70 results about "Knowledge building" patented technology

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 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 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, and particularly discloses a big-data-based question and answer information processing method for AI online education, which comprises the following steps: S1, constructing a subject knowledge graph, taking concept nouns as large nodes, taking question solving methods as small nodes, establishing a derivation relationship between concepts, and forming a structured knowledge base; s2, processing questions of the user, identifying question knowledge points by extracting subject keywords, positioning activation nodes in the knowledge graph, and determining a derivation path; and S3, generating an answer text, generating a connection text according to a node sequence, and performing deviation detection and correction through real-time semantic vector comparison to ensure the answer accuracy and logic continuity. The whole process realizes a complete closed loop from knowledge construction to intelligent question and answer.

The invention discloses a vehicle refitting scheme generation method and system based on a large model and a knowledge graph, and relates to the technical field of electric vehicle networking, and the method comprises the steps: inputting an obtained user refitting demand text into a cloud large model, analyzing the refitting demand text, extracting key information, converting the key information into a query vector, and sending the query vector to a cloud server; the method comprises the following steps: establishing a vehicle refitting knowledge graph, performing matching retrieval with entity embedding in the commercial vehicle refitting knowledge graph, constructing cue words according to the retrieved knowledge, generating a vehicle refitting scheme through cloud big model thinking chain reasoning, calling a 3D component to render a scheme effect graph, and feeding back the scheme effect graph to the local; vehicle data, accessory data, scene data, law and regulation data and historical modification case data are obtained in advance, after data preprocessing, entities and relationships are obtained through named entity recognition and relationship extraction, a commercial vehicle modification knowledge graph is constructed, and entity embedding in the knowledge graph is obtained through graph neural network learning. According to the invention, a refitting scheme which better meets user requirements can be obtained.

The invention relates to the technical field of earth space information, in particular to a super-resolution land coverage mapping method coupled with a large language model knowledge base. Comprising the following steps: acquiring a remote sensing image and geoscience text data of a target area and constructing a geoscience knowledge system; mining geoscience knowledge from the geoscience text data based on a large language model, constructing a structured geoscience knowledge base, and further generating a geoscience knowledge graph; obtaining a deep feature graph of the remote sensing image based on a convolutional neural network, and inputting a knowledge graph into a graph attention network to extract a regional geoscience semantic embedding vector; realizing geoscience semantic embedding vector and deep feature map fusion based on a global-local attention fusion mechanism; and performing up-sampling on the fused feature map to obtain a high-resolution depth feature map, and generating a super-resolution land coverage map of the remote sensing image by adopting a nonlinear activation function. According to the method, dynamic modeling and computable expression of regional global knowledge can be realized, and the problems of surface feature boundary fuzziness and category confusion are remarkably improved.

This invention discloses an intelligent detection method for power monitoring data security risks that integrates knowledge graphs. The method includes: a data acquisition layer that integrates and collects raw power data streams and sends them to a preprocessing layer; a preprocessing layer that standardizes the data, extracts features from the data, groups and associates the data according to the features, and performs a preliminary evaluation; a knowledge construction layer that receives standardized event streams, power equipment features, and abnormal data tags, and updates the knowledge graph in real time based on the above data; an intelligent detection layer that queries the graph display results to ensure reliable graph display, detects threat events and issues warnings, and simultaneously performs risk quantification scoring on threat events, analyzes the risk composition, and determines whether the detection is genuine; and a decision response layer that receives threat reports, integrates and sorts the reports, and predicts subsequent threats. This method solves the technical problems of fragmented power monitoring detection, delayed response, and isolated protection in existing technologies.

The application discloses a feature and knowledge driven optimal parallelism prediction method, system and medium, the method comprises the following steps: obtaining the multivariate runtime features of a target parallel program and embedding and coding to obtain multivariate fusion features; inputting the multivariate fusion features into a pre-trained speedup ratio prediction model to predict the speedup ratios of the target parallel program under a plurality of given parallelisms, the speedup ratio prediction model is a neural network prediction model, and a mapping relationship between the multivariate fusion features of the parallel program and the speedup ratios under the plurality of given parallelisms is established by training the training sample of the multivariate fusion features of the parallel program sample and the speedup ratio prior knowledge under different parallelisms; selecting the parallelism with the maximum speedup ratio as the optimal parallelism obtained by prediction. The application aims to realize efficient, accurate and reliable prediction of the optimal parallelism of the program, improve the computing efficiency, reduce the system resource consumption and reduce the execution time of the program.

This invention discloses a method and system for automatic extraction and knowledge construction of data quality rules for power equipment, belonging to the fields of artificial intelligence and power data governance technology. To address the problems of low parsing accuracy of complex tables in power technology standard documents, lack of physical constraint verification of extraction results, and catastrophic forgetting of models due to standard updates, this invention utilizes a multimodal large language model to parse documents. A structured document object model is obtained through hierarchical table structure encoding, cross-page context preservation, and semantic binding of table annotations. The large language model, fine-tuned with domain knowledge, extracts entity relationships and performs consistency verification in conjunction with a physical constraint knowledge base in the power domain. The verified entity relationships are assembled into structured quality rule records and stored in a graph database. A differential-adaptive elastic weight consolidation strategy is employed for continuous learning, combined with a human-machine collaborative quality inspection optimization model, achieving high-precision complex table parsing, physical consistency verification, and anti-forgetting continuous learning.

The invention discloses an intelligent agent creating and optimizing method for the sintering field, and relates to the technical field of intelligent industries, and the method comprises the specific steps: collecting a sintering field multi-source document, and constructing a structured knowledge base after processing; splitting and preprocessing the document to generate question and answer pairs; building a retrieval framework, processing the text by using a vectorization model, and calculating semantic similarity matching; finely adjusting the base model according to the question-answer pair to obtain a special large language model; finally, the model is deployed in a localized mode, performance is verified through an evaluation algorithm, a knowledge base, the model or retrieval logic is optimized according to a result, and a closed loop is formed; according to the method, a structured knowledge base in the sintering field is constructed by integrating multi-source knowledge, accurate query is realized through hierarchical problem tree and scene perception retrieval, a large language model is optimized by adopting a field adaptation fine tuning algorithm, closed-loop optimization is formed by combining a dual evaluation algorithm, professional and reliable intelligent agent output is ensured, efficient intelligent support is provided for iron and steel enterprises, and the method is suitable for popularization and application. And intelligent development of sintering production is promoted.

The application discloses a retrieval method and a retrieval system. The retrieval method is applied to heterogeneous knowledge data, and the heterogeneous knowledge data includes index type structure knowledge, narrative type structure knowledge and graph type structure knowledge. The retrieval method comprises the following steps: acquiring the heterogeneous knowledge data, constructing an adaptive graph structure for the heterogeneous knowledge data, generating a graph structure representation signal according to the graph structure, wherein a multi-level index graph is constructed for the index type structure knowledge, a keyword index graph is constructed for the narrative type structure knowledge, and a knowledge graph is constructed for the graph type structure knowledge; receiving a retrieval question and the graph structure representation signal, performing retrieval reasoning based on a preset retrieval model, obtaining an evidence chain supporting the retrieval question, outputting a retrieval answer according to the evidence chain, and generating evidence traceability data. The technical scheme of the application can effectively improve the accuracy of the retrieval result, improve the retrieval efficiency, and meet the efficient utilization of the heterogeneous knowledge data.

The invention discloses a feature and knowledge driven optimal parallelism degree prediction method and system and a medium. The method comprises the steps that multivariate runtime features of a target parallel program are obtained, and codes are embedded to obtain multivariate fusion features; inputting the multivariate fusion features into a pre-trained speed-up ratio prediction model to predict the speed-up ratio of the target parallel program under a plurality of given parallelism degrees, the speed-up ratio prediction model being a neural network prediction model, a mapping relation between the multivariate fusion features of the parallel program and the speed-up ratios under a plurality of given parallelism degrees is trained and established by a training sample which is constructed by utilizing the multivariate fusion features of a parallel program sample and the speed-up ratio priori knowledge under different parallelism degrees in advance; and selecting the parallelism degree with the maximum speed-up ratio as the optimal parallelism degree obtained by prediction. According to the method, efficient, accurate and credible prediction of the optimal parallelism degree of the program is achieved, the calculation efficiency is improved, system resource consumption is reduced, and the execution time of the program is shortened.

The invention discloses an operation method of a network knowledge construction system based on big data, and the method comprises the steps: S1, randomly selecting K different basic parts as sampling target parts, the sampling target parts comprise a basic part A, a basic part B,..., and a basic part K, and the K is the number of the sampling target parts obtained by the system through sampling inspection expectation; s2, detecting scheme entries in a plurality of shops and merchants corresponding to the part retrieval result, and detecting equipment assembly variability of selling equipment of the merchants in the shops; s3, after obtaining the equipment assembly variability of a plurality of detection result shop merchants, dividing shops into spot check shops, first priority selection target shops and second priority selection shops; s4, evaluating to obtain the part redundancy rate of the optimal solution of the equipment; s5, the system outputs the recommended spot check shop name to a spot check worker through an output module; the method has the characteristics of low sampling inspection cost and high sampling inspection benefit reliability.

The application provides an OpenAPI multi-step state fuzzy testing method and related device, and relates to the technical field of network security. Through two stages of offline knowledge construction and online state exploration, the problems of too large sequence space, poor request executability and disconnection of exploration are solved, the search space is effectively compressed, semantic guidance is provided, and combination explosion is avoided; through runtime load multiplexing, the executability of deep state paths is ensured; deep-first search scheduling and snapshot rollback are adopted to ensure exploration efficiency and result reproducibility, thereby having the advantages of efficiently detecting multi-step state vulnerabilities.

The invention discloses a highway tunnel precise maintenance dynamic knowledge graph construction method and system. The method comprises the following steps: firstly, extracting corresponding knowledge from highway tunnel maintenance text data by adopting an existing knowledge graph construction method to construct an initial tunnel maintenance knowledge graph; secondly, dividing the initial tunnel maintenance knowledge graph into a plurality of knowledge networks with different dimensions by adopting a manual mode, and respectively expanding each divided knowledge network by taking a corresponding node as a network center; and then, combining the expanded knowledge networks into a fused knowledge graph based on the same entity or concept. And finally, performing knowledge graph reasoning on the fused knowledge graph, and continuously fusing graph knowledge obtained by reasoning into the fused knowledge graph to obtain the highway tunnel precise maintenance dynamic knowledge graph. Therefore, the limitation of the current knowledge graph construction method in understanding the semantic relationship and the complex structure of the tunnel maintenance knowledge is solved, and the correctness and the reliability of the knowledge graph are ensured.

The application provides a knowledge graph construction method and device. The method comprises the following steps: determining the modal category of first data, wherein the first data refers to single-modal data extracted from original data used for constructing a knowledge graph; if the modal category comprises multiple categories, obtaining the representation vector of the first data corresponding to each modal category; constructing a first knowledge graph, wherein the first knowledge graph refers to a knowledge graph constructed by extracted knowledge after knowledge extraction based on all the representation vectors; and constructing a second knowledge graph, wherein the second knowledge graph refers to a knowledge graph obtained by knowledge fusion and knowledge completion on the knowledge contained in the first knowledge graph. The knowledge graph constructed by the method fully contains information associated with various modalities, does not split the association relationship between various modal information, has higher accuracy, and has better applicability.

The invention discloses an electric power measurement large model training method and device, equipment and a storage medium, and relates to the technical field of electric power measurement, and the method comprises the steps: obtaining a plurality of pieces of initial electric power measurement knowledge, and constructing a target electric power measurement knowledge graph based on each piece of initial electric power measurement knowledge; expanding each initial electric power measurement knowledge to obtain a plurality of pieces of to-be-verified electric power measurement knowledge, and constructing an initial sample corpus based on the to-be-verified electric power measurement knowledge; screening out each piece of to-be-verified electric power measurement knowledge in the initial sample corpus based on the target electric power measurement knowledge graph to obtain a plurality of pieces of target electric power measurement knowledge, and determining a target sample corpus based on each piece of target electric power measurement knowledge; and training the initial large model by using each piece of target electric power measurement knowledge in the target sample corpus to obtain a target electric power measurement large model, so as to provide the measurement business service of the electric power industry by using the target electric power measurement large model. According to the method, the large metering model of the power industry with specialty and reliability can be constructed.

This invention discloses a method, apparatus, system, and storage medium for constructing a knowledge graph of academic papers based on a large-scale model. The method for constructing a knowledge graph of academic papers based on a large-scale model includes the following steps: S11, acquiring original paper data and preset extraction rules; S12, preprocessing the original paper data to obtain structured target text; S13, based on the extraction rules, using a large-scale speech model to extract entities from the target text to obtain an entity list; S14, using the large-scale speech model to extract the relationship between any two entities in the entity list to obtain a relationship list; S15, aligning, deduplicating, and detecting conflicts among knowledge from different data sources; S16, based on the entity list and the relationship list, fusing multi-source knowledge to construct a knowledge graph. This invention can efficiently extract structured knowledge from massive amounts of academic papers and reveal the evolution and cross-relationships of disciplines.

The application relates to the field of data processing, and provides an intelligent table processing system for a smart campus. The system comprises a knowledge construction module, which is used for ontology modeling and knowledge construction according to metadata and text data of a smart campus data center, so as to obtain a graph database and a vector knowledge base; a model configuration module, which is used for incremental pre-training based on business-related text materials, and supervised fine-tuning training based on artificial annotation data, so as to obtain large language models respectively meeting multiple different business scenarios; a fusion module, which is used for function fusion of a knowledge graph, a vector knowledge base and a large language model based on an M-RAG mode; and an application module, which is used for taking the functions provided by the fusion module as primary functions, and applying secondary functions to the current business scenario, wherein the secondary functions include one or a combination of data filling, data review and process approval. The application provides an intelligent table processing mode for various business scenarios of a campus.

The application provides a multi-omics graph representation learning method and device based on a multi-head self-attention mechanism, pre-processes multi-omics original data, converts the multi-omics original data into a matrix form, constructs or acquires a multi-source priori knowledge network according to existing priori knowledge, encodes the priori knowledge network by using a graph neural network to obtain a feature-level embedding representation, inputs the multi-omics feature matrix into the multi-head self-attention mechanism to capture potential correlations between different multi-omics data and between different multi-omics data, and performs dimension reduction processing on the original high-dimensional data to obtain a low-dimensional sample-level embedding representation, splices and fuses the feature-level embedding and the sample-level embedding to obtain a unified representation of comprehensive data features and priori knowledge, and finally inputs the fused representation into a task-specific module, performs supervised optimization by combining a cross-entropy loss function of a real label, and learns model parameters. In the application stage, only the multi-omics data to be analyzed needs to be input into the trained model, and a classification result can be output.

The invention relates to a platform for realizing intelligent rule management and control of accounting, and belongs to the technical field of accounting. The platform comprises a background support class module, an application service class module, an accounting knowledge base related module, a plurality of MCP servers, an agent main control module and a client module. The method comprises the following steps: based on an mcp protocol, taking a large language model as an intelligent core, and constructing an accounting knowledge base by integrating knowledge loaded by an approval process and knowledge in an accounting rule base in an accounting system; through a natural language interaction interface, accountants are assisted in interpreting policies, enterprise accounting criteria and related management requirements and matching with accounting rules, the accountants are assisted in establishing and adjusting the accounting rules, the working efficiency is improved, and the consistency and integrity of the accounting rules are guaranteed.

This invention relates to the field of artificial intelligence technology, specifically to an automatic construction method and system for large-scale language model intelligent agents. The system includes: S1 Modular representation and knowledge construction, decomposing the intelligent agent into standardized functional modules and establishing a module knowledge base and a historical experience base; S2 Initial intelligent agent automatic assembly, generating an initial configuration from the language model based on task description and dual-base retrieval results; S3 Execution and hybrid evaluation, collecting execution data and generating performance feedback through holistic evaluation of the language model and game theory module contribution evaluation; S4 Dynamic module reorganization and optimization, performing module replacement or optimization based on feedback to achieve knowledge accumulation; S5 Iterative optimization, cyclically executing evaluation and optimization to output the optimal configuration. The system comprises four main units: module management, assembly, execution and evaluation, and optimization, used to implement the above method. This invention achieves automated construction and dynamic optimization of intelligent agents, improving interpretability, cross-task adaptability, and continuous optimization capabilities, while reducing development costs.

The invention relates to the technical field of Internet of Things and machine learning, and particularly discloses a laboratory safety whole-process dynamic management system based on the Internet of Things. The system comprises an Internet of Things sensing layer, an edge calculation layer, a data fusion and knowledge construction layer, a dynamic risk assessment and decision-making layer and an execution and feedback layer. A dynamic security knowledge graph is constructed by fusing multi-source heterogeneous data, risk assessment is performed by integrating mode recognition and probability graph reasoning, a hierarchical management and control instruction is generated, and finally closed-loop management is formed through an execution layer, so that real-time and comprehensive perception, accurate risk assessment and adaptive dynamic management and control of a laboratory security state are realized.

The invention belongs to the field of motor engineering, and particularly relates to an inference type prior knowledge construction method and device for motor parameter identification, and the method comprises the steps: obtaining basic physical constraint prior based on multi-source parallel data of a target motor; performing cross-task learning on the multi-source parallel data of the target motor between different motor types and different working conditions to obtain hot start prior; based on one or two of basic physical constraint priori and hot start priori, obtaining non-independent identically distributed adaptation priori; collaborative iterative optimization is carried out on the non-independent identically distributed adaptation priori, and enhanced non-independent identically distributed adaptation priori is obtained; the basic physical constraint priori, the hot start priori and the enhanced non-independent identically distributed adaptation priori are integrated, and inference type priori knowledge used for motor parameter identification is obtained; according to the method, reasoning type priori knowledge with physical reasonability, cross-domain adaptability and reasoning reliability can be obtained, and the generalization ability and efficiency of the AI model in a motor parameter identification scene are improved.

Embodiments of the invention provide a retrieval method and apparatus, and a computer readable storage medium, relate to the technical field of artificial intelligence, and can improve the correlation between retrieval enhanced knowledge and user questions in a retrieval enhancement generation process so as to improve the accuracy of generated contents. The method comprises the steps of receiving a user question input by a user; responding to the user question, and displaying an answer corresponding to the user question; wherein the answer is obtained according to the first prompt word; the first prompt word is a prompt word constructed according to at least one piece of first knowledge; the at least one piece of first knowledge comprises at least one of the following knowledge: knowledge associated with a user question and retrieved from first type knowledge in a knowledge base based on a first retrieval mode, or knowledge associated with the user question and retrieved from second type knowledge in the knowledge base based on a second retrieval mode; the first retrieval mode and the second retrieval mode are different retrieval modes. The embodiment of the invention is applied to a retrieval enhancement generation process.

The invention provides a project decision and effect prediction method and device based on expert knowledge extraction and a medium, and belongs to the technical field of power grid enterprise achievement transformation management. The method comprises the following steps: receiving a feasibility research report text and expert review data, and extracting a core text through a keyword matching rule; carrying out word segmentation and segmentation on the extracted content by using a built-in word segmentation device of the pre-training language model, obtaining vector representation of each segment, and distributing a weight to each segment based on an attention mechanism; unifying the dimension of the expert review score in the expert review data; constructing a deep learning model based on pre-training BERT, and adopting a staged progressive training strategy; and collecting a feasibility research report text to be decided, inputting the trained deep learning model, and outputting a project approval decision. According to the method, through intelligent processing of the feasibility research report text and the expert review data of the incubation project, the structured review knowledge is extracted, the deep learning model is constructed, and the project optimization efficiency and consistency are remarkably improved.

The invention discloses a characteristic industry credit-oriented large model lightweight training and scene adaptation method, which comprises the following steps of: firstly, acquiring multi-source heterogeneous data from a public credit information center, performing standardized preprocessing, and constructing a knowledge graph containing entity nodes and relation edges based on industrial characteristic knowledge; the knowledge graph is utilized to perform intelligent data enhancement on credit cases, and then formatting processing is performed based on a task instruction template. A Chinese pre-training large language model is selected as a basis, a Transformer layer is divided into a bottom layer, a middle layer and a top layer, parameters are frozen for the bottom layer, a low-rank adaptation LoRA matrix is inserted into the middle layer, a lightweight Adapter module is inserted into the top layer, a task classification head and a knowledge fusion layer are added, and a hybrid parameter efficient fine tuning framework is constructed. After training, a calibration data set is adopted to carry out mixing precision quantification, structure sparsification processing is executed based on weight importance, a scene perception inference engine is constructed, and finally the data is deployed to a local server, so that efficient and lightweight credit evaluation data processing is realized.

This invention discloses a project compliance audit method and system based on collaborative reasoning using an expert knowledge base and a large model. First, a large model is used to analyze a collection of laws and regulations and audit practice documents, automatically extracting compliance audit-related knowledge. Then, this compliance audit-related knowledge is used to construct an expert knowledge base comprising a legal and regulatory knowledge base, an audit case library, an audit experience library, an audit practice library, and a compliance audit rule library. This invention achieves the function of automatically constructing and dynamically expanding the expert knowledge base by extracting legal and regulatory knowledge and audit knowledge using a large model. Furthermore, the reliability of the expert knowledge base and factual data is ensured through a confidence level mechanism and human-machine collaborative verification. Simultaneously, the use of audit evidence traceability chain information enables full-link traceability from audit conclusions to original evidence, greatly improving knowledge conversion efficiency and enhancing the interpretability and reliability of compliance audits, making it suitable for widespread promotion and use.

The invention discloses a programming education knowledge tracking method fusing logic and grammatical skills, and the method mainly comprises the following steps: S1, carrying out the information extraction of a source code submitted by a student, and obtaining the representation, quality score and grammatical score of the source code; s2, calculating grammar scores and quality scores of the codes, evaluating sequential evolution of logical skills according to code records of the same question continuously submitted by students, and constructing a reweighted logical skill evolution diagram; s3, calculating the similarity of global programming questions, constructing a question similar graph, and constructing a grammar skill interaction graph for a student practice sequence; and S4, comprehensively considering the logic skills and grammatical skills of the students, and predicting the programming behavior sequence of the students. According to the method, by analyzing student codes, the accuracy and interpretability of knowledge tracking in programming practice are remarkably improved, so that teaching workers can deeply understand learning modes and knowledge construction processes of students, and the skill development and knowledge mastering conditions of the students in programming learning are accurately mastered.

The application discloses a Web-based disaster monitoring and early warning spatio-temporal process dynamic evolution visualization method, and specifically comprises the following steps: constructing an expression model to describe disaster component elements and disaster processes as the basis for constructing a disaster spatio-temporal expression ontology, then taking the expression ontology as the top-level guide for constructing a knowledge graph, extracting specific knowledge from a large number of real three-dimensional models to construct a disaster knowledge graph, and designing a disaster data self-adaptive organization mode and a visualization data scheduling optimization mechanism to improve the compactness and relevance of data in a Web partition. The expression model and the disaster knowledge graph enable the computer to understand disaster elements and the multidimensional relationship between them, thereby realizing dynamic adjustment and optimization of three-dimensional data, effectively reducing the overhead of complex disaster scene loading and rendering, and realizing high-fluency and low-time-consumption three-dimensional display of disaster spatio-temporal process dynamic evolution.