76 results about "Knowledge application" patented technology

A method for generating a multimodal forensic report using hybrid metric learning and signature-based models is disclosed herein. The method involves receiving and preprocessing sensory data, extracting features using AI models, applying reasoning for anomaly detection and classification, and integrating spatiotemporal, multimodal AI representation learning, and symbolic knowledge. Dynamic domain-specific knowledge is generated by applying data-driven and ontology knowledge to the model. Explanations are generated using unimodal and multimodal reasoning, and associated features are sorted, prioritized, and indexed in a structured format.

The invention discloses a medical education-oriented knowledge graph construction, retrieval and application system, and the system comprises a data collection and integration module which is used for collecting and integrating various medical professional knowledge, and forming a medical knowledge material library; the intelligent algorithm module is used for constructing a knowledge graph according to the medical knowledge material library and then providing structured knowledge support for learning and reasoning of the knowledge graph by using a graph neural network model; the knowledge retrieval and content generation module is used for performing retrieval in the knowledge graph according to the input content and providing the retrieved knowledge to the large language model to obtain the corresponding content; and the knowledge application service module is used for providing medical education service according to learning and understanding of the graph neural network model on the knowledge graph and integration of the big language model on semantic information in the knowledge graph. The intelligent development of the medical field is better promoted, and more efficient and accurate support and service are provided for medical education, research and the like.

The invention relates to a question and answer method and system fusing a multi-granularity knowledge graph and a self-adaptive agent. The question and answer method comprises the steps of knowledge governance and extraction, knowledge generation and expansion, knowledge retrieval and management, and knowledge rule mining and self-adaptive retrieval. Through cooperation of the multi-granularity knowledge graph and the self-adaptive agent, questions of the user can be more comprehensively understood, and more relevant knowledge can be retrieved from the knowledge base, so that the question and answer accuracy is remarkably improved, and wrong answers are reduced. Meanwhile, the multi-granularity knowledge graph can support more complex knowledge application requirements such as reasoning and prediction, so that the application capability of the question and answer system is enhanced, and the question and answer system can process more complex question and answer tasks such as multi-round dialogues and multi-task question and answer.

The invention belongs to the technical field of risk assessment, and discloses a risk assessment method based on digital economy. The method comprises the following specific steps: S1, cross-modal data federal acquisition; s2, risk factor dynamic deconstruction; s3, dynamic modeling of game theory enabling is carried out; s4, generating a real-time risk thermodynamic diagram; s5, an edge intelligent decision center; s6, dynamic knowledge graph evolution; and S7, sandbox verification and anti-fact deduction are carried out. According to the method, all-around innovation is brought to risk management and control, first, data processing is performed, block chains, 5G and NLP technologies are utilized, multi-source data are collected, public opinions are insighted, privacy compliance is guaranteed based on federal learning, and a safety footstone is built, and second, market strategies are provided, and by means of reinforcement learning, Monte Carlo deduction and a quantum annealing algorithm, subject behaviors are simulated, policy impact is pre-estimated, and a model is optimized, so that risk management and control are realized. And thirdly, knowledge application, leading-edge technology labeling new risk database establishment, federal learning optimization atlas and enhancement of practical operation to cope with complex risks are realized.

The invention provides a large language model knowledge preference alignment method and system based on self-supervised learning. The method comprises the following steps: defining knowledge availability comprising three dimensions; generating and constructing preference data based on retrieval enhancement, obtaining constructed data according to the generated questions, answers and context paragraphs, evaluating the data through a defined knowledge utilization degree, and screening out samples for training; and training samples through a preference optimization method comprising a self-supervision module and knowledge perception to perform preference alignment, wherein the self-supervision module integrates knowledge utilization degree as a supervision signal into model training. Based on the technical scheme of the invention, the data acquisition cost is effectively reduced, the problems of scarcity of field data and difficulty in labeling are solved, and the data quality is improved; and the understanding and application capability of the model on professional knowledge is improved, and effective alignment of the model and domain expert knowledge application is realized.

The invention provides a multi-modal big language model inference engine which comprises a pre-training big language model adaptation layer, a pollution remediation knowledge graph enhancement module, a multi-modal information understanding and fusion unit, a technology implementation optimization module and an inference chain and decision interpretation generator. The system adopts a hierarchical modular design, all components communicate through a standardized API interface, efficient and stable data circulation is ensured, and the problems of knowledge application limitation, opaque reasoning process, difficulty in professional knowledge fusion, knowledge updating lagging, limited multi-modal data processing capability and the like in the existing contaminated site remediation decision process are solved.

The invention provides a multi-stage knowledge enhancement model training method and device, electronic equipment and a storage medium, and relates to the technical field of natural language processing. According to the method, knowledge understanding and memorizing of alternative models can be enhanced through multi-task training, and by training the alternative models, knowledge understanding and memorizing of the alternative models can be enhanced; the obtained target language model can complete answer reasoning of complex knowledge questions. According to the method, through a layer-by-layer progressive mode, the memory, understanding and reasoning process of multi-task training is gradually converted into scene fusion of complex knowledge problems, the knowledge application ability of the target language model is comprehensively improved, and therefore the limitation problem of complex knowledge reasoning in the prior art is solved.

The invention discloses a teaching information management method based on big data, and particularly relates to the field of teaching, comprising data acquisition, behavior characteristic analysis, knowledge mastering evaluation, dynamic feedback adjustment, comprehensive evaluation and management strategy. The teaching behavior analysis depth is improved through multi-dimensional data fusion and non-linear feature analysis, real-time adjustment of an evaluation model along with knowledge mastery is achieved through a dynamic feedback mechanism, limitation of traditional static analysis is broken through, theoretical test and practice project data are integrated through an innovatively constructed three-dimensional evaluation system, and the teaching behavior evaluation efficiency is improved. A comprehensive evaluation model covering knowledge application and innovation ability development is formed, a dynamic path selection mechanism synchronously supports basic ability enhancement and high-order skill development requirements, a resource allocation strategy has instant intervention and long-term optimization characteristics, and adaptability and decision scientificity of a teaching management system are remarkably enhanced.

The invention provides a process design method and system based on a structured process model and knowledge driving, and relates to the technical field of intelligent manufacturing and process design, and the method comprises the steps: obtaining a process design target and a production preparation element, carrying out the objectification structure of the process design target and the production preparation element, and constructing a structured process business model; based on the structured process business model, defining a typical process and application parameters by using an extensible classification characteristic and a combination mechanism; constructing a knowledge-driven process generation engine, and defining a comprehensive knowledge application rule based on a specific target process design in combination with the structured process business model; performing multi-scheme and operation configuration in a knowledge-driven process generation engine by utilizing a comprehensive knowledge application rule; and automatically generating a process design based on the multiple schemes and the operation configuration result, and outputting a final process scheme.

The invention discloses a heterogeneous AI engine interaction system and method based on a cognitive firewall, and relates to the technical field of artificial intelligence, and the system comprises a cognitive isolation channel which is used for building an isolation communication path between an RAG system and an Agent system, and generating standardized knowledge; the three-layer filtering architecture is used for performing grammar layer verification, semantic layer understanding and pragmatic layer evaluation on the standardized knowledge in sequence and outputting safety knowledge passing verification; the bidirectional value alignment module is used for mapping the safety knowledge to a multi-dimensional value space through a forward value alignment mechanism to calculate value conformity, and generating value alignment knowledge according to the value conformity; and the knowledge-instruction conversion engine is used for converting the value alignment knowledge into a structured instruction which can be executed by the Agent system. The method can actively identify, intercept and avoid potential dangerous, misleading, prejudice or ethical inconformity knowledge applications, and greatly reduce negative effects caused by wrong use of knowledge.

The invention relates to the technical field of computers, and discloses a knowledge management method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a knowledge task initiated by a user, analyzing the task intention of the knowledge task, dynamically selecting and combining one or more special intelligent agents from a plurality of predefined special intelligent agents based on the analyzed task intention, and storing the selected special intelligent agents. Generating a workflow for the corresponding knowledge task; the workflow is scheduled and executed, so that the selected special intelligent agents cooperatively work in sequence or in parallel, a task result is generated, and generated derivative knowledge achievements are captured; and processing the derivative knowledge achievement, calling an information extraction class agent to perform entity and relationship extraction on the derivative knowledge achievement, and after entity disambiguation, updating a structured result into the mapping knowledge of the unified knowledge base to form a knowledge closed loop. According to the method, knowledge can be driven to be actively, accurately and efficiently converted into actual productivity in various scenes, and knowledge application ecology capable of being evolved continuously is formed.

The invention discloses a knowledge space control method and system for assisting learning, and relates to the technical field of knowledge space generation methods, and the method comprises the steps: determining a corresponding problem solving path according to the previous problem solving content of a user; and determining error contents of each problem solving node in the problem solving path based on matching of the problem solving path and the knowledge application space, and marking knowledge nodes associated with the error contents. Determining corresponding correct content according to the error content and knowledge content presented by the knowledge node, determining difference content between the correct content and the error content, determining a correct answering thought according to the difference content and the answering thought of the user relative to the error content, and marking knowledge difficult points of the user; the error factors of the user are determined according to the knowledge difficult points of the user, the corresponding mastering level and the answering accuracy of the user, the corresponding auxiliary learning measures are determined based on the error factors of the user and the current learning process of the user, and the accuracy of the auxiliary learning measures is improved.

The invention discloses a wind turbine generator abnormal knowledge association method and device based on a knowledge graph and a medium, and the method comprises the steps: carrying out the collection of distributed multi-source heterogeneous data and knowledge based on the operation and maintenance demands of the wind power industry; establishing an OPC UA information model of the wind power equipment node; the method comprises the following steps: guiding an industrial multi-modal industrial large model to carry out standardized description and identification on node data through cue word engineering, completing entity extraction and attribute extraction, carrying out relation extraction and expression, and importing into a graph database to construct an abnormal knowledge graph; the semantic relation between the node entities is optimized and updated, knowledge merging and processing are completed, and the abnormal knowledge graph is dynamically updated; and for an abnormal knowledge application scene, performing knowledge reasoning based on abnormal knowledge graph mining entity association. According to the multi-modal knowledge graph construction method based on graph structure learning and fine tuning of the multi-modal industrial large model, relevance of different anomalies is explored, the knowledge base rich in abnormal semantics is constructed, and it is ensured that industrial abnormal faults are correctly processed.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: predicting future energy supply profiles at respective ones of a plurality of geographically differentiated computer environments, wherein the respective future energy supply profiles at the respective ones of the plurality of geographically differentiated computer environments express an attribute of supplied energy supplied to a computer environment attributable to renewable power generation; scheduling a plurality of jobs defining an application workflow, wherein the application workflow is characterized by commencement of a second job of the plurality of jobs being dependent on completion of a first job of the plurality of jobs.

The invention provides a multi-agent collaborative semantic transformation framework for a rare disease recognition problem, and belongs to the technical field of artificial intelligence and medical image analysis. According to the method, the problems of scarcity of marked data and difficulty in cross-domain knowledge migration in rare disease recognition are solved. The method comprises the steps that S1, a multi-agent parallel network architecture is constructed, each agent is provided with a special semantic focusing module, and diversified features are extracted from different attribute perspectives; s2, a cooperative gating mechanism with dynamic temperature parameters is realized, and the cooperative and competitive relationship between intelligent agents is balanced in a self-adaptive manner; s3, applying a double-constrained cross-domain semantic alignment strategy to ensure that the converted semantic features are consistent with the original features and keep diversity at the same time; s4, adopting a progressive training strategy and a semantic consistency loss function to reduce an overfitting phenomenon in cross-domain knowledge migration; and S5, classifying and identifying rare diseases through multi-agent cooperation, and applying source domain knowledge to a target domain. The method is prominent in medical image application, the accuracy rate of identifying rare skin diseases by using only common skin disease data reaches 52.13%, and the method is remarkably superior to an existing method. The framework has wide applicability in cross-domain zero sample learning tasks, is particularly suitable for application in the field with definite definition of semantic attributes, and provides a new solution for diagnosis of rare diseases in medical images.

According to the multi-culture medical AI supervision and auditing platform provided by the invention, layered review is carried out on medical AI decisions by introducing a semantic sovereignty framework, so that the accuracy, fairness and compliance of the AI in a cross-culture situation are ensured. A multi-culture knowledge rule base is arranged in the platform, and the whole process of AI from data extraction, knowledge application to decision output is monitored. For each layer of semantics, the auditing unit automatically detects whether the AI has the problems of cultural semantic misunderstanding (such as misunderstanding of traditional terms such as qi deficiency), knowledge bias (such as neglecting local medicine by only adopting western medicine knowledge) or decision-making bias (systematic unfairness exists in suggestions for people with different cultural backgrounds) and the like. Once found, the platform generates an audit report alert and gives adjustment suggestions. Meanwhile, the platform checks ethical suitability of AI suggestions, and the situation that believes of patients and cultural custom taboo are violated is avoided; and through intention layer auditing, the AI decision is ensured to conform to value orientation and medical strategies of the same region. According to the method, the medical AI decision is more transparent and controllable, and the semantic rights and interests of each culture group in the AI era are maintained. As an innovative AI treatment tool, the platform can be used for a supervision institution and a medical institution to continuously audit various intelligent diagnosis and treatment systems so as to realize'controlled autonomous' AI: each cultural context is fully and autonomously learned, human expectation and ethical constraints are followed in output, and a solid guarantee is provided for safe and fair AI medical fusion in a global range.

The application relates to a knowledge-driven multi-agent cooperative reactor scheme demonstration system and a design method, medium and equipment thereof, the system comprising: a knowledge internalization unit for converting unstructured and semi-structured documents into a dynamic knowledge source which can be queried and utilized by a model in real time; a knowledge structuring unit for extracting core entities, relationships and attributes from the knowledge source and constructing a professional knowledge graph of a reactor design field; and a knowledge application unit for constructing intelligent agents and a collaborative working mechanism of multi-professional intelligent agents based on the knowledge source and the knowledge graph, and constructing an intelligent question and answer interface. Through automatic knowledge management and intelligent agent collaborative work, the application significantly reduces the time and effort of manual intervention and improves the efficiency of reactor scheme demonstration.

This invention belongs to the field of artificial intelligence technology. The embodiments of this invention provide a fully automated interview assistance method, system, and device based on a large language model. First, it acquires recruitment requirements, initial interview questions, and multimodal data of job seekers from the recruitment end to construct a multimodal dynamic knowledge base. This multimodal dynamic knowledge base includes content dynamic mechanisms, structure dynamic mechanisms, and iterative dynamic mechanisms, used to update the knowledge base content and sub-question sequences, update the structure of the knowledge graph in the multimodal dynamic knowledge base, and optimize sub-question generation and retrieval strategies, respectively. Then, based on the multimodal dynamic knowledge base, it determines the job seeker's structured question set, and subsequently determines the job seeker's interview assistance strategy. This invention, through three update mechanisms, makes knowledge application more closely aligned with actual interview scenarios, overcoming the shortcomings of traditional static and rigid knowledge bases.

A class-incremental learning method based on knowledge bridging and class anchoring, which alleviates the catastrophic forgetting problem and improves the overall performance of the model class-incremental learning through knowledge bridging and class anchoring respectively; knowledge bridging aims to establish the semantic correlation between old classes and new classes, and applies the learned knowledge to learn new information by using feature-level distillation; class anchoring focuses on learning class-specific feature centers that are crucial for distinguishing all classes. Through the combination of the class-incremental learning method based on knowledge bridging and class anchoring proposed in the application, the original model can achieve better performance in the incremental learning task and effectively alleviate the catastrophic forgetting phenomenon.

This invention discloses an intelligent knowledge extraction system based on natural language processing, comprising: a text data acquisition module that acquires unstructured text and forms raw data; a text data preprocessing module that removes noise and standardizes the format of the raw data, outputting standardized text data; a text feature encoding module that converts the standardized text data into feature vectors that satisfy the input of an improved W2NER model; a knowledge extraction module that processes the feature vectors based on the improved W2NER model to obtain entity extraction results; an entity normalization module that performs ontology mapping and normalization on the entity extraction results; a knowledge fusion module that performs deduplication and fusion on the entity knowledge set; a knowledge storage module that stores the fused knowledge data in a structured manner; and a knowledge application service module that provides user knowledge query and reasoning services. This invention improves the accuracy of knowledge extraction and the efficiency of data application.

The present invention discloses a remarketing advertising effect prediction method and system based on multi-task learning, which belongs to the field of online advertising marketing information. The present invention learns and predicts multiple user behaviors (clicks / browsing / add-to-cart / purchase) after the advertisement is displayed, considers multiple influencing factors at the same time in the display link, automatically learns the correlation between features and targets, and solves the remarketing advertising effect prediction problem with one model. Specifically, knowledge is learned from massive data through offline feature design, target determination, sample construction and model training, and feature extraction and model scoring are completed for requests online in real time. The knowledge trained from the offline model is applied to the production environment to realize real-time remarketing advertising effect prediction. The present invention uses one model to realize the prediction of multiple indicators, which reduces the complexity of traditional multi-model solutions and overcomes the problem of poor training effect caused by sparse data of purchase / add-to-cart and other behaviors.

The invention discloses a knowledge management platform for a petrochemical engineering investigation and design enterprise, which stores knowledge documents of the enterprise into different knowledge bases in the platform for management according to classification, and is also provided with a search module, a knowledge application module, a knowledge community management module, an expert question and answer management module, a knowledge operation module and the like. The method is convenient for enterprises to search, utilize, share and manage knowledge, accumulate enterprise knowledge assets, avoid enterprise knowledge loss, promote reutilization and innovation of enterprise knowledge, improve the working efficiency of enterprise employees and enterprise knowledge management innovation quality, and comprehensively improve the enterprise knowledge informatization management level.

The application discloses a kind of big language model enhanced emission trading knowledge graph construction method and system, it is related to machine learning field.The present application obtains the unstructured text data in the field of emission trading, carries out document analysis and text segmentation to text data, obtains standardized text fragment;Based on the field of emission trading adaptability prompt engineering guides big language model, carries out entity recognition and relationship extraction to the text fragment, obtains initial knowledge triple and processing, obtains high-quality knowledge triple;Text fragment is converted and constructs structured triple with high-quality knowledge triple and semantic vector fusion's bimodal knowledge representation;The bimodal knowledge representation is stored to graph database, and constructs the field of emission trading knowledge graph;Based on double-path retrieval, carry out retrieval and knowledge traceability to knowledge graph, realize the field of emission trading intelligent question and answer and knowledge application.The present application realizes the efficient conversion from unstructured data to structured knowledge.

The application discloses a black box fuzzing method and device based on feedback enhancement. The method divides the field boundary by the structural feature change of the message itself, and constructs an automatic semantic inference method on the basis, learns the abstract connection between the field features and semantic categories from the public protocol, and applies the learned knowledge to infer the semantics of each field in the private protocol. Finally, the feedback message of the device is collected in the fuzzing process to establish a new feedback mechanism, and the variation type and variation strategy are adjusted according to the feedback information. The application can automatically identify the field boundary and semantic information based on the network communication data of the industrial field, without manual participation, and take it as the basis for establishing the feedback mechanism, and has good test effect on the complex and private industrial control protocol.

The invention provides an examination proposition person behavior model construction method and system based on deep learning, and relates to the field of artificial intelligence and educational evaluation, and the method comprises the steps: obtaining proposition records, constructing a knowledge application graph, analyzing proposition preferences based on a time window, training a proposition behavior recognition network, and optimizing a migration path and difficulty distribution. And anomaly detection of proposition behaviors is realized. According to the method, the knowledge preference and the behavior mode of the proposition person can be accurately described, the proposition quality monitoring efficiency is improved, the proposition risk is reduced, and the examination fairness is guaranteed.

The invention belongs to the technical field of medical knowledge maps, and discloses a semantic understanding system and method based on a knowledge map, and the method comprises the steps: obtaining traditional Chinese and western medicine medical data; constructing a medical semantic nebula atlas to obtain a key concept pedigree; constructing a symptom-syndrome type mapping network to obtain a semantic core element set; constructing a double-domain semantic bridging knowledge base; performing space-time context analysis to obtain a context embedding vector and a concept rheological function family; performing semantic alignment to obtain a cross-domain semantic pedigree; performing multi-modal fusion analysis to obtain a modal translation matrix; performing semantic conversion to obtain a semantic resonance model; carrying out concept integrity verification to obtain a target fusion knowledge graph; and monitoring and optimizing to realize dynamic evolution of a traditional Chinese and western medicine semantic comprehension mechanism. According to the method, deep semantic fusion and adaptive optimization of traditional Chinese and western medicine knowledge are realized, and the application efficiency of interdisciplinary medical knowledge is improved.

The present invention relates to a supplier optimal promotion path query method based on a government procurement knowledge graph, and belongs to the field of knowledge graph construction and application technology. The present invention utilizes the advantages of knowledge graphs that are good at processing data with complex relationships, good at integrating data with different structures, and good at using knowledge applications to solve practical problems. The entities in the government procurement and "government procurement loan" financing process are abstracted as nodes, the frequent business transactions between entities are abstracted as the relationship between nodes, and the entire process is abstracted as a data graph model. The financing platform can use which partners to promote the target supplier, which can be converted into a path query problem in the data graph, and the knowledge graph is enabled to promote the government procurement supplier on the financing platform.

This invention provides a method and system for constructing a deep learning-based model of exam question setter behavior, relating to the fields of artificial intelligence and educational assessment. The method includes acquiring question-setting records to construct a knowledge application graph, analyzing question-setting preferences based on time windows, training a question-setting behavior recognition network to optimize transfer paths and difficulty distribution, and achieving anomaly detection in question-setting behavior. This invention can accurately characterize the knowledge preferences and behavioral patterns of question setters, improve the efficiency of question-setting quality monitoring, reduce question-setting risks, and ensure exam fairness.

This invention relates to the field of smart education technology and discloses a knowledge graph-based smart education system and method. The knowledge graph-based smart education system includes: a multi-source data acquisition module, a knowledge graph construction and updating module, a personalized teaching push module, a learning dynamic analysis module, an intelligent interaction module, a permission management module, and a data storage module. These modules work collaboratively to form a complete smart education ecosystem. This invention addresses the problem of fragmented knowledge by constructing a systematic knowledge system. Through the knowledge graph construction and updating module, scattered teaching knowledge points are structurally modeled, clearly presenting the pre-dependencies, parallel relationships, and extensions between knowledge points. This breaks down the knowledge barriers of traditional teaching, helps students grasp the overall logic of the knowledge system, improves knowledge absorption efficiency and application ability, and solves the problem of weak knowledge connections in existing education systems.

The application discloses a kind of information collaborative processing platform and information query method based on knowledge graph, including data acquisition processing layer, knowledge extraction intelligent analysis layer and knowledge application decision layer;The data acquisition processing layer includes data acquisition module, data format conversion module and knowledge base management module;The knowledge extraction intelligent analysis layer includes keyword extraction module, keyword hierarchical module, semantic fusion module and knowledge ontology dynamic evolution module;The knowledge application decision layer includes database module, entity and relationship extraction module, event topic library, organization library, evaluation result acquisition module and military strategy guidance information update module and military strategy graph generation module.The application can realize the effective cooperation between military strategy display, military strategy dynamic adjustment and intelligence data real-time processing, and can also provide more perfect military strategy related information query result.