55 results about "Knowledge application" patented technology

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 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 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.

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 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.

The application discloses a kind of optical communication intelligent equalization algorithm based on physical adjustment, including steps S1: initialization neural network;Step S2: carry out forward propagation;Step S3: carry out loss function calculation;Step S4: carry out gradient calculation;Step S5: carry out back propagation;Step S6: carry out neural network variable value analysis;Step S7: reinitialize neural network by estimated physical parameter.The application discloses a kind of optical communication intelligent equalization algorithm based on physical adjustment, traditional static DSP is parameterized as trainable neural network, and then the intelligent update of neural network variable is carried out using optimization algorithm.At the same time, combined with physical knowledge is applied to neural network and neural network information feedback physics, the effect of mutual promotion of both is achieved, so that high-precision parameter estimation and signal impairment compensation can be carried out on optical communication system with unknown parameters.

The invention relates to the technical field of man-machine interaction, in particular to an rag-fused communication field question-answering system, which comprises the following steps: semantic analysis adopts hierarchical abstraction and non-iterative embedding technologies to convert multi-modal input into structured semantic representation and break through the limitation of traditional modal isolation; according to context adaptation, accurate matching of a query intention and a professional context is achieved through a dynamic alignment algorithm, and the correlation of retrieval results is ensured; knowledge fusion adopts an incremental updating mechanism, the latest industry knowledge is dynamically integrated into the question and answer process, and the information timeliness is kept; feedback is optimized, a continuous learning closed loop based on user interaction is established, and the content of the knowledge base is automatically optimized through intelligent diagnosis. According to the method, full-process intelligent processing from information acquisition to knowledge application is realized particularly aiming at the characteristics of fast knowledge updating, strong specialty and the like in the communication field, and key indexes such as accuracy, response speed and adaptive capacity are remarkably improved.

The application discloses a kind of knowledge mastery degree evaluation methods based on multi-modal learning behavior data, its method includes: the visual, interactive and answering behavior modal data of target user in learning process are collected;Real-time time series feature extraction is carried out to each modal data, and the primary feature sequence respectively representing cognitive concentration, behavior participation and knowledge application degree is obtained;Feature alignment and fusion are carried out by hierarchical attention mechanism, and a comprehensive dynamic learning state vector sequence is generated;The sequence is input into the pre-trained knowledge mastery degree evaluation model, and the mastery degree probability distribution of target user on each knowledge unit evolves with time is output.The application realizes continuous fine-grained characterization to learning process, comprehensively utilizes multi-modal complementary information, overcomes the one-sidedness of evaluation of single data source, so that the evaluation result is more comprehensive and objective, and the change process of knowledge mastery state can be dynamically reflected.

The application discloses a kind of melanoma auxiliary diagnosis methods based on deep learning, using optimized Transform classification network, for melanoma image, ideal auxiliary classification effect is obtained.The application generates malignant melanoma using the way of adversarial generation, makes up the problem of imbalance between classes in data set.The application can judge the benign or malignant of melanoma, the result shows that the accuracy of the application can reach 98.4%, higher than 75% of human eye recognition, can effectively help patients early self-screening, or assist doctors in clinical diagnosis.The application can realize model light weight, reduce the amount of model parameters, reduce the resource and time cost of actual development program, facilitate the application of algorithm to actual development program.The application can apply the related knowledge of computer vision to real life, effectively simplify the process of detecting skin melanoma, so that doctors and patients can obtain detection reference results in real time by simply operating the application program.

The application discloses an education intelligent agent system and method based on multi-engine cooperation and dynamic knowledge evolution, and the system comprises a data acquisition layer, which is responsible for collecting original data from various data sources and providing data support for the upper layer; a data layer is used for storing various data; a model layer is used for providing different levels of large model capabilities; an intelligent analysis layer uses an artificial intelligence layered large model as the technology, analyzes and processes the collected data, and generates high-level abstract data models and knowledge; an application service layer encapsulates the results of the intelligent analysis layer into reusable services; an application layer directly faces the application interface of the end user and provides specific functions; the application has the advantages that: a real personalized learning path planning is realized, through the deep cooperation of the six intelligent engines, the education mode innovation from "one face for thousands of people" to "one person thousands of faces" is realized, and the intelligent education experience of precise adaptation, real-time response and combination of learning and use is provided for each learner.

The invention discloses a multi-modal teaching resource intelligent adaptation and immersive learning interaction system, and belongs to the technical field of intelligent education. The system comprises a multi-modal resource management module, a learner cognition and behavior perception module, an intelligent adaptation engine, an immersive interactive scene generation and rendering module and a learning effect closed-loop feedback module, and all the modules form a complete working closed loop through bidirectional data communication. The system integrates multi-modal resources such as texts, VR / AR, audios and videos and constructs a cross-modal semantic association map, and then real-time cognitive states and interaction data of learners are collected through biosensing and behavior capturing equipment. According to the method, the defects of rigid resource adaptation, weak immersion, lack of collaboration and closed loop and insufficient creative support in the prior art are overcome, precise adaptation, high immersion interaction, creative learning and cross-scene closed loop driven by cognition are realized, and the learning efficiency and knowledge application capability are remarkably improved.

The invention is oriented to the field of data resource construction, aims to provide a self-adaptive dynamic knowledge updating method and system combined with spatio-temporal data, and solves the problem of association and fusion of spatio-temporal data (millisecond-level updating) and static entities (minute / hour-level updating) in a knowledge graph. In practical application, the knowledge graph only having static data is lack of timeliness and is easily disjointed from the real environment, and the prediction reasoning ability is limited. The knowledge graph fused with the spatio-temporal data better meets actual scene requirements, the problem that information of the knowledge graph is outdated can be solved, and dynamic cognitive competence is provided. According to the method, a proper updating mechanism is adaptively selected according to the data updating frequency, the data importance degree, the environment resource state and the like, the updating task is subjected to full-process management, the spatio-temporal data and the atlas entity are associated and fused for use, and the knowledge application requirement under the high timeliness requirement is met.

The invention provides an intelligent knowledge management system and method and a storage medium. The system comprises a knowledge application module which obtains input information and pushes interested knowledge to a current user; the intelligent engine module obtains to-be-queried knowledge corresponding to the input information when the current user has the operation authority corresponding to the input information; the knowledge management module is used for carrying out classified management on knowledge entries in the knowledge graph and carrying out approval, correction and release on newly added knowledge entries; and the basic support module is used for performing authority distribution on the user and performing authority verification on the current user when receiving the query request. For scattered knowledge points in the field of power marketing, multi-source heterogeneous data are integrated, the knowledge graph is established, information input by the user is accurately understood and analyzed based on the intelligent search engine module, the user can conveniently obtain power marketing knowledge by himself / herself, and the requirement of the user for the power marketing knowledge is met.