53 results about "Core Knowledge" patented technology

The invention relates to a method for constructing a subject textbook knowledge graph by using a large language model, and the method is realized through six steps: firstly, introducing a self-prompt framework, generating relation synonyms, synthesizing samples and sentence variants through three rounds of dialogues, and providing rich semantic guidance for subsequent relation extraction; secondly, guiding a large language model to accurately extract core knowledge point entities from teaching materials, exercises and PPT texts by means of a professional field instruction template; then, respectively extracting an attribute triple and a relation triple of the knowledge points by applying a multi-round dialogue mode and combining with a synthetic sample prompt; then, inputting the extracted triad into a verification module, and ensuring the accuracy through iterative verification; and finally, generating an entity embedding vector by utilizing an MPNet model subjected to subject knowledge fine adjustment, calculating entity similarity through a dynamic weighted pooling mechanism, judging entity pairs with high similarity, performing knowledge fusion if the entity pairs represent the same concept, and otherwise, reasoning a potential missing relationship and complementing the knowledge graph. According to the method, the knowledge graph of the course of the specific subject can be automatically constructed from the unstructured text efficiently and accurately, and powerful support is provided for teaching and learning of related subjects.

The invention discloses a scenic spot accompanying service method and a scenic spot accompanying service system based on linkage of a generative AI and a knowledge base, particularly relates to the technical field of artificial intelligence and smart tourism, and is used for solving the problems that core knowledge is forgotten and the answer accuracy is reduced due to dynamic knowledge updating of a generative AI model in an existing scenic spot guide system. The method comprises the following steps: extracting tourist question keywords in real time and retrieving a knowledge base to generate associated data; dynamically weighting the associated data priority based on the tourist aggregation density; generating an initial answer through real-time parameter adjustment; fusing the spatial-temporal clustering and the knowledge graph structure entropy to generate a weight factor; correcting the answer coverage defect and evaluating the result; outputting a final answer after directional addition of core knowledge iterative training; it is guaranteed that core knowledge of the scenic area is stably reserved in the dynamic learning process of the generative AI, meanwhile, the real-time scene requirement is accurately responded, and the reliability, accuracy and individuation level of the navigation service are remarkably improved.

The invention discloses a progressive topic generation method based on semantic analysis and a knowledge graph. Performing preprocessing and semantic analysis on the question setting demand text, and extracting a necessary keyword set corresponding to the core knowledge points and an optional determiner set corresponding to the additional conditions; carrying out concept mapping in a college professional knowledge graph and associating with a course outline, constructing a hierarchical semantic constraint framework containing hard constraint and soft constraint, and carrying out consistency detection; adopting reverse index hard matching recall and knowledge graph soft extension recall to obtain candidate materials, and inputting the candidate materials into a field fine-tuning large language model to generate candidate questions; reordering is performed through multi-target learning ordering, teaching logic verification and quality evaluation are executed, and final questions are output; user feedback is received to form an incremental sample, and the generation model and the sorting model are updated, so that the accuracy, diversity and controllability of question generation are improved, and closed-loop optimization is supported.

The invention discloses an intelligent teaching auxiliary system based on a large model, and belongs to the field of artificial intelligence teaching auxiliary systems. The system comprises three modules: a teaching material content analysis and question-answer pair generation module, which can extract teaching material core knowledge points and generate questions, reference answers and key scoring elements; the intelligent dialogue and dynamic questioning module is used for dynamically questioning when detecting that the user answers and does not cover key scoring elements through semantic understanding until a threshold value is reached or the elements are covered; and the multi-dimensional scoring and optimization feedback module is used for summarizing question and answer records of the user, generating a final answer, performing differential scoring, and pushing an optimal score to be fed back to the user after fine-grained comparison evaluation.

The invention relates to the technical field of storage and retrieval, in particular to a large-model knowledge persistent storage and retrieval method, which comprises the following steps of: capturing version evolution paths of large-model knowledge units in real time, and constructing a four-dimensional knowledge manifold comprising an ontology feature vector and a version association weight for each knowledge unit; extracting a core knowledge skeleton through a topology preserving layer, separating version difference characteristics through an evolution path layer, and generating compressed skeleton-path double codes; constructing a multi-level index structure based on the skeleton-path double coding, wherein the multi-level index structure comprises a static knowledge ontology layer and a dynamic path redirection layer; writing the knowledge unit into a storage device by adopting a hypercube mapping strategy, and establishing a path attenuation model to dynamically recycle a waste version space; according to the method, the long-term storage availability is improved, and the retrieval accuracy and the storage resource utilization rate are improved while efficient evolution management of the knowledge units is guaranteed.

The invention provides a power grid intelligent operation and maintenance multi-mode knowledge base question answering method and system. The method comprises the steps of obtaining natural language query of a user and historical dialogue information of the user in a power grid intelligent operation and maintenance scene; on the basis of the historical dialogue information, keyword preprocessing is conducted on the natural language query, and multi-query input information is obtained; based on a pre-constructed multi-modal knowledge base, carrying out mixed search on the multi-query input information to obtain a core knowledge block set; according to the core knowledge block set, the natural language query and the historical dialogue information, generating question and answer prompt words, inputting the question and answer prompt words into a preset large language model, and outputting target output corresponding to the natural language query; through the method provided by the invention, the multi-modal heterogeneous technical document in the intelligent operation and maintenance scene of the power grid can be effectively processed and utilized, so that the generated answer can be ensured to accurately match the semantic requirement of user query and has high semantic quality.

The invention discloses a learning content determination method and device based on an intelligent education platform and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: extracting a basic knowledge point cluster and a key knowledge point cluster from a student teaching material, and generating a target question bank containing different difficulties and question types; generating a knowledge point association network according to the association relationship and strength of the knowledge points corresponding to the questions in the target question bank; and in combination with the learning track of the student in the learning map, generating the target association strength between the target knowledge points, updating the knowledge point association network according to the target association strength, obtaining a student exclusive target association network, and updating the target question bank to determine the target learning content of the user. According to the scheme, the knowledge points are extracted through the textbook, the target question bank of the multivariate question types is generated, the problem of single question type in the prior art is solved, the question bank is dynamically updated in combination with the learning track and the association network, students can focus on core knowledge points, and the learning efficiency is improved.

The invention belongs to the technical field of data management. According to the material data cleaning method and system fusing the large language model and the knowledge graph, the knowledge graph stores core knowledge such as an enterprise material classification system, term definition and quality rules in a structured triple form, and a clear standard basis is provided for cleaning operation; accurate structured knowledge anchor points and standardized constraints are provided for the large language model, the illusion of the large language model is effectively inhibited, and it is ensured that the output content conforms to industry specifications; the big language model injects strong natural language understanding, fuzzy semantic analysis and unstructured information extraction capabilities into the knowledge graph, makes up for the flexibility deficiency of the knowledge graph, and deeply fuses the two to jointly solve the key problem of what the text semantics is understood and whether the semantics meets the standard or not. And the material data cleaning precision is improved.

The invention relates to the technical field of textbook construction and optimization, in particular to an automatic textbook construction and optimization method and device.The method comprises the following steps that original resources of multiple types of textbooks are collected, and core knowledge elements in the original resources are recognized; constructing a structured mapping relationship between the knowledge elements and the teaching resources based on the core knowledge elements; on the basis of the structured mapping relationship, constructing a knowledge graph covering multidisciplinary knowledge points and a preposition dependency relationship, and automatically identifying knowledge faults and complementing missing association so as to form a knowledge network with self-evolution ability; based on the knowledge network, utilizing a hybrid generation model to cooperatively work, and quickly generating a structured textbook framework containing knowledge veins, teaching cases and exercise resources; learning condition data including classroom behaviors, learning feedback and knowledge mastering conditions are obtained, and a quantity and chemical condition report is obtained after data processing; and updating data based on the quantitative chemical situation report and the knowledge graph.

The invention provides an AI-enabled intelligent teaching personalized service method and system, and relates to the technical field of intelligent teaching, and the method comprises the steps: periodically collecting the multi-modal data of a current student; performing correlation analysis on learning data and behavior data in the multi-modal data to generate an analysis result; constructing a weight matrix according to a group label and an ability prediction model in an analysis result, and performing hierarchical course division on all knowledge nodes according to the weight matrix; respectively determining a core knowledge proportion and an expansion knowledge proportion based on the capability prediction model and the academic wind vector, and fusing the core knowledge proportion and the expansion knowledge proportion to generate a fusion proportion; according to the method, a core module and an expansion module are integrated, an intensified training sequence and an expansion content sequence are screened out from the core module and the expansion module respectively, the intensified training sequence and the expansion content sequence are arranged in a crossed mode according to the fusion proportion, and a dynamic learning path is generated. And precision and individuation of teaching services are realized.

The invention provides a knowledge graph and deep learning fused research course automatic generation method and system. The method comprises the steps of receiving event stream data from a plurality of knowledge sources; performing source identification and theme clustering processing on the event stream, and generating a standardized update demand unit; writing the update demand unit into a temporary change buffer area outside the atlas; inputting the knowledge fragments in the change buffer area into a cross-source semantic alignment model, and executing entity mapping and relation prediction operation; generating a multi-scale updating plan based on the influence hierarchy of the candidate nodes and the edge set, and executing a graph updating operation; based on the updated knowledge graph, determining a learning target and ability dimension of the research course, a required core knowledge point sequence and an association relationship; inputting the core knowledge point sequence into a course structure generation model, and outputting a preliminary study course outline; and generating a final study course by using the course outline. According to the method, the research course can be automatically generated, and the timeliness and accuracy of the course are ensured.

The invention provides an AGI-oriented personalized language model evolution method and device, equipment and a medium, and the method comprises the steps: constructing a three-layer memory system composed of short-term caching, medium-term LoRA increment and long-term solidification, and achieving the dynamic circulation and long-term accumulation of knowledge; a user interaction frequency and context relevancy driving dynamic parameter modulation mechanism is introduced, differential updating is carried out on the matrix, and weight modeling deeply coupled with an individual use mode is formed; in the quantization stage, the bit width of each layer is dynamically allocated by using a user frequency driving function, high-frequency core knowledge is ensured to keep high precision, and low-frequency redundant information is ensured to obtain a high compression ratio; the platform is a micro-training for each user. The method aims at enabling a language model running on a resource-limited terminal device to continuously learn and internalize private knowledge, historical interaction and personalized preference of a user, and gradually evolve into a differentiated and cross-task universal agent.

The present invention provides an AI-enabled smart teaching personalized service method and system, which relates to the field of smart teaching technology. The method includes periodically collecting multimodal data of current students; performing association analysis on learning data and behavioral data in the multimodal data to generate analysis results; constructing a weight matrix based on group labels and ability prediction models in the analysis results, and dividing all knowledge nodes into hierarchical courses based on the weight matrix; determining the core knowledge proportion and the extended knowledge proportion based on the ability prediction model and the style of study vector, and fusing them to generate a fusion ratio; screening out intensive training sequences and extended content sequences from the core module and the extended module respectively, and cross-arranging the intensive training sequences and the extended content sequences according to the fusion ratio to generate a dynamic learning path. The present invention achieves precision and personalization of teaching services through multimodal data fusion, deep association analysis, hierarchical course construction and dynamic path planning.

The application discloses a progressive question generation method based on semantic analysis and a knowledge graph. The question generation demand text is preprocessed and semantically analyzed, the core knowledge point corresponding essential keyword set and the additional condition corresponding optional limiting word set are extracted, the concept mapping is carried out in the college professional knowledge graph, the curriculum outline is associated, the hierarchical semantic constraint framework containing hard constraint and soft constraint is constructed and consistency detection is carried out, the candidate materials are obtained by using the inverted index hard matching recall and the knowledge graph soft expansion recall, the candidate questions are generated by inputting the field fine-tuning large language model, the final questions are output by carrying out multi-objective learning sorting and reordering, teaching logic checking and quality evaluation, the incremental samples are formed by receiving user feedback, the generation model and the sorting model are updated, the accuracy, diversity and controllability of the question generation are improved, and closed-loop optimization is supported.

The invention discloses an intelligent test paper composition method and device, and belongs to the technical field of image or video recognition or understanding. The intelligent test paper composition method comprises the following steps: collecting original data of various questions, and carrying out standardization processing on the collected data to form a standard question bank; wherein the standardization processing comprises adding applicable teaching materials, applicable grades, question types, first-level knowledge point labels, second-level knowledge point labels, difficulty coefficients and score information to the original data; pre-de-duplication processing is carried out on questions in a standard question bank, then the standard question bank is divided into a plurality of first-level question banks according to first-level knowledge point labels, and the first-level question banks are divided into a plurality of second-level question banks according to second-level knowledge point labels; and screening questions from the standard question bank according to set test paper composition information. And when the repeated questions belong to the second-level question bank related to the core knowledge point, replacing the repeated questions with another question in the same second-level question bank, thereby ensuring the accuracy of the test paper composition again.

This invention discloses a shear force distribution demonstration device and measurement method, belonging to the field of structural engineering teaching technology. The device includes a base, a support mechanism, a loading mechanism, a demonstration mechanism, and a measurement mechanism. The support mechanism is a frame structure with sliding rails; the loading mechanism has replaceable supports with switchable constraints; the demonstration mechanism uses replaceable vertical columns with four-jaw chuck sleeves; and the measurement mechanism includes a strain measurement module, an S-shaped tension sensor, and a positioning ruler. This device adopts a fully modular, quick-release design, allowing for rapid switching between series and parallel systems, column constraint forms, and parameters. It simultaneously demonstrates the core knowledge points of inter-story force transmission in series and intra-story force distribution in parallel, verifying the shear force distribution law through measured data. This invention is easy to operate, combining visualization and quantitative measurement, effectively solving the pain points of abstract and difficult-to-understand traditional teaching methods, and significantly improving teaching effectiveness.

The invention relates to the technical field of data processing, and discloses a knowledge security evolution updating method and system oriented to newly-added information, and the method comprises the steps: constructing a multi-granularity time sequence index structure containing business cycle key nodes, marking system input timestamps for collected data, and analyzing business effective timestamps; identifying and inputting an effective time difference distribution zone based on the distribution pattern of the difference value of the two, and calculating a time sequence reversal congestion factor representing the priority reversal risk of the data near the key node; combining the factor, the input aging attenuation degree and the service aging fitting degree to calculate a security evolution sorting weight, and generating a structured suppression parameter by using the factor; and finally, performing incremental data fusion on the target node of the index structure according to the weight, and updating the knowledge base. Through two-dimensional time modeling and an anti-congestion mechanism, the problem that core knowledge is submerged due to high-concurrency window period fragmentation information is effectively solved, and orderly and safe evolution of the knowledge base is achieved.

The present invention discloses a text multi-hop reasoning question answering method using two-stage retrieval, and the field of natural language processing technology, and the method includes two modules. First, in the context-aware knowledge retriever module, a dual encoder framework with a mask mechanism is used for pre-training, and then the representation of the learned hypothesis and knowledge is based on sentence-level dense retrieval to obtain candidate core knowledge. Secondly, in the entity-aware knowledge verification module, the accessibility of the hypothesis and knowledge is determined by the entity-level sparse matrix, which will be used to further verify whether there is a supporting relationship between the core knowledge and the hypothesis. This method combines the context-aware and entity-aware knowledge retrieval methods and applies them to the reasoning process, improves the blindness of entity-centric reasoning, and enables the model to simultaneously consider the contextual semantic matching and entity concept influence between the hypothesis and knowledge. In the field of scientific question answering that requires complex multi-hops, it has obvious performance advantages.

The invention provides a scientific research logic representation-oriented composite knowledge chain construction method, and relates to the technical field of knowledge extraction, and the method comprises the steps: constructing a composite knowledge chain structure model through defining a core knowledge component; constructing an instruction set of the components and the sub-elements, performing instruction fine tuning on the pre-trained large language model, processing the scientific and technical literature through the fine-tuned large language model, and generating a knowledge extraction result; analyzing the internal structure relationship of the component and the cross-component logic relationship; and performing structured organization on the components, the component internal element relationship and the cross-component logic relationship to generate a composite knowledge chain. Through the method and the device, the technical problem that the scientific research knowledge extraction accuracy is relatively low due to the fact that a knowledge organization method is difficult to deeply characterize a complex logic relationship and dynamic dependence in a scientific research activity in the prior art can be solved, and the knowledge extraction accuracy is improved by deeply characterizing scientific research logic and automatically constructing a composite knowledge chain.

The application discloses a kind of based on semantic alignment multimodal data labeling method, system and label integrated machine, belong to artificial intelligence technical field, including: obtaining input multimodal data, the multimodal data is separated into entity representation and environment representation by preset feature decoupling model;Dynamic threshold is determined based on the environment representation, and the conflict value between the entity representation is calculated;According to the comparison result of the conflict value and the dynamic threshold, when the conflict value is greater than the dynamic threshold, corresponding multimodal data is used as to-be-labeled data;According to the cross-modal attention mechanism, complementary mask is generated for the to-be-labeled data, to carry out feature fusion on the to-be-labeled data and obtain artificial correction label;The application can greatly improve the screening purity of high-value semantic conflict difficult example, and realizes parameter smooth transition under the premise of not destroying multimodal big model history core knowledge subspace.

This invention discloses an adaptive dynamic optimization method and system for knowledge bases, relating to the field of knowledge base optimization technology. The invention uses a confidence assessment module to denoise and map user interaction behavior to generate feedback confidence scores, and dynamically adjusts the feedback admission threshold using knowledge structure association values, recognizing only high-confidence feedback as valid signals. It then updates the consolidation weights of knowledge items through a moving average calculation, and finally transfers low-frequency, low-value knowledge to archiving units based on these consolidation weights. This achieves effective retention of historical core knowledge while continuously absorbing new knowledge, avoiding noisy feedback from misleading the optimization direction, and solving the problems in existing technologies where dynamic knowledge base updates cannot effectively retain historical knowledge and user feedback noise interferes with optimization.

The invention discloses a port pollution and carbon cooperative processing method based on multi-model fusion and a dynamic knowledge graph, and the method comprises the steps: constructing and maintaining the dynamic knowledge graph which comprises a port entity and a multi-dimensional relation of the port entity, taking the dynamic knowledge graph as a core knowledge base for cooperative decision making, carrying out the task analysis and reasoning based on the graph when a cooperative processing request is received, and carrying out the task analysis and reasoning based on the graph; a complex task is decomposed into atomic sub-tasks with a logic or time sequence dependency relationship, adaptive professional processing models are matched, a task execution directed graph is constructed, multiple models are scheduled in order for collaborative operation, output is fused and subjected to conflict resolution, a final collaborative scheme is generated, actual effect data are collected, and the task execution directed graph is matched with the atomic sub-tasks. And incremental updating is carried out on the dynamic knowledge graph, so that a complete closed loop from sensing, decision making, execution to feedback learning is formed. According to the invention, information intercommunication and strategy linkage of port pollution prevention and carbon emission control systems are realized, dynamic changes of port operation can be automatically adapted, and the intelligent level and comprehensive benefits of collaborative management are continuously improved.

The present invention relates to the field of machine learning technology, and in particular to a large language model capability assessment method and system based on dynamic data assessment. The method comprises: obtaining a question input by a user and extracting core knowledge points and main content from the question; based on the core knowledge points and main content, using a pre-trained large language model to perform an online search and generate a knowledge description related to the question; generating an assessment question based on the core knowledge points, main content, and knowledge description; performing difficulty control and optimization on the assessment question to obtain a final assessment question; performing multi-dimensional capability assessment and quality testing on the final assessment question, obtaining an assessment result, and completing the capability assessment of the large language model. The present invention can ensure the quality and consistency of assessment data and improve the reliability and fairness of LLMs capability assessment.

The application provides a composite knowledge chain construction method for scientific research logic characterization, and relates to the technical field of knowledge extraction. The method comprises the following steps: constructing a composite knowledge chain structure model by defining core knowledge components; constructing an instruction set of components and sub-elements, and fine-tuning a pre-trained large language model; processing scientific literature through the fine-tuned large language model to generate a knowledge extraction result; executing analysis of internal structural relationships of components and cross-component logical relationships; structurally organizing components, internal element relationships of components and cross-component logical relationships to generate a composite knowledge chain. The application can solve the technical problem of low accuracy of scientific research knowledge extraction in the prior art due to the difficulty of knowledge organization methods in deeply characterizing complex logical relationships and dynamic dependencies in scientific research activities. By deeply characterizing scientific research logic and automatically constructing a composite knowledge chain, the accuracy of knowledge extraction is improved.

The invention belongs to the technical field of computers and artificial intelligence, particularly relates to an auxiliary teaching material generation method and system based on an artificial intelligence technology, and aims to solve the problems that existing teaching materials are low in production efficiency, insufficient in individuation degree and difficult to generate visual materials. The method comprises the steps of obtaining and analyzing a teaching material generation request to determine a teaching range requirement and a core knowledge point; according to the requirements and the core knowledge points, aggregating related contents from a plurality of resource libraries, and extracting and generating a hierarchical knowledge point structure by using an artificial intelligence large model; and generating at least one teaching material including an interactive animation, a knowledge card or a mind map based on the knowledge point structure. According to the invention, the teaching materials can be automatically, intelligently and individually generated, the lesson preparation efficiency is remarkably improved, the expression form of the teaching resources is enriched, and the teaching effect is improved.

The application discloses a government affair knowledge graph ontology construction and optimization method and device, equipment and medium, belongs to the cross technical field of artificial intelligence and knowledge engineering, and the technical problem to be solved by the application is how to provide a unified and standardized knowledge benchmark for ontology construction, realize autonomous perception of ontology change characteristics of a system, accurately match an optimal analysis mode, and guarantee the efficiency and accuracy of ontology construction without manual intervention, and the technical scheme is that: a field knowledge base is constructed; an ontology construction system is started, basic configuration parameters of a target field are automatically loaded, core knowledge elements and unstructured documents of the target field are collected, the unstructured documents are classified, semantically marked and uniformly processed in format, a hierarchical structured field knowledge base is generated, and the field knowledge base is used as a unified knowledge benchmark for ontology construction; a processing data acquisition and analysis process is triggered; and an ontology analysis mode is adaptively selected.

This disclosure presents a method, apparatus, and electronic device for determining learning content based on a smart education platform, relating to the field of data processing technology. The method includes: extracting clusters of basic and key knowledge points from student textbooks to generate a target question bank containing questions of varying difficulty and types; generating a knowledge point association network based on the correlation and strength between knowledge points corresponding to questions in the target question bank; generating the target association strength between target knowledge points by combining the student's learning trajectory on a learning map; updating the knowledge point association network accordingly to obtain a student-specific target association network; and then updating the target question bank to determine the user's target learning content. This solution solves the problem of single question types in existing technologies by extracting knowledge points from textbooks and generating a target question bank with diverse question types. By combining learning trajectories and association networks to achieve dynamic updates of the question bank, it allows students to focus on core knowledge points and improves learning efficiency.

This invention relates to a retrieval enhancement generation method based on multi-level knowledge fusion, belonging to the field of natural language processing technology. It aims to solve two core problems in existing retrieval enhancement generation technologies: fragmented knowledge levels and interference from noise information. The invention includes the following steps: Graph index construction: Segmenting documents into fragments and extracting entities and relations to generate a knowledge graph with key-value pairs, followed by deduplication and optimization; Multi-level knowledge retrieval: Acquiring local and global knowledge separately, and constructing cross-level semantic associations by connecting knowledge modules through an intermediate layer; Dual-dimensional filtering: Filtering core knowledge based on a weighted score of semantic relevance and knowledge importance, combined with a maximum boundary relevance strategy to remove redundant noise; Response generation: Inputting the filtered knowledge into a large-scale language model to generate accurate and coherent responses. This invention significantly improves the generation quality of knowledge-intensive tasks through cross-level knowledge bridging and precise noise filtering, possessing significant theoretical and practical application value.

The application relates to the technical field of online learning, and particularly relates to a learning path generation method, device and equipment for senior members, a medium and a program product. The method comprises the following steps: firstly, in response to a question input by a senior member, extracting a question feature according to a corresponding subject type of the question; secondly, inputting the feature into a knowledge point matching model, obtaining a core knowledge point by combining a preset knowledge point description-feature keyword mapping library; thirdly, obtaining a mastery degree rating by a multi-dimensional weighting algorithm according to historical learning data of the senior member on the core knowledge point; fourthly, screening similar knowledge points from a preset knowledge point graph based on the core knowledge point, and obtaining an initial screening question list by combining a knowledge point ID-question ID list mapping relationship; and finally, secondarily screening the list and preferentially returning a question corresponding to a weak knowledge point, so that a precise and personalized learning path can be generated, and the learning efficiency of the senior member is improved.

A course resource development method. The method comprises: constructing a Chongqing regional culture key element recognition model, and extracting a cultural core having an educational value and an ideological and political orientation function; establishing a mapping logical framework between regional cultures and ideological and political theories, and performing structured correspondence on the cultural core and key knowledge points in course standards for ideological and political education in higher vocational education, so as to form a multi-dimensional mapping relationship matrix taking cultural carriers, value orientation and teaching targets as logical chains; on the basis of student cognitive features and professional backgrounds, dynamically generating contextualized teaching resource units; integrating multi-modal resources such as text, images and virtual scenes to form a reconfigurable multi-modal resource integration platform; and implementing continuous iterative optimization of the resources by means of teaching feedback data.