37 results about "Knowledge resource" patented technology

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 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.

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.

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.

The application discloses an automatic proposition method and system based on structural knowledge resources and reasoning track verification, and the method comprises the following steps: constructing structural knowledge resources for automatic proposition, and forming a double-layer representation of a task skeleton layer and a scene instance layer; receiving and analyzing proposition demand input by a teacher to obtain structured demand information, and compiling to obtain a teaching intention; extracting a task-related subgraph corresponding to a current proposition target from the structural knowledge resources according to the structured demand information; matching a target proposition mode template according to the task-related subgraph and constructing a task skeleton according to the target proposition mode template; planning a candidate reasoning track on the task skeleton, and outputting a verified reasoning track satisfying verification constraints; determining question face given information and hidden information around the verified reasoning track; mapping the task skeleton to a specific question scene, and jointly generating a title, an analysis and an answer based on the same underlying reasoning track. The application can improve the logical rigor of the automatic proposition result, the consistency of the question and the analysis answer.

The application provides an ontology agent autonomous evolution and knowledge updating method, device, equipment, medium and system based on continuous learning, wherein the method comprises the following steps: acquiring baseline configuration records, historical processing records and knowledge resource records of an ontology agent, the baseline configuration records comprising baseline version identification, prompt word constraint templates, replay task sets and fallback anchor points; generating a running evidence frame based on the historical processing records; determining a knowledge gap segment according to the running evidence frame and the knowledge resource records; converting the knowledge gap segment into a knowledge increment index, and generating a resource routing rewrite item according to the running evidence frame; generating a candidate evolution package according to the knowledge increment index and the resource routing rewrite item; loading the candidate evolution package into a shadow service container to obtain a candidate evolution version; calling the candidate evolution version to process the replay task set through a unified service interface, and generating a difference check record; and writing the configuration record to activate the agent when the difference check record meets the takeover condition.

The application discloses a kind of military software defect multimodal knowledge graph construction method, device and system, belong to knowledge graph construction field, comprising: S1, mode layer design;S2, defect knowledge mining: data source is collected, information extraction and data preprocessing;S3, multimodal knowledge graph construction: data entry is carried out in multiple databases, the semantic relationship between nodes is stored in graph database, the multimodal information of graph database node is stored in relational database, and multimodal knowledge graph is constructed.The military software defect knowledge graph of the data driving and knowledge guiding combination constructed by the application changes the traditional knowledge management mode, multidirectional rich semantic information and the defect of other information are shown, the problems of military software defect knowledge resource fragmentation, insufficient utilization, high knowledge acquisition cost and updating difficulty are solved, and foundation is laid for military software defect encyclopedia retrieval, intelligent question and answer, intelligent prediction and intelligent recommendation and the like application.

The invention belongs to the technical field of process approval, and particularly relates to a knowledge-enhanced process approval system and method based on large model assistance. Comprising a data acquisition module, a knowledge base construction module, a large model integration module, a knowledge reasoning engine, an interactive interface and a log and feedback module. The data acquisition module collects and preprocesses various data in the approval process in the production management field and management files related to enterprise production management. The knowledge base construction module establishes a comprehensive multi-mode knowledge base which comprises various types of knowledge resources and carries out tagging processing on the resources in the knowledge base. The method has the beneficial effects that the key information in the approval process can be automatically analyzed, and the pre-trained large model is called to carry out related knowledge pushing, knowledge reasoning and decision suggestion, so that the approver is assisted to make a judgment quickly and accurately, and the approval efficiency of the process can be improved.

The invention discloses a multi-dimensional guiding type teaching assistance method and system based on retrieval enhancement, and the method comprises the steps: collecting questions, answers, practical operation feedback and historical data of students through a heuristic dialogue, recognizing and cognizing weak points, learning demands and question adaptability through a multi-dimensional analysis model, and constructing a personalized learning situation portrait. And on the basis of a large model and a portrait, personalized content adaptive to a teaching scene is customized, a multi-source knowledge resource library traceability identifier is embedded, and the content is ensured to be traceable and accurate through cross validation. Through a visual carrier, multi-round interaction is carried out through'stepped questioning + scene example ', dynamic feedback is collected, and a teaching strategy is optimized. And finally, giving a comprehensive score of a learning result of the student from three-dimensional quantitative evaluation of learning condition positioning accuracy, content generation suitability and guide interaction effectiveness, and improving teaching quality and learning efficiency.

The invention discloses an intelligent writing system and method based on multi-source knowledge base enhancement, and particularly relates to the technical field of intelligent writing, knowledge resources can be efficiently integrated in a writing task by comprehensively collecting structured, semi-structured and non-structured multi-source knowledge data and combining construction of a semantic vector library and a knowledge graph, and the writing efficiency is improved. The key points of each chapter can be identified and accurately extracted by decomposing a writing task, so that the understanding of a writing structure is optimized, the reliability and authority of quoted information are guaranteed by introducing retrieval quality information such as semantic relevancy, field matching degree and source credibility in a retrieval process, and the retrieval efficiency is improved. According to the method, the hierarchical structure and the causal relationship of the multi-source knowledge are combined, a writing outline can be more reasonably constructed, a full text first draft is generated, the quality of the full text can be evaluated in real time based on a self-correction mechanism of a quality optimization coefficient, a segment to be optimized is positioned, and it is ensured that the quality of the generated content meets a preset standard through iterative rewriting.

The invention discloses a personal number knowledge base creation and configuration method supporting multi-dimensional screening, and relates to the technical field of data processing, the method comprises the following steps: creating an exclusive knowledge base bound with a personal number, and establishing data interface connection with a client management system and a communication push system; basic functions of the knowledge base are configured, and initial mapping of files, customer tags and business scenes is established; constructing a multi-dimensional screening logic containing basic attributes, customer behaviors and cross-system feedback dimensions, and presetting a screening dimension self-evolution processing flow, a business conflict calibration processing flow and a cross-system screening dimension generation processing flow; and executing the process to realize real-time linkage of a screening result, a client communication scene and multiple systems, and completing predictive screening and dynamic calibration of the business process. According to the method, the adaptability and efficiency of knowledge resource screening are improved, and support is provided for efficient business development of personal numbers.

The invention provides a vector database retrieval method and device based on an ontology and corpus double-layer structure, and relates to the field of database retrieval, and the method comprises the following steps: performing type division on a knowledge resource text to obtain ontology nodes and corpus nodes; the ontology nodes and the corpus nodes are input into a semantic vectorization model for coding, and ontology vectors and corpus vectors are obtained; according to the cosine similarity between the ontology vector and the corpus vector, performing association mapping on the ontology node and the corpus node to obtain a relateds mapping table; obtaining an initial HNSW structure, and inserting the ontology node and the corpus node into the initial HNSW structure according to the relatedds mapping table to obtain an HNSW index structure; a double-layer database is generated through the relateds mapping table and the HNSW index structure; obtaining a query text, and performing ontology matching and corpus matching on the query text through the double-layer database to obtain a final corpus block sequence; and constructing a language model, and inputting the final corpus block sequence into the language model for semantic retrieval to obtain a retrieval answer text.

The invention discloses an intelligent research interaction platform based on AI, and relates to the technical field of research interaction. Comprising a collaborative interaction module, a task analysis module, a learning condition analysis module and an intelligent pushing module. Through keyword extraction and matching degree calculation, the collaborative interaction module realizes rapid and accurate response of student questions, immediately feeds back knowledge base answers when the matching degree reaches the standard, and circulates teachers and supplements a knowledge base when the matching degree is insufficient, so that the question solving efficiency is improved, and knowledge resources are dynamically enriched; the task analysis module performs multi-dimensional disassembly and hierarchical scoring on research and study task results, comprehensively and accurately evaluates the performance of students in the aspects of knowledge mastering, practical operation, innovative thinking and the like, and provides objective evaluation basis for teachers and students; the learning condition analysis module accurately diagnoses weak points and ability dimension performance of students in combination with the questioning frequency and the score trend, and the intelligent pushing module pushes targeted teaching resources based on the learning condition and resource feature matching degree to assist teachers in implementing personalized teaching.

The invention discloses an information processing method and device, a storage medium and electronic equipment. The method relates to the technical field of financial science and technology, and comprises the steps that a target portrait of a target object is acquired, and the target portrait is constructed at least based on historical code data output by the target object; performing knowledge point matching with a knowledge mapping model according to the target portrait and the code defect to obtain matched knowledge information; according to the matched knowledge information, a learning path card is generated, the learning path card is pushed to the target object, and the learning path card at least comprises a recommended knowledge resource link and predicted learning duration. Through the resource recommendation method and device, the problem that the accuracy of resource recommendation is low due to the fact that learning resources are recommended according to posts of developers in the prior art is solved.

The present application relates to a building pig farm epidemic disease early warning and prevention and control intelligent recommendation method based on LLM-Agent, belongs to the technical field of intelligent breeding and pig farm epidemic disease risk management, through combining abnormal detection algorithm and epidemic disease prevention and control field knowledge resources, the pig farm epidemic disease risk is graded early warning and the corresponding risk cause analysis result is generated, further aiming at different risk grades to generate various prevention and control and management strategies, and from the prevention and control effect, the execution difficulty and the economic cost and so on multiple dimensions are comprehensively evaluated and sorted, the output optimization prevention and control strategy scheme, simultaneously, through introducing user feedback, prevention and control execution result and system cooperative linkage mechanism, the prevention and control strategy is dynamically adjusted and continuously optimized, forms the closed loop control system of '' prevention and control recommendation-execution-feedback-optimization ''. The present application can realize the prospective early warning and scientific decision of building pig farm epidemic disease risk, improves the intelligentization, the explainability and the economic feasibility of epidemic disease prevention and control, has good application value and popularization prospect.

The invention relates to the technical field of power grid knowledge base construction, and discloses a power grid knowledge base construction method, which is used for power grid knowledge base construction of a power grid development knowledge system, and comprises the following steps: obtaining power grid knowledge resources associated with power grid knowledge, identifying and analyzing the obtained power grid knowledge resources, and constructing a multi-level directory structure, orderly collection and grading of power grid knowledge resources are realized; creating knowledge labels used for identifying power grid knowledge contents, wherein the knowledge labels comprise predefined labels uniformly formulated by enterprises and self-defined labels allowing users to configure based on own business characteristics and management requirements; and performing knowledge storage on the obtained power grid knowledge resources, and hooking knowledge labels in a knowledge storage stage. According to the application, centralized management of general knowledge such as national standards, industry specifications, enterprise general methods and the like is realized, characteristic knowledge is also supported to be accumulated in each region and each business unit according to attribution conditions and professional differences, and the collaborative requirements of unified management and attribution management are met.

The invention relates to an LLM-Agent-based building pig raising epidemic disease early warning and prevention and control intelligent recommendation method, belongs to the technical field of intelligent breeding and pig farm epidemic disease risk management, and aims at carrying out graded early warning on pig farm epidemic disease risks and generating corresponding risk cause analysis results by combining an anomaly detection algorithm and epidemic disease prevention and control field knowledge resources; a plurality of prevention and control and management strategies are further generated according to different risk levels, comprehensive evaluation and sorting are carried out from multiple dimensions such as prevention and control effects, execution difficulty and economic cost, and a preferable prevention and control strategy scheme is output; meanwhile, by introducing user feedback, prevention and control execution results and a system collaborative linkage mechanism, a prevention and control strategy is dynamically adjusted and continuously optimized, and a'prevention and control recommendation-execution-feedback-optimization 'closed-loop control system is formed. The building pig farm epidemic disease risk early warning and scientific decision making can be achieved, the intelligence, interpretability and economic feasibility of epidemic disease prevention and control are improved, and the building pig farm epidemic disease risk early warning system has good application value and popularization prospects.

The invention discloses an intelligent operation and maintenance system and method based on multi-model management in the technical field of operation and maintenance. The system comprises a monitoring system which collects the operation state and alarm information of each business system of an office in real time, and displays the operation state and alarm information in a centralized manner through a large visual monitoring screen; the work order system is used for recording, tracking and managing operation and maintenance service requests and forming a structural chemical engineering single flow and a complete operation and maintenance operation log; the operation and maintenance rule processing module is linked with a monitoring system and a work order system respectively and captures operation and maintenance events and structured task information in a full-link manner; the operation and maintenance model library is used for storing multiple types of operation and maintenance models and providing containerized packaging, standardized registration and multi-mode scheduling capabilities; and the operation and maintenance knowledge base is constructed based on an RAG architecture, integrates multi-source knowledge resources, and provides a high-correlation reference basis for model reasoning. According to the system, by constructing a unified multi-model management architecture, accurate matching of operation and maintenance tasks and model capabilities is realized, and the utilization rate of hardware resources is improved.

The application discloses a kind of applied to the computing power resource session response processing method and system of deep learning.The application can accurately extract the key information in session by identifying target session interaction knowledge and constructing information set for computing power resource session data, to avoid the interference of irrelevant information.Initial associated session interaction knowledge vector is obtained by encoding and cross-collision on associated session interaction knowledge information set of computing power resource business keywords, which can deeply mine the knowledge connotation related to business keywords and improve the understanding ability of knowledge in different business scenarios.Target associated session interaction knowledge vector can accurately match the most relevant knowledge resources to improve the accuracy of response according to the correlation eigenvalue.Determined session interaction business theme keyword can quickly and accurately respond to computing power resource session to obtain session response result, effectively improve the session processing efficiency, improve the quality of response, and further optimize the management and utilization of computing power resources.

The invention discloses a game community question and answer method and device, electronic equipment and a storage medium. The method comprises the steps that a first NPC selected by a user and input question content are acquired; analyzing the question content to determine a target question type to which the question content belongs; judging whether the target question type is matched with a preset question type corresponding to the first NPC role or not; if not, prompting the user to switch to a second NPC role matched with the target question type; and determining reply content corresponding to the question content based on preset knowledge resources associated with the second NPC role, and outputting the reply content corresponding to the question content based on role setting corresponding to the second NPC role. According to the method, the accuracy and credibility of question answering of the game community can be improved.