corrected and the reference information into a target template; and correcting factual errors existing in the text to be corrected by using the trained large language model according to the reference information filled in the target template. According to the method, reference information supplemented by the multivariate knowledge base and excellent semantic comprehension and analysis capabilities of the large language model are utilized, so that the text proofreading accuracy is remarkably improved; by cooperating with knowledge resources of the multivariate knowledge base, the accuracy and comprehensiveness of fact retrieval are improved; according to the method, the dependence on rules is avoided, text factual proofreading can be intelligently and accurately carried out, the false alarm rate is effectively reduced, and the text proofreading efficiency and quality are improved.

Disclosed herein are a system, method, and computer program product embodiments for retrieving and ranking knowledge resources relevant to a query from knowledge base(s). For example, a query for resources from knowledge base(s) may be received. Based on the query, a first set of candidate resources are obtained from the knowledge base(s) having a lexical similarity to the query search terms, and a second set of candidate resources are obtained from the knowledge base(s) having a semantical similarity to the search terms. For each of the first and second sets of candidate resources, a confidence level indicating the relevance of the candidate resource to the query is determined. The sets of candidate resources are ranked based on at least the confidence levels to generate a ranked list of candidate resources. A query response comprising at least a subset of the ranked list candidate resources is provided to a GUI.

The invention belongs to the technical field of large model application, and particularly relates to a semantic enhancement text matching method and system based on an achievement transformation large model. The semantic enhancement text matching method based on the achievement transformation large model comprises five parts, namely preparation of data and knowledge, enhancement of multi-level semantic knowledge, learning of semantic difference, intelligent weight distribution of semantic information and prediction of text similarity. The preparation of data and knowledge is used for constructing basic data and knowledge resources required by a text matching task through text preprocessing and knowledge base construction. And enhancement of multi-level semantic knowledge: enriching semantic representation of the text by injecting external knowledge at three levels of word level, phrase level and sentence pattern level. And semantic difference learning: learning fine-grained semantic features by comparing a learning framework, and improving the recognition capability of the model on the text semantic difference. Intelligent weight distribution of semantic information is carried out, and dynamic balance and optimal combination of different source information are realized by evaluating feature importance and dynamically adjusting weights. And predicting the text similarity, calculating the similarity score of the text pair by integrating the feature information of each stage, and providing an interpretable prediction result. The complete text matching method and system are designed based on the achievement transformation large model, the problems of insufficient knowledge utilization, incomplete semantic representation, fixed feature weight and the like of the large model in a text matching task are effectively solved, the text matching effect of the large model in an actual application scene is improved, and the text matching efficiency is improved. And a feasible solution is provided for achievement transformation of a large model technology.

The present disclosure relates to systems and methods for creating a copilot. The copilot uses plugins to provide additional features and functionalities to the copilot. The systems and methods use a large language model (LLM) pipeline to generate a knowledge resource used by the plugins and / or an LLM in the copilot to answer queries from a user.

The invention discloses an online programming system based on large language model assistance and a teaching recommendation method, the system comprises a base model acquisition module, a problem decomposition module, an abstract modeling module, an algorithm design module, a code analysis and assistance module and an evaluation module, and is used for helping students to finish a programming task of teaching design online. And performing problem decomposition, abstract modeling and algorithm design on actual problems by using a large language model, and solving the problems encountered by students during programming. Based on the system, the invention further designs a teaching recommendation method, multi-modal data in the information classroom teaching process is collected, a dynamic three-level teaching interaction diagram of the space-time environment, knowledge resources and cognitive behaviors is established through man-machine, life and teacher-student interaction behaviors, and accurate teaching recommendation and intervention strategies of the human-in-the-loop are provided. According to the invention, a three-dimensional comprehensive teaching field for learning environment intellectual computing is established in a programming scene, and a guarantee is provided for high-quality modern education development.

The invention relates to the technical field of big data education, in particular to a big data teaching resource dynamic allocation practical training system based on AI self-learning, which comprises a teaching scene global data sensing module, a deep reinforcement learning decision core, a dynamic resource scheduling module and a practical training interaction environment layer, and the deep reinforcement learning decision core performs iterative updating on the double-layer resource scheduling model by continuously collecting user learning progress increment and system load change after resource allocation as reward feedback so as to realize collaborative dynamic allocation of computing hardware resources and teaching knowledge resources. The platform breaks the barrier between a traditional teaching system and a cloud computing base, achieves the simultaneous adjustment of computing power supply and teaching guidance according to the practical training progress, guarantees the smooth operation of a high-load task, reduces the operation cost through idle time capacity reduction, and achieves the collaborative optimization of soft and hard resources.

The invention provides a book note digital recycling and sharing method and system based on OCR-NLP collaboration, and relates to the technical field of knowledge management and cyclic utilization. An OCR hybrid recognition model and an NLP note quality evaluation model are constructed; acquiring a book picture uploaded by a seller user; inputting the book picture into an OCR mixed recognition model, and obtaining an OCR recognition coefficient, a book text, a note text and book information; matching corresponding book classifications according to the book information, and carrying out tagging processing and incorporating the book classifications into a knowledge base; inputting the book text and the note text into an NLP note quality evaluation model to obtain a note value coefficient; generating a pricing result according to the OCR identification coefficient, the note value coefficient and the book original price; after the books are attached with corresponding prices, the books are put on the shelf and displayed as commodities, automatic quality evaluation and intelligent pricing of the second-hand books are achieved, fairness and accuracy of pricing are guaranteed, recycling and pricing efficiency is improved, and a knowledge resource platform integrating second-hand book and note recycling and sharing is constructed.

The invention discloses an animal nutrition and feed course resource sharing cloud platform and a knowledge graph construction method. The method comprises the steps of obtaining feeding knowledge resource data of different animal types and constructing a feeding knowledge meta-database; performing multi-granularity entity recognition and analysis on the feeding knowledge resources in the feeding knowledge meta-database to generate four types of entity sets, constructing an entity interaction graph, performing entity relationship reasoning to obtain a triple set, and constructing a feeding resource knowledge graph by using the triple set; constructing a personalized recommendation engine, carrying out user question answering and demand knowledge recommendation through the personalized recommendation engine, and carrying out interest resource recommendation based on user use behaviors; and building an animal nutrition and feed course resource sharing cloud platform by utilizing a micro-service architecture technology based on the personalized recommendation engine, the feeding knowledge meta-database and the feeding resource knowledge graph. Therefore, accurate, real-time and multi-dimensional knowledge service and help are provided for animal husbandry practitioners or interests and amateurs.

The invention discloses a multi-dimensional knowledge base management method and system associated with intelligent tags, and belongs to the technical field of knowledge base data management. Standard documents are analyzed through configuration rules, and semantic tags are generated; for non-standard documents and unstructured data, keywords are intelligently extracted according to the documents or user description to generate semantic tags; based on the knowledge element labels, a multi-dimensional knowledge graph is created, and intelligent association retrieval of the heterogeneous knowledge resources is achieved. According to the method, various documents can be managed in a unified manner, the human input in a knowledge base collection process is remarkably simplified, and the implementation cost is greatly reduced; and associated retrieval is supported, so that the efficiency and practicability of knowledge management are greatly improved.

The invention discloses a computing power resource session response processing method and system applied to deep learning. According to the method and the device, target session interaction knowledge identification and information set construction are carried out on the computing power resource session data, so that key information in the session can be accurately extracted, and interference of irrelevant information is avoided. According to the method, the associated session interaction knowledge information set of the computing power resource business keyword is coded and subjected to cross collision to obtain the initial associated session interaction knowledge vector, so that the knowledge connotation related to the business keyword can be deeply mined, and the understanding ability of knowledge in different business scenes is improved. The target associated session interaction knowledge vector is determined according to the correlation characteristic value, and the most relevant knowledge resources can be accurately matched to improve the response accuracy. The session response result obtained according to the determined session interaction service theme keyword can quickly and accurately respond to the computing power resource session, so that the session processing efficiency is effectively improved, the response quality is improved, and the management and utilization of computing power resources are optimized.

The invention belongs to the technical field of intelligent education and personalized learning, and provides a personalized learning path recommendation method based on a course knowledge point sequence. The method aims at solving the problems that existing learning path recommendation logic association is insufficient, the personalized matching degree is low, and the optimization efficiency is not high. The method comprises the following steps: constructing a knowledge point sequence model by adopting a topological sorting algorithm on the basis of a curriculum knowledge point pre-modification and post-modification relationship, a difficulty level and dependence intensity, and generating a structured knowledge relationship graph; a learner feature model and a knowledge resource model are established, learner features are described from the dimensions of learning targets, cognitive levels, learning styles and the like, and the matching degree with knowledge points is calculated; and constructing an optimization model taking learning cost minimization, mastering degree maximization and path continuity optimization as targets, performing iterative optimization by using an improved intelligent optimization algorithm, and generating a learning path with logic rationality and personalized adaptability. According to the method, the scientificity and learning efficiency of learning path recommendation can be effectively improved.

A simulation method and related equipment for machining process training are disclosed. The method includes: acquiring engineering data and process data of the target workpiece; extracting features from the engineering data and combining them with the process data to construct a case resource library; performing semantic association analysis between the pre-set knowledge resource library and the machining cases in the case resource library to generate a process knowledge network; constructing a virtual training environment based on the process knowledge network; collecting interaction data of learners in the virtual training environment and evaluating the interaction data to obtain evaluation results; and generating guidance animations based on the evaluation results and the process knowledge network. This application can improve the learning efficiency of simulation training.

The invention discloses a network-on-chip debugging method and device, computer equipment and a storage medium, and relates to the technical field of integrated circuit design verification, and the method comprises the steps: generating network-on-chip data files in a preset file format, and generating a network-on-chip database containing network debugging information according to the files; acquiring target information corresponding to the network-on-chip debugging request from a network-on-chip database; and performing on-chip network debugging according to the target information, and writing a debugging result into an on-chip network database. The problem that NoC debugging is low in debugging efficiency and depends on verification of experiences of engineers can be solved. According to the method, the target information corresponding to the debugging request is provided for the user through the network-on-chip database, the dependence on the experience of a verification engineer is eliminated, the network-on-chip debugging difficulty is reduced, and the debugging efficiency is improved. All possible communication scenes and boundary conditions in the debugging process are covered through the target information, and the debugging coverage rate is increased. And continuously improving the database according to the debugging result to form knowledge resources.

1. Name of the product in this design: Artificial Intelligence Question-Answering Interactive Graphical User Interface for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: This interface is used for AI question-and-answer interaction. Clicking "Knowledge Base Management" in the main view will take you to interface state diagram 1, where you can perform unified maintenance of the system's knowledge resources. Clicking "Quick Access Management" in interface state diagram 1 will take you to interface state diagram 2, where you can configure and maintain the system's quick access links.

This invention relates to a fault early warning system for hydro-generator units based on CEGD-FNN. The system includes: a feature extraction module for early fault signals of the hydro-generator unit, a fault diagnosis module for the hydro-generator unit, a status assessment module for the hydro-generator unit, a fault prediction module for the hydro-generator unit, and a human-machine interface module. These modules are sequentially connected. This invention employs a unified data platform integrating heterogeneous source information and a unified platform for open knowledge resource sharing. It can promptly obtain heterogeneous data from distributed systems and knowledge resources from different users, thereby achieving intelligent collaborative diagnosis of equipment faults and reducing losses caused by insufficient timely handling of emergency faults.

The invention provides a business processing method and system based on a large model and retrieval optimization, and the method comprises the steps: constructing a collection knowledge base, and carrying out the fine adjustment of a pre-trained large language model through a quantification low-rank adaptation technology, and obtaining a large language model for the collection business optimization. And a unique strategy of model parameter fixation and only low-rank matrix optimization is adopted in model training, so that the calculation resource occupation is greatly reduced, and the accuracy and performance of the large model in the collection professional field are remarkably improved. Furthermore, dynamic updating of collection related knowledge is achieved through application of the RAG technology, the problem of knowledge updating lag is effectively solved, the retrieval recall rate is remarkably increased through the synergistic effect of synonymous query enhancement and a mixed retrieval strategy, a large language model can accurately obtain related knowledge from a small amount of knowledge resources, and the retrieval efficiency is improved. Therefore, the accuracy and reliability of answering are greatly improved, more powerful support is provided for collection business processing, and the collection effect is effectively improved.

Disclosed herein are a system, method, and computer program product embodiments for recommending knowledge resources to resolve errors at a source and / or target system. For example, a request to post data to a target system is received from a source system. A determination is made that an error occurred with respect to the request. An error context for the error is determined. The error context comprises information describing the error and information describing the system. A representation of the error context is provided as an input to an ML model. The ML model is configured to predict a knowledge resource for resolving the error based on the error context. A prediction indicating the knowledge resource for resolving the error is received from the ML model. A recommendation to apply the knowledge resource on the source and / or target system is provided via a user interface based on the prediction.

The application discloses a kind of personal number knowledge base creation and configuration method supporting multi-dimension screening, it is related to data processing technical field, method includes: creating and the exclusive knowledge base of personal number binding, establish and the data interface connection of customer management system, communication push system;Configuration knowledge base basic function, establish file and the initial mapping of customer label, business scenario;Multi-dimension screening logic containing basic attribute, customer behavior, cross-system feedback dimension is built, preset screening dimension self-evolution process, business conflict calibration process and cross-system screening dimension generation process are handled;The process is executed to realize the real-time linkage of screening result and customer communication scene, multi-system, complete predictive screening and business process dynamic calibration.This method improves the adaptability and efficiency of knowledge resource screening, and provides support for personal number to efficiently carry out business.

This invention discloses an AI-based personalized workplace learning system and method for enterprises, comprising: an enterprise profile construction module, which collects multi-source data and constructs a digital profile of the enterprise through multimodal semantic analysis; a dynamic learning system generation module, which automatically generates a customized workplace learning system based on the enterprise profile using a large language model combined with thought chain and retrieval enhancement generation technology; a multimodal content extraction and AIGC production module, which reconstructs knowledge resources in its proprietary copyright content library into lightweight workplace knowledge units through a RAG and multi-agent collaborative architecture; and a personalized recommendation engine, which adopts an improved DSSM dual-tower model, introduces a dynamic attention feature cross-layer, and combines multi-source user features and enterprise context to achieve accurate push notifications. This invention achieves dynamic matching of the learning system with enterprise strategy, large-scale reconstruction of knowledge assets, and deep personalized recommendation, effectively solving the problems of rigid training systems and single recommendation dimensions in existing technologies.

The invention provides an intelligent decision and recommendation method based on an enterprise knowledge graph. According to the method, the enterprise knowledge graph covering multi-dimensional entities such as enterprise customers, products, supply chains, finance and employees and association relationships is constructed, and heterogeneous data dispersed in each business system in an enterprise is semantically integrated and associated to form a unified knowledge resource pool; the process can comprehensively and accurately reflect the internal relation among the entities in the enterprise operation process, and provides a complete and reliable data basis for subsequent decision analysis and recommendation. A multi-scene intelligent reasoning model is constructed based on an enterprise knowledge graph, and rich semantic association information in the knowledge graph can be utilized to perform deep mining and reasoning analysis on various data in an enterprise operation process.

The application provides a large language model driven open domain natural language reasoning question and answer system and method, a question rewriting module rewrites a user question into a rewritten question; a center calculation and management module manages calculation and knowledge resources of a large language model, and outputs the calculation and knowledge resources of the large language model required by a question core engine module to one or more sub-question and answer modules in a question and answer core engine module according to the type of the rewritten question; the question and answer core engine module reasons to obtain one or more candidate answers of the rewritten question and explainability information of the candidate answers according to the rewritten question and the calculation and knowledge resources of the large language model; and an aggregation reasoning module aggregates and reasons to obtain a final answer of the rewritten question and explainability information of the final answer according to the one or more candidate answers of the rewritten question and the explainability information of the candidate answers, and supports comprehensive question types, is easy to expand, is explainable, and has strong universality by using a large language model.

The application discloses a knowledge resource reliable protection method based on a consortium chain network, and comprises the following steps: an IPFS private network containing seven nodes is built, and an optimal storage node and a backup node are selected; a consortium chain network is established; based on an ERC-1155 standard, a non-fungible token for a knowledge resource file is established and stored in the consortium chain network, and a user is allowed to perform an operation on the non-fungible token, and meanwhile, non-fungible token data and transaction records generated by the operation are stored in nodes of the consortium chain network; all transaction records in the nodes of the consortium chain network are interacted with a public infrastructure network BSN. Therefore, the problem that the rights and interests of a user about a knowledge resource file are infringed is solved.

The invention relates to the technical field of large language models, and discloses a knowledge base questioning method and device, equipment and a storage medium, and the method comprises the steps: obtaining user question information; the user question information is input into a target model, a target knowledge base is referred to based on the user question information through the target model, corresponding answer information is generated and displayed, and the target model comprises at least one large model. By introducing the knowledge base, the large model answers the question information of the user by referring to the knowledge of the target knowledge base, and external knowledge resources are introduced into the reasoning process of the model, so that the mastering of the model on the knowledge in different fields can be effectively improved, and the accuracy and comprehensiveness of answering are further improved.

This application provides a method and system for optimizing large-scale model inference for operation and maintenance (O&M) technical services. First, a set of multiple pending O&M service requests is obtained. Each pending O&M service request carries O&M requirement description information and equipment operating scenario identifiers. Then, based on the above information, the O&M inference tasks are dynamically prioritized, generating a priority list. According to the priority list and inference link requirements, computational resources and O&M knowledge resources for large-scale model inference are allocated to form a collaborative scheduling scheme. The inference process is configured and executed according to the collaborative scheduling scheme, generating O&M inference results. Finally, the results are converted into O&M response instructions based on the equipment operating scenario identifiers and sent to the execution terminal. This achieves accurate processing of O&M tasks, reasonable resource allocation, and efficient response to O&M services.