178 results about "Knowledge building" patented technology

The embodiment of the invention provides a method for constructing a large language model knowledge management system. The method comprises a knowledge construction step, a knowledge retrieval step and an answer production step. According to the scheme, the artificial intelligence large language model technology can be utilized. External knowledge is obtained from multiple data sources, a search technology and a cue word function of a large language model are combined, RAG proposes a problem to the large model, knowledge obtained by searching the problem in the multiple data sources is used as a background context, and information of the problem and the background context is integrated into cue words of the large language model. And therefore, the large language model can make a final accurate answer.

The invention relates to an intelligent operation and maintenance question-answering system for cable manufacturing equipment, and belongs to the technical field of computer systems based on specific calculation models. The system comprises an edge data acquisition module, a predictive map construction module, a semantic perception module, a question-oriented reasoning module and a question and answer generation module. According to the system, multi-dimensional real-time data in the operation process of equipment is collected and structurally processed, a process knowledge graph is constructed in combination with industry knowledge, and the causal relationship and reasoning parameters in the graph are dynamically updated according to the data trend. A semantic perception module is used for recognizing the problem intention of a user, a semantic weight vector is formed to guide the reasoning process, and a problem-oriented reasoning module is made to execute joint reasoning on the basis of combining real-time data and a knowledge graph and generate an explanatory conclusion. Finally, operation and maintenance suggestions with high readability are output through a question and answer generation module, and the targets of equipment fault intelligent diagnosis, process optimization and man-machine efficient interaction are achieved.

The invention relates to an AI-driven document data structured storage and retrieval method, which comprises the following steps of: analyzing and understanding document contents by utilizing a natural language processing technology, extracting structured question and answer knowledge, and constructing a structured question and answer knowledge base; a natural language processing technology and a large pre-training language model are utilized to analyze and understand a query request and a historical dialogue of a user, real intentions and demands of the user are extracted, and structured demand data are generated; and according to the user input and the demand analysis result, vectorizing the user input by using the pre-training language model, extracting related information from the vector database through a dynamic retrieval strategy, and generating and outputting a final answer. According to the method, deep analysis is performed on the document through a natural language processing technology, structured question and answer knowledge is extracted, and the performance and user experience of a question and answer system are improved by utilizing the semantic understanding ability of a large model.

The invention discloses a large model knowledge base construction method based on multi-granularity retrieval. The method comprises the steps of obtaining a target text and analyzing a document structure, splitting and reorganizing text data according to three levels of titles, fragments and sentences, and constructing three levels of knowledge of the titles, the fragments and the sentences; using a large language model to construct a generative index as question-level knowledge according to the sentence-level knowledge; using a vector model to generate a first vector representing index keys of fragment-level knowledge and / or sentence-level knowledge, and a second vector representing index keys of title-level knowledge and / or question-level knowledge; and respectively storing the first vector and the second vector into a vector database, and storing the index values and the index types corresponding to the first vector and the second vector into a relational database. According to the method, multi-granularity document knowledge base construction is achieved, the expression modes of knowledge under different granularities are expanded, the accuracy of user query and knowledge matching is improved, and the problem that information is lost or incomplete in an existing method is solved.

The invention discloses a knowledge construction method and system based on a large model and an RAG technology, and belongs to the technical field of large models.According to the knowledge construction method and system based on the large model and the RAG technology, through cross-text-block mutual information calculation and RAG enhanced reasoning, the system can quantify statistical correlation between entities, and in combination with context information of an external knowledge base, potential incidence relations of cross-paragraphs or documents are mined, so that the knowledge construction efficiency is improved. A dynamic semantic segmentation strategy is adopted to ensure that semantics in text blocks are consistent, context continuity is maintained by retaining overlapped parts, multiple expressions of the same entity are comprehensively judged through a multi-dimensional anaphora resolution mechanism, the error resolution rate is reduced, dynamic segmentation and mixed retrieval are combined, and the problem of large model input length limitation is solved. And through semantic retrieval and keyword matching complementation, the recall rate is improved, and the key problems of semantic fracture, cross-text association missing, low entity alignment precision and the like in traditional knowledge construction are remarkably solved.

The invention provides a multi-agent cooperative processing system and method for multi-scene legal complaint consultation, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a legal or complaint consultation text inputted by a user, and generating structured consultation request data; automatically generating an evidence obtaining template, and forming a standardized evidence obtaining data packet; constructing a demonstration tree and outputting a multi-scheme decision matrix; performing multi-objective optimization by combining the success rate, the timeliness, the cost and the regional qualification index, and outputting a comprehensive recommendation result; generating a knowledge unit set, and updating a regulation index and agent prompt template library; predicting a re-complaint risk and generating a remedy and optimization suggestion; and outputting review result data, and reversely updating the review result data to the agent configuration strategy in the step of the knowledge construction module. According to the method and the device, the technical targets of multi-agent cooperative processing and dynamic optimization management in the whole legal complaint consultation process can be realized, and the technical effects of improving consultation processing efficiency, enhancing scheme recommendation accuracy, reducing manual participation cost and ensuring data compliance and risk controllability are achieved.

The invention belongs to the technical field of computers, and particularly discloses a knowledge graph construction method and device for target culture resources and electronic device.The method comprises the steps that knowledge graphs of multiple different fields are obtained, and a big language model is trained based on the knowledge graphs to obtain a big language model; integrating the structured information of the knowledge graph into the language representation of a large language model; obtaining target culture resource related text data, and performing fine tuning on the large language model based on the target culture resource related text data; performing knowledge extraction on the target cultural resource related text data through the fine-tuned large language model to obtain related knowledge in a triple form; and constructing the knowledge graph based on the related knowledge in the triple form, so that the problem that the knowledge graph construction presentation result is relatively poor due to the sparse target culture resource data sample in the prior art can be solved.

The invention discloses an equipment diagnosis and maintenance method and system based on a large language model, a terminal and a storage medium. The method comprises the steps that multi-source heterogeneous data of target equipment is collected and preprocessed; constructing and continuously updating a historical case vector database containing equipment knowledge; state data of target equipment are monitored in real time, after fault diagnosis is triggered, the most relevant knowledge fragment is retrieved from the historical case vector database according to real-time abnormal data, and a diagnosis context is constructed; inputting the diagnosis context into a large language model for reasoning, and outputting a fault diagnosis report and a maintenance scheme; the fault diagnosis report and the maintenance scheme are presented to a user, and user feedback is collected to optimize the system. According to the method, real-time data, historical records and structured knowledge of equipment can be automatically integrated, a dynamic knowledge base is constructed, accurate and efficient diagnosis of equipment faults is realized by utilizing the powerful reasoning capability of a large language model, an executable maintenance scheme is automatically generated, and the maintenance efficiency is improved.

The invention discloses a knowledge graph generation method based on multi-source data integration, and relates to the technical field of knowledge graph generation. The method comprises the following steps: collecting multi-format data, and performing cleaning, standardization and desensitization preprocessing to ensure that the data is integrated; using the fusion model to extract entities and relationships, and adapting to multi-source data types; similarity is calculated in combination with multi-dimensional features, and entity alignment disambiguation is achieved; carrying out weighted fusion on multi-source knowledge to construct a triple, and processing relation conflicts; evaluating the quality of the atlas through multiple indexes; triggering conditions are set, incremental updating and version management are adopted, and dynamic iteration of the atlas is guaranteed. According to the method, the multi-source data preprocessing quality is improved, the entity recognition and alignment precision is enhanced, relation conflicts are solved, and a high-quality time sequence knowledge graph is constructed; incremental updating is efficient and energy-saving, version management is traceable, and multi-field dynamic application requirements are met.

The invention relates to the technical field of fault diagnosis, and provides a graph network and knowledge driven interpretable fault diagnosis method and system, and the method comprises the following steps: extracting intra-class and inter-class features for obtained operation data, and constructing a data driving graph; constructing a knowledge graph based on prior knowledge, constructing a knowledge-driven graph, and fusing the data-driven graph and the knowledge-driven graph into a unified graph structure; constructing a plurality of graph attention network sub-models, and identifying and fusing the obtained unified graph structures to obtain a diagnosis result; and performing interpretability analysis on the diagnosis result based on an input feature gradient method and a graph neural network interpreter to obtain a comprehensive diagnosis result. According to the method, more accurate, more stable and more explainable fault diagnosis and decision support is provided through data driving, knowledge driving, intra-class and inter-class feature joint extraction, multi-model fusion and fusion of an explainable analysis method.

The invention discloses a multi-priori knowledge fused cerebral apoplexy rehabilitation map convolutional network evaluation method, and belongs to the technical field of cerebral apoplexy rehabilitation evaluation. Firstly, a high-precision prior information matrix is automatically generated through a collaborative and causal relationship automatic reasoning method based on Riemannian manifold geometry and transfer entropy, the problem that in the prior art, engineering depends on artificial features is effectively solved, and interpretable physical prior guidance is provided for a model. Then, through a multi-relation graph construction and attention weighting multi-modal adaptive fusion method, spatio-temporal features and priori knowledge are adaptively fused, an optimized graph structure is constructed, and the representation ability and interpretability of the model to complex joint interaction are improved. And finally, through a space-time diagram convolutional network guided by prior information and a comparative learning collaborative optimization method, the generalization ability and evaluation precision of the model in a small sample scene are remarkably improved through data enhancement and loss function optimization.

The invention belongs to the technical field of power natural language data questions and answers, and particularly relates to a multivariate knowledge link driven NL2SQL question enhancement method. The method comprises the following steps: constructing an electric power data mode by utilizing database table information, and carding electric power domain knowledge; constructing a problem analysis Prompt template, analyzing the structure of an original problem by using a large language model, and extracting a key entity from the original problem; on the basis of the sorted electric power field knowledge and key entities extracted from the original questions, retrieving the electric power field knowledge by adopting a hybrid similarity retrieval mode; obtaining a database table and a data mode related to the original problem through a multi-stage mode linking method; based on the retrieved power field knowledge and the obtained data mode, knowledge standardization is carried out, a problem enhancement Prompt template is designed, reconstruction enhancement is carried out on an original problem by using a large language model, confusion and interference factors are eliminated, and the question and answer accuracy is improved.

The invention discloses an intelligent optimization system for enterprise resource planning, and relates to the technical field of enterprise resource management, and the system comprises a data collection and preprocessing layer, a knowledge construction and reasoning layer, a risk early warning and decision support layer, and a resource scheduling and execution layer. According to the invention, a knowledge graph covering various resources of an enterprise and association relationships thereof is constructed by using preprocessed data through a knowledge construction and reasoning layer, a causal relationship network among the data is established, and meanwhile, an intervention effect evaluation unit combines an anti-factual reasoning result of a causal reasoning unit to evaluate the intervention effect of the enterprise. Quantitative evaluation is carried out on different resource pre-allocation intervention schemes, and a detailed evaluation report is output, so that an enterprise can perceive and take corresponding measures in advance before a risk occurs, and can select an optimal scheme according to a scientific evaluation result when a resource planning decision is made, thereby effectively ensuring stable operation of enterprise resources, and improving the resource planning efficiency. And the operation risk caused by the resource problem is reduced.

The invention provides an expert knowledge-driven large-model customized data processing method and related equipment. The method comprises the following steps: inputting acquired business data into a trained large model capable of dynamically customizing an expert sub-model; in the large model, based on the business data, determining a knowledge embedding vector; based on the knowledge embedding vector, determining the weight of each adapter expert, and customizing an expert sub-model; and processing the business data based on the expert sub-model. According to the embodiment of the invention, the business knowledge hierarchical structure is constructed by using the multi-dimensional business information and the expert knowledge based on the large-scale business data. In combination with a large-model efficient fine tuning technology and a mixed expert strategy, dynamic expert group adaptation is realized under the guidance of business data knowledge, so that different expert models are customized for different business data, automatic adaptation under multiple scenes is realized, and more professional data processing is realized.

The present invention belongs to the technical field of power natural language data question answering, and specifically relates to a method for enhancing NL2SQL questions driven by multi-source knowledge links. The method includes: constructing a power data schema using database table information and sorting out power domain knowledge; constructing a question parsing Prompt template and using a large language model to analyze the structure of the original question to extract key entities from the original question; retrieving power domain knowledge in a hybrid similarity retrieval manner based on the sorted out power domain knowledge and the key entities extracted from the original question; obtaining database tables and data schemas related to the original question through a multi-level schema linking method; standardizing knowledge and designing a question enhancement Prompt template based on the retrieved power domain knowledge and the obtained data schema, and using a large language model to reconstruct and enhance the original question to eliminate confusion and interference factors and improve the accuracy of question answering.

The invention discloses a reasoning application method, system, equipment and medium combining tool creation and evolution, knowledge is abstracted from a collected data set by using a large language model, a knowledge tree structure is constructed, a series of tools are induced based on the knowledge tree structure, and the universality and reusability of the tools are ensured. In the tool evolution search stage, a complete tool evolution mechanism is designed, and a corresponding optimization function is designed in combination with an evaluation index, so that the balance of adaptive exploration and utilization is realized, a tool set is dynamically optimized, and the diversity and adaptability of the tool set are continuously improved. In the reasoning application stage, a tool enhancement agent is constructed, and the tool is flexibly called to solve the complex reasoning problem. According to the method, the reusability and diversity of a tool are remarkably improved, the reasoning accuracy and efficiency of a large language model are enhanced, meanwhile, the interpretability and adaptability of a system are improved, good cross-domain application potential is achieved, and a certain basis is provided for enhancing the complex reasoning ability of artificial intelligence.

The invention discloses an electrical equipment defect identification method and system based on large model tuning, and the method comprises the steps: obtaining multi-modal defect data, and carrying out the defect identification through combining a domain knowledge graph tuning large model; and predicting an evolution trend based on an identification result, and providing a maintenance decision. The system comprises a multi-modal sensing module, a knowledge construction module, a large model tuning module, a defect identification module, an evolution prediction module and a decision support module. According to the method, the completeness, accuracy and generalization ability of defect identification can be improved, evolution prediction is realized, an intelligent prospective maintenance decision is provided, the efficiency is improved, and the cost is reduced.

The invention discloses a training method of a generative language model based on retrieval enhancement and a dialogue generation method and device.The training method comprises the steps that data cleaning is conducted on original historical dialogues, a pre-trained first large language model and a pre-trained second large language model are constructed, the cleaned historical dialogues are input into the first large language model, and the second large language model is input into the second large language model; generating corresponding question description statements and reply statements; inputting the question description statement and the reply statement into a second large language model to generate a corresponding single piece of first knowledge, and then performing data screening to obtain screened data; n pieces of second knowledge retrieved in the database and the generated single piece of first knowledge form n + 1 pieces of knowledge corresponding to each high-quality historical dialogue, and mixed training data is constructed and obtained; and training the generative language model by using the mixed training data. The problems that the training data is difficult to obtain and the reply of the existing RAG system is not accurate enough can be solved.

The invention discloses an automobile industry knowledge base construction method and system based on a large language model in the field of vector knowledge base construction. The automobile industry knowledge base construction method comprises the following steps that S101, public encyclopedia, news financial and knowledge question and answer websites are collected through the Internet; according to the method, based on the large language model, the knowledge of the automobile industry is efficiently extracted and integrated from mass text data, and comprehensiveness and diversity of the knowledge are achieved; and secondly, the system improves the information retrieval accuracy of the knowledge base in the existing automobile industry, and the large language model question and answer service based on the system can provide accurate and detailed information answers for users. In addition, the semantic comprehension and generation capability of the large language model is beneficial to explaining and expressing automobile industry knowledge with higher continuity and accuracy; the construction of the automobile industry knowledge base not only promotes knowledge spreading and sharing in the automobile industry field, but also provides professional field information for decision makers to support intelligent decision making.

The invention discloses a two-stage multiform differential evolution method for dispatching a mine integrated energy system, and belongs to the technical field of intelligent dispatching. The method comprises the following steps: firstly, under a multi-form optimization framework, combining domain knowledge to construct an electricity-heat alternative task and an electricity-cold alternative task, and introducing a feasibility-driven knowledge migration mechanism to realize efficient migration of knowledge between the two tasks; secondly, designing a two-stage optimization strategy, proposing a neighborhood double-variation mechanism, in the first stage, performing parallel evolution on populations corresponding to two alternative tasks through a diversity-driven neighborhood double-variation strategy, and in the second stage, performing evolution on an original task through a convergence-driven neighborhood double-variation strategy to obtain an optimal feasible solution set; and finally, analyzing the time complexity for explaining the rationality of the proposed method for optimizing the scheduling of the mine integrated energy system. The method can deal with the limitation of an existing optimization method when strong coupling constraint and multiple uncertainties are processed, and the economical efficiency, the energy utilization rate and the scheduling stability of the system are improved.

The invention provides a life cycle evaluation automatic modeling method based on a large language model, and relates to the technical field of life cycle evaluation, and the method comprises the steps: extracting the multi-modal information of texts, tables, images and the like from the multi-source data of academic literatures, reports and the like, constructing an instruction data set in combination with LCA process knowledge, carrying out the instruction fine tuning of a base large model, and carrying out the automatic modeling of the life cycle evaluation. And obtaining the LCA field large language model. And generating a standardized prompt by utilizing a prompt word optimizer, driving a model to generate a product LCA model framework, filling list data, and perfecting process data in combination with semantic search and a correction mechanism. And intelligent connection recommendation among unit processes is realized by calculating the semantic similarity of input and output streams. And finally, the verified structured data is output to LCA modeling software, an automatic modeling process is completed, and the modeling efficiency and normalization are improved.

The invention discloses a knowledge intelligent analysis and question-answering method and system based on time sequence knowledge graph retrieval enhancement, relates to the technical field of natural language processing, and aims to solve the problem that a wrong answer is generated due to the fact that a model possibly retrieves semantic-related but time-inconsistent information due to a time-insensitive retrieval mechanism in an existing method. According to the method, a dynamic time sequence knowledge graph modeling and hierarchical interactive retrieval mechanism is introduced. According to the framework, entities and relationships are bound with specific timestamps through time annotation in a knowledge construction stage, and a dynamic graph capable of evolving along with time is formed; in a retrieval stage, through time query decomposition (decomposing a multi-time constraint complex query into single-time sub-queries, and gradually screening related knowledge by a three-layer interactive retriever (time sequence sub-graph retrieval, node-level retrieval and knowledge-level retrieval), time conflicts and redundant interference are reduced, and finally answers with consistent contexts and accurate time are generated.

The invention relates to the technical field of software development, and discloses a method for project process management and quality management through an intelligent agent, and the method comprises the steps: S1, carrying out the multi-protocol access, database direct connection, multi-modal analysis and context memory association enhancement of a communication office platform through an intelligent sensing layer; s2, constructing a dynamic knowledge graph through a knowledge construction layer, configuring a node state, and carrying out relation modeling; s3, an intelligent decision-making layer carries out deep analysis and reasoning through project related knowledge and relationships presented in the dynamic knowledge graph, and provides decision support for project management; s4, realizing data integration and visual report generation through a visual layer; through automatic data acquisition and information analysis, manual check of chat group information one by one is replaced, the project information arrangement time is greatly shortened, key information can be accurately identified and extracted through the intelligent agent, information omission or errors caused by negligence during manual arrangement are avoided, and the accuracy and integrity of project weekly report are ensured.

The invention relates to the field of target intention prediction, and relates to a target intention recognition method based on knowledge and data combined driving, and the method comprises the following steps: 1, collecting behavior variable data of an air flying target, and carrying out the data cleaning, thereby obtaining x; 2, expert knowledge is obtained through a Delphi method, and a confidence rule base is constructed; step 3, inputting the data into a confidence rule base module to obtain an embedded representation xe of expert knowledge, and splicing the embedded representation of the expert knowledge and the data to form lt; x, xegt; all the data are input into the neural network module; and step 4, iteratively optimizing parameters of the belief rule base and the neural network in a collaborative optimization mode until the model converges. According to the method, the effect of expert knowledge in the intention recognition process is fully utilized, and the accuracy of the model is improved. And high intention recognition accuracy can still be obtained under the condition that the data volume is not large.

The invention discloses a question answering system and a construction method for a motor vehicle driving teaching knowledge graph, and aims to overcome the defects of knowledge coverage integrity, scene adaptability, reasoning depth and interaction naturalness in the prior art. A traffic regulation document, coach voice and short videos and vehicle-mounted real-time data are integrated through a data acquisition module to cover rules and experience knowledge, a knowledge construction module constructs a space-time dynamic knowledge graph containing static and dynamic nodes based on Dgraph, space-time association is achieved by means of GeoHash coding, real-time updating is completed through an MQTT protocol, and the time-space dynamic knowledge graph is stored through a storage module. The intelligent reasoning module calls a DoWhy model to construct an'operation, principle and result 'causal chain in combination with a physical formula so as to output deep interpretation, and the interaction module achieves multi-mode immersion teaching through speech synthesis, gesture recognition and 3D animation, dynamically optimizes knowledge pushing according to student operation data, and achieves multi-mode teaching. The comprehensiveness, the real-time performance, the scientificity and the interaction naturalness of driving teaching are improved, and the method is suitable for intelligent driving teaching scenes.

The invention discloses an intention-driven network configuration template generation method, which is characterized in that automatic translation from a user intention to a network configuration instruction is realized by constructing a comprehensive model fusing a graph neural network and a natural language processing model, and specifically comprises the following steps: S1, constructing a network configuration instruction graph; s2, preparing a data set; s3, finely adjusting the BERT model; s4, instruction embedding generation; s5, intention embedding generation; s6, calculating matching similarity; according to the method, knowledge contained in a network configuration document is mined, an instruction graph capable of representing the relation between network configuration instructions is constructed, an incidence relation data set of a private network service deployment intention and the network configuration instructions is prepared, a graph neural network and a natural language processing pre-training model are fused, and through contrast learning training tuning, the network configuration instructions are optimized. The translation from the private network deployment intention to the network configuration instruction sequence is realized, that is, the network configuration template is automatically generated, and the pressure of a service expert on reasoning a configuration scheme in a new scene is relieved.

The invention discloses a multi-dimensional capability construction knowledge tracking method based on learning behavior decoupling, and belongs to the technical field of knowledge tracking. The method comprises the steps of obtaining test question embedding and test question-response embedding, and obtaining learning deviation behavior characterization and multi-scale behavior characterization based on the test question-response embedding; inputting the learning deviation behavior representation and the multi-scale behavior representation into a binary state model to obtain a knowledge construction representation; obtaining a test question sequence structure representation based on test question embedding, and performing multi-scale association on the test question sequence structure representation to obtain a multi-scale test question association representation; the multi-scale test question association representation and the knowledge construction representation are subjected to student ability modeling through frequency domain perception fusion, and differential learning ability is obtained; and predicting the student test question performance based on the differentiated learning ability to obtain a student test question performance prediction value. The method improves the accuracy of student test question performance prediction.

The invention discloses a subject cognition teaching knowledge construction and intelligent navigation system based on a knowledge graph, and relates to the field of education technology and intelligent teaching. The knowledge graph construction module is used for extracting knowledge points and association relationships thereof from subject teaching resources and constructing a structured knowledge graph; the personalized cognitive state vector generation module analyzes the mastering degree of knowledge points by means of a cognitive diagnosis model, and generates a state vector reflecting the cognitive level of an individual; the navigation strategy generation module adopts a hierarchical reinforcement learning method, and dynamically generates a navigation strategy containing knowledge point priorities and an optimal learning path by taking a learning target as guidance; and after the learning effect adjustment module pushes the strategy to the learner, acquiring knowledge point mastering state deviation and learning strategy consistency deviation in real time, and synchronously updating the edge weight of the knowledge graph and cognitive diagnosis model parameters. According to the system, through dynamic evolution of the knowledge graph and self-adaptive updating of the cognitive model, accurate optimization of a teaching strategy is realized, and the personalized learning navigation efficiency is improved.

The invention discloses a hierarchical structured hyper-stereoscopic knowledge base construction method based on video semantic analysis, and relates to the technical field of knowledge base construction.The hierarchical structured hyper-stereoscopic knowledge base construction method comprises the steps that a stable ontology layer frame is generated through a first round of database construction mechanism, and multi-angle details are supplemented for nodes through a second round of database construction mechanism; according to the decoupling type process, common structure drift, concept redundancy and semantic omission in single-round extraction are effectively avoided, and high-quality and controllable knowledge construction is achieved. According to the method, the logic relation is marked for the same-layer nodes and the cross-layer nodes in the ontology layer framework, so that the whole knowledge base not only has the structural property, but also has the rich semantic linkage capability, and the reasoning and visualization effects are greatly improved. In addition, an inference rule is defined based on an explicit logic relationship, so that the knowledge base realizes advanced functions of same-layer recommendation, cross-layer navigation, causal tracing and the like.

The invention belongs to the technical field of artificial intelligence content generation, and discloses an AI GC-based multi-modal picture book material fusion and dynamic presentation method. The method comprises the following specific steps: S1, creating a picture book; s2, illustration matching generation; s3, audio production and addition; s4, preliminarily fusing the materials; s5, interactive logic design; s6, embedding AR elements; and S7, carrying out personalized customization recommendation. According to the method, knowledge picture books are generated by means of AI GC, multi-mode and AR assistance are achieved, abstraction is converted into images, learning potential is stimulated, and knowledge construction, personalized customization, special effect matching, AR and expansion imagination are assisted; a warm bridge is built with the parent-child, the parent-child explores the world together, multiple modes are achieved, AR rich interaction is achieved, the parent-child distance is shortened, the barrier is eliminated, and the parent-child is warm and sweet in light.